Epigenetics and chromatin structure regulate var2csa expression and the placental-binding phenotype in Plasmodium falciparum.

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Learn more: PMC Disclaimer | PMC Copyright Notice eLife . 2026 Apr 15;13:RP93632. doi: 10.7554/eLife.93632 Search in PMC Search in PubMed View in NLM Catalog Add to search Epigenetics and chromatin structure regulate var2csa expression and the placental-binding phenotype in Plasmodium falciparum Todd Lenz Todd Lenz 1 Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, United States Find articles by Todd Lenz 1, † , Madle Sirel Madle Sirel 2 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden Find articles by Madle Sirel 2, † , Hannes Hoppe Hannes Hoppe 2 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden Find articles by Hannes Hoppe 2 , Sulman Shafeeq Sulman Shafeeq 2 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden Find articles by Sulman Shafeeq 2 , Karine G Le Roch Karine G Le Roch 1 Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, United States Find articles by Karine G Le Roch 1, †, ✉ , Ulf Ribacke Ulf Ribacke 2 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden 3 Department of Cell and Molecular Biology (ICM), Uppsala University, Uppsala, Sweden Find articles by Ulf Ribacke 2, 3, † Editors: Urszula Krzych 4 , Dominique Soldati-Favre 5 Author information Article notes Copyright and License information 1 Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, United States 2 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden 3 Department of Cell and Molecular Biology (ICM), Uppsala University, Uppsala, Sweden 4 Walter Reed Army Institute of Research, United States 5 University of Geneva, Switzerland † These authors contributed equally to this work. ✉ Corresponding author. Roles Urszula Krzych : Reviewing Editor Dominique Soldati-Favre : Senior Editor Collection date 2026. © 2024, Lenz, Sirel et al This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited. PMC Copyright notice PMCID: PMC13082790  PMID: 41984626 Previous version available: This article is based on a previously available preprint posted on bioRxiv on January 14, 2026: " Epigenetics and chromatin structure regulate var2csa expression and the placental binding phenotype in Plasmodium falciparum ". Previous version available: This article is based on a previously available preprint with doi: https://doi.org/10.7554/eLife.93632.1 . Previous version available: This article is based on a previously available preprint with doi: https://doi.org/10.7554/eLife.93632.2 . Abstract Plasmodium falciparum is responsible for what appears to be a never-ending public health issue in the developing world. With repeated infections, a gradual semi-immunity to severe malaria can be acquired, but this is disrupted when women become pregnant as the parasite cytoadheres in the placenta to prevent splenic clearance. This change in tissue tropism is due to specific transcription of the antigenically variable adhesin VAR2CSA. To better understand the molecular mechanisms activating var2csa and antigenic variation overall, we used a combination of phenotypic and systems biology assays. We first established phenotypically homogenous populations of VAR2CSA-expressing and placenta-binding parasites that were shown to exclusively transcribe var2csa while all other var genes remained silenced. We also confirmed that the transcriptional activation was strongly associated with distinct depletion of repressive H3K9me3 marks. Further, we used chromatin conformation capture as a high-resolution approach to determine interchromosomal interactions and established that transcriptional activation is linked to a small yet significant repositioning of var2csa relative to heterochromatic telomeric clusters. Lastly, we demonstrated that occupancy of 5-methylcytosine was present in all var genes but independent of transcriptional repression and switching. All together, these findings provide insights at high resolution into the potential role of 5-methylcytosine in P. falciparum and increase our understanding of the mechanisms regulating antigenic variation at the epigenetics and chromatin structure level. Research organism: P. falciparum Introduction The morbidity and mortality associated with Plasmodium falciparum malaria are negatively correlated to the number of infections contracted ( Doolan et al., 2009 ). As a consequence, young children and other malaria-naïve individuals are at greatest risk of developing severe illness and succumbing to the disease. With repeated exposure, a gradual immunity to clinical manifestations is acquired and symptomatic episodes in adults are relatively rare in endemic areas ( Doolan et al., 2009 ). This holds true until women become pregnant, when the built-up immunity is disrupted and they again become highly susceptible to developing severe disease ( Steketee et al., 2001 ). Besides affecting the mother, placental malaria (PM) also results in adverse outcomes for the fetus, including miscarriages, stillbirths, preterm births, and low birth weights, of which the latter two often result in predisposition for morbidities later in life ( Rogerson and Beeson, 1999 ; Nyirjesy et al., 1993 ; Chua et al., 2021 ). Thus, PM represents a devastating public health problem that targets the most vulnerable populations in the resource-scarce malaria-endemic regions. The onset of PM is tightly linked to the appearance of the placenta, which presents a new niche for the parasitized red blood cell (pRBC) to sequester and thrive. To cytoadhere in the microvasculature, the parasite employs antigenically variable adhesins from the var gene encoded P. falciparum Erythrocyte Membrane Protein 1 (PfEMP1) family of proteins, a key virulence feature of the parasite that is directly linked to the pathogenesis of severe disease ( Scherf et al., 2008 ; Wahlgren et al., 2017 ). While harmful for the human host, the cytoadhesion provides the parasite immune evasive opportunities, such as sequestration of pRBC to deep vascular walls to avoid splenic clearance or resetting, the adhesion of pRBCs to uninfected RBCs to mask antigens and prevent immune cells from recognizing the pRBCs. ( Lee et al., 2019 ). The repertoire of PfEMP1 is large, with approximately 60 var genes per parasite genome from which only one is assumed to be expressed at a time, a process referred to as mutually exclusive expression. Transcriptional switching between the different var loci creates antigenic variation, which leads to immune evasion and binding of a plethora of human receptors and variable tissue tropism for the parasite ( Lee et al., 2019 ; Chen et al., 1998 ; Scherf et al., 1998 ). In between parasites, the genetic variation among the absolute majority of var genes is vast, with one important exception, the remarkably well-conserved var2csa . This gene encodes the PfEMP1 VAR2CSA that mediates cytoadhesion to chondroitin sulfate A (CSA) on syncytiotrophoblasts in the placenta and is considered the main culprit behind PM ( Fried and Duffy, 1996 ; Salanti et al., 2004 ). The variable expression of PfEMP1 is thought to be accomplished through several layers of gene regulatory activities to ensure that only one, or very few, var genes are translated to functional adhesins and exported to the surface of the pRBC ( Chen et al., 1998 ; Scherf et al., 1998 ; Brolin et al., 2009 ; Joergensen et al., 2010 ; Hollin and Le Roch, 2020 ). Recent bulk transcriptomics and single-cell RNA-seq have demonstrated that var gene switching doesn’t seem purely random; instead, parasite populations tend to progress through successive waves of parasitemia, with each wave dominated by parasites expressing one (or a few) var genes before the system resolves back to one predominant var gene (16 and 17). The antigenic variation is mainly achieved by transcriptional regulation, where altered DNA accessibility is orchestrated through variations in the nucleosome composition, histone modifications, and organization of the chromatin into active and repressive clusters. In addition, involvement of non-coding RNAs and specific transcription factors has been proposed to compose additional layers in the regulation that governs repression, activation, and switching of transcription into one interconnected regulatory network , enabling coordinated expression patterns across many infected red blood cells ( Hollin and Le Roch, 2020 ; Zhang et al., 2022 ; Florini et al., 2025 ). Lastly, the peculiar VAR2CSA appears also regulated on a translational level ( Mok et al., 2008 ; Amulic et al., 2009 ; Chan et al., 2017 ). While major advances have been made recently to better understand gene transcription of these important virulence factors, mechanisms regulating these events at the molecular level are complex and many questions remain. An improved understanding of the detailed molecular mechanisms that mediate parasite adhesion is vital if we want to grasp the molecular factors that control malaria pathology and to identify new therapeutic strategies. Here, using a combination of advanced phenotypic assays and systems biology approaches including transcriptomics, epigenetics, and chromatin structure features, we provide a detailed view in high-resolution on how expression of the PM mediating var2csa is regulated at transcriptional initiation level. Intriguingly, we also show that occupancy of the epigenetic mark 5-methylcytosine (5mC) is not only found in most gene bodies but is also present at higher levels in highly expressed genes, including antigenically variable genes. However, the 5mC mark appears to be unrelated to transcriptional activation and switching. Results Repeated in vitro selection on CSA results in a highly homogenous population of VAR2CSA-expressing and placenta-binding parasites P. falciparum pRBCs have different receptor preferences for cytoadhesion to diverse host cells. This has been previously established in vitro by panning on antibody-coated beads or either cell-bound or soluble human receptors ( Brolin et al., 2009 ; Roberts et al., 1992 ; Staalsoe et al., 2003 ). While several parasite molecules have been identified as ligands for cytoadhesion, the antigenically variant PfEMP1 proteins, encoded by the var genes, have been demonstrated as the genuine adhesion molecules. In PM, VAR2CSA encoded by a subfamily of the var genes plays a vital role for the cytoadhesion of pRBCs to the CSA expressed on the surface of placental syncytiotrophoblasts. To better understand the molecular mechanisms regulating var2csa transcription, we first employed an approach that entailed several rounds of repeated panning of NF54 pRBCs on chondroitin sulfate A (CSA)-coated plates ( Brolin et al., 2009 ) to select for parasites with homogenous expression of VAR2CSA and a placenta-binding phenotype. Prior to the panning procedure, we confirmed by scanning electron microscopy (SEM) the presence of knob structures, which are protrusions where PfEMP1s are surface exposed on pRBCs in patient isolates but are easily lost upon long-term in vitro cultivation ( Figure 1A ). Several phenotypic analyses were used to confirm the accuracy of our selection process. We analyzed pRBC surface expression of VAR2CSA by flow cytometry-based antibody recognition throughout the trajectory of repeated panning and revealed a gradual transition from a phenotypically heterogeneous to homogeneous parasite population ( Figure 1B ). In addition, we interrogated the cytoadhesive potential using placental sections from malaria non-immune donors ( Flick et al., 2001 ). For the repeatedly panned parasites, substantial numbers of pRBCs were observed binding to the placental syncytiotrophoblasts ( Figure 1C ). We were able to quantify bound pRBCs per mm2 using a VAR2CSA surface negative PTEF knockout parasite ( Chan et al., 2017 ) as control for background binding and revealed an approximate sixfold higher binding of the CSA-selected parasite line ( Figure 1D ). Thus, the repeatedly panned NF54CSA (from here on referred to as NF54CSAh) was deemed phenotypically highly homogenous with a VAR2CSA/PM relevant cytoadhesion profile and considered suitable for downstream analyses of var2csa transcription and regulation. Figure 1. Generation of parasites with a homogenous placental-binding phenotype and consequential dominant expression of var2csa . ( A ) The presence of in vivo relevant knob structures on pRBCs was confirmed by SEM prior to phenotypic selection. Shown is a representative image of a pRBC with knobs appearing as white protrusions. ( B ) Flow cytometry-based antibody recognition of VAR2CSA surface expression on repeatedly selected pRBCs. NF54 ptefKO served as negative control, NF54 was the original and unselected parasite line, NF54CSAm was an intermediately selected line and NF54CSAh was panned enough to achieve a homogenous population of VAR2CSA-expressing pRBCs. ( C ) Representative image of a Giemsa-stained placental section with bound NF54CSAh pRBCs. Tissue boundaries are indicated by white dashed lines. ( D ) Quantification of relative placental binding of NF54CSAh from three biological replicates. pRBCs bound per mm 2 were normalized to the VAR2CSA negative NF54 ptefKO parasite line (n=3). ( E ) Spearman correlation of var gene expression profiles determined by RNA-seq for three bio-replicates each of NF54CSAh and the original, unselected NF54. ( F ) Differential gene expression between NF54CSAh and NF54 identified a limited number of significant genes (FDR <0.05, Log 2 fold change >1) of which the majority were var genes ( var2csa in blue, all other var genes in green). ( G ) Normalized read counts for all var genes revealed NF54 to be phenotypically highly