Searching for IRES

Abstract

The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.

FIGURE 1.

(A) The number of AUGs found upstream of the natural start codon in 5′-UTRs is compared in several nonredundant 5′-UTR data sets. Data come from human transcripts from RefSequation (21589, Release 13), RefSequation 5′-UTRs fully reviewed by NCBI staff (1049), and the 66 mammalian UTRs containing published IRES from Table 1. Data bins are represented as the percentage of the total entries in each data set. (B) The frequency of upstream AUGs in several nonredundant data sets. A histogram in several asynchronous bins containing the relative frequency of upstream AUGs as a percentage of each data set compares the 5′-UTR from human transcripts in RefSeq, and fully reviewed RefSeq UTRs. The distribution shows a selection for no upstream AUGs in all the data sets, but approximately half of all transcripts contain an upstream AUG where ∼10% are considered “strong” AUGs (RNNAUGG) by Kozak (2005). Approximately 45% of annotated start codons in these transcripts are “strong” AUGs.

FIGURE 2.

The lengths of 5′-UTRs of human transcripts from nonredundant data sets. The frequency of different lengths of all the UTRs in each data set is placed in increasing bins of 50 nt and plotted as a percentage of the total number of UTRs in each database. A nonredundant data set of 5′-UTRs from UTRdb, RefSeq, fully reviewed transcripts of RefSeq (see Fig. 1 legend), and the mammalian transcripts containing IRES in their 5′-UTR from Table 1 are compared. The legend gives the median, mean, and third quartile values in each data set with the total number of UTRs in each data set in brackets.

FIGURE 3.

The distribution of the % GC content of human 5′-UTRs. The degree of RNA folding and structure stability can be partially assessed by the percentage of possible G and C base pairing within the UTR sequence and therefore the percent of Gs and Cs within the sequence. The % GC content of mammalian UTRs with published IRES are compared to the human 5′-UTRs from UTRdb and RefSeq and show a similar distribution. Plotted values are grouped in bins of five.

FIGURE 4.

The correctly predicted RNA secondary structure of HCV IRES by MFOLD. (A) The empirically predicted structure of the HCV IRES is shown with correctly predicted basepairs and nonpairing bases with gray background of the lowest energy prediction when using MFOLD with no constraints. (B) The best predicted structure was not the most thermodynamically stable fold, and the predicted base pairs and nonpairing bases shown are in gray. (C) All of the correctly predicted base pairs and nonpairing bases from all of the 36 predicted suboptimal folds using a 50% suboptimal parameter in MFOLD. Structures for figures have been produced with RnaViz2 (De Rijk et al. 2003).

FIGURE 5.

The correctly predicted RNA secondary structure of APAF1 IRES by MFOLD. (A) The empirically predicted IRES structure of Apaf-1 (Mitchell et al. 2003) is shown with the MFOLD correctly predicted base pairs or nonpairing bases shadowed in gray for the most thermodynamically stable structure. (B) All of the nonpairing bases or base pairs that were correctly predicted from all of the suboptimal folds predicted using a 50% suboptimal parameter in MFOLD are presented as gray shadows behind each base. This stresses the need for some empirically derived constraints for large RNA fold predictions when using MFOLD. Structures for figures have been produced with RnaViz2 (De Rijk et al. 2003).

FIGURE 6.

Simultaneous prediction of HCV and CSFV IRES secondary structure with Dynalign. Two phylogenetically related sequences like HCV and CSFV are well predicted by Dynalign. The correctly predicted base pairs and nonpairing bases are shown with gray background. The Matthews Correlation Coefficient (MCC) (Gorodkin et al. 2001) is used to measure the accuracy of the prediction when compared to the empirically derived structures. Structures for figures have been produced with RnaViz2 (De Rijk et al. 2003).

FIGURE 7.

RNAMotif pattern matches to tRNA descriptors of different levels of stringency within 5′-UTR human sequences. Using the eukaryote tRNA descriptor of Tsui et al. (2003) without introns, the number of pattern matches is shown as a percentage of the total number of UTRs in either UTRdb, RefSeq, fully reviewed UTRs in RefSeq, or published IRES from eukaryote transcripts in Table 1 (see data sets in Fig. 1). The first pattern is strict because of the specific sequences required in the pattern matches as well as the overall tRNA structure. The second pattern only requires two of the eight specific base sequences that are standard in tRNAs as well as a maximal −10.0 kCal/mol free energy cut-off for the putative matching RNA structure as calculated by the efn2 program included with RNAMotif software. The last pattern is the same as the second pattern but does not have a free energy cutoff.