Acute exercise rewires the proteomic landscape of human immune cells

Domain: biology
Published: 2026-01-02
Editorial outcome: minor_revision

Abstract
The positive effect of exercise on the immune system is widely acknowledged, but the molecular response of immune cells to exercise remains largely unknown. Here, we perform mass-spectrometry-based proteomic analysis on peripheral blood mononuclear cells (PBMC) at a depth of >6000 proteins. Comparing high-intensity interval exercise (HIIE) and moderate-intensity continuous exercise (MICE), matched for time and workload, we identify versatile changes in the proteomic makeup of PBMCs and reveal profound alterations, related to effector function and immune cell activation pathways within one hour following exercise. These changes are more pronounced after HIIE compared to MICE and occur despite identical immune cell mobilization patterns between the two exercise conditions. We further identify an immunoproteomicsignature that effectively predicts cardiorespiratory fitness, thus allowing insights into potential exercise-triggered adaptations and immunological health benefits that are mediated by exercise. This study provides a reliable data resource that expands our knowledge on how exercise modulates the immune system, and delivers biological evidence supporting the WHO 2020 guidelines, which highlight exercise intensity as a relevant factor to maintain health.

Introduction
Physical exercise is one of the most powerful strategies to prevent and counteract acute and chronic diseases across the entire human lifespan. The health benefits of exercise are demonstrated by numerous clinical and observational trials1,2, but the underlying biological mechanisms are poorly understood. Given the broad implications of the immune system in protecting from disease, there is a clear rationale for comprehensive investigations of the impact of exercise on immune cells.

Both the exercise-induced mobilization of specific immune cells into circulation as well as an increased cytokine release are well-described phenomena in exercise immunology3. However, few investigations have evaluated the molecular alterations in immune cells triggered by exercise. Bulk RNA sequencing revealed a complex interplay of up- and downregulated transcripts that peaked two minutes after exercise and returned to baseline within 30–60 min in peripheral blood mononuclear cells (PBMC). Enriched pathways were predominantly related to inflammatory signaling and immune activation, but also included other processes such as cell growth and mobility4. While the number and kinetics of transcripts suggest that many expected and some novel signaling pathways are initiated by acute exercise, the proteomic response of PBMCs remains unclear.

Given that the proteomic makeup of cells defines their phenotype and function, bulk proteomic analyses depict a suitable starting point to evaluate the impact of exercise on circulating immune cells. Besides offering a comprehensive overview of the proteomic response of PBMCs to exercise, this also paves the way to an unbiased, data-driven discovery of exercise-induced signaling pathways that may underlie immunological adaptations to exercise. In addition, hypothesis-generating, explorative approaches create a malleable foundation for follow-up investigations applying more targeted strategies with a focus on specific immune cell subsets. In analogy to the World Health Organization (WHO) 2020 guidelines on physical activity, which highlight exercise intensity as a crucial variable for health promotion5, and considering that high-intensity interval exercise (HIIE) is associated with greater cardiovascular strain compared to moderate-intensity continuous exercise (MICE)6, we hypothesize that HIIE induces stronger proteomic alterations in PBMCs compared to MICE.

Here, we apply state-of-the-art mass spectrometry-based proteomics and flow cytometry-based immune cell phenotyping in a randomized crossover study to compare how time- and energy-matched HIIE and MICE reshape the immune cell proteome of 23 young healthy adults. We demonstrate that the proteomic makeup of PBMCs is rewired towards immune cell activation and effector function pathways in the recovery phase following exercise and that these changes are indeed more potently induced by HIIE compared to MICE, despite no differences in immune cell mobilization between the two exercise modalities. Using prediction models, we additionally identify an immunoproteomicsignature associated with cardiorespiratory fitness, which enables insights into molecular targets that are potentially responsive to exercise training and might thus be involved in immunological health adaptations mediated by exercise. Overall, our data indicate that exercising at higher intensity is necessary to induce proteomic changes associated with immune function, providing biological support for exercise intensity as a central component of the WHO 2020 physical activity guidelines.

Results
Studydesignandparticipantcharacteristics
We designed a randomized crossover study comparing time- and workload-matched HIIE and MICE to investigate the impact of exercise intensity on the proteome of immune cells. PBMCs were isolated at baseline, immediately after, and 1h after each exercise condition (Fig. 1A). In total, data from 23 overnight-fasted recreationally active runners (12 female, 11 male) was collected. Participants exhibited a mean (±SD) age of 30±4 years, a body mass index (BMI) of 22.2±2.38 kg m−2, and a cardiorespiratory fitness (measured as peak oxygen uptake, V ̇ O ) of 56.64±6.43 ml min−1 kg−1 (Table 1 and Supplementary Data S1).

The collected samples were analyzed via state-of-the-art liquid-chromatography mass-spectrometry (LC-MS/MS) and spectral flow cytometry. Comparable to other studies in the field of immunology, our proteomics analysis yielded an excellent coverage with a total of 7385 identified and 6759 quantified proteins. So far, large-scale proteomic analyses on immune cells have mostly been applied in animal models7 or on resting samples from healthy donors8, making our study the first to apply these methods in a randomized interventional setting with repeated baselines. After data preprocessing, our dataset comprised 6,039 proteins across 23 participants in 2 exercise conditions with 3 measurement timepoints, respectively. This makes our dataset the largest immune cell proteomics data source available in exercise context to date (Fig. 1B). Immune cell phenotyping by spectral flow cytometry was performed on a total of 3,537,855 vital lymphocytes (Supplementary Data S2) to assess exercise-induced shifts within the PBMC compartment (Supplementary Fig. S1A). So far, this has been disregarded in exercise studies applying omics approaches on immune cells4.

Immunecellmobilizationandredistributionisindependentofexerciseintensity
The mobilization and redistribution of immune cells in response to acute exercise is one of the core phenomena of exercise immunology, and it is nowadays agreed upon that the recovery phase following exercise is characterized by a transmigration of lymphocytes from the bloodstream into peripheral tissues, with crucial implications in many disease settings, including anticancer immunity9,10, and immunological defense11,12. A remaining topic of debate, however, is whether exercise intensity influences the magnitude of immune cell mobilization since previous studies on this topic were matched for exercise duration, but not workload13,14. Thus, before dissecting proteomic alterations of PBMCs in response to exercise, we aimed to clarify whether immune cell kinetics differ in dependence on exercise intensity, since this would lead to a different composition of our PBMC samples in response to HIIE and MICE.

By applying unsupervised immune cell clustering using self-organizing maps (SOM), we identified 6 main clusters in our PBMC samples, which were mapped to the corresponding immune cell populations based on their marker expression. Visual inspection of the identified clusters (Fig. 1C) and quantification of exercise-induced cluster shifts resulted in a similar distribution pattern of immune cell clusters between HIIE and MICE with a mean delta of 0.004 ± 0.9% (Fig. 1D and Supplementary Data S2). Confirming these findings, absolute numbers of immune cell populations did not reveal time×condition interaction effects when applying linear mixed models (Supplementary Fig. S1B; Supplementary Data S3) and the proportional contribution of each immune cell population to the PBMC compartment was similar between HIIE and MICE (Fig. 1E and Supplementary Data S4). This suggests that exercise triggers similar mobilization and redistribution patterns independent of exercise intensity and indicates that exercise-induced changes in PBMC composition do not differ between HIIE and MICE.

Measuresofvariabilityindicatehighreliabilityofthegeneratedproteomicsdataset
Inter-individual variability of all quantified proteins resulted in median coefficients of variation (CV) of <5% for all measurement timepoints and conditions (Fig. 2A). This is considerably lower than in other proteomics studies in exercise context4,15 and underlines the homogeneity of our study population and the analytic quality of our proteomics pipeline. The applied crossover design additionally enabled us to calculate intra-individual protein variability. The overall mean difference between the two baselines amounted to 0.13±0.75% for females and 0.06±0.59% for males (Fig. 2B). To assess variability on a per-protein level, we combined multiple measures of variability (i.e., mean CV at baseline, mean CV in response to exercise, and mean difference between the two baselines) into an integrated variability score. 99.34% of all quantified proteins revealed a proteomic variability of <10% and 83.34% achieved a score of <5% (Fig. 2C and Supplementary Fig. S2A, S2B). In summary, the low variability emphasizes the high quality of our study setup, making our generated proteomics dataset highly reliable.

Acuteexercisealterstheimmunecellproteome
To obtain first insights into the proteomic alterations induced by exercise, we performed principal component analysis (PCA). Visual inspection of the PCA suggested that the variation within our samples was mainly accounted for by measurement timepoints but not exercise condition per se (Fig. 2D). PCA also suggested that sex and intervention day had little impact on the variation of our data (Supplementary Fig. S2C). Performing PCA separated by condition and measurement timepoint indicated that HIIE accounted for more variation 1h after exercise than MICE (Supplementary Fig. S2D, E).

Next, we compared the impact of HIIE and MICE on proteomic alterations in PBMCs using linear mixed models. We identified 1408 time effects, 119 group effects, and 27 time×group interaction effects (Fig. 2E). Including sex as a fixed effect in our analysis did not yield significant results. Dissection of the obtained results revealed more time, group, and time×group interaction effects 1h after exercise compared to immediately after exercise and more alterations in HIIE compared to MICE (Fig. 2E). In detail, HIIE was marked by 1377 significantly altered proteins, while MICE only caused significant alterations in 64. The fact that immune cell counts and proportions did not differ between HIIE and MICE 1h after exercise (Fig. 1E and Supplementary S1B) suggests that the proteomic differences are not caused by a distinct PBMC composition. In line with our results obtained by PCA, this suggests that HIIE leads to a more profound reorganization of the PBMC proteome compared to MICE.

