Abstract
Circadian clocks coordinate mammalian behavior and physiology enabling
organisms to anticipate 24-hour cycles. Transcription-translation
feedback loops are thought to drive these clocks in most of mammalian
cells. However, red blood cells (RBCs), which do not contain a nucleus,
and cannot perform transcription or translation, nonetheless exhibit
circadian redox rhythms. Here we show human RBCs display circadian
regulation of glucose metabolism, which is required to sustain daily
redox oscillations. We found daily rhythms of metabolite levels and
flux through glycolysis and the pentose phosphate pathway (PPP). We
show that inhibition of critical enzymes in either pathway abolished
24-hour rhythms in metabolic flux and redox oscillations, and
determined that metabolic oscillations are necessary for redox
rhythmicity. Furthermore, metabolic flux rhythms also occur in
nucleated cells, and persist when the core transcriptional circadian
clockwork is absent in Bmal1 knockouts. Thus, we propose that rhythmic
glucose metabolism is an integral process in circadian rhythms.
Subject terms: Metabolomics, Circadian rhythms
__________________________________________________________________
Red blood cells, which do not possess a nucleus, have circadian redox
rhythms with incompletely understood regulatory mechanisms. Here the
authors show that glucose metabolism plays a crucial role in regulating
circadian redox status of human red blood cells.
Introduction
Circadian rhythms allow species to adapt their physiology to daily
environmental variation tied to the Earth’s rotation^[46]1. They govern
essential processes, including the regulation of hormones, the
sleep-wake cycle, cell division, and immunity^[47]2,[48]3. Disruption
of circadian rhythms is associated with metabolic syndrome, obesity,
type 2 diabetes and various neurological disorders^[49]4,[50]5.
Circadian rhythms are thought to be driven by
transcriptional-translational feedback loops (TTFLs), whereby rhythmic
expression of clock gene products regulate the expression of associated
genes in an approximately 24-h cycle^[51]6. However, genetic studies of
circadian clocks in various model organisms (cyanobacteria, Drosophila,
Arabidopsis, mice, and humans) has shown that most clock genes are not
evolutionarily conserved across distinct phylogenetic kingdoms, and
TTFL components are not shared between organisms^[52]7. Several studies
implicate non-transcriptional components in circadian rhythmicity.
These include post-translational circadian oscillation of
cyanobacterial KaiC phosphorylation and cytosolic mechanisms such as
Ca^2+/cAMP^[53]8,[54]9. However, there is limited homology of these
posttranslational clock components across various model
organisms^[55]10, and thus it has been challenging to study
posttranslational aspects of circadian biology in nucleated mammalian
cells.
We previously found circadian rhythms in reduction-oxidation (redox)
cycles of peroxiredoxin proteins in mammalian red blood cells (RBCs),
which do not have nuclei and do not produce new RNA or protein^[56]11.
We also found similar posttranslational peroxiredoxin rhythms in
various prokaryotes and eukaryotes commonly used as circadian model
systems^[57]10,[58]12. This suggests that redox processes are important
in circadian rhythm generation across phylogenetic boundaries, and may
have evolved ~2.5 billion years ago with specific mechanisms to
counteract the deleterious consequences of oxidative
stress^[59]7,[60]10. However, what drives circadian timing of redox
balance is unclear. Metabolism is one such important player that may be
a connecting node between redox processes and the clockwork.
In this study, we sought to understand the metabolic basis of
peroxiredoxin oscillations in human red blood cells. To do this, we
characterized circadian regulation of metabolites in human red blood
cells using metabolomics and computed metabolic fluxes. Importantly, we
uncovered rhythmic oscillations in glycolysis and pentose phosphate
pathway metabolites and metabolic fluxes with distinct and opposite
phases. Moreover, we find that circadian regulation of metabolic
switching in glycolysis and pentose phosphate pathways is tightly
coupled to redox oscillations. Perturbing key components of these
metabolic pathways results in loss of redox rhythms in human RBCs. We
then examined nucleated mammalian cells and found analogous metabolic
oscillations, with and without the core TTFL clock feedback circuit.
Thus, we find that circadian metabolic flux rhythms persist regardless
of whether the TTFL is present.
Results
Metabolomics reveals circadian regulation of metabolites in human RBCs
Because human RBCs cannot perform transcription and/or protein
synthesis, they completely rely on metabolic processes to combat
reactive oxygen species (ROS), created by auto-oxidation of hemoglobin,
for survival^[61]11,[62]13,[63]14. As such, we hypothesized that
metabolism exhibits non-transcriptional oscillations in RBCs. To
determine whether metabolism exhibits a daily rhythm, we first analyzed
the RBC metabolome in samples from human subjects. We collected fresh
blood samples and incubated purified RBCs in constant conditions (at
37 °C in continuous darkness) ex vivo to exclude any temporal cues that
could drive oscillations (Fig. [64]1A)^[65]11,[66]15–[67]17. Thus, any
measurements made reflect self-sustained rhythms in the isolated cells.
We sampled cells every 3 h over two days and performed untargeted
metabolite profiling by gas chromatography (GC) and liquid
chromatography (LC) mass spectrometry (MS). We detected 1,698 features
that had a coefficient of variation <30% in quality control samples.
This included 533 features from negative mode LC-MS and 1074 from
positive mode, with an additional 91 from GC-MS (Supplementary
Fig. [68]1). We identified 43 rhythmic metabolites (p[adj] < 0.05)
using Rhythmicity Analysis Incorporating Nonparametric methods
(RAIN)^[69]18 (Fig. [70]1B, and Supplementary Table [71]1).
Fig. 1. Redox and metabolic rhythms in human red blood cells (RBCs).
[72]Fig. 1
[73]Open in a new tab
A Experimental scheme for untargeted Human RBCs circadian sampling.
RBCs from n = 3–4 human subjects were incubated with 11 mM glucose and
kept under constant conditions (37 °C in continuous darkness) and
sampling was performed every 3 h. Untargeted metabolite analysis was
performed using GC-MS and LC-MS. B Heatmap of all cycling features
ordered by the phase of the oscillation. Features extracted from
spectra using Progenesis QI (LC-MS) and MassHunter (GC-MS) were used
for the rhythmicity analysis using RAIN algorithm (*P < 0.05,
FDR = 0.2). Data are Z-score normalized log10 intensities. C Principal
components analysis (PCA) of the cycling metabolome. The two major
components separate the data into samples corresponding to the
circadian day (left, orange) and night (right, green) time-points. D
Pathway analysis performed using Metabolite Set Enrichment Analysis
(MSEA) using only identified 24-h rhythmic metabolites. The color bar
represents the significance level, as shown in the table. *FDR < 0.01.
E Table representing the enriched metabolic pathways with the
corresponding metabolite hits, P values and FDR.
The majority of rhythmic metabolites included carbohydrates and redox
metabolites. Overall, 20% (43 of 213 known detected metabolites) of the
RBC metabolome displayed a 24-h cycle. This is consistent with the
circadian metabolome of human blood plasma (∼15–22%)^[74]19,[75]20.
Since RBCs cannot perform fatty acid synthesis, protein synthesis, the
citric acid cycle, or nucleotide synthesis, these aspects of metabolism
are not under circadian control. Principal Components Analysis (PCA)
revealed that the corresponding time points from the first and second
days grouped together, and that the major component separating the
cycling metabolome was time of the day (Fig. [76]1C). Pathway
enrichment analysis of rhythmic metabolites identified an enrichment in
glycolysis and PPP pathways (false discovery rate, FDR < 0.01), but not
others (Fig. [77]1D, E).
Glucose displayed a well-defined 24-h oscillation (P = 0.005 by RAIN),
with glucose-6-phosphate (G6P) showing a similar phase, albeit with
reduced rhythmicity (P = 0.0369 by RAIN) (Fig. [78]2A). Importantly,
ribose-5-phosphate (R5P), a principal metabolite in the PPP, exhibited
daily cycling in anti-phase to G6P (Fig. [79]2A, B). In addition, many
other metabolites in the glycolytic pathway showed robust circadian
cycles over at least two days (Fig. [80]2A, Supplementary Table [81]1).
