Polysome profiling reveals translational control of gene expression in the human malaria parasite Plasmodium falciparum
© Bunnik et al.; licensee BioMed Central Ltd. 2013
Received: 5 September 2013
Accepted: 22 November 2013
Published: 22 November 2013
In eukaryotic organisms, gene expression is regulated at multiple levels during the processes of transcription and translation. The absence of a tight regulatory network for transcription in the human malaria parasite suggests that gene expression may largely be controlled at post-transcriptional and translational levels.
In this study, we compare steady-state mRNA and polysome-associated mRNA levels of Plasmodium falciparum at different time points during its asexual cell cycle. For more than 30% of its genes, we observe a delay in peak transcript abundance in the polysomal fraction as compared to the steady-state mRNA fraction, suggestive of strong translational control. Our data show that key regulatory mechanisms could include inhibitory activity of upstream open reading frames and translational repression of the major virulence gene family by intronic transcripts. In addition, we observe polysomal mRNA-specific alternative splicing events and widespread transcription of non-coding transcripts.
These different layers of translational regulation are likely to contribute to a complex network that controls gene expression in this eukaryotic pathogen. Disrupting the mechanisms involved in such translational control could provide novel anti-malarial strategies.
Malaria is still one of the most deadly infectious diseases worldwide, claiming an estimated 660,000 lives per year . The vast majority of deaths occur among children under the age of 5 years living in sub-Saharan Africa . Over the past decade, malaria control measures have reduced the global incidence and mortality rates by 17% and 26%, respectively . However, the absence of a preventive vaccine and the spread of drug-resistant parasite strains warrant continued investigations into the intricate biology of the malaria parasite, in search of novel anti-malarial drug targets.
The malaria parasite species Plasmodium falciparum is responsible for 90% of all malaria deaths . The complex life cycle of P. falciparum involves multiple stages in both the human and the mosquito host. The symptomatic phase of P. falciparum infection is the erythrocytic stage, where the parasite replicates in red blood cells and progresses through the ring, trophozoite and schizont stages to produce 16 to 32 daughter cells. The release of these daughter cells, or merozoites, into the blood stream after the completion of each 48-hour cycle of cell division causes the typical pattern of recurring fevers. Environmental stress, such as low nutrient levels, induces the formation of gametocytes, the sexual forms of P. falciparum, which can be transferred to a mosquito host when it takes a blood meal.
The multiplication process during the erythrocytic cell cycle of P. falciparum infection is tightly regulated and involves the expression of the majority of its genes [2–4]. However, the regulation of gene expression in P. falciparum is still incompletely understood. Relatively few transcription factors have been identified [5, 6], while changes in chromatin structure seem to play a unique role in transcriptional control [7, 8]. Moreover, for a large proportion of genes expressed in the erythrocytic cycle, transcriptional activity does not correlate well with protein abundance [9, 10], similar to mammalian cells where the initiation of translation, and not transcript abundance, is the main determinant of protein levels . In Plasmodium berghei gametocytes, delayed translation of two transcripts was shown to occur by temporary storage of these transcripts in P-bodies, followed by transfer to ribosomes after ingestion of gametocytes by a mosquito . RNA-binding proteins are likely to be involved in translational repression at this stage . In addition, latency of P. berghei sporozoites is controlled by phosphorylation of eukaryotic initiation factor-2α, resulting in inhibition of translation . However, the mechanisms and the extent of post-transcriptional and translational control have not yet been described for the asexual stage of P. falciparum.
In other eukaryotic organisms, a multitude of mechanisms act in concert to regulate gene expression at a post-transcriptional level, including mRNA splicing, decay, binding of inhibitory proteins and the actions of regulatory mRNA elements. One of the major regulatory mechanisms of mRNA abundance in higher eukaryotes is RNA interference, but homologues of the RNA interference machinery have not been identified in the P. falciparum genome .
In this study, we performed next-generation sequencing of both steady-state mRNA and polysome-associated mRNA, presumed to be actively translated. Our genome-wide approach allowed us to elucidate the extent of translational control during the erythrocytic cell cycle of P. falciparum and to identify key mechanisms likely contributing to the complex regulatory network of gene expression and parasite virulence. Collectively, our results increase our understanding of parasite development throughout the infectious cell cycle, which may contribute to novel antimalarial strategies.
Generation of steady-state mRNA and polysomal mRNA datasets across the P. falciparumasexual cycle
Number of working reads and normalization factor for each library
Time point (hours post-invasion)
mRNA quantity per 109parasites (μg)
Number of working reads
36 h - biological replicate
To compare gene expression levels between the different stages of the asexual cycle, we normalized the total sequence read counts of each library to directly reflect the mRNA levels per parasite at that stage. This was achieved by introducing a scaling factor for the sequence read counts per gene based on the total amount of mRNA isolated per flask (see Materials and methods; Table 1). After normalization, the distribution of read counts per gene for the trophozoite and schizont time points was relatively similar, while the average expression level of genes at the ring stage was much lower (Figure S2 in Additional file 2), corresponding to relative mRNA levels per parasite for each stage.
Cluster analysis of steady-state and polysomal mRNA profiles across the asexual cell cycle
Comparison of steady-state mRNA and polysomal mRNA profiles across the cell cycle
A comparison of steady-state mRNA and polysomal mRNA expression clusters revealed that for 1,749 genes (43.6%), transcription and translation showed similar patterns of upregulation, with transcripts peaking at the same time points in both fractions. For another 738 genes (18.4%), we observed a partial delay in the timing of translation as compared to the moment of transcription. For example, 457 genes were highly abundant in steady-state mRNA at the trophozoite stage but absent at the schizont stage (cluster A.2), while their abundance in polysomal mRNA peaked at the trophozoite stage and continued to the schizont stage (cluster B.3). Considering the 18 h window between the trophozoite and schizont time points, these partial shifts between steady-state mRNA and polysomal mRNA profiles are likely to be biologically relevant. In addition, we identified 1,280 genes (31.9%) for which translation was markedly delayed compared to the time point of transcription (Figure 2C,D). Among genes for which translation was delayed until the schizont stage, we found enrichment of genes involved in energy production (ATP synthesis-coupled proton transport; P = 1.2E-06).
Number of translationally delayed genes for selected groups of parasite-specific proteins
Average normalized read count per 1 kb exon (delay)
Average normalized read count per 1 kb exon (no delay)
Delay to R stageb( n)
No delay ( n)
FIKK kinase family
ApiAP2 transcription factor
Conserved proteins, unknown function
Differences in mRNA landscape between steady-state mRNA and polysomal mRNA
Control of translation by upstream open reading frames
In the 5′ UTR coverage plot, we observed that coverage peaked at the start codon of polysome-associated mRNA (Figure 4A). A similar pattern was previously observed in ribosome-protected fragments from cycloheximide-treated cells . In contrast, apicoplast-encoded genes that are translated in the apicoplast by 70S ribosomes did not show this peak in sequence coverage at the translation start site (Figure 4A). Since 70S ribosomes are insensitive to cycloheximide inhibition , excess coverage at the ATG start codon in our polysome-associated mRNA dataset is likely to be the result of cycloheximide-induced ribosome stalling, indicating that even though our mRNA was not nuclease-digested, the binding sites of ribosomes, initiation factors or RNA binding proteins may be enriched in our sequencing data. If binding of ribosomes or proteins indeed influences gene coverage profiles, the high coverage in 5′ UTR regions in this subset of genes may be explained by protection of this region. High 5′ UTR read coverage in this subset of genes may be explained by protection of this region and may thus be the result of the presence of upstream open reading frames (uORFs) that are actively translated. It has been documented that uORFs can limit the translation of the downstream open reading frame (ORF) in a regulated manner [22, 23] and that re-initiation capacity of the ribosome is strongly reduced after translating an ORF of 35 or more codons . In line with these data, we observed that a larger proportion of transcripts that showed a translational delay from the trophozoite to the ring stage had a high 5′ UTR coverage (19.0%) compared to transcripts that did not show a translational delay (9.7%) (two-tailed Fisher exact test P = 0.005; Figure 4D). Genes with high 5′ UTR coverage and delayed translation also have significantly longer uORFs as compared to genes with low 5′ UTR coverage and no delay in translation (P = 0.008; Figure 4E). Although translation of uORFs may activate the nonsense-mediated mRNA decay pathway [25–27], we did not observe a difference in mRNA half-life  between transcripts with or without evidence of active uORF translation (data not shown). Collectively, these results point towards translational repression and temporal regulation in P. falciparum by uORFs.
