Skip to main content
Log in

Common factors among three types of cells aged in mice

  • Research article
  • Published:
Biogerontology Aims and scope Submit manuscript

Abstract

The greatest risk factor for the formation of numerous significant chronic disorders is aging. Understanding the core molecular underpinnings of aging can help to slow down the inevitable process. Systematic study of gene expression or DNA methylation data is possible at the transcriptomics and epigenetics levels. DNA methylation and gene expression are both affected by aging. Gene expression is an important element in the aging of Homo sapiens. In this work, we evaluated the expression of differentially expressed genes (DEGs), proteins, and transcription factors (TFs) in three different types of cells in mice: antibody-secreting cells, cardiac mesenchymal stromal cells, and skeletal muscle cells. The goal of this article is to uncover a common cause during aging among these cells in order to increase understanding about establishing complete techniques for preventing aging and improving people's quality of life. We conducted a comprehensive network-based investigation to establish which genes and proteins are shared by the three different types of aged cells. Our findings clearly indicated that aging induces gene dysregulation in immune, pharmacological, and apoptotic pathways. Furthermore, our research developed a list of hub genes with differential expression in aging responses that should be investigated further to discover viable anti-aging treatments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All data used in this study is freely available and can be obtained from NCBI using above-mentioned GSE codes “GSE72224”, “GSE129656” and “GSE125815”.

References

  • Agostini S, Costa AS, Mancuso R, Guerini FR, Nemni R, Clerici M (2019) The PILRA G78R variant correlates with higher HSV-1-specific IgG titers in Alzheimer’s disease. Cell Mol Neurobiol 39(8):1217–1221

    Article  CAS  PubMed  Google Scholar 

  • Bastian M, Heymann S, Jacomy M Gephi (2009) An open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media,  vol 1. pp. 361–362

  • Beerman I, Bock C, Garrison BS, Smith ZD, Gu H, Meissner A, Rossi DJ (2013) Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell 12(4):413–425

    Article  CAS  PubMed  Google Scholar 

  • Benayoun BA, Pollina EA, Brunet A (2015) Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat Rev Mol Cell Biol 16(10):593–610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E (2011) Epigenetic predictor of age. PLoS ONE 6(6):e14821

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Campbell CMP, Guan W, Sprung R, Koomen JM, O'Keefe MT, Ingles DJ, Abrahamsen M, Giuliano AR (2013) Quantification of secretory leukocyte protease inhibitor (SLPI) in oral gargle specimens collected using mouthwash. J Immunol Methods 400:117–121

    Article  Google Scholar 

  • Chen B-S, Li C-W (2010) On the interplay between entropy and robustness of gene regulatory networks. Entropy 12(5):1071–1101

    Article  CAS  Google Scholar 

  • Chen B-S, Li C-W (2015) Measuring information flow in cellular networks by the systems biology method through microarray data. Front Plant Sci 6:390

    Article  PubMed  PubMed Central  Google Scholar 

  • Crum CP, McKeon FD (2010) p63 in epithelial survival, germ cell surveillance, and neoplasia. Annu Rev Pathol 5:349–371

    Article  CAS  PubMed  Google Scholar 

  • De Haang G, Gerrits A (2007) Epigenetic control of hematopoietic stem cell aging the case of Ezh2. Ann NY Acad Sci 1106(1):233–239. https://doi.org/10.1196/annals.1392.008

    Article  CAS  Google Scholar 

  • Dozmorov MG (2015) Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes. Epigenetics 10(6):484–495

    Article  PubMed  PubMed Central  Google Scholar 

  • Franceschi C, Garagnani P, Morsiani C, Conte M, Santoro A, Grignolio A, Monti D, Capri M, Salvioli S (2018) The Continuum of Aging and Age-Related Diseases: Common Mechanisms but Different Rates. Front Med (Lausanne) 5:61. https://doi.org/10.3389/fmed.2018.00061

    Article  PubMed  Google Scholar 

  • Gao S, Song Q, Liu J, Zhang X, Ji X, Wang P (2019) E2F1 mediates the downregulation of POLD1 in replicative senescence. Cell Mol Life Sci 76(14):2833–2850. https://doi.org/10.1007/s00018-019-03070-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • García Osorio F, Varela Egocheaga IA, Lara Martín E, Suárez Puente XA, Santoro R, Pérez Freije JM, Fernández Fraga M, López Otín C (2010) Nuclear envelope alterations generate an aging-like epigenetic pattern in mice deficient in Zmpste24 metalloprotease. Aging Cell 9(6):947–957