Together, these data show that there is circadian oscillation of
specific glycolytic and PPP metabolites in human RBCs.
Fig. 2. Rhythmic accumulation of central carbon metabolites in human red
blood cells (RBCs).
[82]Fig. 2
[83]Open in a new tab
A Selected significant rhythmic metabolites (*P < 0.05) in human RBCs
analyzed by an untargeted metabolomics approach using GC-MS and LC-MS
platforms. Rhythmicity analysis was performed using RAIN algorithm
(*P < 0.05, FDR = 0.2). RBCs were synchronized for 24 h and then
sampling was performed for every 3 h over a period of 48 h. Relative
log10 normalized intensities for metabolites are presented. Glycolytic
metabolites are represented in blue and PPP metabolites in red. List of
identified rhythmic metabolites presented in Supplementary Table [84]1.
Glucose (n = 3 replicates), G6P = glucose-6-phosphate (n = 4
replicates), 2,3-DPG = 2,3-diphosphoglycerate (n = 3 replicates),
Ribu5P = ribulose-5-phosphate (n = 4 replicates),
R5P = ribose-5-phosphate (n = 3 replicates), pyruvate (n = 3
replicates), lactate (n = 3 replicates). B Correlation plot comparing
selected glycolytic and PPP metabolites. Data are mean ± s.e.m.
Rhythmic glycolysis and PPP fluxes in human RBCs
Metabolites levels are dependent on the input of fluxes through
metabolic pathways, and these metabolic pathway fluxes are considered a
crucial biological function, and are under strong evolutionary
selection procedure^[85]21,[86]22. To determine how rhythmic abundance
of metabolites occurs, and the enrichment of metabolites in different
circadian phases, we assessed the rate of flow through glycolysis and
PPP. We employed an established labeling model to measure metabolic
fluxes in human RBCs by nuclear magnetic resonance spectroscopy
(NMR)^[87]23. Under constant conditions, as described above, we took
samples at 4 h intervals over three days to determine flux through
glycolysis and PPP (Fig. [88]3A). We collected samples at least 24-h
after incubating cells in 11 mM labeled glucose. When 2-^13C[1]-glucose
is metabolized by glycolysis, it produces lactate labeled at position 2
(2-^13C[1]-lactate; C2-lactate). In contrast, 3-^13C[1]-lactate
(C3-lactate) is produced when the labeled glucose passes through PPP
(Fig. [89]3B). We quantified these differentially labeled lactates at a
chemical shift (δ) of 71.2 (C2-lactate) and 22.8 (C3-lactate) using NMR
(Fig. [90]3B). We then calculated fluxes through PPP and glycolysis. We
found circadian oscillation of glycolysis (P = 0.009 by RAIN), and PPP
in antiphase, for three consecutive days (Fig. [91]3C).
Fig. 3. Circadian regulation of glycolysis and pentose phosphate pathways
(PPP) fluxes in human red blood cells (RBCs).
[92]Fig. 3
[93]Open in a new tab
A Schematic showing experimental protocol used to collect samples. RBCs
from n = 3 human subjects were incubated with either 11 mM
2-^13C[1]-glucose or 1,2-^13C2-glucose and kept under constant
conditions (37 °C in continuous darkness) and sampling was performed
every 4 h over 3 days period. B Schematic showing fate of
2-^13C[1]-Glucose metabolized through glycolysis and the pentose
phosphate pathway (PPP). Metabolic fluxes for glycolysis and PPP were
quantified by ^13C-NMR (nuclear magnetic resonance) spectroscopy. When
2-^13C[1]-glucose is used as a tracer, it is metabolized via glycolysis
and produces singly labeled ^13C[1]-2-lactate with chemical shift,
δ = 71.2. The same 2-^13C[1]-glucose metabolized through the PPP
produces singly labeled ^13C[1]-3-lactate with chemical shift δ = 22.8.
C Rhythmic glycolytic and PPP fluxes (P = 0.009) in RBCs measured by
NMR. P-values were obtained from rhythmicity analysis using RAIN
algorithm. Graph bars present mean ± s.e.m (n = 3 biological
replicates). D Rhythmic PRDX oxidation used as a control for circadian
rhythmicity in RBCs for measuring metabolic fluxes by NMR experiment.
P-values were obtained from rhythmicity analysis using RAIN algorithm.
Data are presented mean ± s.e.m (n = 3 biological replicates). E
Schematic showing fate of 1,2-^13C[2]-Glucose metabolized by glycolysis
and the pentose phosphate pathway (PPP). Metabolic fluxes were measured
with GC-MS. When 1,2-^13C[2]-glucose is used as a tracer, it is
metabolized via glycolysis and produces doubly labeled
2,3-^13C[2]-lactate. The same 1,2-^13C[2]-glucose metabolized through
the PPP produces singly labeled ^13C[1]-lactate. The glycolytic m + 2
isotopomer of ^13C[2]-lactate and the pentose phosphate pathway m + 1
isotopomer of ^13C[1]-lactate were thus used for flux measurements (see
Methods). f Rhythmic regulation of glycolysis and PPP fluxes in RBCs
measured by GC-MS. P-values were obtained from rhythmicity analysis
using RAIN algorithm. Data are presented mean ± s.e.m (n = 3 biological
replicates). G Rhythmic PRDX oxidation used as a control for circadian
rhythmicity in RBCs for measuring metabolic fluxes by GC-MS experiment.
P-values were obtained from rhythmicity analysis using RAIN algorithm.
Data are presented mean ± s.e.m (n = 3 biological replicates). H
Comparison of glycolytic flux measurements in RBCs measured by NMR and
GC-MS methods. This correlation figure is obtained from Fig. 3C, F.
Data are presented mean ± s.e.m (n = 3 biological replicates).
Peroxiredoxin oxidation (PRDX-SO[2/3]) in RBCs from the same samples
demonstrated circadian rhythms (Fig. [94]3D, Supplementary Figs. [95]2
and [96]3). We validated circadian flux measurements by performing an
independent experiment with 11 mM 1,2-^13C[2]-Glucose, assayed by GC-MS
(Fig. [97]3E–G). When 1,2-13C[2]-glucose is administered to RBCs,
glycolysis generates m + 2 lactate, while PPP generates m + 1 lactate
(Fig. [98]3E)^[99]24. GC-MS also demonstrated circadian fluxes through
glycolysis and PPP (P = 0.042 by RAIN; Fig. [100]3F). Corresponding
peroxiredoxin oxidation (PRDX-SO[2/3]) immunoblots also revealed
circadian rhythms (Fig. [101]3G and Supplementary Figs. [102]4 and
[103]5). Indeed, NMR and GC-MS readouts are very similar when compared
side-by-side (Fig. [104]3H).
PPP flux peaked during the circadian day (phase ~11) whereas glycolytic
flux peaked at night (phase ~23). Peroxiredoxin oxidation was highest
during the day, aligned with the PPP flux peak (phase ~13). We saw a
progressive decrease in glucose in the surrounding medium in our single
pulse labeling experiments (Supplementary Fig. [105]6). Because this
could impact measurements in flux models, we next performed a dynamic
glucose pulse experiment using two different tracers: 11 mM
1,2-^13C[2]-glucose or 1-^13C[1]-glucose (Fig. [106]4A, B). In
concordance with our results using a single pulse of glucose at the
beginning of the experiment, we found circadian oscillations of
glycolytic flux using the pulsed glucose time courses (Fig. [107]4A,
B).
Fig. 4. Rhythmic metabolic fluxes in human RBCs subjected to dynamic glucose
labeling.
[108]Fig. 4
[109]Open in a new tab
A Schematic showing the sampling protocol for dynamic glucose pulse
labeling and below calculated fluxes from these labeling experiments.
Oscillations of glycolytic fluxes using either 1,2-^13C[2]-glucose
labeling are shown below the schematic. Data for 1,2-^13C[2]-glucose
flux are mean ± s.e.m (n = 3 biological replicates). Samples were
entrained with 12-h: 12-h 37 °C: 32 °C and maintained at constant
conditions. B Independent validation of metabolic flux with isotope
1-^13C1-Glucose. Schematic of 1-^13C1-Glucose labeling and calculation
of PEP isotopomer ratio to calculate glycolytic flux ratio.
