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I understand what miRNA are, but I'm unsure of what cognate means. Looking at this post, it seems that a cognate miRNA is a known miRNA vs. a recently discovered/possible miRNA. Is this thinking on the right track?
The use of the word cognate in molecular biology reflects adjectival definition 2 from Lexico:
2 formal Related; connected.
'cognate subjects such as physics and chemistry'
The example given shows that this can be used in the most general sense of related, so that there is no requirement for some common antecedent as a surrogate for a human mother or ancestor.
Specifically there is no implication of the involvement of a common DNA sequence, as in the answer from @Dirigible, nor that of a common origin as invoked in an earlier answer on this topic.
This is quite clear if one considers the much earlier usage of cognate in molecular biology in relation to an mRNA codon and the tRNA anticodons that can recognize it, or a tRNA and an aminoacyl tRNA synthetase that can aminoacylate it.
As far as the usage with microRNAs is concerned, a cognate mRNA is one with which the miRNA can interact by base-pairing - no more, no less.
3 Genetic Organization, Variation in MicroRNAs, and Tissue-Specific Expression of MicroRNAs
MicroRNAs (miRNAs) undergo multiple processing events to reach their functional 21–23 ribonucleotide RNA sequence. Canonical miRNAs are generated from protein-coding transcriptional units whereas, other miRNAs (ie, noncanonical miRNAs) are generated from nonprotein-coding transcriptional units. In both cases, the miRNAs can be located either within intronic or exonic regions. A noteworthy mechanistic distinction in canonical versus noncanonical miRNAs is that canonical intronic miRNAs are Drosha dependent and are thus processed cotranscriptionally with protein-coding transcripts in the nucleus. The premiRNA then enters the miRNA pathway, whereas the rest of the transcript undergoes premRNA splicing to produce mature mRNA which will then direct protein synthesis. Noncanonical intronic small RNAs (also called mirtrons) can derive from small introns that resemble premiRNAs, and bypass the Drosha-processing step  . miRNAs tend to be organized in a related cluster and also tend to target multiple mRNA transcripts within common cellular response pathways (eg, proliferation, apoptosis). This organizational thematic provides miR clusters with a capacity for coordinate regulation of multiple steps within a pathway, providing an opportunity for complex and adaptive regulatory control of entire pathways. An interesting class of miRNAs is myomiRs—so-called because they are coded within myosin heavy chain (MYH) genes. myomiRs are transcribed in the same precursor mRNA as the parental MYH gene  . Of special note is the myomiR-499, which despite the absence of a parent mRNA, is one of the most highly expressed miRNAs in heart tissue. In an apparently novel evolutionary phenomenon, alternative splicing in the heart uncouples production of mature miR-499 from expression of parent MYH7b mRNA, meaning that the mRNA has perhaps evolved into a nonfunctional host mRNA for its intronic miR (ie, miR-499).
Comparative studies evaluating the organizational structure of the mammalian genome have identified a wealth of chromosomal insertion–deletions, copy number variants, and single nucleotide polymorphisms (SNPs) that, depending on the environmental context, contribute to the genetic variation that can underlay phenotypic diversity. This diversity is evident in nearly every aspect of human health and disease that has been investigated. Perhaps not surprisingly, there is now a growing recognition that variation in miRNAs and their target genes also contribute to this phenotypic variability. Several solid and hematologic malignancies can be linked to miRNAs located at amplified, deleted, or translocated chromosomal regions in the mammalian genome  . Variation in gene expression and regulation is likely influenced by genetic variants in cis- and trans-acting SNPs (also known as expression quantitative trait loci)  . An interesting observation of miRNA binding is their ability to recognize binding site polymorphism (miRSNPs) in transcribed functional genes. For example, miR-24 appears to be deregulated in human colorectal tumor through a target site polymorphism in the dihydrofolate reductase gene. In another example, a polymorphism within the myostatin gene creates a target site for miR-1 and miR-206, which are highly expressed in skeletal muscle. The binding of these miRs to the polymorphism in myostatin causes translational inhibition of myostatin transcripts and can phenocopy the observed muscle hypertrophy that is observed with genetic knockouts of the myostatin gene  . Given the significant differences in gene expression and genetic variation across human populations, analysis of the role of miRNAs in contributing to population differences in gene expression is likely to provide substantial insights in population based health disparities and physical functionalities  . Indeed, comparative genomic studies indicate that the target mRNA sequences for miRNAs: untranslated regions (UTRs) on mRNAs often display sequence diversity. This may suggest adaptive evolution of coexpressed miRNAs and cognate mRNAs with these UTR variants. Depending on whether the dampening of protein output is beneficial, inconsequential, or harmful, the UTR sites may be selectively conserved, neutral, or avoided during miRNA:mRNA coevolution  .
Studies evaluating the tissue-specific expression of miRNAs illustrate a “cross-regulation” feature of miRNAs that contributes to cell fate specification by repressing alternate cell fates to facilitate commitment to one cell fate and to maintain stability of a differentiated phenotype  . For example the myomiRs miR-1, miR-133, miR-206, miR-208, miR-486, and miR-499 are enriched in skeletal muscle and play crucial roles in the development, growth, and maintenance of skeletal muscle  . Notably, miR-133 prevents osteogenic cell lineage differentiation by repressing Runx2, a factor essential for bone formation  . MiR-7, miR-24, miR-128, miR-134, miR-219, and others are highly expressed in the mammalian brain and regulate neurite outgrowth, neuronal differentiation, and dendritic spine size  . Regionally enriched miRNAs in specific tissues can exert specialized function. For example, miR-7 and miR-24 are highly expressed in the hypothalamus and fine-tuning expression of Fos and oxytocin which play vital roles in the control of water, lactation, and parturition  .
