Malaria parasites regulate intra-erythrocytic development duration via serpentine receptor 10 to coordinate with host rhythms

Abstract

Malaria parasites complete their intra-erythrocytic developmental cycle (IDC) in multiples of 24 h suggesting a circadian basis, but the mechanism controlling this periodicity is unknown. Combining in vivo and in vitro approaches utilizing rodent and human malaria parasites, we reveal that: (i) 57% of Plasmodium chabaudi genes exhibit daily rhythms in transcription; (ii) 58% of these genes lose transcriptional rhythmicity when the IDC is out-of-synchrony with host rhythms; (iii) 6% of Plasmodium falciparum genes show 24 h rhythms in expression under free-running conditions; (iv) Serpentine receptor 10 (SR10) has a 24 h transcriptional rhythm and disrupting it in rodent malaria parasites shortens the IDC by 2-3 h; (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 reveal malaria parasites are at least partly responsible for scheduling the IDC and coordinating their development with host daily rhythms. The mechanism underlying periodicity of Plasmodium’s intra-erythrocytic developmental cycle (IDC) is unclear. Here, Subudhi et al. show that serpentine receptor 10 (SR10) plays a role in regulating the schedule of the IDC in line with the timing of host daily rhythms.

Full text

ARTICLE
Malaria parasites regulate intra-erythrocytic
development duration via serpentine receptor
10 to coordinate with host rhythms
Amit K. Subudhi1, Aidan J. O’Donnell2,9, Abhinay Ramaprasad1,9, Hussein M. Abkallo 3, Abhinav Kaushik 1,
Hifzur R. Ansari 1, Alyaa M. Abdel-Haleem1,8, Fathia Ben Rached 1, Osamu Kaneko 4,
Richard Culleton 3,5✉, Sarah E. Reece 2✉& Arnab Pain 1,6,7✉
Malaria parasites complete their intra-erythrocytic developmental cycle (IDC) in multiples of
24 h suggesting a circadian basis, but the mechanism controlling this periodicity is unknown.
Combining in vivo and in vitro approaches utilizing rodent and human malaria parasites, we
reveal that: (i) 57% of Plasmodium chabaudi genes exhibit daily rhythms in transcription; (ii)
58% of these genes lose transcriptional rhythmicity when the IDC is out-of-synchrony with
host rhythms; (iii) 6% of Plasmodium falciparum genes show 24 h rhythms in expression under
free-running conditions; (iv) Serpentine receptor 10 (SR10) has a 24 h transcriptional rhythm
and disrupting it in rodent malaria parasites shortens the IDC by 2-3 h; (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 reveal malaria
parasites are at least partly responsible for scheduling the IDC and coordinating their
development with host daily rhythms.
https://doi.org/10.1038/s41467-020-16593-y OPEN
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. 5Division of Molecular
Parasitology, Proteo-Science Center, Ehime University, 454 Shitsukawa, Toon, Ehime 791-0295, Japan. 6Center for Zoonosis Control, Global Institution for
Collaborative Research and Education (GI-CoRE), Hokkaido University, N20 W10 Kita-ku, Sapporo 001-0020, Japan. 7Nuffield Division of Clinical Laboratory
Sciences (NDCLS), University of Oxford, Headington, Oxford OX3 9DU, UK.
8
Present address: Computational Bioscience Research Center, King Abdullah
University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
9
These authors contributed equally: Aidan J. O’Donnell,
Abhinay Ramaprasad. ✉email: richard@nagasaki-u.ac.jp;sarah.reece@ed.ac.uk;arnab.pain@kaust.edu.sa
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Many parasite species exhibit daily rhythms in behavior
and/or development that appear scheduled to optimally
exploit periodicities in transmission opportunities and/
or resource availability1,2. The parasitic protozoan Trypanosoma
brucei, for example, possesses an intrinsic circadian clock that
drives metabolic rhythms3. Similarly, rhythms in host feeding and
innate immune responses influence the timing of rhythms in the
intra-erythrocytic developmental cycle (IDC) of rodent malaria
parasites4,5. Specifically, completion of the IDC, a glucose-
demanding process, coincides with host food intake, and quies-
cence 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 responses mounted during malaria infec-
tion4. However, the extent to which malaria parasites or their
hosts are responsible for the synchrony, timing, and duration of
the IDC schedule is unclear6. Either parasites are able to respond
to time-of-day cues provided by the host to organize when they
transition between IDC stages and complete schizogony, or,
parasites are intrinsically arrhythmic and allow the host to impose
rhythms on the IDC that benefit the parasite1.
Establishing how the timing and synchronicity of the IDC is
established is important because temporal coordination with host
rhythms is beneficial for parasite fitness7,8, and because tolerance
to antimalarial drugs is conferred to parasites that pause their
IDCs9–11. 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. First,
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). Second, we show that P. falciparum also has
a transcriptome with 24 h periodicity, even in the absence of host
rhythms. Third, we identify a transmembrane serpentine receptor
with 24 h rhythmic expression in both species and demonstrate it
plays a role in the duration of the IDC. Furthermore, 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 the IDC.
Results
The P. chabaudi transcriptome responds to host rhythms.
Transcriptome analyses of time-series RNA sequencing datasets
were performed with parasites from infections that were in syn-
chrony (phase aligned; “host rhythm matched”) and 12 h out of
synchrony (out-of-phase; “host rhythm mismatched”) with host
daily rhythms (for details see Methods section) (Fig. 1a). After
mismatch to host daily rhythms, the IDC of P. chabaudi becomes
rescheduled to match the host’s rhythms. By the time of sampling
(days 4–5 post-infection, PI), schizogony of mismatched parasites
peaked 6 h after matched parasites (inferred from ring stage
rhythms) (Supplementary Fig. 1a). Parasites in both matched and
mismatched infections remained synchronous throughout the
sampling period (Supplementary Fig. 1a). After quantifying gene
expression at each time point through RNAseq analysis (n=2
per time point), we identified genes that followed ~24 h (“daily”)
rhythms in expression according to two commonly used and
independent algorithms (see Methods section) with a threshold of
p< 0.05. Of a total of 5343 genes (5158 detected and considered
for analysis), 3057 (58%) in matched parasites, and 1824 (34%) in
mismatched parasites, exhibited daily rhythms in expression (p<
0.05; Supplementary Fig. 1b, c and Supplementary Data 1). A
permutation test was performed to empirically determine the
false discovery rate (FDR) in detecting daily rhythmic transcripts
(see Methods section). When the sampling order was permuted
1000 times, it always gave a smaller number of rhythmic genes
compared to the number of observed rhythmic genes predicted
when the samples were kept in correct order, with an overall FDR
of <0.05 for all permutation tests conducted for each algorithm
used for each experimental condition (Supplementary Fig. 1d). A
similar approach was used by Rijo-Ferreira et al.3. We also cal-
culated the Z-score to identify how far the observed value was
from the values obtained in the permutation tests. Z-scores were
found to be high, at between 5.50–16.68, for all four permutation
tests done. This suggests that our observations are higher than the
distribution of all permutations. When we took a range of dif-
ferent q-value cut-offs, we observed that there were always a
greater number of rhythmic transcripts detected in the matched
parasites compared to the mismatched parasites (Supplementary
Fig. 1e).
