How do messenger RNAs regulate each other's expression levels?

How do messenger RNAs regulate each other's expression levels?

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I know that researchers learn gene regulatory networks from microarray experiments that measure the mRNA expression levels. However, I do not understand how mRNAs regulate each other's levels. Do they affect the transcription step or the translation step? I know that there are transcription factors which affect the transcription step, but those are not mRNAs but proteins. How does an mRNA (the product before translation) affect another mRNA's expression level? Is this regulation process very similar to the regulation by microRNAs?

Typically, these experimenters aren't thinking that the mRNAs directly regulate each others' expression levels, but rather that the proteins they code for affect expression. They're just mRNA as a proxy for the corresponding protein.

How do messenger RNAs regulate each other's expression levels? - Biology

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Estimated Diversity of Messenger RNAs in Each Murine Spermatozoa and Their Potential Function During Early Zygotic Development

Peng Fang, 1 Piao Zeng, 1 Zhaoxia Wang, 1 Miao Liu, 2 Wangjie Xu, 1 Jingbo Dai, 1 Xianglong Zhao, 1 Dong Zhang, 1 Dongli Liang, 1 Xiaohui Chen, 1 Shi Shi, 1 Meixing Zhang, 1 Lianyun Wang, 1 Zhongdong Qiao, 1,* Huijuan Shi 1,**

1 5School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republi
2 6National Population and Family Planning Key Laboratory of Contraceptive Drugs and Devices, Shanghai

* Correspondence: Zhongdong Qiao, School of Life Science and Biotechnology, Shanghai Key Laboratory of Reproductive Medicine, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, People's Republic of China. E-mail: [email protected]
** Correspondence: E-mail: [email protected]

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To study the diversity of mRNAs in murine spermatozoa and their potential function during zygotic development, total RNAs in murine spermatozoa were sequenced via RNA-Seq and analyzed through bioinformatics techniques. The delivery and translation of sperm-borne mRNA in fertilized oocyte were detected using RT-PCR (reverse transcription-polymerase chain reaction), Western blot, and immunofluorescence. A total of 35 288 825 reads matching 33 039 transcripts, including 27 310 coding transcripts, were obtained. Based on our analyses, we hypothesized that the transcripts with RPKM (reads per kilobase of exon model per million mapped reads) higher than six may exist in each sperm cell as consistently retained transcripts. There were 4885 consistent transcripts in each sperm, and the remainder were randomly retained. If the baseline RPKM increased, the remaining coding transcripts were more likely related to reproduction and development. The sperm-borne transcripts Wnt4 and Foxg1 were delivered into fertilized oocytes on fertilization. Furthermore, Wnt4 was translated into protein in zygotes, whereas Foxg1 was not translated. In conclusion, approximately 4885 mRNAs were present in each murine spermatozoon, and the spermatozoal mRNAs related to reproduction and development were more likely retained. The sperm-borne mRNA Wnt4 was delivered into the fertilized oocyte and translated, evidence of a paternal effect on zygotic development.

Received: 19 January 2014 Accepted: 1 March 2014 Published: 26 March 2014

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MicroRNA-mediated crosstalk between transcripts

A single microRNA has the potential to lower the levels of many transcripts, and target recognition is thought to decrease microRNA concentration. This suggests that transcripts sharing MREs co-regulate each other through competitive microRNA binding [6]. The first evidence for crosstalk between endogenous RNAs sharing MREs was found in plants. In Arabidopsis thaliana, a non-protein coding transcript, IPS1 (INDUCED BY PHOSPHATE STARVATION1), and a microRNA, miR-399, are induced following phosphorous starvation. The IPS1 transcript contains a conserved MRE for miR-399, which sequesters the microRNA, thereby releasing PHOS2, a protein-coding gene involved in phosphorous response, from its post-transcriptional regulation by this microRNA [7]. Further examples of noncoding RNAs that act as sponges for endogenous microRNAs have been reported and complement earlier observations that microRNA activity can be efficiently and specifically regulated through the expression of exogenous RNA sponges (reviewed in [8]).

More recently, a retrotransposed gene copy, PTENP1, was shown to compete for microRNAs with its parental gene, PTEN, a known tumor suppressor gene [9]. Transcript levels of this paralogous gene pair are highly correlated in human normal and cancer tissues, suggesting their tight co-regulation [9]. Given that PTENP1 arose from the reverse transcription of PTEN mRNA and subsequent insertion within a different chromosome, its transcription is unlikely to be regulated by duplicated cis-regulatory elements. MRE sequences for five of the microRNAs that regulate PTEN are perfectly conserved in PTENP1, suggesting that the observed expression correlation stems from the regulation of the two transcripts by these microRNAs [9]. Two observations are consistent with competition for microRNAs by these two paralogous transcripts: (i) exogenous overexpression of the PTENP1 3' UTR leads to increased PTEN expression in wild-type cells, whereas this is not observed for cells deficient in DICER, an essential component of the microRNA biogenesis pathway [1] and (ii) decreased expression of PTENP1 has an equivalent effect on PTEN expression and results in similar cellular phenotypes [9]. Analysis of other cancer-related protein-coding/retrocopy pairs revealed that, after duplication, microRNA binding sites are commonly preserved. For example, overexpression of the 3' UTR of KRASP1, a KRAS retrocopy with conserved MREs, leads to increased transcript levels of the parental gene [9]. Despite the apparent lack of a protein-coding open-reading frame, some pseudogenes are transcribed and conserved across diverse species, suggesting that they may have important functional roles. Given that they arose through duplication, it is likely that these retrocopies have preserved ancestral noncoding roles, present also in their parental genes. Therefore, many MRE-containing transcripts will be bifunctional, encoding both a primary, sometimes protein-mediated function and a previously unanticipated post-transcriptional trans-regulatory role.

Materials and Methods

Human Breast Tissues and Tumor Cell Lines.

Fifty-three cases were selected from the National Cancer Institute of Canada-Manitoba Breast Tumor Bank (Winnipeg, Manitoba, Canada). As reported previously, all cases in the bank have been processed to provide paraffin-embedded tissue blocks and mirror-image frozen tissue blocks (22) . Histopathological analysis was performed on H&E-stained sections from the paraffin tissue block to estimate, for each case, the proportions of tumor and normal epithelial cells, fibroblasts, and fat as well as to determine the levels of inflammation and Nottingham grade scores (23) . The age of the patients ranged between 39 and 87 years (n = 53, median = 67 years). Tumors spanned a wide range of ER (from 0 to 159 fmol/mg protein, n = 53, median = 9 fmol/mg protein) and PR (ranging from 0 to 285 fmol/mg protein, n = 53, median = 10 fmol/mg protein) levels, as measured by ligand binding assay. These tumors also covered a wide spectrum of grades (Nottingham grading scores from 1 to 9, n = 47, median = 7). Inflammation levels were assessed for 51 cases by scoring the extent of lympho-histocystic infiltrates throughout the section using a semiquantitative scale from 0 (low to minimal infiltration) to 5 (marked infiltrate). For 13 cases, matched adjacent normal tissue blocks were also available. The characteristics of this subset of 13 tumors were as follows: ER status ranged from 0 to 159 fmol/mg protein (median = 3.5 fmol/mg protein), PR status ranged from 4.9 to 134 fmol/mg protein (median = 8.5), Nottingham grade scores ranged from 5 to 9 (median = 7), inflammation levels ranged from 1 to 5 (median = 3), and patients were between 39 and 75 years old (median age = 54 years).