Target-dependent biogenesis of cognate microRNAs in human cells
Extensive research has established how miRNAs regulate target mRNAs by translation repression and/or endonucleolytic degradation in metazoans. However, information related to the effect of target mRNA on biogenesis and stability of corresponding miRNAs in animals is limited. Here we report regulated biogenesis of cognate miRNAs by their target mRNAs. Enhanced pre-miRNA processing by AGO-associated DICER1 contributes to this increased miRNP formation. The processed miRNAs are loaded onto AGO2 to form functionally competent miRISCs both in vivo and also in a cell-free in vitro system. Thus, we identify an additional layer of posttranscriptional regulation that helps the cell to maintain requisite levels of mature forms of respective miRNAs by modulating their processing in a target-dependent manner, a process happening for miR-122 during stress reversal in human hepatic cells.
Conflict of interest statement
The authors declare no conflict of interests.
Figure 1. Reversal of amino acid-starvation-induced stress…
Figure 1. Reversal of amino acid-starvation-induced stress increases miR-122 biogenesis in Huh7 cells.
Figure 2. Target mRNA-dependent increase of mature…
Figure 2. Target mRNA-dependent increase of mature miR-122 in human cells.
Figure 3. Effect of target mRNA concentration…
Figure 3. Effect of target mRNA concentration on substrate-dependent miRNA increase in human cells.
Figure 4. Target mRNA drives increased biogenesis…
Figure 4. Target mRNA drives increased biogenesis of mature miRNA from pre-miRNA.
Figure 5. Increased activity of AGO2-associated DICER1…
Figure 5. Increased activity of AGO2-associated DICER1 contributes to the target mRNA-driven miRNA production.
Figure 6. Increased processivity of AGO2-associated DICER1…
Figure 6. Increased processivity of AGO2-associated DICER1 in the presence of target mRNA.
Trends in the development of miRNA bioinformatics tools
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature (3) most early tools are well maintained and widely used (4) classic machine learning methods retain their utility however, novel ones have begun to emerge (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.
Keywords: bibliometric bioinformatics tools miRNA ranking text mining.
© The Author(s) 2018. Published by Oxford University Press.
miRNA biogenesis of animal/plant and…
miRNA biogenesis of animal/plant and bioinformatics tools associated within each process. The canonical…
Historical time line of miRNA…
Historical time line of miRNA research. The development of experimental and computational aspects…
Standardized miRNA analysis workflow and…
Standardized miRNA analysis workflow and examples of associated tools. The left panel with…
Circular graphic of miRNA identification…
Circular graphic of miRNA identification and miRNA target prediction mentioned in reviews since…
Statistic of miRNA tools. Line…
Statistic of miRNA tools. Line chart: The number of publications of ncRNA-related bioinformatics…
Tags statistic of miRNA tools…
Tags statistic of miRNA tools based on miRToolsGallery. Heat map for top tags…
Word cloud based on literature…
Word cloud based on literature in each year. The word cloud was generated…
ncRNA tools interaction network. The…
ncRNA tools interaction network. The left network was based on the tool’s publication…
RIP and mRNPs
We used RIP in conjunction with either NanoString or high-throughput sequencing to examine endogenous mRNPs. One advantage of this approach is that it allows investigation of mRNAs and RNA-binding proteins expressed from their endogenous loci, and in so doing avoids over-expression of either the mRNA or protein components of mRNPs. Overexpression can lead to spurious binding and, thus, incorrect quantitative and qualitative definition of mRNP components relative to the endogenous situation in cells .
Despite these advantages, the RIP protocol includes in vitro incubation and washes that might perturb the native occupancies of some RNA-binding proteins. Although we modified the protocol to minimize the duration of the in vitro incubation, because we could not eliminate it altogether, the results that we report are for interactions that were sufficiently stable to survive these steps. Thus, any changes in the stability of interactions within an mRNP might have contributed, at least in part, to our observed changes in apparent occupancy. For example, apparent occupancy could have been inflated if remodeling of an mRNP added interactions that decreased the in vitro dissociation rate of the immunoprecipitated protein. Nonetheless, increasing the stability of an interaction often goes hand-in-hand with increasing its occupancy and, in either case, the result would reflect a change in the native mRNP. Overall, the correspondence between our results and those of previous approaches for studying miRNA-mediated regulation confirmed the utility of our approach, extended some of those previous results to endogenous mRNAs, and provided new insight into both miRNA-mediated repression and mRNP organization more generally.
MRNPs in miRNA-mediated repression
Association with eIF4E, eIF4G, and PABP is a hallmark of stable, translationally competent cytoplasmic mRNPs, and a variety of reporter-based studies implicate miRNA-mediated repression in altering association with these key factors [38, 47, 50, 51]. With these results in mind, miRNA-dependent depletion of PABP-bound mRNA, measured using PABP RIP-seq, has been used to identify miRNA targets in human cells, although this was done without distinguishing depletion caused by lowered PABP occupancy from depletion caused by mRNA destabilization . Our study has extended these results to endogenous mRNPs on a transcriptome-wide scale, showing that even after taking into account changes in RNA abundance the loss of PABP and eIF4G is widespread during miRNA-mediated repression, despite the inherently dynamic and diverse nature of mRNPs. The remodeled mRNPs that lost PABP and eIF4G were presumably incapable of supporting translation initiation, consistent with reports of the importance of the eIF4F complex in miRNA-mediated translational repression [49, 51].
