In the developed world, the average calorie intake has steadily risen, as have associated age-related diseases (Lahey and Khan, 2018). Intermittent fasting (IF) eating patterns include frequent periods of 12–24 hr with little or no energy intake sufficient to trigger a metabolic switch from the utilization of liver-derived glucose to fatty acids and ketones. Such IF eating patterns increase health span and lifespan and delay or prevent age-associated diseases in animals (de Cabo and Mattson, 2019; Mattson et al., 2017). Rodents maintained on IF exhibit reduced resting heart rate and blood pressure, increased heart rate variability, and improved cardiovascular adaptations to stress (Mager et al., 2006; Wan et al., 2003a; Wan et al., 2003b; Wan et al., 2014). Age-related increases in oxidative stress, inflammation, and fibrosis in the heart are prevented by every-other-day (EOD) fasting (Castello et al., 2011). IF also improves health indicators and cardiovascular risk factors in human subjects (Harvie et al., 2011; Harvie et al., 2013; Stein et al., 2012; Weiss and Fontana, 2011). In a recent randomized controlled trial, alternate day fasting improved cardiovascular markers, reduced fat mass, fat-to-lean ratio, and increased levels of the ketone β-hydroxybutyrate (BHB) (Stekovic et al., 2019). IF protects the brain and heart against ischemic injury and enhances endurance in animal models (Arumugam et al., 2010; Kim et al., 2018; Marosi et al., 2018; Mattson and Arumugam, 2018; Wan et al., 2010). Animals maintained on EOD fasting prior to experimental myocardial infarction (MI) have a reduced myocardial infarct size compared to the ad libitum (AL) fed controls (Ahmet et al., 2011; Ahmet et al., 2005; Godar et al., 2015). IF also improves survival and recovery of heart function when initiated two weeks after MI (Katare et al., 2009). Recent brain and skeletal muscle studies suggest that IF engages signaling pathways that enhance cellular stress resistance, mitochondrial biogenesis, and autophagy (Marosi et al., 2018; Mattson and Arumugam, 2018). The ketone BHB may mediate some cellular adaptations to fasting, including inducing the expression of the inhibitory neurotransmitter γ-aminobutyric acid, trophic factors, and the cytoprotective mitochondrial deacetylase sirtuin 3 (Cheng et al., 2016; Liu et al., 2019; Marosi et al., 2016; Newman and Verdin, 2017; Yudkoff et al., 2007). However, it is unknown whether similar molecular and cellular mechanisms underlie the beneficial effects of IF on the cardiovascular system.

System-wide interrogation using large-scale omics technologies has proven powerful in capturing snapshots of molecular abundances in an unbiased manner. Previous studies have extensively focused on understanding the metabolic rewiring associated with different modes of fasting and feeding responses in the liver, the primary metabolic and secretory organ, and adipose tissues that functions as a fat sensor (Ng et al., 2019). Though transcriptomics remained the go-to technology for many of the earlier works in this arena, recent technological advancements have positioned mass spectrometry (MS)-based proteomics as a rational choice to simultaneously track the dynamic behavior of thousands of proteins, the prime orchestrators of cellular processes, in a global manner. Accordingly, proteotypes underlying various metabolic diseases, dietary milieu, and fasting regimens including time-restricted feeding or IF have been probed to get holistic molecular portraits of altered metabolic states (Shaik et al., 2016; Hatchwell et al., 2020). Protein post-translational modifications, especially phosphorylation, underlie several energy sensor pathways, glucose homeostasis mechanisms, and insulin secretion process, and hence deconvolution of phosphorylation networks is key to understanding signaling alterations (Sacco et al., 2019; Sacco et al., 2016). While the importance of IF in improving cardiovascular health has been extensively documented, a global investigation documenting the molecular players driven by the metabolic switch would be highly desirable.

In order to understand molecular and cellular remodeling of the heart during IF, we employed advanced mass spectrometry for system-wide profiling of the proteome and phosphoproteome of heart tissues obtained from mice with daily fasting for 12 hr (IF12), 16 hr (IF16) or EOD as well as AL control feeding. We also performed transcriptome analyses using RNA sequencing to evaluate whether the observed molecular responses to IF occur at the transcriptional or post-transcriptional levels. Our results indicated that IF profoundly modifies pathways involved in metabolism, cell signaling, and epigenetic reprogramming in the heart. Of note, we observed that protein network remodeling during IF occurred in an IF duration time-dependent manner, with IF16 and EOD having the most significant effects. In addition, functional studies indicated that IF improves cardiac function compared to AL feeding. Our findings provide novel insight into the genetic and proteomic changes by which IF improves cardiac health and provide a resource for investigators in the fields of metabolism and cardiovascular disease.