Effects of mismatch to the host rhythm. Over 80% of the genes
expressed during the IDC of malaria parasites undergo a tight
temporal expression cascade associated with each parasite
stage12,13. The periodicity of the expression profile of these genes
can be ~24, 48, or 72 h depending on the malaria parasite species:
for P. chabaudi, the expression profile of these genes have a ~24 h
periodicity while for P. falciparum they have ~48 h periodicity.
Thus, genes associated with IDC progression should peak 6 h
later in mismatched compared to matched parasites, but any
genes directly sensitive to the time-of-day of hosts should peak at
the same point in the host’s daily rhythm according to its light:
dark schedule (i.e., ZT), which corresponds to 12 h GMT apart in
mismatched compared to matched parasites (because their hosts
were kept in opposite light:dark schedules).
Comparing rhythmic transcripts detected in both matched and
mismatched parasites revealed three sets of transcripts: (1) 1765
genes (33% of the total) with 24 h rhythmic expression (p< 0.05)
in matched parasites that exhibit a reduction in rhythmicity of
expression profile in mismatched parasites (p> 0.05); (2) 1292
genes with expression profiles with 24 h rhythms in both matched
and mismatched parasites; and (3) 532 genes whose expression
profiles were significantly more rhythmic in mismatched than
matched parasites (p< 0.05, Fig. 1b, c). Hierarchical clustering
analysis identified biological replicates to be tightly clustered
(Supplementary Fig. 2a). 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. 2b), which supports the
hypothesis that the large number of periodic transcripts are
expressed with 24 h (daily) periodicity.
Out of 1292 genes that retained 24 h rhythmicity in both
matched and mismatched parasites, we found 685 genes (53%)
that had a delay of about 6 h (±1.5 h) in mismatched compared to
matched parasites (Supplementary Fig. 2c), complementing the
phase difference between their IDCs detected by morphology
(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 expression between matched and mismatched
parasites. Genes that alter their rhythmic expression in mis-
matched parasites may do so because the IDC and/or homeostasis
are negatively affected by misalignment with host rhythms, and/
or as a consequence of actively rescheduling the IDC to become
coordinated to host daily rhythms.
The amplitude of rhythmically expressed genes is significantly
higher (p< 0.0001, unpaired Student’st-test) in matched
compared to mismatched parasites (Fig. 1d, Supplementary
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Fig. 2d), suggesting a loss of synchronicity in the gene expression
of mismatched parasites that is not severe enough to impact on
IDC synchronicity as measured by stage proportions (Supple-
mentary Fig. 1a). Such a dampening of rhythms is a typical
consequence of misalignment of a circadian clock with its time-
of-day cue (“Zeitgeber”)14. Genes (N=532) with low rhythmicity
in matched parasites that exhibit highly rhythmic expresion in
mismatched parasites were enriched to a single gene ontology
biological process term; RNA processing/splicing. This may
represent the expression of an alternative set of IDC genes during
rescheduling. In humans, a set of transcripts has been shown to
gain rhythmicity in older individuals coinciding with the loss of
canonical clock function15.
Thirty-three percent of genes (n=1765) fell under the
threshold (p> 0.05) for rhythmicity in mismatched parasites. In
matched parasites, a bimodal distribution of expression for these
0/24
7.30
Donor mice
Recipient mice
Room with standard
light regime Room with reverse
light regime
Sampling from day 4 PI
Every 3 h sampling
11 time points
Total RNA
Stranded RNAseq library
RNAseq
Data normalization
JTK cycle and ARSER
Identification of circadian transcripts
Host rhythm matched Host rhythm mismatched
Matched
Mismatched
0/24
0.9
d
200
150
100
Frequency
50
00612
Matched
ZT
18 24
125
100
75
Frequency
50
0
25
12 18 0/24
Mismatched
ZT
612
0.3
0.2
Density
0.1
0.0
20 22 24
N = 1765
Period (h)
26 28
0.6
Density
0.3
0.0
12
Amplitude
Condition
Mismatched
Matched
Condition
6 h_Mismatched
6 h_Matched
3
ZT (h)
0/240/24
N = 1765/5343
ZT (h)
–2 –1 0
Z-score
12
12 12 12
1765
1292
532
Recipient mice
N = 4
N = 4
Donate parasites
12
19.30
0/24
7.30
12
19.30
12
7.30
0/24
19.30
12
7.30
0/24
19.30
ZT
GMT
a
b
ef
c
Mismatched
Matched
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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 expression extremely close to 24 h in matched
parasites (median periodicity =23.89 h), 55% of these transcripts
had shorter periodicities in mismatched parasites (between 20 h
to 24 h), which in turn reduced the overall periodicity by ~1 h
(median periodicity 22.85 h) (Fig. 1f). Period estimates from
genes that lost rhythms in expression profiles are used here to
illustrate an overall trend, rather than provide information on
individual genes. It is possible that the shorter periods observed
for mismatched parasites correlates with a shorter IDC,
explaining how mismatched parasites became rescheduled by
approximately 6 h within four cycles of replication (an average of
1.5 h per cycle).
