Integrated Respirometry and Metabolomics Unveil Circadian Metabolic Dynamics in Drosophila

10 Precise temporal regulation of metabolism by sleep and circadian rhythms is essential for dynamic 11 energy homeostasis, yet the link between systemic metabolism and respiratory demands remains 12 poorly defined. We combined high-resolution respirometry with LC-MS-based metabolomics to 13 characterize respiratory dynamics and metabolic states in Drosophila melanogaster to uncover 14 genotype-specific impacts of sleep and circadian disruption. Wild-type flies under light -dark 15 cycles (WT-LD) showed rhythmic respiratory patterns reflective of anticipatory coordination of 16 mitochondrial energy metabolism, amino acid turnover, and redox cycling. In contrast, short-sleep 17 mutants (fmn, sss) exhibited elevated metabolic rates and reactive shifts of fuel preferences toward 18 lipid and amino acid catabolism and displayed signs of mitochondrial stress. C ircadian-clock 19 disrupted flies (per01, WT -DD) showed reactive and widespread metabolic dysregulation and 20 impaired redox homeostasis . These findings demonstrate that both sleep and circadian systems 21 are essential for aligning metabolic substrate selection with energy demands, offering mechanistic 22 insights into how disruptions in behavioral states compromise metabolic health. 23

24 Respirometry, temporal profiling, circadian rhythms, sleep, metabolism, Drosophila 25 melanogaster, respiratory quotient, mitochondrial function 26 27 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 3

28 Metabolism is a fundamental physiological process responsible for coordinating energy production 29 and substrate utilization in response to changing physiological demands. This process is 30 dynamically regulated by behavioral states, such as sleep and wakefulness, as well as directly by 31 internal circadian clocks that align physiology with the external environment [1-3]. Disruptions in 32 these temporal processes are strongly associated with metabolic disorders, including obesity and 33 type 2 diabetes [4-6] as well as other metabolic diseases such as cardiovascular dysfunction and 34 cancer [7]. 35 With conserved and well-characterized sleep and circadian mechanisms [8, 9], as well as metabolic 36 pathways also found in mammals, Drosophila offers a genetically tractable model for dissecting 37 interactions between these different systems. In particular, sleep mutants with robust phenotypes 38 can be employed to determine how loss of sleep impacts metabolic parameters, including rhythms 39 of metabolic activity. At the same time, additional tools are now available for metabolic 40 measurements previously not possible. Multiple approaches can be combined for a comprehensive 41 understanding of metabolism under precisely controlled environmental conditions and at high 42 temporal resolution [10]. 43 Indirect calorimetry is a non-invasive form of respirometry that enables real-time quantification of 44 whole-organism metabolic rate by measuring oxygen consumption (VO₂) and carbon dioxide 45 production (VCO₂)[11, 12]. These measurements allow for the estimation of energy expenditure 46 and respiratory quotient (RQ), providing insights into substrate preference (carbohydrate vs. lipid 47 use) and metabolic flexibility [13, 14] . Although widely applied in mammalian systems [15], 48 indirect calorimetry in small model organisms such as Drosophila melanogaster has only recently 49 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 4 become feasible with advances in high -resolution, small-volume flow-through systems [16-18]. 50 This is a critical advancement, as dissecting time -of-day-dependent metabolic regulation in 51 mammals is often confounded by overlapping effects of sleep, activity, and feeding rhythms [19, 52 20]. While this can also be true in flies, the genetic tools available allow distinction between these 53 processes to a greater extent. 54 This study focuses on wild -type isogenic control flies alongside three genetically characterized 55 mutants: two short -sleep mutants: fumin (fmn), which displays impaired dopamine transporter 56 function and defective dopamine reuptake [21] and sleepless (sss), which lacks the Sleepless 57 protein, resulting in altered membrane excitability and reduced GABA signaling [22-24]; and the 58 circadian mutant period01 (per01), which lacks a functional molecular clock [25]. To investigate 59 how sleep and circadian disruption independently and interactively influence metabolic regulation, 60 we utilized a small-animal respirometry platform built on the MAVEn-FT system, coupled with a 61 Licor 7000 CO₂ analyzer and an Oxzilla differential O₂ analyzer. This system builds upon the 62 Sleep and Activity Metabolic Monitor (SAMM), which first demonstrated the feasibility of 63 simultaneous CO₂ and O₂ measurements in Drosophila [26]. This MAVEn-based platform 64 enhances temporal resolution and throughput, enabling continuous measurement of respiratory 65 parameters across multiple groups of flies over circadian time, and allowing fine-scale profiling of 66 metabolic rhythms and their disruption across sleep-wake cycles. 67 To provide a more comprehensive perspective on metabolic regulation, we complemented 68 respiratory measurements with steady -state metabolomic profiling using liquid chromatography -69 mass spectrometry (LC-MS) previously published from our group [27]. This integrative approach 70 enables the identification of metabolite -level changes associated with altered energy expenditure 71 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 5 and RQ, providing detailed insights into how sleep and circadian mutations influence metabolic 72 homeostasis [27-29]. By employing this framework, we sought to uncover conserved mechanisms 73 linking temporal biological processes , such as sleep and circadian rhythms , to metabolic 74 homeostasis and to elucidate how genetic and environmental perturbations in these systems 75 influence energy balance and metabolic regulation. 76

77 Drosophila Strains, Entrainment and Collection 78 Drosophila melanogaster isogenic control (iso), two short -sleep mutants, fumin (fmn) [21] and 79 sleepless (sss) [22], as well as a circadian clock mutant (per01) [25] were used for this study. Male 80 flies were collected shortly after eclosion and entrained in light -dark (LD) incubators for a 81 minimum of three days before circadian collection. At the time of collection, flies were between 82 five and seven days old. Wild -type (WT) isogenic control flies were either maintained under 12 83 hrs:12 hrs light v ersus dark conditions (WT-LD) or placed in constant darkness (WT -DD) for at 84 least 24 hours prior to collection to examine light -independent rhythms. Mutant strains (fmn, sss, 85 and per01) were maintained under LD cycles. All flies were reared on a standard 86 cornmeal/molasses medium at 25°C. Zeitgeber time (ZT) 0 was designated as lights -on, with 87 lights-off occurring at ZT12 (12/12 LD). 88 Respirometry Setup 89 All experiments were performed using groups of 25 flies per chamber. To account for genotype -90 specific differences in body size, each group of 25 flies (WT, fmn, sss, and per01) was weighed 91 prior to respirometry, and these weights were used to normalize VCO₂ and VO₂ values for accurate 92 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 6 comparison. Each chamber was provisioned with 2 mL of fly food (2% agar, 5% sucrose) to sustain 93 the flies during the 24-hour continuous recording period. Control chambers containing only food, 94 without flies, were included to account for background microbial metabolism. To simulate natural 95 microbial inoculation, flies were briefly introduced into these control chambers and then removed 96 before data acquisition began. 97 The respirometry system continuously measured CO₂ production and O₂ consumption using a 98 flow-through MAVEn system. It consisted of five main components: (1) a zero -grade air source 99 and scrubbing column to remove residual CO₂, (2) mass -flow controllers to regulate airflow, (3) 100 Sable Systems RC chambers to house the flies, (4) the MAVEn system to direct air sequentially 101 from each chamber, and (5) gas analyzers for measuring CO₂ and O₂ concentrations. The system 102 was calibrated every 3-5 trials using 100% N₂ (CO₂ = 0 ppm) and a known CO₂ standard to ensure 103 measurement accuracy. 104 Zero-Grade Air Supply and Scrubbing System : Zero -grade compressed air (<0.1 ppm 105 hydrocarbons) was first passed through a custom -built scrubbing column (#26800, Drierite) to 106 remove residual CO₂. The column was packed with an inner layer of Ascarite (#81133 –20–2, 107 Acros Organics) flanked by two outer layers of Drierite (#7779–18–9, Drierite), separated by glass 108 wool (#11–388, Fisher Scientific). 109 To re -humidify the air to ~9 parts per thousand (ppt) water vapor content, it was then passed 110 through Nafion tubing (#TT -070, Perma Pure, LLC) submerged in deionized water. Bev -A-line 111 nonpermeable tubing (#56280, United States Plastic Corp.) was used to connect all system 112 components. 113 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 7 Airflow Regulation: Re-humidified, CO₂-free air was split into two streams, each regulated by 114 mass flow controllers to maintain a constant flow rate of 25 mL/min. One stream provided 115