ProteomicalterationsdifferbetweenHIIEandMICE
To evaluate proteins that were distinctly regulated by HIIE compared to MICE, we first dissected the interaction effects of our linear mixed models (Supplementary Data S5). Immediately after exercise, we observed 5 proteins with distinct kinetics in HIIE compared to MICE (Fig. 2F). Among these proteins, synaptotagmin-like protein 2 (SYTL2), a crucial contributor to cytotoxic granule exocytosis in lymphocytes16, displayed a strong increase in response to HIIE, while it remained unaltered in MICE (log FC=1.195, p=3.1×10−2). Similarly, bone marrow stromal antigen 2 (BST2) – known for its role in blocking virus release from infected cells17 – increased in response to HIIE but decreased in MICE (log FC=1.4, p=2.2×10−2; Fig. 2F, G). This gives first insights into the immunomodulatory potential of HIIE and suggests immunological adaptations dependent on exercise intensity immediately after exercise.

In the recovery phase after exercise, we observed 25 interaction effects. Hierarchical clustering yielded two major clusters of proteins marked by opposed kinetics in HIIE compared to MICE (Fig. 2F, G). For instance, we observed an increase 1h after HIIE, but a decrease until 1h after MICE for toll-like receptor 1 (TLR1; log FC=1.06, p=1.6×10−3), BST2 (log FC=1.92, p=3.4×10−9), and cluster of differentiation 302 (CD302; log FC=0.85, p=1.5×10–2). TLR1 is the most abundantly expressed TLR on NK cells18 and serves as a membrane-bound pattern recognition receptor for microbial lipopeptides that triggers cytokine production and NK cell cytotoxicity upon stimulation19,20. Several studies have demonstrated that TLR1 is crucial for antimicrobial defense21,22, suggesting that exercise-induced increases in TLR1 might reinforce NK cell-mediated immunity against invading pathogens. Of note, BST2 was the only protein that continued to increase from post-exercise to 1h post exercise in HIIE, suggesting sustained intensity-dependent adaptions in immunological defense.

In contrast, proteins such as SH2 domain-containing protein 1B (SH2D1B), which serves as a cytoplasmic adapter regulating NK cell effector functions23, or asparagine synthetase (ASNS), which was previously shown to regulate CD8+ T cell activation, differentiation, and effector function24,25 were marked by a decrease in the recovery period following HIIE, while they remained unaltered or increased in MICE (Fig. 2F, G; SH2D1B: log FC=−1.25, p=1.0×10–3; ASNS: log FC=−1.25, p=7.2×10−3). Of note, our statistical analysis also yielded several group differences between HIIE and MICE (Fig. 2H, I and Supplementary Data S5).

In summary, our results suggest that the recovery phase following HIIE is marked by more profound alterations of the immune cell proteome compared to MICE. We provide evidence that several proteins related to immune effector function are differentially expressed over time between HIIE and MICE. Against the backdrop of our flow cytometry results, these effects occur despite identical immune cell mobilization patterns between the two exercise conditions.

Exercisereshapestheimmunecellproteometowardseffectorfunction
To add a functional dimension to our results, we made use of the Gene Ontology (GO) Resource26. GO over-representation analysis yielded 27 enriched GO terms in HIIE and 9 enriched GO terms in MICE. Interestingly, enriched GO terms were centered around immune effector functions in both HIIE and MICE, with biological processes like “disruption of cell in another organism” or “killing of cells of another organisms” yielding high enrichment (Fig. 3A).

For proteins altered by MICE, GO over-representation analysis additionally yielded several biological processes related to lymphocyte effector function, such as “lymphocyte mediated immunity” or “T cell mediated immunity”. Given that the over-representation analysis was conducted with much more proteins for HIIE, we additionally identified several cellular components and molecular functions in this analysis. Semantic evaluation of the identified GO terms underlined their association with immune effector function. For instance, “exogeneous protein binding”, “virus receptor activity”, and “endopeptidase activity” are known molecular functions in the context of immunological defense against viruses27,28. Similarly, “proteasome core complex” and “peroxisomal membrane” depict cellular components associated with such molecular function and biological processes (Fig. 3A). Collectively, our GO over-representation analysis points towards enhanced regulation of immune effector functions in response to both HIIE and MICE (Supplementary Data S6).

Time-resolvedproteinchangesdifferbetweenHIIEandMICE
Within all proteins altered by exercise (n=1408), we found 1344 proteins that were uniquely altered by HIIE, 31 proteins that were uniquely altered by MICE, and 33 proteins that were altered by both exercise conditions (Fig. 3B). Analysis of proteins altered by HIIE suggested two major protein clusters that were characterized by increased or decreased protein abundance 1h after HIIE compared to baseline (Fig. 3C). Considering the large number of proteins altered by HIIE compared to MICE, we took different approaches in analyzing the time effects of each exercise condition.

For proteins altered by both exercise conditions and proteins uniquely altered by MICE, we performed hierarchical clustering to identify proteins displaying similar kinetics. Interestingly, when analyzing proteins that were altered by both exercise conditions, hierarchical clustering yielded 2 major protein clusters: one cluster containing proteins with similar kinetics between HIIE and MICE and one cluster containing proteins with different kinetics (Fig. 3D). In absolute terms, most of the proteins responded similarly with only 4 proteins showing higher values in HIIE, including the antiviral protein BST2, which we previously identified in our analysis of time×group interaction effects (Fig. 2F). In addition, many of the proteins that were shared between HIIE and MICE were associated with immune effector functions, suggesting a shared regulation of several immunological processes by exercise. Examples of such proteins include granzymes (e.g., GZMB, GZMH, GZMM), perforin-1 (PRF1), or guanylate-binding proteins 5 (GBP5; Fig. 3D).

Similarly, hierarchical clustering of proteins uniquely altered by MICE identified 2 major clusters that separated proteins that decreased in response to MICE from proteins that increased, while showing no alterations in HIIE, respectively (Fig. 3E). In line with the observed time×group interaction effects (Fig. 2F) most proteins displayed lower abundance in response to MICE (Fig. 3E). Of note, although the lower number of time effects and the decreased abundance of many proteins might suggest reduced immune effector functions in response to MICE, it is crucial to emphasize that several proteins with immunological functions, especially those jointly regulated between HIIE and MICE, revealed increased abundance in response to MICE as well. Thus, while our data suggests that the immunoproteomic impact of MICE seems to be less pronounced than that of HIIE, there is no conclusive evidence suggesting reduced immune effector function per se in response to MICE.

IdentificationofsharedanduniqueimmuneeffectorfunctionsregulatedbyHIIE
To dissect the proteomic alterations in response to HIIE, we performed fuzzy c-means clustering and mapped the altered proteins (n=1377) to four distinct clusters by means of their relative membership (Fig. 4A and Supplementary Data S7). The four identified clusters confirmed what hierarchical clustering had previously suggested, i.e., two major clusters marked by increased or decreased protein abundance 1h after HIIE (Figs. 3C and 4A). We next leveraged biological theme comparisons29 and identified 576 biological processes, 132 molecular functions, and 187 cellular components associated with the proteins altered by HIIE (Supplementary Data S8). By generating gene-concept networks of the five most significant GO terms in each ontology, we observed both shared and unique GO terms across our four protein clusters (Supplementary Fig. S3A–C).

To quantify functional differences and similarities between the identified protein clusters, we performed gene set enrichment analyses (GSEA)30 and observed a total of 169 enriched GO terms (Fig. 4B and Supplementary Data S9). Interestingly, cluster 4 did not yield any enriched GO terms and re-evaluation of the underlying statistics demonstrated that the individual GO terms did not reach the significance threshold. These findings were validated using the STRING resource31. We then focused our attention on GO terms that were shared across protein clusters 1–3 and obtained 11 shared biological processes. Semantic evaluation confirmed their close connection to immune function, as exemplified by GO terms like “cell killing”, “leukocyte activation”, or “defense response” (Fig. 4B). Analysis of the underlying proteins resulted in a core proteome consisting of 369 proteins, most of which changed in abundance in the recovery phase following HIIE (Fig. 4C). This suggests that the biological processes regulated by HIIE are driven by proteomic alterations in the recovery phase. We observed similar results for the 27 GO terms that were shared between clusters 1 and 2, and the 15 GO terms that were shared between clusters 2 and 3 (Fig. 4B and Supplementary Fig. S4A–C).

Concerning GO terms that were uniquely enriched in a specific protein cluster, we identified 29 GO terms uniquely enriched in cluster 1, 62 GO terms uniquely enriched in cluster 2, and 25 GO terms uniquely enriched in cluster 3 (Fig. 4B, D). Among the GO terms enriched in cluster 1 we found enriched regulation of “endopeptidase activity” and “cellular response to organic substance” (Fig. 4D). Similarly, cluster 2 demonstrated enriched regulation of “glycosaminoglycan binding” and “cell migration”, which are crucial processes in the context of exercise-induced transmigration of immune cells from the bloodstream into peripheral tissues. In contrast, cluster 3 was characterized by a decreased regulation of several cellular components and biological processes 1h after exercise, which can be attributed to the underlying protein kinetic (Fig. 4A). In summary, GSEA suggested a profound regulation of immune effector processes in the recovery phase following HIIE, which were in part shared and in part unique for specific protein kinetics.