We undertook further analysis of the 1765 genes that fell under
the threshold for rhythmicity of expression 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 expression and performed gene ontology (GO)-
based enrichment analysis within each group every 2 h (using the
fitted model output from ARSER). The point when the highest
relative copy numbers of mRNA from these genes was present
was taken to be indicative of an overall trend. A wide range of
biological processes including carbohydrate metabolism, nucleo-
tide 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 2 h phase
clusters, losing daily rhythmicity in host rhythm mismatched
parasites (Fig. 2a and Supplementary Data 2). Many of these
biological processes are under circadian clock control in other
organisms16,17. Disruption of any of these processes could be
detrimental to parasites and may explain the 50% reduction in
parasite densities observed for mismatched parasites by O’Don-
nell et al.7,8. Next, we evaluate how these perturbed processes
might relate to the IDC schedule.
Genes involved in energy metabolism pathways (glycolysis,
fructose, and mannose metabolism) lost robust daily rhythmicity
in mismatched parasites (Fig. 2a, b, Supplementary Data 2).
Given that completing the IDC is glucose demanding, and host
feeding rhythms affect the timing of the IDC, malaria parasites
may be expected to express genes rhythmically to utilize this
energy source efficiently4,5. Furthermore, the maximum relative
number of transcripts of glycolysis-associated genes in matched P.
chabaudi was observed between Zeitgeber (ZT) 22-2, which
corresponds to the ring and early trophozoite stages of parasite
development and also reflects the IDC stages when genes
associated with glycolysis are maximally transcribed in P.
falciparum in vitro12.
Nine out of 25 (36%) genes associated with the ubiquitin
mediated proteolysis pathway, and 25 out of 32 (78%) genes
encoding core and regulatory components of the proteasome
system lost strong rhythmic expression in mismatched parasites
(Fig. 2b, Supplementary Data 2). The ubiquitin-proteasome
system (UPS) plays a direct role in determining the half-life of
core clock components and other clock-controlled protein
functions18. Genes that lost rhythmic expression 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) and the adapter protein SKP1 from
the proteasome pathway. Some of these genes are essential for the
yeast anaphase promoting complex (APC) which is a cell cycle
regulated ubiquitin protein ligase19–22. The majority of UPS genes
have a peak of expression between ZT 7.5–10 in matched
parasites, which corresponds to the late trophozoite/schizont
stage of the IDC (Fig. 2b). During the late IDC stage, the UPS
helps parasites to shift from generic metabolic and cellular
machinery to specialized functions.
Circadian clocks control the timing of DNA replication in
many organisms23–27. We observed that 20 out of 43 (44%) genes
associated with DNA replication lost strong rhythmic expression
in mismatched parasites (Fig. 2a, b and Supplementary Data 2).
This included genes encoding subunits of DNA polymerase,
replication-licensing factors, DNA helicase and the DNA repair
protein RAD51. 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 rhythmic expression in
mismatched parasites. Genes associated with DNA replication
reached peak expression between ZT 8–12 in matched parasites,
which corresponds to the transition from the late trophozoite to
the schizont stage, and is when DNA replication machinery
components are transcribed12.
Seven out of 31 genes associated with cell redox and
glutathione metabolism lost rhythmic expression in mismatched
parasites and are enriched for the term “cell redox homeostasis”
(Padj < 0.001; Supplementary Data 2). All seven genes displayed
maximum expression during ZT 6–8 in matched parasites.
Furthermore, peroxiredoxin proteins show circadian rhythmicity
in oxidation/reduction cycles that are conserved across the tree of
life28. Two out of three genes encoding peroxiredoxin showed
daily rhythmic expression in both matched and mismatched
parasites, while one gene (PCHAS_0511500) lost rhythmic
expression in mismatched parasites. The expression of genes
involved in redox metabolism is driven by an endogenous clock
Fig. 1 P. chabaudi gene expression is sensitive to the phase of host circadian rhythms. a Ring stage parasites from donor mice were used to infect
recipient mice housed in two rooms that differed by 12 h in their light:dark cycle. Blood samples were collected for RNAseq analysis from day 4 post-
infection every 3 h for 11 time points (N=4 mice per group per time point). ZT is Zeitgeber time:hours after lights on. bTime series gene expression
heatmap illustrates daily rhythmicity in matched parasites (left) that lost strong rhythmicity in mismatched parasites (right). Transcripts ordered in the
same sequence based on their phase of expression. Each row 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 and Nrepresents number of genes with 24 h rhythmicity in
expression. Each time point is represented by expression heatmap of two biological replicates. Colors represent the row Z-score. cVenn diagram of the
number of daily rhythmic genes in matched (top) and mismatched (bottom) parasites. dTranscripts with daily 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). This was also the case for transcripts that had ~6 h delayed phase of expression in mismatched compared to matched parasites. eHistogram of the
phase distributions of 1765 genes that displayed daily rhythmicity only in matched parasites. Solid black line indicates the mean circular phase, brown line
represents the standard deviation of the mean of phases. Whilst these genes are not identified as having daily rhythms in mismatched parasites, their
distribution is shown for comparison. fTranscripts that displayed daily 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). Source data are provided as Source Data file.
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in T. brucei3, which may be linked to rhythmicity in oxidation/
reduction cycles resulting from host metabolic rhythms.
Rhythms in the transcriptome of P. falciparum. 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 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. Forty-eight hours, allowing putative 24 h
clock-related genes and their downstream interactors to be
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
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distinguished from IDC genes. Observing a free-running rhythm
of 24 h is consistent with the presence of a circadian clock, but
other factors such as temperature compensation and entrainment
must also be demonstrated to conclusively prove the presence of
a clock.
To determine whether P. falciparum possesses a 24 h free-
running transcriptome, we carried out time-series RNAseq
experiments to observe the expression profile of all P. falciparum
genes at a 2 h resolution from highly synchronized parasites
grown at a constant temperature and in constant darkness (see
Methods). RNAseq data from two biological replicates per time-
point were obtained. We identified 361 transcripts (~6% of the
total genes) with 24 h rhythmicity (common rhythmic genes
detected by ARSER and JTK cycle with q< 0.05) from the
expression profiles of 5702 genes (Fig. 3a, Supplementary Data 3).
