Feeding-regulated glycogen metabolism drives rhythmic liver protein secretion
Meltem Weger, Daniel Mauvoisin, Dominic Hoyle, Jingkui Wang, Eva Martin, James Rae, Charles Ferguson, Glynis Klinke, Michelle Cielesh, Kyle L. Macauslane, Mark Larance, Manfredo Quadroni, Iain Templeman, Jean-Philippe Walhin, Leonidas G. Karagounis, James A. Betts
TL;DR
The liver's protein secretion follows a daily rhythm controlled by feeding and glycogen metabolism, impacting overall health.
Contribution
This study reveals that glycogen metabolism regulates rhythmic liver protein secretion through glycosylation.
Findings
Hepatic protein secretion follows a diurnal rhythm regulated by food intake in humans and mice.
Glycogenolysis provides substrates for protein N-glycosylation, and its disruption causes ER stress and reduced secretion.
Genetic variants related to glycogen storage and glycosylation disorders alter hepatic protein secretion in humans.
Abstract
The liver has a key role in inter-organ communication by secreting most circulating plasma proteins. However, the mechanisms governing hepatic protein secretion remain unclear. Here we show that hepatic protein secretion follows a diurnal rhythm regulated by food intake in humans and mice. Using liver microsomal proteomics, we find that proteins implicated in the early secretory pathway, such as protein glycosylation and folding in the endoplasmic reticulum (ER) and Golgi apparatus, exhibit a rhythmic expression profile, which is abolished in Bmal1-knockout mice. Mechanistically, we show that hepatic glycogenolysis provides substrates for protein N-glycosylation. In mice, perturbing hepatic glycogenolysis with pharmacological or nutritional interventions leads to ER stress and attenuates diurnal protein secretion. We confirm these results in humans, as genetic variants associated with…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9- —https://doi.org/10.13039/501100009708Novo Nordisk Fonden (Novo Nordisk Foundation)
- —https://doi.org/10.13039/501100000925Department of Health | National Health and Medical Research Council (NHMRC)
- —https://doi.org/10.13039/100000002U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- —https://doi.org/10.13039/501100000923Department of Education and Training | Australian Research Council (ARC)
- —https://doi.org/10.13039/100000957Alzheimer's Association
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGlycogen Storage Diseases and Myoclonus · Pancreatic function and diabetes · Endoplasmic Reticulum Stress and Disease
Main
The liver synthesizes and secretes the majority of circulating plasma proteins, which are crucial for inter-organ communication and energy homoeostasis^1^, and it has a central role in systemic metabolic regulation. For instance, during periods of food availability, hepatocytes store glucose as glycogen and release it during fasting to maintain blood glucose levels and meet the energetic demands of other organs^2^.
In turn, liver physiology follows a pervasive diurnal (24 h) rhythm^3,4^, which is governed by the circadian clock system that consists of self-sustained molecular oscillators involving the CLOCK-BMAL1, PER1/2-CRY1/2 and REV-ERBs/RORs transcriptional regulators^5^. These molecular oscillators form a central pacemaker in the suprachiasmatic nuclei of the hypothalamus and peripheral clocks in virtually all tissues^6^. The suprachiasmatic nuclei clock is synchronized by light–dark cycles and coordinates peripheral clocks to maintain phase coherence throughout the body. Feeding–fasting cycles, relying on the rhythmic activity of the suprachiasmatic nuclei, serve as major synchronizers of peripheral clocks, such as in the liver^4,7^. Accordingly, time-restricted feeding (TRF) paradigms, which confine food intake to a specified time window and are aligned with normal activity patterns, have been shown to reduce obesity and lower the risk of diabetes, mainly because of the associated reduction in caloric intake^8^. These benefits are associated with high-amplitude circadian rhythms^8^, highlighting the intricate link between food timing, circadian rhythms and the maintenance of overall health.
Prior studies have shown that circulating proteins and total blood protein levels in both humans and rodents exhibit diurnal fluctuations^9,10^, and our work demonstrated a 24 h rhythmic accumulation of secretory proteins in the mouse liver^11^. However, protein secretion from the liver is still considered constitutive, with blood protein levels, such as that of albumin (ALB), thought to be primarily regulated by synthesis and removal by catabolism and elimination^12^. Protein secretion pathways are generally not regarded as a point of regulating blood protein levels.
Secreted proteins typically traverse the classical secretory pathway, which involves protein processing in the endoplasmic reticulum (ER) and Golgi apparatus (GA) for proper folding and post-translational modification (for example, N-glycosylation) and then trafficking to the plasma membrane for secretion^13^. Quality control mechanisms ensure that properly glycosylated and folded proteins are secreted, while misfolded proteins activate ER stress, leading to the unfolded protein response (UPR) and ER-associated degradation (ERAD) to maintain cellular homoeostasis^14^. Importantly, dysregulation in hepatic protein secretion is associated with multisystem pathologies, including obesity and type 2 diabetes^1^, conditions that are also linked to disruptions in circadian and feeding–fasting rhythms^2^. Critically, the causal links underlying these associations remain poorly understood.
Here, we combined blood proteomics in humans and mice under different feeding regimens with computational prediction of protein secretion and secretion assays to show that liver-derived protein secretion is a highly rhythmic process that is primarily regulated by food intake and glycogen metabolism. Pharmacological inhibition of the rate-limiting enzyme in hepatic glycogenolysis (glycogen phosphorylase L; PYGL) and dietary intervention studies revealed that hepatic glycogen breakdown is required for protein glycosylation and secretion, as well as the associated activation of the ER stress and UPR. We also revealed that variants in genes associated with human glycogen storage disease (GSD) and congenital disorders of glycosylation (CDG) lead to alterations in protein secretion, which could explain the overlapping symptomatology between these diseases and provide evidence for the relevance of our findings in humans.
Results
The human diurnal plasma proteome is modulated by meal timing
To gain insights into the diurnal human plasma proteome and determine the role of the timing of food intake, we conducted a study with two separate cohorts of healthy men (Fig. 1a,b and Extended Data Fig. 1a). These volunteers were assigned to either a ‘meal-fed pattern’, in which participants followed their normal breakfast routine and then received a standardized lunch (12:00 h), dinner (18:00 h) and a snack provided during the day, or a ‘spread-out eating pattern’, in which a nutritionally balanced solution was ingested hourly throughout waking hours from 08:00 h to 22:00 h to meet individually measured metabolic requirements and maintain energy balance. Blood samples were collected every 4 h over a 24 h period (Fig. 1a), and protein concentrations were assessed using a proteomics approach with an aptamer-based multiplexed platform^15^. We used a mixed linear model with harmonic regression in each cohort to identify the 24 h rhythmic accumulation of circulating proteins. Strikingly, we identified diurnally circulating proteins almost exclusively in meal-fed participants (Fig. 1c,d and Supplementary Table 1). This finding was consistent across the entire range of amplitudes (peak-to-trough log_2_ fold change) and was independent of the significance threshold (Fig. 1c).Fig. 1. Diurnal rhythms of blood proteins are influenced by feeding in humans and mice.a, Schematic of study design (created with BioRender.com). Healthy men followed either a meal-fed pattern with two standardized meals at 12:00 h and 18:00 h and a snack that could be consumed freely throughout the day, or a spread-out eating pattern in which a nutritionally balanced solution was provided hourly from 08:00 h to 22:00 h. Red asterisks indicate blood sampling times. b, Overview of study cohort; n = 9 human participants (^a^n = 8 for the chronotype under a spread-out eating pattern). c, Number of rhythmic plasma proteins in human participants following a meal-fed or spread-out eating pattern as a function of minimal amplitude (left). Number of genes that are considered rhythmic with a P value equal to or smaller than the value indicated on the x axis (right). FC, fold change. d, Heatmap representation of rhythmic blood proteins in human participants following a meal-fed (adjusted P < 0.05; 506 proteins) and their corresponding circulating levels under a spread-out eating pattern. Data are row-standardized. e, Phase distribution of rhythmic blood proteins of participants following a meal-fed (left) and spread-out eating pattern (right). f, Examples of temporal profiles of blood proteins. g, Number of organ-specific blood proteins (adjusted P < 0.05) that exhibit 24 h diurnal changes in abundance across a day under a meal-fed pattern in humans. The coloured bars represent the number of rhythmic proteins. h, Scatterplot of acrophase versus amplitude of diurnal blood proteins with an organ-specific origin (adjusted P < 0.05). A blood protein was considered to derive from a specific organ if its gene expression in that organ was at least four times higher than in any other organ. i, Experimental design (created with BioRender.com) and schematic of the three different feeding regimens in WT mice under a 12 h light–dark cycle: AL, food was available all the time; NR, food access was provided for 8 h during the dark period (ZT14–ZT22); DR, food access was provided for 8 h during the light period (ZT2–ZT10). Food availability is indicated by blue boxes. j, Average food consumption per day and WT mouse under AL, NR and DR feeding (n = 2 biological replicates, averaged from four mice per replicate). k, Profiles of insulin and glucagon in WT mouse serum under an AL, NR and DR feeding regimen (n = 24 (6 timepoints × 4 biological replicates)). l, Number of proteins classified in rhythmic models in WT mouse serum under AL, NR and DR feeding. The white boxes indicate that no rhythm has been detected using dryR^20^; the coloured boxes indicate detected rhythmicity. Shared colours show shared rhythmic parameters (amplitudes and phase) between conditions, while different colours indicate different amplitudes and/or phases. A detailed description of the model can be found in Supplementary Table 2. m, Heatmaps of rhythmic blood protein levels under AL, NR and DR feeding. Proteins are ordered according to their assigned rhythmic model. n, Acrophase versus amplitude scatterplot of diurnal blood proteins of the indicated model (m2 and m7) under AL feeding. BICW, Bayesian information criterion Schwarz weight. o, Phase distribution of blood proteins of the indicated model (m2 and m7) under AL feeding. p, Temporal profiles of VEGFC and FN1 in WT mouse blood under AL, NR and DR feeding (n = 24 (6 timepoints × 4 biological replicates)). Data are displayed as means; error bars, s.e.m. A detailed description of the statistical analysis is available in Source Data Fig. 1. See also Extended Data Fig. 1 and Supplementary Tables 1 and 2 for related data.Source data
The rhythmic plasma proteins under the meal-fed pattern exhibited a bimodal phase distribution with peaks at 05:00 h and 17:00 h (Fig. 1e). Circulating proteins that lose rhythmicity under spread-out eating patterns include postprandial metabolic hormones such as INS, GCG, PYY and PPY, along with other feeding-related hormones such as LEP (Fig. 1f and Extended Data Fig. 1b). We also detected diurnal rhythmicity of proteins involved in a broad range of functions, including blood coagulation (Coagulation factor Xa, IX, IXab, X, CRP), the complement system (Complement C2, C4b, C5, factor D and CD55/DAF), molecules and lipids transport (ALB, APOB, APOD) and protease inhibitors (SERPIN A5, C1, E1; Fig. 1f and Extended Data Fig. 1b,c). The TGF-β family also exhibited rhythmicity, peaking between 16:00 h and 20:00 h under the meal-fed pattern (Extended Data Fig. 1c). Nevertheless, a few plasma proteins such as CTSH and IGFBP1 maintained their rhythmicity even under the spread-out eating pattern (Extended Data Fig. 1b). To corroborate this observation, we reanalysed data from a previously published crossover design study^16^ that compared healthy participants under circadian alignment (daytime food intake, nighttime sleep) with circadian misalignment (daytime sleep, nighttime food intake). Consistent with our findings, rhythmicity in the plasma proteome was markedly reduced with misaligned eating (Extended Data Fig. 1d,e).