G6P = Glucose-6-phosphate, G3P = Glyceraldehyde 3-phosphate,
F6P = Fructose-6-phosphate, DHAP = Dihydroxyacetone phosphate,
PEP = Phosphoenolpyruvate, R5P = Ribose-5-phosphate,
6PG = 6-Phosphogluconate. Glycolytic (fgly) flux ratio for
1-^13C[1]-glucose experiments was determined using the mass isotopomer
distribution of Phosphoenolpyruvate (PEP). Data for 1-^13C[1]-glucose
flux ratio are mean ± s.e.m (n = 4 biological replicates). P-values
report significance by ANOVA (effect of time). C Schematic showing the
sampling protocol for calculated fluxes from 1,2-^13C[2]-glucose
labeling experiments. Oscillations of glycolytic fluxes are shown below
the schematic. Data for 1,2-^13C[2]-glucose flux are mean ± s.e.m
(n = 3 biological replicates). P-values were obtained from rhythmicity
analysis using RAIN algorithm Note these data are replotted from
Fig. [110]3F to compare phase of glycolytic flux to Fig. 4A. D
Measurement of RBC flux in circulating mouse RBCs at opposite circadian
phases. Mouse RBCs (n = 3 biological replicates) were harvested at
opposite phases of the circadian cycle (see schematic): CT06 and CT18
(CT, circadian time: subjective day CT00-CT12; subjective night
CT12-24). P-values report significance by one way ANOVA.
Circadian rhythms in mouse RBCs have also been
reported^[111]16,[112]17. To determine if metabolic flux varies over
the day in circulating RBCs in vivo, we took blood from mice at
opposite phases of the circadian cycle—in the middle of the subjective
day (CT06) or night (CT18)—and then labeled RBCs with glucose and
determined flux by GC-MS (Fig. [113]4D). As in our ex vivo experiments,
we found at least a 2-fold difference in flux in labeled cells from
opposite circadian phases (Fig. [114]4D). Collectively, these results
indicate that there is a circadian rhythm of metabolic flux through
glycolysis and PPP in red cells, both in vivo and ex vivo, which may
drive circadian metabolite profiles that we uncovered in these
pathways.
Redox couples cellular metabolism in human RBCs
Peroxiredoxins in red blood cells play a crucial role to maintain redox
balance by neutralizing H[2]O[2] generated in part by auto-oxidation of
hemoglobin^[115]14. Peroxiredoxin oxidation exhibits circadian rhythms,
prompting the question of how coupling between metabolic cycles and
redox state is achieved. To understand this link, we investigated the
interrelationship between glucose-metabolizing pathways in RBCs and
peroxiredoxin redox state. We first inhibited flux through glycolysis
using heptelidic (koningic) acid (HA), a potent inhibitor of
glyceraldehyde 3-phosphate dehydrogenase (GAPDH)^[116]25,[117]26
(Fig. [118]5A). Metabolic inhibition experiments were performed on RBCs
isolated from fresh blood samples collected from human subjects and
metabolic inhibitors were added at the start of the experiment
(Fig. [119]5B). We found that fluxes through glycolysis and PPP became
arrhythmic (P = 0.865 by RAIN) after treatment with 1.9 µM HA
(Fig. [120]5C; only glycolytic flux shown for clarity).
Fig. 5. Metabolic inhibition abolishes circadian metabolic flux and PRDX
oxidation rhythms in human red blood cells (RBCs).
[121]Fig. 5
[122]Open in a new tab
A Schematic showing key steps and metabolites in glycolysis and the
pentose phosphate pathway (PPP) and the points at which the glycolytic
inhibitor, heptelidic acid (HA) and the PPP inhibitor,
6-aminonicotinamide (6AN), act in the pathways.
G6P = Glucose-6-phosphate, F6P = Fructose-6-phosphate,
FBP = Fructose-1,6-biphosphate, DHAP = Dihydroxyacetone phosphate,
6PGL = 6-Phosphogluconolactone, 6PG = 6-Phosphogluconate,
GAP = Glyceraldehyde-3-phosphate, GAPDH = Glyceraldehyde 3-phosphate
dehydrogenase, G6PD = Glucose-6-phosphate dehydrogenase,
TK = Transketolase, TA = Transaldolase. B Schematic for sample
collection. Freshly prepared human RBCs treated with metabolic
inhibitors 1.9 µM HA, 10 mM 6AN, or vehicle control (DMSO) and then
kept under constant conditions (37 °C, continuous darkness) and
sampling was performed every 4 h. C Effect of inhibitors on metabolic
fluxes through glycolysis measured by GC-MS. P-values were obtained
from rhythmicity analysis using RAIN algorithm. Graph bars present
mean ± s.e.m (n = 4 biological replicates). D The effect of metabolic
inhibitors on peroxiredoxin (PRDX) oxidation in RBCs was measured by
immunoblotting for PRDX-SO[2/3] dimer. Blots are shown with their
respective loading controls (Coomassie blue gel images). Graph bars
present mean ± s.e.m (n = 3 biological replicates). Full blots from all
subjects are shown in Supplementary Fig. [123]7. P-values were obtained
from rhythmicity analysis using RAIN algorithm. E Effect of HA and 6AN
on the RBC metabolome. Principal Components Analysis (PCA) plot showing
components 1 and 2 for all metabolomic samples showing good separation
between control, HA and 6AN treated samples. Circadian time dependent
changes were observed in control along PC1 (CT 24 vs. CT 36), while
circadian time dependent changes are abolished in samples treated with
metabolic inhibitors. Ellipses indicate the 95% confidence intervals of
each grouping of samples on the plot. F Time-dependent changes in ATP
and oxidized glutathione at CT24 and CT36. Controls show significant
(P < 0.01) temporal variation of abundance of ATP and oxidized
glutathione, while samples treated with metabolic inhibitors had no
time-dependence (not significant, n.s). P-values report significance by
one way ANOVA. Graph bars present mean ± s.e.m (n = 4 biological
replicates).
We then inhibited flux through PPP using 10 mM 6-aminonicotinamide
(6AN), an inhibitor of the two NADPH-producing enzymes in PPP
(Fig. [124]5A)^[125]27,[126]28. The amplitude of flux oscillations was
reduced and non-rhythmic (P = 0.127 by RAIN; Fig. [127]5C).
Peroxiredoxin oxidation in cells treated with these inhibitors was also
affected, abolishing 24-h patterning (Fig. [128]5D, Supplementary
Fig. [129]7). Interestingly, although HA treatment caused arrhythmicity
(P = 0.231 by RAIN), 6AN prolonged period to 36 h (P < 0.001 by RAIN)
compared to control cells (Fig. [130]5D). Importantly, the inhibitors
did not affect RBC viability at the doses used (Supplementary
Fig. [131]8), and they caused differential changes in the RBC
metabolome, demonstrating their specificity for glycolysis and PPP.
Furthermore, treatment with either pathway inhibitor abolished of time
of day variation in metabolites (Fig. [132]5E). Similarly, temporal
variation of ATP (which reflects glycolytic energy output) was
abolished by HA, and analogously 6AN had an identical effect on
oxidized glutathione (an output of the PPP) (Fig. [133]5F). Together,
these results demonstrate that there is tight coupling between core
glucose metabolism and peroxiredoxin oxidation rhythms in RBCs, and
that perturbing flux through glucose-metabolizing pathways has a potent
effect on redox rhythms.
In addition to PRDX oxidation rhythms in RBCs, we also identified 24-h
oscillations of the redox coenzymes NADH and NADPH previously^[134]11.
Since RBCs do not possess organelles, such as mitochondria, NADH and
NADPH originate from glycolysis and PPP^[135]29. The redox coenzymes
NADH and NADPH produced by glycolysis and PPP are likely links between
glucose metabolism and redox cycling of peroxiredoxins. However, their
respective role in the generation of circadian rhythmicity in RBCs
remains undefined.
