Parasite culture

Plasmodium falciparum 3D7, obtained from the Malaria Research and Reference Reagent Resource Center (MR4), was cultured in O+ human erythrocytes at 4% haematocrit in RPMI 1640 supplemented with 0.25% albumax (Invitrogen), 5% heat-inactivated human serum and 0.25% sodium bicarbonate in gassed chambers at 1% O₂, 3% CO₂, and 96% N₂ [43]. Parasite count was assessed on thin blood smears stained with Hemacolor (Merck).

RNA structure probing and poly(A) enrichment

An asynchronous culture was grown up to 1.2x10¹⁰ parasites as previously described [43]. Erythrocytes were pelleted and then incubated for 30 minutes either with 10 mM DMS (dimethyl sulfate, Sigma) in ethanol or 20 mM NAI (2-methylnicotinic acid imidazolide) in anhydrous DMSO, at 37°C, agitated at 200 rpm, in the same gas mixture described above. The NAI was prepared as previously described [44]. The reaction was stopped with 40 mM dithiothreitol (DTT). Parasites were extracted by erythrocyte lysis using 1v of 0.2% saponin in PBS and then washed 3 times in ice-cold PBS. RNA was extracted using Qiagen RNeasy plus mini kit (Qiagen) following manufacturer’s instructions and then submitted to DNase treatment for 15 minutes (Qiagen). The total RNA recovered was split into samples of 5 μg each and enriched in poly(A) transcripts using NEBNext Poly(A) mRNA magnetic isolation module (NEB) according to manufacturer’s instructions.

Visualisation of 5.8S rRNA structure using RT-stalling

RT stalling was performed using a primer targeting the 5.8S rRNA gene (PF3D7_0531800). Briefly, 1.5 μg of total RNA from DMS, NAI and DMSO-treated cultures in 5.5 μl nuclease-free water was mixed with 1 μl of 5 μM Pfa_5.8S primer labelled at the 5’ end with Cy5 [Cy5] ATTTTCTGTAGGAGTACCACT (Eurofins Genomics) in 3 μl of reverse transcription buffer (20 mM Tris, pH 7.5, 4 mM MgCl₂, 1 mM DTT, 0.5 mM dNTPs, 150 mM LiCl in final). For the sequencing reactions (A/G/C/T), an extra 1 μl of 10 mM dideoxynucleoside triphosphate (Roche) was added to replace 1 μl of nuclease-free water. All samples were heated at 75°C for 3 min, 35°C for 5 min and then held at 50°C. Next, 0.5 μl of Superscript III (200U/μl) (Thermo Scientific) was added and reverse transcription was performed at 50°C for 15 min, followed by addition of 0.5 μl of 2M NaOH at 95°C for 10 min to degrade the RNA template. After reverse transcription, 10 μl of 2x formamide orange G dye (94% deionized formamide, 20 mM Tris pH 7.5, 20 mM EDTA, orange G dye) was added and heated at 95°C for 3 min before loading into a pre-heated (at 90W for 45 min) 8% denaturing polyacrylamide gel. The gel was run at constant power of 90 W for 90 min after loading 3.2 μl of each sample. Fujifilm FLA 9000 was used to scan the gel.