heterogeneous, whereas var2csa was the only var gene expressed by NF54CSAh. Figure 1—figure supplement 1. Genome-wide expression profiles are highly similar among all samples. ( A ) Correlation heatmap of regularized logarithmic read counts displaying strong similarity in expression profiles between replicates and samples. ( B ) Principal component analysis shows similar variance between replicates of each sample. Open in a new tab The P. falciparum placental-binding phenotype is strictly linked to transcription of var2csa Next, we sought to decipher any transcriptomic differences responsible for the phenotypic disparity observed between the unselected NF54 and NF54CSAh. As PfEMP1 encoding var genes are actively transcribed during the first third of the parasite’s 48 h life cycle (also referred to as the ring stage) ( Bozdech et al., 2003 ; Le Roch et al., 2003 ), we harvested NF54 and NF54CSAh from three independent selections at 16±2 h post invasion (p.i.) of RBCs for RNA-seq. We also collected parallel samples for ChIP-seq, MeDIP-seq, and Hi-C. The RNA-seq analysis revealed differential transcription in only 64 of the 5285 protein coding genes of the parasite ( Supplementary file 1 ). Hierarchical clustering and principal component analysis performed on the complete set of data revealed little deviation ( ρ >0.99) between both replicates and samples when accounting for genome-wide transcription ( Figure 1—figure supplement 1A and B ). We, however, observed a very strong correlation between replicates of NF54 (ρ>0.97) and between replicates of NF54CSAh (ρ>0.63) when only taking var genes into account ( Figure 1E ), confirming a high degree of phenotypic maintenance among collected samples. In line with this, we observed a near-exclusive representation of var genes among differentially expressed genes ( Figure 1F ), with var2csa being the only upregulated and almost all other of the 60 var genes downregulated in NF54CSAh compared to NF54 ( Figure 1F and G ). Besides the confirmation of var2csa transcription being closely associated to the placental-binding phenotype of malaria parasites, the high degree of phenotypic homogeneity of NF54CSAh and the extensive heterogeneity of NF54 provided an excellent opportunity for downstream analyses of gene regulation in antigenic variation via epigenetic mechanisms and chromatin structure. Transcriptional activity of var2csa is associated with a near-complete lack of H3K9me3 occupancy Besides its known role in repressing repetitive elements and gene-depleted regions in eukaryotes, the histone H3 lysine 9 trimethylation mark (H3K9me3) has been demonstrated to be an important player in heterochromatin formation, to silence lineage-inappropriate genes and control cell fate. This appears particularly true for Plasmodium spp., where H3K9me3 marks have been identified as potentially critical in regulating parasite-specific genes involved in pathogenicity and sexual commitment ( Ninova et al., 2019 ; Lopez-Rubio et al., 2009 ). We therefore utilized chromatin ChIP-seq as previously described ( Bunnik et al., 2018 ) to assess the distribution of H3K9me3 and examine whether NF54CSAh displays differentially bound heterochromatin/euchromatin marks due to elevated transcription of var2csa . We performed ChIP-seq experiments in triplicates using NF54CSAh and NF54 samples collected at the same time as the samples used for the RNA-seq experiments described above. ChIP-seq libraries were sequenced, processed, and mapped to the genome. Following input normalization, we observed a clear targeted enrichment of H3K9me3 within both subtelomeric and internal var gene clusters as previously described ( Michel-Todó et al., 2023 ; Figure 2A ). H3K9me3 occupancy within the gene body of var2csa of NF54CSAh was almost entirely eliminated, differentiating it from the NF54 control which shows little variation from other var genes, which goes in line with its highly heterogeneous var phenotype and consequently small contribution to H3K9me3 for individual genes on a population level ( Figure 2A and B ). Due to highly homologous sequences preventing unique mapping, several var genes display no H3K9me3 coverage ( Figure 2B ). Figure 2. The var2csa locus is devoid of repressive H3K9me3 in placental-binding parasites. ( A ) Chromosomal distribution of repressive H3K9me3 as determined by ChIP-seq from merged biological triplicates of NF54 and NF54CSAh. Regional clusters of antigenically variable genes are noted with green below each plot. The only noticeable difference between the parasite lines was located to the left arm of chromosome 12, which contains the var2csa locus (dashed box). ( B ) Heatmaps of H3K9me3 occupancy in NF54 and NF54CSAh for all var genes (0.5 kb upstream of the transcription start site (TSS) and 1.5 kb into the gene body) with var2csa displayed with bolded gene ID. ( C, D ) H3K9me3 enrichment in the antigenically variable multigene families var , rif , stevor, and pfmc-2tm partitioned into 5’ UTR, exons, and introns versus the rest of the genomes (all other genes and intergenic regions) of NF54 and NF54CSAh. Figure 2—figure supplement 1. Differential peak calling of H3K9me3 reveals highly focused changes in var2csa region. ( A ) Correlation analysis indicates similarity in global binding of H3K9me3 between replicates and samples. ( B ) Read counts are greater in NF54 than NF54CSAh within all peaks identified by differential binding (FDR<0.05) analysis. ( C ) There are only four significantly differentially bound (FDR <0.05) sites between NF54 and NF54CSAh with a log₂ fold change greater than 1 (black), all of which contain more reads in NF54 than NF54CSAh. ( D ) Read counts for the four differentially bound regions are consistent among replicates within each sample. ( E ) Heatmaps of input-normalized H3K9me3 occupancy for all genes separated by chromosome in NF54 and NF54CSAh (0.5 kb 5’ of the transcription start site (TSS) and 1.5 kb 3’ of the TSS). Genes within each chromosome are sorted by the mean read count within the 2 kb region surrounding the TSS. A majority of genes across all chromosomes contain low levels of H3K9me3 surrounding the TSS. Open in a new tab This observation was confirmed by performing MACS3 broad peak calling of input normalized H3K9me3 enrichment with results showing an average of 2234 significant consensus peaks (q<0.05) per sample and approximately 79% of peaks within the coding sequence (CDS) of protein coding genes ( Supplementary file 2 ). Differential binding analysis via DiffBind showed strong correlation ( r >0.75) between replicates and samples ( Figure 2—figure supplement 1A and B ), with only four differentially bound sites (FDR <0.05) ( Figure 2—figure supplement 1C, D and E ), all within the CDS or just upstream of the transcription start site (TSS) of var2csa ( Supplementary file 2 ). Given that var2csa is significantly downregulated in NF54, these results demonstrate the correlation between loss of var2csa expression and H3K9me3-mediated transcriptional silencing. P. falciparum variant antigen gene families such as var, rif, stevor, and pfmc-2tm cluster within subtelomeric regions of most chromosomes and enrichment of H3K9me3 within the gene body is similarly linked to their repression ( Lopez-Rubio et al., 2007 ; Salcedo-Amaya et al., 2009 ; Howitt et al., 2009 ). NF54CSAh and NF54 both display this same pattern of elevated H3K9me3 within the exons of these multicopy gene families, and after normalizing each region (5’ UTR, exons, introns, and intergenic) the mean H3K9me3 within each is higher than genes outside these families as well as all intergenic regions ( Figure 2C and D ). Increased interchromosomal interaction and perinuclear repositioning are involved in silencing of var genes and activation of var2csa, respectively To further investigate the effect chromatin organization and accessibility has on transcription of highly variant multi-copy gene families such as var and rif , we performed Hi-C experiments on tightly synchronized trophozoites to ensure peak expression of the target genes. Hi-C libraries of three biological replicates for each sample (NF54 and NF54CSAh) were prepared as previously described ( Bunnik et al., 2018 ; Gupta et al., 2021 ) and sequenced to a mean depth of ~155 million reads per replicate. The libraries were processed (aligning, pairing, mapping, and quality filtering) using HiC-Pro ( Servant et al., 2015 ) and resulted in a mean of ~44 million valid interaction pairs per replicate. Due to the P. falciparum genome size and frequency of MboI cut sites, we elected to bin our reads at a 10 kb resolution to identify intrachromosomal and interchromosomal interactions. A high stratum-adjusted correlation coefficient (SCC ≈ 0.87–0.94) suggests that chromatin structures were highly similar between replicates and samples ( Figure 3—figure supplement 1 ). We therefore combined the biological replicates for downstream analyses. Due to variation in sequencing depth between the merged NF54 and NF54CSAh samples, random sampling was performed on NF54CSAh to obtain ~100 million consensus reads for comparative and differential analyses. Heatmaps generated from the ICED normalized matrices show patterns similar to previous studies ( Ay et al., 2014 ; Bunnik et al., 2018 ; Bunnik et al., 2019 ), with a negative log-linear relationship between contact probability and genomic distance demonstrating that our experiment worked as expected ( Figure 3—figure supplement 1 ). Most intrachromosomal interactions occur at a distance less than 10% the total length of each chromosome, with heterochromatin clustering occurring in telomeric regions and internal var gene clusters at a higher frequency than the other distant regions ( Figure 3A–C , Figure 3—figure supplements 2 and 3 ). Figure 3. Changes in chromatin organization and perinuclear repositioning upon var2csa activation and silencing. ( A–C ) Hi-C generated and normalized contact counts representing intrachromosomal interactions for chromosomes 2, 4, and 12 in NF54 (left panels) and NF54CSAh (middle panels). Differential intrachromosomal interactions between the two parasite lines (right panels) displayed a general increase in interactions between subtelomeric and chromosome internal var gene clusters for NF54CSAh compared to NF54. Chromosome 12 ( C ) stood out as the only exception with decreased interactions in NF54CSAh for the subtelomere containing the var2csa locus. ( D ) 3D chromatin modeling for NF54 displays a polarized nucleus with clustering of centromeres (gray) and telomeres (red) in distinct regions. The majority of var genes (green), including var2csa (blue), are located in close vicinity to the telomeric cluster. ( E ) The 3D chromatin model for NF54CSAh displayed a similar topology to NF54 with the exception of decreased distance between var genes (green) and telomere ends (red) overall and an increased distance of var2csa (blue) from the telomeric cluster. Figure 3—figure supplement 1. There is a strong correlation in genome-wide interactions between samples and replicates and between contact count probability and genomic distance. ( A ) Distance normalized correlation heatmap showing similar correlation between replicates of both samples. ( B ) Scatter plot indicating a negative log-linear relationship between contact probability and genomic distance for NF54 and NF54CSAh. Figure 3—figure supplement 2. Hi-C intrachromosomal and interchromosomal contact count heatmaps for NF54. Heatmaps generated from ICED (26619908) and per-million read count normalized intrachromosomal contact count matrices binned at 10 kb resolution for all 14 chromosomes of NF54 with annotated centromeres (gray) and var gene containing bins (green). Whole Genome heatmap depicting the normalized genome-wide interchromosomal contact count matrix for NF54 with intrachromosomal interaction matrices removed. Figure 3—figure supplement 3. Hi-C intrachromosomal and interchromosomal contact count heatmaps for NF54CSAh. Heatmaps generated from ICED (26619908) and per-million read count normalized intrachromosomal contact count matrices binned at 10 kb resolution for all 14 chromosomes of NF54CSAh with annotated centromeres (gray) and var gene containing bins (green). Whole Genome heatmap depicting the normalized genome-wide inter-chromosomal contact count matrix for NF54CSAh with intrachromosomal interactions removed. Figure 3—figure supplement 4. Differential interaction contact count heatmaps show higher interactions between telomeric and internal var gene-containing regions in NF54CSAh. Heatmaps generated from differential interaction matrices for all 14 chromosomes, identifying regions with an increase (red) or decrease (blue) of intrachromosomal interactions in NF54CSAh over NF54. Centromeric bins (gray) and var gene-containing bins (green) are annotated for each chromosome. Whole Genome differential interchromosomal interaction heatmap with intrachromosomal interactions removed. Open in a new tab To evaluate the correlation between transcription and chromatin architecture, we used Selfish ( Ardakany et al., 2019 ) to identify differential intrachromosomal and interchromosomal interactions. Although there were slight variations in the number of intrachromosomal interactions across most regions, the consistent pattern that emerged was the increased number of interactions between subtelomeric regions on most chromosomes (p<0.05, log 2 FC >1) in NF54CSAh over NF54, indicating tighter heterochromatin control of var gene regions ( Figure 3A–C and Figure 3—figure supplement 4 ). The subtelomeric regions on chromosomes 2, 3, 4, and 10 showed the largest increase in interaction frequency, and all the var genes