We next identified proteins derived from specific organs to assess the contribution of individual organs to the rhythmic plasma proteome. The liver, the primary source of circulating proteins, accounted for most organ-specific rhythmic plasma proteins. Specifically, 50% of liver-specific proteins exhibited rhythmicity with acrophases around 17:00 h (Fig. 1g and Extended Data Fig. 1f). However, rhythmic secretion was not exclusive to the liver, as rhythmic organ-specific proteins were also detected from other organs contributing to the diurnal plasma proteome (Fig. 1g,h).
Feeding paradigms shape the diurnal patterns of circulating proteins in mice
To further investigate the role of timed food intake, we next used a mouse TRF model (Fig. 1i). Wild-type (WT) mice were maintained under a 12 h light–dark cycle and subjected to normal chow diet under different feeding paradigms, either with unrestricted food access (ad libitum; AL) or 8 h TRF, including an activity-aligned night-restricted (NR; food access between Zeitgeber time (ZT) 14 and 22, with ZT0 as lights on and ZT12 as lights off), and an activity-misaligned day-restricted (DR; food access between ZT2 and ZT10) feeding. After 2 weeks, metabolic parameters were assessed, and serum samples were collected every 4 h across the day for the aptamer-based proteomic analysis, described above, yielding strong correlations between biological replicates (Extended Data Fig. 1g,h). Comparable with previous reports using a 6–12 h NR feeding window^17–19^, animals exposed to TRF consumed less food (−11% to −16%) and exhibited a reduced body weight change (−55% to −61%) compared with AL-fed mice (Fig. 1j and Extended Data Fig. 1i). Differential rhythmicity analysis of Dbp, Bmal1 and Gys2 in mouse liver confirmed feeding rhythm-dependent entrainment of clock and clock-regulated genes (Extended Data Fig. 1j), consistent with previous reports^19^. Furthermore, the temporal profiles of INS and GCG were aligned with the expected timing of food intake (Fig. 1k).
Analysis of the circulating proteome with the R package dryR^20^ uncovered that 99% (200 out of 202) of rhythmic serum proteins are differentially affected by changes in the feeding regimen (Fig. 1l,m and Supplementary Table 2). Surprisingly, almost all serum proteins that are rhythmic under AL feeding lost their rhythmicity under TRF, independent of whether it was NR or DR feeding, with an acrophase at around ZT6 (rhythmic only in AL feeding (m2); Fig. 1n,o), as exemplified with VEGFC (Fig. 1p). Additionally, 37 serum proteins lost their 24 h rhythm exclusively under DR feeding (rhythmic only in AL and NR feeding (m7); Fig. 1n,o), with fibronectin (FN1) as a notable example (Fig. 1p). Altogether, these findings underscore the pivotal role of food intake in shaping the diurnal blood proteome.
Classical protein secretion is the primary driver of diurnal rhythms in circulating liver blood proteins
Next, we applied an integrated predictive framework that categorizes proteins based on their secretion route or identifies them as non-secreted^21^, revealing that the majority of rhythmically circulating human proteins are classically secreted proteins. Specifically, 59% contained an amino-terminal ‘signal peptide’ (Fig. 2a,b), indicative of classical secretion^13^, while 14% were predicted to be secreted by unconventional protein secretion (Fig. 2a,b), an alternative pathway of protein secretion^22^. This enrichment was even more pronounced among rhythmically circulating liver-derived proteins, with nearly all (39 out of 40) categorized as classically secreted (Fig. 2a,c). A similar distribution (Extended Data Fig. 2a) was observed using an alternative secretory protein annotation^23^, and consistent results were also found in AL-fed mice, in which all liver-derived proteins were categorized as classically secreted (Fig. 2d–f).Fig. 2. Hepatic protein secretion underlies diurnal rhythms in circulating protein levels.a–c, Bioinformatic prediction of rhythmic human plasma proteins containing either an N-terminal signal peptide (indicative of classical secretion; purple), a transmembrane domain (associated with extracellular or membrane localization; blue), no identified N-terminal signal peptide (characteristic of unconventionally secreted proteins (UPS); green) or belonging to the category primarily intracellular (yellow). Type-composition of all rhythmic circulating and liver-derived proteins per category (a) (icon created with BioRender.com), acrophase distribution of all detected rhythmic circulating proteins (b) and liver-derived circulating proteins (c). d–f, Equivalent analysis for WT mouse serum under AL. Type-composition of all rhythmic circulating and liver-derived proteins per category (d) (icon created with BioRender.com), acrophase distribution of all detected rhythmic circulating proteins (e) and liver-derived circulating proteins (f). g, Experimental design of liver ex vivo secretion assay (created with BioRender.com). Protein secretion was measured from WT mouse livers under AL feeding at ZT0 and ZT12. h, Secreted total protein in the medium (n = 4 biological replicates). i, ELISA quantification of secreted ALB and C3 in the medium (n = 4 biological replicates). j, Secreted total protein from liver collected at ZT3 in the medium in the absence (control, CTR) and presence of brefeldin A (BFA) (n = 5 biological replicates). k, ELISA quantification of secreted ALB and C3 in the medium in the absence and presence of BFA (n = 5 biological replicates). Data are displayed as means; error bars, s.e.m. Tukey boxplots show the median and interquartile range, whiskers extend to the most extreme values within 1.5× the interquartile range and outliers are shown as individual points. A detailed description of the statistical analysis is available in Source Data Fig. 2. See also Extended Data Fig. 2 for related data.Source data
Our findings were further corroborated by ex vivo secretion assays using livers from AL-fed WT mice (Fig. 2g), which demonstrated that the secretion of total liver protein (Fig. 2h), as well as of ALB and C3 (Fig. 2i), is higher at ZT0 than at ZT12, confirming that liver protein secretion is time-of-day-dependent. Notably, treatment with brefeldin A, an inhibitor of the classical ER–Golgi dependent secretory pathway^24^, significantly reduced the secretion of these proteins (Fig. 2j,k). By contrast, we did not detect any rhythmic variation in circulating hepatocellular leakage markers such as ALT (official protein name GPT) and AST (official protein name GOT1) in either the human or mouse blood proteomics datasets (Supplementary Tables 1 and 2 and Extended Data Fig. 2b). Furthermore, we observed no significant time-of-day differences in the degradation of circulating proteins (that is, ALB, SERPINA1; Extended Data Fig. 2c–i), thereby excluding hepatocellular leakage or protein catabolism processes as major contributors to the observed rhythmicity in blood proteins. Taken together, these findings suggest that the rhythmicity of liver-derived circulating proteins arises primarily from secretion processes, with classical secretion representing the predominant route.
Diurnal rhythms occur in proteins of the early secretory pathway of mouse liver
We next evaluated the rhythmicity of secreted proteins as defined by UniProt^25^ at various gene expression stages, including transcription, translation and protein accumulation, using RNA sequencing (RNA-seq), ribosomal profiling^26^ and total proteomics^11^ (Extended Data Fig. 3a). This analysis showed that secreted proteins predominantly exhibited rhythmicity at the protein level in mouse liver (Extended Data Fig. 3b). By contrast, non-secreted proteins were more likely to exhibit rhythmicity at the messenger RNA (mRNA) and translational levels (Extended Data Fig. 3b), consistent with previous observations that most rhythmic mRNA accumulation and translation mainly result in non-rhythmic protein levels owing to the generally higher stability of proteins^11,26^. Altogether, these results identify post-translational mechanisms as the primary drivers of hepatic diurnal protein secretion.
We then performed a proteomics study on liver microsomes, which originate from fragmented cell membranes, to assess diurnal rhythms in the secretory pathway. Livers of WT mice were sampled every 3 h for two consecutive days (Fig. 3a and Extended Data Fig. 3c), and relative protein abundance for each of the 16 samples was quantified against a common labelled reference sample using the stable isotope labelling by amino acids (SILAC) method as previously described^11,27^. We qualitatively detected 4,220 proteins in the microsomal fraction across all samples (Extended Data Fig. 3d and Supplementary Table 3), with a strong correlation between biological replicates (Extended Data Fig. 3e). Consistent with the microsome fraction, detected proteins were associated with a variety of membranous organelles (Extended Data Fig. 3f) with a comparable coverage (Extended Data Fig. 3g), excluding technical bias. Of all detected proteins, 249 (6%, false discovery rate of 10%) were identified as rhythmic with a 24 h period (Fig. 3b,c and Extended Data Fig. 3h) and displayed a biphasic distribution (Fig. 3d,e). This included the SNARE protein STX4 and the vesicle trafficking regulator ARFGAP1, which were arbitrarily selected for western blotting validation (Fig. 3f). Notably, rhythmic mRNA levels were poor predictors of rhythmic microsomal protein abundance (Extended Data Fig. 3i), further supporting the involvement of post-translational mechanisms. In addition, we observed a significant overlap of liver-derived circulating proteins in humans that were rhythmically detected in the liver microsomal or total extract fractions (P = 7.45 × 10⁻³, one-tailed hypergeometric test), including ALB and SERPINC1. Enrichment analysis of rhythmic proteins in microsomes revealed a significant presence of proteins associated with the ER and GA, as well as proteins from the plasma membrane, which are key components of the secretory pathway (Extended Data Fig. 3j). Although protein detection coverage was consistent across different stages of the secretory pathway (Extended Data Fig. 3k), rhythmic enrichment was specifically pronounced in the early steps, namely Golgi processing, protein folding and ER glycosylation, with a trend for enrichment detected for ERAD (Fig. 3g and Supplementary Table 3).Fig. 3. Diurnal rhythms in microsomal proteins in mouse liver indicate temporal compartmentalization of the early secretory pathway steps.a, Experimental design (created with BioRender.com). WT mice were maintained under a 12 h light–dark cycle and exposed to NR feeding. Liver tissue was collected every 3 h over two consecutive days (n = 16 (8 timepoints × 2 biological replicates)). b, Heatmap representation of the rhythmic microsomal proteome (adjusted P < 0.1) analysed across two independent days (day 1 and day 2). Data are row-standardized. c, Number of rhythmic microsomal liver proteins as a function of minimal amplitude. The colour code represents different false discovery rate (FDR) values. d, Phase distribution of rhythmic microsomal liver proteins. The colour code represents different FDR values. e, Scatterplot showing the relationship between acrophase and amplitude of rhythmic microsomal proteins. f, Examples of two rhythmic microsomal proteins confirmed by western blot: STX4 and ARFGAP1 (top). Quantification of western blot analysis (bottom). LC, loading control. MS: n = 16, 8 timepoints × 2 biological replicates; WB: n = 23, 8 timepoints × 3 biological replicates, except ZT21 (two biological replicates). g, Gene set enrichment analysis of rhythmic proteins in the secretory pathway. Adjusted P = 0.05, indicated by a dashed line. h, Phase distribution of microsomal rhythmic proteins associated with the ER and GA (statistical difference in phase evaluated by Kolmogorov–Smirnov test, P = 2.2 × 10⁻¹⁶). i, Scatterplot showing the relationship between acrophase and amplitude of rhythmic ER-associated (left) and GA-associated (right) proteins. j, Representative images (left) and quantitative analysis (right) of electron microscopy imaging of mouse livers at one timepoint during the day (ZT4) and night (ZT16) under NR feeding (n = 4 biological replicates with GA ZT16 (n = 3). ER and GA are highlighted in orange and blue, respectively. N, nucleus; mt, mitochondria. Each colour in the graphs represents an independent biological replicate. Several independent liver areas per biological replicate were analysed. k, Heatmap representation of rhythmic proteins involved in protein glycosylation, categorized by function. Data are row-standardized. l, Scatterplot showing the relationship between acrophase and amplitude of N-glycans on the indicated protein. Non-complex (oligomannose) and complex glycosylation modifications. m, Phase difference between rhythmic oligomannose (top) and complex (bottom) N-glycans in mouse liver (two-tailed Kolmogorov–Smirnov test for the difference in phase, P = 0.011). n, Temporal profiles of the microsomal proteins and N-glycans at indicated sites of RAB2 and H2-K1. Temporal profiles of microsomal proteins, oligomannose and complex N-glycans at indicated positions (n = 16 (8 timepoints × 2 biological replicates)). Data are displayed as means; error bars, s.e.m. Tukey boxplots show the median and interquartile range, whiskers extend to the most extreme values within 1.5× the interquartile range and outliers are shown as individual points. A detailed description of the statistical analysis is available in Source Data Fig. 3. See also Extended Data Fig. 3 and Supplementary Tables 3 and 4 for related data.Source data
Temporal dynamics of ER and GA correlate with diurnal protein N-glycosylation
Our studies revealed an anti-phasic pattern in the diurnal abundance of ER and GA-specific proteins in mouse liver microsomes. Specifically, ER-resident proteins such as the chaperones CALR and HSPA5/BiP reached their peak abundance during the light phase (ZT3), while GA-resident proteins such as the Golgi matrix proteins giantin/GOLGB1 and GM130/GOLGA2 peaked during the dark phase (ZT18; Fig. 3h,i and Extended Data Fig. 3l). Although not fully understood, ER-resident and GA-resident proteins have been suggested to have a role in the generation and maintenance of organelle structure and organization, which may be crucial for organelle function^28,29^. This prompted us to investigate the question of whether ER and GA abundance is subject to diurnal regulation. Using electron microscopy, we examined mouse liver hepatocytes collected at ZT4 and ZT16 to assess ER and GA volume density. Consistent with our proteomics data, we observed an anti-phasic variation in ER and GA volumes (Fig. 3j). We also measured the density of mitochondria, which was higher at ZT16, in line with previous studies^30^.