MDA-MB-231, MDA-MB-468, ZR-75, BT-20, T-47D, and MCF-7 breast cancer cells were grown and poly(A) mRNA was obtained as described previously (24) . Total RNA was extracted from frozen breast tissue sections using Trizol reagent (Life Technologies, Inc., Grand Island, NY) according to the manufacturer’s instructions, and quantified spectrophotometrically. One μg of total RNA was reverse-transcribed in a final volume of 25 μl as described previously (25) .

Primers and PCR Conditions.

The primers used consisted of ER-β1U primer (5′-CGATGCTTTGGTTTGGGTGAT-3′ sense, located in exon 7, positions 1400–1420, GenBank accession no. AB006590), ER-β1L primer (5′-GCCCTCTTTGCTTTTACTGTC-3′ antisense, located in exon 8, positions 1667–1648, GenBank accession no. AB006590), and ER-β2L (5′-CTTTAGGCCACCGAGTTGATT-3′ antisense, located in ER-β2 extrasequences, positions 1933–1913, GenBank accession no. AF051428). PCR amplifications were performed, and PCR products were analyzed as described previously, with minor modifications (25) . Briefly, 1 μl of reverse transcription mixture was amplified in a final volume of 15 μl, in the presence of 1 μCi of [α- 32 P]dCTP (3000 Ci/mmol), 4 ng/μl each primer (ER-β1U, ER-β1L, and ER-β2L), and 0.3 unit of Taq DNA polymerase (Life Technologies, Inc.). Each PCR consisted of 30 cycles (30 s at 60°C, 30 s at 72°C, and 30 s at 94°C). PCR products were then separated on 6% polyacrylamide gels containing 7 m urea. Following electrophoresis, the gels were dried and autoradiographed. Amplification of the ubiquitously expressed glyceraldehyde-3-phosphate dehydrogenase cDNA was performed in parallel, and PCR products, separated on agarose gels, were stained with ethidium bromide as described previously (25) . Identity of PCR products was confirmed by subcloning and sequencing, as reported previously (25) .

TP-PCR Validation.

The first series of experiments, performed using cDNAs prepared from breast cancer cell line mRNA, showed that ER-β1, -β2, and -β5 cDNAs can be coamplified, and they led to the production of three PCR products that were subcloned and sequenced as described previously (25) . Spiked cDNA preparations containing 1 fg of purified PCR products, corresponding to ER-β1 and -β5 mRNAs, were amplified together with increasing amounts of ER-β2 PCR product (0, 0.2, 0.4, 1, 4, and 8 fg) in a single PCR tube using the three primers (ER-β1U, ER-β1L, and ER-β2L), as described above. Similar experiments were performed using constant amounts of ER-β1 and ER-β2 or of ER-β2 and ER-β5, with increasing amounts of ER-β5 or ER-β1 PCR products, respectively. In parallel, preparations containing 1 fg of each PCR product alone were also amplified. In every case, PCR products were separated on 6% polyacrylamide gels containing 7 m urea. Following electrophoresis, the gels were dried and autoradiographed. Signals were quantified by excision of the appropriate bands and counting in a scintillation counter (Beckman). For each sample, ER-β1, -β2, and -β5 signals were expressed as a percentage of the sum of all signals measured (ER-β1 + ER-β2 + ER-β5 signals). Experiments have been performed in duplicate and the mean of the relative signals calculated. For each ER-β isoform, regression analyses between the relative signal obtained and the relative initial input (i.e., ER-β isoform input expressed as a percentage of ER-β1 + ER-β2 + ER-β5 input) were performed using GraphPad Prism software.

Quantification and Statistical Analyses.

To quantitate the relative expression of ER-β1, -β2, and -β5 mRNAs within each breast tissue sample, we used the TP-PCR described above. Quantification of ER-β1, -β2, and -β5 signals was carried out by excision of the bands and scintillation counting. For each sample, ER-β1, -β2, and -β5 signals were expressed as a percentage of the sum of all signals measured (ER-β1 + ER-β2 + ER-β5 signals). Three independent PCRs were performed and the mean of the relative expressions was calculated. Differences between ER-β1, -β2 and -β5 relative expression within the cohort studied were tested using the Wilcoxon signed rank test (two-tailed). Correlations with tumor characteristics were tested by calculation of the Spearman coefficient (r).


In order to identify protein complexes of which several distinct components are coordinately regulated by miRNAs, we assembled a miRNA-protein target network for 677 human miRNAs and 18,880 targets which are listed in the TargetScan database. The targets were mapped to a non-redundant set of 2,177 experimentally verified protein complexes from the CORUM database [20]. We compiled the protein complexes, which are more significantly associated with the target sets of miRNAs than expected for random target lists based on Fisher's exact test (see Methods). The analysis resulted in 722 miRNA-regulated protein complexes (P-value < 0.05 Fisher's exact test with Bonferroni correction for multiple testing), which contained at least two targets of an individual miRNA. The entire list of miRNA-regulated protein complexes can be found in Additional file 1, Table S1 online. Furthermore, 140 protein complexes were significantly regulated by miRNA clusters (P-value < 0.05, Fisher's exact test with Bonferroni correction for multiple testing). The list of protein complexes regulated by clusters of miRNA can be found in Additional file 2, Table S2. The highest ranked complexes are listed in Table 1 and Table 2.

Functional spectrum of miRNA-regulated protein complexes

We next analyzed the spectrum of functions covered by our set of miRNA-regulated protein complexes. We identified the biological processes (Gene ontology categories [15]) and pathways representing the molecular interactions and reaction networks (KEGG [16]), which are enriched within the total set of 810 miRNA-targeted components of the protein complexes (Additional file 3, Table S3 and Additional file 4, Table S4 online). In all, as shown in Figure 1a, the miRNA-regulated protein complexes are mainly involved in regulation of RNA metabolic process, regulation of transcription and chromatin modification. Conversely, house-keeping functions, such as translational elongation and ATP synthesis coupled electron transport are underrepresented. The results confirm earlier investigations [21] showing that miRNAs less frequently target genes involved in essential cellular processes. Interestingly, there is an overrepresentation of genes involved in the G1 phase of mitotic cell cycle, while genes that are involved in the S phase and the M/G1 transition of mitotic cell cycle are underrepresented. Experimental evidence has already been reported for the regulation of signal transduction in several metazoan species [22–26] and the cell cycle [27, 28] by miRNAs. The regulation of the cell cycle by miRNAs is further supported by strong correlations of miRNA over-expression with different types of cancer [29].