Despite the readily detectable miRNA-mediated dissociation of PABP and eIF4G, we were unable to detect miRNA-mediated dissociation of eIF4E, which could be a consequence of miRNA-targeted mRNAs that have lost eIF4E undergoing decapping and degradation too rapidly to be detected by our methods. This interpretation is challenged by the results of our transcriptome-wide analysis of eIF4E binding, which revealed a range of eIF4E occupancies that spanned > 100-fold (Fig. 5a), implying that many mRNAs lacking bound eIF4E are not immediately decapped and degraded. We propose that the solution to this apparent paradox rests squarely on the mRNP context—that dissociation of eIF4E might have very different consequences for some mRNAs compared to others. In this model, eIF4E dissociation is necessary for decapping  but is not sufficient, and additional mRNP alterations associated with miRNA targeting would favor both decapping and degradation. Such alterations might include dissociation of PABP, shortening of the poly(A) tail, or recruitment of decay factors , each of which has been observed during miRNA-mediated repression in the present study (Figs. 1 and 2) as well as previous reports [30, 38, 47].
With respect to decay factors that predispose mRNAs for decapping, a top candidate is DDX6. This protein interacts with the decapping complex through adaptor proteins [39, 45] and is implicated in miRNA-mediated repression of reporters [41, 43, 44]. Moreover, we found that DDX6 is recruited to many endogenous mRNAs as they became targeted by miRNAs and that DDX6 is associated with many additional mRNAs undergoing decay, which presumably are not being targeted by miRNAs. DDX6-bound transcripts have poly(A) tails that are on average significantly shorter than those of the steady-state population, suggesting that DDX6 recruitment coincides with deadenylation. This result is consistent with recruitment of DDX6 by the CCR4–NOT deadenylase complex via a direct interaction with CNOT1 [41, 43, 44]. Nonetheless, our analysis of poly(A)-tail lengths on miRNA-site-containing mRNAs indicates that the downstream recruitment of DDX6 is broadly similar in the presence and absence of the cognate miRNA. Together, our results support a model in which miRNA-mediated repression leads to the recruitment of the CCR4–NOT deadenylase complex via direct interactions with TNRC6 [31, 37] and, once the deadenylase has been recruited, the dense network of interactions between mRNA decay factors then leads to the ultimate destruction of the transcript through mechanisms that also act on many other mRNAs and, thus, are not directly orchestrated by the miRNA machinery [42, 61].
Organization of mRNPs: PABP and the poly(A) tail
PABP has long been recognized as a critical factor for post-transcriptional regulation and is essential for the ability of the poly(A) tail to stabilize transcripts. Consistent with this understanding, our results have demonstrated that increased PABP occupancy correlates with increased translatability and stability of the mRNA. The poly(A) tail has long been thought to be central in post-transcriptional regulation, with longer tails leading to increased stability and translation through their ability to bind more PABP. Unexpectedly, however, we found a lack of correlation between PABP occupancy and the length of the poly(A) tail, suggesting that the relationship between the poly(A)-tail length and PABP binding is more complex than previously thought. Indeed, although among transcripts derived from the same gene PABP occupancy was somewhat reduced for mRNAs with the shortest tails, differences in mean poly(A)-tail length could not explain differences in PABP occupancy observed for transcripts from different genes. Thus, although PABP might be critical for signaling that a poly(A) tail is present, in both human and fly cell lines and in yeast, PABP seems to be a very poor “reader” of poly(A)-tail length.
Recent studies have shown that, in contexts other than oocytes and early embryos, steady-state poly(A)-tail length fails to correlate with either mRNA stability or mRNA translation efficiency [36, 76]. These previous results can now be reconciled with the known roles of PABP in promoting mRNA stability and translation [15, 16, 77], in that we have shown here that differences in steady-state poly(A)-tail length do not necessarily cause differences in PABP occupancy.
Our results have also shown that the density of PABP along poly(A) tails can differ substantially for mRNAs from different genes. For instance, mRNAs for ribosomal proteins have very short poly(A) tails yet very high PABP occupancy. Although our current approach cannot determine the absolute number of PABPs bound to these mRNAs, our results are consistent with the density of PABP on these transcripts being higher than on other mRNAs. It will be interesting to explore this possibility further and to determine the extent to which PABP density influences deadenylation, decapping and/or other posttranscriptional processes.
What then might determine how much PABP is bound? Our results, together with those from Drosophila extracts , show that miRNA-mediated loss of PABP may just be the tip of the iceberg. The interplay among various regulatory factors, including miRNAs, and core factors, such as eIF4F, may determine the nature and composition of each mRNP, including the PABP occupancy of its constituent mRNAs. For instance, eIF4G can influence PABP affinity in vitro  and, thus, events in the 5′ UTR might have corresponding effects on PABP binding. This model is consistent with the numerous interactions described between PABP and other regulatory factors [22, 42, 66, 79]. Intrinsic mRNA features might also modulate PABP occupancy. For instance, because PABP can bind to AU-rich stretches with high affinity , differential binding to the 3′ end of the 3′ UTR, which can be quite AU-rich, might influence occupancy. Moreover, because PABP interacts with the termination factor eRF3, translation rate or termination efficiency might also have an impact on PABP binding . Future insights into mRNP organization and remodeling will shed additional light on the mRNA features and factors that trump tail length to determine PABP occupancy.
Mechanisms of miRNA-Mediated Gene Regulation
Most studies to date have shown that miRNAs bind to a specific sequence at the 3′ UTR of their target mRNAs to induce translational repression and mRNA deadenylation and decapping (40, 41). miRNA binding sites have also been detected in other mRNA regions including the 5′ UTR and coding sequence, as well as within promoter regions (42). The binding of miRNAs to 5′ UTR and coding regions have silencing effects on gene expression (43, 44) while miRNA interaction with promoter region has been reported to induce transcription (45). However, more studies are required to fully understand the functional significance of such mode of interaction.
MicroRNA-Mediated Gene Silencing via miRISC
The minimal miRNA-induced silencing complex (miRISC) consists of the guide strand and AGO (46). The target specificity of miRISC is due to its interaction with complementary sequences on target mRNA, called miRNA response elements (MREs). The degree of MRE complementarity determines whether there is AGO2-dependent slicing of target mRNA or miRISC-mediated translational inhibition and target mRNA decay (47). A fully complementary miRNA:MRE interaction induces AGO2 endonuclease activity and targets mRNA cleavage (47). However, this interaction destabilizes the association between AGO and the 3′ end of the miRNA promoting its degradation (48, 49).
