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

Ziyi Wang, Yoshihito Ishihara, Takanori Ishikawa, Mitsuhiro Hoshijima, Naoya Odagaki, Ei Ei Hsu Hlaing, Hiroshi Kamioka

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1 Citation (Scopus)

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.

Original languageEnglish
JournalJournal of Bone and Mineral Metabolism
DOIs
Publication statusAccepted/In press - Jan 1 2018

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Osteocytes
Computational Biology
Genes
Hypergravity
Biological Phenomena
Software
Databases

Keywords

  • Bioinformatics analysis
  • Fluid flow
  • Mechanical stimulation
  • Microarray
  • Osteocyte

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Orthopedics and Sports Medicine
  • Endocrinology

Cite this

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title = "Screening of key candidate genes and pathways for osteocytes involved in the differential response to different types of mechanical stimulation using a bioinformatics analysis",
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.",
keywords = "Bioinformatics analysis, Fluid flow, Mechanical stimulation, Microarray, Osteocyte",
author = "Ziyi Wang and Yoshihito Ishihara and Takanori Ishikawa and Mitsuhiro Hoshijima and Naoya Odagaki and Hlaing, {Ei Ei Hsu} and Hiroshi Kamioka",
year = "2018",
month = "1",
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journal = "Journal of Bone and Mineral Metabolism",
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T1 - Screening of key candidate genes and pathways for osteocytes involved in the differential response to different types of mechanical stimulation using a bioinformatics analysis

AU - Wang, Ziyi

AU - Ishihara, Yoshihito

AU - Ishikawa, Takanori

AU - Hoshijima, Mitsuhiro

AU - Odagaki, Naoya

AU - Hlaing, Ei Ei Hsu

AU - Kamioka, Hiroshi

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Bioinformatics analysis

KW - Fluid flow

KW - Mechanical stimulation

KW - Microarray

KW - Osteocyte

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