Research Article

An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum

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Science  15 May 2020:
Vol. 368, Issue 6492, pp. 754-759
DOI: 10.1126/science.aba4357

Plasmodium's inner clock

Malarial fevers are notably regular, occurring when parasitized red blood cells rupture synchronously to release replicated parasites. It has long been speculated that the Plasmodium parasites that cause malaria must therefore have intrinsic circadian clocks to be able to synchronize like this. Two groups have now probed gene expression in experiments and models using data obtained during the developmental cycles of P. falciparum in vitro and in the mouse model of P. chabaudi malaria. Smith et al. discovered that four strains of P. falciparum have circadian and cell cycle oscillators, each with distinctive periodicities that can be experimentally manipulated. Rijo-Ferreira et al. found that gene expression in P. chabaudi was strikingly rhythmic, persisted during constant darkness and in infections of arrhythmic mice, and synchronized by entraining to the host's periodicity.

Science, this issue p. 754, p. 746


The blood stage of the infection of the malaria parasite Plasmodium falciparum exhibits a 48-hour developmental cycle that culminates in the synchronous release of parasites from red blood cells, which triggers 48-hour fever cycles in the host. This cycle could be driven extrinsically by host circadian processes or by a parasite-intrinsic oscillator. To distinguish between these hypotheses, we examine the P. falciparum cycle in an in vitro culture system and show that the parasite has molecular signatures associated with circadian and cell cycle oscillators. Each of the four strains examined has a different period, which indicates strain-intrinsic period control. Finally, we demonstrate that parasites have low cell-to-cell variance in cycle period, on par with a circadian oscillator. We conclude that an intrinsic oscillator maintains Plasmodium’s rhythmic life cycle.

Malaria and its causal parasite, the Plasmodium genus, are fundamentally rhythmic entities. Patients infected with Plasmodium falciparum often exhibit 48-hour fever cycles, and these cycles coincide with the blood stage of the infection, where the parasite progresses through the asexual intraerythrocytic cycle. After infection of the erythrocyte [red blood cell (RBC)], parasites transit through three morphologically distinct developmental stages that can be visualized by light microscopy: rings, trophozoites, and schizonts. Parasites are in the ring stage immediately after RBC invasion, and they divide asexually multiple times during the schizont stage. At the end of the schizont stage, the RBC bursts and releases merozoites, which quickly invade new host cells and begin the cycle anew. The infecting population of parasites in the host tends to undergo this cycle synchronously, and the subsequent release of merozoites is responsible for the characteristic periodic fevers seen in many patients (1). The human-infecting species of Plasmodium repeat this cycle every 24, 48, or 72 hours (depending on the species), which suggests that cycles could be driven by a host’s circadian cycle or a parasite-intrinsic oscillator with circadian periodicity (2). Notably, multiple animal studies have demonstrated that Plasmodium infections appear to synchronize with their host’s 24-hour circadian rhythms (25).

The source of the parasite’s rhythmic life cycle is a central, unsolved question. It is possible that the intraerythrocytic-cycle periodicity is driven by extrinsic temporal cues in the host environment that trigger the parasite’s developmental cascade (fig. S1A). Several rhythmic host factors have been suggested to affect Plasmodium dynamics, including temperature (2), melatonin (6), glucose, and tumor necrosis factor–α (TNFα) (7). Additionally, recent studies have revealed the existence of an independent 24-hour oscillator in the redox state of peroxiredoxins, a highly conserved family of prominent cellular proteins; these oscillations continue even in isolated RBCs (8). Thus, it is possible that Plasmodium, even grown in culture, merely responds to an extrinsic oscillating program (fig. S1A). An example of such activity can be found in plants, where several rhythmic biological processes are driven by light rather than by the plant’s innate rhythm (9).