Identificationofanimmunoproteomicsignatureassociatedwithcardiorespiratoryfitness
We ultimately leveraged our generated dataset to explore potential long-term immune adaptations to exercise training since this depicts a crucial starting point in understanding the immune-mediated health benefits triggered by exercise. Taking a data-driven approach, we started by pooling the baseline data of all our analyses, including participant characteristics as well as flow cytometry and LC-MS/MS results. This comprehensive dataset was then used to investigate potential pairwise associations with V ̇ O , a gold standard marker of cardiorespiratory fitness that is highly responsive to exercise training. In a first step, we calculated Spearman’s rank correlation coefficients (r ) and preselected features that displayed moderate to high correlation (r >0.4 or <−0.4) with V ̇ O . This resulted in a reduction of our dataset from 6063 to 260 features (Supplementary Data S10).

To establish an elaborate connection between these features and cardiorespiratory fitness, we next performed prediction analyses. Ridge regression yielded an R-squared of 0.61 and a mean squared error (MSE) of 14.1 and visual inspection of the ranked coefficients revealed a homogeneous distribution of features with positive or negative impact on V ̇ O prediction, respectively (Fig. 5A). We next focused on the 20 features with the highest positive or negative impact on V ̇ O prediction and defined these as immunoproteomic signature predictive of cardiorespiratory fitness (Fig. 5B). Interestingly, among these proteins, nicotinamide phosphoribosyltransferase (NAMPT), a key enzyme of nicotinamide adenine dinucleotide (NAD+) metabolism, demonstrated the highest positive impact on V ̇ O prediction. NAMPT plays a crucial role in salvaging intracellular NAD+ and was previously shown to be exercise-responsive in skeletal muscle32–34, but also other target tissues like immune cells35–37. In addition, several studies have suggested a direct antiviral function of NAMPT in host defense38,39. Our results support this notion and suggest that repeated exposure to exercise, which results in greater cardiorespiratory fitness, equips immune cells with a higher metabolic capacity, thereby linking to the immune effector functions previously identified in this work. Similarly, we observed a positive impact on V ̇ O prediction for succinate receptor 1 (SUCNR1), a G-protein coupled receptor that was previously shown to control exercise capacity and systemic glucose homeostasis in mice40,41.

Besides features with positive impact on V ̇ O prediction, our immunoproteomic signature also contained several proteins with a negative impact (Fig. 5B). Among these features, phosphatidylserine decarboxylase (PISD), an enzyme involved in lipid droplet biogenesis42, and caspase recruitment domain family, member 8 (CARD8), a pattern recognition receptor that regulates inflammasome activation and production of pro-inflammatory cytokines43, stood out due to their involvement in metabolism and inflammation. Interestingly, we also observed various inter-feature correlations within our immunoproteomic signature (Fig. 5C).

To confirm that our immunoproteomic signature was associated with cardiorespiratory fitness, we next calculated individual immunoproteomic signature scores using the mean beta coefficients of the ridge regression and individual protein abundancies (see methods for details). Individual scores yielded a significant positive correlation with V ̇ O (R=0.84, p=5.1×10−7) and were higher in participants with greater cardiorespiratory fitness based on a median split performed on our study cohort (p=0.00068, Fig. 5D). Similarly, NAMPT protein levels displayed a positive correlation with V ̇ O (R=0.54, p=0.0073, Fig. 5E). This confirms that the immunoproteomic signature and underlying protein such as NAMPT, are associated with cardiorespiratory fitness.

Having identified this signature, we ultimately aimed to investigate vice versa, whether cardiorespiratory fitness also explains most of the variance in the immunoproteomic signature and the underlying proteins. To assess this, we applied a variance partitioning approach, in which we compared the variance explained by V ̇ O to the variance explained by other participant characteristics such as age, sex, BMI, and participant ID. These comparisons were performed across three different feature sets: a feature set containing all flow cytometry and LC-MS/MS data, a feature set containing the preselected features for V ̇ O prediction (Fig. 5A), and a feature set containing the immunoproteomic signature (Fig. 5B). While participant ID accounted for significantly more variance than V ̇ O in the overall feature set and the feature set containing the preselected features, this difference was no longer apparent in the immunoproteomic signature (Fig. 5F). In addition, in both, the preselected feature set and the immunoproteomic signature, V ̇ O explained significantly more variance than age, sex, or BMI (Fig. 5F and Supplementary Fig. S5A). This demonstrates that cardiorespiratory fitness is closely related to the immunoproteomic signature.

We next dissected the variance explained by participant characteristics for each protein contained in the immunoproteomic signature to evaluate which of the proteins are most strongly influenced by cardiorespiratory fitness. Interestingly, V ̇ O explained 13.62% of the total variance (Supplementary Fig. S5B) and 93.66 % of the explained variance within NAMPT (Fig. 5F). This rendered NAMPT the protein with the highest relative contribution of V ̇ O to the total amount of explained variance, thus confirming our previous finding that PBMC NAMPT levels are dependent on cardiorespiratory fitness.

Collectively, our results suggest that the identified immunoproteomic signature is strongly related to cardiorespiratory fitness and that vice versa, cardiorespiratory fitness also explains most of the variance in the immunoproteomic signature. In addition, many of the underlying proteins, such as NAMPT, are dependent on cardiorespiratory fitness. This suggests a potential involvement of these proteins in reshaping the phenotype of immune cells in response to exercise training. In a broader context, these findings could serve as a molecular foundation for immunological health benefits mediated by regular exercise.

Methods
Participantrecruitmentandinformedconsent
Prior to enrollment of the first participants, the study received ethical approval by the local ethics committee of the German Sport University Cologne, which works according to the World Medical Association’s Declaration of Helsinki. The study meets the National Institutes of Health definition of a clinical trial and was prospectively registered in the German Clinical Trials Register (DRKS00017686). Study eligibility was assessed for 28 healthy adults aged between 18 and 35. To ensure complication-free execution of the high-intensity interval exercise on the treadmill, participants required a weekly running volume of 2–5 h and a body mass index <30. Any previous medical history of muscle disorders, cardiac or kidney diseases, as well as regular intake of medication or nutritional supplements, were treated as exclusion criteria. For female participants, breast-feeding or an ongoing pregnancy were also treated as exclusion criteria. Of the 28 subjects assessed for eligibility, two were considered ineligible due to acute infections. The remaining 26 participants provided written informed consent and were enrolled into the study. After baseline testing, two further participants dropped out due to orthopedic problems while running (Achilles injuries). For one participant, the biomaterial did not suffice to run analyses, which resulted in a total of 23 participants. An overview of all participant characteristics is displayed in Table 1.

Studydesign
Participants enrolled in this randomized crossover study were scheduled for three visits to an exercise physiology laboratory of the German Sport University Cologne: Baseline testing, a HIIE session, and a MICE session. For each visit, participants were asked to arrive overnight-fasted and refrain from alcohol and caffeine intake in the 24 h prior. Water intake was permitted ad libitum. All visits were scheduled between 07:00 and 10:00 am to account for a potential circadian impact on performance and biological outcomes. The minimum timeframe between each of the three visits was 72 h, to prevent potential carryover effects.

Baselinetesting
During baseline testing, written informed consent was obtained from participants and demographic and anthropometric characteristics were recorded. Afterwards, participants underwent cardiopulmonary exercise testing.

Cardiopulmonaryexercisetest(CPET).To standardize the exercise intensity between participants for the HIIE and MICE sessions, respectively, cardiorespiratory fitness was assessed as peak oxygen consumption (V ̇ O ) in an incremental CPET during baseline testing. The CPET was performed on a motorized treadmill (Woodway ELG90, Weil am Rhein, Germany) that was set to 1% incline for all sessions. The warm-up consisted of 5 min at 6–8 km h−1. Afterwards, participants began running at 8 km h−1. The speed of the treadmill was then increased by 1 km h−1 every 60 s until participants reached volitional exhaustion. During the test, heart rate was recorded continuously (Polar FS1C, Kempele, Finland), and rate of perceived exertion was recorded prior to each increase in intensity. Participants were verbally encouraged to continue running by the supervising researcher. After reaching volitional exertion, participants were given a 5 min break before taking up exercise again for a V ̇ O verification test. For this test, the speed of the treadmill was set 1 km h−1 higher than what the participants had finished with. Just before the verification test, participants ran for 3 min at 8 km h−1. The speed was then increased to the target speed within 20 seconds, and participants were instructed to run as long as possible. During the entire CPET, participants wore a facemask that was connected to a spirometer (Cortex Metalyzer 3B, CORTEX Biophysik GmbH, Leipzig, Germany) to collect breathing gases breath-by-breath. The highest 15 s interval during the CPET was used to calculate V ̇ O .

Randomization
To prevent sequence effects arising from the order in which HIIE and MICE were conducted, participants were randomized into one of two exercise intervention sequences after baseline testing: HIIE-MICE or MICE-HIIE. Following the minimization procedure by Pocock and Simon57, randomization was performed via concealed allocation (1:1) using the software Randomization in Treatment Arms (RITA; Evidat, Lübeck, Germany). Age, BMI, and V ̇ O were used as stratification factors. The intervention sequences did not differ in terms of participant characteristics, indicating that our randomization was unbiased (Table 1).

