Abstract
Insulin action on the skeleton is essential for bone development and whole-body energy metabolism, however a global view of signaling in this tissue is lacking. Furthermore, whether there are signaling differences that drive the gene-specific activation under insulin-resistant (IR) or ageing conditions is unknown. Here, we perform a phosphoproteomic analysis of insulin signaling in the bones of young, lean, insulin-sensitive versus old, obese, IR mice revealing a rewiring of phosphorylation. We target dysregulated phosphoproteins in a zebrafish functional genomic screen of bone development and mineralization revealing candidates important for skeletal formation. One of these is ALF Transcription Elongation Factor 4 (AFF4), the core scaffold of the Super Elongation Complex and we show that phosphorylation of S831 on AFF4 is an insulin-dependent substrate of P70S6K and attenuated in aged, IR bone. Phosphorylation of S831 is defective in IR osteoblasts and associated with reduced transcriptional elongation at discrete locations in the genome. Mechanistically, we show phosphorylation of S831 increases recruitment of chromatin remodelers, ENL/AF9 to crotonylated histone via the YEATS domain, and promotes gene-specific activation. Our analysis identifies regulators of insulin action on the skeleton, further uncovering a mechanism of IR via locus-specific changes in transcriptional elongation and gene activation.
Similar content being viewed by others
Data availability
The phosphoproteomics and proteomics data generated in this study are deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/cgi/GetDataset) via the PRIDE113 and can be accessed through the links provided below: PRIDE: PXD054205 (Phosphoproteomic and proteomics of insulin signaling in aged mouse bone). PXD054212 (Zebrafish caudal fin phosphoproteomic of Rps6kb1a/b knockdown). PXD054247 (Analysis of insulin-regulated phosphorylation of AFF4 with Akti or S6Ki and S6K in vitro kinase assay). PXD054250 Affinity purification – mass spectrometry of AFF4 WT or S829/S831/3/5/8 A mutant). PXD054479(Proteomic and secretomic analysis of Kusa 4B10 osteoblasts). The targeted proteomic data can be accessed via Panorama Web Repository114 through the accession code Panorama Web: U of Melbourne – Parker Lab: PRM of mouse AFF4 S831 phosphorylation (Targeted phosphoproteomics of mouse S831 AFF4 phosphorylation in control or insulin resistant osteoblasts). The transcriptomic dataset generated in this study is deposited to NCBI and can be accessed through the accession code below: PRJNA1146056 (Transcriptomics of HEK293T cells expressing AFF4-wild type or AFF4-T829/S831/S833/S834/S835A mutant treated with or without insulin). Full list of Deposited Data is also provided in Supplementary Table 1. Source data are provided with this paper.
References
Wettenhall, R. E., Schwartz, P. L. & Bornstein, J. Actions of insulin and growth hormone on colagen and chondroitin sulfate synthesis in bone organ cultures. Diabetes 18, 280–284 (1969).
Kream, B. E., Smith, M. D., Canalis, E. & Raisz, L. G. Characterization of the effect of insulin on collagen synthesis in fetal rat bone. Endocrinology 116, 296–302 (1985).
Ituarte, E. A., Halstead, L. R., Iida-Klein, A., Ituarte, H. G. & Hahn, T. J. Glucose transport system in UMR-106-01 osteoblastic osteosarcoma cells: regulation by insulin. Calcif. Tissue Int. 45, 27–33 (1989).
Fulzele, K. et al. Insulin receptor signaling in osteoblasts regulates postnatal bone acquisition and body composition. Cell 142, 309–319 (2010).
Ferron, M. et al. Insulin signaling in osteoblasts integrates bone remodeling and energy metabolism. Cell 142, 296–308 (2010).
Zhou, R. et al. Endocrine role of bone in the regulation of energy metabolism. Bone Res. 9, 25 (2021).
Vervoort, S. J. et al. Targeting transcription cycles in cancer. Nat. Rev. Cancer 22, 5–24 (2022).
Chen, R. H., Sarnecki, C. & Blenis, J. Nuclear localization and regulation of erk- and rsk-encoded protein kinases. Mol. Cell Biol. 12, 915–927 (1992).
Deng, T. & Karin, M. c-Fos transcriptional activity stimulated by H-Ras-activated protein kinase distinct from JNK and ERK. Nature 371, 171–175 (1994).
