Disruption of the coordination between host circadian rhythms and malaria parasite development alters the duration of the intraerythrocytic cycle

Abstract

Malaria parasites complete their intra-erythrocytic developmental cycle (IDC) in multiples of 24 hours (depending on the species), suggesting a circadian basis to the asexual cell cycle, but the mechanism controlling this periodicity is unknown. Combining in vivo and in vitro approaches using rodent and human malaria parasites, we reveal that: (i) 57% of Plasmodium chabaudi genes exhibit 24 h circadian periodicity in transcription; (ii) 58% of these genes lose transcriptional rhythmicity when the IDC is out-of-synchrony with host rhythms; (iii) 9% of Plasmodium falciparum genes show circadian transcription under free-running conditions; (iv) Serpentine receptor 10 (SR10) has a circadian transcription profile and disrupting it in rodent malaria parasites shortens the IDC by 2-3 hours; (v) Multiple processes including DNA replication and the ubiquitin and proteasome pathways are affected by loss of coordination with host rhythms and by disruption of SR10. Our results show that malaria parasites are at least partly responsible for scheduling their IDCs explaining the fitness benefits of coordination with host rhythms.

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Disruption*of*the*coordination*between*host*circadian*rhythms*and*malaria*
parasite*development*alters*the*duration*of*the*intraerythrocytic*cycle*
Authors
Amit K. Subudhi1, Aidan J. O’Donnell2†, Abhinay Ramaprasad1†, Hussein M. Abkallo3,
Abhinav Kaushik1, Hifzur R. Ansari1, Alyaa M Abdel-Haleem1, Fathia Ben Rached1,
Osamu Kaneko4, Richard Culleton3,*, Sarah E. Reece2,*, Arnab Pain1,5,6*
1Pathogen Genomics Group, BESE Division, King Abdullah University of Science and
Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
2Institute of Evolutionary Biology, and Institute of Immunology and Infection Research,
University of Edinburgh, Edinburgh EH9 3FL, UK
3Malaria Unit, Department of Pathology, Institute of Tropical Medicine (NEKKEN),
Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
4Department of Protozoology, Institute of Tropical Medicine (NEKKEN), Nagasaki
University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan
5Center for Zoonosis Control, Global Institution for Collaborative Research and
Education (GI-CoRE); Hokkaido University, N20 W10 Kita-ku, Sapporo, 001-0020
Japan
6Lead Contact
Current address: Computational Bioscience Research Center, King Abdullah University
of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
†Contributed equally
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*Correspondence: arnab.pain@kaust.edu.sa, sarah.reece@ed.ac.uk, richard@nagasaki-
u.ac.jp
Malaria parasites complete their intra-erythrocytic developmental cycle (IDC) in
multiples of 24 hours (depending on the species), suggesting a circadian basis to the
asexual cell cycle, but the mechanism controlling this periodicity is unknown.
Combining in vivo and in vitro approaches using rodent and human malaria
parasites, we reveal that: (i) 57% of Plasmodium chabaudi genes exhibit 24 h
“circadian” periodicity in transcription; (ii) 58% of these genes lose transcriptional
rhythmicity when the IDC is out-of-synchrony with host rhythms; (iii) 9% of
Plasmodium falciparum genes show circadian transcription under free-running
conditions; (iv) Serpentine receptor 10 (SR10) has a circadian transcription profile
and disrupting it in rodent malaria parasites shortens the IDC by 2-3 hours; (v)
Multiple processes including DNA replication and the ubiquitin and proteasome
pathways are affected by loss of coordination with host rhythms and by disruption
of SR10. Our results show that malaria parasites are at least partly responsible for
scheduling their IDCs explaining the fitness benefits of coordination with host
rhythms.
Daily fluctuations in environmental parameters such as light and temperature are
assumed to select for the evolution of circadian clocks. In many organisms, circadian
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clocks schedule rhythms in behaviour, physiology and metabolism in coordination with
periodic oscillations in the external environment1. The components of circadian clocks
are diverse2-4. Many clocks function following the principles of transcription-translation
feedback loops (TTFLs); a ‘clock’ protein inhibits the transcription of the gene that
encodes it5 and clocks that operate via non-transcriptional oscillators6 and post-
transcriptional control have also been identified7.
Many parasite species exhibit daily rhythms in behaviour and/or development that are
scheduled to optimally exploit periodicities in transmission opportunities and/or resource
availability8,9. The parasitic protozoan Trypanosoma brucei, for example, possesses an
intrinsic circadian clock that drives metabolic rhythms10. Malaria parasites complete their
intra-erythrocytic developmental cycle (IDC) in 24h (or multiples of 24 h depending on
the parasite species), suggesting a circadian basis to the asexual cell cycle11.
Rhythms in host feeding and innate immune responses influence the timing of rhythms in
the IDC of rodent malaria parasites12,13. Specifically, completion of the IDC, a glucose-
demanding process, coincides with host food intake, and quiescence during the early
phase of the IDC coincides with the daily nadir in host blood glucose that is exacerbated
by the energetic demands of immune responses12. However, the extent to which malaria
parasites or their hosts are responsible for IDC scheduling is unclear14. Either parasites
are able to respond to time-of-day cues provided by the host to organise when they
transition between IDC stages and complete schizogony, or parasites are intrinsically
arrhythmic and allow the host to impose rhythms on the IDC (for example, restricting
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access to a nutrient that is essential to a particular IDC stage to a certain period each day,
starves or kills parasites following the wrong IDC schedule).
Establishing how the timing and synchronicity of the IDC is established is important as
temporal coordination with host rhythms is beneficial for parasite fitness15,16, and because
tolerance to antimalarial drugs is conferred to parasites that pause their IDCs17-19. Here,
we use a combination of rodent malaria parasites in vivo and human malaria parasites in
vitro to investigate the relationship between the IDC and host circadian rhythms.
Firstly, we identify components of the P. chabaudi transcriptome with 24 h periodicities
and determine what happens to them, including the downstream biological processes,
when coordination with host rhythms is disrupted (i.e. when the parasites’ IDC is “out of
phase” with the host). Secondly, we show that P. falciparum also has a transcriptome
with 24 h periodicity, even in the absence of host rhythms. Thirdly, we identify a
transmembrane serpentine receptor with circadian expression in both species and
demonstrate it plays a role in the duration of the IDC. Loss of this serpentine receptor
disrupts many of the same processes affected when coordination to host rhythms is
perturbed. Taken together, our results imply that malaria parasites are, at least in part,
able to control the schedule of their IDCs. Coordinating the IDC with host rhythms could
allow malaria parasites to enhance their fitness by facilitating temporal
compartmentalisation of the IDC processes with respect to each other, by maximizing
exploitation of resources provided by the host in a rhythmic manner, and by protecting
vulnerable IDC stages from encountering peaks in the rhythms of host immune defenses.
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RESULTS
The transcriptome of Plasmodium chabaudi responds to host circadian
rhythms
Transcriptome analyses of time series RNA sequencing datasets were performed with P.
chabaudi parasites from infections that were in synchrony (phase aligned; “host rhythm
matched”) and out of synchrony (out of phase; “host rhythm mismatched”) with host
circadian rhythms (for details see methods section). Briefly, a controlled number of
infected red blood cells (RBC) were sub-inoculated from donor mice into two groups of
recipient mice (n = 43 per group) each housed under different light regimes (12 hours (h)
difference in lights on/off). In one group, the light regime was maintained as for the
donor mouse (i.e. host rhythm matched, lights on: 7.30 (ZT0/24 (Zeitgeber Time: hours
after lights on)) and lights off: 19.30(ZT12)). In the other group, the light-dark cycle was
reversed (host rhythm mismatched, lights on: 19.30 (ZT0/24), lights off: 7.30 (ZT12))
(Fig. 1a). The effect of mismatch to host rhythms on the parasite was assessed by
analyzing the parasite transcriptome every 3 h for 30 h (n=4 mice/group/time point).
After mismatch to host circadian rhythms, the IDC of P. chabaudi becomes rescheduled
within approximately seven cycles to match the host’s rhythms. By the time of sampling
(days 4-5 post infection, PI), the parasites were six hours mismatched to the host’s
rhythm (Supplementary Fig. 1a). Specifically, in mismatched parasites, merozoite
egress from RBCs following schizogony peaked six hours after matched parasites (ZT
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0/24 and ZT 18 respectively; inferred from ring stage rhythms). Parasites in both the
matched and mismatched infections remained synchronous throughout the sampling
period (Supplementary Fig. 1a).
After quantifying gene transcription at each time point through RNAseq analysis, we
identified genes that followed ~24 h rhythms in transcription according to two commonly
used and independent algorithms (see Methods). Genes were considered to have putative
circadian rhythms (hereafter referred to as “circadian”) if both algorithms independently
detected a ~24 h periodicity in their transcription with a threshold of p < 0.05. Of a total
of 5,343 genes in P. chabaudi (5,158 detected and considered for analysis), 3,057 (58%)
in matched parasites, and 1,824 (34%) in mismatched parasites, exhibited circadian
rhythms in transcription (Fig. 1b, Supplementary Data 1). A permutation test was
performed to empirically determine the false discovery rate (FDR) in detecting circadian
transcripts. The original time points were shuffled randomly for 1,000 iterations and
circadian transcripts were identified each time by both the algorithms. The permutation
test identified that the number of circadian transcripts in both matched and mismatched
parasites detected in the original sampling order by both the programs was significantly
higher (FDR < 0.05, Supplementary Fig. 1b) than when the sampling order was
randomly permuted indicating that the circadian genes identified were transcribed in a
rhythmic manner above background noise.
Changes in rhythmicity due to misalignment of parasite and host
rhythms
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Over 80% of the genes expressed during the IDC of malaria parasites undergo a tight
temporal expression cascade associated with development into specific parasite
stages20,21. The periodicity of the transcription profile of these genes can be ~ 24 h, 48 h
or 72 h depending on the malaria parasite species: for P. chabaudi, the transcription
profile of these genes have ~ 24 h periodicity while for P. falciparum they have ~48 h
periodicity. This means that for P. chabaudi, genes that are transcribed in association
with the IDC cascade and genes that encode proteins that could be involved in a circadian
clock or its outputs all display 24 h periodicity in their transcription profiles. However,
IDC genes should follow transcription rhythms that peak six hours later in mismatched
compared to matched parasites, whereas gene transcripts that follow the phased rhythm in
both mismatched and matched parasites with respect to host ZT may be involved in a
circadian clock or its outputs. Genes that lose transcription rhythmicity in mismatched
parasites may do so because the IDC and/or homeostasis are negatively affected by
misalignment with host rhythm, as a consequence of re-aligning the IDC schedule with
host circadian rhythms, or both.
Comparing rhythmic transcripts detected in both matched and mismatched parasites
identified three sets of transcripts: 1) 1,765 genes (33% of the total genes) with 24 h
rhythmic transcription (p < 0.05) in matched parasites that exhibit an arrhythmic
transcription profile in mismatched parasites (p > 0.05); 2) 1,292 genes with transcription
profiles with 24 h rhythm in both matched and mismatched parasites; and 3) 532 genes
whose transcription profiles were arrhythmic in matched parasites and rhythmic in
mismatched parasites (p < 0.05, Fig. 1b and 1c, Supplementary Fig. 1c). Hierarchical
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clustering analysis identified biological replicates to be tightly clustered (Supplementary
Fig. 1d). Comparison of the 11 time points using principal component analysis identified
the first component in both the conditions with a cyclic pattern that accounted for > 85 %
of total variance (Supplementary Fig. 1e), which supports the hypothesis that the large
number of transcripts detected as circadian are truly transcribed at a 24 h periodicity.
Out of 1,292 genes that were rhythmically transcribed in both matched and mismatched
parasites, we found 685 genes (53%) that had a delayed phase of transcription with a
delay of about 6 h (± 1.5 h) in mismatched compared to matched parasites
(Supplementary Fig. 1f) which complements our phenotypic observation of the phase
difference between mismatched and matched parasite IDCs (Supplementary Fig. 1a).
