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10.4: RNA Structure - Biology

10.4: RNA Structure - Biology


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We have learned about different functions of RNA, and it should be clear by now how fundamental the role of RNA in living systems is. Because it is impossible to understand how RNA actually does all these activities in the cell, without knowing what its structure is, in this part we will look into the structure of RNA.

RNA structure can be studied in three different levels:

  1. Primary structure: the sequence in which the bases (U, A, C, G) are aligned.
  2. Secondary structure: the 2-D analysis of the [hydrogen] bonds between different parts of RNA. In other words, where RNA becomes double-stranded, where RNA forms a hairpin or a loop or other similar forms.
  3. Tertiary structure: the complete 3-D structure of RNA, i.e. how the string bends, where it twists and such.

As mentioned before, the presence of ribose in RNA enables it to fold and create double-helixes with itself. The primary structure is fairly easy to obtain through sequencing the RNA. We are mainly interested in understanding the secondary structure for RNA: where the loops and hydrogen bonds form and create the functional attributes of RNA. Ideally, we would like to study the tertiary structure because this is the final state of the RNA, and what gives it its true functionality. However, the tertiary structure is very hard to compute and beyond the scope of this lecture.

Even though studying the secondary structure can be tricky, there are some simple ideas that work quite well in predicting it. Unlike proteins, in RNA, most of the stabilizing]free energy for the molecule comes from its secondary structure (rather than tertiary in case of proteins). RNAs initially fold into their secondary structure and then form their tertiary structure, and therefore there are very interesting facts that we can learn about a certain RNA molecule by just knowing its secondary structure.

Finally, another great property of the secondary structure is that it is usually well conserved in evolution, which helps us improve the secondary structure predictions and also to find ncRNA (non-coding RNA)s. There are widely used representations for the secondary structure of RNA:

Formally: A secondary structure is a vertex labeled graph on n vertices with an adjacency matrix A = (aij) fulfilling:

• ai,i+1 = 1for1 ≤ i ≤ n1 (continuous backbone)
• For each i, 1≤i≤N there is at most one aij =1 where (j gneqq i+/-1)(a base only forms a pair with one other at the time)
• If aij =akl =1andi

RNAstructure, Version 6.3 :

RNAstructure is a complete package for RNA and DNA secondary structure prediction and analysis. It includes algorithms for secondary structure prediction, including facility to predict base pairing probabilities. It also can be used to predict bimolecular structures and can predict the equilibrium binding affinity of an oligonucleotide to a structured RNA target. This is useful for siRNA design. It can also predict secondary structures common to two, unaligned sequences, which is much more accurate than single sequence secondary structure prediction. Finally, RNAstructure can take a number of different types of experiment mapping data to constrain or restrain structure prediction. These include chemical mapping, enzymatic mapping, NMR, and SHAPE data.

RNAstructure is available as a graphical user interface for Windows a JAVA graphical user interface for Mac OS-X or Linux command line interfaces for Max OS-X, Linux, or Windows and source code for local compilation. The source code includes a set of C++ classes for convenient inclusion of the methods into new programs.


Structure, Reactivity, and Biology of Double-Stranded RNA

This chapter focuses on the structure and physicochemical properties of double stranded RNA (dsRNA) on the enzymes that degrade, modify, or otherwise modulate dsRNA structure and function and on protein motifs that specifically recognize dsRNA. It discusses the metabolism and regulatory functions of dsRNA in the prokaryotic cell, as are the functions of dsRNA and dsRNA-specific enzymes in eukaryotic cells. It also covers the mammalian cellular and physiological response to dsRNA and the prospects of dsRNA as a therapeutic agent. The RNA double helix is a ubiquitous structural motif in living organisms. Double stranded RNA is created by a number of biosynthetic pathways and is subsequently degraded, denatured, or specifically modified by enzymatic activities. It also serves as a stable repository of genetic information for many viruses. The diverse functional roles of dsRNA have spurred intensive studies on the biochemical processes that involve dsRNA. The first structural information on dsRNA came from X-ray diffraction analyses of synthetic or naturally occurring dsRNA fibers. Analyses of the structures of prokaryotic antisense RNAs and the precise interactions with their targets provide insight into the dynamics of dsRNA formation and the way gene expression is regulated by RNA–RNA interactions.