Alternatively, Plasmodium may have an intrinsic biological oscillator that generates its rhythms independently from the host. The best-known examples of endogenous oscillators are circadian biological clocks, which are found across a wide range of taxa and affect a substantial array of functions. These 24-hour oscillators emerge from highly interconnected, autoregulatory gene networks that contain transcription-translation feedback loop motifs (10, 11). The oscillators themselves are free-running, but they temporally align to cyclic entrainment signals, driven by the 24-hour cycle imposed by Earth’s rotation. A wide variety of genes may be under circadian control, exhibiting 24-hour cyclic expression. The period length of the circadian rhythm is set by the core gene network underlying the clock (11); mutations may result in shortened, lengthened, or absent rhythms (12, 13). Similar programs of periodic gene expression are observed during cell division and have also been proposed to be driven by oscillating gene-regulatory networks (14, 15). Unlike circadian oscillators, cell cycle oscillators do not necessarily exhibit 24-hour periods and are not necessarily aligned with or entrained by light-dark cycles.

The fact that Plasmodium spp. exhibit rhythms that are usually multiples of 24 hours suggests that they may have an intrinsic oscillator network similar to a circadian oscillator. Although Plasmodium genomes do not appear to contain orthologs of canonical circadian clock genes (5), this does not rule out the possibility of an intrinsic network, similar in structure to either circadian or cell-cycle networks and capable of producing rhythms at multiples of 24 hours.

In this study, we investigate the rhythmic behavior of the P. falciparum intraerythrocytic cycle in an in vitro culture system, where canonical circadian signals from the host were not present. Using high-density time-series transcriptomics and microscopy for four strains of P. falciparum, we compare several key molecular features of these cycles with molecular signatures produced by circadian networks and eukaryotic cell cycle oscillators (14, 16). Our findings provide strong evidence for the existence of an intrinsic oscillator in Plasmodium and suggest that parasites have evolved mechanisms to drive periodicity that may align with host circadian rhythms.

Qualitative similarities to biological oscillator transcriptomes

To assess the molecular features of the P. falciparum temporal transcriptome, we performed high-density time-series transcriptomic analyses for the strains 3D7 (17), FVO-NIH (18), SA250 (19), and D6 (20). These strains were chosen for their diverse geographic origins and cycle lengths, with some strains varying from the wild-type 48-hour cycle. Synchronized parasite populations were cultured in vitro for 60 to 70 hours, with time points sampled every 3 hours for transcriptomic analysis and microscopic observation. This sampling schedule allowed for the completion of one to two intraerythrocytic cycles per strain. RNA was isolated from each time point and subjected to RNA sequencing (RNA-seq) analysis to quantify the abundance of P. falciparum transcripts at each time point (Fig. 1 and data S1). For three of the four strains (D6, FVO-NIH, and SA250), experiments were performed on the same days in the same conditions with blood from a single human donor to eliminate variability caused by growth conditions (materials and methods).

Fig. 1 The majority of P. falciparum genes are periodically transcribed.

Four strains of P. falciparum were cultured in vitro and transcriptionally profiled using time-series RNA-seq. Periodic genes were identified in each strain by JTK_CYCLE, and strain-intrinsic period length is evident. Each vertical line represents a time point, and gene expression is displayed horizontally. Expression values are mean-normalized for each gene and depicted as a z score of standard deviations from the mean. Genes are ordered per strain by peak expression time. The color scale shows z scores, ranging from −1.5 (blue) to 1.5 (yellow).

A key characteristic of circadian or cell cycle oscillators is a well-ordered program of periodic gene expression (14, 16). For each P. falciparum strain, we used the periodicity-detecting algorithm JTK_CYCLE (21) to estimate the number of rhythmic genes (table S1 and data S1 and S2). Periodic genes are largely conserved among the four strains, with the majority of each strain’s periodic genes (79 to 82%) overlapping all those of the other strains (fig. S2), which is a finding similar to previous observations (22, 23). The vast majority of the mapped transcriptome (between 87.3 and 92.5%) in P. falciparum appears to be periodically expressed (Fig. 1, table S1, and data S1 and S2) on the basis of visual inspection. These phase-specific, oscillating genes peak in expression across the entire cycle, forming a cascade of rhythmic genes in a manner that is highly reminiscent of other oscillators.