Morton, S., Davis, R. J., McLaren, A. & Cohen, P. A reinvestigation of the multisite phosphorylation of the transcription factor c-Jun. EMBO J. 22, 3876–3886 (2003).
Xu, R. et al. c-Jun N-Terminal Kinases (JNKs) are critical mediators of osteoblast activity in vivo. J. Bone Min. Res. 32, 1811–1815 (2017).
Chiba, N., Noguchi, Y., Seong, C. H., Ohnishi, T. & Matsuguchi, T. EGR1 plays an important role in BMP9-mediated osteoblast differentiation by promoting SMAD1/5 phosphorylation. FEBS Lett. 596, 1720–1732 (2022).
Zhang, Q. et al. RUNX2 co-operates with EGR1 to regulate osteogenic differentiation through Htra1 enhancers. J. Cell Physiol. 235, 8601–8612 (2020).
Aoi, Y. & Shilatifard, A. Transcriptional elongation control in developmental gene expression, aging, and disease. Mol. Cell 83, 3972–3999 (2023).
Day, D. S. et al. Comprehensive analysis of promoter-proximal RNA polymerase II pausing across mammalian cell types. Genome Biol. 17, 120 (2016).
Vihervaara, A., Duarte, F. M. & Lis, J. T. Molecular mechanisms driving transcriptional stress responses. Nat. Rev. Genet 19, 385–397 (2018).
Core, L. & Adelman, K. Promoter-proximal pausing of RNA polymerase II: a nexus of gene regulation. Genes Dev. 33, 960–982 (2019).
Ohe, S. et al. ERK-mediated NELF-A phosphorylation promotes transcription elongation of immediate-early genes by releasing promoter-proximal pausing of RNA polymerase II. Nat. Commun. 13, 7476 (2022).
Kettenbach, A. N., Sano, H., Keller, S. R., Lienhard, G. E. & Gerber, S. A. SPECHT - single-stage phosphopeptide enrichment and stable-isotope chemical tagging: quantitative phosphoproteomics of insulin action in muscle. J. Proteom. 114, 48–60 (2015).
Small, L. et al. Reduced insulin action in muscle of high fat diet rats over the diurnal cycle is not associated with defective insulin signaling. Mol. Metab. 25, 107–118 (2019).
Needham, E. J. et al. Personalized phosphoproteomics identifies functional signaling. Nat. Biotechnol. 40, 576–584 (2022).
Fedjaev, M. et al. Global analysis of protein phosphorylation networks in insulin signaling by sequential enrichment of phosphoproteins and phosphopeptides. Mol. Biosyst. 8, 1461–1471 (2012).
Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990–995 (2015).
Matsuzaki, F. et al. An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver. Cell Rep. 36, 109569 (2021).
Brandon, A. E. et al. Protein Kinase C epsilon deletion in adipose tissue, but not in liver, improves glucose tolerance. Cell Metab. 29, 183–191.e187 (2019).
Entwisle, S. W. et al. Proteome and phosphoproteome analysis of brown adipocytes reveals that RICTOR loss dampens global insulin/AKT signaling. Mol. Cell Proteom. 19, 1104–1119 (2020).
Fazakerley, D. J. et al. Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance. Nat. Commun. 14, 923 (2023).
Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).
Humphrey, S. J. et al. Dynamic adipocyte phosphoproteome reveals that Akt directly regulates mTORC2. Cell Metab. 17, 1009–1020 (2013).
Yang, P., Humphrey, S. J., James, D. E., Yang, Y. H. & Jothi, R. Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data. Bioinformatics 32, 252–259 (2016).
Kemp, B. E. & Pearson, R. B. Protein kinase recognition sequence motifs. Trends Biochem. Sci. 15, 342–346 (1990).
Stockli, J. et al. ABHD15 regulates adipose tissue lipolysis and hepatic lipid accumulation. Mol. Metab. 25, 83–94 (2019).
Chasman, D. I. et al. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet 5, e1000730 (2009).
Izumi, K. et al. Germline gain-of-function mutations in AFF4 cause a developmental syndrome functionally linking the super elongation complex and cohesin. Nat. Genet 47, 338–344 (2015).