Male C57BL/6 mice were fed a normal chow diet comprised on a caloric basis of 58%, 24%, and 18% carbohydrate, protein, and fat, respectively. Mice were randomly assigned to AL, daily IF12, IF16, or EOD schedules beginning at 3 months of age. The study design, including the timing of experimental interventions and blood and tissue collections, is summarized in Figure 1—figure supplement 1. To determine the extent to which IF affects energy metabolism, we measured the blood glucose and ketone levels, and body weight of all mice during the six-month dietary intervention period. Mice in the AL group exhibited a trend toward increased blood glucose levels at 3 and 6 months. In contrast, mice in the EOD group exhibited a large and highly significant decrease in blood glucose levels throughout the duration of the study (Figure 1—figure supplement 1b). Mice in the IF12 and IF16 groups exhibited glucose levels intermediate to the AL and EOD groups (Figure 1—figure supplement 1b). Ketone levels also displayed variability across different fasting regimens. On fasting days, mice in the EOD group exhibited blood ketone levels that were threefold greater than mice in the AL group. Ketone levels in mice in the IF16 and IF12 groups tended to be higher than those in the AL group but were substantially lower than in the EOD group. Notably, all IF and EOD animals exhibited significantly lower body weight than AL mice (Figure 1—figure supplement 1c). However, despite the differences in the fasting regimen pattern, we did not observe any difference in overall body weight between the IF and EOD groups (Figure 1—figure supplement 1c). Furthermore, no significant difference in the overall energy intake or compositional intake is observed across mice subjected to IF groups compared to the AL group (Figure 1—figure supplement 1d).

IF modifies the heart proteome

To obtain a comprehensive perspective of the mechanisms impacted by IF in heart, we carried out proteome profiling of heart tissues from at least four biological replicates comprising each dietary group (AL = 4, IF12=5, IF16=5, EOD = 4). The AL group served as a control to compare proteomic alterations across different IF regimens. Using tandem mass tag (TMT)-based high resolution liquid chromatography-mass spectrometry (LC-MS) analysis, we identified a total of 6972 proteins (Supplementary file 1). Among the 5578 proteins quantified across all the samples, 9% of the proteins were differentially expressed among the heart tissues from different dietary regimens compared to the control AL group (Dunnett adjusted p-value = 0.01, Supplementary file 2). Among these, only 60 proteins consistently overlapped with altered protein levels across all fasting regimens and were primarily involved in processes associated with glycogen catabolism, fatty acid oxidation, and glycolysis (Figure 1—figure supplement 2a). In comparison with IF12, the proteomes were modulated to a greater extent in the longer fasting durations, IF16 and EOD. The EOD group exhibited the most significant number of protein abundance changes, with 44% modulations exclusively occurring within this group, including apolipoproteins that are important determinants of a healthy heart (Liehn et al., 2018). Approximately one-third of the differentially expressed proteins in IF groups compared to AL were enzymes with catalytic activity involved in energy homeostasis pathways. In addition to the expected fatty acid metabolism and glycolytic pathways, we observed enzymes involved in hypoxia, oxidative phosphorylation, bile acid metabolism, mTOR signaling, and myogenesis to be modulated with IF (Figure 1—figure supplement 2b). We observed that most of the enzymes involved in glycolysis, including the rate-limiting enzyme phosphofructokinase (PFKFKB1/2) displayed decreased expression with IF except for hexokinase 1 (HK1), indicative of suppression of glycolysis (Supplementary file 2). Conversely, the expression of phosphoenolpyruvate carboxykinase (PCK2), which is involved in gluconeogenesis, was enhanced by IF.

Unsupervised hierarchical clustering of the altered heart proteome revealed that IF12, IF16, and EOD mice had different overall protein expression patterns and co-expressed protein clusters compared to mice in the AL group (Figure 1a). Unlike clusters in IF16 and IF12 groups, which exhibited modest differences compared to the AL group, a distinct class of proteins showed increased abundance only in the EOD group. These clusters of proteins with exclusive upregulation in the EOD (FDR <0.05) group belonged to processes such as mRNA processing, fatty acid metabolism, autophagy, and stress response mechanisms. Other processes such as cholesterol transport and phospholipid efflux showed a downward trend in the EOD group. Several other metabolic processes, including oxidative phosphorylation, amine catabolism, and fatty acid oxidation, were overall reduced in all IF groups compared to AL. We also observed distinct modulations of several kinases in a regimen-specific manner, with majority of the alterations occurring only with IF16 and EOD (Figure 1—figure supplement 2c). Several of the kinases involved in the regulation of key metabolic pathways, such as glycolysis, exhibited decreased abundance in comparison with the AL group (Figure 1b). With EOD, major determinants of insulin sensitivity such as AKT1 and mTOR were highly expressed, and several other stress-activated kinases MAPK9 and MAPK10, showed reduced abundance (Figure 1b and Figure 1—figure supplement 2c).