Exerciseinterventions
Exercise intensities for the HIIE and MICE sessions were calculated as percent of V ̇ O for each participant to ensure that all participants exercised at the same intensity. The exercise protocols for HIIE and MICE were designed in a time- and workload-matched manner as previously described58,59 to isolate exercise intensity as the only differing variable between the two exercise conditions. This time- and workload-matched design is crucial to draw unbiased conclusions on the impact of exercise intensity. In addition, we chose these exercise protocols because a duration of 50 min represents a conventional length for acute exercise bouts across different settings (e.g., performance- or health-oriented contexts). Both exercise sessions were performed on the same treadmill that was also used for the CPET at baseline (Woodway ELG90, Weil am Rhein, Germany). During MICE, participants performed a warm-up for 10 min at a self-selected intensity, followed by a 5 min break. Participants then ran for 50 min at 70% of their V ̇ O . During HIIE, participants performed 7 min of warm-up and cool-down at 70% V ̇ O with six bouts of high-intensity running at 90 % V ̇ O in between. Each high-intensity bout lasted 3 min, followed by 3 min of active recovery at 50% V ̇ O . Of note, although our MICE protocol was originally described as “moderate-intensity exercise”58,59, current consensus statements on physical activity and exercise intensity terminology classify exercise at 70% V ̇ O as high-intensity exercise60. However, the continuous character and comparatively long duration of our MICE protocol (50 min), suggests that exercise was performed below metabolic threshold 2, i.e., below maximal lactate steady state. We have thus chosen to adopt the term “moderate-intensity exercise” as originally described by Bartlett and colleagues58,59.

Bloodcollectionandsamplepreparation
Blood was drawn from a median antecubital vein in the supine position at baseline, immediately after exercise, and 1 h after exercise for the HIIE and MICE session, respectively. The timing of blood sampling was selected to capture standardized resting conditions (baseline), effects observable directly post exercise (immediately after exercise), and to build upon work previously published by Contrepois et al.4, in which PBMC transcriptomics indicated a strong response between 2 and 60 min after acute exercise (1 h after exercise). Each blood draw consisted of 24 ml of whole blood collected in EDTA tubes (Vacutainer, BD). After the last blood draw, PBMCs were isolated via density gradient centrifugation. To achieve this, whole blood was first diluted with phosphate-buffered saline (PBS) and then carefully layered on top of a lymphocyte separation medium (Cytiva Ficoll-PaqueTM PLUS, Fisher Scientific). After centrifugation for 30 min at room temperature and 800g−1, the PBMC-containing interphase was collected, washed with PBS, and centrifuged again for 10 min at room temperature and 800g−1. PBMCs were then resuspended in freezing medium (RecoveryTM cell culture freezing medium, Thermo Fisher Scientific) and stored at −80 °C before being transferred to a −150 °C freezer on the next day until further analysis.

Flowcytometry
Sample preparation and data acquisition. Flow cytometry analysis was performed using a Cytek® Aurora full-spectrum flow cytometer (Cytek Biosciences, California, USA). Cryopreserved PBMCs were gently thawed in a water bath at 37 °C with a mean recovery of 81.28% viable cells assessed with the Zombie NIR™ Fixable Viability Kit (BioLegend, San Diego, CA, USA). After incubating 1×106 PBMCs in 2.5 µg Fc block for 10 min at room temperature, cells were stained with anti-CD3 (BUV395, clone SK7), anti-CD4 (PerCP, clone SK3), anti-CD8 (BV750, clone SK1), anti-CD16 (PE-Cy7, clone 3G8), anti-CD25 (BUV805, clone M-A251), anti-CD56 (BUV563, clone NCAM16.2), anti-CD20 (APC, clone L27), and anti-CD19 (BV480, clone SJ25C1) antibodies (all from BD Biosciences, NJ, USA). In brief, cells were incubated in the dark with a master mix containing Brilliant Stain buffer (BD Biosciences) and antibodies against surface antigens for 30 min at 4 °C. After washing with FACS buffer, the BD Pharmingen™ Transcription Factor Buffer Set was used, and cells were fixed for 40 min at 4 °C in the dark. Thereafter, intracellular staining was done by incubating cells with an anti-Foxp3 antibody (PE, clone 259D/C7) for 45 min at 4 °C in the dark. After washing, cells were resuspended in FACS buffer and acquired on the flow cytometer within 2 h after finishing the staining protocol.

Data processing. Gating was performed using FlowJo™ 10.10.0 (Fig. 1C). B cells were phenotyped as CD3-CD56-CD19+CD20+, Natural Killer T (NKT) cells as CD3+CD56+, Natural Killer (NK) cells either as CD56brightCD16- (NKbright) or CD56dimCD16+ (NKdim), and regulatory T cells (Tregs) as CD4+CD25+Foxp3+. The person analyzing the samples was blinded to the participants’ group allocation. Analysis of total blood cell counts was performed from EDTA blood using a hematology analyzer (SYSMEX XP-300, Norderstedt, Germany). The lymphocyte count was then used to calculate the absolute number of peripherally circulating lymphocyte subsets according to the cell proportions derived by flow cytometry.

LC-MS/MS-baseduntargetedproteomics
Samples were processed and measured in a block randomized order61 to prevent any technical bias that might occur during sample preparation or LC-MS/MS measurement.

Sample preparation. Isolated PBMCs were lysed in a RIPA buffer containing 10 mM sodium fluoride, 1 mM sodium orthovanadate, cOmplete™ Protease Inhibitor Cocktail (Merck KGaA), PhosSTOP™ (Merck KGaA), 250 µml−1 benzonase, and 10 µml−1 DNaseI. Samples were incubated on ice for 1 h and then centrifuged at 4 °C and 13,000×g−1 for 15 min. Protein concentration was determined in the supernatant with a BCA assay. An amount of 10 µg of protein per sample was digested (Trypsin) using an AssayMAP Bravo liquid handling system (Agilent Technologies) running the autoSP3 protocol62. After sample preparation, the remaining peptides were vacuum dried and stored at −20 °C until LC-MS/MS analysis.

MS method Orbitrap Exploris 480. The dried peptide sample was reconstituted (97.4 % Water, 2.5 % Hexafluoro-2-propanol and 0.1% trifluoroacetic acid (TFA)), and 10% of the sample were used. The LC-MS/MS analysis was carried out on an Ultimate 3000 UPLC system (Thermo Fisher Scientific) directly connected to an Orbitrap Exploris 480 mass spectrometer for a total of 120 min. Peptides were online desalted on a trapping cartridge (Acclaim PepMap300 C18, 5 µm, 300 Å wide pore; Thermo Fisher Scientific) for 3 min using 30 µl/min flow of 0.1 % TFA in water. The analytical multistep gradient (300 nL/min) was performed using a nanoEase MZ Peptide analytical column (300 Å, 1.7 µm, 75 µm x 200 mm, Waters) using solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). For 102 min the concentration of B was linearly ramped from 4% to 30%, followed by a quick ramp to 78%, after two min the concentration of B was lowered to 2% and a 10 min equilibration step appended. Eluting peptides were analyzed in the mass spectrometer using data-independent acquisition (DIA) mode. A full scan at 120k resolution (380–1400 m/z, 300% AGC target, 45 ms max IT) was followed 47 DIA windows. The DIA acquisition covered a mass range of 400–1000 m/z using windows of variable width with 1 m/z overlap, an AGC target of 1000% with a max IT set to 54 ms and recorded at a resolution of 30k. Each sample was followed by a wash run (40 min) to avoid carry-over between samples. Instrument performance and suitability was monitored by regular (approx. one per 48 h) injections of a standard sample and an in-house shiny application over the whole timeline of the experiment.

Data analysis. Analysis of DIA RAW files was performed with Spectronaut (Biognosys, version 19.1.240724.62635)63 in DIA+ (deep) library-free mode. Default settings were applied with the following adaptions. Within DIA Analysis under Identification, the Precursor PEP Cutoff was set to 0.01, the Protein Q value Cutoff (Run) set to 0.01, and the Protein PEP Cutoff set to 0.01. In Quantification, the Proteotypicity Filter was set to Only Protein Group Specific, the Protein LFQ Method was set to MaxLFQ and the quantification window was set to Not Synchronized (SN17). The data was searched against the human proteome from Uniprot (human reference database with one protein sequence per gene, containing 20,597 unique entries from the ninth of February 2024) and the contaminants FASTA from MaxQuant (246 unique entries from the twenty-second of December 2022).

Data processing. Before further analysis, the obtained dataset was checked for proteins that were identified more than once. For these duplicate results, the event with the highest number of identified precursors across all samples was kept, and all other events were deleted from the dataset. The data was then filtered for proteins that were quantified in ≥70% of the samples in at least one exercise condition and measurement timepoint (i.e., HIIE/MICE baseline, post exercise, 1 h post exercise). Subsequently, we imputed the data separated by exercise condition and measurement time point using the missForest package64. Ultimately, proteins were annotated to match the gene names provided in the org.Hs.eg.db package for subsequent Gene Ontology (GO) analysis. Translation between gene names and Entrez gene identifiers was accomplished with the bitr function from the ClusterProfiler package29,65.

Quantificationandstatisticalanalysis
Samples from a total of 23 participants were available for statistical analyses. For one participant, there was no sample from 1 h after MICE due to difficulties during PBMC isolation. Statistical analysis and visualization were performed in R. If not otherwise noted, data wrangling was achieved using the dplyr66 and tidyr67 package and subsequently visualized with ggplot268 and ggpubr69.

Unsupervisedimmunecellclusteringusingself-organizingmaps.Flow cytometry data of each sample was cleaned using the FlowAI plugin (v3.2.3) in FlowJo™ 10.10.0. The remaining events were gated as described above, and live cells were downsampled to 3000 events per sample using the DownSample plugin (v3.3.1). Subsequently, downsampled events were concatenated to obtain an overall dataset containing all exercise conditions (HIIE, MICE) and measurement timepoints (baseline, post-exercise, 1 h post-exercise). This dataset was then used to perform unsupervised immune cell clustering using self-organizing maps (SOM) with the FlowSOM plugin (v4.1.0). The resulting 6 clusters were identified as CD4+ T cells, CD8+ T cells, NKT cells, CD56dim cells, CD56bright cells and B cells in the build-in ClusterExplorer in FlowJo™ 10.10.0. The overall dataset was visualized using Uniform Manifold Approximation and Projection (UMAP) via the UMAP plugin (v4.1.1), and FlowSOM clusters were superimposed via color-coding. Overall, color-coded immune cell clusters were used as a template map for subsequent clustering per exercise condition and measurement time point. To achieve this, the downsampled events were concatenated for baseline, post-exercise and 1 h post-exercise in HIIE and MICE, respectively. Ultimately, FlowSOM clustering and UMAP were performed on each of these concatenated data frames by applying them on the previously generated map.