Mammalian transcriptomic studies have noted that circadian genes tend to peak in phase clusters (“rush hours”) near dawn and dusk, and this clustering is thought to represent the activation of expression in anticipation of metabolic events (16, 24). To evaluate the timing of gene peaks in P. falciparum, 3703 periodic genes shared between all strains were mapped to a single representative cycle using a wrapping procedure that averaged overlapping measurements, similar in principle to phase dispersion minimization (25) (materials and methods). This approach allowed comparison between parasites with different cycle times and, using microscopic assessments of intraerythrocytic-cycle phase (fig. S3), allowed the mapping of the timing of gene peaks throughout the cycle (Fig. 2). Notably, we did not observe any evidence of rush hours in any of the phases of the intraerythrocytic cycle (Fig. 2).

Fig. 2 The periodic genes of P. falciparum are expressed in multiple phases of the intraerythrocytic cycle.

Polar graphs depict the relative numbers of genes peaking per hour of each strain’s cycle. The boundaries between phases (ring, trophozoite, and schizont) were determined by microscopy (fig. S3) and marked in hours (table S5). Wrapped, interpolated expression data for the set of shared periodic genes (materials and methods) were used to assign peak times.

Multiple studies have shown that in both vertebrate and invertebrate circadian rhythms, a subset of genes oscillate at 8- and 12-hour periods (26, 27). Although the precise mechanisms of these harmonic rhythms have not been dissected, evidence from Clock gene mutants in mice indicates that they arise from the core circadian clock mechanism (28). To search for such harmonics in P. falciparum, we took advantage of JTK_CYCLE’s period prediction features by running the algorithm with a period search range of 6 to 60 hours (6 to 54 hours in the case of D6). A minority of genes (between 3.5 and 4.3%) in each strain have a predicted period length that is roughly half that of the strain (Fig. 3 and table S2). Visualizing the expression of these genes confirms that they peak twice per cycle (Fig. 3 and fig. S4). Among this set of harmonic genes, most are specific to one strain; only three harmonic genes were identified in all four strains (fig. S5).

Fig. 3 P. falciparum strains exhibit periodic genes at half the normal cycle length (harmonic expression).

JTK_CYCLE was used to search for genes in a wide range of predicted period lengths; the distribution of predicted period lengths is shown for each strain. A minority of genes oscillate at approximately half the dominant period in each strain, indicated with an arrow: 21 hours in 3D7, 24 hours in FVO-NIH, 27 to 33 hours in SA250, and 18 hours in D6. These genes are plotted by heat map; each vertical line represents a time point, and gene expression is displayed horizontally. Expression values are mean-normalized for each gene and depicted as a z score of standard deviations from the mean.

Quantitative characteristics of biological oscillators

Cell cycle and circadian oscillators tend to produce well-ordered programs of transcription; however, substantial evolutionary divergence between species may yield unexpected ordering of orthologous periodic genes, correlating with substantial phenotypic changes that may reflect rewired networks (29). We assessed the temporal ordering of the transcriptome between strains using a set of periodic genes shared between all four strains. For each heatmap in Fig. 4A, the genes are plotted in the order of peak time of expression in the strain 3D7. Visual inspection indicates that the ordering of peak gene expression is globally well conserved between all four strains (Fig. 4A). Similar qualitative levels of conservation were observed when the analysis was repeated using each of the remaining strains as the ordering standard (fig. S6).

Fig. 4 Ordering of periodic gene expression is broadly conserved among four strains of P. falciparum, comparable to mouse circadian genes.

(A) The ordering of genes for 3D7, as determined by peak expression time, was applied to the remaining three strains (FVO-NIH, SA250, and D6). The resulting heat maps show highly conserved ordering (see Fig. 1 for comparison). Each vertical line represents a time point, and gene expression is displayed horizontally. Expression values are mean-normalized for each gene and depicted as a z score of standard deviations from the mean. (B and C) A set of periodic genes from parasite (119 genes) and mouse circadian data (107 genes) was identified that peaked in very similar cycle phase to the reference strain (3D7) or tissue (liver). A null baseline distribution was created, and both the baseline and in-phase genes were sampled 5000 times in sets of six genes to produce estimates of gene ordering similarity across strains or tissues. (B) Mean and standard deviation of ordering similarity to reference strain or tissue, averaged across all samples and all applied levels of noise (6 to 10% ε). (C) Percent of in-phase samples above baseline.