Results

The current most commonly used sgRNA design has the duplex shortened by 10 bp compared with the native crRNA–tracrRNA duplex (Fig. 1a), which does not seem to reduce its functionality in vitro [6]. Hsu et al. [9] also showed that extending the duplex appeared to have no effect on knockout efficiency in cells. However, Chen et al. [10] showed that extending the duplex significantly enhances imaging efficiency of the dCas9–GFP fusion protein in cells. We suspected that extending the duplex might increase knockout efficiency in cells. To test this hypothesis, we extended the duplex in two sgRNAs targeting the CCR5 gene, as shown in Fig. 1a, and determined the knockout efficiency of these mutants in TZM-bl cells. Extending the duplex by 1, 3, 5, 8, or 10 bp significantly increased the knockout efficiency in both sgRNAs tested, and extending the duplex by 5 bp appeared to yield the highest efficiency at the protein level (Fig. 1b Figure S2 in Additional file 1). The modification rate at the DNA level was also confirmed by deep sequencing of target sites (Additional file 2), and the results correlated well with the results determined at the protein level (Fig. 1b Figure S2 in Additional file 1). Since measuring the modification rate by deep sequencing is more expensive and labor intensive, we mainly relied on fluorescence-activated cell sorting (FACS) to determine the CCR5 disruption rate in this study. When the effect of extending the duplex was tested for another sgRNA (sp2), the results were consistent with those for sp1 (Fig. 1c Figure S2 in Additional file 1). Thus, extending the duplex appears to increase the knockout efficiency of the CRISPR-Cas9 system.

Knockout efficiency can be increased by extending the duplex and disrupting the continuous sequence of Ts. a The duplex extension. Green indicates the 3’ 34 nucleotides, which are not required for sgRNA functionality in vitro but are required in cells red indicates the extended base pairs. b Extension of the duplex increased knockout efficiency. Constructs harboring sgRNAs targeting the CCR5 gene were co-transfected with a Cas9-expressing plasmid into TZM-bl cells. An sgRNA targeting the HIV genome served as mock control. The GFP-positive cells were sorted out 48 hours after transfection, and the gene modification rates were determined at the protein and DNA levels, respectively. Protein level disruption: the expression of CCR5 was determined by flow cytometry analysis. The raw data are shown in Figure S2 in Additional file 1. DNA level modification rate: the genomic DNA was extracted, and the target sites were amplified and deep-sequenced with a MiSeq sequencer. The raw data are provided in Additional file 2. c The experiment in (b) at the protein level was repeated for another sgRNA, sp2. The difference with (b) is that the cells were not sorted, but the CCR5 disruption rate was measured in GFP-positive cells. The raw data are shown in Figure S2 in Additional file 1. d Mutation of the RNA polymerase (Pol III) pause signal significantly increased knockout efficiency. The mutated nucleotides are shown in bold. The raw data are shown in Figure S3 in Additional file 1. The graphs represent biological repeats from one of three independent experiments with similar results, shown as mean ± standard deviation (n = 3). Significance was calculated using Student's t-test: *P < 0.05 **P < 0.01 ***P < 0.005 ****P < 0.001. O original, M mutant

Because the continuous sequence of Ts after the guide sequence is the pause signal for RNA polymerase III [11], the effect of its disruption in sgRNAs has been previously studied [9, 10]. We suspected that mutating the continuous sequence of Ts might also improve knockout efficiency in cells. Accordingly, we mutated this sequence at different positions and determined the knockout efficiency of the mutants (Fig. 1d Figure S3 in Additional file 1). The knockout efficiency was increased in all mutants, and the mutation at position 4 had the greatest effect.

Next, we systematically investigated the effect of extending the duplex while mutating the fourth T in the sequence of Ts (Fig. 2a Figure S4 in Additional file 1). Consistent with the result shown in Fig. 1b, mutating the fourth T increased the knockout efficiency significantly for all four sgRNAs tested (Fig. 2a). On top of the increase due to mutation, extending the duplex also increased the knockout efficiency, reaching a peak at around 5 bp but then declining with longer extensions, although the pattern appears to be slightly different for different sgRNAs (Fig. 2a), which is consistent with Chen et al.’s results showing that modifying both elements significantly enhances the imaging efficiency of a dCas9–GFP fusion protein in cells [10].

Knockout efficiency can be further increased by combining duplex extension with disruption of the continuous sequence of Ts. a The effect of duplex extension when mutating the fourth T to an A in four sgRNAs. The raw data are shown in Figure S4 in Additional file 1. b The effect of mutation of Ts at the indicated positions to A, C, or G when also extending the duplex by 5 bp. The raw data are shown in Figure S5 in Additional file 1. The graphs represent biological repeats from one of three independent experiments with similar results, shown as mean ± standard deviation (n = 3). Significance was calculated using Student's t-test: *P < 0.05 **P < 0.01 ***P < 0.005 ****P < 0.001. M mutant

We previously tested the effect of mutating T→A on knockout efficiency without extending the duplex (Fig. 1c). Next, we also wanted to test the effect of mutating T→A, C, or G while also extending the duplex. Consistent with previous observations, mutations at position 4 generally had the highest knockout efficiency, although mutating T→C at position 1 had a similar effectiveness. In addition, mutating T→C or G generally had higher knockout efficiency than mutating T→A at various positions (Fig. 2b Figure S5 in Additional file 1). Thus, mutating T→C or G at position 4 yielded the highest knockout efficiency.