In animal cells, the majority of miRNA:MRE interactions are not fully complementary (50). Most MREs contain at least central mismatches to their guide miRNA, preventing AGO2 endonuclease activity. Consequently, AGO2 acts as a mediator of RNA interference, similar to the non-endonucleolytic AGO family members (AGO1, 3, and 4 in humans). In many cases, a functional miRNA:MRE interaction occurs via the 5' seed region (nucleotides 2𠄸) (42, 51). However, additional paring at the 3′ end aids in the stability and specificity of the miRNA-target interaction (15).
The formation of a silencing miRISC complex starts with the recruitment of the GW182 family of proteins by miRISC GW182 provides the scaffolding needed to recruit other effector proteins (52), such as the poly(A)-deadenylase complexes PAN2-PAN3 and CCR4-NOT, following miRNA:target mRNA interaction (50, 53). Target mRNA poly(A)-deadenylation is initiated by PAN2/3 and completed by the CCR4-NOT complex. The interaction between the tryptophan (W)-repeats of GW182 and poly(A)-binding protein C (PABPC) promotes efficient deadenylation (50). Subsequently, decapping takes place facilitated by decapping protein 2 (DCP2) and associated proteins (52), followed by 5′𢄣′ degradation by exoribonuclease 1 (XRN1) (54) (Figure 1).
MicroRNA-Mediated Translational Activation
Although most studies are focused on how miRNAs inhibit gene expression, some have also reported up-regulation of gene expression by miRNAs. In serum starved cells, AGO2 and another protein related to the miRNA-protein complex (microRNPs), Fragile-x-mental retardation related protein 1 (FXR1), were associated with AU-rich elements (AREs) at 3′ UTR to activate translation (55). Several miRNAs, including let-7, were found to be associated with AGO2 and FXR1 to activate translation during cell cycle arrest, but they inhibit translation in proliferating cells (55). Upregulation of gene expression by miRNAs was also observed in quiescent cells, such as oocytes (56, 57). The miRNA-mediated activation of translation involves AGO2 and FXR1 instead of GW182 (56). Other examples of gene activation by miRNAs include binding to the 5′ UTR of mRNAs encoding ribosomal proteins during amino acid starvation (58) thus suggesting that miRNA-mediated upregulation of gene expression occurs under specific conditions.
MicroRNA-Mediated Transcriptional and Post-transcriptional Gene Regulation Within the Nucleus
Through Importin-8 or Exportin-1, human AGO2 shuttles between the nucleus and cytoplasm via its interaction with TNRC6A (a GW182 family protein) which contains a nuclear localization and export signal (59). Nuclear localized miRISC was found to regulate both transcriptional rates and post-transcriptional levels of mRNA (59) and associate with euchromatin at gene loci with active transcription (62). However, our understanding of when and how miRNAs exert their functions in the nucleus is still limited.
It has been reported that low molecular weight miRISC can interact with mRNAs within the nucleus and induce nuclear mRNA degradation, although the mechanism behind this is unclear (59, 61, 63). Enrichment of miRNA at actively transcribed genes may suggest that miRISC interacts with target mRNA co-/post-transcriptionally. The involvement of AGO and Drosha in mRNA splicing (64, 65) further supports co-transcriptional miRISC:mRNA interactions. miRISC may also regulate transcription directly. A study showed that AGO2 was concentrated in the nucleus of senesced fibroblasts and interacted with miRISC and retinoblastoma (Rb) to suppress the transcription of proliferation-promoting genes regulated by Rb/E2F. It was noted that let-7f was bound to MREs found in the promoters of two E2F target genes, CDCA8 and CDC2, in an AGO2-dependent manner (60). Also, a subset of AGO-promoter bound genes was upregulated following senescence, and AGO2 was found to co-immunoprecipitate with euchromatin (60). A more recent study by Miao et al. (66) found nuclear miR-522 interacting with a DNA cruciform structure (a stem-loop on sense and antisense DNA strands) within the promoter of CYP2E1 and suppressing its transcription (66). miRNAs have also been shown to interact at genomic loci, where enhancer-derived RNA (eRNA) are transcribed, and increase mRNA levels of adjacent genes by promoting a transcriptionally active chromatin state (67) while altering alternative splicing profiles (64). The overall role of miRISC in the regulation of chromatin state and structure and transcriptional control remain to be determined, but these current data suggest a transcription factor-like role. It is also possible that miRISC may be involved in the establishment of de novo methylation, and by extension, the compactification of chromatin into nuclear compartments, and mediation of genomic remodeling.
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Circular RNAs: formation and function
As “new kids on the block” in the non-coding RNA family, the circular RNAs (circRNAs) are a large group of uniquely alternatively spliced RNA molecules, which are ubiquitous among eukaryotes and are proving functional in a plethora of contexts. Historically much maligned because of their relatively low abundance and high diversity, it may be surprising that five individuals’ presentations focused predominantly or exclusively on discrete aspects of circRNA biogenesis, recognition and regulation. Some presented on circRNA functions hitherto unheard-of for any ncRNA.
Mariangela Morlando (Universita di Roma, Rome, Italy) showed that, while not widespread, certain circRNAs are not strictly non-coding RNAs at all. An example being circ-ZNF609, these circRNAs retain endogenous internal ribosome entry sites from the cognate mRNA, which enables their translation into protein. She also identified a new circRNA biogenesis factor, FUS, whose pathogenic mutations in amyotrophic lateral sclerosis and autism are linked to altered circRNA formation. Gregory Goodall (Centre for Cancer Biology, Adelaide, Australia) reported on the role of Quaking, an RNA binding protein that regulates circRNA biogenesis in epithelial–mesenchymal transition, a model of cancer metastasis. Nikolaus Rajewsky (Max Delbrück Center for Molecular Medicine, Berlin, Germany) described how CRISPR-Cas9-based genome editing could be used to remove the expression of one brain-specific circRNA, CDR1as. This is a ‘sponge’ for human microRNA-7 (hsamiR-7), which yields mice with a normal phenotype but mildly altered neurological responses.
