Phosphoproteomics Uncovers Exercise Intensity-Specific Skeletal Muscle Signaling Networks Underlying High-Intensity Interval Training in Healthy Male Participants

Abstract

Background: In response to exercise, protein kinases and signaling networks are engaged to blunt homeostatic threats generated by acute contraction-induced increases in skeletal muscle energy and oxygen demand, as well as serving roles in the adaptive response to chronic exercise training to blunt future disruptions to homeostasis. High-intensity interval training (HIIT) is a time-efficient exercise modality that induces superior or similar health-promoting skeletal muscle and whole-body adaptations compared with prolonged, moderate-intensity continuous training (MICT). However, the skeletal muscle signaling pathways underlying HIIT's exercise intensity-specific adaptive responses are unknown.

Objective: We mapped human muscle kinases, substrates, and signaling pathways activated/deactivated by an acute bout of HIIT versus work-matched MICT.

Methods: In a randomized crossover trial design (Australian New Zealand Clinical Trials Registry number ACTRN12619000819123; prospectively registered 6 June 2019), ten healthy male participants (age 25.4 ± 3.2 years; BMI 23.5 ± 1.6 kg/m²; $\dot{V}{\text{O}}_{2}max$ 37.9 ± 5.2 ml/kg/min, mean values ± SD) completed a single bout of HIIT and MICT cycling separated by ≥ 10 days and matched for total work (67.9 ± 10.2 kJ) and duration (10 min). Mass spectrometry-based phosphoproteomic analysis of muscle biopsy samples collected before, during (5 min), and immediately following (10 min) each exercise bout, to map acute temporal signaling responses to HIIT and MICT, identified and quantified 14,931 total phosphopeptides, corresponding to 8509 phosphorylation sites.

Results: Bioinformatic analyses uncovered exercise intensity-specific signaling networks, including > 1000 differentially phosphorylated sites (± 1.5-fold change; adjusted P < 0.05; ≥ 3 participants) after 5 min and 10 min HIIT and/or MICT relative to rest. After 5 and 10 min, 92 and 348 sites were differentially phosphorylated by HIIT, respectively, versus MICT. Plasma lactate concentrations throughout HIIT were higher than MICT (P < 0.05), and correlation analyses identified > 3000 phosphosites significantly correlated with lactate (q < 0.05) including top functional phosphosites underlying metabolic regulation.

Conclusions: Collectively, this first global map of the work-matched HIIT versus MICT signaling networks has revealed rapid exercise intensity-specific regulation of kinases, substrates, and pathways in human skeletal muscle that may contribute to HIIT's skeletal muscle adaptations and health-promoting effects. Preprint: The preprint version of this work is available on medRxiv, https://doi.org/10:1101/2024.07.11.24310302 .

Fig. 1

Preliminary testing and randomized crossover trial design, participant baseline characteristics, and plasma lactate and glucose responses to HIIT and MICT. As detailed in the overall study schematic (A), participants first underwent preliminary testing and dietary control prior to each experimental HIIT or MICT trial day. Participants arrived at the laboratory following overnight fasting for baseline measurements, a dual-energy X-ray absorptiometry (DXA) body composition scan and resting metabolic rate (RMR) testing. Each participant then completed an incremental fitness test to volitional fatigue on a cycle ergometer to determine peak oxygen uptake ($\dot{V}{\text{O}}_2\text{peak}$) and maximal aerobic power (MAP) to calculate the work rate for the subsequent two workload (67.9 ± 10.2 kJ) and total duration (10 min) matched HIIT and MICT exercise trials. Participants’ food and fluid intake for all meals and snacks was recorded over a 3-day period using a mobile phone application and analyzed by an accredited research dietician. A standardized dinner was consumed by each participant the evening prior to each exercise trial, with no caffeine or alcohol consumed 20 or 24 h prior, respectively. In a randomized crossover design, participants were randomly assigned their first exercise trial (i.e., HIIT or MICT) prior to commencing trial days and did not perform any exercise in the 72 h prior to each trial day. HIIT and MICT exercise trials were separated by at least a 10-day recovery period. On each experimental day, participants reported to the laboratory following overnight fasting, and vastus lateralis skeletal muscle biopsies and venous blood samples were collected pre-exercise (0 min), mid-exercise (5 min), and immediately post-exercise (10 min). Participant characteristics are listed in (B). Plasma (C) lactate and (D) glucose concentrations across the acute HIIT and MICT exercise bouts were determined using a YSI Analyzer. No interaction effect was observed for plasma lactate in (C); P = 0.0573. Heart rate (E) and rating of perceived exhaustion (F; RPE; Borg RPE scale out of 20) were recorded at 1 min (i.e., following completion of the first HIIT “on” interval), 5 min, and 10 min during HIIT or MICT trials. Data are presented as mean ± SD; two-way ANOVA with repeated measurements, Tukey’s test for multiple comparisons; **P < 0.01 versus 0 min (or 1 min in E and F); ***P < 0.001 versus 0 min (or 1 min in E and F); ****P < 0.0001 versus 0 min (or 1 min in E and F); ^#P < 0.05 versus 5 min; n = 10 for each exercise intensity and timepoint