Based on these results, mutating T→G or C at position 4 and extending the duplex by

5 bp appears to achieve the optimal sgRNA structure, with the highest knockout efficiency. Therefore, we compared the knockout efficiency of the original and optimized structures for 16 sgRNAs targeting CCR5. A typical optimized structure had a T→G mutation at position 4 and extended the duplex by 5 bp. In 15 out of 16 sgRNAs, the optimized structure increased the knockout efficiency significantly and for sp10, 14, 15, 17, and 18 did so dramatically (Fig. 3a Figure S6 in Additional file 1).

The optimized sgRNA structure is superior to the original version. a CCR5 knockout efficiency was determined for the indicated sgRNAs targeting CCR5 with either an optimized sgRNA structure or the original structure. The knockout efficiency was determined in the same way as in Fig. 1b. The raw data are sown in Figure S6 in Additional file 1. b CD4 knockout efficiency was determined for the indicated sgRNAs targeting the CD4 gene, with two versions of the sgRNA structure in Jurkat cells. Cells were analyzed for CD4 expression by flow cytometry 72 hours after transfection. The raw data are shown in Figure S7 in Additional file 1. c T→C and T→G mutations are superior to the T→A mutation. Eleven sgRNAs targeting CCR5 were randomly selected. The knockout efficiency of sgRNAs with different mutations at position 4 in the sequence of continuous Ts were determined as in Fig. 1c. The raw data are shown in Figure S9 in Additional file 1. The graphs represent biological repeats from one of three independent experiments with similar results, shown as mean ± standard deviation (n = 3). Significance was calculated using Student's t-test: *P < 0.05 **P < 0.01 ***P < 0.005 ****P < 0.001

To exclude the possibility that the increase in knockout efficiency using the optimized sgRNA structure is limited to TZM-bl cells or the CCR5 gene, we also tested eight sgRNAs targeting the CD4 gene in Jurkat cells. Consistent with the results observed in TZM-bl cells for the CCR5 gene, the optimized sgRNA design also significantly increased the efficiency of knocking out the CD4 gene in the Jurkat cell line (Fig. 3b Figure S7 in Additional file 1). Thus, the optimized sgRNA structure appears to generally increase knockout efficiency.

The beneficial effect of extending the duplex generally reached a peak at around 5 bp of added length (Fig. 2a). To test whether extending the duplex by 5 bp is superior to extending it by 4 bp or 6 bp, we extended the duplex by 4 bp or 6 bp and compared the resulting knockout efficiencies for the 16 sgRNAs in Fig. 3a. As shown in Figure S8 in Additional file 1, extending the duplex by 4 bp or 6 bp appeared to yield similar knockout efficiency as 5 bp in most cases.

Previously, Chen et al. [10] showed that mutating T→A at position 4 in combination with extending the duplex by 5 bp significantly enhanced the imaging efficiency of the dCas9–GFP fusion protein in cells. Our results showed that extending the duplex by 4–6 bp and mutating T→C or G at position 4 significantly increased knockout efficiency. To compare the effect of two sgRNA designs on increasing the knockout efficiency, we randomly selected ten sgRNAs targeting CCR5 and compared their knockout efficiencies with different mutations. As shown in Fig. 3c, all of the T→C and most (nine out of ten) of the T→G mutations had significantly higher knockout efficiency than the T→A mutation. It is noteworthy that, although in most cases the T→C mutation had a similar level of knockout efficiency as the T→G mutation, it had a significantly higher knockout efficiency in sp11 (+11 %, P = 0.006) and sp19 sgRNAs (+6 %, P = 0.026) (Fig. 3c Figure S9 in Additional file 1), suggesting that the T→C mutation might be the best choice.

Creation of a frame-shift mutation with an sgRNA is generally insufficient to investigate the loss of function of noncoding genes, such as long noncoding RNAs (lncRNAs) or microRNA genes. A better strategy is to excise all or part of the gene of interest, which requires cutting at two positions simultaneously and linking the two breakpoints together. The efficiency of generating this type of deletion mutation is very low with current sgRNA design templates however, the deletion efficiency was improved dramatically (around tenfold) in all four pairs of sgRNAs tested here (Fig. 4). If the original sgRNA structure, in which the deletion efficiency ranged from 1.6–6.3 % (Fig. 2c), was used to delete target genes, one would have to screen hundreds of colonies to identify the colonies with the deletion, which is a daunting task. Using the optimized sgRNAs, in which the deletion efficiency ranged from 17.7–55.9 % (Fig. 4), the number of colonies that would need to be screened to identify those with the deletion would be within the limits of feasibility. Thus, the optimized sgRNA template would simplify the genome-editing procedure, thereby enhancing its potential utility.