Rhythmic hepatic microsomal proteins were particularly enriched for functions related to protein glycosylation (Fig. 3k and Extended Data Fig. 3m), suggesting that protein glycosylation may be a diurnally regulated process. Although our proteomics approach was not designed to enrich glycosylated proteins, we identified and quantified the mass of detectable glycosylated peptides (Supplementary Table 4) and found that N-glycosylation (Fig. 3l), similar to O-glycosylation^31^, exhibits a diurnal pattern, and its temporal profile aligns with the day–night variations in ER and GA protein abundance (Fig. 3h,i). Specifically, peptides with oligomannose N-glycosylation, which is initially transferred to proteins primarily processed in the ER, peaked during the light phase (ZT9; Fig. 3l–n and Extended Data Fig. 3n). By contrast, peptides with a complex type of N-linked glycosylation processed in the GA peaked some hours later (Fig. 3l–n and Extended Data Fig. 3n). In summary, these findings demonstrate that the secretory pathway in the liver is not a constitutive but a temporally dynamic process.
Bmal1 deletion impairs glycogen metabolism and alters hepatic protein secretion
N-glycosylation is crucial for the correct folding of secretory proteins^13,14^ and, in this way, can impact protein secretion (Fig. 4a). Based on our observation that protein N-glycosylation is rhythmic (Fig. 3k–n), we reasoned that diurnal liver protein secretion may be driven by fluctuations in the availability of glycosylation substrates, particularly uridine diphosphate (UDP)-sugars, which are essential precursors for N-glycoprotein synthesis^32^. UDP-sugars are synthesized from glucose-1-phosphate, resulting from glucose or glycogen metabolism (Fig. 4a). Notably, both glucose and glycogen levels in mouse liver exhibit rhythmicity, peaking around ZT0 (Fig. 4b), with glycogen showing a higher amplitude than glucose (log_2_(fold change) = 2.8 vs 0.7). Strikingly, UDP-sugars, specifically UDP-glucose and UDP-galactose, showed pronounced rhythmicity (Fig. 4c), with markedly higher amplitudes (log_2_(fold change) = 7.2 and 5.0, respectively) and a peak in anti-phase with glucose and glycogen. Given that mice primarily feed during the dark phase under AL feeding, this result strongly suggests that UDP-sugars are derived from glycogenolysis rather than from dietary carbohydrates. In line with this interpretation, rate-limiting enzymes involved in glycogen synthesis and glycogenolysis show strong rhythmic expression (Fig. 4d), consistent with the rhythm of UDP-sugars.Fig. 4Bmal1 deficiency impacts hepatic glycogen metabolism and protein secretion in addition to circadian disruption.a, Simplified schematic of hepatic glycogen metabolism as a potential upstream regulator of protein glycosylation and secretion. Glycogen synthase 2 (GYS2) and glycogen phosphorylase L (PYGL) are the rate-limiting enzymes. GBE1, 1,4-alpha-glucan branching enzyme 1; UGP2, UDP-glucose pyrophosphorylase 2; GAA, alpha-glucosidase; STDBD1, starch-binding domain-containing protein 1; GABARAPL1, gamma-aminobutyric acid receptor-associated protein-like 1; AGL, amylo-alpha-1,6-glucosidase and 4-alpha-glucanotransferase; UDPG, UDP-glucose; G6P, glucose-6-phosphate; PPP, pentose phosphate pathway. b, Temporal profiles of hepatic glycogen (n = 11 (6 timepoints × 2 replicates, with ZT4 n = 1)) and glucose under AL feeding in WT mice (raw glucose data sourced from prior work^100^, n = 12 (6 timepoints × 2 replicates)). c, Temporal profile of hepatic UDP-glucose and UDP-galactose under AL feeding in WT mice (raw UDP-sugar data sourced from prior work^100^) n = 12 (6 timepoints × 2 replicates). d, Glycogen metabolism-associated enzymes in the microsomal fraction oscillate in abundance in NR-fed WT mice (n = 16 (8 timepoints × 2 biological replicates)). e, Experimental design (created with BioRender.com). Cry1/2-KO and *Bmal1-*KO mice were maintained under a 12 h light–dark cycle and exposed to NR feeding. Liver tissue was collected every 6 h over two consecutive days. f, Pearson correlation analysis of secretory proteins in liver microsomes between Cry1/2-KO profiles (left) and Bmal1-KO mice (right) and their respective WTs (n = 4 biological replicates per genotype). g, Examples of three diurnal microsomal proteins (ALB, C3 and SERPINA1D) showing higher correlation between Cry1/2-KO and Bmal1-KO mice and their respective WT counterparts (Cry1/2 WT: n = 16 (8 timepoints × 2 biological replicates); Cry1/2-KO, Bmal1 WT and Bmal1-KO mice: n = 4 (4 timepoints × 1 biological replicate)). h, Changes in overall abundance of blood proteins in Bmal1-KO mice presented as a volcano plot. i, Scatterplot of differences (log_2_FC) between Bmal1-KO and WT mice in blood and liver total extracts (TE; left) and liver microsomes (right). j, Temporal profiles of serum levels of FN1 and C3 in Bmal1-KO and WT mice determined by ELISA (FN1: Bmal1 WT mice, n = 16 (6 timepoints × 3 biological replicates, except ZT0 and ZT20 with two biological replicates); Bmal1-KO mice, n = 12 (6 timepoints × 2 biological replicates). C3: Bmal1 WT mice, n = 17 (6 timepoints × 3 biological replicates, except ZT20 with two biological replicates); Bmal1-KO mice, n = 12 (6 timepoints × 2 biological replicates)). k, Temporal gene expression pattern of Alb in the liver of Bmal1-KO and WT mice. Sourced from prior work^20^ (n = 12 (6 timepoints × 2 biological replicates)). Data are shown as means; error bars, s.e.m. Tukey boxplots show the median and interquartile range, whiskers extend to the most extreme values within 1.5× the interquartile range and outliers are shown as individual points. A detailed description of the statistical analysis is available in Source Data Fig. 4. See also Extended Data Fig. 4 and Supplementary Tables 3 and 5 for related data.Source data
Given the critical role of the circadian clock in glucose metabolism^33^, we next investigated its contribution to rhythmic liver glycogen metabolism using two clock-disrupted mouse models, Bmal1-knockout (KO) and Cry1/2-KO mice, which are deficient in the transcriptional activator and repressor complexes of the circadian clock, respectively^20^. The circadian clock component CLOCK, the heterodimerization partner of BMAL1, has been shown to directly regulate the transcription of Gys2 (ref. ^34^), the rate-limiting enzyme of glycogen synthesis (Fig. 4a). Our data showed that BMAL1 regulates the rhythmic transcription of multiple enzymes involved in glycogen metabolism, and that Bmal1-KO mice exhibit distinct expression patterns of these enzymes compared to Cry1/2-KO mice (Extended Data Fig. 4a–c). For example, mean expression levels of Gys2 were reduced by 2.6-fold in Bmal1-KO mice, whereas no significant reduction was observed in Cry1/2-KO mice (Extended Data Fig. 4a–c). By contrast, the mean expression levels of Pygl were significantly elevated and non-rhythmic in Bmal1-KO mice but were less affected in Cry1/2-KO mice (Extended Data Fig. 4a–c). Consistent with these findings, the diurnal rhythmic patterns of hepatic glycogen levels observed in AL-fed WT mice were attenuated in Bmal1-KO mice but remained intact in Cry1/2-KO mice (Extended Data Fig. 4d), probably because of a rhythmic flux of glucose into glycogen through dietary carbohydrate uptake. These results highlight that although both Bmal1-KO mice and Cry1/2-KO mice exhibit disrupted circadian clocks, only Bmal1-KO mice show impairments in glucose storage and present a unique metabolic phenotype in addition to circadian function^35^.
To investigate these findings in the context of hepatic protein secretion, we examined microsomal liver fractions from Cry1/2-KO mice and Bmal1-KO mice across the day (Fig. 4e). The mice were maintained on a 12 h light–dark cycle with 12 h NR feeding to avoid confounding effects of altered feeding behaviour^20^. Comparative analysis of temporal profiles of the expression of proteins involved in the different aspects of the secretory pathway between Cry1/2-KO animals and their WT counterparts revealed a strong correlation (Fig. 4f). This finding was further confirmed by a correlated rhythmic accumulation of secreted proteins such as ALB, C3 and alpha-1-antitrypsin (SERPINA1D) between Cry1/2-KO and WT mice (Fig. 4g), extending previous work^11^. By contrast, Bmal1-KO mice showed either no correlation or a negative correlation with their WT counterparts (Fig. 4f,g), altogether providing evidence that perturbed rhythms in secretory proteins are Bmal1-KO-specific. We next analysed serum proteins of Bmal1-KO mice and found that the levels of liver-derived blood proteins, including ALB, FN1 and C3, were altered in both the serum and liver of Bmal1-KO mice (Fig. 4h,i and Supplementary Table 5). The reduced circulating protein levels of FN1 and C3 were confirmed using ELISA (Fig. 4j). Notably, the changes observed for mouse liver ALB were also evident at the gene expression level (Fig. 4k), with Alb being a target of the CLOCK/BMAL1-regulated transcription factor DBP^36,37^. Taken together, these findings point to the BMAL1-regulated hepatic glycogen metabolism as a potential driver of rhythmic protein secretion.
