Justin P. Sheehy, Amber R. Davis, and Brent M. Znosko
Department of Chemistry, Saint Louis University, Saint Louis, Missouri 63103, USA
Reprint requests to : Brent M. Znosko, Department of Chemistry, Saint Louis University, Saint Louis, Missouri 63103, USA, e-mail: znoskob@slu.edu fax: 314 977 2521
Although tetraloops are one of the most frequently occurring secondary
structure motifs in RNA, less than one-third of the 30 most frequently
occurring RNA tetraloops have been thermodynamically characterized. Therefore,
24 stem–loop sequences containing common tetraloops were optically melted,
and the thermodynamic parameters DH°, DS°,
DG°37,
and TM for each stem–loop were determined. These new experimental
values, on average, are 0.7 kcal/mol different from the values predicted
for these tetraloops using the model proposed by Vecenie
CJ, Morrow CV, Zyra A, Serra MJ. 2006. Biochemistry 45: 1400–1407.
The data for the 24 tetraloops reported here were then combined with the
data for 28 tetraloops that were published previously. A new model, independent
of terminal mismatch data, was derived to predict the free energy contribution
of previously unmeasured tetraloops. The average absolute difference between
the measured values and the values predicted using this proposed model
is 0.4 kcal/mol. This new experimental data and updated predictive model
allow for more accurate calculations of the free energy of RNA stem–loops
containing tetraloops and, furthermore, should allow for improved prediction
of secondary structure from sequence. It was also shown that tetraloops
within the sequence 5'-GCCNNNNGGC-3' are, on average, 0.6 kcal/mol
more stable than the same tetraloop within the sequence 5'-GGCNNNNGCC-3'.
More systemic studies are required to determine the full extent of non-nearest-neighbor
effects on tetraloop stability.
Keywords:
hairpin, RNA, secondary structure, tetraloops
INTRODUCTION:
Most biological RNA is single stranded. In order to fold into active secondary and tertiary structures, these single strands of RNA must fold back onto themselves. In doing so, hairpin loops are created at the end of most base-paired regions. Over 50% of these hairpins are tetraloops (Antao and Tinoco 1992; Wolters 1992). Therefore, RNA tetraloops are widespread and found quite frequently in nature. For example, tetraloops are found in the 16S rRNA of Thermus thermophilus, the 23S rRNA of Deinococcus radiodurans, the selenocysteine insertion sequence within the mRNA of prokaryotes (Fourmy et al. 2002), the 5'-UTR of coxsackievirus B3 (Du et al. 2003), the P5b stem–loop from a group I intron ribozyme (Kieft and Tinoco 1997), the recognition site for Saccharomyces cerevisiae RNase III (Wu et al. 2001), and the encapsidation signals of duck and heron Hepatitis B virus (Girard et al. 2007), to name a few. It is important to note, however, that RNA tetraloops not only occur in a variety of different RNAs and in different organisms, but they also serve functional roles within the RNA beyond allowing secondary structure formation. In general, tetraloops are extensively involved in RNA tertiary interactions with other RNAs and RNA interactions with proteins (Varani 1995; Tinoco and Bustamante 1999). Tetraloops also serve a variety of more specific functional roles, such as preventing reverse transcriptase from reading through mRNAs (Tuerk et al. 1988).
RNA tetraloops have been extensively studied structurally (Cheong et al. 1990; Heus and Pardi 1991; Varani et al. 1991; Allain and Varani 1995; Jucker and Pardi 1995; Cate et al. 1996; Jucker et al. 1996; Ennifar et al. 2000; Wimberly et al. 2000) and thermodynamically (Groebe and Uhlenbeck 1988; Tuerk et al. 1988; Antao et al. 1991; Heus and Pardi 1991; Varani et al. 1991; Antao and Tinoco 1992; Serra et al. 1997; Giese et al. 1998; Dale et al. 2000; Williams and Hall 2000; Proctor et al. 2002), especially the stable tetraloops 5'-GNRA-3' and 5'-UNCG-3'. Nevertheless, less than one-third of the 30 most frequently occurring RNA tetraloops (when considering the identity of the nucleotides in the hairpin loop as well as the closing base pair) have been thermodynamically characterized. This work focuses on those frequently occurring tetraloops that have not yet been studied and provides thermodynamic data for frequently occurring tetraloops and a new model to predict the stability of tetraloops that do not yet have experimental data.
Non-nearest-neighbor effects have been observed previously for a
wide variety of RNA secondary structure motifs (Longfellow
et al. 1990; Kierzek et al. 1999; Badhwar
et al. 2007; Davis and Znosko 2007; Siegfried
et al. 2007; Wright et al. 2007); however, current
algorithms that predict the stability of RNA from sequence (Zuker
1989; Mathews et al. 1999, 2004; Hofacker
2003; Zuker 2003; Lu et al.