We first measured NADH and NADPH in RBCs every 4 h over a period of
24 h after treating cells with the glycolytic and PPP inhibitors, HA
and 6AN, respectively (Fig. [136]6A). We did not find discernible 24-h
rhythms in either NADH or NADPH when cells were treated with the
inhibitors (Fig. [137]6B, C), although a residual non-circadian (12-h)
NADH rhythm appeared to persist in cells treated with HA
(Fig. [138]6B). This suggests coupling between cellular metabolism and
redox factors NAHD and NADPH. Thus, rhythmic glucose flux is necessary
for robust redox oscillations (PRDX-SO[2/3], NADH, and NADPH). To test
whether the converse is true, that redox oscillations are needed for
flux rhythms, we treated RBCs with a compound that inhibits PRDX
oxidation (conoidin A)^[139]30–[140]33 and one that abolishes PRDX
oxidation rhythms (MG-132)^[141]17 in RBCs (Fig. [142]6D). Treatment
with either inhibitor resulted in non-rhythmic flux (Fig. [143]6E).
These data indicate that rhythmic glucose metabolism is a crucial
determinant of circadian redox rhythms, and reciprocally, redox rhythms
are required for flux rhythms in RBCs.
Fig. 6. Metabolic inhibition abolishes rhythmic redox cofactor accumulation
in human red blood cells (RBCs).
Fig. 6
[144]Open in a new tab
A Schematic showing experimental protocol used to collect samples. RBCs
from n = 3–4 human subjects were incubated with 11 mM 1, 2-13C2-glucose
and kept under constant conditions (37 °C in continuous darkness) and
sampling was performed every 4 h. Cells were treated with metabolic
inhibitors, HA and 6AN at the starting of the experiment. B Effect of
metabolic inhibitors HA and 6AN on the rhythmicity of the redox
coenzyme, NADH. Graph bars present mean ± s.e.m (n = 6 biological
replicates) C Effect of metabolic inhibitors HA and 6AN on the
rhythmicity of the redox coenzyme, NADPH. Graph bars present
mean ± s.e.m (n = 6 biological replicates). D Schematic showing
experimental protocol used to collect samples after treating with
inhibitors Conoidin A and MG-132. E RBCs were treated with inhibitors
of PRDX oxidation (Conoidin A) or PRDX oxidation rhythms (MG-132) and
their effect on glucose flux was assessed. All data are mean ± s.e.m.
(n = 4 biological replicates). The 24-h rhythmicity P-value (determined
by RAIN) for each profile is shown with each plot, in addition to the
best fit period of the rhythm if this was not 24 h. ns, not significant
(P > 0.05).
Metabolic flux rhythms in nucleated mammalian cells
We next tested whether metabolic flux oscillations also occur in
nucleated cells. Human U2OS cells have been extensively used to
investigate molecular clock mechanisms, including small molecule
screening^[145]28,[146]34,[147]35. Unlike RBCs, U2OS cells exhibit
autonomous circadian rhythms in gene transcription and translation.
U2OS cells also exhibit circadian PRDX oxidation rhythms^[148]28 and
these redox rhythms are intrinsically coupled to clock components
through reversible redox modifications^[149]36. As well as being
essential redox factors in RBCs, NADH and NADPH influence the activity
of transcription factors in nucleated cells by modulating the binding
of circadian components such as CLOCK and BMAL1 to the
genome^[150]37,[151]38.
We therefore determined glycolytic and PPP fluxes in U2OS cells. Cells
were grown to confluence (i.e., non-dividing to exclude contributions
from the cell cycle), cultured for three days in standard glucose
(25 mM) medium, and then synchronized with a 15 min 100 nM
dexamethasone pulse^[152]28. Then, we replaced medium with that
containing 25 mM 1,2-^13C[2]-glucose. Beginning 24 h after
synchronization, we took samples for 48 h and measured metabolic flux
(Fig. [153]7A). GC-MS analysis of cellular extracts showed that
glycolytic and PPP flux exhibit circadian oscillations, as in RBCs
(P < 0.001 by RAIN; Fig. [154]7B; only glycolytic flux shown for
clarity).
Fig. 7. Circadian regulation of metabolic flux in nucleated cells.
[155]Fig. 7
[156]Open in a new tab
A Human U2OS cells were synchronized with a 15 min dexamethasone pulse
treatment and their medium replaced with DMEM medium containing 25 mM
1,2-^13C[2]-glucose. They were then kept under constant conditions
(37 °C, continuous darkness) and sampled every 3 h, starting 24 h after
the dexamethasone pulse (and glucose labeling). B Glycolytic and
pentose phosphate pathway (PPP) flux in U2OS cells was measured by
GC-MS using the glycolytic m + 2 isotopomer of ^13C[2]-lactate and the
pentose phosphate pathway m + 1 isotopomer of ^13C[1]-lactate (see
“Methods” section). Data are mean ± s.e.m. (n = 3 biological
replicates). The 24-h rhythmicity P-value (determined by RAIN) is shown
with the plot. C Treatment of U2OS cells with 50 µM of SR9011 resulted
in severe damping of bioluminescence rhythms that was reversible on
drug wash out (indicated by arrow). Traces for cells treated with
vehicle control (DMSO), and untreated cells, are also shown. Data are
mean of n = 4 biological replicates (see Supplementary Fig. [157]9 for
traces with error boundaries). D Metabolic flux in cells treated with
SR9011 (or vehicle control). P-values were obtained from rhythmicity
analysis using RAIN algorithm. E Adult skin fibroblasts from Bmal1^+/+
and Bmal1^−/− mice were synchronized for a 15 min with dexamethasone
and their medium replaced with DMEM medium containing 25 mM
1,2-^13C[2]-glucose. Cells were then kept under constant conditions
(37 °C, continuous darkness) and sampled every 3 h, starting 24 h after
the dexamethasone pulse (and glucose labeling. F Glycolytic flux in
adult skin fibroblasts from Bmal1^+/+ and Bmal1^−/− mice. Metabolic
flux measured by GC-MS. Flux data are mean ± s.e.m. (n = 3 biological
replicates).
Glycolytic and PPP fluxes are independent of BMAL1 in nucleated cells
Metabolic regulation is closely associated with the circadian clock
machinery, and REV-ERB proteins play critical roles in feedback
regulation of the core TTFL oscillator because they are direct targets
of BMAL1 and CLOCK transcription factors^[158]39. Accordingly, recently
developed REV-ERB agonists, such as SR9011, alter circadian behavior
and metabolism in mice by affecting core clock genes^[159]40. We
treated U2OS cells with 50 µM SR9011, which resulted in severe blunting
of circadian oscillations of the reporter Bmal1:luciferase (Bmal1:luc).
The effect was reversible on washing out the compound (Fig. [160]7C,
Supplementary Fig. [161]9). Of note, although higher concentrations of
SR9011 (e.g., 100 µM) completely abolished rhythms, recovery of rhythms
was not apparent on wash out because of cell death at higher
concentrations (Supplementary Fig. [162]9). Despite compromised
transcriptional rhythms in SR9011-treated cells, we found persistent
circadian rhythms of glycolytic and PPP fluxes (Fig. [163]7D; only
glycolytic flux shown for clarity).
To validate these results in a different model, we performed time
courses on skin fibroblasts from adult Bmal1^−/− and Bmal1^+/+ mice.
BMAL1 (MOP3) is considered to be an essential component of the
circadian transcriptional clockwork, and is necessary for behavioral
rhythms^[164]41. Bmal1^+/+ and Bmal1^−/− fibroblasts were grown to
confluence, cultured for three days in standard glucose (25 mM) medium,
and then synchronized for 15 min with a 100 nM dexamethasone
pulse^[165]28,[166]42. We then replaced medium with that containing
25 mM 1,2-^13C[2]-glucose (Fig. [167]7E). Twenty-four hours after
synchronization, we took samples every three hours for 48 h and
measured metabolic flux (Fig. [168]7E). We found robust rhythms in
relative glucose flux in both Bmal1^+/+ and Bmal1^−/− cells
(Fig. [169]7F). These findings are consistent with data suggesting
residual, but non-circadian, oscillations of glycolytic metabolites
(biphosphoglycerate, ATP, ADP, and lactate) in U2OS cells when Bmal1
was knocked down by RNA interference (RNAi)^[170]35. Moreover,
non-canonical circadian mRNA, protein and post-translational rhythms
(including PRDX-SO[2/3]) were also reported in Bmal1^−/− cells and
tissue^[171]42, showing that metabolic flux oscillations are an
additional type of BMAL1-independent circadian rhythm.