Stop codon readthrough
Similar to the 5′ UTR, we observed higher average gene coverage in the 3′ UTR region in the polysome-associated mRNA as compared to steady-state mRNA (Figure 4F). While a bias towards the 3′ end of transcripts could be introduced by library preparation, the use of both oligo-dT and random hexamer primers during cDNA preparation should have minimized this effect. Furthermore, as this differential pattern was observed in polysomal mRNA but not in steady-state mRNA, it is likely to be biologically relevant. Since higher coverage may be indicative of translational activity, we studied the coding potential of the 3′ UTR regions. In particular, we searched for the presence of in-frame ORFs that could be translated as a result of stop codon readthrough. Numerous stop codon readthrough gene candidates have been identified in other eukaryotes, some of which have been experimentally verified [29–34]. In addition, multiple double-readthrough gene candidates have been detected in Drosophila and other metazoa , indicating that this might be a common event in eukaryotic genomes. In P. falciparum, we identified 133 genes with a substantial ORF (>195 nucleotides) directly downstream of the stop codon. In addition, we found another 85 genes with large downstream ORFs that had a second stop codon within the first 10 codons downstream of the annotated stop codon. The average 3′ UTR coverage for these stop codon readthrough candidates was slightly increased as compared to genes with an ORF smaller than 75 nucleotides (Figure 4G), suggesting that ribosome binding and thus translation of the 3′ UTR may indeed occur for these genes.
To validate our finding, we studied one of the double-readthrough candidates in more detail. PF3D7_1345500, a ubiquitin conjugating enzyme, encodes an annotated gene product of 278 amino acids, including a 28 amino acid signal peptide that is cleaved off after translocation of the protein to the apicoplast . A double-stop codon readthrough event would result in a protein that is 142 amino acids longer (Figure S4A in Additional file 2). Importantly, our polysome sequencing data confirmed that this potential second ORF was part of the full-length transcript and was highly covered by sequence reads (Figure S4B in Additional file 2). Enrichment of the 3′ UTR in polysomal samples was validated by RT-PCR on an independent biological replicate (Figure S4C in Additional file 2). For this gene, three protein bands were observed by western blot analysis using a specific antibody, of which the lowest molecular weight band corresponded to the expected protein size of 33 kDa . Interestingly, the highest band exactly matches the size of a potential double-readthrough product (51 kDa), while the middle band of approximately 48 kDa may represent a ubiquitinylated form of the processed protein, although this remains to be experimentally verified. Taken together, these observations suggest that stop codon readthrough occurs in P. falciparum and that the currently annotated proteome is incomplete, although this will need to be verified by mass spectrometry and ribosome-footprinting experiments.
Alternative splice variants
To further study alternative splicing events, we focused on gene PF3D7_0807700, for which we observed reads mapping to intronic regions as well as reads that mapped to a truncated second exon (Figure 5D). We cloned and sequenced a cDNA fragment from total steady-state RNA corresponding to the region covering exon 2 to exon 5 of gene Pf3D7_0807700. Only one transcript out of 12 clones was identical to the currently known exon model (Figure 5E). The remaining 11 clones consisted of three different alternative transcripts, all of which contained the fourth intron, in some cases in combination with a retained third intron and/or a truncated second exon. These alternative transcripts all contained a premature stop codon, and could thus produce truncated versions of the full-length gene, or be subject to nonsense-mediated mRNA decay.
Polysome-associated intronic transcripts from vargenes
The var gene family consists of approximately 60 genes each coding for a different variant of the adhesion protein Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), which is expressed on the surface of the infected erythrocyte. PfEMP1 allows the parasite to adhere to the microvasculature, thus preventing clearance by the spleen, and causing severe disease symptoms associated with cerebral and placental malaria. In addition, the process of antigenic variation, or var gene switching, prolongs parasite survival and mediates immune escape. Although each parasite transcribes multiple (possibly all) var genes early in its erythrocytic cycle, only one PfEMP1 variant is eventually translated while the remaining variants are silenced . Multiple control mechanisms are likely to be involved in this process of mutually exclusive expression, including upstream and intronic regulatory elements, localization of non-expressed var genes in nuclear repressive centers [37–39] and gene silencing by repressive histone marks [40, 41]. In addition, two long non-coding RNAs were shown to be transcribed from the intronic regions and to be associated with chromatin , which is likely to also contribute to the selective expression of var genes. However, these mechanisms are still incompletely understood and a better understanding of var gene expression would greatly facilitate novel prevention or intervention strategies for the symptomatic stage of P. falciparum infection.
In this study, we aimed to gain a better understanding of mechanisms that control gene expression at the translational level during the asexual cell cycle of P. falciparum by comparing next-generation sequencing data from steady-state mRNA and polysome-associated mRNA. We determined that more than 50% of genes expressed during the asexual cycle of the malaria parasite exhibit some form of translational control, ranging from a partial shift in translation levels as compared to transcriptional activity, to a delay in translation of 18 hours or more. The results from this genome-wide analysis confirm and extend previous findings from smaller scale comparisons of the P. falciparum transcriptome and proteome [9, 10]. In comparison with other eukaryotic genomes, P. falciparum encodes relatively few transcription-associated proteins, while it was found to be enriched for proteins involved in chromatin remodeling, translation rates and mRNA stability .
Based on these observations, we propose a gene expression model for P. falciparum in which the absence of a tight regulatory network for transcription is compensated by post-transcriptional and translational control mechanisms, resulting in a just-in-time translation of proteins. In particular, the parasite seems to have developed a mechanism of delayed translation for proteins that are needed early in the cell cycle, many of which play important roles in erythrocyte remodeling and metabolism. By retaining these transcripts until after re-invasion, the parasite can quickly translate proteins that are critical for its establishment inside the erythrocyte, before it starts a new round of massive transcription and replication. Translational repression by temporary storage of two transcripts in ribonucleoprotein particles has previously been described for the gametocyte stage . The presence of the DDX-6 class RNA helicase DOZI is essential for the formation of these complexes. This protein was recently shown to be present in granular bodies in the cytoplasm of asexual parasites , and it is tempting to speculate that DOZI may also be involved in storage of transcripts during the asexual cell cycle.