The efficiency of gene deletion is increased dramatically using optimized sgRNAs. a The CCR5 gene deletion. b sgRNA pairs targeting CCR5 with the original or optimized structures were co-transfected into TZM-bl cells with a Cas9-expressing plasmid. The gene deletion efficiency was determined by amplifying the CCR5 gene fragment. Note that the truncated fragments of CCR5, with a smaller size than wild-type CCR5, are a consequence of gene deletion using paired sgRNAs. The numbers below each lane indicate the percentage deletion

Mutating the contiguous Ts is likely to increase the production of sgRNAs. Thus, to understand how modifications increase the knockout efficiency, we measured the RNA level of different sgRNA structures. First, we checked the CCR5 knockout efficiency of the sgRNA with the extended duplex or a mutated continuous sequence of Ts or with both. Consistent with our previous study, both modifications individually increased knockout efficiency, and in combination further increased knockout efficiency (Fig. 5a Figure S10 in Additional file 1). Next, we measured the sgRNA levels in transfected cells. Mutating the continuous sequence of Ts significantly increased the sgRNA level, and it appears that extending the duplex also slightly increased the sgRNA level (Fig. 5b). To ascertain if increased sgRNA production or the sgRNA structure or both is responsible for increased knockout efficacy, we transfected activated CD4+ T cells with Cas9 protein preloaded with in vitro transcribed sgRNAs, which excludes the effect of RNA level change because in this case the amount of sgRNA remains the same. In initial experiments, the results using the in vitro transcribed sgRNAs were highly variable, because these molecules form dimers to variable extents which interfered with their functionality (Fig. 5c). Cas9 can only bind to the monomers but not the dimers, in which the sgRNA structure is not maintained. The ratio of monomers to dimers was not fixed between samples, which led to highly variable results. However, this problem was solved by a heating and quick cooling step (Fig. 5c), as we have previously shown for other small RNAs with duplex structures [12]. With pure monomer sgRNAs, it appeared that Cas9 preloaded with sgRNAs with an extended duplex has higher knockout efficiency (Fig. 5d Figure S11 in Additional file 1), suggesting that the structural change of extending the duplex can by itself increase Cas9 functionality. Next, we transfected in vitro transcribed sgRNAs into cells stably expressing Cas9 and showed that extending the duplex by itself increases knockout efficiency (Fig. 5e Figure S11 in Additional file 1), most likely because of the structural change and not because of changes in RNA levels.

How modifications increase knockout efficiency. a Knockout efficiency of sp3 from Fig. 2a with the indicated modifications was determined as in Fig. 1b. The raw data are shown in Figure S10 in Additional file 1. Mut mutant, O original. b sgRNA levels were determined by real-time PCR. The relative expression level was normalized to U6 small RNA. c In vitro transcribed sgRNA formed dimers (upper panel), which can be transformed into monomers by a heating and quick cooling step (lower panel). d sp7 from Fig. 3b was transcribed in vitro and preloaded into Cas9. The complex was electroporated into activated primary CD4+ T cells. Knockout efficiency was determined as in Fig. 3b. The raw data are shown in Figure S11 in Additional file 1. e In vitro transcribed sp7 was electroporated into TZM-Cas9 cells. Knockout efficiency was determined as in Fig. 3b. The raw data are shown in Figure S11 in Additional file 1. The graphs represent biological repeats from one of three independent experiments with similar results, shown as mean ± standard deviation (n = 3). Significance was calculated using Student's t-test: *P < 0.05 **P < 0.01

We performed all our experiments with transient plasmid transfection, in which the copy number of the Cas9 and the sgRNA can vary considerably. Low multiplicity of infection (MOI) of lentivirus vector harboring the Cas9 or the sgRNA should provide relatively consistent copy numbers of Cas9 and sgRNA in infected cells. Therefore, to determine sgRNA functionality more rigorously, we first created cell lines stably expressing Cas9 by infecting TZM-bl or JLTRG-R5 cells with lentivirus harboring a Cas9-expressing cassette and selecting the cells stably expressing Cas9. We then infected these cells with lentivirus harboring sgRNAs with different structures at low MOI. The results were similar to the experiments done with plasmids in both cell lines. In fact, the difference between structures shown for lentiviral infection was even greater than what we observed with plasmids (Fig. 6 Figure S12 in Additional file 1), suggesting that the optimized sgRNAs are indeed superior to commonly used sgRNA (+85 nucleotides). These results also suggest that the optimized sgRNAs would perform better for CRISPR-Cas9-based genome-wide pooled screenings, which use lentivirus to deliver sgRNAs at low MOI [13–20].