    Article  Google Scholar 

  • Garcia S, Nissanka N, Mareco EA, Rossi S, Peralta S, Diaz F, Rotundo RL, Carvalho RF, Moraes CT (2018) Overexpression of PGC-1α in aging muscle enhances a subset of young-like molecular patterns. Aging Cell 17(2):e12707. https://doi.org/10.1111/acel.12707

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gems D (2015) The aging-disease false dichotomy: understanding senescence as pathology. Front Genet 6:212

    Article  PubMed  PubMed Central  Google Scholar 

  • Glass D, Viñuela A, Davies MN, Ramasamy A, Parts L, Knowles D, Brown AA, Hedman ÃK, Small KS, Buil A (2013) Gene expression changes with age in skin, adipose tissue, blood and brain. Genome Biol 14(7):1–12

    Article  Google Scholar 

  • Hardy K, Mansfield L, Mackay A, Benvenuti S, Ismail S, Arora P, O'Hare M, Jat P (2005) Transcriptional networks and cellular senescence in human mammary fibroblasts. Mol Biol Cell 16(2):943–953

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • He S, Sharpless NE (2017) Senescence in health and disease. Cell 169(6):1000–1011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Heath L, Earls JC, Magis AT, Kornilov SA, Lovejoy JC, Funk CC, Rappaport N, Logsdon BA, Mangravite LM, Kunkle BW (2022) Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90. Sci Rep 12(1):1–17

    Article  Google Scholar 

  • Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):1–20

    Article  Google Scholar 

  • Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    Article  CAS  PubMed  Google Scholar 

  • Huang Y, Wu J, Li R, Wang P, Han L, Zhang Z, Tong T (2011) B-MYB delays cell aging by repressing p16INK4αtranscription. Cell Mol Life Sci 68(5):893–901. https://doi.org/10.1007/s00018-010-0501-9

    Article  CAS  PubMed  Google Scholar 

  • Jugdutt BI, Palaniyappan A, Uwiera RRE, Idikio H (2008) Role of healing-specific-matricellular proteins and matrix metalloproteinases in age-related enhanced early remodeling after reperfused STEMI in dogs. Mol Cell Biochem 322(1):25. https://doi.org/10.1007/s11010-008-9936-9

    Article  CAS  PubMed  Google Scholar 

  • Kannan S, Dawany N, Kurupati R, Showe LC, Ertl HC (2016) Age-related changes in the transcriptome of antibody-secreting cells. Oncotarget 7(12):13340–13353. https://doi.org/10.18632/oncotarget.7958

    Article  PubMed  PubMed Central  Google Scholar 

  • Kirkwood T (1989) DNA, mutations and aging. Mutat Res/DNAging 219(1):1–7

    Article  CAS  Google Scholar 

  • Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, Ma'ayan A (2010) ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26(19):2438–2444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lee S, Choi E, Cha MJ, Park AJ, Yoon C, Hwang KC (2015) Impact of miRNAs on cardiovascular aging. J Geriatr Cardiol 12(5):569–574. https://doi.org/10.11909/j.issn.1671-5411.2015.05.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li C-W, Chen B-S (2010) Identifying functional mechanisms of gene and protein regulatory networks in response to a broader range of environmental stresses. Comp Funct Genomics. https://doi.org/10.1155/2010/408705

    Article  PubMed  PubMed Central  Google Scholar 

  • Lin D, Fiscella M, O’Connor PM, Jackman J, Chen M, Luo LL, Sala A, Travali S, Appella E, Mercer WE (1994) Constitutive expression of B-myb can bypass p53-induced Waf1/Cip1-mediated G1 arrest. Proc Natl Acad Sci 91(21):10079–10083

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013) The hallmarks of aging. Cell 153(6):1194–1217

    Article  PubMed  PubMed Central  Google Scholar 

  • Lüders J, Demand J, Höhfeld Jr (2000) The ubiquitin-related BAG-1 provides a link between the molecular chaperones Hsc70/Hsp70 and the proteasome. J Biol Chem 275(7):4613–4617