air to the CO₂ and O2 analyzers (controlled by a Side-Trak 840 Series, Sierra Instruments, 116 Inc. MFCV), while the second stream provided flow to the fly chambers (controlled by the 117 MAVEn’s internal mass flow-controlled system). 118 Respirometry Chambers and MAVEn System’s Airflow Management: Flies were housed in Sable 119 Systems RC chambers (70 mm × 20 mm borosilicate glass tubes), integrated into a continuous 120 flow through MAVEn system. The MAVEn splits airflow into 16 channels for fly chambers and 121 one dedicated baseline channel. Airflow is constantly and continuously maintained through all fly 122 chambers while the MAVEn’s multiplexing system will switch the ‘active’ chamber’s airflow 123 toward the analyzer chain for a preset dwell time of 120 seconds per chamber, and 60 seconds for 124 baseline, with a baseline interleave ratio of 4 to ensure accuracy in O₂ measurement (i.e a baseline 125 measurement after every four chambers). In this system the MAVEn manages air flow to allow for 126 the sequential measurements of individual chamber gas parameters and air flow rates. 127 Experimental respirometry recording were conducted for 24-hour periods. System control and data 128 acquisition were handled via Sable Systems MAVEn Controller software. 129 CO₂ Production Measurement: Baseline CO₂ levels were recorded from the reference air prior to 130 entering the chambers. As flies respired, the CO₂ released in each chamber was subsequently 131 flushed to the CO₂ analyzer (Li-7000, LI-COR Biosciences) via the MAVEn system. 132 O₂ Consumption Measurement: Similarly, O2 consumed by flies was measured immediately using 133 the Oxzilla differential O 2 analyzer (Oxzilla II Oxygen Analyzer, Sable Systems International) 134 placed downstream from the CO 2 analyzer in the analyzer chain. In this setup -controlled flow 135 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 8

air exiting the CO 2 analyzer’s reference cell was routed into the Oxzilla’s reference 136 channel, while animal air exiting the CO2 analyzer’s sample cell was routed to the Oxzilla’s sample 137 channel. To prevent dilution effects, water vapor was removed prior to O₂ analysis using small 138 scrubbing columns. 139 Water Vapor Scrubbing Columns: Separate scrubbing columns were used for reference and animal 140 airflows and placed in -line directly prior to the Oxzilla differential O2 analyzer’s reference and 141 sample channel inlet ports. Each column was made from a 10 mL syringe body (#14955459, Fisher 142 Scientific) filled with an inner layer of Ascarite and two outer layers of magnesium perchlorate 143 (M54, Fisher Scientific), separated by glass wool. Bev -A-line tubing was secured with rubber 144 stoppers (#14 –135E, Fisher Scientific). Columns were replaced twice during each 24 -hour 145 experiment to maintain performance. 146 Carbon Dioxide and Oxygen Analysis and Calculations 147 O₂ consumption was quantified by measuring the decrease in O₂ concentration as air passed 148 through chambers containing flies. To ensure accuracy, a Savitzky -Golay filter ( 25-second 149 window) was applied to smooth the raw signal , followed by a lag correction of 127 seconds 150 accounted for delay between chamber exit and O 2 analyzer input. To match dry atmospheric air , 151 the reference air was baseline corrected to 20.95% O₂. For both reference and animal air the %O2 152 values were converted to O 2 fractional contents by dividing percentage values by 100 (thus 153 baseline air has a fractional content of 0.2095). The VO₂ value from an empty chamber was 154 subtracted from experimental values [30]: 155 VO₂ = (Animal O₂ − Reference O₂) × Flow rate / (1 − Reference O₂) 156 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 9 VO₂ values were expressed in μL/hr by summing all 5-minute bins into hourly averages. 157 CO2 production was quantified by measuring the increase in CO₂ concentration as air passed 158 through chambers containing flies. To ensure accuracy, a Savitzky -Golay filter (25 -second 159 window) was applied to smooth the raw signal, followed by a lag correction of 22 seconds with Z 160 correction and 28 seconds without Z correction accounted for delay between chamber exit and O2 161 analyzer input. To match dry atmospheric air , the reference air was baseline corrected to 0 parts 162 per million (ppm) CO₂. For both reference and animal air the ppm CO 2 values were converted to 163 CO2 fractional contents by dividing percentage values by 1000000. The VCO₂ value from an empty 164 chamber was subtracted from experimental values [30]: 165 VCO2= (Reference CO2 + Animal CO2) × Flow rate/1-Reference CO2 166 VCO₂ values were expressed in μL/hr by summing all 5-minute bins into hourly averages. 167 Respiratory quotient (RQ) was calculated as: 168 RQ= VCO2 / VO2. This provided a ratio of CO₂ produced to O₂ consumed, reflecting macronutrient 169 oxidation and metabolic flexibility. 170 Metabolite Extraction and LCMS Measurements 171 The metabolomics dataset analyzed in this study was previously published and is publicly 172 available, as described in detail in [27, 31]. Briefly, polar metabolites were extracted from fly 173 bodies sampled every 2 hours from ZT0 to ZT22 using a modified Bligh-Dyer extraction method, 174 as previously reported [31, 32]. The polar fraction of each extract was dried under vacuum and 175 reconstituted in 100 μL of acetonitrile:Milli -Q water, followed by vortexing for 20 seconds. 176 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 10 Samples were analyzed using an ion -switching LC-MS method, and peak integration and data 177 processing were performed according to previously published procedures [27]. 178 Statistical Analysis 179 All statistical analyses were conducted using GraphPad Prism. Non -parametric Spearman 180 correlation was used to evaluate relationships among metabolic parameters. Metabolites with p-181 values below 0.05 were considered statistically significant and selected for further investigation. 182 To assess genotype-specific differences, one-way ANOVA was performed followed by a pairwise 183 unpaired two-tailed Student’s t-tests comparing the control group (WT -LD) with each genotype 184 (fmn, sss, per01, and WT-DD). 185 Rhythmicity analysis 186 Rhythmicity analysis was performed using Nitecap, a tool for circadian and rhythmic analyses 187 [33]. Time-series metabolic data were analyzed to compute rhythmic parameters using the RAIN 188 algorithm [34]. Rhythms were assessed for statistical significance using false discovery rate 189 (FDR)-adjusted p -values, ensuring robust detection while minimizing false positives. The lag 190 parameters of each rhythm were computed using JTK cycle algorithm [35]. 191 Pathway Analysis 192 Significant metabolites identified from univariate analyses were used for metabolic pathway 193 analysis via MetaboAnalyst 5.0. Metabolites were uploaded using HMDB identifiers and analyzed 194 using the hypergeometric enrichment method , with relative-betweenness centrality applied for 195 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 11 topology analysis based on the Drosophila melanogaster (KEGG) pathway library. Pathways with 196 a p-value less than 0.05 were considered statistically significant. 197