Raible, S. E. et al. Clinical and molecular spectrum of CHOPS syndrome. Am. J. Med Genet A 179, 1126–1138 (2019).
Badolato, J., Gardiner, E., Morrison, N. & Eisman, J. Identification and characterisation of a novel human RNA-binding protein. Gene 166, 323–327 (1995).
Rose, J. P. et al. Deep coverage and quantification of the bone proteome provides enhanced opportunities for new discoveries in skeletal biology and disease. PLoS One 18, e0292268 (2023).
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).
Geiger, T. et al. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell Proteom. 12, 1709–1722 (2013).
Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
Wisniewski, J. R. & Mann, M. A proteomics approach to the protein normalization problem: selection of unvarying proteins for MS-based proteomics and western blotting. J. Proteome Res 15, 2321–2326 (2016).
Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016).
Keele, G. R. et al. Global and tissue-specific aging effects on murine proteomes. Cell Rep. 42, 112715 (2023).
Feskanich, D., Singh, V., Willett, W. C. & Colditz, G. A. Vitamin A intake and hip fractures among postmenopausal women. JAMA 287, 47–54 (2002).
Lim, L. S., Harnack, L. J., Lazovich, D. & Folsom, A. R. Vitamin A intake and the risk of hip fracture in postmenopausal women: the Iowa Women’s Health Study. Osteoporos. Int 15, 552–559 (2004).
Caire-Juvera, G., Ritenbaugh, C., Wactawski-Wende, J., Snetselaar, L. G. & Chen, Z. Vitamin A and retinol intakes and the risk of fractures among participants of the Women’s Health Initiative Observational Study. Am. J. Clin. Nutr. 89, 323–330 (2009).
de Jonge, E. A. et al. Dietary vitamin A intake and bone health in the elderly: the Rotterdam Study. Eur. J. Clin. Nutr. 69, 1360–1368 (2015).
Keenan, A. B. et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Res. 47, W212–W224 (2019).
Liu, B., Cheng, S., Xing, W., Pourteymoor, S. & Mohan, S. RE1-silencing transcription factor (Rest) is a novel regulator of osteoblast differentiation. J. Cell Biochem 116, 1932–1938 (2015).
Morris, J. A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet 51, 258–266 (2019).
Zhang, D., Wang, J., Zhou, C. & Xiao, W. Zebrafish akt2 is essential for survival, growth, bone development, and glucose homeostasis. Mech. Dev. 143, 42–52 (2017).
Toyoshima, Y. et al. The role of insulin receptor signaling in zebrafish embryogenesis. Endocrinology 149, 5996–6005 (2008).
Huitema, L. F. et al. Entpd5 is essential for skeletal mineralization and regulates phosphate homeostasis in zebrafish. Proc. Natl. Acad. Sci. USA 109, 21372–21377 (2012).
Zhao, Y. G. et al. The p53-induced gene Ei24 is an essential component of the basal autophagy pathway. J. Biol. Chem. 287, 42053–42063 (2012).
Reimann, E., Koks, S., Ho, X.D., Maasalu, K. & Martson, A. Whole exome sequencing of a single osteosarcoma case--integrative analysis with whole transcriptome RNA-seq data. Hum. Genomics. 8, 20 (2014).
Rivadeneira, F. et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat. Genet. 41, 1199–1206 (2009).
Xu, Y. et al. STARD3NL inhibits the osteogenic differentiation by inactivating the Wnt/beta-catenin pathway via binding to Annexin A2 in osteoporosis. J. Cell Mol. Med 26, 1643–1655 (2022).
Parichy, D. M., Elizondo, M. R., Mills, M. G., Gordon, T. N. & Engeszer, R. E. Normal table of postembryonic zebrafish development: staging by externally visible anatomy of the living fish. Dev. Dyn. 238, 2975–3015 (2009).
Singleman, C. & Holtzman, N. G. Growth and maturation in the zebrafish, Danio rerio: a staging tool for teaching and research. Zebrafish 11, 396–406 (2014).
Strumillo, M. J. et al. Conserved phosphorylation hotspots in eukaryotic protein domain families. Nat. Commun. 10, 1977 (2019).
Tremblay, F. et al. Identification of IRS-1 Ser-1101 as a target of S6K1 in nutrient- and obesity-induced insulin resistance. Proc. Natl. Acad. Sci. USA 104, 14056–14061 (2007).