Testing the effect of modifications by lentiviral infection. TZM-bl cells (a) or JLTRG-R5 cells (b) were infected with Cas9-expressing lentivirus, and cells stably expressing Cas9 were selected. The indicated sgRNA (sp3 from Fig. 2a)-expressing cassettes were packaged into lentivirus and used to infect cells stably expressing Cas9 at MOI = 0.5. Knockout efficiency was determined as in Fig. 1b on the indicated days. The raw data are shown in Figure S12 in Additional file 1. O original, Mut mutant


Conclusions

I have argued that the RNA world hypothesis, while certainly imperfect, is the best model we currently have for the early evolution of life. While the hypothesis does not exclude a number of possibilities for what – if anything – preceded RNA, unfortunately the evolution of coded protein synthesis has drawn a veil over the previous history of proteins. The situation is different in the case of non-coding RNAs such as ribosomal RNA and tRNA, as these were able to replicate prior to the evolution of ribosomal protein synthesis.

As we have noted previously [5], the proposal that the RNA world evolved in acidic conditions [5, 6] offers a plausible solution to Charles Kurland's criticism [57] that the RNA world hypothesis makes no reference to a possible energy source. As de Duve [87] has noted, "the widespread use of proton-motive force for energy transduction throughout the living world today is explained as a legacy of a highly acidic prebiotic environment and may be viewed as a clue to the existence of such an environment" [87]. Although Russell, Martin and others [23–26] have argued that proton and thermal gradients between the outflow from hot alkaline (pH 9-11) under-sea hydrothermal vents and the surrounding cooler more acidic ocean may have constituted the first sources of energy at the origin of life, the lack of RNA stability at alkaline pH ([5] and references within) would appear to make such vents an unlikely location for RNA world evolution.

Although possible, it seems unlikely that the A-C base pair 'mismatches' found in the tRNA genes of Ferroplasma acidarmanus and Picrophilus torridus (two species of archaebacteria with a reportedly acidic internal pH) [5] are corrected by C to U RNA editing that occurs, for example, with some - but not other - plant chloroplast tRNAs [88, 89]. Such editing of secondary structure A-C base pair mismatches has so far not been found to occur in archaebacteria however, in a single archaeal species (Methanopyrus kandleri) a tertiary structure A-C base pair found in 30 of its 34 tRNAs undergoes C to U editing catalyzed by a cytidine deaminase CDAT8 [90]. M. kandleri is a unique organism that contains many 'orphan' proteins. CDAT8, which contains a cytidine deaminase domain and putative RNA-binding domain, has no homologues in other arachaeal species, including F. acidarmanus and P. torridus (L Randau, pers. commun. [90]). Definitive proof, however, that the A-C base pairs in these two species are not modified would of course require e.g. cDNA sequencing of the tRNAs.


DISCUSSION

The regulation of VEGF mRNA stability is of particular interest, not only because VEGF is a member of the class of hypoxia-stabilized mRNAs, but also because of its importance as a potential target for antiangiogenic therapy (Kim et al., 1993 Millauer et al., 1994). The design of agents to inhibit or promote the activity of VEGF requires elucidation of the specific mechanisms controlling its expression. For this reason there is considerable interest in identifying the determinants in the VEGF mRNA involved in its lability under normoxic conditions and its stabilization in response to hypoxia.

In this study we investigated the role of various regions in the VEGF mRNA in regulating its lability in vivo. The use of a reporter mRNA driven from the fos promoter allowed us to determine the effect of different regions of the VEGF mRNA on the stability of a reporter gene, rather than using indirect means such as changes in the steady-state levels of reporter enzymes or the use of nonspecific inhibitors of transcription. In all of our constructs we maintained the positional and translational context of the inserted region in the reporter mRNA. We found that the VEGF mRNA contains multiple, independently functional destabilizing elements and that the rapid degradation of the VEGF mRNA under normoxia is due to the combined action of these elements. The normoxic half-life of the entire reconstructed mRNA was not significantly different from those calculated for each pairwise combination of the three regions, suggesting that any two of the three regions suffices to recapitulate the lability of the mRNA however, this finding could reflect the fact that the fos promoter, which produces a transcriptional pulse of ∼30 min duration (Rivera et al., 1990), cannot resolve half-lives significantly <1 h.

A number of other mRNAs have been shown to contain destabilizing elements in both the 3′UTR and coding region, including thefos and myc mRNAs (Kabnick and Housman, 1988Shyu et al., 1989 Wisdom and Lee, 1991 Herrick and Ross, 1994) however, the ability of the VEGF 5′UTR to destabilize the reporter mRNA in our studies suggests that a destabilizing element is also present in the 5′UTR. The presence of a destabilizing element in the 5′UTR is highly unusual, although we note that the first 40 bases of the fos mRNA are able to weakly destabilize a globin reporter mRNA (Kabnick and Housman, 1988). We found that the VEGF 5′UTR, coding region, and 3′UTR were also potently destabilizing in NIH3T3 cells, indicating that the destabilizing activity of each region was not restricted to the BALB/c3T3 cells used in our study. Although the mechanism of destabilization promoted by each of the destabilizing elements in the VEGF mRNA remains to be determined, the existence of multiple destabilizing elements in the VEGF mRNA indicates that multiple degradation pathways have evolved to ensure rapid turnover of the mRNA under normoxic conditions however, it has also become apparent that a number of tumor-derived cell lines constitutively express elevated levels of VEGF caused by persistent stabilization of the mRNA (White et al., 1995 Iliopoulos et al., 1996 Levy et al., 1996a,b). The existence of multiple instability elements in the VEGF mRNA and the ability of any two of the three regions to recapitulate the lability of the mRNA suggests that the persistent stabilization of the VEGF mRNA in these tumor-derived cells may be due to the constitutive activation of a stabilizing pathway rather than the loss of function of two distinct degradation pathways.