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To understand the molecular events accompanying each IF regimen, we performed 1-D annotation enrichment (Cox and Mann, 2012) identifying processes enriched at higher and lower protein abundances (Figure 1c and Supplementary file 3). We observed upregulation of structural proteins involved processes such as focal adhesion and cell adhesion across all fasting regimens suggesting possible re-organization of the extracellular matrix interaction in heart tissues with IF. In addition, proteins regulating lipid kinase activity were observed in higher abundance in the hearts of mice from all three IF regimens compared to AL. We also observed specific signaling pathways to be selectively enriched in one IF regimen compared to the other IF regimens. For example, the IF12 group showed enrichment of proteins associated with cyclase activity, while the IF16 group displayed elevation of ERK signaling proteins. Signaling pathways involving the second messenger cAMP, however, were activated only with longer periods of fasting. This supports the notion that different IF regimens differentially modulate protein expression changes in the heart and impact signaling rewiring (Figure 1c and Supplementary file 3). Apart from components of lipid metabolism and transport that were reduced across all IF groups, our data also showed that branched-chain amino acid (BCAA) metabolism, which is known to regulate several anabolic and catabolic cellular cascades (Gannon et al., 2018), was significantly lower in all IF groups compared to the AL group (Figure 1c).

Regimen-specific modulation of heart functional processes

The distinct and overlapping proteomic profiles for the three IF regimens suggested a progressive molecular and cellular remodeling of the heart in response to increasing fasting durations. While the overall metabolic responses were modulated across all regimens in comparison with AL, we further probed into regimen-specific differences to understand selective processes that were rewired with an increase in fasting duration (Figure 2a–c). Compared to AL, IF12 enhanced the processes that maintain the structural integrity of the heart, such as adhesion, extracellular structure, and cytoskeletal organization (Figure 2a). We also observed increased expression of proteins involved in the immune and stress response, and endocytosis and exocytosis events in IF12. Along with significant changes to the circulatory process, which displayed increased protein abundances, protein networks associated with heart rate regulation were also altered by IF12. As expected, proteins involved in metabolic processes such as sterol metabolism and transport, glucose utilization, and protein synthesis were downregulated (Figure 2a). While the protein trafficking network was enhanced in IF16, other immune functional networks (NF-κB and Wnt signaling, innate immune response, and T cell receptor signaling) and amino acid biosynthetic processes were downregulated in IF16 in comparison with IF12 (Figure 2b). Prolonged activation of NF-κB in immune cells occurs in several major cardiovascular diseases, including cardiac hypertrophy and heart failure, and aberrant Wnt signaling contributes to cardiac hypertrophy (Foulquier et al., 2018; Gordon et al., 2011). Changes in ion homeostasis and cardiac morphogenesis networks were also evident with an increase in fasting duration from IF12 to IF16. EOD introduced a wide spectrum of changes in the heart proteome. In addition to metabolic changes commonly, EOD led to epigenetic changes compared to IF16 (Figure 2c). Proteins associated with chromatin remodeling and organization, and those involved in histone and other protein modifications showed increased abundance. In parallel, proteins that regulate transcription initiation and termination, transcript splicing, and nuclear import were also upregulated in the hearts of EOD mice. Also, an increase in the mitotic and nuclear division regulatory networks was observed. This starkly contrasted to reductions in cell cycle-related pathways observed specifically in IF12 or IF16 groups. Additionally, DNA repair and organelle assembly protein networks were upregulated by EOD. Along with reduced immune response and ion and sterol transport, EOD exhibited a reduced abundance of components involved in mitochondrial electron transport chain (ETC), pyruvate metabolism, and ATP generation compared to IF16 mice (Figure 2c). EOD fasting also downregulated blood coagulation which may be crucial to reducing risk of pathological clot formation.

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Time-resolved protein network remodeling during IF

To elucidate how functional protein networks are remodeled in response to IF, we curated protein-protein interactions (PPI) of all IF-responsive proteins and explored their functional crosstalk (Figure 3, Figure 3—figure supplement 1 and Supplementary file 6). Since many of the altered proteins were enzymes, we also considered functional interactions such as those that regulate gene expression, mediate enzymatic cascades or regulate protein phosphorylation in addition to physical interactions. To this end, we used the Pathway Commons database, a compendium of pathway-centric biologically relevant interactions (Cerami et al., 2011), and mapped interactions for 441 (81%) of the IF-responsive proteins. To infer duration-specific effects, we specifically marked those proteins that were first altered with shorter fasting duration and those that appeared with longer fasting hours, such as IF16 or EOD (Figure 3a). Changes in around 79 proteins appeared early within the IF12 regimen and some of these alterations were conserved until longer fasting durations at both IF16 and EOD (Supplementary file 6). Highly populated network clusters exclusively emerged with EOD that contributed to 189 proteins in the overall IF-modulated network (Figure 3a), suggesting enhanced network rewiring with longer fasting duration.