198 Metabolic Fuel Utilization Across Genotypes Based on Respiratory Quotient (RQ) 199 To assess whole -body metabolic activity, we performed respirometry on groups of 25 flies per 200 genotype, measuring oxygen consumption (VO₂) and carbon dioxide production (VCO₂) across 201 the circadian cycle. The respiratory quotient (RQ), calculated as the ratio of VCO₂ to VO₂, provides 202 insight into the predominant metabolic substrate being utilized: an RQ of 1.0 reflects pure 203 carbohydrate metabolism, values below 1.0 indicate lipid and protein oxidation, and values above 204 1.0 suggest lipogenesis (the conversion of carbohydrates into fats) [14]. RQ, VCO₂, and VO₂ were 205 continuously recorded from Zeitgeber Time (ZT) 0 to 24 at one-second intervals and subsequently 206 averaged into 5-minute bins for analysis. Figures 1a-c shows the 5-minute binned VCO₂, VO₂ and 207 RQ profiles across the circadian cycle for all genotypes (separate individual plots for VCO₂, VO₂, 208 and RQ for each genotype are provided in the figure S1. a-e, S2. a-e and S3. a-e respectively). 209 The average values of VCO₂, VO₂ and RQ across the full 24-hour cycle are summarized in Figure 210 1d-f. 211 Among the genotypes tested, WT -LD, WT-DD, and per01 flies exhibited similarly elevated RQ 212 values (1.20, 1.19, and 1.20, respectively), consistent with active lipogenesis. Group differences 213 were assessed using the Kruskal–Wallis test, followed by Dunn’s multiple comparisons post hoc 214 test revealed no statistically significant differences in RQ among these three genotypes ( Figure 215 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 12 1f, ns), suggesting that circadian disruption in constant darkness or in the per01 mutant does not 216 alter the dominant fuel utilization strategy under basal conditions. 217 In contrast, short -sleeping mutants displayed lower RQ values. The fmn mutant exhibited a 218 moderate reduction (RQ = 1.0 9), reflecting increased reliance on carbohydrate metabolism. The 219 sss mutant showed the lowest RQ (0. 94), indicating a shift toward lipid and protein catabolism. 220 Both mutants were significantly different from WT-LD (p < 0.001), highlighting genotype-specific 221 alterations in fuel utilization associated with sleep loss (Figure 1f). 222 These results demonstrate that while WT and circadian -disrupted genotypes maintain a lipogenic 223 profile, sleep -deficient mutants exhibit altered substrate utilization, favoring catabolism of 224 carbohydrates or lipids depending on the severity of sleep disruption. 225 Furthermore, to account for potential differences in body size that could confound VCO₂ and VO₂ 226 measurements, fly weights were recorded for each genotype before respirometry. Notably, fmn 227 and sss mutants weighed significantly less than WT controls, whereas per01 flies had comparable 228 weights (Figure 1g). Accordingly, VCO₂ and VO₂ values were normalized to fly weight, and the 229 genotype-specific differences remained robust after normalization ( Figure 1h -i). These data 230 confirm that the observed metabolic differences are not attributable to body size but reflect inherent 231 alterations in metabolic fuel utilization. 232 Diurnal Variation in CO₂ Production, O₂ Consumption, and Respiratory Quotient Across 233 Genotypes 234 To investigate how sleep and circadian regulation influence temporal patterns of metabolism, we 235 measured carbon dioxide production (VCO₂), oxygen consumption (VO₂), and respiratory quotient 236 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 13 (RQ) across day and night phases in wild-type flies maintained under light-dark cycles (WT-LD), 237 wild-type flies kept in constant darkness (WT-DD), short-sleep mutants (fumin [fmn] and sleepless 238 [sss]), and the circadian clock mutant period01 (per01). The average values of VCO₂, VO₂ and RQ 239 during the day (ZT0-12) and night (ZT12-24) are presented in Figure S4. 240 The sss mutant exhibited the highest metabolic rates among all genotypes, with significantly 241 elevated VCO₂ and VO₂ during both day and night relative to WT -LD (p < 0.001). A pronounced 242 day-night difference in sss further indicated a strong phase -dependent increase in respiratory 243 activity ( Figure S4). The fumin mutant also showed significantly elevated VCO₂ and VO₂ 244 compared to WT-LD (p < 0.001), with a detectable but less marked diurnal variation than in sss 245 (Figure S4). WT-LD flies exhibited a robust diurnal rhythm, with significantly higher respiratory 246 rates during the day (p < 0.001), consistent with normal circadian regulation of energy expenditure 247 (Figure S4). 248 The circadian mutant per01, lacking a functional molecular clock, displayed an attenuated and 249 phase-altered respiratory pattern. Both VCO₂ and VO₂ were significantly reduced during the day 250 (p < 0.05), with no significant changes at night, indicative of disrupted or misaligned metabolic 251 rhythmicity (Figure S4). WT-DD flies, maintained in constant darkness, exhibited the lowest 252 overall respiratory activity. Despite the absence of environmental light cues, VCO₂ and VO₂ 253 showed significant day -night differences, while RQ remained unchanged during the day but 254 displayed a modest difference at night (p < 0.05), indicating limited circadian control over 255 metabolic rhythms in constant conditions (Figure S4). 256 Circadian Variations in CO₂ Production, O₂ Consumption, and Respiratory Quotient Across 257 Genotypes 258 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 14 To evaluate genotype-dependent circadian regulation of metabolism, we assessed rhythmicity in 259 carbon dioxide production (VCO₂), oxygen consumption (VO₂), and respiratory quotient (RQ) in 260 wild-type flies under light -dark conditions (WT -LD), wild-type flies in constant darkness (WT -261 DD), short-sleep mutants (fumin [fmn] and sleepless [sss]), and the circadian clock mutant period01 262 (per01). Time -course data were analyzed using JTK CYCLE and RAIN algorithms, with 263 rhythmicity classified as statistically significant (p ≤ 0.05), trending (0.05 < p < 0.1), or not 264 significant (p ≥ 0.1). Circadian phase was estimated using JTK lag values (Figures 2-3, Table 1). 265 VCO₂ rhythms were statistically significant in WT-LD, WT-DD, fmn, sss and per01 (Figure 2, 266 Table 1). These findings demonstrate that VCO₂ exhibits robust circadian oscillations under both 267 light-dark and constant darkness conditions, and that rhythmicity persists even in the absence of a 268 functional period gene. VCO₂ peak occurred near ZT ~4 in WT-LD, ZT ~7.5 in WT-DD, ZT ~3.25 269 in fmn, ZT ~3 in per01, and ZT ~2 in sss, indicating genotype-specific shifts in respiratory phase 270 (Figure 3, Table 1). 271 VO₂ rhythmicity was statistically significant in WT-LD, WT -DD, sss and per01 while not 272 significant in fmn (Figure 2, Table 1). These findings indicate that circadian regulation of oxygen 273 consumption is evident in constant darkness and in the absence of a functional period gene but is 274 reduced in sleep mutant. VO₂ peaked near ZT ~4 in WT-LD, ZT ~7 in WT -DD, ZT ~1.25 in sss 275 and ZT ~6 in per01, suggesting conserved phase alignment (Figure 3, Table 1). 276 RQ also showed genotype -dependent rhythmicity, with significant oscillations observed in fmn, 277 sss and per01, while no significant rhythms were observed in WT-LD or WT-DD (Figure 2, Table 278 1). In the rhythmic genotypes, RQ peaked at ZT ~4.25 in fmn, ZT ~2 in per01 and ZT ~12.5 in sss 279 indicating distinct phase alignment compared to respiratory output (Figure 3, Table 1). 280 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 15 Temporal Profiling of Respiratory Quotient in Wild-Type Flies Under Light-Dark Conditions 281 To align respiratory quotient (RQ), which was collected at one-second intervals and averaged into 282 5-minute bins, with steady -state metabolite measurements taken at 2 -hour intervals, RQ values 283 corresponding to each 2-hour Zeitgeber Time (ZT) point were extracted from the 5-minute binned 284 dataset. Building on this alignment, we next explored temporal relationships at higher resolution 285 by performing a 2-hour lag analysis, systematically shifting the RQ time series by −120, −60, −30, 286 −15, −5, +5, +15, +30, +60, and +120 -minutes relative to the metabolite dataset. This analysis 287 revealed a distinct circadian rhythmicity in RQ, characterized by oscillatory patterns indicative of 288 coordinated substrate utilization across the day-night cycle (Figure 4). 