Hepatic glycogenolysis is required for protein N-glycosylation and protein secretion
We aimed to mechanistically test whether hepatic glycogen is a driver of protein glycosylation and secretion. To this end, we pharmacologically targeted liver glycogenolysis in WT mice by injecting the PYGL inhibitor CP-91149 (ref. ^38^) at ZT0, when glycogen levels are at their peak (Fig. 5a). CP-91149 treatment had no effects on food intake (Fig. 5b) or body weight in these animals (Extended Data Fig. 5a) but increased overall levels of hepatic glycogen in the liver (Fig. 5c), which coincided with decreased blood glucose levels 12 h after injection (Extended Data Fig. 5b). Given that the mice were under a 12 h NR feeding regimen and did not feed during this period, this observation confirms effective inhibition of glycogenolysis. By contrast, the rhythmicity of hepatic glycogen levels was maintained upon CP-91149 treatment (Fig. 5c), suggesting a PYGL-independent mechanism driving diurnal glycogen levels, such as glycophagy, a glycogen-specific autophagy process^39^. Consistently, several key enzymes of this pathway are rhythmic in mouse liver microsomes, in anti-phase to hepatic glycogen levels (Extended Data Fig. 5c,d). Of note, glycophagy generates free glucose^39,40^ and therefore, in contrast to PYGL-mediated glycogenolysis (Fig. 4a), does not directly contribute to the hepatic glucose-1-phosphate pool used to synthesize UDP-glucose.Fig. 5PYGL-dependent glycogenolysis modulates glycosylation and protein secretion.a, Experimental design (created with BioRender.com). Inhibition of PYGL-mediated glycogenolysis by CP-91149 in vivo. WT mice under a regular 12 h light–dark cycle and NR feeding were subcutaneously injected with either vehicle (Veh) or CP-91149 at ZT0 for tissue collections at ZT3, ZT6, ZT9, ZT12 and ZT24. b, Average food consumption of WT mice (n = 4 biological replicates) 24 h after CP-91149 injection. c, Temporal profiles of glycogen in the liver of Veh-injected and CP-91149-injected mice (n = 20 (5 timepoints × 4 biological replicates)). d, Glycosylation levels of proteins in CP-91149-treated and vehicle-treated mouse liver were determined by lectin blot analysis with concanavalin A (ConA, n = 20 (5 timepoints × 4 biological replicates)). Amido black staining of the membranes was used as a loading control and serves as a reference for normalization of the quantified values (right). e, C3 levels in mouse serum as assessed by ELISA (n = 20 (5 timepoints × 4 biological replicates)). f, Experimental design (created with BioRender.com). Inhibition of PYGL-mediated glycogenolysis by CP-91149 in AML12 mouse hepatocytes. Treatment with Veh or CP-91149 (67 µM) was performed for 3, 6, 12 and 24 h. g,h, Kinetic profiles of UDP-glucose + UDP-galactose levels (g), cytidine 5′-monophospho-N-acetyl neuraminic acid (CMP-Neu5Ac) (h, left) and uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) (h, right) in AML12 cells upon CP-91149 treatment (n = 16 (4 timepoints × 4 biological replicates)). i, Glycosylation levels of proteins in CP-91149-treated and vehicle-treated AML12 cells were determined by lectin blot analysis with ConA (n = 16 (4 timepoints × 4 biological replicates)). Amido black staining of the membranes was used as a loading control and serves as a reference for normalization of the quantified values (bottom). j, ALB, FN1 and C3 levels in cell medium determined by ELISA (n = 22–24 (4 timepoints × 6 biological replicates, with ALB 14 h Veh and 24 h CP-91149 n = 5)). k, Experimental design (created with BioRender.com). AML12 cells were treated with Veh or CP-91149 for 14 h in the absence or presence of supplemental UDP-glucose (UDPG; 2 mM). l,m, ALB and C3 levels in cell medium (l) and lysates (m) as determined by ELISA (n = 6 biological replicates, except for ALB under CP-91149 treatment (n = 5)). Data are displayed as means; error bars, s.e.m. Boxplots show the median (centre line), interquartile range (box) and minimum to maximum values (whiskers). A detailed description of the statistical analysis is available in Source Data Fig. 5. See also Extended Data Fig. 5.Source data
Subsequent analysis of N-glycoproteins with terminal α-D-mannosyl and α-D-glucosyl groups in mouse liver revealed a transient decrease in protein N-glycosylation upon CP-91149 treatment (Fig. 5d). In line with these changes, circulating C3 levels were altered (Fig. 5e), altogether confirming a role of PYGL-mediated glycogenolysis for protein N-glycosylation and secretion. To further study the downstream effects of CP-91149, we performed RNA-seq analysis on treated mouse livers and identified 130 differentially expressed genes at 6 h post treatment, which decreased to only nine differentially expressed genes after 24 h (Supplementary Table 6). A gene ontology enrichment analysis revealed that drug detoxification pathways were upregulated in the liver as early as 6 h following CP-91149 treatment (Extended Data Fig. 5e), suggesting an induction of drug metabolizing enzymes and, consequently, a rapid breakdown of CP-91449 and reduction of its effects.
To circumvent potential compensatory effects in vivo, we extended our investigations to AML12 mouse hepatocytes (Fig. 5f). CP-91149 treatment did not cause adverse effects (Extended Data Fig. 5f,g), but effectively reduced UDP-glucose + UDP-galactose levels within 3 h (Fig. 5g) and altered other glycosylation substrates (Fig. 5h) and downstream metabolites of glucose or glycogen metabolism (Extended Data Fig. 5h). Additionally, CP-91149 treatment resulted in a rapid decreased phosphorylation of ribosomal protein S6 (RPS6-P; Extended Data Fig. 5i), a target of the glucose-sensing mTOR pathway^41^, which further supports effective PYGL inhibition. Similar to in vivo, CP-91149 treatment significantly decreased the abundance of N-glycoproteins in AML12 cells (Fig. 5i). It also impaired the secretion of ALB (a non-glycosylated protein), as well as FN1 and C3 (both glycosylated proteins), into the culture medium (Fig. 5j). Altogether, these results confirm that PYGL-mediated hepatic glycogenolysis is required for the synthesis of glycosylation substrates and, subsequently, protein N-glycosylation and secretion. To confirm the role of UDP-glucose in the mechanism, we added exogenous UDP-glucose in addition to CP-91149 (Fig. 5k). Our observation that UDP-glucose supplementation only restored C3 secretion (Fig. 5l) and partially rescued intracellular C3 levels (Fig. 5m), while ALB secretion remained suppressed by CP-91149 treatment (Fig. 5l,m), underscores the differential dependence of glycosylated versus non-glycosylated proteins on UDP-glucose for liver protein secretion and suggests that additional mechanisms are involved in the regulation of the secretion of non-glycosylated proteins.
Hepatic glycogenolysis modulates ER stress, the UPR and the cellular abundance of secretory proteins
To gain additional insights into PYGL-mediated hepatic protein secretion, we performed RNA-seq analysis on CP-91149-treated AML12 cells (Supplementary Table 6). In line with the observed changes in protein N-glycosylation (Fig. 5i) and reflecting the link between impaired protein N-glycosylation and the induction of ER stress^42,43^, ER stress-responsive genes were upregulated in AML12 cells following PYGL inhibition (Extended Data Fig. 6a). Specifically, within 3–6 h, CP-91149 treatment increased the mRNA expression of all three major transcriptional regulators of the UPR (Fig. 6a), namely Atf4, Atf6 (Fig. 6b) and spliced Xbp1 (Fig. 6c; total Xbp1 in Extended Data Fig. 6b), as well as the UPR downstream target Ddit3/Chop (Fig. 6d). This was also confirmed at the protein level for ATF4 (Extended Data Fig. 6c). Additionally, CP-91149 treatment upregulated ATF4 target genes (Fig. 6e) but showed enrichment for downregulation or no clear direction for ATF6 and XBP1 target genes (Extended Data Fig. 6d), suggesting that inhibition of PYGL-mediated glycogenolysis may primarily activate the ATF4 pathway. Consistent with our in vitro findings, activation of the ATF4 and XBP1 pathways was also observed in mouse liver 6 h post treatment (Fig. 6f and Extended Data Fig. 6e). However, this response was diminished by 24 h (Extended Data Fig. 6f), probably because of the transient in vivo activity of the compound (Extended Data Fig. 5e) and/or resolution of ER stress by that time. Further reflecting the activation of ER stress and the UPR, CP-91149-treated AML12 cells showed a global upregulation of ERAD-related gene expression (Fig. 6g), suggesting enhanced protein degradation, which was further supported by our observations that CP-91149 treatment reduced the abundance of ALB, FN1 and C3 proteins in cell lysates (Fig. 6h). Moreover, UDP-glucose treatment of AML12 cells (Extended Data Fig. 6g) induced opposing effects to glycogenolysis inhibition by increasing C3 levels in both cell medium and lysate (Extended Data Fig. 6h,i) while decreasing expression of ER stress sensors Atf4 and Ddit3/Chop (Extended Data Fig. 6j). FN1 protein levels (Extended Data Fig. 6i) remained unchanged with a tendency for reduced secretion following UDP-glucose treatment, suggesting the involvement of additional regulatory mechanisms of hepatic protein secretion. Collectively, these results suggest that PYGL-mediated glycogenolysis involves ER stress and UPR-related mechanisms for the modulation of protein secretion.Fig. 6PYGL-dependent glycogenolysis modulates ER stress-mediated UPR and ERAD pathways.a, Schematic representation of the three signalling pathways of ER stress and the UPR and its transcriptional regulators. The activation of ER stress through perturbed protein glycosylation and folding is well established^14^. b, Gene expression of activating transcription factor 4 (Atf4) and 6 (Atf6) in AML12 cells determined by BRB-seq (n = 23–24 (4 timepoints × 6 biological replicates), except for 14 h Veh (n = 5)). c, Gene expression measured by reverse transcription-quantitative PCR (RT–qPCR) of the spliced X-box binding protein 1 mRNA (Xbp1 spliced) in AML12 cells (n = 23–24 (4 timepoints × 6 biological replicates), except for 14 h Veh (n = 5)). d, Gene expression of Ddit3/Chop transcript in AML12 cells determined by BRB-seq (n = 23–24 (4 timepoints × 6 biological replicates), except for 14 h Veh (n = 5)). e, Barcode plots depicting the upregulation of ATF4 downstream target genes in AML12 cells upon CP-91149 treatment. Genes are ordered by t-statistics from the most upregulated to the most downregulated gene upon CP-91149 treatment. f, Barcode plots depicting the upregulation of ATF4 downstream target genes in mouse liver following CP-91149 treatment at 6 h post CP-91149 injection. Genes are ordered by t-statistics, from the most upregulated to the most downregulated. g, Heatmap showing transcript levels of differentially expressed genes with a role in the ERAD in vehicle-treated and CP-91149-treated cells. Data are row-standardized. h, ALB, FN1 and C3 levels in cell lysates determined by ELISA (n = 22–24 (4 timepoints × 6 biological replicates), except for C3 under 24 h CP-91149 treatment (n = 5, FN1 under 3 h Veh treatment (n = 5) and FN1 under 24 h Veh treatment (n = 4)). i, Experimental design (created with BioRender.com). Pre-treatment of AML12 mouse liver cells with Veh or SAL (15 µM) for 14 h, and then post-wash for an additional 6 h in the absence or presence of CP-91149 (CP; 134 µM). j,k, ALB and C3 levels in cell medium (j) and in cell lysates (k) determined by ELISA (n = 6 biological replicates). l, Gene expression measured by RT–qPCR of ERAD components (Herpud1 and Derl1), ER chaperones (Hspa5/Bip and calreticulin (Calr)) and the glycosylation enzyme alpha-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase (Mgat1; n = 6 biological replicates). Data are displayed as means; error bars, s.e.m. Boxplots are either Tukey boxplots indicating the median (centre line) and interquartile range (box), with whiskers extending to the most extreme values within 1.5× the interquartile range and outliers shown as individual points (b and d) or boxplots showing the median (centre line), interquartile range (box) and minimum to maximum values (whiskers; c, h, j, k and l). A detailed description of the statistical analysis is available in Source Data Fig. 6. See also Extended Data Fig. 6 and Supplementary Table 6 for related data.Source data
Therefore, we aimed to test whether alleviating ER stress would restore protein secretion. To this end, AML12 cells were treated with salubrinal (SAL; Fig. 6i), a selective inhibitor of eIF2α phosphatases that stabilizes phosphorylated eIF2α and modulates the PERK–ATF4 arm of the UPR, thereby mitigating ER stress^44,45^. SAL-treatment markedly increased the secretion of non‑glycosylated ALB (Fig. 6j) and, to a lesser extent, intracellular ALB content (Fig. 6k). CP-91149 treatment in the presence of SAL did not differ from CP-91140 treatment alone, indicating that glycosylation stress overrides eIF2α-mediated ER stress mitigation. For glycosylated C3, both SAL and CP-91149 reduced secretion (Fig. 6j) and intracellular levels (Fig. 6k), with combined treatment resulting in additive inhibition (Fig. 6j,k). Analysis of gene expression further corroborated these observations. As expected, CP‑91149 induced robust upregulation of canonical UPR and ERAD markers (Fig. 6l and Extended Data Fig. 6k), whereas SAL alone selectively increased only Herpud1 (Fig. 6l), an ERAD adaptor that recruits misfolded proteins for degradation, without affecting expression of protein glycosylation-associated genes (Fig. 6l). Notably, Calr and Mgat1 transcripts were downregulated in CP‑91149-treated AML12 cells, both in the absence and presence of SAL. Together, these data support a model in which glycosylation defects elicit ER stress and activate UPR and ERAD pathways that differentially impact the secretion and degradation of glycosylated and non-glycosylated proteins. Although glycosylated proteins such as C3 appear to be directly targeted by ER quality control and degradation mechanisms, non-glycosylated proteins such as ALB are indirectly affected through a global UPR-mediated increase in protein-folding factors, which are known to enhance protein secretion^46,47^.
