Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNAs in failing human heart and remodeling with mechanical circulatory support

Abstract

Background: Microarrays have been used extensively to profile transcriptome remodeling in failing human heart, although the genomic coverage provided is limited and fails to provide a detailed picture of the myocardial transcriptome landscape. Here, we describe sequencing-based transcriptome profiling, providing comprehensive analysis of myocardial mRNA, microRNA (miRNA), and long noncoding RNA (lncRNA) expression in failing human heart before and after mechanical support with a left ventricular (LV) assist device (LVAD).

Methods and results: Deep sequencing of RNA isolated from paired nonischemic (NICM; n=8) and ischemic (ICM; n=8) human failing LV samples collected before and after LVAD and from nonfailing human LV (n=8) was conducted. These analyses revealed high abundance of mRNA (37%) and lncRNA (71%) of mitochondrial origin. miRNASeq revealed 160 and 147 differentially expressed miRNAs in ICM and NICM, respectively, compared with nonfailing LV. Among these, only 2 (ICM) and 5 (NICM) miRNAs are normalized with LVAD. RNASeq detected 18 480, including 113 novel, lncRNAs in human LV. Among the 679 (ICM) and 570 (NICM) lncRNAs differentially expressed with heart failure, ≈10% are improved or normalized with LVAD. In addition, the expression signature of lncRNAs, but not miRNAs or mRNAs, distinguishes ICM from NICM. Further analysis suggests that cis-gene regulation represents a major mechanism of action of human cardiac lncRNAs.

Conclusions: The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support. These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.

Keywords: RNA, long noncoding; deep sequencing; heart failure; ventricular assist device.

Figure 1

Workflow of comprehensive quantification of miRNAs, mRNAs and lncRNAs in human LV. Total RNA was isolated from non-failing (n=8) LV, and from ICM (n=8) and NICM (n=8) LV, before and after LVAD support; the latter were matched samples from the same patient. Poly-A(+) RNA libraries were constructed for pair-end RNA sequencing, whereas small RNA libraries were prepared for single-end sequencing. Pair-end sequencing reads went through transcriptome reconstruction and computational prediction pipelines to identify novel transcripts; among the 2049 novel transcripts uncovered by RNASeq, 113 were identified as novel lncRNAs. A compiled lncRNA annotation database, including NONCODE3.0, the Human Body Map lincRNAs catalog and the 113 novel lncRNAs identified in this study, was generated and utilized for quantitatve analyses of lncRNAs. RefSeq and the Ensemble database were used for quantification of mRNAs, and miRBase v.18 was used for miRNA quantification analyses. Results from RNASeq were subjected to differential expression and co-expression network analyses. In addition, quantitative PCR analysis of selected transcripts was conducted and compared with the results from RNASeq.

Figure 2

RNASeq reveals distinct expression pattern of different RNA species in human hearts. (A-C)Pie charts showing read count distributions of mitochondrial and non-mitochondrial mRNAs (A), lncRNAs (B) and miRNAs (C) in human LV as percentages of the total read counts. (D-F)Pie charts illustrating the percent distributions of non-mitochondrial mRNAs (D), lncRNAs (E) and miRNAs (F) in human LV based on abundance. (G-H)The top 20 most abundant non-mitochondrial mRNAs (G), lncRNAs (H) and miRNAs (I) account for markedly different percentages of the total mRNA, lncRNA or miRNA species identified in human LV.

Figure 3

Expression signature of lncRNAs, but not mRNAs or miRNAs, distinguishes failing human hearts of ischemic and non-ischemic origin. (A-C)Unsupervised hierarchical clustering of the expression profiles of human cardiac mRNAs (A), miRNAs (B) and lncRNAs (C) reveals that all three distinguish failing from non-failing LV samples. In addition, the lncRNA, but not the mRNA or miRNA, expression profiles discriminate ICM from NICM LV samples. (D-F)Principal component analyses of mRNA (D), miRNA (E) and lncRNA (F) expression profiles showed similar findings. The variance explained by the principal components chosen is shown on top of each plot. (G)Heat map and unsupervised hierarchical clustering of lncRNA expression profiles derived from the microarray data of an independent cohort of human cardiac samples (11 NF, 11 ICM and 15 NICM) showed that lncRNA expression signature correctly classified all but one sample in all three groups.

Figure 4

Higher proportion of HF-associated lncRNAs, compared with mRNAs and miRNAs, are improved or normalized by LVAD support. (A-D)The LV end-diastolic dimension (LVEDD) (A), systolic (B) and diastolic (C) pulmonary arterial pressures (PAP), as well as myocardial expression levels of the heart failure marker ANF (D), are all reduced significantly (^‡P<0.05) in HF patients with LVAD support. (E)A significantly (^#P<0.01,^*P<0.001) higher percentage of differentially expressed lncRNAs, compared to mRNAs and miRNAs, are improved or normalized by LVAD in both ICM and NICM LV samples.

Figure 5

lncRNA expression signature distinguishes HF samples before and after mechanical circulatory support. (A,B)Heat maps and unsupervised hierarchical clustering of lncRNA expression profiles in NF, NICM (A) and ICM (B) samples before and after LVAD support. (C,D)Principal component analyses of lncRNA expression profiles in NF, NICM (C) and ICM (D) before and after LVAD support. Both analyses showed that lncRNA expression signature distinguishes HF samples before and after LVAD support.

Figure 6

Expression levels of many cardiac lncRNAs are positively correlated with neighboring coding genes. (A-C)Histograms of Pearson correlations between random gene pairs (A) mRNA:cis-mRNA (B) and lncRNA:cis-mRNA gene pairs (C) reveals a strong positive correlation between lncRNAs and cis-mRNAs (C), compared with either mRNA:cis-mRNA (B) or random gene pairs (A). (D)Read distributions of a HF-associated lncRNA, n340651 (blue boxes), and its neighboring coding gene RARA (brown boxes), in NF, ICM and NICM LV samples. (E,F)Scatter plots of n340651 and RARA expression levels in individual human LV samples determined by RNASeq (E) and qPCR (F); both analyses reveal strong correlations for this lncRNA:cis-mRNA gene pair.

Figure 7

The trans-acting potential of lncRNAs is similar to that of mRNAs. Histograms of Pearson correlations between (A) lncRNA:trans-mRNA and (B) mRNA:trans-mRNA gene pairs reveals that the percentage of lncRNA:trans-mRNA gene pairs that show a significant positive (r>0.5) or negative (r<-0.5) correlation is actually slightly lower than that between mRNA:trans-mRNA gene pairs, suggesting that the potential of lncRNAs co-expressing/interacting with remote coding genes in trans is not higher than that of background trans mRNA-mRNA interaction.