Simon Conn (Centre for Cancer Biology, Adelaide, Australia) reported another seminal finding by demonstrating that circRNAs are stoichiometrically favoured to bind certain targets in the cell. Specifically, interactions between a circRNA from Arabidopsis and its cognate DNA locus via complementary base-pairing to produce an RNA:DNA hybrid, or R-loop, is sufficient to promote alternative splicing of its cognate mRNA, altering floral morphology. The capacity of circRNAs to bind DNA was supported by Ingrid Grummt, who reported that circRNAs, as well as other ncRNAs and linear RNAs, associate with DNA without complementary base-pairing in DNA–RNA triplexes. Yet, the hyperstability of circRNAs means they are potent mediators in any landscape, particularly in dynamic chromatin.
It has long been recognised that single-stranded RNA viruses, recognised as foreign entities, can drive host immune responses. However, the fact that circRNAs are structurally identical led opening speaker Howard Chang (Stanford University, Stanford, USA) to reason that circRNAs might do the same. As shown by ChiRP–MS (comprehensive identification of RNA binding proteins by mass spectrometry), recognition of self versus non-self circRNAs depends on their biogenesis and marking by endogenous protein co-factors. Importantly, the use of circRNAs seems as effective as current metal ion adjuvants in promoting immune responses to vaccination and to block viral transduction.
Taken together, we have learned much about circRNAs since their systematic identification by NGS in 2013. It is increasingly clear that they are not accidental by-products of splicing. The talks summarized above provide insights into the ways in which circRNAs are involved in neurodevelopment and degeneration, plant development, and cancer. Studies of how circRNA is generated and its functions at the organismal level will surely proceed rapidly in the coming years.
Highly specific detection of microRNA
To demonstrate the specificity of Mir-X miRNA quantitation, we used a series of 8 highly similar synthetic Let7 miRNA variants. We first spiked each of the Let7 miRNAs into separate samples of yeast polyA + RNA and generated cDNA using the Mir-X single-tube reaction. We then tested a panel of variant-specific primers with each cDNA sample to determine each primer&rsquos ability to specifically and individually quantify the Let7 subtypes in the cDNA sample. Despite the high degrees of similarity among the variants and the primers, Mir-X qPCR was highly specific in detecting each Let7 variant.
Cell culture and transfections
Both Huh7 and HEK293 cells were obtained from the laboratory of Witold Filipowicz. The cells were free of mycoplasma contamination and were cultured in DMEM medium containing 2 mM L -glutamine and 10% heat-inactivated FCS as described earlier 25,31,33 . All cell culture reagents were from Life Technologies. All plasmid transfections were carried out with Lipofectamine 2000 and siRNAs with RNAiMax (Life Technologies) using the manufacturer’s instructions.
In HEK293 cells, 1 μg target mRNA encoding plasmids were transfected in six-well format with 5 pmol pre-miR-122 (Life Technologies) or 500 ng pre-miR-122 encoding plasmid. Four hundred nanograms of synthetic in vitro-transcribed target mRNAs were transfected in a 24-well format, unless described otherwise. Transcription blockage was done with 10 μg ml −1 α-Amanitin (Calbiochem). For experiments using IRE constructs, 50 μM Hemin (Sigma) or 100 μM DFMO (Calbiochem) was added to cell culture media for desired time. For experiments using Tetracycline-inducible constructs, TET-ON HEK293 cells with the inducble expression constructs were cultured in DMEM supplemented with 10% TET-approved FCS (Clontech) and induction of expression of respective gene product was carried out for indicated time durations using 300 ng ml −1 Doxycycline (SIGMA) 47 .
siDICER1 were transfected at 30 nM concentration in a 24-well format and siCAT-1 at 50 nM in the same format. CAT-1 siRNA (5′- CCAGCCUUAUAGCUGUUCU -3′) was purchased from Eurogentec. Dharmacon SMARTpool ON-TARGETplus siRNAs against DICER1 were purchased from Thermo Scientific. All the plasmids used for the study have been listed in Supplementary Table 1.
Huh7 starvation and feeding
For starvation experiments, Huh7 cells grown in normal DMEM were incubated in Hanks’ balanced salt solution (HBSS) supplemented with 10% dialysed FCS for 4 h followed by ‘re-feeding’ cells with HBSS supplemented with 10% normal FCS for an additional 2 h. HBSS supplemented with 10% normal FCS was used for ‘fed’ control.
Renilla luciferase (RL) and Firefly luciferase (FF) activities were measured using a Dual-Luciferase Assay Kit (Promega), following the manufacturer’s instructions, on a VICTOR X3 Plate Reader with injectors (Perkin Elmer). The ratio of Firefly luciferase normalized Renilla luciferase expression levels for control is to reporter were used to calculate fold repression.
In the experiment using GFP target reporters, HEK293 cells were transfected in a 24-well format with 250 ng pmiR-122, 100 ng firefly luciferase, 500 ng of GFP-con or GFP-3 × bulge-miR-122 along with 10 ng of luciferase reporter RL-con or RL-3 × bulge-miR-122, or RL-3 × bulge-let-7a (additional control). After 24 h cells were split and at 48 h luciferase activity measured. Firefly normalized RL values were plotted. For GFP-3 × bulge-let-7a reporter, exactly the same transfections were performed with an exception of the GFP-3 × bulge-miR-122 reporter.