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We next performed a cluster assessment using the Cluster ONE graph-clustering algorithm (Nepusz et al., 2012) to identify densely connected network regions that may be functionally relevant. This highlighted several tightly connected clusters that included proteins responsive to different IF regimens and showed functional preference for specific metabolic processes (Figure 3b, Figure 3—figure supplement 2, and Supplementary file 6). The cluster that constituted proteins associated with fatty acid beta-oxidation and PPAR was the most highly enriched cluster exemplifying the impact of IF on lipid oxidation. It was evident that many of the proteins associated with this cluster tend to be downregulated during IF, particularly in EOD, except for a few proteins such as ACOT1, ACOT2, and EHHADH that showed high abundances exclusively with EOD (Figure 3b). Indeed, cardioprotective roles have been attributed to the fatty acyl-CoA thioesterase ACOT1 via its activation of PPARα/PGC1α signaling (Yang et al., 2012). Another lipid metabolism-associated cluster impacted by IF12, IF16, and more by EOD, included proteins involved in cholesterol efflux, triglyceride homeostasis, and chylomicron clearance (Figure 3b). The most prominent effects of IF on lipid metabolism were on the apolipoproteins APOA1/4, APOE, and APOC1/2/3, all of which declined in the EOD group. An increase in the level of NPC2, an intracellular transporter mediating the export of cholesterol from lysosomes (Pfeffer, 2019), was observed in the EOD group. PLTP, the increased activity of which is known to be associated with atherosclerosis, systolic dysfunction, obesity, and diabetes (Chowaniec and Skoczyńska, 2018), was observed to be suppressed explicitly in all three IF groups.

Apart from these lipid-linked clusters, we also observed other clusters differentially modulated in an IF group-dependent manner. Signal transduction primarily via the insulin receptor and MAPK pathways emerged as one of the prominent clusters primarily comprising proteins modulated with longer fasting duration (Figure 3b). B2M, a risk marker for cardiovascular disease (Shi et al., 2021), was found within this cluster. The network comprising RNA processing and splicing proteins mostly constituted proteins changing with EOD (Figure 3—figure supplement 2 and Supplementary file 6). The cluster with proteins involved in ubiquitination and proteolytic processes exhibited mostly included proteins altered with IF16. Upregulated by IF16 within this cluster was LNPEP, a zinc-dependent aminopeptidase that cleaves several peptide hormones and also catalyzes the conversion of angiotensinogen to cardioprotective angiotensin IV (AT4) (Stragier et al., 2008; Yang et al., 2011). From the network, we infer that other metabolic processes such as pyruvate and amino acid metabolism and glycolysis show gradual modulations in protein abundance with increasing fasting duration (IF12<IF16<EOD) (Figure 3—figure supplement 2). Further, based on their centrality, we inferred kinases including AKT1, CDC42, PKM, EGFR, and CSNK2A1 as IF-responsive hubs that effectively relay signaling modulations during IF-induced network rewiring.

To determine how the IF-induced network rewiring impacts downstream transcriptional programs, we assessed transcription factors enriched at each IF regimen using ChIP enrichment analysis (ChEA) database (Figure 3c). Several key transcription factors with metabolic relevance including PPARα/γ, FOXA1/2, NUCKS1, HNF4A among others were found to be enriched. While some such as ELKF, TAF7L, and PPARα were modulated irrespective of the IF regimen, others, including HOXC9, YY1, and JARID1A showed exclusive enrichment in the EOD group.

IF impacts phosphosignaling pathways in a cardioprotective manner

To gain deeper insight into IF-induced signaling alterations in myocardial cells, we investigated changes in the phosphorylation landscape that accompanies IF (Figure 4, Figure 4—figure supplement 1 and Supplementary file 8a). We enriched phosphopeptides from heart tissue homogenates (n=3 from each group) and then profiled them using label-free phosphoproteome analysis. Among the different groups, we identified 12,540 phosphorylated sites on 2931 proteins of which, ~80% and~19% sites were phosphorylated on serine and threonine respectively, with only 1% being tyrosine phosphorylated. Peptides with single phosphorylation sites represented 81% of the phosphopeptides analyzed. Among the sites identified, 66% corresponded with those curated in the PhosphoSitePlus database (Hornbeck et al., 2012). There was high reproducibility of phosphopeptide quantifications among the biological replicates with an average intensity correlation of 0.86.