Discussion
Although gene expression cycles are essential for the temporal
coordination of physiology, we and others have previously shown that
rhythms in redox balance is a cell-intrinsic phenomenon that persists
without any gene expression cycles in non-nucleated mammalian red blood
cells^[172]11,[173]17. This circadian rhythm in redox balance is seen
across the domains of life, and cells have evolved a number of defense
mechanisms to counteract the deleterious consequences of oxidative
stress, including the use of peroxiredoxin
proteins^[174]10,[175]43,[176]44. Among these processes, metabolism is
a key player, and is also evolutionarily conserved to maintain cellular
energy and survival^[177]45. However, how cytosolic rhythmic redox
balance interacts with metabolism has not been elucidated. At different
levels of biological organization, from the whole body to single cells,
a significant portion of metabolism is under circadian control, leading
to the prevailing view that biological cycles drive metabolic
rhythms^[178]46,[179]47. Indeed, recent reports suggest rhythmic
metabolites from lipid metabolism, and NAD+ biosynthesis from
mitochondrial metabolism, are controlled by the clock^[180]48.
However, such circadian control of metabolism cannot happen in human
red blood cells, where there are no gene expression cycles and/or
organelles such as mitochondria. We uncovered a circadian metabolome in
human RBCs and also showed circadian rhythms of glycolysis and PPP
metabolites, indicating that cellular metabolism may be an integral
part of non-transcriptional circadian clocks. RBCs depend on glycolysis
for ATP, and on the PPP to maintain the redox balance through
NADPH^[181]29. Because metabolites are functionally dependent on input
of metabolic fluxes through a number of enzyme pathways, the
contribution of fluxes in generating cycling metabolites is important.
Our labeling experiments with human RBCs revealed rhythmic regulation
of glycolytic and PPP flux. These fluxes have opposite phases, with the
PPP reaching its peak during the day, aligned with peroxiredoxin peak
oxidation, while glycolysis is active during the night (Supplementary
Fig. [182]10).
It is likely that RBC redox rhythms are synchronized to human
physiology through blood oxygen levels. Indeed, the latter exhibit a
24-h pattern, and oxygen levels reach their maximum in the day (during
biophysical peak activity), and decrease to their lowest levels during
at night^[183]49. This leads to greater oxygenation of hemoglobin in
RBCs, and the generated ROS are likely to entrain cellular redox state
and oxidation of peroxiredoxin to synchronize the RBC 24-h oscillations
in oxidative stress to body physiology^[184]11,[185]17. To counteract
cellular oxidative damage, rhythmic NADPH production by the PPP may
thus be required to prevent cellular damage caused by the daily
auto-oxidation of hemoglobin^[186]50–[187]52.
Our results show that during the circadian night, when oxygen is
lowest, glycolysis reaches its peak. Active dynamic re-routing of
carbohydrate flux is key to counteracting oxidative stress, and our
results indicate that switching metabolic flux through PPP and
glycolysis over the circadian cycle could fulfill this goal
(Supplementary Fig. [188]10). GAPDH functions as a metabolic switch for
re-routing carbohydrate^[189]52. We have shown that inhibiting GAPDH
results in arrhythmic metabolic fluxes, which leads to arrhythmic redox
balance as shown by flat NADPH, NADH, and peroxiredoxin oxidation
profiles. Thus, our data suggest daily cross-talk of glucose metabolism
and redox factors is required to maintain circadian oscillations in
human RBCs.
In eukaryotes, prevailing clock models revolve around
transcription-translation feedback loops (TTFLs)^[190]6. How cell
metabolism couples to rhythmic peroxiredoxin state and whether these
metabolic rhythms are independent of TTFL was unclear. Our experiments
with U2OS cells reveal rhythmic regulation of glycolysis and PPP fluxes
in nucleated human cells. Furthermore, experiments using Bmal1 knockout
mouse cells show that circadian flux rhythms persist in the absence of
a functional TTFL network. Indeed, glycolysis and PPP fluxes have
higher amplitudes in comparison to control (Bmal1^+/+) cells. We
recently reported circadian oscillations of the transcriptome, proteome
and phosphoproteome of Bmal1 knockout mouse cells and tissue^[191]42.
The flux data in Bmal1 knockout cells presented in this study thus
strongly support the notion of TTFL-independent circadian timekeeping
mechanisms. Given that metabolic oscillations are present in both
nucleated cells genetically lacking a functional TTFL^[192]53, and in
the complete absence of transcription or translation (as shown here),
the most parsimonious explanation is that rhythmic metabolism is at the
core of circadian timekeeping. Thus, our work paves the way to explore
the role of metabolic components in regulating circadian rhythms in
diverse model systems.
Methods
Resource availability
Contact and materials availability
Further information and requests for resources and reagents should be
directed and will be fulfilled by the senior author, Akhilesh B Reddy
(areddy@cantab.net). This study did not generate new unique reagents.
Experimental model and subject details
Human participants and ethics statement
Studies were conducted in accordance with the principles of the
Declaration of Helsinki, with approval from the Health Research
Authority’s (UK) Research Ethics Committee (Reference number
12/EE/0370) and local ethical approval by The Francis Crick Institute’s
Ethics Review Board. The Francis Crick Institute is licensed by the
Human Tissue Authority (HTA) to store human samples for the purposes of
research (License number 12650). The research complies with all
requirements of the relevant HTA Code of Practice. All volunteers
provided written informed consent after receiving a participation
information sheet containing detailed information of the study
procedures. Participants were screened for self-reported health issues
including sleep disorders, night shift work, and regular high alcohol
consumption, and excluded if they met any of these criteria. Sample
size for all experiments mentioned at corresponding experimental figure
legends.
Human red blood cells (RBC) culture
Fresh blood samples (9–10 ml) were collected from each healthy
volunteer in 10 ml tubes containing trisodium citrate (Sarstedt,
S-Monovette 02.1067.001). RBCs from each donor were separately isolated
using 5 ml of Histopaque (Histopaque-1007, density 1.077 g/ml, sigma)
by density gradient centrifugation for 15 min at 700 × g. The obtained
RBC pellet was washed three times using 10 ml sterile PBS
(Sigma-Aldrich). The washed pellet was diluted to 9 ml with
Krebs–Henseleit Buffer (290 mOsm, pH 7.40) supplemented with 100 U/ml
penicillin and 100 μg/ml streptomycin (Sigma-Aldrich) and 0.1% BSA
(Sigma-Aldrich). 500 μl of anti-CD15 Dynabeads (Life Technologies
11137D) were added to each tube to remove any remaining nucleated
cells, which were extracted after 15 min incubation with the aid of a
magnet (Life Technologies). Krebs-Henseleit Buffer containing 11 mM
D-Glucose (Sigma-Aldrich K3753) was used for culturing RBC and
un-targeted metabolite profiling experiments. For metabolic labeling
experiments, unlabeled D-glucose was replaced with either 11 mM
U-^13C[6]-D-glucose (99%, CLM-1396, CK Isotopes Ltd) or
2-^13C[1]-D-glucose (99%, CLM-504, CK Isotopes Ltd). Different volumes
of purified RBCs were used for untargeted metabolite analysis (1 ml of
RBC per sample), metabolic flux assay experiments with NMR (1 ml of RBC
per sample) and GC-MS (200 μl of RBC per sample). RBC samples were
maintained at 37 °C in a microprocessor-controlled incubator (Eppendorf
Galaxy 170 R) in complete darkness, in sealed tubes, for sampling. For
metabolic inhibition experiments, heptelidic acid was dissolved in DMSO
to make a stock solution. Compounds were diluted in Krebs-Henseleit
Buffer to their final concentration. Drugs were added to RBCs to reach
a final concentration of 10 mM 6-Aminonicotinamide (CAS 329-89-5, Sigma
A68203), or 1.9 µM heptelidic acid (CAS 74310-84-2, Biovision
2215–1000), or 3 µM MG-132, or 5 µM conoidin A. Control samples were
treated with 0.5% DMSO.