RNA isolation and northern blotting
Total RNA was isolated using TRIzol reagent (Life Technologies). Small RNA was isolated using mirVANA miRNA isolation kit (Life Technologies) according to the manufacturer’s instructions. Northern blotting of total cellular RNA (5–10 μg) was performed as described by Pillai et al. 48 . In short, equal amount of total RNA was electrophoresed in 15% Urea–PAGE followed by elecrtrophoretic transfer to nylon membrane. For detection, γ 32 P-labelled 22-nt miRCURY complementary LNA probes for miR-122 and let-7a (Exiqon) or complementary DNA probe for miR-16, U6 snRNA were used. PhosphorImaging of the blots was performed in Cyclone Plus Storage Phosphor System (Perkin Elmer). Uncropped versions of all blots are given in Supplementary Fig. 7.
Real-time (reverse transcriptase) PCR for mRNA was done with the Mesa Green qPCR Mastermix Plus for SYBR Assay-Low ROX (Eurogentec) by using the 100–200 ng of the total RNA with the primers listed in Supplementary Table 2. The endogenous control used was 18S ribosomal RNA. For the quantification of miRNA, 30 ng of total RNA was taken. Following primers were used for the amplifications of different miRNAs. For example, human let-7a (assay ID 000377), human miR-122 (assay ID 000445), human miR-122* (assay ID 002130), human miR-16 (assay ID 000391), human miR-21 (assay ID 000397), human miR-24 (assay ID 000402), human miR-125b (assay ID 000449), U6 snRNA (assay ID 001973) and Applied Biosystem Taqman chemistry-based miRNA assay system were used for the experiment. All reactions were done in 7500 Applied Biosystem Real Time System or BIO-RAD CFX96 Real-Time system. Cycles were set as per the manufacturer’s protocol.
Copy number determination
To determine copy number of miR-122, we generated a standard curve using PAGE-purified synthetic miR-122. Five nanograms of total RNA from HEK293 cells (does not contain miR-122) were spiked with 10 6 –10 10 molecules of synthetic miR-122 and analysed by real-time PCR using Taqman miRNA assays (Life Technologies). No Ct was obtained in a control where synthetic miR-122 was not added. The Ct values obtained were plotted to get the standard curve. Ct-values obtained from fed, starved or re-fed samples using 5 and 25 ng of Huh7 RNA were converted to molecule copy number using the standard curve. To calculate copy number of miR-122 per Huh7 cell, we determined total amount of RNA obtained per Huh7 cell under fed, starved and re-fed conditions by again generating a standard curve of total RNA versus number of Huh7 cells. To do that, we counted cells using haemocytometer and isolated RNA by TRIzol reagent and quantified total RNA using NanoDrop. The values obtained were as follows: 34.69 pg (fed 4 h), 21.55 pg (starved 4 h) and 23.19 pg (re-fed 2 h). This value was fairly consistent with Chang et al. 24 who obtained 25 pg of RNA per cell. The copy number obtained by our real-time-based method for non-starved Huh7 (fed) was in that order of 10 4 ( ∼ 1.2 × 10 4 ). This was lower than that obtained by Siegrist et al. 49 , (3 × 10 4 ) using bead array technology.
Similarly, for CAT-1 mRNA standard curve was generated using 100 ng RAW264.7 (mouse cell line) RNA spiked with 10 6 –10 10 molecules of in vitro-transcribed RL-CatA mRNA that contains Renilla-coding region fused to the 3′-UTR (bases 2,169–4,646) of human CAT-1 mRNA. A minus RL-catA control yielded no amplification 3′-UTR-specific primers were used and the same set of primers were used to quantify CAT-1 from 500 ng RNA of Huh7 cells.
Immunoprecipitation of proteins was done essentially as per published protocols 25,50 . Cells were lysed in a lysis buffer [20 mM Tris-HCl pH 7.4, 200 mM KCl, 5 mM MgCl2, 1 mM dithiothreitol (DTT), 1 × EDTA-free protease inhibitor (Roche), 5 mM Vanadyl ribonucleoside comples (Sigma), 0.5% Triton X-100, 0.5% sodium deoxycholate] at 4 °C for 15 min, followed by clearing the lysate at 3,000 g for 10 min. Protein G agarose beads (Invitrogen) were blocked with 5% BSA in lysis buffer for 1 h and then incubated with required primary antibody for another 3–4 h before the lyaste was added. A final dilution of 1:50 (antibody:lysate) was used for immunoprecipitation. Immunoprecipitation was carried out for 16 h at 4 °C. Post washing with IP buffer (20 mM Tris-HCl pH 7.4, 150 mM KCl, 5 mM MgCl2, 1 mM DTT, 1 × EDTA-free protease inhibitor (Roche)), the beads were divided in two halves: one subjected to RNA isolation with TRIzol LS and another for western blotting.
Western blotting of proteins was essentially performed as described previously 48 . For immunoblotting, the cell lysates or immunoprecipitated proteins were subjected to SDS–PAGE, transferred to nitrocellulose membrane and probed with specific antibodies. Antibodies used were as follows: anti-AGO2 (Novus Biologicals Cat# H00027161-M01 1:1,000), anti-DICER (Bethyl Cat# A301-936A 1:5,000), anti-HA (Roche Cat# 11867431001 1:1,000), β-Actin (Sigma Cat# A3854 1:10,000) and phospho-eIF2α (Cell Signaling Technology Cat# 9721 1:500). Imaging of all western blottings was done in UVP BioImager 600 system equipped with VisionWorks Life Science software (UVP) V6.80 and quantification of bands done using the same software. Uncropped versions of all blottings along with molecular-weight marker positions are included in Supplementary Fig. 7.
