Assessment of the QT interval started to receive increased regulatory attention in the late 1980s.The heightened safety concern was precipitated by repeated reports on torsade de pointes (TdP) and other arrhythmias occurring in patients treated with an antihistamine drug (terfenadine) . ECG meaSimilarly, in early 1990s, attempts to decrease sudden cardiac death by novel antiarrhythmic drugs , have demonstrated that a certain degree of arrhythmia suppression was paralleled by a proarrhythmic effect, translated in 3-fold increase in mortality rate among patients treated with encainide or flecainide ) known to be derived from a large patient population and thereby to be considered the most rigorous from an epidemiological perspective ,61.α) they demonstrated a large variability of the α exponent (range: 0.233 – 0.485) in 50 healthy subjects. The same parameter in Fridericia's and Bazett's formulas is 0.33 and 0.50 respectively. Malik and colleagues concluded that correction of QT interval by heart rate may be misleading, regardless of the method used.The main limitation in the aforementioned formulas is that each of them attempts to correct for heart rate only, while leaving into play a number of other known confounders . Disappointingly, analysis done on ECGs sampled from periods of stable heart rate provided no better results . AccordiQT/RR regression models ,65 can bReporting of results becomes mostly informative if tabular frequency distribution and frequency histograms are used to display PR, QRS and QTc data  for individuals and/or groups. For the hypothetic example captured in Fig. baseline, mean and mean maximum values for all parameters measured/computed for one group  and displays the difference (D1) between the mean value of each parameter "on-treatment" and the corresponding mean value at baseline. Given that a D2 value is to be computed for the second group (comparator), their difference (D2 – D1), for all parameters and the resulting p value (Bomferoni adjusted) could be displayed in Risk-benefit assessment with respect to a drug's propensity to prolong the QT/QTc interval entails a careful judgement of the frequency and magnitude of QT changes encountered in the preclinical and/or clinical program and balancing the potential risks against the drug's benefit.The large variability in the prolonged QT/QTc behaviour as to the potential risk for a TdP ensuing, makes this task difficult and requires individual characterisation of a specific drug's effects on repolarization.Amiodarone, for example, is known to prolong repolarization but to cause rarely TdP. Sotalol which prolongs repolarization through the same mechanism of action as Amiodarone (blockade of the IKr channel) causes a more frequent occurrence of TdP .Some agents may cause slight QTc prolongation but when combined with other drugs that inhibit the metabolism of the suspected drug , marked prolongation can occur . A typicIt is estimated that about 40–50% of the cases of drug-induced QT interval prolongation and/or TdP, result from drug-drug interactions with metabolic inhibitors (as in the example of dofetilide-cimetidine) and that only 10% are associated with electrolyte imbalance, some 10% with concurrent use of other QT-prolonging drugs and approximately 10–20% of cases have no obvious risk factors .As a general rule, it is recommended that any prolongation should be considered as a potential toxicity . In thisIdeally, therapy should be individualized on the basis of patient's genotype/phenotype determined through pharmacogenetic studies performed in the early stages of a drug's development and through application of that information while exploring the drug's pharmacokinetic and pharmacodynamic properties, its drug interaction potential as well as when ethnical-based bridging data is generated.While genotyping of individual cases, where prior informed consent is obtained, based on strong suspicion of genetic substrate having caused substantial QT/QTc prolongation is highly recommendable , large-scale genotyping in early stages of drug development or pre-prescription genotyping are still controversial.Consequently, the clinical and scientific community is facing the need to apply classical "individualizing therapy" approaches  in reducObviously, the most elementary requirement in this respect is that prescribing physicians should fully comply with contraindications regarding co-prescription of interacting drugs and with the recommendation on appropriate monitoring of targeted patients. More specifically, attention should be given to pharmacokinetic and pharmacodynamic factors that constitute important risk factors .Liver and/or renal diseases act as risk factors at pharmacokinetic level. Likewise, a multitude of metabolic inhibitors see , when tePharmacodynamic risk factors include diseases that are associated with QT interval prolongation see .sine qua non condition for preventing SAE in patients known to be treated with QT-prolonging drugs. QT interval should be monitored in these patients: (i) at baseline; (ii) at steady-state post-dose and at each incremental dose; (iii) when there is an inter-current change in level of risk, and (iv) if the patient develops symptoms of tachycardia or impaired cerebral circulation [Obviously, appropriate monitoring is a culation . TreatmeOccurrence of typical AE suggestive of eventual QT-prolongation, should prompt careful investigation of this possibility even in cases where initial QT/QTc assessment has shown to be negative. In such instances, it is recommended that screening for risk factors shall be employed and genotyping performed after receipt of informed consent. Furthermore, consideration should be give to "re-challenge" with the investigational drug under appropriate monitoring conditions, with the aim of obtaining an accurate assessment of the situation at hand as well as for getting useful information on dose- and concentration-response relationship.Compelling evidence has accrued during the past years on the potential of several cardiac and non-cardiac drugs to prolong cardiac repolarization (reflected as prolonged QT on surface ECG) and to predispose to life-threatening arrhythmias.This evidence has a major impact on the risk-benefit ratio of any drug, currently carefully considered from early stages of clinical drug development by pharmaceutical companies, by ethics committees as well as by regulatory agencies.The broad spectrum of risk factors that may interplay in the increased propensity toward malignant arrhythmias of any new chemical entity is just increasing  and adding to the complexity of the problem.This calls for standardized methodologies to deal with the multifaceted aspects that the QT/QTc prolongation poses in practice, meant to ensure that drugs awarded market approval have undergone appropriate quality assurance scrutiny and, where necessary, further post-marketing surveillance is systematically planned and reported on, in a timely manner.The author(s) declare that they have no competing interests.Frequency distribution of TU morphology changes across two groups.Click here for fileTU morphology changes in individual subjects.Click here for fileFrequency distribution of TU morphology changes across two groups.Click here for fileFrequency distribution of the PR/QRS/QTc(B/F/L) data matching PK sampling .Click here for fileSummary of PR/QRS/QTc(B/F/L) data .Click here for fileNormal ranges for the PR/QRS/QTc(B/F/L) data and for the QTc(B/F/L) relative changes to baseline.Click here for fileFrequency distribution of the baseline and on-treatment values pertaining the PR, QRS, QT, QTcB, QTcF and QTcL parameters as well as the D1 difference.Click here for fileSummary of outcome differences between the two groups regarding key ECG parameters.Click here for fileAlert criteria based on ECG findings (measurements) and rational for subject withdrawal from study.Click here for fileCharacteristics of the cross-over and parallel study designs.Click here for fileDisease associated with prolonged QT/QTc interval.Click here for fileAbbreviations (Not mentioned in the text!)Click here for file


An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms.Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance.We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research. RNA, once considered a passive carrier of genetic information, is now known to play a more active role in nature. Many recently discovered RNAs are catalytic, for example RNase P which is involved in tRNA maturation and the self-splicing introns involved in mRNA maturation . In addiA fundamental tenet of biology is that a stable tertiary structure is essential for biological function. In the case of RNA the secondary structure (the base-pair set for an RNA molecule) provides a scaffold for the tertiary structure ,10. Yet,O(nm3) in time, and O(nm2) in space, where n is the sequence length and m is the number of sequences). Current implementations, Foldalign [This has motivated plan B, the use of the "Sankoff-Algorithm", an algorithm designed for the simultaneous alignment, folding and inference of a protosequence for a set of homologous structural RNA sequences . The recoldalign ,28, Dynaoldalign  and PMcooldalign , are resThe final approach (plan C) applies when no helpful level of sequence conservation is observed. We may exclude the sequence alignment step, predict secondary structures for each sequence (or sub-group of sequences) separately, and directly align the structures. Because of the nested branching nature of RNA structures, these are adequately represented as trees. The concept of a similarity measurement via edit operations, a standard procedure for string comparisons, has been generalised to trees -33. Tree• the viability of plan A, B, or C given tools available today, and• the relative performance of the tools used within each plan..We shall explicitly not evaluate computational efficiency, which (by necessity) differs widely between the tools. We also do not evaluate user friendliness  except for some remarks in the discussion section. Data-sets, documentation and relevant scripts are freely available from in vivo structure, given only the sequence of bases. Three general considerations apply: (1) The in vivo structure is not only predetermined by the primary structure, but also by cellular components such as chaperones, base modifications, and even by the transcriptional process itself. There are currently no computational tools available that assess these effects. (2) There are 'ribo-switches', whereby two or more functional structures exist for a given sequence [RNA secondary structure inference is the prediction of base-pairs which form the sequence -57. Suchsequence -60. (3) in-vivo structure for each of them, as well as a consensus structure that captures the common, relevant structural aspects. The consensus structure per se does not exist in vivo, and so some mathematical rigour should be applied when working with this notion.The comparative approach to structure inference is initiated from a set of homologous RNA sequences. Attempts are made to infer the A, C, G, U}. An RNA sequence B = b1,...,bn contains n bases, but no structural information. For comparative analysis, we are given the RNA sequences B1,...,Bk. A secondary structure can be associated with each sequence B as a string S over the alphabet {""}, where parentheses in S must be properly nested, and B and S must be compatible: If  are matching parentheses, then  must be a legal base-pair. A base-pair is also denoted as bi·bj, si·sj, or simply i·j when the sequence is clear from the context. Both sequences and structures may be padded with a gap symbol "-", in order to align sequences and structures of different lengths. For compatibility of padded sequences and structures, we require that bi = "-" iff si = "-".An RNA sequence is a string over the RNA alphabet {structural alignment is a multiple sequence alignment of the 2 * k sequences, B1, S1,..., Bk, Sk, such that Bi is compatible with Si, and the following consistency criterion is satisfied: For any Si and Sj and any base-pair , we have  ≠ ")" and  ≠ "", then . This means that if one partner of a base-pair in Sj is aligned to one partner in Si, their partners must also be aligned to each other  which is the best thermodynamic folding for B that exhibits the base-pairs specified by C, plus additional ones that do not conflict with the former. Refolding can be achieved by