The median amplitude of oscillations of the 24 h free-running
genes was 0.93. This is lower than the amplitude of the rhythmic
genes detected in P. chabaudi in vivo (1.22). A higher amplitude
in P. chabaudi may be due to reinforcement by host rhythms and/
or the contribution of genes expressed due simply to 24 h IDC
developmental progression—neither of which apply to P.
falciparum in vitro. Genes identified in P. falciparum with a
24 h expression pattern are enriched for most of the processes
that exhibit reduced rhythmicity in mismatched P. chabaudi
infections (Ubiquitin proteasome system, regulation of cell cycle,
nuclear division, protein localization, and transmembrane trans-
port) (Fig. 3b) but not redox processes. This suggests that
circadian rhythms in the redox state of RBCs29, which persist
when RBCs are cultured in constant conditions, are not
important for the timing of developing parasites.
Comparing the expression profiles of orthologues of P.
falciparum that were rhythmic in matched P. chabaudi (333
one to one orthologues identified) but lost rhythmicity in
mismatched P. chabaudi (N=1765) identified 103 common
daily rhythmic genes. Taken together, these observations suggest
that a set of genes whose expression is sensitive to the timing of
host daily rhythms and which are also rhythmic under constant
conditions are potentially driven by an endogenous oscillator.
Role of serpentine receptor 10 in the IDC. If malaria parasites
are able to schedule the IDC they must respond to time-of-day
information either through receptors or transporters. Seven
transmembrane domain-containing receptors/serpentine recep-
tors/G protein-coupled receptors (GPCRs) are the largest and
most diverse group of membrane receptors and participate in a
variety of physiological functions30–33.
The P. falciparum proteome contains four serpentine receptor
(SR) proteins; SR1, SR10, SR12, and SR2534. Of these, only Pfsr10
showed a 24 h expression rhythm (Fig. 3c) while the rest showed
periodicity closer to 48 h (Supplementary Fig. 3a). Serpentine
receptor 10 (sr10: PF3D7_1215900), was also the top ranked
receptor in the 24 h rhythmic P. falciparum gene list (ranked 28
out of all 361 genes sorted based on q-values from JTK output)
(Supplementary Data 3). Additionally, SR10 has been
bioinformatically classified as a member of Class A serpentine
receptors belonging to the hormonal receptor subclass based on
the length of the N-terminal domain34 and classification by Inoue
et al.35. Its orthologue in P. chabaudi (PCHAS_1433600) also has
a 24 h expression rhythm in both matched and mismatched
parasites (Fig. 3c, Supplementary Data 1). In P. falciparum, peak
sr10 expression corresponds to the ring (8 h post-invasion) and
late trophozoite stages (32 h) of the IDC. In P. chabaudi,
expression peaked at ZT 14, corresponding to the late trophozoite
stage. SR10 is also the only receptor shared between malaria
parasites and other apicomplexans and also with distantly related
organisms such as Caenorhabditis elegans,Drosophila melanoga-
ster,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.
Serpentine receptor 10 influences IDC duration. To investigate
whether SR10 may be part of a receptor-mediated signaling sys-
tem in malaria parasites that influences the IDC based on time-
of-day information from the host, we disrupted the sr10 gene in
P. chabaudi by a double crossover homologous recombination
strategy to generate sr10 deficient parasite clones (sr10KO)
(Supplementary Fig. 3b). Functional disruption of sr10 was ver-
ified by RNAseq analysis (Supplementary Fig. 3c). We compared
the IDC schedule of wild type and two sr10KO clones by
examination of thin blood smears (n=4 per group), every three
hours over 48 h, starting from day 1 PI at ZT 13.5 (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 suggest the
IDC duration of both sr10KO clones is ~2–3 h shorter (IDC
duration 22.4 h) than the wild type (IDC duration 25.15; p<
0.0001, Fig. 3f). We repeated this using the same strategy for P.
yoelii (Supplementary Fig. 3d). The proportion of parasites at
early trophozoite stage displayed weak daily rhythmicity in both
the wild type and sr10KO clone (amplitude for P. yoelii wild type:
0.30 ± 0.02 and P. yoelii sr10KO: 0.31 ± 0.01, Fig. 3g, Supple-
mentary Table 1), yet the IDC duration was shorter in the sr10KO
clone (sr10KO IDC duration 24.45 h; wild type IDC duration
~28 h, Fig. 3h). Observing similar 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.
SR10 regulates expression of multiple pathways. To explore
how disruption of sr10 affects IDC progression 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 number
of mapped reads i.e., < 1 million paired ends mapped reads)
sampled every 3 h (n=2 per time point), starting from day 2 PI
Fig. 2 Biological pathways affected by mismatch to the phase of host rhythms. a Time series gene expression view of genes that displayed with 24 h
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 h phase clusters. Line plots along the sides of the heat map represent expression profiles of individual genes
significantly enriched to gene ontology terms (FDR corrected p< 0.05, hypergeometric test, one-sided) representing few crucial biological processes. The
Yaxis 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. bHeatmaps illustrating the expression patterns of 24 h rhythmic 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 rhythmicity in mismatched parasites. Genes have been sorted
based on the phase of maximum expression. The color scheme represents the row Z-score. Each time point is represented by the expression heatmap of
two biological replicates.
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(05.30 h, ZT 22.5). Biological replicates were found to be tightly
clustered (Supplementary Fig. 3e). 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. 3f). A total of 3620 and 2886 genes showed ~ 24 h rhyth-
micity in expression in P. chabaudi wild type and sr10KO para-
sites respectively (q< 0.05; Supplementary Fig. 4, Supplementary
Data 4). 1015 genes (19% of the total genes) had dampened
rhythms in expression in sr10KO parasites (Fig. 4a, b,
Supplementary Data 4). Furthermore, 85% of the genes identified
as being transcribed with daily rhythms in matched P. chabaudi
parasites from our first experiment also had a daily rhythm in
wild-type parasites in this dataset (which are also matched).