289 Correlation of Respiratory Quotient with Metabolite Profiles and Temporal Lag Analysis in WT -290 LD Flies 291 To investigate the association between respiratory quotient (RQ) and metabolite in WT-LD flies, 292 we initially performed Spearman correlation analysis (ρ) across a range of temporal lags (−120 to 293 +120 minutes). Several metabolites demonstrated strong correlations with RQ (|ρ| > 0.7, p < 0.05), 294 suggesting tight coupling between metabolic fluctuations and respiratory output. To minimize the 295 influence of outliers and enhance data visualization, correlation patterns were subsequently 296 presented as rank -based scatter plots . Representative examples are shown in Figure 5a : 297 Hydroxyhexadecenoylcarnitine exhibited a significant positive correlation with RQ (ρ = +0.78, p 298 < 0.05) at a +120-minute lag, while Quinolinate showed a strong negative correlation (ρ = -0.77, 299 p < 0.05) at a −120-minute lag. 300 To further characterize these associations, metabolites were categorized based on the timing of 301 their peak changes relative to RQ fluctuations, exhibiting either positive or negative lag. To enable 302 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 16 direct comparison of temporal dynamics between RQ and metabolite profiles across ZT 0 to ZT 303 24, z-scoring was performed to normalize differences in absolute magnitude and measurement 304 units. Within this framework, a positive lag indicates that metabolite changes occur after shifts in 305 RQ, requiring the metabolite profile to be shifted forward (rightward) along the time axis for 306 optimal alignment. Conversely, a negative lag signifies that metabolite changes precede RQ 307 fluctuations, necessitating a backward (leftward) shift of the metabolite profile to achieve 308 alignment (Figure 5b). 309 Metabolite Response Dynamics Relative to RQ and Functional Pathway Enrichment of RQ -310 Associated Metabolites in WT-LD Flies 311 To further explore the temporal relationship between the metabolites and respiratory quotient 312 (RQ), we generated a clustered heatmap of metabolites significantly correlated with RQ. 313 Metabolites were filtered based on statistical significance (|ρ| > 0.7, p < 0.05 at any timepoint), and 314 their temporal profiles were realigned relative to respiration, anchoring RQ at t = 0 (Figure 6). 315 This flipping step was essential to standardize the visualization of metabolites either preceding or 316 following respiratory changes, facilitating clearer interpretation of biological timing relationships. 317 To identify key metabolic pathways associated with circadian regulation of respiration, we next 318 performed pathway enrichment analysis on the RQ -correlated metabolites using MetaboAnalyst. 319 Enriched pathways were identified at a significance threshold of p < 0.05. WT -LD-specific 320 pathway enrichment results are presented in Figure 6. Consistent with these findings, WT flies 321 under LD conditions exhibited circadian -regulated metabolic rhythms, characterized by 322 coordinated activity in amino acid metabolism, redox balance, and energy -generating pathways 323 such as the TCA cycle and glyoxylate metabolism. 324 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 17 RQ-Linked Metabolic Shifts and Pathway Enrichment in Sleep Mutants 325 We applied the same analysis method to sleep mutant flies ( fmn and sss) to identify metabolic 326 pathways associated with altered respiratory rhythms. Metabolites significantly correlated with 327 RQ (|ρ| > 0.7, p < 0.05) were identified, and their temporal profiles were realigned relative to 328 respiration by anchoring RQ at t = 0. Clustered heatmaps were generated to visualize metabolite 329 response dynamics, and pathway enrichment analysis of RQ-associated metabolites was conducted 330 using MetaboAnalyst ( p < 0.05) (Figure 7 ). This analysis revealed that sleep mutants exhibit 331 genotype-specific metabolic disruptions implicating mitochondrial and energy -related pathways. 332 In fmn flies, alterations were observed in arginine biosynthesis, purine and nitrogen metabolism, 333 and butanoate pathways, suggestive of dysregulated nitrogen balance and mitochondrial 334 dysfunction. In contrast, sss mutants showed perturbations in glycine, serine, and threonine 335 metabolism, as well as carbohydrate and antibiotic biosynthetic pathways, reflecting altered carbon 336 utilization and mitochondrial-associated metabolic stress. 337 RQ-Linked Metabolic Shifts in Circadian Mutants and WT Flies in Constant Darkness 338 Circadian clock mutant flies (per01) and wild-type flies maintained under constant darkness (WT-339 DD) were analyzed using the same approach. Metabolites exhibiting strong correlations with RQ 340 (|ρ| > 0.7, p < 0.05) were identified and temporally aligned by anchoring RQ at t = 0. Clustered 341 heatmaps were constructed to visualize the dynamic responses of these metabolites, and pathway 342 enrichment analysis of RQ-associated metabolites was performed using MetaboAnalyst (p < 0.05) 343 (Figure 8 ). Both per01 and WT -DD flies exhibited disrupted temporal coordination of 344 mitochondrial metabolism. per01 mutants showed dysregulation in amino acid metabolism, purine 345 turnover, and redox-associated pathways such as glutathione and nicotinate metabolism, consistent 346 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 18 with impaired redox buffering and energy imbalance. In contrast, WT -DD flies exhibited 347 alterations in sulfur- and nitrogen-containing amino acid pathways, indicative of desynchronized 348 but intact metabolic cycling in the absence of external light cues. 349

350 Temporal Misalignment Alters Fuel Utilization and Respiratory Rhythms 351 Our integrative approach, combining whole -organism respirometry [18, 26] with LC-MS-based 352 metabolomics [27, 36 -40], reveals how sleep and circadian disruptions distinctly alter energy 353 metabolism in Drosophila melanogaster . By comparing wild -type flies with mutants affecting 354 sleep (fumin and sleepless) [21, 22, 41, 42] and circadian clock function ( per01) [24], we have 355 captured genotype -specific differences in respiratory dynamics and substrate usage across the 356 circadian cycle under tightly controlled environmental conditions. These findings establish a 357 functional framework for understanding how behavioral state and clock integrity shape metabolic 358 homeostasis. 359 Our data suggest that sleep deprivation and circadian disruption drive distinct changes in whole -360 organism respiratory physiology and fuel selection. Metabolites in wild-type LD typically showed 361 peak correlations preceding respiratory changes (negative lag), reflecting anticipatory circadian 362 control. In contrast, mutants displayed a shift to reactive correlations (positive or zero lag), 363 indicating loss of temporal synchronization and proactive metabolic adjustments. These 364 phenotypes closely parallel metabolic outcomes reported in mammalian systems, where 365 disruptions in behavioral states such as sleep deprivation or circadian misalignment are linked to 366 increased energy expenditure, altered substrate utilization patterns, and systemic metabolic 367 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 19 imbalance [3, 43]. In both rodents and humans, sleep loss leads to elevated basal metabolic rate 368 and a shift toward carbohydrate oxidation and lipid/protein catabolism [3, 43, 44]. A similar shift 369 is evident in our study, where Drosophila short-sleep mutants -fmn flies display moderately 370 reduced RQ values (1.09) and sss mutants show a more pronounced shift (0.94)-exhibit enhanced 371 catabolic metabolism. 372 Likewise, the dampened and phase-shifted respiratory rhythms observed in per01 and WT-DD flies 373 resemble patterns seen in mammalian models of circadian misalignment, which exhibit blunted 374 respiratory oscillations, disrupted glucose and lipid regulation, and increased risk for metabolic 375 disease [45, 46]. These conserved physiological responses underscore the translational value of 376 Drosophila as a model system for dissecting the interplay between sleep, circadian timing, and 377 metabolic regulation. 378 Wild-Type Flies Exhibit Circadian-Regulated Biosynthesis and Redox Balance Under LD Cycles 379 In wild -type flies maintained under a 12:12 light -dark (LD) cycle, RQ -correlated metabolites 380 revealed tightly coordinated circadian regulation of biosynthetic and redox pathways. An increase 381 in nicotinate and nicotinamide metabolism -highlighted by changes in quinolinate, nicotinamide 382 riboside, and L-aspartic acid-suggests a direct link between mitochondrial respiration and NAD⁺ 383 biosynthesis [47, 48]. These NAD⁺ precursor changes occur before respiration increases (negative 384 lag), highlighting anticipatory NAD⁺ regulation. Concurrent alterations in alanine, aspartate, and 385 glutamate metabolism (via citric acid and carbamoyl phosphate) and arginine biosynthesis indicate 386 dynamic alignment of amino acid turnover with energy demand and nitrogen disposal [49, 50]. 