Functional analysis and validation of miRNA-regulated protein complexes. Functional analysis: Enrichment of Gene Ontology (GO) terms and KEGG pathways in the target subunits of protein complexes. The size of the bars for each term indicates the negative logarithm of the P-value. Only meaningful and non-redundant terms were selected for illustration. See Additional file 3 &4, Table S3&S4 for a complete and detailed list of significant terms.

These observations correspond with the overrepresentation of targeted genes contained in pathways from KEGG (see Figure 1b). A high overrepresentation of genes could be observed in "Pathways in cancer". Also many signaling pathways are overrepresented, namely Wnt signaling, TGF-beta signaling, Insulin signaling, Notch signaling, ErbB signaling, MAPK signaling, T and B cell receptor signaling and Chemokine signaling. Genes involved in house-keeping functions were underrepresented also in KEGG pathways, namely RNA polymerase, RNA transport, Proteasome, Oxidative phosphorylation and Ribosome.

Validating predicted miRNA targets in protein complexes

Two recent proteomics studies measured the changes in synthesis of proteins in response to miRNA over-expression or knockdown on a genome-wide scale for selected miRNAs [6, 7]. We incorporated the data of these studies in order to validate our predictions. To determine the impact of protein downregulation by miRNAs, which have targets in protein complexes, the level of downregulation of targeted components and non-targeted components was compared. We considered both significantly and insignificantly regulated complexes, since the amount of significantly regulated complexes for the examined miRNAs in the proteomics study is too low to provide statistical significance. The negative fold changes of the targeted components were significantly higher than the negative fold changes of the non-targeted components (see Table 3 and Figure 2) for every analyzed miRNA. For example, our data showed that the LARC (LCR-associated remodelling) complex [30] has two (out of 19) components, which are computationally predicted targets of let-7. These two components, namely DPF2 (Zinc finger protein ubi-d4) and SMARCC1 (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily C member 1) were modestly down-regulated (fold changes of -0.38, and -0.2, respectively), when let-7b was over-expressed in HeLa cells [7]. LARC binds to the DNase hypersensitive 2 site in the human β-globin locus control region (LCR) and transactivates β-like globin genes [30]. By simultaneously down-regulating two components of the LARC complex, let-7b might contribute to the overall transcriptional repression of the human β-globin locus.

Validation of targeted complex components. Fold change distributions of targeted and non-targeted proteins in complexes for each investigated miRNA. The (*) indicates high significance in the Kolmogorov-Smirnov test.

PAR-CLIP (Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immuno-precipitation) is a powerful tool to detect segments of RNA bound by RNA-binding proteins (RBPs) and ribonucleoprotein complexes (RNPs). We corroborated the miRNA target sites identified by PAR-CLIP [13] with the proteomics data [6, 7]. 55% of the proteins with miRNA targets sites predicted based on PAR-CLIP data were moderately down-regulated (log2-fold change < -0.1). 413 protein complexes contained miRNA target sites in at least two subunits (Additional file 5, Table S5 online). Interestingly, of the 5,185 unique proteins with miRNA target sites identified based on PAR-CLIP data, 607 (12%) are members of protein complexes (with at least two distinct targets of one miRNA in the same protein complex). For comparison, the manually curated collection of human protein complexes in the CORUM database covers 2,780 unique proteins (2% of UniProt proteins). This implies miRNA targets identified from PAR-CLIP data are more likely to be in a protein complex from the CORUM database (12%) as compared to proteins in general (2%). While miRNAs frequently target multiple genes with isolated functions, these independent data, though only by a simple estimate, suggest that there is also a significant proportion of miRNA targets, which are distinct members of protein complexes (hypergeometic P-value 1.23e-11).

Protein complexes and miRNA expression

We next tested whether miRNAs, which target different components of the same protein complex, are more likely to be co-expressed. The average expression correlation (Co-expression as calculated by Pearson correlation coefficients, hereafter termed PC values) of miRNAs was examined based on pairwise correlation calculations of miRNA expression profiles obtained for 26 different organ systems and cell types [31]. To test for statistical significance, we combined all pairwise PC values obtained from the sets of miRNAs which significantly target the same complex. These PC values were then compared to all other pairwise PC values that were present in the data set from [31]. We performed a one-sided Kolmogorov-Smirnov (KS) test for the two PC value distributions and obtained a significantly (P-value 6.106e-24) higher co-expression within the sets of miRNAs that target the same complex. Since we are interested in coexpression of miRNAs that are not in one transcription unit, we also tested for increased correlation only for miRNAs of different transcription units. Only a few (3.3%) of the correlated miRNAs were actually contained in one transcription unit. Therefore, the result remains highly significant (P-value 2.11e-18). Another bias of our results might occur due to fact that all miRNAs from one family must target the same complex since they target the same set of mRNA. We compared only miRNAs within one complex that belong to different families. The KS test resulted in a P-value of 0.0058. Taken together, our statistical test indicates that miRNAs targeting different components of a protein complex are significantly co-expressed. The average Pearson correlations of miRNAs that simultaneously target a specific complex can be found in Additional file 6, Table S6 online1).

Protein complex networks co-ordinately regulated by clusters of miRNAs

We systematically characterized the protein complex networks, which are simultaneously regulated by clustered miRNAs in 154 transcription units gained from miRBase [1]. The interconnectivity of the target sets of the miRNA gene clusters was first assessed as follows: the number of protein-protein interactions between the target sets of each pair of miRNAs in the cluster was counted, and these values were compared to 1,000 randomly sampled sets of miRNAs. To avoid miRNA target prediction bias arising from redundant prediction of clustered miRNA family members, only targets of one family member were counted within each cluster. The statistical analysis revealed 35 clusters, whose targets are significantly interconnected in the protein-protein interaction network (P-value < 0.05, permutation test, 1,000 samples, Table 1). Comparing the observed number of interactions (Figure 3b) with the corresponding distributions of randomly sampled sets of miRNAs provides a strong indication that a significant fraction of miRNAs in clusters might co-ordinately regulate targets (P-Value < 0.02, Wilcoxon signed rank test, Additional file 7, Table S7 online). In order to support this finding, we also applied Fisher's exact test to test if the global number of target interactions from miRNA clusters is higher than expected by chance. This test resulted in a P-value < 2e-16.

Statistical evidence of coordinate regulation by miRNAs. a, Pearson correlation distributions of miRNAs that target the same complex (red line) is plotted against the distribution of all observed Pearson correlation values (black dotted line). Also the distributions of excluded Pearson correlations of miRNAs from the same family (blue) and the same cluster (green) are plotted. b, Boxplot for direct interactions of proteins targeted by N miRNAs within a cluster as compared to a null model of N randomly sampled miRNAs, respectively.

CtBP/ZEB complex regulated by the miR-141-200c cluster

The network perspective provides fascinating insights of gene regulation by miRNA gene clusters, whose target sets have not yet been analyzed at a systems-level. To explore this in detail we examined the protein complexes predicted to be co-ordinately regulated by the miR-141-200c cluster. The miR-141 and miR-200c genes are located on chromosome 12p 13.31, separated by a 338bp spacer sequence miR-141 and miR-200c belong to the miR-200 family. The seed region of miR-141 differs to that of miR-200c by one nucleotide at position 4 of the miRNA therefore, miR-141 and miR-200c have, based on the "seed" rule, different computationally predicted targets. Nevertheless, we found that the targets of the miR-141-200c cluster are significantly interconnected (P-value < 0.02, Table 4).

