PGC-1 alpha overexpression in the skeletal muscle results in a metabolically active microbiome which is independent of redox signaling

PGC-1 alpha overexpression in the skeletal muscle results in a metabolically active microbiome which is independent of redox signaling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PGC-1 alpha overexpression in the skeletal muscle results in a metabolically active microbiome which is independent of redox signaling Erika Koltai, Soroosh Mozaffaritabar, Lei Zhou, Attila Kolonics, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5982826/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract In this study, we investigated the potential relationship between the mitochondrial network and the microbiome using wild-type and skeletal muscle-specific PGC-1α (Pparg coactivator 1 alpha) overexpressing mice, both with and without exercise training. PGC-1α levels were significantly elevated in skeletal muscle and, notably, in the colon, which is anatomically proximal to the microbiome. However, no significant changes were observed in cell signaling or mitochondria-related proteins within the colon. On the other hand, mitochondrial H₂O₂ production in the colon decreased in the PGC-1α overexpressing group. The relative abundance of several bacterial taxa differed between wild-type and PGC-1α overexpressing groups, indicating a shift in the microbiome milieu probably to cope with the increased metabolism, enhanced short-chain fatty acid utilization, and improved endurance capacity. Ten weeks of exercise training differentially modulated the host microbiome in PGC-1α overexpressing and wild-type mice, facilitating adaptations to a broad range of exercise-induced challenges. The results of this study provide new insights into the possible cross-talk between mitochondria and the microbiome. Biological sciences/Microbiology Biological sciences/Molecular biology Mitochondria Microbiota PGC-1α Physical Exercise Host-Microbial Interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The microbiota of the gut is crucial for breaking down dietary nutrients, regulating intestinal and systemic immune responses, producing small molecules critical for intestinal metabolism, and generating several gases that can modulate cellular function 1 . Due to the complex function of the gut microbiome, microbial diversity can be defined as the variety of different unicellular organisms, including bacteria, archaea, protists, and fungi 2 . The evolutionary interactions between eukaryotes and bacteria have fostered mutual benefits, resulting in a dynamic yet safe system known as symbiosis 3 . It is suggested that mitochondria were once free-living bacteria, based on shared structural and functional features. Over time, they transitioned into an endosymbiotic state and became an integral organelle within early eukaryotic cells. 3 . This highly probable endosymbiosis enabled unique characteristics of mitochondria, including their role in extracellular communication. It is suggested that one of the targets of this communication is the ancient family of eukaryote-hosted bacterial species. 4 . Indeed, it has been shown that mutations in mtDNA and the mitochondrial genotype are associated with the diversity of bacterial species in the gut microbiome of mice. 4 . One possible mode of communication could be via reactive oxygen species (ROS), as mitochondria-produced ROS play an important role in the innate immune response, which is often targeted by pathogenic bacteria, leading to altered regulation of the gut epithelial barrier 5 . To better understand the potential interactions between the mitochondrial network and the gut microbiome, we employed transgenic mice with muscle-specific overexpression of PGC-1α, performed shotgun metagenomic analysis of the microbiome, and investigation of mitochondria-associated cellular pathways in the intestine. Given that PGC-1α overexpression is thought to affect the motor activity of mice, we included trained groups to distinguish the effects of increased mitochondrial mass due to PGC-1α overexpression from those resulting from exercise-induced adaptive responses. 2. Materials and Methods 2.1 Animal model Twenty 10-month-old C57BL/6-Tg(Ckm-Ppargc1a)31Brsp/J mice with skeletal muscle-specific PGC-1α overexpression and twenty age-matched wild-type littermates were randomly assigned to four experimental groups: Wild-type Control (Wt-C), PGC-1α Control (PGC-1α-C), with 11 animals per control group (n = 11), and two exercise groups: Wild-type Exercise (Wt-Ex) and PGC-1α Exercise (PGC-1α-Ex), each consisting of 9 animals (n = 9). Animals were purchased from The Jackson Laboratory (Bar Harbor, Maine, U.S https://www.jax.org/strain/008231 ) in this mice model PGC-1α overexpression is driven by the mouse muscle creatine kinase (MCK) promoter 6 . The mice were housed under a 12-hour light/dark cycle with ad libitum access to standard laboratory chow and water. The study protocol was approved by the National Animal Research Ethical Committee of Hungary (PE/EA/62 − 2/2021), and all methods were performed in accordance with the relevant national and international guidelines and regulations. Additionally, all experiments and procedures were conducted in compliance with the ARRIVE guidelines. 2.2 Training protocol: After familiarization with the treadmill, mice in the training groups underwent a fatigue endurance test to assess their maximal running capacity, following the protocol described by Dougherty et al. 7 . Briefly, the protocol consists of three days of running habituation, followed by one day of rest and a final test day. Based on the results, the training protocol was initiated at 60% of the animals’ average maximal running capacity, with the exercise intensity progressively increased on a weekly basis. The training regimen lasted for 10 weeks, consisting of 5 days of 30-minute sessions per week as reported by Mozaffaritabar et al. 8 . Fecal samples were collected before the start and after the completion of the exercise intervention. After overnight fasting, animals were deeply anesthetized with intraperitoneal Ketamine (100 mg/kg) and Xylazine (10 mg/kg) injection, followed by euthanasia via cervical dislocation; subsequently, their intestines were harvested, flash-frozen in liquid nitrogen, and stored at − 80°C for further analysis. 2.3 Western blots: Western blots were performed as previously described 9 using the following antibodies: PGC-1α (nbp1-04676), CS (ab96600), Mfn1 (sc50330), GAPDH (9001–50 − 7), β-Actin (sc69879), p-MTOR/MTOR (cst5536, 2983), p-AKT/AKT (cst9271, 4691), p-CREB/CREB (cst9198, 9197s), p-AMPKα/AMPKα (2535, 2532), CBS (14782), TFAM (PA5-27865), and PCNA Antibody FL-261 (sc-7907). 2.4 Mitochondrial, cytosolic, and nuclear fraction preparation Cell fractionation was performed according to Scoranno et al. 10 with minor modifications. Every step was performed at 4 o C. Briefly, the fresh, fat, and connective tissue-free colon tissue was immersed in ice-cold PBS supplemented with 10 mM EDTA and minced into small pieces. Samples were digested by 0.05% trypsin for 30 min with gentle shaking, then centrifuged at 1000 g for 5 min. The pellet was resuspended in a 10-fold buffer volume of IB m 1 (50 mM Tris-HCl, 50 mM KCl, 10 mM EDTA, 0.2% BSA and 0.067 M Sucrose pH 7.4) and homogenized by 3–4 times gentle stroke. The homogenate was centrifuged at 600 g for 10 minutes. Part of the nucleus including pellet and cytosolic supernatant was reserved for Western blot analysis. The supernatant was centrifuged at 8000 g for 10 minutes which resulted in the mitochondrial pellet. The centrifugation step was repeated after IB m 1 buffer homogenization to gain high-quality intact mitochondria. The mitochondrial pellet was suspended in the least volume of possible IB m 2 buffer (10 mM Tris-HCl, 3 mM Tris-EGTA, and 0.25 M Sucrose pH 7.4). Protein concentration was measured using the Bradford assay 11 . 