Gene ontology enrichment analysis revealed that biological processes associated with
these transcripts include DNA metabolic processes and cellular responses to stress (FDR
< 0.05). The other 607 genes display broad differences in their phase of transcription
between matched and mismatched parasites, and so might also be IDC associated genes
affected by a loss of scheduling forces and / or genes undergoing readjustment of their
phase of transcription to realign the IDC with the host rhythm. The amplitude of
rhythmically transcribed genes is significantly higher (p < 0.0001, unpaired student t test)
in matched compared to mismatched parasites (Fig. 1d), suggesting a loss of
synchronicity in the transcription of mismatched parasites that is not severe enough to
impact on IDC synchronicity as measured by stage proportions (Supplementary Fig.
1a). Such a dampening of rhythms is a typical consequence of misalignment of a
circadian clock with its time-of-day cue22.
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Arrhythmically transcribed genes (N=532) in matched parasites that exhibit rhythmic
transcription in mismatched parasites enriched to a single gene ontology biological
process term; RNA processing/splicing. Why genes should gain rhythmicity of
transcription in mismatched parasites is unclear; it is possible that they compensate for
stresses imposed by being misaligned to host rhythms or they may represent an
alternative set of IDC genes that the parasite expresses during its rescheduling period.
Such a phenomenon is proposed to operate in humans, by which a set of transcripts gains
rhythmicity in older individuals coinciding with the loss of canonical clock function23.
The most striking observation is that 1,765 genes (33%) lose rhythmicity of transcription
in mismatched parasites. In matched parasites, a bimodal distribution of transcription for
these genes was observed with peaks at two different times of the day (ZT 8 and ZT 20,
Fig. 1e) corresponding to the late trophozoite and ring stages of the IDC, respectively.
Whilst these genes have a periodicity of transcription extremely close to 24 h in matched
parasites (median periodicity =23.89h), 55% of these transcripts had shorter periodicities
in mismatched parasites (between 20h to 24h), which in turn reduced the overall
periodicity by ~ 1 hour (median periodicity 22.85h) (Fig. 1f). Period estimates from
genes that lost rhythms in transcription profiles are used here to illustrate an overall trend,
rather than provide information on individual genes. It is possible that the shorter periods
for mismatched parasites correlates with a shorter IDC, thus explaining how mismatched
parasites become rescheduled by approx. six hours within four cycles of replication (an
average of 1.5 h per cycle).
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We undertook further analysis of the 1,765 genes that lose rhythmicity in their
transcription profile in mismatched parasites to examine which biological processes are
affected. We divided genes into 12 groups based on the time of day (“phase”) of their
maximal transcription and performed gene ontology (GO) based enrichment analysis
within each group every two hours (using the fitted model output from ARSER). The
point at which there were the highest relative copy numbers of mRNA from these genes
present in samples was taken to be indicative of an overall trend. A wide range of
biological processes including carbohydrate metabolism, nucleotide and amino acid
metabolism, DNA replication, oxidation-reduction processes, translation, RNA transport,
aminoacyl-tRNA biosynthesis and ubiquitin mediated proteolysis and proteasome
pathways were enriched in different phase clusters and so appear to be under the
influence of host rhythms (Fig. 2a and Supplementary Data 2). Many of these
biological processes are under circadian clock control in other organisms24,25. Disruption
to any (or all) of these processes could explain the 50% reduction in parasite densities
observed for mismatched parasites by O’Donnell et. al.15,16. To gain insight into these
perturbed processes, we assessed these findings on a gene-by-gene basis in the context of
the IDC and how it might be scheduled.
Energy metabolism
Blood glucose levels are rhythmic during the circadian cycle in mice26. This rhythm is
exacerbated in malaria-infected mice due to the alterations in energy metabolism
experienced by inflammatory leukocytes12. Given that completing the IDC is glucose
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demanding, malaria parasites may be expected to express genes rhythmically to utilize
the energy source efficiently. This could be achieved via the recently discovered nutrient
sensing mechanism and that allows parasites to respond to alterations in glucose
availability through transcriptional rearrangement27. In support of this, genes involved in
energy metabolism pathways (glycolysis, fructose and mannose metabolism) showed
circadian transcription patterns in the matched parasites while this rhythmicity was lost in
mismatched parasites (Figs. 2a and 2b, Supplementary Data 2). The maximum relative
number of transcripts of glycolysis-associated genes in matched P. chabaudi was
observed between ZT 22-ZT 2, which corresponds to the ring and early trophozoite
stages of the parasite and are also when genes associated with glycolysis are maximally
transcribed in P. falciparum in vitro20. Genes associated with
glycolysis pathways in Trypanosoma brucei are under the control of an endogenous
circadian clo10. If malaria parasites also possess such an oscillator, or another controller
of the IDC, misalignment to the host rhythm should disrupt glucose metabolism.
The ubiquitin-proteasome system
In chronobiology, the ubiquitin-proteasome system (UPS) plays a direct role in
determining the half-life of core clock components and other clock controlled protein
functions28. The conserved UPS system works by tagging ubiquitin (Ub) to a protein
destined for enzymatic degradation and the tagged proteins are then recognized by a
multi-catalytic protease complex called the 26S proteasome system that eventually
degrades the tagged proteins. A total of nine out of 25 (36%) genes associated with the
ubiquitin mediated proteolysis pathway and 25 out of 32 (78%) genes encoding core and
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regulatory components of the proteasome system lost transcriptional rhythmicity in
mismatched parasites (Fig. 2b, Supplementary Data 2). Notably, genes that lost
transcriptional rhythmicity include those associated with one E1 Ub-activating enzyme,
four E2 Ub-conjugating enzymes, and three ring finger type E3 Ub-ligases (RBX1,
SYVN and Apc11) from the ubiquitin mediated proteolysis system. Furthermore,
rhythmicity was also lost in RBX1, Apc11 and the adaptor protein SKP1 from the
proteasome pathway. These genes are part of the anaphase promoting complex (APC)
which is a cell cycle regulated ubiquitin protein ligase. Mutation studies in budding yeast
show that rbx1and apc11 are essential for APC activity29-32.
The majority of ubiquitin-proteasome associated genes have a peak of transcription
between ZT 7.5- 10 in matched parasites, which corresponds to the late trophozoite/
schizont stage of the IDC (Fig. 2b). The UPS plays a crucial role for the liver, blood and
sexual stages of the parasite. In the blood stage, particularly during the late IDC stage, the
UPS helps parasite to shift from its involvement in generic metabolic and cellular
machinery to specialized parasite function. Loss of rhythms of UPS associated genes
suggests that part of the UPS system that plays a role in regulating important events in
cell division and in protein homeostasis is detrimentally affected by mismatch with the
host rhythm.
DNA replication associated genes
GO enrichment analysis revealed that 19 out of 43 (44%) genes associated with DNA
replication lost transcriptional rhythmicity in mismatched parasites (Figs. 2a and 2b and
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Supplementary Data 2). These included genes encoding subunits of DNA polymerase,
replication-licensing factors, DNA helicase and the DNA repair protein RAD51. Genes
associated with DNA replication reached peak transcription between ZT8-12 in matched
parasites, which corresponds to the transition from the late trophozoite to the schizont
stage during the IDC, and is when DNA replication machinery components are
transcribed20.
Other cell cycle associated genes encoding cdc2-related protein kinase 4 and 5, anaphase-
promoting complex 4, cyclin 1, cullin like protein, regulator of initiation factor 2 and
replication termination factor also lost transcriptional rhythmicity in mismatched
parasites. Biological clocks control the timing of DNA replication in many organisms33-
37. If the parasite uses a clock that follows host circadian rhythm to initiate its own DNA
replication to remain in synch with host rhythms, then when the cue to begin DNA
replication is received, mismatched parasites will be at the wrong IDC stage to achieve
the task, resulting in disorganized DNA replication.
The redox system
Organisms employ multiple antioxidant mechanisms such as the use of super oxide
dismutase, peroxiredoxins, glutathione and thioredoxin systems to remove harmful
reactive oxygen species radicals that include hydrogen peroxides and super oxide ions.
Seven genes out of 31 associated with cell redox and glutathione metabolism lost
transcriptional rhythmicity in mismatched parasites and are enriched for the term ‘cell
redox homeostasis’ (Padj < 0.001; Supplementary Data 2). All seven genes displayed
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maximum transcription during ZT6-8 in matched parasites. Peroxiredoxin proteins show
circadian rhythmicity in oxidation/reduction cycles and these are conserved across the
tree of life3. Although genes that encode peroxiredoxins are not necessarily expressed in a
circadian manner during circadian oxidation/reduction cycles38, two out of three genes
encoding peroxiredoxin in P. chabaudi showed circadian transcription in both matched
and mismatched parasites, while one gene (PCHAS_0511500) lost transcriptional
rhythmicity in mismatched parasites. As for glycolysis, the expression of genes involved
in redox metabolism is also driven by an endogenous clock in T. brucei10. It is possible
that redox metabolism associated genes are also driven by a clock in malaria parasites
and misalignment with host rhythms interferes with the parasites’ ability to prepare for
and cope with redox challenges.
24-hour endogenous rhythms in the transcriptome of Plasmodium
falciparum
We used P. falciparum to investigate whether malaria parasites schedule their IDC to
coincide with host rhythms. Parasites may set the timing of IDC transitions using a
circadian clock that is entrained by a host rhythm (i.e. a Zeitgeber). One of the criteria for
demonstrating clock control of a rhythm is that the rhythm persists (“free-runs”) under
constant conditions. In vitro culture can provide constant conditions and in contrast to P.
chabaudi, P. falciparum has an IDC of approx. 48 h, allowing putative clock genes and
their downstream interactors to be distinguished from IDC genes. Observing a rhythm of
24 h is consistent with the presence of a circadian clock, but other criteria such as
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temperature compensation and entrainment must be fulfilled to conclude the presence of
a clock.
We analysed the published 48-hour time-series IDC microarray expression profile of P.
falciparum captured at a 1 h time-scale resolution by20. Temperature is the Zeitgeber for
trypanosome clocks10, but P. falciparum experienced a constant temperature in the study
of Bozdech et al. (2003)20. Furthermore, Bozdech et al. (2003)20 cultured P. falciparum in
constant darkness, though light:dark rhythms do not influence the schedule of the P.
chabaudi IDC13. Expression data from two odd time-points are missing in this dataset so
we considered only data from the even time-points, giving the dataset a 2 h resolution.
We identified 494 transcripts (9% of the genes) with ~24 h rhythmicity (q < 0.05) from
the expression profiles of 4,818 genes (Fig. 3a, Supplementary Data 3). The median
amplitude of oscillations of the 24 h free-running genes was 0.20, which is lower than the
amplitude of the rhythmic genes detected in P. chabaudi in vivo (1.22). A lower
amplitude of 24 h expression rhythms in P. falciparum in vitro is expected as it is devoid
of exposure to host circadian rhythms, whereas a higher amplitude of 24 h expression
rhythms in P. chabaudi in vivo may be maintained by exposure to host rhythms.
Genes identified in P. falciparum with a circadian transcription profile are enriched for
most of the processes that lost rhythmicity in mismatched P. chabaudi infections
(carbohydrate metabolism, DNA replication and the ubiquitin proteasome system (Fig.
3b). In contrast to mismatched P. chabaudi, no gene transcripts related to redox processes
were enriched. This suggests that if P. falciparum has a clock, it is not used to anticipate
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daily redox challenges and that instead redox genes are transcribed only in response to a
change in redox levels inside the host (possibly as a consequence of host metabolism).
Furthermore, this finding suggests that circadian rhythms in the redox state of RBCs7,
which persist when RBCs are cultured in constant conditions, are either not important for
malaria parasites or are dampened by the developing parasite.
We also identified individual members of different sub-telomeric multigene families that
are transcribed in a circadian manner in both P. falciparum and P. chabaudi datasets
(Supplementary Data 3). Multigene families whose members are present in the
subtelomeric regions of the P. falciparum chromosomes39 and P. chabaudi
chromosomes40, and have at least three paralogous members in the genome were
considered for this analysis. These included members of major antigenically variant gene
families such as var, riffins and other families such as (Maurer’s cleft two-
transmembrane protein encoding genes and Plasmodium helical interspersed
subtelomeric protein encoding genes). Further analysis of host circadian rhythm-matched
and mismatched P. chabaudi transcriptome revealed circadian expression of a large sub-
set of RMP (rodent malaria parasites) sub-telomeric multigene families such as cir, RMP-
fam-a and etramp (early transcribed membrane protein), a large proportion of which lose
their rhythmic expression pattern in mismatched parasites (Supplementary Data 3).