Very recent reports have shown that the miR-200 family regulates epithelial to mesenchymal transition (EMT) by targeting the transcriptional repressor zinc-finger E-box binding homebox 1 (ZEB1) and ZEB2[4, 32–35]. During EMT, the miR-141-200c cluster and the tumor invasion suppressor gene E-cadherin are downregulated by ZEB1/2[35]. ZEB1 and ZEB2 repress transcription through interaction with corepressor CtBP (C-terminal binding protein) [36]. Interestingly, several essential components of the CtBP/ZEB complex, namely ZEB1/2, CtBP2, RCOR3 (REST corepressor 3) and CDYL (Chromodomain Y-like protein), are predicted targets of the miR-141-200c cluster. CtBP2 has one miR-141 target site and one miR-200c target site, while ZEB1 and CDYL have two miR-200c target sites. RCOR3 has one miR-141 target site. The CtBP/ZEB complex mediates the transcriptional repression of its target genes by binding to their promotors and altering the histone modification [37].

We showed that overexpression of miR-141 and miR-200c led to reduced expression of CtBP2 and ZEB1 in human pancreatic carcinoma (PANC-1) cells (Figure 4a). Luciferase reporter assay showed reduced activity of the CtBP2 and ZEB1 3'UTR-luciferase reporters with increased levels of miR-141 and miR-200c (Additional file 8, Figure S1 online). These results are also confirmed on protein level by immunoblots (Figure 4b). In order to rule out the possibility that the stability of ZEB1 and CtBP2 are dependent on each other, we separately knocked down ZEB1 and CtBP2 by siRNAs in PANC-1 cells and observed no change in protein levels of the respective complex partner (Figure 4c). Although the expression of CDYL and RCOR3 is less obviously affected by overexpression of miR-141 and miR-200c in PANC-1 cells as compared to CtBP2 and ZEB1 (data not shown), we observed a downregulation of CDYL and RCOR3 on the protein level, when miR-141 or miR-200c were transiently transfected in PANC-1 cells (Figure 4d), suggesting that CDYL and RCOR3 are also targets of the miR141-200c cluster. Together, these experiments demonstrate, for the first time, that CtBP2, CDYL and RCOR3 can be regulated by miR141-200c cluster post-transcriptionally. As the functional consequence of miRNA overexpression, the expression of E-cadherin mRNA is greatly upregulated (Figure 4a), indicating that the repression activity of CtBP/ZEB complex is compromised. The interaction between the miR-141-200c cluster and multiple components of the CtBP/ZEB complex suggests a coordinated regulation of the repression activity for the CtBP/ZEB complex. Intriguingly, the miR-141-200c cluster also targets β-catenin, which is a shared component of cell adhesion and Wnt signalling [38]. β-catenin is found in the plasma membrane, where it promotes cell adhesion by binding to E-cadherin, in the cytoplasm, where it is easily phosphorylated and degraded in the absence of a Wnt signal, and in the nucleus, where it binds to TCF transcription factors and induces the transcription of Wnt target genes. Most protein-interacting motifs of β-catenin overlap in such a way that its interactions with each of its protein partners are mutually exclusive [38]. Since the miR-141-200c cluster and E-cadherin are both downregulated during EMT, it is tempting to speculate that more β-catenin would be made available for participating in transactivating downstream genes, which may contribute to the progress of cancer [4].

Protein complexes regulated by the miR-141-200c cluster. a, Real-time reverse transcription-PCR of CtBP2 and ZEB1 after transfection of the indicated miRNAs in undifferentiated cancer cells (PANC-1). The expression levels of E-cadherin (of which the transcription is repressed by CtBP/ZEB complex) are included as positive controls. b, Confirmation of the regulation of CtBP2 and ZEB1 by miR-141 and miR-200c on protein levels by immunoblots. c, ZEB1 and CtBP2 knock down by siRNAs, no change in protein levels of the respective complex partner is oberserved. e, Downregulation of CDYL and RCOR3 on protein level when miR-141 or miR-200c was transiently transfected.

Conclusions and future directions

Due to the essential physiological function of PTEN, the ncRNAs controlling PTEN expression play crucial roles in various biological activation, such as autophagy and cell stemness. PTEN induces autophagy through repressing PI3K/Akt pathway, while miR-21 elevation was found in human degenerative nucleus pulposus tissues, which inhibits autophagy and induces ECM degradation via repressing PTEN expression [137] Human aortic smooth muscle cell-derived exosomal miR-221/222 suppressed the autophagy in human umbilical vein endothelial cells by regulating PTEN/Akt signaling pathway in a co-culture system [138] MiR-21-5p significantly increases cell stemness of keloid keratinocytes, mediated by PTEN repression and AKT activation, which may account for the invasion and recurrence of keloids [139]. MiR-10b promotes cellular self-renewal and expression of stemness markers in breast cancer stem cells through negative regulation of PTEN and sustained activation of AKT [140].

Actually, therapeutic strategies for multiple diseases focus on PI3K/Akt pathway inhibitors. However, the therapeutic benefit is modest due to the network complexities [141, 142]. PTEN modulation has been considered as a possible approach to tumor and other diseases. NcRNAs including lncRNAs and miRNAs act alone or interact with each other to regulate PTEN expression. Elucidation of the details that ncRNAs modulate PTEN expression may provide novel insights into the regulation network of PTEN, which could suggest possible strategies to target PI3K/Akt pathway.

Primary therapeutic attempts targeting ncRNAs to alter the PTEN expression have shown effects. Sophocarpine, a tetracyclic quinolizidine alkaloid derived from Sophora alopecuroides L, has shown inhibitory effects on HNSCC progression via the downregulation of miR-21 and the upregulation of PTEN in vivo and in vitro [53]. Ursolic acid exerted protective action on high glucose-induced cell podocyte injury via decreasing miR-21 expression, which resulted in an increase of PTEN level [143]. Combination of STAT3 inhibitor and DDP treatment led to a notable reduction of STAT3/miR-21 axis and an increase of PTEN level, repressing oral squamous cell carcinoma (OSCC) cell proliferation, migration and invasion [144].

As-miR-21 treatment presented an obvious inhibition on established glioma tumor growth and an increase in PTEN expresson. Coincidently, in a prostate xenograft model, injection of as-miR-4534 led to a significant reduction in tumor volume, which increased the expression level of PTEN [145]. In a spontaneously developed lung tumors mouse model, treatment with the miR-214 antisense oligonucleotides microvesicles displayed promotion of PTEN levels and reduction of growth of spontaneous lung tumors [68]. Furthermore, administration of LNA-antimiR-19a increased the sensitivity of multidrug resistant MCF-7 cells to Taxol in vivo, with an upregulation of PTEN verified [146]. The growth of human LSCC xenograft was remarkably inhibited by HOTAIR shRNA lentivirus treatment [135], and injection of the PTENP1-expressing baculovirus effectively mitigated the HCC xenograft tumor growth, which was associated with the increase of PTEN [97].