387 RQ-associated enrichment of citrate cycle intermediates such as malate and citric acid further 388 supports circadian gating of oxidative phosphorylation [51]. Elevated levels of D -glucose were 389 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 20 mapped to the KEGG pathway for neomycin, kanamycin, and gentamicin biosynthesis. While 390 Drosophila does not synthesize these antibiotics, the enrichment likely reflects the presence of 391 shared carbohydrate intermediates (e.g., glucose, glucose-6-phosphate, UDP-glucose) common to 392 multiple biosynthetic processes. This mapping is more indicative of altered carbohydrate and 393 amino sugar metabolism, or potentially reflects microbial contributions, rather than actual 394 antibiotic production by the host [52, 53] . Additional contributions from glyoxylate and 395 dicarboxylate metabolism reinforce the role of anaplerotic flux and redox cycling [54]. Notably, 396 persistent correlations between RQ and pantothenate precursors suggest synchronized CoA 397 biosynthesis, potentially coordinating fatty acid oxidation and TCA cycle input [55]. Together, 398 these results demonstrate that under LD conditions, wild -type flies sustain temporal coordination 399 between respiration and mitochondrial metabolism, integrating redox homeostasis, nitrogen 400 metabolism, and substrate availability in a circadian-dependent manner. 401 Altered Amino Acid Metabolism and Mitochondrial Dysregulation in fmn Mutants 402 The fumin (fmn) mutant, characterized by chronic sleep loss, exhibited genotype -specific 403 disruptions in metabolic coordination, as revealed by metabolites whose temporal dynamics were 404 strongly correlated with respiratory quotient (RQ). In fmn flies, significant RQ correlations ( p < 405 0.05) were identified in pathways including arginine biosynthesis (citrulline, glutamine), purine 406 metabolism (inosine, glutamine), and nitrogen metabolism-suggesting a decoupling of amino acid 407 turnover and nucleotide cycling from respiratory demand [56, 57]. Glutamine’s strong association 408 with RQ highlights its central role in buffering nitrogen flux and supporting biosynthetic processes 409 under energetic stress [58]. Correlated dynamics of 2 -hydroxyglutarate, a key intermediate in 410 butanoate metabolism, point to impaired TCA cycle flux and redox imbalance [59]. Likewise, RQ-411 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 21 associated shifts in methylhistidine suggest altered histidine metabolism, potentially impacting 412 mitochondrial protein turnover and methylation processes . Acylcarnitine metabolites, indicators 413 of lipid oxidation, consistently emerged as strong correlates across all genotypes but particularly 414 in fmn, underscoring lipid metabolism’s critical role in respiratory coupling. Particularly in 415 mutants, increased reliance on fatty acid oxidation may reflect compensatory responses to 416 disrupted carbohydrate metabolism and sustained energetic demands. Together, these findings 417 indicate a failure in aligning substrate oxidation with mitochondrial respiratory output in fmn 418 mutants. Building on prior evidence of mitochondrial stress in sleep -deprived fmn flies [60], our 419 integrative respirometry -metabolomics analysis reveals disrupted coupling between energy 420 metabolism and amino acid pathways, underscoring the physiological cost of chronic sleep loss on 421 mitochondrial homeostasis. 422 Disrupted Carbon Metabolism and Mitochondrial Stress in sss Mutants 423 The sleepless (sss) mutant displayed significant disruptions in carbon and amino acid metabolism, 424 as indicated by strong correlations between respiratory quotient (RQ) and metabolites involved in 425 glycine, serine, and threonine metabolism (choline, dimethylglycine, glycine), butanoate 426 metabolism (acetoacetate, succinate), and carbohydrate pathways [22]. Notably, RQ -associated 427 shifts in choline and dimethylglycine suggest dysregulation of one-carbon metabolism and methyl 428 group transfer, which can impair mitochondrial function and epigenetic stability [61, 62] . 429 Correlated dynamics of acetoacetate, a ketone body often found in starvation, and succinate (both 430 key intermediates of butanoate metabolism ) point to altered TCA cycle input and mitochondrial 431 redox imbalance [63, 64]. In parallel, strong RQ correlations with sucrose and D -glucose (from 432 both starch/sucrose and galactose metabolism) indicate disrupted carbohydrate processing and 433 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 22 inefficient substrate utilization [65]. cAMP and biotin were also found to associate with 434 respiration, both unique in metabolic regulation through altered neuromodulatory signaling and 435 cofactor metabolism. These findings suggest that sss mutants exhibit altered metabolic routing in 436 response to respiratory demand, shifting substrate utilization toward carbohydrate and amino acid 437 pathways under sleep-deprived conditions, consistent with metabolic stress responses observed in 438 other models of sleep loss [66]. 439 Loss of Temporal Coupling of Mitochondrial Metabolism in Clock Mutants 440 In period (per01) mutants lacking a functional circadian clock, the temporal coordination between 441 metabolism and respiration was disrupted across nearly every measured metabolite through 442 intermittent strong correlations with respiration, reflecting broad metabolic chaos [1, 67]. Strong 443 correlations with RQ were observed in alanine, aspartate, and glutamate metabolism (L -alanine, 444 glutamine, fumarate, oxoglutarate) and arginine biosynthesis, suggesting inefficient routing of 445 amino acid-derived substrates into mitochondrial energy production [50]. Delayed RQ-associated 446 dynamics of glutamine, oxoglutarate, and fumarate indicate a mismatch between substrate 447 availability and respiratory output [2]. Additionally, metabolites from arginine and proline 448 metabolism (creatine, spermidine, N -acetylputrescine, 4 -hydroxyproline) exhibited altered RQ 449 correlations, suggesting impaired mitochondrial redox buffering and compromised integrity [68]. 450 Perturbations in purine metabolism -including adenosine, deoxyadenosine monophosphate, 451 xanthosine, and ADP-ribose-further point to dysregulated ATP turnover and nucleotide imbalance. 452 Correlations with quinolinic acid and niacinamide reflect altered nicotinate and nicotinamide 453 metabolism, implicating disrupted NAD⁺ biosynthesis and redox state [48]. Additional RQ -454 associated changes in NADPH and TCA intermediates (fumarate, oxoglutarate) align with 455 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 23 impaired glutathione metabolism and reduced mitochondrial capacity. Microbial-derived 456 metabolites (trigonelline, phenylacetylglycine) were also identified in the correlation analysis, 457 suggesting potential gut microbiome interactions that become evident in circadian disruption. 458 Together, these findings suggest that the loss of circadian timing in per01 mutants lead to 459 widespread uncoupling of substrate utilization and mitochondrial respiration. This breakdown in 460 temporal regulation disrupts energy homeostasis, highlighting the essential role of the circadian 461 clock in coordinating metabolic flux with respiratory demand [1, 51, 69]. 462 Metabolic Desynchrony and Redox Imbalance in Wild-Type Flies Under Constant Darkness 463 In wild-type flies maintained under constant darkness (DD), the absence of environmental light 464 cues led to disrupted temporal alignment between mitochondrial respiration and metabolic 465 pathways [70, 71]. Generally, we note a shift towards reactive metabolic adjustments (positive 466 lags) compared to WT -LD instead of anticipatory preparation, indicating reduced metabolic 467 efficiency. RQ-correlated metabolites showed significant enrichment in arginine biosynthesis 468 (citrulline, ornithine, urea), glycine, serine, and threonine metabolism (L -cystathionine, 469 phosphoserine, pyruvic acid), and cysteine and methionine metabolism -indicating altered 470 integration of nitrogen and sulfur amino acid metabolism with respiratory activity [49]. Notably, 471 the convergence of pyruvic acid across multiple pathways suggests dysregulated entry points into 472 the TCA cycle, while the consistent association of L -cystathionine highlights impaired redox 473 buffering and methylation capacity [72, 73] . Altered correlations in arginine and proline 474 metabolism (ornithine, pyruvic acid) further underscore compromised coupling between amino 475 acid turnover and mitochondrial energy production [29]. Altered lipid metabolism and increased 476 nitrogen catabolism are likely compensatory mechanisms in the absence of environmental cues. 477 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 24 These findings build on previous reports of circadian desynchrony by providing direct functional 478 evidence that environmental light cues are essential for maintaining coherent coordination between 479 respiration and key metabolic circuits [50, 74] . The resulting metabolic misalignment under 480 constant darkness emphasizes the circadian clock’s dependence on external entrainment to regulate 481 energy homeostasis at the organismal level [54]. 482