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To elucidate IF-induced changes in protein phosphorylation, we focused on 6935 unique phosphorylation sites from 7615 phosphopeptides that were quantified from at least two biological replicates in the dietary groups (Supplementary file 8b). Approximately 84% of these belonged to class I phosphorylation sites with a localization probability ≥0.75, and overall displayed an average localization probability of 0.90, indicative of high phosphosite confidence. On assessing those sites that were significantly altered (Dunnett adjusted p-value <0.05) with IF, we observed that phosphorylation changes varied with increasing duration of the fasting period with EOD exhibiting the greatest number of phosphosite abundance changes, similar to what was observed in the proteome (Figure 4a and Supplementary file 8b). However, in contrast to proteome remodeling, phosphoproteome profiling revealed increased phosphosite abundance on a majority of the differentially modulated phosphoproteins, and several of these were independent of proteome changes. This is intriguing as it points to differential exploitation of translational and post-translational mechanisms to remodel cardiac cells in response to IF. Site-specific phosphorylation changes occurred on several proteins involved in structural remodeling of the heart, such as titin, which was observed with multiple changes (Figure 4a), and on regulatory proteins, including kinases, phosphatases, and enzymes in metabolic pathways (Figure 4—figure supplement 1). For instance, phosphorylation changes were observed on several MAPK associated kinases including MAPK1/3 and MAPK12, PKC family kinases including PKC-alpha, -beta, and -theta kinases that coordinate muscle contraction, including MYLK, MYLK3, and PAK2, and sphingolipid signaling kinase SPHK2 (Supplementary file 8b). Enzymes involved in lipid metabolism such as ACOT1 and ACSL1, glycolytic enzyme triose phosphate isomerase (TPI1) and fumarate hydratase (FH) in the TCA cycle, phosphorylase kinase (PHKB) involved in glycogen breakdown, all showed IF-responsive changes in phosphosite abundance emphasizing phosphorylation-dependent remodeling of energy homeostasis pathways with IF.

While phosphorylation signatures reflect kinase activities, we noticed that only 2.8% of the phosphosites we quantified had experimentally validated kinases. Hence, we defined kinase-substrate relationships through motif mapping for each IF regimen. Accordingly, we focused on all the modulated phosphosites and identified motifs specifically enriched (p-value <0.05) within distinct phosphosite expression clusters. The clustering revealed discrete patterns of phosphorylation changes that occur in response to each IF regimen (Figure 4b). PKA/PKC were commonly activated across all IF regimens. CSNK and p38 signaling were exclusive to the IF16 group, while AMPK, the master regulator of energy metabolism, exhibited activation in longer fasting groups. Overall, the kinase activation profiles were more pronounced in IF16 and EOD groups compared to the AL group. Pathway-based assessment of the modulated phosphoproteins highlighted the conserved modulation of pathways associated with insulin signaling, focal adhesion, and cGMP-PKG signaling across all IF groups (Figure 4c–d). cGMP-PKG signaling is known to mediate vasorelaxation, and activation of this pathway is regarded as cardioprotective (Kukreja et al., 2012). In line with the kinase prediction, components of AMPK signaling showed significant modulation in both IF16 and EOD groups (Figure 4c–d). In summary, our phosphoproteome analyses revealed that IF impacts different signaling pathways that influence the heart’s structural and functional plasticity.

To further validate our proteomics data, we carried out immunoblot validation to confirm a few key findings we inferred based on IF-induced proteome and phosphoproteome alterations. For this and subsequent functional studies, we chose the IF16 group as it showed IF-specific rewiring of several kinase-induced pathway rewiring events and is also a more realistic fasting regimen in human. We evaluated the expression levels of selected proteins from various pathways, namely p-AMPKα/AMPKα, PKG-1, MYPT1, PPARγ, p-GSKαβ/GSKαβ, AKT, Insulin receptor β, and p-IRS1/IRS-1, in IF16 and AL using immunoblotting (Figure 5a–b). In line with our proteomic inferences, compared to the AL group, expression levels of pAMPK/AMPK, PPARγ, MYPT1, and IRS1 were significantly higher in the IF16 group. In addition, we observed increasing trends of PKG-1, AKT and decreasing trend of p-GSKαβ/GSKαβ in the IF16 group compared to the AL group (Figure 5a–b). Overall, these results further support the findings of IF-modulated pathways observed from our omics analyses.

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Western blot uncropped membranes.