In term of the importance of PTEN expression level in physiological situation and pathogenesis of various diseases, modulating PTEN level could be considered as potential approaches for multiple diseases, while clarifying the regulation network of PTEN including ncRNAs is predicted to be able to provide novel strategies.

Functions of circRNAs

Given their dynamic and cell type-specific expression patterns during development, many studies have focused on the potential developmental roles and functions of circRNAs. Although some studies are beginning to point towards some generalized functions for circRNAs, a unified explanation for the functions of the vast majority of circRNAs is lacking.

The function of specific circRNAs in development

Almost a decade ago, the Drosophila muscleblind (mbl) gene was found to give rise to developmentally regulated, highly abundant transcriptional products now known to be circRNAs (Ashwal-Fluss et al., 2014 Houseley et al., 2006). Because mbl is an essential gene in Drosophila and human (Artero et al., 1998 Begemann et al., 1997), future studies may reveal if and how the circular isoform contributes to this essentiality or plays a role in development. As mentioned above, circMbl might exert these effects by tuning the expression of the mbl transcript, competing with the production of mRNA and binding to MBL protein.

CircRNA from the mouse Sry gene is another early key example of developmentally regulated circRNA splicing. In this case, the linear Sry isoform is expressed in the developing genital ridge where it plays a fundamental role as a transcription factor in sex determination, whereas the circular isoform is expressed in adult testes (Capel et al., 1993). The circularization of the Sry transcript is dictated by promoter usage: the use of a promoter proximal to the coding region gives rise to a linear translated transcript, whereas the use of a distal promoter gives rise to a linear RNA containing long inverted repeats that is spliced to form a circRNA (Fig. 6) (Hacker et al., 1995). These complementary sequences are required for splicing the Sry circRNA (Dubin et al., 1995). Indeed, complementary sequence-mediated circularization appears to be a more general phenomenon it has been corroborated for several circRNAs in recent years and is now the basis of many circRNA overexpression vectors (Kramer et al., 2015 Liang and Wilusz, 2014 Zhang et al., 2014). Although no function could be ascribed to the Sry circRNA at the time of its discovery, there is now evidence that it might function as a miRNA sponge for miR-138, binding up to 16 molecules of this miRNA per circRNA (Fig. 6 Fig. 7A) (Hansen et al., 2013).

Developmentally regulated expression of SRY. (A) In the genital ridge of the developing mouse embryo, the Sry transcription start site (TSS) occurs proximal to the open reading frame (ORF), yielding a translatable mRNA that gives rise to SRY protein, a transcription factor involved in sex determination. (B) In the adult testis, the TSS occurs far upstream, yielding a long transcript containing large inverted repeats (green arrows). This transcript is backspliced to form a circRNA, which might function as a miR-138 sponge. SA, splice acceptor SD, splice donor.

Developmentally regulated expression of SRY. (A) In the genital ridge of the developing mouse embryo, the Sry transcription start site (TSS) occurs proximal to the open reading frame (ORF), yielding a translatable mRNA that gives rise to SRY protein, a transcription factor involved in sex determination. (B) In the adult testis, the TSS occurs far upstream, yielding a long transcript containing large inverted repeats (green arrows). This transcript is backspliced to form a circRNA, which might function as a miR-138 sponge. SA, splice acceptor SD, splice donor.

Another circRNA named ciRS-7, or sometimes simply CDR1as, which is derived from the Cdr1 antisense locus, also probably functions as a miRNA sponge (Hansen et al., 2013 Memczak et al., 2013). ciRS-7 is very highly expressed in the mammalian brain, is induced during neuronal development (Rybak-Wolf et al., 2015) and has >70 potential miRNA-binding sites for miR-7, most of which are conserved across eutherian mammals (Hansen et al., 2013 Memczak et al., 2013). When ciRS-7 is ectopically expressed in zebrafish, which normally do not express this circRNA but do express miR-7, defects in midbrain development are observed (Memczak et al., 2013), suggesting that this RNA might play a role in the development of the mammalian brain where ciRS-7 and miR-7 are co-expressed (Hansen et al., 2013).

Broad classes of circRNA function

The discovery that ciRS-7 and Sry may serve as miRNA sponges (Fig. 7A) generated great excitement that circRNAs might play a general role in post-transcriptional regulation. However, the analysis of AGO2 crosslinking to circRNAs, as well as computational searches for enrichment of miRNA seed matches in exons contained in circRNA versus neighboring non-circularized exons, has revealed only a few other candidate circRNAs that might function in this manner, none of which has yet been validated (Guo et al., 2014 You et al., 2015). Indeed, both Sry and ciRS-7 are exceptional in their primary sequence: both are circular RNAs hosted in genes with single exons, and both are derived from genomic regions with a highly repetitive sequence. The analysis of circRNA expression in organisms lacking siRNA pathways, namely, S. cerevisiae and P. falciparum, also supports additional functions for circRNA aside from a function as a miRNA sponge (Wang et al., 2014).

circRNA functional classes. (A) circRNAs can function as a sponge containing several binding sites for a particular miRNA/RBP and can compete a miRNA/RBP away (dashed arrows) from its mRNA targets, altering gene expression. (B) Through an interaction with U1 snRNP, exon-intron circRNAs (EIciRNAs) can interact with transcription complexes at host genes to induce their transcription (adapted from Li et al., 2015).

circRNA functional classes. (A) circRNAs can function as a sponge containing several binding sites for a particular miRNA/RBP and can compete a miRNA/RBP away (dashed arrows) from its mRNA targets, altering gene expression. (B) Through an interaction with U1 snRNP, exon-intron circRNAs (EIciRNAs) can interact with transcription complexes at host genes to induce their transcription (adapted from Li et al., 2015).

Similarly, the function of circMbl in Drosophila to sequester the MBL protein might also be exceptional (Ashwal-Fluss et al., 2014). Supporting the function of circRNAs as an RBP sponge, an analysis of mined photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) data from 20 RBPs revealed slightly higher cluster density for circularized exons than for a control cohort of neighboring exons (Guo et al., 2014). However, a bioinformatic analysis of 38 RBP sequence motifs found that circularized exons contained a lower RBP-binding density than did the coding sequence or 3′ UTRs of mRNAs (You et al., 2015). It is important to note that these results are not necessarily contradictory as circRNAs are not translated, RBPs may not be easily displaced, accounting for high experimentally observed cluster densities despite bioinformatic predictions. Still, in order to have an appreciable effect on the concentration of an RBP in the cell without an enrichment of RBP-binding sites, a circRNA would have to be very highly expressed, making an RBP or miRNA sponge function unlikely for most circRNAs (Denzler et al., 2014), but, of course, rare cases may exist.