2.5 ROS production measurement Mitochondria (0.3 mg/ml) were incubated in experimental buffer (10 mM Tris/HCl, 5 mM MgCl 2 , 2 mM KH 2 PO 4 , 20 mM EGTA/Tris, 250 mM Sucrose pH 7.4) supplemented with 1 µM Amplex Red (excitation: 560 nm; emission: 584 nm) and horseradish peroxidase (10 IU) to assess ROS production by monitoring H 2 O 2 -induced fluorescence according to Votyakova et al. with minor modifications 12 . After measuring basal ROS production, 10 mM succinate (Succ) and/or 1 µM (Rote) were sequentially added. With succinate as a substrate, ROS production is augmented due to reverse electron transport (RET) at complex I. This can be estimated by its sensitivity to inhibition by rotenone. Under these conditions, the addition of rotenone has two effects at complex I: it enhances ROS production linked to the forward electron flux while reducing ROS production associated with reverse electron flux. 13 . Calibration of H 2 O 2 production was obtained by the addition of a known amount of H 2 O 2 . Fluorimetric assays were performed at 30 ◦ C with Fluorskan Ascent FL fluorimeter on 96 well plates. Each sample was measured in triplicate. 2.6 Microbiome Assay Fecal samples were collected for analysis of gut microbiota in cryo tubes and stored at -80°C until subsequent analysis. A frozen aliquot (200 mg) of each fecal sample was suspended in 250 ml of guanidine thiocyanate solution, 0.1 M Tris, pH 7.5, and 40 ml of 10% N-lauroyl sarcosine. DNA extraction was then performed as previously described 14 and the DNA concentration and molecular size were estimated using a nanodrop (Thermo Scientific) and agarose gel electrophoresis. 2.7 Illumina Sequencing Extracted fecal DNA was used as input for the Illumina Nextera® XT DNA Library Preparation Kit to construct indexed paired-end libraries, following previously established protocols 15 . DNA library preparation followed the manufacturer's instructions (Illumina). The workflow indicated by the provider was used for cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturing, and hybridization of the sequencing primers. The base-calling pipeline (version IlluminaPipeline-0.3) was used to process the raw fluorescent images and call sequences. 2.8 Bioinformatics Analysis The quality of raw and trimmed reads was assessed using FastQC and MultiQC. Low-quality sequences were filtered and trimmed with Trimmomatic, removing sequences with a minimum length < 36 bp and low-quality base calls (Phred score < 30). Reads aligning to the human reference genome (GRCh38) were removed to eliminate host contamination using Bowtie2 (v2.4.2). Shotgun metagenomic sequencing data were analyzed for microbiome composition using Kraken2- Bracken, as previously described 16 and functional genomic analysis as described by FMAP 17 . Taxa with an average relative abundance of less than 1% across all samples were excluded from further analysis. 2.9 Statistical analysis Data distribution was tested using the Shapiro-Wilk test to assess normality. After confirming normal distribution factorial ANOVA was conducted to assess timepoint and group differences with Tukey HDS to compare means. For microbiome data and other non-normally distributed variables, the Kruskal–Wallis test was used. Benjamini-Hochberg correction was used to adjust for multiple comparisons, with false discovery rate set at FDR < 0.05. Taxa with log₂ fold changes greater than 0.5 or less than − 0.5 were considered biologically relevant. 3. Results The baseline running distance of the PGC-1α-Ex group was 2.9-fold longer than that of the wt-Ex group, showing a significant improvement. After 10 weeks of exercise training, the running distance of the PGC-1α-Ex group increased significantly compared to both the PGC-1α-Ex baseline and wt-Ex baseline. Figure 1 presents the results of the exhaustive running test (1a), representative PGC-1α levels from quadriceps muscle for control mice in both PGC-1α overexpressing and wt mice(1b), and skeletal muscle visuals of the hindlimb (1c) including the plantaris, gastrocnemius and tibialis anterior muscle representing the wt-C and PGC-1α-C groups. Our results show similar tendencies as published by Mozaffaritabar et al 8 with similar mouse model. We examined the microbiome in close proximity to the colon. The biochemical analysis revealed that exercise and PGC-1α overexpression significantly decreased basal and succinate-induced ROS production in colon mitochondria (Fig. 2 ). Except for PGC-1α, no significant alterations were detected in the main exercise-associated adaptive proteins in the colon related to PGC-1α overexpression or exercise training, as measured after the exercise intervention (Fig. 3 ). The intestine is in proximity to the gut microbiome, and shotgun metagenomic analysis was performed to investigate the effects of PGC-1α muscular overexpression and exercise training on microbiome plasticity. The relative abundance of several bacterial genera showed significant differences related to PGC-1α overexpression (Fig. 4 , Supplementary table S1 ). | This finding suggests an interaction between the mitochondrial network in the skeletal muscle and microbiome . The relative abundance of Campanilactobacillus, Marinomonas, Gracilibacilus, Cloacibacterium, Glutamicibacter, Providencia, Anoxybacillus, Syntrohomonas, Borrelia, Enterobacter, Methonobacterium , and Turicimonas differed at baseline between wild-type and PGC-1α overexpressing mice (Fig. 4 panel a). Moreover, we detected exercise-induced alterations in the microbiome, revealing distinct adaptability between wild-type and PGC-1α-overexpressing animals. Indeed, following exercise training, the relative abundance of Micropruina, Limosilactobacillus, Aeromicrobium, Phycicoccus, Dermacoccus , and Adlercreutzia was lower, while Bacteroides , Paraglaciecola , Niastella , Anaerolinea , and Exiguobacterium were increased in the PGC-1α overexpressing group compared to wild-type mice (Fig. 4 , Panel B).This suggests that exercise training may enhance the dynamics of the microbiome in PGC-1α overexpressing animals compared to wild-type controls. Exercise training increased the relative abundance of Mycobacterium and Arcanobacterium in the wild-type group (Fig. 4 , Panel c), and Turicimonas in the PGC-1α overexpressing group, while the abundance of Desulfovibrio and Ligilactobacillus decreased in the PGC-1α overexpressing group (Fig. 4 , Panel c, d). Genotype and regular exercise also appear to influence molecular pathways, with a greater number of significantly altered microbiome-related pathways observed between the muscle-specific PGC-1α overexpression group and the control group, both before and after the intervention, compared to the differences observed within each group before and after exercise. (Supplementary Fig S1 , Table S2 ). In total, 127 pathways differed significantly at baseline, with 28 pathway-associated bacterial groups showing increased representation and 99 showing decreased representation in the PGC-1α overexpressing mice compared to wild-type controls. Following the exercise intervention, 129 pathways were significantly different, with 90 showing increased and 39 showing decreased representation. Notably, 45 pathways exhibited persistent differences across the exercise treatment period, the majority of which (41 pathways) showed elevated representation in the overexpression group, while only 4 showed lower representation. (Fig. 4 Panel e). 4. Discussion It is suggested, based on the evolutionary origins of the cell organelle, that communication exists between gut-hosted bacterial flora and the mitochondria. However, a definitive link has yet to be fully established. To the best of our knowledge, this study provides preliminary evidence that may support the existence of communication between mitochondria and the microbiome. PGC-1α overexpression has been linked to significant changes in the gut microbiome, particularly in the relative abundance of specific microbial genera. This suggests that the host's metabolic environment may undergo substantial shifts, as PGC-1α is crucial for regulating energy metabolism, mitochondrial biogenesis, and oxidative metabolism 18 , 19 . The physiological effects of PGC-1α overexpression in skeletal muscle appear to be reflected in the gut microbiota, suggesting a metabolic environment characterized by enhanced energy extraction, fermentation, and short-chain fatty acid (SCFA) production. These changes likely contribute to enhanced metabolic efficiency and improved gut health, which aligns with the significantly higher baseline endurance observed in PGC-1α overexpressing mice. Recent studies have highlighted that PGC-1α overexpression in skeletal muscle is linked to increased levels of GPR41, a receptor specialized for SCFA uptake, suggesting a direct relationship between enhanced muscle mitochondrial