Library preparation and sequencing details

Random fragmentation of polyA-enriched RNA was performed with 150 ng polyA-enriched RNA in fragmentation buffer (40 mM Tris-HCl pH 8.2, 100 mM LiCl, 30 mM MgCl2) at 95°C for 60s to generate average fragment size of ~250 nt, and then purified with RNA Clean & Concentrator (Zymo Research). 3’ dephosphorylation reactions included 7 μl fragmented sample, 1 μl of 10x PNK buffer (NEB), 1 μl of rSAP enzyme (NEB) and 1 μl of PNK enzyme (NEB), carried out at 37°C for 30 min. 3’ adapter ligation was performed by adding 3’ adapter (5’-/5rApp/NNNNNNAGATCGGAAGAGCACACGTCTG/3SpC3/-3’ with 1:5 molar ratio of RNA to 3’ adapter), PEG 8000 (17.5% final) and T4 RNA ligase 2 (NEB) in 1x T4 RNA ligase buffer at 25°C for 1 h. Excess adapter was digested by adding 1 μl RecJf (NEB) and 1 μl 5’deadenylase (NEB) at 30°C for 30 min and removed by RNA Clean & Concentrator (Zymo Research). Reverse transcription reactions including the ligated RNA above, reverse primer (5’-CAGACGTGTGCTCTTCCGATCT-3’ with 1:2.5 molar ratio of RNA to reverse primer), reverse transcription buffer (see RTS method above) and Superscript III (1U/μl final) (Thermo Scientific) were performed at 75°C for 3 min, 35°C for 5 min, then 50°C for 50 min. In this step, Superscript III should be added before the 50°C and after reverse transcription, NaOH (0.1 M final) was added at 95 °C for 10 min for RNA degradation, followed by mixing with 5 μl 1M Tris-HCl (pH 7.5) before RNA Clean & Concentrator (Zymo Research). The purified cDNA was then ligated with 5’ adapter (5’/5Phos/AGATCGGAAGAGCGTCGTGTAGCTCTTCCGATCTN10/3SpC3/-3’ with 1:20 molar ratio of RNA to 5’adapter) using Quick Ligation Kit (NEB) at 37°C overnight. Ligated reaction mixture was heated to 95°C for 10 min for inactivation and mixed with 1 volume of 2x formamide orange G dye before purifying with 10% denaturing urea-TBE acrylamide gel (Thermo Scientific) at 300V for 20 min. The size of 100–400 nt was sliced, crushed and soaked in 1x TEN 250 buffer (1x TE pH 7.4, 0.25 M NaCl) with incubation at 80°C for 30 min with 1300 rpm shaking, and then purified with RNA Clean & Concentrator (Zymo Research). The purified ssDNA was mixed with forward primer (5’-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT-3’, 0.5 μM final) and reverse primer with different indexes (5’-CAAGCAGAAGACGGCATACGAGAT-(6 nt index seq)-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3’, 0.5 μM final) for PCR amplification by using KAPA HiFi HotStart ReadyMix (KAPA Biosystems). The PCR program included 95°C for 3 min, 16 cycles of 3 steps (98°C: 20s, 68°C: 15s, 72°C: 40s), 72°C for 90s, then cooled to 4°C for size selection. PCR samples were resolved on a 1.8% TAE agarose gel at 120 V for 55 min. The size of 150–400 nt was cut and recovered by Zymo DNA agarose gel extraction kit (Zymo Research). DNA libraries were quantified, pooled and sequenced on the Illumina Hiseq System in 150 bp paired-end (PE) configuration. A more detail protocol was described in reference [45].

Structurome analysis

Forward and reverse reads from each sample were cleaned of adapter sequence contamination using reaper (v15-065) from the kraken package [46]. Paired reads were then aligned to an indexed reference transcriptome (PlasmoDB-45_Pfalciparum3D7) using Bowtie2 (v2.4.2) in paired-end mode and converted to SAM and sorted BAM files [47]. Reverse transcription stops were assessed using StructureFold2 for each individual sample [48]. Coverage and overlap of RT stop data were then computed along with cross-replicate coverage and overlap and stop correlation was assessed among all samples via StructureFold2. These data were normalised and structural reactivity data generated for each transcript with enough supporting data across samples using StructureFold2. These reactivities were supplied to RNAStructure for experimental reactivity constrained folding [49]. Resulting structures were then assessed for structural features using Forgi from the ViennaRNA package [50]. Unconstrained folding of every transcript was also performed using RNAStructure with the same parameters and assessed similarly to generate a cohort of unconstrained purely in silico folded transcripts. Parameters for the number of reads and mapping efficiency for each library are shown (Table 1).

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Table 1. Sequencing parameters for each NGS library generated in this study.

https://doi.org/10.1371/journal.pone.0270863.t001

Functional dataset analysis

Microarray datasets from parasites exposed to hyperoxia were extracted from [51], and the chloroquine-exposed dataset was extracted from [52]. Lists of genes were updated to the current genome annotation. Ribosome profiling data were obtained from [53] and translational efficiency was calculated as ribosome density per messenger RNA. The RNA decay dataset was extracted from [54] at 10hpi for rings, 24hpi for early trophozoites, 30hpi for late trophozoites and 46hpi for schizonts. Structural parameters per transcript (numbers of nucleotides involved in stems, hairpins, multi-stem loop and bulges) were normalized to transcript length before comparing these structural parameters with the translational efficiency of each gene.