RNAfold with option -C . If B and S contain gaps, we remove them before refolding and reintroduce them in the same positions afterwards.A consensus structure exhibits base-pairs shared by the majority of structures under consideration, but has no sequence information associated with it. Each individual structure for a concrete sequence typically has additional base-pairs which are properly nested between those that constitute the consensus. Given a consensus structure Given a consistent structural alignment, it is easy to derive a consensus structure, as we can count majorities at individual positions. If the 5' partner of a base-pair passes the majority threshold, consistency implies that its 3' partner also makes it into the consensus.without structural information, we can approximate a structural alignment by computing Si = refold. We call this structural alignment reconstruction. While all Si will be consistent with C, and with each other as far as the base-pairs of C are concerned, they may be inconsistent for the base-pairs introduced in refolding. This is tolerable, since if we trust the consensus to capture the relevant common structural features, there is no need to require that all members of a family agree upon extra-consensus features.Given a consensus structure and a sequence alignment We note in passing that it seems worthwhile to study the conditions under which consensus derivation and structural alignment reconstruction are mutually inverse operations, but such theoretical issues are outside our present scope.consensus by example: They provide a reference sequence, say B1, with an experimentally proved structure S1, and provide a multiple sequence alignment of B1, S1 and additional sequences B2,..., Bn in the family under consideration. The sequence alignment is chosen to exhibit structural similarities between the reference structure and the other family members, but in general, we do not know the precise model of achieving similarity, nor do we know whether this model has been solved to optimality.While the plans A, B and C we are about to evaluate strive to find a good consensus structure from sequence data, the "truth" available to us comes in a different form. Structural databases only convey a consensus reconstruction.One consequence of this situation would be to conclude that the reference structure is the only reliable anchor point available to us for evaluation. Comparative analysis tools would then be evaluated by the capacity to predict this particular structure by using family information. This would be a meaningful way to proceed, however, the effect of structural homogeneity within a sequence family would go unmeasured, and so would the difficulty or success of exploiting it. We therefore proceed in a different way which we call S1 need not be compatible with any Bi except for i = 1. However, we can still compute Si := refold by treating bases as unpaired where they violate compatibility.  What we obtain in this way is a reconstructed structural alignment, which will be consistent to the extent that the reference structure indeed describes the common structural features, and to the extent that the database sequence alignment reflects these. In all our test cases, this alignment was overall consistent, an indicator that the families and their structural features are in fact well defined. From this alignment, we derive a consensus structure as explained above using a threshold p = 0.5, which will serve as the standard of truth in our evaluation.The reference structure One may argue that our approach to reconstruct the truth is somewhat ad-hoc and should be replaced by a more systematic method. However, this is what the tools we evaluate try to achieve, and we should not add one of our own as the standard of truth. Hence, our consensus reconstruction is designed to stay as close as possible to the database information.Results of observations based on the above measures must be interpreted with care. We list a number of caveats that must be kept in mind when proceeding to the subsequent sections.In all tests, one could possibly obtain better predictions by tuning the program's parameters. We felt that it would be inappropriate to do so, since in the evaluation, we know the correct result and could use this knowledge in the tuning, whereas in a true application context, one does not have such guidance. Hence we used the recommended defaults in all cases.In some cases we apply a tool to data where we know that the model structure has features not recognised by the tool. An example is a structure with multiloops or pseudoknots, searched for with a tool that explicitly excludes such structures. We permit such cases, because again, in a true application context one does not know whether the tool is appropriate or not, and it is still of interest to see how close to the correct structure one can get.We take for granted the correctness of structural alignments taken from the literature, and the consensus reconstructed thereof. Should one of the tested algorithms produce a result that is actually better , it may be penalised. Also, we do not consider a large number of data-sets here, it is possible that performance of some algorithms improves on a different selection of data-sets.Our data reflect the state of the art in 2004. Most of the tools tested are very recent, and their authors are still improving them. Hence, not all observations will remain reproducible. In fact, we hope this study helps to obtain better results in the future.B1 with (preferably) an experimentally verified secondary structure S1. Experimental verification of a structure may be from a variety of sources: x-ray crystallography, NMR, enzymatic structure probing or phylogenetic inference. A comparison of phylogenetic with x-ray crystallographic structures has shown the phylogenetic predictions of rRNA to be very reliable (sensitivity > 97%) [We have compiled RNA sequence alignments consisting of up to 11 sequences derived from reliable sources (see table y > 97%) . This daTo avoid results bias, we constructed test alignments, with corresponding phylogenies that, wherever possible, were free of highly similar clades. In addition, we endeavoured to ensure that the reference sequence was central to the phylogeny, or more specifically, not an out group. To meet these requirements, sequences from large data-sets were sorted into high-similarity and medium-similarity groups (with respect to the model sequence), from which maximum-likelihood phylogenies  were conOur data-sets are quite diverse and must for the purposes of this study be considered difficult to analyse in structural terms. The shape of ribosomal RNA is believed to be influenced by interaction with ribosomal proteins. The shape of RNase P shows relatively little sequence and structure conservation, and furthermore, it contains pseudoknots which are generally excluded by prediction algorithms. Transfer RNAs are known to be a hard case for thermodynamic folding, primarily due to the propensity of modified bases which influence structure formation. All tools tested may perform better upon less complex data-sets, but the purpose of this study is not to show how good the algorithms are but to compare relative performance when prediction is difficult.Sensitivity (X) and selectivity (Y) are common measures for determining the accuracy of prediction methods [X and Y for examining RNA secondary structure prediction: methods . Selecti methods  and "pos methods ,65. We uTP is the number of "true positives" (correctly predicted base-pairs), FN is the number of "false negatives" (base-pairs in the reference structure that were not predicted) and FP is the number of "false positives" (in-correctly predicted base-pairs). However, not all FP base-pairs are equally false! We classify FP base-pairs as either inconsistent, contradicting or compatible. Predicted base-pairs which conflict with a base-pair in the reference structure are labelled inconsistent (i.e. i·j is predicted where either i·k and/or h·j are paired in the reference structure (h ≠ i and j ≠ k)). Predicted base-pairs (i·j) which are non-nested with respect to the reference structure are labelled contradicting (i.e. there exists base-pairs k·l in the reference satisfying k <i <l <j). Note that some base-pairs may both contradict and be inconsistent with the reference structure. Predicted base-pairs which are neither true positive, contradicting or inconsistent are labelled compatible and can be considered neutral with respect to algorithm accuracy. Hence these are subtracted in the selectivity evaluation, their number is ξ in the above equation. It is of interest to note that the base-pair metric [dBP is the sum of FN and FP, and hence is different from the measure used here.where r metric ,67 betweMatthews correlation coefficient [A measure combining both selectivity and sensitivity is useful for ranking algorithms. For this we employ the fficient  defined MCC ranges from -1 for extremely inaccurate (TP = TN = 0) to 1 for very accurate predictions (FP - ξ = FN = 0). When comparing RNA structures TN = 0 occurs only in extreme examples, hence MCC generally ranges from 0 to 1. Furthermore, for the specific case of RNA structure comparisons, MCC can be approximated by the arithmetic-mean or geometric-mean of X and Y [ X and Y .The accuracy of the MFE single sequence method has been evaluated elsewhere and was found to have an accuracy of 73% when averaged over many different RNAs and "base-pair slippage" was tolerated in the evaluation . A recenO(n3) in time and O(n2) in memory where n is the sequence length. Both employ the same thermodynamic parameters [Mfold ,72 and Rrameters . Hence, S. cerevisiae tRNA-PHE which the free energy methods favour. Mfold infers 'suboptimal' structures by calculating minimum free energy structures with the restriction that every possible base-pair is forced in a one-by-one fashion. Unique structures are then ranked by energy. Investigating the top two suboptimal structures from Mfold resulted in an overall increase in the range of sensitivity, selectivity and correlation, 22–69%, 20–67% and 0.18–0.68 respectively. The predictions shown here are used to illustrate the potential advantages of using comparative analyses over single sequence methods.The sensitivity, selectivity and correlation of MFE methods (for the four data-sets considered here) ranged from 22–63%, 20–60% and 0.18–0.61 respectively  and a covariation score matrix, augmented with penalties for inconsistent sequences, Bij. A standard trace-back procedure is performed to recover a consensus structure with the optimal sum-of-average-energy-and-covariation-score . The algorithm is remarkably efficient O(N·n2 + n3) in time and O(n2) in memory.RNAalifold ,76 impleThe sensitivity, selectivity and correlation of the RNAalifold predictions ranged from 57–91%, 57–100% and 0.57–0.95 respectively, showing a significant increase in the accuracy measures when compared to the MFE-methods.Pfold implements a "stochastic context free grammar" (SCFG) designed to produce a "prior probability distribution of RNA structures" for an RNA alignment input ,24,77. AThe algorithm is generally accurate and efficient. Hence, the over-all sensitivity, selectivity and correlation of the Pfold predictions ranged from 0–100%, 0–100% and 0.0–1.0, respectively. But removing those points where Pfold predictions were empty structures (LSU rRNA (H & M) and SSU rRNA (M), see figure must decrease when a significant proportion of the true base-pairs are engaged in pseudo-knots.ILM (iterated loop matching) is one of the few comparative RNA folding algorithms which can return pseudo-knotted structures ,78. It uThe inclusion of pseudo-knots prediction vastly increases the number of possible secondary structures, this is why they are generally excluded from exhaustive folding algorithms. In addition, there is a general lack of experimentally derived thermodynamic parameters which include pseudo-knots. ILM is a method still under development, hence the performance may improve once pseudo-knots can be more accurately modelled.O(nm3) in time, and O(nm2) in space for sequence length n and m sequences) to be practical [The Sankoff algorithm is a dynamic programming approach to obtain a common base-pair list with maximal sum of base-pair weights. Basically, this is a merger of sequence alignment and Nussinov   in time (where N is the number of sequences and n