Several reviews of published cluster randomised trials have reported that about half did not take clustering into account in the analysis, which was thus incorrect and potentially misleading. In this paper I ask whether cluster randomised trials are increasing in both number and quality of reporting.British Medical Journal over 20 years.Computer search for papers on cluster randomised trials since 1980, hand search of trial reports published in selected volumes of the British Medical Journal contained more such reports than any other journal. In this journal there was a corresponding increase over time in the number of trials where subjects were randomised in clusters. In 2003 all reports showed awareness of the need to allow for clustering in the analysis. In 1993 and before clustering was ignored in most such trials.There has been a large increase in the numbers of methodological papers and of trial reports using the term 'cluster random' in recent years, with about equal numbers of each type of paper. The Cluster trials are becoming more frequent and reporting is of higher quality. Perhaps statistician pressure works. Cluster randomised trials are those where research subjects are not allocated to treatments independently, but as a group. For example, in a study of counselling patients on physical activity in general practice, practices were allocated to counselling or control and patients aged 40–79 years who attended during a five day period and who did not take regular exercise were invited to take part. Patients in the same practice received the same treatment, counselling or usual care, depending on how the practice was allocated.  The grouMembers of a cluster will be more like one another than they are like members of other clusters and we need to take this into account in the analysis, and preferably the design, of the study. Methods which ignore clustering may mislead, because they assume that all subjects provide independent observations. Applying simple statistical methods to such data, without taking the clustering into account, can lead to confidence intervals which are too narrow and P values which are too small.British Medical Journal Statistics Notes on this topic [There has been an increasing interest in cluster randomised trials over the past 20 years. For example, by the end of 2003 the is topic  had beenet al. [et al. [British Medical Journal website found that only 3 out of 36 ignored clustering. The review of the American Journal of Public Health and Preventive Medicine in 1998 – 2002 [There have been several reviews of published cluster randomised trials -13  that once we have carried out a study, the sample size calculations are not particularly informative. Analysis which ignores the clustering, however, can be highly misleading, finding significant differences where there are none. We may have incorrect conclusions in the literature, which are then uncritically repeated and become false knowledge.We should not be surprised that clustering is ignored. In the past, few textbooks have cautioned against this and the assumption of independence of observations is seldom stressed. Many statisticians will admit to having incorrectly ignored clustering in the analysis of clustered designs, including myself when I was younger and more ignorant than today. However, it can be very important.In this paper I attempt to chart the changes in both the number of cluster randomised trials reported and the proportion of these reports where clustering has been taken into account in the analysis.I first carried out a search on the ISI Web of Science, looking for papers on cluster randomisation and reports of trials. I classified these by type , year of publication, and journal.British Medical Journal. I identified and scanned all papers reporting trials for the years 1983, 1988, 1993, 1998, and 2003, recording any where subjects were allocated in clusters. I excluded any studies where subjects were not allocated to groups by the investigator, for example several comparisons of fund-holding and non-fund-holding general practices.To identify cluster randomised trials we have to read the papers. We cannot tell whether a trial is cluster randomised from title, keywords, or abstract. Many authors are not aware of the importance of clustering and do not mention it. In this paper I report the results of a hand search of the For each trial identified, I noted whether clustering had been taken into account in the analysis. There are several approaches which can be used to allow for clustering. The easiest is to calculate a summary statistic for each cluster.  This is I also noted whether ignoring the possible effects of clustering might have an important effect on the conclusions. Clustering may result in P values and confidence intervals which are sufficiently biased to have a major effect if any of the following are true: the cluster size is large, the number of clusters is small, or the intra-cluster correlation coefficient is large. Whether any of these applies in a trial which ignores clustering is a matter of judgement.Figure There are many potential biases. In the first part of the period, the Web of Science database did not include abstracts, so there was less opportunity to pick up the search terms. More recently, several journals began to include a description of the trial design in the title of the paper, for example 'Effect on hip fractures of increased use of hip protectors in nursing homes: cluster randomised controlled trial'.  