within those regions were significantly down-regulated in the RNA-seq data generated in this study. Most interesting is perhaps the mix of increased and decreased interactions between subtelomeric regions on chromosome 12, due to the proximity of var2csa to other nearby var genes within that region confirming a potential role of the chromatin 3D structure in gene regulation. Additional analysis of these small-scale structural changes was performed by 3D chromatin modeling using the Poisson-based algorithm, PASTIS ( Varoquaux et al., 2014 ). Modeling shows co-localization of centromeres and telomeric clustering in distinct regions within the nucleus ( Figure 3D and E ). The overall 3D structure of the genome was similar between NF54 and NF54CSAh. Using the coordinates output from PASTIS, we computed the distance between various bins/regions and found that the mean distance between telomeres containing var genes decreased by 6% in NF54CSAh, indicating overall telomeric compaction. What is more significant is that although there is a 52% increase in distance of var2csa to the nearest bin in spatial proximity and 19% greater distance from the telomeric cluster, the var gene nearest to the end of chromosome 12 only shows a 3% increase in distance from the telomeric cluster. This stark difference in distances for two regions only separated by 20 kb shows that small and localized changes in the chromatin structure are enough to allow transcriptional activation of var2csa . Distribution of DNA methylation may influence gene expression overall but does not mediate transcriptional activation and switching in antigenic variation DNA methylation of cytosine residues serves yet another epigenetic transcriptional antagonist and is found in many model eukaryotic organisms; however, the context and level of methylation differs among plants and animals ( Zhang et al., 2006 ; Lister et al., 2009 ; Feng et al., 2010 ; Zemach et al., 2010 ). In higher eukaryotes, while DNA methylation of promoter sequences has been shown to be a repressive epigenetic mark that down-regulates transcription, DNA methylation is more prevalent within gene bodies and positively correlated with transcript levels ( Jjingo et al., 2012 ). Recent studies into P. falciparum erythrocytic stages have identified the possible presence of low levels of 5-methylcytosine (5mC) and under-characterized 5-hydroxymethylcytosine (5hmC)-like marks throughout the genome ( Ponts et al., 2013 ; Hammam et al., 2020 ; Nardella et al., 2020 ). Presence of low levels of 5mC has also been detected in sporozoites and liver stage forms in P. vivax ( Maher et al., 2023 ). Due to the GC-poor nature of the P. falciparum genome, and the consequential low levels of 5-methylcytosine (5mC) coverage, there remains uncertainty as to what level of transcriptional control is conferred through DNA methylation ( Ponts et al., 2013 ). Recent findings suggest perturbation of levels of the DNA methyltransferase PfDNMT2 to have significant impact on transcription and cell proliferation ( Lucky et al., 2023 ), but any potential role in antigenic variation and cytoadhesive phenotypes remains to be elucidated. To deduce the potential gene regulatory relevance of different DNA methylation marks, we first analyzed 5mC and 5hmC levels in NF54CSAh using MeDIP-seq, which allows for differentiation of 5mC from 5hmC due to antibody specificity, compared to prior efforts that have used bisulfite sequencing as the method of choice ( Hammam et al., 2020 ). We also investigated the presence of 6-methyladenine (6mA), a common DNA modification in prokaryotes that has only recently been studied in humans and other eukaryotic organisms ( Wion and Casadesús, 2006 ; Zhang et al., 2015 ; Luo et al., 2015 ; Liang et al., 2018 ; Xiao et al., 2018 ). Mapping and peak calling of genome-wide 5mC coverage shows ~94% of a mean 6876 significant consensus peaks mapping to genomic coordinates within the CDS of protein coding genes ( Figure 4A , Supplementary file 3 ). 5hmC and 6mA coverages were detected at considerably lower levels with only 269 and 0 significant peaks, respectively ( Figure 4—figure supplement 1 , Supplementary file 3 ). We therefore considered that 6mA modifications are most likely absent from the P. falciparum genome. 5hmC distribution across the genome does not preferentially map to specific gene families or regions within each gene ( Figure 4—figure supplement 1B ). It is therefore likely that the weak 5hmC signal detected across the genome could be attributed to background noise or 5hmC coverage is non-specific and serves a more generalized function rather than being key to transcriptional regulation. Figure 4. Regional differences in 5mC occupancy track with global transcriptional activity but are dissociated from var2csa regulation. ( A ) Genome-wide distribution of input normalized MeDIP-seq counts for 5mC in NF54 and NF54CSAh reveal a high level of concordance between the analyzed parasite lines. Regional clusters of antigenically variable genes are denoted in green below each plot with an area of chromosome 12 marked by a dashed box and a zoomed-in insert displaying minor differences in 5mC occupancy in the var2csa locus between NF54 and NF54CSAh ( var2csa in blue, other var genes in green and rif in black). ( B ) Levels of 5mC marks binned in 5’ flanks and first exon for the top 20% highest expressed genes and 20% lowest expressed genes in NF54 and NF54CSAh. ( C ) Distribution of 5mC across exon-intron boundaries for the 5mC enriched var , rif, and other two-exonic genes. Figure 4—figure supplement 1. The P. falciparum genome contains very little 5hmC and 6mA compared to 5mC and no pattern between gene type or feature. ( A ) Genome-wide distribution of input-normalized 5-methylcytosine (5mC, top), 5-hydroxymethylcytosine (5hmC, middle), and 6-methyladenosine (6mA, bottom), with centromeres (gray) and var genes (green) annotated within each track. ( B, C ) Bar graphs depicting per-million read count normalized 5mC and 6mA per cytosine partitioned into the 500 bp 5’ region, exons, and introns for four antigenically variable gene families ( var , rifin , stevor, and pfmc-2tm ), as well as the remaining protein coding genes and intergenic regions. Open in a new tab Due to the more abundant and distinct distribution of 5mC, we then examined the potential correlation between 5mC coverage and transcription. Because no significant difference in exon coverage was detected between NF54CSAh and NF54 in var2csa or any other var gene ( Figure 4A ), we investigated the difference in distribution of 5mC marks within high versus lowly transcribed genes. We detected a negative correlation for 5mC marks in the promoter of highly transcribed genes, whereas 5mC across the rest of the gene body displays the opposite correlation ( Figure 4B ). Functional differentiation of methylated cytosines within these two distinct regions has already been observed in P. falciparum and in other eukaryotic organisms ( Zhang et al., 2006 ; Zemach et al., 2010 ; Ponts et al., 2013 ) but not in the context of antigenic variation. Over the years, genome-wide DNA methylation studies have confirmed this ‘DNA methylation paradox’ and demonstrated that methylation in exons is found in constitutively expressed genes and has an important role in maintaining transcriptional fidelity by preventing spurious transcription initiation and can affect histone modification and alternative splicing ( Zilberman, 2017 ). We therefore investigated the DNA methylation pattern across the exon/intron junction ( Figure 4C ). Altogether, these results confirm the presence of 5mC in the parasite genome within gene bodies of highly expressed genes as well as var genes and their introns. While additional experiments will be needed to further validate the role of DNA methylation in P. falciparum , this epigenetic mark does not seem to directly control transcriptional activation and switching of var2csa expression but is likely essential in maintaining transcriptional elongation and fidelity, guiding histone modification enzymes and controlling splicing. Discussion The complex and highly variable repertoire of var genes constitutes an essential tool for P. falciparum pRBCs to express different PfEMP1 proteins in order to cytoadhere to vascular endothelium and avoid the host immune system. The ability to cytoadhere and thereby withdraw from the circulation is crucial for the highly rearranged, rigid, and immunogenic pRBCs to avoid splenic clearance and for the intracellular parasites to survive ( Del Portillo et al., 2012 ; Maier et al., 2008 ). Although the prevention of splenic clearance helps the parasite to escape the immune defense of the host, the exposure of the cytoadhesive proteins presents another issue, namely the immune recognition of the adhesins themselves. Therefore, varied expression of these surface proteins through antigenic variation is a necessity, both for the sake of immune escape but also for altered receptor preference and tissue tropism. Excessive cytoadhesion of P. falciparum in the microvasculature of various organs has been strongly associated with malaria disease severity and death. Perhaps the most striking example is the VAR2CSA-mediated sequestration of pRBCs to syncytiotrophoblasts in the placental intervillous space that render previously semi-immune women at risk of severe malaria while pregnant ( Salanti et al., 2004 ). The causative var2csa gene is unique from other var genes in that it is relatively conserved between parasite genomes and is regulated at both a transcriptional and translational level. Parasites cultured continuously in vitro often modify their patterns of var gene expression by transcribing var2csa without translating the mRNA, as this additional layer of regulation presents the parasites with energy saving and competitive advantages in the absence of the need to cytoadhere for its survival ( Mok et al., 2008 ; Amulic et al., 2009 ). Similarly, the complex regulation of the gene allows for the parasites to not express VAR2CSA until infecting a pregnant woman and being presented with the new placental niche. In the work presented here, we were adamant about analyzing the regulation of var2csa in parasites with an appropriate in vivo phenotype instead of parasites with in vitro artifactual and non-productive transcription of the gene. Therefore, several downstream analyses were performed post-phenotypic selection, where we were able to confirm the placental-binding parasites with abundant surface expression of VAR2CSA. In concordance with the adhesive phenotype observed, the selected parasite NF54CSAh showed exclusive and abundant transcription of var2csa , which was in stark contrast to the original line which displayed transcript evidence from a vast number of var genes. These parasite lines, therefore, presented an excellent opportunity to scrutinize the molecular requirements for var2csa selection and activation on a population level, similar to what would occur for isogenic parasites in the in vivo context of PM. Several studies have previously and conclusively linked gene activation in P. falciparum to alterations in DNA accessibility through the change from heterochromatic to euchromatic state ( Lopez-Rubio et al., 2009 ; Bunnik et al., 2018 ; Michel-Todó et al., 2023 ; Lopez-Rubio et al., 2007 ; Salcedo-Amaya et al., 2009 ). In line with our transcriptomic findings, which revealed very limited and var exclusive differences, we observed a remarkable similarity in global H3K9me3 occupancy between the two analyzed parasite lines. We found H3K9me3 primarily in antigenic variable gene contexts with over-representation in var , rif, and stevor , which highlights the high degree of heterochromatic gene silencing required for these genes. Differential binding analysis revealed only four significant differences between the analyzed lines all centered within or around var2csa , with a near absence in H3K9me3 occupancy for NF54CSAh. This clearly confirms the pivotal role of epigenetic regulation through histone modifications in the establishment of the placental-binding phenotype in P. falciparum . Besides histone modifications, other molecular events are known to be involved in the transition of a gene from a heterochromatic to euchromatic state in eukaryotes. One such factor is the macromolecular events that involve physical repositioning of genes within the nuclear context to allow for their activation or repression ( Ferrai et al., 2010 ). In P. falciparum , nuclear repositioning has previously been suggested to be involved in regulating the activation of the gametocyte-specific transcription factor locus, pfap2-g, in the transition from the asexual to the early sexual stages in the parasite life cycle progression ( Bunnik et al., 2018 ). Nuclear repositioning has also been suggested in antigenic variation ( Ralph et al., 2005 ; Duraisingh et al., 2005 ; Freitas-Junior et al., 2005 ; Coleman et al., 2012 ) but its importance has remained somewhat unclear. This is partially due to how well the nuclear context of the parasite is resolved upon interrogation. Previous studies have most often relied on fluorescent in situ hybridizations (FISH) to determine the proximity of genes of interest to a single marker of heterochromatin, such as a subtelomeric repetitive DNA sequence. Considering the small size of the P. falciparum nucleus, the naturally close proximity of many var genes to these subtelomeric repeats and the limited resolution of fluorescent probe approaches, this is a non-trivial task even in the case of robustly selected parasites with discrete var phenotypes. This indeed applies to the subtelomerically located var2csa