To make a quantitative determination of the similarity in ordering, a recently developed method was used to measure the conservation of gene ordering between datasets (30). In brief, extrema (peaks and troughs) for each gene’s normalized expression pattern were assigned a time interval, and a partial order was computed. The use of partial orders allows a quantitative assessment while acknowledging that the order of some extrema cannot be distinguished because they both fall at the same time point. To account for stochastic behavior of gene expression and the relatively coarse sampling, partial orders were evaluated in the presence of 6 to 10% noise (ε) (Fig. 4, B and C). A similarity score for each ε is computed between partial orders of the same subset of genes from two datasets (30). We calculated the conservation of gene ordering among our strains of P. falciparum and compared this conservation to a collection of time-series data obtained from an established circadian oscillator. For the latter, we used the high-density time-series data of Zhang et al. (16), which profiled the circadian transcriptomes of 12 mouse organ tissues every 2 hours for 48 hours. In both species, we used subsets of periodic genes that peaked at similar times across parasite strains or mouse tissues, which we call “in-phase” subsets, and computed a null baseline by randomizing the corresponding time series within each dataset (materials and methods).

We found that the ordering of similarity scores of the null baselines for circadian and parasite data were comparable, with in-phase similarity scores equivalent or higher in P. falciparum (Fig. 4, B and C, and table S3). Furthermore, the percentage of samples above the null baseline in all three parasite strains (75 to 95%) was greater than in both mouse circadian tissues (61% in lung and 68% in kidney; Fig. 4C and table S3). Although it has been observed that circadian genes are not perfectly ordered across mouse tissues (16), the observation that P. falciparum strains exhibit ordering comparable to or better than that of circadian genes suggests that the mechanisms guiding the parasite’s transcriptional cycle are capable of maintaining high-fidelity ordering.

The analyses thus far indicate that P. falciparum’s transcriptome dynamics share features with known, cell-intrinsic biological oscillators, but the results could still be consistent with an extrinsic mechanism for the control of periodicity. Although most conventional rhythmic host cues are absent in RBC culture conditions, a 24-hour peroxiredoxin clock identified in RBCs (8) could be sufficient to drive intraerythrocytic-cycle periodicity by repeated cascades of gene expression.

To ascertain the period length of the intraerythrocytic cycle for each of our cultured strains, we used several distinct metrics to avoid bias from any one method. We interpolated and wrapped each strain’s transcriptome (fig. S7) and microscopic culture progression curves for ring, trophozoite, and schizont stages (fig. S8) to find the periods that minimized overlapping error for each strain (materials and methods and table S4). We also measured the distances between recurring expression peaks and expression troughs for each rhythmic gene and identified the modal value in these peak-distance and trough-distance distributions, providing two more estimates of genetic period length (figs. S9 and S10 and table S2). Because of some disagreement between metrics in certain cases, we established a final estimated period by using a weighted average of all four metrics (table S2). We observe that the strains differ in period regardless of the method of estimation and that the rank order of strains in terms of period length is the same.

Although the typical in vivo P. falciparum infection exhibits a 48-hour periodicity, we observed that each cultured strain had a different period length that sometimes varied substantially from 48 hours (table S2). These variations are also apparent in the visualization of each strain’s periodic transcriptomes (Fig. 1), and the observed variations are not due to growth conditions, as three of four strains were cultured in parallel in blood from a single donor (materials and methods and data S3). Strain SA250 has the longest estimated cycle at 54 hours, whereas strain D6’s cycle is a mere 36 hours long. These results are incompatible with the hypothesis that the 24-hour peroxiredoxin clock is responsible for controlling the parasite’s rhythm; if that were the case, period lengths in all four strains would be roughly 48 hours long. The observations are, however, consistent with what is known about cell-intrinsic oscillators, in which period length is genetically controlled (11, 12, 31).

The pronounced diversity in cycle period lengths between P. falciparum strains raises the question of how such shortening and lengthening from the wild-type’s 48-hour cycle period has occurred. In the eukaryotic cell cycle, the length of the G1 phase is the most flexible because factors such as cell size and the availability of nutrients or growth factors all affect the ability of the cells to move into the S phase. If P. falciparum’s intraerythrocytic cycle stages are analogous to the cell cycle phases, as has been suggested (32), then the bulk of the changes in period length between strains may be confined to particular stages of the cell cycle.