biogenesis and SCFA utilization 8 . Our observation of differences in the gut microbiome composition between PGC-1α overexpressed and wild-type mice aligns with the findings of an earlier study, which showed that AC5KO mice—known for their improved longevity, increased glucose metabolism, insulin sensitivity, and exercise tolerance—also exhibit distinct changes in their gut microbiome profile 20 . This supports the notion that metabolic adaptations—such as those mediated by PGC-1α overexpression—may influence the composition of the gut microbiome. The differential response of the microbiome to exercise between wild-type and PGC-1α-overexpressing animals suggests that PGC-1α influences the way the microbiome responds to physical activity, potentially impacting host physiology. The decreased abundance of Micropruina, Limosilactobacillus, Aeromicrobium, Phycicoccus, Dermacoccus , and Adlercreutzia might indicate a reduction in certain metabolic activities or immune responses. For example, Limosilactobacillus is known for its probiotic properties and its role in maintaining gut health 21 , 22 . Micropruina has been suggested to were use to carbon from sugars and amino acids, under anaerobic conditions, to fermentation to lactic acid, acetate, propionate, and ethanol, and partly stored as glycogen for potential aerobic use 23 . Recent data revealed that Aeromicrobium , is important part of immune system since it acts effectively against H9N2 influenza virus in mice 24 . It has been reported that one of the metabolites of Dermacoccus bacterium, the demacozines play a role in the regulation of redox homeostasis 25 . A decrease in these bacteria could suggest a shift away from these functions, potentially impacting gut homeostasis and immune regulation. The increased abundance of Bacteroides, Paraglaciecola, Niastella, Anaerolinea , and Exiguabacterium could be associated with enhanced metabolic activities related to energy production and nutrient absorption. Bacteroides are crucial for breaking down complex molecules in the gut, which can influence energy balance and metabolic health 26 . In deed, Bacteroidetes efficiently breaks down poly- and mono-saccharides into beneficial SCFAs 27 like acetate and propionate that could play a role in the prevention of colon cancer 28 and it could enhace endurance capacity 29 , 30 . The fact that exercise training differently modulates the microbiome of wild-type and PGC-1α-overexpressing mice indicates that exercise training provides a different physiological signal to the microbiome than the increased levels of mitochondrial formation. Exercise training provides intermittent metabolic challenges, while higher mitochondrial content in the skeletal muscle could imply continuous cross talks between mitochondria and microbime. Interestingly, it has been noted that different lipid metabolism-related pathways were influenced by PGC-1α overexpression and exercise training 8 . Exercise training increased the relative abundance of Mycobacterium and Arcanobacterium in wild, and Turicimonas , in PGC-1α overexpressed group while the Desulfovibro and Ligalactobacillus content decreased by training in this group. Mycobacterium and Arcanobacterium, which showed increased abundance with exercise training in wild-type mice, are not typically prominent in the gut microbiome but can be transiently present. Some species from these taxa may contribute to immune modulation, potentially supporting exercise-induced anti-inflammatory effects. Research suggests that Mycobacterium can influence T-cell responses and enhance immune resilience, potentially benefiting metabolic and immune adaptations to exercise 31 . Arcanobacterium is less well-studied in the intestinal tract, but its presence may indicate shifts in niche microbial dynamics due to exercise, possibly favoring microbes with versatile metabolic functions that respond positively to exercise-induced alterations in host physiology. The role of Arcanobacterium in gut health remains unclear, but its relative expansion may be linked to exercise-induced changes in nutrient availability and gut pH. Turicimonas , which showed increased abundance following exercise training, is associated with butyrate production—a beneficial factor for colonic health and energy metabolism. 32 . Butyrate has anti-inflammatory properties and contributes to maintaining gut barrier integrity. The increase in Turicimonas abundance could support the anti-inflammatory and metabolic benefits associated with PGC-1α overexpression, enhancing the host’s endurance capacity and energy regulation 32 . This may also align with findings that PGC-1α overexpression leads to greater mitochondrial biogenesis, complementing the energetic requirements of exercise adaptation. Concurrently, the decreased abundance of Desulfovibrio , which is known for producing hydrogen sulfide 33 and Ligilactobacillus , involved in immune regulation 34 , in the transgenic group suggests a shift to a bacterial environment that maintains a more stable and less pro-inflammatory microbiome, especially under the high metabolic demands of PGC-1α overexpression. Ligilactobacillus species, previously classified as Lactobacillus, are generally regarded as beneficial probiotics. The relative decline in Ligilactobacillus due to exercise may suggest that PGC-1α overexpression alters gut ecology, potentially reducing the niche for these bacteria. This shift could be attributed to an altered metabolic environment, and there is a possibility that PGC-1α, due to its enhancing effect on fatty acid oxidation, may reduce the need for lactate-producing bacteria. Our data suggest that PGC-1α overexpression decreases mitochondria-derived ROS production in the colon, possibly ruling out ROS-associated signaling pathways that could account for the different compositions of the microbiome between wild-type and PGC-1α overexpressed animals. Overall, our data indicate that PGC-1α overexpression in skeletal muscle could contribute to a physiological environment—such as improved oxygen utilization and reduced ROS production—that may lead to changes in the microbiome, potentially supporting metabolic activity, SCFA utilization, and improved endurance capacity. Exercise training seems to differentially modulate the host microbiome in PGC-1α overexpressing and wild-type mice, which may reflect coping mechanisms to exercise-induced physiological challenges. The results of the present investigation, together with recent advances in the field, suggest a potential cross-talk between mitochondria and the microbiome; however, further studies are required to further elucidate the underlying biological mechanisms. Abbreviations PGC-1α peroxisome proliferator-activated receptor gamma coactivator 1-alpha ROS reactive oxygen species mtDNA mitochondrial DNA Wt wild type SCFA short-chain fatty acid GPR41 free fatty acid receptor 3 FMAP Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies Succ succinate Rote rotenone CS citrate synthase Mfn1 Mitofusin-1 GAPDH glyceraldehyde 3-phosphate dehydrogenase MTOR mammalian target of rapamycin AKT Protein kinase B CREB cAMP response element-binding protein AMPKα 5' AMP-activated protein kinase alpha CBS cystathionine-β-synthase TFAM mitochondrial transcription factor A PCNA proliferating cell nuclear antigen Declarations Funding Open access funding provided by Hungarian University of Sports Science. ZR acknowledges support from the National Excellence Program (126823) National Science and Research Found (OTKA142192) and Scientific Excellence Program TKP2021-EGA-37 at the Hungarian University of Sports Science, Innovation and Technology Ministry, Hungary, as well as TEKA grant from Hungarian University of Sport Science. Data availability The datasets generated during the current study are available in the The European Nucleotide Archive (ENA) repository, with accession number ERP169114: https://www.ebi.ac.uk/ena/browser/view/PRJEB85739. Author contribution The authors declare that all data were generated in-house and that no paper mill was used. Conceptualization, Z.R. S.M. E.K. Methodology, E.K., S.M. L.Z., A.K., A.K., J.P., F.T., and Z.R; Investigation, E.K., S.M., L.Z., K.T., A.K., A.K., and Z.R. Formal Analysis, J.P., F.T., and K.T. Writing – Original Draft, Z.R., and E.K.. Writing – Review & Editing, E.K., T.F., K.T. Z.R., Supervision, Z.R. 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Gut Microbes . 