Total polysome isolation was carried out as described 51 . For total polysome isolation, around 6 × 10 6 HEK293 cells were lysed in a buffer containing 10 mM HEPES pH 8.0, 25 mM KCl, 5 mM MgCl2, 1 mM DTT, 5 mM vanadyl ribonucleoside complex, 1% Triton X-100, 1% sodium deoxycholate and 1 × EDTA-free protease inhibitor cocktail (Roche) supplemented with Cycloheximide (100 μg ml −1 Calbiochem). The lysate was cleared at 3,000 g for 10 min followed by another round of pre-clearing at 20,000 g for 10 min at 4 °C. The clarified lysate was loaded on a 30% sucrose cushion and ultracentrifuged at 100,000 g for 1 h at 4 °C. The non-polysomal supernatant was collected from the top. The sucrose cushion was washed with a buffer (10 mM HEPES pH 8.0, 25 mM KCl, 5 mM MgCl2, 1 mM DTT), ultracentrifuged for additional 30 min and the polysomal pellet was finally resuspended in polysome buffer (10 mM HEPES pH 8.0, 25 mM KCl, 5 mM MgCl2, 1 mM DTT, 5 mM vanadyl ribonucleoside complex, 1 × EDTA-free protease inhibitor cocktail) for RNA isolation.
RISC cleavage assay
RISC cleavage assay was essentially performed as described elsewhere with minor modifications 50,51 . HEK293 cells stably expressing FH-AGO2 were transfected with pmiR-122 and target mRNA expression plasmids. For miRISC isolation, cells were lysed, 48 h after transfection, in lysis buffer [10 mM HEPES pH 7.4, 200 mM KCl, 5 mM MgCl2, 1 mM DTT, 1 × EDTA-free protease inhibitor (Roche), 5 mM vanadyl ribonucleoside comples (Sigma), 1% Triton X-100] at 4 °C for 15 min, followed by clearing the lysate at 3,000 g for 10 min. The clarified supernatant was subjected to immunoprecipitation with anti-FLAG M2 affinity gel (Sigma) for 16 h at 4 °C. Beads were washed in IP buffer [20 mM Tris-HCl pH 7.4, 150 mM KCl, 5 mM MgCl2, 1 mM DTT, 1 × EDTA-free protease inhibitor (Roche)] for three times at 4 °C. The affinity-purified miRISC was eluted from the beads using 3 × FLAG Peptide (Sigma) as per the manufacturer’s instructions in a RISC purification buffer (30 mM HEPES pH 7.4, 100 mM KCl, 5 mM MgCl2, 0.5 mM DTT, 3% glycerol).
Affinity-purified miRISC-122 were assayed for target RNA cleavage using a 36 nt RNA 5′- AAAUUCAAACACCAUUGUCACACUCCACCAGAUUAA -3′ bearing the sequence complementary to mature miR-122. Target RNA cleavage assays were carried out in a total volume of 30 μl with 10 fmol of 5’ 32 P-labelled RNA in an assay buffer (100 mM KCl, 5.75 mM MgCl2, 2.5 mM ATP, 0.5 mM GTP) and protein equivalent amounts of RISC at 30 °C for 30 min. RNA isolation was carried out from the reaction mixture, cleaved products were analysed on a 12% denaturing 8 M Urea–PAGE and visualized by autoradiography.
In vitro RISC loading assay
FH-AGO2 affinity purified on agarose beads from FH-AGO2-stable HEK293 cells was incubated with 10 nM synthetic 5′-phosphorylated pre-miR-122 or pre-let-7a, and 500 ng in vitro-transcribed target mRNA in a 20-μl reaction in assay buffer [20 mM Tris-HCl pH 7.5, 200 mM KCl, 2 mM MgCl2, 5% glycerol, 1 mM DTT, 40 U RNase inhibitor (Fermentas)] for 1 h at 37 °C followed by washing of the beads three times with IP buffer. Each reaction was then divided into two halves: one for RNA isolation and the other for western blotting of AGO2.
For assays with recombinant proteins, 50 ng of rAGO2 (Sino Biologicals) was used with 0.1 U of rDICER1 (Recombinant Human Dicer Enzyme kit, Genlantis) keeping the rest of the reaction parameters same as above.
To measure the association of AGO2 and DICER1 along the 3′-UTR, FH-AGO2 stably expressing HEK293 cells was transfected with NHA-DICER1 transiently and FH-AGO2 immunoprecipitated with anti-FLAG agarose beads as described before. FH-AGO2 was eluted from anti-FLAG beads with 3 × FLAG Peptide (Sigma) and isolated miRISC was incubated with RL-3 × bulge-miR-122 at 37 °C with 10 nM pre-miR-122 for 60 min. The reaction was then divided in two halves and immunoprecipitated with anti-AGO2 and anti-DICER1 antibodies for 3 h at 4 °C. Beads were washed and RNA isolated using TRIzolLS followed by quantitative reverse transcriptase–PCR using a single forward and two sets of reverse primers.
For scoring DICER1 processivity, HEK293 cells transiently expressing FH-AGO2 and pre-let-7a were lysed and FH-AGO2 immunoprecipitated as described earlier. FH-AGO2 was eluted from anti-FLAG beads with 3 × FLAG Peptide (Sigma) and isolated miRISC was incubated with RL-con or RL-3 × bulge-let-7a at 37 °C with 10 nM pre-miR-122 for 30 min. RNA was isolated from the reaction and mature miR-122 formed quantified by real-time PCR.
In vitro transcription
In vitro transcription was carried out using mMESSAGE mMACHINE kit (Life Technologies) and poly A tailing of transcripts done with Poly (A) tailing kit (Life Technologies) as per the manufacturer’s protocol. For preparation of plasmid DNA template, RL-con, RL-1 × bulge-miR-122, RL-3 × bulge-miR-122, RL-3 × bulge-miR-122 (W5′), RL-3 × bulge-miR-122 (W3′) and RL-CatA were digested for 4 h at 37 °C with DraI (New England Biolabs) and RL-3 × bulge-let-7a was digested with HpaI (New England Biolabs) the rest of the protocol was as per the manufacturer’s instructions. All transcripts obtained were analysed and size verified by 6% Urea–PAGE and ethidium bromide-based detection.
