Dynamic regulation of LncRNAs and mRNAs in key metabolic organs under multiple age conditions
Because all coding and noncoding transcripts were quantified in parallel, our expression profile allows the assessment and comparison of the developmental stage on the lncRNA and mRNA transcriptomes in the liver. We first performed hierarchical clustering analyses on all transcripts expressed. As expected, mRNA expression profiles readily separate all samples into three distinct groups on the basis of their age (1 day, 15 months, 5 years), and samples cluster tightly within each treatment group (Figure 1A, top left). Interestingly, a nearly identical pattern of sample clustering was observed for lncRNAs (Figure 1A, top right), indicating that expression profiles of lncRNAs could serve as a metabolic signature in a manner similar to those of protein coding mRNAs. Using RNA-seq platform, we detected 35,216 mRNA and 10,073 non-redundant lncRNA transcripts (Figure 2B). From transcriptomes in the livers of yak aged from three diverse age (LD: Liver 1 Day, LM: Liver 15 Month, LY: Liver 5 Year) (Figure 2B).
Consistently, PCAs on all regulated transcripts, either for mRNAs or lncRNAs, readily separate all samples into distinct groups (Figure 1C). These patterns suggest that regulated lncRNA and mRNA transcriptomes might function coordinately in related physiological processes, and their intrinsic functional connections could be defined by performing correlation analyses of samples under multiple metabolic conditions in which the lncRNA-mRNA networks are sufficiently dynamically regulated. 433 mRNAs and 152 lncRNAs were identified to be regulated by LD versus LM, as well as 412 mRNAs and 160 lncRNAs by LD versus LY (Figure 1D). And 263 mRNAs and 66 lncRNAs were completely LM versus LY (Figure 1D), suggesting that their expression levels are stringently governed by age states of the animals.
Functional analysis of differentially expressed mRNAs in each age condition in liver
At LD versus LM, the differentially expressed genes of many functional gene ontology categories enriched for terms related to metabolism, ion binding, developmental process and so on. While in KEGG pathway analysis, differentially expressed genes were also enriched for terms such as metabolism, biosynthesis, and ECM-receptor interaction so on (Figure 2A). At LD versus LY, there were functional gene ontology categories enriched for metabolism, tissue remodeling and developmental process and so on. While in the KEGG pathway analysis, differentially expressed genes were also enriched for terms like metabolism, biosynthesis, PI3K-Akt signaling pathway and so on, indicating that (Figure 2B).
Similar functional gene ontology categories of metabolism, biosynthetic process and oxidation reduction process and so on were enriched at LM versus LY. While in the KEGG pathway analysis, differentially expressed genes were also enriched for terms like metabolism, biosynthesis, focal adhesion and so on, indicating that (Figure 2C). All these results suggest metabolism related function are developed during the aged process (Figure 2).
Functional prediction of metabolically related differentially expressed LncRNAs by LncRNA-mRNA co-expression correlation under at least two age conditions
Those more dynamically regulated lncRNAs would be ideal for functional prediction by lncRNA-mRNA expression correlations across different age conditions. Therefore, we filtered and selected all lncRNAs that are regulated by at least two of the three age conditions from the total 288 regulated lncRNAs, and obtained 88 highly regulated non-redundant lncRNAs (Figure 1D; Figure 4). To provide an example of examining the specific roles of these metabolically sensitive lncRNAs, we have selected six metabolically sensitive lncRNAs for further analyses.
Our work exampled six lncRNAs that our correlation analyses provide insights into their functions in cell differentiation and metabolism. ENSBMUG00000000490 and XLOC_045379 was predicted to be associated with fat single organism metabolic process (Figure 3A and 3B), and XLOC_021536 and XLOC_041441 associated with collagen metabolic processes and protein metabolic processes in liver, respectively (Figure 3C and 3D). Additionally, ENSBMUG00000026019 and XLOC_183608, have been shown to be enriched in cell proliferation and transport (Figure 3E and 3F). These results indicate that the co-expression network we built can efficiently predict the potential metabolic functions for age regulated and metabolically sensitive lncRNAs. In addition, the dynamic regulation of several randomly selected lncRNAs by different age conditions was confirmed by qRT-PCR, attesting to the quality of this method (Figure 4).