is the length of the longest sequence). A simple match-based scoring scheme is used to rank putative conserved structure elements.Foldalign  can be iThe Tool Abuse Caveat generally applies to the tool Foldalign as all of our data-sets contain multi-loops. The use of Foldalign for the prediction of global, multi-looped secondary structures is not recommended-as Foldalign is specifically designed for the location of short regulatory motifs such as IREs  where thi and j of aligned nucleotides (where i indexes positions in sequence 1 and j indexes sequence 2) to be less than M. In addition, Dynalign uses the same method employed by MFold to reduce the conformation space, by limiting the size of internal loops [O(n3M3).Dynalign ,85 is a al loops ,86. The  pairwise foldings we compared all sequences with the reference structure. Due to the computational expense of this algorithm it could only be used to predict tRNA and RNase P structures. Dynalign performed well on the tRNA, medium sequence homology data-set . With this one high-scoring point removed, averaged sensitivity, selectivity and correlation values ranged from 32–54%, 33–54% and 0.32–0.54 respectively. Comparing the performances of MFold and Dynalign showed that MFold performance was always superior on the RNase P data-set, Dynalign however did much better on the shorter and more diverse tRNA sequences. Performance gains could be made by investing more computer time and refolding RNase P with larger ' maximum insert size', which was set to 10 during this study. The use of Dynalign on the RNase P data-sets in this study is therefore a case of tool-abuse, as the parameters recommended by the authors of Dynalign were not used .The current Dynalign implementation is restricted to pair-wise sequence comparisons. Rather than compute all The Carnac algorithm, as mentioned previously, is not strictly an implementation of the Sankoff algorithm. A set of filters are employed through which sets of sequences are passed in a pair-wise fashion ,81,87. SThe Carnac algorithm was remarkably selective at base-pair predictions. However, the sensitivity of the algorithm was generally low, although when evaluated with the correlation coefficient it is comparable to RNAalifold and Pfold. Sensitivity, selectivity and correlation values for Carnac predictions ranged from 45–71%, 92–100% and 0.65–0.82 respectively. The sensitivity of Carnac can be increased by constraining a minimum free energy fold (i.e. with "RNAfold-C") with the Carnac predicted structure, but this cost in terms of selectivity. On average this increased the sensitivity by 22.5, decreased the selectivity by 17.2 and slightly increased the correlation by 0.05.RNAforester ,88 impleWe used the tRNA and RNase P data-sets and generated structure single sequence predictions with RNAfold. All predicted structures were aligned pairwise and a neighbour-joining approach used to cluster and align high similarity sequences and structure profiles. The highest scoring alignment was used to derive a predicted consensus that was evaluated against the consensus tRNA model structures. Sensitivity, selectivity and correlation ranges of consensus structures computed from the highest scoring RNAforester alignments were 29–67%, 27–67% and 0.26–0.66 respectively. It seems likely that much of the inaccuracy of this approach is due to MFE structure prediction, however the structure-clustering approach frequently separates mis-folded MFE predictions from the accurate folds.The MARNA algorithm ,89 proceQij > 1, where , , and pij are pair-probabilities computed using McCaskilPs partition function [Sensitivity, selectivity and correlation values of consensus structures computed from MARNA alignments of MFE structures ranged from 29–52%, 32–84% and 0.30–0.65 respectively. We also tried trimming high entropy base-pairs from the MFE predictions using the bound function . The newWe have evaluated three different strategies for comparative structure prediction, and altogether eight tools (not counting the single sequence methods). The results of which are summarised in figures For well aligned short sequences, both Pfold and RNAalifold generally perform well, PFold performed marginally better than RNAalifold. It is likely that some moderate refinements to RNAalifold would improve accuracy without altering the efficiency, for example, if gaps were not penalised in the free-energy evaluation and a more sophisticated model for scoring mutations was employed, perhaps ribosum matrices  could beCarnac produced highly selective structures for all the test data-sets, which if used to constrain a free energy fold produced sensitive predictions with a cost to selectivity. The consistency of Carnac performance is remarkable, for all the data-sets considered here this heuristic approach performed well. It is however unclear how Carnac will perform on highly diverse data-sets.For advocates of plan C, we have an encouraging message: Both MARNA and RNAforester perform better on the medium similarity data than on high similarity data. This seems paradoxical at first glance, but one must understand that for an approach purely based on predicted structures, high sequence similarity can be a curse rather than a blessing: If sequences are very similar, they may jointly fold into the wrong MFE structure. With more sequence variation, it becomes more likely that at least some family members have good predictions, which by their mutual similarity can be picked out from the rest. This means that especially in the case of low sequence similarity, where nothing else works, plan C, currently the least explored strategy of all, has a certain promise.Finally, let us outline some directions for future research.An implementation of the single sequence pseudoknot algorithms ,43,94 emAgain allowing constrained foldings and alignments would be useful. The further development of "BLAST-like" folding heuristics for this should be a priority, obviously Carnac is a good start. The MARNA approach for producing structurally enhanced multiple alignments produced rather selective results after trimming high-entropy base-pairs from MFE predictions. This suggests that weighting edit-distances with partition-function derived probabilities or entropies will produce reasonable RNA alignments. A consensus structure could then be derived from MFE-structures or from PFold or RNAalifold predictions on the resultant alignment. This approach would effectively decouple the Sankoff algorithm into manageable structure-enhanced-alignment and folding stages.Two further developments are likely to increase the power of plan C. Pure multiple structure alignment  presented in  may leavMore training data is essential for this field to progress, for this homology search tools are essential. Infernal ,97 used PPG carried out the experiments, the analysis and drafted the manuscript. RG suggested comparing comparative structure prediction methods and assisted in the manuscript preparation. All authors read and approved the final manuscript.


The prevalence of mental disorders is so high that members of the public will commonly have contact with someone affected. How they respond to that person  may affect outcomes. However, there is no information on what members of the public might do in such circumstances.In a national survey of 3998 Australian adults, respondents were presented with one of four case vignettes and asked what they would do if that person was someone they had known for a long time and cared about. There were four types of vignette: depression, depression with suicidal thoughts, early schizophrenia, and chronic schizophrenia. Verbatim responses to the open-ended question were coded into categories.The most common responses to all vignettes were to encourage professional help-seeking and to listen to and support the person. However, a significant minority did not give these responses. Much less common responses were to assess the problem or risk of harm, to give or seek information, to encourage self-help, or to support the family. Few respondents mentioned contacting a professional on the person's behalf or accompanying them to a professional. First aid responses were generally more appropriate in women, those with less stigmatizing attitudes, and those who correctly identified the disorder in the vignette.There is room for improving the range of mental health first aid responses in the community. Lack of knowledge of mental disorders and stigmatizing attitudes are important barriers to effective first aid. Surveys in many countries have found that mental disorders have a high prevalence and are a major cause of disability in the population -3. For eHow people initially respond to others with a mental disorder may influence their recovery. For example, it is known that many people with mental disorders get no professional help  and thatThere has been no previous research on mental health first aid knowledge in the population. Previous mental health literacy surveys have assessed knowledge and beliefs about mental disorders and their treatment , but theIn 2003–2004 a household survey was carried out of Australian adults aged 18 or over by the company AC Nielsen. Households were sampled from 250 census districts covering all states and territories and metropolitan and rural areas. Up to 5 call backs were made to metropolitan selections and 3 to non-metropolitan selections. To achieve a target sample of 4,000 interviews with adults aged 18 years or over, visits were made to 28,947 households. The outcome of these visits was: no contact after repeated visits 14,630; vacant house or lot 306; refused 7,815; person sampled within household temporarily unavailable 1,132; no suitable respondent in household 287; did not speak English 383; incapable of responding 213; and unavailable for the duration of the survey 181. The achieved sample was 3998 persons, with 1001 receiving the depression vignette, 999 the depression with suicidal thoughts vignette, 997 the early schizophrenia vignette, and 1001 the chronic schizophrenia vignette.The interview was based on a vignette of a person with a mental disorder. On a random basis, respondents were shown one of four vignettes: a person with major depression, one with major depression together with suicidal thoughts, a person with early schizophrenia, and one with chronic schizophrenia. All vignettes were written to satisfy the diagnostic criteria for either major depression or schizophrenia according to DSM-IV and ICD-10. The vignette with depression and the one with early schizophrenia were written to satisfy these diagnostic criteria at a minimal level, so that we could ascertain the public's reaction to cases of a developing disorder which had reached the point where intervention was needed. The vignette of the person with depression together with suicidal thoughts was identical to the depression vignette in all respects except the suicidal thoughts and was designed to assess how this symptom affected the public's response. The chronic schizophrenia vignette was designed to assess the response to someone with a severe long-standing disorder, where acceptance seemed less likely. Respondents were also randomly assigned to receive either male ("John") or female ("Mary") versions of the vignette. These vignettes (John version) are shown in Table After being presented with the vignette, respondents were asked a series of questions to assess their recognition of the disorder in the vignette, their beliefs about treatment and long-term outcomes, beliefs about causes and risk factors, stigmatizing attitudes, awareness of mental disorders in the media, contact with people like those in the vignette, and the health and sociodemographic characteristics of the respondent. The questions relevant to the present paper are described below.other people believe. Please indicate how strongly you agree or disagree with the following statements. Most people believe that people with a problem like John's could snap out of it if they wanted. Strongly agree, Agree, Neither agree nor disagree, Disagree, Strongly disagree". Sociodemographic characteristics recorded included age group , gender, and education .To assess recognition of the problem in the vignette, respondents were asked: "What would you say, if anything, is wrong with John?". Responses of "depression" were counted as correct for the first two vignettes above, and responses of "schizophrenia" or "psychosis" for