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IF improves cardiac performance after stress

With the enhanced cardiac resilience inferred from the omics data, we next investigated the effects of IF on heart function and dynamics in the IF mice compared to the AL mice. For this, we performed echocardiographic measurements in mice that had been maintained on AL or IF16 diets for 6 months. While no differences in heart function were observed between IF and AL groups, AL mice started to show signs of age deterioration, such as prolonged E/A and shortened deceleration time (de Lucia et al., 2019; Figure 6—figure supplement 1, Figure 6—videos 1–4). To evaluate cardiac functional reserve, dobutamine stress tests were performed after basal parameter acquisition. As expected, administration of dobutamine resulted in significant inotropic, lusitropic, and chronotropic cardiac responses Tyrankiewicz et al., 2013 in both groups. However, the increase in heart rate, as well as ejection fraction, fractional shortening and left ventricular dimensions after stress were significantly higher in IF16 mice compared with AL control mice (Supplementary file 9, Figure 6a, Figure 6—videos 1–4), suggesting that IF preserves myocardial functional reserve. To confirm these findings, strain analyses were performed. Longitudinal and radial strain significantly improved after dobutamine administration in IF16 mice but not in AL mice, suggesting that IF preserved systolic contractile reserve, which deteriorated with age in the AL mice (Supplementary file 9, Figure 6b).

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Increasing evidence from animal and human studies suggests that IF reduces the risk for cardiovascular disease and bolsters myocardial function and resilience (Mattson et al., 2017; Mattson and Wan, 2005; Sutton et al., 2018). However, except for enhancement of parasympathetic tone (Mager et al., 2006; Griffioen et al., 2013) and upregulation of protein chaperones (Ma et al., 2019), the underlying mechanisms are unknown. Collectively, our comprehensive datasets provide a window into the molecular, cellular, structural, and functional remodeling of the heart in mice adapted to IF eating patterns. To address the question of how the duration of the fasting period in specific IF regimens affects the molecular remodeling of the heart, we compared the molecular effects of IF12, IF16, and EOD fasting. All three IF regimens affected the pathways that regulate cellular carbohydrate, lipid and protein metabolism, cell-cell interactions, and myocardial cell contractility. Previous findings suggested that the beneficial effects of IF on organ function and stress resistance require 2–4 weeks to be fully engaged (Mager et al., 2006; Liu et al., 2019; Bruce-Keller et al., 1999; Johnson et al., 2007). If and to what extent the molecular remodeling of the heart in response to IF reverts to the pre-IF state upon return to ad libitum feeding remains to be determined.

There are many similarities in the effects of aerobic exercise and IF on the cardiovascular system that beg the question of the extent of overlap in the underlying molecular mechanisms. For example, both IF and regular exercise training increase parasympathetic tone and heart rate variability, and both can protect the myocardium against ischemic injury (Rengo et al., 2013; Voulgari et al., 2013). Moreover, both exercise and IF stimulate mitochondrial biogenesis in skeletal muscle (Marosi et al., 2018; Drake et al., 2016). Exercise training also stimulates mitochondrial biogenesis in myocardial cells (Bernardo et al., 2018). Studies of the effects of IF on the brain and skeletal muscle suggest that IF activates several major pathways engaged by ischemic and metabolic preconditioning, including upregulation of protein chaperones, anti-apoptotic proteins, and autophagy, and suppression of the mTOR pathway and protein synthesis (Marosi et al., 2018; Mattson and Arumugam, 2018). Previous research has led to the development of several treatments that mimic one or more effects of exercise, caloric restriction, and IF. Examples include agents that induce mild transient metabolic stress such as 2-deoxyglucose and mitochondrial uncoupling agents (Wan et al., 2003b; Liu et al., 2019; Liu et al., 2015; Wan et al., 2004; Yu and Mattson, 1999), ketone esters (Cox et al., 2016; Kashiwaya et al., 2013), and mTOR inhibitors such as rapamycin (Sciarretta et al., 2014).

Our analysis of functional interactions and IF-induced alterations provides a global view of molecular and metabolic rewiring influencing various aspects of cardiac performance and bioenergetics. We observed IF-induced changes in expression of several enzymes functioning in key energy pathways, as well as kinases involved in cellular signaling. While IF triggered the downregulation of several lipid metabolism enzymes, thioesterases that regulate fatty acid flux, such as ACOT1, ACOT2, and ACOT7, were upregulated, especially in the EOD group. ACOT1, for instance, functions as a molecular rheostat to impede excessive fatty acid oxidation during fasting and dampens associated inflammation and oxidative stress by inducing protective PPARα signaling (Franklin et al., 2017). Such an induction of PPARα activity with IF is indeed supported by increased protein abundance of several of its transcriptional targets, including HMGCS2 and PDK4. It is noteworthy that signaling alterations driving such PPAR-associated modulations were also evident from IF-induced phosphorylation changes, including activation of PKA, one of the key modulators of PPAR transcriptional activity, especially in the context of lipid oxidation during fasting and exercise (Lazennec et al., 2000). In addition to modulating fatty acid flux, PKA also triggers glycogen breakdown by activating phosphorylase kinase PHKB, which in turn triggers glycogen phosphorylase to release glucose 1 phosphate from glycogen (Brushia and Walsh, 1999). Our data point to an increase in phosphorylation site abundance on PHKB with IF suggesting increased turnover of glycogen with IF. The PKA effect of IF was accompanied by changes in associated cAMP signaling which also showed enhanced activation with prolonged durations of fasting as indicated by proteome modulation. The cAMP/PKA axis plays crucial modulatory roles in various aspects of cardiac physiology, including cardiomyocyte contractility via excitation–contraction coupling (ECC), control of heart rate, and downstream relay of β-adrenergic receptor signaling, is known to be perturbed in instances of heart failure (Bockus and Humphries, 2015; Callaghan, 2016; Lissandron and Zaccolo, 2006).