The wealth of information obtained from RNA-Seq experiments allowed us to subsequently perform an in-depth comparison between these two mRNA subpopulations. We identified major differences in mRNA landscape between steady-state mRNA and polysomal mRNA, which provide important clues for potential regulatory mechanisms. Compared to other eukaryotes, P. falciparum genes contain relatively long 5′ UTRs . Recently, the 5′ UTR of the household gene phosphoglucomutase-2 (PGM2; PFD0660w) was shown to play an important role in translation efficiency . In addition, the length and sequence context of the uORF of P. falciparum var gene variant var2csa was demonstrated to influence the balance between translational repression and translation initiation at the main coding sequence after uORF translation . In line with knowledge from other well-studied eukaryotic organisms [20, 22, 23, 47], uORFs in the 5′ mRNA leaders of P. falciparum transcripts are likely to be important regulatory elements that control the level and timing of translation of the main coding sequence. The translation of uORFs by itself does not necessarily influence translation of downstream coding sequences, since the ribosome can continue scanning the mRNA and re-initiate translation at a downstream AUG [20, 48, 49]. However, the association we observed between uORF translation, translational delay and uORF length suggests that the main inhibitory mechanism of uORFs in P. falciparum entails the prevention of ribosomes from reaching the main ORF, possibly by activation of the nonsense-mediated decay pathway [25–27]. Alternatively, many uORFs in the high A/T-biased P. falciparum genome contain poly-A tracts that encode lysine repeats, which may significantly slow down the translating ribosome . Furthermore, it is known that uORFs can code for functional peptides that can even exert translational control by themselves . Future studies will have to elucidate the exact nature of these uORFs and their impact on translation.
Another interesting feature of translation that had not been previously described in P. falciparum is stop codon readthrough. A recent in-depth study in Drosophila classified about 2% of all genes as stop codon readthrough candidates , indicating that this may be a relatively common event in eukaryotes. Although further studies will need to validate the occurrence of stop codon readthrough at similar levels in P. falciparum, this process could possibly explain the unexpected large size of at least one protein. Phylogenetic analysis of evolutionary constraints on 3′ UTRs and computational methods for the identification of coding regions could shed more light on this mechanism, although currently available tools may have to be adapted for the highly A/T-rich genome of P. falciparum. In this respect, it is also interesting to mention that relatively long 3′ UTRs, as frequently observed in P. falciparum transcripts, may also harbor binding sites for long non-coding RNAs (lncRNAs) that could influence translation efficiency, similar to what is observed in mammalian neuronal tissues .
We detected novel alternative splice variants in the asexual cell cycle of P. falciparum, thereby expanding the number of alternative splice variants that are currently annotated or have previously been described in multiple independent RNA-Seq datasets from the same stages [4, 18, 53]. In addition, we also observed that a large proportion of genes contained sequence reads that mapped to introns. Since the majority of introns were completely devoid of reads, this is unlikely to be caused by DNA contamination of our mRNA samples. Intron coverage can be the result of intron retention in the transcript, or the transcription of overlapping RNAs, either in a sense or anti-sense direction, as is known to occur in P. falciparum[53, 54]. Alternatively, introns are known to contain many non-protein-coding RNAs (ncRNAs) , that can be independent transcripts or be derived from the pre-mRNA. While a number of small nucleolar RNAs (snoRNAs), RNAs of unknown function (RUFs) and other ncRNAs encoded by intronic regions have previously been described for the P. falciparum genome [56, 57], the widespread detection of intronic coverage is suggestive of a much larger number of regulatory RNAs encoded by introns. In addition, the preferential association of some intronic transcripts with polysomal fractions may point to functions in translational regulation. The apparent role of intron-encoded transcripts in translational repression of the var genes demonstrates the potent regulatory function of such RNAs, although the exact mechanism of control remains to be determined. The lack of coverage in the exons suggests that the intronic transcript involved in translational repression is distinct from the two var intronic ncRNAs that have previously been described and that either partially overlapped exon 1 or completely overlapped exon 2 . Instead, it may suggest that the intron itself is retained and functional after splicing. Given the purity of our polysome fractions (<2.0% protein contamination), it is unlikely that intronic transcripts were obtained by co-purification of other protein-RNA complexes. Furthermore, even though ribosomes are known to be sticky complexes, the high yield of intronic transcripts in polysomal fractions for virtually all var gene variants at the ring stage suggests that this is not the result of mere non-specific adherence, but of specific targeting of intronic var transcripts to ribosomes. A better understanding of the role of intronic transcripts or intron-encoded peptides in translational repression of the var genes would contribute to strategies for disrupting the mutually exclusive var gene expression, thus preventing escape of the parasite from adaptive immune responses.
Collectively, this study has shown that the regulation of translation in P. falciparum is a multi-faceted process of high complexity. Several control mechanisms that have previously been described in higher eukaryotes are also likely to be active in the malaria parasite, including translational repression by upstream ORFs, widespread transcription of non-coding transcripts, and alternative splicing events. This study clearly demonstrates that steady-state mRNA levels are often not predictive for translational activity and that translated transcript variants may differ from the general mRNA population, as recently also shown for human cells , indicating that the compartment of actively translated transcripts should not be overlooked. A detailed understanding of the regulatory network that determines gene expression in the different stages of the P. falciparum life cycle will not only increase our knowledge of parasite biology, but may ultimately result in the identification of novel antimalarial drug targets.
Materials and methods
The P. falciparum strain 3D7 was cultured in human O+ erythrocytes at 5% hematocrit as previously described . Cultures were synchronized twice at ring stage with 5% D-sorbitol treatments performed 8 hours apart . Cultures (8% parasitemia in 5% hematocrit in a total volume of 25 ml) were harvested 48 hours after the first sorbitol treatment (ring stage), and then 18 hours (trophozoite stage) and 36 hours thereafter (schizont stage).
Total RNA extraction
Total RNA was isolated from parasites by adding 5 volumes of pre-warmed Trizol LS Reagent (Life Technologies, Carlsbad, CA, USA) to pelleted infected erythrocytes, followed by a 5 minute incubation at 37°C. RNA isolation was then continued according to the manufacturer’s instructions.
Polysome-associated RNA isolation
Polysomes were isolated from P. falciparum according to a recently published protocol . Briefly, cycloheximide was added to parasite-infected red blood cell cultures to a final concentration of 200 μM, followed by a 10 minute incubation at 37°C. Erythrocytes were then pelleted (4 minutes at 660 x g) and washed twice in phosphate-buffered saline containing 200 μM cycloheximide. After the last wash, pellets were kept on ice and were subsequently lysed by adding 2.2 volumes of lysis buffer (1% (v/v) Igepal CA-360 and 0.5% (w/v) sodium deoxycholate in polysome buffer (400 mM potassium acetate, 25 mM potassium HEPES pH 7.2, 15 mM magnesium acetate, 200 μM cycloheximide, 1 mM dithiothreitol (DTT), and 1 mM 4-(2-aminoethyl)benzenesulfonyl fluoride HCl (AEBSF))). After a 10 minute incubation on ice, lysates were centrifuged for 10 minutes at 20,000 x g at 4°C. The clarified lysates were then loaded on top of a sucrose cushion (1 M sucrose in polysome buffer) to concentrate the ribosomes. For large cultures volumes, 20 ml lysate was loaded on top of 6 ml of sucrose cushion in 26 ml polycarbonate ultracentrifuge tubes and then centrifuged for 3 h at 50,000 rpm at 4°C in a Type 70 Ti rotor (Beckman Coulter, Brea, CA, USA). For small culture volumes, 4 ml lysate was loaded atop 1.25 ml of sucrose cushion in 5 ml polyallomer ultracentrifuge tubes and then centrifuged for 123 minutes at 50,000 rpm at 4°C in an SW 55 Ti rotor (Beckman Coulter). Ribosome pellets were resuspended in polysome buffer, incubated for at least 30 minutes at 4°C to allow complete ribosome resuspension and centrifuged for 10 minutes at 12,000 x g at 4°C. The ribosome suspension was layered on top of a 4.5 ml continuous linear 15 to 60% sucrose (w/v) gradient in polysome buffer and centrifuged for 1.5 h at 50,000 rpm at 4°C in an SW 55 Ti rotor. Fractions of 400 μl were collected using an UA-5 UV detector and model 185 gradient fractionator (ISCO, Lincoln, NE, USA). Polysome fractions were digested with 200 μg Proteinase K (New England Biolabs (NEB), Ipswich, MA, USA) for 1 h at 37°C. RNA was extracted with acid-phenol:chloroform:isoamylalcohol, pH 4.5 (Life Technologies), extracted twice with chloroform and then precipitated using isopropanol.
