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    Multi-organ proteomic atlas of obesity regression in male mice

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    Multi-organ proteomic atlas of obesity regression in male mice

    Abstract

    Obesity drives systemic metabolic dysfunction, yet how the body adapts and recovers at a system-wide level remains unclear. Here we generate a multi-organ proteomic atlas of diet-induced obesity and its regression in male mice. Using a standardized, semi-automated sample preparation workflow, we quantify 12,936 unique proteins across 15 organs and four timepoints. We find proteomic changes to be highly tissue-dependent, with a small number of proteins displaying shared changes across multiple tissues. While proteomes of most tissues revert to lean levels after weight loss, white adipose tissue retains strong phagocytic and inflammatory responses, and the brain, kidney, bone, thymus and spleen display delayed obesity-induced alterations. We map the adipose tissue-specific immune regulators using ligand-target inference and show reduced expression of proteasomal subunits in brown adipose tissue, resulting in stalled ubiquitin turnover. Finally, we make all datasets open access and create an interactive webtool to facilitate further community-driven discovery.

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    Fig. 1: Data acquisition pipeline, data quality and general overview.
    Fig. 2: Systemic proteomic signatures across tissues.
    Fig. 3: Tissue-specific alterations in mitochondrial fatty acid β-oxidation.
    Fig. 4: Proteomic signatures and late-onset adaptations in LTR of obesity.
    Fig. 5: System-wide immune responses during obesity and regression.
    Fig. 6: Adipose tissue-specific immune regulation and proteasomal remodelling.

    Subjects

    • Metabolism
    • Proteomics
    • Translational research

    Data availability

    The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE66 partner repository with the dataset identifier PXD066875. All relevant processed data can also be found in Supplementary Data 1–22 (descriptions in Extended Data Table 1). Raw RNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession no. GSE338985. The Shiny-based webtool can be accessed at https://computproteomics.bmb.sdu.dk/app_direct/obesity-atlas/ and a dockerized container can be pulled from Docker Hub at felixboel/proteomic-atlas-of-obesity. Source data are provided with this paper.

    Code availability

    Code for our Shiny-based webtool, along with necessary datasets, is availableesity (ref. 67). Remaining data analysis pipelines come from popular well-documented R packages and can be found briefly described in the Methods

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    Acknowledgements

    We thank V. Schwämmle for hosting the Shiny-based webtool associated with this study

    Funding

    This research received funding from the Danish National Research Foundation (grant no. DNRF141) awarded to the Center for Functional Genomics and Tissue Plasticity (ATLAS) and the Independent Research Fund Denmark (grant no. 1026-00013B). The proteomics work was supported by the INTEGRA MS research infrastructure established at SDU by a generous grant from the Novo Nordisk Foundation (grant no. NNF20OC0061575)

    Author information

    Authors and Affiliations

    1. Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark

      Felix Boel, Ellen Gammelmark Klinggaard, Vyacheslav Akimov, Daniel Hansen, M. L. Uthpala, Babukrishna Maniyadath, Flora Reincke Fuglsang, Kim Ravnskjær, Susanne Mandrup & Blagoy Blagoev

    2. Center for Functional Genomics and Tissue Plasticity (ATLAS), University of Southern Denmark, Odense, Denmark

      Felix Boel, Ellen Gammelmark Klinggaard, Vyacheslav Akimov, Daniel Hansen, M. L. Uthpala, Babukrishna Maniyadath, Flora Reincke Fuglsang, Kim Ravnskjær, Susanne Mandrup & Blagoy Blagoev

    3. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

      Marc Pielies Avellí & Simon Rasmussen

    Authors

    1. Felix BoelView author publications

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    2. Ellen Gammelmark KlinggaardView author publications

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    3. Vyacheslav AkimovView author publications