The precise biological significance of the rhythmic expression of these genes needs
detailed future experimentations.
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Finally, comparison of the transcriptional profile of genes that lost rhythm in mismatched
P. chabaudi (N =1,765) to orthologues of “free running” P. falciparum rhythmic genes
(N = 404, one to one orthologues out of 495 genes) identified 110 common genes. The
transcription of these genes is sensitive to the timing of host circadian rhythms and is
rhythmic under constant conditions suggesting they may be outputs of a circadian clock.
Circadian expression of serpentine receptor 10
If malaria parasites are able to schedule the IDC they must respond to time-of-day
information either through receptors or transporters. Serpentine receptor 10 (sr10:
PF3D7_1215900), a member of a seven-transmembrane receptor family, was the top
ranked receptor in the P. falciparum circadian gene list (ranked 21 out of all 495 genes
sorted based on q-values) (Fig. 3c, Supplementary Data 3). Its orthologue in P.
chabaudi (PCHAS_1433600) was also circadian in transcription in both matched and
mismatched parasites (Fig. 3c, Supplementary Data 1). In P. falciparum, SR10
expression peaked at 8 h and 32 h post invasion, which corresponds to ring and late
trophozoite stages of the IDC. In P. chabaudi, expression peaked at ZT14, corresponding
to the late trophozoite stage.
Seven transmembrane domain-containing receptors/serpentine receptors/G protein
coupled receptors (GPCRs) are the largest and most diverse group of membrane receptors
and participate in a variety of physiological functions41-44. P. falciparum contains four
serpentine receptor (SR) proteins; SR1, SR10, SR12 and SR2545. Of these, only Pfsr10
showed circadian transcription while the rest showed periodicity closer to 48 h
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(Supplementary Fig. 2a). Expression profile of Pfsr25 is not available in the microarray
dataset from Bozdech et al (2003)20. However, microarray-based expression data (~8h
resolution) from another study46 revealed a single peak of expression during the entire
IDC for Pfsr25 with a periodicity closer to 48 h. Additionally, SR10 has been classified
as a member of Class A serpentine receptors belonging to the hormonal receptor subclass
based on the length of the N-terminal domain45 and classification by Inoue et al (2004)47.
Of the four serpentine receptors, SR10 is also the only receptor that is present, not only in
different Plasmodium spp., but also in other apicomplexans and distantly related
organisms such as Caenorhabditis elegans, Drosopihila melanogaster, Gallus gallus,
Mus musculus, Homo sapiens and Arabidopsis thaliana (data retrieved from OrthoMCL
DB), although it has not been linked to circadian clocks in these organisms. The circadian
transcription of sr10 in both parasite species and in free-running conditions, coupled with
its phylogenetic conservation across taxa, suggests the presence of a receptor-mediated
signaling system in malaria parasites that receives time-of-day information from the host.
We tested this hypothesis via a detailed analysis of SR10.
Serpentine receptor 10 influences IDC duration
To investigate the functional role of SR10 in vivo, we disrupted the sr10 gene in P.
chabaudi by a double crossover homologous recombination strategy to generate sr10
deficient parasite clones (sr10KO) (Supplementary Fig. 2b). Functional disruption of
sr10 was verified by RNAseq analysis (Supplementary Fig. 2c). We then compared the
IDC of wild type and two sr10KO clones by microscopic examination of thin blood
smears (n= 4 per group), sampled every three hours over 48 h, starting from day 1 PI at
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20.30 h (ZT13.5) (Fig. 3d). All infections of wild type and sr10KO clones (sr10KOA
and B) were highly synchronous (amplitude ±(SE) for P. chabaudi wild type: 0.94±0.02,
P. chabaudi sr10KOA: 0.79±0.02 and sr10KOB 0.93±0.03, Fig. 3e, Supplementary
Table 1). Period estimates for the proportion of parasites at early trophozoite stage
identified the IDC duration of both sr10KO clones to be ~2h shorter (IDC duration 22.4
h) than the wild type (IDC duration 25.15; p < 0.0001, Fig. 3f).
Next, we investigated whether sr10 also influences the IDC duration in another rodent
malaria species i.e. P. yoelii. We generated an sr10KO clone in P. yoelii using a double
crossover homologous recombination strategy (Supplementary Fig. 2d). As for P.
chabaudi, we then compared wild type and sr10KO P. yoelii clones by microscopic
examination of thin blood smears. The proportion of parasites at early trophozoite stage
displayed weak circadian rhythmicity in both the wild type and sr10KO infections
(Amplitude for P. yoelii wild-type: 0.30±0.02 and P. yoelii sr10KO: 0.31±0.01, Fig. 3g,
Supplementary Table 1). As we find for the P. chabaudi sr10KO clones, knocking out
sr10 in P. yoelii shortens the duration of the IDC (sr10KO IDC duration 24.45 h; wild
type IDC duration ~28 h, Fig. 3h). Observing such similar biologically reproducible
changes to IDC duration in two different experiments using two different malaria species
strongly implicates sr10 in the control of developmental progression through the IDC.
To explore how disruption of sr10 affects the duration of the IDC we repeated the time-
series RNAseq experiments on both wild type and sr10KO P. chabaudi parasites from 17
time points (however, data from the first three time points were excluded owing to a low
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number of mapped reads i.e. < 1 million paired ends mapped reads) sampled every three
hours (n= 2 per time point) starting from day 2 PI (05.30 h, ZT22.5). Hierarchical
clustering analysis identified biological replicates to be tightly clustered (Supplementary
Fig. 2e). Transcripts with 24 h periodicity were identified following the approach
deployed for the experiment using matched and mismatched P. chabaudi infections. A
total of 3,620 and 2,886 genes showed ~24 h rhythmicity in transcription in P. chabaudi
wild type and sr10KO parasites respectively (q < 0.05; Fig. 4a, Supplementary Data
4). Principal component analysis of 14 time points identified that the first and third
components of PCA (with a cyclic pattern in both the wild type and sr10KO parasites),
accounted for > 85 % of total variance (Supplementary Fig. 2f). Comparison of wild
type and sr10KO parasites revealed that 1,015 genes (19% of the total genes) lost
transcriptional rhythmicity in sr10KO parasites (Fig. 4b, Supplementary Data 4).
Further, 85% of the genes identified as being transcribed in a circadian manner in
matched (wild type) P. chabaudi parasites from our first experiment were also circadian
in the wild type parasites in this dataset (which are also matched). Whilst the additional
rhythmically transcribed genes identified in wild type parasites in this dataset could be
due to a longer time series, we found generally high concordance between the
transcriptomes of infections initiated in the same way but in different laboratories. This
lends support to the inference that the genes losing rhythmicity of transcription
(henceforth called SR10-linked circadian genes or “SLCGs”) in sr10KO parasites is due
to the loss of SR10.
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Examination of the SLCGs reveals a bimodal distribution pattern for peak transcription in
wild-type parasites in which they are rhythmic (peaking at ZT 8 and ZT 19). This pattern
was partially lost in sr10KO parasites in which the early peak displays a broader
distribution (Fig. 4c). Further, the SLCGs exhibit a shorter periodicity in sr10KO (24 h)
compared to wild type (25.81 h) parasites (Fig. 4d). Our intention is not to draw
inference from the quantitative difference of periods (which is of limited utility for genes
that lose rhythmicity), but to ascertain a qualitative comparison. However, the shorter
periods in sr10KO parasites reflects the shorter periods observed in genes that lose
rhythmicity in mismatched parasites. As genes that retained transcriptional rhythmicity in
both matched and mismatched parasites, the genes that retained transcriptional
rhythmicity in both wild type and sr10KO parasites (N=2,620) exhibited a significant
reduction (p < 0.0001, unpaired student t test) of amplitude in sr10KO (1.15) compared
to wild-type parasites (1.53) (Fig. 4e).
SR10 regulates expression of multitude of pathways
Gene ontology (GO) analysis of the SLCGs showed enrichment for terms related to
translation, RNA splicing, RNA and vesicle-mediated transport and purine and
pyrimidine metabolism, indicating a broad effect of SR10 loss on parasite biology
(Supplementary Fig. 3, Supplementary Data 5). Comparing differentially regulated
genes for four different IDC stages (i.e. four time points: Day 2 ZT 16.5, Day 2 ZT 22.5,
Day 3 ZT 4.5 and Day 3 ZT 10.5) in wild-type and sr10KO parasites (Fig. 5a,
Supplementary Data 6) reveals that genes associated with: (i) protein translation are
perturbed in ring stages; (ii) DNA replication and cell cycle associated processes are
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perturbed in early and late trophozoite stages; and (iii) microtubule based movement are
perturbed in schizonts (Fig. 5b). Disruption of genes involved in DNA replication and
cell cycle associated processes suggests SR10 influences IDC duration by altering the
developmental rate of trophozoites. However, the perturbations in expression of rings and
schizonts opens up the possibility that some, or all, of the IDC stages exhibit a shorter
duration in sr10KO parasites.
We then compared the transcripts that lost rhythmicity in mismatched parasites (N =
1765) to the SLCGs (N = 1,015) to identify common genes. A total of 326 genes were
shared (Supplementary Fig. 4a) suggesting that their transcription is shaped by both
host rhythms and how the IDC is scheduled by the parasite. The shared genes were
enriched for biological processes including energy metabolism, heme metabolic
processes, and translation (Supplementary Fig. 4b).
Disruption of sr10 affects rhythmic expression of spliceosome machinery associated
genes
The spliceosome is a large and dynamic ribonucleoprotein complex of five small nuclear
ribonucleoproteins (snRNP) and over 150 multiple additional proteins that catalyze
splicing of precursor mRNA in eukaryotes48,49. Out of 85 genes (based on the Kyoto
Encyclopedia of Genes and Genomes (KEGG) database mapping) expressing different
spliceosomal proteins in P. chabaudi, 44 genes showed circadian transcription in wild
type parasites, of which 26 are in the SLCG group (lost rhythmicity in sr10KO parasites)
(Fig. 6a). They represent proteins of major spliceosome components including core
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spliceosomal protein members of snRNPs, prp19 complex and prp 19 related complexes.
Alternative splicing can regulate gene expression in signal dependent and tissue-specific
manners50 and an emerging body of evidence links alternative splicing with the control of
circadian regulatory networks in a variety of organisms, including Drosophila
melanogaster51, Neurospora crassa52-54, Arabidopsis55,56 and Mus musculus57.
Alternative splicing has been reported to occur in Apicomplexans (including malaria
parasites) for relatively few genes, covering only several percent of the total genes58.
Having observed that the loss of sr10 modulates the transcription of genes associated
with the spliceosome, we investigated whether it also affects the alternative-splicing (AS)
signature using RNAseq analysis of AS in both wild type and sr10KO parasites. We
considered two consecutive time-points, day 3 PI, time 05.30 GMT (ZT 22.5) and 08.30
(ZT 1.5) because they follow the time point when genes associated with spliceosome
machinery are expressed maximally (ZT 16.5-19.5). Comparison of sr10KO and wild-
type parasites identified 320 differential alternative splicing events covering 214 genes (P
< 0.05) for ZT 22.5 and 708 differential alternative splicing events covering 409 genes (p
< 0.05) for ZT1.5 (Fig. 6b, Supplementary Data 7). In a separate analysis, we
compared two consecutive time points (ZT 22.5 and ZT 1.5) within each strain as
controls, with an expectation of less differential alternative splicing events within
compared to between wild-type and sr10KO parasites for the same time point. As
expected, only 72 (54 genes) and 151 (114 genes) differential alternative splicing events
were detected in each strain (Figure 6B, Supplementary Data 7). This suggests that our
alternative splicing analysis was robust and that SR10 impacts the spliceosome
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machinery, resulting in differential alternative splicing patterns. GO-enrichment analysis
of genes that showed differential alternative splicing events enriched with biological
process terms such as translation, intracellular signal transduction and protein
sumoylation (Padj < 0.05) of sr10KO compared to wild-type parasites. These
observations collectively suggest that SR10 links host derived time-of-day information
with how parasites schedule the IDC and regulate alternative splicing.
