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Screening of key candidate genes and pathways for osteocytes involved in the differential response to different types of mechanical stimulation using a bioinformatics analysis

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Abstract

This study aimed to predict the key genes and pathways that are activated when different types of mechanical loading are applied to osteocytes. mRNA expression datasets (series number of GSE62128 and GSE42874) were obtained from Gene Expression Omnibus database (GEO). High gravity-treated osteocytic MLO-Y4 cell-line samples from GSE62128 (Set1), and fluid flow-treated MLO-Y4 samples from GSE42874 (Set2) were employed. After identifying the differentially expressed genes (DEGs), functional enrichment was performed. The common DEGs between Set1 and Set2 were considered as key DEGs, then a protein–protein interaction (PPI) network was constructed using the minimal nodes from all of the DEGs in Set1 and Set2, which linked most of the key DEGs. Several open source software programs were employed to process and analyze the original data. The bioinformatic results and the biological meaning were validated by in vitro experiments. High gravity and fluid flow induced opposite expression trends in the key DEGs. The hypoxia-related biological process and signaling pathway were the common functional enrichment terms among the DEGs from Set1, Set2 and the PPI network. The expression of almost all the key DEGs (Pdk1, Ccng2, Eno2, Egln1, Higd1a, Slc5a3 and Mxi1) were mechano-sensitive. Eno2 was identified as the hub gene in the PPI network. Eno2 knockdown results in expression changes of some other key DEGs (Pdk1, Mxi1 and Higd1a). Our findings indicated that the hypoxia response might have an important role in the differential responses of osteocytes to the different types of mechanical force.

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Acknowledgements

The present work was supported by Grant-in-Aid for Scientific Research (to H. Kamioka [16H05549] and [16K15837], to Y. Ishihara [17H04413]) from the Japan Society for the Promotion of Science, Japan. Lastly, Ziyi Wang would like to dedicate this article to his wife, Yao Weng, who saw too much of the back of his head as he looked at the computer screen while he was coding, processing data and revising the manuscript before the deadline. His wife’s tolerance is best described as remarkable.

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Authors and Affiliations

  1. Department of Orthodontics, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, 2-5-1 Shikata, Kita-ku, Okayama, 700-8525, Japan

    Ziyi Wang, Mitsuhiro Hoshijima, Ei Ei Hsu Hlaing & Hiroshi Kamioka

  2. Department of Orthodontics, Okayama University Hospital, Okayama, Japan

    Yoshihito Ishihara, Takanori Ishikawa & Naoya Odagaki

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  1. Ziyi Wang
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Electronic supplementary material

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Online Resource 1

The work flow of the present study. DEGs: differentially expressed genes (PDF 155 kb)

Online Resource 2

The sequences of primers (PDF 10 kb)

Online Resource 3

The complete list of DEGs of Set1 (PDF 44 kb)

Online Resource 4

The complete list of DEGs of Set2 (PDF 54 kb)

Online Resource 5

The complete list of the functional enrichment of Set1. The height of each slice represents the log2 value (fold enrichment). The radian of each slice represents the percentage of DEGs for the corresponding term to all queried DEGs; the exact percentage is shown by the label of each slice. The common terms between Set1 and Set2 are in yellow highlight (PDF 811 kb)

Online Resource 6

The complete list of the functional enrichment of Set2. The height of each slice represents the log2 value (fold enrichment). The radian of each slice represents the percentage of DEGs for the corresponding term to all queried DEGs; the exact percentage is shown by the label of each slice. The common terms between Set1 and Set2 are in yellow highlight (PDF 1456 kb)

Online Resource 7

Protein-protein interaction (PPI) network was constructed from all the 316 DEGs from both Set1 and Set2. This network contained 298 nodes and 2476 edges with enrichment p-value of <1.0×10-16 (PDF 531 kb)

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Wang, Z., Ishihara, Y., Ishikawa, T. et al. Screening of key candidate genes and pathways for osteocytes involved in the differential response to different types of mechanical stimulation using a bioinformatics analysis. J Bone Miner Metab 37, 614–626 (2019). https://doi.org/10.1007/s00774-018-0963-7

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