16 , 2418415. 10.1080/19490976.2024.2418415 (2024). Additional Declarations No competing interests reported. Supplementary Files tableS1.html tableS2.html KoltaietalsupplementarySciReprevised.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 01 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 24 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5982826","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447483790,"identity":"7f558504-8749-41b0-8a81-e82df17cfa36","order_by":0,"name":"Erika Koltai","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Koltai","suffix":""},{"id":447483791,"identity":"01eacb4c-e080-42ad-bf0a-bc2dc9655db3","order_by":1,"name":"Soroosh Mozaffaritabar","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Soroosh","middleName":"","lastName":"Mozaffaritabar","suffix":""},{"id":447483792,"identity":"d9e019f3-35cd-45a4-823a-0d43e5f887f4","order_by":2,"name":"Lei Zhou","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhou","suffix":""},{"id":447483793,"identity":"656d7ce1-e98e-4770-91ed-5b8a0224f7d9","order_by":3,"name":"Attila Kolonics","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Attila","middleName":"","lastName":"Kolonics","suffix":""},{"id":447483794,"identity":"ad364ef7-dcd9-48eb-ad1c-04aed401415c","order_by":4,"name":"Atsuko Koike","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Atsuko","middleName":"","lastName":"Koike","suffix":""},{"id":447483795,"identity":"f69936e5-5933-41db-9136-a26403727cca","order_by":5,"name":"Kumpei Tanisawa","email":"","orcid":"","institution":"Faculty of Sport Sciences, Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Kumpei","middleName":"","lastName":"Tanisawa","suffix":""},{"id":447483796,"identity":"5c609dd6-c3d2-45c7-9d64-e2a97a6b6410","order_by":6,"name":"Jonguk Park","email":"","orcid":"","institution":"Artificial Intelligence Center for Health and Biomedical Research","correspondingAuthor":false,"prefix":"","firstName":"Jonguk","middleName":"","lastName":"Park","suffix":""},{"id":447483797,"identity":"48cdad56-4d52-455e-ae77-44536751192f","order_by":7,"name":"Ferenc Torma","email":"","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":false,"prefix":"","firstName":"Ferenc","middleName":"","lastName":"Torma","suffix":""},{"id":447483798,"identity":"764b4cbf-07f0-4fb3-8c2d-3bd686c5846c","order_by":8,"name":"Zsolt Radak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHACA8YGEMXOwPiAwYYhgQQtzMzMBgfSSNTCJkGUFvP2wxs/zmCos+dn5j9W/SGBIc/gAAEtMmfSiiU3MLAlzmxmZrtxIIGhmKAWCYYcA8kHDDwJBoeBWg7+YEjcQFAL/xvjnw8YJOxBWgqAthChRSLHDOgwA8YNQC0MRGp5VmY5wyAB5BdjiTMJEokzCTssefPNngpgiLE3PvxQkWCT2EdICwQYIIwgSv0oGAWjYBSMAgIAAG4yPX1QKrZqAAAAAElFTkSuQmCC","orcid":"","institution":"Research Institute of Sport Science, Hungarian University of Sport Science","correspondingAuthor":true,"prefix":"","firstName":"Zsolt","middleName":"","lastName":"Radak","suffix":""}],"badges":[],"createdAt":"2025-02-07 16:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5982826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5982826/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-05594-w","type":"published","date":"2025-07-01T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81521264,"identity":"8d0a5be8-74fc-4d03-b5f3-2169acf744ca","added_by":"auto","created_at":"2025-04-28 08:05:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotype and running distance of PGC-1 alpha and wild-type of mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePGC-1 alpha overexpressed animals had significantly better endurance performance than that of wild-type mice. Ten weeks of exercise training increased the endurance performance in both groups (a).Representative plantaris, gastrocnemius and tibialis anterior muscles (left to right) of the PGC-1α transgenic and wt control mice (c). Representative PGC-1α the protein levels were showed higher tendencies in the quadriceps of transgenic mice (b). Bars depict mean ± SEM.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1revised.png","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/663ed67e62029f141759a783.png"},{"id":81520208,"identity":"28603548-90e1-43b1-b7aa-237fc1ddbbf8","added_by":"auto","created_at":"2025-04-28 07:57:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of muscle-specific PGC-1α overexpression and exercise on mitochondrial ROS production in the intestine.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIntact mitochondria were isolated from the intestine, (see Materials and Methods section) and incubated ( 0.1 mg protein) in 0.2 mL medium (30◦C) containing\u0026nbsp; 250 mM sucrose, 0.1 mM EGTA, 20 mM Tris–HCl (pH 7.4), 2.5 mM Pi and 1 mM MgCl\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e. Basal and 10 mM\u0026nbsp; succinate (Succ) induced as well as 1μM \u003c/em\u003erotenone (Rote) inhibited\u003cem\u003e hydrogen peroxide (ROS) production was assessed by monitoring H\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-induced\u0026nbsp; fluorescence of 1μM Amplex Red\u0026nbsp; in the presence of horseradish peroxidase. Results are mean ± SEM\u0026nbsp; (n = 4 - 5). Statistical significance was assessed using Oneway ANOVA .\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/786112f67eae3615478c0784.png"},{"id":81522567,"identity":"ad5fc1f9-db91-47f1-bc07-d47c37ee0bdc","added_by":"auto","created_at":"2025-04-28 08:13:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of exercise and muscle-specific PGC-1α overexpression on protein levels and phosphorylation in the large intestine. \u003c/strong\u003e\u003cem\u003eExcept for PGC-1α (h), protein and phosphorylation levels in colon samples did not show significant differences between the exercise (Control, Exercise) and genotype (Wt, PGC-1α) groups (a–g, i–j) after the exercise intervention period. wC: Wilt type control, wE: wild type exercise, PC: PGC-1α control, PE: PGC-1α exercise.Bars depict mean ± SEM.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/e9d4fe439d8391c28c8b70ac.png"},{"id":81520205,"identity":"c5e16382-2141-4441-be09-cd2ffd41c951","added_by":"auto","created_at":"2025-04-28 07:57:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of muscle specific PGC-1α over expression and exercise on gut microbiome composition. \u003c/strong\u003e\u003cem\u003ea-d panels show the changes in relative abundance density at the genus taxonomic level. Red dots indicate significantly increased relative abundance, while blue dots significantly decreased relative abundance between the groups depicted in the upper right corner of each panel. The green dashed line marks the Benjamini-Hochberg (BH) correction threshold, and gray lines indicate ±0.5 Log2 fold change. Panel e depicts the number of significantly different biological pathways between the PGC-1α and Wt mice before and after the exercise intervention.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/7c021e8000186367282f03e2.png"},{"id":86178969,"identity":"d0d3d4e3-f6bf-4d9f-be21-6e179ff3294c","added_by":"auto","created_at":"2025-07-07 16:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1805494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/e6845d69-d273-4057-8f5f-25fe1144872b.pdf"},{"id":81520207,"identity":"998b1982-9e07-48b2-bf33-5b4c4896ebb4","added_by":"auto","created_at":"2025-04-28 07:57:56","extension":"html","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":215819,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.html","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/5b2a3e0928022708e3c08588.html"},{"id":81520214,"identity":"45af5079-822c-4d51-9923-6219b274647d","added_by":"auto","created_at":"2025-04-28 07:57:56","extension":"html","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":871932,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2.html","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/c56d64cff119984dd5e25980.html"},{"id":81520215,"identity":"fbd28570-4237-4284-814f-c1e7e11c91ea","added_by":"auto","created_at":"2025-04-28 07:57:56","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1081358,"visible":true,"origin":"","legend":"","description":"","filename":"KoltaietalsupplementarySciReprevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5982826/v1/018616c00af5db263371e311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PGC-1 alpha overexpression in the skeletal muscle results in a metabolically active