483 This study demonstrates that sleep loss and circadian disruption impair metabolic homeostasis in 484 Drosophila melanogaster via distinct yet converging mechanisms. Wild-type flies under light-dark 485 cycles (WT-LD) maintained coordinated anticipatory respiratory and metabolic rhythms, while 486 sleep mutants ( fmn, sss) and circadian -disrupted flies ( per01, WT-DD) showed altered substrate 487 utilization, redox imbalance, and uncoupling of mitochondrial pathways , likely reactive to 488 respiration. These findings highlight the essential role of both sleep and circadian timing in 489 regulating energy metabolism. The presence of neuromodulatory metabolites (e.g., cAMP in 490 sleepless mutants) and microbial -derived compounds (trigonelline, phenylacetylglycine in per01 491 mutants) points toward additional layers of metabolic regulation influenced by neuronal activity 492 and gut microbiota interactions, revealing more complex metabolic dysregulation in sleep -493 deprived and circadian-disrupted states. By combining high-resolution respirometry with targeted 494 metabolomics, we establish Drosophila as a powerful model for investigating the molecular basis 495 of sleep- and clock-related metabolic dysfunction and potential therapeutic interventions. 496 Author Contributions 497 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 25 Conceptualization, F.A., and A.M.W.; methodology, F.A., D.M.M., P.H., J.K. and A.M.W.; 498 formal analysis, F.A., A.S.G., and A.M.W.; investigation, F.A. and D.M.M.; resources, A.S. 499 and A.M.W.; writing-original F.A.; writing-review and editing, F.A., A.S.G., J.K., A.S., and 500 A.M.W.; visualization, F.A., A.S.G, and A.M.W.; supervision, A.M.W.; and funding 501 acquisition, A.S. and A.M.W. 502 Funding 503 This work is supported by NIDDK and NHLBI of the National Institutes of Health under award 504 numbers R01- DK120757 and R01-HL142981. 505 Notes 506 The authors declare no competing financial interest. 507 ACKNOWLEDGMENTS 508 We thank Pinky Kain for assistance with Drosophila stock maintenance and insightful discussions 509 on Drosophila mutants. We are also grateful to Sara B. Noya for her support during respirometry 510 calibration. 511 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 26 Figure legends 512 Figure 1. 513 a-c. VCO₂, VO₂ and Respiratory quotient (RQ) profiles across the circadian cycle in different 514 genotypes. 515 VCO₂, VO₂ and RQ (VCO₂/VO₂) was continuously recorded at one -second intervals from 516 Zeitgeber Time (ZT) 0 to 24 and subsequently averaged into 5 -minute bins for analysis. Traces 517 represent mean VCO₂, VO₂ and RQ values across the circadian cycle for wild -type flies under 518 light-dark conditions (WT -LD), short -sleep mutants fumin (fmn) and sleepless (sss), circadian 519 clock mutant period01 (per01), and wild-type flies maintained in constant darkness (WT-DD). 520 d-f. Genotype-specific differences in VCO2, VO 2 and RQ measured over a full circadian 521 timecourse. 522 Boxplots show average values of (left to right) respiratory quotient (RQ), carbon dioxide 523 production (VCO₂), and oxygen consumption (VO₂) across genotypes and lighting conditions. 524 Measurements were taken continuously over a 24 -hour period using a flow -through MAVEn 525 system. Genotypes include wild-type under light-dark (WT-LD) and constant darkness (WT-DD), 526 short-sleep mutants fumin (fmn) and sleepless (sss), and circadian mutant per01. Group differences 527 were assessed using the Kruskal–Wallis test, followed by Dunn’s multiple comparisons post 528 hoc test . Significance is denoted as: p < 0.05 (* ), p < 0.01 ( **), p < 0.001 ( ***); ns = not 529 significant. 530 g-i. Body Weight and Normalized VCO₂ and VO₂ Across Different Genotypes. 531 Body weight (mg) and respiratory parameters (VCO₂ and VO₂) normalized to body weight (mg) 532 were measured in wild type (WT), fmn, sss, and per01 mutant flies. Statistical significance was 533 assessed using one-way ANOVA followed by Dunnett’s multiple comparisons test against WT. p 534 < 0.05 (*), p < 0.01 (**), p < 0.001 (***); ns = not significant. 535 Figure 2. Circadian timecourse of respiratory parameters across genotypes. 536 Normalized temporal profiles of carbon dioxide production (VCO₂) (top), oxygen consumption 537 (VO₂) (middle), and respiratory quotient (RQ) (bottom) plotted over a 24 -hour circadian cycle 538 (ZT0–24) for genotypes: wild-type in light-dark (WT-LD) and constant darkness (WT-DD), short-539 sleep mutants fumin (fmn) and sleepless (sss), and circadian mutant per01. Curves reveal genotype-540 specific rhythmicity and metabolic dynamics across circadian time. 541 Significant genotype × time interactions were observed for all variables (VCO₂, VO₂, RQ), 542 indicating altered circadian rhythmicity in mutants. p < 0.05 considered significant. 543 Figure 3. Circadian phase distribution of respiratory rhythms across genotypes. 544 Polar plots depicting the phase (peak timing) of rhythmic expression for carbon dioxide production 545 (VCO₂), oxygen consumption (VO₂), and respiratory quotient (RQ) over a 24 -hour cycle (ZT0–546 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 27 24) for each genotype. Genotypes include wild-type in light-dark (WT-LD) and constant darkness 547 (WT-DD), short -sleep mutants fumin (fmn) and sleepless (sss), and circadian mutant per01. 548 Statistical significance of rhythmicity was determined using the RAIN algorithm: darker -colored 549 bars indicate significant rhythms (p 0.05). 551 Figure 4. Temporal Profiling of Respiratory Quotient in Wild -Type Flies Under Light -Dark 552 Conditions. 553 Respiratory quotient (RQ) was extracted every 2 h across a 24 -hour light-dark (LD) cycle in WT 554 flies. Lag analyses were performed by advancing or delaying the RQ data by −120, −60, −30, −15, 555 −5, +5, +15, +30, +60-, and +120-minutes relative to Zeitgeber Time (ZT). Each panel represents 556 the RQ profile corresponding to a specific time shift, illustrating the phase-dependent dynamics of 557 respiration across the circadian cycle. 558 Figure 5. 559 560 a. Rank-based correlation between Respiratory Quotient (RQ) and selected metabolites. 561 562 Scatter plots depict the rank -order relationships between RQ and (A) 563 Hydroxyhexadecenoylcarnitine and (B) Quinolinate. Each point represents a timepoint after 564 aligning data with respective time shifts. Spearman’s rank correlation coefficient (ρ) and 565 corresponding p -values are indicated on each panel. A positive lag (+120 minutes for 566 Hydroxyhexadecenoylcarnitine) or negative lag (−120 minutes for Quinolinate) denotes the 567 temporal shift in RQ relative to the metabolite dataset. Statistical significance was defined as p < 568 0.05. 569 570 b. Temporal alignment of metabolite with respiratory quotient (RQ) in WT-LD flies. 571 572 Z-scored time series of RQ, Hydroxyhexadecenoylcarnitine, and Quinolinate across a 24 -hour 573 light-dark cycle. Top Panel: Hydroxyhexadecenoylcarnitine shows a positive lag of +120 minutes 574 and strong positive correlation with RQ (ρ=+0.78), suggesting its rise after changes in RQ. Bottom 575 Panel: Quinolinate shows a negative lag of –120 minutes and strong negative correlation with RQ 576 (ρ=-0.77), indicating it precedes changes in RQ. 577 Figure 6. Metabolite-Respiratory Quotient Correlations and Pathway Enrichment in WT -LD 578 Flies. 