Using microscopy data rewrapped to the final estimated cycle lengths (table S4 and fig. S11), stages are labeled on the basis of dominant parasite phase (>50% of parasites), and the length of each stage is calculated as a percent of the total period length. We are unable to detect a single stage or stages that show particular conservation or flexibility in length, whether measured in hours or in percent of the period length (table S5). Although the ring stage shows the most variability and the trophozoite stage appears to be the most stable, there is no consistent correlation between total period lengths and stage lengths. The intraerythrocytic cycle appears to be plastic in terms of lengthening and shortening throughout all stages.

Variance in period length in culture

When Trager and Jensen published the first protocol for in vitro culture of P. falciparum in 1976, they noted that an initially synchronous parasite population from a clinical malaria sample eventually desynchronizes in culture, becoming a heterogeneous mix of ring, trophozoite, and schizont stages (33). It has come to be broadly accepted in the field that synchronized parasites lose synchrony rapidly in culture, an observation that would appear to be largely inconsistent with a robust intrinsic oscillator. However, cells synchronized in the cell cycle lose synchrony over time (34), and circadian oscillators also lose synchrony in cell-based systems in the absence of entrainment cues (35) because of variance in the individual cycle times.

To determine whether synchrony loss owing to the cell-to-cell variance observed in populations of P. falciparum is compatible with the variance in periods observed in cell-intrinsic circadian oscillations, we fit a simple phase-oscillator model (see materials and methods) to the microscopic staging data for the four strains along with an additional strain, HB3, from a prior study (23) (fig. S1, B and C, and fig. S12). We calculated the coefficient of variation (CoV) for all P. falciparum strains from this model and compared these values to the calculated CoV of single-cell traces of a circadian reporter gene in human fibroblast cells (36) (table S6). The CoV of circadian cycles in the fibroblast population was estimated to be 0.0845, which means that the period lengths exhibit a standard deviation of roughly 8.45% of the average period length. Notably, the estimated CoVs of the five P. falciparum strain cycles were similar or smaller, ranging between 0.23 and 10.18% of the estimated mean cycle period, depending on the strain (Fig. 5 and table S6). Increasing or decreasing the mean cycle period by 1 hour does not appreciably change the estimated CoV (Fig. 5 and table S6).

Fig. 5 Variation of period lengths between cycles in P. falciparum is comparable to a circadian model.

Microscopic times-series data from this study, along with additional data from strain HB3 (23), were fit to a phase-oscillator model, yielding an estimated standard deviation of cycle length as percent of the mean cycle length. The dashed line represents the empirical standard deviation (std. dev.) of the circadian cycle derived from single-cell imaging of circadian reporters in fibroblast cells (36). Estimates are also shown if the mean cycle length is lengthened or shortened by 1 hour (table S6).

These results were produced using best-fit estimates for parameters in the phase-oscillator model, such as strain-specific period length and initial population synchrony (table S6). To ensure that the model was not artificially lowering estimates for period variability by adjusting other parameters, we recomputed optimal model parameters while enforcing a minimum-allowed period variability that is larger than the empirically determined circadian oscillator (materials and methods and table S7). This alteration substantially decreases the model’s fit to the experimental data, particularly in the second round of replication, where dampening due to synchrony loss becomes more apparent (fig. S12).

It has recently been suggested that microscopic staging curves overestimate parasite population synchrony of P. falciparum in culture (37), because parasite replication during the schizont stage enforces synchrony on the schizont-ring transition. To make sure that our models were not unfairly biasing our estimates of variance, we explicitly added replication to the model at the schizont-ring transition, assuming a parasite multiplication rate of N = 4, 8, or 16 (materials and methods). We find that, with the addition of replication, the dynamics of the experimental staging curves are better matched by modeling populations with smaller variance in period length than those found in the simple oscillator model (fig. S13). Thus, including replication in our model bolsters the finding that P. falciparum’s estimated period length variability, and therefore the rate at which the population loses synchrony, is comparable to a circadian oscillator.