Validation of circadian transcription pattern of genes through high-throughput
real-time qPCR
We independently verified the transcription patterns of 87 genes that lost rhythmicity
either in the mismatched parasites or in sr10KO parasites through high-throughput real-
time qPCR using the BioMarkTM HD system (Fluidigm). A total of 58 genes, from the
initial experiment comparing matched and mismatched P. chabaudi parasites, covering
11 randomly selected genes that represent multiple affected pathways were tested for
their transcription in both groups (Supplementary Fig. 4c). Similarly, a total of 36
genes from the sr10KO experiment covering 12 randomly selected genes and
representing multiple affected pathways were tested for their transcription in both P.
chabaudi wild-type and sr10KO strains (Supplementary Fig. 4d). The gene datasets,
generated by high-throughput real-time qRT-PCR using the BioMarkHD platform tightly
correlated with the RNAseq transcription values (Spearman-rank correlation between
0.67 -0.95 for all the genes tested), thus independently validating our RNAseq analysis.
DISCUSSION
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How malaria parasites interact with host rhythms to establish and maintain rhythms
during the IDC remain unknown. Our analyses, which were carried out using the rodent
malaria parasites P. chabaudi and P. yoelii in vivo, and the human malaria parasite, P.
falciparum in vitro, reveal an extensive transcriptome with 24 hour rhythmicity and
suggest that coordination of the IDC with host rhythms is important for the parasites’
ability to undertake key cellular processes. This includes metabolic pathways, DNA
replication, redox balance, the ubiquitin proteasome system, and alternative splicing (Fig.
6c). Rhythmicity in almost all of these processes persists in conditions in which parasites
are not exposed to host rhythms, suggesting the presence of an endogenous time-keeping
mechanism. We propose that, given its role in determining the duration of IDC and being
a GPCR class of receptor, serpentine receptor 10 acts as a link between host circadian
rhythms and parasite’s endogenous time-keeping / IDC scheduling mechanism.
Most genes (3,057 of 5,343) in the transcriptome of P. chabaudi in synchrony (matched)
with the host circadian rhythm are transcribed with 24 hour periodicities, whilst this
number drops to 1,824 when parasites are mismatched against the host circadian rhythm.
Genes that lose rhythmicity are involved in diverse biological processes including
glycolytic process, DNA replication, translation, ubiquitin/proteasome pathway and
redox metabolism (Supplementary Data 2). This is not simply a consequence of
mismatched parasites becoming desynchronised as they maintain synchrony during
rescheduling (Supplementary Fig. 1a). Instead, disruption to these processes could be a
result of stresses resulting from the IDC being misaligned to the host. If, for example,
rescheduling parasites are unable to coincide the appearance of a particular IDC stage
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with a rhythmically provided resource it needs from the host, the parasite may be
physiologically compromised and alternative pathways upregulated.
Regardless of the proximate cause, if such disruption impacts on the likelihood of
completing the IDC or the fitness of merozoites produced by schizonts, it could explain
the reduced replication rate previously observed in mismatched P. chabaudi15,16. Whilst
caution needs to be employed in interpreting the periodicities of genes that lost
rhythmicity of transcription, on the whole, they were approximately 1 hour shorter in
mismatched than matched parasites. This difference is supported by the observation that
mismatched parasites were rescheduling by on average approximately 1.5 hour every
IDC. Why some genes retain rhythmicity (1,292) and others don’t (1,765) is unclear.
Further, why 532 genes – all associated with RNA processing/splicing - became rhythmic
as a consequence of mismatch is unclear but raises the possibility of an alternative set of
IDC and/or timekeeping genes.
The IDC of P. chabaudi is approximately 24 hours in duration, making it difficult to
distinguish genes associated simply with particular IDC stages from genes associated
with an endogenous time-keeping mechanism or its outputs. Thus, P. falciparum cultured
under constant conditions was used to separate IDC from putative clock/clock controlled
genes. We found that 495 P. falciparum genes exhibit ~ 24 hour transcriptional rhythms
and many of these genes are associated with metabolic processes (carbohydrate
metabolism, DNA replication and the ubiquitin proteasome system) affected in P.
chabaudi by mismatch with host rhythm.
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The presence of “free-running” rhythms is consistent with an endogenous oscillator. That
the transcription of these genes becomes disrupted by mismatch is analogous to “jet lag”;
the temporal misalignment of processes in different organs/tissues whilst a clock regains
coordination (entrains) to a phase-shift in its time-cue (Zeitgeber). Such misalignment
can manifest as a dampening (reduction in amplitude) of rhythmicity of transcription of
genes driven by the clock22,59. In concordance with this, we observed a reduction in the
mean amplitude of transcripts of genes that remain rhythmic in matched, mismatched
(Fig. 1d), and in wild type and sr10KO parasites (Fig. 4e).
Observing that the ubiquitin-proteasome system is disrupted in mismatched parasites is
also consistent with parasites possessing an endogenous oscillator as the ubiquitin-
proteasome system plays a role in regulating clock components and their outputs in many
taxa28. The timing of transcription of ubiquitin-proteasome system genes in mismatched
P. chabaudi corresponds to the final stage of the IDC, and so could be disrupted as a
mechanism to facilitate a shorter IDC or as consequence of the wrong IDC stage
receiving time-of-day information from the host, or both. Similarly, if the signal to begin
DNA replication, acquire glucose, and / or manage redox states, is host derived,
mismatched parasites will be at the wrong IDC stage to respond appropriately.
The strongest suggestion that the parasite is capable of scheduling the IDC comes from
observations that SR10; (i) determines IDC duration in P. chabaudi and P. yoelii (Fig.
6c); (ii) has a 24 h transcription rhythm in P. falciparum and in both matched and
mismatched P. chabaudi; (iii) peaks at trophozoite stage in both P. falciparum and P.
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chabaudi; (iv) regulates rhythmicity in gene transcription for several of the processes
whose genes lost transcription rhythmicity in mismatched P. chabaudi (Fig. 6c); and (v)
determines the period of transcripts for genes that lose rhythmicity when it is disrupted.
We also observed rhythmic transcription of genes associated with histone modification
and the control of transcription and translation (Figs 2a, 3a, 5b and Supplementary fig.
3). These processes are considered central to the circadian organization of the
transcriptome60-62. Thus, we propose that SR10 acts as a link between time-of-day
information provided by host rhythms and the parasites’ endogenous time-keeping
mechanism that schedules the IDC.
In summary, we reveal that coordination with host circadian rhythms is central to the
schedule of transcription of genes associated with diverse processes underpinning IDC
progression, and ultimately, replication. Our data are consistent with some of the criteria
required to demonstrate an endogenous time-keeping ability, suggesting that malaria
parasites are at least in part responsible for scheduling their IDC. Further work should
examine whether other features of an endogenous clock exist and identify the interaction
partners of SR10 that link it with host time-of-day.
Taking all of our observations together, we propose that: (i) malaria parasites benefit
from coordination with the hosts circadian rhythm, (ii) malaria parasites are capable of
sensing the timing of their host’s feeding rhythm and adjust the speed of the IDC,
possibly by altering the duration of the trophozoite stage, to facilitate coordination with
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nutrient supplies. The IDC underpins malaria parasites’ capacity to undergo rapid asexual
replication and cause severe disease, and fuels transmission of the disease. Thus,
uncovering the components of the parasites’ time-keeping mechanisms and the signaling
system that links it to IDC progression may uncover novel intervention strategies.
METHODS
Ethics statement
All animal procedures for the parasite mismatching study were performed in accordance
with the UK Home office regulations (Animals Scientific Procedures ACT 1986; project
license number 70/8546) and approved by the University of Edinburgh. All the
procedures for the sr10KO study were performed in strict accordance with the Japanese
Humane Treatment and Management of Animal Law (Law No. 105 dated 19 October
1973 modified on 2 June 2006), and the Regulation on Animal Experimentation at
Nagasaki University, Japan. The protocol was approved by the Institutional Animal
Research Committee of Nagasaki University (permit: 12072610052). The animal
experiments conducted in Edinburgh and Nagasaki were officially exempted from
additional IBEC clearances in KAUST. All the procedures to perform work on different
parasite materials used in this study was approved by IBEC in KAUST (IBEC number:
19IBEC12)
Parasites and hosts
Experimental design and data collection for host rhythm mismatching
experiments
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Hosts were 6-8 weeks old female MF1 mice housed in groups of five at 21°C with food
and drinking water supplemented with 0.05 % para-aminobenzoic acid (PABA, to
supplement parasite growth) provided ad libitum. All experimental infections were
initiated by intravenous injection of 1 x 107 P. chabaudi chabaudi clone AS63 parasitized
red blood cells (per ml). Parasites at ring stage were collected at ZT0.5 (08:00 GMT)
from donor mice housed in standard LD light conditions (Lights ON 07:30; Lights OFF
19:30 GMT) and immediately used to infect two groups of experimental mice. One group
(termed “matched”) were entrained to the same photoperiod as the donor mice,
generating infections in which parasite and host rhythms were in the same phase. The
other group (termed “mismatched”) were entrained to reverse light (Lights OFF 07:30
and Lights ON 19:30 GMT) resulting in infections in which the parasite is out of phase
with the host by 12 hours.
Samples were collected every three hours, over a period of 30 hours, from 09:00 GMT on
day 4 to 15:00 GMT on day 5 post infection. This corresponds to a starting time of ZT1.5
for matched, and ZT13.5 for mismatched infections. At each sampling point, four mice
from each group (matched/mismatched) were sacrificed and thin blood smears and RBC
counts (via flow cytometry; Beckman Coulter) were taken by tail bleeds, and 50µl of
blood was taken via cardiac puncture (added to 200µl of RNAlater and frozen at -80C)
for RNAseq analysis. The developmental rhythm of parasites was assessed from blood
smears, in which the number of parasites at each of three morphologically distinct stages
(ring stage trophozoite (hereby referred to as ‘ring stage’), early (small) trophozoite stage,
late (large) trophozoites and schizont; differentiated based on parasite size, the size and
number of nuclei and the appearance of haemozoin) was recorded, as described before13.
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Experimental design and data collection for sr10 knockout experiment
The sr10 gene knockout and subsequent experiments were performed with the P.
chabaudi AS clone. Routine maintenance of the parasites was performed in ICR female
mice (6-8 weeks old) and rhythmicity was assessed in groups of female CBA inbred mice
(6-8 weeks old). Mice (SLC Inc., Shizuoka, Japan) were housed at 23°C with 12hr light-
dark cycle (lights-off: 19:00 h and lights-on: 07:00 h) and fed on a maintenance diet with
0.05% PABA-supplemented water.
Ring stage sr10KO (clone A and B) and wild type P. chabaudi parasites were sub-
inoculated into groups of four CBA mice each (1 x 106 parasitized RBCs per mouse) by
intravenous injection at 20:30 hrs corresponding to ZT13.5, day 0. Starting at ZT13.5 on
day 1 post-infection, blood smears were taken for both groups every three hours for 48
hrs producing a total of 17 time points. Blood smears were briefly fixed with 100%
methanol and stained with Giemsa’s solution. The parasitic patterns were then recorded
based on classification of the parasitic forms into four stages- rings (ring stage
trophozoite), early (small) trophozoites, late (large) trophozoites and schizonts. Same
procedures were adopted for P. yoelii wild type (17X1.1pp) and sr10KO clones to obtain
time series phenotype data using blood smears. Blood microsamples were also collected
at these timepoints for time-series gene expression analysis64. Briefly, 20µl of blood was
collected via tail snip at each time point, washed in PBS and immediately treated with
500 uL TRIzol reagent and stored shortly at 4°C and for long term at -80°C.