In contrast to the additive effects of individual regions in destabilizing the mRNA, we found that hypoxic stabilization of the VEGF mRNA occurs only if the 5′UTR, coding region, and 3′UTR are all present in the mRNA. Based on the results of a previous study in which hypoxic stabilization of the VEGF 3′UTR was observed in a cell-free extract system (Levy et al., 1996a,b), our expectation was that the 3′UTR would be sufficient to confer correct regulation on a reporter mRNA in vivo however, our observation in intact cells demonstrates that the regulation of VEGF mRNA stability is more complex than previous studies have suggested and involves considerable complexity in the molecular interactions. This could be due to the requirement for three distinct RNA binding proteins each interacting with a discrete element, or it could reflect the need to form a particular RNA structure from various RNA sequences that is recognized by a single protein.

The ability to recapitulate hypoxic stabilization is significant for a number of reasons. It will allow further delineation of the elements required for stabilization and therefore provide a route to the isolation of factors involved in stabilization of the mRNA. It also provides the ability to study the effect of various conditions or factors on mRNA stability directly, without the complication of associated changes in transcription from the VEGF promoter. Furthermore, our demonstration of the need for the cooperation of multiple elements for hypoxic stabilization of the VEGF mRNA could have important implications for antiangiogenic therapy. Inhibition of the function of any of the cooperating elements should prevent accumulation of the mRNA in response to hypoxia.

Although the VEGF 3′UTR is not sufficient to confer hypoxic stabilization, our results show that elements in the 3′UTR are essential for stabilization. This is supported by other studies that show that RNA elements in the 3′UTR play a role in hypoxic stabilization. Antisense expression of HuR, a protein that binds to a distal AU-rich region in the VEGF 3′UTR, prevents hypoxic stabilization of the VEGF mRNA (Levy et al., 1998). HuR is a member of the Elav-like family of binding proteins and has been implicated in the function of AU-destabilizing elements (Myer et al., 1997). Additionally, one of the hypoxia-inducible proteins found to bind to a pyrimidine-rich element in the VEGF 3′UTR in vitro also binds to a similar element found in the erythropoietin and tyrosine hydroxylase mRNAs (Czyzyk-Krzeska and Beresh, 1996 Iliopoulos et al., 1996 Levy et al., 1996a,b McGary et al., 1997). Mutation of this element in the erythropoietin mRNA destabilizes the mRNA in vivo under normoxic conditions and prevents hypoxic stabilization of the mRNA (McGary et al., 1997).

We found that the endogenous VEGF mRNA showed a pattern of serum stimulation and decay under normoxic conditions, indicating that the VEGF promoter is also subject to a pulse of transcription in response to serum stimulation (Figure 8). A similar pattern of VEGF transcription in response to serum has also been seen in human fibroblasts (Enholm et al., 1997). The pattern of decay of the endogenous mRNA under hypoxia was consistent with stabilization of the mRNA, although nuclear run-on experiments are required to verify that the shut-off of transcription under hypoxia and normoxia occurs at the same rate. Nevertheless, the degree of hypoxic stabilization (twofold) of the endogenous mRNA determined from these apparent half-lives was similar to that determined from the reconstructed mRNA. If the pulse of transcription generated from the VEGF promoter in response to serum stimulation is similar under normoxia and hypoxia, this method may allow the direct determination of the effect of various agents on stabilization of the endogenous VEGF mRNA.

The degree of complexity of the molecular interactions required for the regulation of stability of some mRNAs is only now becoming apparent. Our finding that multiple regions are required for VEGF mRNA stabilization parallels similar findings with the interleukin (IL)-2 and IL-11 mRNAs. Stabilization of the IL-2 mRNA in T cells by the c-Jun amino-terminal kinase (JNK) pathway requires elements at the junction of the 5′UTR and coding region as well as elements in the 3′UTR (Chenet al., 1998), and stabilization of the IL-11 mRNA in bone marrow stromal cells in response to phorbol ester requires all three regions of the mRNA (Yang et al., 1996) however, the VEGF mRNA appears to be unique in the complexity of the interactions required for both its lability and stabilization. Such complexity most likely reflects the need for expression of the molecule to be very tightly regulated and is most apparent in the failure in vascular development in mice carrying a single defective VEGF gene (Carmelietet al., 1996 Ferrara et al., 1996). This heterozygous lethality is unprecedented in an autosomal gene and indicates that the level of VEGF is critical for correct embryonic vascular development. The ability to regulate the stability of the VEGF mRNA allows a level of control in addition to regulation of transcription, and this may be important for coordinating expression of VEGF with the many other factors that also play a role in the angiogenic process (Folkman and Shing, 1992).