for which FISH-based endeavors have been inconclusive ( Brolin et al., 2009 ; Ralph et al., 2005 ). Our current Hi-C experiments, performed on parasites with vastly different var phenotypes, proved able to provide the resolution needed to resolve this matter. We observed a significant decrease in differential interchromosomal interactions for NF54CSAh in the subtelomere of chromosome 12 where var2csa is located, whereas interactions around other silent var genes were increased. Upon 3D structural modeling, it became evident that the genome of NF54CSAh displays an overall compaction of telomere ends and var gene-containing intrachromosomal regions, which is in line with the observed heterochromatic silencing of var genes. On the contrary, var2csa was distanced from the nearest telomeric cluster to a transcription permissive area of the nucleus. This conclusively suggests that, besides alteration of epigenetic marks, nuclear repositioning and small and localized changes in the chromatin structure are indeed required for activation of var2csa transcription. It is important to note that the NF54CSAh line consists of a homogeneous parasite population with respect to var gene expression, whereas the NF54 line is heterogeneous, with parasites expressing distinct var genes. Consequently, interactions surrounding silent var genes and the overall compaction of telomeric ends may be similar in individual parasites within the heterogeneous NF54 population but may appear tighter or more compact in the var2csa-expressing line simply because that population is homogeneous. Lastly, we investigated the differences in DNA methylation between NF54CSAh and NF54. DNA methylation exists in most eukaryotic organisms, including plants, and is involved in various physiological processes and has a complex correlation with gene expression. While the role of promoter hypermethylation in repressing gene transcription has been well documented, emerging evidence indicates that gene body methylation correlates with transcriptional activity in up to 60% of genes in some species. While the presence of low levels of 5mC and 5hmC marks has been identified throughout the P. falciparum ( Ponts et al., 2013 ; Hammam et al., 2020 ) and P. vivax ( Maher et al., 2023 ) genomes, the function and the molecular mechanism underlying its regulation have remained obscure. However, recent studies where perturbations of the methylation process by either small molecules ( Nardella et al., 2020 ; Maher et al., 2023 ) or reverse genetics ( Lucky et al., 2023 ) suggest essentiality and a role in gene regulation. Here, using MeDIP-seq, we show that DNA methylation is mostly absent from the promoter regions of most var genes ( Figure 4B ) but is present throughout the CDS of most genes but at a higher level in highly expressed genes ( Figure 4B ). Although there is no clear indication that 5mC regulates the transcriptional activation of var2csa , mechanistically, it may be a required modification to guide and stabilize RNA polymerases as well as histone modifying enzymes, and ultimately control transcription at the elongation level. We propose that, in Plasmodium , DNA methylation may be associated with priming genes for transcriptional activity, rather than repressing transcription. Specifically, higher methylation levels may facilitate recruitment of RNA polymerase II and associated transcriptional machinery to support gene transcription. In Figure 4B , we observe higher levels of DNA methylation in the first exon of highly expressed genes in both the NF54 and NF54CSAh lines. Interestingly, we also detect high levels of methylation across most introns of var genes, introns that must be transcribed and are essential for var gene regulation, suggesting a possible sequence-recognition function. While the presence and function of gene body methylation in some species remains controversial, a growing body of evidence suggests that its effects, while minor, may be shaped by natural selection ( Dixon et al., 2018 ) and correlated with fitness ( Muyle et al., 2022 ). A high level of DNA methylation in genes known to be involved in antigenic variation in malaria parasites may also be crucial for parasite adaptation and survival in vivo and deserves additional investigation. Collectively, our data confirm a strong association between epigenetics, genome organization, and gene expression in regulating parasite transcripts involved in malaria pathogenicity, but also what appears as a fundamentally different role for DNA methylation through 5mC. Materials and methods Parasites and in vitro culturing The P. falciparum strains NF54 and NF54CSA-ptefKO ( Chan et al., 2017 ) were cultivated according to standard methods ( Trager and Jensen, 1976 ). All parasite cultures were maintained in blood group O RBCs at 4% hematocrit in RPMI-1640 medium (Gibco) containing 27 nM NaHCO 3 (Sigma), 2 mM L-glutamine (Hyclone), and 2.5 µg/mL gentamicin (Gibco) and supplemented with 10% human A+ serum. The microaerophilic environment was kept constant by gassing of culture flasks with a mixture of 90% N 2 , 5% O 2 and 5% CO 2 and cultures were kept in suspension by orbital shaking at 37°C. Parasite cultures were regularly synchronized by treatments with 5% sorbitol as described previously ( Lambros and Vanderberg, 1979 ). Scanning electron microscopy To confirm proper pRBC surface morphology prior to the establishment of cytoadhesive phenotype, scanning electron microscopy was used. Magnetically enriched pRBCs were fixed in 2.5% glutaraldehyde (Polysciences), 1% paraformaldehyde (Santa Cruz) in PBS for 1 h, rotating at 4°C. The samples were washed twice with cold PBS. Following the fixation, specimens were adhered onto alcian blue pre-treated 0.45 mm Supor PES membrane filter (Pall Corp.) and washed with 0.1 M phosphate buffer pH7.4 followed by MilliQ water. The membranes were then subjected to stepwise ethanol dehydration, transferred to acetone, and critical-point-dried using carbon dioxide (Leica EM CPD030). The membranes were finally mounted on specimen stubs using carbon adhesive tabs and sputter coated with a 10 nm layer of platinum (Quorum Q150T ES). SEM images were acquired using an Ultra 55 field emission scanning electron microscope (Zeiss) at 3 kV and the SE2 detector. Establishment and preservation of parasite phenotypes The chondroitin sulfate A (CSA) and placental-binding phenotype of NF54CSA was maintained by bi-weekly panning on CSA-coated plastic plates with minor modifications from Brolin et al., 2009 . Briefly, 100 μg/mL CSA from bovine trachea (Sigma) in phosphate-buffered saline (PBS) was coated on plates overnight leaving a fraction of the plate without CSA-coating. Thereafter, to prevent non-specific binding, plates were blocked with 2% bovine serum albumin (BSA, fraction V, HyClone) in PBS for 1 h. Mature trophozoites were purified using a MACS magnetic cell sorter (Miltenyi BioTec) and resuspended in RPMI-1640 with 10% human serum. Parasites were added to CSA-coated plates and incubated for 1 h at 37°C in microaerophilic conditions with occasional gentle swirling. Plates were thereafter washed with RPMI-1640 until background binding was low, as confirmed by comparison with the non-CSA coated area. Thereafter, the bound pRBCs were recovered from the plates and returned to culture. It is well known that parasites selected to bind to CSA (and other cytoadhesion receptors) revert back to a non-CSA binding state over time. With the selected panning interval, minor reversion was noted. Prior to any downstream analyses, phenotypes were nevertheless confirmed by flow cytometry (see below). Flow cytometric analysis of pRBC surface levels of VAR2CSA For pRBC surface staining of VAR2CSA, cultures with late-stage pRBCs (24–40 hpi) were blocked in PBS with 2% bovine serum albumin (BSA, fraction V, HyClone) for 30 min at room temperature. Primary antibody against the VAR2CSA (goat anti-DBL1-6) was added to blocked cultures at a concentration of 100 μg/mL in PBS with 2% BSA and incubated for 1 h at room temperature. Non-immune goat IgG (Jackson ImmunoResearch) was used for non-specific binding control. Cells were washed twice with 90 µL of PBS before being incubated with a secondary rabbit anti-goat antibody coupled to Alexa647 (Invitrogen) at 1:100 dilution, 5 μg/mL Dihydroethidium (DHE) (Invitrogen) and 10 μg/mL Hoechst 33342 (Invitrogen) in PBS for 1 h at room temperature. Unbound secondary antibody was washed away by 2 × 90 µL washes with PBS before cells were resuspended in PBS to a final hematocrit of 0.3%. 3000 pRBCs per sample were analyzed using a BD FACSVerse (BD Bioscience) and surface positivity was analyzed in FlowJo version 10 by first gating on intact cells on SSC-A and FSC-A, followed by FSC-H and FSC-A for single cells and from there, the Hoechst-DHE double positive pRBC population was selected. This population was analyzed for percentage of Alexa647 positive cells based on a cutoff from the non-immune IgG background binding. The parasite NF54CSA-ptefKO ( Chan et al., 2017 ) was used as negative control as the knock-out of the gene encoding the Plasmodium Translation Enhancing Factor (PTEF) has previously been shown to be essential for the translation of VAR2CSA. Thus, NF54CSA-ptefKo pRBCs are devoid of VAR2CSA and therefore non-adherent to CSA. Placental binding A placental-binding assay was adapted from Flick et al., 2001 . Cryo-sections (8 μm in thickness and approximately 6 mm in diameter) from placentas donated by healthy Swedish pregnant women were prepared in a Microm HM 560 cryotome (Thermo Scientific). Sections were mounted onto 10-well microscopy slides (Novakemi AB) and stored at –80°C. Prior to use, the placental sections were submerged in ice-cold PBS. Late-stage pRBCs were enriched using magnetic columns (Miltenyi Biotec) and washed three times before resuspended in binding medium RPMI-1640 (Gibco) with HEPES adjusted to 20 mM, pH 6.8, and 10% human serum at a density of 2.4 × 10 5 pRBCs/μL. 25 μL (0.6 × 10 7 cells) of pRBC suspension was added to each placental section and was incubated at 37°C for 1 h in a humid chamber. Unbound cells were washed away by dipping the slides 3 × 5 min in RPMI-1640 with HEPES. Preparations were thereafter fixed with 100% methanol for 15 s and stained with 5% Giemsa (Merck) for 15 min, washed with distilled water, and air-dried. Samples were visualized using an Eclipse 80i (Nikon) microscope at 1000× magnification and blinded for counting. Samples were imaged using an Infinity 3 color camera (Teledyne Lumenera). Number of bound cells per experimental condition was counted for 30–45 fields containing on average 29 pRBCs per field and relative binding was determined compared to the one of VAR2CSA non-expressing pRBCs. RNA purification, library preparation, and sequencing For RNA sequencing, NF54 and NF54CSAh were harvested from three independent selections at 16±2 h post invasion (p.i.) of RBCs. Cell pellets were lysed using five volumes of TRIzol Reagent (Life Technologies) before RNA was purified using the NucleoSpin miRNA Mini kit for miRNA and RNA purification (Macherey-Nagel) without size fractionation, according to the instructions of the manufacturer. The high retention of RNA on the columns of the kit allows for sequential purification without any apparent loss of RNA (not shown). Therefore, all RNA samples were passed over columns in total three times, with the first two elutions being treated with Turbo DNAse (Invitrogen) in solution to ensure a complete lack of contaminating gDNA in the preparations. Strand-specific RNA sequencing (RNA-seq) libraries were generated using the KAPA Stranded RNA-Seq kit (Roche) and were amplified for five PCR cycles (45 s at 98°C for initial denaturing and cycles of 15 s at 98°C, 30 s at 60°C, 30 s at 68°C before final extension for 60 s at 68°C) using the KAPA HiFi HotStart ready mix (KAPA Biosystems) before purification twice over 1X KAPA Pure beads (KAPA Biosystems). Prior to sequencing, the quality of libraries was evaluated using the Agilent 2100 Bioanalyzer (Agilent) and a high sensitivity DNA kit (Agilent). Libraries were quantified using the Collibri Library Quantification kit (Invitrogen) and thereafter sequenced in 75 bp paired-end reads on an Illumina NextSeq550 instrument. PhiX library (Illumina) was used for sequencing control and to diversify libraries. RNA-seq data processing and differential expression analysis Sequenced RNA-seq libraries were assessed for quality using FastQC (v0.11.9). In addition to the index adapters, 12 bp were trimmed from the ends of reads based on sequence quality using Trimmomatic (v0.39) ( Bolger et al., 2014 ). Trimmed reads were then aligned to the P. falciparum 3D7 genome assembly (PlasmoDB v58) using HISAT2 (v2.1.0) ( Kim et al., 2015 ). Samtools (v1.10) ( Li et al., 2009 ) was used to filter and sort aligned reads using a quality score of 30 and retrieve properly mapped and paired reads. The output high-quality, properly paired reads were mapped to protein coding genes to retrieve total read count using HTseq-count (v1.99.2) ( Anders et al., 2015 ). Differential gene expression analysis between NF54CSAh and NF54 was performed using DESeq2 (v1.32.0) ( Love et al., 2014 ). Chromatin immunoprecipitation (ChIP) and ChIP-seq library preparation For the ChIP-seq experiment, we harvested three biological replicates of NF54 and NF54CSAh (16±2 h p.i) in parallel with the samples collected for RNA-seq. Parasites were freed from RBCs using 0.1% saponin (from quillaja bark, Sigma) and washed thrice with ice-cold PBS. Parasites were cross-linked in 1% methanol-free formaldehyde (Polysciences Inc) at 37°C for 10 min while kept in suspension on an orbital shaker (50 rpm). The cross-linking was thereafter quenched using 125 mM glycine (Sigma) for 5 min at 37°C before cell pellets were washed two times with PBS at 4°C. Nuclei were extracted using a nuclear extraction buffer (10 mM HEPES, 10 mM KCl, 0.1 mM EDTA, 0.1 mM EGTA, 1 mM DTT, 0.5 mM AEBSF, and 1× Halt protease inhibitor cocktail from Thermo Fisher) and incubated for 30 min on ice before addition of Nonidet P 40 substitute (Roche) to a final concentration of 0.5%. Samples were homogenized by passage through 23 G needles three times and 25 G needles seven times. Parasite nuclei were thereafter collected and resuspended in shearing buffer (1% SDS, 10 mM EDTA, 50 mM Tris HCl pH 8.1, and 1x Halt protease inhibitor cocktail, Thermo Fisher). Chromatin was fragmented using the Covaris ultra sonicator (ME220) for 5 min with the following settings: 25% duty cycle, 75 W intensity peak incident power, 1000 cycles per burst. Insoluble material was removed by centrifugation for 10 min at 15700 rcf at 4°C before samples were diluted 10-fold in ChIP dilution buffer (16.7 mM Tris–HCl pH 8, 1.2 mM EDTA, 0.01% SDS, 150 mM NaCl, 1.1% Triton, and 1× Halt protease inhibitor cocktail from Thermo Fisher). To reduce non-specific background, samples were precleared with ChIP-grade protein A/G magnetic beads (Thermo Scientific)/0.2 mg/mL Salmon Sperm DNA (Invitrogen) for 2 h at 4°C with end-to-end rotation. To retrieve H3K9 trimethylated histones, samples were incubated with 2 µg anti-H3K9me3 antibodies (ab8898, Abcam) or a non-immune rabbit IgG as negative control (12-370, Upstate) overnight at 4°C. An input sample was saved at 4°C until the de-crosslinking step. Antibody–protein complexes were recovered during 2 h incubation at 4°C with protein A/G magnetic beads (Thermo Scientific), followed by duplicate washes of 15 min with low-salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl pH 8.1, 150 mM NaCl), high-salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl pH 8.1, 500 mM NaCl), LiCl wash buffer (0.25 M LiCl, 1% NP-40, 1% sodium deoxycholate, 1 mM EDTA, 10 mM Tris–HCl pH 8.1), and TE buffer (10 mM Tris–HCl pH 8, 1 mM EDTA). Chromatin immuno complexes were thereafter eluted from the beads twice with elution buffer (1% SDS, 0.1 M NaHCO 3 ) for 15 min at room temperature, and elutes were combined. RNA was removed by incubation with 0.48 μg/μL RNase A (Invitrogen) for 30 min at 37°C, followed by a 2 h incubation at 45°C with proteinase K (final concentration 0.24 μg/μL, Ambion). Samples were de-crosslinked overnight at 65°C by adding NaCl to a final concentration of 200 mM. DNA was thereafter extracted using phenol:chloroform:isoamyl alcohol (25:24:1, Sigma) and ethanol precipitation and was further purified with KAPA Pure beads (KAPA Biosystems). ChIP-seq libraries were prepared using the KAPA LTP library preparation kit (KAPA Biosystems). Immunoprecipitated and input libraries were amplified for 16 and 5 PCR cycles, respectively (45 s at 98°C for initial denaturing and cycles of 15 s at 98°C, 30 s at 60°C, 30 s at 68°C before final extension for 60 s at 68°C) using the KAPA HiFi HotStart ready mix (KAPA Biosystems) before purification twice over 1X KAPA Pure beads (KAPA Biosystems). Prior to sequencing, the quality of libraries was evaluated using the Agilent 2100 Bioanalyzer (Agilent) and a High Sensitivity DNA kit (Agilent). Libraries were quantified using the Colibri Library Quantification kit (Invitrogen) and thereafter sequenced in 75 bp paired-end reads on an Illumina NextSeq550 instrument. PhiX library (Illumina) was used for sequencing control and to diversify libraries. Methylated DNA immunoprecipitation (MeDIP) and MeDIP-seq library preparation Biological triplicates of NF54 and NF54CSAh (16±2 h p.i) were harvested for MeDIP-seq in parallel with the samples collected for RNA-seq and ChIP-seq. Parasites were initially released from host RBCs by 0.1% saponin (from quillaja bark, Sigma) and washed thrice with ice-cold PBS. Thereafter, genomic DNA was extracted using QIAamp DNA Blood Kit (QIAGEN) following the instructions of the manufacturer and including the optional RNase A treatment. Extracted DNA was eluted in NE buffer (Macherey-Nagel) and quantified with dsDNA HS assay kit (Invitrogen) on a Qubit 3 fluorometer (Invitrogen). gDNA was fragmented in TE buffer using the Covaris ultra sonicator (ME220) for 130 s with the following settings: 20% duty cycle, 70 W intensity peak incident power, 1000 cycles per burst, resulting in average fragment size of approximately 300 bp. Thereafter, gDNA was precipitated and concentrated by addition of 0.1 volumes of sodium acetate (Alfa Aesar) and 3 volumes of 100% ethanol. gDNA was quantified with dsDNA HS assay kit (Invitrogen) for Qubit 3 fluorometer (Invitrogen) and 100/30 ng (immunoprecipitation/input) used for library preparation using KAPA LTP library preparation kit (KAPA Biosystems). Library preparation was stopped prior to PCR amplification. The quality of libraries was assessed with Agilent 2100 Bioanalyzer (Agilent) and High Sensitivity DNA kit (Agilent) and quantity with the Collibri Library Quantification kit (Invitrogen). Thereafter, equal amounts of libraries from NF54 and NF54CSAh were pooled for the same bio-replicate for the same antibody. Libraries were denatured at 95°C for 10 min and then incubated on ice for 10 min. The Methylated DNA Immunoprecipitation (MeDIP) Kit (Active Motif) was used according to the instructions by the manufacturer with some modifications. 3 μg of polyclonal rabbit anti-5mC (#61255, Active Motif) and rabbit IgG (#103524, Active Motif) were added to the denatured libraries. For the elucidation of potential presence of 5hmC and 6mA in NF54CSAh only, 3 μg of polyclonal rabbit anti-5mhC (#39069, Active Motif) and polyclonal rabbit anti-6mA (#202003, Synaptic Systems) was used respectively. Immune complexes were collected using 20 μL of protein G magnetic beads (Active Motif). After elution from the beads, samples were purified using 1X KAPA Pure beads (KAPA Biosystems) and PCR amplified using the KAPA HiFi HotStart ready mix (KAPA Biosystems) for eight PCR cycles (45 s 98°C of initial denaturing and amplification at 15 s at 98°C, 30 s at 60°C, 30 s at 68°C and final extension 60 s at 68°C). Libraries were analyzed for quality using an Agilent 2100 Bioanalyzer (Agilent) and the High sensitivity DNA kit (Agilent), quantified using Collibri Library Quantification kit (Invitrogen) and thereafter sequenced in 75 bp paired-end reads on an Illumina NextSeq550 instrument. PhiX library (Illumina) was used for sequencing control and to diversify libraries. ChIP-seq and MeDIP-seq data processing and peak calling ChIP-seq reads in fastq format were assessed for quality using FastQC (v0.11.9). Index adapters and an additional 8 bp were trimmed from the ends of reads based on sequence quality using Trimmomatic (v0.39) ( Bolger et al., 2014 ). Bowtie2 (v2.3.5.1) ( Langmead and Salzberg, 2012 ) was used to align reads to the P. falciparum 3D7 genome (PlasmoDB v58). PCR duplicates were tagged using Picardtools (v2.26.11) ( http://broadinstitute.github.io/picard ). Quality filtering using a mapping score of 30, sorting, and indexing were performed using Samtools (v1.10) ( Li et al., 2009 ) keeping only properly mapped and paired reads. Output properly paired, high-quality, deduplicated reads were then mapped to the genome using Bedtools (v2.27.1) ( Quinlan and Hall, 2010 ) to retrieve per base coverage genome wide. All three sets of reads for each sample (H3K9me3, IgG, and input) were then normalized by read count at each locus per million mapped reads before subtracting IgG and input read counts from H3K9me3 to remove background noise for visualizing chromosomal coverage. Genome-wide coverage was binned at 10 bp resolution prior to mapping. Significant peaks were called using MACS3 (v3.0.0a7) ( https://github.com/macs3-project/MACS ; Liu et al., 2023 ) with FDR <0.05 for calling composite broad peaks. TSS coverage profiles were mapped using deeptools2 (v3.5.1) ( Ramírez et al., 2016 ) bamCompare to subtract input reads and regions from 500 bp 5’ of the TSS to 1500 bp 3’ of the TSS were scored with a 1 bp bin size. DiffBind (v3.2.7) was utilized for differential peak calling between NF54CSAh and NF54. MeDIP-seq data processing was performed the same as above, using the same versions of FastQC, Trimmomatic, Picardtools, Samtools, Bedtools, and MACS3. Counts per million normalization was performed prior to subtracting IgG and input reads from 5mC, 5hmC, and 6mA for genome-wide visualization at 10 bp resolution. MACS3 peak calling was performed using the default narrow peak calling for 5mC, 5hmC, and 6mA, but minimum fold coverage for model building was reduced to 2. Default DiffBind parameters were used for differential peak calling between NF54CSAh and NF54 for 5mC, and between NF54CSAh 5mC, 5hmC, and 6mA. Chromosome conformation capture sequencing (Hi-C) library preparation Three biological replicates of NF54 and NF54CSAh (at 20±2 h.pi.) were fixed with 1.25% methanol-free formaldehyde (Polysciences Inc) for 25 min at 37°C and quenched with 150 mM glycine for 15 min at 37°C. Thereafter, samples were incubated at 4°C on a rocking platform for 15 min. Parasites were collected and washed with ice-cold PBS. The supernatant was removed and parasite pellets stored at –80°C. Crosslinked parasite pellets were resuspended in lysis buffer (10 mM Tris–HCl, pH 8.0, 10 mM NaCl, 2 mM AEBSF, 0.10% Igepal CA-360 [v/v], and 1X protease inhibitor cocktail) and incubated for 30 min on ice. Samples were homogenized by a 26.5-gauge needle, then washed in lysis buffer. Pellets were resuspended in 0.5% SDS and incubated at 62°C to solubilize the chromatin. DNA was digested overnight with 100 units MboI (NEB) restriction enzyme, then the ends filled using dTTP, dGTP, dATP, biotinylated dCTP, and 25 units DNA polymerase I. Blunt ends were then ligated using 4000 units T4 DNA ligase and chromatin de-crosslinked using decrosslinking buffer (50 mM Tris–HCl, pH 8.0, 1% SDS, and 500 mM NaCl) and RNase A. Ligated DNA was purified and sheared to a length of ~300–500 bp using the Covaris S220 sonicator (settings: 10% duty factor, 140 peak incident power, and 200 cycles per burst for 65 s). Fragments were pulled down using streptavidin T1 beads (Invitrogen) then library prepped by end-repair, A-tailing, and adapter ligation all in lo-bind tubes. Libraries were PCR amplified with NEB multiplex oligos (45 s at 98°C, 12 cycles of 15 s at 98°C, 30 s at 55°C, 30 s at 62°C and a final extension step of 5 min at 62°C) and sequenced using the NOVASeq platform (Illumina). Hi-C data processing, differential interaction analysis and 3D modeling Paired-end Hi-C libraries were processed (mapping, read pairing, quality filtering, binning, and normalizing) using HiC-Pro (v3.1.0) ( Servant et al., 2015 ) with minimum mapping quality of 30 at 10 kb resolution when aligning to the P. falciparum 3D7 genome (PlasmoDB v58). Interaction heatmaps were generated using the ICED-normalized interaction matrices after an additional per-million read count normalization to allow for direct comparison between NF54CSAh and NF54. Correlation among biological replicates was evaluated with HiCRep ( Yang et al., 2017 ), then replicates were merged to generate a single representative sample. To enhance visualization, all intra-bin contacts and contacts within a two-bin distance were set to the 90th percentile, reducing the range of the data needing to be color mapped. Regions of differential interactions between NF54CSAh and NF54 were identified using Selfish ( Ardakany et al., 2019 ) for all intrachromosomal and interchromosomal matrices. Coordinate matrices were generated by PASTIS ( Varoquaux et al., 2014 ) from raw read count matrices and then visualized as 3D chromatin models in ChimeraX ( Goddard et al., 2018 ) while highlighting bins containing var genes, telomeres, and centromeres. Acknowledgements The authors would like to thank Lars Haag at the Electron Microscopy Unit (Emil), Karolinska Institutet, for excellent technical assistance relating to the scanning electron microscopy. This work was funded by The Knut and Alice Wallenberg Foundation (KAW 2017.0055 to UR); The Swedish Research Council (VR 2018-05814, VR 2016-02917 to UR); The National Institutes of Allergy and Infectious Diseases and the National Institutes of Health (R01 AI136511, R21 AI142506 to KLR); and The University of California, Riverside (NIFA-Hatch-225935 to KLR). Funding for open access charge: The Swedish Research Council. Funding Statement The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Contributor Information Karine G Le Roch, Email: [email protected]. Urszula Krzych, Walter Reed Army Institute of Research, United States. Dominique Soldati-Favre, University of Geneva, Switzerland. Funding Information This paper was supported by the following grants: National Institutes of Health

R01 AI136511 to Karine G Le Roch. National Institutes of Health

R21 AI142506 to Karine G Le Roch. Knut and Alice Wallenberg Foundation

KAW 2017.0055 to Ulf Ribacke. Swedish Research Council

VR 2018-05814 to Ulf Ribacke. Swedish Research Council

VR 2016-02917 to Ulf Ribacke. University of California, Riverside