Exercise-induced mobilization of immune cells. Exercise-induced alterations in cell counts of ten different immune cell populations (Supplementary Fig. S3) were analyzed by applying linear mixed models to the flow cytometry results (N=22). Measurement timepoint and exercise condition were implemented as fixed effects, and participant ID as random effect using the lmer function from the lme4 package70. Results of the linear mixed models were then analyzed for time and time×condition interaction effects via analyses of variance (ANOVA) with the built-in ANOVA function from R stats without correcting for testing of multiple immune cell populations. In case of significant results, pairwise comparisons of measurement timepoints and/or exercise conditions were performed by applying the emmeans function from the emmeans package. P-values of pairwise comparisons were Bonferroni-corrected for multiple testing. Immune cell proportions were analyzed in the same manner (Supplementary Data S4).

Measures of variability. All measures of variability were calculated with unimputed data to avoid potential bias arising from imputation. Inter-individual variability was calculated as the coefficient of variation (CV) for each protein across all participants separated by exercise condition (HIIE, MICE), and measurement timepoint (baseline, post exercise, 1 h post exercise). CVs were calculated as the ratio of the standard deviation σ to the mean µ. Intra-individual variability was assessed by comparing the baseline values of the two intervention days. Relative differences between day 1 and day 2 were calculated (in percent) for each protein separated by study participant. Proteomic variability was quantified for each protein by calculating (i) the mean CV across all participants in HIIE and MICE at baseline, (ii) the mean CV across all participants in HIIE and MICE post exercise and 1 h post exercise, and (iii) the mean difference between the two baselines across all participants. Proteomic variability was also calculated, separated by exercise conditions and measurement timepoints (see Supplementary Fig. S2A, B).

Principal component analysis. Principal component analysis (PCA) was carried out using the built-in prcomp function from R stats. All samples were plotted with the fviz_pca_ind function from the factoextra package. Exercise condition, measurement timepoint, intervention day, and sex were used as metadata to color-code PCA results. PCAs were also computed on datasets separated by exercise condition or measurement timepoint to visually assess the impact of these variables on each other (see Fig. S2C–E).

Linear mixed models to identify proteins altered by HIIE and/or MICE. To identify proteins altered by HIIE and/or MICE, a linear mixed model was fitted on the log2-transformed, normalized, and imputed protein intensities via the limma R package71. Intra-individual correlation was estimated via the duplicateCorrelation function. The model included the exercise condition (HIIE, MICE), the measurement timepoint (baseline, post-exercise, 1 h post-exercise), and the interaction between both as fixed factors. A moderated t statistic72 was obtained for each contrast of interest via the eBayes function with estimated variance trend and robustification. The resulting p-values for each contrast were adjusted with the Benjamini-Hochberg procedure73 to control the false discovery rate, and significance was declared at the adjusted 5% two-sided level. The model was subsequently extended to include sex and all two-way interactions.

Gene ontology (GO) over-representation analysis. Time effects of the statistical analysis with limma71 were used to map proteins that were significantly altered by HIIE and MICE to GO terms, respectively. GO over-representation analysis was performed with the ClusterProfiler package29,65. For HIIE and MICE, significantly altered proteins were compared with the entire dataset of quantified proteins, applying Benjamini-Hochberg correction of p-values with a p-value cutoff of 0.05 and a q-value cutoff of 0.2.

Fuzzy c-means clustering. Fuzzy c-means clustering was performed with the Mfuzz package74,75. Data was standardized using the standardize function, and the optimal number of clusters was determined by calculating the minimum centroid distance for a range of cluster numbers using the Dmin function. The optimal fuzzifier was identified with the mestamiate function.

Biological theme comparison. Biological theme comparison was carried out using the compareCluster function from the ClusterProfiler package29,65. Entrez gene identifiers of the proteins contained in the identified clusters were used as input with the function command set to “enrichGO”. Benjamini-Hochberg correction was applied to p-values with a cutoff of 0.05 and the minimum gene set size was set to 10. The results were simplified via the simplify function using a cutoff of 0.7 and visualized separated by ontology with the cnetplot function from the enrichplot package76.

Gene ontology (GO) gene set enrichment analysis. Gene set enrichment analysis was performed using the gseGO function from the ClusterProfiler package29,65. Entrez gene identifiers and fold changes from baseline of the proteins contained in the identified clusters were used as input, with the minimum gene set size set to 10. In case fold changes were only positive or negative, the “scoreType” command was set to “pos” or “neg”, respectively. P-values were corrected using the Benjamini-Hochberg procedure with a p-value cutoff of 0.05. The underlying proteins mapping to each significant GO term were identified using the select function from the AnnotationDbi package. Shared and unique GO terms across the identified clusters were visualized with the UpSetR package77.

Immunoproteomicsignature
Preselection of features. To identify features with high association to V ̇ O , we conducted a preselection in Python (v.3.9)78. The features were standardized using z transformation and included the average of mass spectrometry-based proteomics data and flow cytometry-based immunophenotyping data at baseline of intervention day 1 and 2, as well as sex, height, weight and BMI. V ̇ O was scaled to body weight. Data from 2 participants were excluded from the analysis due to incomplete feature sets. Pairwise Spearman’s rank correlations between all features and V ̇ O were calculated (Supplementary Data S10), and features with a correlation coefficient of >0.4 or <−0.4 were included in the subsequent analysis. From a total of 6063 initial features, 260 remained after this selection.

V ̇ O prediction. We ran LASSO79,80 and ridge regression81 as well as a random forest82 as a non-linear, tree-based approach. A leave-one-out (LOO) cross-validation was performed in Python (v.3.9) to assess the predictive performance of these methods based on the 260 features. To optimize the hyperparameters for each model by grid search, a second inner cross-validation was performed. For each training set, we selected the model that had the lowest test error. The predicted output value resulted from the cross-validation iteration, where the corresponding output data point and its associated features were not included in the training set. These predicted values were used to calculate the mean squared error (MSE) and the r-squared. Ridge regression outperformed the other models. To preclude that our prediction model was affected from overfitting, we ran the model with permuted outcome variables to simulate data without any true signal. The resulting model yielded an R-squared of 0.0062 and an MSE of 36.4, suggesting that the predictive performance under random conditions is close to zero. This comparison supports the conclusion that our original model does not appear to suffer from overfitting. All features with coefficients from the ridge regression are listed in the supplementary information (Supplementary Data S10).

Immunoproteomicsignature. The immunoproteomic signature was defined as the 10 features displaying the most positive ridge regression coefficients and the 10 features displaying the most negative ridge regression coefficients in V ̇ O prediction. Proteins included in the immunoproteomic signature were used to create a weighted, undirected network using Spearman’s rank correlations. The network was visualized in R (v.4.4.1) with the packages Hmisc (v.5.2.1) and igraph (v.2.1.1.). The immunoproteomic signature score for each participant i was calculated as the weighted sum of the 20 z-transformed protein abundances (z p) using the corresponding average beta coefficients (β p) from ridge regression models:

Immunoproteomicsignature score i = Σ 20 p=1 β p * z i,p

Variance partitioning. Variance partitioning was performed using the variancePartition package in R83. V ̇ O , BMI and age were included as fixed effects and participant ID and sex were included as random effects in the linear mixed models. Variance partitioning was performed on three different feature sets containing either all flow cytometry and LC-MS/MS data, the 260 preselected features from V ̇ O prediction or the proteins contained in the immunoproteomic signature. To compare the variance explained by each participant characteristic to the variance explained by V ̇ O , two-sided pairwise Wilcoxon rank-sum tests with Bonferroni correction were conducted. The variance explained by participant characteristics was then assessed for each protein contained in the immunoproteomic signature. In addition, the relative contribution of participant characteristics to the explained variance of each protein was calculated.