Attenuation of diurnal rhythms in hepatic glycogen, ER stress and circulating proteins in obesity and caloric restriction
To evaluate the relevance of these findings in the context of metabolic disease and TRF, we extended our mouse studies (Fig. 1i) by analysing the temporal profiles of circulating proteins in leptin-deficient Ob/Ob mice (Fig. 7a), a well-established model for obesity with alterations in hepatic glucose and glycogen metabolism^48,49^. Consistent with their overfeeding and obesity phenotype, Ob/Ob mice exhibited a higher food intake than WT mice but, similar to WT mice, consumed less food (−22% to −29%) (Figs. 1j and 7b) and showed a reduced body weight change (−58% to −60%) under TRF (that is, NR and DR) compared to AL feeding (Extended Data Figs. 1i and 7a). We then applied the same proteomics approach as used for our WT mice to measure circulating proteins (Supplementary Table 2 and Extended Data Fig. 7b). Although INS and GCG were overall higher in Ob/Ob mice (Fig. 7c) than in WT mice (Fig. 1k), consistent with known metabolic changes, we detected an anti-phasic relationship between these hormones under NR and DR feeding that aligned with expected feeding times (Fig. 7c). Differential rhythmicity analysis of Ob/Ob mouse serum proteins showed that, like in WT mice (Fig. 1l,m), the rhythmicity of all circulating proteins was impacted by the feeding regimen (Fig. 7d,e). Notably, however, rhythmicity was overall lower in Ob/Ob mice across all tested feeding regimens compared to AL-fed WT mice (Fig. 7d–f and Extended Data Fig. 7c).Fig. 7Ob/Ob mice and WT mice under TRF exhibit attenuated rhythms in glycogen metabolism, ER stress and secretory proteins.a, Experimental design (created with BioRender.com). Schematic of the three different feeding regimens in obese (Ob/Ob) mice under a 12 h light–dark cycle: AL, NR and DR feeding. Food availability is indicated by blue boxes. (n = 24 per group (6 timepoints × 4 biological replicates)). b, Average food consumption per day and per Ob/Ob mouse under AL, NR and DR feeding (n = 2 biological replicates per group, averaged from four mice per replicate). c, Temporal profiles of insulin and glucagon in Ob/Ob mouse serum under an AL, NR and DR feeding regimen (n = 24 (6 timepoints × 4 biological replicates)). d, Number of proteins classified in rhythmic models in Ob/Ob mouse serum under AL, NR and DR feeding. White indicates no rhythm detected, the same colour indicates shared rhythmic parameters (amplitudes and acrophase) between conditions and different colours indicate different phase and/or amplitudes. A detailed description of the models can be found in Supplementary Table 2. e, Number of rhythmic serum proteins in WT and Ob/Ob mouse serum under AL, NR and DR feeding as a function of minimal amplitude. f, Temporal profiles of VEGFC and FN1 in Ob/Ob mouse blood under AL, NR and DR feeding (n = 24 (6 timepoints × 4 biological replicates)). g, Temporal profiles of hepatic glycogen levels for indicated feeding regimens in WT mice (left, n = 11–12 (6 timepoints × 2 biological replicates, except AL at ZT4 (n = 1)) and Ob/Ob mice (right, n = 24 (6 timepoints × 4 biological replicates)). Glycogen measurements for WT AL are reproduced from Fig. 4b. h, Fitted mesor and trough glycogen levels in the livers of WT and Ob/Ob mice derived from the data shown in g under the indicated feeding regimens. i, Gys2 and Pygl transcript levels in Ob/Ob and WT mouse liver measured by RT–qPCR (n = 24 (6 timepoints × 4 biological replicates)). The WT Gys2 time course data is reused from Extended Data Fig. 1j. j, Hspa5/Bip, Ddit3/Chop, synoviolin 1 (Syvn1/Hrd1), Xbp1 spliced transcript levels in Ob/Ob and WT mouse liver measured by RT–qPCR (n = 23–24 (6 timepoints × 4 biological replicates, except for Xbp1 spliced in WT mice under NR feeding (n = 3)). Data are displayed as means; error bars, s.e.m. A detailed description of the statistical analysis is available in Source Data Fig. 7. See also Extended Data Fig. 7 and Supplementary Table 2 for related data.Source data
Next, we examined hepatic glycogen levels and found that their diurnal rhythms were maintained across all conditions (Fig. 7g), with reverse-phase (DR) feeding causing a 12 h shift in the glycogen peak in both WT and Ob/Ob mice. Nevertheless, Ob/Ob mice exhibited a markedly lower amplitude (Fig. 7g,h and Extended Data Fig. 7d), probably resulting from the strong attenuation of rhythmic Gys2 expression and the increased expression of Pygl observed in these animals (Fig. 7i and Extended Data Fig. 7e). Accordingly, glycogen levels were significantly elevated in Ob/Ob mice, with higher trough levels compared to AL-fed WT mice (Fig. 7g,h), consistent with other reports in genetically obese animals^48,50^. Interestingly, mean glycogen levels were also elevated in WT mice under TRF (Fig. 7h), probably because of the associated caloric restriction (Figs. 1j and 7b), which promotes glucose storage via glycogen synthesis rather than glycogenolysis^51^. The calorie-restriction effect of TRF on mean or trough levels of glycogen was less pronounced in Ob/Ob mice (Fig. 7h), which are metabolically skewed to favour glucose storage. Strikingly, elevated glycogen levels in Ob/Ob mice and WT mice under TRF negatively correlated with the number of rhythmic blood proteins, thus linking reduced glycogenolysis to a loss of rhythmicity in secretory proteins (Fig. 7d,e).
We then investigated ER stress/UPR gene expression, which typically exhibit 12 h ultradian rhythms in AL-fed WT mice^52,53^, a pattern we likewise observed in our study (Fig. 7j and Extended Data Fig. 7f). However, Ob/Ob mice showed overall lower expression levels of several ER stress/UPR markers, and the characteristic expression peaks at ZT0–4 and ZT16 observed under AL feeding were lost (Fig. 7j and Extended Data Fig. 7f). WT mice also lost the ER stress-activation peak at ZT0–4 and ZT16 when exposed to NR and DR feeding, respectively. Notably, the detected loss of ER stress-activation peaks in these mice coincides with troughs in secretory liver protein levels and their ubiquitination (Extended Data Fig. 7g), an observation that, together with our PYGL inhibition studies, supports the involvement of protein degradation processes. Altogether, these findings confirm that impaired glycogen metabolism, whether caused by obesity or calorie restriction, is associated with altered protein secretion and impaired initiation of rhythmic physiological ER stress. Consistently, Bmal1-KO mice, which exhibit defects in both glycogen metabolism and protein secretion (Fig. 4e–k), lack the ER stress-activation peak at ZT0–4, resulting in a single remaining peak at ZT14 (Extended Data Fig. 7h). This provides a mechanistic explanation for the reported loss of 12 h ER stress-activation rhythms and their shift to 24 h rhythms in Bmal1-KO mice^26,52,53^.
Variants in glycogen and glycosylation metabolism-associated disease genes affect circulating blood proteins in humans
Finally, we examined how diurnal hepatic protein secretion relates to glycogen metabolism and protein glycosylation in humans. We focused on GSD, inherited inborn errors of carbohydrate metabolism, and CDG, inherited defects in glycan biosynthesis and metabolism, which are both metabolic disorders with multisystemic pathological effects^54,55^. Given that blood proteomics data from such patients are lacking, we reasoned that genetic variants associated with GSD and CDG in a healthy cohort might induce subclinical changes in glycogen metabolism and protein glycosylation and thus allow us to test whether these genes influence circulating proteins. To this end, we used a recent dataset identifying protein quantitative trait loci (pQTLs), which are regions of the genome associated with variation in circulating protein levels^56^. We identified pQTLs associated with genes related to protein glycosylation and CDG (Fig. 8a), validating our hypothesis that genetic variants in a healthy cohort can be used to test for effects on protein secretion. Similarly, pQTLs associated with glycogen metabolism genes, including those implicated with GSD, significantly impacted circulating protein levels (Fig. 8b and Extended Data Fig. 8a–c). The number of impacted circulating proteins associated with GSD variants was 2.3 times higher than the ones associated with CDG, suggesting that the effect of glycogen deficiency might be broader than that of glycosylation (Fig. 8c). Although only partial, the overlap of circulating proteins affected by glycosylation-associated and glycogen metabolism-associated pQTLs was significant (Fig. 8c and Supplementary Table 7) and remained so even after excluding genes only associated with glycogen metabolism (Extended Data Fig. 8d), supporting the connection between glycogen metabolism and protein glycosylation and secretion in humans. In line with our previous observations, the vast majority of these shared pQTLs map to classically secreted proteins (Extended Data Fig. 8e), and most of the rhythmic proteins among them show oscillations under the meal-fed regimen that are abolished under the spread-out eating pattern (Extended Data Fig. 8f). Furthermore, and consistent with our observation in mice, we confirmed that ultradian rhythms in ER stress activation are also present in the human liver (Extended Data Fig. 8g,h), although they differ from those observed in mice, probably because of species-specific differences in eating/feeding behavior.Fig. 8. Natural genetic variants associated with genes linked to glycogen metabolism, GSD and CDG affect the levels of overlapping blood proteins.a,b, Effect size of target blood protein concentration for significant trans pQTLs. Each trans pQTL is colour-coded according to the annotated gene associated with CDG (a) or GSD (b). Triangles indicate pQTLs with an effect size (beta) that exceeds the maximum limit of the y axis; these values have been projected onto the plot boundary. Test statistics (two-sided, unadjusted) are derived from REGENIE genome-wide association study regression. Data obtained from prior work^56^. c, Venn diagram showing the overlap between affected blood proteins in subjects with significant trans pQTLs linked to genes involved in glycogen metabolism, including those related to GSD and CDG. P value derived from hypergeometric testing. Data are displayed as means; error bars, s.e.m. A detailed description of the statistical analysis for panel c is available in Source Data Fig. 8. See also Extended Data Fig. 8 for related data.Source data
Discussion
Current understanding of rhythmic liver physiology is still limited, and it remains largely unknown how the dysregulation of these processes is associated with pathology. Here, we showed that the early secretory pathway of the liver, a fundamental cellular process involving protein glycosylation in the ER and GA, exhibits 24 h rhythms. Specifically, our studies established that diurnal liver plasma protein secretion is driven by the timing and quantity of food intake and the circadian clock regulator BMAL1. This rhythmic control operates through hepatic glycogenolysis, driving the synthesis of glycosylation precursors and, thus, protein N-glycosylation, the activation of ER stress and the subsequent secretion of both glycosylated and non-glycosylated hepatic proteins. Therefore, contrary to previous assumptions, hepatic protein secretion is a temporally dynamic and actively regulated process. This pathway may extend to other organs, given the fundamental role of physiological ER stress and the UPR in secretory tissues^14^ and our finding that other organ-specific plasma proteins show food timing-dependent rhythmicity in humans.