Iovino, S. et al. Genetic insulin resistance is a potent regulator of gene expression and proliferation in human iPS cells. Diabetes 63, 4130–4142 (2014).
Gardini, A. et al. Integrator regulates transcriptional initiation and pause release following activation. Mol. Cell 56, 128–139 (2014).
Li, Y. et al. Molecular Coupling of Histone Crotonylation and Active Transcription by AF9 YEATS Domain. Mol. Cell 62, 181–193 (2016).
Garnar-Wortzel, L. et al. Chemical inhibition of ENL/AF9 YEATS domains in acute leukemia. ACS Cent. Sci. 7, 815–830 (2021).
Burchfield, J. G., Diaz-Vegas, A. & James, D. E. The insulin signalling network. Nat. Metab. 7, 1745–1764 (2025).
O’Brien, R. M., Streeper, R. S., Ayala, J. E., Stadelmaier, B. T. & Hornbuckle, L. A. Insulin-regulated gene expression. Biochem Soc. Trans. 29, 552–558 (2001).
Kim, S. J. & Kahn, C. R. Insulin stimulates p70 S6 kinase in the nucleus of cells. Biochem Biophys. Res. Commun. 234, 681–685 (1997).
Hornbeck, P. V. et al. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. 40, D261–D270 (2012).
Parker, B. L. et al. Structural basis for phosphorylation and lysine acetylation cross-talk in a kinase motif associated with myocardial ischemia and cardioprotection. J. Biol. Chem. 289, 25890–25906 (2014).
Alpy, F. et al. Functional characterization of the MENTAL domain. J. Biol. Chem. 280, 17945–17952 (2005).
Alpy, F. et al. STARD3 or STARD3NL and VAP form a novel molecular tether between late endosomes and the ER. J. Cell Sci. 126, 5500–5512 (2013).
Dai, Q. et al. mTOR/Raptor signaling is critical for skeletogenesis in mice through the regulation of Runx2 expression. Cell Death Differ. 24, 1886–1899 (2017).
Clancy, D. J. et al. Extension of life-span by loss of CHICO, a Drosophila insulin receptor substrate protein. Science 292, 104–106 (2001).
Kimura, K. D., Tissenbaum, H. A., Liu, Y. & Ruvkun, G. daf-2, an insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science 277, 942–946 (1997).
Bluher, M., Kahn, B. B. & Kahn, C. R. Extended longevity in mice lacking the insulin receptor in adipose tissue. Science 299, 572–574 (2003).
Debes, C. et al. Ageing-associated changes in transcriptional elongation influence longevity. Nature 616, 814–821 (2023).
Gyenis, A. et al. Genome-wide RNA polymerase stalling shapes the transcriptome during aging. Nat. Genet 55, 268–279 (2023).
Papadakis, A. et al. Age-associated transcriptional stress due to accelerated elongation and increased stalling of RNAPII. Nat. Genet 55, 2011–2012 (2023).
Selman, C. et al. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science 326, 140–144 (2009).
Haider, N. et al. Signaling defects associated with insulin resistance in nondiabetic and diabetic individuals and modification by sex. J. Clin. Invest. 131, e151818 (2021).
Sharma, A. et al. Sexing bones: improving transparency of sex reporting to address bias within preclinical studies. J. Bone Min. Res 38, 5–13 (2023).
Robles, M. S., Humphrey, S. J. & Mann, M. Phosphorylation is a central mechanism for circadian control of metabolism and physiology. Cell Metab. 25, 118–127 (2017).
Larsen, J. K. et al. High-throughput proteomics uncovers exercise training and type 2 diabetes-induced changes in human white adipose tissue. Sci. Adv. 9, eadi7548 (2023).
Humphrey, S. J., Karayel, O., James, D. E. & Mann, M. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat. Protoc. 13, 1897–1916 (2018).
Blazev, R. et al. Phosphoproteomics of three exercise modalities identifies canonical signaling and C18ORF25 as an AMPK substrate regulating skeletal muscle function. Cell Metab. 34, 1561–1577.e1569 (2022).
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Willforss, J., Chawade, A. & Levander, F. NormalyzerDE: online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis. J. Proteome Res 18, 732–740 (2019).