Multidimensional protein identification technology
Pooled polysome fractions from a mixed-stage P. falciparum culture were analyzed for protein content using MudPIT. Proteins were precipitated with 20% trichloroacetic acid (TCA). The resulting pellet was washed once with 10% TCA and twice with cold acetone. TCA-precipitated protein pellet (approximately 50 μg) was solubilized in Tris–HCl pH 8.5 and 8 M Urea. TCEP (Tris(2-Carboxylethyl)-Phosphine Hydrochloride; Thermo Fisher Scientific, Rockford, IL, USA) and CAM (chloroacetamide; Sigma-Aldrich, St. Louis, MO, USA) were added to a final concentration of 5 mM and 10 mM, respectively. The protein suspension was digested overnight at 37°C using Endoproteinase Lys-C at 1:50 w/w (Roche, Basel, Switzerland). The sample was brought to a final concentration of 2 M urea and 2 mM CaCl2 before performing a second overnight digestion at 37°C using Trypsin (Promega, Madison, WI, USA) at 1:100 w/w. Formic acid (5% final) was added to stop the reactions. The sample was loaded on split-triple-phase fused-silica micro-capillary column  and placed in-line with LQT Velos Pro mass spectrometer (Thermo Fisher Scientific), coupled with quaternary Agilent 1260 series high-performance liquid chromatography. A fully automated 10-step chromatography run (for a total of 20 hours) was carried out, as described in . Each full mass spectrometry (MS) scan (400 to 1,600 m/z) was followed by 10 data-dependent tandem MS (MS/MS) scans. The number of the micro scans was set to 1 for both MS and MS/MS. The dynamic exclusion settings used were as follows: repeat count 2; repeat duration 30 s; exclusion list size 500 and exclusion duration 90 s, while the minimum signal threshold was set to 500. The MS/MS dataset was searched using SEQUEST  against a database of 72,358 sequences, consisting of 5,487 P. falciparum non-redundant proteins (downloaded from PlasmoDB on 12 July 2012), 30,536 H. sapiens non-redundant proteins (downloaded from NCBI on 27 August 2012), 177 usual contaminants (such as human keratins, IgGs, and proteolytic enzymes), and, to estimate false discovery rates, 36,179 randomized amino acid sequences derived from each non-redundant protein entry. To account for alkylation by CAM, 57 Da were added statically to cysteine residues. To account for the oxidation of methionine residues to methionine sulfoxide (that can occur as an artifact during sample processing), 16 Da were added as a differential modification to methionine residue. Peptide/spectrum matches were sorted, selected using DTASelect/CONTRAST . Proteins had to be detected by one peptide with two independent spectra, leading to false discovery rates at the protein and spectral levels of 2.89% and 0.26%, respectively. To estimate relative protein levels and to account for peptides shared between proteins, Normalized Spectral Abundance Factors (dNSAFs) were calculated for each detected protein, as described in . Lists of all proteins that were detected in our sample and individual peptide/spectral counts are provided in Table S1 in Additional file 1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  via the PRIDE partner repository  with the dataset identifier PXD000553. The MS .RAW files, .ms2 files created by RawDistiller, the .sqt files generated by SEQUEST, and the DTASelect output files for this analysis are also available to download from The Stowers Institute Original Data Repository .
mRNA isolation and cDNA preparation
To remove potential DNA contamination, RNA samples were treated twice with 1 U DNase I (Life Technologies) per 10 μg of RNA for 30 minutes at 37°C, followed by inactivation of the DNase I enzyme. The absence of DNA was confirmed by performing a 40-cycle PCR on P. falciparum gene PF05_139450 using 200 to 500 ng total RNA as input. Messenger RNA was then purified from total RNA samples using the GenElute mRNA Miniprep kit (Sigma-Aldrich) according to the manufacturer’s instructions.
The preparation of double-stranded cDNA from steady-state mRNA and polysome-associated mRNA samples was adapted from a previously published method . Up to 500 ng of mRNA was diluted 1:5 in RNA storage solution (Life Technologies) and was fragmented by a 50 minute incubation at 98°C. The fragmented mRNA was added to 3 μg of random hexamers (Integrated DNA Technologies, Coralville, IA, USA), 1 μg of anchored oligo(dT)20 (Integrated DNA Technologies) and 1 μl 10 mM dNTP mix (Life Technologies) in a total volume of 10 μl. The mixture was incubated for 10 minutes at 70°C and chilled on ice for 5 minutes. Next, a mix of 2 μl 10X RT buffer, 4 μl 20 mM MgCl2, 2 μl 0.1 M DTT, 1 μl 40 U/μl RNaseOUT and 1 μl 200 U/μl SuperScript III Reverse Transcriptase (all from Life Technologies) was added. First-strand cDNA was synthesized by incubating the sample for 10 minutes at 25°C, 50 minutes at 50°C, and finally 5 minutes at 85°C. The first-strand cDNA was then purified using Agencourt AMPure XP beads (Beckman Coulter) and eluted in 47 μl of nuclease-free water. Second-strand cDNA was prepared by adding 2 μl 5X first-strand buffer (Life Technologies), 1 μl 0.1 M DTT (Life Technologies), 15 μl second-strand buffer (Life Technologies), 4 μl 10 mM dNTP mix (Life Technologies), 4 μl 10 U/μl E. coli DNA Polymerase (NEB), 1 μl 10 U/μl E. coli DNA ligase (NEB), and 1 μl 2 U/μl E. coli RNase H (Life Technologies) and incubating the mixture for 2 h at 16°C. Finally, double-stranded cDNA was purified using Agencourt AMPure XP beads.
Library preparations and sequencing
Libraries from steady-state mRNA samples were prepared using the Encore Multiplexing System (NuGEN, San Carlos, CA, USA) according to the manufacturer’s instructions, with the following modifications for the high AT content of the P. falciparum genome: the libraries were amplified for a total of 15 PCR cycles (5 cycles of 15 s at 98°C, 30 s at 55°C, 30 s at 62°C followed by 10 cycles of 15 s at 98°C, 30 s at 63°C, 30 s at 72°C) using KAPA HiFi HotStart Ready Mix (Kapa Biosystems, Woburn, MA, USA). Libraries from polysome-associated mRNA samples were prepared using the NEBNext ChIP-Seq Library Preparation kit (NEB) according to the manufacturer’s instructions, with the exception of the use of the KAPA HiFi Hotstart Ready Mix for the amplification of the libraries. Depending on the amount of input DNA, libraries were amplified for a total of 11 to 15 PCR cycles (3 to 5 cycles of 15 s at 98°C, 30 s at 55°C, 30 s at 62°C followed by 8 to 10 cycles of 15 s at 98°C, 30 s at 63°C, 30 s at 72°C). Libraries of steady-state mRNA samples and of polysomal mRNA samples were multiplexed and were sequenced on two separate lanes with a HiSeq 2000 (Illumina, San Diego, CA, USA), generating 50 bp paired-end sequence reads. By multiplexing all libraries of one sample type into one lane, we attempted to minimize differences in cluster generation and other sequencing artifacts between samples of the same type. The selection of library preparation kits for the construction of sequence libraries was solely based on availability. In our hands, we have not noticed any differences or biases between library preparation kits used in this study. RNA-Seq and polysome-Seq sequence reads are available from the Short Read Archive under accession number SRP021890.