Assessment of NAI and DMS as RNA structure probing chemicals for P. falciparum inside the erythrocyte host cell

We conducted in vivo structure-seq on Plasmodium falciparum: an organism with a >80% AT-rich genome, and one that lives for the majority of its lifecycle inside another host cell. It was necessary first to test and optimise the parameters for in vivo RNA probing in this unusual organism. We used two different probing agents with different nucleotide reactivity: NAI (2-methylnicotinic acid imidazolide) and DMS (dimethyl sulfate). NAI reacts with all four unpaired nucleotides while DMS selectively reacts with unpaired adenosine and cytosine. Neither of these chemicals had been used in P. falciparum before. In order to check if the chemicals could penetrate inside the parasite inside the host erythrocyte, we exposed an asynchronous culture of P. falciparum to NAI or DMS. After RNA extraction we performed a primer extension with a Cy5-conjugated primer targeting the 5.8S rRNA transcript. When a nucleotide has been modified by one of the chemicals, meaning that the nucleotide was unpaired in the original RNA, the reverse transcriptase will stall one nucleotide beforehand, stopping the elongation reaction (Fig 1A). This can be seen on a denaturing polyacrylamide sequencing gel (Fig 1B, S1 Raw images). Reading the sequence confirmed that it was identical to the PF3D7_0531800 gene sequence available on PlasmoDB [55]. In NAI and DMS probed parasites, base reactivity was seen with all four nucleotides after NAI-probing, and with only adenosine and cytosine after DMS-probing. (Modification of adenosine and cytosine was not, however, identical at all positions with DMS and NAI, because these chemical probes react with different moieties of the RNA nucleotides, and with different efficiencies.) This indicated that both chemicals could penetrate into P. falciparum, and our probing strategy could investigate the RNA structures in P. falciparum.

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Fig 1. In vivo probing of RNA structures in P. falciparum.

(A) Schematic of RNA structure probing. An asynchronous parasite culture was split into two, one exposed to NAI (+NAI) and the other exposed to DMSO as control (-NAI). Reverse transcription (RT) was performed using a 5’-Cy5 conjugated primer (dark red star) specific for the 5.8S rRNA of P. falciparum (in green). During RT, nucleotides that have been modified by NAI in the +NAI samples (red dots) will stall the reverse transcription, interrupting the elongation process (black line). Finally, elongation cDNA products are resolved on a PAGE gel. (B) Sequencing gel showing the 5.8S rRNA gene of P. falciparum. Lanes 1 to 4 are sequencing lanes where one of the four nucleotides in the reaction mix has been switched with the dideoxynucleotide, provoking RT-termination. Lanes 5 and 6 represent respectively DMS and NAI modifications on 5.8S rRNA.

https://doi.org/10.1371/journal.pone.0270863.g001

To prepare the structurome, we then extracted the total RNA from two independent asynchronous parasite cultures treated with NAI, DMS, or DMSO only, as in Fig 1, and used poly(T) magnetic beads to enrich poly(A) RNA. The enriched poly(A) fraction represented 2–3.4% of the total RNA, which is commensurate with the literature for eukaryotic cells [56]. The enriched fractions were then used to generate NGS libraries (Fig 2A). The sequencing outputs were processed using StructureFold2 and RNAStructure for experimental reactivity constrained folding [57] to determine the secondary structure of the RNA molecules. Each replicate for each chemical treatment was analysed separately. After preliminary analysis, because the distribution of structure differences between replicates was marginal and the correlation between replicates and pooled samples was good (S1 Fig), reads from both replicates were merged for subsequent analysis. To ensure structure calls of a high quality, mappings were filtered such that there were no more than 3 mismatches/indels from the reference and the first 5’ base was matching. Coverage filtering also required transcripts with complete coverage between samples (i.e. meeting the StructureFold2 default filtering threshold of an average coverage >1.0 per nucleotide throughout the transcript (S2 Fig). Structural information was more comprehensive (and agreement between replicates was also better), for the NAI-probed datasets because all four unpaired nucleotides were detected. Subsequent analysis was therefore focused on the NAI dataset as the primary in vivo structurome.