microbiome which is independent of redox signaling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe microbiota of the gut is crucial for breaking down dietary nutrients, regulating intestinal and systemic immune responses, producing small molecules critical for intestinal metabolism, and generating several gases that can modulate cellular function \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Due to the complex function of the gut microbiome, microbial diversity can be defined as the variety of different unicellular organisms, including bacteria, archaea, protists, and fungi \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe evolutionary interactions between eukaryotes and bacteria have fostered mutual benefits, resulting in a dynamic yet safe system known as symbiosis \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It is suggested that mitochondria were once free-living bacteria, based on shared structural and functional features. Over time, they transitioned into an endosymbiotic state and became an integral organelle within early eukaryotic cells. \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This highly probable endosymbiosis enabled unique characteristics of mitochondria, including their role in extracellular communication. It is suggested that one of the targets of this communication is the ancient family of eukaryote-hosted bacterial species. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Indeed, it has been shown that mutations in mtDNA and the mitochondrial genotype are associated with the diversity of bacterial species in the gut microbiome of mice. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. One possible mode of communication could be via reactive oxygen species (ROS), as mitochondria-produced ROS play an important role in the innate immune response, which is often targeted by pathogenic bacteria, leading to altered regulation of the gut epithelial barrier \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo better understand the potential interactions between the mitochondrial network and the gut microbiome, we employed transgenic mice with muscle-specific overexpression of PGC-1α, performed shotgun metagenomic analysis of the microbiome, and investigation of mitochondria-associated cellular pathways in the intestine. Given that PGC-1α overexpression is thought to affect the motor activity of mice, we included trained groups to distinguish the effects of increased mitochondrial mass due to PGC-1α overexpression from those resulting from exercise-induced adaptive responses.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003e2.1 Animal model\u003c/strong\u003e\u003c/div\u003e\n\u003cp\u003eTwenty 10-month-old C57BL/6-Tg(Ckm-Ppargc1a)31Brsp/J mice with skeletal muscle-specific PGC-1\u0026alpha; overexpression and twenty age-matched wild-type littermates were randomly assigned to four experimental groups: Wild-type Control (Wt-C), PGC-1\u0026alpha; Control (PGC-1\u0026alpha;-C), with 11 animals per control group (n\u0026thinsp;=\u0026thinsp;11), and two exercise groups: Wild-type Exercise (Wt-Ex) and PGC-1\u0026alpha; Exercise (PGC-1\u0026alpha;-Ex), each consisting of 9 animals (n\u0026thinsp;=\u0026thinsp;9). Animals were purchased from The Jackson Laboratory (Bar Harbor, Maine, U.S \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jax.org/strain/008231\u003c/span\u003e\u003c/span\u003e ) in this mice model PGC-1\u0026alpha; overexpression is driven by the mouse muscle creatine kinase (MCK) promoter \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The mice were housed under a 12-hour light/dark cycle with ad libitum access to standard laboratory chow and water. The study protocol was approved by the National Animal Research Ethical Committee of Hungary (PE/EA/62\u0026thinsp;\u0026minus;\u0026thinsp;2/2021), and all methods were performed in accordance with the relevant national and international guidelines and regulations. Additionally, all experiments and procedures were conducted in compliance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Training protocol:\u003c/h2\u003e\n \u003cp\u003eAfter familiarization with the treadmill, mice in the training groups underwent a fatigue endurance test to assess their maximal running capacity, following the protocol described by Dougherty et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Briefly, the protocol consists of three days of running habituation, followed by one day of rest and a final test day. Based on the results, the training protocol was initiated at 60% of the animals\u0026rsquo; average maximal running capacity, with the exercise intensity progressively increased on a weekly basis. The training regimen lasted for 10 weeks, consisting of 5 days of 30-minute sessions per week as reported by Mozaffaritabar et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Fecal samples were collected before the start and after the completion of the exercise intervention. After overnight fasting, animals were deeply anesthetized with intraperitoneal Ketamine (100 mg/kg) and Xylazine (10 mg/kg) injection, followed by euthanasia via cervical dislocation; subsequently, their intestines were harvested, flash-frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Western blots:\u003c/h2\u003e\n \u003cp\u003eWestern blots were performed as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e using the following antibodies: PGC-1\u0026alpha; (nbp1-04676), CS (ab96600), Mfn1 (sc50330), GAPDH (9001\u0026ndash;50\u0026thinsp;\u0026minus;\u0026thinsp;7), \u0026beta;-Actin (sc69879), p-MTOR/MTOR (cst5536, 2983), p-AKT/AKT (cst9271, 4691), p-CREB/CREB (cst9198, 9197s), p-AMPK\u0026alpha;/AMPK\u0026alpha; (2535, 2532), CBS (14782), TFAM (PA5-27865), and PCNA Antibody FL-261 (sc-7907).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Mitochondrial, cytosolic, and nuclear fraction preparation\u003c/h2\u003e\n \u003cp\u003eCell fractionation was performed according to Scoranno et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e with minor modifications. Every step was performed at 4\u003csup\u003eo\u003c/sup\u003eC. Briefly, the fresh, fat, and connective tissue-free colon tissue was immersed in ice-cold PBS supplemented with 10 mM EDTA and minced into small pieces. Samples were digested by 0.05% trypsin for 30 min with gentle shaking, then centrifuged at 1000 g for 5 min. The pellet was resuspended in a 10-fold buffer volume of IB\u003csub\u003em\u003c/sub\u003e1 (50 mM Tris-HCl, 50 mM KCl, 10 mM EDTA, 0.2% BSA and 0.067 M Sucrose pH 7.4) and homogenized by 3\u0026ndash;4 times gentle stroke. The homogenate was centrifuged at 600 g for 10 minutes. Part of the nucleus including pellet and cytosolic supernatant was reserved for Western blot analysis. The supernatant was centrifuged at 8000 g for 10 minutes which resulted in the mitochondrial pellet. The centrifugation step was repeated after IB \u003csub\u003em\u003c/sub\u003e1 buffer homogenization to gain high-quality intact mitochondria. The mitochondrial pellet was suspended in the least volume of possible IB\u003csub\u003em\u003c/sub\u003e2 buffer (10 mM Tris-HCl, 3 mM Tris-EGTA, and 0.25 M Sucrose pH 7.4). Protein concentration was measured using the Bradford assay \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 ROS production measurement\u003c/h2\u003e\n \u003cp\u003eMitochondria (0.3 mg/ml) were incubated in experimental buffer (10 mM Tris/HCl, 5 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 2 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 20 mM EGTA/Tris, 250 mM Sucrose pH 7.4) supplemented with 1 \u0026micro;M Amplex Red (excitation: 560 nm; emission: 584 nm) and horseradish peroxidase (10 IU) to assess ROS production by monitoring H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e-induced fluorescence according to Votyakova et al. with minor modifications \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. After measuring basal ROS production, 10 mM succinate (Succ) and/or 1 \u0026micro;M (Rote) were sequentially added. With succinate as a substrate, ROS production is augmented due to reverse electron transport (RET) at complex I. This can be estimated by its sensitivity to inhibition by rotenone. Under these conditions, the addition of rotenone has two effects at complex I: it enhances ROS production linked to the forward electron flux while reducing ROS production associated with reverse electron flux. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Calibration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production was obtained by the addition of a known amount of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. Fluorimetric assays were performed at 30\u003csup\u003e◦\u003c/sup\u003eC with Fluorskan Ascent FL fluorimeter on 96 well plates. Each sample was measured in triplicate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Microbiome Assay\u003c/h2\u003e\n \u003cp\u003eFecal samples were collected for analysis of gut microbiota in cryo tubes and stored at -80\u0026deg;C until subsequent analysis. A frozen aliquot (200 mg) of each fecal sample was suspended in 250 ml of guanidine thiocyanate solution, 0.1 M Tris, pH 7.5, and 40 ml of 10% N-lauroyl sarcosine. DNA extraction was then performed as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and the DNA concentration and molecular size were estimated using a nanodrop (Thermo Scientific) and agarose gel electrophoresis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Illumina Sequencing\u003c/h2\u003e\n \u003cp\u003eExtracted fecal DNA was used as input for the Illumina Nextera\u0026reg; XT DNA Library Preparation Kit to construct indexed paired-end libraries, following previously established protocols \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. DNA library preparation followed the manufacturer\u0026apos;s instructions (Illumina). The workflow indicated by the provider was used for cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturing, and hybridization of the sequencing primers. The base-calling pipeline (version IlluminaPipeline-0.3) was used to process the raw fluorescent images and call sequences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Bioinformatics Analysis\u003c/h2\u003e\n \u003cp\u003eThe quality of raw and trimmed reads was assessed using FastQC and MultiQC. Low-quality sequences were filtered and trimmed with Trimmomatic, removing sequences with a minimum length\u0026thinsp;\u0026lt;\u0026thinsp;36 bp and low-quality base calls (Phred score\u0026thinsp;\u0026lt;\u0026thinsp;30). Reads aligning to the human reference genome (GRCh38) were removed to eliminate host contamination using Bowtie2 (v2.4.2). Shotgun metagenomic sequencing data were analyzed for microbiome composition using Kraken2- Bracken, as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and functional genomic analysis as described by FMAP \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Taxa with an average relative abundance of less than 1% across all samples were excluded from further analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eData distribution was tested using the Shapiro-Wilk test to assess normality. After confirming normal distribution factorial ANOVA was conducted to assess timepoint and group differences with Tukey HDS to compare means. For microbiome data and other non-normally distributed variables, the Kruskal\u0026ndash;Wallis test was used. Benjamini-Hochberg correction was used to adjust for multiple comparisons, with false discovery rate set at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Taxa with log₂ fold changes greater than 0.5 or less than \u0026minus;\u0026thinsp;0.5 were considered biologically relevant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe baseline running distance of the PGC-1\u0026alpha;-Ex group was 2.9-fold longer than that of the wt-Ex group, showing a significant improvement. After 10 weeks of exercise training, the running distance of the PGC-1\u0026alpha;-Ex group increased significantly compared to both the PGC-1\u0026alpha;-Ex baseline and wt-Ex baseline. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the exhaustive running test (1a), representative PGC-1\u0026alpha; levels from quadriceps muscle for control mice in both PGC-1\u0026alpha; overexpressing and wt mice(1b), and skeletal muscle visuals of the hindlimb (1c) including the plantaris, gastrocnemius and tibialis anterior muscle representing the wt-C and PGC-1\u0026alpha;-C groups. Our results show similar tendencies as published by Mozaffaritabar et al \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e with similar mouse model.\u003c/p\u003e\n\u003cp\u003eWe examined the microbiome in close proximity to the colon. The biochemical analysis revealed that exercise and PGC-1\u0026alpha; overexpression significantly decreased basal and succinate-induced ROS production in colon mitochondria (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e ).\u003c/p\u003e\n\u003cp\u003eExcept for PGC-1\u0026alpha;, no significant alterations were detected in the main exercise-associated adaptive proteins in the colon related to PGC-1\u0026alpha; overexpression or exercise training, as measured after the exercise intervention (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe intestine is in proximity to the gut microbiome, and shotgun metagenomic analysis was performed to investigate the effects of PGC-1\u0026alpha; muscular overexpression and exercise training on microbiome plasticity. The relative abundance of several bacterial genera showed significant differences related to PGC-1\u0026alpha; overexpression (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary table\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). |\u003cem\u003eThis finding suggests an interaction between the mitochondrial network in the skeletal muscle and microbiome\u003c/em\u003e. The relative abundance of \u003cem\u003eCampanilactobacillus, Marinomonas, Gracilibacilus, Cloacibacterium, Glutamicibacter, Providencia, Anoxybacillus, Syntrohomonas, Borrelia, Enterobacter, Methonobacterium\u003c/em\u003e, and \u003cem\u003eTuricimonas\u003c/em\u003e differed at baseline between wild-type and PGC-1\u0026alpha; overexpressing mice (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e panel a). Moreover, we detected exercise-induced alterations in the microbiome, revealing distinct adaptability between wild-type and PGC-1\u0026alpha;-overexpressing animals. Indeed, following exercise training, the relative abundance of \u003cem\u003eMicropruina, Limosilactobacillus, Aeromicrobium, Phycicoccus, Dermacoccus\u003c/em\u003e, and \u003cem\u003eAdlercreutzia\u003c/em\u003e was lower, while \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParaglaciecola\u003c/em\u003e, \u003cem\u003eNiastella\u003c/em\u003e, \u003cem\u003eAnaerolinea\u003c/em\u003e, and \u003cem\u003eExiguobacterium\u003c/em\u003e were increased in the PGC-1\u0026alpha; overexpressing group compared to wild-type mice (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel B).This suggests that exercise training may enhance the dynamics of the microbiome in PGC-1\u0026alpha; overexpressing animals compared to wild-type controls.\u003c/p\u003e\n\u003cp\u003eExercise training increased the relative abundance of \u003cem\u003eMycobacterium\u003c/em\u003e and \u003cem\u003eArcanobacterium\u003c/em\u003e in the wild-type group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel c), and \u003cem\u003eTuricimonas\u003c/em\u003e in the PGC-1\u0026alpha; overexpressing group, while the abundance of \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eLigilactobacillus\u003c/em\u003e decreased in the PGC-1\u0026alpha; overexpressing group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel c, d).\u003c/p\u003e\n\u003cp\u003eGenotype and regular exercise also appear to influence molecular pathways, with a greater number of significantly altered microbiome-related pathways observed between the muscle-specific PGC-1\u0026alpha; overexpression group and the control group, both before and after the intervention, compared to the differences observed within each group before and after exercise. (Supplementary Fig\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table\u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). In total, 127 pathways differed significantly at baseline, with 28 pathway-associated bacterial groups showing increased representation and 99 showing decreased representation in the PGC-1\u0026alpha; overexpressing mice compared to wild-type controls. Following the exercise intervention, 129 pathways were significantly different, with 90 showing increased and 39 showing decreased representation. Notably, 45 pathways exhibited persistent differences across the exercise treatment period, the majority of which (41 pathways) showed elevated representation in the overexpression group, while only 4 showed lower representation. (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e Panel e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt is suggested, based on the evolutionary origins of the cell organelle, that communication exists between gut-hosted bacterial flora and the mitochondria. However, a definitive link has yet to be fully established. To the best of our knowledge, this study provides preliminary evidence that may support the existence of communication between mitochondria and the microbiome.