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The genetic information of RNA viruses is organized very ef®ciently. Practically every nucleotide of their genome is utilized, either as protein-coding sequence or as cis-acting signals for translation, RNA synthesis or RNA encapsidation. As part of their genome expression strategy, several groups of positive-strand RNA (+RNA) viruses produce subgenomic (sg) mRNAs (reviewed by Miller and Koev, 2000) . The replication of their genomic RNA, which is also the mRNA for the viral replicase, is supplemented with the generation of sg transcripts to express structural and auxiliary proteins, which are encoded downstream of the replicase gene in the genome. Sg mRNAs of +RNA viruses are always 3¢-co-terminal with the genomic RNA, but different mechanisms are used for their synthesis. Some viruses, such as brome mosaic virus, initiate sg mRNA synthesis internally on the full-length minus strand RNA template (Miller et al., 1985) . Others, exempli®ed by red clover necrotic mosaic virus (RCNMV), may rely on premature termination of minus strand synthesis from the genomic RNA template, followed by the synthesis of sg plus strands from the truncated minus strand template (Sit et al., 1998) . Members of the order Nidovirales, which includes coronaviruses and arteriviruses, have evolved a third and unique mechanism, which employs discontinuous RNA synthesis for the generation of an extensive set of sg RNAs (reviewed by Brian and Spaan, 1997; Lai and Cavanagh, 1997; Snijder and Meulenberg, 1998) . Nidovirus sg mRNAs differ fundamentally from other viral sg RNAs in that they are not only 3¢-coterminal, but also 5¢-co-terminal with the genome ( Figure 1A) . A 5¢ common leader sequence of 65±221 nucleotides, derived from the 5¢ end of the genomic RNA, is attached to the 3¢ part of each sg RNA (thè mRNA body'). Various models have been put forward to explain the cotranscriptional fusion of non-contiguous parts of the nidovirus genome during sg RNA synthesis ( Figure 1B and C). Central to each of these models are short transcription-regulating sequences (TRSs), which are present both at the 3¢ end of the leader and at the 5¢ end of the sg RNA body regions in the genomic RNA. The TRS is copied into the mRNA and connects its leader and body part (Spaan et al., 1983; Lai et al., 1984) . Synthesis of sg mRNAs initially was proposed to be primed by free leader transcripts, which would base-pair to the complementary TRS regions in the full-length minus strand, and would be extended subsequently to make sg plus strands ( Figure 1B ; Baric et al., 1983 Baric et al., , 1985 . This model, however, was based on the report that sg minus strands were not present in coronavirus-infected cells (Lai et al., 1982) . The subsequent discovery of such molecules (Sethna et al., 1989) resulted in reconsideration of the initialleader-primed transcription' model. Sawicki and Sawicki (1995) have proposed an alternative model ( Figure 1C ), in which the discontinuous step occurs during minus instead of plus strand RNA synthesis. In this model, minus strand synthesis would be attenuated after copying a body TRS from the plus strand template. Next, the nascent minus strand, with the TRS complement at its 3¢ end, would be transferred to the leader TRS and attach by means of TRS±TRS base pairing. RNA synthesis would be reinitiated to complete the sg minus strand by adding the complement of the genomic leader sequence. Subsequently, the sg minus strand would be used as template for sg mRNA synthesis, and the presence of the leader complement at its 3¢ end might allow the use of the same RNA signals that direct genome synthesis from the fulllength minus strand. Sequence requirements for RNA strand transfer during nidovirus discontinuous subgenomic RNA synthesis The EMBO Journal Vol. 20 No. 24 pp. 7220±7228, 2001 Using site-directed mutagenesis of TRSs of the arterivirus equine arteritis virus (EAV), we have shown previously that base pairing between the sense leader TRS and antisense body TRSs is crucial for sg mRNA synthesis (van Marle et al., 1999a) . However, base pairing is only one step of the nascent strand transfer process and is essential in both models outlined in Figure 1 . The EAV genomic RNA contains several sequences that match the leader TRS precisely, but nevertheless are not used for sg RNA synthesis (den Boon et al., 1996; Pasternak et al., 2000) . This suggests that leader±body TRS similarity alone is, though necessary, not suf®cient for the strand transfer to occur. To gain further insight into the cis-acting signals regulating sg RNA synthesis, we performed a comprehensive site-directed mutagenesis study of the EAV leader and body TRSs. Every nucleotide of the TRS (5¢-UCAACU-3¢) was substituted with each of the three alternative nucleotides. Our analysis revealed a number of striking similarities with the process of copy-choice RNA recombination, as it occurs in RNA viruses. Whereas the leader TRS plays only a targeting role in translocation of the nascent strand, body TRS nucleotides appear to ful®l diverse position-speci®c and base-speci®c functions. In addition, the sequence of the leader±body junctions of the sg mRNAs produced by these mutants provided strong evidence for the discontinuous minus strand extension model. EAV genome replication is not signi®cantly affected by leader TRS and body TRS mutations To dissect EAV RNA synthesis, we routinely use a fulllength cDNA clone (van Dinten et al., 1997) , from which infectious EAV RNA is in vitro transcribed. Following transfection of the RNA into baby hamster kidney (BHK-21) cells, intracellular RNA is isolated and analysed by northern blot hybridization and RT±PCR (van Marle et al., 1999a) . Due to differences in transfection ef®ciency, the total amount of virus-speci®c RNA (genomic RNA and sg mRNA) isolated from transfected cell cultures is somewhat variable. Thus, the accurate quantitation of sg mRNA synthesis by TRS mutants requires an internal standard for transfection ef®ciency. The amount of viral genomic RNA can be this standard, but only if its ampli®cation is not dramatically affected by the TRS mutations. To prove that this is the case, we used the previously described mutants L4, B4 and LB4 (van Marle et al., 1999a) , in which ®ve nucleotides of the TRS (5¢-UCAAC-3¢) were replaced by the sequence 5¢-AGUUG-3¢, either in the leader TRS (L4), RNA7 body TRS (B4) or both TRSs (LB4). The three mutants were tested in three independent experiments. Intracellular RNA was isolated at 14 h posttransfection, early enough to prevent spread of the wildtype control virus to non-transfected cells (®rst cycle analysis). Transfection ef®ciencies were determined by immuno¯uorescence assays (see Materials and methods) and varied between 10 and 23% (data not shown). Prior to RNA analysis, the amount of isolated intracellular RNA was corrected for the transfection ef®ciency of the sample, so that each lane in Figure 2 represents EAV-speci®c RNA from an approximately equal number of EAV-positive cells. Phosphoimager quantitation revealed that genomic RNA replication of mutants L4, B4 and LB4 varied by not more than 30% (Table I) . These differences could re¯ect, for example, a slight in¯uence of RNA secondary structure changes in the TRS regions on genomic RNA synthesis. Remarkably, however, the genomic RNA level of the leader±body TRS double mutant LB4 was not affected by more than 10%. In view of the results obtained with these pentanucleotide TRS mutants, we assumed that the amount of genomic RNA could indeed be used as an internal standard during the analysis of mutants containing only single nucleotide replacements in leader TRS and/or RNA7 body TRS. The regions of the genome specifying the leader (L) sequence, the replicase gene (ORFs 1a and 1b) and the structural genes are indicated. The nested set of EAV mRNAs (genome and sg mRNAs 2±7) is depicted below. The black boxes in the genomic RNA indicate the position of leader and major body TRSs. (B and C) Alternative models for nidovirus discontinuous sg RNA synthesis. The discontinuous step may occur during either plus strand (B) or minus strand (C) RNA synthesis. In the latter case, sg mRNAs would be synthesized from an sg minus strand template. For details see text. Northern analysis of EAV-speci®c RNA isolated from cells transfected with RNA transcribed either from the wild-type EAV infectious cDNA clone or from TRS pentanucleotide mutants (UCAAC to AGUUG). The results of two independent experiments are shown. The RNA±RNA interaction between the leader and body TRSs is not the only factor that regulates EAV sg RNA synthesis There are numerous examples of regulatory RNA±RNA interactions in both eukaryotic and prokaryotic cells, as well as in RNA viruses. Essential processes such as translation, replication and encapsidation of RNA virus genomes frequently depend on RNA±RNA interactions and higher order RNA structures. Regulation of sg RNA synthesis of +RNA viruses by RNA±RNA interactions is also not without precedent. In tomato bushy stunt virus, an RNA element located 1000 nucleotide upstream of the sg RNA2 promoter base-pairs with the promoter and is necessary for sg RNA production (Zhang et al., 1999) . Similarly, base pairing interactions between complementary sequences in the 5¢ end of the potato virus X genomic RNA and sequences upstream of two major sg RNA promoters are required for ef®cient sg RNA synthesis (Kim and Hemenway, 1999) . In RCNMV, an intermolecular RNA±RNA interaction is required for sg RNA synthesis (Sit et al., 1998) . Recently, we have established the pivotal role of an interaction between sense and antisense RNA sequences in the life cycle of EAV (van Marle et al., 1999a) . In that study, the role of TRS nucleotides C 2 and C 5 was tested by substituting them with G. It was concluded that base pairing between the sense leader TRS and the antisense body TRS plays a crucial role in nidovirus sg RNA synthesis. We now took a more systematic approach and performed an extensive site-directed co-variation mutagenesis study of the entire leader TRS and RNA7 body TRS, which directs the synthesis of the most abundant EAV sg RNA. Every nucleotide of the TRS (5¢-UCA-ACU-3¢) was replaced with each of the other possible nucleotides. As in the study of van Marle et al. (1999a) , every mutation was introduced into leader TRS, RNA7 body TRS and both TRSs, resulting in 54 mutant constructs. Each mutant was given a unique name: e.g. BU 1 A refers to a mutant in which a U has been changed to A at position 1 of the body TRS; LU 1 A refers to the same substitution in the leader TRS; and DU 1 A means that these two substitutions were combined in one double mutant construct. The amount of sg RNA7 was quantitated by phosphoimager scanning of hybridized gels and was corrected for the amount of genomic RNA in the same lane (as outlined above). Figure 3 shows the relative sg RNA7 level of the 54 mutants, compared with the RNA7 level of the wild-type control. For a selection of 11 interesting mutants (see below), the analysis was repeated three times (Figure 4 ), without observing signi®cant variations in sg RNA synthesis. The comprehensive analysis of the effects of TRS mutations considerably expanded our understanding of van Dinten et al., 1997) was taken along as a positive control. For every mutant, the level of sg RNA7 synthesis was calculated as [(sg/g)/(sg/g) wt ] 3 100%: it was corrected for the level of genomic RNA (used as an internal standard; see text) and subsequently was related to the level of sg RNA7 produced by the wild-type control in the same experiment, which was also corrected for the corresponding genomic RNA level. The relative sg RNA7 level of the wild-type control was set at 100%. A.O. Pasternak et al. discontinuous sg RNA synthesis. Remarkably, the effects of single (leader or body) TRS mutations were mostly base speci®c, i.e. different nucleotide substitutions at the same position affected sg RNA7 synthesis to different extents. For example, at position 1, the BU 1 A mutant retained 44% of the wild-type RNA7 synthesis level, whereas both the BU 1 C and BU 1 G mutants lost RNA7 synthesis almost completely. Conversely, when U 1 of the leader TRS was changed to A or G, RNA7 synthesis was completely abolished, whereas 13% of the wild-type level was still maintained by LU 1 C. For position 2, only the BC 2 U mutant retained 30% of the wild-type RNA7 synthesis level, while all the other position 2 single mutants have lost 90% or more of wild-type RNA7 synthesis. Another example is position 6: BU 6 C left only 5% of wild-type RNA7 synthesis, whereas BU 6 A produced much higher RNA7 levels. This implied that for some positions (1, 2 and 6), certain mismatches in the duplex between plus leader TRS and minus body TRS, such as U±U (BU 1 A and BU 6 A) or C±A (LU 1 C and BC 2 U), are allowed to a limited extent. In contrast, no mismatches were allowed for position 5, where all single nucleotide substitutions abolished RNA7 synthesis almost completely. Surprisingly, both body TRS U to C substitutions at positions 1 and 6 (BU 1 C and BU 6 C) resulted in low levels of RNA7, despite the fact that these mutations allow the formation of a G±U base pair between the plus leader TRS, providing the U nucleotide, and the minus body TRS, providing the G. On the other hand, for positions 3 and 4, G±U base pairing was shown to be functional, because mutants LA 3 G and LA 4 G, which can form G±U base pairs between the G in the plus leader TRS and U in the minus body TRS, were the only position 3 and 4 single mutants that produced reasonable levels of RNA7. Taken together, these ®ndings suggest that other factors, besides leader± body base pairing, also play a role in sg RNA synthesis and that the primary sequence (or secondary structure) of TRSs may dictate strong base preferences at certain positions. Our analysis of the degree of complementation by the double mutants provided strong support for this assumption. Differentiating between effects at the level of primary TRS sequence and the level of leader±body duplex formation For some TRS nucleotides (2, 5 and 6, except in the case of DU 6 C), the RNA7 level of double mutants was clearly higher than that of the corresponding single mutants. This means that base pairing between these leader and body TRS nucleotides is involved in sg RNA synthesis. However, none of these double mutants reached the wild-type sg RNA7 level. In the other double mutants (all position 1, 3 and 4 mutants, and DU 6 C), in clear contradiction to the predictions of thebase pairing model', RNA7 synthesis was not signi®cantly restored. Moreover, a comparison of the values for the B and D mutants in Figure 3 showed that, for almost all of these mutants (e.g. the position 1 mutants), the amount of sg RNA7 produced by the double mutant appeared to be limited by the level allowed by the body TRS mutation. Sometimes the RNA7 level of the double mutant was even less than that of the leader mutant (DU 1 C, DA 3 G, DA 4 G or DU 6 C). Clearly, for these substitutions, restoration of the possibilities for leader±body duplex formation did not restore sg RNA synthesis. Apparently this is because the effect of body TRS mutations at the level of primary sequence or secondary structure can bedominant' over the duplex-restoring effects of the double mutations. Body TRS mutants thus fell into two distinct types, determined by the position and chemistry of the substitution. In mutants of the ®rst type, sg RNA synthesis was impaired mainly because of the disruption of the leader± body TRS duplex. This effect could be compensated for by introduction of the corresponding mutation in the leader TRS and, in the double mutant, sg RNA synthesis was restored compared with the corresponding single mutants. In mutants of the second type, sg RNA synthesis was down-regulated as a consequence of both TRS duplex disruption and disruption of the primary sequence (or secondary structure) of the body TRS. Obviously, the latter effect could not be compensated for by mutating the leader TRS, and the corresponding double mutants did not show restoration of sg RNA synthesis. In contrast to our ®ndings with the body TRS mutants, we did not obtain leader TRS mutations that appeared to determine the level of sg RNA7 synthesis of the corresponding double mutant (Figure 3) . Thus, effects of mutations in the leader TRS were notdominant' over the duplex-restoring effects of the double mutations, suggesting that they only affected duplex formation. This indicated that the leader TRS probably does not have an additional, sequence-speci®c function in sg RNA synthesis in addition to its participation in TRS±TRS base pairing. The fact that single leader TRS mutations at all six Nidovirus discontinuous subgenomic RNA synthesis positions severely repressed RNA7 synthesis indicated that base pairing of every TRS nucleotide contributes to sg RNA production. In this respect, it was signi®cant that the two leader TRS mutants with the highest RNA7 levels, LA 3 G and LA 4 G, can form G±U base pairs to maintain the duplex. The observation that leader TRS mutations could bè rescued' by introducing complementary mutations in the body TRS, but that many body TRS mutations could not bè rescued' by corresponding changes in the leader TRS, is clearly illustrated by the U 1 A mutants. Due to the restoration of TRS base pairing possibilities, the RNA7 synthesis of double mutant DU 1 A was signi®cantly increased compared with that of LU 1 A, but not above the level of BU 1 A. Thus, restoration of the leader±body duplex in DU 1 A exerted a clear effect on sg RNA7 production compared with LU 1 A, but had no effect on sg RNA synthesis compared with BU 1 A. This exempli®ed the dominant nature of a mutation in the primary sequence of a body TRS. In contrast, for instance, the BC 2 U mutation probably affected duplex formation only, because RNA7 synthesis was restored almost to wildtype levels in the DC 2 U double mutant. These results indicate that there are strong base preference constraints for some body TRS positions. To interpret these base preferences accurately, it is necessary to limit the analysis to the double mutants only, because in these mutants the down-regulation of sg RNA synthesis was only due to the sequence changes in the body TRS, and not to the disruption of the leader±body TRS duplex. There were strict preferences for positions 1, 3 and 4 of the body TRS: at position 1, only the U to A substitution allowed for a signi®cant RNA7 level (~40% of wild-type); and at positions 3 and 4, only the A to U mutants retained 15±20% of the wild-type level. For positions 2 and 5, the sequence constraints were less stringent (all substitutions allowed for >20% of wild-type level), but still only DC 2 A and DC 2 U reached >50%. At position 6 of the body TRS, only U to C was not allowed, whereas the other two double mutants still produced 50% or more of RNA7. In other words, the functional EAV RNA7 body TRS (based on the analysis of our single nucleotide substitutions) can be described as U 1 (C/u/a) 2 A 3 A 4 C 5 (U/a/g) 6 , with wild-type nucleotides shown in upper case and nucleotides that allowed for at least 50% of the wild-type RNA7 level shown in lower case. Remarkably, TRS nucleotides A 3 , A 4 and C 5 are conserved in the TRSs of all other arteriviruses (Snijder and Meulenberg, 1998) . Also the fact that DC 2 U retained 80% of RNA7 synthesis corresponded nicely to the presence of a U at this position in other arteriviruses. Until recently (Almazan et al., 2000; Thiel et al., 2001) , infectious cDNA clones were lacking for coronaviruses. Consequently, most studies on coronavirus sg RNA synthesis were carried out using defective interfering (DI) RNAs. These replicons carried body TRSs from which moderate levels of sg mRNAs could be produced in the presence of helper virus. Using this system, Joo and Makino (1992) and van der Most et al. (1994) performed body TRS mutagenesis studies for the murine coronavirus (MHV). Joo and Makino systematically mutagenized the core of the MHV body TRS. In contrast to our results, they found that in only two of 21 body TRS mutants was sg RNA synthesis from the DI RNA genome abolished, whereas all others supported normal levels of sg RNA production. Thus, it is possible that the MHV TRS which was used in that study is more tolerant to single-nucleotide mismatches than the EAV sg RNA7 TRS. In a similar study, van der Most et al. (1994) observed that U to C substitutions at positions 1 and 3 of the MHV body TRS, which maintained the duplex by changing a U±A base pair into a U±G base pair, reduced sg RNA levels more strongly than substitutions that disrupted the duplex (van der Most et al., 1994) . This implies that, as in the case of EAV, leader±body TRS duplex formation is not the only factor that determines coronavirus sg RNA synthesis. However, because of the limitations of the DI RNA system, the leader TRS could not be mutagenized in these studies, and body TRS-speci®c effects could not be distinguished from effects at the level of leader±body duplex formation. The discontinuous step in nidovirus sg RNA synthesis occurs during minus strand RNA synthesis Due to recent studies of arterivirus and coronavirus sg RNA synthesis (van Marle et al., 1999a; Baric and Yount, 2000; Sawicki et al., 2001) , the discontinuous minus strand extension model ( Figure 1C ) has been gaining more and more ground. This model predicts that the TRSderived sequence that forms the leader±body junction in the sg mRNA is a copy of the body TRS, and not of the leader TRS. The leader-primed transcription model predicts the opposite ( Figure 1B) . Therefore, determining the origin of the leader±body junction of sg mRNAs would help to distinguish between the two models. However, in the wild-type situation, EAV leader and body TRSs are identical and consequently one cannot determine the origin of the sg mRNA leader±body junction. This problem could be overcome by tracing the mutations introduced in leader or RNA7 body TRS mutants, most of which retained part of their ability to produce mRNA7. In a previous study (van Marle et al., 1999a) , we found that nucleotides 2 and 5 of the mRNA7 leader±body junction sequence were derived exclusively from the body TRS, and not from the leader TRS. This was shown by direct sequencing of RT±PCR products obtained from the residual mRNA7 produced by mutants BC 2 G, LC 2 G, BC 5 G and LC 5 G ( van Marle et al., 1999a) . Using the same approach, we analysed mRNA7 from mutants BC 2 A and BC 2 U, and these transcripts also contained the mutated nucleotide derived from the body TRS (data not shown). Assuming that only one crossover event occurs during leader±body joining, we could thus map this crossover between positions ±1 and +2 of the sg RNA junction sequence. This left the intriguing question of whether the crossover site could be mapped even more precisely. In other words, was nucleotide +1 of the junction sequence derived from the body TRS or the leader TRS? Using the position 1 mutants described above, we could answer this question ( Figure 5) . The most striking result was that mRNA7 of mutants BU 1 A, BU 1 G and LU 1 C contained exclusively the body TRS-derived nucleotide at position +1. Thus, for these mutants, the crossover site could be mapped precisely between TRS nucleotide positions ±1 and +1, meaning that the complete leader± body junction sequence in an EAV sg mRNA can be body TRS derived. On the other hand, sg RNAs from mutants LU 1 A, BU 1 C and LU 1 G contained mixed populations of leader TRS-and body TRS-derived nucleotides at position +1 ( Figure 5 ): A and U for LU 1 A, C and U for BU 1 C, and G and U for LU 1 G. Remarkably, this pattern correlated with the relative amounts of sg mRNA7 produced by these mutants (Figure 3 ). Mutants that produced populations of sg RNAs that were mixed with respect to the origin of the nucleotide at position +1 of the leader±body junction had lost RNA7 synthesis almost completely. On the other hand, mutants that contained exclusively the body nucleotide at position +1 retained higher levels of RNA7 synthesis. This observation may be explained as follows: in the wild-type situation, the large majority of the crossovers probably occur between positions ±1 and +1, leading to a body TRS-derived nucleotide at position +1 in the sg RNA; however, a low number of crossovers take place between nucleotides +1 and +2, resulting in a leader TRS-derived nucleotide at position +1. Mutants in which almost all sg RNA synthesis is blocked by a substitution at position +1 may somehow be de®cient in the crossover between ±1 and +1, but may have retained the ability for crossovers between +1 and +2, which were detected by sequence analysis. Conversely, in position +1 mutants that retain reasonable sg RNA synthesis, most crossovers occur between positions ±1 and +1, and they obscure the minority of crossovers between +1 and +2 in the sequencing electropherogram. Alternatively, position +1 TRS mutations that strongly interfere with sg RNA synthesis may force a shift of the crossover site in the remaining molecules. We believe that our present ®ndings strongly support the discontinuous minus strand extension model. Indeed, the fact that a complete body TRS can be copied into the sg RNA is very dif®cult to reconcile with the alternative model, in which sg RNA synthesis from the genomic minus strand template is primed by free plus strand leader transcripts that contain the leader TRS at their 3¢ end ( Figure 1B) . To explain the presence of a complete copy of the body TRS in the sg mRNA in this model, one would have to assume that a 3¢±5¢ exonuclease activity trims back the free leader transcript prior to its extension into an sg mRNA (Baker and Lai, 1990) . Note that there would not be a single base pair left to hold thesetrimmed' leader molecules on the template. Such an enzymatic activity, which is unprecedented in +RNA viruses, exists in yeast retrotransposon Ty5 (Ke et al., 1999) , in which reverse transcription is primed by an internal region in a tRNA. However, in this system, it is not a part of the duplex that is removed, but the single-stranded 3¢ tail of the tRNA, which cannot base-pair with the Ty5 RNA. Removal of the TRS at the 3¢ end of the nidovirus leader, which has already base paired with the template, would be very energetically unfavourable for the RdRp. Instead of starting elongation using the intact and properly positioned leader as a primer, it would have to disrupt the newly formed duplex, degrade part of the leader RNA and then reinitiate polymerization, without any base pairing between primer and template. It has been shown that in¯uenza virus transcription does not require a sequence match between the (cellular) RNA primer and the (viral) template (Plotch et al., 1981) . However, if in the nidovirus system thetrimmed' leader RNA could also be ®xed on the template solely by RNA±protein interactions, the targeting of the nascent strand by TRS base pairing would be extremely puzzling. Sequence data of sg RNA leader±body junctions from other arteriviruses are also dif®cult to reconcile with the leader-primed transcription model. For the porcine and simian arteriviruses (Meulenberg et al., 1993; Godeny et al., 1998) , the leader±body junctions of some sg RNAs mapped two nucleotides upstream of the body TRS, which again would not leave a single nucleotide to hold the putative free leader on the template after the hypothetical back trimming'. On the other hand, these ®ndings and our data can be explained readily by the discontinuous minus strand extension model ( Figure 1C ). The six-nucleotide Fig. 5 . Sequence analysis of mRNA7 leader±body junctions from position 1 TRS mutants. Sequences were determined directly from sg mRNA7-speci®c RT±PCR products. For the U 1 A and U 1 C mutants, the sequence shown corresponds to the plus strand of sg RNA7. For sequencing-related technical reasons, the minus strand sequence was determined for the U 1 G mutants; a mirror image of the electropherogram is shown with the corresponding plus strand sequence listed at the top of the panel. For every mutant, a sequence alignment of the leader (red) and body (blue) TRSs and surrounding sequences is shown (TRSs are boxed). The mRNA7 leader±body junctions detected by our sequence analysis are shown in yellow. duplex formed between the body TRS complement at the 3¢ end of the leaderless sg minus strand and the leader TRS in the genomic RNA template should suf®ce to position the nascent minus strand properly for subsequent elongation to add the complement of the leader sequence. In most cases, the nascent minus strand contains the entire body TRS complement at its 3¢ end at the moment of strand transfer, leading to a body TRS-derived leader±body junction sequence in the sg mRNA molecule. In a small number of transcripts, however, minus strand synthesis appears to be interrupted before nucleotide +1 of the body TRS is copied and, after strand transfer, resumes by incorporating the complement of the +1 nucleotide of the leader TRS. As stated above, we postulate that the detection of this phenomenon is determined by the level of crossovers between the ±1 and +1 position that is allowed by the mutations introduced at the +1 position of body TRS or leader TRS. We cannot, however, formally exclude that aback trimming' activity degrades the 3¢-terminal nucleotide of the minus strand before or after strand transfer. However, note that in the discontinuous minus strand extension model ( Figure 1C ), such an activity would not disturb the proper positioning of the nascent minus strand on the leader template, because the TRS± TRS duplex would be shortened by one nucleotide only. Nidovirus discontinuous minus strand extension resembles similarity-assisted, copy-choice RNA recombination Due to their discontinuous sg RNA synthesis, nidoviruses occupy a specialniche' in the +RNA virus world. Their mode of sg RNA production is clearly different from that of other +RNA viruses and resembles another welldocumented +RNA virus feature: RNA recombination (for recent reviews see Nagy and Simon, 1997; Aaziz and Tepfer, 1999; Worobey and Holmes, 1999) . Most of the experimental evidence supports an RdRp template switch (Kirkegaard and Baltimore, 1986) as the main mechanism of RNA recombination. Mechanistically, such a template switch involves the transfer of a nascent strand from one RNA template (donor) to the other (acceptor). Also, nidovirus discontinuous sg RNA synthesis involves transfer of a nascent RNA strand, the sg RNA, but now from one site to another in the same template. Based on the data currently available, we refer to the discontinuous minus strand extension model as our working model for nidovirus sg RNA synthesis. If one applies therecombination terms' to this model (Chang et al., 1996; Brian and Spaan, 1997; van Marle et al., 1999a) , the donor strand would be the body part of the genomic RNA template, the acceptor strand would be the leader part of the genomic RNA template and the nascent strand would be the discontinuously synthesized minus strand. Nagy and Simon (1997) have de®ned three main classes of RNA recombination: similarity-essential, similarity-non-essential and similarity-assisted recombination. The latter is de®ned as a mechanism in which strand transfer is determined by both sequence similarity between the parental RNAs and additional RNA determinants, present in only one of the parental RNAs. The results of our present study strongly suggest that nidovirus discontinuous sg RNA synthesis can be considered a special case of high-frequency similarity-assisted RNA recombination. While the only obvious function of the leader TRS is to ensure the ®delity of the strand transfer by base pairing with the 3¢ end of the nascent strand, the body TRS in the donor template indeed has additional, sequence-speci®c functions. One of these functions apparently is to pause (or terminate) nascent strand synthesis and thereby provide the opportunity for strand transfer. In addition, body TRS-derived nucleotides may play a role in the reinitiation of nascent strand synthesis on the acceptor template. Given the compact nature of the EAV TRS, it is quite possible that some nucleotides ful®l multiple tasks. RNA secondary structure of the body TRS may regulate sg RNA synthesis The sequence-speci®c function of the body TRS, revealed in this study, may be exerted at the level of either primary sequence or secondary structure. For a number of +RNA viruses, RNA secondary structure motifs located in the (proximity of) sg RNA promoters are vital for sg RNA synthesis. In alfalfa mosaic virus (Haasnoot et al., 2000) , turnip crinkle virus (TCV) (Wang et al., 1999) and barley yellow dwarf virus (Koev et al., 1999) , stem±loop structures in sg RNA promoter regions of the template strand are required for sg RNA synthesis. The sg RNA1 promoter of the latter virus is especially interesting, since it contains two stem±loop domains. For one of them, secondary structure, but not the primary sequence, is important for sg RNA synthesis, whereas the other domain acts through primary sequence, and not secondary structure (Koev et al., 1999) . Similarly, RNA secondary structure may play only a minor role in the sequence-speci®c recognition of the BMV sg RNA promoter by the RdRp Siegel et al., 1997) . We have suggested previously that RNA secondary structure of body TRS regions contributes to their attenuating potential and thereby determines the relative portion of the nascent minus strands that is transferred to the leader TRS in the template (Pasternak et al., 2000) . At present, it is unknown whether EAV body TRSs are part of an RNA structural motif that is essential for body TRS function, or whether they are recognized by a protein factor in a sequence-speci®c manner. However, the latter seems less likely than the former, since even LB4 (Figure 2 ), in which ®ve TRS nucleotides were substituted, still produced some sg RNA7, although~30-fold less than the wild-type control. The fact that some sequences in the EAV genome match the leader TRS perfectly, but are not used for sg mRNA synthesis, also argues against the recognition of a speci®c sequence (Pasternak et al., 2000) . More probably, mutagenesis of the RNA7 body TRS disturbed an RNA structure that is necessary for its function. This could, for example, explain the fact that the BU 6 C substitution reduced the amount of RNA7 by 20-fold (and could not be rescued by the same mutation in the leader TRS), whereas the wild-type RNA6 body TRS contains a C at the same position. If a protein factor were involved in sequence-speci®c TRS recognition, then one would expect it to recognize all TRSs similarly. If RNA structure is important for recognition by such a protein, then the BU 6 C substitution probably disturbs a structural motif of the RNA7 TRS, which is not present in the RNA6 TRS. On the other hand, conservation of part of the TRS in other arteriviruses suggests a sequence-speci®c recognition. Further studies are required to distinguish between these possibilities. In the TCV satellite RNA recombination system, the hairpin structure in the acceptor strand, as well as the donor±acceptor homology region, are necessary for the template switch . The hairpin has been postulated to bind the RdRp, whereas the homology region targets the nascent strand to the crossover site. The TCV RdRp probably recognizes the secondary and/or tertiary structure of the hairpin, while individual nucleotides play a less important role . In EAV, the leader TRS in the acceptor template is predicted to reside in the loop of an extensive hairpin, and its base pairing interaction with the body TRS complement at the 3¢ end of the nascent minus strand would resemble certain antisense RNA-regulated control mechanisms that are based on interactions between single-stranded tails and hairpin loops (van Marle et al., 1999a, and references therein) . It is possible that the EAV RdRp, or its accessory proteins, also binds to the stem of the long hairpin that presents the leader TRS. In any case, the leader TRS itself does not seem to be recognized by a protein in a sequence-speci®c manner. The body TRS is a better candidate to serve as a protein recognition site. This protein would then mediate the pausing of the nascent strand synthesis and/or nascent strand transfer. This would resemble the DNA-dependent RNA polymerase I termination system, in which speci®c DNA-binding terminator proteins bind to termination sequences (Reeder and Lang, 1997) , or a function of the HIV nucleocapsid protein, which promotes the minus strand strong-stop DNA transfer (Guo et al., 1997) . The EAV replicase component nsp1, which recently was shown to possess an sg RNA synthesis-speci®c activity (Tijms et al., 2001) , may be a good candidate for such a regulatory role. Residues predicted to form a zinc ®nger structure in nsp1 were shown to be necessary for sg RNA synthesis. Interestingly, zinc ®nger structures in the HIV nucleocapsid protein facilitate strand transfer (Guo et al., 2000) . Finally, it should be noted that the RNA structure of the nascent strand may also in¯uence pausing, strand transfer or reinitiation, as illustrated by the fact that stable hairpin structures in the nascent strand promote termination of transcription by Escherichia coli RNA polymerase (Wilson and von Hippel, 1995) . Site-directed mutagenesis, RNA transfections and immuno¯uorescence analysis Site-directed mutagenesis of EAV leader and body TRSs was carried out as described by van Marle et al. (1999a) , and all mutant constructs were sequenced. Following in vitro transcription from infectious cDNA clones, full-length EAV RNA was introduced into BHK-21 cells by electroporation, as described by van Dinten et al. (1997) . Immuno¯uorescence assays with EAV-speci®c antisera were performed at 14 h posttransfection as described by van der Meer et al. (1998) . To visualize the nuclei for cell counting, nuclear DNA was stained with 5 mg/ml Hoechst B2883 (Sigma). Cells were counted using the Scion Image software (Scion Corporation) and the percentage of transfected cells was calculated on the basis of the number of cells positive for the EAV replicase component nsp3 (Pedersen et al., 1999) . For RNA analyses, cells were lysed at 14 h post-transfection. Intracellular RNA isolation was performed using the acidic phenol method as described by Pasternak et al. (2000) . Total intracellular RNA was resolved in denaturing agarose±formaldehyde gels. Hybridization of dried gels with the radioactively labelled oligonucleotide probe E154, which is complementary to the 3¢ end of the EAV genome and recognizes all viral mRNA molecules (genomic and subgenomic), and phosphoimager quantitation of individual bands were performed as described by Pasternak et al. (2000) . To determine the leader±body junction sequence of sg mRNA7, mRNA7-speci®c RT±PCRs were carried out as described by van Marle et al. (1999b) using an antisense (RT and PCR) primer from the RNA7 body region and a sense PCR primer matching a part of the leader sequence. RT±PCR products were sequenced directly as described by Pasternak et al. (2000) using the leader-derived primer, an ABI PRISMÔ sequencing kit (Perkin Elmer) and an ABI PRISMÔ 310 Genetic Analyser (Perkin Elmer).