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Fig 2. RNA structurome of P. falciparum using Structure-seq.

(A) Schematic showing the Structure-seq pipeline. An asynchronous parasite culture was split into two, one exposed to NAI (+NAI) and the other exposed to DMSO as control (-NAI). Polyadenylated-enriched RNA fractions were then used to prepare the sequencing libraries in parallel. Fragmentation was performed to generate an average length (~250 nt) of RNA for sequencing followed by 3’ dephosphorylation, which replaces the 2’3’ cyclic phosphate group with a 3’-OH group for 3’ adapter ligation. Then a 3’ adapter (black line) was ligated to the 3’-OH group of dephosphorylated RNA. Next, RT of RNA molecules (orange line) was carried out using a designed reverse primer (dark blue line) at the 5’ end. During RT, nucleotides that had been modified by NAI in the +NAI samples (red dots) would stall the reverse transcription, interrupting the elongation process (pink line). Later, a 5’ adaptor (light blue line) was added by ssDNA ligation at the 3’ end of the newly synthesised cDNA strand (pink line). Finally, forward primers (brown+light blue line) and reverse primers (dark blue+red+green line) with different indexes were added by PCR for NGS and bioinformatic analysis. Reads were aligned to the PF3D7 reference genome and subsequently analysed with StructureFold2 to determine the structural reactivity sequence (transcribed as a dot/bracket sequence denoting paired/unpaired nucleotides), and with RNAStructure to predict the folding. (B) Representation of the diversity of the dataset regarding RNA families. (C) Structure-seq derived structures of glutamic acid (PF3D7_0411600), methionine (PF3D7_1339100), asparagine (PF3D7_0714700), alanine (PF3D7_0620800) and proline (PF3D7_1339200) tRNAs, all of which are similar to canonical tRNA structures, and of serine (PF3D7_041000) tRNA, which is non-canonical.

https://doi.org/10.1371/journal.pone.0270863.g002

Structure of the NAI-probed dataset

After filtering for low-coverage genes, the NAI structurome covered 51.23% of the total transcriptome of P. falciparum (2699 transcripts detected out of 5268 total). We first looked at the representation of the various families of RNA molecules. Within our dataset 95.5% of the transcripts were protein coding transcripts, 1.27% corresponded to mitochondrial transcripts (1.16% mitochondrial rRNA, 0.11% mitochondrial mRNA) and 0.94% were uncharacterised non-protein coding transcripts. Other types of non-coding RNA were also detected, 1.31% of snoRNA, 0.19% of snRNA and 0.34% of tRNA (Fig 2B). Within the protein coding transcripts, nine were encoded in the apicoplast, a subcellular organelle that is unique to apicomplexans and represents a relic chloroplast (S1 Table).

As well as the different families of transcripts, we detected 114 transcripts with splice variants (S2 Table). However, since we used a short-read technology to sequence the library, it was impossible to associate all the reads within a variant transcript to one variant. A second limitation of short-read sequencing was that it resulted in very limited coverage of untranslated regions, which in P. falciparum tend to be extremely A/U-biased (often over 90%), and also highly repetitive with many low-complexity regions. This precluded any comprehensive assessment of RNA structure in the UTRs versus CDSs of mRNAs. Nevertheless, this overall analysis indicates that our in vivo chemical probing, RNA extraction and polyA selection were robust because all the different RNA families in P. falciparum were detected, with a strong bias towards mRNAs, and if a long-read sequencing technique had been used, even the detection of transcript variants would likely have been possible.