Whilst the additional rhythmically expressed genes identified in
wild type parasites in this dataset could be due to a longer time
series, we found generally high concordance between the tran-
scriptomes of infections initiated in the same way but in different
laboratories. This lends support to the inference that the genes
losing rhythmicity of expression (henceforth called SR10-linked
N = 4
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f
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Fig. 3 Serpentine receptor 10 maintains the duration of the intra-erythrocytic developmental cycle. a Time series RNAseq gene expression heatmap
view of 24 h rhythmic genes identified in P. falciparum in vitro in “free running”(constant temperature and darkness) conditions. Genes sorted based on the
phase of maximum expression starting from time T:0 that corresponds to 3 h post merozoite invasion. bManually curated gene ontology terms enriched
for P. falciparum genes with 24 h expression (FDR corrected p< 0.05, hypergeometric test, one-sided). cLine 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.
dP. 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 h. Expression data from two biological replicates over 14 time points (from day 2 PI, ZT 22.5) were analyzed to identify putative “circadian”
transcripts. PVM parasitophorous vacuole membrane, PPM parasite plasma membrane, RBC red blood cell. eProportion of parasites in the blood at early
trophozoite stage in P. chabaudi wild type (WT) and sr10KO clones (Mean ± SEM, N=4/clone). fIDC duration of P. chabaudi wild type (WT) and sr10KO
clones (Mean ± SEM, N=4/clone). gProportion of parasites in the blood at early trophozoite stage in P. yoelii wild type (WT) and sr10KO clones (Mean ±
SEM, N=4/clone). hIDC duration of P. yoelii wild type (WT) and sr10KO clones (Mean ± SEM, N=4/clone). Source data are provided as Source Data file.
abc
de
Fig. 4 Disruption of sr10 affects daily rhythms in P. chabaudi gene expression. a Time series gene expression heatmap view of genes expressed with daily
rhythmicity in P. chabaudi wild type that lost rhythmicity in 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. Nrepresents number of genes with 24 h rhythmicity identified. Each time point is represented by an expression heatmap of two
biological replicates. bVenn diagram of number of genes with 24 h rhythmic expression identified by both JTK and ARSER in wild type and sr10KO
parasites. cPhase distributions of genes with 24 h expression in wild type that lost 24 h rhythmicity in sr10KO parasites. The mean circular phase for each
condition is indicated by a solid black line. Nrepresents the number of cycling transcripts. Pink lines represent standard deviation of the mean circular
phases. dTranscripts that displayed with 24 h (putative “circadian”) rhythmicity only in wild type parasites have median periods close to 26 h in wild-type
parasites (blue dashed line) and 24 h in sr10KO parasites (brown dashed line). eGenes 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 type parasites (1.53, p< 0.00001). Source data are
provided as Source Data file.
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rhythmic genes or “SLRGs”)insr10KO parasites is due to the loss
of sr10.
Examination of SLRGs revealed a bimodal distribution pattern
for peak expression in wild type parasites (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 SLRGs 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 rather to ascertain a qualitative comparison.
Nonetheless, the shorter periods in sr10KO parasites reflects the
shorter periods observed in genes that lose rhythmicity in
mismatched parasites in our first experiment. As for genes that
retained rhythmic expression in both matched and mismatched
parasites, the genes that retained rhythmic expression in only
sr10KO parasites (N=2620) exhibited a significant reduction
(p< 0.0001, unpaired Student’st-test) of amplitude in sr10KO
(1.15) compared to wild type parasites (1.53) (Fig. 4e).
GO analysis of the SLRGs revealed 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. 5, 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)
revealed that (i) genes associated with protein translation are
perturbed in ring stages; (ii) DNA replication and cell cycle
associated processes are perturbed in early and late trophozoite
stages; and (iii) microtubule-based movements are perturbed in
schizonts (Fig. 5b). We then compared the transcripts that lost
rhythmicity in mismatched parasites (N=1765) to the SLRGs
(N=1015). A total of 326 genes were shared (Supplementary
Fig. 6a) suggesting that their expression is shaped by both host
rhythms and how the IDC is scheduled by the parasite’s
expression of sr10. The shared genes were enriched for biological
processes including energy metabolism, heme metabolic pro-
cesses, and translation (Supplementary Fig. 6b).
SR10 affects rhythmic expression of spliceosome machinery.
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 eukaryotes36,37. Out of 85 genes (based on
the Kyoto Encyclopedia of Genes and Genomes (KEGG) database
mapping) expressing different spliceosomal proteins in P. cha-
baudi, 44 genes showed 24 h expression rhythms in wild type
parasites, of which 26 (q< 0.005, hypergeometric test) are in the
SLRG group (Fig. 6a). They represent proteins of major spliceo-
some components including core spliceosomal protein members
of snRNPs, prp19 complex and prp 19 related complexes.
Alternative splicing can regulate gene expression in signal
dependent and tissue-specific manners38 and an emerging body
of evidence links alternative splicing with the control of circadian
regulatory networks in a variety of organisms, including Droso-
phila melanogaster39,Neurospora crassa40,41,Arabidopsis42,43,
and Mus musculus44. Alternative splicing has been reported to
occur in Apicomplexans (including malaria parasites) for rela-
tively few genes, covering only several percent of the total genes45.
Having observed that the loss of sr10 modulates the expression
of genes associated with the spliceosome, we investigated whether
it also affects the alternative-splicing (AS) signature using
RNAseq analysis of both wild type and sr10KO parasites.
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 ZT 1.5 (Fig. 6b, Supplementary
Data 7) (for details see Methods section). In a separate analysis
(see Methods), we found only 72 (54 genes) and 151 (114 genes)
differential alternative splicing events in each strain when we
compared two consecutive time points (i.e., ZT 22.5 vs. ZT 1.5)
(Fig. 6b, Supplementary Data 7). This suggests that SR10 impacts
the spliceosome 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 may link host derived time-of-day information with the
IDC schedule and regulate alternative splicing.
High-throughput real-time qPCR based validation. We inde-
pendently verified the expression 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. cha-
baudi parasites, covering 11 randomly selected genes that repre-
sent multiple affected pathways were tested for their expression in
both groups (Supplementary Fig. 6c). Similarly, a total of 36 genes
from the sr10KO experiment covering 12 randomly selected genes
and representing multiple affected pathways were tested for their
expression in both P. chabaudi wild type and sr10KO strains
(Supplementary Fig. 6d). The gene datasets, generated by high-
throughput real-time qRT-PCR using the BioMarkHD platform
tightly correlated with the RNAseq expression values (Spearman-
rank correlation between 0.67 −0.95 with p< 0.05 for all the
genes tested), thus independently validating our RNAseq analysis.