Correlation analyses. Correlation analyses were performed for the association between immunoproteomic signature scores and V ̇ O and NAMPT z-scores and V ̇ O . Pearson correlation coefficients were calculated and reported with corresponding two-sided p-values. A median split of our study cohort based on V ̇ O was performed (median V ̇ O = 55.6 ml min−1 kg−1) and used to compare immunoproteomic signature scores between participants with a cardiorespiratory fitness above vs. below the median. Cardiorespiratory fitness groups were compared using Welch’s two-sample t tests.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This noteworthy manuscript reports findings of a translational study of the effects of aerobic exercise intensity on immune cell proteomic profiles in younger, healthy, physically fit persons. In a randomized, cross-over design, participants completed treadmill running acute bouts of high intensity interval exercise or moderate intensity continuous state exercise and had blood drawn for peripheral blood mononuclear cell (PMBC) isolation and storage pre-exercise, post-exercise, and post-one hour recovery. Stored PBMC where then used for bulk proteomic analyses. The main findings of this study are that high intensity exercise—compared to moderate intensity—leads to greater proteomic changes in PBMCs linked to immune response, independent of changes in specific immune cell concentrations after exercise. The authors also identified baseline PBMC profiles that are highly associated with and predictive of cardiorespiratory fitness. The strengths of this manuscript are the clear and concise writing, reporting, and data presentation, use of human samples, robust study design and statistical analyses plan, and novel analyses and findings of exercise-induced immune cell-specific proteomic changes. The weaknesses of the manuscript include lack of data integration/analyses of with other level -omics (e.g., transcriptomics, epigenetics) or immune function assessments, and some lack of clarity regarding the goals and hypotheses of this exploratory project. Overall, the novel findings of this manuscript considerably add to the field of exercise immunology. The study holds promise to inspire future work aimed at understanding the effects of exercise on immune function, which may have significant translational impact in the health sciences and clinical care. My comments for suggested minor revisions are as follows:\n1. The Introduction and background leading to the present study are well presented. However, the rationale and goals of the project are minimally described. The addition a more detailed rationale for study, including hypotheses regarding possible differential effects of high vs moderate intensity exercise, would strengthen the manuscript.\n2. Though implied, the rationale to investigate bulk vs targeted proteomics is not clearly discussed. Please specify in the Introduction and/or Methods.\n3. The findings of strong relationships between baseline PMBC proteomics and cardiorespiratory fitness and possible predictive value are fascinating. However, there is minimal rationale for why these analyses are included in the current study and how these analyses fit within the context of the rest of the analyses discussed (i.e., focus on acute bout changes). Please discuss.\n4. The findings of this study give support to a concept that exercise boosts immune responses to pathogens and shows differential effects of high versus moderate intensity exercise. Consider adding further comment in the Discussion regarding how findings of the study add to current understanding of how acute exercise modulates infection risk (e.g., compared to previous notion that prolonged, high intensity exercise may be immunosuppressive).\n5. The findings of differential protein effects of time and group focus on immune effector function. Consider adding further discussion on other biologic processes impacted in PMBCs after acute exercise. For example, it is interesting that cluster 4 proteins—which decreased at both post-exercise and recovery time points—strongly associated with mitochondrial/cellular metabolism pathways, yet VO2 peak was linked to greater baseline PBMC mitochondrial/metabolic protein abundance.\n6. It may also be helpful to add discussion for what next steps are needed to better understand and apply the findings of the study.\n7. In the Discussion, recommend clarifying that high intensity aerobic exercise was needed to stimulate changes in PBMC effector function specifically in healthy, young, trained individuals (i.e., findings may be evidence of a training or age effect, as it is still unclear if moderate intensity may produce a robust response in older, sedentary, untrained, and chronic disease populations).\n8. The addition of a “limitations” paragraph is needed to strengthen the Discussion. For example, as above, it is unclear if findings apply to older, sedentary, or chronic disease populations; there is lack of integration of proteomic data with transcriptomic or epigenetic data to better understand molecular signaling pathways; there is no connection of proteomic data with immune function assessments or immunometabolic assessments.\n9. In the Methods, some clarification of experimental procedures is needed. For example, 70% of V02 peak can be considered to steady state high-intensity (as opposed to moderate intensity as described in the manuscript). Please provide a brief clarification and rationale.\n10. Please also provide brief rationale for acute exercise bout duration.\n11. Please provide brief rationale for the timing of blood sample collection. For example, why inclusion of 1 hour post-acute bout compared to a different time point (e.g., 30 minutes, 4 hours, 24 hours).", "reviewer_2": "This study presents a rigorous and thoughtfully designed investigation into how acute aerobic exercise shapes the molecular landscape of peripheral blood mononuclear cells (PBMCs), filling a critical gap in the field of exercise immunology from a proteomics perspective. By employing a randomized crossover design, carefully matched for workload and duration, and combining high-resolution LC-MS/MS proteomics with spectral flow cytometry, the authors provide a solid experimental foundation. Notably, the discovery that high-intensity interval exercise (HIIE) induces a much stronger proteomic response than moderate-intensity continuous exercise (MICE) adds biological weight to WHO’s recommendations highlighting the importance of exercise intensity for health promotion.\nBeyond its depth of proteomic analysis, the study stands out for its integration of functional annotation through Gene Ontology, hierarchical and fuzzy clustering, and its correlation with cardiorespiratory fitness (V̇O₂peak). The identification of NAMPT and other proteins related to metabolism and immune function as potential predictors of V̇O₂peak is particularly exhilarating, offering new avenues to understand how immunometabolic health relates to systemic fitness.\nThat said, a few refinements could further enhance the clarity, depth, and translational relevance of this important work:\n1. Streamlining the narrative: Some parts of the text are quite dense. Condensing and restructuring these sections could make the key findings easier to grasp and visually more accessible for the reader.\n2. Incorporating sex- and age-based comparisons: Including analyses based on sex and age could offer valuable insights, especially given the well-established hormonal and metabolic differences across sexes and life stages. Such stratification may reveal distinct molecular responses to exercise and aid in tailoring interventions.\n3. Linking molecular pathways to clinical relevance: A brief discussion on how the activated pathways might be beneficial in metabolic conditions such as type 2 diabetes or how they might protect organs from chronic disease progression would elevate the translational impact of the findings and support differential applications of MICE and HIIE.\n4. Adding functional validation: Although functional predictions were made using Gene Ontology, no direct functional assays (e.g., cytokine secretion, cytotoxicity, or antigen presentation) were performed. Including or proposing these validations, particularly for key markers like NAMPT, would strengthen confidence in the interpretations and reduce the risk of overfitting.\n5. Integrating additional omics layers: While the correlation of proteomic changes with V̇O₂peak is valuable, incorporating transcriptomic, metabolomic, or cytokine data would have provided a more holistic systems biology view. Cytokine profiling, in particular, would have been a strong complement to the immune-related findings.\n6. Quantifying effect sizes: The observation that HIIE induces stronger changes could be bolstered by reporting effect sizes, which would add statistical weight and practical context to the biological findings.\n7. Website access issue: The resource link (https://sportsmedicine-dortmund.shinyapps.io/beat) appears to require a password and was not functional with the provided materials.\nAddressing these points would help the study not only read more clearly but also broaden its impact—both for the scientific community and in real-world health and exercise applications.", "reviewer_3": "I had the opportunity to review the manuscript \"Acute exercise rewires the proteomic landscape of human immune cells\" by D. Walzik et al. The manuscript describes results of a randomized crossover trial comparing effects of high-intensity interval exercise and of moderate-intensity continuous exerceise on proteomic makeup of PBMCs. I reviewed the manuscript with a focus on the statistical methods used, but I am not an expert in the analysis of high-dimensional OMICS data and am not fully familiar with the statistical conventions in that field. I have a few concerns about the methods and would ask authors to comment on them.\nLinear mixed effects models were used for analysis of immune cells and for proteins. The analytical approaches differ in some certain aspects, as e.g. the adjustment for multiple testing. THe authors sould comment on the differences in the approaches and it should also be clear in the results sections and in the figures, which approach was used. For the immune cells, it is unclear which factor was used for the Bonferroni adjustment - was the correction applied for multiple tests over different timepoints or was the number of tested cells also considered?\nWas the randomization sequence considered in the regression model or was it used in the analysis in any other way?\nFor the predcition model presented in the last part of the results, I would be really interested how this model performs compard to a model derived from data of the same structure without any true signal - which can be generated e.g. by permuting the outcome variable. This would allow to assess how much the reulting R-squared is affected from overfitting.\nGenerally, I believe that some of the wording used in the manuscrit implies a causality that cannot be confirmed by the data (\"... rules out the possibility ...\", \"... that were ... altered by ...\", etc.). The authors should carefully review the manuscript in this regard and adjust the wording accordingly."}, "author_response": "Point by point response\nDear Reviewers,\nThank you very much for your constructive and positive feedback on our submission. Please\nfind the point-by-point response addressing your comments below.\nWe believe that the revision process and your input have improved our manuscript and we are\nlooking forward to receiving feedback.\nBest regards on behalf of our team,\nPhilipp Zimmer\nThis noteworthy manuscript reports findings of a translational study of the effects of aerobic\nexercise intensity on immune cell proteomic profiles in younger, healthy, physically fit persons.