the second two. To assess mental health first aid responses, respondents were asked the open-ended question: "Imagine John is someone you have known for a long time and care about. You want to help him. What would you do?". Answers were recorded verbatim by the interviewer. To assess contact with people like those in the vignette, respondents were asked: "Has anyone in your family or close circle of friends ever had problems similar to John's?"; "Have they received any professional help or treatment for these problems?'; "Have you ever had problems similar to John's?"; "Have you received any professional help or treatment for these problems?"; and "Have you ever had a job that involved providing treatment or services to a person with a problem like John's?". Those that said "yes" to these questions were respectively labelled in the analyses reported below as "carers", "consumers" or "professionals". To assess stigma, respondents were asked a series of nine questions designed to elicit their attitudes towards the person in the vignette  and nine items concerning what they thought others in the community would believe about the person in the vignette (perceived stigma) . PersonaResponses were coded according to the categories identified from an earlier study where the same question was administered as part of a randomized controlled trial of Mental Health First Aid . ResponsA. Encourage professional help-seekingB. Listen to / talk to / support personC. Listen to / talk to / support familyD. Assess problem / assess risk of harmE. Give or seek informationF. Encourage self-helpA. Encourage professional help-seeking, were subcoded into multiple categories to identify the type of professional help recommended. These categories were:Responses coded into category A1. GP / doctor unspecifiedA2. CounsellorA3. PsychiatristA4. PsychologistA5. Mental health team / servicesA6. Other mental health professionalsA7. Unspecified professionals and other professionalsA8. Accompany person (eg. Offer to go with him/her)A9. Contact help on their behalfExamples of responses coded into category B included "support, understanding and caring, someone to talk to him", "talk to her about it", "listen", "be there for him".Responses coded into category C were the same for category B, but referred to giving support, listening to, or talking to the sufferer's family. For example, "talk to her family", "contact relatives", "ask advice of parents", "support his parents".Responses coded into category D included "keep an eye on her, make sure she is safe", "make a contract with her so if she wants to harm herself she rings me first", "find out what is the real problem behind the behaviour, focus on the problem".Responses coded into category E included "ring the local health authority to get advice", "talk to other people who have been in the situation", "speak to health professionals and get best advice", "get some brochures from community health and give them to him". Responses coded into category F included "I suggest that he have a holiday/exercise/change jobs", "try and help him get into something he is interested in", "support groups", "do something for herself to get out of the situation".Inter-rater reliability of the coding was assessed by a second rater who independently coded 100 responses which were randomly selected using the SPSS Select Cases procedure .Inter-rater reliability of the content coding was assessed using kappa. Kappa values were interpreted according to Altman  as folloThe frequency of codings was analysed by pooling across male and female versions of each vignette and percent frequencies calculated. Percentages were calculated applying survey weights to give better population estimates. Standard errors of these percentages were estimated using the Complex Samples procedure in SPSS 12.0 . This prMultiple logistic regressions were then conducted to examine the levels of association between participants suggesting particular treatment options and their sociodemographic and mental health experience attributes. The following predictor variables were included: age group; consumer, carer and professional status, including whether or not professional help was obtained; and levels of perceived stigma and personal stigma. Two vignette measures – type of vignette provided and whether respondents correctly identified the problem portrayed in that vignette – were also included in the analyses. Each logistic regression was also adjusted to take into account of sampling weights and clustering method applied in this survey. These analyses were undertaken using STATA 8 .Inter-rater reliability was assessed for a randomly chosen 100 responses. Kappa was very good or good for encourage professional help-seeking (0.89), listen/talk/support person (0.70), listen/talk/support family (1.00), encourage seeing doctor (0.98), encourage seeing counsellor (0.93), encourage seeing psychiatrist (0.94), encourage seeing psychologist (0.88), and accompanying the person to a professional (0.95). It was moderate for give or seek information (0.48), encourage seeing unspecified and other professionals (0.56), and contact professional on their behalf (0.56). Kappa was fair for encourage self-help (0.34) and poor for assess problem/risk of harm (0.15). Kappa could not be calculated because of zero frequencies from the first rater for the categories of mental health team/services and other mental health professional.To better understand the reasons for the fair and poor agreement with two of the codes, positive and negative agreement were examined separately . In bothTable Table Table Table Table Table The most common first aid responses were found to be encouraging professional help-seeking and listening/talking/supporting the person. Nevertheless, these responses were far from universal, with 32–44% not mentioning professional help and 27–34% not mentioning listening/talking/supporting. Given the likely helpfulness of these first aid responses, they need greater promotion in the community.Other first aid responses were mentioned only by a minority. Of particular concern is the low percentage assessing risk of harm for the person in the depression/suicidal vignette. Asking about suicidal intentions is often recommended as a response ,14, althEncouraging self-help was another minority response, but was associated with stigma and lack of recognition of the mental disorder in the vignette. Respondents appear to have suggested self-help as an alternative to professional help, rather than as a complement to it. We have previously reviewed the evidence on self-help interventions for depression and anxiety disorders and found that some have support ,17. SuchWhen correlates of first aid responses were examined, most variables had at least one significant association. However, the variables that most often predicted first aid responses were female gender, low personal stigma and correct recognition of the disorder in the vignette. The latter two predictors indicate potential barriers to providing first aid. Respondents who saw the person in the vignette as having negative attributes were less likely to respond by encouraging professional help-seeking or providing personal support. Efforts to reduce stigma in the community may therefore facilitate greater first aid. People who did not recognize the disorder showed a similar pattern of responses. These people lack knowledge of mental disorders, at least to the extent of being able to apply a psychiatric label. Therefore community education about how to recognize these disorders may also facilitate helpful first aid responses.One approach to improving public responses to people with a mental disorder is an individual training course in mental health first aid . Such a The study has two limitations which must be acknowledged. The major one is that the study has assessed intended first aid to a hypothetical person in a case vignette. Whether these intentions would be implemented in practice is unknown. Intentions might be seen as placing an upper limit on responses, such that if a respondent fails to state an intention, this is unlikely to be seen in practice. An alternative approach would have been to ask the respondent how they had treated actual people they knew with mental health problems. However, the disadvantage of this alternative would have been the lack of standard situations. A second limitation is that the inter-rater reliability of coding some of the first aid responses was low. Conclusions about these responses must be viewed with caution. On the other hand, the strengths of the study are the large representative sample, the open-ended responses which did not constrain the respondents to particular alternatives, and the ability to compare responses to a series of standard scenarios.There is room for improving the range of mental health first aid responses in the community. Lack of knowledge of mental health and stigmatizing attitudes are important barriers to effective first aid.The author(s) declare that they have no competing interests.AFJ was involved in securing funding for the survey, had a major role in the design of the survey and the interview questionnaire, carried out the descriptive statistical analysis and had a major role in writing the manuscript.KAB provided research assistance with the survey, coded all the responses, and wrote the method relevant to this coding.KMG was involved in the design of the study, developed the stigma scales and wrote some of the manuscript.BAK developed the coding scheme, was the second rater for inter-rater reliability, and wrote some of the manuscript.RAP did the regression analyses and wrote the section of the Method describing this.All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


The clinical course of breast cancer is difficult to predict on the basis of established clinical and pathological prognostic criteria. Given the genetic complexity of breast carcinomas, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. The use of gene expression profiles could result in more accurate and objective prognostication.BRCA2, DNMT3B and CCNE1) as an independent prognostic marker . This "poor prognosis" signature was then tested on an independent panel of ERα-positive breast tumors from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone: although this "poor prognosis" signature was associated with shorter relapse-free survival in univariate analysis (P = 0.029), it did not persist as an independent prognostic factor in multivariate analysis (P = 0.27).To this end, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of a large panel (n = 47) of genes previously identified as candidate prognostic molecular markers in a series of 100 ERα-positive breast tumor samples from patients with known long-term follow-up. We identified a three-gene expression signature (Our results confirm the value of gene expression signatures in predicting the outcome of breast cancer. Breast carcinoma is the most common female cancer and is showing an alarming year-on-year increase. Most patients do not die as a result of the primary tumor but from metastatic invasion. The mean 5-year relapse-free survival rate is about 60% overall, but differs significantly between patients with forms that rapidly metastasize and those with less aggressive forms.i.e. age, menopausal status, lymph-node status, macroscopic tumor size, histological grade and estrogen receptor status, fail to accurately predict clinical behavior.Current clinical, pathological and biological parameters, ERBB2, CCDN1, MYC, UPA and PAI1 [Breast cancer initiation and progression is a process involving multiple molecular alterations, many of which are reflected by changes in gene expression in malignant cells. Many clinical studies have attempted to identify correlations between altered expression of individual genes and breast cancer outcome, but often with contradictory results. Examples of such genes include and PAI1 . It is tThe recent development of effective tools for monitoring gene expression on a large scale is providing new insights into the involvement of gene networks and regulatory pathways in various tumor processes . It has BRCA2, DNMT3B and CCNE1) associated with poor clinical outcome. We then tested this "poor prognosis" signature on an independent panel of ERα-positive breast tumor samples from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone with known long-term follow-up.In this study, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of 47 candidate prognostic molecular markers in a series of 100 ERα-positive breast tumor samples. We identified a three-gene expression signature  were pre- or post-menopausal . Sixty patients received adjuvant therapy, consisting of chemotherapy alone in 14 cases, hormone therapy alone in 15 cases, and both treatments in 31 cases. The standard prognostic factors are presented in Table The second series consisted of 104 post-menopausal women whose breast tumors were excised at Centre René Huguenin from 1980 to 1994. The patients  all received post-operative adjuvant hormone therapy consisting of tamoxifen (20 mg daily for 3–5 years) and no other treatment. The standard prognostic factors are reported in Table Complete clinical, histological and biological information was available for the two series of breast cancer patients; no radiotherapy or chemotherapy was given before surgery, and full follow-up took place at Centre René Huguenin. The histological type of the tumor and the number of positive axillary nodes were established at the time of surgery. The malignancy of infiltrating carcinomas was scored according to Scarff Bloom and Richardson's (SBR) histoprognostic system.Both series of tumor samples were placed in liquid nitrogen until total RNA extraction immediately following surgery.Quantitative values are obtained from the cycle number  at which the increase in fluorescent signal associated with an exponential growth of PCR products starts to be detected by the laser detector of the ABI Prism 7700 Sequence Detection System  using the PE Biosystems analysis software according to the manufacturer's manuals.TBP (Genbank accession NM_003194) encoding for the TATA box-binding protein (a component of the DNA-binding protein complex TFIID) as an endogeneous RNA control, and normalized each sample on the basis of its TBP content.The precise amount of total RNA added to each reaction  and its quality (i.e. lack of extensive degradation) are both difficult to assess. We therefore also quantified transcripts of the gene TBP gene, termed "Ntarget", were determined by the formula: Ntarget = 2sampleΔCt , where ΔCt value of the sample was determined by subtracting the average Ct value of the target gene from the average Ct value of the TBP gene.Results, expressed as N-fold differences in target gene expression relative to the target values of the samples were subsequently normalized such that the Ntarget value of the tumor sample which contained the smallest amount of target gene mRNA in each tumor series would equal a value of 1.The NTBP and the 47 target genes were chosen with the assistance of the computer programs Oligo 5.0 . We conducted searches in dbEST, htgs and nr databases to confirm the total gene specificity of the nucleotide sequences chosen for the primers and probes, and the absence of single nucleotide polymorphisms. In particular, the primer pairs were selected to be unique when compared with the sequences of the closely related family member genes or of corresponding retropseudogenes. To avoid amplification of contaminating genomic DNA, one of the two primers or the probe was placed at the junction between two exons. Agarose gel electrophoresis allowed us to verify the specificity of PCR amplicons. The list of the 47 target genes tested in this study is indicated in Table Primers and probes for Total RNA was extracted from frozen tumor samples by using the acid-phenol guanidinium method. The quality of the RNA samples was determined by electrophoresis through agarose gels and staining with ethidium bromide, and the 18S and 28S RNA bands were visualized under ultraviolet light.Reverse transcription of total RNA was done in a final volume of 20 μL containing 1X RT buffer , 20 units of RNasin RNase inhibitor , 10 mM DDT, 100 units of Superscript II RNase H- reverse transcriptase , 3 μM random hexamers  and 1 μg of total RNA. The samples were incubated at 20°C for 10 min and 42°C for 30 min, and reverse transcriptase was inactivated by heating at 99°C for 5 min and cooling at 5°C for 5 min.® PCR Core Reagents kit or the SYBR® Green PCR Core Reagents kit (Perkin-Elmer Applied Biosystems). The thermal cycling conditions comprised an initial denaturation step at 95°C for 10 min and 50 cycles at 95°C for 15 s and 65°C for 1 min.All PCR reactions were performed using a ABI Prism 7700 Sequence Detection System (Perkin-Elmer Applied Biosystems). PCR was performed using either the TaqManU test  and the Spearman rank correlation test (link between 2 quantitative parameters). Differences between the two populations were judged significant at confidence levels greater than 95% (p < 0.05).The distributions of the gene mRNA levels were characterized by their median values and ranges. Relationships between mRNA levels of the different target genes and comparison between the target gene mRNA levels and the clinical parameters were estimated using nonparametric tests: the Mann-Whitney Y axis against 1 – the specificity  on the X axis, considering each value as a possible cutoff value. The AUC (area under curves) was calculated as a single measure for the discriminate efficacy of a molecular marker. When a molecular marker has no discriminative value, the ROC curve will lie close to the diagonal and the AUC is close to 0.5. When a test has strong discriminative value, the ROC curve will move up to the upper left-hand corner (or to the lower right-hand corner) and the AUC will be close to 1.0 (or 0).To visualize the efficacy of a molecular marker to discriminate two populations , we summarized the data in a ROC (receiver operating characteristic) curve . This cuHierarchical clustering was performed using the GenANOVA software .Relapse-free survival (RFS) was determined as the interval between diagnosis and detection of the first relapse .Survival distributions were estimated by the Kaplan-Meier method , and theThe results for the 47 genes are summarized in table BRCA2, DNMT3B, CCNE1, HMMR/RHAMM, MKI67, TERT and CCND1. The prognostic performance of these 7 genes was also assessed using ROC-AUC analysis. BRCA2 emerged as the most discriminatory marker of relapse status . The mRNA expression of this gene, as well as DNMT3B, CCNE1, HMMR/RHAMM, MKI67 and TERT, was higher in patients who relapsed than in patients who did not relapse, while only CCND1 mRNA expression was lower in patients who relapsed.Seven genes showed significantly different expression according to relapse status (P < 0.05), namely i.e. the genes with the highest ROC-AUC values , the patient population fell into two subgroups  with significantly different relapse-free survival curves  in univariate analysis, us Table  and the us Table . Only thBRCA2, DNMT3B and CCNE1 mRNA levels were significantly higher in patients who relapsed than in those who did not relapse.The results for each of the three genes are summarized in table On hierarchical clustering of the samples, the three-gene expression signature dichotomized the 104 patients into two subgroups  of similar sizes to those of the initial patient population .The "poor prognosis" signature was again associated with shorter relapse-free survival in this independent tumor series  in univariate analysis,  in univaze Table , only SBBRCA2, DNMT3B and CCNE1) with independent prognostic significance in breast cancer . This "poor prognosis" signature was then tested on an independent set of 104 ERα-positive breast tumors from a well-defined cohort of postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone. It was found to be significant in univariate analysis (P = 0.029), but not in multivariate analysis (P = 0.27). We have previously published individual data for 18 of these 47 genes, namely ERBB1-4 [MYC [TERT [CCND1 [CGB, CGA, ERα, ERβ, PR, PS2 [AR [DNMT3B [PAI1, PAI2 and UPA [We used real-time quantitative RT-PCR assays to quantify the mRNA expression of 47 genes previously identified as candidate prognostic molecular markers in 100 ERα-positive breast tumor samples. We identified a three-gene expression signature , and for distinguishing among closely related family member genes or alternatively spliced specific transcripts . Finally, real-time quantitative RT-PCR assay is a reference in terms of its performance, accuracy, sensitivity and throughput for nucleic acid quantification, and is more appropriate for routine use in clinical laboratories, being simple, rapid and yielding good inter-laboratory agreement and statistical confidence values.These discrepancies may be due to the clinical, histological and ethnic heterogeneity of breast cancer, but also to the fact that breast tumors consist of many different cell types – not just tumoral epithelial cells, but also additional epithelial cell types, stromal cells, endothelial cells, adipose cells, and infiltrating lymphocytes. Real-time RT-PCR requires smaller starting amounts of total RNA (about 1–2 ng per target gene) than do cDNA microarrays, making it more suitable for analyzing small tumor samples, cytopuncture specimens and microdissected samples. Real-time RT-PCR also has a linear dynamic range of at least four orders of magnitude, meaning that samples do not need to contain equal starting amounts of RNA. Real-time RT-PCR is also more suitable than cDNA microarrays for analyzing weak variations in gene expression and weakly expressed genes , confirming that the ERBB2 mRNA expression level is not a major prognostic factor in breast cancer; (b) ESR1/ERα mRNA levels were not different between the two subgroups , suggesting that the ESR1/ERα mRNA expression level in ERα-positive tumors is not predictive of outcome.The comparison of median target gene mRNA levels between patients who did and did not relapse provided two interesting results: (a) CCNE1), DNA methylation (DNMT3B) and DNA damage repair (BRCA2). This gene expression signature is an interesting candidate for routine clinical use, especially as the three genes encode well-characterized proteins for which specific antibodies are already commercially available. Furthermore, the three protein products are amenable to pharmacological control.The three-gene expression signature predictive of subsequent relapse status comprised genes involved in cell cycle control . We also observed a strong positive link between BRCA2 and MKI67, which encodes the proliferation-related Ki-67 antigen . The observed strong associations between BRCA2, HMMR/RHAMM and MKI67 mRNA expression explain why four- and five-gene expression signatures, comprising HMMR/RHAMM alone or together with MKI67, showed no additional prognostic value relative to the three-gene signature. control . We founBRCA2 expression ex vivo are in keeping with reports from several authors [BRCA2 mRNA expression is upregulated in rapidly proliferating cells in vitro. Our results are also in agreement with those of Egawa et al. [BRCA2 expression carries a poor prognosis in breast cancer. This link between BRCA2 overexpression and poor outcome should be taken into account when evaluating future BRCA2-based therapeutic approaches to breast cancer.Our results for  authors ,34 showia et al.  showing DNMT3B, the third gene in our expression signature, codes for one of the three functional DNA methyltransferases  that catalyze the transfer of methyl groups to the 5-position of cytosine (DNA methylation). We previously showed that, among the three DNA methyltransferases , only DNMT3B overexpression is associated with poor outcome in breast cancer [DNMT3B (like DNMT3A) is known to be a de novo methylator of CpG sites. Abnormal DNA methylation is thought to be a major early event in the development of tumors characterized by widespread genome hypomethylation leading to chromosome instability and localized DNA hypermethylation; the latter may be important in tumorigenesis by silencing tumor suppressor genes [Finally, t cancer . DNMT3B or genes .BRCA2, DNMT3B and CCNE1) with prognostic significance. The practical value of this signature remains to be validated in large prospective randomized studies.In conclusion, by studying the expression of 47 genes previously identified as candidate prognostic markers in breast cancer, we identified a three-gene expression signature (ERα, estrogen receptor alpha; RT-PCR, reverse transcriptase-polymerase chain reaction.Real-time RT-PCR have been carried out by ST and IG. IB and RL interpreted the result, performed bioinformatics and statistical analyses.