Materials and methods

Plasmids and DNA analysis

The lentiviral vector plasmid pSIN-EGFP containing an EGFP gene, IRES and Puromycin gene was generated from pSIN-EF2-Lin28-Puro (Addgene plasmid #16580) using EcoRI and BamHI restriction enzyme sites. SaCas9 plasmid was a gift from Feng Zhang (Addgene plasmid #61591) VPR expression plasmid (GP230) and mCherry reporter plasmid (ZP30) were gifts from Dr. Yang, Hui (Shanghai Institutes for Biological Sciences, CAS). CRISPR-Cas9 plasmids were constructed as described online (http://www.genome-engineering.org/crispr/). The oligonucleotide sequences for sgRNA construction are summarized in S2 Table. Plasmid DNA and genomic DNA were isolated by standard techniques. The DNA sequencing confirmed the desired specific sequence in the constructs.

Cells and cell culture

HEK-293 cells were obtained from ATCC (CAT#CRL-1573) and grown at 37°C in 5% CO2 in Dulbecco’s Modified Eagle Medium (Life Technologies, Carlsbad, CA), 10% heat-inactivated fetal bovine serum, penicillin/streptomycin. HEK-293 cells expressing EGFP were described previously [29]. Drug-resistant single colonies of transduced HEK-293 cells were isolated and named 293-SC1. To maintain EGFP expression, the medium for 293-SC1 culture includes puromycin.

Construction of SaCas9 library

The library was generated by error-prone PCR (primers sequence in S1 Table). Specifically, Plasmid (pX601) harboring SaCas9 coding sequence was digested with AgeI/HindIII or HindIII/BamHI, respectively. The AgeI/HindIII or HindIII/BamHI fragments were mutated by random mutagenesis kit (CAT#101005, TIANDZ) and then purified, in-fusion with linearized pX601 backbone without the corresponding fragment. The individual colonies from LB plate were then manually picked. The plasmids from individual colonies were isolated. The concentration of each plasmid was adjusted as 100 ng/μL.

Targeted deep sequencing

Off-target sites (S1 Table) were predicted by Cas-OFFinder software [40], and off-target sites with fewer than 5 mismatched nucleotides were screened. In addition, the off-target sites of OT1 and OT2 for EMX1_1 were reported [5]. Off-target sites for chr2:156,968,467–156,968,493, DLGAP2, and DUS2 were screened by algorithm for fewer than 2 mismatched nucleotides in whole genome. Targeted deep sequencing experiments were performed with WT SaCas9 and Mut268 for different loci at human genome. Briefly, 1.8 × 10 5 HEK-293 cells were transfected with 750 ng of all-in-one expression plasmids. Seventy-two hours after transfection, genomic DNA was extracted using standard phenol/chloroform extraction protocols. For the construction of the NGS library, the primary PCR was performed to amplify 100 to 230 bp on/off-target sites from approximately 60 ng of genomic DNA using Phanta Super-Fidelity DNA Polymerase (Vazyme Biotech Co., Ltd). The secondary amplification was to fix barcodes, index, and adaptor sequences into the primary PCR products (S2 Table). Amplification products were purified (Thermo Fisher Scientific) and pooled into one tube. After the removal of adaptors and low-quality reads, paired-end reads were merged and then mapped to the template. Base substitution and in/del were analyzed using open-sourced “CRISPResso” software (version 1.0.10) with read quality above Q30 [41].

PEM-seq for SaCas9

The protocol has been previously described [42]. Specifically, 3.2 μg pX601 plasmids were transfected into HEK-293 cells in 6-cm dishes. Cell were harvested 48 h post transfection followed by standard PEM-seq procedure. Hiseq reads were processed by “SuperQ” pipeline, and off-target hotspots were identified by “MACS2” callpeaks mode. MACS2 results were further filtered to remove sites with fewer than 2 junctions and no target site-similar sequence by “Bedtools” and “Needle.”

T7EI assay for gene editing

Briefly, 293-SC1 were plated at a density of 1.8 × 10 5 cells per well in a 12-well plate on day 0 and transfected with 750 ng CRISPR-SaCas9-sgRNA plasmids with Turbofect on day 1. Fresh medium was added to the transfected 293-SC1 cells on day 2. Cells were harvested on day 3. For T7EI assay, 150 ng purified PCR products were mixed with 1.5 μL 10× NEB#2 buffer and ultrapure water to a final volume of 14.5 μL and were subjected to re-annealing process to enable heteroduplex formation. After re-annealing, products were treated with 0.5 μL T7 Endonuclease I for 45 min.