Our findings establish hepatic glycogen metabolism as a circadian regulator of protein secretion, revealing an additional layer of metabolic control over the timing of protein release. The rhythmic nature of hepatic glycogen levels and their regulation by the feeding–fasting cycle has been well established for decades^57^ and is driven by alternating phases of glycogenesis during the feeding period and glycogenolysis during fasting. This temporal compartmentalization prevents these opposing metabolic processes from occurring simultaneously, thus avoiding inefficient energy production and use, highlighting the importance of rhythmic regulation in liver physiology^58^. We also detected temporal compartmentalization in the liver at the organelle level. Although mitochondria have been previously shown to undergo diurnal regulation^30^, the dynamics of the ER and GA were primarily studied under stress or pathological conditions^29,59^, and little is known about their behaviour under normal physiological conditions. Our findings revealed that ER and GA abundance in hepatocytes is rhythmic and aligns temporally with ER-specific and GA-specific functions, with ER-associated processes preceding those in the GA. This sequential timing may be influenced by rhythmic transcription and translation of GA-associated genes^26,60^. Altogether, our findings suggest that protein secretion, from protein synthesis to N-glycosylation, quality control, ER and GA dynamics and transport, is a sequential process that unfolds over a 24 h period, as similarly reported for ribosome biogenesis^27,61^. The organization of essential, energy-intensive biological processes in a sequential manner highlights another principle of temporal compartmentalization, distributing metabolic load across the 24 h cycle in alignment with resource availability.
However, although glycogen metabolism and protein secretion processes follow a 24 h rhythm, ER stress in mouse liver exhibits a 12 h rhythm, with one peak at the start of the fasting period and a second peak at the start of the feeding period^52,53,62,63^. We found that only the first peak in ER stress activation is linked with rhythms in protein secretion and glycogen metabolism, whereas the activation of the second peak appears to involve other mechanisms. Consistent with this observation, studies in ClockΔ19 mutant mice showed that a brain-specific rescue of Clock restores only the second peak in 12 h gene expression^64^. As the second peak in ER stress activation persists in our Bmal1-KO mice, it is likely that systemic cues, rather than a functional circadian oscillator in the brain, are sufficient for its generation.
Notably, the pathway described here is altered under conditions of obesity, which is a major contributor to various metabolic comorbidities^65^. Specifically, using the Ob/Ob mouse model, we observed that hepatic glycogen rhythms are attenuated in these mice, which we associated with a reduction in the rhythmic secretion of liver proteins and a concurrent failure to fully induce physiological ER stress in the liver. Although not studied in the context of hepatic glycogen levels or protein secretion, a failure to build an ER stress response in the liver may be a causal factor in obesity-related comorbidities^66^, and Xbp1 deletion results in the development of fatty liver disease^67^, a condition linked to altered levels of liver-secreted proteins^68^. In this context, the failure to properly establish an ER stress response in the liver under conditions of obesity could explain the observed perturbations in circulating proteins in both Ob/Ob mice (our data) and obese humans^69^, and in this way may contribute to disease pathology. Strikingly, we also detected a failure in transcriptional ER stress activation at ZT0 in healthy WT mice under TRF, an intervention leading to reduced calorie intake and health benefits^8^. Although the impact of TRF—a type of intermittent fasting—on protein secretion has not yet been studied, every-other-day intermittent fasting alters plasma protein composition^70^, and calorie restriction modifies their N-glycosylation^71^, suggesting that similar processes may occur in humans. Given these findings, it is essential to consider how disrupted rhythms in circulating proteins may influence the effects of TRF and its potential impact on inter-organ communication. Moreover, although clinical bloodwork for metabolism-associated markers is commonly performed under standardized conditions like fasting, our findings suggest that the timing of food and calorie intake may broadly influence plasma protein levels. This highlights the need to consider eating schedules more carefully when interpreting blood protein biomarkers for medical diagnostics.
Furthermore, we showed that genetic variants of genes associated with GSD and CDG alter levels of circulating proteins, with a significant overlap between both pathologies. Clinical data indicate that certain GSD subtypes present abnormal glycosylation^72,73^, further linking glycogen metabolism with protein glycosylation in humans. However, the potential role of protein secretion has not been extensively studied in the context of GSD or CDG. Yet common symptoms of GSD include abnormal glucose homoeostasis, hepatomegaly, hyperlipidemia, growth deficiency and liver fibrosis/cirrhosis^54^, and CDG symptoms feature coagulation, immune and endocrine abnormalities, as well as growth deficiency^55^. All of those symptoms could, at least partly, be explained by alterations in hepatic protein secretion. For example, growth deficiency may result from a reduction of hepatic IGF1 secretion, the primary mediator of growth hormone regulating body growth, while coagulation issues may arise from insufficient secretion of coagulation factors. In addition, altered hepatic protein secretion can lead to hepatomegaly and liver damage^74,75^, a feature also observed in a mouse model of GSD with deletion of Pygl^76^. Future studies involving systematic serum protein analyses in CDG and GSD patients will help clarify the impact of altered protein secretion on similar disease phenotypes and progression.
Our findings demonstrate that rhythmic regulation of the classical secretory pathway is a principal driver of diurnally oscillating plasma proteins; however, other mechanisms, such as organ-specific internalization and catabolism or the rhythmic release of extracellular vesicles^77^, may also contribute and warrant further investigation. Similarly, the low-amplitude rhythms observed in ‘housekeeping’ proteins may result from a continuous, concurrent cycle of synthesis, secretion and degradation, as recently demonstrated at the global proteome level in fibroblasts^78^. Overall, our study underscores that although circadian regulation at the mRNA level is becoming increasingly well characterized, the impact of circadian rhythms at the protein level remains far from fully understood.
Methods
Ethics statement
All experimental studies were conducted in accordance with the regional committee for ethics in the regulations of the veterinary office of the Canton of Vaud, Switzerland (VD2459, VD2801, VD2720), and the University of Queensland Animal Ethics Committee (2021/AE000004, 2022/AE000274). The studies involving human participants were conducted in compliance with the Declaration of Helsinki and received ethical approval from both the Health Research Authority (Cornwall & Plymouth National Health Service Research Ethics Committee; reference 14/SW/0123) and the Commission Cantonale d’Éthique de la Recherche of Canton de Vaud (CER-VD; Protocol 337/12). Participants provided written informed consent.
Experimental model and subject details
For investigating the human diurnal secretome, blood samples were collected from two studies, each study containing nine healthy male volunteers (Fig. 1a,b and Extended Data Fig. 1a). The volunteers had no history of sleep disorder, as confirmed by questionnaires MCTQ^79^ and Horne-Ostberg^80^. For the meal-fed pattern study, nine healthy men aged between 22 and 27 years were recruited. Participants could follow their normal breakfast routine before participating in the study. At 08:00 h, each participant had a catheter installed for blood sampling. Blood samples were collected every 4 h throughout the day. Standardized meals were provided at 12:00 h and 18:00 h, with participants allowed one snack during the day. Throughout the study period, participants were instructed to maintain normal daily activities but were restricted from engaging in sports and consuming alcohol. Lights were turned off at 23:00 h to standardize sleep conditions. The design and metadata on the spread-out eating pattern study were previously published in detail^81^. In brief, nine healthy men, aged 22 to 54 years, arrived at the laboratory at 19:00 h the day before the testing. They remained semi-recumbent throughout the study with a standardized dinner provided the evening before testing. Lights were on from 07:00 h to 22:00 h. Participants were woken at 07:00 h, and a catheter was placed at 08:00 h to collect blood samples. On the day of testing, participants ingested Fortisip (Nutricia) hourly during waking hours (08:00 h to 22:00 h) to maintain energy balance. The amount of Fortisip matched each participant’s daily energy expenditure, which had been assessed upon waking that morning by indirect calorimetry^82^.
Mouse strains
Experiments with WT mice were conducted using C57BL/6J (RRID:IMSR_JAX:000664) purchased from Charles River (L’Arbresle, France) or Ozgene ARC (Australia). The generation of SILAC C57BL/6J mice was previously described^11^. Studies involving clock-depleted animals were carried out using Bmal1-KO and Cry1/2-KO mice, along with their respective WT controls, generated as previously described^83,84^. C57BL/6J Ob/Ob mice were obtained from Charles River (RRID:IMSR_JAX:000632). Unless otherwise noted, all animals were maintained under a standard 12 h light–dark cycle (ZT0, lights on; ZT12, lights off) in regular housing conditions (temperature of 23 ± 1 °C; humidity ranging between 30% and 70%), with free access (AL) to food (sterile chow diet; SAFE, U8213G10R; Specialty Feeds, SF00-100) and water. All mice used in the experiments were adult males, aged 9–15 weeks.
Cell line
AML12 (alpha mouse liver 12) hepatocyte cells from a 3-month-old mouse donor (CD1 strain, line MT42) were purchased from ATCC (CRL-2254). Cells were maintained in an incubator (37 °C, 5% CO_2_) in AML12 base medium (DMEM/F-12 medium with L-glutamine and 15 mM HEPES (Gibco, 11330032), supplemented with 10% FBS, 10 µg ml^−1^ insulin, 5.5 µg ml^−1^ transferrin, 5 ng ml^−1^ selenium and 40 ng ml^−1^ dexamethasone).
Feeding experiments
For TRF experiments, mice were subjected to either 8 h or 12 h NR or DR feeding schedules, whereby food access was limited to the respective day or night periods; food was provided in bulk at the start and removed at the end of each feeding window. In the 12 h NR feeding regimen, food was provided from ZT12 to ZT24, starting 4 days before tissue collection. For the 8 h TRF, food access was available between ZT2 and ZT10 for DR feeding, or between ZT14 and ZT22 for NR feeding, beginning 2 weeks before tissue collection. Organs were snap-frozen in liquid nitrogen and stored at −80 °C until further processing.
PYGL inhibition in vivo
C57BL/6J mice were exposed to 12 h NR feeding for 4 days before receiving a subcutaneous injection (40 mg kg^−1^; volume of 10 µl per g body weight) of CP-91149 (Sigma-Aldrich, PZ0104) or vehicle (5% PEG400/5% Tween80/5% DMSO) at ZT0. Body weight and food intake of mice were measured after 24 h of CP-91149 or vehicle treatment. Blood glucose was measured with the Breeze2 system (Bayer).