579 (A) Heatmap depicting Spearman correlations (|ρ| > 0.7, p < 0.05) between respiratory quotient 580 (RQ) and metabolite across the circadian cycle in wild-type flies maintained under light-dark (LD) 581 conditions. Metabolites were clustered based on correlation patterns, highlighting groups with 582 similar temporal associations with respiratory activity. ( B) Pathway enrichment analysis of 583 significantly correlated metabolites, illustrating metabolic pathways most closely linked to 584 respiratory dynamics. Pathways with a p -value less than 0.05 were considered statistically 585 significant. 586 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 28 Figure 7. Correlation of Metabolites with Respiratory Quotient and Pathway Enrichment in 587 Short-Sleep Mutants. 588 (A, C) Heatmaps showing Spearman correlations (|ρ| > 0.7, p < 0.05) between respiratory quotient 589 (RQ) and metabolites in the short-sleep mutants fmn (A) and sss (C). (B, D) Pathway enrichment 590 analyses of metabolites significantly correlated with RQ in fmn (B) and sss (D). Pathways with p-591 values less than 0.05 were considered statistically significant. 592 Figure 8. Correlation of Metabolites with Respiratory Quotient and Pathway Enrichment in 593 Circadian Mutant and Wild-Type Flies in Constant Darkness. 594 (A, C) Heatmaps displaying Spearman correlations (|ρ| > 0.7, p < 0.05) between respiratory 595 quotient (RQ) and metabolites in the circadian mutant per01 (A) and wild-type flies maintained in 596 constant darkness (WT-DD) (C). 597 (B, D) Pathway enrichment analyses of metabolites significantly correlated with RQ in per01 (B) 598 and WT-DD (D). Pathways with p-values less than 0.05 were considered statistically significant. 599 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 29

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PLoS Genet, 756 2009. 5(4): p. e1000442. 757 758 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 33 759 Figure 1. 760 0 2 4 6 8 10 12 14 16 18 20 22 24 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 ZT (Zeitgeber Time) VCO2 WT-LD fmn sss per01 WT-DD 0 2 4 6 8 10 12 14 16 18 20 22 24 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 ZT (Zeitgeber Time) VO2 0 2 4 6 8 10 12 14 16 18 20 22 24 0.75 1.00 1.25 1.50 ZT (Zeitgeber Time) Respiratory Quotient (VCO2/VO2) (a) (b) (c) WT-LD fmn sss per01 WT-DD 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 VCO2 ✱✱✱✱ ✱✱✱✱ ✱ ✱✱✱✱ WT-LD fmn sss per01 WT-DD 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 0.0018 0.0020 VO2 ✱✱✱✱ ✱✱✱✱ ns ✱✱✱✱ WT-LD fmn sss per01 WT-DD 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 Respiratory Quotient (VCO2/VO2) ✱✱✱✱ ✱✱✱✱ ns ns (d) (e) (f) Genotype-specific differences in VCO2, VO2 and RQ over a full circadian timecourse WT fmn sss per01 0.00000 0.00002 0.00004 0.00006 0.00008 0.00010 0.00012 Normalize VCO2 to fly weight in mg ✱✱✱✱ ✱✱✱✱ ns WT fmn sss per01 0.00000 0.00002 0.00004 0.00006 0.00008 0.00010 0.00012 Normalize VO2 to fly weight in mg ✱✱✱✱ ✱✱✱✱ ns WT fmn sss per01 0 5 10 15 20Weight (in mg) ✱✱ ✱✱ ns (g) (h) (i) Body Weight and Normalized VCO₂ and VO₂ Across Different Genotypes VCO₂, VO₂ and Respiratory quotient (RQ) profiles across the circadian cycle in different genotypes .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 34 761 Figure 2. 762 Temporal trend of RQ across genotypes fmn per01 sss WT−DD WT−LD Temporal trend of VO2 across genotypes fmn per01 sss WT−DD WT−LD Temporal trend of VCO2 across genotypes fmn per01 sss WT−DD WT−LD 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 −0.0001 0.0000 0.0001 0.0002 −0.0001 0.0000 0.0001 0.0002 −0.05 −0.10 −0.15 0.00 0.05 0.10 0.15 −0.0001 0.0000 0.0001 0.0002 0.0000 0.0001 0.0002 −0.10 −0.05 0.00 0.05 0.10 −0.0002 −0.0001 0.0000 0.0001 −0.0001 −0.0002 −0.0003 0.0000 0.0001 0.0002 −0.05 0.00 0.05 0.10 −0.0001 0.0000 0.0001 0.0000 0.0001 0.0002 −0.1 0.0 0.1 0.2 −0.00010 −0.00005 0.00000 0.00005 0.00010 −0.0001 0.0000 0.0001 0.0002 −0.05 −0.10 0.00 0.05 0.10 Normalized VCO2Normalized VO2Normalized RQ ZT (Zeitgeber Time) ZT (Zeitgeber Time) ZT (Zeitgeber Time) p value << 0.001 p value << 0.001 p value << 0.001 p value << 0.001 p value << 0.001 p value= 0.69 p value << 0.001 p value << 0.001 p value << 0.001 p value < 0.05 p value << 0.001 p value < 0.05 p value << 0.001 p value= 0.02 p value= 0.12 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 35 763 764 Figure 3. 765 2 4 6 8 14 12 10 16 18 20220/24 2 4 6 8 14 12 10 16 18 20220/24 2 4 6 8 14 12 10 16 18 20220/24 2 4 6 8 14 12 10 16 18 20220/24 2 4 6 8 14 12 10 16 18 20220/24 WT-DD WT-LD fmn per01 sss ZT (Zeitgeber Time) Variable RQ VCO2 VO2 RAIN p value not significant (p value>0.05) significant (p value<0.05) .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 36 766 767 Figure 4. 768 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT + 5 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT + 15 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT + 30 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT + 60 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT + 120 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 RQ (Respiratory Quotient) ZT (Zeitgeber Time) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT - 5 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT - 15 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT - 30 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT - 60 min RQ (Respiratory Quotient) 0 2 4 6 8 10 12 14 16 18 20 22 24 1.0 1.1 1.2 1.3 1.4 1.5 ZT - 120 min RQ (Respiratory Quotient) .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 37 769 Figure 5. 770 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Rank of RQ (Respiratory quotient) Rank of Hydroxyhexadecenoylcarnitine Spearman ρ (rho) =+0.78, p value <0.05, +120 min 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Rank of RQ (Respiratory quotient) Rank of Quinolinate Spearman ρ (rho) =-0.77, p value <0.05, -120 min 0 2 4 6 8 10 12 14 16 18 20 22 24 -3 -2 -1 0 1 2 ZT (Zeitgeber Time) Z-Score Hydroxyhexadecenoylcarnitine +120 min, ρ (rho) = +0.78 0 2 4 6 8 10 12 14 16 18 20 22 24 -3 -2 -1 0 1 2 ZT (Zeitgeber Time) Z-Score Quinolinate -120 min, ρ (rho) = -0.77 RQ Hydroxyhexadecenoylcarnitine Quinolinate a. Rank-based correlation between Respiratory Quotient (RQ) and selected metabolites b. Temporal alignment of metabolite with respiratory quotient (RQ) in WT-LD flies .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 38 771 772 773 Figure 6. 774 Clustered Heatmap of Metabolites (|ρ| >= 0.7 , p < 0.05 ) 0.20 0.78 0.75 0.76 0.76 0.75 −0.78 −0.74 −0.87 −0.71 −0.73 −0.33 0.71 −0.10 0.41 0.48 0.13 0.14 0.10 −0.41 −0.21 −0.30 −0.11 −0.34 0.34 0.07 −0.17 −0.13 −0.04 −0.03 −0.03 −0.01 0.06 0.08 0.01 −0.03 0.00 0.55 0.22 −0.19 −0.03 −0.06 −0.15 −0.06 0.13 0.13 0.20 0.05 −0.06 −0.07 0.59 0.37 −0.10 −0.03 −0.13 −0.12 0.04 0.18 0.22 0.30 −0.03 −0.03 −0.02 0.64 0.38 0.00 −0.18 −0.35 −0.23 −0.01 −0.02 0.41 0.41 0.03 0.16 0.12 0.76 0.19 −0.01 −0.11 −0.31 −0.27 −0.10 0.08 0.29 0.32 0.06 0.03 0.08 0.64 0.36 −0.04 −0.17 −0.43 −0.22 −0.08 −0.01 0.33 0.34 −0.02 0.13 0.22 0.62 0.24 −0.21 −0.22 −0.51 −0.07 −0.06 −0.13 0.20 0.07 −0.06 0.13 0.24 0.42 0.23 −0.06 −0.28 −0.32 0.03 0.02 −0.06 0.31 0.31 −0.05 0.21 0.36 0.31 −0.02 −0.77 −0.32 −0.11 −0.10 −0.17 −0.27 0.08 0.18 0.26 0.22 0.11 0.28 −0.31 −120 −60 −30 −15 −5 0 5 15 30 60 120 Quinolinate Hydroxyhexadecenoylcarnitine NADP+ FADH LPC 20:5 Nicotinamide Riboside Aspartate Carbamoyl Phosphates Betaine Malate Methionine Citrate/Isocitrate Glucose −1 −0.5 0 0.5 1 Pyrimidine metabolism Cysteine and methionine metabolism Glycine, serine and threonine metabolism Galactose metabolism Pyruvate metabolism beta−Alanine metabolism Pantothenate and CoA biosynthesis Starch and sucrose metabolism Histidine metabolism Nitrogen metabolism Glyoxylate and dicarboxylate metabolism Neomycin, kanamycin and gentamicin biosynthesis Citrate cycle (TCA cycle) Arginine biosynthesis Alanine, aspartate and glutamate metabolism Nicotinate and nicotinamide metabolism 1 2 3 4 −log10 (p−value) Enrichment Ratio 20 40 60 80 P−value 0.00 0.05 0.10 0.15 0.20 Overview of Enriched Metabolite Sets (Top 25) WT-LD[A] [B] .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 39 775 776 Figure 7. 