The ef®ciency of +1 ribosomal frameshifting at a speci®c codon is used as a sensor to regulate polyamine levels in mammalian cells. The frameshifting occurs in decoding the gene antizyme 1, which has two partially overlapping open reading frames (ORFs). Protein sequencing showed that the reading-frame shift occurs at the last codon of ORF1, causing a proportion of ribosomes to enter ORF2 to synthesize a transframe protein (Matsufuji et al., 1995) . ORF2 encodes the main functional domains (Matsufuji et al., 1990; Miyazaki et al., 1992) of antizyme but has no ribosome initiation site of its own. The antizyme 1 protein binds to ornithine decarboxylase (ODC) (Murakami et al., 1992a; Cof®no, 1993, 1994) , inhibits it (Heller et al., 1976) and targets it for degradation by the 26S proteosome without ubiquitylation (Murakami et al., 1992b (Murakami et al., , 1999 . ODC catalyzes the ®rst and usually ratelimiting step in the synthesis of polyamines, conversion of ornithine to putrescine. Putrescine is a substrate for the synthesis of spermidine and spermine. Because of its inhibition of ODC, antizyme 1 is a negative regulator of the synthesis of polyamines. In addition, antizyme 1 is a negative regulator of the polyamine transporter (Mitchell et al., 1994; Suzuki et al., 1994; Sakata et al., 1997) . As discovered by Matsufuji and colleagues (Gesteland et al., 1992) and Rom and Kahana (1994) , increasing polyamine levels elevate frameshifting in decoding antizyme 1 mRNA and so increase the level of antizyme 1. Since antizyme 1 negatively regulates the synthesis and uptake of polyamines, the frameshifting is the sensor for an autoregulatory circuit. A second mammalian paralog of antizyme, antizyme 2, has very similar properties to antizyme 1, including the regulatory frameshifting, but does not stimulate degradation of ODC under certain conditions where antizyme 1 is active (Ivanov et al., 1998a; Zhu et al., 1999; Y.Murakami, S.Matsufuji, I.P.Ivanov, R.F.Gesteland and J.F.Atkins, in preparation) . Just like antizyme 1, antizyme 2 mRNA is ubiquitously expressed in the body but is 16 times less abundant than mRNA of antizyme 1 (Ivanov et al., 1998a) . In addition to antizyme 1 and 2, mammals have a third paralog of the gene, antizyme 3 (also encoded by two ORFs), which is expressed only during spermatogenesis (Ivanov et al., 2000) . Zebra®sh also have multiple antizyme genes, which differ in their expression patterns and activities (Saito et al., 2000) . Numerous studies have addressed the regulation of fungal ODC in response to exogenously added polyamines. In the cases examined, Physarum polycephalum (Mitchell and Wilson, 1983) , Saccharomyces cerevisiae (Fonzi, 1989; Toth and Cof®no, 1999) and Neurospora crassa (Barnett et al., 1988; Williams et al., 1992) , added polyamines, especially spermidine, result in signi®cant repression of ODC activity. The mechanisms of repression seem to vary from fungus to fungus and are apparently different from the mechanism of polyamine-dependent regulation of ODC in higher eukaryotes. In some cases, the existence of an antizyme-like protein has been suggested but has either been disproved, as in the case of N.crassa (Barnett et al., 1988) , or has never been substantiated, as is the case with S.cerevisiae. As expected from their small cationic nature and ability to neutralize negative charges locally, polyamines play key roles in processes ranging from the functioning of certain ion channels (Williams, 1997) , nucleic acid packaging, DNA replication, apoptosis, transcription and translation. The role of polyamines can be complex as illustrated by the transfer of the butylamine moiety of spermidine to a lysine residue to form hypusine in mammalian translation initiation factor eIF-5A, the only known substrate for this reaction (Tome et al., 1997; Lee et al., 1999) . Spermine negatively regulates the growth of prostatic carcinoma cells at their primary site (Smith et al., 1995) , but at later stages of tumor progression it fails to induce antizyme, which correlates with cells becoming refractory to spermine (Koike et al., 1999) . Lack of antizyme function is also important in the early deregulation of cellular proliferation in oral tumors (Tsuji Conservation of polyamine regulation by translational frameshifting from yeast to mammals The EMBO Journal Vol. 19 No. 8 pp. 1907±1917, 2000 ã European Molecular Biology Organization et al., 1998) and probably others. The levels of polyamines are altered in many tumors, and inhibitors of polyamine synthesis are being tested for antiproliferative and cell death effects. The synthesis of ODC varies during the cell cycle in normal cells (Linden et al., 1985; Fredlund et al., 1995) . It is induced by many growth stimuli and is constitutively elevated in transformed cells (Pegg, 1988; Auvinen et al., 1992) with some phosphorylated ODC being translocated to the surface membrane where it is important for mitotic cytoskeleton rearrangement events (Heiskala et al., 1999) . Antizyme is one example of certain mRNA-contained signals that can elevate speci®c frameshifting >1000-fold above the background level of normal translational errors. In addition to antizyme, frameshifting is also involved in the decoding of some bacterial and yeast genes and especially in many mammalian Retroviruses and Coronaviruses, plant viruses and bacterial insertion sequences (Atkins et al., 1999) . The site of frameshifting in both mammalian antizyme 1 and 2 mRNAs is UCC UGA, where quadruplet translocation occurs at UCCU (underlined) to shift reading to the +1 frame, immediately before the UGA stop codon of the initiating frame (Matsufuji et al., 1995; Ivanov et al., 1998a) . For the frameshifting to occur with an ef®ciency of 20% or more, it is important that the 3¢ base of the quadruplet is the ®rst base of a stop codon. Other important features are a pseudoknot just 3¢ of the shift site and a speci®c sequence 5¢ of the shift site (Matsufuji et al., 1995; Ivanov et al., 1998a) . A pseudoknot 3¢ of the shift site is a common stimulator for eukaryotic ±1 frameshifting, but the synthesis of antizyme is the only known case utilizing +1 frameshifting. Comparative analysis of RNA sequences from different organisms is informative about important features and the different options selected by evolution. Since most of the known examples of programmed frameshifting are in viruses or chromosomal mobile elements, the opportunity for comparison of frameshift cassettes in divergent organisms where the time of divergence can be approximated is limited. A start has been made with the frameshifting required for bacterial release factor 2 expression (Persson and Atkins, 1998) , but antizyme provides the ®rst opportunity for such a comparison in eukaryotes. Antizyme genes in genetically tractable lower eukaryotes would be helpful for understanding the functionally important interactions responsible for autoregulatory programmed frameshifting. Identi®cation of an antizyme gene in Schizosaccharomyces pombe A search for DNA sequences encoding protein sequences homologous to Drosophila melanogaster antizyme (Ivanov et al., 1998b) and Homo sapiens antizyme 1 identi®ed the same S.pombe anonymous cDNA clone (DDBJ/EMBL/GenBank accession No. D89228). The similarity is limited (~10% identity, 24% similarity to both human antizyme 1 and D.melanogaster antizyme); however, it is highest in regions that are most highly conserved among the previously identi®ed antizymes ( Figure 1A ). Closer examination of the cDNA nucleotide sequence provided further evidence that it encodes an S.pombe homolog of antizyme. The initiating AUG codon for the ORF that is similar to higher eukaryotic antizymes (ORF2 of those genes) is not the 5¢-most AUG in this cDNA. In fact, there are eight AUGs closer to the 5¢ end. The ®rst or the second AUGs would initiate translation of an ORF (ORF1) that overlaps the longer downstream ORF (ORF2) such that a +1 translational frameshifting event in the overlap would generate a protein product analogous to the products of antizyme genes from higher eukaryotes. Furthermore, the last 12 nucleotides of ORF1 (UGG-UGC-UCC-UGA) are identical to the last 12 nucleotides of mammalian antizyme 1 ORF1s, including the frameshift site. Eleven of these 12 nucleotides are identical to the corresponding regions of all previously identi®ed antizyme genes ( Figure 1B ). Previous experiments with the mammalian frameshift sequence tested in S.pombe have shown that this short 12 nucleotide sequence, by itself, is suf®cient to stimulate measurable levels (up to 0.5%) of +1 frameshifting (Ivanov et al., 1998c) . To con®rm the ORF con®guration of the putative S.pombe antizyme gene, a region corresponding to the two overlapping ORFs plus~80 nucleotides of the 5¢ UTR and 370 nucleotides of the 3¢ UTR, was ampli®ed from both S.pombe genomic DNA and a cDNA library. The sequence of the ampli®ed DNA con®rmed that there are indeed two overlapping ORFs with the deduced con®guration. This sequence (DDBJ/EMBL/GenBank accession No. AF217277) differs from the previously sequenced cDNA clone by three nucleotides (two in the coding region and one in the 3¢ UTR); one changes an alanine codon to proline, another is a silent mutation within a proline codon. Since the sequences from the cDNA library and genomic DNA are identical, we conclude that the differences with clone No. D89228 are most likely due to strain variation. This gene contains no introns within the ampli®ed region. The S.pombe protein was tested for antizyme activity using a gene fusion with glutathione S-transferase (GST). In this construct, ORF1 and ORF2 of antizyme are fused in-frame by deleting the T nucleotide that encodes U of the stop codon of ORF1. This GST±antizyme fusion gene was expressed in Escherichia coli and the protein was puri®ed by af®nity chromatography. ODC inhibitory activity was tested by incubating the recombinant antizyme protein with an S.pombe crude extract and then assaying the mixture for ODC activity. The results ( Figure 2) show that the recombinant protein can inhibit S.pombe ODC. GST alone (1 mg) does not inhibit S.pombe ODC (data not shown). In light of these results, the S.pombe gene will be called S.pombe ODC antizyme (SPA). Interestingly, the S.pombe ODC was also inhibited by mouse antizyme 1 and antizyme 2 (both expressed as GST fusions); however, the yeast fusion protein did not inhibit mouse ODC (data not shown). Deletion and overexpression of SPA Although the effects of overexpression of antizyme on cellular physiology have been tested previously in mammalian cells, the physiological changes associated with complete absence of antizyme activity have not yet been investigated because of the complication of multiple antizymes. The single S.pombe antizyme provides the chance to explore a knockout. SPA deletion strains were I.P. Ivanov et al. generated by replacing the two ORFs of the gene with the ORFs of either URA4 or LEU2 (see Materials and methods). Complete deletion of SPA (both ORFs) did not affect the viability of S.pombe cells in rich (YE) or minimal (MM) media. Temperature had no differential effect on mutant and wild-type cell growth. Similarly, the growth rates, mating ef®ciencies and overall morphology of the knockout strains are apparently indistinguishable from those of wild-type cells (results not shown). In wild-type S.pombe cells the most abundant polyamine is spermidine followed by putrescine ( Figure 3 ). Spermine and cadaverine are found in much smaller amounts. This distribution of polyamine content is very similar to that in other fungi for which polyamine concentrations have been measured (for references, see review by Tabor and Tabor, 1985) . The effect of SPA deletion on cellular polyamine contents was examined in both exponentially growing and stationary phase cells ( Figure 3 ). The cellular concentrations of putrescine, spermidine and cadaverine (but not spermine) were higher in the knockout strains than in wild-type cells. The greatest effect was seen on putrescine and cadaverine content, with smaller effects on spermidine, presumably because eukaryotic ODC activity directly catalyzes decarboxylation of both ornithine and lysine to produce putrescine and cadaverine, respectively (Pegg and McGill, 1979) , but subsequent regulatory events affect homeostasis of spermidine and spermine. The effect of inactivating antizyme on the polyamine contents in exponentially growing cells is modest (<2-fold in all cases). The effect becomes very pronounced in cells in stationary phase with up to 40-and 10-fold increases of putrescine and cadaverine contents, respectively, in the knockout strains. To test overexpression of SPA, two versions of the gene were cloned into pREP3 expression vector behind a strong, thiamine-repressible promoter (nmt1). One had the wild- type SPA sequence while in the second, ORF1 and ORF2 are fused in-frame. SPA wild type and an SPA deletion strain were transformed with each of the overexpression constructs. Derepression of the nmt1 promoter is a gradual process since it requires dilution of the intracellular pool of thiamine (the repressor) through cell division. After 2.5 days of exponential growth under derepressed conditions, yeast strains transformed with either SPA overexpression construct show signi®cant increases in doubling time ( Figure 4A ). The growth inhibition is greater with the construct expressing the in-frame version of SPA and after prolonged incubation (5±7 days); these cells cease growth and accumulate in G 1 as determined bȳ ow cytometry (data not shown). The fact that the inframe overexpression construct, which differs by a single nucleotide from the wild-type construct, confers a more severe phenotype is consistent with the hypothesis that translational frameshifting is required for expression of SPA. The growth phenotype associated with SPA overexpression is only partially relieved by adding 100 mM putrescine to the media (1 mM had no further effect) (data not shown). To see whether the slower growth is correlated with aberrant polyamine levels the polyamine contents of the deletion strain carrying in-frame SPA overexpression vector were measured under derepressed and repressed conditions, in both cases after 2 days of exponential growth ( Figure 4B ). As expected, overexpression of SPA results in signi®cant reduction in the intracellular levels of all four polyamines. After longer (4±5 days) incubation under derepressed conditions, no putrescine and cadaverine can be detected (data not shown). Translational frameshifting during expression of SPA Previously, we developed an assay for measuring antizyme translational frameshifting in both S.cerevisiae (Matsufuji et al., 1996) and S.pombe (Ivanov et al., 1998c) . Brie¯y, the nucleotide sequence to be assayed is inserted between GST and lacZ, such that ORF1 of the assayed sequence is fused in-frame to GST, while ORF2 is fused in-frame to lacZ. b-galactosidase activity provides a measure of frameshifting ef®ciency. To determine whether translational frameshifting occurs in the overlap of ORF1 and ORF2 of SPA, a region of SPA including all but the ®rst codon of ORF1 plus 180 nucleotides downstream of the ORF1 stop codon was tested. +1 frameshifting occurred at 2.2% compared with a construct in which ORF1 and ORF2 are fused in-frame. This result is consistent with +1 frameshifting being crucial for expression of SPA. Previous experiments have shown that the frameshift cassette of mammalian antizyme 1 can direct ef®cient +1 frameshifting when tested in S.pombe. The reverse experiment was conducted here. The SPA gene was translated in vitro in rabbit reticulocyte lysate and its resulting frameshift ef®ciency measured. With no addition of polyamines, frameshifting ef®ciency is~1.5%. Addition of spermidine to the translation mixture to a ®nal concentration of 1 mM results in a 3.7-fold increase in frameshifting to~5.5%, a level even higher than that observed in the endogenous system in vivo (autoradiogram not shown). The observed ef®ciency of frameshifting with the SPA frameshifting cassette in vivo in S.pombe is signi®cantly more than that expected from its limited nucleotide similarity to the antizyme frameshift sites of higher eukaryotes. This prompted a search for additional stimulatory elements within the SPA frameshift cassette. The following experiments were done in a strain carrying deletion of SPA (high polyamines) because it gives higher frameshifting and higher b-galactosidase activity in general; however, we obtained similar ratios for mutant to wild-type frameshifting ef®ciency in a strain with the intact SPA gene. Deleting 5¢ sequences up to the third to last sense codon of ORF1 has little or no effect on frameshifting ef®ciency. Deleting all but the last sense codon (UCC) of ORF1 leads to a 4-to 5-fold reduction in frameshifting ef®ciency ( Figure 5A ). This implies that the conservation of the six nucleotides 5¢ of the UCC-UGA frameshift site is due to their importance for stimulating +1 frameshifting. It also suggests that no additional ORF1 sequences of SPA stimulate the +1 recoding event. The 180 nucleotide 3¢ region was searched for possible structure by computer RNA folding algorithms plus visual inspection. The algorithms predicted several minimal structures in that region. 3¢ deletion constructs (constructs del.3,3¢±81,3¢) tested the importance of any putative structure on the frameshifting ef®ciency. The results ( Figure 5B and C) show that all of these deletions lead to a signi®cant (~10-fold) reduction in +1 frameshifting, indicating the presence of a major 3¢ stimulatory element in the 180 nucleotide region immediately following the frameshift site of SPA. However, the results indicate that none of the putative RNA structures in this region are suf®cient for the activity of this element. Several additional 3¢ deletions delineated the boundaries of this stimulatory element from the frameshift site to 150 and 180 nucleotides downstream (since construct del.150,3¢ stimulates 5.5-fold more +1 frameshifting than del.129,3¢, 150 nucleotides downstream probably contain most of the 3¢ stimulator). In the experiments described above, two of the characteristics of the autoregulatory circuit of mammalian antizyme 1 were con®rmed: SPA inhibition of ODC and the +1 translational frameshifting. The key question left is whether the recoding event is responsive to polyamine levels in cells. As shown above, overexpression of SPA leads to signi®cant reduction of polyamine levels in S.pombe. An SPA + strain was co-transformed with an SPA wild-type overexpressing plasmid (cells overexpressing wild-type SPA grow slowly but continuously) and a construct that monitors the +1 frameshifting from an SPA frameshift sequence. The +1 frameshifting was compared with that in SPA non-overexpressing cells (in both cases frameshifting was measured relative to in-frame control). The results ( Figure 6 ) show a signi®cant reduction (6.5-fold) in frameshifting ef®ciency in SPA-overproducing cells that correlates with a decrease of polyamine content (4.5-fold for putrescine and 3.9-fold for spermidine). This indicates that polyamines modulate the frameshifting ef®ciency of SPA. An alternative but less likely possibility is that SPA overexpression reduces frameshifting because high levels of SPA transcript titrate some factor limiting for frameshifting. The SPA frameshift signals direct 2-fold more frameshifting in Dspa::LEU2 cells (4.4%) than in SPA + cells (in both cases the measurement is done during stationary phase); however, the relatively high standard deviations for both measurements make it dif®cult to draw ®rm conclusions from this particular result. A search of Caenorhabditis elegans expressed sequence tag (EST) sequences with mammalian antizyme 1 sequence identi®ed 20 clones. These sequences could be deconvoluted into a contiguous cDNA sequence. Primers designed on the basis of this sequence were used to PCR amplify and subclone this cDNA from a C.elegans cDNA library. The sequence of the subcloned cDNA was con®rmed (DDBJ/EMBL/GenBank accession No. AF217278); the subsequently released genomic sequence of this C.elegans gene (DDBJ/EMBL/GenBank accession No. AF040659) con®rms our cDNA data. The amino acid sequence deduced from the cDNA sequence revealed that the longer ORF has similarity to previously reported antizyme sequences (overall 27% identity, 39% similarity to human antizyme 1; 19% identity, 34% similarity to Drosophila antizyme). These similarities are higher than that of SPA to these two antizyme genes and again are concentrated in the regions most highly conserved among previously identi®ed antizymes ( Figure 1A ). Just like mammalian antizymes, the longer ORF (ORF2) lacks an appropriate in-frame initiation codon, and expression could be provided by initiation in a short upstream overlapping ORF (ORF1) leading to +1 ribosomal frameshifting in the overlap. The putative C.elegans antizyme frameshift site (the nucleotides proximal to the end of ORF1) has 18 of 26 nucleotides identical to the consensus sequence for antizyme frameshift sites ( Figure 1B) . Frameshifting for expression of C.elegans antizyme was investigated in heterologous systems. Two constructs containing the entire antizyme cDNA, one with the wildtype sequence and one with a single nucleotide deletion that fuses ORF1 to ORF2 in-frame (in-frame control), were transcribed in vitro and the RNA was translated in rabbit reticulocyte lysate. The products were examined by SDS±PAGE (Figure 7) . The main product from both constructs has an apparent M r of 21 kDa, slightly greater than the predicted M r of 17.7 kDa [aberrant, slower than expected, mobility is observed with antizyme proteins from other species (Ivanov et al., 1998a) ]. From the ratio of wild-type to in-frame product, we estimate that the ef®ciency of frameshifting of C.elegans antizyme in reticulocyte lysate is~0.8%, which is somewhat lower than SPA frameshifting in the same system. Addition of spermidine to the translation reactions almost doubles the ef®ciency of frameshifting to~1.5% (the exact numbers are not easy to determine because of dif®culty in de®ning background values). The frameshifting properties of C.elegans antizyme mRNA were also tested in vivo in S.pombe cells. A sequence including all but the ®rst codon of ORF1 plus 180 nucleotides downstream was inserted between GST and lacZ of the PIU-LAC plasmid. Comparison of the b-galactosidase activity of cells (Dspa::LEU2 strain) transformed with the wild-type construct and the in-frame control constructs indicated 3.5% +1 frameshifting. From the frameshifting observed in the heterologous systems, as well as the sequence considerations discussed above, we conclude that expression of this C.elegans gene requires ribosomal frameshifting. Searching the EST database with the newly discovered C.elegans antizyme identi®ed antizyme orthologs in four other nematode species. In two cases (Necator americanus and Haemonchus contortus), the cDNA sequences in the database were suf®cient to make contigs of the complete coding regions. In the other two cases [Onchocerca volvulus (DDBJ/EMBL/GenBank accession No. AF217279) and Pristioncus paci®cus (DDBJ/EMBL/ GenBank accession No. AF217280)] the complete cDNA sequences were obtained by PCR amplifying and sequencing the full genes from cDNA libraries. As with the previously identi®ed eukaryotic antizyme genes, the ORF con®guration of the newly found nematode orthologs implies the necessity for +1 frameshifting for synthesis of full-length protein. The C.elegans antizyme mRNA frameshift site UUU-UGA is unique, differing from the UCC-UGA of previously known antizyme mRNAs. The C.elegans antizyme gene shares this feature with N.americanus and H.contortus but not with P.paci®cus and O.volvulus antizymes. The phylogenetic tree of nematode antizyme protein sequences matches exactly the phylogenetic relationship (Blaxter, 1998) of the nematodes expressing them, indicating that these gene sequences are the result of divergent evolution within the nematode lineage (data not shown). These results also show that the UUU-UGA frameshift site evolved after the last common ancestor of P.paci®cus and C.elegans but before the divergence of C.elegans, N.americanus and H.contortus (probably 450± 500 million years ago). The ability of UUU-UGA sequence to direct +1 frameshifting was further tested in a mammalian system in the context of the mammalian antizyme mRNA (i.e. in the presence of the 3¢ RNA pseudoknot and 5¢ stimulator). A BMV-coat-protein±antizyme 1 gene fusion construct, which has a TCC-TGA to TTT-TGA substitution, was transcribed and then translated in a rabbit reticulocyte lysate. Eleven percent frameshift ef®ciency was seen in the absence of exogenously added polyamines, 2.2 times the ef®ciency seen with the UCC-UGA transcript. The frameshift ef®ciency becomes 18% when 0.6 mM spermidine is added, which is 1.3 times that with the wild type (Matsufuji et al., 1995) . Similar results were obtained in cultured mammalian (Cos7) cells transfected with TTT-TGA mutant construct, the frameshift being higher than that of wild-type construct in both high-and lowpolyamine conditions (our unpublished results). These results demonstrate that the putative C.elegans frameshift site (UUU-UGA) is, if anything, shiftier than UCC-UGA in the antizyme 1 context and is subject to polyamine stimulation. The results presented show that the yeast S.pombe has a homolog of mammalian antizyme. This is the ®rst documented example of antizyme-type regulation of ODC in a lower eukaryote. Deleting SPA from the yeast genome has no detectable effect on viability or any other overt phenotypic effect but, as expected, it results in altered accumulation of polyamines in the cell. Interestingly, the effect is most pronounced in cells in stationary phase, where the knockout cells accumulate up to 40 times more putrescine than wild-type counterparts. This compares with a <2-fold increase of putrescine in exponentially growing cells. A likely explanation for this observation is that the usual rate of ornithine decarboxylation in exponentially growing cells is close to capacity givennormal' concentrations of substrate, enzyme and product. At the same time, all newly synthesized polyamines are continuously diluted through Fig. 6 . Effect of polyamine depletion on SPA +1 frameshifting. Polyamine depletion is achieved by overexpression of the wild-type version of SPA. The same cultures were assayed both for frameshifting and polyamine content. Numbers above columns indicate fold reduction of frameshifting and polyamine content compared with cells that do not overexpress SPA. Antizyme genes in S.pombe and C.elegans cell growth and division at a rate that is almost identical to the rate of maximum capacity synthesis. Cells in stationary phase can no longer dilute newly synthesized polyamines, and more importantly lack an effective antizymeindependent mechanism of shutting off ODC. This suggests that SPA is the primary regulator of ODC activity in S.pombe, not only during cell growth (short term regulation) but also in non-dividing cells (longer term regulation). Overexpression of SPA (5±7 days derepression) leads to complete depletion of intracellular putrescine. This result implies that in S.pombe ornithine decarboxylation is the only source of putrescine synthesis (the pathway from arginine via agmatine is not utilized). The complete depletion of cadaverine in SPA overexpressing cells suggests that ODC is the only enzyme in S.pombe that can decarboxylate lysine, which is also the case in rat tissues (Pegg and McGill, 1979) . It is somewhat perplexing that addition of putrescine to the media leads to only partial relief of the growth phenotype associated with SPA overexpression. There are two likely explanations. (i) Perhaps S.pombe imports putrescine poorly. (ii) Alternatively, like the mammalian system, maybe SPA inhibits not only ODC but also the polyamine transporter. Further experiments will help to distinguish between these two models. It is unclear how widespread the antizyme gene is within the fungal kingdom. We have identi®ed and cloned antizyme homologs from two other ®ssion yeasts (Schizosaccharomyces octosporus and Schizosaccharomyces japonicus) and from two distantly related fungi (Botryotinia fuckeliana and Emericella nidulans) (our unpublished results). The antizyme frameshift site of the latter two fungi has evolved in a unique way different from all other known antizymes, but nevertheless even these two distantly related fungi have conserved the autoregulatory +1 frameshifting. The fact that the yeast S.pombe has an antizyme gene suggests the possibility that the higher eukaryotic metazoans may all have an antizyme gene. The only previously reported antizyme activity in unicellular organisms is from E.coli, but recent analyses suggest that E.coli does not have a true antizyme (Ivanov et al., 1998d) . This makes SPA the ®rst bona ®de antizyme in a unicellular organism. The remarkable similarity of the core sequence important for antizyme frameshifting from S.pombe to humans could be due to convergent or divergent evolution. The near identity of this sequence in worms, Drosophila, Xenopus, zebra®sh and humans argues against convergent evolution, as if antizyme frameshifting arose in a common ancestor perhaps more than one billion years ago. Three cis-acting RNA elements are known to stimulate mammalian antizyme 1 frameshifting. One is a 50 nucleotide sequence immediately 5¢ of the shift site (Matsufuji et al., 1995; our unpublished results) . A second stimulator is the UGA stop codon of ORF1 and the third is an RNA pseudoknot starting 3 nucleotides 3¢ of the UGA stop codon. Among frameshift sites of the previously identi®ed antizymes from mammals all the way to Drosophila, there is substantial similarity in the sequences immediately 5¢ of the shift site. Sixteen of the last 18 nucleotides of ORF1 are completely conserved in these genes. Schizosaccharomyces pombe and C.elegans antizymes have 9 of 9 and 6 of 9 (14 out of 19 in O.volvulus) nucleotides identical to the consensus, respectively. For the 5¢ sequences, generally, the more distantly related two antizymes are, the more the similarity is con®ned to the 3¢ end of that region. Our SPA ORF1 deletion data show that mutation of nucleotides that are part of the 5¢ consensus sequence leads to reduced frameshifting ef®ciency. This is another indication that conservation of nucleotide sequence in this region is because of its importance for stimulating ef®cient +1 frameshifting. It is quite striking that in all antizyme gene sequences identi®ed so far, including a number of unpublished ones, ORF1 ends with a UGA stop codon. This is particularly surprising since any of the other two stop codons can substitute for UGA to stimulate antizyme 1 frameshifting, although slightly less ef®ciently, in vitro (Matsufuji et al., 1995) and in vivo (our unpublished results). The 3¢ pseudoknot that stimulates frameshifting in antizyme 1 is highly conserved in all known vertebrate antizymes, including mammalian antizyme 2 ( Figure 1B) . None of the invertebrate antizyme mRNAs identi®ed so far, including those presented here, has a sequence in the equivalent region that can be simply folded to a comparable RNA structure. However, sequences immediately 3¢ of the frameshift site are conserved between invertebrates and vertebrates. The conservation of this region between Drosophila and the vertebrate counterparts has already been noted (Ivanov et al., 1998b) . The C.elegans antizyme gene contains the sequence YGYCCCYCA (Y = pyrimidine) in this region, which is identical to the consensus. The antizyme genes from the other four nematodes also have a similar sequence ( Figure 1B) . The signi®cance of this similarity is not clear [in fact, sequences in this region appear to play no role in antizyme 1 in vitro frameshifting outside of the RNA pseudoknot context (Matsufuji et al., 1995) ]. Only two examples are known where RNA elements 3¢ of the frameshift site stimulate +1 frameshifting. One is the RNA pseudoknot of mammalian antizyme 1 and the second is a short RNA sequence immediately following the frameshift site of Ty3 (Farabaugh et al., 1993) . Additional examples would be very helpful in deciphering the role such elements play in the mechanism of +1 frameshifting. It is currently not known how many and which of the invertebrate antizyme genes contain 3¢ frameshift stimulators. The results presented here show that an S.pombe 3¢ stimulator enhances frameshifting 10-fold. This stimulator appears completely different from the 3¢ RNA pseudoknot in vertebrates. Our deletion experiments indicate that none of the predicted RNA structures contained within the minimally required 3¢ region [up to 150±180 nucleotides downstream of the frameshift site ( Figure 5C )] are suf®cient to confer the stimulatory effect. The SPA 3¢ stimulator may act directly through sequence or may have an unusual RNA structure involving non-Watson±Crick base pairing. More detailed mutagenesis combined with phylogenetic analysis would be required to discern the nature of the 3¢ stimulator of SPA. The nematode antizymes were analyzed for the presence of possible 5¢ or 3¢ stimulators¯anking the core frameshift site. Computer RNA folding programs did not identify any potentially interesting structure. More importantly, phylogenetic analysis with the ®ve identi®ed nematode antizymes failed to identify any conservation of primary RNA sequence (or for that matter potential secondary structure) outside of the core region that is shared between two or more members. This could indicate that no such extra cis-acting stimulators exist in nematode antizymes or that they are located in a very different place within the mRNA, for example the 3¢ untranslated region (the latter suggestion is not supported by our sequence analysis). A common mechanism for frameshifting is re-pairing of the peptidyl tRNA in the new reading frame. However, an alternative mechanism whereby the peptidyl tRNA merely occludes the ®rst base of the next codon, has been documented for yeast Ty3 frameshifting (Farabaugh et al., 1993) . Results of experiments with some mutants of the mammalian antizyme 1 shift site pointed to an occlusion mechanism (Matsufuji et al., 1995) . However, the mechanism with the wild-type, UCC-UGA, shift site is not clear. For C.elegans antizyme the UUU-UGA sequence would be an obvious candidate for a re-pairing since Phe-tRNA could pair perfectly with UUU in both frames. But with UCC-UGA the Ser-tRNA ®rst reading UCC could at best pair two out of three with CCU. This important problem warrants further investigation. The frameshift ef®ciency of SPA frameshift site is lower than that observed with mammalian antizyme 1 even when both are tested in the same organism (S.pombe) [for the frameshift ef®ciency of antizyme 1 cassette in S.pombe, see Ivanov et al. (1998c) ]. It is possible that the observed ef®ciencies for S.pombe antizyme are arti®cially low because the constructs do not include all the cis-acting stimulatory elements. On the other hand there is no reason why a lower level of frameshifting does not correctly re¯ect the evolved balance with the other characteristics of the complex system such as relative protein stabilities. Like other core cellular processes, the antizyme polyamine regulatory scheme is conserved from yeast S.pombe to human. It is not obvious why this very special mechanism is so exquisitely preserved over vast evolutionary time. Perhaps there is another whole aspect to the system that our experiments do not yet detect. From this viewpoint it would seem very important to exploit the genetics systems of S.pombe and C.elegans to understand more thoroughly the physiological effects of perturbing the antizyme system. The SPA gene was ampli®ed using the following primers: 5¢-CAAAACAAGTTTTCATTATTGGTTTTTTTTAAATCAATCCCC (sense) and 5¢-CGTAAATCCAATCTAAATTTAATCTTCAACTAA-ATCATGAAAAGCCTC (antisense). The S.pombe cDNA library used as a template in the ampli®cation was kindly provided by R.Rowley (University of Utah). The C.elegans antizyme gene was ampli®ed using the following primers: 5¢-CCCAGGAATTCCTCGAGTATTTTGA-GTATAATTTTAC (sense) and 5¢-CGGCCGCTCGAGTTAGACCTT-GTAGCTCATGATG (antisense). This same ampli®ed DNA was used to make the constructs for in vitro transcription and translation of C.elegans antizyme by cloning it into pTZ18U plasmid using the SacI and HindIII sites incorporated in the two primers. The in-frame construct was made using a two-step PCR. The cDNA sequences of O.volvulus and P.paci®cus antizyme genes were obtained by performing 5¢ and 3¢ RACE PCR with cDNA libraries, which were kindly provided by Ralf Sommer (P.paci®cus) and Susan Haynes (O.volvulus). The SPA overexpression constructs were made by amplifying the gene with the primers 5¢-GCATCCGAATTCCCAAATCCAAGCATCATACGCC (sense) and 5¢-GCATCCGGATCCGCCAGTGTTCTTACTTTGAGA-TGC (antisense), and then inserting BamHI-digested product between the MscI and BamHI sites of pREP3 plasmid. The in-frame construct was made by two-step PCR and subsequently all in-frame SPA constructs described below were made by one-step PCR using this plasmid's DNA as a template. To make the constructs for frameshift assays in S.pombe, DNA fragments with a given nucleotide length (as described in the main text), were ampli®ed from both the SPA and C.elegans antizyme constructs described above. These fragments were then cloned between the KpnI and BstEII sites of PIU-LAC plasmid (Ivanov et al., 1998c) . The PCR primers included anAC' spacer between the 5¢ cloning site (BstEII) and the antizyme sequences in order to correct the reading frame. The in vivo frameshifting assays in S.pombe (strains ura4-D18 leu1-32 ade6-M216 h ± and Dspa::LEU2 ura4-D18 leu1-32 ade6-M216 h ± ) were done as described (Ivanov et al., 1998c) . The plasmid for GST±SPA expression was made by PCR amplifying SPA (all but the ®rst codon of ORF1 through the downstream ORF2) from an in-frame template and cloning the product into the EcoRI and XhoI restriction sites of pGEX-5X-3 plasmid. The antizyme frameshift site in the BMV-coatantizyme fusion construct (C3NE) (Matsufuji et al., 1995) was mutated with a two-step PCR. To generate the two knockout strains, Dspa::URA4 and Dspa::LEU2, both ORFs of SPA were replaced exactly with the ORF of either URA4 or LEU2. To accomplish this, two pairs of primers ampli®ed URA4 and LEU2 such that 50±60 nucleotides, which normallȳ ank the two ORFs of SPA,¯ank the ORFs of the two genes. The ampli®ed DNA products were gel puri®ed and 2 mg of each were used to electroporate into ura4-D18 leu1-32 ade6-M216 h ± cells. URA + and LEU + transformants were selected by growth on URA ± and LEU ± media, respectively. PCR screen and partial sequencing, with primers¯anking the regions used for the homologous recombination, con®rmed the SPA disruptions. All DNA clones were sequenced with automated sequencing machines (ABI 100). Schizosaccharomyces pombe ODC active crude extracts were prepared as follows: S.pombe (strain 1519, leu1-32, h ± ) provided by R.Rowley was grown to OD 600 0.7 in 50 ml of minimal media + LEU. Ten milligrams of lysing enzymes (Sigma) were added, followed by continued incubation for 30 min at 30°C. Cells were harvested and washed once with cold homogenization buffer [25 mM Tris±HCl pH 7, 0.25 M sucrose, 1 mM dithiothreitol (DTT), 20 mM pyridoxal-5-phosphate, 2 mM EDTA] then resuspended in 0.75 ml of homogenization buffer. Cells were broken open and the lysate was clari®ed by centrifugation at 10 000 r.p.m. for 15 min at 4°C. Extracts were dialyzed overnight in dialysis buffer (25 mM Tris± HCl pH 7.4, 1 mM DTT, 20 mM pyridoxal-5-phosphate, 0.1 mM EDTA). A volume of 25 ml of extract was used for each ODC assay. ODC activity was assayed by measuring the release of 14 CO 2 from L-[1-14 C]ornithine (Amersham) as described (Nishiyama et al., 1988) . Each reaction took 1 h. Pre-incubation of S.pombe extract with 0.1 mM di¯uoromethyl ornithine (DFMO) for 15 min led to >99% inhibition of 14 CO 2 release. The cells were collected by centrifugation, washed twice with 1 ml of phosphate-buffered saline (PBS) and then the pellet was frozen at ±80°C until use. The pellet was resuspended in 0.1 ml of PBS. An aliquot of the suspension was mixed with an equal volume of 8% perchloric acid, vortexed for 1 min, kept on ice for 5 min and centrifuged at 15 000 r.p.m., 4°C for 5 min. Ten microliters of the supernatant were subjected to polyamine analysis using¯uorometry on high-performance liquid chromatography as described previously (Murakami et al., 1989) . Protein concentrations were determined with the BCA protein assay kit (Pierce). The experiments with the BMV-coat-antizyme fusion constructs were performed as described previously (Matsufuji et al., 1995) . All other plasmid DNA templates were prepared using QIAGEN Miniprep Kit and then digested with HindIII. Transcripts for SPA in vitro translation were made from PCR templates that had a T7 promoter incorporated into the PCR primers. Linearized DNA (1 mg) was used as a template for in vitro transcription with Ambion MEGAshortscript TM T7 Kit. The DNasetreated RNAs were recovered and resuspended in 40 ml of RNase-free water. One microliter of each speci®ed transcript suspension was used in each in vitro translation reaction [0.5 ml of 1 mM amino acid mix ±Met, 7 ml of reticulocyte lysate (Promega), 0.5 ml of [ 35 S]Met (Amersham)] to a total volume of 10 ml. The reactions were stopped by adding 1 ml of RNase (10 mg/ml). The frameshift ef®ciencies were quanti®ed as described (Ivanov et al., 1998a) .