Non-neuronal cells, including those derived from lung, are reported to express nicotinic acetylcholine receptors (nAChR). We examined nAChR subunit expression in short-term cultures of human airway cells derived from a series of never smokers, ex-smokers, and active smokers.At the mRNA level, human bronchial epithelial (HBE) cells and airway fibroblasts expressed a range of nAChR subunits. In multiple cultures of both cell types, mRNA was detected for subunits that constitute functional muscle-type and neuronal-type pentomeric receptors. Two immortalized cell lines derived from HBE cells also expressed muscle-type and neuronal-type nAChR subunits. Airway fibroblasts expressed mRNA for three muscle-type subunits  significantly more often than HBE cells. Immunoblotting of HBE cell and airway fibroblast extracts confirmed that mRNA for many nAChR subunits is translated into detectable levels of protein, and evidence of glycosylation of nAChRs was observed. Some minor differences in nAChR expression were found based on smoking status in fibroblasts or HBE cells. Nicotine triggered calcium influx in the immortalized HBE cell line BEAS2B, which was blocked by α-bungarotoxin and to a lesser extent by hexamethonium. Activation of PKC and MAPK p38, but not MAPK p42/44, was observed in BEAS2B cells exposed to nicotine. In contrast, nicotine could activate p42/44 in airway fibroblasts within five minutes of exposure.These results suggest that muscle-type and neuronal-type nAChRs are functional in airway fibroblasts and HBE cells, that prior tobacco exposure does not appear to be an important variable in nAChR expression, and that distinct signaling pathways are observed in response to nicotine. Nicotine, the addictive component of tobacco smoke, signals through its family of receptors, the nicotinic acetylcholine receptors (nAChR). Acetylcholine is the endogenous ligand for these receptors, and has been found in many tissues outside of the nervous system. Non-neuronal nAChR have also been identified in tissues such as the skin, vasculature, and nasal mucosa . nAChR aThe ionic permeability of nAChR is dependent upon the subunit composition of the receptor, with some receptors showing preference to either calcium or sodium . HoweverThe primary route of exposure to nicotine is through inhalation, either by active smokers or non-smokers exposed to environmental tobacco smoke. Through inhalation, the lung, in particular, would be exposed to pharmacological doses of nicotine. In addition, receptor inactivation is likely to occur in sensitive receptors, due to the extended length of time that smokers use tobacco ,5. This In vitro data has indicated that airway epithelial cells release GM-CSF upon exposure to nicotine, and activate Akt, a signaling molecular important in cell survival [Using radiolabeled agonist, we have shown that saturable nicotine binding sites exist in the lung . Other psurvival ,9. Howevsurvival  airway tIn this study, a series of 37 short-term human bronchial epithelial cultures, 25 airway fibroblast cultures, and 2 immortalized bronchial epithelial cell lines were examined by RT-PCR for nAChR expression. We also examined protein expression by immunoblot to determine which subunits are most highly expressed and to determine if appropriate combinations are present at the protein level to form functional receptors. We determined that the nAChR present are functional by examining calcium influx after agonist exposure, and blockade by antagonists. We also show that exposure of airway cells to nicotine leads to activation of downstream signaling pathways. Finally, we examined the nAChR present in HBE and airway fibroblasts derived from smokers, ex-smokers, and never smokers to determine if alterations in nAChRs based on tobacco exposure can be detected.HBE cells are cultured from airway biopsies using standard methodology in serum-free medium . BrieflyBEAS2B were all purchased from ATCC and cultured according to ATCC instructions. IB3-1 cells were derived from a cystic fibrosis patient and cultured in Hams medium with 10% serum .All chemicals used were from Sigma  and all supplies were from either Fisher Scientific  or PGC Scientific , unless otherwise indicated.RNA is isolated from cultures using standard guanidinium thiocyanate method . RNA is 12–18 (Invitrogen) was annealed to 1 μg total RNA and reverse transcribed with Superscript II (Invitrogen). The reaction contained 1 μg RNA, 500 ng Oligo dT12–18, 50 mM Tris-HCl, pH 8.3, 75 mM KCl, 3 mM MgCl2, 10 mM DTT, 1 mM each dNTP, 200 U superscript. Briefly, total RNA was incubated with oligo dT12–18 at 70°C for 10 min. The cDNA produced was then used as a template for PCR using specific primers. Table Primers were developed to unique regions of each subunit and were tested on human muscle and brain RNA purchased from Clontech . Optimized protocols were then used on RNA from airway cells. All primers span introns and do not amplify DNA. GAPDH or actin is always used as a positive control for RNA integrity. Oligo dT50 μg protein with loading buffer was denatured using 50 mM DTT and heated at 80°C for 15 min. Protein was loaded onto 10% Bis-Tris gels (Invitrogen). Brain lysate  and lysates from myotubes cultures  were used for positive control. Protein was transferred to PVDF  or Multiblots . PVDF membranes were blocked for 1 hr with 5% blocker (Biorad) in TBS-T ). Primary antibody was diluted in carrier protein, 5% blocker for PVDF or 0.5% casein (Pierce) for Multiblots and incubated at 4°C overnight, see Table Airway cells were grown in 24-well dishes, with 10,000 cells per well. After 24 hours, medium was replaced with serum-free, prewarmed medium spiked with calcium-45, with a final specific activity of approximately 60 μCi/mM calcium. Drug was added to the wells as indicated, in triplicate. If nAChR antagonists or channel blockers were used, they were added 20 min before the addition of agonist. The calcium ionophore A23187  was used as a positive control for calcium influx. After incubation at 37°C, plates were put on ice and washed 3 times with ice-cold PBS. Lysis buffer  was added. Lysates were transferred to scintillation vials, scintillation fluid added, and counted. Results are presented as percent control, with untreated control normalized to 100%.For phospho-PKC, phospho-p42/44, and phospho-p38, 10 minute exposure to 20 ng/ml EGF was used as the control. These conditions have been published as optimal for signaling by EGF in several published articles ,17. ImmuDifferences in expression frequency of nAChR subunits at either the RNA or protein level were analyzed by Fisher's Exact Test. In all other experiments, differences from control were determined using Student's T-test. All p-values reflect two-tailed tests.Using RT-PCR, we repeatedly detected mRNA for nAChR subunits in short-term cultures of human airway cells from bronchial biopsies. Figure The subunits that are expressed by HBE cells could potentially combine to form muscle-type (α1/ β1/ δ /ε) heteropentamers, neuronal α7 or α9 homopentamers, and neuronal heteropentamer receptors α3/ α5/ β2 or β4 and α6/β2 or β4. By examining the pattern of mRNA expression in each individual culture, combinations were observed that would produce a functional muscle-type receptor in 7 of 33 (21%) of cultures, a functional α3-containing neuronal type receptor in 10 of 28 (35%) of cultures, α6-containing neuronal type receptor in 13 of 28 (46%), α7 homopentamer receptors in 25 of 37 (68%), and homopentamer α9 receptors in 27 of 29 cultures (93%). Only 2 of 35 (6%) HBE cultures did not express at least one functional nAChR subunit combination.Two immortalized airway epithelial cell lines also expressed mRNA for many of these nAChR subunits, including muscle-type subunits Figure , Table 3Airway fibroblasts also expressed nAChR Table . We founThe mRNA expression of four nAChR subunits is significantly different when comparing airway fibroblasts and HBE cells. The muscle-type receptor subunits α1, δ, and ε are all expressed more frequently in airway fibroblasts  than in HBE cells . In addition, the β3 subunit is also expressed more frequently in fibroblast cultures than in HBE cells . The subunit combinations that could form functional receptors were also examined and compared between cell types. Consistent with the individual subunit data, the combination of all subunits for the muscle-type receptor is expressed significantly more frequently in human airway fibroblasts (74%) than in HBE cultures (21%)(p = 0.0001).The nAChR subunit α9 was frequently expressed by both HBE cells and airway fibroblasts. Although we find this nAChR frequently and it may have physiological significance, it is unlikely that signaling through this receptor would be responsible for the immediate downstream effects seen in our studies, which focus on the effects of nicotine, since nicotine does not act as an agonist for this receptor type.We next examined nAChR subunit protein expression using immunoblotting. All the cultures examined expressed protein for nAChR subunits, in general agreement with mRNA expression data . Figure We examined functionality of the AChR on HBE cells by measuring calcium influx. After treatment with nicotine, extracellular radioactive calcium Ca-45) is internalized by BEAS2B cells and short-term HBE cultures .To test the specificity of this response, we used the nicotinic antagonists α-bungarotoxin and hexamethonium with BEAS2B cells and nicotine. α-Bungarotoxin will block muscle-type nAChR as well as α7 homopentamers and hexamethonium will block neuronal heteropentamer nAChR such as α3- and α6- containing receptors. We used ionophore alone and with the nicotinic antagonists as a control in these experiments. Antagonists had no effect on ionophore-induced calcium influx (data not shown). In this experiment we found that α-bungarotoxin could completely prevent the calcium-45 influx seen after nicotine treatment, while hexamethonium could only slightly inhibit the effect of nicotine in BEAS2B cells  responds to nicotine because it is commonly activated by calcium influx. Calcium influx is an immediate effect of nicotine exposure to BEAS2B cells Figures  and 5. TUsing phosphorylation as a marker of activation, we find that PKC and p38, but not p42/44 are activated after treatment with nicotine in BEAS2B Figure . As showIn contrast, signaling experiments done with short-term airway fibroblast cultures show that nicotine caused phosphorylation of p42/44 within 10 minutes of exposure Figure . After dTogether, these data indicate that the muscle-type (α1/β1/δ/ε) nAChR is consistently present on airway epithelial cells, while airway fibroblasts consistently demonstrate both a muscle-type and an α7 homomeric nAChR. Normal airway epithelial cells may also sometimes express the neuronal α3/α5/β2 or α6/β2 nAChR. The nicotinic receptors are functional, regulate calcium influx upon ligand binding, and lead to downstream activation of the signaling pathways MAPK or PKC when bound by nicotine. Downstream effects can be blocked by use of nicotinic antagonists.We examined the relationship of prior smoking to nAChR expression on airway cells. To do so, we compared subunits expressed at the mRNA and protein level in HBE cultures from never smokers, active smokers, and ex-smokers to determine if long-term exposure to nicotine was a factor in the type of nAChR expressed. As shown in