AMPK emerged as another prominent signaling cascade that was activated with longer hours of fasting (IF16 and beyond) based on the modulated phosphorylation sites. We observed that the substrates of AMPK were associated with multiple aspects of energy usage that are known to enhance the metabolic flexibility of the heart. TBC1D1, another substrate that is important in AMPK-induced GLUT4 translocation, also showed a consistent increase in functional phosphorylation with increasing fasting duration (Jessen et al., 2011). Disruption of GLUT4 trafficking is associated with impaired glucose uptake and ensuing insulin resistance (Mueckler, 2001), and the phosphorylation change on TBC1D1 and GLUT4 along with the predicted increase in AKT activity suggest that IF improves insulin sensitivity in the heart by modulating glucose uptake and resetting aberrations in insulin signaling via increasing phosphorylation of such proteins. The effects of insulin signaling are primarily mediated by AKT. We identified AKT1 to be differentially expressed based on protein abundance, AKT2 to have specific alterations in IF triggered association modulations, and also predicted AKT to be induced based on substrate signatures from our phosphoproteome data. Collectively, our data suggest that AKT may be an important mediator of heart cell responses to IF. In addition, there was a striking increase in phosphorylation of epitopes on numerous proteins known to regulate heart development and cardiomyocyte contraction, particularly that is a crucial determinant of myocardial passive stiffness and whose malfunction is associated with various myopathies (LeWinter and Granzier, 2014; Makarenko et al., 2004). In all, these highlight varying degrees of kinase and metabolic rewiring in the heart induced by different durations of IF, with the most protective effects accompanying prolonged fasting.

Whilst our data provide a comprehensive molecular snapshot of the changes accompanying various courses of IF, there is a limitation that our landscape is reflective of net changes occurring across the entire heart. Thus, it is challenging to understand specific changes within the different cell types that comprise the heart. Such a limitation is exaggerated, particularly for the transcriptome analysis, as cardiomyocytes account for only 30% of cells in the mammalian heart and yet account for the majority of the cardiac mass (Zhou and Pu, 2016). Knowledge of cell type-specific responses to IF will therefore be important to delineate distinct responses of heart cells to various IF regimens, and future studies exploring single-cell sequencing will add new dimensions to our understanding of cardiac dynamics with IF.

Further experimentation with different aspects of our study is required for detailed understanding of our findings. The timing of fasting cycles in this study may not promote synchronization of the natural circadian rhythm of nocturnal rodents, which may impact the potential for translating these to other model systems and understanding human patho-physiology. It has been established that time-restricted feeding/intermittent fasting cycles impact and can reset secondary tissue-specific peripheral clocks independent of the master circadian clock that is generally phase-locked to light-dark cycles (Damiola et al., 2000; Hara et al., 2001; Stokkan et al., 2001). While such mechanisms have been well documented in the context of liver and hepatic clocks, the impact of feeding on cardiac clock rhythmicity, however, remains to be deciphered. Evidence from our data suggests that fasting regimens affect circadian oscillations in heart cells, at least in part, independently of the central light-dark cycle-responsive clock. In addition, our analysis of cardiac function only included the AL and IF16 groups. We selected the IF16 fasting regimen to perform proof of principle functional experiments to further understand the functional outcome of proteomic changes observed. Echocardiographic analysis of the EOD group may provide significant information on the effect of IF, as the EOD group had the largest effect sizes from multi-omics analyses. With our study highlighting several kinase rewiring and signaling pathways impacted by IF based on modulations in proteome and phosphorylation site modulations, it will be interesting to follow-up on several of these to further assess their functional significance in the context of heart health and function. It is noteworthy that previous studies established that the EOD regimen leads to hearts with thinner walls and a lower left ventricular end-diastolic volume (LVEDV) (Ahmet et al., 2005) and lower blood pressure and heart rate (Wan et al., 2003a) than those of control animals. The latter effects of EOD were associated with cardioprotection in a rat model of myocardial infarction (Ahmet et al., 2005). Furthermore, our multi-omics study was limited to mice 6–9 months old, and it is, therefore, difficult to draw conclusions as to the effects of IF on ageing heart functions. All three IF groups maintained similar average body weights throughout the study period and were significantly lower than the AL group. Therefore, the different transcriptomes, proteomes, and phosphoproteomics observed among the three IF groups were independent of body weight. Our data suggest overlapping but distinct molecular adaptations of the heart to different intermittent fasting regimens.