Plasmid construction to modify the sr10 gene locus in Plasmodium
chabaudi and Plasmodium yoelii
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Plasmids were constructed using the MultiSite Gateway cloning system (Invitrogen). For
P. chabaudi, One thousand base pair long regions at the 5′ and 3′ UTRs of Pchsr10 were
PCR-amplified from P. chabaudi with attB-flanked primers, Pchsr10-5U.B1.F and Pch-
5U.B2.R to yield attB1-Pchsr10-5U-attB2 fragment, and Pchsr10-3U.B4.F and Pchsr10-
3U.B1r.R to yield attB4-Pchsr10-3U-attB1r fragment. The attB1-Pchsr10-5U-attB2 and
attB4-Pchsr10-3U-attB1r products were then subjected to independent BP recombination
reactions with pDONR221 (Invitrogen) and pDONRP4P1R (Invitrogen) to generate
pENT12-Pchsr10-5U and pENT41-Pchsr10-3U entry clones, respectively. All BP
reactions were performed using the BP Clonase II enzyme mix (Invitrogen) according to
the manufacturer’s instructions. pENT12-Pchsr10-5U, pENT41-Pchsr10-3U and linker
pENT23-3Ty entry plasmids were subjected to LR recombination reaction (Invitrogen)
with a destination vector pDST43-HDEF-F3 (that contains the pyrimethamine resistant
gene selection cassette hDHFR) to yield knockout construct pKO-Pchsr10. LR reactions
were performed using the LR Clonase II Plus enzyme mix (Invitrogen) according to the
manufacturer’s instructions. P. yoelii 17x1.1pp sr10 knockout parasites were also
generated using the same procedures adopted for P. chabaudi using Pysr10-5U.B1.F and
Pysr10-5U.B2.R to yield attB1- Pysr10-5U-attB2 fragment , and Pysr10-3U.B4.F
PySR10-3U.B1r.R to yield attB4- Pysr10-3U-attB1r fragment from P. yoelii 17×1.1pp
gDNA . These fragments were then used in independent BP reactions and subsequent LR
reactions as above to generate pKO- Pysr10 construct. All primers used are listed in
Supplementary Data 8.
Parasite transfection
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Schizonts from P. yoelii and P. chabaudi-infected mice were enriched by centrifugation
over a Histodenz density cushion. Histodenz™ (Sigma-Aldrich, St. Louis, MO) solution
was prepared as 27.6 g/100 mL in Tris-buffered solution (5 mM Tris-HCl, 3 mM KCl,
and 0.3 mM CaNa2-EDTA, pH 7.5) and then diluted with equal volume of RPMI1640-
based incomplete medium containing 25 mM HEPES and 100 mg/L of hypoxanthine65.
The schizont-enriched parasites were transfected using a Nucleofector™ 2b device
(Lonza Japan) as described66 using 20 μg of linearized plasmids for each transfection.
Stable transfectants were selected by oral administration of pyrimethamine (0.07mg/mL)
and cloned by limiting dilution in mice. Stable integration of plasmids in the parasite
genome was confirmed by PCR and sequencing.
Time series gene expression analysis of parasites using RNAseq
Total RNA was isolated from TRIzol treated samples according to the manufacturer’s
instructions (Life Technologies). Strand-specific mRNA libraries were prepared from
total RNA using TruSeq Stranded mRNA Sample Prep Kit LS (Illumina) according to the
manufacturer’s instructions. Briefly, at least 100ng of total RNA was used as starting
material to prepare the libraries. PolyA+ mRNA molecules were purified from total RNA
using oligo-T attached magnetic beads. First strand synthesis was performed using
random primers followed by second strand synthesis where dUTP were incorporated in
place of dTTP to achieve strand-specificity. Ends of the double stranded cDNA
molecules were adaptor ligated and the libraries amplified by PCR for 15 cycles.
Libraries were sequenced on Illumina HiSeq 4000 platform with paired-end 100/150bp
read chemistry according to manufacturer’s instructions.
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Real-time quantitative reverse transcriptase PCR analysis
Total RNA was treated with TURBO Dnase according to the manufacturer’s instructions
(Thermo Fischer Scientific) to eliminate DNA contamination. The absence of DNA in
RNA samples was confirmed by inability to detect DNA after 40 cycles of PCR with
HSP40, family A (PCHAS_0612600) gene primers in a 7900HT fast real-time PCR
system (Applied Biosystems) with the following cycling conditions: 95°C for 30 sec
followed by 40 cycles of 95°C 2 sec; 60°C for 25 sec. P. chabaudi DNA was used as
positive control. For both the mismatching and SR10 experiments, gene expression
profiles were obtained for a total of 87 genes from eight time-points. Two biological
replicates per experimental condition and two technical replicates per biological replicate
were run on a Biomark HD microfluidic quantitative RT-PCR platform (Fluidigm) to
measure the expression level of genes. For the SR10 experiment, first strand cDNA
synthesis was performed using reverse transcription master mix according to the
manufacturer’s instructions (Fluidigm) and for the mismatching experiment, first strand
cDNA synthesis was performed using a High-Capacity cDNA reverse transcription kit
according to the manufacturer’s instructions (Thermo Fisher Scientific). Pre-
amplification of target cDNA was performed using a multiplexed, target-specific
amplification protocol (95°C for 15 sec, 60°C for 4 min for a total of 14 cycles). The pre-
amplification step uses a cocktail of forward and reverse primers of targets (genes of
interest) under study to increase the number of copies to a detectable level. Products
were diluted 5-fold prior to amplification using SsoFast EvaGreen Supermix with low
ROX and target specific primers in 96.96 Dynamic arrays on a Biomark HD microfluidic
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quantitative RT-PCR system (Fluidigm). Expression data for each gene were retrieved in
the form of Ct values. Normalization of transcript expression level was carried out using
P. chabaudi HSP40, family A (PCHAS_0612600) gene that was found to be non-
circadian in expression in all the strains used. All primers used are listed in
Supplementary Data 8.
Analysis of parasite developmental rhythmicity and periodicity
The early trophozoite stage was used as the marker stage because this stage is both easily
identifiable and has developmental duration similar to other stages. Rhythmicities in the
proportion of parasites at early trophozoite stages was determined by Fourier transformed
harmonic regression in Circwave67. A cosine wave was fitted to data from each individual
infection and compared to a straight line at the mean via an F-test. The period was
allowed to change between 18 and 30 hours (i.e. 24hr ± two sampling periods for the host
rhythm mismatch experiment and one sampling period for the analysis of sr10KO strains)
and the fit was considered significant if the adjusted (to account for multiple tests for
different periods) p value was greater than the alpha of 0.017. General linear models were
used to determine if the characteristics of rhythms varied according to treatment and
parasite strain (using the package lme4 in R version 3.4.0).
Transcriptome sequencing and analysis
RNASeq read quality was assessed using FASTQC quality control tool
(http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Read trimming tool
Trimmomatic68 was used to remove low quality reads and Illumina adaptor sequences.
Reads smaller than 36 nucleotides long were discarded. Quality trimmed reads were
mapped to P. chabaudi chabaudi AS reference genome (release 28 in PlasmoDB-
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36!
http://www.plasmoddb.org) using TopHat2 (version 2.0.13)69 with parameters “ –no-
novel-juncs –library-type fr-firststrand”. Gene expression estimates were made as raw
read counts using the Python script ‘HTSeq- count’ (model type – union, http://www-
huber.embl.de/users/anders/HTSeq/)70. Count data were converted to counts per million
(cpm) and genes were filtered if they failed to achieve a cpm value of 1 in at least 30% of
libraries per condition. Library sizes were scale-normalized by the TMM method using
EdgeR software71 and further subjected to linear model analysis using the voom function
in the limma package72. Differential expression analysis was performed using DeSeq273.
Genes with fold change greater than two and false discovery rate corrected p-value
(Benjamini-Hochberg procedure) < 0.05 were considered to be differentially expressed.
Identification of circadian transcripts in Plasmodium chabaudi
Circadian transcripts were identified using two programs, JTK-Cycle74 and ARSER75
implemented in MetaCycle76, an integrated R package with parameters set to fit time-
series data to exactly 24-h periodic waveforms. While JTK Cycle uses a non-parametric
test called the Jonckheere-Terpstra test to detect rhythmic transcripts74, ARSER uses
“autoregressive spectral estimation to predict an expression profile’s periodicity from the
frequency spectrum and then models the rhythmic patterns by using a harmonic
regression model to fit the time-series” 75. For both the programs voom-TMM normalized
count data was used as input data. A gene was considered cyclic if both the programs
identified it as a circadian transcript with significance bounded by p < 0.05 for the
parasite and host rhythms mismatching experiment where 11 time points separated by 3h
were used and by q < 0.05 for the SR10 experiment where 14 time points separated by 3h
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were used. We used the data from only 14 out of 17 time points. The first three time-
points having been excluded owing to low numbers of mapped reads to P. chabaudi (< 1
million). The output from ARSER concerning amplitude, phase and period of circadian
transcripts was used for further analysis.
Time points and biological replicates were clustered using hierarchical clustering
in R software environment with Pearson correlations from normalized count values as
input and ‘ward.D2’ agglomerative hierarchical clustering procedure was used for cluster
generation. The circular and linear histogram plots representing phase distribution of
cycling transcripts were generated using Oriana (www.kovcomp.co.uk/oriana/). PCA was
performed on voom-TMM normalized data using princomp in R software environment.
To determine the FDR of circadian transcripts in the host-circadian rhythm
mismatching experiment, the time points of collection were randomly permuted 1000
times and the number of circadian transcripts was assessed for each of the permutations
by both the programs used. A similar approach to determine the FDR was used by10. This
was done for both matched and mismatched parasite datasets.
Identification and analysis of circadian transcripts in Plasmodium
falciparum
Microarray based stage-specific expression data covering P. falciparum intra-erythrocytic
developmental stages sampled at 1 hour resolution for 48 hours was obtained from a
previous study20. The data had two odd time points (TP) missing (TP 23 and TP 29) so
only even time points were considered. The final data had 24 time points with a 2 h
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sampling rate. Circadian transcripts were identified following the same protocol as for P.
chabaudi.
P. chabaudi and P. falciparum gene ontology terms were downloaded from the UniProt
gene ontology annotation database (https://www.ebi.ac.uk/GOA). Circadian genes were
segregated into 12 groups based on their phase of maximum expression as determined
from ARSER output and gene ontology enrichment analysis was performed on each
groups using GOstats R package77. In the case of P. falciparum, GO-enrichment analysis
was performed on all the identified circadian transcripts. GO terms were considered only
if statistical tests showed FDR corrected p < 0.05. Odds ratio was calculated by dividing
the occurrence for GO term in the input list to the occurrence for GO term in the
reference set (i.e. whole genome).
Identification of differential alternative splicing events
Differential alternative splicing events in terms of differential exon usage were detected
using the DEXSeq program v 1.20.0278 with modified scripts as reported in Yeoh et al.
(2015)79. The p value significance level was set to 0.05 for the identification of
differential exon usage. Comparison was made between P. chabaudi wild and the SR10
knocked out strains for two time points i.e. Day 3, ZT 21 and Day 3, ZT 0/24 post-
infection. For these two time points, RNASeq read depth was increased by performing
additional rounds of sequencing in order to detect AS events more reliably. Two
biological replicates per time-point were used.
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DATA AND SOFTWARE AVAILABILITY
RNASeq data sets associated with this study have been submitted to the gene expression
omnibus under accession number GSE132647. See also Supplementary Table 1 and 3.
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REFERENCES
!
!
1! Sharma,!V.!K.!Adaptive!significance!of!circadian!clocks.!Chronobiol)Int!20,!
901-919!(2003).!
2! Bell-Pedersen,!D.)et)al.!Circadian!rhythms!from!multiple!oscillators:!lessons!
from!diverse!organisms.!Nat)Rev)Genet!6,!544-556,!doi:10.1038/nrg1633!
(2005).!
3! Edgar,!R.!S.)et)al.!Peroxiredoxins!are!conserved!markers!of!circadian!rhythms.!
Nature!485,!459-464,!doi:10.1038/nature11088!(2012).!
4! Rosbash,!M.!The!implications!of!multiple!circadian!clock!origins.!PLoS)Biol!7,!
e62,!doi:10.1371/journal.pbio.1000062!(2009).!
5! Young,!M.!W.!&!Kay,!S.!A.!Time!zones:!a!comparative!genetics!of!circadian!
clocks.!Nat)Rev)Genet!2,!702-715,!doi:10.1038/35088576!(2001).!
6! O'Neill,!J.!S.)et)al.!Circadian!rhythms!persist!without!transcription!in!a!
eukaryote.!Nature!469,!554-558,!doi:10.1038/nature09654!(2011).!