Intracellular bacterial and viral pathogens have evolved numerous mechanisms to appropriate and exploit different systems of the host during their life cycles in order to facilitate their spread during entry and exit from the host (Cudmore et al., 1997; Finlay and Cossart, 1997; Dramsi and Cossart, 1998) . In the case of viruses, perhaps the best studied example is the exploitation of the actin cytoskeleton by vaccinia virus during its exit from infected cells (Cudmore et al., 1997) . Vaccinia virus is a large DNA virus with a genome of~191 kb encoding 260 open reading frames (ORFs) that is a close relative of variola virus, the causative agent of smallpox (Johnson et al., 1993; Massung et al., 1993) . Vaccinia virus morphogenesis is a complex process which occurs in the cytoplasm of infected cells and results in the formation of the intracellular mature virus (IMV) and the intracellular enveloped virus (IEV). IMV consist of a viral core of DNA and protein enveloped in a membrane cisterna derived from the intermediate compartment (Sodeik et al., 1993) . The IMV core contains ®ve major proteins, A3L, A4L, A10L, F17R and L4R (Vanslyke and Hruby, 1994; Jensen et al., 1996a) , while 12 proteins, A12L, A13L, A14L, A14.5L, A17L, A27L, D8L, G4L, G7L, H3L, I5L and L1R, are associated with the membranes around the virus particle (Jensen et al., 1996a; Betakova et al., 2000) . Depending on the virus strain and cell type, a proportion of IMV can become enwrapped by a membrane cisterna derived from the trans-Golgi apparatus to give rise to IEV particles (Schmelz et al., 1994) . To date, six IEV-speci®c proteins, A33R (Roper et al., 1996) , A34R (Duncan and Smith, 1992) , A36R (Parkinson and Smith, 1994) , A56R (Payne and Norrby, 1976; Shida, 1986) , B5R (Engelstad et al., 1992; Isaacs et al., 1992) and F13L (Hirt et al., 1986) , have been identi®ed. Studies using recombinant viruses have shown that A33R, A34R, B5R and F13L play an important role in IEV assembly (Blasco and Moss, 1991; Engelstad and Smith, 1993; Wolffe et al., 1993 Wolffe et al., , 1997 Roper et al., 1998; Sanderson et al., 1998a; Ro Èttger et al., 1999) . Vaccinia virus is thought to leave the cell by fusion of the outer IEV membrane with the plasma membrane, to give rise to the extracellular enveloped virus (EEV) (Morgan, 1976; Payne, 1980; Blasco and Moss, 1991) or the cell-associated enveloped viruses (CEV) which remain associated with the outer surface of the plasma membrane (Blasco and Moss, 1992) . During the complex vaccinia infection process, the actin cytoskeleton is dramatically reorganized and numerous actin comet-like tails are induced by IEV particles (Cudmore et al., 1995; Ro Èttger et al., 1999) . Using actin polymerization as the driving force, IEV particles are propelled on actin tails until they contact the plasma membrane and extend outwards, thereby facilitating infection of neighbouring cells (Cudmore et al., 1995) . In addition, vaccinia infection results in stimulation of cell motility, loss of contact inhibition and changes in cell adhesion (Sanderson and Smith, 1998; Sanderson et al., 1998b) . Vaccinia virus-induced cell motility can be subdivided further into cell migration and extension of neurite-like projections, the latter of which is dependent on microtubules (Sanderson et al., 1998b) . The dependence of neurite-like projection formation on microtubules suggests that the microtubule cytoskeleton may also play a role during the life cycle of vaccinia virus. Indeed, recently, the vaccinia A27L protein and microtubules have been shown to be required for ef®cient IMV dispersion (Sanderson et al., 2000) . Furthermore, in the absence of vaccinia actin-based motility, cell to cell spread still occurs although it is less ef®cient (Wolffe et al., 1997 Sanderson et al., 1998a) , suggesting that additional transport mechanisms must exist. Given these observations, we wondered whether the microtubule cytoskeleton has a function during the life Vaccinia virus infection disrupts microtubule organization and centrosome function The EMBO Journal Vol. 19 No. 15 pp. 3932±3944, 2000 cycle of vaccinia virus. We now report that the microtubule cytoskeleton and the dynein±dynactin complex play an important role during the early stages of vaccinia infection. However, later during the infection cycle, loss of centrosome function and accumulation of viral-encoded microtubule-associated proteins (MAPs) result in a dramatic rearrangement of the microtubule cytoskeleton. Vaccinia localization in the vicinity of the MTOC depends on microtubules and the dynein±dynactin complex Indirect immuno¯uorescence labelling shows that by 6 h post-infection the majority of vaccinia virus particles are concentrated in the area coinciding with the centre of the microtubule aster ( Figure 1A and C). To examine whether this localization is indeed microtubule dependent, we infected cells pre-treated with nocodazole to depolymerize microtubules. In the absence of microtubules, virus particles were distributed throughout the cytoplasm ( Figure 1B and D) . The accumulation of virus particles in the area around the centre of the microtubule aster suggested that a microtubule minus end-directed motor may be involved in establishing the position of the virus in this location. To examine this possibility, we infected cells overexpressing p50/dynamitin which acts as a dominantnegative for dynein±dynactin function (Echeverri et al., 1996) . We found in cells overexpressing p50/dynamitin that virus particles did not accumulate at the centre of the microtubule aster but rather throughout the cytoplasm, as occurs in the absence of microtubules (compare Figure 2B with Figure 1D ). As vaccinia morphogenesis involves wrapping by host membranes, it was possible that the effects of nocodazole and p50/dynamitin on virus localization were in fact due to disruption of the intermediate compartment and Golgi apparatus by these reagents (Burkhardt et al., 1997) . However, two independent experiments showed that this is not the case. First, in cells infected in the absence of microtubules, the Golgi apparatus as well as vaccinia virus particles are dispersed throughout the cytoplasm but do not co-localize ( Figure 3F and O). Secondly, vaccinia particles remain in the vicinity of the microtubule-organizing centre (MTOC) when the Golgi but not the microtubules was disrupted by treatment with brefeldin A ( Figure 3G and P). Similar results were obtained using other markers: A17L for vaccinia, galactosyltransferase for the Golgi or ERGIC53 for the intermediate compartment (data not shown). Taken together, our data indicate that the microtubule cytoskeleton is required for the localization of newly assembled virus particles in the vicinity of the MTOC during vaccinia infection. Formation of functional IEV, but not IMV, is microtubule dependent Given the requirement for microtubules in vaccinia localization, we subsequently examined whether this localization has a role in morphogenesis of the two different intracellular forms of vaccinia virus, IMV and IEV. From electron microscopic examination of cells infected in the presence of nocodazole, it became clear that IMV particles which are morphologically indistinguishable from controls are formed ( Figure 4 ). Although IMV particles are assembled in the absence of microtubules, we wondered whether their number is reduced and whether those that are formed are infectious, since the integrity of the intermediate compartment depends on microtubules (Burkhardt et al., 1997) . To address this question, three independent virus stocks were prepared in the presence or absence of nocodazole. To simplify the interpretation of the data, we used the recombinant vaccinia virus mutant DF13L, which is unable to form IEV (Blasco and Moss, 1991) . The ®nal concentration of virus particles produced, Vaccinia uses and abuses the microtubule cytoskeleton as determined by the method of Joklik (1962) , was 30.2 6 5.2 3 10 10 particles/ml in the presence of microtubules and 9.0 6 6.7 3 10 10 particles/ml in the absence of microtubules. Although there is a 3-fold decrease in the number of virus particles formed in the absence of microtubules, the particles that are formed are infectious (data not shown). While infectious IMV are formed in the absence of microtubules, we found no evidence for IEV formation, based on electron microscope examination of cells infected in the presence of nocodazole ( Figure 4 ). We did, however, observe IMV particles partially wrapped in trans-Golgi membranes most probably in the process of abortive IEV formation ( Figure 4D ). Given these data, we examined by indirect immuno¯uorescence whether low amounts of IEV particles are formed in the absence of microtubules. However, we could ®nd no evidence for colocalization of the IEV protein markers A36R, A34R or A33R with vaccinia particles formed in the presence of nocodazole ( Figure 5F ). We also found no evidence for IEV formation, based on their ability to nucleate actin tails ( Figure 5O ). As IEV particle assembly involves wrapping by the Golgi apparatus (Schmelz et al., 1994) , we examined the effects of only disrupting this membrane compartment using brefeldin A. We could ®nd no evidence for IEV formation, based on co-localization of IEV protein markers with virus particles and actin tails in cells infected in the presence of brefeldin A ( Figure 5G±I and P±R). Indeed, in brefeldin A-treated cells, the IEV membrane proteins required for assembly were observed in the endoplasmic reticulum and not the trans-Golgi ( Figure 5H ). In summary, our data indicate that the microtubule cytoskeleton is required for ef®cient IMV assembly and is essential for IEV formation. In the course of our experiments, it became obvious that the Golgi apparatus becomes progressively dispersed during infection co-concominantly with disruption of the microtubule network ( Figure 6 ). Further analysis showed that during infection the normal morphology of the microtubule cytoskeleton is replaced by morphologically aberrant microtubule forms, which vary among each other but have in common the absence of a discrete MTOC ( Figure 7 ). These aberrant forms can be broadly classi®ed into three types: (i) cells with a disorganized microtubule network where microtubules seem randomly oriented ( Figure 7E ); (ii) cells in which microtubules form rings around the nucleus and throughout the cytoplasm ( Figure 7H ); or (iii) cells with long projections consisting of microtubule bundles ( Figure 7K ). We quanti®ed the appearance of the different morphological forms in ®ve independent infection experiments, in which 200 cells were counted for each time point for each experiment ( Figure 7C , F, I and L). Small compact cells, representing 20.7 6 2.6, 21.8 6 12.4 and 29.7 6 15.2% for 5, 8 and 24 h post-infection, respectively, in which the microtubule cytoskeleton morphology was not evident were not included in the analysis. Already by 5 h post-infection, when virus particle assembly has occurred, the normal aster microtubule con®guration has been disrupted and replaced in the majority of cells by microtubules without obvious organization from the MTOC ( Figure 7F ). Furthermore,~10% of cells have microtubule rings and 5% of cells have long projections by this time point ( Figure 7I and L). As the infection proceeds, microtubules become progressively more disrupted and bundled ( Figure 7I and L). From our observations, there seems to be no obvious connection between the disruption and changes in the actin and the microtubule cytoskeletons ( Figure 7) . Moreover, the same reorganization of the microtubule network occurs in cells infected with the vaccinia deletion mutants DF13L and DA36R which do not make actin tails (data not shown). The effects of vaccinia virus infection on the reorganization of the microtubule cytoskeleton were also observed in all cell lines we examined (BHK-21, C 2 C 12 , PtK2, RK 13 and Swiss 3T3) to varying degrees (data not shown). Our data show that vaccinia infection results in severe disruption of the normal morphology of the microtubule cytoskeleton. The formation of microtubule bundles and the loss of organization from the MTOC in vaccinia-infected cells is strongly reminiscent of the phenotype observed in cells overexpressing a MAP (Weisshaar et al., 1992; Togel et al., 1998) . As overexpression of MAPs stabilizes microtubules, we examined whether the microtubule cytoskeleton in infected cells was more resistant to depolymerization by nocodazole or cold treatment ( Figure 8 ). This was indeed the case, suggesting that the virus genome may encode viral proteins with MAP-like properties. To identify viral proteins which exhibit microtubule-binding properties, we performed microtubule co-sedimentation assays using extracts prepared from uninfected and vaccinia-infected cells (Figure 9 ). Initial experiments, however, revealed that intact virus particles in the extracts were prone to pellet even in the absence of microtubules, making identi®cation of viral MAPs impossible. To avoid this problem, we prepared extracts from cells infected in the presence of rifampicin, a drug that inhibits vaccinia virus particle assembly but does not affect viral protein expression (Moss et al., 1969; Tan and McAuslan, 1970) . The morphological effects of vaccinia infection on the microtubule cytoskeleton were the same in the presence or absence of rifampicin (data not shown). Comparison of the proteins present in pellets from microtubule co-sedimentation assays reveals that a number of additional prominent and minor bands are present in extracts prepared from vaccinia-infected but not from uninfected cells (Figure 9 ). Co-sedimentation assays Vaccinia uses and abuses the microtubule cytoskeleton performed in the presence of nocodazole or with coldtreated extracts reveal that the majority of these additional bands disappear in the absence of microtubules. To identify the viral proteins co-sedimenting with microtubules, we performed in-gel protease digestion followed by analysis of the resulting peptides by MALDI mass spectrometry. Using this approach, we identi®ed a number of potential vaccinia-encoded MAPs: A10L (a structural protein), I1L and L4R (which are DNA-binding proteins), all of which are associated with viral cores (Vanslyke and Hruby, 1994; Jensen et al., 1996a; Klemperer et al., 1997) , and A6L which is conserved in all poxvirus genomes but is of unknown function (Figure 9 ). A10L and L4R associate with microtubules in vivo and mediate binding of viral cores to microtubules in vitro Using available antibodies, we examined the localization of A10L, L4R and I1L in infected cells to see whether they associate with microtubules in vivo, in addition to their essential role in the virus core (Vanslyke and Hruby, 1994; Jensen et al., 1996a) . As a negative control, we also examined the localization of the A3L core protein which was identi®ed as the prominent 70 kDa protein pelleting in the absence of microtubules (Figure 9 ). Indirect immunouorescence analysis showed that A10L and L4R are associated with microtubules, in both the presence and absence of rifampicin ( Figure 10 ). As expected, A10L and L4R were also associated with viral particles (data not shown). In contrast, I1L and A3L were never observed in association with microtubules, regardless of the ®xation conditions, but were localized to viral factories and viral particles, respectively (data not shown). Interestingly, A10L and L4R were not associated with all microtubules but were co-localized with a subset of acetylated microtubules ( Figure 10) . The association of A10L and L4R with virus particles and microtubules raises the question of whether there is a role for this microtubule-binding activity during infection. We wondered whether these two proteins mediate the interaction of incoming viral cores with microtubules at the beginning of infection, as cores and not virus particles are released in the cytoplasm at the start of the infection cycle (Ichihashi, 1996; Vanderpasschen et al., 1998; Pedersen et al., 2000) . To examine this possibility, we investigated whether puri®ed viral cores would bind microtubules in vitro. We found that viral cores were able to bind microtubules, while protease-treated cores showed no association ( Figure 11A and B) . Pre-incubation of puri®ed viral cores with antibodies against A10L and L4R speci®cally inhibited the interaction of viral cores with microtubules ( Figure 11C and D); in contrast, IgG or antibodies against A3L had no inhibitory effect ( Figure 11E and F). Taken together, our data suggest that A10L and L4R have MAP-like properties and may play a role in mediating interactions of incoming viral cores with microtubules. The dramatic rearrangement of the microtubule cytoskeleton which occurs during vaccinia infection is unlikely to be attributed exclusively to the action of A10L and L4R since they only associate with a subset of microtubules ( Figure 10) . Furthermore, the loss of microtubule organization precedes detectable association of A10L and L4R with microtubules, which occurs from~8 h post-infection. We therefore wondered whether vaccinia infection disrupts centrosome function, given the loss of microtubule aster con®guration during infection (Figure 7 ). Since microtubules are nucleated by the centrosome in animal cells, we examined whether vaccinia infection affects g-tubulin, which is critically required for this process (Stearns and Kirschner, 1994) . We observed that g-tubulin labelling of the centrosome is greatly reduced from as early as 2 h post-infection ( Figure 12 ). The same result was obtained when we infected PtK1 cells stably expressing green¯uorescent protein (GFP)-labelled g-tubulin (Khodjakov and Rieder, 1999) . In addition, the centrosomal and centriolar components pericentrin, C-Nap 1, Nek 2 and centrin are reduced by immuno¯uorescence in the centrosomes/centrioles of vaccinia-infected cells ( Figure 12) . Furthermore, the reduction of centrosomal markers requires viral protein synthesis as their levels are not affected when cells are infected in the presence of cycloheximide (data not shown). The dramatic reduction of g-tubulin from the centrosome implies that vaccinia infection perturbs centrosome Vaccinia uses and abuses the microtubule cytoskeleton function. To test this hypothesis, we examined whether the centrosome in vaccinia-infected cells could re-nucleate microtubules, following their depolymerization by nocodazole. We found that by 2 h post-infection, when we already see a reduction in g-tubulin, microtubule nucleation from the centrosome was very inef®cient, as compared with uninfected controls, indicating that vaccinia has disruptednormal' centrosome function (Figure 13 ). At later times post-infection, microtubule re-nucleation ef®ciency from the centrosome was even lower (data not shown). However, following nocodazole washout, microtubules eventually are repolymerized throughout the cytoplasm of infected cells but do not display any organization from the MTOC, as do controls (compare Figure 13I and K). The size of virus particles is such that they are unlikely to move within and between cells by diffusion alone, suggesting that their movements will require interactions with the host cytoskeleton. Previous data have shown that vaccinia virus both disrupts and hijacks the actin cytoskeleton to facilitate movement of the intracellular enveloped form of vaccinia virus (Cudmore et al., 1995; Ro Èttger et al., 1999) and of the infected cell itself (Sanderson and Smith, 1998; Sanderson et al., 1998b) . The data described here now show that vaccinia also uses and subsequently disrupts the microtubule cytoskeleton during its infection cycle. It is clear from our experiments and the previous observations of Ulaeto et al. (1995) that microtubules are required to maintain the integrity of the Golgi apparatus which is in turn required for IMV wrapping to form IEV (Schmelz et al., 1994) . In contrast, IMV are assembled in the absence of microtubules, albeit at reduced levels. While microtubules are not required for IMV assembly, they are required together with the dynein±dynactin complex for virion accumulation in the vicinity of the microtubule aster. One can envisage that minus enddirected microtubule-dependent movements of IMV particles from their site of assembly in the viral factory towards the MTOC, by the dynein±dynactin complex, would enhance the possibility of wrapping with the Golgi apparatus and subsequent IEV formation. Recently it has been shown that the IMV protein A27L and microtubules are required for ef®cient IMV dispersion from the viral factories (Sanderson et al., 2000) . In the absence of A27L, mature IMV particles accumulate at the periphery of the virus factory but do not subsequently wrap to form IEV, presumably because they are unable to move on microtubules (Sanderson et al., 2000) . The microtubule-and dynein±dynactin-dependent accumulation of vaccinia in the vicinity of the MTOC is analogous to the microtubule-dependent movements required for herpes simplex virus 1 (HSV-1) and adenovirus to reach their site of replication in the nucleus (Sodeik et al., 1997; Suomalainen et al., 1999; Leopold et al., 2000) . In the case of HSV-1, the UL34 protein, 9 . Vaccinia encodes proteins that co-sediment with microtubules. Analysis of pellets from in vitro microtubule co-sedimentation assays performed with protein extracts from vaccinia-infected (inf.) and uninfected (uninf.) cells. Twice the amount of pellet has been loaded in control assays performed in the absence of microtubules (nocodazole or 4°C). Proteins co-sedimenting with microtubules that were only present in extracts from infected cells are indicated by an asterisk. The identity of proteins determined by in-gel proteolysis MALDI mass spectrometry is indicated (arrowheads). which is associated with the incoming nucleocapsids, interacts with the intermediate chain of cytoplasmic dynein (IC-1a) (Ye et al., 2000) . It has also been reported that incoming nucleocapsids of pseudorabies virus, an alphaherpes virus closely related to HSV-1, are associated with and dependent on microtubules for their movement to the nucleus (Kaelin et al., 2000) . This interaction may be mediated by the UL25 protein, a minor but essential component of the capsid, which co-localizes with microtubules and accumulates at the MTOC (Kaelin et al., 2000) . The accumulation of UL25 at the MTOC is consistent with a possible interaction with the dynein± dynactin motor complex which is known to be localized at the MTOC (Echeverri et al., 1996) . It would not be surprising, based on observations with HSV-1 and pseudorabies, if microtubules and dynein±dynactin were also involved in establishing the infection cycle of cytomegalovirus (CMV), Epstein±Barr virus and varicella-zoster virus, all of which are herpes viruses. The other clear example of microtubule-dependent virus movements during the establishment of infection is that of incoming human foamy virus (HFV) which is dependent on microtubules and presumably a minus end-directed microtubule motor to get to its nuclear replication site (Saib et al., 1997) . In the absence of protein expression, HFV Gag proteins, which are associated with the viral genome, accumulate at the centrosome in a microtubuledependent fashion prior to nuclear import (Saib et al., 1997) . The centrosomal accumulation of Gag proteins of HFV, however, appears to be unique for this class of retroviruses as no similar localization has been reported for human immunode®ciency virus (HIV) or other retroviruses. On the other hand, the Gag protein of murine leukaemia virus and HIV has been shown to interact with KIF4, a microtubule plus end-directed kinesin motor, both in vitro and in vivo (Kim et al., 1998; Tang et al., 1999) , suggesting that additional roles may exist for microtubules and motors during the outward movement of virus particles. Indeed, vaccinia virus particles are able to reach the cell periphery in the absence of actin-based motility (see images in Wolffe et al., 1997; Sanderson et al., 1998a Sanderson et al., , 2000 Ro Èttger et al., 1999) , suggesting that viral particles can also move out on microtubules (Sanderson et al., 2000) . Microtubule-dependent motordriven movements of virus particles represent an ef®cient mechanism to achieve a peri-nuclear localization, required to facilitate entry into the nucleus during establishment of infection. They also provide an excellent way for newly assembled virus particles to reach the cell periphery, facilitating the continued spread of infection. Our data show that although vaccinia virus uses the microtubule cytoskeleton to achieve a peri-nuclear localization, microtubule and Golgi organization becomes disrupted later during the infection process. Interestingly, HSV-1 and CMV have also been reported to disrupt the microtubule cytoskeleton and Golgi organization in their infection cycles (Avitabile et al., 1995; Fish et al., 1996) . While disruption of the microtubule network might at ®rst sight not appear to be bene®cial to the virus, it may not actually hinder viral spread but could enhance it. First, extensive virus assembly and spread to the cell periphery have already occurred by the time the microtubule cytoskeleton and Golgi organization are disrupted. Secondly, disruption of microtubule organization may overcome potential microtubule motor anchoring effects at the MTOC, thus allowing viral spread to the periphery to occur more easily. Lastly, the formation of long projections of up to 200 mm supported by extensive microtubule bundles provides a means to achieve long range spread of virus particles (Sanderson et al., 1998b) . It is clear that disruption and reorganization of the microtubule cytoskeleton by vaccinia virus is mediated by the combined effects of viral proteins with MAP-like properties and loss of microtubule-organizing function from the MTOC. The same may also be true for HSV-1, although disruption of centrosome function remains to be established, as late in infection microtubules are organized in bundles around the nucleus and do not show MTOCorchestrated organization (Avitabile et al., 1995) . The identi®cation of viral proteins with MAP-like properties is not unique to vaccinia virus. The VP22 tegument protein from HSV-1 co-localizes with microtubules in infected cells and induces microtubule bundles when expressed in uninfected cells (Elliott and O'Hare, 1998) . Other examples of viral MAPs based on their in vivo localization Fig. 11 . Vaccinia cores bind directly to microtubules in vitro. Puri®ed viral cores labelled by DAPI (green) bind to rhodamine-labelled microtubules (red) in the absence of ®xation (A). Binding to microtubules is not observed if cores are pre-treated with protease (B) or pre-incubated with antibodies against the A10L (C) or L4R (D) proteins. In contrast, pre-incubation of puri®ed viral cores with control IgG (E) or antibody against the A3L protein (F) does not inhibit their interaction with microtubules. Scale bar = 5 mm. Vaccinia uses and abuses the microtubule cytoskeleton or in vitro association with microtubules are the N protein from murine coronavirus (Kalicharran and Dales, 1996) , the movement protein from tobamovirus (Heinlein et al., 1995) , the aphid transmission factor from cauli¯ower mosaic virus (Blanc et al., 1996) , the UL25 protein from pseudorabies virus (Kaelin et al., 2000) , the VP4 spike protein from rotavirus (Nejmeddine et al., 2000) and the M protein of vesicular stomatitis virus (VSV) (Melki et al., 1994) . The identi®cation of A10L and L4R, two viral core proteins, as MAP-like proteins was, however, unexpected given their previously characterized role in viral morphogenesis (Vanslyke and Hruby, 1994) . The interaction of A10L and L4R with microtubules in vivo, together with the in vitro microtubule-binding data, suggest a potential mechanism for the association of viral cores with microtubules. One could envisage that viral cores which are released into the cytoplasm at the beginning of infection (Ichihashi, 1996; Vanderpasschen et al., 1998; Pedersen et al., 2000) bind directly to microtubules in a manner analogous to adenovirus or HSV-1 nucleocapsids. Further work is required to determine whether incoming cores do in fact move towards the MTOC by the dynein± dynactin complex and/or use the complex for anchoring on microtubules. The loss of centrosome function must enhance disruption of the microtubule cytoskeleton during infection. Indeed, the loss of microtubule organization from the MTOC precedes detectable association of A10L and L4R with microtubules, which occurs from~8 h post-infection. Vaccinia-induced loss of centrosomal proteins is inhibited by cycloheximide, indicating that viral protein expression is required for disruption of the centrosome microtubule nucleation activity. To our knowledge, vaccinia virus infection represents the ®rst example of virus-induced disruption of centrosome function, although we would predict that HSV-1 may have a similar effect. The mechanism by which vaccinia virus disrupts the centrosome requires further study; nevertheless, it is clear that understanding the molecular basis of this disruption will provide important insights into the regulation and stability of centrosome function which currently is the subject of intense research (Ohta et al., 1993; Lane and Nigg, 1997; Karsenti, 1999) . HeLa cells (ATCC CCL2) were infected with the wild-type vaccinia virus strain Western Reserve (WR) or with the vaccinia deletion mutants DF13L (vRB12) (Blasco and Moss, 1991) or DA36R (Parkinson and Smith, 1994) at a multiplicity of infection of 1 p.f.u. (plaque-forming unit) per cell, as described previously (Ro Èttger et al., 1999) . Nocodazole dissolved in dimethyl sulfoxide (DMSO) and brefeldin A dissolved in ethanol were added to the culture medium to ®nal concentration of 10 mM and 5 mg/ml, respectively unless otherwise stated. In non-treated controls, an equal volume of DMSO or ethanol was added. Cells transfected with a myc-tagged p50/dynamitin expression construct (Echeverri et al., 1996) were infected 24 h later with WR and subsequently ®xed 6 h postinfection. All experiments described have been repeated 3±10 times. The following antibodies were kindly provided: anti-a-tubulin by Dr E.Karsenti, anti-centrin (20H5) by Professor J.L.Salisbury (Sanders and Salisbury, 1994; Paoletti et al., 1996) , anti-Nek2 and anti-C-Nap1 by Professor E.Nigg (Fry et al., 1998a,b) , anti-myc and anti-gp27 by Dr T.Nilsson (Fu Èllekrug et al., 1999) and antibodies against the corresponding vaccinia proteins: A3L, A10L and L4R by Professor D.Hruby (Vanslyke and Hruby, 1994) , I1L by Professor P.Traktman (Klemperer et al., 1997) , A27L (C3) by Dr M.Esteban (Rodriguez et al., 1985) , A33R, A34R and A36R (Ro Èttger et al., 1999) . In addition, the following antibodies were obtained from commercial sources: anti-a-tubulin (N356) (Amersham International, UK), anti-acetylated a-tubulin (6-11B-1) (Sigma, USA), anti-g-tubulin (GTU-88; Sigma), anti-pericentrin and anti-TGN46 (BAbCO, USA), and rabbit IgG (Sigma). Actin was visualized with¯uorescently labelled phalloidin derivatives (Molecular Probes, USA). Cells were ®xed in ±20°C methanol or in 5% paraformaldehyde in BRB80 (80 mM PIPES pH 6.8, 1 mM MgCl 2 , 1 mM EGTA) followed by 0.1% Triton X-100 permeabilization. Fixed cells were processed for immuno¯uorescence, viewed and images recorded as described previously (Ro Èttger et al., 1999) . HeLa cells were pre-incubated with 25 mM nocodazole in the medium for 1 h to depolymerize microtubules, prior to infection with vaccinia DF13L at 1 p.f.u./cell. Nocodazole was kept in the medium throughout the infection, while an equal volume of DMSO was added to the controls. At 24 h post-infection, the cells were scraped from the¯asks into the medium and sedimented by centrifugation (300 g, 7 min, 4°C). The cell membranes were disrupted and the nuclei were removed by centrifugation. The resulting post-nuclear supernatant was centrifuged through a 36% sucrose cushion (76 000 g, 30 min, 4°C). The virus pellet was resuspended in 10 mM Tris pH 9; the virus was collected by centrifugation (76 000 g, 30 min, 4°C), resuspended in 10 mM Tris pH 9 and stored at ±80°C. The concentration of the virus (elemental bodies) was determined by OD 260 measurement (Joklik, 1962) . Fig. 13 . Vaccinia infection reduces centrosome microtubule nucleation ef®ciency. In uninfected cells, microtubules (A, E and I) nucleate from centrosomes (B, F and J) after nocodazole washout for the times indicated. In contrast, 2 h after infection with vaccinia, microtubules (C, G and K) are nucleated inef®ciently from centrosomes (D, H and L). All images were collected with identical camera settings, to allow comparison of uorescence intensity between centrosomes. Inserts (B, D, F, H, J and L) are adjusted as in Figure 12 to facilitate visualization of the weak g-tubulin centrosomal labelling. Arrowheads indicate the position of the centrosome. Scale bar = 10 mm. In vitro microtubule binding assays Puri®ed EEV particles were prepared as described previously (Ro Èttger et al., 1999) and subsequently were used to prepare virus cores following the method of Cudmore et al. (1996) . Rhodamine-labelled microtubules were prepared according to Hyman et al. (1991) . Vaccinia virus cores were incubated with rhodamine-labelled microtubules in BRB80 buffer containing 10 mM taxol for 5 min at room temperature. 4¢,6-diamidino-2phenylindole (DAPI) was added subsequently to a ®nal concentration of 0.1 mg/ml to label the virus cores. Finally, the mixture was diluted 1:1± 1:10 with antifade solution (0.1 mg/ml catalase, 0.1 mg/ml glucose oxidase, 10 mM glucose) and viewed without ®xation. Proteinase K or trypsin treatment of core particles prior to incubation with microtubules was performed as described previously (Roos et al., 1996) . Anti-A3L, A10L, L4R or control IgG antibodies were incubated with puri®ed cores for 1 h at room temperature prior to incubation with microtubules. Cell extracts and microtubule co-sedimentation assay Extracts from HeLa cells infected for 24 h or uninfected controls maintained in the presence of 0.1 mg/ml rifampicin were prepared as described previously (Ro Èttger et al., 1999) . The extract was clari®ed by centrifugation at 150 000 g for 20 min at 4°C and cytochalasin D added to a ®nal concentration of 1 mg/ml to depolymerize actin ®laments. Endogenous tubulin in the extract was polymerized in a two-step procedure. First, the extract supernatant was supplemented with protease inhibitors, 2 mM MgGTP and 5 mM taxol and incubated for 5 min at room temperature; subsequently, an additional 15 mM taxol was added to the mix and the reaction incubated at 33°C for 30 min. For controls, no taxol was added at any stage and microtubule polymerization was inhibited either by the addition of nocodazole to a ®nal concentration of 40 mM or by maintaining the extract at 4°C throughout the experiment. Following microtubule assembly, each 400 mg extract reaction was diluted 5-fold in BRB80 buffer (containing protease inhibitors and 20 mM taxol) and centrifuged through a 10% sucrose cushion containing protease inhibitors and 20 mM taxol at 165 000 g for 20 min at 25°C. The microtubule pellet was solubilized in SDS±PAGE sample buffer and analysed by SDS± PAGE. In-gel proteolytic cleavage was performed automatically in theProgest' as described (Houthaeve et al., 1997) [Genomic Solutions Cambridge (http://www.genomicsolutions.com)] and the peptides obtained were analysed on a Bruker REFLEX MALDI mass spectrometer (Bruker Analytik, Germany) (Jensen et al., 1996b) . Proteins were identi®ed by peptide mass ®ngerprinting (Jensen et al., 1997) using the program PeptideSearch (http://www.narrador.embl-heidelberg.de/Services/ PeptideSearch/PeptideSearchIntro.html. At 1 h post-infection, nocodazole was added to the culture medium to a ®nal concentration of 25 mM to depolymerize microtubules. At 2 h postinfection, the cells were washed 3±4 times in warm medium to remove nocodazole. Washed cells were incubated in medium without nocodazole for the indicated time at 37°C to re-initiate microtubule polymerization; they were then washed brie¯y in warm phosphate-buffered saline (PBS) and immediately ®xed. In parallel, samples were also removed at the same time point, brie¯y rinsed in ice-cold PBS, ®xed and processed for immuno¯uorescence to con®rm complete microtubule depolymerization before initiation of microtubule assembly. Uninfected control HeLa cells were treated and processed in an identical fashion. The same numbers of images were integrated using identical camera settings to allow direct comparison between infected and uninfected samples from the same experiment.