2006) ignore non-nearest-neighbor effects. Hairpins are a convenient
motif to investigate non-nearest-neighbor effects on motif stability because
they are situated at the end of a helix and have nonnearest neighbors in
only one direction. Conversely, internal loops and bulges are located
within duplexes and contain nonnearest neighbors both 5' and 3' of the
motif. Therefore, this work investigates non-nearest-neighbor effects
on the stability of RNA tetraloops by studying frequently occurring
tetraloops that have already been thermodynamically characterized. These
tetraloops (along with their closing base pair) are placed within a different
stem sequence of the same length and recharacterized. It was concluded
that tetraloop stability does depend upon the sequence of the stem.
...
DISCUSSION:
Database searching
Due to the size and diversity of the RNA secondary structure database that was searched, we have assumed that the number and type of tetraloops found in this database are representative of tetraloops found in naturally occurring RNA.
It is clear from the first set of data in Table 1 that only one previous thermodynamic study (Dale et al. 2000) has focused on the tetraloop-closing base-pair combinations that occur most frequently in nature. When looking at the second set of data in Table 1, however, it appears as if the most frequent tetraloops (considering the loop nucleotides only) have already been studied. It is well known that the 5'-GNRA-3' and 5'-UNCG-3' tetraloops are found frequently in nature, and this is confirmed by the fact that nine out of the top 10 tetraloops fall into one of these two categories. However, it has been shown that the stability of hairpin loops depends not only on the identity of the nucleotides in the loop, but also on the stacking of the first mismatch on the closing base pair (Mathews et al. 1999, 2004; Vecenie and Serra 2004; Vecenie et al. 2006). Therefore, this work focuses on frequently occurring tetraloops when considering both the nucleotides in the loop and the closing base pair.
It is interesting to note that a closing base pair of C–G occurs as frequently as all five of the remaining base pairs combined (see Table 1, data set 3). Recently, Blose et al. (2009) have investigated the molecular basis for the enhanced stability of tetraloops with C–G closing base pairs. Although they may be more stable, it is unclear why C–G closing base pairs are so common in nature, as nature does not select tetraloop sequences based solely on stability (see Table 2).
When categorizing the loop nucleotides as purines and pyrimidines (Table 1, data set 4), it is interesting to note that 5'-RRRR-3' and 5'-RYRR-3', representing all of the 5'-GNRA-3' tetraloops, occurs three and two times as often as the third most frequent type (5'-YYYR-3', a type of 5'-UNCG-3'), respectively, and eight and six times as often as the fourth most frequent type (5'-RRYR-3'), respectively.
Thermodynamic contributions of tandem mismatches to duplex thermodynamics
From the data in Table 2, it is evident that the stability of a tetraloop alone does not determine its frequency of occurrence. For example, the most stable tetraloop (5'-CUUCGG-5', DG°37,tetraloop = 1.2 kcal/mol) is only the third most common in the database. Also, one of the most stable tetraloops measured, 5'-GACCAU-3' (Giese et al. 1998) (DG°37,tetraloop = 2.4 kcal/mol), is the 95th most common tetraloop in the database. Similarly, a tetraloop that contributes an unfavorable 4.1 kcal/mol toward stem–loop stability (5'-GGUGAC-5') appears in the top 10 in frequency of occurrence. However, stability may play a partial role in determining frequency of occurrence; the measured tetraloops in the top 30, on average, contribute 3.4 kcal/mol toward stem–loop stability, while the measured tetraloops outside the top 30 contribute 4.2 kcal/mol toward stem–loop stability.
Non-nearest-neighbor effects on the stability of tetraloops
Previous studies have shown that the stability of the iron responsive element hairpin loop is dependent upon the stem sequence. For example, the hexaloop contributes 2.4 kcal/mol to stem–loop stability when in the sequence 5'-GAAGACAGUGCUCUUC-3' (Laing and Hall 1996). When placed in the sequence 5-'GGACAGUGCUCC-3', the hexaloop contributes 3.8 kcal/mol to stem–loop stability (Dale et al. 2000). The effect of the stem sequence on tetraloop stability was studied here. One tetraloop reported here was synthesized twice within the same stem (5'-GCCGCAAGGC-3'), and each sample was purified, melted, and analyzed separately. The DG°36,tetraloop values for these two samples varied by only 0.2 kcal/mol, showing that the data are reproducible. Eight tetraloops in the top 30 that were previously characterized (Dale et al. 2000) were remeasured here in a different stem. The sequence used earlier was 5'-GGCNNNNGCC-3', and the sequence used here is 5'-GCCNNNNGGC-3'. When comparing the two stem sequences, they have the following in common: three G–C pairs, the number and type of nearest-neighbor combinations, a closing C–G pair, and melting conditions. The only difference is that a G–C pair 2 base pairs (bp) away from the hairpin loop in the tetraloops studied previously is switched to a C–G pair in the tetraloops studied here. Interestingly, when tetraloops were placed within the sequence studied here, 5'-GCCNNNNGGC-3', tetraloops were on average 0.6 kcal/mol more stable than the sequence used previously. It appears as if the orientation of the nonnearest neighbors plays a role in the stability of tetraloops. The DG°37,nonnearest neighbors term in Equation 5 takes into account this added stability. Because non-nearest-neighbor effects are not well understood and a significant amount of additional data are required, more studies are needed in order to more fully examine the effects of nonnearest neighbors on the stability of tetraloops.