The first five bases and the last base were systematically removed from the sequence reads using FastQ Trimmer, part of the FASTX-Toolkit . Contaminating adaptor reads were removed using Scythe . Reads were then trimmed for bases with a quality score below 30, and reads containing any Ns as well as reads shorter than 18 bases were discarded using Sickle . Subsequently, the trimmed sequence reads were mapped to P. falciparum genome v9.0 (downloaded from PlasmoDB ) using tophat v2.0.3 , allowing a maximum of one mismatch per read segment and no insertions or deletions. We removed all reads that were non-uniquely mapped (using Samtools v0.1.18 ), not properly paired (Samtools), PCR duplicates (using Picard Tools v1.78 ) or mapped to either ribosomal DNA or to DNA encoding transfer RNA (using Bedtools v2.17.0 ). The final number of working reads for each library is listed in Table 1.
For each gene, the number of reads mapping to its exons was calculated (Bedtools). Exon read counts per gene were normalized for GC content and gene length using the open-source Bioconductor R package EDASeq . In our experience, expression values of short genes with low read counts (<5 reads per gene) are highly inflated using this package. To minimize overestimating expression levels of such genes, genes that did not reach five mapped reads at any time point in both steady-state mRNA and polysomal mRNA were removed from the datasets before applying the normalization algorithm. For genes with annotated alternative splice variants, only the first variant (annotated with .1 in its gene name) was included. Non-protein coding transcripts (annotated as ‘transcript’, not ‘mRNA’ in PlasmoDB-9.0_Pfalciparum3D7.gff) and small nuclear RNAs (snRNAs) were also excluded. Next, to normalize the exon read counts to the mRNA levels per parasite, a scaling factor was calculated for each stage based on the mRNA yield per flask of P. falciparum-infected culture (Table 1). For each stage, the total number of working reads was divided by the total number of working reads from the smallest library for that sample type (that is, steady-state mRNA or polysomal mRNA), and was subsequently multiplied by the ratio between the mRNA yield per flask for the stage of the smallest library and the mRNA yield per flask for that particular stage. The exon counts per gene were then divided by this scaling factor. The final normalized abundance values were expressed as counts per kilobase of exon model. Finally, for both steady-state mRNA and polysomal mRNA datasets, genes that were not expressed were excluded from further analysis. Non-expressed genes were defined as having <15% of the median counts per kilobase of exon model (18.9 counts for the steady-state mRNA dataset and 4.0 counts for the polysomal mRNA dataset) at all stages. Because of differences in library sizes, RPKM cutoff values differed for each library, but were at least 0.7. An overview of exon read counts before, during, and after the different normalization steps is provided in Additional file 5.
Gene clustering and gene ontology analysis
Cluster analysis was performed for genes that were differentially expressed during the cell cycle, defined as a more than two-fold change in exon read counts at any time during the cell cycle, in both steady-state mRNA and polysomal mRNA datasets. For both datasets, genes were subsequently clustered based on scaled expression levels (z-score) using the k-means clustering algorithm with a maximum of 1,000 iterations in R v2.14.2. Several independent clustering runs were performed with increasing numbers of clusters. Determination of the optimal number of clusters was guided by the percentage of variance that was captured by the clusters. We selected the smallest number of clusters that captured more than 90% of the variance (expressed as within group sum of squares) and for which an increase in clusters did not yield a cluster with a novel expression profile (as determined by eye). For both steady-state mRNA and polysomal mRNA, more than 90% of variance could be explained by five or more clusters. Adding a sixth cluster to the polysomal mRNA dataset resulted in a novel cluster (B.6; genes expressed at ring and schizont stage) that was not observed with five or less clusters. The optimal number of clusters was therefore determined to be five clusters for the steady-state mRNA dataset and six clusters for polysomal mRNA dataset. GO analysis was performed for each cluster using the Bioconductor R package goseq . Enriched GO terms were identified using a false discovery rate cutoff of 0.05.
Only genes that are located at least 1,000 bp from neighboring genes were included in analyses of 5′ UTR and 3′ UTR coverage. The extent of 5′ UTR coverage was calculated as the ratio between the number of reads that map to the first 500 bp upstream of the start codon and the number of reads that mapped to the coding sequence (expressed per 500 bp to correct for gene size). The numbers of reads mapping to the different gene regions are provided in Additional file 5.
Coverage plots were prepared by extracting the normalized read counts for the region of interest for all genes included in the analysis, scaling the read counts for each gene (z-score) and subsequently calculating the average value for each nucleotide position. Coverage profiles were smoothed in R using the function smooth.spline with a smoothing parameter of 0.35, and were subsequently plotted using bioconductor R package ggplot2. For the var genes, normalized read counts for exon 1, intron, and exon 2 were extracted separately and were divided into bins of approximately equal length (that is, 650 bins for exon 1, 100 bins for intron and 150 bins for exon 2). The average coverage of each bin was calculated and used for subsequent scaling and averaging across the total length of all var genes.
Semi-quantitative reverse transcription PCR
Reverse transcription was performed for unfragmented steady-state or polysome-associated mRNA using random hexamers and oligo-dT(20) as described above. For directional reverse transcription, three separate cDNA synthesis reactions were performed using 2 pmole of either a forward or a reverse gene-specific primer, or no primer as a control for self-priming (primers are listed in Table S6 in Additional file 2). Subsequently, semi-quantitative PCRs were performed using the KAPA HiFi Hotstart Ready Mix supplemented with 10 ng of cDNA and 10 pmole of both forward and reverse primers. DNA was amplified by incubation for 5 minutes at 95°C, followed by 35 cycles of 30 s at 98°C, 30 s at 55°C, 30 s at 62°C. Samples of 5 μl obtained after cycles 25, 30 and 35 were analyzed by agarose gel electrophoresis. For each primer set, a separate amplification reaction using genomic DNA was performed to control for differences in PCR efficiency.
Northern blotting analysis
Samples analyzed by northern blot were obtained from independent biological experiments as replicates of sequenced samples. Each RNA sample was loaded in two concentrations, containing 2 μg (left lane) and 8 μg (right lane) of total RNA, respectively. RNA was separated on a 1.2% formaldehyde-agarose gel for 3.5 hours at 40 V. After rinsing the gel twice for 15 minutes in 20X SSC, RNA was transferred for 2.5 hours to Hybond N + membrane (GE Healthcare, Waukesha, WI, USA) using Northern Max Transfer Buffer (Life Technologies), according to the manufacturer’s instructions. After transfer, RNA was cross-linked to the membrane by UV exposure. RNA detection was performed using the DIG Northern Starter Kit (Roche) according to the manufacturer’s instructions with minor modifications for the highly A/T-rich P. falciparum genome. Briefly, PCR products were amplified in advance using primers that included the sequence of the SP6 polymerase promoter (primers are listed in Table S6 in Additional file 2). DIG-labeled RNA probes were prepared by incubation of the PCR product with SP6 polymerase in the presence of DIG-labeled nucleotides for 2 hours at 42°C. RNA probes were diluted in ethanol, titrated, stored at -20°C and boiled for 5 minutes just before use. RNA blots were blocked for 30 minutes at 50 to 55°C in pre-warmed 1X DIG Easy Hyb solution and were then incubated O/N at 50 to 55°C in pre-warmed 1X DIG Easy Hyb buffer supplemented with 100 ng/ml of DIG-labeled probe. Blots were washed twice for 5 minutes in 2X SCC, 0.1% SDS at room temperature under constant agitation, followed by two 15 minute washes in pre-warmed 0.1X SSC, 0.1% SDS at 50 to 55°C under constant agitation. After these stringency washes, blots were rinsed in washing buffer, incubated for 30 minutes in blocking solution, incubated for 30 minutes in antibody solution, followed by two 15 minute washes in washing buffer and a 2 minute equilibration in detection buffer. Blots were developed using CDP-Star solution, and were exposed to X-ray film for approximately 25 minutes.