Table Interestingly, in airway fibroblasts mRNA patterns for combinations of neuronal heteropentamers containing the α3 subunit were downregulated with smoking Table . Cultureet al. [et al. [et al. [Recent evidence suggest that endogenous acetylcholine is a local signaling molecule in non-neuronal tissue, and that nicotinic acetylcholine receptors are found outside the nervous system. Our data show that HBE cells can express mRNA for the neuronal α3/α5/β2 or β4, α6/β2 or β4, and α7 and α9 pentamers, as well as the muscle type α1/β1/δ/ε nAChR. Previous data in the human airway examined small numbers of HBE cultures for a limited number of nAChR subunits. Maus et al.  used RT- [et al. . Our datInstead, our results suggest that the muscle-type nAChR present in HBE cells may have a functional role in HBE cells that has not previously been considered. The muscle-type receptor was more recently characterized than the neuronal type and previous literature never examined HBE cultures for the muscle-type receptor ,10,12. TSimilarly to HBE, mRNA and protein for nAChR subunits are commonly expressed by airway fibroblast cultures. Airway fibroblasts have never been examined for the presence of nicotinic receptors. Dermal fibroblasts have been shown to express mRNA for some receptors, although they were not examined for muscle-type receptors , and ginCalcium influx is a hallmark of the opening of the nAChR ion channel. An increase in intracellular calcium from the extracellular milieu can occur either by a direct influx of calcium through the nAChR channel, as occurs in α7 receptors, or by an influx of sodium that leads to depolarization of the cell and the opening of L-channels, as occurs after agonist binding to heteropentamer nAChR ,25. We dNicotine has previously been shown to affect signaling in human airway cells, and acetylcholine, the endogenous ligand, causes proliferation of HBE cells ,9. In ouOver the same time period, the MAPK family member p38 but not p42/44 is phosphorylated, a required step for activation. The activation of p38 is associated with regulation of apoptosis in response to cellular stress. The MAPK family kinases, such as p38, are not known to be directly activated by calcium; however there are several indirect pathways that lead to rapid phosphorylation of these pathways. These include signaling through the calcium/calmodulin-dependent protein kinases CaMKI, CaMKII, and CaMKIV as well as the calcium-activated signaling molecule PYK2.et al [This finding is in contrast to signaling activated by nicotine in airway fibroblasts. These cells phosphorylate p42/44 immediately upon treatment with nicotine. Like p38, p42/44 is not directly activated by calcium, but could be phosphorylated by calcium-activated signaling molecules. However, the pathways that lead to phosphorylation of different MAPK family members in the two cell types in response to nicotine have not been elucidated. It is possible that the nAChR type differences are responsible for the differential MAPK effects. For example, the α7 receptor is highly expressed on airway fibroblasts but not on HBE cells. The additional nAChR type on these fibroblasts may change the signaling pathways activated by cells in response to nicotine. This is consistent with a previous study by Jull et al . This stet al . A similFinally, we found that smoking had only modest effects on nAChR expression in the airway. Previous studies in the brain indicate that certain nAChR may be increased in frequency in smokers as compared to non-smokers , and a pIn contrast, there was a significant decrease in the frequency of the expression of the functional combination of subunits for the α3-containing receptors in airway fibroblasts of smokers and ex-smokers compared to never-smokers. This nAChR type is a sodium channel that undergoes inactivation upon long-term exposure to agonist, and, in airway fibroblasts, appears to be downregulated with long-term exposure to nicotine. This change in receptor expression remains even after exposure to nicotine ceases, as evidenced by the reduced frequency of expression in ex-smokers.We have shown that short-term cultures of normal airway fibroblasts as well as normal human bronchial epithelial cells from a number of different human donors consistently express functional nAChR and that these cell types differ in the type of nAChR they express. It is likely that the muscle-type nAChR plays a major role in the response of HBE cells to nicotine, and that the neuronal heteropentamers play a more minor role. Calcium influx as well as initiation of downstream signaling pathways indicate that receptors are functional and that both human bronchial epithelial cells and airway fibroblasts respond to nicotine, and those signaling responses may differ due to the difference nAChR present on the cell type. Together, these data suggest that exposure of the human airway to nicotine through tobacco smoke may have physiological consequences on airway homeostasis involving both the airway mucosa and the underlying submucosal mesenchymal cells. As such, nicotine may act to promote lung disease by acting to change cell growth and apoptosis. In airway fibroblasts this may leading to thickening of the airway wall seen in the pathogenesis of COPD. In the bronchial epithelium this may lead to preneoplasia or development of frank cancer.nAChR: nicotinic acetylcholine receptorHBE: human bronchial epithelial cellPKC: protein kinase CMAPK: mitogen activated protein kinaseBEGM: bronchial epithelial growth mediumEGF: epidermal growth factorP42/44: Extra-cellular signal regulated kinase isoforms 1 and 2DLC designed and performed the majority of the experiments and data analysis, and wrote the manuscript. TMH designed and performed RT-PCR with the assistance of MJS. JDL and NAC contributed the tissues that were grown into primary cultures and provided information on smoking history. AGD cultured the primary cells. JMS conceived of the study and participated in its design and coordination. All authors read and approved the manuscript.


DNA-directed synthesis represents a powerful new tool for molecular discovery. Its ultimate utility, however, hinges upon the diversity of chemical reactions that can be executed in the presence of unprotected DNA. We present a solid-phase reaction format that makes possible the use of standard organic reaction conditions and common reagents to facilitate chemical transformations on unprotected DNA supports. We demonstrate the feasibility of this strategy by comprehensively adapting solid-phase 9-fluorenylmethyoxycarbonyl–based peptide synthesis to be DNA-compatible, and we describe a set of tools for the adaptation of other chemistries. Efficient peptide coupling to DNA was observed for all 33 amino acids tested, and polypeptides as long as 12 amino acids were synthesized on DNA supports. Beyond the direct implications for synthesis of peptide–DNA conjugates, the methods described offer a general strategy for organic synthesis on unprotected DNA. Their employment can facilitate the generation of chemically diverse DNA-encoded molecular populations amenable to in vitro evolution and genetic manipulation. A method is presented that makes possible the use of standard organic reaction conditions and common reagents in the presence of unprotected DNA, an important step in enabling DNA-directed chemical synthesis and drug discovery A number of strategies have been proposed recently to enable the in vitro selection and evolution of chemical libraries . These nOne approach takes advantage of hybridization to induce proximity between reactants covalently attached to oligonucleotides. “Reading” is accomplished by the hybridization of the reactant conjugates to a DNA template, whereas synthetic execution results from the reactants being positioned closely together. The strategy has been demonstrated for several types of chemistries . HoweverRather than tailoring reactions to the narrow window of hybridization conditions, DNA reading and chemical transformation can be carried out in chronologically distinct steps . The DNAWe first had to choose a solid-phase material that exhibited several critical properties: reversible, efficient binding and release of unprotected DNA; robust solvent integrity; and resistance to chemical modification. The first requirement narrowed our focus to resins that noncovalently bind DNA. A number of resins were tested and excluded due to poor bind–release properties . Others exhibited extensive compression in organic solvent  or poor reswelling during organic to aqueous solvent transitions (Poros 50 HQ). Reverse-phase resins were excluded because they would presumably not retain DNA in many organic solvents.2O, methanol (MeOH), dimethyl sulfoxide, N,N-dimethylformamide (DMF), ethyl acetate, and dichloromethane. Lastly, Sepharose has been used previously as a material for solid-phase synthesis  assay, oligonucleotides were immobilized and eluted quantitatively in small volumes . Single-ynthesis . All subWe studied 9-fluorenylmethyoxycarbonyl (Fmoc)–based peptide synthesis because it has a well-established solid-phase precedent and offers a challenge in diverse chemical functionality A. As a DN-hydroxysuccinimide, 1-hydroxy-7-azabenzotriazole (HOAt), or N-hydroxybenzotriazole -3-ethylcarbodiimide hydrochloride (EDC) out-performed other activating reagents examined; other reagents gave poor coupling yields (benzotriazole-1-yl-oxy-tris-pyrrolidino-phosphonium hexafluorophosphate [PyBOP]), resulted in the formation of undesired side products , or led to poor recovery of DNA (dicyclohexylcarbodiimide [DCC] and diisopropylcarbodiimide [DIC]). Thirty-minute EDC coupling reactions were typically less than 50% efficient without the addition of acylation catalysts such as triazole . Of thesHOAt see . ImportaHOAt see , which mHOAt see .To examine whether the coupling conditions generalized to longer DNA fragments, we used an aminated 340-base ssDNA as the support. After coupling, the eluted amino acid–DNA conjugates were digested with nuclease P1, a 3′-to-5′ exonuclease that cleaves all but the 5′ phosphodiester bond of our ssDNA constructs. The 5′-terminal nucleotide, which maintains the linker and synthetic peptide product, was separated from the other nucleoside monophosphates by HPLC and verified by electrospray ionization mass spectrometry. Amino acid coupling to the 340-base ssDNA proceeded with efficiencies comparable to those observed with oligonucleotides (data not shown). In some cases, the sensitivity of this HPLC assay was increased using fluorescence detection. For these experiments, we synthesized a fluorescent lysine derivative, Fmoc-Lys(coumarin)-OH  esters, which are normally cleaved with trifluoroacetic acid, could be removed at pH 6.5 in an aqueous solution at 70 °C. This gentle condition offers a convenient approach for acid deprotection.The carboxylic acid side chains of aspartic and glutamic acid are usually protected as esters during Fmoc peptide synthesis. Sterically bulky esters are required to suppress piperidine-induced imide formation, which leads to undesired side chain peptide bonds. We discovered that the conventional tert-butyl ester deprotection did not proceed through intramolecular imide formation, we coupled either Fmoc-Asp(tBu)-OH or Fmoc-Asp-OtBu to Leu-NC20, followed by Fmoc-Phe-OH. The main and side chain isomers of these tripeptide–DNA conjugates were resolved by HPLC after removal of the t-butyl group. Interconversion of the isomers during deprotection was undetectable (less than 5%). When the experiment was repeated using a 10 mM NaOH solution for tert-butyl ester deprotection (where imide formation would be expected), interconversion of the side and main chain isomers was observed. These results indicate that the thermolytic deprotection maintains the regiochemistry of the initial peptide bonds. At this time, we have little other data that speak to the mechanism of deprotection.To verify that the thermolytic Protection of the primary amine side chain of lysine prevents the formation of branched peptides. 