NIFA-Hatch-225935 to Karine G Le Roch. Additional information Competing interests No competing interests declared. Author contributions Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing. Investigation, Methodology. Investigation. Investigation. Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Writing – original draft, Project administration, Writing – review and editing. Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing. Additional files Supplementary file 1. RNA-seq analysis. elife-93632-supp1.xlsx (314.1KB, xlsx) Supplementary file 2. ChiP-seq analysis. elife-93632-supp2.xlsx (526.5KB, xlsx) Supplementary file 3. MeDIP-seq analysis. elife-93632-supp3.xlsx (1.1MB, xlsx) MDAR checklist elife-93632-mdarchecklist1.docx (90.9KB, docx) Data availability No new algorithms or tools were created to process or visualize data contained in this manuscript and any scripts used in the analysis of data use previously established packages. Scripts used to process data and generate figures for this manuscript are freely available on the personal GitHub page of Todd Lenz ( https://github.com/tlenz88/ ). Data processing and plotting of RNA-seq, ChIP-seq, and MeDIP-seq data was accomplished using the SimpleSeq pipeline with accompanying tools EasyDGE and EasyPeaks found on the SimpleSeq repository ( Lenz, 2026 ). Plotting of HiC heatmaps and 3D models was accomplished using the scripts found on the HiCplotter ( Lenz, 2025a ) and Chrom3D ( Lenz, 2025b ) repositories, respectively. The sequencing datasets generated and analyzed during the current study are available in the SRA repository under the accession number PRJNA947327. The following dataset was generated: Le Roch Lab 2023. Epigenetics and chromatin structure regulate var2csa expression and the placental binding phenotype in Plasmodium falciparum. NCBI BioProject. PRJNA947327 References Amulic B, Salanti A, Lavstsen T, Nielsen MA, Deitsch KW. An upstream open reading frame controls translation of var2csa, a gene implicated in placental malaria. PLOS Pathogens. 2009;5:e1000256. doi: 10.1371/journal.ppat.1000256. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–169. doi: 10.1093/bioinformatics/btu638. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ardakany AR, Ay F, Lonardi S. Selfish: discovery of differential chromatin interactions via a self-similarity measure. Bioinformatics. 2019;35:i145–i153. doi: 10.1093/bioinformatics/btz362. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ay F, Bunnik EM, Varoquaux N, Bol SM, Prudhomme J, Vert J-P, Noble WS, Le Roch KG. Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression. Genome Research. 2014;24:974–988. doi: 10.1101/gr.169417.113. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120. doi: 10.1093/bioinformatics/btu170. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bozdech Z, Llinás M, Pulliam BL, Wong ED, Zhu J, DeRisi JL. The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLOS Biology. 2003;1:E5. doi: 10.1371/journal.pbio.0000005. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Brolin KJM, Ribacke U, Nilsson S, Ankarklev J, Moll K, Wahlgren M, Chen Q. Simultaneous transcription of duplicated var2csa gene copies in individual Plasmodium falciparum parasites. Genome Biology. 2009;10:R117. doi: 10.1186/gb-2009-10-10-r117. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bunnik EM, Cook KB, Varoquaux N, Batugedara G, Prudhomme J, Cort A, Shi L, Andolina C, Ross LS, Brady D, Fidock DA, Nosten F, Tewari R, Sinnis P, Ay F, Vert JP, Noble WS, Le Roch KG. Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages. Nature Communications. 2018;9:1910. doi: 10.1038/s41467-018-04295-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bunnik EM, Venkat A, Shao J, McGovern KE, Batugedara G, Worth D, Prudhomme J, Lapp SA, Andolina C, Ross LS, Lawres L, Brady D, Sinnis P, Nosten F, Fidock DA, Wilson EH, Tewari R, Galinski MR, Ben Mamoun C, Ay F, Le Roch KG. Comparative 3D genome organization in apicomplexan parasites. PNAS. 2019;116:3183–3192. doi: 10.1073/pnas.1810815116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Chan S, Frasch A, Mandava CS, Ch’ng J-H, Quintana MDP, Vesterlund M, Ghorbal M, Joannin N, Franzén O, Lopez-Rubio J-J, Barbieri S, Lanzavecchia A, Sanyal S, Wahlgren M. Regulation of PfEMP1-VAR2CSA translation by a Plasmodium translation-enhancing factor. Nature Microbiology. 2017;2:17068. doi: 10.1038/nmicrobiol.2017.68. [ DOI ] [ PubMed ] [ Google Scholar ] Chen Q, Fernandez V, Sundström A, Schlichtherle M, Datta S, Hagblom P, Wahlgren M. Developmental selection of var gene expression in Plasmodium falciparum. Nature. 1998;394:392–395. doi: 10.1038/28660. [ DOI ] [ PubMed ] [ Google Scholar ] Chua CLL, Hasang W, Rogerson SJ, Teo A. Poor birth outcomes in malaria in pregnancy: recent insights into mechanisms and prevention approaches. Frontiers in Immunology. 2021;12:621382. doi: 10.3389/fimmu.2021.621382. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Coleman BI, Ribacke U, Manary M, Bei AK, Winzeler EA, Wirth DF, Duraisingh MT. Nuclear repositioning precedes promoter accessibility and is linked to the switching frequency of a Plasmodium falciparum invasion gene. Cell Host & Microbe. 2012;12:739–750. doi: 10.1016/j.chom.2012.11.004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Del Portillo HA, Ferrer M, Brugat T, Martin-Jaular L, Langhorne J, Lacerda MVG. The role of the spleen in malaria. Cellular Microbiology. 2012;14:343–355. doi: 10.1111/j.1462-5822.2011.01741.x. [ DOI ] [ PubMed ] [ Google Scholar ] Dixon G, Liao Y, Bay LK, Matz MV. Role of gene body methylation in acclimatization and adaptation in a basal metazoan. PNAS. 2018;115:13342–13346. doi: 10.1073/pnas.1813749115. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Doolan DL, Dobaño C, Baird JK. Acquired immunity to malaria. Clinical Microbiology Reviews. 2009;22:13–36. doi: 10.1128/CMR.00025-08. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Duraisingh MT, Voss TS, Marty AJ, Duffy MF, Good RT, Thompson JK, Freitas-Junior LH, Scherf A, Crabb BS, Cowman AF. Heterochromatin silencing and locus repositioning linked to regulation of virulence genes in Plasmodium falciparum. Cell. 2005;121:13–24. doi: 10.1016/j.cell.2005.01.036. [ DOI ] [ PubMed ] [ Google Scholar ] Feng S, Cokus SJ, Zhang X, Chen PY, Bostick M, Goll MG, Hetzel J, Jain J, Strauss SH, Halpern ME, Ukomadu C, Sadler KC, Pradhan S, Pellegrini M, Jacobsen SE. Conservation and divergence of methylation patterning in plants and animals. PNAS. 2010;107:8689–8694. doi: 10.1073/pnas.1002720107. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ferrai C, de Castro IJ, Lavitas L, Chotalia M, Pombo A. Gene positioning. Cold Spring Harbor Perspectives in Biology. 2010;2:a000588. doi: 10.1101/cshperspect.a000588. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Flick K, Scholander C, Chen Q, Fernandez V, Pouvelle B, Gysin J, Wahlgren M. Role of nonimmune IgG bound to PfEMP1 in placental malaria. Science. 2001;293:2098–2100. doi: 10.1126/science.1062891. [ DOI ] [ PubMed ] [ Google Scholar ] Florini F, Visone JE, Hadjimichael E, Malpotra S, Nötzel C, Kafsack BFC, Deitsch KW. scRNA-seq reveals transcriptional plasticity of var gene expression in Plasmodium falciparum for host immune avoidance. Nature Microbiology. 2025;10:1417–1430. doi: 10.1038/s41564-025-02008-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Freitas-Junior LH, Hernandez-Rivas R, Ralph SA, Montiel-Condado D, Ruvalcaba-Salazar OK, Rojas-Meza AP, Mâncio-Silva L, Leal-Silvestre RJ, Gontijo AM, Shorte S, Scherf A. Telomeric heterochromatin propagation and histone acetylation control mutually exclusive expression of antigenic variation genes in malaria parasites. Cell. 2005;121:25–36. doi: 10.1016/j.cell.2005.01.037. [ DOI ] [ PubMed ] [ Google Scholar ] Fried M, Duffy PE. Adherence of Plasmodium falciparum to chondroitin sulfate A in the human placenta. Science. 1996;272:1502–1504. doi: 10.1126/science.272.5267.1502. [ DOI ] [ PubMed ] [ Google Scholar ] Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, Ferrin TE. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Science. 2018;27:14–25. doi: 10.1002/pro.3235. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gupta MK, Lenz T, Le Roch KG. Chromosomes conformation capture coupled with next-generation sequencing (Hi-C) in Plasmodium falciparum. Methods in Molecular Biology. 2021;2369:15–25. doi: 10.1007/978-1-0716-1681-9_2. [ DOI ] [ PubMed ] [ Google Scholar ] Hammam E, Ananda G, Sinha A, Scheidig-Benatar C, Bohec M, Preiser PR, Dedon PC, Scherf A, Vembar SS. Discovery of a new predominant cytosine DNA modification that is linked to gene expression in malaria parasites. Nucleic Acids Research. 2020;48:184–199. doi: 10.1093/nar/gkz1093. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hollin T, Le Roch KG. From genes to transcripts, a tightly regulated journey in Plasmodium. Frontiers in Cellular and Infection Microbiology. 2020;10:618454. doi: 10.3389/fcimb.2020.618454. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Howitt CA, Wilinski D, Llinás M, Templeton TJ, Dzikowski R, Deitsch KW. Clonally variant gene families in Plasmodium falciparum share a common activation factor. Molecular Microbiology. 2009;73:1171–1185. doi: 10.1111/j.1365-2958.2009.06846.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jjingo D, Conley AB, Yi SV, Lunyak VV, Jordan IK. On the presence and role of human gene-body DNA methylation. Oncotarget. 2012;3:462–474. doi: 10.18632/oncotarget.497. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Joergensen L, Bengtsson DC, Bengtsson A, Ronander E, Berger SS, Turner L, Dalgaard MB, Cham GKK, Victor ME, Lavstsen T, Theander TG, Arnot DE, Jensen ATR. Surface co-expression of two different PfEMP1 antigens on single Plasmodium falciparum-infected erythrocytes facilitates binding to ICAM1 and PECAM1. PLOS Pathogens. 2010;6:e1001083. doi: 10.1371/journal.ppat.1001083. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature Methods. 2015;12:357–360. doi: 10.1038/nmeth.3317. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lambros C, Vanderberg JP. Synchronization of Plasmodium falciparum erythrocytic stages in culture. The Journal of Parasitology. 1979;65:418–420. [ PubMed ] [ Google Scholar ] Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lee W-C, Russell B, Rénia L. Sticking for a cause: the falciparum malaria parasites cytoadherence paradigm. Frontiers in Immunology. 2019;10:1444. doi: 10.3389/fimmu.2019.01444. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lenz T. HiCplotter. ca5c777GitHub. 2025a https://github.com/tlenz88/HiCplotter Lenz T. Chrom3D. e0ddea4GitHub. 2025b https://github.com/tlenz88/Chrom3D Lenz T. SimpleSeq. GitHub. 2026 https://github.com/tlenz88/SimpleSeq Le Roch KG, Zhou Y, Blair PL, Grainger M, Moch JK, Haynes JD, De La Vega P, Holder AA, Batalov S, Carucci DJ, Winzeler EA. Discovery of gene function by expression profiling of the malaria parasite life cycle. Science. 2003;301:1503–1508. doi: 10.1126/science.1087025. [ DOI ] [ PubMed ] [ Google Scholar ] Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079. doi: 10.1093/bioinformatics/btp352. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liang Z, Shen L, Cui X, Bao S, Geng Y, Yu G, Liang F, Xie S, Lu T, Gu X, Yu H. DNA N-adenine methylation in Arabidopsis thaliana. Developmental Cell. 2018;45:406–416. doi: 10.1016/j.devcel.2018.03.012. [ DOI ] [ PubMed ] [ Google Scholar ] Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462:315–322. doi: 10.1038/nature08514. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Liu T, Schiller B, Doherty P. MACS: model-based analysis for chip-seq. GitHub. 2023 https://github.com/macs3-project/MACS/ Lopez-Rubio JJ, Gontijo AM, Nunes MC, Issar N, Hernandez Rivas R, Scherf A. 5’ flanking region of var genes nucleate histone modification patterns linked to phenotypic inheritance of virulence traits in malaria parasites. Molecular Microbiology. 2007;66:1296–1305. doi: 10.1111/j.1365-2958.2007.06009.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lopez-Rubio JJ, Mancio-Silva L, Scherf A. Genome-wide analysis of heterochromatin associates clonally variant gene regulation with perinuclear repressive centers in malaria parasites. Cell Host & Microbe. 2009;5:179–190. doi: 10.1016/j.chom.2008.12.012. [ DOI ] [ PubMed ] [ Google Scholar ] Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15:550. doi: 10.1186/s13059-014-0550-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lucky AB, Wang C, Li X, Chim-Ong A, Adapa SR, Quinlivan EP, Jiang R, Cui L, Miao J. Characterization of the dual role of Plasmodium falciparum DNA methyltransferase in regulating transcription and translation. Nucleic Acids Research. 2023;51:3918–3933. doi: 10.1093/nar/gkad248. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Luo GZ, Blanco MA, Greer EL, He C, Shi Y. DNA N(6)-methyladenine: a new epigenetic mark in eukaryotes? Nature Reviews. Molecular Cell Biology. 2015;16:705–710. doi: 10.1038/nrm4076. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Maher SP, Bakowski MA, Vantaux A, Flannery EL, Andolina C, Gupta M, Antonova-Koch Y, Argomaniz M, Cabrera-Mora M, Campo B, Chao AT, Chatterjee AK, Cheng WT, Chuenchob E, Cooper CA, Cottier K, Galinski MR, Harupa-Chung A, Ji H, Joseph SB, Lenz T, Lonardi S, Matheson J, Mikolajczak SA, Moeller T, Orban A, Padín-Irizarry V, Pan K, Péneau J, Prudhomme J, Roesch C, Ruberto AA, Sabnis SS, Saney CL, Sattabongkot J, Sereshki S, Suriyakan S, Ubalee R, Wang Y, Wasisakun P, Yin J, Popovici J, McNamara CW, Joyner CJ, Nosten F, Witkowski B, Le Roch KG, Kyle DE. A drug repurposing approach reveals targetable epigenetic pathways in plasmodium vivax hypnozoites. bioRxiv. 2023 doi: 10.1101/2023.01.31.526483. [ DOI ] [ PMC free article ] [ PubMed ] Maier AG, Rug M, O’Neill MT, Brown M, Chakravorty S, Szestak T, Chesson J, Wu Y, Hughes K, Coppel RL, Newbold C, Beeson JG, Craig A, Crabb BS, Cowman AF. Exported proteins required for virulence and rigidity of Plasmodium falciparum-infected human erythrocytes. Cell. 2008;134:48–61. doi: 10.1016/j.cell.2008.04.051. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Michel-Todó L, Bancells C, Casas-Vila N, Rovira-Graells N, Hernández-Ferrer C, González JR, Cortés A. Patterns of Heterochromatin transitions linked to changes in the expression of Plasmodium falciparum clonally variant genes. Microbiology Spectrum. 2023;11:e0304922. doi: 10.1128/spectrum.03049-22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mok BW, Ribacke U, Rasti N, Kironde F, Chen Q, Nilsson P, Wahlgren M. Default pathway of var2csa switching and translational repression in Plasmodium falciparum. PLOS ONE. 2008;3:e1982. doi: 10.1371/journal.pone.0001982. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Muyle AM, Seymour DK, Lv Y, Huettel B, Gaut BS. Gene body methylation in plants: mechanisms, functions, and important implications for understanding evolutionary processes. Genome Biology and Evolution. 2022;14:evac038. doi: 10.1093/gbe/evac038. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nardella F, Halby L, Hammam E, Erdmann D, Cadet-Daniel V, Peronet R, Ménard D, Witkowski B, Mecheri S, Scherf A, Arimondo PB. DNA methylation bisubstrate inhibitors are fast-acting drugs active against artemisinin-resistant Plasmodium falciparum parasites. ACS Central Science. 2020;6:16–21. doi: 10.1021/acscentsci.9b00874. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ninova M, Fejes Tóth K, Aravin AA. The control of gene expression and cell identity by H3K9 trimethylation. Development. 2019;146:dev181180. doi: 10.1242/dev.181180. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nyirjesy P, Kavasya T, Axelrod P, Fischer PR. Malaria during pregnancy: neonatal morbidity and mortality and the efficacy of chloroquine chemoprophylaxis. Clinical Infectious Diseases. 1993;16:127–132. doi: 10.1093/clinids/16.1.127. [ DOI ] [ PubMed ] [ Google Scholar ] Ponts N, Fu L, Harris EY, Zhang J, Chung D-WD, Cervantes MC, Prudhomme J, Atanasova-Penichon V, Zehraoui E, Bunnik EM, Rodrigues EM, Lonardi S, Hicks GR, Wang Y, Le Roch KG. Genome-wide mapping of DNA methylation in the human malaria parasite Plasmodium falciparum. Cell Host & Microbe. 2013;14:696–706. doi: 10.1016/j.chom.2013.11.007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–842. doi: 10.1093/bioinformatics/btq033. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ralph SA, Scheidig-Benatar C, Scherf A. Antigenic variation in Plasmodium falciparum is associated with movement of var loci between subnuclear locations. PNAS. 2005;102:5414–5419. doi: 10.1073/pnas.0408883102. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research. 2016;44:W160–W165. doi: 10.1093/nar/gkw257. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Roberts DJ, Craig AG, Berendt AR, Pinches R, Nash G, Marsh K, Newbold CI. Rapid switching to multiple antigenic and adhesive phenotypes in malaria. Nature. 1992;357:689–692. doi: 10.1038/357689a0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rogerson SJ, Beeson JG. The placenta in malaria: mechanisms of infection, disease and foetal morbidity. Annals of Tropical Medicine and Parasitology. 1999;93 Suppl 1:S35–S42. doi: 10.1080/00034989957727. [ DOI ] [ PubMed ] [ Google Scholar ] Salanti A, Dahlbäck M, Turner L, Nielsen MA, Barfod L, Magistrado P, Jensen ATR, Lavstsen T, Ofori MF, Marsh K, Hviid L, Theander TG. Evidence for the involvement of VAR2CSA in pregnancy-associated malaria. The Journal of Experimental Medicine. 2004;200:1197–1203. doi: 10.1084/jem.20041579. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Salcedo-Amaya AM, van Driel MA, Alako BT, Trelle MB, van den Elzen AMG, Cohen AM, Janssen-Megens EM, van de Vegte-Bolmer M, Selzer RR, Iniguez AL, Green RD, Sauerwein RW, Jensen ON, Stunnenberg HG. Dynamic histone H3 epigenome marking during the intraerythrocytic cycle of Plasmodium falciparum. PNAS. 2009;106:9655–9660. doi: 10.1073/pnas.0902515106. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Scherf A, Hernandez-Rivas R, Buffet P, Bottius E, Benatar C, Pouvelle B, Gysin J, Lanzer M. 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. 1998;17:5418–5426. doi: 10.1093/emboj/17.18.5418. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Scherf A, Lopez-Rubio JJ, Riviere L. Antigenic variation in Plasmodium falciparum. Annual Review of Microbiology. 2008;62:445–470. doi: 10.1146/annurev.micro.61.080706.093134. [ DOI ] [ PubMed ] [ Google Scholar ] Servant N, Varoquaux N, Lajoie BR, Viara E, Chen CJ, Vert JP, Heard E, Dekker J, Barillot E. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biology. 2015;16:259. doi: 10.1186/s13059-015-0831-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Staalsoe T, Nielsen MA, Vestergaard LS, Jensen ATR, Theander TG, Hviid L. In vitro selection of Plasmodium falciparum 3D7 for expression of variant surface antigens associated with severe malaria in African children. Parasite Immunology. 2003;25:421–427. doi: 10.1111/j.1365-3024.2003.00652.x. [ DOI ] [ PubMed ] [ Google Scholar ] Steketee RW, Nahlen BL, Parise ME, Menendez C. The burden of malaria in pregnancy in malaria-endemic areas. The American Journal of Tropical Medicine and Hygiene. 2001;64:28–35. doi: 10.4269/ajtmh.2001.64.28. [ DOI ] [ PubMed ] [ Google Scholar ] Trager W, Jensen JB. Human malaria parasites in continuous culture. Science. 1976;193:673–675. doi: 10.1126/science.781840. [ DOI ] [ PubMed ] [ Google Scholar ] Varoquaux N, Ay F, Noble WS, Vert JP. A statistical approach for inferring the 3D structure of the genome. Bioinformatics. 2014;30:i26–i33. doi: 10.1093/bioinformatics/btu268. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wahlgren M, Goel S, Akhouri RR. Variant surface antigens of Plasmodium falciparum and their roles in severe malaria. Nature Reviews. Microbiology. 2017;15:479–491. doi: 10.1038/nrmicro.2017.47. [ DOI ] [ PubMed ] [ Google Scholar ] Wion D, Casadesús J. N6-methyl-adenine: an epigenetic signal for DNA-protein interactions. Nature Reviews. Microbiology. 2006;4:183–192. doi: 10.1038/nrmicro1350. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Xiao CL, Zhu S, He M, Chen D, Zhang Q, Chen Y, Yu G, Liu J, Xie SQ, Luo F, Liang Z, Wang DP, Bo XC, Gu XF, Wang K, Yan GR. N6-Methyladenine DNA modification in the human genome. Molecular Cell. 2018;71:306–318. doi: 10.1016/j.molcel.2018.06.015. [ DOI ] [ PubMed ] [ Google Scholar ] Yang T, Zhang F, Yardımcı GG, Song F, Hardison RC, Noble WS, Yue F, Li Q. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Research. 2017;27:1939–1949. doi: 10.1101/gr.220640.117. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zemach A, McDaniel IE, Silva P, Zilberman D. Genome-wide evolutionary analysis of eukaryotic DNA methylation. Science. 2010;328:916–919. doi: 10.1126/science.1186366. [ DOI ] [ PubMed ] [ Google Scholar ] Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SWL, Chen H, Henderson IR, Shinn P, Pellegrini M, Jacobsen SE, Ecker JR. Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell. 2006;126:1189–1201. doi: 10.1016/j.cell.2006.08.003. [ DOI ] [ PubMed ] [ Google Scholar ] Zhang G, Huang H, Liu D, Cheng Y, Liu X, Zhang W, Yin R, Zhang D, Zhang P, Liu J, Li C, Liu B, Luo Y, Zhu Y, Zhang N, He S, He C, Wang H, Chen D. N6-methyladenine DNA modification in Drosophila. Cell. 2015;161:893–906. doi: 10.1016/j.cell.2015.04.018. [ DOI ] [ PubMed ] [ Google Scholar ] Zhang X, Florini F, Visone JE, Lionardi I, Gross MR, Patel V, Deitsch KW. A coordinated transcriptional switching network mediates antigenic variation of human malaria parasites. eLife. 2022;11:e83840. doi: 10.7554/eLife.83840. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zilberman D. An evolutionary case for functional gene body methylation in plants and animals. Genome Biology. 2017;18:87. doi: 10.1186/s13059-017-1230-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] eLife. doi: 10.7554/eLife.93632.3.sa0 eLife Assessment Urszula Krzych Urszula Krzych 1 Walter Reed Army Institute of Research, United States Reviewing Editor Find articles by Urszula Krzych 1 Author information Article notes Copyright and License information 1 Walter Reed Army Institute of Research, United States Roles Urszula Krzych : Reviewing Editor Keywords: Convincing Keywords: Valuable PMC Copyright notice This interesting study presents a multi-OMICs approach to unify different lines of evidence regarding the epigenetic regulation of the key virulence factor causing placental malaria during P. falciparum infection. Most results are confirmatory of previous observations; nonetheless, the claims are supported by convincing evidence. The combinatorial approach chosen here is unprecedented and therefore provides valuable new data. In addition, the comparative investigation of different DNA methylation modifications is novel and disproves a direct role in var gene regulation. eLife. doi: 10.7554/eLife.93632.3.sa1 Reviewer #2 (Public review): Anonymous Anonymous Reviewer Find articles by Anonymous Author information Copyright and License information Roles Anonymous : Reviewer PMC Copyright notice Summary: Dr Lenz and colleagues report on their in vitro studies comparing gene transcription and epigenetic modifications in Plasmodium falciparum NF54 parasites selected or not selected for adhesion of the infected erythrocytes (IEs) to the placental IE adhesion receptor chondroitin sulfate A (CSA). The authors report that selection led to preferential transcription of var2csa, the gene that encodes the VAR2CSA-type PfEMP1 well-established as the PfEMP1 mediating IE adhesion to CSA. They confirm that transcriptional activation of var2csa is associated with distinct depletion of H3K9me3 marks and that transcriptional activation is linked to repositioning of var2csa. Finally, they provide preliminary evidence potentially implicating 5mC in transcriptional regulation of var2csa. Strengths: The study confirms previously reported features of gene transcription and epigenetic modifications in Plasmodium falciparum . Weaknesses: No major new finding is reported. Comments on revisions: I suggest replacing the term "pregnancy-associated malaria (PAM)" with the more current and more precise term "placental malaria (PM)" throughout the manuscript. L. 59-60: "... shielding of the parasite antigens expressed on pRBC surfaces by leukocytes...". It is unclear to me what this means - I suggest a rephrasing for improved clarity. L. 144-6: Please provide a reference for the primary antibody reagent used. eLife. doi: 10.7554/eLife.93632.3.sa2 Reviewer #3 (Public review): Anonymous Anonymous Reviewer Find articles by Anonymous Author information Copyright and License information Roles Anonymous : Reviewer PMC Copyright notice Summary: The manuscript by Lenz et al. seeks to investigate molecular mechanisms directing virulence gene expression in the malaria parasite Plasmodium falciparum . The report provides a detailed characterization of the phenotypic and epigenetic features of a var2csa expressing parasite population, the key virulence gene causing the clinical syndrome of placental malaria. Novel evidence supporting the concept that active expression of this gene is associated with nuclear repositioning away from suppressive regions of chromatin is presented. In addition, the authors conducted a preliminary characterization of different forms of DNA methylation, suggesting that 5-methylcytosine is enriched in virulence genes, but does not correlate with their activation or repression. However, a trend towards higher enrichment of 5-methylcytosine in highly active as opposed to inactive genes from the core genome was reported, although this observation requires further validation. Strengths: The concise study provides a well documented and controlled set of experiments utilizing state-of-the-art OMICs methodologies including ChIPseq, RNAseq, chromatin-conformation capture (Hi-C) and DNA methylation (MeDIPseq) to generate deep insight into the epigenetic regulation of the key virulence factor of P. falciparum . The study unifies different lines of evidence and thereby contributes to a clearer understanding of the mechanisms underlying active expression of var2csa. Weaknesses: Although all experiments appear to have been rigorously conducted and documented with appropriate replicates and controls, the study is overall lacking statistical support from individual analyses of the biological replicates. In particular, the key novel result suggesting increased distance of the active var2csa gene from regions of heterochromatin as assessed by chromatin conformation capture would benefit from further analysis by comparison with other genetic loci. This also applies to the differential DNA methylation patterns, which should be dissected in more detail to support any association with gene expression or intron function. eLife. 