\nIn a randomized, cross-over design, participants completed treadmill running acute bouts of\nhigh intensity interval exercise or moderate intensity continuous state exercise and had blood\ndrawn for peripheral blood mononuclear cell (PMBC) isolation and storage pre-exercise, post-\nexercise, and post-one hour recovery. Stored PBMC where then used for bulk proteomic\nanalyses. The main findings of this study are that high intensity exercise—compared to\nmoderate intensity—leads to greater proteomic changes in PBMCs linked to immune\nresponse, independent of changes in specific immune cell concentrations after exercise. The\nauthors also identified baseline PBMC profiles that are highly associated with and predictive\nof cardiorespiratory fitness. The strengths of this manuscript are the clear and concise writing,\nreporting, and data presentation, use of human samples, robust study design and statistical\nanalyses plan, and novel analyses and findings of exercise-induced immune cell-specific\nproteomic changes. The weaknesses of the manuscript include lack of data\nintegration/analyses of with other level -omics (e.g., transcriptomics, epigenetics) or immune\nfunction assessments, and some lack of clarity regarding the goals and hypotheses of this\nexploratory project. Overall, the novel findings of this manuscript considerably add to the field\nof exercise immunology. The study holds promise to inspire future work aimed at\nunderstanding the effects of exercise on immune function, which may have significant\ntranslational impact in the health sciences and clinical care. My comments for suggested minor\nrevisions are as follows:\nThank you for reviewing our manuscript and providing helpful and constructive comments. We\nfully agree with your comments and revised our manuscript accordingly.\n1. The Introduction and background leading to the present study are well presented. However,\nthe rationale and goals of the project are minimally described. The addition a more detailed\nrationale for study, including hypotheses regarding possible differential effects of high vs\nmoderate intensity exercise, would strengthen the manuscript.\nWe added a clear rationale including a hypothesis on the possible differential effects of HIIE\nvs. MICE at the end of the introduction section and expanded the first sentence of the abstract\nby a rationale for doing proteomic analyses on PBMCs.\n2. Though implied, the rationale to investigate bulk vs targeted proteomics is not clearly\ndiscussed. Please specify in the Introduction and/or Methods.\nWe added the rationale and an explanation for investigating bulk proteomics in the introduction\nsection.\n3. The findings of strong relationships between baseline PMBC proteomics and\ncardiorespiratory fitness and possible predictive value are fascinating. However, there is\nminimal rationale for why these analyses are included in the current study and how these\nanalyses fit within the context of the rest of the analyses discussed (i.e., focus on acute bout\nchanges). Please discuss.\nThank you for drawing attention to the lack of rationale regarding the relationship between\nbaseline proteomics and cardiorespiratory fitness. We added further pieces of information in\nthe abstract and the introduction section and elaborated on the reasons for doing this analysis\nin the results and discussion sections.\n4. The findings of this study give support to a concept that exercise boosts immune responses\nto pathogens and shows differential effects of high versus moderate intensity exercise.\ncurrent understanding of how acute exercise modulates infection risk (e.g., compared to\nprevious notion that prolonged, high intensity exercise may be immunosuppressive)\nAs suggested, we added a paragraph in the discussion section on the question how acute\nexercise modulates infection risk and thus further contextualized our findings.\n5. The findings of differential protein effects of time and group focus on immune effector\nfunction. Consider adding further discussion on other biologic processes impacted in PMBCs\nafter acute exercise. For example, it is interesting that cluster 4 proteins—which decreased at\nboth post-exercise and recovery time points—strongly associated with mitochondrial/cellular\nmetabolism pathways, yet VO2 peak was linked to greater baseline PBMC\nmitochondrial/metabolic protein abundance.\nThank you for suggesting this. We have highlighted the differential response of\nmitochondrial/metabolic proteins (Figure S3 vs. Figure 5B) in the discussion.\n6. It may also be helpful to add discussion for what next steps are needed to better understand\nand apply the findings of the study.\nAt the end of the discussion section, we added an outlook paragraph on the future\nmethodological steps that need to be taken.\n7. In the Discussion, recommend clarifying that high intensity aerobic exercise was needed to\nstimulate changes in PBMC effector function specifically in healthy, young, trained individuals\n(i.e., findings may be evidence of a training or age effect, as it is still unclear if moderate\nintensity may produce a robust response in older, sedentary, untrained, and chronic disease\npopulations).\nAs suggested, we clarified in the discussion/limitations that our findings only apply to young\nhealth trained individuals, and that future studies in other cohorts are needed for further\nvalidation.\n8. The addition of a “limitations” paragraph is needed to strengthen the Discussion. For\nexample, as above, it is unclear if findings apply to older, sedentary, or chronic disease\npopulations; there is lack of integration of proteomic data with transcriptomic or epigenetic data\nto better understand molecular signaling pathways; there is no connection of proteomic data\nwith immune function assessments or immunometabolic assessments.\nA paragraph on the limitations including the limited generalizability of the findings to other\npopulations and the lack of other omics layers (also in line with comment 5 from Reviewer#2)\nand immune function assessments was added at the end of the discussion section.\n9. In the Methods, some clarification of experimental procedures is needed. For example, 70%\nof V02 peak can be considered to steady state high-intensity (as opposed to moderate intensity\nas described in the manuscript). Please provide a brief clarification and rationale.\nThank you for this comment. We acknowledge that there are some discrepancies in exercise\nscience literature concerning the terminology used for intensity domains. We based our\nterminology on the original articles that described the time- and energy-matching between\nexercise sessions performed at different intensities (PMIDs 22267390 and 21360405). In these\narticles the authors use the terms “high-intensity exercise” and “moderate-intensity exercise”\nto distinguish between a 6 x 3-min HIT protocol at 90% VO2max and a 50-min continuous\nrunning protocol at 70% VO2max.\n2peak\ndefined by the joint American College of Sports Medicine (ACSM) Expert Statement and\nExercise and Sport Science Australia (ESSA) Consensus Statement (PMID 41085254).\nHowever, the same consensus statement also defines moderate intensity as exercise\nperformed between metabolic threshold (MT)1 and MT2. The continuous character and\ncomparatively long duration of our moderate-intensity exercise protocol (50 min) implies that\nexercise was performed below metabolic threshold 2, i.e., below maximal lactate steady state.\nConversely, exercise above MT2 – which would be referred to as high intensity exercise\naccording to the consensus statement – could not have been sustained for a duration of 50\nmin since per definition this intensity is above maximal lactate steady state. To refer to this\ntopic in a transparent manner, we added a short section and a reference to the\nexpert/consensus statement in the methods section to explain our use of terminology.\n10. Please also provide brief rationale for acute exercise bout duration.\nA rationale for the exercise bout duration was added in the methods section. We decided to\nuse a conventional exercise duration that is often used in different exercise settings and\nselected a previously published protocol comparing the effects of HIIE vs. MICE.\n11. Please provide brief rationale for the timing of blood sample collection. For example, why\ninclusion of 1 hour post-acute bout compared to a different time point (e.g., 30 minutes, 4\nhours, 24 hours).\nWe added a brief rationale for the timing of blood sampling. Samples were collected at baseline\n(as standardized reference point), immediately post exercise (to capture effects most directly\ninduced by acute exercise), and 1h post exercise (to be consistent with other works published\nin the field and particularly to building upon the work of Contrepois et al. 2020, Cell\n(PMID: 32470399), in which PBMC transcriptomics indicated a strong response in between 2-\n60 min. after acute exercise).\nReviewer #2 (Remarks to the Author):\nThis study presents a rigorous and thoughtfully designed investigation into how acute aerobic\nexercise shapes the molecular landscape of peripheral blood mononuclear cells (PBMCs),\nfilling a critical gap in the field of exercise immunology from a proteomics perspective. By\nemploying a randomized crossover design, carefully matched for workload and duration, and\ncombining high-resolution LC-MS/MS proteomics with spectral flow cytometry, the authors\nprovide a solid experimental foundation. Notably, the discovery that high-intensity interval\nexercise (HIIE) induces a much stronger proteomic response than moderate-intensity\ncontinuous exercise (MICE) adds biological weight to WHO’s recommendations highlighting\nthe importance of exercise intensity for health promotion.\nBeyond its depth of proteomic analysis, the study stands out for its integration of functional\nannotation through Gene Ontology, hierarchical and fuzzy clustering, and its correlation with\ncardiorespiratory fitness (V̇ O₂peak). The identification of NAMPT and other proteins related to\nmetabolism and immune function as potential predictors of V̇ O₂peak is particularly exhilarating,\noffering new avenues to understand how immunometabolic health relates to systemic fitness.\nThat said, a few refinements could further enhance the clarity, depth, and translational\nrelevance of this important work:\nThank you very much for your positive and constructive feedback on our manuscript. We agree\nwith your comments and believe that the corresponding revisions helped to further improve the\nquality of the manuscript.\n4\n1. Streamlining the narrative: Some parts of the text are quite dense. Condensing and\nrestructuring these sections could make the key findings easier to grasp and visually more\naccessible for the reader.\nThis comment is partly consistent with some comments from Reviewer 1 (comment 1, 7, 8).\nWe sharpened our rationale in the introduction, including a hypothesis on comparing the\neffects of HIIE vs. MICE, and further contextualized the findings of study in the discussion\nsection. If you have any specific remarks on how to condense the results section, we are happy\nto adapt.\n2. Incorporating sex- and age-based comparisons: Including analyses based on sex and age\ncould offer valuable insights, especially given the well-established hormonal and metabolic\ndifferences across sexes and life stages. Such stratification may reveal distinct molecular\nresponses to exercise and aid in tailoring interventions.\nThank you for this comment. We agree that meta-variables like sex and age can offer valuable\ninsights into immunological differences in response to exercise. In fact, we have already\nevaluated sex differences in our analysis and did not find any significant interaction effects\nwhen including sex as a fixed effect in our linear mixed models (see page 7, last paragraph).\nRegarding age, we think our study is not suited to address this question since our recruited\ncohort was characterized by a very homogenous age distribution (30 ± 4 years), also because\nage between 18 and 35 was defined as an inclusion criterium. However, we added a paragraph\nat the end of the discussion that highlights the need of future studies to investigate other\npopulations including cohort of different age and fitness.\n3. Linking molecular pathways to clinical relevance: A brief discussion on how the activated\npathways might be beneficial in metabolic conditions such as type 2 diabetes or how they\nmight protect organs from chronic disease progression would elevate the translational impact\nof the findings and support differential applications of MICE and HIIE.\nIn accordance with your suggestion, we added a brief statement in the discussion section to\nfurther elevate the translational impact of our findings.