To validate the accuracy of P. falciparum time-series RNAseq
datasets, the expression profiles of nine randomly chosen genes
were validated using real-time qPCR; expression patterns were
tightly correlated (Supplementary Fig 6e).
Discussion
How malaria parasites interact with host rhythms to establish and
maintain a schedule for IDC development is unknown. Our
analyses, which were carried out using the rodent malaria para-
sites P. chabaudi and P. yoelii in vivo, and the human malaria
parasite, P. falciparum in vitro, reveal an extensive transcriptome
with 24 h 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 path-
ways, DNA replication, redox balance, the ubiquitin proteasome
system, and alternative splicing (Fig. 6c). Twenty-four hour
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 also
found that the IDC duration is, at least in part, controlled by
serpentine receptor 10 (SR10). Given its role in determining the
duration of IDC and being a GPCR class of receptor, we propose
that SR10 acts as a link between host circadian rhythms and the
parasite’s endogenous time-keeping/IDC scheduling mechanism.
In support of this hypothesis, SR10: (i) has a 24 h rhythmic
expression in P. falciparum, and in both matched and mis-
matched P. chabaudi; (ii) regulates rhythmicity in gene expres-
sion for several of the processes whose genes lost rhythmic
expression in mismatched P. chabaudi (Fig. 6c); and (iii) deter-
mines the duration (period) of gene expression patterns when it is
disrupted.
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Most genes (3057 of 5343) in the transcriptome of P. chabaudi
in synchrony (matched) with the host circadian rhythm are
transcribed with 24 h periodicities, whilst this number drops to
1824 when parasites are mismatched to the host circadian
rhythm. Genes that lose rhythmicity are involved in diverse
biological processes including glycolytic process, DNA replica-
tion, translation, the ubiquitin/proteasome pathway and redox
metabolism (Supplementary Data 2). This is not simply a con-
sequence of mismatched parasites becoming desynchronized
because they maintain morphological synchrony during resche-
duling (Supplementary Fig. 1a). Instead, disruption to these
processes could be a result of stresses resulting from the IDC
being misaligned to host rhythms. If, for example, rescheduling
parasites are unable to coincide the appearance of a particular
IDC stage with a rhythmically provided resource it needs from
the host4–6, the parasite may be physiologically compromised and
alternative pathways upregulated. Such stresses may explain the
reduced parasite densities previously observed in mismatched P.
chabaudi7,8. Whilst caution needs to be employed in interpreting
periodicities from short time-series and from genes that exhibit
dampened rhythmicity, on the whole, the expression patterns of
such genes were approximately 1 h shorter in mismatched than
matched parasites. This difference coincides with the observation
that mismatched parasites were rescheduling by on average
approximately 1.5 h every IDC.
The IDC of P. chabaudi is approximately 24 h in duration,
making it difficult to distinguish genes associated with features of
particular IDC stages from genes associated with a time-keeping
mechanism or its outputs. Thus, P. falciparum cultured under
constant conditions was used to separate IDC genes from putative
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 sr10 KO
Row Z-score
Early trophozoite Late trophozoiteSchizont 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
0 –1 1
Fig. 5 Knock out of sr10 affects multiple 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 Log
2
fold change < −1 for downregulated genes and > 1 for upregulated 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. bGene ontology analysis of the differentially regulated genes within each
time point. Manually curated functional annotations of biological processes (False discovery rate corrected p< 0.05, hypergeometric test, one-sided) are
represented and the color spectrum represents the odds ratio.
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time-keeping associated genes. We found that 361 P. falciparum
genes exhibit ~24 h rhythmic expression and many of these genes
are associated with processes (regulation of cell cycle, nuclear
division, transmembrane transport, and the ubiquitin proteasome
system) affected in P. chabaudi by mismatch to host rhythms.
Finding that the ubiquitin–proteasome system is disrupted in
mismatched parasites is of particular interest because this system
plays a role in regulating clock components and their outputs in
many taxa18.
We also observed rhythmic expression of genes associated with
histone modification and the control of transcription and trans-
lation (Figs. 2a, 3a, 5b and Supplementary Fig. 3). These processes
are considered central to the circadian organization of the tran-
scriptome in other taxa46–50.
ab
c
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In summary, we reveal that coordination with host daily
rhythms is central to the schedule of expression of genes asso-
ciated with diverse processes underpinning IDC progression, and
ultimately, within-host replication. Our data are consistent with
some of the criteria required to demonstrate an endogenous time-
keeping ability (free-running rhythm), and further work is
required to examine whether other features of an endogenous
clock exist, as well as to identify the interaction partners that link
SR10 with host time-of-day. The IDC underpins malaria para-
sites’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 pro-
gression may reveal novel intervention strategies.
Methods
Ethics statement. All animal procedures for the parasite mismatch 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 studies were performed
in 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 pro-
tocol was approved by the Institutional Animal Research Committee of Nagasaki
University (permit: 12072610052). The animal experiments conducted in Edin-
burgh 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 were approved by IBEC in KAUST (IBEC number: 19IBEC12).
Experimental design and data collection. For the host rhythm mismatching
experiment, hosts were 6–8 weeks old female MF1 mice housed in groups of five in
open top cages at 21 °C and 65% humidity with food and drinking water supple-
mented with 0.05% para-aminobenzoic acid (PABA, to supplement parasite
growth) provided ad libitum. All experimental infections were initiated by intra-
venous injection of 1 × 10 7P. chabaudi chabaudi clone AS51 parasitized red blood
cells (per ml). Parasites at ring stage were collected at ZT 0.5 (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”) was 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 h.
Samples were collected every 3 h, over a peri od of 30 h, from 09:00 GMT on day
4 to 15:00 GMT on day 5 post infection. This corresponds to a starting time of ZT
1.5 for matched, and ZT 13.5 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 50 µl of blood was taken via cardiac puncture (added to 200 µl of
RNAlater and frozen at −80 °C) 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.
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) housed at 23 °C with 12 h 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. 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
12 h 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 fou r CBA mice each (1 × 106parasitized RBCs per
mouse) by intravenous injection at 20:30 h corresponding to ZT 13.5, day 0.