2-Nitrobenzenesulfonamide (nosylamide) protection was particularly attractive as a means for lysine protection because the protecting group is base-stable and removed under conditions known to be DNA-compatible . The lysArginine does not absolutely require side chain protection . Howevertert-butyl esters. However, the trityl group is not ideal in aqueous conditions because of its hydrophobicity and extreme lability at high temperatures. To offer a more robust solution, we sought an H2O-compatible histidine protecting group. The 2,4-dinitrophenyl group, widely used in Boc peptide synthesis, is more hydrophilic than trityl and is removed under the nosyl-deprotection conditions. Unfortunately, 2,4-dinitrophenyl is not stable to the piperidine used for Fmoc removal  group of His(Trt) is rapidly removed under the thermolytic conditions used to deprotect  removal . After tHistidine racemization is a well-recognized concern in peptide synthesis. To assay the extent of racemization occurring during histidine coupling, we synthesized the oligonucleotide–dipeptide conjugate His(CNP)-Ala-NC20 using either L- or D-Fmoc-His(CNP)-OH. The diasteriomeric dipeptide–oligonucleotide products were resolvable by reverse-phase HPLC. Neither L-His nor D-His coupling resulted in detectable racemization. The experiment was repeated using L- and D-Fmoc-His(Trt)-OH with the same result.tBu)-OH was employed as the protected form of cysteine. The tert-butyl thioether coupled efficiently (Fmoc-Cys(Siciently  and was n amino acids exceeded (0.9)n n . The HPLd (0.9)n , ruling Beyond the physical and chemical characterization of the peptide–oligonucleotide conjugates, it was important to examine their behavior in a biochemical setting. Thus, the [Leu]enkephalin pentapeptide was synthesized on an aminated 340-base ssDNA support, which was subsequently converted to duplex form. The conjugate exhibited a peptide-dependent electrophoretic mobility gel shift when incubated with the 3-E7 antibody , demonstDNA-directed synthesis requires “chemical translation” . Rather The proximity approach to chemical translation uses hybridization to induce proximity-driven chemical transformations. Because the DNA “reading” and chemical execution steps are simultaneous, reactions are necessarily performed in aqueous solutions with solute, pH, and temperature conditions that promote DNA–oligonucleotide hybridization. These conditions limit the generality, efficiency, and speed of possible organic transformations.2O, MeOH, ethanol, isopropanol, DMF, dimethyl sulfoxide, dichloromethane, and ethyl acetate (data not shown). We also carry out reactions at high temperatures. For example, difficult peptide couplings are facilitated with elevated temperature, and the BME-mediated deprotection of lysine, arginine, histidine, and cysteine is carried out at 60 °C. We have recently used microwave-assisted methods  submonomer synthesis on unprotected DNA (data not shown). The chemistry used for peptoid synthesis is entirely different from peptide chemistry, illustrating the generality of the strategy. The potential for adapting other chemistries is essentially limitless. Wittig reactions, azide reductions, 1,3 dipolar cycloadditions, reductive aminations, Heck couplings, and a wide variety of other useful chemical transformations have been carried out in the presence of unprotected DNA without modification of DNA . Nuclease P1 (#27–0852-01), DEAE Sepharose Fast Flow (#17–0709-01), and Medium Grade G-25 Sephadex (#17–0033-01) were purchased from Pharmacia-LKB Technology . Xba1 was purchased from New England Biolabs . DEAE Sepharose columns were poured in Empty TWIST synthesis columns . Kendall Monoject syringes  and a Promega  manifold with chemically resistant PFTE stopcocks  were used. All other chemical reagents or solvents were purchased from either Sigma-Aldrich  or Fisher Scientific International .Fmoc amino acids were purchased from Novabiochem , Chem-Impex International , or Fluka . EDC was purchased from Omega Chemical . 2N-X-AGCAGGCGAATTCGTAAGCC, where X represents a C12 linker (NC20) or a longer PEG linker (NP20). NC20 was synthesized using the Glen Research 5′-Amino-Modifier C12 (#10–1922). NP20 was synthesized using the Glen Research Spacer Phosphoramidite 18 (#10–1918) followed by the 5′-Amino-Modifier 5 (# 10–1905).The internal control 10-base oligonucleotide had the sequence CGGACTAGAG. The reactive 20-base oligonucleotides had the sequence H260nm ≈ 224.5 mM−1cm−1) and were not considered in efficiency and yield determination. Reaction products were collected, concentrated to approximately 50 μM using centrifugal evaporation, and desalted over G-25 Sephadex. A mixture of 1 μl of desalted oligonucleotide and 1 μl of a freshly prepared saturated matrix solution was spotted on a matrix-assisted laser desorption/ionization target and allowed to air dry before mass spectrometry analysis. The matrix solution was made from 250 μl of H2O, 250 μl of acetonitrile, 25 mg of THAP, and 10 mg of ammonium tartrate. Peptide sequences (five or more amino acids) were verified by Edman degradation peptide sequencing.Coupling reactions were monitored by HPLC mobility shift using a C18 analytical column  and UV detection at 260 nm and 280 nm . Linear gradients from 0%–90% acetonitrile in 100 mM triethylammonium acetate (pH 5.5) were employed. Coupling efficiencies  and yields  were determined by integration of elution peaks from the 260-nm channel. Chromophores added or removed during reactions cause changes in extinction coefficients less than the sensitivity (5%) of our HPLC assay . Yields were determined by integration of elution peaks from the 260-nm channel, using the P1 digestion product of unreacted starting material as a reference. Approximately 1 nmol of material was required for accurate UV detection. Products were collected, concentrated by centrifugal evaporation, and applied to a C18 SepPak cartridge  in 25 mM triethylammonium acetate (pH 5.5). The cartridge was washed with 3 ml of 25 mM triethylammonium acetate (pH 5.5) and 1 ml of H2O. The products were eluted with 1 ml of 50/50 MeCN/H2O, concentrated to 100 μl by centrifugal evaporation, and analyzed by electrospray ionization mass spectrometry. For coumarin-labeled products, fluorescence was monitored (320 nm excitation/380 nm emission) with a scanning fluorescence detector , and less than 50 pmol of material was necessary for accurate fluorescence detection.DEAE elute buffer containing the peptide–DNA conjugates was neutralized and brought to 100 mM sodium acetate (pH 5.2) and 400 μM ZnSO5/pixel). The intensity of full-length control and [Leu]enkephalin bands were similar to within 1% (S/N approximately 600). Upon peak integration along the entire lane, the full-length band represented a similar percentage of total intensity in the control (83%) and [Leu]enkephalin (81%) samples. The data suggest that, in the worst case, 3% of the DNA could have been modified during the course of peptide synthesis.A 5′-aminated 340-base ssDNA support was generated as described . After l2O followed by 12 ml of DEAE bind buffer (10 mM acetic acid and 0.005% Triton X-100) using a syringe or a syringe barrel, a male-male luer adapter, and a vacuum manifold  using a syringe. Long DNA molecules (340mers) were eluted with 4 ml of 1.5 M NaCl, 10 mM NaOH, and 0.005% Triton X-100 heated to 80 °C.2O, and 7.5 μl of diisopropylethylamine. Fmoc-Asn-OSu required four couplings rather than two to achieve quantitative yields.The following process was carried out twice. The column, with DNA bound, was washed with 3 ml of DMF. Using two syringes see , the colAfter the second amino acid incubation, the column was washed with 3 ml of DMF. Fmoc deprotection was carried out as follows: 3 ml of 20% piperidine in DMF was applied to a 3-ml syringe barrel attached to the top of the column. 1.5 ml was pushed through the column, followed by a 3-min incubation. An additional 1 ml was pushed through the column, followed by a 17-min incubation. The procedure was completed with a final 3-ml DMF wash.2O and 3 ml of MeOH and then incubated for 30 min at room temperature with a freshly prepared 500-μl solution of 50 mM Fmoc–amino acid-OH, 50 mM EDC, and 5 mM HOAt in MeOH. These conditions were derived directly from n amino acids exceeded (0.9)n. Fmoc deprotection was carried out as described for succinimidyl ester coupling. See Supporting Information for a more detailed description of peptide coupling.The column was washed with 500 μl of HtBu), and His(CNP), the column was washed with 3 ml of DMF and subsequently incubated for 30 min with 700 μl of DMF containing 500 mM BME and 250 mM DBU while submerged in a 60 °C H2O bath. The column was then washed with 3 ml of DMF and 12 ml of DEAE bind buffer. Lys(Ns) can also be deprotected quantitatively with a DMF solution containing 5% thiophenol and saturated K2CO3 at 37 °C for 90 min. These conditions deprotect Arg(Ns) inefficiently, and have not been tested for Cys(StBu) or His(CNP).For Lys(Ns), Arg(Ns), Cys(StBu), Glu(tBu), and His(Trt), after HPLC purification, the tert-butyl ester and/or trityl containing oligonucleotide–peptide hybrid was incubated in a 20-mM MgCl2 solution at 70 °C, yielding quantitative deprotection in 3 h  or 12 h (Glu). Deprotection can alternatively be carried out before HPLC purification: after eluting from the DEAE Sepharose column, NaOAc (pH 5.2) and MgCl2 were added to final concentrations of 30 mM and 200 mM, respectively, and the solution was then incubated at 70 °C for the appropriate time. In contrast, acid deprotection on solid support was inefficient.For Asp and after  peptide coupling. Calculated masses are noted to the left of the mass peaks.(149 KB PDF).Click here for additional data file.Figure S2(A) Reaction scheme for synthesis of GLFYG-NC20. Coupling efficiencies for individual steps are noted in black, and absolute yields from NC20 are noted in red. MALDI-MS results for all species are denoted under each species as “Observed .” See Protocol S1 for precise coupling procedures.(B) HPLC analysis of sequential couplings during peptide synthesis monitored at 260 nm. Load and elutes from columns 1–5 are indicated. Sequential coupling efficiencies (black) were calculated by integration of recovered aminated DNA peaks. Absolute yields (red) were calculated by integration of intended product peak relative to load. A nonaminated 10-base oligonucleotide (10mer) was included as a control for nonspecific DNA loss and modification. Percent recovery of 10mer is noted in red. The HPLC analysis employed a 60-min gradient of 0%–45% MeCN in100 mM TEAA (pH 5.5).(368 KB PDF).Click here for additional data file.Protocol S1(1.0 MB PDF).Click here for additional data file.