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).
Molendijk, J., Yip, R. & Parker, B. L. urPTMdb/TeaProt: upstream and downstream proteomics analysis. J. Proteome Res. 22, 302–310 (2023).
Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).
Kall, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).
Taus, T. et al. Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res 10, 5354–5362 (2011).
Bruderer, R. et al. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell Proteom. 16, 2296–2309 (2017).
Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 787 (2020).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma. 10, 421 (2009).
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinforma. 14, 128 (2013).
Frewen, B. & MacCoss, M. J. Using BiblioSpec for creating and searching tandem MS peptide libraries. Curr. Protoc. Bioinforma. 13, 13 17 11–13 17 12 (2007).
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Wu, R. S. et al. A rapid method for directed gene knockout for screening in G0 zebrafish. Dev. Cell 46, 112–125.e114 (2018).
Gagnon, J. A. et al. Efficient mutagenesis by Cas9 protein-mediated oligonucleotide insertion and large-scale assessment of single-guide RNAs. PLoS One 9, e98186 (2014).
Untergasser, A. et al. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 35, W71–W74 (2007).
Kent, W. J. BLAT-the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
Spoorendonk, K. M. et al. Retinoic acid and Cyp26b1 are critical regulators of osteogenesis in the axial skeleton. Development 135, 3765–3774 (2008).
Sakata-Haga, H. et al. A rapid and nondestructive protocol for whole-mount bone staining of small fish and Xenopus. Sci. Rep. 8, 7453 (2018).
Tarasco, M., Cordelieres, F. P., Cancela, M. L. & Laize, V. ZFBONE: an ImageJ toolset for semi-automatic analysis of zebrafish bone structures. Bone 138, 115480 (2020).
Niemisto, A., Dunmire, V., Yli-Harja, O., Zhang, W. & Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. IEEE Trans. Med Imaging 24, 549–553 (2005).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
Sharma, V. et al. Panorama: a targeted proteomics knowledge base. J. Proteome Res. 13, 4205–4210 (2014).
Acknowledgements
We thank Nicholas Williamson, Ching-Seng Ang, Shuai Nie, Swati Varshney and Michael Leeming for instrument support in the Bio21 Mass Spectrometry and Proteomics Facility. We thank Mr. Cameron Mackey and Mr. Bryan Ko for providing technical support for zebrafish maintenance. This research was supported by access to the Melbourne Mouse Metabolic Phenotyping Platform at The University of Melbourne. This work was funded by a University of Melbourne Driving Research Momentum Grant, an NHMRC Emerging Leader Investigator Grant APP2009642 and Australian Research Council Discovery Grant DP250100201 to B.L.P. G.T.D is funded by NHMRC Grants 2022/GNT2021126, 2020/GNT2002427, 2018/GNT1160043, Australian Research Council Grant DP220102910, Diabetes Australia Grants Y23G-DodG, Y20G-DodG and The University of Melbourne Deans Innovation Award. S.J.V. is supported by a CSL Centenary Fellowship and SNOW Medical Fellowship. We thank Prof Benjamin M. Hogan and Mr. Scott Paterson from the Peter MacCallum Cancer Center, Australia for assistance with the zebrafish functional screen. We thank Dr Marco Tarasco from The University of Algarve, Portugal, for designing the ZFBONE brightfield ImageJ macro used in the zebrafish bone mineralization analysis.
Author information
Authors and Affiliations
Contributions
B.L.P. conceptualized the study. M.D., L.L., R.B., A.C., A.B.-G., L.D., I.S.R., N.K.Y.W., V.R.H., V.U., J.P.H.W., B.L.P. performed the experiments. M.D., L.L., H.J.K., R.B., A.C., H.K., A.B.-G., J.M., P.Y., B.L.P. analyzed the data. G.S.L., K.A.S., M.K.M., M.J.W., G.T.D., S.J.V., N.A.S., B.L.P provided resources, guided experimental design, supervised and funded the research.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Wenbiao Chen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Dutt, M., Liao, L., Kim, H.J. et al. Phosphoproteomics of aged insulin-resistant bone identifies P70S6K phosphorylation of AFF4 as a gene-specific transcriptional regulator. Nat Commun (2025). https://doi.org/10.1038/s41467-025-68106-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-025-68106-4