Analysis of coding potential
The coding potential of a region in the genome (5′ UTR, intron, or 3′ UTR) was determined by scanning for ORFs in all three translation frames. Out of all possible ORFs, only the longest was recorded. For var gene introns, this analysis was repeated for the antisense strand.
Detection of unannotated introns and alternative splice variants
The intron/exon boundaries of all intron-spanning reads (defined by samtools CIGAR string dMdNdM where d is one or more digits) were compared to annotated intron/exon boundaries and were selected for further inspection when one or both intron boundaries were different from current PlasmoDB (version 9.0) annotations. All introns with one unannotated intron/exon boundary supported by at least 5 sequence reads and all novel introns (both intron/exon boundaries unannotated) with at least 10 supporting reads were manually verified in a genome browser and were compared to previously reported alternative splice variants [4, 18, 53].
Analysis of intronic reads
Analysis of intronic reads was performed for genes without annotated alternative splice variants. For each intron, we determined the number of intron-spanning reads and the number of sequence reads for which at least 90% of the read length mapped to the intron itself. The ratio of intronic versus intron-spanning reads was then calculated by dividing the number of intronic reads per 100 nucleotides of intron length by the number of intron-spanning reads (one read was added to this number for calculation purposes). To reduce the chance that reads mapping to an intron were derived from an overlapping neighboring gene, only genes that are located at least 750 bp from their nearest annotated neighboring genes were analyzed. Introns with at least 10 reads per 100 nucleotides and at least twice the number of intronic reads versus intron-spanning reads were reported. The numbers of intronic and intron-spanning reads are provided in Additional file 5.
Tandem mass spectrometry
Multidimensional protein identification technology
New England Biolabs
Open reading frame
Upstream open reading frame
We thank Piyada Juntawong, Maureen Hummel, and Julia Bailey-Serres for their help in polysome profiling experiments, and John Weger, Rebecca Sun, and Glenn Hicks (Institute for Integrative Genome Biology, University of California Riverside) for their assistance in the library preparation and sequencing process. We thank Julie Bailey-Serres, Frances Sladek and Fedor Karginov for critical reading of the manuscript. The 3D7 parasite strain was obtained through the MR4 (MRA-102) deposited by DJ Carucci. This study was financially supported by the Human Frontier Science Program (grant LT000507/2011-L to EMB) and the National Institutes of Health (grant R01 AI85077-01A1 to KLR).
- WHO: World Malaria Report. 2012, http://www.who.int/malaria/publications/world_malaria_report_2012/en/,Google Scholar
- Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J, DeRisi JL: The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol. 2003, 1: E5-PubMedPubMed CentralView ArticleGoogle Scholar
- 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. 10.1126/science.1087025.PubMedView ArticleGoogle Scholar
- Otto TD, Wilinski D, Assefa S, Keane TM, Sarry LR, Bohme U, Lemieux J, Barrell B, Pain A, Berriman M, Newbold C, Llinás M: New insights into the blood-stage transcriptome of Plasmodium falciparum using RNA-Seq. Mol Microbiol. 2010, 76: 12-24. 10.1111/j.1365-2958.2009.07026.x.PubMedPubMed CentralView ArticleGoogle Scholar
- Balaji S, Babu MM, Iyer LM, Aravind L: Discovery of the principal specific transcription factors of Apicomplexa and their implication for the evolution of the AP2-integrase DNA binding domains. Nucleic Acids Res. 2005, 33: 3994-4006. 10.1093/nar/gki709.PubMedPubMed CentralView ArticleGoogle Scholar
- Coulson RM, Hall N, Ouzounis CA: Comparative genomics of transcriptional control in the human malaria parasite Plasmodium falciparum. Genome Res. 2004, 14: 1548-1554. 10.1101/gr.2218604.PubMedPubMed CentralView ArticleGoogle Scholar
- Ponts N, Harris EY, Lonardi S, Le Roch KG: Nucleosome occupancy at transcription start sites in the human malaria parasite: a hard-wired evolution of virulence?. Infect Genet Evol. 2011, 11: 716-724. 10.1016/j.meegid.2010.08.002.PubMedPubMed CentralView ArticleGoogle Scholar
- Ponts N, Harris EY, Prudhomme J, Wick I, Eckhardt-Ludka C, Hicks GR, Hardiman G, Lonardi S, Le Roch KG: Nucleosome landscape and control of transcription in the human malaria parasite. Genome Res. 2010, 20: 228-238. 10.1101/gr.101063.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Foth BJ, Zhang N, Mok S, Preiser PR, Bozdech Z: Quantitative protein expression profiling reveals extensive post-transcriptional regulation and post-translational modifications in schizont-stage malaria parasites. Genome Biol. 2008, 9: R177-10.1186/gb-2008-9-12-r177.PubMedPubMed CentralView ArticleGoogle Scholar
- Le Roch KG, Johnson JR, Florens L, Zhou Y, Santrosyan A, Grainger M, Yan SF, Williamson KC, Holder AA, Carucci DJ, Yates JR, Winzeler EA: Global analysis of transcript and protein levels across the Plasmodium falciparum life cycle. Genome Res. 2004, 14: 2308-2318. 10.1101/gr.2523904.PubMedPubMed CentralView ArticleGoogle Scholar
- Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M: Global quantification of mammalian gene expression control. Nature. 2011, 473: 337-342. 10.1038/nature10098.PubMedView ArticleGoogle Scholar
- Mair GR, Braks JA, Garver LS, Wiegant JC, Hall N, Dirks RW, Khan SM, Dimopoulos G, Janse CJ, Waters AP: Regulation of sexual development of Plasmodium by translational repression. Science. 2006, 313: 667-669. 10.1126/science.1125129.PubMedPubMed CentralView ArticleGoogle Scholar
- Hall N, Karras M, Raine JD, Carlton JM, Kooij TW, Berriman M, Florens L, Janssen CS, Pain A, Christophides GK, James K, Rutherford K, Harris B, Harris D, Churcher C, Quail MA, Ormond D, Doggett J, Trueman HE, Mendoza J, Bidwell SL, Rajandream MA, Carucci DJ, Yates JR, Kafatos FC, Janse CJ, Barrell B, Turner CM, Waters AP, Sinden RE: A comprehensive survey of the Plasmodium life cycle by genomic, transcriptomic, and proteomic analyses. Science. 2005, 307: 82-86. 10.1126/science.1103717.PubMedView ArticleGoogle Scholar
- Zhang M, Fennell C, Ranford-Cartwright L, Sakthivel R, Gueirard P, Meister S, Caspi A, Doerig C, Nussenzweig RS, Tuteja R, Sullivan WJ, Roos DS, Fontoura BM, Ménard R, Winzeler EA, Nussenzweig V: The Plasmodium eukaryotic initiation factor-2alpha kinase IK2 controls the latency of sporozoites in the mosquito salivary glands. J Exp Med. 2010, 207: 1465-1474. 10.1084/jem.20091975.PubMedPubMed CentralView ArticleGoogle Scholar