In conclusion, our comprehensive multi-omics analysis sheds light on the intricate molecular mechanisms underlying the beneficial effects of intermittent fasting (IF) on cardiac health and disease prevention. Our findings demonstrate that various IF regimens induce distinct and time-dependent changes in the cardiac proteome, with longer fasting periods resulting in more significant alterations. The EOD group, in particular, exhibited the most significant protein abundance changes. The unsupervised hierarchical clustering analysis of the altered heart proteome highlights unique protein expression patterns and co-expressed protein clusters in all IF groups compared to the AL group. Collectively, these results provide valuable insights into potential pharmacological targets for developing novel therapeutic strategies for cardiovascular diseases. Future studies may further explore these targets and the underlying pathways to advance the development of effective interventions to improve cardiac health.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015452. High throughputRNA sequencing data from this manuscript have been submitted to the NCBI Sequence Read Archive (SRA) under accession number GSE133290.

1. 1. Alli-Shaik A
   2. Gunaratne J

   (2023) PRIDE

   ID PXD015452. Multiomics Analyses Reveal Dynamic Bioenergetic Pathways and Functional Remodeling of the Heart During Intermittent Fasting.

   https://www.ebi.ac.uk/pride/archive/projects/PXD015452
2. 1. Arumugam TV
   2. Kim J

   (2023) NCBI Gene Expression Omnibus

   ID GSE133290. Transcriptome analysis of mouse heart under different intermittent fasting conditions.

   https://www.ncbi.nlm.nih.gov/geo/GSE133290

1. 1. Ahmet I
   2. Wan R
   3. Mattson MP
   4. Lakatta EG
   5. Talan M

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Author details

1. Thiruma V Arumugam

   1. Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy, Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Australia
   2. Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore
   3. School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Asfa Alli-Shaik

   For correspondence

   g.arumugam@latrobe.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3377-0939

2. Asfa Alli-Shaik

   Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Thiruma V Arumugam

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7477-0495

3. Elisa A Liehn

   1. National Heart Research Institute, National Heart Centre Singapore, Singapore, Singapore
   2. Institute for Molecular Medicine, University of Southern Denmark, Odense, Denmark
   3. National Institute of Pathology "Victor Babes", Bucharest, Romania

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Sharmelee Selvaraji

   1. Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore
   2. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore

   Contribution

   Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Luting Poh

   Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Vismitha Rajeev

   Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Yoonsuk Cho

   School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

8. Yongeun Cho

   School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

9. Jongho Kim

   School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

10. Joonki Kim

    1. Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore
    2. Natural Products Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea

    Contribution

    Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

11. Hannah LF Swa

    Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore

    Contribution

    Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

12. David Tan Zhi Hao

    Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Investigation

    Competing interests

    No competing interests declared

13. Chutima Rattanasopa

    1. Translational Laboratories in Genetic Medicine, Agency for Science, Technology and Research, Singapore, Singapore
    2. Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore, Singapore, Singapore

    Contribution

    Investigation

    Competing interests

    No competing interests declared

14. David Yang-Wei Fann

    Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. David Castano Mayan

    Translational Laboratories in Genetic Medicine, Agency for Science, Technology and Research, Singapore, Singapore

    Contribution

    Investigation

    Competing interests

    No competing interests declared

16. Gavin Yong-Quan Ng

    Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Investigation

    Competing interests

    No competing interests declared

17. Sang-Ha Baik

    Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Investigation

    Competing interests

    No competing interests declared

18. Karthik Mallilankaraman

    Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9492-9050

19. Mathias Gelderblom

    Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

20. Grant R Drummond

    Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy, Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Australia

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

21. Christopher G Sobey

    Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy, Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Australia

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6525-9097

22. Brian K Kennedy

    1. Department of Physiology, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore
    2. Department of Biochemistry, Yong Loo Lin School Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

23. Roshni R Singaraja

    Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3418-3867

24. Mark P Mattson

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States

    Contribution

    Conceptualization, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

25. Dong-Gyu Jo

    School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea

    Contribution

    Conceptualization, Resources, Supervision, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

26. Jayantha Gunaratne

    1. Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
    2. Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    jayanthag@imcb.a-star.edu.sg

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5377-6537

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