Experiments with U2OS cells
Stably-transfected Bmal1:luc U2OS cells were a kind gift of Dr Andrew
Liu (University of Memphis). Cells were cultured in a humidified 5%
CO[2] incubator in Dulbecco’s Modified Eagle’s medium (DMEM) (Sigma
D6546) containing 4.5 g/l glucose, 1× Glutamax (Life Technologies
35050-038, 10% Newborn Calf Serum (Sigma 12023 C), 100 U/ml penicillin
and 100 μg/ml streptomycin (Sigma P0781), 1× MycoZap Plus-PR (Lonza)
and blasticidin 2 μg/ml. For metabolic flux experiments, unlabeled
glucose was replaced with 25 mM 1,2-^13C[2]-D-glucose (99%, CLM-504, CK
Isotopes Ltd). For bioluminescence recordings, U2OS cells were grown to
confluence in 96-well plates in the above medium and synchronized by
changing the medium to Air Medium^[193]28,[194]54: DMEM (Sigma D5030)
supplemented with 4.5 g/L glucose (Sigma G8644), 1× Glutamax (Life
Technologies 35050-038), 10% Newborn Calf Serum (Sigma 12023 C), 100 U
penicillin/ml and 100 μg/ml streptomycin, 0.5× B-27® Supplement (Life
Technologies 17504-044), 20 mM HEPES (Sigma H0887), 1 mM Luciferin
(Biosynth L8220), 1× Non-Essential Amino acids (Sigma M7145), 0.035%
NaHCO[3] (Sigma S8761), 1× MycoZap Plus-PR (Lonza) and blasticidin
2 μg/ml. For experiments using the REV-ERB agonist SR9011 (Caymen
Chemical 11930; CAS 1379686-29-9), the compound was solubilized in DMSO
and diluted in Air Medium to a final concentration of 50 or 100 μM.
Control cells were treated with a matched concentration of DMSO.
Bioluminescence assays were conducted in custom-made light-tight
Alligator bioluminescence recording systems (Carin Research Ltd,
Faversham, UK) composed of a CCD camera (Andor iKon-M 934) placed on
the top of Galaxy 170 R incubator (Eppendorf). All bioluminescence
experiments were performed at 37 °C in darkness. Plate luminescence
images were captured every 30 min over seven days.
Experiments with mice
Animal studies were carried out in concordance with an approved
protocol from Institutional Animal Care and Use Committee (IACUC) at
Perelman School of Medicine at the University of Pennsylvania, or under
license by the United Kingdom Home Office under the Animals (Scientific
Procedures) Act 1986, with Local Ethical Review by the Francis Crick
Institute Animal Welfare & Ethical Review Body Standing Committee
(AWERB). Male C57BL/6J mice, 8–10 weeks old, were purchased from
Charles River and allowed to acclimatize and entrain in a 12-h
light-dark cycle (LD). Light intensity during light and dark periods
was 200 and <3 lux, respectively. Humidity and temperature (21 ± 1 °C)
were kept within standard ranges. After 3 weeks, mice were transferred
to constant darkness (DD; dim red light, <3 lux) and on the second day
of DD blood was collected by cardiac puncture under terminal anesthesia
(sodium pentobarbital, 170 mg/kg, intraperitoneal) at CT24 and CT36.
Experiments with Bmal1^−/− mouse skin fibroblasts
Bmal1^+/+ and Bmal1^−/− adult mouse skin fibroblasts (MSF) cells were
grown in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% (v/v)
HyClone III Serum (Analab; Cat # SH30109.03), 1/100 Glutamax-I
(Invitrogen; Cat # 35050-038), 1/100 Penicillin-Streptomycin (SIGMA;
Cat # P4333) and 1/500 MycoZap™ (Lonza; Cat # VZA-2022) in multiple
six-well plates until fully confluent (n = 3, per time-point, per
genotype). Confluent MSF cells were treated with 100 nM (final
concentration) of dexamethasone (DEX) for 15 min to synchronize the
cells. MSF cells were then washed three times with PBS (37 °C) and were
incubated in HEPES-buffered Medium; 1× DMEM powder (SIGMA; Cat #
D5030), 5 mg/ml 1, 2-13C2 (99%) D-glucose (Cambridge Isotope
Laboratories, Cat # CLM-504-1), 0.35 mg/mL sodium bicarbonate, 0.01 M
HEPES, 5% (v/v) HyClone III Serum, 1/100 Glutamax-I, 1× B-27 supplement
(LifeTech Cat # 17504-044), 1× Non-Essential Amino acids, and 1/500
MycoZap™, pH 7.4 (adjusted with HCl) and osmolality 350 mOsm (adjusted
with NaCl) at 37 °C under DD cycle. Twenty-four hours after the DEX
treatment, MSF cells were harvested at every three-hour interval for
two days for subsequent metabolic flux analysis.
Metabolite extraction for untargeted metabolite profiling
At each time point, 1.5 ml Eppendorf tubes containing 1 ml of purified
RBCs in Krebs-Henseleit Buffer were collected from the incubator and
samples were immediately centrifuged for 2 min at 375 × g at 4 °C. The
supernatant was removed and the RBC pellet washed twice with ice-cold
PBS. RBC metabolite extractions were performed using a reported
protocol^[195]55 with slight modifications. Briefly, metabolites were
extracted from RBCs by adding 450 μl of methanol. Vortex mixed for 10 s
to lyse the cells in methanol. Immediately, 200 μl of chloroform and
200 μl of water was added to the pellet at 4 °C, followed by sonication
for 10 min and then vortexing for 20 min. Samples were then centrifuged
at 18,400×g for 20 min at 4 °C. The upper aqueous layer was collected
and lower chloroform layer (containing non-polar metabolites) was
discarded. Two more extractions were performed on the same RBC pellet
with 50% methanol:water with vortexing for 20 min, and centrifugation
at 18,400×g for 20 min at 4 °C. ^13C[5]-^15N-Valine was used as an
internal standard during all extractions. The three extracts were
combined and dried with a vacuum concentrator (Concentrator plus,
Eppendorf). The dried extracts were subsequently used for GC-MS and
LC-MS analysis for metabolite profiling.
UPLC-MS based data acquisition for untargeted metabolite profiling
Dried samples were reconstituted in 500 μl methanol:water (1:1, v/v).
LC-MS analysis was conducted using a Dionex UltiMate Liquid
Chromatography (LC) system coupled to a Q-Exactive Orbitrap mass
spectrometer (both Thermo Scientific), adapted from a reported
method^[196]56. LC separation was performed using hydrophilic
interaction chromatography (HILIC) on a ZIC-pHILIC column
(150 mm×4.6 mm, 5 μm particle size; Merck Sequant) with a gradient
solvent A (20 mM ammonium carbonate in water; Optima HPLC grade, Sigma
Aldrich) and solvent B (acetonitrile; Optima HPLC grade, Sigma
Aldrich). A 15 min elution gradient of 80 to 20% Solvent B was used,
followed by a 5 min wash of 5% Solvent B and 5 min re-equilibration.
Other LC parameters were: flow rate 300 μl/min; column temperature
25 °C; injection volume 10 μl; auto sampler temperature 4 °C. MS was
performed with positive/negative polarity switching using a HESI II
probe. MS parameters were: spray voltage 3.5 kV and 3.2 kV for positive
and negative modes, respectively; probe temperature 320 °C; sheath and
auxiliary gases were 30 and 5 arbitrary units, respectively; full scan
range: 70 to 1050 m/z with settings of AGC target and resolution as
Balanced and High (3 × 10^6 and 70,000) respectively. Data was recorded
using Xcalibur 3.0.63 software (Thermo Scientific). To enhance
calibration stability, lock-mass correction was also applied to each
analytical run using ubiquitous low-mass contaminants. Parallel
reaction monitoring (PRM) parameters: resolution 17,500; collision
energies were set individually in high-energy collisional dissociation
(HCD) mode.