Fig. 2

Human skeletal muscle phosphoproteomic analysis reveals effective pre-exercise standardization and distinct signaling profile clusters in response to HIIT versus MICT after 5 min and 10 min. Vastus lateralis skeletal muscle biopsies were collected pre-exercise (0 min), mid-exercise (5 min), and immediately post-exercise (10 min) from each participant during the HIIT and MICT exercise trials (A). The 10 min HIIT cycling session consisted of alternating 1 min intervals at 85 ± 0.1% of individual MAP (176 ± 34 W) and 1 min active recovery intervals at 50 W. The total duration- and work-matched MICT cycling session consisted of 10 min cycling at 55 ± 2% of individual MAP (113 ± 17 W). Each of the 60 total muscle biopsy samples were prepared and subjected to LC–MS/MS analysis to accurately identify and quantity skeletal muscle protein phosphorylation sites at 0 min (pre-exercise), 5 min (mid-exercise), and 10 min (post-exercise) for both the HIIT and MICT exercise trials (A). Principal component analysis (B) and hierarchical clustering (C) of the phosphoproteomic datasets resulting from LC–MS/MS analysis of the 60 muscle biopsy samples were performed using the PhosR phosphoproteomic data analysis package (Kim et al. 2021 Cell Reports). Each individual point (B) or line (C) represents a unique biological sample, and samples are color-coded by exercise intensity and timepoint. Overall, 19% of the total variance in the overall phosphoproteomic dataset was explained by principal component (PC)1, while PC2 explained 6% of the variance. The total number of phosphopeptides and phosphosites identified and quantified using MS are shown (D), in addition to the number of differentially regulated phosphosites (± 1.5-fold change and adjusted P < 0.05) from each timepoint and/or exercise intensity comparison (F). Volcano plot shows the median phosphopeptide log₂ fold change (x-axis) plotted against the − log₁₀ P-value (y-axis) for each pre-exercise condition, with no differentially regulated phosphosites at rest between crossover trials (E)

Fig. 3

Human skeletal muscle phosphorylation sites differentially regulated by an acute bout of work- and duration-matched HIIT and/or MICT. Volcano plots showing the median phosphopeptide log₂ fold change (x-axis) are plotted against the − log₁₀ P value (y-axis) for each individual exercise intensity versus the respective timepoint (A–F). From the ~ 15,000 total phosphopeptides detected, significantly up-regulated (red dots) and down-regulated (blue dots) phosphosites are shown (± 1.5-fold change and adjusted P < 0.05), with black dots representing phosphosites that were detected but not significantly regulated by exercise. Volcano plots comparing signaling responses with each exercise intensity (i.e., HIIT versus MICT) at each timepoint are shown in G, H

Fig. 4

Kinase and pathway enrichment uncovers common and unique kinases and pathways regulated by HIIT and/or MICT. Kinase activity (A) was inferred via direction analysis using kinase perturbation analysis (KinasePA; [28]) to annotate and visualize how kinases and their known substrates are perturbed by each exercise intensity and timepoint. Pathway enrichment analysis (B) was performed using the Reactome database [30] to determine biological pathways that are enriched within the lists of significantly regulated genes (corresponding to their respective phosphoproteins) for each exercise intensity and timepoint relative to its respective pre-exercise control. For kinase activity inference (A) and pathway enrichment (B), z-scores above and below the dotted lines (corresponding to |z-score|> 1.64) were considered as increased or decreased by exercise, respectively, as they correspond to a one-tailed P value of ~ 0.05 in normally distributed data

Fig. 5

Kinase–substrate predictions and pathway enrichment analysis identify differential regulation of downstream substrates and pathways in response to HIIT versus MICT. Kinase–substrate associations were predicted in response to HIIT and MICT via the phosphoproteome signaling profiles and kinase recognition motif of known substrates using PhosR [22]. This analysis generated prediction matrices, with columns corresponding to kinases, rows corresponding to phosphosites, and values in the heatmaps denoting how likely a phosphosite is phosphorylated by a given kinase in response to HIIT (A) and MICT (B). Pathway enrichment analysis was performed using kinase–substrate predictions (i.e., phosphosites with a high prediction score for each kinase) to determine how kinases regulate common and/or distinct signaling pathways in response to HIIT (C) and MICT (D)

Fig. 6

Signalome network highlights distinct HIIT and MICT kinase clusters and differential signaling trajectories in response to each exercise intensity. Signalome networks for HIIT (A) and MICT (B) exercise were constructed using the PhosR phosphoproteomic data analysis package [22]. This “signalome” construction method utilized the phosphoproteome signaling profile and kinase recognition motif of known substrates to visualize the interaction of kinases and their collective actions on signal transduction. Kinases clustered together are highly correlated in terms of kinase–substrate predictions. Visualization of five phosphoprotein clusters from the phosphoproteomic dataset highlights distinct kinase–substrate regulation within the HIIT and MICT signaling networks, with shared and unique signaling trajectories shown for a panel of kinases in response to HIIT (C) and MICT (D)

Fig. 7

Correlation of HIIT and MICT phosphosites and plasma lactate levels identifies > 3000 lactate-correlated sites including functional phosphosites that govern protein activity and metabolic regulation. Spearman correlation of individual phosphorylation sites (n = 8509 total) with plasma lactate concentrations at each timepoint and exercise intensity (n = 60 total plasma samples analyzed) are shown for four of the most significantly correlated sites (q < 0.05 with Benjamini–Hochberg FDR) with annotated functional roles in governing their respective phosphoprotein’s activation state and regulating a range of key metabolic processes (e.g., glycolysis, glucose transport, and mitochondrial biogenesis) including PDHA1 S201 (A), RPTOR S859 (B), TFEB S123/S128/S136 (C), and TBC1D4 S588 (D)