- Aravind L, Iyer LM, Wellems TE, Miller LH: Plasmodium biology: genomic gleanings. Cell. 2003, 115: 771-785. 10.1016/S0092-8674(03)01023-7.PubMedView ArticleGoogle Scholar
- Lacsina JR, LaMonte G, Nicchitta CV, Chi JT: Polysome profiling of the malaria parasite Plasmodium falciparum. Mol Biochem Parasitol. 2011, 179: 42-46. 10.1016/j.molbiopara.2011.05.003.PubMedPubMed CentralView ArticleGoogle Scholar
- Ponts N, Saraf A, Chung DW, Harris A, Prudhomme J, Washburn MP, Florens L, Le Roch KG: Unraveling the ubiquitome of the human malaria parasite. J Biol Chem. 2011, 286: 40320-40330. 10.1074/jbc.M111.238790.PubMedPubMed CentralView ArticleGoogle Scholar
- Sorber K, Dimon MT, DeRisi JL: RNA-Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts. Nucleic Acids Res. 2011, 39: 3820-3835. 10.1093/nar/gkq1223.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.PubMedPubMed CentralView ArticleGoogle Scholar
- Ingolia NT, Lareau LF, Weissman JS: Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell. 2011, 147: 789-802. 10.1016/j.cell.2011.10.002.PubMedPubMed CentralView ArticleGoogle Scholar
- Franklin TJ, Snow GA: Inhibitors of protein biosynthesis. Biochemistry and Molecular Biology of Antimicrobial Drug Action. Volume 6. 2005, New York: Springer, 104Google Scholar
- Calvo SE, Pagliarini DJ, Mootha VK: Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans. Proc Natl Acad Sci U S A. 2009, 106: 7507-7512. 10.1073/pnas.0810916106.PubMedPubMed CentralView ArticleGoogle Scholar
- Kozak M: Structural features in eukaryotic mRNAs that modulate the initiation of translation. J Biol Chem. 1991, 266: 19867-19870.PubMedGoogle Scholar
- Rajkowitsch L, Vilela C, Berthelot K, Ramirez CV, McCarthy JE: Reinitiation and recycling are distinct processes occurring downstream of translation termination in yeast. J Mol Biol. 2004, 335: 71-85. 10.1016/j.jmb.2003.10.049.PubMedView ArticleGoogle Scholar
- Gaba A, Jacobson A, Sachs MS: Ribosome occupancy of the yeast CPA1 upstream open reading frame termination codon modulates nonsense-mediated mRNA decay. Mol Cell. 2005, 20: 449-460. 10.1016/j.molcel.2005.09.019.PubMedView ArticleGoogle Scholar
- Nyiko T, Sonkoly B, Merai Z, Benkovics AH, Silhavy D: Plant upstream ORFs can trigger nonsense-mediated mRNA decay in a size-dependent manner. Plant Mol Biol. 2009, 71: 367-378. 10.1007/s11103-009-9528-4.PubMedView ArticleGoogle Scholar
- Arribere JA, Gilbert WV: Roles for transcript leaders in translation and mRNA decay revealed by Transcript Leader Sequencing. Genome Res. 2013, 23: 977-987. 10.1101/gr.150342.112.PubMedPubMed CentralView ArticleGoogle Scholar
- Shock JL, Fischer KF, DeRisi JL: Whole-genome analysis of mRNA decay in Plasmodium falciparum reveals a global lengthening of mRNA half-life during the intra-erythrocytic development cycle. Genome Biol. 2007, 8: R134-10.1186/gb-2007-8-7-r134.PubMedPubMed CentralView ArticleGoogle Scholar
- Bonetti B, Fu L, Moon J, Bedwell DM: The efficiency of translation termination is determined by a synergistic interplay between upstream and downstream sequences in Saccharomyces cerevisiae. J Mol Biol. 1995, 251: 334-345. 10.1006/jmbi.1995.0438.PubMedView ArticleGoogle Scholar
- Cassan M, Rousset JP: UAG readthrough in mammalian cells: effect of upstream and downstream stop codon contexts reveal different signals. BMC Mol Biol. 2001, 2: 3-10.1186/1471-2199-2-3.PubMedPubMed CentralView ArticleGoogle Scholar
- Firth AE, Wills NM, Gesteland RF, Atkins JF: Stimulation of stop codon readthrough: frequent presence of an extended 3′ RNA structural element. Nucleic Acids Res. 2011, 39: 6679-6691. 10.1093/nar/gkr224.PubMedPubMed CentralView ArticleGoogle Scholar
- Jungreis I, Lin MF, Spokony R, Chan CS, Negre N, Victorsen A, White KP, Kellis M: Evidence of abundant stop codon readthrough in Drosophila and other metazoa. Genome Res. 2011, 21: 2096-2113. 10.1101/gr.119974.110.PubMedPubMed CentralView ArticleGoogle Scholar
- Williams I, Richardson J, Starkey A, Stansfield I: Genome-wide prediction of stop codon readthrough during translation in the yeast Saccharomyces cerevisiae. Nucleic Acids Res. 2004, 32: 6605-6616. 10.1093/nar/gkh1004.PubMedPubMed CentralView ArticleGoogle Scholar
- Berry MJ, Banu L, Larsen PR: Type I iodothyronine deiodinase is a selenocysteine-containing enzyme. Nature. 1991, 349: 438-440. 10.1038/349438a0.PubMedView ArticleGoogle Scholar
- Agrawal S, Chung DW, Ponts N, van Dooren GG, Prudhomme J, Brooks CF, Rodrigues EM, Tan JC, Ferdig MT, Striepen B, Le Roch KG: An apicoplast localized ubiquitylation system is required for the import of nuclear-encoded plastid proteins. PLoS Pathog. 2013, 9: e1003426-10.1371/journal.ppat.1003426.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen Q, Fernandez V, Sundstrom A, Schlichtherle M, Datta S, Hagblom P, Wahlgren M: Developmental selection of var gene expression in Plasmodium falciparum. Nature. 1998, 394: 392-395. 10.1038/28660.PubMedView ArticleGoogle 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. 10.1016/j.cell.2005.01.036.PubMedView ArticleGoogle Scholar
- Dzikowski R, Li F, Amulic B, Eisberg A, Frank M, Patel S, Wellems TE, Deitsch KW: Mechanisms underlying mutually exclusive expression of virulence genes by malaria parasites. EMBO Rep. 2007, 8: 959-965. 10.1038/sj.embor.7401063.PubMedPubMed CentralView ArticleGoogle 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. 10.1016/j.chom.2008.12.012.PubMedView ArticleGoogle Scholar
- Freitas-Junior LH, Hernandez-Rivas R, Ralph SA, Montiel-Condado D, Ruvalcaba-Salazar OK, Rojas-Meza AP, Mancio-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. 10.1016/j.cell.2005.01.037.PubMedView ArticleGoogle Scholar
- 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. Mol Microbiol. 2007, 66: 1296-1305.PubMedPubMed CentralGoogle Scholar
- Epp C, Li F, Howitt CA, Chookajorn T, Deitsch KW: Chromatin associated sense and antisense noncoding RNAs are transcribed from the var gene family of virulence genes of the malaria parasite Plasmodium falciparum. RNA. 2009, 15: 116-127.PubMedPubMed CentralView ArticleGoogle Scholar
- Tarique M, Ahmad M, Ansari A, Tuteja R: Plasmodium falciparum DOZI, an RNA helicase interacts with eIF4E. Gene. 2013, 522: 46-59. 10.1016/j.gene.2013.03.063.PubMedView ArticleGoogle Scholar
- Russell K, Hasenkamp S, Emes R, Horrocks P: Analysis of the spatial and temporal arrangement of transcripts over intergenic regions in the human malarial parasite Plasmodium falciparum. BMC Genomics. 2013, 14: 267-10.1186/1471-2164-14-267.PubMedPubMed CentralView ArticleGoogle Scholar
