Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that play an important role in the post-transcriptional regulation of gene expression. miRNAs are involved in the regulation of many biological processes such as differentiation, apoptosis, and cell proliferation. miRNAs are expressed in embryonic, postnatal, and adult hearts, and they have a key role in the regulation of gene expression during cardiovascular development and disease. Aberrant expression of miRNAs is associated with abnormal cardiac cell differentiation and dysfunction. Tbx5 is a member of the T-box gene family, which acts as transcription factor involved in the vertebrate heart development. Alteration of Tbx5 level affects the expression of hundreds of genes. Haploinsufficiency and gene duplication of Tbx5 are at the basis of the cardiac abnormalities associated with Holt–Oram syndrome (HOS). Recent data indicate that miRNAs might be an important part of the regulatory circuit through which Tbx5 controls heart development. Using high-throughput technologies, we characterized genome-widely the miRNA and mRNA expression profiles in WT- and Tbx5-depleted zebrafish embryos at two crucial developmental time points, 24 and 48 h post fertilization (hpf). We found that several miRNAs, which are potential effectors of Tbx5, are differentially expressed; some of them are already known to be involved in cardiac development and functions, such as miR-30, miR-34, miR-190, and miR-21. We performed an integrated analysis of miRNA expression data with gene expression profiles to refine computational target prediction approaches by means of the inversely correlation of miRNA–mRNA expressions, and we highlighted targets, which have roles in cardiac contractility, cardiomyocyte proliferation/apoptosis, and morphogenesis, crucial functions regulated by Tbx5. This approach allowed to discover complex regulatory circuits involving novel miRNAs and protein coding genes not considered before in the HOS such as miR-34a and miR-30 and their targets.
Highlights
MicroRNAs are small non-coding RNAs of about 20–23 nucleotides that play an essential role in a variety of biological important pathways from development and physiology to diseases such as cancer (Chen and Rajewsky, 2007; Small and Olson, 2011). miRNAs are mostly known to function by targeting complementary sequences in mRNA transcripts, usually in the 3′ untranslated region (3′ UTR) and so inhibiting the translation and altering the stability of mRNA (Bartel, 2004; Yates et al, 2013)
We detail the expression profiles of both miRNAs and annotated genes, which resulted altered by Tbx5a depletion during early zebrafish developmental stages (24 and 48 hpf)
From experiments reported in our previous work (Chiavacci et al, 2015), the downregulation of miR-19a at 48 hpf was not significant. This miRNA was included in the list of miRNAs modulated by Tbx5 because: (1) Q-RT PCR analysis performed in four different sets of experiments confirmed miR-19a downregulation (Figure 2B), (2) this downregulation was clearly supported by physiological data and by in situ hybridization experiments already presented (Chiavacci et al, 2015)
Summary
MicroRNAs (miRNAs) are small non-coding RNAs of about 20–23 nucleotides that play an essential role in a variety of biological important pathways from development and physiology to diseases such as cancer (Chen and Rajewsky, 2007; Small and Olson, 2011). miRNAs are mostly known to function by targeting complementary sequences in mRNA transcripts, usually in the 3′ untranslated region (3′ UTR) and so inhibiting the translation and altering the stability of mRNA (Bartel, 2004; Yates et al, 2013). Several computational methods and tools have been developed in the last years (Yue et al, 2009; Peterson et al, 2014) They encompass a range of different computational approaches, from the modeling of physical interactions, exploiting common features like seed match, conservation, free energy, and site accessibility to the incorporation of less common features extracted through machine learning techniques. One possibility to reduce the false positive rate is to combine high-throughput experimental data with sequence-based predictions (Huang et al, 2007; Muniategui et al, 2013). This approach does not allow to identify miRNA targets that are repressed exclusively at the translational level. Since many miRNAs cause degradation of their targets (Baek et al, 2008; Hendrickson et al, 2009; Guo et al, 2010; Subtelny et al, 2014), the integration of expression profiles has been proposed to be an effective strategy to discover true miRNA–target interactions (Gennarino et al, 2009; Nazarov et al, 2013; Albert et al, 2014)
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