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    Contributions

    Conceptualization—F.B., E.G.K., S.M. and B.B.; proteomics laboratory work—F.B. and V.A.; animal work—E.G.K., B.M. and F.R.F.; immunohistochemical staining—D.H.; serum cholesterol measurements— M.L.U.; exploratory analysis—F.B. and M.P.A.; main data analysis and figures—F.B.; supervision of the study—B.B., S.M., K.R. and S.R.; and manuscript preparation

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    Peer review information

    Nature Metabolism thanks Nathaniel Vacanti, Matthew Watt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Revati Dewal, in association with the Nature Metabolism team. Peer reviewer reports are available

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    Extended data

    Extended Data Table 1 Description of supplementary data tables
    Full size table

    Extended Data Fig. 1 Overview of mouse cohort

    Body mass in grams (left), fat mass in grams (center), and body fat percentage (right). x-axis shows the number of weeks after the regression cohort reverted from a high-fat diet to a low-fat diet. Each time point shows the group mean, along with the standard error of the mean. Statistical comparisons were performed independently at each time point using a two-sided Welch’s t-tests with Benjamini-Hochberg correction. Adjusted P < 0.05 is indicated by an asterisk

    Extended Data Fig. 2 Differential expression overview

    Percentage of DEPs at each timepoint between the obesity regression group and age-matched lean control mice across all tissues using a free y-axis scale. OBE obese, STR short-term regression, MTR medium-term regression, LTR long-term regression

    Extended Data Fig. 3 Overview of processes in most regulated tissues

    Results from Reactome-based pathway enrichment analysis of DEPs found in obese mice

    Extended Data Fig. 4 Transcriptomics- and protein-level agreement and enrichment in eWAT and iWAT

    a,b, Scatter plots showing the gene-wise log2FC values (RNA vs protein) at each time point. Linear model R2 values are reported for each panel. Representative discordant proteins identified in obesity (OBE), Serpina1e, Apoc3, and Saa4, are labelled across time points. c, Pathway enrichment analysis of proteins increased at the protein level but decreased in RNA (‘protein up & RNA down’) across tissues and timepoints. Dot size indicates the number of proteins per pathway. Color represents BH-adjusted p-value. X axis shows the fold enrichment of pathways. d, STRING network plot of proteins that were upregulated at protein-level & downregulated in RNA. Nodes represent proteins, edges indicate STRING interactions (confidence > 0.4). The three largest interaction modules are shown in color where, (i) red shows a module of acute phase/lipoproteins, (ii) green shows a module of cytoskeleton/adhesion proteins, and (iii) blue shows a module of histones/chromatin proteins, while sparsely connected proteins are grouped as ‘Other’. Isolated proteins are omitted for clarity.

    Extended Data Fig. 5 Discordant apolipoprotein regulation at the protein and RNA levels in adipose tissue and associated changes in circulating cholesterol

    a, Protein-level and b, RNA-level log2 fold changes of selected apolipoproteins in eWAT and iWAT during obesity (Ob) and long-term regression (LTR), relative to age matched controls. c, Fed serum LDL/VLDL, HDL, and total cholesterol concentrations measured in lean and obese mice before regression of obesity and at after long-term regression. Bars represent mean ± SD, dots represent individual biological replicates (lean start, n = 3; lean long term, n = 3; obese start, n = 3; obese regressed, n = 4).

    Extended Data Fig. 6 Validation of Hippo pathway-associated protein changes across tissues during long-term regression

    Representative western blots and quantification of total and phosphorylated YAP and MOB proteins in a, bone, b, iBAT, c, kidney, and d, spleen from long-term regression (LTR) and age-matched lean control (LTC) mice. Phosphorylated YAP (p-YAP Ser127) and phosphorylated MOB1 (p-MOB1 Thr35) were assessed alongside corresponding total protein levels. Panels to the right show the quantification relative to the levels in LTR mice. Bars represent mean ± SD, dots represent individual biological replicates (n = 3 mice per group). GAPDH was used as loading control.