7! O'Neill,!J.!S.!&!Reddy,!A.!B.!Circadian!clocks!in!human!red!blood!cells.!Nature!
469,!498-503,!doi:10.1038/nature09702!(2011).!
8! Reece,!S.!E.,!Prior,!K.!F.!&!Mideo,!N.!The!Life!and!Times!of!Parasites:!Rhythms!
in!Strategies!for!Within-host!Survival!and!Between-host!Transmission.!J)Biol)
Rhythms!32,!516-533,!doi:10.1177/0748730417718904!(2017).!
9! Westwood,!M.!L.)et)al.!The!evolutionary!ecology!of!circadian!rhythms!in!
infection.!Nat)Ecol)Evol!3,!552-560,!doi:10.1038/s41559-019-0831-4!(2019).!
10!Rijo-Ferreira,!F.,!Pinto-Neves,!D.,!Barbosa-Morais,!N.!L.,!Takahashi,!J.!S.!&!
Figueiredo,!L.!M.!Trypanosoma!brucei!metabolism!is!under!circadian!control.!
Nat)Microbiol!2,!17032,!doi:10.1038/nmicrobiol.2017.32!(2017).!
11! Hawking,!F.!The!clock!of!the!malaria!parasite.!Sci)Am!222,!123-131!(1970).!
12!Hirako,!I.!C.)et)al.!Daily!Rhythms!of!TNFalpha!Expression!and!Food!Intake!
Regulate!Synchrony!of!Plasmodium!Stages!with!the!Host!Circadian!Cycle.!Cell)
Host)Microbe,!doi:10.1016/j.chom.2018.04.016!(2018).!
13!Prior,!K.!F.)et)al.!Timing!of!host!feeding!drives!rhythms!in!parasite!replication.!
PLoS)Pathog!14,!e1006900,!doi:10.1371/journal.ppat.1006900!(2018).!
14! Reece,!S.!E.!&!Prior,!K.!F.!Malaria!Makes!the!Most!of!Mealtimes.!Cell)Host)
Microbe!23,!695-697,!doi:10.1016/j.chom.2018.05.015!(2018).!
15! O'Donnell,!A.!J.,!Schneider,!P.,!McWatters,!H.!G.!&!Reece,!S.!E.!Fitness!costs!of!
disrupting!circadian!rhythms!in!malaria!parasites.!Proc)Biol)Sci!278,!2429-
2436,!doi:10.1098/rspb.2010.2457!(2011).!
16! O'Donnell,!A.!J.,!Mideo,!N.!&!Reece,!S.!E.!Disrupting!rhythms!in!Plasmodium!
chabaudi:!costs!accrue!quickly!and!independently!of!how!infections!are!
initiated.!Malar)J!12,!372,!doi:10.1186/1475-2875-12-372!(2013).!
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
41!
17! Hott,!A.)et)al.!Artemisinin-resistant!Plasmodium!falciparum!parasites!exhibit!
altered!patterns!of!development!in!infected!erythrocytes.!Antimicrob)Agents)
Chemother!59,!3156-3167,!doi:10.1128/AAC.00197-15!(2015).!
18!Mok,!S.)et)al.!Artemisinin!resistance!in!Plasmodium!falciparum!is!associated!
with!an!altered!temporal!pattern!of!transcription.!BMC)Genomics!12,!391,!
doi:10.1186/1471-2164-12-391!(2011).!
19!Mok,!S.)et)al.!Drug!resistance.!Population!transcriptomics!of!human!malaria!
parasites!reveals!the!mechanism!of!artemisinin!resistance.!Science!347,!431-
435,!doi:10.1126/science.1260403!(2015).!
20!Bozdech,!Z.)et)al.!The!transcriptome!of!the!intraerythrocytic!developmental!
cycle!of!Plasmodium!falciparum.!PLoS)Biol!1,!E5,!
doi:10.1371/journal.pbio.0000005!(2003).!
21!Hoo,!R.)et)al.!Integrated!analysis!of!the!Plasmodium!species!transcriptome.!
EBioMedicine!7,!255-266,!doi:10.1016/j.ebiom.2016.04.011!(2016).!
22! Archer,!S.!N.)et)al.!Mistimed!sleep!disrupts!circadian!regulation!of!the!human!
transcriptome.!Proc)Natl)Acad)Sci)U)S)A!111,!E682-691,!
doi:10.1073/pnas.1316335111!(2014).!
23!Chen,!C.!Y.)et)al.!Effects!of!aging!on!circadian!patterns!of!gene!expression!in!
the!human!prefrontal!cortex.!Proc)Natl)Acad)Sci)U)S)A!113,!206-211,!
doi:10.1073/pnas.1508249112!(2016).!
24!Bailey,!S.!M.,!Udoh,!U.!S.!&!Young,!M.!E.!Circadian!regulation!of!metabolism.!J)
Endocrinol!222,!R75-96,!doi:10.1530/JOE-14-0200!(2014).!
25! Milev,!N.!B.!&!Reddy,!A.!B.!Circadian!redox!oscillations!and!metabolism.!
Trends)Endocrinol)Metab!26,!430-437,!doi:10.1016/j.tem.2015.05.012!
(2015).!
26! Kalsbeek,!A.,!la!Fleur,!S.!&!Fliers,!E.!Circadian!control!of!glucose!metabolism.!
Mol)Metab!3,!372-383,!doi:10.1016/j.molmet.2014.03.002!(2014).!
27!Mancio-Silva,!L.)et)al.!Nutrient!sensing!modulates!malaria!parasite!virulence.!
Nature!547,!213-216,!doi:10.1038/nature23009!(2017).!
28! Stojkovic,!K.,!Wing,!S.!S.!&!Cermakian,!N.!A!central!role!for!ubiquitination!
within!a!circadian!clock!protein!modification!code.!Front)Mol)Neurosci!7,!69,!
doi:10.3389/fnmol.2014.00069!(2014).!
29! Kamura,!T.)et)al.!Rbx1,!a!component!of!the!VHL!tumor!suppressor!complex!
and!SCF!ubiquitin!ligase.!Science!284,!657-661!(1999).!
30! Ohta,!T.,!Michel,!J.!J.,!Schottelius,!A.!J.!&!Xiong,!Y.!ROC1,!a!homolog!of!APC11,!
represents!a!family!of!cullin!partners!with!an!associated!ubiquitin!ligase!
activity.!Mol)Cell!3,!535-541!(1999).!
31!Seol,!J.!H.)et)al.!Cdc53/cullin!and!the!essential!Hrt1!RING-H2!subunit!of!SCF!
define!a!ubiquitin!ligase!module!that!activates!the!E2!enzyme!Cdc34.!Genes)
Dev!13,!1614-1626!(1999).!
32!Skowyra,!D.)et)al.!Reconstitution!of!G1!cyclin!ubiquitination!with!complexes!
containing!SCFGrr1!and!Rbx1.!Science!284,!662-665!(1999).!
33!Dong,!G.)et)al.!Elevated!ATPase!activity!of!KaiC!applies!a!circadian!checkpoint!
on!cell!division!in!Synechococcus!elongatus.!Cell!140,!529-539,!
doi:10.1016/j.cell.2009.12.042!(2010).!
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
42!
34!Feillet,!C.)et)al.!Phase!locking!and!multiple!oscillating!attractors!for!the!
coupled!mammalian!clock!and!cell!cycle.!Proc)Natl)Acad)Sci)U)S)A!111,!9828-
9833,!doi:10.1073/pnas.1320474111!(2014).!
35!Kowalska,!E.)et)al.!NONO!couples!the!circadian!clock!to!the!cell!cycle.!Proc)
Natl)Acad)Sci)U)S)A!110,!1592-1599,!doi:10.1073/pnas.1213317110!(2013).!
36!Laranjeiro,!R.)et)al.!Cyclin-dependent!kinase!inhibitor!p20!controls!circadian!
cell-cycle!timing.!Proc)Natl)Acad)Sci)U)S)A!110,!6835-6840,!
doi:10.1073/pnas.1217912110!(2013).!
37!Masri,!S.,!Cervantes,!M.!&!Sassone-Corsi,!P.!The!circadian!clock!and!cell!cycle:!
interconnected!biological!circuits.!Curr)Opin)Cell)Biol!25,!730-734,!
doi:10.1016/j.ceb.2013.07.013!(2013).!
38! Reddy,!A.!B.!&!Rey,!G.!Metabolic!and!nontranscriptional!circadian!clocks:!
eukaryotes.!Annu)Rev)Biochem!83,!165-189,!doi:10.1146/annurev-biochem-
060713-035623!(2014).!
39!Sargeant,!T.!J.)et)al.!Lineage-specific!expansion!of!proteins!exported!to!
erythrocytes!in!malaria!parasites.!Genome)Biol!7,!R12,!doi:10.1186/gb-2006-
7-2-r12!(2006).!
40!Otto,!T.!D.)et)al.!New!insights!into!the!blood-stage!transcriptome!of!
Plasmodium!falciparum!using!RNA-Seq.!Mol)Microbiol!76,!12-24,!
doi:10.1111/j.1365-2958.2009.07026.x!(2010).!
41! Buck,!L.!&!Axel,!R.!A!novel!multigene!family!may!encode!odorant!receptors:!a!
molecular!basis!for!odor!recognition.!Cell!65,!175-187!(1991).!
42! Firestein,!S.!The!good!taste!of!genomics.!Nature!404,!552-553,!
doi:10.1038/35007167!(2000).!
43! Howard,!A.!D.)et)al.!Orphan!G-protein-coupled!receptors!and!natural!ligand!
discovery.!Trends)Pharmacol)Sci!22,!132-140!(2001).!
44!Lee,!D.!K.,!George,!S.!R.,!Evans,!J.!F.,!Lynch,!K.!R.!&!O'Dowd,!B.!F.!Orphan!G!
protein-coupled!receptors!in!the!CNS.!Curr)Opin)Pharmacol!1,!31-39!(2001).!
45!Madeira,!L.)et)al.!Genome-wide!detection!of!serpentine!receptor-like!proteins!
in!malaria!parasites.!PLoS)One!3,!e1889,!doi:10.1371/journal.pone.0001889!
(2008).!
46!Rovira-Graells,!N.)et)al.!Transcriptional!variation!in!the!malaria!parasite!
Plasmodium!falciparum.!Genome)Res!22,!925-938,!
doi:10.1101/gr.129692.111!(2012).!
47! Inoue,!Y.,!Ikeda,!M.!&!Shimizu,!T.!Proteome-wide!classification!and!
identification!of!mammalian-type!GPCRs!by!binary!topology!pattern.!Comput)
Biol)Chem!28,!39-49!(2004).!
48!Jurica,!M.!S.!&!Moore,!M.!J.!Pre-mRNA!splicing:!awash!in!a!sea!of!proteins.!Mol)
Cell!12,!5-14!(2003).!
49! Will,!C.!L.!&!Luhrmann,!R.!Spliceosome!structure!and!function.!Cold)Spring)
Harb)Perspect)Biol!3,!doi:10.1101/cshperspect.a003707!(2011).!
50! Lim,!C.!&!Allada,!R.!Emerging!roles!for!post-transcriptional!regulation!in!
circadian!clocks.!Nat)Neurosci!16,!1544-1550,!doi:10.1038/nn.3543!(2013).!
51!Cheng,!Y.,!Gvakharia,!B.!&!Hardin,!P.!E.!Two!alternatively!spliced!transcripts!
from!the!Drosophila!period!gene!rescue!rhythms!having!different!molecular!
and!behavioral!characteristics.!Mol)Cell)Biol!18,!6505-6514!(1998).!
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
43!
52! Brunner,!M.!&!Schafmeier,!T.!Transcriptional!and!post-transcriptional!
regulation!of!the!circadian!clock!of!cyanobacteria!and!Neurospora.!Genes)Dev!
20,!1061-1074,!doi:10.1101/gad.1410406!(2006).!
53! Colot,!H.!V.,!Loros,!J.!J.!&!Dunlap,!J.!C.!Temperature-modulated!alternative!
splicing!and!promoter!use!in!the!Circadian!clock!gene!frequency.!Mol)Biol)Cell!
16,!5563-5571,!doi:10.1091/mbc.E05-08-0756!(2005).!
