A Quartet Network Analysis Identifying Mechanically Responsive Long Noncoding RNAs in Bone Remodeling

Mechanical force, being so ubiquitous that it is often taken for granted and overlooked, is now gaining the spotlight for reams of evidence corroborating their crucial roles in the living body. The bone, particularly, experiences manifold extraneous force like strain and compression, as well as intrinsic cues like fluid shear stress and physical properties of the microenvironment. Though sparkled in diversified background, long noncoding RNAs (lncRNAs) concerning the mechanotransduction process that bone undergoes are not yet detailed in a systematic way. Our principal goal in this research is to highlight the potential lncRNA-focused mechanical signaling systems which may be adapted by bone-related cells for biophysical environment response. Based on credible lists of force-sensitive mRNAs and miRNAs, we constructed a force-responsive competing endogenous RNA network for lncRNA identification. To elucidate the underlying mechanism, we then illustrated the possible crosstalk between lncRNAs and mRNAs as well as transcriptional factors and mapped lncRNAs to known signaling pathways involved in bone remodeling and mechanotransduction. Last, we developed combinative analysis between predicted and established lncRNAs, constructing a pathway-lncRNA network which suggests interactive relationships and new roles of known factors such as H19. In conclusion, our work provided a systematic quartet network analysis, uncovered candidate force-related lncRNAs, and highlighted both the upstream and downstream processes that are possibly involved. A new mode of bioinformatic analysis integrating sequencing data, literature retrieval, and computational algorithm was also introduced. Hopefully, our work would provide a moment of clarity against the multiplicity and complexity of the lncRNA world confronting mechanical input.