- Hasenkamp S, Russell K, Ullah I, Horrocks P: Functional analysis of the 5′ untranslated region of the phosphoglutamase 2 transcript in Plasmodium falciparum. Acta tropica. 2013, 127: 69-74. 10.1016/j.actatropica.2013.03.007.PubMedView ArticleGoogle Scholar
- Bancells C, Deitsch KW: A molecular switch in the efficiency of translation reinitiation controls expression of var2csa, a gene implicated in pregnancy associated malaria. Mol Microbiol. 2013, 90: 472-488. 10.1111/mmi.12379.PubMedPubMed CentralView ArticleGoogle Scholar
- Oliveira CC, McCarthy JE: The relationship between eukaryotic translation and mRNA stability, A short upstream open reading frame strongly inhibits translational initiation and greatly accelerates mRNA degradation in the yeast Saccharomyces cerevisiae. J Biol Chem. 1995, 270: 8936-8943. 10.1074/jbc.270.15.8936.PubMedView ArticleGoogle Scholar
- Cao J, Geballe AP: Translational inhibition by a human cytomegalovirus upstream open reading frame despite inefficient utilization of its AUG codon. J Virol. 1995, 69: 1030-1036.PubMedPubMed CentralGoogle Scholar
- Mize GJ, Ruan H, Low JJ, Morris DR: The inhibitory upstream open reading frame from mammalian S-adenosylmethionine decarboxylase mRNA has a strict sequence specificity in critical positions. J Biol Chem. 1998, 273: 32500-32505. 10.1074/jbc.273.49.32500.PubMedView ArticleGoogle Scholar
- Charneski CA, Hurst LD: Positively charged residues are the major determinants of ribosomal velocity. PLoS Biol. 2013, 11: e1001508-10.1371/journal.pbio.1001508.PubMedPubMed CentralView ArticleGoogle Scholar
- Rahmani F, Hummel M, Schuurmans J, Wiese-Klinkenberg A, Smeekens S, Hanson J: Sucrose control of translation mediated by an upstream open reading frame-encoded peptide. Plant Physiol. 2009, 150: 1356-1367. 10.1104/pp.109.136036.PubMedPubMed CentralView ArticleGoogle Scholar
- Miura P, Shenker S, Andreu-Agullo C, Westholm JO, Lai EC: Widespread and extensive lengthening of 3′ UTRs in the mammalian brain. Genome Res. 2013, 23: 12-25. 10.1101/gr.139469.112.View ArticleGoogle Scholar
- Lopez-Barragan MJ, Lemieux J, Quinones M, Williamson KC, Molina-Cruz A, Cui K, Barillas-Mury C, Zhao K, Su XZ: Directional gene expression and antisense transcripts in sexual and asexual stages of Plasmodium falciparum. BMC Genomics. 2011, 12: 587-10.1186/1471-2164-12-587.PubMedPubMed CentralView ArticleGoogle Scholar
- Gunasekera AM, Patankar S, Schug J, Eisen G, Kissinger J, Roos D, Wirth DF: Widespread distribution of antisense transcripts in the Plasmodium falciparum genome. Mol Biochem Parasitol. 2004, 136: 35-42. 10.1016/j.molbiopara.2004.02.007.PubMedView ArticleGoogle Scholar
- Rearick D, Prakash A, McSweeny A, Shepard SS, Fedorova L, Fedorov A: Critical association of ncRNA with introns. Nucleic Acids Res. 2011, 39: 2357-2366. 10.1093/nar/gkq1080.PubMedPubMed CentralView ArticleGoogle Scholar
- Chakrabarti K, Pearson M, Grate L, Sterne-Weiler T, Deans J, Donohue JP, Ares M: Structural RNAs of known and unknown function identified in malaria parasites by comparative genomics and RNA analysis. RNA. 2007, 13: 1923-1939. 10.1261/rna.751807.PubMedPubMed CentralView ArticleGoogle Scholar
- Mourier T, Carret C, Kyes S, Christodoulou Z, Gardner PP, Jeffares DC, Pinches R, Barrell B, Berriman M, Griffiths-Jones S, Ivans A, Newbold C, Pain A: Genome-wide discovery and verification of novel structured RNAs in Plasmodium falciparum. Genome Res. 2008, 18: 281-292. 10.1101/gr.6836108.PubMedPubMed CentralView ArticleGoogle Scholar
- Sterne-Weiler T, Martinez-Nunez RT, Howard JM, Cvitovik I, Katzman S, Tariq MA, Pourmand N, Sanford JR: Frac-seq reveals isoform-specific recruitment to polyribosomes. Genome Res. 2013, 23: 1615-1623. 10.1101/gr.148585.112.PubMedPubMed CentralView ArticleGoogle Scholar
- Trager W, Jensen JB: Human malaria parasites in continuous culture. Science. 1976, 193: 673-675. 10.1126/science.781840.PubMedView ArticleGoogle Scholar
- Lambros C, Vanderberg JP: Synchronization of Plasmodium falciparum erythrocytic stages in culture. J Parasitol. 1979, 65: 418-420. 10.2307/3280287.PubMedView ArticleGoogle Scholar
- McDonald WH, Ohi R, Miyamoto DT, Mitchison TJ, Yates JR: Comparison of three directly coupled HPLC MS/MS strategies for identification of proteins from complex mixtures: single-dimension LCMS/MS, 2-phase MudPIT, and 3-phase MudPIT. Int J Mass Spectrom. 2002, 251: 245-251.View ArticleGoogle Scholar
- Florens L, Washburn MP: Proteomic analysis by multidimensional protein identification technology. Methods Mol Biol. 2006, 328: 159-175.PubMedGoogle Scholar
- Eng JK, McCormack AL, Yates JR: An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom. 1994, 5: 976-989. 10.1016/1044-0305(94)80016-2.PubMedView ArticleGoogle Scholar
- Tabb DL, McDonald WH, Yates JR: DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res. 2002, 1: 21-26. 10.1021/pr015504q.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang Y, Wen Z, Washburn MP, Florens L: Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal Chem. 2010, 82: 2272-2281. 10.1021/ac9023999.PubMedView ArticleGoogle Scholar
- ProteomeXchange. http://proteomecentral.proteomexchange.org,
- Vizcaino JA, Cote RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell J, O’Kelly G, Schoenegger A, Ovelleiro D, Pérez-Riverol Y, Reisinger F, Rios D, Wang R, Hermjakob J: The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013, 41: D1063-D1069. 10.1093/nar/gks1262.PubMedPubMed CentralView ArticleGoogle Scholar
- Stowers Institute Original Data Repository. http://srdr.stowers.org/,
- Yassour M, Pfiffner J, Levin JZ, Adiconis X, Gnirke A, Nusbaum C, Thompson DA, Friedman N, Regev A: Strand-specific RNA sequencing reveals extensive regulated long antisense transcripts that are conserved across yeast species. Genome Biol. 2010, 11: R87-10.1186/gb-2010-11-8-r87.PubMedPubMed CentralView ArticleGoogle Scholar
- FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/index.html,
- Scythe. https://github.com/ucdavis-bioinformatics/scythe,
- Sickle. https://github.com/ucdavis-bioinformatics/sickle,
- PlasmoDB. http://plasmodb.org/plasmo/,
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.PubMedPubMed CentralView ArticleGoogle Scholar
- Picard. http://picard.sourceforge.net/,
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033.PubMedPubMed CentralView ArticleGoogle Scholar
- Risso D, Schwartz K, Sherlock G, Dudoit S: GC-content normalization for RNA-Seq data. BMC Bioinforma. 2011, 12: 480-10.1186/1471-2105-12-480.View ArticleGoogle Scholar
- Young MD, Wakefield MJ, Smyth GK, Oshlack A: Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010, 11: R14-10.1186/gb-2010-11-2-r14.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.