54! Diernfellner,!A.!C.,!Schafmeier,!T.,!Merrow,!M.!W.!&!Brunner,!M.!Molecular!
mechanism!of!temperature!sensing!by!the!circadian!clock!of!Neurospora!
crassa.!Genes)Dev!19,!1968-1973,!doi:10.1101/gad.345905!(2005).!
55! James,!A.!B.)et)al.!Alternative!splicing!mediates!responses!of!the!Arabidopsis!
circadian!clock!to!temperature!changes.!Plant)Cell!24,!961-981,!
doi:10.1105/tpc.111.093948!(2012).!
56! Park,!M.!J.,!Seo,!P.!J.!&!Park,!C.!M.!CCA1!alternative!splicing!as!a!way!of!linking!
the!circadian!clock!to!temperature!response!in!Arabidopsis.!Plant)Signal)
Behav!7,!1194-1196,!doi:10.4161/psb.21300!(2012).!
57!McGlincy,!N.!J.)et)al.!Regulation!of!alternative!splicing!by!the!circadian!clock!
and!food!related!cues.!Genome)Biol!13,!R54,!doi:10.1186/gb-2012-13-6-r54!
(2012).!
58!Yeoh,!L.!M.,!Lee,!V.!V.,!McFadden,!G.!I.!&!Ralph,!S.!A.!Alternative!Splicing!in!
Apicomplexan!Parasites.!MBio!10,!doi:10.1128/mBio.02866-18!(2019).!
59!Moller-Levet,!C.!S.)et)al.!Effects!of!insufficient!sleep!on!circadian!rhythmicity!
and!expression!amplitude!of!the!human!blood!transcriptome.!Proc)Natl)Acad)
Sci)U)S)A!110,!E1132-1141,!doi:10.1073/pnas.1217154110!(2013).!
60! Etchegaray,!J.!P.,!Lee,!C.,!Wade,!P.!A.!&!Reppert,!S.!M.!Rhythmic!histone!
acetylation!underlies!transcription!in!the!mammalian!circadian!clock.!Nature!
421,!177-182,!doi:10.1038/nature01314!(2003).!
61!Jouffe,!C.)et)al.!The!circadian!clock!coordinates!ribosome!biogenesis.!PLoS)Biol!
11,!e1001455,!doi:10.1371/journal.pbio.1001455!(2013).!
62!Koike,!N.)et)al.!Transcriptional!architecture!and!chromatin!landscape!of!the!
core!circadian!clock!in!mammals.!Science!338,!349-354,!
doi:10.1126/science.1226339!(2012).!
63! Falanga,!P.!B.,!D'Imperio!Lima,!M.!R.,!Coutinho,!A.!&!Pereira!da!Silva,!L.!
Isotypic!pattern!of!the!polyclonal!B!cell!response!during!primary!infection!by!
Plasmodium!chabaudi!and!in!immune-protected!mice.!Eur)J)Immunol!17,!
599-603,!doi:10.1002/eji.1830170504!(1987).!
64! Ramaprasad,!A.,!Subudhi,!A.!K.,!Culleton,!R.!&!Pain,!A.!A!fast!and!cost-effective!
microsampling!protocol!incorporating!reduced!animal!usage!for!time-series!
transcriptomics!in!rodent!malaria!parasites.!Malar)J!18,!26,!
doi:10.1186/s12936-019-2659-4!(2019).!
65!Mutungi,!J.!K.,!Yahata,!K.,!Sakaguchi,!M.!&!Kaneko,!O.!Isolation!of!invasive!
Plasmodium!yoelii!merozoites!with!a!long!half-life!to!evaluate!invasion!
dynamics!and!potential!invasion!inhibitors.!Mol)Biochem)Parasitol!204,!26-
33,!doi:10.1016/j.molbiopara.2015.12.003!(2015).!
66! Janse,!C.!J.,!Ramesar,!J.!&!Waters,!A.!P.!High-efficiency!transfection!and!drug!
selection!of!genetically!transformed!blood!stages!of!the!rodent!malaria!
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
44!
parasite!Plasmodium!berghei.!Nat)Protoc!1,!346-356,!
doi:10.1038/nprot.2006.53!(2006).!
67! Oster,!H.,!Damerow,!S.,!Hut,!R.!A.!&!Eichele,!G.!Transcriptional!profiling!in!the!
adrenal!gland!reveals!circadian!regulation!of!hormone!biosynthesis!genes!
and!nucleosome!assembly!genes.!J)Biol)Rhythms!21,!350-361,!
doi:10.1177/0748730406293053!(2006).!
68! Bolger,!A.!M.,!Lohse,!M.!&!Usadel,!B.!Trimmomatic:!a!flexible!trimmer!for!
Illumina!sequence!data.!Bioinformatics!30,!2114-2120,!
doi:10.1093/bioinformatics/btu170!(2014).!
69! Kim,!D.)et)al.!TopHat2:!accurate!alignment!of!transcriptomes!in!the!presence!
of!insertions,!deletions!and!gene!fusions.!Genome)Biol!14,!R36,!
doi:10.1186/gb-2013-14-4-r36!(2013).!
70! Anders,!S.,!Pyl,!P.!T.!&!Huber,!W.!HTSeq--a!Python!framework!to!work!with!
high-throughput!sequencing!data.!Bioinformatics!31,!166-169,!
doi:10.1093/bioinformatics/btu638!(2015).!
71! McCarthy,!D.!J.,!Chen,!Y.!&!Smyth,!G.!K.!Differential!expression!analysis!of!
multifactor!RNA-Seq!experiments!with!respect!to!biological!variation.!Nucleic)
Acids)Res!40,!4288-4297,!doi:10.1093/nar/gks042!(2012).!
72!Ritchie,!M.!E.)et)al.!limma!powers!differential!expression!analyses!for!RNA-
sequencing!and!microarray!studies.!Nucleic)Acids)Res!43,!e47,!
doi:10.1093/nar/gkv007!(2015).!
73! Love,!M.!I.,!Huber,!W.!&!Anders,!S.!Moderated!estimation!of!fold!change!and!
dispersion!for!RNA-seq!data!with!DESeq2.!Genome)Biol!15,!550,!
doi:10.1186/s13059-014-0550-8!(2014).!
74!Hughes,!M.!E.,!Hogenesch,!J.!B.!&!Kornacker,!K.!JTK_CYCLE:!an!efficient!
nonparametric!algorithm!for!detecting!rhythmic!components!in!genome-
scale!data!sets.!J)Biol)Rhythms!25,!372-380,!doi:10.1177/0748730410379711!
(2010).!
75! Yang,!R.!&!Su,!Z.!Analyzing!circadian!expression!data!by!harmonic!regression!
based!on!autoregressive!spectral!estimation.!Bioinformatics!26,!i168-174,!
doi:10.1093/bioinformatics/btq189!(2010).!
76! Wu,!G.,!Anafi,!R.!C.,!Hughes,!M.!E.,!Kornacker,!K.!&!Hogenesch,!J.!B.!MetaCycle:!
an!integrated!R!package!to!evaluate!periodicity!in!large!scale!data.!
Bioinformatics!32,!3351-3353,!doi:10.1093/bioinformatics/btw405!(2016).!
77! Falcon,!S.!&!Gentleman,!R.!Using!GOstats!to!test!gene!lists!for!GO!term!
association.!Bioinformatics!23,!257-258,!doi:10.1093/bioinformatics/btl567!
(2007).!
78! Anders,!S.,!Reyes,!A.!&!Huber,!W.!Detecting!differential!usage!of!exons!from!
RNA-seq!data.!Genome)Res!22,!2008-2017,!doi:10.1101/gr.133744.111!
(2012).!
79!Yeoh,!L.!M.)et)al.!A!serine-arginine-rich!(SR)!splicing!factor!modulates!
alternative!splicing!of!over!a!thousand!genes!in!Toxoplasma!gondii.!Nucleic)
Acids)Res!43,!4661-4675,!doi:10.1093/nar/gkv311!(2015).!
!
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was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
45!
ACKNOWLEDGEMENTS
The project was supported by a faculty baseline funding (BAS/1/1020-01-01) from the
King Abdullah University of Science and Technology (KAUST) to AP. RC is supported
by Japanese Society for the Promotion of Science (JSPS), Japan Grant-in-Aid for
Scientific Research Nos. 24255009, 25870525, 16K21233 and 19K07526. SER and
AJOD are supported by Wellcome (202769/Z/16/Z; 204511/Z/16/Z), the Royal Society
(UF110155; NF140517) and the Human Frontier Science Program (RGP0046/2013). The
authors thank the staff of the Bioscience Core Laboratory in KAUST for sequencing
RNAseq libraries and all members of the Reece lab at the University of Edinburgh and
pathogen genomics lab at KAUST for assistance during the experiments. This work was
partly conducted at the Joint Usage / Research Center for Tropical Disease, Institute of
Tropical Medicine, Nagasaki University, Japan.
AUTHOR CONTRIBUTIONS
Conceptualization, A.P. and S.E.R.; Methodology, A.P., S.E.R., A.K.S., A.J.O.D.,
H.M.A., A.R., R.C. and O.K.; Investigation, A.K.S., A.R., A.J.O.D., R.C., and H.M.A.;
Formal analysis, A.K.S., A.R., A.J.O.D., H.M.A., A.K., A.M.A.H., F.B.R. and H.R.A.;
Writing- Original Draft, A.K.S., S.E.R and A.P.; Writing – Review & Editing, A.K.S.,
S.E.R, R.C. and A.P.; Funding Acquisition, S.E.R. and A.P.; Resources, S.E.R., R.C. and
A.P.; Supervision, A.P.
DECLARATION OF INTERESTS
The authors declare no competing interests.
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
46!
FIGURES
Fig. 1 | The transcriptome of Plasmodium chabaudi is sensitive to the phase of host
circadian rhythms.
a) Ring stage parasites from donor mice housed at a standard light regime were used to infect recipient
mice housed in two rooms that differed by 12 hours in their light:dark cycle. Blood samples were collected
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
47!
for RNAseq analysis from day 4 post-infection every 3 hours for 11 time points (N = 4 mice per group per
time point). ZT is Zeitgeber Time: hours after lights on.
b) Time series gene expression heatmap views of transcripts with 24 hour (putative “circadian”)
rhythmicity in matched and mismatched parasites. Right most side heatmap shows the expression pattern of
24 h transcripts in mismatched parasites that lost rhythmicity. Each row in each heatmap represents a single
gene, sorted according to the phase of maximum expression starting from first sample time point. The
phase of expression of each gene was obtained from ARSER output and N represents number of genes
identified by both JTK74 and ARSER75 as fluctuating in expression in a 24 h manner. Each time point is
represented by expression heatmap of two biological replicates.
c) Venn diagram of number of 24 h (putative “circadian”) genes identified by both JTK and ARSER
programs in matched (top) and mismatched (bottom) parasites.
d) Transcripts with 24 h (putative “circadian”) rhythmicity in both matched and mismatched parasites had
lower median amplitude (0.86, brown dashed line) in mismatched parasites compared to matched parasites
(1.22, blue dashed line).
e) Phase distributions of genes that displayed 24 h (putative “circadian”) rhythmicity only in matched
parasites in the form of a histogram. The mean circular phase is indicated by a solid black line. N represents
the number of cycling transcripts. The pink line represents the standard deviation of the mean of phases.
Whilst these genes are not identified as having 24 h rhythms in mismatched parasites, their distribution is
shown for comparison.
f) Transcripts that displayed 24 h (putative “circadian”) rhythmicity only in matched parasites have median
period close to 24 h (blue dashed line) and (for comparison) 23 h in mismatched parasites (brown dotted
line).
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which. http://dx.doi.org/10.1101/791046doi: bioRxiv preprint first posted online Oct. 2, 2019;

!
48!