Another potential function of circRNAs could be to compete with the splicing of an mRNA, as in the case of mbl (Fig. 5A) (Ashwal-Fluss et al., 2014). As circRNAs almost always consist of exons that are also included in mRNA, the production of a circRNA would be expected to interrupt or compete with the splicing of the linear mRNA in most cases (unless a stable exon-skipped transcript can be formed). Whether this ‘function’ is merely a by-product of circRNA biogenesis remains to be tested. Although not a strict requirement, a feedback mechanism between the gene product and the splicing of its pre-mRNA would argue in favor of such a function.

Some circRNAs have also been implicated in transcriptional or post-transcriptional gene regulation of their host genes. The CDR1as circRNA is purported to promote the expression of CDR1 sense mRNA, but the precise mechanism by which this is achieved is unknown (Hansen et al., 2011). More recently, a class of regulatory circRNAs, named exon-intron circRNAs (EIciRNAs), has been identified and appears to play a role in transcriptional regulation (Li et al., 2015). Such EIciRNAs are multiexon circRNAs containing one or more unspliced intervening introns. Unlike most circRNAs, some EIciRNAs have been shown to be localized to the nucleus, and through an interaction with the U1 small nuclear ribonucleoprotein (snRNP), a spliceosomal component, can promote transcription of their parental genes (Fig. 7B). In this way, circRNA might function as a scaffold for RBPs regulating transcription. This example suggests a provocative hypothesis for a potential broader role for circRNAs as stable molecular scaffolds, much like some long noncoding RNAs (e.g. HOTAIR) (Tsai et al., 2010 Yoon et al., 2013).

LncRNAs in ES cell maintenance and differentiation

Embryonic stem (ES) cells are unique in their ability to generate all terminally differentiated cells derived from all three primary germ layers—ectoderm, endoderm and mesoderm. Maintaining this pluripotent state requires precise and delicate transcriptional regulation mediated by key transcription factors, such as Oct4, Sox2 and Nanog [ 70 ]. In addition to these protein components, lncRNAs are also involved in maintaining the ‘stemness’ of ES cells (Fig 2A). Guttman et al systematically performed loss-of-function studies on 147 lincRNAs expressed in mouse ES cells by using lentiviral-based shRNAs [ 11 ]. For 90% of the lincRNAs, knockdown resulted in significant changes in ES cell gene expression. Interestingly, 26 lincRNAs were found to be involved in the maintenance of ES cell pluripotency, whereas 30 lincRNAs acted to repress specific gene expression programmes associated with differentiation. Importantly, expression of most of these lincRNAs is regulated by ES-cell-specific transcription factors, and many lincRNAs seem to bind to diverse chromatin regulatory proteins, potentially giving rise to specific nuclear RNA–protein complexes. Similarly, Lipovich's group identified four conserved lincRNAs that are regulated by the ES-cell-specific transcriptional factors Oct4 and Nanog [ 10 ]. Importantly, inhibition or misexpression of two of these lincRNAs impaired the ‘stemness’ state of ES cells. Collectively, these results implicate lincRNAs in the regulatory networks that maintain ES cell identity. One caveat in interpreting these data, however, is the uncertainty as to whether all of the studied transcripts are truly non-coding. Although the coding potential of these transcripts was computationally evaluated, experiments are required to verify this important point.

LncRNAs also regulate differentiation of ES cells (Fig 2A). One well-characterized example is the role of Xist in X-chromosome inactivation in mammalian females [ 51 ]. To equalize the dosage of X-chromosome-encoded genes between female and male cells, expression from one of two copies of the X chromosome must be silenced this is achieved by formation of heterochromatin early during ES cell differentiation. A non-coding RNA transcript, Xist, is important in this developmental process. Xist is exclusively expressed from a region of the inactive X chromosome called the X-inactivation centre. Despite undergoing splicing, Xist remains in the nucleus to coat the inactive X chromosome, and to recruit—through its structured RNA domain termed Repeat A—the chromatin regulator PRC2 to this chromosome [ 71 ]. PRC2 facilitates the formation of heterochromatin through histone modifications, specifically H3K27. Thus, Xist-mediated recruitment of PRC2 contributes significantly to X-chromosome inactivation. Interestingly, the generation and the activity of Xist are regulated by several other lnc transcripts, such as Tsix and Xite [ 51 ]. Clearly, regulatory networks of lncRNAs are important in this differentiation process, even when the transcripts themselves show limited conservation in primary sequence between species [ 72 ].

In addition to modulating ES cell differentiation, lncRNAs are also involved in the generation of induced pluripotent stem (iPS) cells (Fig 2A). iPS cells can be derived from terminally differentiated somatic cells by ectopic expression of key pluripotency-associated transcription factors, such as Oct4, Nanog, Sox2 and c-Myc [ 73 ]. This cellular reprogramming is accompanied by an extensive global remodelling of the epigenome [ 74 ]. Loewer et al found that several lincRNAs contribute to this dedifferentiation process [ 75 ]. By comparing lincRNAs expressed in iPS cells with those expressed in ES cells, they identified 28 lincRNAs that are specifically enriched in iPS cells. These lincRNAs are regulated by pluripotency-associated master transcription factors (Oct4 and Nanog), suggesting a potential involvement in the generation of iPS cells. In particular, inhibition of one such lincRNA, lincRNA-RoR, impairs iPS cell formation. Conversely, overexpression of this lincRNA leads to an approximate 2.5-fold increase in cellular reprogramming, a modest yet significant effect. Collectively, these observations indicate that lncRNAs can activate or repress transcriptional programmes associated with ES cell pluripotency or differentiation, and that their impact on these processes can range from essential to subtle but detectable.

Dynamic regulation of Gata factor levels is more important than their identity

Three Gata transcription factors (Gata1, -2, and -3) are essential for hematopoiesis. These factors are thought to play distinct roles because they do not functionally replace each other. For instance, Gata2 messenger RNA (mRNA) expression is highly elevated in Gata1-null erythroid cells, yet this does not rescue the defect. Here, we test whether Gata2 and -3 transgenes rescue the erythroid defect of Gata1-null mice, if expressed in the appropriate spatiotemporal pattern. Gata1, -2, and -3 transgenes driven by beta-globin regulatory elements, directing expression to late stages of differentiation, fail to rescue erythropoiesis in Gata1-null mutants. In contrast, when controlled by Gata1 regulatory elements, directing expression to the early stages of differentiation, Gata1, -2, and -3 do rescue the Gata1-null phenotype. The dramatic increase of endogenous Gata2 mRNA in Gata1-null progenitors is not reflected in Gata2 protein levels, invoking translational regulation. Our data show that the dynamic spatiotemporal regulation of Gata factor levels is more important than their identity and provide a paradigm for developmental control mechanisms that are hard-wired in cis-regulatory elements.


