Mapping the osteocytic cell response to fluid flow using RNA-Seq

• Published in: Journal of biomechanics Vol. 48; no. 16; pp. 4327 - 4332
• Main Authors: Govey, Peter M, Kawasawa, Yuka Imamura, Donahue, Henry J
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 16.12.2015; Elsevier Limited
• Subjects: Angiogenesis; Animals; Biocompatibility; Biomedical materials; Bone Remodeling; Bones; Cell Line; Fluid dynamics; Fluid flow; Fluids; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Ontology; Gene Regulatory Networks; Genes; Genomes; Hematology; Hypoxia; Kinases; Mechanotransduction; Mechanotransduction, Cellular; Mice; Ontology; Osteocyte; Osteocytes - physiology; Physical Medicine and Rehabilitation; Quality; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA-Seq; Sequence Analysis, RNA; Shear stress; Signaling; Systems development; Transcriptome; Up-Regulation; Signaling; Mechanotransduction; Gene expression; RNA-Seq; Osteocyte; Fluid flow
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2015.10.045

Abstract Bone adaptation to mechanical loading is regulated via signal transduction by mechano-sensing osteocytes. Mineral-embedded osteocytes experience strain-induced interstitial fluid flow and fluid shear stress, and broad shifts in gene expression are key components in the signaling pathways that regulate bone turnover. RNA sequencing analysis, or RNA-Seq, enables more complete characterization of mechano-responsive transcriptome regulation than previously possible. We hypothesized that RNA-Seq of osteocytic MLO-Y4 cells reveals both expected and novel gene transcript regulation in cells previously fluid flowed and analyzed using gene microarrays. MLO-Y4 cells were flowed for 2 h with 1 Pa oscillating fluid shear stress and post-incubated 2 h. RNA-Seq of original samples detected 55 fluid flow-regulated gene transcripts ( p -corrected <0.05), the same number previously detected by microarray. However, RNA-Seq demonstrated greater dynamic range, with all 55 transcripts increased >1.5-fold or decreased <0.67-fold whereas 10 of 55 met this cut-off by microarray. Analyses were complimentary in patterns of regulation, though only 6 transcripts were significant in both RNA-Seq and microarray analyses: Cxcl5 , Cxcl1 , Zc3h12a , Ereg , Slc2a1 , and Egln1 . As part of a broad inflammatory response inferred by gene ontology analyses, we again observed greatest up-regulation of inflammatory C-X-C motif chemokines, and newly implicated HIF-1α and AMPK signaling pathways. Importantly, we detected both expected fluid flow-sensitive transcripts (e.g. Nos2 [iNOS], Ptgs2 [COX-2], Ccl7 ) and transcripts not previously identified as flow-sensitive, e.g. Ccl2 . We found RNA-Seq advantageous over microarrays because of its greater dynamic range and ability to analyze unbiased estimation of gene expression, informing our understanding of osteocyte signaling.