Some RNA families have their function dictated by their structure. This is especially the case of tRNA. Within our dataset 9 tRNAs were identified. Using RNAStructure we were able to call their structures and 5 were similar to canonical eukaryotic tRNA molecules. Those tRNAs were for glutamic acid (PF3D7_0411600, PF3D7_0527700), methionine (PF3D7_1339100), asparagine (PF3D7_0714700), alanine (PF3D7_0620800) and proline (PF3D7_1339200) (Fig 2C). In the other 3 tRNAs, for valine (PF3D7_0312600), serine (PF3D7_0410100) and leucine (PF3D7_0620900), the lowest-free-energy structures predicted by RNAStructure were not canonical (S3 Fig). For some but not all of these, alternative folding with less favourable energy produced canonical structures, albeit less compatible with the data defining free/flexible bases (e.g. S3A Fig). It is theoretically possible that dynamic structural variants of some tRNAs exist in vivo, although it is unclear why some tRNAs would differ from others in the dataset. Importantly, however, tRNA structure is also dependent on nucleotide base modification such as m²A, m²G, pseudouridine, etc. [46]. Those modifications have not been assessed in this study, but it is possible that a) they would influence the apparently-noncanonical tRNA structures and b) they might cause extra RT-stalling and thus appear erroneously as ‘probed’ bases, impeding the determination of the true structures of hyper-modified RNAs such as tRNAs.

In general, opportunities to ‘verify’ the structurome were limited because of the lack of Plasmodium RNA structures obtained independently, by orthogonal experimental methods. The canonical structural model of a blood-stage P. falciparum rRNA is available from the Comparative RNA Web (CRW) site and this was used to map base reactivities obtained in vivo (S4 Fig). The result demonstrates visually the A/C reactivity of DMS versus the all-base reactivity of NAI, and makes it clear that many bases modelled as unpaired are nevertheless not highly reactive in vivo. Other factors such as protein binding and base modification probably affect their reactivity, emphasising the over-simplification imposed by in silico modelling and the extra level of information obtained by in vivo structural probing.

Divergence between in vivo RNA structures and structures modelled in silico

Using the RNAStructure algorithm, RNA structures can be predicted from primary sequence alone, or they can be predicted with the addition of base-pairing information gained experimentally. We generated both types of structure, transcriptome-wide, taking only the lowest-free-energy structure in all cases where the algorithm generated several alternatives. This was simply because, in the absence of other constraints, the lowest-free-energy structure is the most likely to form.

Firstly, we sought to discover how much these two types of structures would differ in P. falciparum, i.e. how much new information was gained by performing the in vivo structurome? We calculated the ratio of bases that differed (i.e. that were paired versus unpaired) between each in-silico-predicted and probing-informed structure, and termed this ratio the ‘divergence’. Most of the non-protein coding transcripts had relatively low divergence, meaning that they were minimally informed by probing (Table 2). This was expected for highly conserved, stereotypical structures like rRNAs and tRNAs—although the divergence for mitochondrial rRNAs, which are encoded in a highly fragmentary fashion in the P. falciparum mitochondrial genome, was notably greater than for nuclear-encoded rRNAs. By contrast, the divergence of protein coding transcripts was much higher: a median of 0.7067 for the nuclear-encoded mRNAs and 0.8117 for mitochondrial mRNAs (Table 2).

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Table 2. Distribution of ‘divergence’ normalised by transcript length between base pairing determined by Structure-seq and in silico.

https://doi.org/10.1371/journal.pone.0270863.t002

Since structure and function in non-protein coding transcripts, like tRNAs, are deeply intertwined, we then sought any relationship between folding complexity and biological function. Using the ‘divergence’ as a metric, we split the protein coding transcripts into 3 tiers: minimal value to 25^th percentile, 25^th to 75^th percentile and 75^th percentile to the highest value (Table 2). The list of transcripts was then submitted for gene ontology enrichment and using Revigo [58] we obtained broad families of GO terms and networks of terms belonging to a similar biological pathway. In tier 1, with the lowest ‘divergence’, we observed an enrichment of 21 terms, with the strongest enrichment in terms related to ribosomal functions (see GO term 7, ‘translation’, in Fig 3A, S3 Table). This was expected, since ribosomal RNA structures are highly conserved and are likely to be predicted accurately in silico with little alteration in P. falciparum. Twenty-one terms were found in tier 2. Two terms were again strongly enriched: ‘translation’ and ‘cellular amide metabolism’, and these fell into a loosely-associated collection of similar terms around ‘protein’ and ‘protein regulation’. There was a second cluster restricted to two terms around RNA splicing, and a third cluster around mitochondrian/organellar organisation (Fig 3B, S3 Table). In tier 3, with the lowest level of identity between in vivo structures and in silico predictions, only seven terms were enriched and there was only one cluster, composed of four terms strongly enriched around different kinds of amino acid metabolism (Fig 3C, S3 Table).