Extensive-Existing Coding Transcripts in Murine Spermatozoa

Compared with conventional strategies, RNA-Seq can provide a much more intact picture of the transcripts in sperm, allowing for the identification, quantification, and characterization of both known and unknown RNAs, including coding RNAs and noncoding RNAs [ 22]. RNA-Seq has been used to survey the selective retention of transcripts in human sperm [ 16]. In human mature sperm, approximately 22 302 unique transcripts were surveyed, including transcripts linked to the testes, spermatids, spermatogenesis, and infertility [ 15]. In our RNA-Seq results of pooled mouse sperm collected from 200 adult mice, more than 33 000 different transcripts, including 27 310 coding transcripts, were obtained, a number that is far beyond our original prediction. Considering the sensitivity and depth of RNA-Seq, some extremely low abundance transcripts could be sequenced [ 23]. To confirm the sequencing accuracy, sperm were purified strictly to eliminate contaminant cells, and all the sequencing processes were under strict quality control. The results of the real-time PCR used to verify the RNA-Seq data indicated that they generally corresponded with each other. In addition, we did another RNA-Seq of mouse sperm treated with a certain chemical, and the result was consistent with that of the normal sperm except for some transcripts (unpublished results). As a result, the RNA-Seq data were reliable.

According to the RNA-Seq results of human spermatozoa, more than 80% of the RNAs were fragmented ribosomal RNAs [ 15]. However, our RNA-Seq results and a previous report [ 24] showed that murine spermatozoa contained abundant transcripts with few or even no rRNA background. We divided these transcripts into two groups: randomly remnant transcripts and persistent transcripts. In a simplified mathematical model, the average numbers of these two types of transcripts in a single sperm cell are less than one and more than one. Based on two estimated results, namely, that a single sperm cell contains approximately 84 310 mRNAs (previously mentioned methods) and that RPKM indicates the abundance of a transcript without the effect of its length [ 20], we hypothesized that a transcript with RPKM ≥ 6 is a persistent transcript in sperm. In our RNA-Seq results, the number of transcripts with RPKM ≥ 6 was 5826, including 4885 coding transcripts, which corresponded with Ostermeier's [ 13] estimation. Although 4885 coding transcripts were estimated to exist in each sperm cell, it should be noticed that 4885 was an average number. Thus, the transcript with RPKM slightly more than six was possibly not retained in each sperm cell. The number of transcripts with RPKM six to eight was 1216, and the number of transcripts with RPKM 6–10 was 1923, which is a greater number of transcripts than found with RPKM of slightly more than six. Thus, the number of actually stable mRNA in a single sperm was surely less than 4885.

Our analyses focused mainly on the coding transcripts. Through gene ontology and phenotype analyses of the coding transcripts, we found that some of the transcripts were involved in reproduction and development, but these showed no significant bias compared with other biological processes. Through pathway analysis, 54 mRNAs were linked to the gonadotropin-releasing hormone receptor pathway, which plays a critical role in fertility [ 25], and 52 mRNAs were linked to the Wnt signaling pathway, which is related to the differentiation of Sertoli cells and spermatogonial stem/progenitor cells [ 26, 27]. The pathway analysis results indicated that these transcripts may encode proteins that play an important role in spermatogenesis and reproduction.

Because the transcripts with RPKM < 6 most likely did not exist in every spermatozoon, these were assumed to be randomly residual transcripts that did not function in postspermatogenesis. Among the transcripts with RPKM ≥ 6, we hypothesized that most of them were also residual of spermatogenesis-function transcripts. Those two types of spermatozoal transcripts may simply be remnants of prior functional transcripts involved in spermatogenesis and sperm maturation [ 28, 29].

It was noteworthy that 142 of the transcripts with RPKM ≥ 6 were expressed in spermatids during postmeiosis according to the results of Spermatogenesis online. In the last phase of spermatogenesis, both the nucleus and the cytoplasm of spermatids are condensed to form elongated spermatids [ 30]. During this process, the transcription in spermatids is shut down, and almost all of the cytoplasm is discarded meanwhile, the spermatids undergo morphological transformations, which still require the participation of many proteins [ 31]. These two processes are paradoxical in time thus, some transcripts are transcribed long before translation and wait in the cytoplasm to be translated [ 32]. In our opinion, the 142 transcripts involved in postmeiosis remained in the spermatozoa due to the limited time for discarding or degrading those transcripts. Of these transcripts, some showed a higher RPKM value compared with others. For example, Prm1 (RPKM = 273) and Prm2 (RPKM = 1667), which code for protamine-1 and protamine-2, respectively, are specifically expressed before and during the DNA condensation phase in spermatids. The condensation phase is first characterized by abundant protamine expression and later results in the transition of DNA chromatin with protamine to replace histone [ 31]. Another example is Tnp1 (RPKM = 56) and Tnp2 (RPKM = 4365), encoding transition nuclear protein-1 and -2, which are arginine- and lysine-rich basic proteins, respectively, that strongly bind to DNA and are expressed exclusively in postmeiotic spermatids. In mouse, Tnp2 transcript is first detected in step 7 round spermatids and then degraded at steps 13 and 14 [ 33]. TNP1 and TNP2 play an important role in the spermatid chromatin condensation process through their phosphorylation by sperm-specific protein kinase A [ 34]. Similarly, the transcripts Fank1 (RPKM = 2027) [ 35], Spem1 (RPKM = 75) [ 36], and Klhl10 (RPKM = 130) [ 37] are also involved in spermiogenesis, such as in the processes of chromatin condensation and cytoplasm removal. Meanwhile, the works of Kobayashi et al. [ 38] showed that the gene expression level in mouse sperm was negatively correlated to the promoter methylation but positively correlated to the gene-body methylation in the genome [ 38]. As a consequence, the expression level of gene was also related to the methylation level in the genome.

Another group of highly retained transcripts is related to mitochondria, which are among the few organelles retained in mature spermatozoa. In total, 464 transcripts with RPKM ≥ 6 are related to the mitochondria. Of these, some transcripts showed an extremely high expression level, such as Mrpl27 (RPKM = 2500), Cox7b (RPKM = 2496), and Lars2 (RPKM = 2324). Although there is still debate on whether sperm mitochondria can translate nucleus-transcribing mRNA into protein via mitochondrial ribosomes, it has been reported that some coding transcripts are translated in mature spermatozoa after capacitation by mitochondrial ribosomes [ 39, 40].

The subset of transcripts that interested us most were those related to zygotic and/or embryonic development. It has been reported that the sperm-borne mRNA Akap4, which encodes for AKAP4, a protein involved in signaling cascades that are likely relevant in initial oocyte activation after fertilization, entered the fertilized oocyte [ 41]. In 2004, Ostermeier et al. [ 17] found that some human spermatozoal transcripts coding for proteins involved in fertilization, stress response, embryogenesis, morphogenesis, and implantation were delivered into the fertilized oocyte. In our RNA-Seq results, there were also many transcripts linked to development in particular, 42 transcripts with a relatively high expression level (RPKM ≥ 30) were involved in zygotic and/or embryonic development, and these included some of the transcripts reported by Ostermeier et al., such as Foxg1 and Clusterin, though the results of mouse were not the same as with human. We thus performed some in-depth studies of these transcripts to determine whether they entered the fertilized oocyte and were further translated these studies are described in detail below.