\u003c/p\u003e \u003cp\u003ePGC-1α overexpression has been linked to significant changes in the gut microbiome, particularly in the relative abundance of specific microbial genera. This suggests that the host's metabolic environment may undergo substantial shifts, as PGC-1α is crucial for regulating energy metabolism, mitochondrial biogenesis, and oxidative metabolism \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The physiological effects of PGC-1α overexpression in skeletal muscle appear to be reflected in the gut microbiota, suggesting a metabolic environment characterized by enhanced energy extraction, fermentation, and short-chain fatty acid (SCFA) production. These changes likely contribute to enhanced metabolic efficiency and improved gut health, which aligns with the significantly higher baseline endurance observed in PGC-1α overexpressing mice. Recent studies have highlighted that PGC-1α overexpression in skeletal muscle is linked to increased levels of GPR41, a receptor specialized for SCFA uptake, suggesting a direct relationship between enhanced muscle mitochondrial biogenesis and SCFA utilization \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Our observation of differences in the gut microbiome composition between PGC-1α overexpressed and wild-type mice aligns with the findings of an earlier study, which showed that AC5KO mice\u0026mdash;known for their improved longevity, increased glucose metabolism, insulin sensitivity, and exercise tolerance\u0026mdash;also exhibit distinct changes in their gut microbiome profile \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This supports the notion that metabolic adaptations\u0026mdash;such as those mediated by PGC-1α overexpression\u0026mdash;may influence the composition of the gut microbiome.\u003c/p\u003e \u003cp\u003eThe differential response of the microbiome to exercise between wild-type and PGC-1α-overexpressing animals suggests that PGC-1α influences the way the microbiome responds to physical activity, potentially impacting host physiology. The decreased abundance of \u003cem\u003eMicropruina, Limosilactobacillus, Aeromicrobium, Phycicoccus, Dermacoccus\u003c/em\u003e, and \u003cem\u003eAdlercreutzia\u003c/em\u003e might indicate a reduction in certain metabolic activities or immune responses. For example, \u003cem\u003eLimosilactobacillus\u003c/em\u003e is known for its probiotic properties and its role in maintaining gut health \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMicropruina\u003c/em\u003e has been suggested to were use to carbon from sugars and amino acids, under anaerobic conditions, to fermentation to lactic acid, acetate, propionate, and ethanol, and partly stored as glycogen for potential aerobic use \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Recent data revealed that \u003cem\u003eAeromicrobium\u003c/em\u003e, is important part of immune system since it acts effectively against H9N2 influenza virus in mice \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It has been reported that one of the metabolites of \u003cem\u003eDermacoccus\u003c/em\u003e bacterium, the demacozines play a role in the regulation of redox homeostasis \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A decrease in these bacteria could suggest a shift away from these functions, potentially impacting gut homeostasis and immune regulation.\u003c/p\u003e \u003cp\u003eThe increased abundance of \u003cem\u003eBacteroides, Paraglaciecola, Niastella, Anaerolinea\u003c/em\u003e, and \u003cem\u003eExiguabacterium\u003c/em\u003e could be associated with enhanced metabolic activities related to energy production and nutrient absorption. \u003cem\u003eBacteroides\u003c/em\u003e are crucial for breaking down complex molecules in the gut, which can influence energy balance and metabolic health \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In deed, \u003cem\u003eBacteroidetes\u003c/em\u003e efficiently breaks down poly- and mono-saccharides into beneficial SCFAs \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e like acetate and propionate that could play a role in the prevention of colon cancer \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and it could enhace endurance capacity \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe fact that exercise training differently modulates the microbiome of wild-type and PGC-1α-overexpressing mice indicates that exercise training provides a different physiological signal to the microbiome than the increased levels of mitochondrial formation. Exercise training provides intermittent metabolic challenges, while higher mitochondrial content in the skeletal muscle could imply continuous cross talks between mitochondria and microbime.\u003c/p\u003e \u003cp\u003eInterestingly, it has been noted that different lipid metabolism-related pathways were influenced by PGC-1α overexpression and exercise training \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Exercise training increased the relative abundance of \u003cem\u003eMycobacterium\u003c/em\u003e and \u003cem\u003eArcanobacterium\u003c/em\u003e in wild, and \u003cem\u003eTuricimonas\u003c/em\u003e, in PGC-1α overexpressed group while the \u003cem\u003eDesulfovibro\u003c/em\u003e and \u003cem\u003eLigalactobacillus\u003c/em\u003e content decreased by training in this group. Mycobacterium and Arcanobacterium, which showed increased abundance with exercise training in wild-type mice, are not typically prominent in the gut microbiome but can be transiently present. Some species from these taxa may contribute to immune modulation, potentially supporting exercise-induced anti-inflammatory effects. Research suggests that \u003cem\u003eMycobacterium\u003c/em\u003e can influence T-cell responses and enhance immune resilience, potentially benefiting metabolic and immune adaptations to exercise \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eArcanobacterium\u003c/em\u003e is less well-studied in the intestinal tract, but its presence may indicate shifts in niche microbial dynamics due to exercise, possibly favoring microbes with versatile metabolic functions that respond positively to exercise-induced alterations in host physiology. The role of \u003cem\u003eArcanobacterium\u003c/em\u003e in gut health remains unclear, but its relative expansion may be linked to exercise-induced changes in nutrient availability and gut pH. \u003cem\u003eTuricimonas\u003c/em\u003e, which showed increased abundance following exercise training, is associated with butyrate production\u0026mdash;a beneficial factor for colonic health and energy metabolism. \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Butyrate has anti-inflammatory properties and contributes to maintaining gut barrier integrity. The increase in \u003cem\u003eTuricimonas\u003c/em\u003e abundance could support the anti-inflammatory and metabolic benefits associated with PGC-1α overexpression, enhancing the host\u0026rsquo;s endurance capacity and energy regulation \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This may also align with findings that PGC-1α overexpression leads to greater mitochondrial biogenesis, complementing the energetic requirements of exercise adaptation. Concurrently, the decreased abundance of \u003cem\u003eDesulfovibrio\u003c/em\u003e, which is known for producing hydrogen sulfide \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eLigilactobacillus\u003c/em\u003e, involved in immune regulation \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, in the transgenic group suggests a shift to a bacterial environment that maintains a more stable and less pro-inflammatory microbiome, especially under the high metabolic demands of PGC-1α overexpression. \u003cem\u003eLigilactobacillus\u003c/em\u003e species, previously classified as Lactobacillus, are generally regarded as beneficial probiotics. The relative decline in \u003cem\u003eLigilactobacillus\u003c/em\u003e due to exercise may suggest that PGC-1α overexpression alters gut ecology, potentially reducing the niche for these bacteria. This shift could be attributed to an altered metabolic environment, and there is a possibility that PGC-1α, due to its enhancing effect on fatty acid oxidation, may reduce the need for lactate-producing bacteria.