Transcriptional activation assay

The ChIP-seq assay was performed as described previously [7]. For the fluorescence reporter assay, 1.0 × 10 5 HEK-293 cells of each well (24-well plates) were seeded on day 0. On day 1, each well of was transfected with 375 ng of dCas9-VPR plasmid, 150 ng of plasmid containing sgRNA, and 250 ng of miniCMV-mCherry plasmid. Fresh medium was added to the transfected cells on day 2. Cells were harvested for FCM on day 3.

Western blotting

HEK-293 cells were plated at a density of 5.0 × 10 5 cells per well in a 6-well plate on day 0 and transfected with 1.5 μg plasmids (pX601, Mut268, and efSaCas9) via TurboFect transfection reagent on day 1. Fresh medium was added after 12 h. Cells were harvested on day 3. Proteins were analyzed on SDS-PAGE after quantification. Membranes were blotted with antibodies directed at the following proteins: HA (Mouse-1F5C6, Proteintech Cat#66006-2-Ig, 1:2,500 dilution) and β-Actin (Mouse-8H10D10, Cell Signaling Technology Cat#3700, 1:1,000). An HRP-conjugated secondary antibody (Goat, Abcam Cat#ab97023, 1:5,000) was used for chemiluminescent detection.


Preface

The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year. We have had invariably an interdisciplinary audience with backgrounds from physics, biology, medicine, math, computer science or other quantitative fields. We want this book to be a starting point for computational genomics students and a guide for further data analysis in more specific topics in genomics. This is why we tried to cover a large variety of topics from programming to basic genome biology. As the field is interdisciplinary, it requires different starting points for people with different backgrounds. A biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. In the same manner, a more experienced person might want to refer to this book when needing to do a certain type of analysis, but having no prior experience.


The online version of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


The Intrinsic Determinants of Axon Regeneration in the Central Nervous System

Kin-Sang Cho , . Dong Feng Chen , in Neural Regeneration , 2015

5.3.4 KLF-9

As a repressor , KLF-9 exerts its effects through its SID, which recruits the mSin3a repressor complex. Overexpression of KLF-9 causes dramatic reduction in neurite length to less than 40% of the control length. The expression profile of KLF-9 also supports its role as a neurite growth repressor in the adult, as messenger RNA expression of KLF-9 increases 250-fold from E19 to P21. Although KLF-9 has a marked effect on neurite growth, its developmental role is more confined. KLF-9 knockout mice do not show any deficits in axon targeting or dendrite growth. However, these knockout mice show deficiencies in dentate granule neuron maturation in the hippocampus, and they display behavioral deficits in rotorod and contextual fear-conditioning tests [71] .


10.4: RNA Structure - Biology

Recent paper

Recent breakthroughs in genomics and in biotechnology provide new unprecedented capabilities to study RNA structure and function at a whole transcriptome scale and at high resolution and throughput. These advances warrant the development of innovative computational methods to process, interpret, and analyze these massive amounts of genomic data. Computational approaches can also leverage this new wealth of information to improve upon current structure analysis capabilities and thereby enhance our ability to address a range of biological questions.

Our interdisciplinary approach draws on methods and principles from machine learning, statistics, applied math, and biophysics. Through collaborations as well as in-house wet lab studies, we are interested in applying our methods to the engineering of novel RNAs for a range of biotechnology and therapeutic applications and to improving our understanding of the relationship between RNA sequence, structure, and function.

We develop computational methods for inferring RNA dynamics from experiments and theory, with applications ranging from basic research to bimolecular engineering and synthetic biology.

Feb. 2018: New paper on reconstruction of complex RNA structure landscapes in Nature Communications. ———————————————- March 2018: PATTERNA, a new algorithm that mines RNA structures from large-scale structure profiling datasets, in Genome Biology.


Figure 4

Figure 4. Targeting the RIB1 gene with the scgRNA/dCas9 system. (A) Overview of the tested target sequences (R1–R4). Arrowheads indicate the position of the PAM sequence and the positions of the last nucleotide of the PAM sequences in respect to the TSS. Riboflavin productivities after 23 and 53 h of (B) MS2 VP64 and (C) NAC strains cultivated using glucose surplus conditions. (D) Log2 fold changes of relative transcript levels of the RIB1 gene compared to the wt measured after 23 h of cultivation using MeOH conditions. Riboflavin productivities measured after 23 and 53 h of (E) MS2 VP64 and (F) NAC strains cultivated using MeOH conditions. The number of biological replicates is indicated on top of each bar. Error bars indicate the standard deviation of the biological replicates for B, C, E, and F and the sum of squared errors of the Ct values of the target gene and the housekeeping gene of all technical replicates (4 per biological replicate) for D.


Watch the video: RNA structure prediction lecture (September 2022).


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