There are hundreds of viruses that infect different human organs and cause diseases. Some fatal emerging viral infections have become serious public health issues worldwide. Early diagnosis and subsequent treatment are therefore essential for fighting viral infections. Current diagnostic techniques frequently employ polymerase chain reaction (PCR)-based methods to quickly detect the pathogenic viruses and establish the etiology of the disease or illness. However, the fast PCR method suffers from many drawbacks such as a high false-positive rate and the ability to detect only one or a few gene targets at a time. Microarray technology solves the problems of the PCR limitations and can be effectively applied to all fields of molecular medicine. Recently, a report in Retrovirology described a multi-virus DNA array that contains more than 250 open reading frames from eight human viruses including human immunodeficiency virus type 1. This array can be used to detect multiple viral co-infections in cells and in vivo. Another benefit of this kind of multi-virus array is in studying promoter activity and viral gene expression and correlating such readouts with the progression of disease and reactivation of latent infections. Thus, the virus DNA-chip development reported in Retrovirology is an important advance in diagnostic application which could be a potent clinical tool for characterizing viral co-infections in AIDS as well as other patients. Microarray technology has been proven to be a powerful tool with great potential for biological and medical uses. In this technique, recombinant DNA fragments or synthesized oligonucleotides affixed on the surface of glass slides or nylon membranes are used for detecting complementary nucleic acid sequences (frequently representing a few hundred to >10,000 genes/expressed sequence tags) as well as for genotyping microorganisms and for profiling the gene-expression patterns in cells from higher organisms [1]. A new report by Ghedin, et al. [2] in Retrovirology describes the successful use of a multi-virus array (termed multivi-rus-chip) to detect multiple viral co-infections in cultured cells as well as to study viral gene expression and promoter activities (Figure 1 ). Ghedin's multivirus-chip contains genes from eight human viruses including human immunodeficiency virus type 1 (HIV-1). Conceptually, this chip can be used to detect viral co-infections in AIDS patients who are frequently rendered susceptible to additional opportunistic infections. In developing their multivirus-chip, Ghedin, et al. tested more than 250 ORFs from HIV-1, human T cell leukemia virus types 1 (HTLV-1) and 2 (HTLV-2), hepatitis C virus (HCV), Epstein-Barr virus (EBV), human herpesvirus 6A (HHV6A) and 6B (HHV6B), and Kaposi's sarcoma-associated herpesvirus (KSHV) which were PCR-amplified and spotted on glass slides. They then hybridized their slides with Cy3-or Cy5labeled genomic DNA or cDNAs derived from various virus-infected cells. Their multivirus-chip was found to be highly specific and sensitive for detecting different viral genomic sequences in cell lines. Moreover, the chip could also detect the effect of various drugs on viral gene expression. In such instance, cell lines latently infected with HIV-1 and KSHV were used to generate profiles of viral gene expression in the presence of cyclin-dependent Schematic drawing of the multivirus-chip that possesses multiple functions kinase inhibitor (CKI), Roscovitine, which was applied to cells to suppress the reactivation of latently infected viruses. Ghedin, et al. [2] also studied the role of cellular chromatin structure on viral gene expression using their multivirus-chip. They employed the chromatin immunoprecipitation technique (ChIP) [3] to isolate cellular DNA fragments that were bound to phosphorylated histone H3 (P-H3). These DNA fragments were hybridized to the viral ORFs contained on the multivirus-chip to investigate the role of phospho-H3 on viral gene expression. They showed that whether transcriptionally active or silent the chromatin state played a role in regulating the expression of KSHV genes under the different cellular context. Current routine clinical diagnostics employ PCR, Southern blotting, Northern blotting, DNA sequencing and microarray hybridization to detect and characterize genes of interest in biomedicine. PCR is generally regarded as the most sensitive diagnostic method. However, Iyer, et al. [4] have shown that the sensitivity of cDNA-chip hybridization is comparable to that of TaqMan-driven quantitative PCR assay, and that the microarray hybridization technique is less likely to be complicated by high false positive rates due to carry-over contaminations. Furthermore, using microarrays, the viral gene transcripts in infected cells can be easily detected by hybridization without any prior amplification steps, and the microarray technique requires much less experimental material when compared to Southern or Northern blotting and can provide high sensitivity in the setting of large throughput. In view of the above, the multivirus-chip described in Retrovirology [2] holds several advantages over other more commonly used techniques (e.g. PCR, DNA sequencing) for the diagnosis of viral infections. First, this chip provides a more accurate diagnosis of viral infection by simultaneously evaluating the transcription of all viral genes, and can use such cumulative data to correlate infection with clinical disease manifestations. Second, the high throughput and flexible synthesis nature of DNA microarray construction can allow scientists to tailor-make and rapidly alter arrays to match evolving emergence of new pathogens. The SARS genome chip made by the US NIAID, NIH is a good example [5] of how diagnostic arrays can be developed quickly and be used in a timely manner. Finally, the most novel application described by Ghedin, et al. is their use of microarrays to correlate the cellular "histone code" [6] with the promoter activity of KSHV. Usually the transcription of a gene located on chromosomal DNA is influenced not only by the cis-acting ele-ments (or DNA-binding motifs), but also by the structure of chromatin. The latter can be vary depending on the post-translational modifications of histone proteins. Methylation, acetylation, and/or phosphorylation of certain amino acid residues at the amino terminal "tails" of histone H3 and/or H4 can indeed influence chromatin structure. Thus accumulating evidence has shown that chromatin-associated proteins and their modifications play vital roles in many physiological processes such as growth, differentiation, and development in mammals, plants and fungi [6, 7] . Many studies have used DNA array technology to investigate viral gene expression or to genotype viral isolates; however, none has used this technique to study the influence of cellular chromatin structure on viral gene expression [1]. Ghedin, et al. [2] demonstrated that only DNA fragments derived from ChIP of latent BCBL-1 cell genomic DNA captured using phospho-H3 antibody bound specifically to the KSHV ORF on the multivirus-chip. This result suggests that latent KSHV genome in BCBL-1 cells is packed into a nucleosomal structure and that histone H3 proteins near the viral promoter can be phosphorylated at serines to make the DNA at the promoter region less tightly packed with histones and more easily accessible to transcription factors. In conclusion, the multivirus-chip improvements developed by Ghedin, et al. [2] provide versatile clinical and basic uses. In the near future, such chips are likely to be used to detect viral co-infections in many different clinical settings.
It is now widely admitted that actual genomes have a common ancestor (LUCA, Last Universal Common Ancestor). Their current diversity results from events that have modified genomes during evolution. While some of these events happen at the nucleotide level (point mutation, indel of few nucleotides), others [strand inversion, duplications, repetitions, transpositions and horizontal transfers (HTs)] may concern significant parts of the genome. It has been postulated that HTs (exchange of genetic material between two different species) were very frequent during the first stages of evolution and are essentially subsisting nowadays in prokaryotes (1) (2) (3) (4) . As a consequence, the detection of HTs appears crucial to the understanding of the evolutionary processes and to the qualitative and quantitative evaluation of exchange rate between species (5) (6) (7) (8) (9) . The recent complete sequencing of several genomes allows to systematically search for the presence of DNA transfers in species, especially in prokaryotes where the probability of occurrence is higher (10) (11) (12) (13) (14) . It has been reported in particular that (i) HTs in bacteria account for up to 25% of the genome (8, (14) (15) (16) ; (ii) archaebacteria and non-pathogenic bacteria are more prone to transfers than pathogenic bacteria (15, 16) ; and (iii) operational genes are more likely transferred than genes dealing with information management (15) (16) (17) . The HT concept has been originally coined to explain the dramatic homologies between genes of unrelated species (18, 19) . An 'unusual' match is subsequently the criteria for the detection of HTs (20, 21) . While this approach allows detection of gene transfers with only a partial knowledge of genomes, it requires the sequencing of homologous genes in a number of species and consequently cannot be used for HT screening. Genes from a given species are very similar to one another with respect to base composition, codon biases and short oligonucleotide composition (15, 16, (22) (23) (24) . As a general rule, usage of oligonucleotides varies less along genomes than among genomes (24) (25) (26) (27) . In addition, it has been observed that transferred DNA retains (at least for some time) characteristics from its species of origin (8, 14) . These particularities are used alone or in conjunction to detect DNA transfers between species (8, 12, 13) . Transferred DNA is consequently detected on the basis of some of its singularities with respect to the sequence characteristics of the recipient species. However, these techniques suffer several drawbacks and weaknesses (28) (29) (30) that led us to consider generalizing the above approach for the screening of atypical regions in sequences. In fact, the genomic signature that accounts for all possible biases in DNA sequences has been shown to be speciesspecific (26, 27, 31, 32) . The signature is approximately invariant along the genome in such a way that the species of origin of DNA segments as small as 1 kb could be identified with a surprisingly high efficiency by means of their signatures (25, 27) . As a consequence, the sequence signature may be most often (at least in bacteria) considered a valuable estimation of the genomic signature. Assuming that (i) transferred DNA fragments exhibit signature of the species they come from and (ii) recipient and donor signatures are different, the screening of local variations of signature along genomes is expected to reveal regions of interest where HTs might be located. In addition, the status of HT is strongly suggested if the signatures of these regions of interest are found close to the signature of other species. The sequence signature is defined as the frequencies of the whole set of short oligonucleotides observed in a sequence (26, 31) . It can be easily obtained thanks to a very fast algorithm derived from the Chaos Game Representation (CGR) (33) , which allows coping with a 1 Mb sequence in a few seconds on a laptop computer. Signatures may be visualized as square images where the color (or gray level) of each pixel represents the frequency of a given oligonucleotide (called word thereafter) (31) (for examples of signatures, see Supplementary Materials 2, 4 and 6). DNA sequences are gathered from GenBank. The genomes of 22 prokaryotes are scanned for HTs, B.subtilis, E.coli and H.influenzae genomes being given a special attention to illustrate our approach. In particular, B.subtilis and E.coli provide valuable benchmark thanks to the set of previous works addressing that very issue (12, 14, 16, (34) (35) (36) (37) . Signatures of about 12 000 species are obtained from genomic sequences longer than 1.5 kb. Sequences derived from the same species are concatenated for accuracy purposes. Species from the three domains of life, archaea ($260 species), bacteria ($3950 species) and eukarya ($6750 species) as well as viruses ($1300 species), are represented for a total amount of 1.0 Gb. The detection of atypical regions is based on the observation of deviation of local signatures (i.e. signature of small fragments of DNA) from the genomic signature of the recipient species. Genomes are consequently sampled by means of a sliding window with an appropriate size. In fact, it would be interesting to have windows the smallest as possible for highest sampling accuracy. However, intra-genomic variability of signature increases for small windows. In addition, variability depends on species and word length. Base composition (1-letter word), 2-and 3-letter words are poorly speciesspecific: they do not allow a good discrimination between species (25, 27) . As a general rule, the longer the words (up to 9-letter long), the higher the specificity of the signature (25, 27, 31) . However, counts of long words in small windows are too low to allow a reliable estimation of the parameters. In our hands, the analysis of 4-letter words in a sliding window of 5 kb (with a 0.5 kb step) offers a good trade-off between reliability of count, file size and computational charge, whatever the species. In addition, a double-strand signature (called local signature thereafter) is computed for each window to get rid of variations induced by strand asymmetry (38) (39) (40) (41) (42) . For illustration purposes, local signatures are developed as vertical vectors and stacked together in genome order to give an overall picture of word usage variations along each genome. In such plots, horizontal lines show the variation in frequency of words along the genome, whereas local changes in word usage appear as vertical breaks ( Figure 1 ). Figure 1 . Signatures (4-letter words and 5 kb windows) along genome for Clostridium acetobutylicum, Deinococcus radiodurans and Mycobacterium tuberculosis. In this kind of displays, lines represent the frequency of words along genome, columns represent signature of windows. Considering that the greatest part of the genome is speciestypical, the signature of the recipient species might have been estimated from the analysis of the whole sequence. Although the vast majority of local signatures look mostly the same (believed to be instances of the recipient species signature), some of them may greatly differ. In order to avoid potential biases linked to these outliers, it has been subsequently decided to select typical local signatures on the basis of their similarities, observed after clustering. The underlying idea is that typical local signatures aggregate in few large groups, whereas outliers are found in small complementary groups at a great distance from the recipient genome signature. Groups were consequently determined with the K-means clustering tool, using every scheme of clusters between 3 and 8 for each species. Finally, the best scheme of clusters was obtained by a decision tree-based partition [CART algorithm (43) ]. The purpose of the CART algorithm is to predict values of a categorical dependent variable (clusters of local signatures in this work, each signature being characterized by its distance to the estimated genomic signature) from one or more continuous and/or categorical predictor variables [the different clustering schemes (3-8 clusters) in this work]. The CART algorithm thus provides an optimal split between groups collecting signatures close to the estimated recipient genome signature and the others groups. For each species, a clustering scheme is selected (e.g. the 5-group clustering) and a partition offered (continued example: group 2 and 3 on one side; 1, 4 and 5 on the other). The recipient species signature is subsequently calculated as the mean of the signatures of the groups belonging to the partition with the smallest distance to the estimated genomic signature. Comparison of signatures is made possible, thanks to an Euclidian metric, accounting for differences in word usage. It must be pointed out that distances between signatures are calculated for high dimensional data (256 dimensions corresponding to the 256 different 4-letter words) and are consequently subjected to the so-called 'concentration of measure phenomenon' (44) . All distances in a high dimension space seem to be comparable since they increase with the square root of the dimension of the space, whereas the variance of their distribution remains unchanged. In fact, the radius of the hyper sphere holding 99% of the signatures of our database is only seven times the nearest neighbor distance (smallest distance between two species). Small differences in distance may consequently be considered highly significant. For each species, a set of recipient-specific distances is obtained, every local signature belonging to the large clusters being given a distance to the host signature. In order to select outlying signatures, a cut-off distance is chosen on the basis of the distribution of distances observed for each species. It appears that the 99% percentile offered a good trade-off between sensibility and specificity for outlier detection (for impact of the threshold on detection of atypical regions, see Results). Most signatures from minority clusters are detected in this way. Isolated signatures are detected as well, while very few signatures from the recipient species clusters are selected (1%). Outliers together with the flanking regions on the genome are later on reanalyzed with smaller window and step (1/10 th of the original size typically) in order to more accurately determine their limits, when signal-to-noise ratio allows it. Finally, the gene content of all detected regions is analyzed with the help of species dedicated databases [Genome Information Broker, http://gib.genes.nig.ac.jp/]. A BlastN search (GenBank, default settings) is carried out for each atypical region in order to identify the origin of potential HTs if homology is high enough. Search for the origin of atypical regions About 12 000 species (including chromosomal, plasmidic, mitochondrial and chloroplastic DNA) from GenBank are found eligible for a genomic signature. Given the signature of an atypical DNA fragment, species with a close signature might be considered as potential donors. Such a screening is performed for every atypical region of the 22 species under consideration. The first five nearby species are retained when their distance to the outlier was donor-compatible. A total of 22 genomes are screened for atypical regions (Table 1 and Supplementary Material 1). On the average, the 6-cluster scheme offers the best partition. However, in a single case (Aeropyrum pernix), nine clusters are required. In general, a single cluster is devoted to rRNA. The mean distance of windows to host varies over species from 121 to 145 (mean = 132, coefficient of variation = 3%). It is tightly correlated (P-value for the Pearson correlation coefficient <10 À4 ) with the cut-off distance that varies from 178 to 289 (mean = 234, coefficient of variation = 14%). Such large variations can hardly be explained on the mere basis of statistical fluctuations. As already observed (31, 45, 46) , variation of oligonucleotides usage along genome depends on species and can consequently be considered as a species property. Segmentation quality of atypical regions can be tested using rRNA genes. About 94% of rRNA is detected as atypical ( Table 1) . Borders of rRNA genes are accurate to within 130 nt (0.5 kb window and 50 bp step, threshold 99%). Meanwhile, adjacent tRNAs are identified as well. As a general rule, it can be concluded that rRNA has a specific signature that is consistently at variance with the host signature. In this context, it is worth noticing that rRNA and the remaining outliers lie at comparable distances from the species they belong to, but they are clearly different from one another, rRNAs being consistently found in their own cluster. The percentage of RNA-free outliers (at the nucleotide level) varies from 1.3 to 13% as a function of species (threshold 99%, Table 1 ). B.subtilis shows the highest percentage of atypical regions, whereas Pyrococcus abyssi has the lowest. Percentages among species are found correlated with the cut-off distance: the higher the cut-off distance, the lower the percentage of outliers (P = 0.007). In fact, a high cutoff distance takes place in species that display a high intragenomic variability, also expressed by a high mean distance to the host (Table 1) . Whether the actual percentage of atypical DNA is an intrinsic property of the species or a mere consequence of the resolution power of nucleotide biases-based methods remains consequently an open question. In addition, as already observed (13, 14) , the percentage of outliers is significantly higher for longer genomes (P = 0.004), whereas the cut-off distance is not related to the length of the genome (P = 0.69). The mean cut-off distance for the 22 species is 234 (Table 1) . This value is chosen to select credible donors. About 50% of atypical regions are subsequently given credible donors (Supplementary Material 1). Each species has it own set of (Table 1) . Many plasmids and viruses are also found in agreement with the known molecular mechanisms of horizontal transfer (Table 1 and Supplementary Material 1). A clustering with three classes allows assessing the signature of B.subtilis. The most populated class (collecting 84% of the segments) is chosen to represent B.subtilis. For this subpopulation, the mean distance (arbitrary unit) to the recipient (centroid of the class) and the cut-off distance are 126 and 204, respectively ( Table 1 ). Runs of contiguous outlying windows sharing the same cluster are considered as single transfer events. As a consequence, 58 regions (Figure 2a and Supplementary Material 2) fall beyond the cut-off distance and are thus potential candidates for hosting foreign DNA (for a segmentation of the B.Subtilis genome in terms of genes, see Supplementary Material 3). Figure 2b illustrates the accuracy of segmentation of an atypical region obtained by using a sliding window of 0.5 kb with a 50 bp step. rRNA genes make up $1.1% of B.subtilis genome ( Table 1) . All rRNA genes are found in the outlier population. In addition, all windows containing rRNA are assigned to a specific cluster. In fact, it is known that rRNA has its own signature, which is at variance from the host signature (12) . rRNA genes account for 7% of the outliers (tRNAs are not considered in this study, because their size is too small to generate a significant deviation from the host signature if they are isolated). A total of 86% of the B.subtilis genome should be considered as B.subtilis typical (Table 1) . When looking for the origin of B.subtilis segments in the 12 000 signature database, B.subtilis appears in the 10 first potential donors for 84% of the whole set of 5 kb sequences that can be derived from its genome. This result confirms that segments having signatures belonging to the predominant clusters are good representatives of the recipient species signature. The 49 rRNA-free atypical regions vary in size from 1.5 to 135 kb and make up 13% of the total genome (Table 1) . About 50% of atypical regions are less than (or around) 6 kb long. Distances of outlier from first potential donor often fall within the intra-genomic range ( However, in some instances, the outlier-to-donor distance is too great to consider the 'closest' species as potential donor. In contrast, unusual small values deserve a specific attention. In particular, the very small distance between bacteriophage SPBc2 and '2150751-2285750' atypical region (d = 2) allows to spot the part of B.subtilis genome where bacteriophage SPBc2 is incorporated (12, 47) . Other regions in the genome are also found similar (in terms of signature) to bacteriophage SPBc2. Most of them correspond to bacteriophages, imbedded in B.subtilis genome, whose free forms are not sequenced (12, 47) . Observed similarities with SPBC2 are, however, expected since signatures of phages usually share some characteristics with the species they infect (48) . The SPBc2 sequence is the only foreign sequence identified in B.subtilis, using homology as criterion (BlastN, with parameters set to default). In fact, Blast analysis of B.subtilis outliers leads to contrasted results. Besides SPBc2 and 7 out of 9 prophages imbedded in the genome, the only atypical regions identified are those containing the 30 rRNA genes coded in B.subtilis genome. The only few genes that are homologous to parts of atypical regions are found in species belonging to the Bacillus genus. It is interesting to note that no house-keeping genes (except rRNA) are detected in atypical regions. In fact, a great number of genes in atypical regions (except bacteriophage genes and rRNA) have no known function. A clustering with five classes is required to determine the recipient species signature of H.influenzae. The three most populated classes (collecting 94% of the segments) are chosen to calculate the H.influenzae signature. Mean distance to host and cut-off distance is subsequently found equal to 130 and 239, respectively (Table 1) . Similarly to B.subtilis, one cluster (1.5% of H.influenzae genome) is devoted to the 18 rRNA gene copies (Table 1) . A total of 91% of rRNA is labeled atypical and account for 29% of the outliers. Analysis of Table 1 shows that 95% of the H.influenzae genome should be considered as H.influenzae typical. In fact, H.influenzae is one of the 10 first potential donors for 92% of all 5 kb sequences that can be derived from its genome. As already observed for B.subtilis, the concordance of these two percentages corroborates the partition procedure used for the selection of typical/atypical fragments. The 13 rRNA-free atypical regions vary in size from 1.5 to 19.5 kb and make up 3.3% of the genome (Table 1 , Annex 4 and Figure 3 , see Annex 5 for a segmentation of the H.influenzae genome in terms of genes). About 50% of atypical regions are less than (or around) 2.5 kb long. Numbers for H.influenzae are clearly at variance with those for B.subtilis: a smaller percentage of the genome qualifies as atypical and the average size of atypical regions is also smaller. This result is examined below in the context of intra-species signature variability (see Discussion). A clustering with six classes is required to determine the recipient species signature of E.coli. The main features are summarized in Table 1 . The potential donors of the 84 RNAfree atypical regions are given in Annex 6 (for a segmentation of the E.coli genome in terms of genes, see Annex 7). It is worth noticing that 56% of E.coli potential donors belong to the Enterobacteriales family. Segmentation in terms of genes is displayed in Annex 7. The analysis of this genome is particularly useful for the comparison with literature (see below). Numerous approaches for detecting horizontal gene transfers have been proposed in the last 2 decades. Phylogenetic trees of protein or DNA sequences, unusual distribution of genes, nucleotide composition (including codon biases) are some of the HT features that are considered within the framework of these models (16, 34) , Hidden Markov Models (HMMs) (12, 14, 35) and Factorial Correspondence Analysis (FCA) (37) are some criteria that are currently employed. Each of the resulting models has its own advantages and caveats (28) (29) (30) . As it has been recently pointed out by Ragan (49) and Lawrence and Ochman (50) , each approach deals with a particular subset of HTs, being for example more efficient for detecting recent transfers, or more effective for the detection of ancient HTs. Our approach, which is clearly based on oligonucleotide composition, assumes that different species have different signatures but does not rely on any other assumption. It is not surprising, therefore, that the genomic signature approach provides results (in terms of % of DNA transferred) in reasonable agreement with those proposed by Garcia-Vallve (16) and Nakamura et al. (14) for the 22 species that were analyzed in common. Correlations between percentages of HTs found by these three methods are highly significant Two species are extensively studied for HT content: B.subtilis (five methods including ours) and E.coli (six methods including ours). H.influenzae is also analyzed by Garcia-Vallve (16) and Nakamura (14) . Comparisons of methods are presented in Tables 2-4 and detailed in Supplementary Materials 3, 5 and 7. A voting procedure (majority rule) has been implemented to determine the status of genes with respect to atypicality. For that task, our initial analysis is converted in terms of genes (Supplementary Materials 3, 5 and 7). Degree of agreement between methods is subsequently observed using the statistical Kappa coefficient (51) . Kappa measures the degree of agreement on a scale from minus infinity to 1. A Kappa of one indicates full agreement, a Kappa of zero indicates that there is no more agreement than expected by chance and negative values are observed if agreement is weaker than expected by chance (a very rare situation). (14, 13, 11, 13 and 15%, respectively). The number of detected genes per method is close, ranging from 457 for Nakamura (14) to 599 for this work (median 537). Detailed votes are given in Table 2 . Among the 4100 genes of B.subtilis genome, 1011 genes are detected by at least one method (about 25% of B.subtilis genes). The number of 'single vote' genes ranges from 116 for Garcia-Vallve (16) to 47 for Nicolas (12) . A total of 470 genes make up the majority consensus set and we detected 453 of them, which is the best score of the five methods. The best agreement with the majority consensus (in terms of Kappas) is reached by Nicolas (12), followed by our method and Moszer (36) ( Table 2 ). Our method gets the best agreement with Nicolas (12) and the worst with the other HMM method used by Nakamura (14) (pairwise Kappa comparison, Table 2 and Supplementary Material 3). In fact, Nakamura approach is at variance with every other approach (14) . It gets the lowest Kappa with the Garcia-Vallve (16) Hayes (35) Lawrence (34) Nakamura (14) Medigue (49) This work majority consensus or with whatever other methods. From Table 2 , the probable number of HT genes in B.subtilis would range from 230 to 1011 with a 'reasonable' estimation around 470 corresponding to the majority consensus. It is to be noted that our method is unable to find two genes that are detected by every other methods (Supplementary Material 3) . These genes are 338 and 236 nt long, respectively, as compared with 2500 nt, the median size of atypical regions detected by our method (Table 1) . Clearly, our method is not appropriate for detecting short isolated atypical genes. H.influenzae. Garcia-Vallve (16), Nakamura et al. (14) and we are the voters concerned with the analysis of the H.influenzae genome (Supplementary Material 5 and Table 3 , H.influenzae). The originality of results obtained by Nakamura (14) is the salient feature of this comparison. The number of detected HT genes is more than twice higher for Nakamura et al., whereas the part belonging to the majority consensus is the smallest ( Table 3) . Eleven genes are detected both by Garcia-Vallve and Nakamura (14, 16) but not by our method; however, the small number of voters precludes any specific comment in this respect. The probable number of HT genes in H.influenzae would range between 11 and 273, with a 'reasonable' estimation around 60 (majority consensus of 57) ( Table 4 ). The results obtained by Hayes and Borodovsky (35) are clearly at variance with the others (Table 4 ). Although the proportion of claimed outliers is within the range of published numbers for E.coli (14, 16, 24, 34, 35, 37) , 37% of them are method-specific, and the agreement with other methods is weak (Table 4 ). Hayes and Borodovsky have obviously developed an approach based on HMM dealing with specific outliers. Lawrence and Ochman (34) also get a poor rating especially because they detect about twice as many genes as the other authors do (Table 4) . It is worth noting that if the cut-off distance for our method is lowered, i.e. 95% instead of 99% for instance, some of the 'single vote' genes are dug out (for details about the impact of the cut-off distance, see Supplementary Material 7). Meanwhile, the percentage of outliers as reported by our approach rises to 20% and the percentage of 'single vote' genes reaches 24%. As expected, a high cut-off distance provides few single vote genes at the risk of missing some potentially transferred genes. Lowering the cut-off increases the proportion of single vote genes with the advantage of detecting most of the potential transfers (Supplementary Material 7) . There is obviously a continuous grading in gene 'atypicality'. It is suggested to first consider most 'consensual' genes as potential HTs and then apply amelioration models to explain the grading. It is difficult to assess the relevancy of proposed donors, because genes detected as potential HT have generally undergone amelioration (8) . The comparison of recently diverged genomes (species or strains) provides the opportunity to find recent HTs, for which corresponding homologous genes in the donor species may be detected (52) . Such a study is performed for five E.coli strains (two K12 strains: E.coli MG1655, E.coli W3110, one uropathogenic strain: E.coli CFT073, two enterohaemorrhagic strains: E.coli O157-H7 RIMD 0509952, E.coli O157-H7_EDL933) and two Shigella flexneri strains (S.flexneri 2a 2457T, S.flexneri 2a 301). These seven strains/ species have recently diverged, genome sizes are different and the proportion of horizontally transferred genes varies from one strain/species to another (14, 52) . For instance, only$40% of the non-redundant set of proteins is common to E.coli strains CFT073, 0157-H7 EDL 9333 and MG1655 (53) . These strains/species can be clustered in four groups with respect to phylogeny (Table 5) . Two criteria are used to searching for 'recent horizontally transferred genes': atypical regions (window size 1 kb, step 0.5 kb) (i) must have a signature that differs greatly from that of the host [distance to host must be at least >325, 2.5 times the E.coli intrinsic mean distance (Table 1) ] and (ii) must be present in a limited number of strains/species to ascertain their recentness. In fact, outliers meeting the first criterion generally aggregate into several heterogeneous clusters (K-means clustering) that usually include samples from each strain/species. In some instances, however, some strains/species were absent from the cluster. It was subsequently considered that the corresponding regions might have been recently acquired by the relevant strains/ species. Table 5 shows a selection of potential recently transferred genes. Each cluster of atypical regions contains genes present in a specific set of strains. Some atypical genes are strainspecific, some are only absent in the non-pathogenic K12 strains and intermediate situations are also encountered. FASTA and Blast searches confirm that these genes are absent from some of the tested strains as already observed in the analysis complete genomes (53) (54) (55) . In a large number of cases, we are able to find a well-conserved homologous gene in another species (Table 5) . It is interesting to note that some of the suggested donors using our 12 000 signature database are in agreement with the species found by alignment methods. When no homologous gene is found, the proposed donors give credit to the known mechanisms of gene transfer (bacteriophages or plasmids) ( Table 5) . It is worth noticing that most of the selected genes that are absent in K12 strains are involved in the pathogenicity of the other strains (52) . E.coli 0157-H7 is the strain exhibiting the greatest number of genes absent in K12 strains [about 1400 (54) ]. It has the greatest number of genes for which no homolog can be found (Table 5) . Moreover, we are unable to propose a donor for a great part of these genes (Table 5) . Many selected genes for E.coli 0157-H7 lie in the Ter region of the genome (between positions 2 000 000 and 2 500 000) in agreement with the published results (56). We have observed that most genomic regions are typical of the genome they belong to, using the signature as endpoint. Considering that the genomic signature is species-specific, atypicality of a region in terms of oligonucleotide usage has been promoted as a criterion for the detection of HTs. However, atypicality-based methods suffer several caveats that reduce their effectiveness in such a way that only a part of HTs can be detected. In fact, transfers between species with close signatures cannot be detected: significant differences between characteristics of transferred DNA and recipient species DNA are required. For similar reasons, HTs that were drastically ameliorated following their introduction cannot be detected either (8, 14) . The most stringent constraint, however, results from the size of the screening window. On the one hand, ideally, the best signal-to-noise ratio would be obtained when windows and HTs have a comparable size. On the other hand, the window size must be large enough to provide significant word counts, a requirement that strengthens with the size of the words under consideration and the intrinsic variability of the genomic signature along the genome. All together, the trade-off that has been implemented in this paper allows detecting atypical regions as small as 1 kb. In fact, rRNA regions sharing this characteristic were consistently detected. It must be pointed out that smaller fragments can be eventually detected if their signatures are radically atypical. G+C% atypicality has often been considered as criterion for detecting HTs (8, 24) , but this approach suffered several drawbacks (28) (29) (30) . It is to be noted that our signature-based method detects regions for which the G+C% lies within one standard deviation from the mean G+C% of the species (for instance, regions 2675251-2676250 in B.subtilis or 534751-535250 in H.Influenzae, see also Supplementary Materials 2 and 4). As already observed by Nicolas et al. (12) for B.subtilis, rRNA has definitely an atypical signature. It is systematically classified as outlier, whatever the species (Table 1) . Although transfer of rRNA from one species to another is unlikely (11, 57) , it cannot be firmly ruled out. However, it is clear that the atypical signature of rRNA does not imply that they are horizontally transferred. The signature approach has an interesting property (that it shares with HMM) (7, 12, 28) : detection is not bound to any specific function in the genome. In contrast with most other methods, the signature approach not only detects genes, but whole transferred regions as well, in agreement with the described mechanisms of DNA exchange between species. It is to be noticed that the method allows detecting several atypical non-coding regions (Supplementary Materials 3, 5 and 7). One major difference between HMM and signature method lies beyond the time required for the learning process, in the few resources that HMM can mobilize to deal with a short 'one of its kind' HT. On the other hand, HTs shorter than 1 kb can hardly be detected by a signature-based approach. An innovative HT detector is likely to result from an adequate fusion of both methods. Several factors contribute to the efficiency of the search for donors. Of course, distance between putative HT and donor signatures is essential. Accuracy of signatures, linked to the length of available sequences, density of signatures in the 'vicinity' of HT, amount of amelioration sustained by HT during its presence in the host are also of importance [P. Deschavanne, S. Lespinats and B. Fertil, unpublished results; (25, 27, 31) ]. Distance between the signature of a putative HT and the closest species varies to a large extent, but usually the shortest ones fall within the intra-genomic range ( Table 1 , Supplementary Materials 1, 2, 4 and 6) . In some cases, the distance between the closest donor signature and the atypical segment signature is so great that no potential donor can be proposed (Supplementary Materials 1, 2, 4 and 6) . When strong similarities between a given DNA sequence and a foreign species are observed, the hypothesis for an underlying transfer is highly strengthen. However, the 'true' donor has to be previously sequenced and included in our bank of signatures to allow such a situation to occur. Moreover, we must take into account the intrinsic variability of short DNA segment signature (which is a function of their size, but also species-specific) when compared with the signature of a complete genome or any other large species sample (25, 27, 31) . In the present state, our signature database is in no way representative of the diversity and richness of life. However, it must be noticed that there is already an obvious structure (in terms of distances between signatures) expressing taxonomy relationships between species in our signature database (31, (58) (59) (60) (61) . Related species are often found close to one another. Clusters of potential donors may consequently provide pertinent information about the origin of HTs. The diversity of signatures of putative HTs that can be observed for most of the species analyzed in this paper reveals the multiplicity of transfer events and donors (Supplementary Materials 2, 4 and 6). However, several outliers, not necessarily neighbors in the genome, are given the same set of potential donors (Table 1 , Supplementary Materials 1, 2, 4 and 6). In general, the potential donors belong to few sets of taxonomically close species (Table 1 ) and share the biotope of the host (Supplementary Materials 1, 2, 4 and 6). For instance, B.subtilis, H.Influenzae and E.coli live in distinct biotopes; their potential donors do so as well. It is particularly encouraging to find that most of the potential donors that our approach has pointed out have had the opportunity to exchange DNA material with the recipient species. Numerous viruses and plasmids qualify as potential donors (Tables 1 and 5 , Supplementary Materials 1, 2, 4 and 6 ). It is not really surprising since they are known as HT vectors. They are often totally or partially inserted together with transferred genes in the host genome (14) . Some atypical DNA segments are particularly peculiar. They are isolated, have a specific signature (distances from neighbors are great), so that they cannot be given a credible set of donors (Supplementary Materials 1, 2, 4 and 6) . Lack of data in the search domain, shift of signature features after a substantial amelioration process, structural constraints serving special functions or roles (14,62) (as it is for rRNA coding regions) are some of the tracks that remain to explore in these circumstances. It would be interesting to localize the region the transfer may come from when the complete genome of the donor is available. However, homology (at the DNA level) is not a pertinent criterion for the comparison of sequences as soon as amelioration has taken place (8, 14) . In fact, homology is sometimes weak, e.g. between genes of Escherichia and Salmonella although these species have 'recently' diverged (34) . It is clear that a more powerful search for the origin of putative HTs would have to embody models of amelioration [such as the one designed by Lawrence and Ochman (8) ]. When searching for very recent horizontally transferred genes, in different strains of a species for instance, it was possible to find a great homology between detected genes and some genes from other species (Table 5 ). In numerous cases, the selection of donors is consistent with FASTA results ( Table 5 ). This confirms the pertinence, beyond the similarity of signature between putative HTs and donors, of the proposed method to retrieve the species of origin of a transferred region. It seems that the search for origin of HTs on the basis of genomic signature is a powerful approach to understand some of the mechanisms of evolution (13, 63) . Oligonucleotide usage is known to be species-specific and to suffer only minor variations along the genome (25, 27) . Considered together, these properties allow searching for atypical local signatures that may point out DNA transfers. Results obtained with the 22 genomes analyzed in this paper are found in good agreement with literature (Tables 2-4 , Supplementary Materials 3, 5 and 7) (12, (14) (15) (16) 24, 34, 35) . The species specificity of signature allows searching for donor species. Quite often, sets of donor species with common taxonomic features are obtained. With the help of environmental considerations, it is subsequently possible to identify (or collect clues about) potential donors. The search for donor makes use of non-homologous sequences. Partially sequenced species become consequently eligible, inasmuch 1.5 kb of the genome is available (25, 27) . Thanks to the exponentially growing rate of nucleotide databanks, the search for donor species by means of the sequence signature will turn more and more pertinent and fruitful in the future. In this context, it is worth noticing that computational power is clearly not an issue since the CGR algorithm described in this paper is fast and of 0 order (calculation time is proportional to the number of nucleotides). Several methods are proposed to look for HTs. The signature method, based on different hypotheses, is complementary to those already described. It seems that each method detects preferentially certain types of HTs (49, 50) . In agreement with many authors (1, 16, 49, 50, 64) , it appears that the conjunction of several methods is required to obtain an overview of HT extent in a genome. The signature method described in this paper generalized many approaches that ground the detection of outliers on the basis of the bias in oligonucleodides. The strong species specificity of the signature not only allows detecting various kinds of outliers but also provides clues about their possible origin. Obviously, the detection of HTs remains an open question; a consensus has still to emerge. Additional materials and experimentation with the genomic signature are available from the GENSTYLE site (http:// genstyle.imed.jussieu.fr).