2026 Apr 15;13:RP93632. doi: 10.7554/eLife.93632.3.sa3 Author response Todd Lenz Todd Lenz 1 University of California, Riverside, Riverside, United States Author Find articles by Todd Lenz 1 , Madle Sirel Madle Sirel 2 Karolinska Institutet, Stockholm, Sweden Author Find articles by Madle Sirel 2 , Hannes Hoppe Hannes Hoppe 3 Karolinska Institutet, Stockholm, Sweden Author Find articles by Hannes Hoppe 3 , Sulman Shafeeq Sulman Shafeeq 4 Karolinska Institutet, Stockholm, Switzerland Author Find articles by Sulman Shafeeq 4 , Karine G Le Roch Karine G Le Roch 5 University of California, Riverside, Riverside, United States Author Find articles by Karine G Le Roch 5 , Ulf Ribacke Ulf Ribacke 6 Uppsala University, Stockholm, Sweden Author Find articles by Ulf Ribacke 6 Author information Article notes Copyright and License information 1 University of California, Riverside, Riverside, United States 2 Karolinska Institutet, Stockholm, Sweden 3 Karolinska Institutet, Stockholm, Sweden 4 Karolinska Institutet, Stockholm, Switzerland 5 University of California, Riverside, Riverside, United States 6 Uppsala University, Stockholm, Sweden Roles Todd Lenz : Author Madle Sirel : Author Hannes Hoppe : Author Sulman Shafeeq : Author Karine G Le Roch : Author Ulf Ribacke : Author Collection date 2026. PMC Copyright notice The following is the authors’ response to the original reviews Public Reviews: Reviewer #1 (Public Review): Summary: The manuscript by Lenz and colleagues describes a detailed examination of the epigenetic changes and alterations in subnuclear arrangement associated with the activation of a unique var gene associated with placental malaria in the human malaria parasite Plasmodium falciparum . The var gene family has been heavily studied over the last couple of decades due to its importance in the pathogenesis of malaria, its role in immune avoidance, and the unique transcriptional regulation that it displays. Aspects of how mutually exclusive expression is regulated have been described by several groups and are now known to include histone modifications, subnuclear chromosomal arrangement, and in the case of var2csa, regulation at the level of translation. Here the authors apply several methods to confirm previous observations and to consider a possible role for DNA methylation. They demonstrate that the histone mark H3K9me3 is found at the promoters of silent genes, var2csa moves away from other var gene clusters when activated, and while DNA methylation is detectable at var genes, it does not seem to correlate with transcriptional activation/silencing. Overall, the data and approach appear sound. Strengths: The authors employ the latest methods for epigenetic analysis of histone marks, transcriptomic analysis, DNA methylation, and chromosome conformation. They also use strong selection pressure to be able to examine the gene var2csa in its active and silent state. This is likely the only paper that has used all these methods in parallel to examine var gene regulation. Thus, the paper provides readers with confidence in the interpretation of independent methods that address a similar subject. We thank the reviewer for this positive assessment. We appreciate the recognition that our study combines complementary approaches including histone mark profiling, transcriptomic analysis, DNA methylation mapping, and chromosome conformation capture in parallel to the use of strong population selection that enables a controlled comparison of var2csa in active versus silent states. We agree that the convergence of independent methods strengthens confidence in the interpretation. Weaknesses: The primary weakness of the paper is that none of the conclusions are novel and the overall conclusions do not shed much new light on the topic of var gene regulation or antigenic variation in malaria parasites. The paper is largely confirmatory. The roles of H3K9me3 and subnuclear localization in var gene regulation are well established by many groups (including for var2csa), albeit in some cases using alternative methods. The only truly unique aspect of the manuscript is the description of 5mC at var2csa when the gene is transcriptionally active or silent. Here the authors demonstrate that the mark has no clear role in transcriptional activation or silencing, however, this will not be surprising to many in the field who have previously cast doubt on a regulatory role for this modification. While we agree that some individual features of var gene regulation, including H3K9me3 enrichment, have been described previously, our study integrate for the first time several layer of gene regulation on the clinically important var2csa locus using phenotypically homogeneous placental-binding parasite populations. As expected, var2csa activation coincided with a loss of H3K9me3 at the locus. However, using high-resolution chromatin conformation capture (to our knowledge, this experiment had never been applied to phenotypically homogeneous parasite populations), we quantified the repositioning of var2csa relative to heterochromatic telomeric clusters. We further assessed DNA methylation in this framework and show that 5-methylcytosine is broadly present at var genes and may correlate with transcript level, but is uncoupled from transcriptional activation, repression, and switching. Together, these findings integrate transcriptional state, chromatin marks, and 3D genome organization at var2csa and argue against models in which 5mC acts as a primary regulatory switch for var gene expression. Reviewer #2 (Public Review): Summary: Dr Lenz and colleagues report on their in vitro studies comparing gene transcription and epigenetic modifications in Plasmodium falciparum NF54 parasites selected or not selected for adhesion of the infected erythrocytes (IEs) to the placental IE adhesion receptor chondroitin sulfate A (CSA). The authors report that selection led to preferential transcription of var2csa, the gene that encodes the VAR2CSA-type PfEMP1 well-established as the PfEMP1 mediating IE adhesion to CSA. They confirm that transcriptional activation of var2csa is associated with distinct depletion of H3K9me3 marks and that transcriptional activation is linked to repositioning of var2csa. Finally, they provide preliminary evidence potentially implicating 5mC in the transcriptional regulation of var2csa. Strengths: The study confirms previously reported features of gene transcription and epigenetic modifications in Plasmodium falciparum . As stated in our response to Reviewer 1, our study combines, for the first time, complementary approaches, including transcriptomic analysis, histone mark profiling, DNA methylation mapping, and chromosome conformation capture, together with strong population selection to enable a controlled comparison of var2csa in active versus silent states. Weaknesses: No major new finding is reported. The strength of the evidence presented is mostly solid, although certain elements, e.g., the role of 5mC in transcriptional regulation of var2cs, appear preliminary and incomplete. While we agree that no major new finding is reported, we were able to use for the first time a high-resolution chromatin conformation capture method to quantify the repositioning of var2csa relative to heterochromatic telomeric clusters. We also further assessed that 5-methylcytosine is present at var genes and may correlate with transcript level, but is uncoupled from transcriptional activation, repression, and switching. Together, these findings integrate for the first time transcriptional state, chromatin marks, and 3D genome organization at var2csa and argue against models in which 5mC acts as a primary regulatory switch for var gene expression. Recommendations for the authors: Reviewer #1 (Recommendations for the Authors): (1) In the second paragraph of the introduction, the authors state "....such as the shielding of the parasite antigens expressed on pRBC surfaces by other cells and the evasion of splenic clearance (8)." What does "other cells" mean here? We thank the reviewer for this comment. We have clarified the cell type in the text. (2) In their interpretation of the Hi-C data, the authors conclude that the var2csa expressing parasites display "tighter heterochromatin control of var gene regions" and "interactions around other silent var genes were increased" and "an overall compaction of telomere ends and var gene-containing intrachromosomal regions". While the data appear to show that this is true when they compare the two parasite populations, I am concerned that the authors might be misinterpreting the data. It is important to note that the NF54CSAh line is heavily selected to be nearly entirely homogeneous for var gene expression while the NF54 line is exceptionally heterogeneous. This is shown in Figure 1G. Thus, any chromosomal arrangement specific for var gene expression in the unselected NF54 population will be similarly heterogeneous and therefore could appear less tight. In other words, interactions around silent var genes and overall compaction of telomere ends might be identical between individual parasites within these populations, but appear tighter or more compact in the var2csa expressing line simply because it is a homogeneous population. Perhaps this is what the authors meant to convey, however as currently written, it seems that they conclude the expression of var2csa results in a unique change in chromosome organization. A better comparison would be two populations homogeneously expressing different var genes, one expressing var2csa and one expressing an alternative var gene. Such lines can be generated through clonal isolation or selection for binding to a different host receptor. We thank the reviewer for this comment. The reviewer is correct, and we have revised the Discussion section of the manuscript to clarify this issue. (3) The title of the last section of the Results is "Distribution of DNA methylation influences gene expression overall but does not mediate transcriptional activation and switching in antigenic variation". This is an overstatement. The authors show that DNA methylation is absent at var gene promoter regions and enriched in coding regions, but there they provide no evidence that it "influences gene expression overall". This is speculation. Lastly, when the authors examined 5mC occupancy across genes, did they normalize for GC content of the DNA sequences? GC content is known to increase dramatically in coding regions (particularly in var genes) and thus could explain the distribution of this mark. If the authors corrected for this, they should directly state this in the results section. If they did not, they should explain why they don't think this property of the P. falciparum genome explains the distribution of 5mC. There is often a misconception in the field that DNA methylation is primarily confined to CpG islands in promoter regions and functions mainly as a repressor of transcription. However, in contrast to promoter methylation, methylation within gene bodies is generally associated with higher levels of gene expression, suggesting a role in facilitating transcription elongation. Gene-body methylation can also repress internal promoters, thereby preventing spurious transcription initiation within the gene. In addition, it has been shown to influence alternative splicing by affecting RNA polymerase II elongation kinetics. We propose that, in Plasmodium , DNA methylation may be associated with priming genes for transcriptional activity rather than repressing transcription. Specifically, higher methylation levels may facilitate recruitment of the RNA polymerase II transcriptional machinery to enable transcription. In Figure 4B, we observe higher levels of DNA methylation in the first exon of highly expressed genes in both the NF54 and NF54CSAh lines. Interestingly, we also detect high levels of methylation across most introns of the var genes, introns that must be transcribed, cannot be degraded, and are essential for var gene regulation, suggesting a possible sequence-recognition function. We have edited the manuscript to improve clarity. (4) In the legend to Figure 3D, the authors state that the centromeres are shown in blue, however in the figure they appear to be grey while var2csa is blue. We have revised the figure legend accordingly. Reviewer #2 (Recommendations For The Authors): I recommend using the term "transcription" rather than "expression" when discussing events at the gene level. We have revised the manuscript accordingly. I also recommend using the term "adhesion" to describe the physical interaction between infected erythrocytes and adhesion receptors rather than adherence", which should be reserved to describe non-physical affinity (e.g., beliefs, faith). We have revised the manuscript accordingly. Important new evidence regarding transcriptional regulation of var genes in general and var2csa in particular should be discussed and cited. We have revised the manuscript accordingly. Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Citations Le Roch Lab 2023. Epigenetics and chromatin structure regulate var2csa expression and the placental binding phenotype in Plasmodium falciparum. NCBI BioProject. PRJNA947327 [ DOI ] [ PMC free article ] [ PubMed ] Supplementary Materials Supplementary file 1. RNA-seq analysis. elife-93632-supp1.xlsx (314.1KB, xlsx) Supplementary file 2. ChiP-seq analysis. elife-93632-supp2.xlsx (526.5KB, xlsx) Supplementary file 3. MeDIP-seq analysis. elife-93632-supp3.xlsx (1.1MB, xlsx) MDAR checklist elife-93632-mdarchecklist1.docx (90.9KB, docx) Data Availability Statement No new algorithms or tools were created to process or visualize data contained in this manuscript and any scripts used in the analysis of data use previously established packages. Scripts used to process data and generate figures for this manuscript are freely available on the personal GitHub page of Todd Lenz ( https://github.com/tlenz88/ ). Data processing and plotting of RNA-seq, ChIP-seq, and MeDIP-seq data was accomplished using the SimpleSeq pipeline with accompanying tools EasyDGE and EasyPeaks found on the SimpleSeq repository ( Lenz, 2026 ). Plotting of HiC heatmaps and 3D models was accomplished using the scripts found on the HiCplotter ( Lenz, 2025a ) and Chrom3D ( Lenz, 2025b ) repositories, respectively. The sequencing datasets generated and analyzed during the current study are available in the SRA repository under the accession number PRJNA947327. The following dataset was generated: Le Roch Lab 2023. Epigenetics and chromatin structure regulate var2csa expression and the placental binding phenotype in Plasmodium falciparum. NCBI BioProject. 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