Fig. 2 | Multiple key biological pathways are affected by mismatch to the phase of
the host’s circadian rhythm in the rodent malaria parasite Plasmodium chabaudi.
a) Time series gene expression view of genes that displayed with 24 h (putative “circadian”) rhythmicity in
matched parasites but lost rhythmicity in mismatched parasites. Genes were sorted based on phase of
maximum expression and segregated into 12 groups with each group representing 2 hours (h) phase
clusters. Line plots along the sides of the heat map represent expression profiles of individual genes
significantly enriched to gene ontology terms (p<0.05, hypergeometric test) representing few crucial
biological processes. Each plot has information about the false discovery rate corrected p value of
0-2
2-4
4-6
6-8
8-10
10-12
12-14
14-16
16-18
18-20
20-22
22-24
DNA
replication
Protein
phosphorylation
Regulation of
transcription
Gene
expression
Phase(ZT)
Glycolytic process
Ubiquitin process
Translation
Transmembrane
transport
0.75
1.15
p = 0.0001
p =0. 0001
0.6
1.2
p = 0.02
1.3
0.4
p = 4.8E-09
1.4
0.5
p = 7.9E-08
1.8
0.2
p =0. 006
4.0
-1.0
p =0. 0003
1.4
0.6
p =0. 004
1.4
0.5
-1
Relative expression
Relative expression
0/24 12 0/24
-2 0 2
1
0/24 0/24
12
Matched Mismatched
DNA replication
Ubiquitin
Proteasome
Row Z score
PCHAS_0107700
PCHAS_0209900
PCHAS_0621000
PCHAS_0808500
PCHAS_1130000
PCHAS_1210500
PCHAS_1223700
PCHAS_1233700
PCHAS_1304600
PCHAS_1338700
PCHAS_0203800
PCHAS_0305800/RPN1
PCHAS_0411200/RPN12
PCHAS_0514600
PCHAS_0832900
PCHAS_0927100/RPN7
PCHAS_1040400/RPN6
PCHAS_1129900
PCHAS_1143500/RPN11
PCHAS_1227100
PCHAS_1334300/RPN2
PCHAS_1347900
PCHAS_1356400/RPN3
PCHAS_1411900/RPT1
PCHAS_1464100/RPT6
PCHAS_1021700/HRD1
PCHAS_0822200
PCHAS_1142400/SKP1
PCHAS_1460800/UBC12
PCHAS_0517800
PCHAS_0722900/UBA3
PCHAS_0806500/RBX1
PCHAS_1122900/APC11
PCHAS_0806300
PCHAS_1108100/SSB
PCHAS_0407200
PCHAS_0316800/RFC2
PCHAS_0501400
PCHAS_0727400/MCM9
PCHAS_0904800/RAD51
PCHAS_1220700
PCHAS_1330200
PCHAS_1443400/PCNA2
PCHAS_1451200
PCHAS_0611900/MCM5
PCHAS_0613300
PCHAS_0614900
PCHAS_0803300/ORC2
PCHAS_1131100/MCM6
PCHAS_1457400/RFC4
PCHAS_0314600/ORC5
PCHAS_0514000
PCHAS_1242200/MCM3
PCHAS_1353400
Glycolysis
PCHAS_1125100
PCHAS_1311800
PCHAS_0916200/PGM1
PCHAS_1122400
PCHAS_1215000/ENO
PCHAS_1329700/GAPDH
PCHAS_0806800
PCHAS_0920600
PCHAS_0816700/PFK9
0/24 12 0/24
0/24 12 0/24 12 0/24 12
0/24 12 0/24 12 0/24 12
0/24 12 0/24 12 0/24 12
0/24 12 0/24 12 0/24 12
Matched Mismatched
a
b
ZT ZT
ZT ZT
ZT ZT
ZT ZT
ZT ZT

!
49!
representing GO term. The Y axis represents relative expression of genes at each time point determined by
count level expression of each gene normalized by its mean across 11 time points.
b) Heatmaps illustrating the expression patterns of circadian genes for matched and mismatched parasites
that are involved in the ubiquitin and proteasome systems, and the DNA replication and glycolysis
pathways. These genes lost circadian rhythmicity in mismatched parasites. Genes have been sorted based
on the phase of maximum expression. The colour scheme represents the row Z score. Each time point is
represented by the expression heatmap of two biological replicates.

!
50!
Fig. 3 | Expression of serpentine receptor 10 is “circadian” in Plasmodium
falciparum in vitro and maintains the duration of the intra-erythrocytic
developmental cycle of P. chabaudi and P. yoelii in vivo
a) Time series gene expression heat map view of circadian genes identified in P. falciparum in vitro in
“free running” (constant) conditions. Genes sorted based on the phase of maximum expression starting
from time T:0 post merozoite invasion. P. falciparum IDC expression data was obtained from Bozdech et
al., (2003)20.
b) Manually curated gene ontology terms enriched for P. falciparum genes with 24 h expression (Padj <
0.05).

!
51!
c) Line graphs represent the expression of serpentine receptor 10 in P. falciparum over its 48 h IDC (top
plot) and in P. chabaudi during its 24 h IDC (bottom plot). Dotted lines show the best-fit sinusoidal curves.
P. falciparum IDC expression data was obtained from20 and P. chabaudi expression data from this study.
d) P. chabaudi wild-type and sr10KO parasites were used to initiate infections in CBS mice. Blood was
collected from day 1 (ZT 13.5) every 3 h during the following 48 hrs. Expression data from two biological
replicates over 14 time points (from day 2 PI, ZT22.5) were analyzed to identify “circadian” transcripts.
Abbreviations: PVM, parasitophorous vacuole membrane; PPM, parasite plasma membrane; RBC, red
blood cell.
e) Proportion of parasites in the blood at early trophozoite stage in P. chabaudi wild type and sr10KO
clones (Mean ± SEM, N=4/clone)
f) IDC duration of P. chabaudi wild type and sr10KO clones (Mean ± SEM, N=4/clone).
g) Proportion of parasites in the blood at early trophozoite stage in P. yoelii wild type and sr10KO clones
(Mean ± SEM, N=4/clone).
h) IDC duration of P. yoelii wild type and sr10KO clones (Mean ± SEM, N=4/clone).

!
52!
Fig. 4 | Disruption of SR10 affects the circadian transcriptome of Plasmodium
chabaudi.
a) Time series gene expression heatmap view of transcripts with 24 h rhythmicity in P. chabaudi wild type
and sr10KO parasites. Right most heatmap shows the expression pattern of transcripts that lost rhythmicity
in sr10KO parasites. Each row represents a single gene, sorted according to phase of maximum expression
starting from first time point of sample collection. N represents number of circadian genes identified.
b) Venn diagram of number of genes with 24 h rhythmic expression identified by both JTK and ARSER in
wild type and sr10KO parasites.
c) Phase distributions of genes with 24 h expression in wild type that lost 24 h rhythmicity in sr10KO
parasites (SLCGs). The mean circular phase for each condition is indicated by a solid black line. N
represents the number of cycling transcripts. Pink lines represent standard deviation of the mean circular
phases.
d) Transcripts that displayed with 24 h (putative “circadian”) rhythmicity only in wild parasites have
median periods close to 26 h in wild parasites (blue dashed line) and 24 h in sr10KO parasites (brown
dashed line).
N =3620/5343
N =2886/5343
N =1015/3620
22.5 10.5 22.5 10.5
22.5 10.5 22.5 10.5
22.5 10.5 22.5 22.5
a b
c
e
P. chabaudi wild P. chabaudi sr10KO Lost rhythm in sr10KO
Density of periods of SLCGs
0.0
0.1
0.2
0.3
Period (h)
sr10KO
Wild
20 22 24 26 28
N= 1015
P. chabaudi wild
P. chabaudi sr10KO
Frequency
0612 18 24
0
10
20
30
40
50
60
N=1015
ZT(h)
0612 18 24
0
10
20
30
40
50
60 N=1015
ZT(h)
ZT(h)
ZT(h)
ZT(h)
sr10KO
Wild
d
0.0
0.2
0.4
0.6
0.8
012345
Amplitude
density
Condition
SR10KO
Wild
Amplitude
012 3 45
0.0
0.2
0.4
0.6
0.8
sr10KO
Wild
Density of amplitudes of
cycling genes Frequency
N= 2620
2620
1015
296

!
53!
e) Genes that were rhythmic in both wild type and sr10KO parasites had a significantly lower mean
amplitude in sr10KO parasites (1.15, brown dashed line) compared to wild parasites (1.53, p < 0.00001).
Fig. 5 | Knock out of sr10 affects many biological processes.
a) Differentially regulated genes were identified by comparing four matching time points of sr10KO and
wild-type Plasmodium chabaudi parasites. Up and down represent differentially regulated genes with the
false discovery rate corrected p < 0.05 and Log2 fold change < -1 for down-regulated genes and > 1 for up-
regulated genes at each time point. The four time points analyzed represent four IDC stages as derived
from examination of parasite morphology in thin blood smears.
Time:D2ZT16.5
Down:73
UP:214
Down:79
UP:376
Down:271
UP:334
Down:98
UP:423
Wild SR10KO SR10KO
Wild
Time:D2ZT22.5
Wild SR10KO
Time:D3ZT4.5 Time:D3ZT10.5
Wild SR10KO
Row Z-score
0
Early trophozoite Late trophozoite
Schizont Ring
a
Schizont
Ring
Early
trophozoite
Late
trophozoite
b
P value
Log2 odds ratio
Histone H2A acetylation
Histone H4 acetylation
Proteolysis
Nucleobase-containing compound biosynthetic process
Cellular response to stimulus
Pathogenesis
Protein modification process
Cell cycle process
Chromosome segregation
Protein phosphorylation
DNA replication
Regulation of nulceobase compound metabolic process
Translational termination
Regulation of transcription
Organonitrogen compound biosynthetic process
Translation
Peptide metabolic process
Lipid transport
Oxidation-reduction process
Glycosil compound biosynthetic process
Carbohydrate derivative metabolic process
Purine ribonucleoside metabolic process
Pentose-phosphate shunt
Cellular modified amino acid metabolic process
rRNA process
Cofactor biosynthetic process
GMP metabolic process
Organophosphate metabolic process
Carbohydrate derivative transport
Cellular aldehyde metabolic process
Nucleoside metabolic process
Microtubule based movement
D2ZT16.5 D2ZT22.5 D3ZT4.5 D3ZT10.5
5
4
3
2
1
0.04
0.03
0.02
0.01
-1 1

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b) Gene ontology analysis of the differentially regulated genes within each time point. Manually curated
functional annotations of biological processes (FDR <0.05) are represented and the colour spectrum
represents the odds ratio.
Fig. 6 |*The*cross-talks*between*the*intraerythrocytic*developmental*cycle*of*P.#
chabaudi*and*the*host*rhythms.*
a) sr10 knockout affects parasite spliceosome machinery. Heatmap illustrating the expression pattern of
sr10 knockout affected circadian genes involved in spliceosome pathway in P. chabaudi wild and sr10KO
parasites. The list of genes was obtained by mapping the SLCGs to P. chabaudi spliceosome pathway
represented in the KEGG database. Genes have been sorted based on phase of maximum expression. The
colour scheme represents the row Z score.
b) sr10 knockout affects alternative splicing signature of the transcriptome. sr10 knock out affects the
alternative splicing signature of the parasite transcriptome. Two consecutive time points were compared

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between wild and sr10KO parasites to identify differential usage of exons. As a control two consecutive
time points (Day 2 ZT 22.5 and Day 3 ZT1.5) from the same parasite strain were also compared. The
number shown depict the number of differential exon usage events detected (p < 0.05). Two biological
replicates per time point were used. Differential exon usage events were identified using DEXSeq78.
c) Schematic figure summarizing the cross-talks between the intraerythrocytic developmental cycle
schedule of P. chabaudi and the host rhythms. Parasite reschedules its IDC cycle when its developmental
rhythms are mismatched with the host rhythms. Due to the mismatch, the parasites are devoid of host
rhythmic cues on anticipated time. The parasites respond by losing rhythmic expression of genes associated
with multiple biological processes as depicted in the pie-charts on the left. P. chabaudi serotonin receptor
10 (sr10) expresses rhythmically during the IDC and knocking out sr10 in P. chabaudi reduces the IDC
duration by ~ 3h and also affects the rhythmic expression of genes associated with multiple biological
processes as depicted in pie charts on the right. We speculate that SR10 may serve as one of the receptors
through which the parasite receives the rhythmic cues of the host that controls the IDC duration of parasite.
Black section within the pie-charts represent the percentage of rhythmic genes in each biological process
that lost the rhythmic expression in the host-mismatched and sr10KO parasites.
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