GC-MS based data acquisition for untargeted metabolite profiling
Dried sample extracts were used for GC-MS analysis. Untargeted
metabolite profiling was performed by GC-MS using an Agilent
7890A-5975C GC-MSD after derivatization of metabolites with
methoxyamine hydrochloride (20 mg/ml in pyridine, both Sigma) and
N,O-bis-(trimethylsilyl)trifluoroacetamide (containing 1%
trimethylchlorosilane)^[197]57. GC separation was achieved using an
Agilent DB-5 MS column (30 m × 0.25 mm × 0.5 μm). The GC oven
temperature program was: 70 °C, 2 min hold; ramp 12.5 °C/min to 295 °C,
0 min hold; ramp 25 °C/min to 320 °C, 3 min hold. Other GC parameters
were: injection volume 1 μl; inlet temperature 270 °C; Helium was used
as a carrier gas at a flow rate of 0.9 ml/min; transfer line
temperature 280 °C. Electron impact ionization was used for mass
spectrometry detection with scan range m/z 50–565.
Metabolic flux measurements with NMR
For RBC metabolic flux experiments, cultures were incubated with
Krebs-Henseleit Buffer containing 11 mM 2-^13C[1]-D-glucose and at each
time point a 1.5 ml Eppendorf tube containing 1 ml RBCs was collected
from the incubator, and immediately centrifuged for 2 min at 4 °C at
375 × g. The supernatant was collected and the pellet washed with
ice-cold PBS two times and centrifuged again for 2 min at 4 °C and at
375 × g. The RBC pellet and was then immediately flash frozen in liquid
nitrogen. Frozen samples stored at −80 °C till the analysis. Frozen
samples were thawed at 4 °C, and then placed in a boiling water bath
for 9 min to lyse cells and halt enzymatic activity^[198]23,[199]58.
Boiled lysates were sonicated for 10 min at 4 °C on ice, followed by
vortex mixed for 10 min. Samples were then centrifuged for 20 min at
18,400 × g at 4 °C. Nine-hundred microliter of each supernatant was
dried in a vacuum concentrator (Concentrator plus, Eppendorf). Dried
sample extracts were suspended in 750 μl D[2]O (Sigma 151882)
containing 0.05% of 3-(trimethlysilyl)-1-propanesulfonicacid-d[6]
sodium salt (Sigma 613150). ^13C-NMR spectroscopy was performed on a
Bruker AM-500 MHz spectrometer (Chemistry Department, University of
Cambridge) or a Bruker Avance III 600 MHz spectrometer equipped with a
5 mm TCI Cryoprobe (MRC Biomedical NMR Centre, Francis Crick
Institute). Spectra were recorded using a 30° ^13C excitation pulse,
1 s acquisition time, and 3 s relaxation delay. The spectra were
^1H-broadband de-coupled using WALTZ16, which was also employed during
the relaxation delay to exploit the {^1H}^13C heteronuclear Overhauser
enhancement. Labeled C2-lactate, C3-lactate and alpha-anomers,
beta-anomers of glucose were identified and peak integrals were
evaluated using the Bruker NMR software package TopSpin 3.5.
Metabolic flux measurements with GC-MS
For RBC metabolic flux experiments, cultures were incubated with
Krebs–Henseleit Buffer containing 11 mM 1,2-^13C[2]-D-glucose and at
each time point a 1.5 ml Eppendorf tube containing 200 µl RBCs was
collected from the incubator, and immediately centrifuged for 2 min at
4 °C at 375 × g. The supernatant was collected and the pellet washed
with ice-cold PBS two times and centrifuged again for 2 min at 4 °C and
at 375 × g. The RBC pellet and was then immediately flash frozen in
liquid nitrogen and metabolites extracted with 80% methanol:water
followed by vortexing for 10 min, and then centrifugation at 18,400 × g
for 15 min at 4 °C. The aqueous layer was collected and dried using a
vacuum concentrator (Concentrator plus, Eppendorf).
For U2OS cell experiments, cultures were incubated with medium
containing 25 mM 1,2-^13C[2]-D-glucose and at each time point, culture
supernatant was removed and cells washed with ice-cold PBS three times.
Immediately, 1 ml of ice-cold 80% methanol:water (pre-cooled to −80 °C)
was added and cells collected by scraping into 1.5 ml Eppendorf tubes.
Cells were flash frozen in liquid nitrogen and stored at −80 °C.
For lactate isotopomer analysis, samples were derivatized as above and
analyzed on an Agilent GC-MSD. Parameters were as for untargeted GC-MS
analyses above, but with the following GC temperature gradient: 120 °C,
2 min hold; ramp 8 °C/min to 180 °C; ramp 20 °C/min to 290 °C, 3 min
hold. Selective ion monitoring (SIM) for lactate isotopomers m + 0
(unlabeled ^12C-lactate), m + 1 (^13C[1]-lactate), m + 2
(^13C[2]-lactate), m + 3 (^13C[3]-lactate).
Extracellular glucose from the media obtained from incubating RBCs with
U-13C-Glucose over a period of 72 h were analyzed after drying of 1 μl
of medium. Derivatization and GC-MS conditions were the same as
mentioned above.
Pulse labeling experiments
Pulsed isotopic labeling was performed by feeding RBCs with either
1,2-^13C[2]-glucose or 1-^13C-glucose. To minimize potential clock
resetting and perturbation of cells due to replacement of normal
glucose medium with labeled medium, we used conditioned medium, which
was obtained by incubating RBCs for 1 h in labeled medium and storing
immediately at 4 °C. Metabolic measurements for 1,2-13C2-glucose
experiments were performed as follows. Briefly, 800 μl of the upper
fraction was dried in a speed-vacuum for 4.5 h. The resulting dried
sample was reconstituted in 200 μl of acetonitrile:water, vortexed for
20 s, and centrifuged at 13,300 rpm for 15 min. The undiluted sample
was used for glucose measurements while a 30-fold diluted sample was
utilized for lactate measurements. Samples were measured in analytical
duplicates with each sample set run in a randomized manner with pooled
quality control samples measured at the start of the run, after every
10th sample, and at the end of the run. For both glucose and lactate
measurements, 2 μl of each sample was analyzed in a manner similar to
the methods previously described^[200]59,[201]60, modified for
isotopomer analysis. Targeted multiple reaction monitoring (MRM)
methods were utilized to detect lactate and glucose isotopologues in
each respective run. Data were processed and integrated using Waters
TargetLynx software (version 4.1) with natural abundance correction and
further processing of ion counts performed in R.
Metabolic measurements for 1-^13C-glucose experiments was measured with
Agilent LC-QTOF 6545. Metabolites were confirmed by running standards
in parallel. Isotopic labeling was measured using Agilent MassHunter
Profinder v.B.08.00 (Batch Isotopologue extraction) and the personal
compound database and library (PCDL).
Flux measurements in Bmal1^−/− mouse skin fibroblasts
MSF cells collected at each time point were washed with ice-cold PBS
three times and then homogenized in 1 ml 80% methanol (precooled at
−80 °C). Samples were then flash frozen in liquid nitrogen and were
stored at −80 °C until extraction. In the extraction process, mild
sonication was applied to the samples for 10 min (30 s on, 30 s off;
medium power) using a Bioruptor Standard (Diagenode) instrument. Then
the samples were vortexed for 20 mins at 4 °C and the lysates were
centrifuged at 14,000 rpm for 20 min at 4 °C. Supernatants were
carefully separated and transferred into new microcentrifuge tubes.
Samples were dried by vacuum centrifugation and were stored at −80 °C
until GC-MS analysis for flux measurements.