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Fig 3. GO terms enrichment scatterplot for protein coding transcripts, tier 1 to 3.

All plots represent the GO enrichment provided by REVIGO for (A) tier 1, (B) tier 2 and (C) tier 3. Detailed values are available in S3 Table. Each GO term is represented by a numbered circle on the plot, with placement indicating its similarity to nearby GO terms. The colour scale represents the adjusted p-value of the GO term and the size of each circle represents the log10 of the number of annotations per GO term.

https://doi.org/10.1371/journal.pone.0270863.g003

Secondly, we assessed the value of performing Structure-seq instead of just in silico prediction for modelling the shape of RNA molecules. We used ViennaFold on the P. falciparum transcriptome to fold the structures with or without reactivity data. We first aligned the dot/bracket sequences obtained from in silico folding with the Structure-seq-determined sequence of the U2 snRNA (PF3D7_1137000). It showed a difference in the pairing of 74 nucleotides out of 198, meaning a 37.4% discrepancy in the structure determined in silico compared to in vivo (Fig 4A). Structurally, the difference between the two transcripts is major and only 3 loops are common to both structures (Fig 4B). More interestingly, an RNA-appropriate measurement of Minimum Free Energy, MFEden [59], revealed that the Structure-seq U2 snRNA structure had a lower MFEden (-24.76 versus 15.50). Extending this to the whole dataset, we observed a similar effect in each RNA category (Table 3). Thus, structures called from Structure-seq are more likely to form and are more stable than the ones predicted only using an in silico prediction.

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Fig 4. Difference in U2 snRNA structure determined by Structure-seq versus in silico prediction.

(A) U2 snRNA (PF3D7_1137000) reactivity sequence alignment, comparing in vitro probing using NAI and in silico prediction using the same constraint parameters. The first line shows the nucleotide sequence, the second line shows the in silico predicted dot/bracket sequence and the third line, the Structure-seq dot/bracket sequence extracted from reactivity measurement. A dot shows a nucleotide not paired with any other nucleotide. An open bracket shows a nucleotide paired with another and starting a structural motif, while a closed bracket shows a nucleotide paired with another but finishing a structural motif. Red-highlighted dot or bracket indicates a change of pairing between the NAI and in silico predicted structural sequence. A green-highlighted bracket indicates a change in motif orientation between the NAI and in silico predicted structural sequence. Boxes with roman numerals indicate the conserved structures between the two shapes. (B) U2 snRNA structure predicted in silico from RNA sequence alone, on the left, and with the addition of reactivity data on the right. The red and green colouring have the same meaning as in (A).

https://doi.org/10.1371/journal.pone.0270863.g004

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Table 3. MFEden average for every type of RNA molecule.

https://doi.org/10.1371/journal.pone.0270863.t003

Effect of A/U bias in the structurome of P. falciparum by comparison with an A/U-balanced Plasmodium species

The Plasmodium genus offers the possibility to compare very different genome compositions. The genome of P. falciparum is ~80% A/T rich while P. knowlesi, a macaque Plasmodium species which also infects humans, is 62.5% A/T rich [60]. In order to assess whether the base composition of the genome, and hence the transcriptome, could affect the structurome, we compared the in silico folding of P. falciparum and P. knowlesi structuromes. Firstly, we assessed the stability of the two predicted structuromes by comparing the average free energy (MFEden [59]) of all folded structures: P. falciparum had a significantly higher MFEden than P. knowlesi (respectively 14.03 and 7.1, t-test p-value <0.0001). Secondly, we looked at the occurrence of various structures by comparing the length-normalised number of nucleotides participating in a structure (e.g. a hairpin or bulge) in each P. falciparum transcript with their P. knowlesi orthologs. The structurome of P. falciparum had significantly more nucleotides involved in hairpins and multi-stem loops than the structurome of P. knowlesi (t-test, p-value <0.0001), whereas P. knowlesi had significantly more nucleotides involved in stems and bulges (t-test, p-value <0.0001) (S5 Table).