Abundant Coding Transcripts in Spermatozoa

A total of 635 coding transcripts with RPKM ≥ 60 were considered to be representative of the set of highly abundant transcripts. Of these highly abundant transcripts, 257 were located in reproductive tissues (testes and epididymis) and cells (spermatocyte, spermatids, and spermatozoa), indicating that these mRNAs encoded proteins with critical functions in spermatogenesis and sperm maturation, including the previously described processes of spermatid chromosome packing and cytoplasm discarding. It was notable that 55 were related to male infertility, which indicated that these transcripts played a critical role in male function and suggested that these might provide a resource of markers for the diagnosis of male infertility [ 42]. The gene ontology analysis showed that more than one in six transcripts were related to reproduction, including multicellular organismal reproductive process, gamete generation, fertilization, and the development of primary sexual characteristics, indicating that these transcripts might encode proteins that function in spermatogenesis.

More than one in four transcripts were related to development in particular, 33 were linked to embryonic development, including embryonic morphogenesis embryo development ending in birth or egg hatching and embryonic organ development, hinting that these transcripts may encode proteins that function in zygotic and/or embryonic development [ 43]. The increase in the ratio of development and reproduction in the gene ontology and phenotype analyses with an increase in the RPKM baseline (6, 60, and 600) indicated that the transcripts related to development and reproduction were more likely to be retained in sperm. Furthermore, it could be speculated that the transcripts in sperm are selectively rather than randomly reserved.

Delivery and Translation of Sperm-Borne mRNAs in the Fertilized Oocyte

To study the biological function of coding transcripts in zygotic development, the transcripts (RPKM ≥ 30) that may participate in zygotic and/or embryonic development were screened. In total, 11 of these were found to exist in sperm but not in oocyte. Furthermore, these 11 transcripts were tested in one- and two-cell zygotes, showing that only two coding transcripts were present in the one-cell zygote: Wnt4 and Foxg1. Because the transcription of zygote was shut down until it reached the two-cell stage [ 44, 45] and due to the absence of Wnt4 and Foxg1 in the oocyte, those two coding transcripts were confirmed to be delivered by sperm. The sperm-delivered Wnt4 was detected in the one-cell zygote but not in the two-cell zygote, whereas the coded WNT4 protein was found in both one- and two-cell zygotes. Because both the spermatids in testes and mature spermatozoa do not contain WNT4 protein (see Fig. 3, C and D), it was confirmed that the WNT4 protein in the fertilized oocyte was not delivered by sperm. Summarizing the previously described results, it could be speculated that the spermatozoal Wnt4 mRNA was sent into the oocyte, where it was immediately translated and degraded, and that the coded protein existed until at least the two-cell-zygote stage. Nevertheless, the sperm-delivered Foxg1 transcript was not translated in the fertilized oocyte but existed until the two-cell-zygote stage thus, it might be translated in a later development stage, which was not studied in this work.

Franco et al. [ 46] proved that WNT4 plays a critical role in progesterone signaling during embryo implantation and decidualization. Jordan et al. [ 47] showed that WNT4 inhibits steroid biosynthesis in mouse testicular Leydig cells. In our study, the results indicated that Wnt4 transcript delivered by sperm is translated in the zygote and then is degraded and that the coded WNT4 protein may function in initial zygotic development. The WNT4/β-catenin signaling pathway has been demonstrated to be a regulator in myogenic proliferation and vascular smooth muscle cell proliferation [ 48], suggesting that WNT4 in the zygote may play a similar role in cell division and proliferation. As a fibroblast growth factor signaling-regulating protein, FOXG1 has been reported to be an important regulator in embryo neurogenesis [ 49]. However, in this study, Foxg1 mRNA was not translated into protein in the one- and two-cell zygotes. Further studies are required to determine whether Foxg1 is translated and during which stage of development this occurs. It must be emphasized that although only two sperm-delivered transcripts were detected in the zygote in this study, there might be more transcripts delivered by spermatozoa during fertilization. It is possible that some transcripts were missed in our study due to the extremely low amount of sperm-delivered transcripts. The delivery of some transcripts that exist in both oocyte and sperm cannot be detected either because it is hard to determine whether these transcripts are contributed by sperm or by oocyte.

In classical concepts, oocyte provides almost all the necessary cytoplasmic components for early zygotic development, and paternal contribution is thought to be no more than genomic DNA. Recently, the paternal effects on embryonic development have attracted more and more attention, and the contribution of the parent in fertilization may be more than paternal genomic DNA. The entire contents of the sperm are released into the ooplasm on fertilization [ 50], followed by degradation of paternal mitochondria and sperm tail structures [ 51], whereas some sperm components are required for further development, such as the spermatozoal centriole [ 52], various transcription factors, and signaling molecules. However, the function of sperm-borne transcripts in regulating zygotic development has rarely been reported, though 380 RNAs have been proven to be delivered into the oocyte by sperm through comparing the RNA profiles of fertilized oocytes and parthenogenetic oocytes [ 53]. Sone et al. [ 54] demonstrated that sperm-specific phospholipase Cζ (PLCζ) transcript injected into the mouse oocyte could be translated into a functional protein to yield a functional calcium oscillator. Yao et al. [ 55] found that sperm-borne Dby mRNA was transferred into the oocyte during fertilization and regulated zygotic development. Meanwhile, some noncoding RNAs that appear during the last stage of sperm maturation play a potential role in early zygotic and development [ 56]. The results of present and previous studies and our results here strongly hint that some of the sperm-delivered transcripts may have paternal effects that are associated with the regulation of zygotic development, which remains to be explored.

Although Wnt4 was delivered and translated in the fertilized oocyte, it was unclear whether WNT4 was involved in zygotic development and in which process. Some efforts have been made to investigate the effect of the loss of WNT4 on early zygotic development. We tried to prepare conditional Wnt4-knockout mice in testis but failed because we could not get suitable Cre-mouse for conditional knockout in testis. Perhaps some work will be carried out on interfering WNT4 and FOXG1 expression, such as shRNA and siRNA, to confirm the function in early zygotic development. On the other hand, some work is now being carried out to seek clinical implications. Human sperm samples from both fertile and infertile donors have been collected for RNA-Seq to analyze the differences of the RNA profiles, and the two transcripts Foxg1 and Wnt4, identified in mouse sperm, have also been detected in human sperm.

In conclusion, the transcripts with RPKM ≥ 6 in our RNA-Seq results may exist in each murine sperm. A total of 5826 RNAs, including 4885 mRNAs, are consistently retained in each sperm, whereas the remaining 27 213 RNAs with RPKM < 6 are randomly retained. Most of the spermatozoal transcripts are residual transcripts with no biological function in postspermatogenesis, whereas the transcripts related to reproduction and development, which represent the function of spermatozoal RNA pre- and postfertilization, respectively, were more likely to be retained. Thus, the transcripts in sperm are selectively rather than randomly retained. In particular, the sperm-borne Wnt4 mRNA was found to be delivered and translated in the fertilized oocyte. This result indicates that some sperm-borne transcripts can enter the oocyte and be translated, which is new evidence of paternal effects on zygotic development. However, the function of sperm-delivered transcripts during early zygotic development requires further investigation.

Watch the video: ncRNAs - all types of non-coding RNA lncRNA, tRNA, rRNA, snRNA, snoRNA, siRNA, miRNA, piRNA (June 2022).


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