    Article  PubMed  Google Scholar 

  • Marcotte R, Lacelle C, Wang E (2004) Senescent fibroblasts resist apoptosis by downregulating caspase-3. Mech Ageing Dev 125(10–11):777–783

    Article  CAS  PubMed  Google Scholar 

  • Martinez I, DiMaio D (2011) B-Myb, Cancer, Senescence, and MicroRNAsRegulation of B-Myb. Cancer Res 71(16):5370–5373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Martini H, Iacovoni JS, Maggiorani D, Dutaur M, Marsal DJ, Roncalli J, Itier R, Dambrin C, Pizzinat N, Mialet-Perez J, Cussac D, Parini A, Lefevre L, Douin-Echinard V (2019) Aging induces cardiac mesenchymal stromal cell senescence and promotes endothelial cell fate of the CD90 + subset. Aging Cell 18(5):e13015. https://doi.org/10.1111/acel.13015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Maslov AY, Vijg J (2009) Genome instability cancer and aging. Biochim et Biophys Acta (BBA)-Gen Subj 1790:963–96910

    Article  CAS  Google Scholar 

  • Mihalas BP, Camlin NJ, Xavier MJ, Peters AE, Holt JE, Sutherland JM, McLaughlin EA, Eamens AL, Nixon B (2019) The small non-coding RNA profile of mouse oocytes is modified during aging. Aging 11(10):2968–2997. https://doi.org/10.18632/aging.101947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mowla SN, Lam EW-F, Jat PS (2014) Cellular senescence and aging: the role of B-MYB. Aging Cell 13(5):773–779. https://doi.org/10.1111/acel.12242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mozhui K, Pandey AK (2017) Conserved effect of aging on DNA methylation and association with EZH2 polycomb protein in mice and humans. Mech Ageing Dev 162:27–37. https://doi.org/10.1016/j.mad.2017.02.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 9(5):471–472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Patel T, Brookes KJ, Turton J, Chaudhury S, Guetta-Baranes T, Guerreiro R, Bras J, Hernandez D, Singleton A, Francis PT (2018) Whole‐exome sequencing of the BDR cohort: evidence to support the role of the PILRA gene in Alzheimer's disease. Neuropathol Appl Neurobiol 44(5):506–521

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Przytycka TM, Kim Y-A (2010) Network integration meets network dynamics. BMC Biol 8(1):1–3

    Article  Google Scholar 

  • Reynolds LM, Taylor JR, Ding J, Lohman K, Johnson C, Siscovick D, Burke G, Post W, Shea S, Jacobs DR Jr (2014) Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat Commun 5(1):1–8

    Article  Google Scholar 

  • Ribeil J-A, Zermati Y, Vandekerckhove J, Cathelin S, Kersual J, Dussiot M, Coulon S, Cruz Moura I, Zeuner A, Kirkegaard-Sørensen T (2007) Hsp70 regulates erythropoiesis by preventing caspase-3-mediated cleavage of GATA-1. Nature 445(7123):102–105

    Article  CAS  PubMed  Google Scholar 

  • Rovillain E, Mansfield L, Caetano C, Alvarez-Fernandez M, Caballero OL, Medema RH, Hummerich H, Jat PS (2011) Activation of nuclear factor-kappa B signalling promotes cellular senescence. Oncogene 30(20):2356–2366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sáez-Freire MdM, Blanco-Gómez A, Castillo-Lluva S, Gómez-Vecino A, Galvis-Jiménez JM, Martín-Seisdedos C, Isidoro-García M, Hontecillas-Prieto L, García-Cenador MB, García-Criado FJ, Patino-Alonso MC, Galindo-Villardón P, Mao J-H, Prieto C, Castellanos-Martín A, Kaderali L, Pérez-Losada J (2018) The biological age linked to oxidative stress modifies breast cancer aggressiveness. Free Radic Biol Med 120:133–146. https://doi.org/10.1016/j.freeradbiomed.2018.03.012

    Article  CAS  PubMed  Google Scholar 

  • Sala A, Calabretta B (1992) Regulation of BALB/c 3T3 fibroblast proliferation by B-myb is accompanied by selective activation of cdc2 and cyclin D1 expression. Proc Natl Acad Sci 89(21):10415–10419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Saxon SV, Mary Jean Etten EDGNPCMPFT, Elizabeth A, Perkins PDRFF (2021) Physical Change and Aging, Seventh Edition: A Guide for Helping Professions. Springer Publishing Company

  • Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sikora-Wohlfeld W, Ackermann M, Christodoulou EG, Singaravelu K, Beyer A (2013) Assessing computational methods for transcription factor target gene identification based on ChIP-seq data. PLoS Comput Biol 9(11):e1003342

    Article  PubMed  PubMed Central  Google Scholar 

  • Soundararajan R, Stearns TM, Czachor A, Fukumoto J, Turn C, Westermann-Clark E, Breitzig M, Tan L, Lockey RF, King BL, Kolliputi N (2016) Global gene profiling of aging lungs in Atp8b1 mutant mice. Aging 8(9):2232–2252. https://doi.org/10.18632/aging.101056

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122(6):957–968

    Article  CAS  PubMed  Google Scholar 

  • Stirewalt DL, Choi YE, Sharpless NE, Pogosova-Agadjanyan EL, Cronk MR, Yukawa M, Larson EB, Wood BL, Appelbaum FR, Radich JP, Heimfeld S (2009) Decreased IRF8 expression found in aging hematopoietic progenitor/stem cells. Leukemia 23(2):391–393. https://doi.org/10.1038/leu.2008.176

    Article  CAS  PubMed  Google Scholar 

  • Sun D, Luo M, Jeong M, Rodriguez B, Xia Z, Hannah R, Wang H, Le T, Faull KF, Chen R (2014) Epigenomic profiling of young and aged HSCs reveals concerted changes during aging that reinforce self-renewal. Cell Stem Cell 14(5):673–688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P (2016) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw937

    Article  PubMed  PubMed Central  Google Scholar 

  • Tu C-T, Chen B-S (2013a) New measurement methods of network robustness and response ability via microarray data. PLoS ONE 8(1):e55230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tu C-T, Chen B-S (2013) On the increase in network robustness and decrease in network response ability during the aging process: a systems biology approach via microarray data. IEEE/ACM Trans Comput Biol Bioinform 10(2):468–480

    Article  PubMed  Google Scholar 

  • Uchitomi R, Hatazawa Y, Senoo N, Yoshioka K, Fujita M, Shimizu T, Miura S, Ono Y, Kamei Y (2019) Metabolomic Analysis of Skeletal Muscle in Aged Mice. Sci Rep 9(1):10425. https://doi.org/10.1038/s41598-019-46929-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Waldera-Lupa DM, Kalfalah F, Florea AM, Sass S, Kruse F, Rieder V, Tigges J, Fritsche E, Krutmann J, Busch H, Boerries M, Meyer HE, Boege F, Theis F, Reifenberger G, Stühler K (2014) Proteome-wide analysis reveals an age-associated cellular phenotype of in situ aged human fibroblasts. Aging 6(10):856–878. https://doi.org/10.18632/aging.100698

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang Y-C, Lin C, Chuang M-T, Hsieh W-P, Lan C-Y, Chuang Y-J, Chen B-S (2013) Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study. BMC Syst Biol 7(1):1–11

    Article  CAS  Google Scholar 

  • Wu Q, Zhan J, Pu S, Qin L, Li Y, Zhou Z (2017) Influence of aging on the activity of mice Sca-1 + CD31- cardiac stem cells. Oncotarget 8(1):29–41. https://doi.org/10.18632/oncotarget.13930

    Article  PubMed  Google Scholar 

  • Yamauchi T, Ishidao T, Nomura T, Shinagawa T, Tanaka Y, Yonemura S, Ishii S (2008) AB-Myb complex containing clathrin and filamin is required for mitotic spindle function. EMBO J 27(13):1852–1862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The author thanks Dr. Rasoul Godini (Monash University, Australia) for his help.

Funding

This work has been conducted with no specific funding to declare.

Author information

Authors and Affiliations

Authors

Contributions

MR and HF: designed the experiment and analyzed the data. NG: performed the calculations. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Corresponding author

Correspondence to Hossein Fallahi.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial interest.

Consent to participations

Not applicable.

Consent for publications

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Radak, M., Ghamari, N. & Fallahi, H. Common factors among three types of cells aged in mice. Biogerontology 24, 363–375 (2023). https://doi.org/10.1007/s10522-023-10035-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s10522-023-10035-0

Profiles

  1. Nakisa Ghamari
  2. Hossein Fallahi