Updated model for predicting thermodynamics of tetraloops
Because we have collected thermodynamic data for 15 tetraloops that previously did not have experimental values, when predicting the free energy contributions of these tetraloops in an RNA stem–loop, the experimental values can be used. These new experimental values, on average, are 0.8 kcal/mol different from the values predicted for these mismatches using the previous model (Vecenie et al. 2006). For tetraloops that still do not have experimental values, the predictive model can be utilized.
Using the data reported here and the data for previously measured tetraloops (Groebe and Uhlenbeck 1988; Antao and Tinoco 1992; Serra et al. 1997; Giese et al. 1998; Dale et al. 2000), the accuracy of the current model (Vecenie et al. 2006) (Eqs. 1, 2) to predict tetraloop stability was tested. On average, this model predicted the experimental free energies within 0.8 kcal/mol. Because this model was derived from data for hairpins of various sizes, and because the database of tetraloop experimental data has nearly doubled with the data reported here, the values for this model were recalculated using tetraloop data only. On average, this model (Eqs. 3, 4) predicted the experimental free energies within 0.6 kcal/mol.
Other models to predict tetraloop stability were also investigated. The model that gave the closest prediction to the experimental free energies is shown in Equation 5. Although more studies are needed to determine if additional DG°37, nonnearest neighbors parameters should be included, this model, on average, predicted the experimental free energies within 0.4 kcal/mol. Interestingly, this model does not depend upon the terminal mismatch data as does previous models. The conformation of a terminal mismatch and the interaction between the terminal mismatch and adjacent base pair is likely different from the conformation of the first mismatch in a tetraloop and the interaction between the first mismatch and the closing base pair. Therefore, using terminal mismatch data to predict tetraloop stability may not be the best approach. Not only does the model proposed here (Eq. 5) result in more accurate free energy predictions, but because it relies on free energy bonuses derived from tetraloop data and not on data from terminal mismatches, the new model may be a more realistic approach.
Because this model is derived from tetraloop data only, and most
of the tetraloops that have been studied thermodynamically are 5'-GNRA-3'
and 5'-UNCG-3', this model may be biased for these loops. In order to test
for this bias, the available thermodynamic database for tetraloops was
broken down into 5'-GNRA-3', 5'-UNCG-3', and all other tetraloops. For
the 28 5'-GNRA-3' tetraloops, the average difference between the predicted
and experimental values are 0.7, 0.4, and 0.3 kcal/mol for the previous
model (Eqs. 1, 2), updated previous model (Eqs. 3, 4), and the model proposed
here (Eq. 5), respectively. For the six 5'-UNCG-3' tetraloops, the average
difference between the predicted and experimental values are 0.7, 0.7,
and 0.5 kcal/mol for the previous model, updated previous model, and the
model proposed here, respectively. For the 18 other tetraloops, the average
difference between the predicted and experimental values are 0.9, 0.8,
and 0.7 kcal/mol for the previous model, updated previous model, and the
model proposed here, respectively. This shows that although 54% of the
tetraloops used to derive the proposed model are 5'-GNRA-3' tetraloops,
it predicts the stability of 5'-GNRA-3', 5'-UNCG-3', and all other tetraloops
better than both the previous model and updated previous model.
...
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1. Each cell retains all of its embryonic genes for a lifetime.
2. Controls for embryonic genes are often absent in adults.
3. Uncontrolled embryonic genes can replicate wildly.
4. Replicating genes participate in intra-cellular competition.
5. The basis for gene competition is selective transcription.
6. MicroRNAs can reprogram embryomic transcription.
7. Gene reprogramming can produce normal phenotypes.
8. Normal phenotypes can by-pass chromosomal lesions.
9. MicroRNA therapy may need to be permanent.
10. Transplantation of microRNAs could be preferred.
1. Pathways within cell genomes involve a flow of information.
2. Information can flow by direct contact or by third parties.
3. Direct contact within whole genomes is difficult to regulate.
4. DNA-DNA direct contects are influenced by agents.
5. Nuclear agents include hydrophilic ionic and hydrophobic conforming ligands.
6. Third parties within genomes involve RNAs and proteins.
7. RNAs and proteins are easy to regulate or reverse.
8. Information can be shared, lost, or transformed.
9. System information can be hidden during system isolation.
10. Local information can be permanently lost during system entropy.
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