RAS BiologyЦитология Cell and Tissue Biology

  • ISSN (Print) 0041-3771
  • ISSN (Online) 3034-6061

Osteogenic Differentiation in vitro off Human Osteoblasts is Associated with Only Slight Shift in Their Proteomics Profile

PII
10.31857/S0041377123010066-1
DOI
10.31857/S0041377123010066
Publication type
Status
Published
Authors
Volume/ Edition
Volume 65 / Issue number 1
Pages
20-27
Abstract
Fracture healing is a complex process in which the periosteum and endosteum become the main sources of osteoblast progenitor cells. However, cellular mechanisms and signaling cascades underlying the early stages of osteoblast progenitors differentiation in adult bone are still not well understood. Therefore, we performed shotgun proteomics analysis of primary culture of isolated human osteoblasts from femur of adult donors in undifferentiated conditions and on the sixth day of osteogenic differentiation in vitro. This is an early timepoint in which we have observed no extracellular matrix mineralization yet. 1612 proteins identified with at least two unique peptides were included in proteomics analysis. Data are available via ProteomeXchange with identifier PXD033697. Despite the fact, that matrix mineralization starts only after induction of osteogenic differentiation, we revealed unexpectedly weak physiological shift associated with a decrease of cells proliferative activity and changes in proteins inVved in extracellular matrix secretion and organization. We demonstrated that osteoblasts were positive for markers of later osteogenic differentiation stages during standard cultivation: osteopontin, osteocalcin, BMP-2/4 and RUNX2. Therefore, further differentiation required for matrix mineralization needs minimal physiological changes.
Keywords
остеобласты остеогенная дифференцировка протеомика дробовика кость масс-спектрометрия
Date of publication
01.01.2023
Year of publication
2023
Number of purchasers
0
Views
39

References

  1. 1. Bahney C., Zondervan R., Allison P., Theologis A., Ashley J., Ahn J., Miclau T., Marcucio R., Hankenson K. 2019. Cellular biology of fracture healing. J. Orthop. Res. V. 37. P. 35. https://doi.org/10.1002/jor.24170
  2. 2. Blighe K., Sharmila R., Myles L. 2022. EnhancedVcano: publication-ready Vcano plots with enhanced colouring and labeling. https://bioconductor.org/packages/devel/bioc/vignettes/EnhancedVcano/inst/doc/EnhancedVcano.html
  3. 3. Bragdon B., Bahney C. 2018. Origin of reparative stem cells in fracture healing, Curr. Osteoporos. Rep. V. 16. P. 490. https://doi.org/10.1007/s11914-018-0458-4
  4. 4. Cleland T., Vashishth D. 2015. Bone protein extraction without demineralization utilizing principles from hydroxyapatite chromatography. Anal. Biochem. V. 472. P. 62. https://doi.org/10.1016/j.ab.2014.12.006
  5. 5. Cleland T., Voegele K., Schweitzer M. 2012. Empirical evaluation of bone extraction protocols. PLoS One. V. 7. P. e31443. https://doi.org/10.1371/journal.pone.0031443
  6. 6. Florencio-Silva R., Sasso G., Sasso-Cerri E., Simões M., Cerri P. 2015. Biology of bone tissue: structure, function, and factors that influence bone cells. Biomed. Res. Int. V. 2015. P. e421746. https://doi.org/10.1155/2015/421746
  7. 7. Hastie T., Tibshirani R., Narasimhan B., Chu G. 2022. Impute: imputation for microarray data. Bioconductor version: Release (3.14). https://www.bioconductor.org/packages/release/bioc/html/impute.html
  8. 8. Jiang X., Ye M., Jiang X., Liu G., Feng S., Cui L., Zou H. 2007. Method development of efficient protein extraction in bone tissue for proteome analysis. J. Proteome Res. V. 6. P. 2287. https://doi.org/10.1021/pr070056t
  9. 9. Lobov A., Malashicheva A. 2022. Osteogenic differentiation: a universal cell program of heterogeneous mesenchymal cells or a similar extracellular matrix mineralizing phenotype? Bio. Comm. V. 67. P. 32. https://doi.org/10.21638/spbu03.2022.104
  10. 10. Matthews B., Novak S., Sbrana F., Funnell J., Cao Y., Buckels E., Grcevic D., Kalajzic I. 2021. Heterogeneity of murine periosteum progenitors inVved in fracture healing. Elife. V. 10. P. e58534. https://doi.org/10.7554/eLife.58534
  11. 11. Perez-Riverol Y., Bai J.; Bandla C., García-Seisdedos D., Hewapathirana S., Kamatchinathan S., Kundu D.J., Prakash A., Frericks-Zipper A., Eisenacher M., Walzer M., Wang S., Brazma A., Vizcaíno J.A. 2022. The PRIDE database resources in 2022: A hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. V. 50. D543–D552. https://doi.org/10.1093/nar/gkab1038
  12. 12. Pitkänen S. 2020. In vitro and in vivo osteogenesis and vasculogenesis in synthetic bone grafts. Doctoral dissertation: Tampere University.
  13. 13. Raouf A., Ganss B., McMahon C., Vary C., Roughley P., Seth A. 2002. Lumican is a major proteoglycan component of the bone matrix. Matrix Biol. V. 21. P 361. https://doi.org/10.1016/s0945-053x (02)00027-6
  14. 14. Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., Smyth G.K. 2015. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. V. 43. P. e47. https://doi.org/10.1093/nar/gkv007
  15. 15. Rohart F., Gautier B., Singh A., Cao K. 2017. mixOmics: an R package for ‘omics’ feature selection and multiple data integration. PLoS Comput. Biol. V. 13. P. e1005752. https://doi.org/10.1371/journal.pcbi.1005752
  16. 16. Rutkovskiy A., Stensløkken K., Vaage I. 2016. Osteoblast differentiation at a glance. Med. Sci. Monit. Basic Res. V. 22. P. 95. https://doi.org/10.12659/MSMBR.901142
  17. 17. Wickham H. 2016. ggplot2. Cham: Springer Int. Publishing.
  18. 18. Yan L. 2021. ggvenn: Draw Venn Diagram by “ggplot2”. https://cran.r-project.org/web/packages/ggvenn/
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