Although pain is the presenting symptom of most patients with chronic pancreatitis, its neurobiological basis remains poorly understood [1] . In the past, investigators have focused on the role of anatomical abnormalities such as a strictured pancreatic duct or narrowed intraparenchymal ducts. However, mechanical decompression techniques such as endoscopic stent placement or even surgical pancreatojejunostomy frequently do not provide a permanent solution to the pain [1] . More recently, investigators have begun focusing on the role of neurotransmitters and neurotrophins such as substance P and nerve growth factor with known or suspected roles in nociceptive signaling and/or sensitization and have reported an increased expression of several of them in the pancreas of patients with painful chronic pancreatitis [2] . Mast cells are also increased in both acute and chronic pancreatitis [3, 4] but their role in the generation of pain in pancreatitis has not been investigated. We hypothesized that mast cells are involved in the pathogenesis of pain in chronic pancreatitis. This hypothesis is based on the following observations. First, mast cells have been associated with human conditions in which pain is a predominant symptom. Interstitial cystitis and irritable bowel syndrome are both conditions in which pain is out of proportion to the objective pathological findings [5, 6] . In both conditions, an increase in the number of mast cells has been described in the bladder and the colon, respectively [5, 6] . Further, mast cells are frequently found in close proximity to nerves in the intestinal mucosa and the bladder [7] [8] [9] . This has also been observed in the pancreas -the total number of mast cells was significantly higher in pancreatic tissue from patients with chronic pancreatitis than in the normal pancreatic controls [3] . One of the preferential locations of mast cells was around and within the perineurium of nerve fibers in tissue samples of patients with chronic pancreatitis, suggesting the potential for interactions between mast cells and the nervous system. Lastly, there is evidence for bi-directional functional communication between mast cells and nerves [10] [11] [12] . Mast cells can not only release mediators that increase excitability of neurons but in turn, neurotransmitters such as substance P can trigger mast cell degranulation [10] . Mast cells may therefore contribute to the pathogenesis of pain in pancreatitis through degranulation products that can sensitize pancreatic afferent neurons in an ongoing vicious circle of neuronally mediated mast cell degranulation. Our first aim was to analyze the presence and distribution of mast cells in autopsy specimens of chronic pancreatitis and study the correlation, if any, with historical documentation of pain. We then explored our hypothesis further using an experimental model of trinitrobenzene sulfonic acid (TNBS)-induced chronic pancreatitis in both wild type and Kit W /Kit W-v mice, a strain deficient in mast cells (MCDM). Autopsy records from the University of Texas Medical Branch from the years 1993 to 2000 were searched electronically for the term "pancreatitis." One-hundred sixtysix patients were identified of which 26 patients carried an autopsy diagnosis of chronic pancreatitis and 140 patients carried a diagnosis of acute pancreatitis. The medical charts from patients with an autopsy diagnosis of chronic pancreatitis were reviewed for documentation of a medical history of chronic pancreatitis. If no such documentation was present in the chart, patients were excluded from the study (12/26) . Thus, 14/26 patients with both a documented history and an autopsy based diagnosis of chronic pancreatitis, were included in the study. Patients were categorized as painful chronic pancreatitis (8/26) when they fulfilled one of the following criteria: a documented history of chronic abdominal pain clinically attributed to chronic pancreatitis that required the use of narcotics, and/or frequent admissions for recurrent abdominal pain clinically attributed to chronic pancreatitis, and/or a surgical or endoscopic procedure for refractory abdominal pain clinically attributed to chronic pancreatitis. Patients were categorized as non-painful chronic pancreatitis (6/ 26) if patients did not fit any of the criteria listed under painful chronic pancreatitis. In addition, the following data were collected: demographic factors (age and race), cause of death, comorbidities, clinical history of pancreatitis, etiology of pancreatitis, diagnostic studies supporting a diagnosis of pancreatitis (amylase, lipase, calcifications on abdominal plain film, CT-scan, ultrasound or ERCP). Human pancreatic control tissue was obtained from 8 arbitrarily chosen patients of whom the autopsy records recorded acute myocardial infarction as the cause of death. Their medical records were reviewed to ensure that they did not have a clinical history of pancreatitis. Therefore there were three categories of patients: one with painful chronic pancreatitis, one with non-painful chronic pancreatitis and non-pancreatitis controls. A pathologist, blinded to the group assignment, verified all histological diagnoses and counted mast cells on a Giemsa stained tissue section (average of 10 high-power randomly chosen (40X) fields per specimen). The protocol was approved by the Institutional Review Board of the University of Texas Medical Branch. All mice were purchased from the Jackson Laboratory (Bar Harbor, ME). Male mice were used from the following strain: WBB6F1/j-Kit W /Kit W-v (MCDM) and the respective littermate control mouse strain, Kit W-v -+/+ (WT). The mice were 3 months of age at the onset of the experiment with body weights of 25-30 gram. Experimental protocols involving mice were approved by our Institutional Animal Care and Use Committee (IACUC) in accordance with the guidelines provided by the National Institutes of Health. Mice were anesthetized with sodium Nembutal (50 mg/kg body weigh, i.p.) Following a midabdominal laparotomy, a canula was introduced into the common pancreato-biliary duct; the duct was ligated proximally and distally to ensure perfusion into the pancreas and prevent entry of the injected substance into the liver or duodenum. 0.1 ml of 1% TNBS in phosphate buffered saline (PBS)-10% ethanol, pH 8, was infused into the pancreas (modified after Puig-Divi [13] ). The canula was removed and the abdomen closed. Control mice were treated in the exact same fashion but were perfused with saline instead (Figure 1 ). Mice were sacrificed 8 weeks after surgery. VFF hairs consist of a series of filaments of increasing diameter that produce increasing sensations of touch when applied to the skin. When the tip of a fiber of given length and diameter is pressed against the skin, the force of application increases until the fiber bends. After the fiber bends, continued advance creates more bend, but not more force of application. This principle makes it possible to apply a reproducible force to the skin surface. VFF testing is an established behavioral pain assay used to determine mechanical pain thresholds in somatic pain. More recently, VFF testing has been used as a surrogate marker for visceral pain [14, 15] . Mice were placed in a cage with a mesh floor and habituated to the environment for 30-60 minutes. Measurements were taken from the abdomen and the plantar surface of both hindpaws over a period of three weeks prior to the surgery and for a total of three weeks after the surgery ( Figure 1 ). VFF filaments of various caliber were applied to the mid-abdomen in ascending order 10 times, each for 1-2 seconds with a 10 second interval. A response was defined as: a) sharp retraction of the abdomen; b) immediate licking or scratching of site of application of hair; or c) jumping. The response frequency was defined as the total number of responses out of 10 applications (expressed as a percentage) to the skin per filament. An investigator blinded to the different treatment groups performed the behavioral testing. Fresh specimens of the mouse pancreas were fixed in 10% formaldehyde in PBS pH 7.4 containing 1 mM MgCl 2 at 4°C overnight. Sections from paraffin-embedded specimens were stained with hematoxyline and eosin and observed under a light microscope. Pathological changes were scored based on a scale described by Tito et al. by a pathologist blinded to the different treatment groups [16] . Comparisons of the number of mast cells in autopsy specimen were analyzed using the Mann-Whitney U test. For each behavioral experiment (see figure 1), the average response frequency was calculated as the mean of the mean response frequencies for each mouse (across four measures). The "post-pre response frequency" was calculated by subtracting the pre-surgical average response frequency from the post-surgical average response frequency. To assess the independent effect of TNBS on VFF response (ie. to control for the effect of the surgery itself), the postpre response frequency for TNBS infusion was compared with the post-pre response frequency for saline infusion. This comparison was performed using analysis of variance for a two-factor experiment with repeated measures on time at each level of force for each type of mice (WT and MCDM). The two factors were induction of pancreatitis or not (TNBS or saline, respectively) and time (pre-surgical or post-surgical). TNBS infusion was considered to have had an independent effect on the VFF response if the postpre response frequency was greater for TNBS than for saline infusion. Fisher's least significant difference procedure was used for multiple comparisons of least squares means with Experimental design Figure 1 Experimental design All mice underwent pre and post surgical VFF testing. For the VFF testing, 4 measures were taken for each mouse. WT and MCDM were randomized to either saline or TNBS perfusion into the pancreatic duct. Patient demographics are summarized in Table 1 . Alcohol abuse was the most common cause for pancreatitis in both groups. Analysis of our results, using the Mann-Whitney U test, revealed significantly more mast cells in patients with a history of painful chronic pancreatitis (n = 8) when compared to patients with either non-painful chronic pancreatitis (n = 6) (33.8 vs 9.4 average mast cell count/10 high power fields; p < 0.01) or controls (n = 8), (33.8 vs 6.1 average mast cell count/10 high power fields; p < 0.01) ( Figure 2 ). The increased number of mast cells in patients with painful pancreatitis was noted predominantly in interstitial areas and, to a lesser degree, in the periacinar space. Figure 3 shows the post-pre surgical response frequency for both WT and MCDM. TNBS had a significant independent effect on abdominal VFF response in WT mice at the force levels 4 and 8 mN (p = 0.007 and 0.037, respectively) ( Figure 3A ). There was a trend towards a significant effect at the force level of 16 mN (p = 0.066). In contrast, for MCDM, TNBS had no significant effect on abdominal VFF response at any force level ( Figure 3B ). There was no significant TNBS effect on VFF response in the left A g e Pancreatic histology confirmed the presence of chronic pancreatitis in both WT and MCDM with marked fibrosis, inflammatory infiltrates and ductular proliferation mimicking changes seen in human chronic pancreatitis ( Figure 5A ). The pancreas of saline treated controls was normal. There was no significant difference in the overall inflammatory scores between the WT and MCDM ( Figure 5B ). An increased number of mast cells were counted in WT mice with chronic pancreatitis compared to saline Histology (Giemsa) of mice with chronic pancreatitis (Figure 6 ). As to be expected, no mast cells were present in pancreas of MCDM. Chronic pancreatitis has been defined as a continuing inflammatory disease of the pancreas characterized by irreversible morphologic changes that typically cause pain and/or permanent loss of function [17] . The pathogenesis of pain in this condition remains to be satisfactorily established. We examined the association, if any, of pain with mast cells as quantified in autopsy specimens of patients with a history of painful and non-painful chronic pancreatitis and normal controls. Significantly more mast cells were present in pancreatic tissue from patients with a history of painful chronic pancreatitis, indicating an association with this condition and a potential role for these cells in the pathogenesis of pain in painful chronic pancreatitis. There are clearly limitations to a retrospective, autopsybased study such as the one we report here. For instance, we do not know whether pain was present at the time of death and there was incomplete information on the different patterns of pain. Also, our findings pertain mainly to patients with a history of alcoholic pancreatitis. Nevertheless, our findings do suggest an association of painful chronic pancreatitis with an increased number of mast cells. This observation provided the rationale for further experimental testing, which we performed in mice. We first developed a model of chronic pancreatitis in mice following a modified protocol first described by Puig-Divi et al. [13] . Histological changes consisted of periductal and lobular fibrosis, duct stenosis, chronic inflammatory cell infiltrates, and gland atrophy, mimicking features of chronic pancreatitis in humans. Significantly more mast cells were present in WT mice with chronic pancreatitis, adding to the validity of this model for use in studies on the role of mast cells in pancreatitis. Both WT and MCDM developed histological changes consistent with chronic pancreatitis, indicating that the elimination of mast cells did not modulate the animals' ability to mount an inflammatory response. Therefore, any changes observed in pain behavior are unlikely to stem from differences in underlying inflammation. Next we determined whether this mouse model could be used to study behavioral differences associated with chronic pancreatitis. The assessment of spontaneous pain in a visceral organ presents significant difficulties. We have used a behavioral method to assess this, which relies on the association of visceral pain with sensitization of somatic regions of the body that share segmental innervation at the level of the spinal cord (referred pain). This somatic sensitization can be quantified using VFF to stim-ulate the somatotopically appropriate abdominal region and measuring the abdominal withdrawal response. Thus, VFF testing of the anterior abdominal wall can be used as a surrogate marker for visceral pain. Although this is the first time that this technique has been used for the measurement of referred visceral hyperalgesia in a mouse model of chronic pancreatitis, this method has previously been described and validated to assess the severity of referred visceral pain for models of colonic hypersensitivity [14] as well as rat models of acute necrotizing pancreatitis [15] and chronic pancreatitis [18] . The abdominal VFF response was compared to the hind paw response to assess the specificity of the interventions to the pancreas. TNBS treated mice, but not the saline control, developed increased abdominal wall withdrawal responses to VFF testing when compared to baseline, suggesting the development of force-dependent referred hyperalgesia of the abdominal wall in WT mice. There was no evidence of referred hyperalgesia in the hindpaws, suggesting that the measured effect on abdominal withdrawal is specific for an intra-abdominal origin of the pain. Vera-Portocarrero et al. previously described similar findings, increased withdrawal frequency after VFF stimulation to the abdominal area, in a rat model of chronic pancreatitis [18] . These behavioral changes were abrogated by morphine. Rats that demonstrated behavioral changes also expressed increased substance P expression in the nociceptive layers of the spinal cord, suggestive of central nociceptive changes. Mast cells produce a variety of degranulation products in the setting of inflammation that may activate and/or sensitize primary nociceptive neurons. The neurotrophin growth factor (NGF) is one such product [19] [20] [21] [22] . NGF is released in the setting of inflammation and can not only function as a chemoattractant for other mast cells, but it can also trigger mast cell degranulation [23] . We are speculating that NGF production in the inflamed pancreas is responsible for plastic changes in the sensory neurons by activating proalgesic receptors and channels such as the NGF receptor tyrosine kinase A (TrkA) and Transient Receptor Potential Family V receptor 1 (TRPV1; previously known as VR1) thereby contributing to the generation of pain [24] [25] [26] . Similarly, other mast cell degranulation products such as tryptase and histamine are capable of modulating neuronal function [27] [28] [29] [30] [31] [32] . Tryptase may directly activate the proteinase-activated receptor-2 (PAR-2), a G-protein coupled receptor expressed by pancreatic nerves, important in the pathogenesis of pain in pancreatitis [33, 34] . Although the role for mast cells in the mediation of visceral nociceptive signaling needs to be explored further, we speculate that mast cell products released in pancreatitis, contribute to the development of pain by direct effects on nociceptors located on pancreatic afferent neurons (Figure 7 ). However, before concluding a definite role for mast cells from our experimental data, it should be noted that MCDM carry a spontaneous mutation for tyrosine kinase receptor c-kit which not only produces a deficiency of mast cells but may have an independent effect on the function of sensory neurons, which are known to express it [35] . Therefore, it remains to be determined whether the detected differences in nociceptive responses is due to the absence of mast cells per se or a yet unknown change in the responsiveness of sensory neurons due to a congenital lack of the c-kit receptor. Reconstitution of mast cells into the MCDM mice should restore their nociceptive responses close to the wild type phenotype. Our data should increase awareness of the importance of mast cells in the pathogenesis of painful inflammatory Proposed involvement of mast cells in nociceptive signaling in pancreatitis Figure 7 Proposed involvement of mast cells in nociceptive signaling in pancreatitis In pancreatitis, mast cells may migrate to sites of inflammation, in response to release of mast cell chemoattractants. Mast cell degranulation products may modulate neurotransmission directly by activating proalgesic receptors and channels such as trka (NGF), TRPV1 (NGF) and PAR-2 (tryptase and trypsin). The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-230X/5/8/prepub
The structure of an RNA molecule is widely recognized to play a role in many processes, including structurally organizing complex RNAs, the assembly of ribonucleoprotein complexes, and in translational recoding and regulation [reviewed in (1) ]. One common RNA folding motifs is a pseudoknot, the folding back of a single-stranded RNA onto itself to form two helical structures with single-stranded loops joining them (2) . Many such structures can be inferred from RNA sequences and frameshifting function has been demonstrated for some of these [reviewed in (3) (4) (5) ]. However, though much theoretical progress has been made in understanding how mRNA pseudoknots promote efficient À1 ribosomal frameshifting (6), a complete understanding of this mechanism remains untested. Programmed À1 ribosomal frameshift signals are typically divided into three components. From 5 0 to 3 0 these are (i) a 'slippery site' in the form N NNW WWH, where N must be a stretch of any three identical nucleotides, where W is either three A or U residues, and H is A, C or U (spacing indicates the unshifted zero frame), (ii) a spacer region and (iii) an mRNA structural element, most often a pseudoknot. The general model posits that upon encountering the mRNA pseudoknot, an elongating ribosome is forced to pause such that the anticodons of its A-and P-site tRNAs are base-paired with the zero-frame codons of the slippery site. The nature of the tRNA-mRNA interactions is such that a relative slip of À1 nucleotide still allows base-pairing in the non-wobble positions. The slippage occurs during the ribosomal pause, and it has been shown that changes affecting ribosome pause times affect frameshift efficiencies [reviewed in (7) ]. An important observation is that even though mRNA pseudoknots and energetically equivalent stem-loop structures appear to promote ribosome pausing with equal effectiveness, mRNA pseudoknots are more efficient at promoting À1 PRF (8). Our '9 Å ' model (6) provided a refinement of the original 'simultaneous slippage' (9, 10) model of frameshifting by suggesting that rather than the entire ribosome having to slip one base in the 5 0 direction, slippage could be accomplished by moving the small section of mRNA in the downstream tunnel by one base in the 3 0 direction. We have proposed that this is accomplished by the bulky and difficult to unwind mRNA pseudoknot structures becoming wedged in the downstream entrance tunnel of the ribosome, preventing the downstream region of the mRNA from being pulled into the ribosome by the equivalent of one base during the accommodation step of elongation. This blockage would introduce tension into the spacer region, which could be resolved by unpairing the mRNA from the tRNAs, allowing the mRNA to slip 1 nt backwards, resulting in a net shift of reading frame by À1 base. Though the 9 Å model provides a partial explanation for why mRNA pseudoknots promote programmed À1 ribosomal frameshifting (À1 PRF) more efficiently than simple *To whom correspondence should be addressed. Tel: +1 301 405 0981; Fax: +1 301 314 9489; Email: dinman@umd.edu ª The Author 2005. Published by Oxford University Press. All rights reserved. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org stem-loop structures, it does not answer the question of how the mRNA pseudoknot directs the ribosome to pause at the correct position along the mRNA. A complementary 'torsional restraint' model addresses this issue (11) . When a stem-loop structure is unwound by an elongating ribosome, unwinding of the stem forces the loop to rotate. Since a simple stem-loop is not restrained, the loop can rotate freely and only the base pairs within the stem resist ribosomal movement, and thus the potential energy of unwinding should be distributed along the length of Stem 1 ( Figure 1A ). However, if the loop is anchored or restrained, as it is in a pseudoknot by Stem 2, since the intrinsic ribosomal helicase is processive (12) , Stem 1 cannot be fully unwound until Stem 2 is first denatured. Mechanically, as the ribosome begins to unwind the base of Stem 1, Stem 2 forces the supercoiling in the remainder of Stem 1, providing extra resistance to ribosome movement. At some specific point, the resistance to ribosome movement provided by the supercoiling counteracts the forward movement of the ribosome, increasing the likelihood that the ribosome will stop at a precise point along the mRNA. Energetically, since full unwinding of Stem 1 is dependent on complete denaturation of Stem 2, the potential energy of unwinding of the pseudoknot structure should similarly be directed toward one point. Viewed either mechanically or energetically, this point is where ribosomes will be directed to specifically pause on the mRNA. If it occurs with the tRNAs in ribosomal A-and P-sites positioned at the slippery site, then frameshifting is stimulated. This is summarized in Figure 1B . The efficiency of À1 PRF can thus be viewed as a function of (i) the fraction of ribosomes paused over the slippery site and (ii) the rate at which the structure can be denatured. There is increasing evidence from single molecule experiments that unfolding occurs in quick 'rips' at a particular force (13) , suggesting that in the case of unfolding pseudoknots, frameshifting efficiency is related to both the energy barriers to unfolding the pseudoknot structure and the resistance of the structure against the force of the ribosome. In the context of the torsional restraint model, this resistance is dependent on the ability of Stem 2 to remain intact while Stem 1 is being unwound. There is experimental data that indirectly support this model: (i) disruption of the first 3 bp of Stem 1, which would displace the ribosome's pause site to a point 3 0 of the slippery site, has been shown to eliminate frameshifting (14) ; (ii) destabilizing Stem 2, which would allow it to be unwound more readily, has been shown to result in decreased frameshifting efficiency (15) (16) (17) ; (iii) replacing bulges in Stem 1 with base pairs would increase the energy required to unwind the first three bases, and a longer ribosomal pause over the slippery site would follow, yielding increased efficiencies in À1 frameshifting (15, 18) ; (iv) destabilizing the base of Stem 1 by replacing G:C base pairs with A:U pairs decreases À1 frameshifting efficiencies (19, 20) ; (v) the model eliminates the need for a 'pseudoknot recognizing factor', the evidence of which has not been forthcoming in either competition assays in in vitro translation systems (21) or by gel retardation assays (J. D. Dinman, unpublished data); and (vi) elimination of a potential torsion-restraining Stem 2, but not of a non-torsion-restraining Stem 2 in HIV-1, resulted in decreased À1 PRF efficiencies (22) . Though all of the cited studies support the torsional restraint model, none has directly addressed it. In the experiments presented in this study, a series of 'pseudo-pseudoknot' containing reporter constructs were used to test the torsional restraint hypothesis. In vitro frameshifting assays show that frameshifting can be significantly stimulated by limiting the rotational freedom of the loop region of a stem-loop structure, and that the degree of rotational freedom of Stem 1 is important in determining the extent of À1 PRF. Furthermore, mRNA toeprint analyses reveal a pseudo-pseudoknot-specific strong stop 16 nt 3 0 of the slippery site, consistent with this structure being able to direct ribosomes to pause with their A-and P-sites positioned at the slippery site. All synthetic DNA oligonucleotides were purchased by IDT (Coralville, IA). The modified L-A viral À1 PRF signal containing the G GGU UUA slippery site followed by a simple stem-loop was amplified from pJD18 (23) using the primers luc5 0 b (5 0 -CCCCAAGCTTATGACTTCTAGGCAGGGTTT-AGG-3 0 ) and luc3 0 b (5 0 -CCCCCCATGGGACGTTGTAAA-AACGACGGGATC-3 0 ). These were digested with HindIII and NcoI (restriction sites are underlined) and cloned into the firefly luciferase reporter plasmid pT7-LUC minus 3 0untranslated region-A50 (24) . In the resulting reporter construct (pJD214-18), expression of firefly luciferase requires a À1 frameshift, and the 5 0 sequence of the Stem 2 is not able to base pair with the 3 0 sequence, so that only a stem-loop rather than a pseudoknot is able to form. The same primers were used to amplify DNA from pJDRC (23) to make pJD214-Ry. In this construct, complementary mutations (5 0 -GCUGGC-3 0 to 5 0 -CGACCG-3 0 ) in the 3 0 acceptor sequence of the pseudoknot-forming region of Stem 2 allow the formation of an mRNA pseudoknot that has previously been shown to promote frameshifting at the same frequency as the wild type (23) . The primer luc5 0 CON (5 0 -CCCCAAGCTTATGACTTC-TAGGCAAGGGTTTAGG-3 0 ) contains an additional A nucleotide upstream of the slippery site and was used to make pJD214-0, the zero-frame control. To eliminate the possibility of internal initiation occurring at the luciferase initiation codon downstream of the frameshift signals, the AUG codon was changed to AUA. The Stratagene Quik-Change kit was used to mutate pJD214-18 and pJD214-Ry into pJD336-18 and pJD366-Ry, respectively, using the oligonucleotides 5 0 -GGCGTTCTTCTATGGGACGTTGTA-AAAACGGATC-3 0 and 5 0 -GATCCGTCGTTTTTACAACG-TCCCATAGAAGACGCC-3 0 (the mutated codon is underlined). pJD366-18 was further mutated to make a zero-frame control by placing an A upstream of the slippery site using the oligonucleotides 5 0 -TGACTTCTAGGCAAGGGTTTAGGAG-TG and 5 0 -CACTCCTAAACCCTTGCCTAGAAGTCA (the inserted base is underlined). A series of synthetic DNA oligonucleotides were designed to join the loop acceptor region of mRNA transcribed from pJD366-18 to the downstream region that forms the pseudoknot in the wild-type L-A À1 PRF signal. In the J-oligos, the 3 0 sequences base pair with the loop of the mRNA transcribed from pJD366-18, and the 5 0 regions of these oligos base pair with the downstream sequence. This orientation is reversed for the R-oligos. These general orientations are shown in Figure 3B . The naming of the oligonucleotide names refers to the number of additional residues placed between the regions of complementarity. The bases complementary to the pJD366-18 sequence are underlined. Plasmid DNAs were prepared using the Qiagen mini-prep kits and were linearized with DraI in a total volume of 20 ml. Proteins were eliminated by the addition of 2 ml of 1 mg/ml proteinase K and SDS to a final concentration of 0.5% followed by digestion at 50 C for 30 min. Volumes were then increased to 100 ml, extracted twice with phenol/chloroform, and DNA was precipitated with 10 ml NH 4 Ac and 250 ml ethanol. The purified DNA was resuspended in DEPCtreated H 2 O. To prepare synthetic mRNAs, 2 ml of purified linear DNAs were used for in vitro transcription using the Ambion T7 mMachine mMessage kit. RNAs were precipitated using 30 ml DEPC H 2 O and 25 ml LiAc. The RNA was resuspended in 11 ml DEPC H 2 O (1 ml in 500 would give an OD 260 of 0.05-00.1; 1-2 mg/ml). To anneal the oligonucleotides with the mRNA, J-oligos, R-oligos or the equivalent volumes of dilution buffer alone (20 mM Tris, pH 7.4, 2 mM MgCl 2 and 50 mM EDTA final concentration) were added to synthetic mRNA (0.5 mg), and the mixtures were first incubated in a 70 C heating block for 10 min; the block was then removed and allowed to cool to 37 C (30 min), after which they were briefly spun down and incubated on ice. In all experiments, the molar ratios of J-and R-oligos to synthetic mRNAs were 100:1. In experiments using the competing oligonucleotide (C-oligo), this was added to either 0.5:1 or 1:1 molar ratios with either J-or R-oligonucleotides. Reticulocyte lysates were thawed on ice, 15 ml of Àmet and 15 ml of Àleu master mixes plus 20 ml of H 2 O were added to 400 ml of lysate, and 19 ml of this was added to each annealed reaction to start the in vitro translation reactions. These were incubated at 30 C for 60 min (the reaction reached a plateau after 30-35 min where the greatest difference was seen between the zero-frame controls and the frameshifting plasmids) (data not shown), and the reactions were then placed on ice. An aliquot of 7.5 ml from each in vitro translation reaction was added to 50 ml of the prewarmed luciferase reagent, and luminescence readings were taken after a 3 s delay for 15 s in triplicate using a Turner 20/20 Luminometer. Synthetic transcripts generated from DraI-digested pJD366-18 ($1.7 kb) were 5 0 end labeled using [g-32 P]CTP. These RNAs (4 ml) were incubated with 1 ml of annealing buffer and either 1 ml of H 2 O or 1 ml of an oligo (0.25 ng) at 70 C. The heating block was allowed to cool at room temperature for 40 min before 8 ml of RNaseH buffer was added (20 mM HEPES, 50 mM KCl, 10 mM MgCl 2 and 1 mM DTT). An aliquot of 1 ml of enzyme was added (mung bean nuclease, RNaseH or RNaseT1) and the reactions incubated at 37 C for 1 h. The reactions were stopped by adding 4 ml of stop solution, the products separated through a 6% polyacrylamide-urea denaturing gel and visualized by autoradiography. mRNA toeprinting JD366-18 mRNA (1 mg in 8 ml) was annealed with 2 ml of 3 0 end-labeled toeprinting primer (5 0 -CGTACGTGATCTTCA-CC-3 0 , complementary to sequence 240 bp 3 0 of the slippery site) as described above. This was added to 15 ml of lysate (200 ml Ambion retic lysate, 7.5 ml of each master mix [Àleu and Àmet] and 70 ml of 250 mM KCl), except for 2 ml, which was added to 15 ml of RT buffer [50 mM Tris-HCl (25), 40 mM KCl, 6 mM MgCl 2 , 5 mM DTT and 575 mM dNTPs] to be used as a no-ribosome control. In vitro translation reactions were incubated at room temperature for 10 min, which was empirically determined to provide the optimum amount of time to allow ribosomes to initiate translation and pause at the frameshift signal. Subsequently, 15 ml of RT buffer containing RNasin inhibitor and cycloheximide (to a final concentration of 100 ng/ml) was added to stop translation. To this, 2 ml of Superscript II (Invitrogen) was added and the reaction incubated at room temperature for 10 min. Reactions were terminated by phenol:chloroform extraction and 15 ml of stop solution added. The toeprinting primer was also used in conjunction with pJD366-18 to produce sequencing ladders by standard dideoxynucleotide chain termination methods using Sequenase (USB). Products were separated though 6% polyacrylamide-urea denaturing gels and visualized using a Storm phosphorImager (Pharmacia). Pseudo-pseudoknots stimulate frameshifting, and frameshifting efficiency changes with the degree of pseudo-pseudoknot rotational freedom We previously showed in intact yeast cells that the pseudoknot containing mRNA produced from pJDRC was able to promote efficient À1 PRF, whereas one in which only a stem-loop can form, transcribed from pJD18, could not (23) . As a first step in this study, we tested the ability of synthetic mRNAs produced from pJD366-RC and from pJD366-18, two plasmids derived from these parental constructs, to promote À1 PRF. Total luciferase activities produced from these synthetic mRNAs were divided by the luciferase activity produced from the zero-frame control plasmid, pJD366-0, and multiplied by 100% to determine À1 PRF efficiencies. The results show that the trends observed in yeast were replicated in vitro, i.e. JD366-RC mRNA promoted$8% efficiency of À1 PRF as compared with $1.1% promoted by JD366-18 mRNA (Figure 2 ). The 'torsional restraint' model predicts that conditions that would inhibit the rotational freedom of the loop region of the pJD366-18-derived mRNA should result in enhanced À1 PRF efficiency. The strategy used in this study was to anneal this mRNA with synthetic oligonucleotides complementary to both the loop region and to the sequence downstream that is normally involved in pseudoknot formation. These 'pseudopseudoknots' would be predicted to restore a pseudoknot-like structure to the mRNA. This is diagrammed in Figure 3A . Two different classes of oligonucleotides having different orientations relative to the mRNA were used to this end: 'joining' (J-) and 'reverse' (R-) oligos. The orientation of the J-oligos promotes the formation of a structure containing the equivalent of a Loop 2 region, while that of the R-oligos promotes a Loop 1 equivalent. The model also predicts that pseudo-pseudoknots having different degrees of rotational freedom should promote different frequencies of ribosome pausing over the slippery site, resulting in different efficiencies of À1 PRF. In order to control this parameter, increasing numbers of nucleotides were inserted between the mRNA hybridizing regions of the J-and R-oligos. The additional non-complementary bases in the J-oligos are 3 0 to the stem-loop residues involved in Stem 2, thus effectively increasing Loop 2. Similarly, the additional non-complementary bases in the R-oligos are 5 0 to the loop acceptor residues and correspond to an increased Loop 1. The structure of the stem-loop of pJD366-18 and its maximum base-paired interactions with representative J-and R-oligos are shown in Figure 3B . To demonstrate that an oligonucleotide-mRNA hybrid was capable of forming under the assay conditions, the J1-oligo was incubated with 5 0 [ 32 P]labeled JD366-18 mRNA and subjected to RNaseH digestion. Digestion of the RNA-DNA hybrid resulted in a labeled 110 nt fragment, demonstrating that the oligonucleotide bound to the mRNA at the position of the pseudoknot (Figure 4) . Having demonstrated the utility of the in vitro frameshifting assay and that the J-and R-series of oligonucleotides were able to hybridize with synthetic mRNA produced from pJD366-18, the next step was to monitor frameshifting efficiencies promoted by these hybrid species. Significant increases in frameshifting were observed with the incubation of pJD366-18 mRNA with oligonucleotides J1 ($10%) and J2 ($35%), while only modest increases were seen with J3 and J4 ( Figure 5 ). These findings are consistent with the notion that changes in the degree of rotational freedom of the structure would affect the distribution of paused ribosomes in the vicinity of the slippery site. One potential complication with the J-oligos is the possibility that they could interact with the Loop 2-Stem 1 region. In the R-oligos, the additional bases are distal to any possible Loop 2-Stem 1 interactions and would be more analogous to increasing Loop 1. The R-oligos stimulated À1 PRF to an even higher extent than the J-oligos ( Figure 5 ). Importantly, increasing the length of the bridging regions in these oligonucleotides (R1 to R3), which is predicted to increase the rotational freedom of the stem-loop, resulted in decreased frameshifting activity as predicted by the torsional resistance model. However, addition of three residues between the two binding regions of the R-oligo (R4) resulted in an unexpected increase in frameshifting with a very large amount of variation. In a series of control experiments, 8 nt oligos complementary to the 5 0 (Loop 1) and 3 0 Stem 2 forming regions of the pseudo-pseudoknot were hybridized to the SL mRNA and À1 PRF assays were performed. Neither of these were able to stimulate À1 PRF, even at concentrations in 100-fold molar excess to the mRNA template (data not shown). Though supportive of our central hypothesis, it is also possible that these results were due to the thermodynamic instability of the RNA:DNA duplexes through the course of the experimental protocol. To determine whether the stimulation of frameshifting was specifically due to the bridging of the stem-loop with downstream sequence (the pseudo-pseudoknot), as opposed to the nonspecific presence of an RNA:DNA hybrid, the competing oligonucleotide (C-oligo) was designed to form a 15 bp duplex with JD366-18 mRNA, including the 3 0 Stem 2 forming region, which was expected to significantly out compete either the J-or R-oligos from binding to this site, thus disrupting formation of the pseudo-pseudoknot (see Figure 3A ). Additionally, in the presence of the C-oligo, the J-and R-oligos were still predicted to hybridize with the 5 0 Stem 2 forming region, enabling us to address the question of whether this interaction alone was able to stimulate frameshifting. The results demonstrate that the addition of the C-oligo severely inhibited the abilities of both the J-and R-oligos to promote efficient frameshifting ( Figure 6 ). These findings demonstrate that (i) frameshifting was specifically stimulated by bridging of the 5 0 and 3 0 Stem 2 forming regions by the J-and R-oligos, and (ii) that the presence of an RNA:DNA hybrid at the 5 0 Stem 2 forming region was not sufficient to stimulate frameshifting by itself. The torsional restraint model predicts that pseudoknots should direct elongating ribosomes to pause at one specific location 1 2 3 4 5 6 7 8 . Efficient frameshifting is stimulated by pseudo-pseudoknots. In vitro translation assays were performed in retic lysates with mRNAs derived from pJD366-18 (SL) to which J-or R-oligos were annealed. Luciferase activities were divided by those obtained using mRNAs generated from pJD366-0, and the resulting ratios were multiplied by 100 to calculate percent frameshifting. The averages of three independent experiments performed in triplicate are shown. Error bars denote standard deviation. on the mRNA, rather than being distributed along Stem 1. We used mRNA toeprint assays to test this hypothesis. In mRNA toeprint reactions, the movement of reverse transcriptase is blocked by paused ribosomes, resulting in a strong stop positioned$16-18 nt 3 0 of the P-site of eukaryotic ribosomes (25) . Synthetic JD366-18 mRNAs were annealed with the sequencing oligonucleotide and either J1, R1 or no second oligo, and these were then used for in vitro translation reactions. After a period of time (10 min were empirically determined to be optimal), elongation reactions were stopped by the addition of cycloheximide, and reverse transcription reactions were initiated on the sequencing oligonucleotides. In parallel, control reverse transcription reactions were carried out using synthetic JD366-18 mRNA and oligonucleotides, but without in vitro translation. The results are consistent with the model, showing that the J1-and R1-oligos specifically promoted one strong reverse transcriptase stop 16 nt 3 0 of the P-site of the slippery site only the in the in vitro translation reactions ( Figure 7 ). As further predicted by the model, a broad distribution of stops of equal intensities was observed in this region with JD366-18 mRNA alone (Figure 7, lane 1) . Importantly, the +16 stop was not observed when toeprint reactions were carried in the absence of ribosomes. Additional strong stops were also of interest. One corresponding to the 3 0 end of the base of Stem 1 was observed in all samples, consistent with the presence of this structure. Both J-and R-oligo-specific pauses were also observed. The reason for the strong pause in the J-oligo is unknown. The R-oligo-specific pause is perhaps more revealing. It occurs at the 3 0 end of the RNA:DNA hybrid formed by this oligo and the mRNA, a structure that should also promote pausing of reverse transcriptase. The results presented in this study provide strong support for the torsional restraint model of programmed À1 frameshifting. Specifically, we demonstrated that RNA:DNA hybrids that mimic mRNA pseudoknots can significantly stimulate frameshifting. As predicted by the model, changing the rotational freedom of the structure by altering the lengths in the J1-and R1-oligos between the 5 0 and 3 0 mRNA hybridizing regions resulted in changes in their abilities to stimulate À1 frameshifting. The demonstration that these 'pseudopseudoknot' structures cause elongating ribosomes to specifically pause with their A-and P-sites positioned at the slippery site provides independent evidence in support of the model. In the case of the J-oligo series, frameshifting was best stimulated by J2, suggesting the structure created and the rotational freedom allowed by it was optimal for À1 PRF. The experimental design is such that we assume a similar rate of unfolding for each oligo as the predicted maximum base pairing is the same for them all. However, we do note that the type of nucleotides separating the two, separately paired regions of the oligos, and their presentation, may play a role in À1 PRF efficiency. The recent NMR structural solution of the SRV-1 pseudoknot revealed a highly structured Loop 2-Stem 1 interface including base triples involving an A residue at the 3 0 end of Loop 2 (26) . The additional base in the J2oligonucleotide is also an A. Mutagenesis experiments in this region by other groups showed, for example, that replacing the 3 0 base in Loop 2 of IBV with an A residue promoted a significant increase in frameshifting efficiency (27) , and mutation or removal of the A residue at the 3 0 base in Loop 2 of the BWYV pseudoknot reduced frameshifting levels (17) . This part of the pseudoknot has been proposed to be important in a frameshifting model where differential transition state energy barriers (due to small differences in local structure, stability or dynamics) are the primary determinant of frameshifting efficiency (3). Indeed, a Loop 2-Stem 1 triplex interaction seen in smaller frameshifting pseudoknots from luteoviruses has been shown to be critical for À1 PRF, and that similar pseudoknots lacking the triplex are less efficient at frameshifting [(28) and references therein]. This extra structural feature would limit the rate of unfolding and provide extra anchoring of Stem 2 as the ribosome attempts to unwind Stem 1, i.e. it too would help to provide additional torsional restraint. It is also possible that although the J3-and J4oligonucleotides also help to form a pseudo-pseudoknot, the additional bases may interfere with the stabilization of Stem 1. With the R-oligos, a general correlation was observed between minimization of rotational freedom and frameshifting efficiency, though this was not the case of the R4-oligo. Since the stability of the pseudo-pseudoknot generated with R4 should be similar to that of the other oligonucleotides based on the base-pairing, this result suggests that there are additional considerations to be uncovered with regard to the Frameshifting (% stimulated by R1) Figure 6 . Competition for J-or R-oligo binding sites inhibits its ability to promote efficient frameshifting. mRNA transcribed from pJD366-18 (SL) was annealed with either J-or R-oligos alone, or in combination with different concentrations of competing (C-) oligos (in ratios of 2:1 or equimolar as indicated). Sample marked SL is mRNA alone. Luciferase activities generated from in vitro translation reactions in rabbit reticulocyte lysates were divided by those obtained using mRNAs generated from pJD366-0, and the resulting ratios were multiplied by 100 to calculate percent frameshifting. pseudoknot structure influencing frameshifting. Addition of residues in the R-oligos was analogous to lengthening Loop 1, which is typically short in À1 frameshifting pseudoknots. Limited and conflicting data are available on the importance of Loop 1 in À1 frameshifting pseudoknots. In one study, addition of three A bases to Loop 1 did not affect frameshifting efficiency (15) , while in another all the mutations made in this region were detrimental to frameshifting efficiency (17) . Given the complex interactions occurring between the helices and loops in this region, we cannot yet account for why the R4-oligo stimulated frameshifting so efficiently and with such variable results. Examination of the RNA toeprint data presented here reveals that both of the pseudo-pseudoknot structures formed by the J1-and R1-oligos promoted strong stops of the reverse transcriptase $16 nt 3 0 of the P-site codon of the slippery site, consistent with the hypothesis that the presence of Stem 2 forces ribosomes to pause with their A-and P-sites positioned over the slippery site. Previous studies mapping the lagging edge of paused ribosomes, i.e. mRNA heelprint studies, did not reveal any striking differences between the effects of pseudoknots versus stem-loops (8, 16) . Interestingly, using this method, the ribosomal pauses appeared distributed over a broader stretch of mRNA ($4 nt) than observed here. It is possible that some critical level of resolution is lost in the requirement for many additional manipulations of substrates using the mRNA heelprint as compared with the toeprint methods. A remaining question centers on whether the role of the RNA pseudoknot in À1 PRF is passive or active. In the '9 Å solution' (6), the frameshift mechanism is activated by movement of the A-site codon-anticodon complex by 1 base in the 5 0 direction upon accommodation. As currently described, the mRNA pseudoknot merely passively blocks entry of the downstream message into the ribosome, resulting in stretching of the segment of mRNA located between the codon-anticodon complex and the pseudoknot. By this model, all of the energetic input for the frameshift is derived from hydrolysis of GTP by eEF1A. However, it is possible that the pseudoknot may also actively contribute to the frameshift mechanism. Specifically, pulling the downstream message into the ribosome at accommodation could result in unwinding of Stem 1 of the pseudoknot by one additional base pair. The energetic cost of so doing would be to introduce an equivalent amount of torsional resistance into Stem 2. If Stem 2 were to release this resistance by 'pulling back', the base pair in Stem 1 would be re-formed, which in turn would contribute to the energy required to dissociate the A-and P-site codonanticodon complexes from the zero-frame. This would be followed by slippage of the mRNA by 1 base in the 3 0 direction relative to the ribosome, followed by the formation of À1 frame codon-anticodon complexes. As such, the proposed active role for the mRNA pseudoknot would further reduce the energetic barrier to À1 PRF. In sum, we suggest that the 'torsional restraint model' can be combined with the '9 Å solution' to mechanistically explain the original 'simultaneous 3' mRNA + Ribos. mRNA Figure 7 . Pseudo-pseudoknots direct ribosomes to pause over the slippery site. mRNAs generated from pJD366-18 (SL) were annealed with the sequencing oligonucleotide and either J1-, R1-or no oligo (lanes 1, 2 and 3, respectively), and these were then used for in vitro translation reactions. Reactions were stopped after 10 min by the addition of cycloheximide, and reverse transcription reactions were initiated on the sequencing oligonucleotides. In parallel, control reverse transcription reactions were carried out using synthetic JD366-18 mRNA and oligonucleotides, but without in vitro translation (lanes 4-6). The positions of the slippery site, loops and stems of the pseudo-pseudoknots are indicated next to a sequencing reaction. Arrowheads indicate positions of reverse transcriptase strong stops and these are mapped to a representation of the stem-loop structure of pJD366- 18. slippage' model of À1 PRF (9, 10) . In other words, the 9 Å solution + torsional restraint = simultaneous slippage. Two recent publications have also shown that oligonucleotide:mRNA duplexes can stimulate efficient À1 ribosomal frameshifting (29, 30) . These studies differed from the present one in a number of ways, particularly insofar as they examined the effects duplex structures immediately 3 0 of the slippery site rather than addressing mRNA pseudoknot related questions. The findings support the notion that the specific location of ribosome pausing on the mRNA plays a critical role in determining frameshifting, though they do come with caveats, e.g. neither study directly mapped ribosomal pausing, and the use of different slippery sites and downstream contexts likely contributed to disparate findings for the optimal distances between the 3 0 ends of slippery sites and 5 0 ends of frameshift-stimulating oligonucleotides. Although potentially useful therapeutically there are no known natural examples of frameshifting stimulated in this manner, and thus these results do not affect the hypothesis presented here. However, these studies are important in that they raise the possibility for a new role for micro-RNAs in regulating gene expression, and for therapeutic approaches to correcting inborn errors of metabolism due to the presence of frameshift mutations.