Periodic biological processes can arise from cell- or organism-intrinsic oscillators or may be imposed by extrinsic rhythmic factors. Classic examples of cell-intrinsic biological oscillators include circadian rhythms and the eukaryotic cell cycle (11, 14). In this work, we investigated the origin of periodicity in the intraerythrocytic cycle of P. falciparum using an in vitro culture system where extrinsic rhythmic cues are largely eliminated. We find that P. falciparum shares many molecular signatures of well-characterized cellular oscillators and that the data are most consistent with a model in which rhythmic behaviors are driven by a parasite-intrinsic oscillator.

Consistent with the observations of previous studies (22, 23, 38), we find that the majority (87.3 to 92.6%) of P. falciparum’s mapped genes are rhythmic in their expression during the cycle. This large periodic program of phase-specific transcription is also observed in cell cycle (~15 to 20% of the genome) (14) and circadian systems (~40 to 80% of the genome) (16, 24), although rhythmicity itself does not indicate an intrinsic oscillator. Circadian control of gene expression has been proposed to link various physiological processes with light-dark cycles (e.g., sleep-wake cycles) (11) or to temporally separate incompatible biochemical processes (39), whereas the temporal program of expression in the cell cycle is a foundational mechanism for ordering cell cycle events (40). Unlike circadian gene expression, which tends to cluster roughly near dawn and dusk, there does not appear to be a consistent pattern of phase clustering in P. falciparum. Given the ordered nature of P. falciparum development during the intraerythrocytic cycle, it is likely that the temporal program of transcription serves a purpose similar to the cell cycle transcriptional program, where just-in-time gene expression helps maintain temporal ordering. It has been suggested that these parasite stages may be analogous to the familiar G1, S, G2, and M phases of the eukaryotic cell cycle (32, 41), with the ring stage specifically suggested to be the equivalent of G1. In a typical cell cycle, the cell cycle period is adjusted mostly by expanding or shortening in the G1 phase (42). However, in the four P. falciparum strains we examined, there was no discernible pattern of stage expansion or contraction to explain the diversity in strain cycle lengths.

Like circadian systems from several organisms (2628), genes that oscillate at the first harmonic of the period length (i.e., twice as fast) are observed in all four P. falciparum strains. Notably, the harmonic genes are half-period regardless of the different period lengths of the strains, which indicates that, like circadian oscillators, harmonic expression is driven by the core network and not by an alternative oscillator. The fact that the strains have different periods, despite three of the four strains growing in RBC cultures from the same donor, indicates that periodicity is not driven by the peroxiredoxin oscillations reported to function with a 24-hour period in RBCs. Moreover, the periods are not exact multiples of 24 hours, so they are unlikely to result from some alternative coupling to the peroxiredoxin cycle. These findings are supported by studies that indicate genetic variation or mutation of Plasmodium genes can lead to altered cycle period lengths (32, 41).

It has been suggested that synchronized populations of P. falciparum in in vitro cultures lose synchrony rapidly. Rapid synchrony loss argues that without input from the host, the parasite is unable to maintain a robust period. However, synchronized populations of cells under circadian and cell cycle oscillator controls have also been shown to lose periodicity because of variation in period (34, 35), which raises the question of how variable the P. falciparum intraerythrocytic cycle is when compared with cell cycle– or circadian-controlled systems. To examine this question, we used a phase-oscillator model to estimate the variation in cycle period lengths between parasites in culture and found that it is comparable to the variation found in circadian cell lines. Thus, the degree of synchrony loss observed for synchronized P. falciparum is completely compatible with a model in which the intraerythrocytic cycle is driven by a robust intrinsic clock network.

Collectively, our findings are incompatible with a mechanism in which P. falciparum’s intraerythrocytic cycle is controlled only by extrinsic cues. Our findings point to a mechanism in which the cycle is driven by an intrinsic oscillator with molecular characteristics of circadian or cell cycle oscillators. The control mechanisms for circadian and cell cycle oscillators have been identified as gene regulatory networks composed of transcriptional regulators, kinases, and ubiquitin ligases with negative feedback loops that drive oscillation. Of the genes we have identified as periodic, we found ApiAP2 transcription factor genes (43) along with genes annotated as kinases, which suggests the possibility that gene regulatory networks resembling circadian or cell cycle oscillators may be present. It is not surprising that orthologs to known circadian clock genes have not been identified in Plasmodium spp. (5), as even gene networks with similar oscillating functions often do not share genes. Genes in oscillating networks from different organisms may not be highly conserved, but network motifs and topologies are (11, 44).