Gel electrophoresis and immunoblotting
At each time point during a time course, samples were removed from the
incubator and 75 μl of RBCs (and medium) from each sample tube lysed in
250 μl of 2× LDS buffer (Life Technologies) and placed in a thermomixer
heating block (Grant Instruments, model PHMT) for 10 min at 70 °C at
800 rpm. Immediately, samples were flash frozen in liquid nitrogen and
stored at −80 °C until analysis. Samples were allowed to reach room
temperature before analysis, which was performed as described
previously^[202]11,[203]15,[204]28. Briefly, NuPAGE Novex 4–12%
Bis-Tris gradient gels (Life Technologies) were loaded and run with a
non-reducing MES SDS running buffer as per the manufacturer’s
guidelines. Protein transfer to nitrocellulose membrane was performed
using the iBlot system (Life Technologies) with a standard P3, 7 min
protocol. The nitrocellulose membrane was blocked for 1 h at room
temperature in blocking buffer, composed of 0.5% w/w BSA, (Sigma
A7906)/non-fat dried milk (Marvel) in Tris buffered saline/0.05%
Tween-20 (TBST). After blocking, membranes were incubated with 1:10,000
anti-PRDX-SO[2/3] antiserum (Abcam ab16830), diluted in blocking
buffer, overnight at 4 °C. The following day, blots were washed for
3 × 5 min in TBST and then incubated for 1 h at room temperature with a
1:10,000 HRP-conjugated secondary antiserum (Sigma A6154). Blots were
washed 4 × 10 min in TBST before performing chemiluminescence detection
using Immobilon Forte reagent (Millipore). Equal protein loading in
each lane was checked with the aid of gels stained with Coomassie
SimplyBlue SafeStain (Life Technologies). Coomassie stained gel images
were obtained using an Odyssey system (Licor Biosciences). Immunoblot
membranes were scanned using an Amersham Imager 600 (GE Healthcare).
Quantification of images was performed using NIH Image J
software^[205]11,[206]16.
NADH and NADPH assays
At each time point, samples were collected from the incubator and
centrifuged for 2 min at 375 × g at 4 °C. RBC pellets were washed with
ice-cold PBS twice and then centrifuged for 2 min at 375 × g at 4 °C.
Supernatant was discarded and the RBC pellet flash frozen in liquid
nitrogen and stored at −80 °C. Extraction of metabolites was performed
with the buffers supplied with commercial assay kits following the
manufacturer’s instructions (Abcam ab65348 for NADH and ab65349 for
NADPH measurements). Colorimetric measurements were made at 450 nm
absorbance and at 25 °C using a PerkinElmer Ensight multimode plate
reader. NADH and NADPH concentrations were determined from a standard
calibration curve according to the manufacturer’s instructions.
NADH/NADPH absorbance measurements were acquired using Kaleido pate
reader software.
Cell viability assays for metabolic inhibitor experiments
RBCs were isolated from donors as mentioned above. The RBC pellet was
made up to 9 ml with Krebs-Henseleit Buffer. 420 µl of RBCs were
aliquoted into the wells of a 96 deep well plate for drug addition.
2.1 µl of diluted drug (1:200 dilution; 0.5% DMSO final) was added to
each well and mixed thoroughly. For 6AN, concentrations were screened
from 0 to 10 mM. For heptelidic acid, concentrations were screened over
the range of 0 to 1 mM. Fifty microliter of RBCs preparation with
compounds added (and control RBCs with only 0.5% DMSO added) were
aliquoted into 96-well plates. The plates were incubated at 37 °C in
constant darkness. Sampling was then performed every 24 h over a period
of 96 h. At each time point, RBCs in a plate were re-suspended by
pipetting up and down. The 96-well plates were then spun for 5 min at
375 × g and 25 µl of the supernatant transferred into a 384-well plate
for assaying. A standard curve was prepared by lysing untreated cells
with water (hypotonic lysis) and then performing serial dilutions in a
96-well plate. The absorbance of the sample supernatants was measured
at 480 nm (in a 384-well plate) and using a Tecan M1000 plate reader.
Red cell absorbance measurements were acquired using Tecan M1000 plate
reader software.
Red cell microscopy
During the circadian time course with metabolic inhibitors, 10 µl of
sample was collected at specific time points and spread onto a
microscope slide (Thermo Scientific Superfrost Plus) and air dried.
Microscopic images were captured using a Leica DM IL LED inverted
microscope with LAS software version 4.8.
Quantification and statistical analysis
Mass spectrometry data processing, identification and metabolite enrichment
analysis
Vendor specific LC-MS raw data files from the mass spectrometer were
extracted using Progenesis QI for metabolomics using parameters:
feature detection = high resolution, peak processing = centroid data
with 70,000 (FWHM) resolution. In positive ionization mode, M + H,
M + 2H, M + Na, M + NH[4], in negative ionization mode, M-H, M-2H,
M + Na-2H were considered. Agilent Mass Hunter software v B.07.00 was
used to extract GC-MS data. Features having a coefficient of variation
(CV) lower than 30% among quality control samples were selected for
downstream analyses and features having CV more than 30% were dropped
from the dataset. We detected 1,698 features that had a coefficient of
variation <30% in quality control samples. This included 533 features
from negative mode LC-MS and 1074 from positive mode, with an
additional 91 from GC-MS. Metabolite Set Enrichment Analysis was
performed using Metaboanalyst software v 4.0, implemented in the R
programming language.
Rhythmic metabolites were identified using retention time and MS/MS
spectra of metabolite standards for LC-MS samples. Retention time and
MS spectra from GC-MS analyzed samples were compared with metabolite
standards and the National Institute of Standards and Technology (NIST)
mass spectral library to confirm identification. The maximum number of
metabolites detected in the RBC metabolome until now is 213^[207]57.
The percentage of rhythmic metabolites in this study is 21% (46
rhythmic metabolites from total known RBC metabolome, i.e., 213
metabolites).
Metabolic flux calculations
Metabolic fluxes were estimated by using previously well-established
models^[208]23,[209]58,[210]61,[211]62. Briefly, the Pentose Cycle (PC)
can be estimated using NMR by measuring differential enrichment of
C3-lactate and C2-lactate after feeding cells with 2-^13C[1]-Glucose
with the formula:
[MATH: C3−LactateC2−Lactate=
2PC1+2
PC :MATH]
1
Where PC refers to fraction of glucose used to produce pentose
phosphate pathway-derived Glyceraldehyde-3-phosphate.
Differential enrichment of ^13C[1]-lactate (m + 1) and ^13C[2]-lactate
(m + 2) determined using GC-MS can be used to estimate fluxes after
feeding cells with 1,2-^13C[2]-Glucose with the formula:
[MATH: M+1LactateM+2Lactate=
3PC1−PC :MATH]
2
Metabolic fluxes for various metabolic pathways can be calculated from
PC and glucose uptake as follows:
[MATH: PPPflux=PC×Glucoseconsumption :MATH]
3
[MATH: Glycolyticflux=1−PC×Glucoseconsumption :MATH]
4
Rhythmicity analysis
Analysis of circadian waveforms was performed using two independent
statistical methods^[212]22, the Rhythmicity Analysis Incorporating
Nonparametric method (RAIN)^[213]18 and a harmonic regression method
(ARSER)^[214]22,[215]63, which are implemented in the R programming
language. P-value outputs are shown for each plot, along with the
best-fitting period (e.g., for short or long period oscillations that
deviate from 24 h). Only P-values for RAIN analyses are shown in the
text and figures, but the alternative rhythm detection algorithm ARSER
yielded results matching RAIN. Plots were produced in either R or
GraphPad Prism (version 7 and 8). Statistical parameters such as
details of replication and error bar meaning were reported in the
figure legends.
Bioluminescence assay analysis
Exported images were quantified in a time series using NIH Image J
software. Circadian rhythmicity in bioluminescence data was measured
using a modified version of the R script
“CellulaRhythm”^[216]28,[217]64.
Reporting summary
Further information on research design is available in the [218]Nature
Research Reporting Summary linked to this article.
Supplementary information
[219]Supplementary Information^ (27.8MB, docx)
[220]Peer Review File^ (1.4MB, pdf)
[221]Reporting Summary^ (367.8KB, pdf)
Acknowledgements