Methods for the automated chemical synthesis of oligonucleotides (1, 2) and their assembly into long double-stranded DNA (dsDNA) sequences by PCR (3, 4) and LCR (5) have enabled the chemical synthesis of genes and even entire viral genomes (6, 7) . These technological advances have helped spur the formation of the new field of synthetic biology (8) , which aims at defining the functional units of living organisms through the modular engineering of synthetic organisms. In addition, the demand for fully synthetic gene length DNA fragments of defined sequence has dramatically increased in recent years for use in applications such as codon optimization (9), construction of DNA vaccines (10) , de novo synthesis of novel biopolymers (11) , or simply to gain access to known DNA sequences when original templates are unavailable. The future demand for long synthetic DNA is likely to dramatically increase when it becomes cheaper/faster to synthesize a desired sequence than to obtain it by other means. The assembly of DNA is currently limited by the presence of random sequence errors in synthetic oligonucleotides that arise from side reactions during synthesis (incomplete couplings, misincorporations, etc.) and resulting in 1-3 errors/kb (7, 12, 13) . The deleterious impact of these errors becomes more significant as the desired lengths of synthetic DNA increase. Indeed, in the remarkable assembly of the PhiX 174 bacteriophage genome (5386 bp) using gel-purified, synthetic oligonucleotides, the products contained an average of $2 lethal errors/kb resulting in 1 plaque-forming genomes per 20 000 clones (7) . A functional selection (plaque formation) was required in this study to identify a clone with the correct sequence. Thus, error reduction/correction is a requirement for the efficient production of long synthetic DNA of defined sequence. However, the process of sequencing multiple clones and manual correction of errors is both costly and time consuming. Several methods have been reported for the removal of error-containing sequences in populations of DNA. These methods rely upon the selective destruction (14, 15) or physical separation (16, 17) of mismatch-containing heteroduplexes. Smith and Modrich (14) reported the selective destruction of error-containing sequences in PCR products by generating dsDNA breaks upon overdigestion with the Escherichia coli mismatch-specific endonuclease MutHLS (18) . Gel purification and cloning of the remaining full-length DNA resulted in an apparent 10-fold reduction in the error rate for PCR products. However, the existing approaches are not well suited for error removal in long synthetic DNA sequences where virtually all members in the population contain multiple errors. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org Error correction with MutS is outlined in Figure 1 . The population of DNA molecules containing random errors is first re-hybridized to expose synthesis errors as mismatches ( Figure 1A ). Duplexes containing mismatches can then be removed from the population by affinity capture with immobilized MutS ( Figure 1B) , a process we term coincidence filtering, since both strands of the duplex must match to pass this filtering step. For long synthetic DNA sequences or for sequences with high error rates, coincidence filtering is ineffective, since the likelihood of both strands being perfectly matched after re-hybridization is very low. To generalize MutS error filtering for application on synthetic DNA, the synthetic DNA is cleaved into small overlapping fragments before MutS filtering. Fragments containing mismatches are selectively removed through absorption to an immobilized maltose-binding protein (MBP)-Thermus aquaticus (Taq) MutS-His 6 fusion protein (MBP-MutS-H6) (18) (19) (20) . The remaining mixture of fragments (enriched with fragments of the correct sequence) serves as a template for assembly PCR to produce the full-length product ( Figure 1C ). This process can be iterated until the consensus sequence emerges as the dominant species in the population. This approach is equivalent to DNA shuffling (21) with additional mismatch exposure and removal steps. In this report, we assemble GFPuv from synthetic oligonucleotides and apply both coincidence filtering and consensus shuffling protocols to reduce errors in the resultant DNA populations. The error rates are characterized by gene function (fluorescence) and by DNA sequencing. We also provide a mathematical model describing the error reduction protocols to aid predictions about parameters influencing their effectiveness. Chemicals were from Sigma. Restriction enzymes were from Promega and New England Biolabs. KOD Hot Start DNA Polymerase was from Novagen. Amylose resin was from NEB (catalog no. E8021S). Ni-NTA resin was from Novagen (catalog no. 70666). Ultrafiltration device from Millipore (catalog no. UFC900524). Slide-A-Lyzer dialysis membrane was from Pierce (catalog no. 66415). Full-length Taq MutS was amplified from template pETMutS (22) with primers 5 0 -AAA AAA CAT ATG GAA GGC ATG CTG AAG G-3 0 and 5 0 -AAA AAT AAG CTT CCC CTT CAT GGT ATC CAA GG-3 0 and cloned into the Nde1/HindIII sites of vector pIADL14 (23) to give plasmid pMBP-MutS-H6. E.coli strain BL21(DE3) transformed with pMBP-MutS-H6 was grown to OD 600$1.0 and induced using 1 mM isopropylb-D-thiogalactopyranoside for 4 h at 37 C. Cells from 4 l of culture were pelleted and resuspended in 60 ml of buffer A (20 mM Tris-HCl, pH 7.4, 300 mM NaCl, 1 mM EDTA, 1 mM DTT and 1 mM phenylmethlysulfonyl fluoride). Cell suspension was sonicated on ice and insoluble material was removed by centrifugation at 50 000 g for 10 min at 4 C. Supernatant was applied to 5 ml amylose resin pre-equilibrated in buffer A. Bound MBP-MutS-H6 was washed three times using 20 ml buffer B (20 mM Tris-HCl, pH 7.4, 300 mM NaCl) and stored The re-hybridized gene synthesis products are fragmented, and error containing fragments are precipitated by MBP-MutS-H6 immobilized on amylose support. Error reduced fragments (orange, blue and red) are reassembled into the full-length gene followed by PCR amplification to generate error reduced products. Primers: black lines. overnight at 4 C. MBP-MutS-H6 was eluted using 20 ml buffer B + 10 mM maltose. Eluate was applied to $4 ml of Ni-NTA resin pre-equilibrated in buffer B. Bound MBP-MutS-H6 was washed four times using 20 ml buffer B + 25 mM imidazole. Bound MBP-MutS-H6 was eluted using buffer B + 1 M imidazole. Eluate was concentrated via ultrafiltration using Amicon Ultra 5 kDa MWCO at 4 C. Concentrated sample was dialyzed extensively against 2· storage buffer (100 mM Tris-HCl, pH 7.5, 200 mM NaCl, 0.2 mM EDTA and 0.2 mM DTT) using a Slide-A-Lyzer 10 kDa MWCO cassette at 4 C. Protein concentration was determined using A 280 and a calculated extinction coefficient of 119 070 M À1 cm À1 . Dialyzed sample was diluted using an equal volume of glycerol and stored at À20 Oligonucleotides were purchased from Qiagen with 'salt-free' purification. Sequence 261-1020 of pGFPuv (GenBank accession no. U62636 with T357C, T811A and C812G base substitutions) was assembled using 40mer (37) and 20mer (2) oligonucleotides with 20 bp overlap (Supplementary Table 1 ). Assembly reactions contained the following components: 64 nM each oligonucleotide, 200 mM dNTPs, 1 mM MgSO 4 , 1· buffer and 0.02 U/ml KOD Hot Start DNA Polymerase. Assembly was carried out using 25 cycles of 94 C for 30 s, 52 C for 30 s and 72 C for 2 min. PCR amplification of assembly products contained the following components: 10-fold dilution of assembly reaction, 25 mM of 20 bp outside primers, 200 mM dNTPs, 1 mM MgSO 4 , 1· buffer and 0.02 U/ml KOD Hot Start DNA Polymerase. PCR was carried out using 35 cycles of 94 C for 30 s, 55 C for 30 s and 72 C for 1 min followed by a final extension at 72 C for 10 min. PCR products were purified using the Qiagen QIAquick PCR purification kit with elution in dH 2 O followed by speed-vac concentration. Assuming an error rate of 1 · 10 À6 /bp/duplication for KOD DNA polymerase (24) , 35 cycles of PCR would be expected to introduce$0.053 mutations per assembled GFPuv molecule. Assembled GFPuv was diluted to 250 ng/ml in 10 mM Tris-HCl, pH 7.8, 50 mM NaCl and heated to 95 C for 5 min followed by cooling 0.1 C/s to 25 C. Heteroduplex for consensus filtering was split into three pools and digested to completion with NlaIII (NEB), TaqI (NEB) or NcoI plus XhoI (Promega) for 2 h following the manufacturer's protocols. Digests were purified using the Qiagen QIAquick PCR purification kit with elution in dH 2 O. Samples were pooled and the concentration was determined by measuring A 260 . MBP-MutS-H6 binding reactions contained $11.5 ng/ml DNA and$950 nM MBP-MutS-H6 dimers in 1· binding buffer (20 mM Tris-HCl, pH 7.8, 10 mM NaCl, 5 mM MgCl 2 , 1 mM DTT and 5% glycerol). Reactions were allowed to incubate at room temperature for 10 min before incubation for 30 min with an equal volume of amylose resin pre-equilibrated in 1· binding buffer. Protein-DNA complexes were removed by low-speed centrifugation and aliquots of supernatant were removed for subsequent processing. Supernatant (50 ml) from consensus filtering experiments was desalted using Centri-Sep spin columns (Princeton Separations) and concentrated. Purified and concentrated DNA fragments were reassembled as above with aliquots removed at varying cycles. Aliquots of assembly reactions were resolved on 2% agarose gels to monitor the reassembly process. Aliquots showing predominantly reassembled fulllength GFPuv were PCR amplified as above. Aliquots of supernatant from coincidence filtering experiments were diluted 10-fold and PCR amplified as above. PCR products were digested with BamHI/EcoRI and ligated into the 2595 bp BamHI-EcoRI fragment of pGFPuv. Ligations were transformed into E.coli DH5 and fluorescent colonies were scored using a handheld 365 nm ultraviolet (UV) lamp. Preparation of substrate for consensus shuffling from 10 non-fluorescent GFPuv clones Ten non-fluorescent GFPuv clones were pooled in equal amounts. The nature and location of the mutations in these clones is shown in Figure 4 . The GFP coding region was PCR amplified from the mixture and submitted to the consensus shuffling protocol with and without the application of the MBP-MutS-H6 error filter. To create an error filter, we constructed a fusion protein between MBP (19) and the mismatch binding protein from T.aquaticus (22) with a C-terminal His 6 tag (MBP-MutS-H6). MBP-MutS-H6 was overexpressed and purified from E.coli to >95% purity (Supplementary Figure 1) . MBP-MutS-H6 immobilized on amylose resin was shown to selectively retain a 40mer heteroduplex containing a deletion mutation over wild-type homoduplex (Supplementary Figure 2) . To demonstrate error correction, unpurified 40mer oligonucleotides were assembled by PCR (3) to produce a 760 bp gene encoding green fluorescent protein (25) (GFPuv). Two independent preparations of GFPuv containing typical gene synthesis errors (Figure 3 and Table 1 ) were re-hybridized and subjected to two iterations of coincidence filtering or consensus shuffling. For consensus shuffling, the GFPuv assembly product was split into three pools and digested into sets of overlapping fragments using distinct Type II restriction enzymes ( Figure 2 ). The digests were pooled and subjected to error filtering with or without added MBP-MutS-H6. The unbound fragments were reassembled into full-length products and PCR amplified. For coincidence filtering, unbound fulllength GFPuv was PCR amplified following treatment with the error filter. After cloning in E.coli, error rates were estimated by scoring colonies for fluorescence under a handheld UV lamp (Figure 3) . The actual error rates of the input and consensus shuffled populations were determined by sequencing plasmid DNA from randomly selected colonies (Figure 3) . The results show that two rounds of consensus shuffling increased the percentage of fluorescent colonies from $60 to >90% and Table 1 . Sequence errors in input and consensus shuffled DNA Table 1 . Although DNA shuffling has traditionally been used to create diversity through the combinatorial shuffling of mutations in a population, DNA shuffling also creates a sub-population of sequences with a reduction in diversity, as correct fragments can recombine to produce error-free sequences. Indeed, with consensus shuffling, it is possible to start with a population of DNA molecules wherein every individual in the population contains errors and create a new population where the dominant sequence is error free. To demonstrate this, 10 nonfluorescent GFPuv clones, each containing a deletion mutation (Figure 4) , were pooled and subjected to either DNA shuffling alone or two iterations of consensus shuffling. Products were cloned in E.coli, and the percentage of fluorescent colonies was monitored as an indication of progress toward the consensus sequence. DNA shuffling alone (no MBP-MutS-H6) increased the percentage of fluorescent colonies to 30% (387 colonies total) similar to a previous report (26) . Two rounds of consensus shuffling gave a new population that was 82% fluorescent (551 colonies total), indicating that the dominant species was likely the consensus sequence of the input population. Both consensus shuffling and coincidence filtering protocols were effective in reducing errors in synthetic GFPuv populations ( Figure 3 ). In both cases, two iterations of either consensus shuffling or coincidence filtering increased fluorescent colonies from average values of$60 to >90%. Sequencing data from two independent experiments showed a 4.3-and 3.5-fold reduction in the error rate for the consensus shuffled populations compared with the input populations giving final error rates of 0.3 and 0.28 errors/kb, respectively. These results demonstrate the usefulness of the MBP-MutS-H6 error filter in both consensus shuffling and coincidence filtering protocols. Taq MutS has previously been shown to bind to deletion mutations with high affinity (27) , a mutation common in synthetic DNA. However, it is important to note that Taq MutS has lower affinity for specific point mutations and binds weakly to homoduplex DNA (27) . These factors may limit the stepwise efficiency of the error filter. Moreover, specific point mutations may be refractory to removal even after multiple rounds of consensus shuffling. Two rounds of consensus shuffling using the MBP-MutS-H6 error filter proved most effective in reducing deletions and G/C to A/T transitions, consistent with previous reports for the selectivity of Taq MutS (27) . However, it must be emphasized that each synthetic oligonucleotide point mutation would generate two heteroduplex DNA molecules containing unique mismatches after PCR amplification and re-hybridization ( Figure 1A and Table 1 ). For example, a G to A transition mutation in a synthetic oligonucleotide would generate heteroduplexes with G-T or A-C mismatches after PCR amplification and re-hybridization. For consensus shuffling, either of these mismatch containing heteroduplexes could evade precipitation by the MBP-MutS-H6 error filter and participate in the reassembly of full-length GFPuv. Therefore, Table 1 lists the pair of mismatches that could give rise to the observed transition or transversion mutation. These results show that the MBP-MutS-H6 error filter was most effective at removing insertion/deletion loops and G-T/A-C mismatches from the population. It should be possible to generalize the consensus shuffling protocol to a large number of synthetic DNA constructs. GFPuv was chosen as the synthetic construct in this study for its advantages as a fluorescent reporter gene. This allowed easy optimization of our protocol without the need to sequence thousands of base pairs of DNA. We expect the results reported here for consensus shuffling to readily translate to synthetic DNA constructs of varied sequence, greater overall length and/ or higher initial errors/kb. Synthetic DNA constructs of varied sequence can be digested into a defined set of fragments using Type II restriction enzymes or fragmented into any desired size range using controlled DNase I digestion (26) . Digestion and reassembly of a large number of different genes is expected to be as robust as the protocol of DNA shuffling (28) , which has been broadly applied to a variety of gene sequences. Synthetic DNA constructs larger than GFPuv are expected to be amenable to error correction by consensus shuffling, as the error filtering is conducted on gene fragments before reassembly of the full-length gene. Thus, the errors/kb data presented in this study are expected to translate to larger genes with similar initial errors/kb (excepting mutations introduced by PCR amplification following the final application of the error filter). Synthetic DNA constructs of higher initial errors/kb are expected to be amenable for error correction by consensus shuffling. However, these constructs will require digestion into smaller sized gene fragments that may affect the efficiency of error correction. In contrast to consensus shuffling, an increase in the size of the synthetic DNA product or an increase in errors/kb would preclude the use of the coincidence filtering protocol, as every molecule in the population would contain one or more errors. As proof of the utility of the consensus shuffling protocol, 10 non-fluorescent GFPuv clones containing one or more errors (Figure 4 ) were converted into a population where 82% of the clones were fluorescent. It is important to note that DNA shuffling alone shows an improvement in percent fluorescent colonies in this example (from 0 to 30%). For synthetic DNA populations, DNA shuffling alone shows no improvement in percent fluorescent colonies (see Figure 3 'no MutS' treatments). DNA shuffling alone improves the overall number of correct sequences only for small DNA populations with low error rates. For example, when shuffling 10 clones with a unique mutation in each clone, one would expect the fraction of correct products to be (9/10) 10 = 35% (26), very close to the value of 30% that we observed. A mathematical model describing the error rates for shuffling and error filtering of synthetic DNA populations is presented below. To estimate some parameters of consensus shuffling and coincidence filtering, a simple mathematical model (Equations 1-6) was constructed. An input population of dsDNA molecules of length N, containing E errors/base is re-hybridized, fragmented into shorter dsDNA fragments of average length S, error filtered and reassembled. P(F) is the probability a fragment of length S will have a correct sequence. We determine the probability that re-hybridized duplexes will have zero (C), one (H ) or both (I ) strands with errors. Equation 5 estimates the probability that a fragment will be correct after a cycle of MutS filtering, P(F 0 ), by applying a MutS selectivity factor (M ) to adjust the relative amounts of mismatch containing duplexes (I, H ) while accounting for the total fraction of correct strands in the re-hybridized duplexes. The probability of obtaining an error free assembly product, P(A), is then given by Equation 6 . From our consensus shuffling error rate data (Figure 3 ), we estimate the MutS selectivity factor M to be $2.2. Figure 5 shows some predictions that emerge from this model assuming typical length (2 kb), fragment sizes (200 bp) and error rates (1.8 errors/kb). Consensus shuffling is predicted to be most effective with smaller fragment sizes ( Figure 5A ). As mentioned above, smaller fragment sizes could be obtained by controlled digestion with DNase I (21) . In addition, multiple iterations of MutS filtering can have dramatic results on populations with few correct sequences ( Figure 5B ), although the model does not account for the differing specificity of MutS toward the various types of mismatches. The model also predicts that even modest improvements in the MutS selectivity factor through optimization of the MutS-DNA binding conditions and/or the use of a combination of MutS homologs with varying mismatch specificity (29) could dramatically improve the consensus shuffling protocol ( Figure 5C ). Coincidence filtering (N = S) is predicted to be effective for populations with low error rates per clone ( Figure 5D ) but becomes ineffective when the majority of re-hybridized duplexes contain mismatches. We have demonstrated consensus shuffling and coincidence filtering as experimental methods to significantly reduce errors in synthetic DNA. Consensus shuffling should be generally applicable for error correction on synthetic genes of typical lengths and error rates. Two iterations of consensus shuffling ($6 h/iteration) generated a population with $1 error/3500 bp. This reduction in error rate will allow the identification of a correct clone after sequencing DNA from a reduced number of colonies. Coincidence filtering is a simple and effective procedure to reduce errors in synthetic DNA populations with low error rates per clone. These methods should significantly increase the speed and decrease the cost of production of synthetic genes. Note: While this manuscript was under review, Carr et al. (30) independently reported the application of Taq MutS in protocols for error reduction on synthetic DNA. Purity and integrity of RNA are critical elements for the overall success of RNA-based analyses, including gene expression profiling methods to assess the expression levels of thousands of genes in a single assay. Starting with low quality RNA may strongly compromise the results of downstream applications which are often labor-intensive, time-consuming and highly expensive. However, in spite of the need for standardization of RNA sample quality control, presently there is no real consensus on the best classification criteria. Conventional methods are often not sensitive enough, not specific for single-stranded RNA, and susceptible to interferences from contaminants present in the sample. For instance, when using a spectrophotometer, a ratio of absorbances at 260 and 280 nm (A 260 :A 280 ) greater than 1.8 is usually considered an acceptable indicator of RNA purity (1, 2) . However, the A 260 measurement can be compromised by the presence of genomic DNA leading to over-estimation of the actual RNA concentration. On the other hand, the A 280 measurement will estimate the presence of protein but provide no hint on possible residual organic contaminants, considered at 230 nm (3) (4) (5) . Pure RNA will have A 260 :A 230 equal to A 260 :A 280 and >1.8 (1) . A second check involves electrophoresis analysis, routinely performed using agarose gel electrophoresis, with RNA either stained with ethidium bromide (EtBr) (6) (7) (8) (9) , or the more sensitive SYBR Green dye (10) . The proportion of the ribosomal bands (28S:18S) has conventionally been viewed as the primary indicator of RNA integrity, with a ratio of 2.0 considered to be typical of 'high quality' intact RNA (1) . However, these methods are highly sample-consuming, using 0.5-2 mg total RNA and often not sensitive enough to detect slight RNA degradation. Today, microfluidic capillary electrophoresis with the Agilent 2100 bioanalyzer (Agilent Technologies, USA) has become widely used, particularly in the gene expression profiling platforms (11, 12) . It requires only a very small amount of RNA sample (as low as 200 pg), the use of a size standard during electrophoresis allows the estimation of sizes of RNA bands and the measurement appears relatively unaffected by contaminants. Integrity of *To whom correspondence should be addressed. Tel: The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org the RNA may be assessed by visualization of the 28S and 18S ribosomal RNA bands ( Figure 1A and B); an elevated threshold baseline and a decreased 28S:18S ratio, both are indicative of degradation. A broad band shows DNA contamination ( Figure 1C ). As it is apparent from a review of the literature, the standard of a 2.0 rRNA ratio is difficult to meet, especially for RNA derived from clinical samples, and it now appears that the relationship between the rRNA profile and mRNA integrity is somewhat unclear (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) . On the one hand, this may reflect unspecific damage to the RNA, including sample mishandling, postmortem degradation, massive apoptosis or necrosis, but it can reflect specific regulatory processes or external factors within the living cells. Altogether, it appears that total RNA with lower rRNA ratios is not necessarily of poor quality especially if no degradation products can be observed in the electrophoretic trace ( Figure 1D ). For all these reasons, the development of a reliable, fully integrated and automated system appropriate for numeric evaluation of RNA integrity is highly desirable. Standardized RNA quality assessment would allow a more reliable comparison of experiments and facilitate exchange of biological information within the scientific community. With that prospect in mind, and with the aim of anticipating future standards by pre-normative research, we identified and tested two software packages recently developed to gauge the integrity of RNA samples with a user-independent strategy: one open source, the degradometer software for calculation of the degradation factor and 'true' 28S:18S ratio based on peak heights (24) and the freely available RIN algorithm of the Agilent 2100 expert software, based on computation of a 'RNA Integrity Number' (RIN) (25) . Both tools were developed separately to extract information about RNA integrity from microcapillary electrophoretic traces and produce a userindependent metrics. Using these tools, we assessed the purity and integrity of 414 RNA samples, derived from 14 different human adult tissues and cell lines, many of which representing tumors. Those results were compared with conventional RNA quality measurement approaches as well as with highly expert human interpretation. We evaluated the simplicity for users and examined the potential, accuracy and efficiency of each method to contribute to standardization of RNA integrity assessment upstream of biological assays. These procedures were further validated by real-time RT-PCR quantitation of the expression levels of three housekeeping genes, using the same RNA samples, at different levels of degradation. Total RNA was prepared from human cell lines (especially from the ATCC bio-resource center, N = 50) and tissue samples (clinical samples, N = 285) from 13 different human adult tissue types, i.e. blood, brain, breast, colon, epithelium, kidney, lymphoma, lung, liver, muscle, prostate, rectum and thyroid. RNA purification was performed by cesium chloride ultracentrifugation according to Chomczynski and Sacchi (26) , by phenol-based extraction methods (TRIzol reagent, Invitrogen, USA), or silica gel-based purification methods (RNeasy Mini Kit, Qiagen, Germany; Strataprep kit, Stratagene, USA or SV RNA isolation kit, Promega, USA) according to the manufacturer's instructions with some modifications. Material was maintained at À80 C with minimal handling. RNA extraction was carried out in an RNase-free environment (see Supplementary Table 1 online) . The commercially available RNA samples were the 'Universal Human Reference' (N = 75) distributed by Stratagene (USA), and human brain (N = 2) and muscle (N = 2) RNAs supplied by Clontech (USA). Once extracted, RNA concentration and purity was first verified by UV measurement, using the Ultrospec3100 pro (Amersham Biosciences, USA) and 5 mm cuvettes. The absorbance (A) spectra were measured from 200 to 340 nm. A 230 , A 260 and A 280 were determined. A 260 :A 280 and A 260 :A 230 ratios were calculated. For microcapillary electrophoresis measurements, the Agilent 2100 bioanalyzer (Agilent Technologies, USA) was used in conjunction with the RNA 6000 Nano and the RNA 6000 Pico LabChip kits. In total, 39 assays were run in accordance with the manufacturer's instructions (see Supplementary Notes online). To evaluate the reliability of the classifier systems described in this study, replicate runs were done on a set of 56 RNA samples loaded on different chips, resulting in 2 (N = 41), 3 (N = 12), 7 (N = 2) and 50 (N = 1) data points per sample. Human RNA integrity categorization RNA integrity checking was performed by expert operators who classified each total RNA sample within a predefined discrete category from 1 to 5, examining the integrity of the RNA from electropherograms (see Supplementary Table 2 online). A low number indicates high integrity. Reference criteria parameters include ribosomal peaks definition, baseline flatness, existence of additional or noise peaks between ribosomal peaks, low molecular weight species contamination and genomic DNA presence suspicion. A smearing of either 28S and 18S peaks, or a decrease in their intensity ratio indicate degradation of the RNA sample and results in the classification into the higher categories. To evaluate the robustness of this human interpretation, five highly experienced operators, trained in these cataloging steps, separately classified a subset of 33 samples from breast cancers. It included samples with varying levels of integrity: intact RNA (33%), low quality samples (20%) and a wide range of degradation (47%). Bioanalyzer electrophoretic data were exported in the degradometer software folder (.cld format). For comparison of samples, the original data were re-scaled by the classifier system, first along the time-axis to compensate for differences in migration time, then along the fluorescence intensity-axis to compensate for variation in total RNA amount. As a result, fluorescence curves that have the same shape will have the same peak heights after re-scaling. Then, Degradation Factors (DegFact) and corrected 28S:18S ratios were calculated (see Supplementary Table 3 online) using the mathematical model developed by Auer et al. (24) , examining additional 'degradation peak signals' appearing in the lower molecular weight range and comparing them to ribosomal peak heights. Calculation of the DegFact is based on a numbering of continuous metrics, ranging from 1 to ¥; increasing DegFact values correspond to more degradation, and a new group of integrity is defined after 8 graduation steps. Once the classification of the RNA samples is completed, 4 groups of integrity are displayed, 3 showing an alert warning indicative of some measurable degradation (Yellow: 8-16, Orange: 16-24 and Red: >24), while all non-reliable data come together and form the fourth group (Black). We introduced a fifth class labeled White (<8), when no alert was produced by the software. Software and manual are freely available at http://www. dnaarrays.org/downloads.php. Degradometer version 1.4.1 (released in May 2004) of the software was used. Bioanalyzer electrophoretic sizing files (.cld format) collected with biosizing software version A.02.12.SI292 (released in March 2003) were imported in the Agilent 2100 expert software (RIN beta release). The RIN algorithm allows calculation of RNA integrity using a trained artificial neural network based on the determination of the most informative features that can be extracted from the electrophoretic traces out of 100 features identified through signal analysis. The selected features which collectively catch the most information about the integrity levels include the total RNA ratio (ratio of area of ribosomal bands to total area of the electropherogram), the height of the 18S peak, the fast area ratio (ratio of the area in the fast region to the total area of the electropherogram) and the height of the lower marker. A total of 1300 electropherograms of RNA samples from various tissues of three mammalian species (human, mouse and rat), showing varying levels of degradation and an adaptive learning approach were used in order to assign a weight factor to the relevant features that describe the RNA integrity. A RIN number is computed for each RNA profile (see Supplementary Table 4 online) resulting in the classification of RNA samples in 10 numerically predefined categories of integrity. The output RIN is a decimal or integer number in the range of 1-10: a RIN of 1 is returned for a completely degraded RNA samples whereas a RIN of 10 is achieved for intact RNA sample. In some cases, the measured electropherogram signals are of an unusual shape, showing for example peaks at unexpected migration times, spikes or abnormal fluctuation of the baseline. In such cases, a reliable RIN computation is not possible. Several separate neural networks were trained to recognize such anomalies and display a warning to the user or even suppress the display of a RIN number. Combining the results of the neural network for the RIN computation and the neural networks to detect anomalies, the RIN algorithm achieves a mean square error of 0.1 and a mean absolute error of 0.25 on an independent test set. The beta release of the software and manual are freely available at http://www.agilent.com/chem/RIN. Agilent 2100 expert version B.01.03.SI144 (released in November 2003) of the software was used. Expression levels of three housekeeping genes (HKG)-GAPD, GUSB and TFRC-were measured by quantitative PCR using the TaqMan Gene Expression Assays according to the manufacturer's instructions (Applied Biosystems, USA). Sixteen aliquots of a unique batch of RNA sample (Universal Human Reference RNA, Stratagene, USA) of various levels of integrity (cf. Table 1 ) were used to test the influence of RNA quality on the relative expression of those three genes. In parallel, a 5 0 to 3 0 comparison was done using two separate GUSB and TFRC TaqMan probes. An homogeneous quantity (0.8-1 mg) of the RNA samples was subjected to a reverse transcription step using the highcapacity cDNA archive kit (Applied Biosystems, USA) as described by the manufacturer. Single-stranded cDNA products were then analyzed by real-time PCR using the TaqMan Gene Expression Assays according to the manufacturer's instructions (Applied Biosystems, USA). Single-stranded cDNA products were analyzed using the ABI PRISM 7700 Sequence Detector (Applied Biosystems, USA). The efficiency and reproducibility of the reverse transcription were tested using 18S rRNA TaqMan probes. Five assays were used, GAPDH-5 0 (Hs99999905_m1), GUSB-5 0 (Hs00388632_gH), GUSB-3 0 (Hs99999908_m1), TFRC-5 0 (Hs00951086_m1) and TFRC-3 0 (Hs00951085_m1). In each case, duplicate threshold cycle (Ct) values were obtained and averaged; then expression levels were evaluated by a relative quantification method (27) . The fold change in one tested HKG (target gene) was normalized to the 18S rRNA (reference gene) and compared to the highest quality sample (calibrator sample), using the following formula: Fold change = 2 ÀDDCt , where DDCt = (C t-target À C t-reference ) sample-n À (C t-target À C t-reference ) calibrator-sample . Sample-n corresponds to any sample for the target gene normalized to the reference gene and calibrator-sample represents the expression level (1·) of the target gene normalized to the reference gene considering the highest quality sample. Mean 2 ÀDDCt and SD were calculated, considering the samples either individually or grouped by quality metrics categories, based on RIN metrics or DegFact values, together with the lower and upper bound mean of 95% Intervals of Confidence (IC). Using this analysis, if the expression levels of the HKG are not affected by the RNA degradation, the values of the mean fold change at each condition should be very close to 1 (since 2 0 = 1) (27) . Descriptive statistics were executed using the XLSTAT software, version 7.1 (Addinsoft, USA), P = 0.05. Mean, SD and coefficient of variation (variation or CV) between and within groups of samples were calculated, together with a measure of the dispersion (range), inter-quartile range (1st and 3rd quartiles, Q1-Q3) and evaluation of the lower and upper bound mean of 95% Interval of Confidence (IC). Comparative statistical analyses between groups were completed, P = 0.05, using non-parametric statistical tests: two-independent Mann-Whitney U-test and k-independent Kruskal-Wallis test. We analyzed 414 total RNA sample profiles from various human tissues (69%) and cell lines (31%) of either tumoral (85%) or normal (15%) origin, with varying levels of RNA integrity. Supplementary Table 1 online for details). Significant differences in A 260 :A 280 ratios were observed between specific groups of samples (i.e. tumoral versus normal or tissues versus cell lines). For instance, RNA extracted from normal samples displayed an improved ratio of 1.97, with 97% falling within the desired range ( Figure 2A ). In contrast, the distribution of A 260 :A 280 ratios was not found to correlate with either purification methods or tissues of origin. RNA integrity was further assessed by resolving the 28S and 18S ribosomal RNA bands using the Agilent 2100 bioanalyzer and the RNA 6000 protocol. The analysis was done on 399 RNA profiles; data from 15 samples was not obtained due to device problems during the runs. The system automatically provided 28S:18S ratios for 348 (87%) of the 399 profiles. Figure 2B shows the distribution of the 28S:18S computed values, with a median ratio around 1.7 and a variation of 54% from the mean (IC 1.9-2.1 and Q1-Q3 1.4-2.5). In addition, a significant degree of variability of the 28S:18S ratio (19-24%) was found for identical samples from replicate runs (2-50 times). Among those RNA samples, 28S:18S ratios of 2.0 or greater were rare, less than 44% of the values measured being within the theoretically desired range, except for the samples prepared from cultured cells ( Figure 2B ). The integration failed in the remaining 51 cases, displaying an atypical migration, with no clear 28S and 18S rRNA bands, and no 28S:18S ratio was computed (data not shown). Expert operators categorized the set of RNA samples by inspecting the electrophoretic traces of successful assays. Over the 399 RNA profiles checked, 379 (95%) were scored within predefined categories ( Figure 2C ), namely good [Human Categorization (HC)-level 1], regular (HC-level 2), moderate (HC-level 3), low (HC-level 4) and degraded (HC-level 5). The remaining 20 (5%) were flagged as displaying a temperature-sensitive profile: RNA samples initially found intact became highly degraded when heated, although no RNase contamination was observed (data not shown). Estimation of the robustness of this cataloging was done through comparison of qualifying criteria using a set of 33 breast cancer samples (see Materials and Methods). Integrity of the samples was evaluated independently by five expert operators, and categorization was found highly reliable with a coefficient variation (CV)$16%. This is low considering that individual interpretation is involved, but can be explained by the fact that very experienced operators accomplished the scoring based on a clearly defined set of instructions, thus limiting frequently observed subjective visual interpretation and inconsistency of human categorization. Predictably, a 28S:18S ratio of 2.0 denoted high quality for a majority of RNA samples, 91% being classified in HC-levels 1 to 3. However, 83% of total RNAs with 28S:18S > 1.0 but a low baseline between the 18S and 5S rRNA or front marker were also classified in HC-levels 1-3 (see Figure 1D ) and could be considered suitable for most downstream applications. RNA degradation was first assessed using the degradometer software (see Materials and Methods). Over the 399 RNA profiles checked, all were scored in one of the five predefined classes ( Figure 3A) . Altogether, 334 (84%) Degradation Factors (DegFact) values were computed, the remaining 65 RNA samples (16%) displaying profiles that could not be interpreted reliably; no DegFact values could be scored, and samples were flagged in the Black category ( Figure 3A ). Most of them (80%) correspond to samples previously classified by our operators as degraded (HC-level 5). The remaining cases had an average degradation factor of 7.5 (IC 6.7-8.3) with large variations over the entire set of samples (over 103% from the mean, range 1-52). A lower variability was persistently found when identical samples from replicate runs were considered, resulting in observed DegFact values with a 26-32% CV. In addition, statistically significant differences were found between DegFact values of samples sorted by types. The highest DegFact values were found characteristic of tissue samples, 41% of them displaying a DegFact > 8, as compared with 6% for the cell lines (data not shown). Remarkably, we found a significant linear relationship between the DegFact values distribution and the explicit human categorization. Most HC classes corresponded to an unambiguous DegFact distribution ( Figure 3B ), while HClevels 2 and 3 form a single class: HC-level 1, mean DegFact of 3.3, SD of 2.8 (IC 2.8-3.7); HC-level 2 and 3, mean Deg-Fact of 8.8, SD of 6.8 (IC 7.5-10.2); HC-level 4, mean DegFact of 15.9, SD of 7.8 (IC 12.7-19.1); HC-level 5, mean DegFact of 26.0, SD of 7.5 (IC 21.9-30.1). It is worth mentioning that the normalized heights of 18S and 28S peaks, and the interval between them after rescaling gradually decrease and then reverse with increasing degradation ( Figure 3B ). Integrity of RNA samples was measured in parallel based on the RNA Integrity Number metrics using an artificial neural network trained to distinguish between different RNA integrity levels by examining the shape of the microcapillary electrophoretic traces (see Materials and Methods). Over the 399 RNA profiles checked, 363 (91%) were scored successfully ( Figure 4A) , with an average RIN of 7.7 (IC 7.4-8.0). The remaining 36 (9%) samples were associated with various unexpected signals, disturbing computation of the RIN using default anomaly detection parameters. In each case, a flag alert was added corresponding to critical anomalies including unexpected data in sample type, (or) ribosomal ratio, (or) baseline and signal in the 5S region (data not shown). RIN categorization was found regular, variability between replicate runs, compared to the other methods, being consistently very small (CV 8-12%). As expected, the highest RIN were characteristic of cell line samples, 72% of them displaying a RIN > 9, as compared with 47% for the tissue samples (data not shown). A first group, corresponding to 295 (82%) of the 363 RNA profiles, was analyzed using the default settings of the RIN system, but with a lower threshold of RNA quantity loaded (20 ng) for reliable detection of anomalies than that recommended by the manufacturer (50 ng). A significant linear relationship was found between the RIN number and both the explicit human classification provided by our operators, Figure 3 . RNA degradation characterization. Integrity of 399 RNA sample profiles was scored using the degradometer software. (A) A total of 334 RNA profiles were successfully categorized into 5 predefined alert classes using a mathematical model that quantifies RNA degradation and computes a degradation factor (DegFact). Four classes (White, Yellow, Orange and Red) are associated with different levels of degradation. A fifth class, Black alert corresponds to samples that the system was not able to qualify with accuracy (n.d.). The distribution is represented by the number of records in each class. (B) Comparative analysis was done using human evaluation (x-axis) based on electrophoresis analysis as a reference for RNA integrity classification; observations of rRNA peak heights and DegFact values were taken at each of the 5 HC levels. Histograms refer to the mean 28S and 18S rRNA peak heights and 95% confidence intervals (fluorescence intensities; left scale). Mean DegFact values and 95% confidence intervals (arbitrary unit, right scale) are plotted with the means joined. and the DegFact values calculated by the degradometer software ( Figure 4B ). Each distinct HC class corresponds to an explicit RIN number, with HC-levels 2 and 3 forming once again a single class: HC-level 1, mean RIN of 9.6, SD of 0.7 (IC 9.5-9.7); HC-level 2 and 3, mean RIN of 8.6, SD of 0.9 (IC 8.4-8.9); HC-level 4, mean RIN of 6.1, SD of 1.5 (IC 5.2-7.1); HC-level 5, mean RIN of 3.7, SD of 2.0 (IC 2.9-4.5). For the remaining 68 samples (assay done with <20 ng of RNA), two separate groups were considered: 41 samples with a computed RIN below 5.0, and 27 above 7.0. All samples in the first group were derived from RNA 6000 Nano assays, with mean RNA quantities loaded below 10 ng (Q1-Q3, 5-12 ng), i.e. below the lower limit of quantitation indicated by the manufacturer. All but 8 of these samples were estimated by our operators to be of poor quality (HC-level 4; N = 3) or degraded (HC-level 5; N = 30), and all but 4 were flagged Black by the degradometer software and no DegFact values were scored. These RNA profiles could not be interpreted reliably, possibly due to either the low RNA concentration or the unusual migration behavior and shifted baseline values of degraded samples. Thus, the two automated systems were in disagreement for these samples; while human interpretation was in most cases in agreement with the RIN system, with less than 20% of inconsistency. In the second group of 27 samples, 20 of the profiles were derived from RNA 6000 Pico assays with RNA quantities loaded being on average below 4 ng (Q1-Q3, 0.5-0.8 ng), which is within the manufacturer specifications. All but 3 of them were estimated by our operators to range from high (HC-level 1; N = 12) to correct (HC-level 2 and 3; N = 12) quality levels. In addition, all RNA profiles except 1 were scored by the degradometer software, most of them displaying an alert flag (N = 20); some slight degradation was detected, associated to a low mean DegFact value of 9.7 (IC 8.1-11.3; Q1-Q3, 6.2-12.6). Thus, both automated systems and human interpretations agreed in most of these cases, with <11% of inconsistency. The influence of RNA quality categorization obtained with both user-independent classifiers on gene expression profiling was explored using real-time RT-PCR. The expression levels of three housekeeping genes (HKG)-GAPDH, GUSB and TFRC-were measured in 16 aliquots of a unique RNA displaying various integrity metrics ( Table 1 ). The mean correlation coefficient (r) between the threshold cycle (Ct) among the 16 samples and both quality metrics was found high: r = À0.87 considering the RIN metrics and r = 0.85 considering the DegFact values. The values of the mean fold changes, calculated according to the 2 ÀDDCt quantification method (see Materials and Methods), were found lower than 1.0, corresponding to the expression level (1·) in the sample exhibiting the highest RNA quality (Table 2 and Figure 5 ). Considering that HKG expression was measured relative to the reference sample, an obvious decline of the relative expression levels was observed, up to 24, 70 and 82%, in samples categorized according to the RIN metrics ( Figure 5A) and DegFact values ( Figure 5B ). These results indicate that 2-to 7-fold differences may be expected in the relative expression levels of genes in samples that differ only by their quality (Table 2 ). These fold differences are much larger than those measured for RNA samples of comparable integrity, consistently lower than 1.6 (Table 2 and Figure 5 ). In addition, an unambiguous gap in the distribution may be defined ( Figure 5A and B) , distinguishing the RNA samples of the higher quality categories (RIN > 8 and DegFact values < 7) from those of the lower categories (RIN < 8 and DegFact values > 12). It would be expected that measuring expression of an intact mRNA would yield approximately equal results regardless of the region being probed, and if mRNA fragmentation had occurred, then some sequences may be more abundant than others. We thus tested the effect of PCR probe location on the RNAs. The 5 0 and 3 0 GUSB probes, separated by 1209 nt, were associated with highly similar threshold cycle (Ct) measures (r = 0.98, b parameter = 0.88) ( Figure 5C ). Similar results were obtained for TFRC, with probes separated by 2066 nt (r = 0.84, b parameter = 0.92, data not shown). It seems therefore that the region being probed is not a source of variation in our results. It is universally accepted that RNA purity and integrity are of foremost importance to ensure reliability and reproducibility of downstream applications. In the biomedical literature (PubMed, November 2004), from the 485 090 articles that relate to RNA, and the 287 515 or 40 395 including respectively the 'quality' or 'integrity' term, less than 100 were found to contain 'RNA quality' or 'RNA integrity' terms. Interestingly, half of them were published between 2001 and 2004; but none is proposing a standard operational procedure for RNA quality assessment to the scientific community. Except for two studies (24, 25) , those reports are based on 10 to 15 years old methods (1), indicating that they represent the established and currently mostly used methods. Our results strongly challenge the reliability and usefulness of those conventional methods, demonstrating their inconsistency to evaluate RNA quality. First, the A 260 :A 280 and A 260 :A 230 ratios are reflecting RNA purity, but are not informative regarding the integrity of the RNA. Available RNA extraction and purification methods yield highly pure RNA with very little DNA or other contaminations, resulting most often in both ratios )1.8, although 18% of the samples were found degraded and 7% more of poor quality. The high A 260 :A 280 ratios are indicative of limited protein contaminations, whereas high A 260 :A 230 ratios are indicative of an absence of residual contamination by organic compounds such as phenol, sugar or alcohol, which could be highly detrimental to downstream applications. Nonetheless, samples displaying low A 260 :A 230 ratios ((1.8) did not exhibit any inhibition during downstream applications, such as cDNA synthesis and labeling or in vitro transcription (data not shown). Second, due to a lack of reliability, the 28S:18S rRNA ratios may not be used as a gold standard for assessing RNA integrity. When ribosomal ratios were calculated from identical samples but through independent runs, a large degree of variability (CV 19-24%) was observed. Moreover, using the biosizing software, we found 28S:18S rRNA ratios evaluation compromised by the fact that their calculation is based on area measurements and therefore heavily dependent on definition of start and end points of peaks. In 13% of the cases, the system was unable to localize the ribosomal peaks, and therefore no 28S:18S ratios were computed. For the remaining samples, no clear correlation between 28S:18S ratios and RNA integrity was found although RNAs with 28S:18S >2.0 were usually of high quality. Most of the RNAs we studied (83%), displaying a 28S:18S > 1.0, could be considered of good quality. Interestingly, Auer et al. (24) in a study on 19 tissues from seven organisms, reported that an objective measurement of the RNA integrity may possibly be done through comparison of re-scaled 28S and 18S peak heights, but not of the corresponding areas. Actually, we observed a linear relationship between RNA integrity and differences in normalized 28S and 18S peak heights. Increased degradation resulted in a significant decrease in the scaled corrected heights of the ribosomal peaks, with inversion of the ratio at the highly degraded stages (cf. Figure 3B ). In comparison to the area computation, 28S:18S rRNA re-scaled peak height measurement produced more consistent values, with a CV reduced to 12-14%, and displayed clear concentration-independent values (see Supplementary Tables 1 and 3 online) . Human evaluation of the integrity of RNA through visual inspection of the electrophoresis profiles provided very consistent data. Variability between classifications produced by five independent expert operators (CV 16%) was lower than with automated management of more conventional control 28S:18S area values (CV 19-24%). It is, however, very time-consuming and strongly dependent on individual competence. Even with highly trained specialists, 5% of the set of RNA samples could not be allocated to any of the five predefined categories; their corresponding profiles were considered by our experts as atypical, displaying a temperature-sensitive shape (data not shown). These strategies appear unsuitable for standardization and quality control of RNA integrity assessment, which require simple but consistent expert-independent classification, facilitating information exchanges between laboratories. The N-value corresponds to the number of samples by category. The mean quality metrics, i.e. RIN and DegFact and the mean fold change (2 ÀDDCt ) relative to the reference sample are indicated, together with the 95% confidence intervals. Observed technical variation (IC-rep, P = 0.05) is also specified, considering duplicate (two per gene per target sample) and replicate (six per gene per calibrator sample) measures. The reference sample exhibits a RIN of 9, a DegFact value of 4.9 and by default mean fold change set to 1. The observed decrease in the expression (relative expression, %) relative to the reference sample is calculated. The fold differences refer to the fold-ratios that are expected in the expression levels for a gene, across categories (between categories), given that the samples only differ by their quality, and within each category (within categories), considering RNA of comparable integrity. The fold-ratios (technical variation) that may be expected by chance in the gene expression levels, P = 0.05, from some technical reasons, are also considered. We therefore investigated the performance of two recently developed user-independent software algorithms (24, 25) . The degradometer software provided a reliable evaluation of RNA integrity based on the identification of additional 'degradation peak signals' and their integration in a mathematical calculation together with the ribosomal peak heights. It allowed characterization of the integrity of 84% of the samples tested, one-third with an alert flag, which was first found to be fairly informative, as it strongly reduces the complexity of the metrics by introducing three distinct classes labeled Yellow, Orange and Red, and can be used as a first straightforward simple filtering step. However, degradation factors (DegFact) metrics yield precise measures with less than 32% CV and are much more valuable than flag alerts for the purpose of standardization. The same is true for the RNA Integrity Number 'RIN' software which allowed the characterization of the integrity of 91% of the RNA samples tested, with a RIN value for 363 RNA sample profiles with less than 12% CV. In general, there was a good agreement between the human classification, the degradation factor and the RIN (see Figure 4B ). This provided a cross-validation of the user-independent qualification systems tested. Both resulted in the refinement of human interpretations, validating four statistically relevant classes of samples, namely good (HC-level 1), regular/ moderate (HC-level 2 and 3), poor (HC-level 4) and degraded (HC-level 5). Moreover, the 5% RNA samples previously flagged by the operators as displaying an atypical temperature-sensitive shape were unambiguously assigned to one or the other category of samples [RIN = 7.3 (IC 6.8-7.8); DegFact = 11.9 (IC 9.5-14.2); data not shown]. Altogether, we found the degradometer and RIN algorithms to be highly reliable user-independent methods for automated assessment of RNA degradation and integrity. The RIN system is a slightly more informative tool, able to compute assessment metrics for 91% of the RNA profiles, compared to 84% with the degradometer software; the remaining being flagged respectively as N/A or Black alert. For samples available below a low limit of 20 ng (N = 80) the RIN system provided Figure 6 . Workflow of operational procedure for RNA quality assessment. Integrity of the RNA, once extracted and purified from cell lines, clinical or biological tissues samples, is controlled from the widely used bioanalyzer electrophoretic traces. As standard part of the Agilent analysis software (25), a RIN metrics is first calculated, scoring each RNA sample into 10 numerically predefined categories of integrity (RIN, from 1 to 10; N is a threshold value). As an independent control, a degradation factor metrics (DegFact, from 1 to ¥; N 0 is a threshold value) may optionally be allocated to each RNA sample using the bioanalyzer-independent degradometer software (24) . In a standard operating procedure, RIN and/or DegFact metrics will first be used as a standard exchange language to document RNA integrity and degradation, second to classify the RNA in homogeneous groups, and finally to select samples of comparable RNA integrity to improve the scheme of meaningful downstream experiments. The standard operating procedure will benefit from feedback information that will help users to define threshold integrity metrics values based on the results of RNA-based analyses. metric values for 85% of them, compared to only 46% with the degradometer software. Similarly, the RIN system was able to provide metric values for 81% of poor quality samples (including low quality and degraded samples; N = 96), whereas the degradometer software could classify only 44% of them. Another advantage with the RIN classifier is that, if there are critical anomalies detected (including genomic DNA contamination, wavy baseline, etc.), threshold settings may be changed and a reliable RIN value computed. This was the case for 25 of the 363 RNA sample profiles successfully classified by the system. While intact RNA obviously constitutes the best representation of the natural state of the transcriptome, there are situations in which gene expression analysis may be desirable even on partially degraded RNA. Some studies report collection of reasonable microarray data from RNA samples of impaired quality (28) , leading to meaningful results if used carefully. Moreover, Auer et al. (24) recently concluded that degradation does not preclude microarray analysis if comparison is done using samples of comparable RNA integrity. We confirmed the direct influence of the RNA quality on the distribution of gene expression levels, by detecting using Q-PCR a significant (up to 7-fold) difference in the relative expression of genes in samples of slightly decreased RNA integrity, which is much larger than the variation within comparable RNA quality categories (cf. Figure 5 and Table 2 ). This may correlate with ratio discrepancies in gene expression experiments, and therefore with false positive and false negative rates of differential gene expression when comparing two samples. Therefore, computing reliable metrics of RNA integrity, even if the RNA is found to be partially degraded, may be highly valuable. The straight and unambiguous relationships established between human interpretations and both RIN and DegFact distributions indicates that, using these metrics, it should be possible to distinguish specific samples that are too disparate to be included in comparative gene expression analyses without compromising the results. Although the information provided by these user-independent classifiers is not a guarantee for successful downstream experiments, it gives a more comprehensive picture of the samples and can be used as a safeguard against performing useless and costly experiments. Thus, the RIN system may be used as simple metrics that can be easily integrated in any sample tracking information system for definition of standard operating procedures under quality assurance following a scheme such as the one described in Figure 6 . In this context, we suggest that the growing number of laboratories performing RNA Quality Control by microcapillary electrophoresis should be offered the option to report objective RNA quality metrics as part of the 'Minimum Information About a Microarray Experiment' MIAME standards (29) . Through registration of RNA profiles in a public electronic repository, such standardized information should enable and facilitate comparisons of RNA-based bioassays performed across laboratories with RNA samples of similar quality, in much the same way as sequencing traces are compared.
The principal mechanism of translation is the accurate decoding of the triplet codon sequences in one reading frame of mRNA. Specific signals built into the mRNA sequences can cause deviations from this rule. Viruses exploit several translational 'recoding' mechanisms, including translational hopping, stop codon readthrough and programmed ribosomal frameshifting (PRF) [reviewed in (1, 2) ], for regulating the amount of proteins produced from their polyproteins. For positive-stranded RNA viruses, À1 PRF is the prevailing recoding mechanism and an essential determinant of the stoichiometry of synthesized viral proteins. Most viral À1 PRF signals are regulating the production of replication-associated proteins. Depending on the virus, the efficiency of À1 PRF can vary between 1 and 40% (3) , and changes in the efficiency can inhibit virus assembly and replication (4) (5) (6) . Therefore, À1 PRF can be regarded as a potential target for antiviral agents (4, 7) . However, the development of efficient antiviral drugs is still hindered, since little is known about the trans-acting factors and the biophysical parameters affecting the À1 PRF efficiencies. Database searches have identified putative frameshift signals from a substantial number of chromosomally encoded eukaryotic mRNAs (8) . Thus, À1 PRF may also have an impact on the complexity of the proteome of several eukaryotic organisms. Two cis-acting signals, a slippery heptamer X XXY YYZ (the incoming reading frame indicated) and a downstream secondary structure, direct the slippage and are therefore essential for this event (9) . À1 PRF takes place after the accommodation step in the slippery sequence by simultaneous slippage of both tRNAs into the overlapping À1 frame XXX YYY (9, 10) . The sequence of the heptamer allows postslippage base-pairing between the non-wobble bases of the tRNAs and the new À1 frame codons of the mRNA. Downstream RNA secondary structures [reviewed in (11) ] force the ribosomes to pause, and place the ribosomal A-and P-sites correctly over the slippery sequence (12) . However, the pausing of the ribosomes is not sufficient for À1 PRF to occur (13) ; in fact, the duration of the halt does not necessarily correlate with the level of the À1 PRF observed (12) . Crystallographic, molecular, biochemical and genetic studies suggest that a pseudoknot restricts the movement of the mRNA during the tRNA accommodation step of elongation by filling the entrance of the ribosomal mRNA tunnel (14) . This restriction can be eased either by unwinding the pseudoknot, which allows the mRNA to move forward, or by a slippage of the mRNA one nucleotide backwards. Chemical agents such as *To whom correspondence should be addressed. Tel: +358 9 19158342 ; Fax: +358 9 19158633; Email: kristiina.makinen@helsinki.fi ª The Author 2005. Published by Oxford University Press. All rights reserved. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org antibiotics, certain mutations in the translation apparatus, and in translation elongation factors that change the translation fidelity and kinetics, have been shown to influence À1 PRF efficiency [(10,15) ; reviewed in (16) ]. The parameters known to contribute to the efficiency of À1 PRF are the sequence of the slippery heptamer, the downstream secondary structure, and the length and sequence of the spacer between the two cis-acting signals. Up-and downstream sequences such as termination codons in the vicinity of the À1 PRF signals, or even several kilobases away from them, can affect the À1 PRF efficiencies (3, (17) (18) (19) (20) (21) (22) . A specific sequence in the Barley yellow dwarf virus (BYDV) 3 0 untranslated region (UTR), 4 kb downstream from the slippage site, is vital for À1 PRF (6, 19) . A stimulating effect is achieved through the formation of a tertiary structure, where complementary nucleotides from the 3 0 UTR base pair with a single-stranded bulge in the cis-acting stem-loop (6). Human immunodeficiency virus (HIV) was also shown to require a more complex secondary structure instead of a simple stemloop for optimal À1 PRF in vivo (21, 22) . These investigations suggest that À1 PRF studies carried out with minimal frameshift signals may lead to inaccurate estimates of the stoichiometry of synthesized viral protein products during infection. Cocksfoot mottle virus (CfMV; genus Sobemovirus) infects a few monocotyledonous plant species such as barley, oats and wheat. It has a monopartite, single-stranded, 4082 nt long, positive-sense RNA genome (23, 24) . The polyprotein of CfMV is translated from two overlapping open reading frames (ORFs) 2A and 2B by a À1 PRF mechanism (25) . In this study, we wanted to determine the in vivo À1 PRF efficiency guided by the CfMV U UUA AAC heptamer and the stem-loop structure. In addition to the minimal signal (18) , we decided to test the effect of flanking CfMV sequences for their ability to contribute to À1 PRF. We found that the surrounding viral sequences promoted more efficient À1 PRF than the minimal signal sequence in vivo when measured with the dual reporter vector system developed by Stahl et al. (26) . Therefore, we carried out an expression pattern and deletion analysis to understand the molecular basis of the observed upregulation. In addition, we critically analysed the suitability of the implemented experimental system for this type of a recoding study. An interesting possibility is that the viral proteins produced via À1 PRF could regulate À1 PRF. This hypothesis was tested by co-expressing the CfMV proteins P27 and replicase together with the dual reporter vectors. Three regions from the CfMV polyprotein ORFs ( Figure 1A) were cloned into the NheI and BclI sites between the lacZ and the luc ORFs in pAC74 (26) . This dual reporter vector was a generous gift from Dr J. Rousset of the Universite Paris-Sud, France. The inserted sequences 1602-1720 (A region), 1386-2137 (B region) and 1551-1900 (C region), were amplified by PCR using pAB-21 as a template (18) . Primers were used to introduce NheI and BglII sites to the flanking ends of the inserts. Since NheI digestion removed lacZ ORF, it was reintroduced into the plasmids as a final cloning step. The resulting plasmids were named pAC-A, pAC-B and pAC-C. Corresponding inframe controls, where one nucleotide was added in front of the slippery heptamer, were generated by PCR-based mutagenesis (Exsite, Stratagene) and named as pAC-Am, pAC-Bm and pAC-Cm, respectively. Deletion plasmids pAC-AB/ABm (1602-2137), pAC-AC/ACm (1602-1900), pAC-BA/BAm (1386-1720) and pAC-CA/ CAm (1551-1720) were also generated. The target sequences are shown in Figure 1B . The base numbering refers to the CfMV genome as in (23) . Transcription was driven from SV40 promoter. Plasmids encoded leucine (LEU2) and b-lactamase (ampicillin resistance) as selective markers. Plasmids were transformed into Saccharomyces cerevisiae H23 [MATa hsp150::URA3 ura3-1 his3-11 15leu2-3 112trp1-1 ade2-1 can100]. Dual reporter plasmid pAC1789 and the inframe control pAC1790 containing a 53 bp sequence from the HIV-1 frameshift region (26) were used as a positive control for monitoring the À1 PRF efficiency. To analyse the proteins produced during À1 PRF, lacZ-A/ Am/B/Bm/C/Cm-Fluc fragments were cloned inframe with the N-terminal 6xhistidine-tag in pYES2/NT KpnI and XhoI sites (Invitrogen). Reporter fusions were amplified by PCR using pAC-A/Am, pAC-B/Bm or pAC-C/Cm as templates. The resulting plasmids were named pYES2/NT-A/ Am, B/Bm and C/Cm. Protein expression was regulated from GAL1 promoter. Two CfMV encoded proteins, P27 (C-terminal end of ORF2A) and replicase (ORF2B), were cloned into pYES2 (Invitrogen). Translation initiation codons were introduced within the oligonucleotides during PCR. The resulting plasmids were named pYES-P27 and pYES-Rep. Control plasmids, which lacked the translation initiation codons were prepared by PCR-based mutagenesis (pYES-P27DAUG and pYES-RepDAUG) and the resulting plasmids were verified by sequencing. Plasmids encoded auxotrophic marker for uracil (URA3). All cloning steps were performed using standard protocols. Plasmids were amplified either in Escherichia coli DH5a or JM110, and purified with Qiagen columns. Inserts were verified by sequencing. Yeast transformations were done using the LiAc method (27) , and transformants were selected on a synthetic minimal defined medium (SC) lacking the corresponding auxotrophic marker(s) encoded by the used plasmid(s). Bacteria (E.coli DH5a) were grown in LB-medium containing ampicillin, whereas yeast cells were grown either in YPD, or in an SC medium. Protein expression from GAL1 promoter was repressed during growth at SC medium containing 2% glucose. Expression was induced by replacing glucose with 2% galactose and 1% raffinose. Reporter fusions were expressed in S.cerevisiae INVSc1 (his3D1/his3D1, leu2/leu2 trp1-289/trp1-289 ura3-52/ura3-52) (Invitrogen) overnight. Protein fusions were purified in denaturing conditions using Ni-NTA agarose (Qiagen), and analysed in 6% SDS-PAGE gels. Proteins were visualized either by Coomassie staining, or by using antisera raised against the CfMV polyprotein region 1386-1724 encoding CfMV VPg (28) . Protein antibody complexes were visualized with horseradish peroxidase-conjugated anti-rabbit antibodies (Sigma) and ECL chemiluminescent reagents (Amersham). Plasmids pYES-P27, pYES-Rep, pYES-P27DAUG, pYES-RepDAUG, or empty pYES2 were co-expressed with pAC-A or with the corresponding pAC-Am inframe control in S.cerevisiae EGY48 strain (MATa, ura3, trp1, his3, 6lexAop-LEU2) (Invitrogen). Transformants were grown overnight in SC-Leu-Ura media in non-inducing conditions, and used to inoculate induction medium. Cells were harvested at late logarithmic phase. Expression of the CfMV proteins was confirmed by western blotting using polyclonal antisera against the CfMV ORF 2a and 2b proteins (28) . Determining the enzymatic activities as described below monitored the effect of CfMV P27 and replicase on À1 PRF. For the in vitro analysis, the lacZ-gene of pAC-A/Am, -B/Bm and -C/Cm vectors was replaced with PCR-amplified Renilla luciferase (Rluc) gene from pRLnull vector (Promega). The resulting pACRF plasmids were used as templates for PCR in order to add T7 promoter upstream of the Rluc gene. These PCR products were used for RNA synthesis with RiboMax kit (Promega). Transcripts were treated with RQ1-DNase (Promega), purified with Qiagen RNeasy columns, and quantified spectrophotometrically. The integrity of the transcripts was checked in agarose gels. In vitro translations were carried CfMV À1 PRF test and control sequences were cloned between the b-galactosidase (LacZ) and firefly luciferase (Luc) genes into a dual reporter vector pAC74 (26) . Inframe control constructs had one extra nucleotide inserted in front of the slippery heptamer, which fused the reporters into the same reading frame. Thus, translation of the inframe control results in the production of a b-galactosidase-CfMV-firefly luciferase fusion. Translation of the test constructs in the incoming 0-frame yields a b-galactosidase-CfMV fusion, whereas À1 PRF produces a b-galactosidase-CfMV-firefly luciferase fusions identical to those produced from the inframe controls. À1 PRF efficiencies were calculated from the firefly luciferase activities after b-galactosidase normalization with the given formula. (B) CfMV polyprotein is encoded by two overlapping ORFs, 2A and 2B via À1 PRF. Sequence regions tested in the dual reporter vectors for their activity to promote À1 PRF are indicated. The numbering refers to the CfMV RNA sequence as published in (23) . out in wheat germ extract (WGE) according to the manufacturer's protocols (Promega). Reactions were incubated in room temperature for 60 min, and stopped on ice prior to enzymatic measurements. Cell cultures were started from at least three independent clones and grown until the late exponential phase. Cells were collected by centrifugation, frozen in liquid nitrogen and stored at À70 C. Bacterial cells were lysed by sonication (3 · 15 s), and yeasts by vortexing with glass beads (0.5 vol) in +4 C for 30 min. Lysates were cleared by centrifugation, and enzymatic activities were determined immediately. Total protein concentrations were measured by using a Bradford protein assay reagent (Bio-Rad). b-Galactosidase (LacZ) and firefly or Renilla luciferase (LUC or RUC) activities were measured with commercial kits from Promega according to the manufacturer's instructions. LacZ activity was determined as the colour intensity at A414 nm. Luciferase activities were measured as relative light units (RLUs) with luminometer (Biohit or ThermoLabsystems). À1 PRF efficiencies were calculated from normalized firefly luciferase activities with the following formula: [(LUC activity from the test construct)/(LacZ or RUC activity from the test construct)]/[(LUC activity from the inframe control)/(LacZ or RUC activity from the inframe control)] · 100%. In CfMV, the motif for À1 PRF is the slippery heptamer U UUA AAC and a stem-loop structure 7 nt downstream (25) . The efficiency of À1 PRF directed by CfMV cis-acting signals was assayed in vivo using a dual reporter vector system ( Figure 1A ). Since reporters are produced from one single mRNA, factors that affect the stability of the mRNA as well as the rate of translation initiation have a similar influence on the expression of both reporters, and these variations can be monitored as changes in the activity of the upstream reporter. We quantified À1 PRF by comparing the b-galactosidase normalized firefly luciferase activities derived from the test constructs via À1 PRF to those obtained from the inframe controls, in which identical b-galactosidase-CfMV-firefly luciferase fusions are produced without À1 PRF due to the added nucleotide in front of the slippery heptamer (see Figure 1A ). Similar vectors have been shown to detect even small changes in the recoding efficiencies resulting from alterations in the cis-or trans-acting factors (26, (29) (30) (31) (32) . Three inserts of varied lengths from the CfMV polyproteinencoding region (ORF2A/2B) were introduced between the two reporters ( Figure 1B) . The A-region, which at 119 bp was the shortest, represented approximately the minimal frameshift signal proven to be functional in vitro (18) . The longest region was the B-insert. At 752 bp, it started from the 5 0 -terminus of the 12 kDa viral genome-linked protein (VPg) gene and continued to the end of ORF2A. This region encodes CfMV protein P27 with an unknown function (28) . Since the minimal requirements for the functional frameshift signal in vivo were not known, an intermediate 349 bp C-sequence was also selected for the analysis. A well-characterized 53 bp frameshift cassette derived from HIV-1 RNA was used as a positive control. Our results regarding the HIV À1 PRF efficiency, 0.7 -0.1% in bacteria, and 4.5 -1.1% in yeast (Figure 2) , are corroborated by those published earlier (26, 33, 34) indicating that our dual reporter system was fully functional. b-Galactosidase has been shown to retain its specific activity well, irrespective of the C-terminal fusions (35) . This is important, since the first reporter serves to control the variations among the abundance and translation rates of the studied mRNAs (26, 30) . In addition to changes in specific activities, heterologous fusions can cause alterations in the solubility and conformation, which can expose cryptic protease target sites and reduce the stability of the proteins (36) . Therefore, for a reliable quantification of À1 PRF, it was important to test that equimolar amounts of fusions produced from the corresponding test and control constructs had similar enzymatic activities. Most inframe controls and the analogous test constructs had equal absolute b-galactosidase activities ( Table 1) . Comparable results were obtained, if activities were normalized with total protein concentration (data not shown). These results indicated that the length of the fusion as such did not affect the specific activities. The b-galactosidase activity from pAC-Am inframe control was also comparable to activity obtained from an empty pAC74, where this enzyme has no fusion (data not shown). This further supported the view that the few observed variations in the b-galactosidase activities more likely resulted from the changes in translatability or stability of the transcripts. In addition to pAC-Cm, two inframe controls pAC-Am and pAC-ACm showed $25% lower b-galactosidase activities when compared to the equivalent test constructs ( Table 1 ), indicating that the productivity from these constructs was reduced. Taken together, b-galactosidase seemed to fit well to be used as the first reporter and thus normalization factor in the in vivo experiments of this study. CfMV frameshift signals generated significant À1 PRF in yeast. À1 PRF level measured from pAC-A was 3-fold higher than from HIV RNA (Figure 2A and B) . The extent of À1 PRF directed by the minimal region A in yeast, 14.4 -1.9%, was at the same level as that reported for the CfMV minimal frameshift signal in vitro (12.7%) (18) . In contrast to our earlier in vitro observations (18) , the longer CfMV sequences upregulated À1 PRF in vivo. In yeast, the level of upregulation was 2-fold for pAC-B, the À1 PRF frequency being 26.3%, and almost 5-fold for pAC-C resulting in efficiency close to 70% (Figure 2A) , which is an extremely high value, if compared to the other values published earlier (3). CfMV frameshift signals directed À1 PRF at a lower level in bacteria than in yeast ( Figure 2B ). The extent of À1 PRF directed by region A in bacteria was 2.4 -0.7%. As in yeast, the longest B region stimulated À1 PRF 2-fold in bacteria when compared with pAC-A. However, region C did not further improve À1 PRF, but programmed À1 PRF to similar levels as pAC-B, the percentages being 4.7 -1.6% for pAC-C and 5.5 -1.5% for pAC-B. To identify the sequence(s) responsible for the enhancement of À1 PRF in vivo, a deletion analysis was carried out. The 5 0 -or the 3 0 -sequences flanking the A-region were deleted from pAC-B/Bm or pAC-C/Cm as indicated in Figure 1B , which generated vectors pAC-AB/ABm, pAC-BA/BAm, pAC-AC/ ACm and pAC-CA/CAm. À1 PRF frequencies were determined in yeast ( Figure 2C ). Increased À1 PRF was observed in all deletion constructs in comparison to the À1 PRF directed by the A region. The BA and AB regions promoted À1 PRF as efficiently as the B region, whereas regions CA and AC were better than region A, but not as good as region B. In other words, the presence of nucleotides 1386-1720, or downstream nucleotides 1602-2137, was sufficient to increase À1 PRF to the level directed by the region B. Thus, the deletion analysis did not identify single specific sequence region as being responsible for the increased À1 PRF frequencies. The expression pattern of the test and control constructs was analysed to understand the basis for the observed upregulation in yeast. Cassettes containing the reporters and the studied intercistronic sequences were expressed and purified as N-terminal histidine fusions. This allowed us to capture all the N-terminally intact products. The affinity-purified proteins were separated in SDS-PAGE gels, and visualized either by Coomassie staining (data not shown), or by western blotting with the CfMV-specific anti-VPg antibodies. The expected b-galactosidase-CfMV fusions terminating at the end of the 0-frame in the test constructs were detected. Also, the longer transframe b-galactosidase-CfMV-firefly luciferase fusion proteins were present in both the test and the inframe constructs ( Figure 3) . Comparison of the Coomassie-stained gels with the western blots revealed that the antisera recognized the products terminating at the CfMV-encoding regions better than the transframe products. Furthermore, the small size of the CfMV-specific region in the pYES2/NT-Am decreased the binding of the antibodies to these inframe control fusions. Thus, this data were not suitable for quantitative analysis of À1 PRF. Interestingly, an additional protein, which reacted with CfMV-specific antisera, was co-purified from the cells expressing pYES2/NT-Bm and pYES2/NT-Cm inframe controls ( Figure 3 ). The size of these fusions suggested that translation had terminated approximately at the site for À1 PRF signals. If such putative termination products were also present in cells expressing the test constructs, the correctly terminated 0-frame products in the western blots masked these products. A closer look at the absolute b-galactosidase and firefly luciferase activities revealed that firefly luciferase expression from pAC-Cm was clearly reduced (data not shown). In fact, expression from the inframe control was comparable to the corresponding pAC-C test construct. This was also obvious when the firefly luciferase activities were normalized with the total protein amount. After setting the activity from pAC-Am to a relative value of one, the corresponding values from pAC-Bm and pAC-Cm were 0.80 and 0.28. Although the b-galactosidase measurements (Table 1) suggested that the overall translatability of the pAC-Cm mRNA was also reduced to some extent, it explained the decrease in firefly luciferase expression only partially. In the light of these findings, the extremely high À1 PRF frequency estimate calculated for the C-region could be explained with