Starting at ZT 13.5 on day 1 post-infection, blood smears were taken for both
groups every three hours for 48 h producing a total of 17 time points. Blood smears
were brieflyfixed with 100% methanol and stained with Giemsa’s solution. IDC
stages were recorded based on classification of the parasitic forms into four stages
—rings (ring stage trophozoite), early (small) trophozoites, late (large) trophozoites
and schizonts. The same procedures were adopted for P. yoelii wild type (17 × 1.1
pp) 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 analysis52. Briefly, 20 µl of blood was collected via tail snip at each time
point, washed in PBS and immediately treated with 500 µl TRIzol reagent and
stored shortly at 4 °C and for long term at −80 °C.
Plasmodium falciparum culture.Plasmodium falciparum II3 strain53 (a DiCre
recombinase expressing parasite derived from 3D7 clone) was maintained at 37 °C,
5% O
2
, and 5% CO
2
in AB+human RBCs in RPMI 1640 medium containing
Albumax II (Invitrogen) supplemented with 2 mM L-glutamine. Culture medium
was changed in every two days to avoid providing unknown 24 h rhythmic cues
that might be present in the culture medium. Cultures were routinely monitored by
Giemsa’s solution-stained thin blood smears and synchronized by treating the
cultures with 5% sorbitol (w/v) to kill any late stage asexual stages. For the time-
series experiment, mature segmented schizonts were isolated by centrifugation over
cushions of 70% (v/v) isotonic Percoll (GE healthcare Life Sciences), washed twice
with RPMI 1640 without Albumax and allowed to invade fresh RBCs for about 2 h
in a shaker maintained at 37 °C. Following invasion, the remaining schizonts were
removed from the culture by overlaying the culture over cushions of 70% (v/v)
isotonic Precool (GE healthcare Life Sciences). The pellet containing ~2 h post-
invasion early ring stage infected RBCs and uninfected RBCs was then treated with
5% sorbitol (w/v) to kill contaminating mature schizonts. The culture containing
highly synchronous early ring stage parasites was then split into six-well plates.
Over the course of 48 h, 1–2 ml of infected RBC culture was collected every 2 h,
media was removed by centrifugation for 2 min at 900 × g, and cells were lysed by
adding 1 ml of TRIzol and immediately stored at −80 °C. The T0 time point was
considered to be 3 h after the addition of Percoll cushion isolated segmented
mature schizonts to the fresh RBCs.
Time series gene expression analysis 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 100 ng 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
Fig. 6 Cross-talk between the intraerythrocytic developmental cycle and host rhythms. a sr10 knockout affects parasite spliceosome machinery.
Heatmap illustrating the expression pattern of sr10 knockout affected 24 h rhythmic genes involved in spliceosome pathway in P. chabaudi wild type and
sr10KO parasites. The list of genes was obtained by mapping the SR10 linked rhythmic genes (SLRGs) to P. chabaudi spliceosome pathway represented in
the KEGG database. Genes have been sorted based on phase of maximum expression. The color scheme represents the row Z-score. bsr10 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 between wild type and sr10KO parasites to identify differential usage of exons. As a control two consecutive time
points (Day 2 ZT 22.5 and Day 3 ZT 1.5) from the same parasite strain were also compared. The number shown depicts 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 DEXSeq68.
cSchematic figure summarizing the cross-talk between the IDC schedule of P. chabaudi and host rhythms. The parasite can reschedule its IDC when its
developmental rhythms are mismatched with the host rhythms. The parasite responds to mismatch by losing rhythmic expression of genes associated with
multiple biological processes as depicted in the pie-charts on the left. P. chabaudi serpentine receptor 10 (sr10) is expressed rhythmically during the IDC,
and knocking out sr10 in P. chabaudi reduces the IDC duration by ~2–3 h and also affects the rhythmic expression of genes associated with multiple
biological processes as depicted in the pie charts on the right. We speculate that SR10 may serve as one of the receptors through which the parasite
receives rhythmic cues from the host that influence the IDC schedule, permitting rescheduling to recover from mismatch. Black section within the pie-
charts represent the percentage of rhythmic genes in each biological process that fell under the threshold for rhythmic expression in mismatched and
sr10KO parasites.
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double stranded cDNA molecules were adapter ligated and the libraries amplified
by PCR for 15 cycles. Libraries were sequenced on Illumina HiSeq 4000 platform
with paired-end 100/150 bp read chemistry according to manufacturer’s
instructions.
RNAseq read quality was assessed using FASTQC quality control tool (http://
www.bioinformatics.babraham.ac.uk/projects/fastqc). Read trimming tool
Trimmomatic54 was used to remove low quality reads and Illumina adapter
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—http://www.plasmoddb.org) using TopHat2 (version 2.0.13)55
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/)56. 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 software57 and
further subjected to linear model analysis using the voom function in the limma
package58. Differential expression analysis was performed using DeSeq259. 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 daily rhythmic transcripts. We used two programs, JTK-Cycle60
and ARSER61 implemented in MetaCycle62, an integrated R package with para-
meters 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 transcripts60, 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-ser-
ies”61. 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
rhythmic transcript with significance bounded by p< 0.05 for the parasite and host
rhythms mismatching experiment , where 11 time points separated by 3 h were used
and by q< 0.05 for the SR10 experiment where 14 time points separated by 3 h
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). For the P. falciparum time-series experiment, a gene was
considered cyclic if both programs identified it as a rhythmic transcript with sig-
nificance bounded by q< 0.05 where a total of 25 time-points separated by two
hours were used. The output from ARSER concerning amplitude, phase and period
of rhythmic transcripts was used for further analysis. Median amplitude of
rhythmically expressed genes was calculated by taking the amplitude information
from ARSER output. Two biological replicates per time point were used for
RNAseq analysis for both host rhythm mismatch and sr10KO data.
Time points and biological replicates (n=2) were clustered using hierarchical
clustering in R software environment with Pearson correlations from normalized
count values as input and agglomerative hierarchical clustering function “hclust”
(option ward.D2) was used for cluster generation. The 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 overall FDR of transcripts with daily rhythms in the host-
circadian rhythm mismatching experiment, the time points of collection were
randomly permuted 1000 times and the number of transcripts with daily rhythms
was assessed for each of the permutations by both the programs used. We observed
that in every permutation, the number of rhythmic transcripts detected was less
than the observed number of rhythmic transcripts detected when the samples were
in correct order of sampling. A similar approach to determine the FDR was used by
Rijo-Ferreira et al.3. This was done for both matched and mismatched parasite
datasets, where p< 0.05 was used as a cut-off to identify genes with daily rhythms.