\n4. Adding functional validation: Although functional predictions were made using Gene\nOntology, no direct functional assays (e.g., cytokine secretion, cytotoxicity, or antigen\npresentation) were performed. Including or proposing these validations, particularly for key\nmarkers like NAMPT, would strengthen confidence in the interpretations and reduce the risk\nof overfitting.\nThank you for highlighting this. With our analysis we aimed to provide an overview of proteomic\nalterations in PBMCs and the subsequent triggered molecular pathways. From our point of\nview functional/mechanistic validation of our results is much better suited for analyses that\nfocus on specific immune cell populations. We have addressed the lack of functional assays\nlike cytokine secretion or cytotoxicity assays in the discussion section and provided guidance\nfor future steps to be taken in this direction.\nRegarding NAMPT, we revisited the data obtained in this study to strengthen the confidence\nin the interpretation that higher NAMPT levels in PBMCs are observed in persons with higher\ncardiorespiratory fitness (i.e., VO2peak). For this purpose, we have expanded the manuscript\nby the following aspects:\n1. We calculated immunoproteomic signature score for each participant and included a\ncorrelation analysis and a median split of our cohort based on VO2peak values to\ndemonstrate that immunoproteomic signature scores correlate with VO2peak and that\nthis metric differs between participants with VO2peak above the median (55.6\nml/min/kg) vs. below the median (Figure 5D).\n5\n2. We provided an internal validation that NAMPT z-scores obtained via LC-MS/MS are\ncorrelated with VO2peak (Figure 5E).\n3. We included a variance partitioning approach in Figure 5 and Figure S5 to demonstrate\nthat:\na. VO2peak accounts for the largest fraction of explainable variance in the\nimmunoproteomic signature (Figure 5F & S5A).\nb. from all proteins included in the immunoproteomic signature, VO2peak provides\n93.66% of the explainable variance for NAMPT (Figure 5G & S5B). This\nindicates that cardiorespiratory fitness contributes more to explainable variance\nin NAMPT than in other proteins and that other participant characteristics like\nsex, age or BMI play an inferior role in explaining the variance within NAMPT\nvalues.\nBased on these additions to Figure 5 and Figure S5, we have provided details in the methods\nsection for variance partitioning, calculation of immunoproteomic signature scores, and\ncorrelation analyses. We have also rewritten the results section on the immunoproteomic\nsignature with the overall aim to strengthen the confidence in our results.\nRegarding the risk of overfitting in our VO2peak prediction (see also second last comment of\nReviewer 3), we reran the prediction model with permuted outcome variables to simulate data\nwithout any true signal. The resulting model yielded an R2 of 0.0062 and a mean square error\n(MSE) of 36.4, suggesting that the predictive performance under random conditions is close\nto zero. This comparison supports the conclusion that our original model does not appear to\nsuffer from overfitting. We added a few sentences on this in the results section.\n5. Integrating additional omics layers: While the correlation of proteomic changes with\nV̇ O₂peak is valuable, incorporating transcriptomic, metabolomic, or cytokine data would have\nprovided a more holistic systems biology view. Cytokine profiling, in particular, would have\nbeen a strong complement to the immune-related findings.\nWe fully agree with this comment and mentioned this aspect as key limitation at the end of the\ndiscussion section.\n6. Quantifying effect sizes: The observation that HIIE induces stronger changes could be\nbolstered by reporting effect sizes, which would add statistical weight and practical context to\nthe biological findings.\nThank you for this comment. We have added log2 fold changes as standardizes effect sizes\ntogether with FDR-adjusted p values for all time × condition interaction effects reported in the\ntext (see section Proteomic alterations differ between HIIE and MICE). A full report of all\nstatistical results, including all log2 FCs is given in Table S5, which is cross-referenced several\ntimes throughout the paragraphs on interaction effects.\n7. Website access issue: The resource link (https://sportsmedicine-dortmund.shinyapps.io/beat) appears to require a\npassword and was not functional with the provided materials.\nThank you for letting us know about this. We had provided the editors with a password, which\nwas apparently not forwarded to the reviewers. The website is now openly available without\nthe need of a password (https://sportsmedicine-dortmund.shinyapps.io/beat). We have also\nadded a reference to the website in the abstract to promote interactive data mining for\nresearchers interested in our results.\nAddressing these points would help the study not only read more clearly but also broaden its\nimpact—both for the scientific community and in real-world health and exercise applications.\nmanuscript.\nReviewer #3 (Remarks to the Author):\nI had the opportunity to review the manuscript \"Acute exercise rewires the proteomic landscape\nof human immune cells\" by D. Walzik et al. The manuscript describes results of a randomized\ncrossover trial comparing effects of high-intensity interval exercise and of moderate-intensity\ncontinuous exerceise on proteomic makeup of PBMCs. I reviewed the manuscript with a focus\non the statistical methods used, but I am not an expert in the analysis of high-dimensional\nOMICS data and am not fully familiar with the statistical conventions in that field. I have a few\nconcerns about the methods and would ask authors to comment on them.\nThank you for reviewing our manuscript and your constructive comments. Please find our\nreplies to your comments below.\nLinear mixed effects models were used for analysis of immune cells and for proteins. The\nanalytical approaches differ in some certain aspects, as e.g. the adjustment for multiple testing.\nTHe authors sould comment on the differences in the approaches and it should also be clear\nin the results sections and in the figures, which approach was used. For the immune cells, it is\nunclear which factor was used for the Bonferroni adjustment - was the correction applied for\nmultiple tests over different timepoints or was the number of tested cells also considered?\nThank you for noting this. We acknowledge that immune cell counts and proteomics data were\nanalyzed differently and have specified which approach was used in the results and methods\nsection as well as the figure legends.\nSince immune cell counts were only analyzed for a total of 10 immune cell populations, we\nrefrained from correcting linear mixed model and subsequent ANOVAs for multiple testing. In\ncontrast, pairwise comparisons of timepoints within each group and pairwise comparisons of\ngroups within each timepoint were Bonferroni-corrected. Against the backdrop of the higher\nnumber of statistical tests for pairwise comparisons ((6 time effect + 3 interaction effects) * 10\noutcome variables = 90 pairwise comparisons) compared to ANOVAs (1 ANOVA per outcome\nvariable = 10 ANOVAs), this is how, to the best of our knowledge, correction for multiple testing\nis classically handled in exercise science research with a manageable amount of outcome\nvariables.\nIn contrast, proteomics data comprised 6039 proteins across 23 participants in 2 conditions\nwith 3 measurement timepoints each. For large datasets with thousands of outcome variables\nmore sophisticated analytical pipelines with correction for multiple testing are urgently required.\nIn consultation with our colleagues from the proteomics core facility and biostatistics\ndepartment at the German Cancer Research Center, we have thus decided to implement linear\nmixed models with Benjamini-Hochberg correction for multiple testing using the limma R\npackage (PMID 25605792). Limma is especially powerful for statistical analyses in complex\nstudy designs as the cross-over design applied by us.\nWas the randomization sequence considered in the regression model or was it used in the\nanalysis in any other way?\nWe did not include randomization sequence in the regression models for statistical analysis of\nimmune cell counts or protein abundances. We acknowledge that if exercise sequences were\nnot randomized, it would be crucial to include the sequence of interventions as a fixed effect\nin the statistical analyses. However, with randomized exercise sequences as employed by us,\nwe do not expect any differences in immune cell counts/protein abundances. This notion is\nsupported by the fact that randomization sequences did not differ in terms of participant\ncharacteristics (see section on Randomization in Methods and Table S1). We also performed\nprincipal component analysis (PCA) to assess the impact of randomization sequence on the\nvariation within our data but did not include it in the manuscript since it offers little additional\n7\ninformation compared to the PCAs performed for intervention day (which was not randomized)\nand sex (Figure S2C). All three figures are appended below for your information.\nFor the prediction model presented in the last part of the results, I would be really interested\nhow this model performs compared to a model derived from data of the same structure without\nany true signal - which can be generated e.g. by permuting the outcome variable. This would\nallow to assess how much the resulting R-squared is affected from overfitting.\nWe appreciate this great suggestion. In line with the reviewer's comment, we tested the model\nperformance using a permuted version of the outcome variables to simulate data without a true\nsignal. The resulting model yielded an R2 of 0.0062 and a mean square error (MSE) of 36.4,\nsuggesting that the predictive performance under random conditions is close to zero. This\ncomparison supports the conclusion that our original model does not appear to suffer from\noverfitting. We added a few sentences on this in the methods section.\nGenerally, I believe that some of the wording used in the manuscrit implies a causality that\ncannot be confirmed by the data (\"... rules out the possibility ...\", \"... that were ... altered by ...\",\netc.). The authors should carefully review the manuscript in this regard and adjust the wording\naccordingly.\nThank you for this comment. We have reworded sections of our manuscript that imply false\ncausality.\n8"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for your detailed revisions and responses. All of my comments have been adequately addressed.", "reviewer_2": "This manuscript presents a well-designed and carefully executed investigation into how acute aerobic exercise influences the proteomic landscape of PBMCs. The combination of a randomized crossover design, controlled exercise workloads, and high-resolution LC-MS/MS proteomics provides a solid methodological foundation. The analytical approaches—ranging from Gene Ontology enrichment to clustering analyses and correlations with V̇O₂peak—are thoughtfully applied and add depth to the interpretation. Notably, the finding that high-intensity interval exercise elicits a more pronounced proteomic response than moderate-intensity exercise is convincing, and the additional analyses involving NAMPT and variance partitioning strengthen the study’s connection to cardiorespiratory fitness.\nThe authors have responded well to prior reviewer comments by clarifying dense narrative sections, addressing sex- and age-related considerations, expanding statistical reporting, and improving translational context. The manuscript is clear, scientifically rigorous, and supported by appropriate methodological detail. Although some results sections could still benefit from slight condensation for readability, and future work incorporating functional assays or multi-omics layers would enrich mechanistic insight, these points do not detract from the quality of the present study. Overall, the work makes a meaningful contribution to exercise immunology and is well-positioned for publication.", "reviewer_3": "I would like to thank the authors for taking my comments into consideration and for their clarifications."}, "author_response": ""}]

Decision letter