more frequent translation termination at the frameshift signals of the pAC-Cm mRNA, which reduced firefly luciferase activity in relation to b-galactosidase. À1 PRF was also assayed in vitro in WGE. Although LacZ-encoding gene is suitable for the in vivo studies, it is an unsuitable first reporter for the in vitro determination of À1 PRF efficiencies due to its big size (30) . In good agreement with this, we observed several unexpected products in the in vitro translations programmed with LacZ-CfMV-luc mRNAs (data not shown). Renilla luciferase has been shown to retain its specific activity irrespective of the C-terminal fusions (30) . Therefore, we decided to use Rluc-CfMV-luc transcripts to determine the À1 PRF efficiencies in the cellfree system. First, we verified the suitability of Renilla luciferase for the intended in vitro experiments as described in (30) . Transcripts encoding monocistronic Renilla luciferase and Renilla luciferase fused to firefly luciferase (Rluc-Am/ Cm-luc) were mixed in different ratios and used to program the in vitro translations. Increasing concentrations of transcripts encoding the Rluc-Am-luc fusion resulted in linearly growing firefly luciferase activities. At the same time Renilla luciferase activities remained constant, which showed that its enzymatic activity was not sensitive to the C-terminal fusions ( Figure 4A ). Similar results were obtained with Rluc-Cm-luc mRNA (data not shown). À1 PRF efficiencies were then determined with transcripts that contained CfMV regions A, B and C, and their corresponding inframe controls. In all cases, slightly higher À1 PRF frequencies were obtained than in vivo. In nice correlation with the in vivo results, enhanced À1 PRF was observed with the region B, although the effect was weaker than in vivo. In this context, region C did not differ from the minimal region A in its capacity to program À1 PRF ( Figure 4B) . The ratio between the CfMV P27 and replicase is regulated by À1 PRF during CfMV infection (28) . We studied whether these proteins could regulate the À1 PRF process. P27, replicase, or an empty expression vector was co-expressed in yeast together with the dual reporter vectors containing the minimal À1 PRF test and inframe control regions as intergenic sequences (pAC-A and -Am). P27 and replicase expression was verified by a western blot analysis ( Figure 5) . A faint band having nearly the same mobility as the replicase was detected in cells grown under repressing conditions. However, due to the small size difference, this protein was not regarded as replicase. Enzymatic activities were measured from yeast lysates prepared from induced cultures. Measurements showed comparable levels of b-galactosidase in all the samples, indicating that P27 or replicase expression did not affect the stability of the dual reporter mRNA or the translatability of the first reporter ( Table 2 ). The effect of P27 or replicase expression was monitored by comparing the reporter activity ratios to those measured from cells harbouring the empty expression plasmids (Table 2) . Co-expression of CfMV replicase did not affect the normalized firefly luciferase expression (LUC/LacZ) from the inframe control, whereas slightly increased luciferase expression from the test construct was observed. In contrast, P27 expression reduced firefly luciferase expression both from the test and the inframe constructs. The effect was stronger in the presence of inframe control as normalized firefly luciferase levels reached only 54% of expression measured from the empty vector control. To verify that the observed differences in firefly luciferase production depended on the studied CfMV proteins, we co-expressed the dual reporter vectors with plasmids having the first translation initiation codons of P27 and replicase deleted (pYES-P27DAUG and pYES-RepDAUG). Western blot analysis with antisera against ORF2A or 2B did not detect any proteins produced from these vectors (data not shown). The obtained LUC/LacZ ratios were compared to those measured from cells expressing the CfMV proteins (pYES-P27 or pYES-Rep). LUC/LacZ ratios measured from cells expressing replicase were slightly lower than the ratios calculated from cells harbouring pYES-RepDAUG plasmids, being$90% when co-expressed with pAC-A and $84% when co-expressed with pAC-Am. In the presence of P27, LUC/ LacZ ratio of pAC-A reached$81% of expression measured from cells transformed with pYES-P27DAUG. Again the effect of P27 expression was more evident with pAC-Am inframe control as P27 expression reduced LUC/LacZ ratio to half ($48%) when compared to the corresponding value measured from the cells harbouring pYES-P27DAUG. This verified that CfMV P27 was able to reduce the downstream reporter expression from dual reporter mRNAs. Since CfMV P27 had a proportionally stronger effect to firefly luciferase production from the inframe control mRNAs in comparison to the test mRNAs (Table 2) , the calculated À1 PRF efficiency increased from 14.7 to 22.4%. Since À1 PRF studies are affected by a huge number of different parameters, it is not an easy task to determine the real ratio between the proteins produced via this mechanism in vivo. However, in viral systems, the efficiency of À1 PRF is an essential determinant of the stoichiometry of synthesized viral protein products, which must be rigidly maintained for efficient propagation of the virus. For example, frameshifting in retroviruses determines the ratio of structural (Gag) to enzymatic (Gag-Pol) proteins, and plays a critical role in viral particle assembly (5) . In this study, the capacity of CfMV frameshift signals to direct efficient À1 PRF was analysed in vivo by using dual reporter vectors. The length of the CfMV sequence clearly affected the actual efficiency percent in vivo. The PRF efficiency was elevated when longer viral sequences were directing the À1 PRF, but the deletion analysis did not identify any specific region as being solely responsible for the enhancement. Up-and downstream sequences nearby or far away from the cis-acting signals have been reported to enhance À1 PRF in other viruses, such as HIV, human T-cell leukaemia virus and BYDV (6, 19, 20) . Also out-of-frame stop codons have been shown to influence À1 PRF frequency in vitro in retroviruses (17) and in CfMV (18) . A study on the spacer sequences located between the cis-acting signals showed that high slippage frequencies were obtained when the first three nucleotides were G/U, G/A and G/A, the first two being the most important (37) . In CfMV, the spacer starts with UAC, which partially explains the capacity of the CfMV sequence to promote high slippage levels. In this study, the observed enhancement of À1 PRF was, however, caused by sequences that were not in the immediate vicinity of the Figure 5 . Co-expression of CfMV P27 or replicase simultaneously with the minimal frameshift signal construct pAC-A or the corresponding inframe control pAC-Am in yeast. Yeast total protein samples were separated in 12% SDS-PAGE gels, transferred onto PVDF membranes, and immunocomplexes detected by ECL chemiluminescent system. CfMV P27 expression was verified by western blotting with antisera raised against ORF2A (A), and CfMV replicase expression was detected with antisera raised against ORF2B (B). Abbreviations: À, repressed; +, induced; C1, pMAL-VPg$53 kDa; and C2, baculovirus expressed CfMV replicase. slippery sequence thus indicating that CfMV sequences further away also have an influence on the level of frameshifting in vivo. We conclude that the most reliable estimates for À1 PRF and consequently for the amount of replicase versus the 0-frame translation product P27 can be obtained only by using the full-length viral sequences. In reality, such a study would however be hampered by the non-quantitative nature of the western blot analysis, the presence of different polyprotein processing intermediates, and the differences in the stabilities of the end products in the infected cells. The overall competence of CfMV signals to direct À1 PRF was high, when compared to related plant viruses, such as Potato leaf roll virus and BYDV. À1 PRF values of $1% have been reported for these viruses when measured with reporter-based assays (6, 38) . We can hypothesize that one reason for the high efficiency is the slippery tRNA Asn encoding the AAC triplet of the CfMV heptamer. Equal U UUA AAC slippery heptamer has been measured to induce 20-40% of À1 PRF in a diversity of animal viruses [(39); reviewed in (3)]. The low fitness of CfMV À1 PRF signals in bacteria is in agreement with the poor functioning of the eukaryotic slippery heptamers of the order X XXA AAC in prokaryotes (40) (41) (42) . IBV RNA, having an identical shifty heptamer, has been shown to direct À1 PRF at similar 2-3% level in bacteria (41) . A recent study reported that XXXAAAC heptamers dictate À1 PRF to occur via the slippage of two adjacent tRNAs placed over the heptamer, irrespective of whether the host is an eukaryote or a prokaryote (42) . Therefore, the inability of prokaryotic translation systems to direct efficient À1 PRF from this heptamer is not an inherited property of prokaryotic tRNA Asn , but results from differences in the ribosomes (42) . Paused ribosomes can pass the À1 PRF site by À1 frameshifting, resumption of 0-frame translation, or termination (43) . Transient polypeptide intermediates that result from the pausing of ribosomes in the slippery sequences have been observed during IBV and S.cerevisiae L-A virus polyprotein synthesis (12, 13, 43, 44) . A pseudoknot structure formed by IBV mRNA causes a translational pause at fixed position upstream the secondary structure regardless of whether the slippery heptamer is present or absent (12) . Based on the findings of this study, we propose that also here a certain percent of ribosomes stalled at the secondary structure of the frameshift site in our inframe control and test mRNAs in yeast, and this led to the prematurely terminated products observed with the inframe control constructs pAC-Bm and -Cm. Although not unambiguously proven by this study, high frequency of termination of translation especially at the frameshift site of the pAC-Cm mRNA would nicely explain the extremely high calculated À1 PRF efficiency. Factors that change the translation fidelity and kinetics have been shown to influence À1 PRF efficiency [ (10, 15) ; reviewed in (16) ]. Autoregulation of +1 frameshifting by mammalian ornithine decarboxylase antizyme has been reported (45) . This mechanism allows modulation of frameshifting frequency according to the cellular concentration of polyamines. One could speculate that such a regulation mechanism could also be useful to adjust the amounts of the replicationassociated proteins to match the requirements of different phases in viral replication cycle. This hypothesis was studied by expressing CfMV proteins P27 and replicase together with pAC-A and pAC-Am in yeast cells. Since b-galactosidase production remained constant regardless of the presence or absence of CfMV proteins, they did not interfere with translation initiation from pAC-A/Am mRNAs per se. However, P27 expression caused a reduction in the firefly luciferase production especially from the inframe control, whereas replicase production only slightly increased the firefly luciferase production from pAC-A, but not from pAC-Am. Since replicase expression had only a faint effect on the normalized firefly luciferase production via À1 PRF, our conclusion is that CfMV replicase had no pronounced effect on translation at the frameshift site. Co-expression of the non-translatable form of P27 with the dual reporter vectors verified that P27 truly affected firefly luciferase expression on the protein level. Therefore, we propose that CfMV protein P27 may influence translation at the frameshift site. If CfMV P27 indeed interferes with viral protein synthesis during CfMV infection, the mechanism, its specificity and the possible biological role needs to be elucidated in the future. Public health is a small component of the health system, both in terms of budgetary allocation at either state or national level and in terms of the number of practitioners. It incorporates a myriad of activities; legislation and regulation for health protection, preventive services directed at specific diseases and populations, and health promotion programs geared towards particular risk factors and vulnerable groups in the community. As such, it looks like a disparate collection of programs and investments. In Australia, there is also confusion about the very terminology of 'public health'. Despite its extensive history and global understanding, in Australia the term is used variously; to refer to publicly funded health services, and interventions (regardless of the funding source) which are aimed at primary prevention and the promotion and protection of the public health ('rats and drains'). This has led to an increasing number of jurisdictions adopting the label 'population health'. Renovation of the public health system has been on the international agenda for some years. In the US, the Institute of Medicine released reports during 2003 about the public health workforce required for 21 st century challenges [1], as well as re-visited and updated its landmark report, The Future of Public Health in the 21st Century [2] . In the UK, following the path-breaking review of the NHS by Derek Wanless [3] the Treasury commissioned him, in 2003, to undertake a review of whole-of-government effort in public health. Arising in part from the challenges that confronted Canada during the outbreak of sudden acute respiratory syndrome (SARS) in 2003, a new public health agency, at arms length from government, is being created. Public health in Australia, meanwhile, remained fragmented -by programs, across jurisdictions (particularly the states and territories) -and without a systematic approach to funding, organisation, or conceptualisation. In 2003/04, the gap between rhetoric and funding continued to be noticeable, along with the tension between framing priorities for popular appeal versus the technical language of the evidence base. This article will examine some of the indicative developments of public health in Australia in 2003/04. The key developments are identified, and a number of them are selected for in-depth analysis. In this article, we use the traditional meaning of the term 'public health' and focus on activities which are usually designed to promote and protect the health of the population. The drivers for these developments, their short term implications and some signposts for the future are suggested. While early global anxiety over SARS occupied headlines between February and May, the more persistent popular headline in 2003 focused on obesity. Summits were held in NSW and Victoria, while the National Obesity Taskforce was convened under the auspice of the Australian Health Ministers Council (AHMC). When Kay Patterson was the Federal Health Minister, she declared that prevention was the fourth pillar of Medicare and she wanted to be 'Minister for Prevention'. Indeed, the 2003/04 federal budget, although limited, contained a bundle of initiatives entitled "Prevention on the Health Agenda". In particular, a number of immunisation and health promotion programs were included. Significant amongst the funding initiatives for public health announced in 2003/04 was government support for the meningococcal vaccine. Although this was the culmination of many months of careful planning, a perception existed that this only occurred after considerable public interest in and anxiety about deaths from outbreaks of this disease. Further changes to the recommended schedule in 2003 were made by the Australian Technical Advisory Group on Immunisation (ATAGI), in particular the inclusion of pneumococcal and varicella vaccines; however, these did not result in similar prescribed vaccine programs or in similar funding. These three developments are reviewed in greater detail in the next section. The National Public Health Partnership (NPHP) and the AHMC adopted the influenza pandemic plan in October 2003, and with the advent of the newly-identified disease SARS, as well as outbreaks of meningococcal disease, management and prevention of communicable diseases was prominent. Following on from the significant funding boost for bioterrorism preparedness in 2002/03, public health preparedness became a more generic theme. The arrival of SARS occupied the national popular and political imagination as well as tested the infrastructure capacity of public health. Australia fared well during the outbreak. Apart from escaping with only six Australian cases, it provided an opportunity to establish a coordinated approach between the Commonwealth and the states/territories and also contributed to the global epidemiological investigation and prevention effort. SARS also prompted amendments to the Quarantine Act [4] . While the recall following the Pan Pharmaceutical crisis put the Therapeutic Goods Administration (TGA) under the spotlight, it also managed to conclude negotiations that had been in train for several years on a Trans-Tasman regulatory regime and authority. Also on the regulatory front, the Australian New Zealand Food Regulation Ministerial Council endorsed a nutrition, health and related claims policy guidelines and established a review of genetically modified (GM) labeling of foods [5] . All these developments pointed to the global nature of public health, and the intersection between public health activities and the economy. Policy development in public health has never been confined to a set of health programs, and in 2003/04, the lead was often taken from outside the health sector. Most significant was the adoption of the National Agenda for Early Childhood [6] , pushed by public health advocates for child health since the mid 1990s. The National Public Health Partnership responded by coordinating a scoping of child health strategies across Australia. Elsewhere in Government, "Promoting and Maintaining Good Health" was adopted as one of the National Research Priorities [7] . Healthy ageing also emerged as a policy theme in Ageing Research. Public health workforce development was pursued outside the mainstream education and training arrangements for public health in universities. The Community Services and Health Training Board commissioned a consultative process to develop population health competencies for the Vocational Education and Training (VET) sector [8] . New population health qualifications and competencies were proposed for incorporation into the Health Training Package -including certificates in population health and in environmental health, and diplomas in population health and in indigenous environmental health. The release in 2003 of the report "Returns on Investments in Public Health: an epidemiological and economic analysis" [9] (often referred to as the Abelson report), may have a significant impact in subsequent years. Commissioned several years earlier by the Population Health Division of the Department of Health and Ageing (DoHA), the report experienced a relatively low profile until Derek Wanless visited from the UK. Having chaired a review that contributed to a significant budgetary increase for the NHS, Wanless had been commissioned by the British Treasury to examine prevention across government. In September 2003, at a meeting in Canberra with senior officials across key agencies, Wanless marveled at the value of the Abelson report, described in more detail below. Although 2004 was an election year, public health policy was neither visible during the campaign or in policy development more generally. The Federal Government's initiative to wind up the National Occupational Health and Safety Commission received little publicity and comment, even though it indicated the Commonwealth's increasing tendency to pursue its own pathway, separate from states and territories, and to bring the functions of statutory bodies into departments. Jurisdictional and annual reports show that across the states and territories, there were multiple plans, draft guidelines, meetings, episodic training and programs across a broad range of areas. Some health issues are being taken up across jurisdictions -particularly tobacco control, sexually transmitted infections, Aboriginal health, and vaccination. Innovative activities were reported in some jurisdictions, such as a new Health Impact Assessment Branch and a new public health training program in Western Australia. There was, however, no apparent consistency in health priorities across the nation, and an apparent divergence in the interests of the states/territories and the federal government. While the "prevention and management of overweight and obesity" agenda may have appeared to many observers as a new issue in 2003, its arrival was preceded by several years of intensive work. The NHMRC had released Acting on Australia's Weight: Strategic plan for the prevention of overweight and obesity in 1997 [10] , the same year the ABS published the findings from the 1995 National Nutrition Survey, revealing that 45% of men and 29% of women in Australia were overweight, with an additional 18% of men and women classified as obese [11] . Furthermore, overweight and obesity were more common in lower socio-economic groups, in rural populations, in some immigrant groups, and in Aboriginal and Torres Strait Islander (ATSI) peoples. Despite longstanding national cooperation on nutrition (since the days of the National Better Health Program in the late 1980s), and even more recent national cooperation on physical activity, public and political imagination was not captured until the same issues were recast as 'obesity', with a focus in particular on childhood obesity. Following from the NSW Childhood Obesity Summit in late 2002, the Australian Health Ministers agreed that a national approach was required and established a National Obesity Taskforce [12] . In 2003, NSW Health released it's response to the Summit recommendations and supported the vast majority of the 145 resolutions [13] . The Victorian Department of Human Services also held a summit [14] , while Healthy Weight 2008 -Australia's Future was released by the Commonwealth [15] . The NHMRC joined in with release in late 2003 of clinical practice guidelines for general practitioners and other health professionals [16] . While the specifics vary, the major themes and strategies are captured in Healthy Weight 2008. These are summarised in the Table 1. The Commonwealth strategy is, however, relatively weak on intersectoral policy and regulatory measures. As an illustrative example of the contrast at the state level, implementation in NSW now ranges from school physical activity and nutrition survey, to a school canteen strategy, to negotiating with Commercial Television Australia about their code of practice on advertising in peak children's viewing hours. The Commonwealth apparently chose not to consider how it might exercise its relevant taxation or legislative powers, despite the history of health promotion pointing to the importance of public policy measures beyond the health system. An examination of the manner in which the obesity issue was framed, and the details contained in the national strategy, raises a number of issues and questions: -Why was framing the issues as 'obesity' more successful than the focus on 'nutrition' and 'physical activity'? Why did 'obesity' gain traction while the other terms did not? -Why did the Commonwealth opt for the softer programmatic approach, rather than tackle obesity with stronger public policy measures (such as taxation and regulation), and demonstrate its national leadership capacity? -Was the absence of stronger public policy measures because 'obesity' is regarded as largely a health issue, rather than a whole-of-government issue? Or was the Government waiting to see if the US opposed the WHO Global Strategy on account of the strength of the industry lobby? -After a number of years of public concern about eating disorders and whether they arise in part because of promotion of certain types of body image, was the 'obesity' label a backward step for mental health and a return to traditional images of beauty? -Is there a risk that people, including children, who are labeled as 'overweight and obese' will be stigmatised? To what extent have the voices of affected communities been incorporated into the development of national strategies, if at all? -Given the correlation between obesity and socioeconomic disadvantage, how would the proposed strategy not exacerbate those inequalities? -Were children targeted because they are a "captive audience" and therefore easy targets or did the evidence suggest the best return on investment (in terms of health gain and managing demand on the health care system) would come from a focus on children? -Was the move to appeal to a populist agenda, while simultaneously progressing the longer-term agenda of tackling health inequalities through multi-sectoral partnerships, a triumph for public health advocates? These complex threads are interwoven. For the moment, the publicly enunciated agenda represents a confluence of a number of rationales. During 2003-4 three new vaccines were added to the schedule of recommended vaccines for Australians (an additional change to the schedule, recommending that polio immunisation be changed from oral to injected (IPD) vaccine, will not be discussed here). These vaccines protect against serogroup C meningococcal disease, some strains of Pneumococcal disease, and chicken pox [17] . For the first time, not all of these recommended vaccines will be funded by Government. Prior to the introduction of these vaccines, the quality of information about the epidemiology and burden of disease caused by these three infections was extremely variable. Meningococcal disease has been notifiable for many years, and in Australia almost all is caused by serogroups B and C. Whilst serogroup B predominantly occurs in young children, a new strain of serogroup C [18] was causing increasing anxiety amongst public health professionals, microbiologists, staff of accident and emergency departments, intensive care units and of course the public and media. The cause of anxiety amongst health professionals was based on the fact that this new strain carried a high fatality rate with severe after-effects in a high proportion of survivors. The attack rate, although still small, was increasing exponentially each year and reaching an important trigger point, and the majority of cases were now healthy teenagers and young adults. Although an initial accelerated catch-up programme was introduced for teenagers (the major risk group), the new conjugated vaccine was also introduced to the childhood schedule at age one, as from that age, only one dose (at a cost of$30-$60) was considered necessary for full protection from serogroup C disease. Pneumococcal disease became notifiable in 2001, however, with such a short surveillance history, not much is certain locally, epidemiologically speaking, about risk groups and effects (although there is no reason to suppose that it has a different epidemiological pattern from other developed countries). Pneumococcal disease is thought to occur at least four times as often as meningococcal disease, is known to carry major sequelae and has a high case fatality rate. For some time it has been known to be even more common amongst the indigenous Australian population with attack rates of up to 1 in 500 each year, knowledge which underpinned the 1999 decision to target Aboriginal people for free vaccination as soon as the new vaccines became available. Unfortunately at about$120 per dose, conjugate pneumococcal vaccine is very expensive and, for the protection of the very young children who bear the brunt of this disease, it is licensed only to be given as a three dose course, making provision of this vaccine to all Australian children prohibitively expensive. Varicella, predominantly a childhood disease, is caused by a Herpes virus known as herpes virus 3 or varicella-zoster virus or VZV. It is not notifiable in Australia; therefore no epidemiological population data are available. A reliable varicella vaccine has been available since the mid 1990s in the USA and is part of American routine immunisation schedule. This vaccine became available in Australia in 2000, at a cost of about $75-$90 per dose, with two doses being required for full protection. In 2003 the Commonwealth provided its periodic update on the Australian Standard Vaccination Schedule, the list of vaccines it provides as appropriate at no cost to all Australians [19] . For the first time it differed from the National Immunisation Program recommendations in that besides meningococcal serogroup C conjugate vaccines, pneumococcal vaccine, varicella vaccine and also inactivated polio (injected) vaccine were also recommended: however, funding was only secured for meningococcal conjugate vaccines, with a continuation of the provision of pneumococcal vaccines for indigenous children. As a result, although recommended, pneumococcal and varicella vaccines were not funded and parents would have to decide whether or not to pay for them. These funding decisions had important implications. Vaccines protect most of their recipients from unpleasant and sometimes life-threatening disease. One view, subscribed to in the UK, is that ethically, children should not be denied access because of their parents' inability to pay. These vaccines have been the subject of several cost-benefit studies, with generally favourable to extremely favourable pro-vaccination results. Table 2 summarises the various models for framing policy. The policy of funding meningococcal serogroup C vaccine was built on a sustained program of epidemiological evidence, ethical decision-making and public support (and was arguably honed by public pressure). Pneumococcal disease and varicella vaccination programs however, were neither supported by good local epidemiological evidence nor respectable levels of public awareness about these diseases. There had not been a similar program of sustained policy building to support or drive a decision to fund these vaccines. As a funding policy, this was noteworthy in that it marked a departure from previous policies where all recommended vaccines were fully funded by governments. National vaccination policy is designed to advise vaccination policy makers and practitioners of the most up-to-date thinking about optimal vaccination schedules for Australian children, and is not therefore proscriptive, unlike the United Kingdom (UK). Changing or adding vaccines to the recommended schedule is therefore an advisory matter, and the question of funding the vaccination program is decided separately. Cost benefit studies indicate pneumococcal polysaccharide and conjugate vaccines can be cost-effective although vaccine costs clearly affect ratios of cost to benefit greatly [20, 21] . Varicella vaccine is more contentious, because this disease is more severe in older cases, and it is possible that one result of a vaccination program could be an increase in older cases (and therefore severe disease). Whilst the vaccine undoubtedly works, there is no consensus about precisely who should be vaccinated for maximum population health as well as cost benefit, and again potential financial savings are highly dependant upon vaccine costs [22, 23] . The costs of preventive vaccine programs and curative medicine are funded from different sources. Vaccines are currently funded by the Commonwealth and subsidised through the states according to local vaccination policies, whilst the costs of curing cases of these diseases is broadly funded through the Medicare and private health insurance systems. Savings to Medicare and health insurance funds, as a result of successful vaccination programs, are not automatically transferred to the Commonwealth to fund the vaccine programs. Savings -or costs -in one area are of little interest or importance to other program areas. In 2004 the Government revised this funding policy, providing funding for conjugate pneumococcal vaccines population immunisation program for all children under seven years of age (as well as specific people in other risk groups) to commence in January 2005. The Australian Technical Advisory Group on Immunisation (ATAGI) completed Ministerial reports on both varicella and polio (injected as well as oral) vaccination late in 2004, and it is possible that programs for these vaccines will also be funded in the future. The 2002/2003 Federal Budget papers stated that "the Government is committed to making disease prevention and health promotion a fundamental pillar of the health system": however, this was not evident in the subsequent 2003/2004 budget. The Government's Focus on Prevention Package in 2002/03 aimed to incorporate disease prevention into the core business of the primary health care system and was reflective of how the public health agenda was evolving at the national level [24] . The package was comprised largely of a range of measures directed at specific diseases, plus a bundle of initiatives for general practitioners, also referred to as the "primary health care system". Amongst health conditions affecting Australians, breast cancer received the most attention, with the National Breast Cancer Centre being funded to develop a partnership approach to the review and dissemination of new information, along with information, support and management initiatives for rural women diagnosed with breast cancer. Hepatitis C also received some attention, with funding for national education and prevention projects. Financial support was offered for the SARS efforts that had been undertaken by states and territories, in particular for providing medical personnel at international airports. A clear process for assessing priorities under the broad banded National Public Health Program was also flagged. For purposes of the budget, primary health care was defined as general practitioners, and the measures funded included: • "Lifestyle prescriptions" to help GPs "raise community awareness and understanding of benefits of preventive health"; • Collaborative approach to learning, training education and support systems; • Coordinated care plans for people with chronic or terminal conditions; and • Involvement in multidisciplinary case conferencing. The budget did not adopt a comprehensive approach to the primary health care system, perhaps because many community health services, which represent the other important arm for delivery of public health services, are the responsibility of states. The timetable for renewing Public Health Outcome Funding Agreements (PHOFAs) between the Commonwealth and states and territories in 2004 raised in the minds of some stakeholders, the possibility that the Commonwealth might adopt a more comprehensive and strategic approach, linking public health and primary health care funding streams. Judging by the actual quantum of funds made available in the 2003/2004 budget, it would seem that most elements from the package did not actually receive additional funding, as shown in Table 3 . Indeed, many of the GP initiatives, previously cast as improving primary health care, were subsequently packaged as 'prevention'. The combination of these measures reflected a tight fiscal climate, with little growth in the overall health budget, as well as that of other portfolios. It was also a package that demonstrated relatively limited imagination, with support for established issues (such as breast cancer) and repackaging general practice measures that were already in train. With Medicare spending "uncapped" (and targeted public health programs "capped"), attaining more prevention dollars through the GP sector may appear to be one of the few ways to 'grow' dollars for prevention. Although this could be considered to be consistent with the Ottawa Charter of "reorienting health services", many GPs are not trained in a population-based approach to practice, and simply providing new for payments to all represents an undifferentiated, uncoordinated and untargeted approach to prevention. If there is limited support to GPs, and little monitoring, then these measures are unlikely to translate into improved health outcomes. Funding for the Tough on Drugs strategy was announced outside the Focus on Prevention package; perhaps due to Source: [28] the fact that the Tough on Drugs was the responsibility of the Parliamentary Secretary therefore requiring a separate communications strategy, or because the Prime Minister has a strong personal interest in the illicit drug strategy. The range of measures funded (which included introduction of retractable needle and syringe technology, addressing problems related to increased availability and use of psycho stimulants, establishing a research fund, supporting alcohol and drug workforce development needs, promoting access to drug treatment in rural areas, and tackling problems faced by drug users with concurrent mental health problems) certainly suggested more serious government interest and commitment to illicit drugs. During the course of the Howard Government, there has been a gradual process of re-casting the "landscape" of interest groups and policy constituencies. Strong support for breast cancer and zero-tolerance on illicit drugs contrasts sharply with the delays experienced in renewal of the National HIV/Hepatitis C Strategy. The new prominence given to meningococcal vaccine, child health and obesity creates space for other interest groups: even if the re-framing was shaped by nutrition and physical activity lobbies, other clinical interests have been brought into the picture. These developments illustrate how 'political' considerations are important in determining 'public health policy'. It was interesting however, to observe the interest in prevention from outside the health portfolio, particularly from Treasury. This was motivated in part by the Intergenerational Report and concerns about both the sustainability of Medicare as well as the social and economic cost burden arising from an ageing society. This helped to ensure interest in the Abelson Report [9] . Few countries have conducted research on return of investment from prevention efforts. Australia was praised by Derek Wanless at a high-level consultation for completing such an analysis, during his visit to Canberra while conducting a review for the UK Treasury, "Securing Good Health for the Whole Population" [26] . His final report pointed to Australia and Netherlands as two countries that were increasingly using economic evaluation in public health programs. It will be interesting to see if public health policy analysts and Treasury officials draw on this report in future years. In the future it will be interesting to see if the focus on high-visibility programs can demonstrate short-term economic returns. Given 2004 was an election year, the "political economy" of prevention programs could arguably have become a focus of future public health policy, with the 2003/4 agenda providing the Government with the opportunity to gauge public reaction to this new positioning and design their election campaign appropriately. This was, however, not the case. The American emphasis on 'preparedness' appears not to resonate with the Australian public in the same way. From the perspective of public health policy advocates, some lessons that can be drawn from 2003/04 are: • Government's response to public health proposals are shaped by its understanding of the popular interest and desire to communicate directly with the general public; • Longer term public health issues which have struggled to gain support can be progressed if they are cleverly shaped to fit the Government's "formula"; • Develop and nurture new advocates, particularly in seeking to engage with the broader health system; and • Work with the media as partners rather than adversaries These lessons need to be learned well and quickly, to assist with moving the forum for public health policy debate more into the public domain; beyond an essentially "in house" discourse between politicians, researchers and public health advocates. If a more engaged and informed community takes up a public health issue, government will be more likely to respond.

# Dataset Card for [pritamdeka/cord-19-fulltext]

## Dataset Description

### Dataset Summary

This is a modified cord19 dataset which contains only the fulltext field. This can be used directly for language modelling tasks.

English

### Citation Information

@article{Wang2020CORD19TC,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and
Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and
D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
`