    Source data

    Extended Data Fig. 7 Altered PIP3 distribution in eWAT during obesity and regression

    Representative images of PIP3 staining in epididymal white adipose tissue (eWAT) from lean control (LC), obese (OBE), and long-term regression (LTR) mice. Arrowheads indicate representative regions with detectable PIP3 signal. Obese animals displayed reduced PIP3 staining compared with lean controls, with partial recovery observed following regression. Nuclei are counterstained in blue. Scale bars, 100 µm (top panels) and 50 µm (bottom panels). The experiment was performed independently twice with similar results using biological replicates (LC, n = 3; OBE, n = 3; LTR, n = 2). Representative images are shown.

    Extended Data Fig. 8 Reduced AKT phosphorylation during long-term regression

    Representative western blots and quantification of phosphorylated AKT (p-AKT Ser473) and total AKT levels in epididymal white adipose tissue (eWAT) from long-term regression (LTR) and long-term lean control (LTC) mice. Panel to the right show the quantification relative to the levels in LTR mice. Bars represent mean ± SD, dots represent individual biological replicates (n = 3 mice per group). β-Actin was used as loading control

    Source data

    Supplementary information

    Supplementary Information (download PDF )

    Supplementary Figs. 1–17

    Reporting Summary (download PDF )

    Peer Review File (download PDF )

    Supplementary Data 1 (download XLSX )

    Metadata for all proteomic samples included in the study

    Supplementary Data 2 (download XLSX )

    Cohort characteristics for all mice included in the study

    Supplementary Data 3 (download XLSX )

    Proteomics data for bone

    Supplementary Data 4 (download XLSX )

    Proteomics data for brain

    Supplementary Data 5 (download XLSX )

    Proteomics data for duodenum

    Supplementary Data 6 (download XLSX )

    Proteomics data for eWAT

    Supplementary Data 7 (download XLSX )

    Proteomics data for heart

    Supplementary Data 8 (download XLSX )

    Proteomics data for iBAT

    Supplementary Data 9 (download XLSX )

    Proteomics data for iWAT

    Supplementary Data 10 (download XLSX )

    Proteomics data for kidney

    Supplementary Data 11 (download XLSX )

    Proteomics data for liver

    Supplementary Data 12 (download XLSX )

    Proteomics data for skeletal muscle

    Supplementary Data 13 (download XLSX )

    Proteomics data for pancreas

    Supplementary Data 14 (download XLSX )

    Proteomics data for serum

    Supplementary Data 15 (download XLSX )

    Proteomics data for small intestine

    Supplementary Data 16 (download XLSX )

    Proteomics data for spleen

    Supplementary Data 17 (download XLSX )

    Proteomics data for thymus

    Supplementary Data 18 (download XLSX )

    Merged proteomics dataset containing all tissues

    Supplementary Data 19 (download XLSX )

    Metadata corresponding to Supplementary Data 18

    Supplementary Data 20 (download XLSX )

    Summary of differential expression test statistics across all tissues and timepoints

    Supplementary Data 21 (download XLSX )

    Protein group annotations, including corresponding gene names and protein descriptions

    Supplementary Data 22 (download XLSX )

    GlyGly proteomics data from iBAT comparing OBE and LC mice

    Source data

    Source Data Extended Data Fig. 6 (download PDF )

    Unprocessed western blots for Extended Data Fig. 6

    Source Data Extended Data Fig. 8 (download PDF )

    Unprocessed western blots for Extended Data Fig. 8

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    Cite this article

    Boel, F., Klinggaard, E.G., Akimov, V. et al. Multi-organ proteomic atlas of obesity regression in male mice.
    Nat Metab (2026). https://doi.org/10.1038/s42255-026-01599-5

    • Received:21 July 2025

    • Accepted:28 July 2026

    • Published:21 August 2026

    • Version of record:21 August 2026

    • DOI
      :https://doi.org/10.1038/s42255-026-01599-5

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