RNase protection assay
RNase Protection Assay was performed as described earlier 52 . The synthetic miR-122 RNA probe was 5′-end labelled with γ 32 P ATP using T4 Polynucleotide kinase (Ferementas) according to the manufacturer’s protocol. Labelled miR-122 (1.5 pmol 100 fmol radiolabelled and 1.4 pmol 5′-end labelled with cold ATP) was mixed with 500 fmol of target mRNA and precipitated with 0.1 vol sodium acetate (pH 5.2) and 2.5 vol ice-cold ethanol for 1 h at −80 °C. The RNA recovered was dissolved in 30 μl Hybridization Buffer (40 mM PIPES pH 6.8, 1 mM EDTA pH 8.0, 0.4 M NaCl, 80% deionized formamide), denatured at 85 °C for 5 min and then annealed at 35 °C overnight. The mixture was next cooled to room temperature and then RNase digestion was carried out in 300 μl of RNase digestion mixture (300 mM NaCl, 10 mM Tris pH 7.4, 5 mM EDTA pH 7.5, 30 μg RNase A (Fermentas)) at 30 °C 1 h. ProteinaseK treatment was done (10% SDS and 20 mg ml −1 Proteinase K (Roche) at 37 °C for 30 min) followed by RNA extraction with acid phenol:chloroform pH 4.5 in the presence of 20 μg carrier transfer RNA (Sigma). The samples were run in an 18% Urea–PAGE followed by drying the gel and image was obtained using phosphorimager. For partial digestion, the reaction was incubated on ice instead of 30 °C.
Nuclear run-on transcription
Nuclear run-on transcription was performed as described by Roberts et al. 53 . For isolation of intact nucleus from Huh7 cells, ∼ 4 × 10 6 cells were lysed in NP-40 lysis buffer [10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2 and 0.5% (vol/vol) NP-40]. Fractionation verified by western blotting for nuclear and cytosolic markers. The nuclei were resuspended in 30 μl Nuclei Storage Buffer [50 mM Tris-HCl pH 8.3, 0.1 mM EDTA, 5 mM MgCl2, 40% (vol/vol) glycerol] and subjected to run-on transcription in transcription buffer (10 mM Tris-HCl pH 8.3, 2.5 mM MgCl2, 150 mM KCl, 2 mM DTT, 1 mM ATP, 1 mM CTP, 1 mM GTP, 1 mM GTP, 100 U RNase inhibitor) at 30 °C for 30 min. Immediately after the reaction, RNA was isolated using TRIzolLS and subjected to DNaseI (New England Biolabs) treatment at 37 °C for 15 min. DNaseI was inactivated by heating at 75 °C for 5 min. Six hundred nanograms of RNA was used for cDNA preparation in a 10-μl reaction. Minus reverse transcriptase control was done to verify the absence of any genomic DNA contamination.
SLA was prepared using published protocol 54 . Briefly, for SLA preparation, around 10 10 Leishmania donovani cells were washed thrice with 1 × PBS followed by five to six cycles of liquid nitrogen freeze thawing. The cells were then sonicated in PBS with 0.04% NP-40 for six times. Lysate was cleared at 12,000 g for 10 min at 4 °C and the supernatant collected was SLA. Protein estimation of SLA was done using Bradford method at 595 nm.
The in vitro DICER1 cleavage assay was carried out as mentioned elsewhere 32 . Lysate of HEK293 cells stably expressing FH-AGO2 were prepared as for immunoprecipitation. For each cleavage reaction, 30 μg cell lysate was incubated with 20 μg SLA in assay buffer (10 mM Tris-HCl pH 7.5, 1 mM DTT, 100 mM KCl, 1 × EDTA-free protease inhibitor (Roche)) and 1 mg ml −1 BSA for 30 min at 37 °C. This was followed by immunoprecipitation of the mixture with anti-FLAG M2 agarose beads. Post immunoprecipitation, the beads were subjected to in vitro DICER activity assay.
All graphs and statistical analyses were done in GraphPad Prism 5.00 (GraphPad, San Diego, CA, USA). Nonparametric paired t-test were used for analysis and P-values were determined. Error bars indicate mean±s.d.
The authors declare that all data supporting the findings of this study are available within the article and its Supplementary Information files or on request.
The miRvestigator web server provides biologists with a powerful suite of algorithms that together identify miRNAs that bind and regulate genes discovered to be co-expressed in transcriptome profiling studies. The website takes as input a list of gene identifiers (Entrez gene, Ensembl gene, RefSeq transcript or official gene symbol) for one of six different species ( C. elegans , D. melanogaster , G. gallus , H. sapiens , M. musculus or R. norvegicus ). For a given set of genes, miRvestigator: (i) extracts their relevant 3′-UTR sequences (ii) scans these 3′-UTR sequences for an overrepresented motif using the Weeder algorithm (iii) identifies putative binding sites for the overrepresented motif and (iv) applies a HMM and the Viterbi algorithm to identify the miRNA(s) that is most likely to bind to the overrepresented motif. The user friendly web interface makes it easy for biologists to tune parameters and submit jobs to miRvestigator. The output from miRvestigator is a ranked list of miRNAs presented in tabular format with links to corresponding records in miRBase, statistical assessment of complementarity quality, and an alignment of the motif to the miRNA seed sequences. The output also includes a second table with the putative binding locations of the miRNA within the 3′-UTRs of query genes. The miRvestigator provides a valuable tool for users to identify miRNAs regulating biological processes of interest and the information required to design experiments to test these predictions.