777 Clustered Heatmap of Metabolites (|ρ| >= 0.7 , p < 0.05 ) 0.39 0.70 0.68 0.84 0.43 −0.23 −0.57 −0.71 −0.71 0.44 0.69 0.78 0.72 0.71 −0.36 −0.44 −0.48 −0.78 0.71 0.90 0.85 0.55 0.50 −0.32 −0.41 −0.52 −0.61 0.30 0.43 0.45 0.78 0.56 −0.30 −0.76 −0.36 −0.31 0.44 0.56 0.82 0.27 0.49 −0.55 −0.15 −0.38 −0.52 0.25 0.40 0.62 0.64 0.55 −0.64 −0.58 −0.24 −0.43 0.00 0.15 0.47 0.39 0.62 −0.69 −0.29 0.03 −0.29 0.01 0.17 0.47 0.38 0.56 −0.71 −0.47 −0.02 −0.34 0.22 0.25 0.51 0.39 0.68 −0.58 −0.54 −0.13 −0.42 0.13 0.13 0.31 0.41 0.62 −0.54 −0.40 −0.07 −0.15 0.22 0.30 0.41 0.22 0.23 −0.67 −0.17 −0.23 −0.15 −120 −60 −30 −15 −5 0 5 15 30 60 120 Glutamine Methylhistidines Inosine Citrulline LPC 18:0 LPC 20:1 2−Hydroxyglutarate 2−Aminopimelic Acid Butylcarnitine −1 −0.5 0 0.5 1 Pyrimidine metabolism Glyoxylate and dicarboxylate metabolism Alanine, aspartate and glutamate metabolism Histidine metabolism Butanoate metabolism Nitrogen metabolism Purine metabolism Arginine biosynthesis 1.0 1.5 2.0 2.5 3.0 −log10 (p−value) Enrichment Ratio 10 20 30 40 50 P−value 0.00 0.03 0.06 0.09 0.12 Overview of Enriched Metabolite Sets (Top 25) −0.68 −0.24 −0.43 −0.15 −0.22 −0.23 −0.41 0.16 0.64 0.32 0.00 −0.03 −0.08 0.13 0.07 −0.85 −0.62 −0.71 −0.08 −0.15 −0.67 −0.38 0.73 0.73 0.44 −0.04 0.15 0.12 0.15 0.17 −0.70 −0.69 −0.72 −0.29 −0.51 −0.59 −0.52 0.36 0.69 0.75 0.40 0.11 0.19 0.24 0.29 −0.66 −0.62 −0.73 −0.46 −0.54 −0.62 −0.33 0.47 0.50 0.61 0.43 0.04 0.11 0.10 0.19 −0.71 −0.66 −0.77 −0.38 −0.40 −0.68 −0.34 0.69 0.53 0.51 0.29 0.10 0.14 0.15 0.20 −0.70 −0.71 −0.78 0.00 −0.42 −0.62 −0.29 0.67 0.56 0.53 0.29 0.06 0.08 0.13 0.14 −0.69 −0.76 −0.69 −0.26 −0.32 −0.69 −0.44 0.72 0.70 0.54 0.22 0.20 0.20 0.34 0.24 −0.43 −0.56 −0.62 −0.48 −0.37 −0.65 −0.33 0.63 0.54 0.42 0.41 0.27 0.20 0.48 0.32 −0.34 −0.52 −0.63 −0.52 −0.55 −0.47 −0.25 0.36 0.48 0.47 0.48 0.19 0.06 0.42 0.22 −0.36 −0.52 −0.45 −0.73 −0.71 −0.64 −0.59 0.29 0.43 0.57 0.74 0.27 0.34 0.53 0.45 −0.06 −0.34 −0.03 −0.36 −0.36 −0.74 −0.83 0.26 0.41 0.39 0.52 0.72 0.74 0.88 0.80 −120 −60 −30 −15 −5 0 5 15 30 60 120 Ribitol/Xylitol Monosaccharide cAMP 4−Guanidinobutanoic acid Biotin Citrulline Sucrose Dimethylglycine/2−Aminobutyrate Glycine Stearoylcarnitine LPC 20:1 Acetylcarnitine Succinate Acetoacetate Choline −1 −0.5 0 0.5 1 Primary bile acid biosynthesis Tyrosine metabolism Valine, leucine and isoleucine degradation Glycerophospholipid metabolism Arginine and proline metabolism Porphyrin metabolism Glyoxylate and dicarboxylate metabolism Lipoic acid metabolism Glutathione metabolism Alanine, aspartate and glutamate metabolism Propanoate metabolism Citrate cycle (TCA cycle) Arginine biosynthesis Biotin metabolism Neomycin, kanamycin and gentamicin biosynthesis Galactose metabolism Starch and sucrose metabolism Butanoate metabolism Glycine, serine and threonine metabolism 1 2 3 −log10 (p−value) P−value 0.00 0.05 0.10 0.15 0.20 0.25 Enrichment Ratio 20 40 60 Overview of Enriched Metabolite Sets (Top 25) fumin [A] [B] [C] Clustered Heatmap of Metabolites (|ρ| >= 0.7 , p < 0.05 ) [D]sss .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 40 778 779 Figure 8. 780 781 782 783 784 785 786 787 788 0.73 0.45 0.25 0.31 0.38 0.29 0.15 −0.03 0.76 0.83 −0.03 0.08 0.14 0.24 −0.01 0.00 0.52 −0.07 −0.36 −0.40 −0.09 −0.17 −0.21 −0.13 −0.05 −0.24 0.17 0.29 0.00 0.00 −0.30 −0.73 −0.18 −0.78 −0.06 −0.21 −0.15 0.00 0.08 0.03 0.16 0.27 0.09 −0.03 −0.27 −0.44 −0.03 0.15 0.16 −0.34 −0.19 −0.04 0.08 0.15 −0.01 0.00 −0.01 −0.10 −0.20 −0.36 0.03 0.03 0.28 0.28 −0.24 0.52 0.73 0.54 −0.26 −0.15 −0.22 −0.25 −0.13 −0.08 −0.24 0.06 −0.11 0.00 −0.23 −0.71 0.00 −0.27 −0.29 −0.72 −0.49 −0.59 0.34 0.40 0.24 0.26 0.31 0.57 0.40 0.22 0.31 0.43 0.70 0.56 0.12 0.15 0.26 0.35 −0.36 −0.08 −0.15 −0.35 −0.27 −0.08 −0.25 −0.06 −0.22 −0.10 −0.11 −0.58 −0.67 −0.36 −0.38 −0.86 −0.62 −0.71 0.36 0.41 0.36 0.36 0.40 0.72 0.53 0.47 0.51 0.57 0.76 0.59 0.00 0.02 0.15 0.37 −0.31 −0.05 −0.17 −0.34 −0.34 −0.13 −0.30 −0.24 −0.09 0.08 −0.13 −0.37 −0.71 −0.44 −0.51 −0.69 −0.46 −0.76 0.36 0.48 0.38 0.46 0.45 0.77 0.60 0.53 0.57 0.58 0.77 0.71 0.00 −0.18 −0.04 0.13 −0.30 −0.04 −0.16 −0.35 −0.36 −0.15 −0.26 −0.31 −0.07 0.07 −0.10 −0.35 −0.74 −0.46 −0.55 0.00 −0.45 −0.75 0.35 0.45 0.42 0.50 0.48 0.76 0.59 0.56 0.60 0.61 0.00 0.00 0.35 −0.19 0.00 0.12 −0.24 −0.23 −0.13 −0.38 −0.40 −0.38 −0.21 −0.16 −0.15 −0.20 −0.41 −0.48 −0.76 −0.41 −0.45 −0.69 −0.74 −0.53 0.39 0.47 0.50 0.55 0.57 0.66 0.55 0.50 0.45 0.71 0.69 0.53 0.00 −0.13 0.20 0.27 −0.32 −0.42 −0.45 −0.62 −0.60 −0.57 −0.43 −0.21 0.00 −0.07 −0.33 −0.38 −0.81 −0.72 −0.75 −0.55 −0.57 −0.59 0.64 0.62 0.73 0.77 0.78 0.77 0.78 0.69 0.61 0.76 0.59 0.77 0.00 −0.25 0.11 0.15 −0.50 −0.63 −0.69 −0.78 −0.83 −0.72 −0.71 −0.27 0.10 −0.02 −0.18 0.07 −0.76 −0.84 −0.78 −0.35 −0.43 −0.41 0.74 0.76 0.76 0.81 0.81 0.85 0.85 0.71 0.76 0.57 0.53 0.64 0.73 −0.31 −0.08 0.15 −0.52 −0.71 −0.72 −0.55 −0.64 −0.54 −0.53 −0.17 0.02 −0.07 0.07 0.48 −0.15 −0.59 −0.57 0.05 0.08 0.06 0.56 0.63 0.36 0.45 0.48 0.36 0.38 0.28 0.40 −0.18 −0.04 0.31 0.49 0.17 −0.08 0.22 0.13 −0.36 −0.25 −0.08 0.06 −0.22 −0.16 0.73 0.12 0.13 −0.73 −0.45 0.10 −0.02 0.10 0.01 −0.16 −0.13 0.29 0.32 0.08 0.03 0.08 0.01 0.08 −0.22 −0.18 0.13 0.12 0.20 −0.10 0.00 0.15 0.05 −120 −60 −30 −15 −5 0 5 15 30 60 120 Creatine Alanine Glutamine Spermidine dAMP Lauroylcarnitine Methylhistidines Octadecadienoylcarnitine Creatinine NADPH Adenosine Xanthosine LPC 14:0 Acetylputrescine cis−5−Tetradecenoylcarnitine trans−4−Hydroxyproline ADP−ribose Quinolinate Methionine Propionylcarnitine Trigonelline Inositols Norepinephrine 2−Aminopimelic acid Fumarate Homocysteine Phenylacetylglycine Itaconic acid Alpha−ketoglutarate Coenzyme A (CoA) Leucine/Isoleucine Nicotinamide Aminosugar phosphates dUMP −1 −0.5 0 0.5 1 0.13 −0.19 −0.41 0.45 0.78 0.23 0.39 −0.07 0.04 −0.01 −0.48 −0.72 −0.25 −0.06 0.13 −0.22 0.59 0.17 0.11 0.10 −0.04 0.05 −0.19 −0.22 −0.15 −0.36 −0.14 −0.02 −0.18 0.45 0.24 0.13 −0.04 −0.21 0.03 −0.29 −0.05 −0.19 −0.57 −0.01 −0.06 −0.13 0.45 0.08 0.08 0.05 −0.20 0.02 −0.71 −0.46 −0.24 −0.46 0.10 0.03 0.05 0.42 0.03 0.01 −0.01 −0.10 0.01 −0.59 −0.52 −0.18 −0.41 0.22 0.06 0.17 0.30 −0.13 −0.06 −0.06 0.03 0.03 −0.51 −0.61 −0.12 −0.27 0.52 0.06 0.10 0.41 −0.09 −0.10 0.06 0.11 0.03 −0.25 −0.71 −0.25 −0.20 0.64 0.22 0.23 0.39 −0.17 −0.05 0.00 0.13 −0.09 −0.02 −0.63 −0.24 −0.17 0.72 0.29 0.31 0.46 0.00 0.07 0.15 0.03 −0.19 0.26 −0.52 −0.35 −0.29 0.41 0.65 0.53 0.71 0.22 0.81 0.71 −0.76 −0.72 0.15 −0.11 −0.32 −0.71 0.47 0.71 0.79 0.19 −0.14 0.42 0.40 −0.41 −0.63 0.43 0.15 0.15 −0.11 −120 −60 −30 −15 −5 0 5 15 30 60 120 UDP Cystathionine Urea Citrulline Phosphoserine Ornithine Sucrose 3−isopropylmalic acid Pyruvate 22:0−Carnitine Stearoylcarnitine 2−hydroxyglutarate 3−Indoleacetic Acid −1 −0.5 0 0.5 1 Valine, leucine and isoleucine degradation Glycine, serine and threonine metabolism Glyoxylate and dicarboxylate metabolism Inositol phosphate metabolism Lipoic acid metabolism Galactose metabolism Pyruvate metabolism beta−Alanine metabolism Selenocompound metabolism Histidine metabolism Butanoate metabolism Ascorbate and aldarate metabolism Tyrosine metabolism Valine, leucine and isoleucine biosynthesis Pyrimidine metabolism Nitrogen metabolism Cysteine and methionine metabolism Glutathione metabolism Citrate cycle (TCA cycle) Nicotinate and nicotinamide metabolism Purine metabolism Arginine and proline metabolism Arginine biosynthesis Alanine, aspartate and glutamate metabolism 1 2 3 −log10 (p−value) P−value 0.0 0.1 0.2 0.3 0.4 Enrichment Ratio 5 10 15 Overview of Enriched Metabolite Sets (Top 25) Purine metabolism Tyrosine metabolism Tryptophan metabolism Pyrimidine metabolism Glyoxylate and dicarboxylate metabolism Lipoic acid metabolism Glutathione metabolism Alanine, aspartate and glutamate metabolism Galactose metabolism Glycolysis / Gluconeogenesis Pyruvate metabolism Citrate cycle (TCA cycle) Starch and sucrose metabolism Butanoate metabolism Arginine and proline metabolism Cysteine and methionine metabolism Glycine, serine and threonine metabolism Arginine biosynthesis 1 2 3 4 −log10 (p−value) P−value 0.0 0.1 0.2 0.3 Enrichment Ratio 10 20 30 Overview of Enriched Metabolite Sets (Top 25) per01 [A] Clustered Heatmap of Metabolites (|ρ| >= 0.7 , p = 0.7 , p < 0.05 ) [D] .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint 41 Table 1. Circadian rhythmicity metrics for respiratory parameters across genotypes 789 790 Genotype RQ JTK Lag RQ JTK Period RQ RAIN p-value VCO₂ JTK Lag VCO₂ JTK Period VCO₂ RAIN p-value VO2 JTK Lag VO2 JTK Period VO2 RAIN p-value WT-LD 19.75 20 0.12 4 24 1.1143e-05 4 24 0.001 fumin 4.25 22.5 9.54e-08 3.25 24 0.0002 14 20 0.69 sss 12.5 20 1.41e-07 2 24 1.33e-09 1.25 24 1.14e-10 per01 2 20 0.006 3 24 1.05e-13 6 21.5 1.82e-07 WT-DD 19.25 23 0.02 7.5 24 0.0002 7 24 9.4145e-06 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 25, 2025. ; https://doi.org/10.1101/2025.07.22.665580doi: bioRxiv preprint