Cell viability assays
Cell cytotoxicity was assessed using the LDH-Glo Cytotoxicity Assay according to the manufacturer’s protocol (Promega Corporation, J2380), and cell viability was measured using resazurin (Sigma-Aldrich, R7017) as previously described^85^. Untreated cells and Triton X-100 (TX100) were used as controls.
Cell experiments
For metabolite analysis, AML12 cells (6,500,000 cells per dish) were seeded into sterile 150 mm dishes and incubated overnight at 37 °C. The following day, cells were washed twice with 1× PBS, and the medium was replaced with AML12 base medium containing either 67 μM CP-91149 or vehicle (0.03% DMSO). At 3 h, 6 h, 14 h and 24 h post treatment, cells were washed twice with ice-cold 0.9% NaCl and collected using a cell scraper in 800 µl of the same solution. Cell pellets were collected by centrifugation at 400g for 5 min at 4 °C, weighed to determine wet mass and snap-frozen in liquid nitrogen. For all other cell experiments, AML12 cells (300,000 cells per well) were seeded into sterile six-well plates. Treatments were carried out in AML12 base medium and included the following conditions: 67 μM CP-91149 or 0.03% DMSO as vehicle for 3–24 h; 2 mM UDP-glucose (Merck Life Science, U4625) or 2% water as vehicle for 14 h in the absence or presence of 67 μM CP-91149. For the SAL (Merck Life Science, SML0951) experiments, AML12 cells were first pre-incubated for 14 h with 15 μM SAL and then, following a wash in 1× PBS, were treated for 6 h with 15 μM SAL in the absence or presence of 134 μM CP-91149.
Liver and cell protein extractions
For total protein extraction from mouse livers, 40–100 mg of snap-frozen liver tissue was homogenized in a buffer containing 20 mM HEPES (pH 7.6), 100 mM KCl, 0.1 mM EDTA, 1 mM NaF, 1 mM NaVO_4_, 1% Triton X-100, 0.5% NP-40, 0.15 mM spermine, 0.5 mM spermidine, 1 mM dithiothreitol, 1.6 µM aprotinin, 1 µM pepstatin A, 1.7 µM leupeptin and 0.5 mM PMSF. The homogenates were incubated on ice for 30 min, followed by centrifugation at 16,000g for 10 min to collect the supernatant.
To measure protein in cell culture medium, the media were centrifuged at 10,000g for 10 min at 4 °C to remove debris and collect the supernatant. For cell lysates, cells were first incubated on ice in 200 µl of RIPA lysis buffer supplemented with a protease and phosphatase inhibitor mixture (0.2 tablet cOmplete and one tablet PhosSTOP per 10 ml; Roche) for 15 min. The cells were then scraped from the dish and incubated for an additional 30 min on ice. The lysates were centrifuged at 10,000g for 10 min at 4 °C to collect the supernatant. Protein concentrations in the supernatants were determined using a Bradford assay.
Microsomal protein extraction from mouse liver
For microsomal protein extraction, liver tissue was homogenized in 0.2 M sodium phosphate buffer supplemented with a protease and phosphatase inhibitor mixture (0.2 tablet cOmplete and one tablet PhosSTOP per 10 ml; Roche). The homogenate was clarified by centrifugation at 9,000g for 20 min at 4 °C. Microsomal fractions were recovered from the supernatant by centrifugation at 105,000g for 1 h. The resulting pellet was resuspended in 8 M urea buffer with inhibitors. After a 20 min incubation on ice, the extracts were centrifuged at 20,000g for 10 min to collect the supernatant. Protein concentrations were determined using a BCA protein assay kit.
SILAC-based mass spectrometry analysis of microsomal proteomics
Tandem mass spectrometry (MS/MS)-based SILAC analysis was conducted as previously described^11,27^, with the modification that microsomal proteins were analysed. A common microsomal fraction reference SILAC protein mix was prepared from 16 SILAC protein samples (six male and ten female SILAC livers) collected at ZT0 and ZT12 and added in a ratio of 1:1 to each sample. In brief, protein samples were precipitated with trichloroacetic acid and deoxycholate, redissolved in 8 M urea and digested with trypsin. Peptides were fractionated by off-gel isoelectric focusing, and all 12 fractions of each mix were analysed on a Fusion tribrid mass spectrometer (Thermo Fisher Scientific) on 60 min gradients. MS data were analysed with MaxQuant (v.1.5.1.2)^86^ against the UniProt database (release 2014_10) restricted to mouse (Mus musculus) taxonomy. The same procedure was applied to protein samples from Cry1/2-KO and Bmal1-KO mice using the same SILAC mix as the reference.
Glycopeptide analysis
Glycopeptides in mouse liver microsomal fractions were identified in the Byonic (Protein Metrics v.4.3.4) node of Proteome Discoverer (Thermo Fisher Scientific, v.2.5.0.400) searching against a database of 16,964 mouse proteins (UniProt reviewed, downloaded 20 April 2018) and a database provided by Byonic containing 83 mouse plasma N-glycans. The cleavage enzyme was specified as trypsin with no missed cleavages. Precursor mass tolerance was set to 6 ppm, and fragment mass tolerance was set to 0.5 Da. Modifications were set as follows: carbamidomethyl at cysteine (fixed), SILAC heavy label at lysine (common 1) and all *N-*glycans (rare 1).
Somalogic analysis and blood serology
Human plasma and mouse serum proteins were quantified using the SomaScan assay (v.3.2). SomaScan uses dilution groups in its assays to ensure that the measurement for each individual aptamer remains within the linear range, accommodating the wide range of endogenous protein concentrations present in the blood. The most abundant proteins are found in the highest dilution groups^87^. Samples were processed according to the manufacturer’s protocol at Somalogic, and the resulting relative fluorescence units were normalized and log_2_-transformed for downstream statistical analysis. The SomaScan assay (v.3.2) is optimized for human proteins (1,305 protein targets); thus, we only considered 896 SOMAmers identified as having medium to high quality in mouse serum samples, based on performance in dilution linearity and assay variability in technical replicates. We identified organ-specific human plasma proteins by analysing gene expression patterns using the GTEx project RNA-seq dataset^88^. A plasma protein was classified as organ-specific if its encoding gene was four times more highly expressed in one organ compared to others. Serum AST and ALT activities were measured by an IFCC kinetic UV assay, using Cobas 8000 (Roche).
Non-depleted blood proteomic analysis
Mouse serum protein samples were digested, and peptides were purified using previously established methods^70^. Liquid chromatography–MS/MS analysis of these samples used the scanning SWATH methodology on a Sciex 6600 mass spectrometer with a 5 min gradient as described previously^89^. Raw MS data were analysed using quantitative DIA proteomics software DIA-NN (v.1.8). The complete mouse proteome database from Uniprot was used for neural network generation, with deep spectral prediction enabled. Protease digestion was set to trypsin, allowing for one missed cleavage and one variable modification. Oxidation of Met and acetylation of the protein N-terminus were set as variable modifications. Carbamidomethyl on Cys was set as a fixed modification. Match between runs and remove likely interferences were enabled. The neural network classifier was set to double-pass mode. Protein interferences were based on genes. Quantification strategy was set to any liquid chromatography (high accuracy). Library profiling was set to smart profiling.
Ex vivo protein secretion measurements
For each time point (ZT0 and ZT12), livers were dissected from C57BL/6J mice under AL feeding. Each liver was cut into two ~100 mg pieces, which were individually washed three times with 1× PBS, transferred into 25 cm^2^ tissue flasks (one tissue piece per flask) containing 5 ml CHO medium (Gibco, 10743029) and incubated at 37 °C for 30 min. Medium was then collected, centrifuged at 200g for 5 min at 4 °C and supernatants were stored at −80 °C until analysis. For each mouse liver, the secretion measurements from two tissue pieces were averaged to generate a single biological replicate. For the brefeldin A experiments, the same procedure was followed as described above, with the modification that the two liver pieces per mouse were incubated in parallel with CHO medium containing either 2 mM brefeldin A or vehicle (2% DMSO).
Western blot analysis
Protein samples were immersed in Bolt LDS Sample Buffer (1×) substituted with 50 mM dithiothreitol and subsequently heat-denatured at 70 °C for 10 min. Denatured protein samples (10–30 µg) were fractionated using an Invitrogen 4–12% Bis-Tris plus gel. Proteins were then transferred to an Immobilon-FL low-fluorescence PVDF membrane, and western blot analysis was performed using the Odyssey CLx system according to the manufacturer’s protocol. Intercept blocking buffer (LI-COR, LCR-927-60001) was the standard blocking buffer, but for biotinylated lectin detection, Intercept Protein-Free Blocking buffer (LI-COR, 927-80001) was used. Primary antibodies were used at the following dilutions: 1:1,000 for ATF4 (Cell Signaling Technologies, 11815), ARFGAP1 (Cell Signaling Technologies, 14608), Phospho-RPS6 (Cell Signaling Technologies, 2211), Total-RPS6 (Cell Signaling Technologies, 2217), GABARAPL1 (Genetex, GTX132664) and ConA Lectin (Vector Laboratories, B-1005) and 1:2,000 for STX4 (ProteinTech, 14988-1-AP). Secondary anti-Mouse (Thermo Fisher Scientific, SA5-35521) and anti-Rabbit polyclonal (Thermo Fisher Scientific, SA5-35571) antibodies were used at a 1:10,000 dilution. For biotinylated SERPINA1 or lectin detection, LI-COR IRDye 800CW Streptavidin (LI-CORbio, 926-32230) was used at a 1:5,000 dilution.
ELISA
ELISAs targeting ALB (ab108792), FN1 (ab210967) and C3 (ab157711) were performed according to the manufacturer’s protocols (Abcam). Dilutions of ex vivo supernatants were 1:20 for ALB and undiluted for C3. For the cell media, dilutions were 1:10 for ALB, 1:20 for FN1 and 1:10 for C3. For cell lysates, dilutions were 1:20 for ALB, 1:32 for FN1 and 1:16 for C3. For mouse serum, a 1:40,000 dilution was used for C3.
Serum protein stability assay
SERPINA1D was synthesized by in vitro translation using the TNT Quick Coupled Transcription/Translation Kit (Promega, L1170) with 1 µg of a T7 promoter-driven plasmid encoding mouse Serpina1d and containing an SV40 polyadenylation signal (VectorBuilder, T7::Serpina1d_SV40polyA) and biotinylated tRNA (Promega, L5061) at 30 °C for 90 min. The translation mix was combined 1:1 (v/v) with serum and incubated at 37 °C for the indicated times; biotinylated SERPINA1D degradation was quantified by densitometric analysis of western blots. Serum-protein stability (including ALB) was assessed by incubating serum 1:1 with PBS at 37 °C, resolving by SDS–PAGE, staining with Coomassie Blue and quantifying band intensities by densitometry.
Glycogen measurements
Glycogen extractions from the liver were performed using a phenol-sulfuric acid quantification as previously described^90^. In brief, 40–100 mg of snap-frozen liver tissue was immersed in a 30% KOH solution and incubated for 20–30 min at 100 °C, with intermittent vortexing, until fully homogenized. A 95% ethanol solution was then added, and the samples were vortexed thoroughly before being incubated on ice for 30 min. This was followed by centrifugation at 2,000g for 10 min at 4 °C. The pelleted glycogen was resuspended in water, and debris was sedimented at 10,000g for 30 s before glycogen was measured in a phenol-sulfuric acid solution (67.8–70% (v/v) sulfuric acid 0.71% (w/v) phenol) at an absorbance of 490 nm.