\u003c/p\u003e \u003cp\u003eOur data suggest that PGC-1α overexpression decreases mitochondria-derived ROS production in the colon, possibly ruling out ROS-associated signaling pathways that could account for the different compositions of the microbiome between wild-type and PGC-1α overexpressed animals.\u003c/p\u003e \u003cp\u003eOverall, our data indicate that PGC-1α overexpression in skeletal muscle could contribute to a physiological environment\u0026mdash;such as improved oxygen utilization and reduced ROS production\u0026mdash;that may lead to changes in the microbiome, potentially supporting metabolic activity, SCFA utilization, and improved endurance capacity. Exercise training seems to differentially modulate the host microbiome in PGC-1α overexpressing and wild-type mice, which may reflect coping mechanisms to exercise-induced physiological challenges. The results of the present investigation, together with recent advances in the field, suggest a potential cross-talk between mitochondria and the microbiome; however, further studies are required to further elucidate the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePGC-1α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperoxisome proliferator-activated receptor gamma coactivator 1-alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emtDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emitochondrial DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWt\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewild type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshort-chain fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPR41\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efree fatty acid receptor 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSucc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esuccinate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRote\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erotenone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecitrate synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMfn1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitofusin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAPDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglyceraldehyde 3-phosphate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emammalian target of rapamycin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein kinase B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCREB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecAMP response element-binding protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMPKα\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5' AMP-activated protein kinase alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecystathionine-β-synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTFAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emitochondrial transcription factor A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eproliferating cell nuclear antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding provided by Hungarian University of Sports Science. ZR acknowledges support from the National Excellence Program (126823) National Science and Research Found (OTKA142192) and Scientific Excellence Program TKP2021-EGA-37 at the Hungarian University of Sports Science, Innovation and Technology Ministry, Hungary, as well as TEKA grant from Hungarian University of Sport Science.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the The European Nucleotide Archive (ENA) repository, with accession number ERP169114: https://www.ebi.ac.uk/ena/browser/view/PRJEB85739.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that all data were generated in-house and that no paper mill was used. Conceptualization, Z.R. S.M. E.K. Methodology, E.K., S.M. L.Z., A.K., A.K., J.P., F.T., and Z.R; Investigation, E.K., S.M., L.Z., K.T., A.K., A.K., and Z.R. Formal Analysis, J.P., F.T., and K.T. \u0026nbsp; Writing \u0026ndash; Original Draft, Z.R., and E.K.. Writing \u0026ndash; Review \u0026amp; Editing, E.K., T.F., K.T. \u0026nbsp;Z.R., Supervision, Z.R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and code are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in compliance with local, state, and national regulations regarding the use of animals in research. 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Lactobacillaceae differentially impact butyrate-producing gut microbiota to drive CNS autoimmunity. \u003cem\u003eGut Microbes\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 2418415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19490976.2024.2418415\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2024.2418415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mitochondria, Microbiota, PGC-1α, Physical Exercise, Host-Microbial Interactions","lastPublishedDoi":"10.21203/rs.3.rs-5982826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5982826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, we investigated the potential relationship between the mitochondrial network and the microbiome using wild-type and skeletal muscle-specific PGC-1α (Pparg coactivator 1 alpha) overexpressing mice, both with and without exercise training. PGC-1α levels were significantly elevated in skeletal muscle and, notably, in the colon, which is anatomically proximal to the microbiome. However, no significant changes were observed in cell signaling or mitochondria-related proteins within the colon. On the other hand, mitochondrial H₂O₂ production in the colon decreased in the PGC-1α overexpressing group. The relative abundance of several bacterial taxa differed between wild-type and PGC-1α overexpressing groups, indicating a shift in the microbiome milieu probably to cope with the increased metabolism, enhanced short-chain fatty acid utilization, and improved endurance capacity. Ten weeks of exercise training differentially modulated the host microbiome in PGC-1α overexpressing and wild-type mice, facilitating adaptations to a broad range of exercise-induced challenges. The results of this study provide new insights into the possible cross-talk between mitochondria and the microbiome.\u003c/p\u003e","manuscriptTitle":"PGC-1 alpha overexpression in the skeletal muscle results in a metabolically active microbiome which is independent of redox signaling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 07:57:51","doi":"10.21203/rs.3.rs-5982826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T07:13:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T20:46:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T04:51:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334849192909139574686686115422872464254","date":"2025-04-28T15:15:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174230952934941684415496026639838638447","date":"2025-04-24T12:46:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-24T12:11:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T11:07:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-09T14:03:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69caf68b-fa3b-4200-a8af-6d4f8c0f159f","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47630552,"name":"Biological sciences/Microbiology"},{"id":47630553,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-07-07T16:01:44+00:00","versionOfRecord":{"articleIdentity":"rs-5982826","link":"https://doi.org/10.1038/s41598-025-05594-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:57:24","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-28 07:57:51","video":"","vorDoi":"10.1038/s41598-025-05594-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-05594-w","workflowStages":[]},"version":"v1","identity":"rs-5982826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5982826","identity":"rs-5982826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}