Analysis of parasite developmental rhythmicity. The early trophozoite stage was
used as the marker stage because it 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 Circwave63. 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 h (i.e., 24 h ± 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) pvalue 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).
Plasmid construction to modify the sr10 gene locus. 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 indepen-
dent 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 instruc-
tions. pENT12-Pchsr10-5U, pENT41-Pchsr10-3U, and linker pENT23-3Ty entry
plasmids were subjected to LR recombination reaction (Invitrogen) with a desti-
nation 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 17 × 1.1 pp 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 × 1.1 pp 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. Schizonts from P. yoelii and P. chabaudi-infected mice were
enriched by centrifugation over a Histodenz density cushion. HistodenzTM (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 hypoxanthine64. The schizont-enriched parasites
were transfected using a NucleofectorTM 2b device (Lonza Japan) using 20 μgof
linearized plasmids for each transfection65. Stable transfectants were selected by
oral administration of pyrimethamine (0.07 mg/ml) and cloned by limiting dilution
in mice. Stable integration of plasmids in the parasite genomes were confirmed by
PCR and sequencing (Supplementary Fig. 7a, b). Genomic forward and reverse
primers (F1 and R1) were designed using the conserved 5′and 3′UTR regions of
sr10 in P. chabaudi and P. yoelii. Primers were also designed corresponding to
internal sequence regions of drug resistant cassette (R2 and F1) (Supplementary
Fig 7a). Amplification from primer pairs F1 and R2 confirmed the 5′integration of
plasmid and from F2 and R2 confirmed the 3′integration of plasmid in P. chabaudi
sr10KO parasites (Supplementary Fig. 7a) and P. yoelii sr10KO parasites (Sup-
plementary Fig. 7b). To confirm the absence of sr10 in the P. chabaudi and P. yoelii
sr10KO parasites primer pairs internal to sr10 sequences of P. chabaudi and P.
yoelii were designed and PCRs were performed on both wild type and sr10KO
P. yoelii and P. chabaudi parasites. Supplementary Fig. 7c shows the absence of sr10
in the sr10KO parasites while a single band of desired size was detected in wild type
P. chabaudi and P. yoelii parasites. All primers used are listed in Supplementary
Data 8 and sanger sequening results are provided in Supplementary Data 9.
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 s
followed by 40 cycles of 95 °C 2 s; 60 °C for 25 s. 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 s, 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 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. We chose P. chabaudi HSP40 to nor-
malize transcript expression because it was found to arrhythmic in expression as
determined from JTK-Cycle60 and ARSER61 output (expressed constantly
throughout the IDC) in all the time-series RNAseq data from all the strains used in
this study, including host rhythm matched and mismatched P. chabaudi parasites
(Supplementary Data 1 and Supplementary Data 4). For P. falciparum the same
steps were followed up to cDNA synthesis. The housekeeping gene Seryl tRNA
ligase (PF3D7_0717700) was used to check DNA contamination in RNA samples
and for normalization of Ct values. Seryl tRNA ligase was used a control gene
because it was also found to be arrhythmic in our P. falciparum IDC time-series
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RNAseq data as determined from JTK-Cycle60 and ARSER61 output. We have also
verified the expression of both P. chabaudi HSP40, family A and P. falciparum
Seryl tRNA ligase visually and found it to be arrhythmic. Seryl tRNA ligase has also
been widely used as a control gene in previous P. falciparum studies66. Twelve
randomly selected genes were initially slected for validating the time-series RNAseq
data out of which nine genes gave specific PCR products observed through dis-
sociation curve analysis. These nine genes (Supplementary Fig. 6e) were further
used for validating the time-series RNAseq data. qPCR-based quantification was
carried out using Fast SyBr green (Thermo) with the following cyling conditions:
95 °C for 30 s followed by 40 cycles of 95 °C 2 s; 55 °C for 30 s, and 60 °C for 25 s.
All primers used are listed in Supplementary Data 8.
Gene ontology enrichment analysis.Plasmodium chabaudi and P. falciparum
gene ontology terms were downloaded from the UniProt gene ontology annotation
database (https://www.ebi.ac.uk/GOA). Genes with daily rhythms 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 package67. 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 occur-
rence for GO term in the reference set (i.e., filtered list of all detected genes).
Identification of differential alternative splicing events. We considered two
consecutive time-points, day 3 PI, time 05.30 GMT (ZT 22.5) and 08.30 (ZT 1.5) to
compare between sr10KO and wild type parasites because they follow the time
point when genes associated with spliceosome machinery are expressed maximally
(ZT 16.5–19.5). 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.
Differential alternative splicing events in terms of differential exon usage were
detected using the DEXSeq program v 1.20.0268 with modified scripts as reported
in Yeoh et al.69. The pvalue significance level was set to 0.05 for the identification
of differential exon usage. Comparison was made between P. chabaudi wild type
and the SR10 knock 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.
Reporting summary. Further information on research design is available in
the Nature Research Reporting Summary linked to this article.
Data availability
RNAseq data sets are available in Gene Expression Omnibus under accession numbers
GSE132647,GSE144976. See also Supplementary Data 1, 3, and 5. The source data
underlying Figs. 1d, e, f, 3e, f, g, h, 4d, e, Supplementary Figs. 1d, 2c, 6c, d are provided as
Source Data file.
Received: 18 August 2019; Accepted: 4 May 2020;
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Acknowledgements
The project was supported by a faculty baseline fund(BAS/1/1020-01-01) and a Com-
petitive Research Grant (CRG) award from OSR (OSR-2018-CRG6-3392) from the King
Abdullah University of Science and Technology (KAUST) to A.P. R.C. 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 and 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. All authors read and approved the final manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41467-
020-16593-y.
Correspondence and requests for materials should be addressed to R.C., S.E.R. or A.P.
Peer review information Nature Communications thanks Nicolas Cermakian, Ricardo
Gazzinelli and Timothy Myers for their contribution to the peer review of this work. Peer
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