Metabolite analysis
Frozen cell pellets (7–14 mg wet mass) were processed using a modified method for targeting energy carriers and nucleotide sugars^91^, including polarity switching and additional metabolites of interest. In brief, cell pellets were extracted on ice using 250 µl of cooled extraction buffer (acetonitrile:MeOH:15 mM ammonium acetate in H_2_O (3:1:1), pH 10). The samples were then sonicated in a sonication bath (Transsonic 460, Elma) for 5 min at the highest frequency on ice to ensure complete cell disruption. Afterwards, the samples were centrifuged at 13,000g for 15 min at 4 °C, and the supernatant was transferred to a new liquid chromatography–MS grade autosampler vial and immediately frozen at −80 °C.
Metabolite separation and detection were carried out using an ACQUITY I-class PLUS UPLC system (Waters) coupled with a QTRAP 6500+ (AB SCIEX) mass spectrometer equipped with an electrospray ionization source. Metabolites were separated on an ACQUITY Premier BEH Amide Vanguard Fit column (100 mm × 2.1 mm, 1.7 µm; Waters) with a constant column temperature of 35 °C. Separation of nucleotide sugars was achieved using the following liquid chromatography gradient scheme with mobile phase A (5 mM ammonium acetate in H_2_O + 0.05% (v/v) ammonium hydroxide, pH 10) and mobile phase B (acetonitrile + 0.05% (v/v) ammonium hydroxide, pH 10). Data acquisition was performed using Analyst (v.1.7.2) (AB SCIEX) and processed with the OS software suite (v.2.0.0) (AB SCIEX).
Electron microscopy imaging
C57BL/6J mice were exposed to 12 h NR feeding for 3 weeks and then sacrificed for tissue collection at ZT4 and ZT16. Small pieces of fresh liver tissue were transferred to ice-cold PBS and then into 2.5% glutaraldehyde in PBS. Fixed tissue was processed as described previously^92^ (method A), and sections were viewed without further on-grid staining or with a modified protocol involving incubation in 1% osmium tetroxide, followed by 1% aqueous uranyl acetate, before dehydration (method B). For the latter method, sections were stained with aqueous uranyl acetate, then with Reynolds lead citrate stain. The two different methods were used to ensure that different compartments were readily distinguishable at low magnification (method B providing optimal staining of rough ER-associated ribosomes). Random images of hepatocytes were captured across the section on a JEOL1011 transmission electron microscope at a primary magnification of ×10,000 for each biological replicate. Images were then analysed (46–105 per condition) by two independent investigators who were blind to the identity of the samples using the stereology quantification method in ImageJ to assess the volume density of the ER, GA and mitochondria relative to the cell volume.
RNA extraction
Cells were lysed on ice in QIAzol Lysis Reagent, collected using a cell scraper and homogenized by passing several times through a syringe. Snap-frozen liver tissue was homogenized in 2 ml tubes with QIAzol Lysis Reagent and 1.4 mm ceramic beads (Cappella Science, 19-627) using a Precellys tissue homogenizer. RNA from cells and liver tissue was extracted using the RNeasy Plus Universal Mini Kit (Qiagen, 73404), following the manufacturer’s protocol. The purity, integrity and quality of the total RNA were assessed using an agarose gel and a NanoDrop spectrometer (Thermo Scientific). Only RNA with an A260/280 ratio of ≥2 and an A260/230 ratio of ≥1 were used for subsequent analysis.
Reverse transcription-quantitative PCR
cDNA synthesis was performed using 1 μg of RNA with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814). Gene expression levels were quantified using ABI SYBR GREEN PCR MASTERMIX (Applied Biosystems, 4312704) according to the manufacturer’s protocol. Primer sequences for all analysed genes are listed in Supplementary Table 8.
Bulk RNA barcoding and sequencing
Transcriptomic analysis of RNA extracted from AML12 cells and mouse livers was performed and analysed using bulk RNA barcoding and sequencing (BRB-seq) performed at Alithea Genomics (Epalinges, Switzerland)^93^. Raw reads were mapped using STAR on the GRCm38/mm10 genome assembly (Ensembl release 102). The BRB-seqTools suite (http://github.com/DeplanckeLab/BRB-seqTools) was subsequently applied to retrieve raw counts per gene.
Omics data analysis
Differential rhythmicity analysis was performed using the R package dryR^20^. We fitted nested cosinor models using the functions dryseq (negative-binomial generalized linear model for count data) or drylm (ordinary linear regression) and selected the optimal model per gene by minimizing the Bayesian information criterion. Schwarz weights, calculated from the change in Bayesian information criterion values, were interpreted as approximate posterior probabilities for each model (for more details, see previous work^20^). If only one condition was tested for rhythmicity, we used the f_24 function of the dryR package. Human somalogic data were fitted with a mixed linear model as previously described^94^. Specifically, we used the lmer function from the lme4 R package on log_2_-normalized relative fluorescence unit values. A 24 h harmonic regression model with a subject-specific random effect on the baseline was applied as follows: log_2_(y_i,t) ~ cos(2π / 24t) + sin(2π / 24t) + (1|i), where yi,t_ represents the log_2_-normalized relative fluorescence units for subject i at time t. Model comparison between this full model and a null model (excluding harmonic terms) was performed using the likelihood ratio test from the lmtest R package. P values were corrected using the Benjamini–Hochberg method.
To analyse BRB-seq data, we used DESeq2 (ref. ^95^) to statistically evaluate differences between the two treatment groups, with time included as a covariate in the model. For cell experiments, we also accounted for batch effects by including batch as a covariate. StageR^96^ post hoc analysis was performed to further evaluate the statistical significance of treatment within timepoints. Wet-mass-normalized metabolite abundances from metabolic profiling of AML12 cells were analysed using a mixed linear model (~time × treatment + (1|batch)), where time represents the duration after treatment and treatment denotes CP-91149-treated or vehicle-treated samples. The model accounted for batch effects by including batch as a random effect. Post hoc analysis was performed using emmeans and stageR. Total paired-end RNA-seq data from previous work^26^ was mapped using kallisto^97^ to GRCm39/mm10 (Ensembl release 110) quantify transcript levels.
For non-depleted plasma proteomic analysis of Bmal1 WT and Bmal1-KO mice, we used maxLFQ to assess mean differences by comparing a full linear model (~time + genotype) with a reduced model (~time), where time is time of tissue collection.
Functional and gene set enrichment analysis
For functional enrichment analysis, we performed hypergeometric testing or a mean-rank gene set enrichment analysis for standard gene sets, such as Gene Ontology Cellular Component and Biological Process, and custom gene sets based on a previous publication^98^.
Statistical analysis
Unless otherwise stated, comparisons between two independent groups were made by unpaired two-tailed Student’s t-test for normally distributed data and by Wilcoxon rank-sum test for non-normal data. Rhythmicity within a single condition was assessed by cosine-fit linear regression using the f_24 function from the dryR package^20^. To assess rhythmicity in temporally resolved measurements from human subjects, we used a linear mixed-effects model with harmonic regression, as previously described^94^. Experiments with two factors (for example, time × genotype) were analysed by two-way ANOVA with Šidák/Šidák–Holm’s post hoc test, or with P value adjustment by the Benjamini–Hochberg method, as indicated. Phase distributions were compared by a two-sample Kolmogorov–Smirnov test. Correlations were evaluated by Pearson’s correlation coefficient. One-sample t-tests were used to test whether group means differed from zero. Overlap between protein, pQTLs or gene sets was assessed by hypergeometric testing, and when sample sizes were small, contingency tables were analysed by Fisher’s exact test. In cell-based and ex vivo assays, batch effects were modelled using mixed-effects linear models. We used StageR correction for post hoc comparisons if more than two timepoints were assessed. Unless otherwise specified, P < 0.05 was considered statistically significant. Details of the statistical analysis are available in Source Data figures and Source Data Extended Data Figs. 1–8.
No statistical methods were used to pre-determine sample sizes, but our sample sizes for animal studies were designed to capture 24 h rhythms follow previously published guidelines^99^. The number of human participants was comparable to those reported in a previous blood proteomics study with similar sampling intervals and objectives for 24 h rhythm analysis^16^. For in vitro experiments, a minimum of n = 4 biological replicates was used. Mice were randomized to experimental conditions based on body weight, except for experiments involving Bmal1-KO and Cry1/2-KO animals, in which mice were randomly assigned. Experimental conditions were interleaved to avoid clustered treatment or sampling and order effects. Data collection and analysis were not performed blind to experimental conditions, except for electron microscopy image analysis, which was conducted by analysts blinded to experimental conditions. In addition, investigators were blinded to the injection treatments, so they were unaware whether a mouse received the vehicle or CP-91149 injection. All data were included in the analyses unless exclusion was required owing to documented technical errors during sample processing or non-concordant technical replicates; additionally, limited material precluded inclusion in certain analyses. Stereological analyses of electron microscopy images were assumed to be non-normally distributed and were analysed using non-parametric tests. Proteomics and SomaLogic data distributions were assessed using diagnostic visualizations, including Q–Q plots. BRB-seq count data were modelled using a negative binomial distribution as implemented in DESeq2, with standard quality control procedures applied. For all other data, including RT–qPCR, western and lectin blotting, ELISA, cytotoxicity assays, metabolite measurements and blood glucose analyses, data distribution was assumed to be normal but was not formally tested.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Supplementary information
Reporting Summary Supplementary Table 1. SomaScan assay human plasma dataset and analysis Supplementary Table 2. SomaScan assay mouse serum dataset and analysis Supplementary Table 3SILAC-based MS microsomal mouse liver dataset and analysis Supplementary Table 4. Glycosylated microsomal mouse liver peptide mass dataset and analysis Supplementary Table 5. Bmal1 KO and WT mouse serum, total extract, and microsomal proteomics datasets and analysis Supplementary Table 6. Mouse liver and AML12 cell BRB-sequencing datasets and analysis Supplementary Table 7. List of affected circulating proteins associated with GSD and CDG Supplementary Table 8RT–qPCR primer sequences
Source data
Source Data Fig. 1. Source Data for Figure 1 Source Data Fig. 2. Source Data for Figure 2 Source Data Fig. 3. Source Data for Figure 3 Source Data Fig. 4. Source Data for Figure 4 Source Data Fig. 5. Source Data for Figure 5 Source Data Fig. 6. Source Data for Figure 6 Source Data Fig. 7. Source Data for Figure 7 Source Data Fig. 8. Source Data for Figure 8 Source Data Extended Data Fig. 1. Source Data for Extended Data Figure 1 Source Data Extended Data Fig. 2. Source Data for Extended Data Figure 2 Source Data Extended Data Fig. 3. Source Data for Extended Data Figure 3 Source Data Extended Data Fig. 4. Source Data for Extended Data Figure 4 Source Data Extended Data Fig. 5. Source Data for Extended Data Figure 5 Source Data Extended Data Fig. 6. Source Data for Extended Data Figure 6 Source Data Extended Data Fig. 7. Source Data for Extended Data Figure 7 Source Data Extended Data Fig. 8. Source Data for Extended Data Figure 8 Source Data Extended Data Fig./Table 9. Unprocessed western blots and gels for Figures 1F, 5D, 5I, Source Data Extended Data Fig./Table 10. Unprocessed western blots and gels for ED Data Figures 1C, 1D, 5D, 5I, 6C
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Freeze, H. H., Boyce, M., Zachara, N. E., Hart, G. W. & Schnaar, R. L. Glycosylation precursors in Essentials of Glycobiology 4th edn (eds Varki, A. et al.) 53–66 (Cold Spring Harbor Laboratory Press, 2022).35536946 · pubmed ↗
- 2Candia, J. Soma Scan Bioinformatics: normalization, quality control, and assessment of pre-analytical variation in Methods in Molecular Biology Vol. 2929 (eds. Ruiz-Romero, C. et al.) 107–127 (Humana, 2025).10.1007/978-1-0716-4595-6_940601147 · doi ↗ · pubmed ↗