Farnert et al. have found that, in chronic infections with P. falciparum, 48-hour rhythms in parasitemia levels could be observed in patients who did not exhibit fever cycles, which led them to speculate that “mechanisms other than fever might be involved” (45). The existence of an intrinsic oscillator in P. falciparum is a likely mechanism, and this hypothesis reframes the models of periodicity in malarial disease as the coupling between a parasite oscillator with a 48-hour period and the host circadian oscillator with a 24-hour period. There is evidence for phase alignment between Plasmodium parasites and animal models (2, 7). Plasmodium’s oscillator may entrain to circadian rhythms in the host, much as circadian clocks themselves ultimately entrain to light (46). The host signal(s) that the parasite uses for entrainment are, as yet, unsettled (6, 7). It is also possible that the parasite can manipulate the host’s circadian cycle to achieve better alignment. In mouse experiments, entrainment of the intraerythrocytic cycle to the host shows benefits for the parasite (4, 47). The benefit of phase alignment to the host’s circadian rhythm is a likely explanation for why mammalian-infecting Plasmodium species have cycle lengths that are multiples of 24 hours. The lack of this selective pressure in vitro (and/or genetic bottlenecking during culture establishment) may also explain why cultured P. falciparum strains, such as those in this study, can vary substantially from their 48-hour wild-type period length.

Plasmodium is not the only parasite with a highly rhythmic life cycle, as several parasites exhibit time-of-day elements in their life cycles (5). Moreover, an innate circadian rhythm was recently identified in the Trypanosoma brucei, the causal organism of sleeping sickness, using traditional metrics of circadian clocks (48). Recent reports indicate that nearly 80% of all genes in a primate genome are under circadian control in at least one tissue (24). Given that animal physiology is so broadly controlled by circadian rhythm, it makes sense that pathogens have evolved to take advantage of the 24-hour periodicity of the host, and it is likely that many pathogens will display periodic behaviors.

Supplementary Materials

Materials and Methods

Figs. S1 to S18

Tables S1 to S8

References (5258)

MDAR Reproducibility Checklist

Data S1 to S4

References and Notes

Acknowledgments: We thank D. K. Welsh (Department of Psychiatry, University of California, San Diego) for providing us with data from the primary fibroblasts. We also thank R. Moseley and S. Campione for critical reading of the manuscript and S. Campione for technical help with figures. Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author and are not to be construed as official or as reflecting true views of the Department of the Army or the Department of Defense. Funding: F.C.M., S.B.H., J.H., A.R.L., and C.M.K. were funded by the Defense Advanced Research Projects Agency, D12AP00025. S.B.H., L.M.S., T.G., and B.C. were also funded by NIH 1R01GM126555-01. T.G. and B.C. were funded by NSF DMS-1839299. R.R.N. was funded by NIH R01 GM126555-01. S.B.H. and J.H. are members of Mimetics, LLC. J.H. is CEO of Geometric Data Analytics, Inc. T.G. and B.C. are on the board of Kanto, Inc. Author contributions: S.B.H. and J.H. conceived of the study. S.B.H., J.H., A.R.L., N.C.W., and G.C. collaborated on the experimental design. N.C.W., G.C., and J.K.M. performed parasite synchrony and release time-series experiments. A.R.L. processed samples for RNA-seq. C.M.K. designed the RNA-seq alignment and analysis pipeline and performed preliminary analyses. L.M.S. analyzed the transcriptomes for periodicity, presence of harmonics, and qualitative ordering conservation. K.E.R. developed the data-wrapping approach and assisted in period-length estimates. T.G., R.R.N., and B.C. developed the quantitative partial ordering approach and determined quantitative ordering conservation. F.C.M. constructed models of cell-to-cell variance and determined the effect on synchrony loss. L.M.S., S.B.H., F.C.M., and B.C. wrote the manuscript. Competing interests: The authors declare no competing interests. Data and materials availability: All data used in the paper are available in the supplementary materials. RNA sequences are deposited at the Gene Expression Omnibus under series GSE141653. All code used in the analyses is available in public repositories (4951).

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