RNA structures affect translation efficiency in a stage specific manner

Fig 2 shows that various families of RNAs display very conserved structures that are related to their particular functions. This is clearly true for structural RNAs (tRNAs, rRNAs, etc.), but it may also be true for mRNAs, whose main function is to be translated into proteins. The stability of the transcript and the efficiency of its translation could both be affected by mRNA structure. Therefore, if mRNA structures can vary with varying cellular conditions, transcript and protein abundance may both vary accordingly.

We first explored the concept that transcript abundance could vary with cellular conditions due to changes in mRNA structure. For this, we used published microarray and transcriptomic datasets from P. falciparum under physiological stresses like hyperoxia [51], or drug exposure like chloroquine treatment [52]. These datasets reveal which transcripts are upregulated and downregulated in each condition. We compared the most differentially expressed genes in each condition with the structurome dataset, to see if they were particularly structured or unstructured. Neither the overall degree of ‘divergence’ nor the representation of diverse structures (the proportion of hairpins, loops, etc), correlated with differential expression under stress (S6 Table).

Secondly, we looked at the effect of RNA structures on translation efficiency (TE). To establish the translation efficiency for each transcript in our dataset, we extracted from previously published work [53] the average level of ribosome attachment per transcript, determined by Ribo-seq. We obtained TE for the transcriptome of rings, early and late trophozoites and schizonts. Applying a linear regression model, we compared TE with transcript structuredness (‘NAI ratio’, i.e. the ratio of paired to unpaired bases) and also the proportion of different motifs such as stems, hairpins, multi-loop stems and bulges (Fig 5). We observed no correlation between the transcript-length-normalised ‘structuredness’ and TE in any of the four stages (Fig 5A). Therefore, there was no overall relationship between the proportion of paired bases in a transcript and the efficiency of its translation. However, breaking down the different possible structures (Fig 5B–5E) we observed a positive correlation of TE with the presence of stems and a negative correlation between TE and hairpins, multi-loop stems and bulges. The correlations were only seen in the more mature parasite stages, late trophozoites and schizonts.

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Fig 5. Impact of various RNA motifs on transcription efficiency across the erythrocytic cycle of P. falciparum.

The columns represent the different erythrocytic stages of P. falciparum and the rows represent the correlation between transcription efficiency and (A) the transcript structuredness normalised by the transcript length, (B) the number of stems, (C) the number of hairpins, (D) the number of multi-stem loops and (E) the number of bulges. Statistically significant positive correlations have a p-value framed in green and statistically significant negative correlations have a p-value framed in red.

https://doi.org/10.1371/journal.pone.0270863.g005

RNA decay is mediated by the same structures across the cell cycle

The final critical part of an RNA molecule’s life inside the cell is its rate of decay. Following a similar reasoning as for translation efficiency, we extracted RNA decay values from data published by Painter et al. [54] and tested for any correlation between RNA decay and RNA secondary structures in the different cell cycle stages (rings, early and late trophozoites and schizonts). For this, we applied a linear regression model to compare RNA decay (the higher the value, the less stable the molecule is) to the number of different structures per transcript. We observed no correlation between RNA decay and RNA secondary structures involving high amounts of base pairing: stems and hairpins (Fig 6A and 6B). However, when nucleotides were more exposed, as in bulges and multi-stem loops (Fig 6C and 6D) we observed correlations between RNA decay and the presence of those structures in all stages (except for multi-stem loops in ring-stage) (Fig 6C).

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Fig 6. Correlation between RNA secondary structures and RNA decay across the erythocytic cycle of P. falciparum.

The columns represent the different erythrocytic stages of P. falciparum and the rows represent the correlation between RNA decay and (A) the number of stems, (B) the number of hairpins, (C) the number of multi-stem loops and (D) the number of bulges. Statistically significant negative correlations have a p-value framed in red.

https://doi.org/10.1371/journal.pone.0270863.g006