This wilStatistics in Medicine and Statistical Methods in Medical Research on cluster randomisation, so there were a larger number of methodological papers than might be expected in those years. The numbers of papers found in the two categories were similar in each year before 2001: as many papers were about how to do such trials as were reports of actual trials. It is hard to believe that there are so few such trials being reported and it is likely that many have been reported without any acknowledgement of the importance of clustering.I divided the papers into those which were methodological, either educating researchers into the appropriate design and analysis of cluster randomised trials or developing new methods of analysing such trials, and those reporting actual trials. The data for 2000 and 2001 includes special issues of The Design and Analysis of Group-Randomized Trials.All the papers up to 1990 are due to Donner and his colleagues. ,15,16 HoBritish Medical Journal was the journal most frequently represented in the survey, no fewer than 43 of the 332 publications found, 13%, appearing there. The next was Statistics in Medicine with 39 publications (12%), all methodological, then the British Journal of General Practice with 17 (5%), Controlled Clinical Trials with 16 (5%), and Family Practice with 10 (3%). Figure British Medical Journal reflect the literature as a whole. The first paper in the BMJ was a report of a trial, [The a trial,  which waBritish Medical Journal had most reports and had been published for many years, this was the obvious journal with which to begin.When I first did the search reported in Figure BMJ papers in Figure BMJ Statistics Notes on clustering. [The results of the search are shown in Table stering. -72 analysis. The role of potential covariates was explored using linear regression specified as a two level model  using the software package MLn'. [2 tests. This was a trial of community based management in failure to thrive by babies. Thirty eight primary care teams were randomly allocated to intervention or control and all children identified in the practice were offered the same intervention, so clearly clustering should be taken into account.The noted query relates to a paper in which the authors stated that 'Univariate comparisons were calculated by t test and χge MLn'.  I could et al. [The trials which I regarded as failing to take the clustering into account were as follows. Russell et al.  investiget al. [Rink et al.  investiget al. [In a trial of clinical guidelines to improve general-practice management and referral of infertile couples, Emslie et al.  randomisThe trial where I judged ignoring clustering to be unimportant had many very small clusters. Wetsteyn and Degeus  comparedOnly five of the 18 trials had been found in the Web of Science search, showing that that was indeed an underestimate. However, the growth in numbers of trials is indicated by both electronic and hand searches.British Medical Journal. A hand search of the British Medical Journal has confirmed this increase, at least in this journal. Although the effects of clustering have often been ignored in trials, producing potentially misleading conclusions, the situation has certainly improved in the British Medical Journal. This has followed many articles on the topic in the Journal. Perhaps statistician pressure works.A bibliometric survey has suggested a rapid increase in the number of cluster randomised trials, many of which appeared in the et al. [et al. [British Medical Journal, is valid. Whether we would find a similar improvement in other journals is less certain. It is likely that reporting of cluster randomised trials in the British Medical Journal is especially good, as the journal reports many such trials, has carried many articles on their correct analysis and reporting, has a fairly rigorous statistical refereeing system, [BMJ's current statistical checklist [Identification of cluster allocation is subjective. I included one year, 1998, also searched by Puffer et al.  and iden [et al.  identifi system, -27 and ihecklist  does notThere are still many other aspects of trial reporting where improvement is possible ,12 but tCluster trials have become much more frequent since the mid 1990s. Reporting of these trials has improved and in the journal which publishes more than any other the quality had improved greatly. This improvement has followed a large number of articles advocating methods of analysis which take clustering into account, Perhaps statistician pressure works.British Medical Journal, including articles written for payment.The author has been published frequently in the J. M. Bland is the sole contributor.The pre-publication history for this paper can be accessed here:Papers in the survey List of papers in the survey of the BMJ, Word file.Click here for file


Rats were sacrificed 4 hours after LPS treatment.SP600125 or PD98059 inhibited LPS-induced phosphorylation of JNK and ERK, total protein and LDH activity in BAL fluid, and neutrophil influx into the lungs. In addition, these MAP kinase inhibitors substantially reduced LPS-induced production of inflammatory mediators, such as CINC, MMP-9, and nitric oxide. Inhibition of JNK correlated with suppression of NF-κB activation through downregulation of phosphorylation and degradation of IκB-α, while ERK inhibition only slightly influenced the NF-κB pathway.JNK and ERK play pivotal roles in LPS-induced acute lung injury. Therefore, inhibition of JNK or ERK activity has potential as an effective therapeutic strategy in interventions of inflammatory cascade-associated lung injury. This puediators ,3. Throuediators ,5. HowevCytokine-induced neutrophil chemoattractant (CINC) has been shown, in rodent models of lung injury, to play an important role in neutrophil migration into the lung . Matrix In vivo activation of NF-κB, but not other transcription factors, has also been demonstrated in alveolar macrophages from patients with acute respiratory distress syndrome (ARDS) [NF-κB activation is regulated by phosphorylation of the inhibitor protein, IκB-α, which dissociates from NF-κB in the cytoplasm. The active NF-κB can then translocate to the nucleus, where it binds to the NF-κB motif of a gene promoter and functions as a transcriptional regulator. e (ARDS) . Our pree (ARDS) . Therefo2-terminal activation domain of c-Jun, activating AP-1 by phosphorylating its component c-Jun. AP-1 can then translocate into the nucleus to promote transcription of downstream genes. However, action of MAP kinases on the upstream of NF-κB activation remains controversial [Once activated, MAP kinases appear to be capable of further signal transduction through kinase phosphorylation, as well as modulating phosphorylation of transcription factors -17. Actioversial -22. HereSpecific pathogen-free male Sprague-Dawley rats (280–300 g) were purchased from Daehan Biolink Co. (Eumsung-Gun, Chungbuk, Korea). The Animal Care Committee of the Ewha Medical Research Institute approved the experimental protocol. The rats were cared for and handled according to the National Institute of Health (NIH) Guide for the Care and Use of Laboratory Animals.Escherichia coli lipopolysaccharide, 055:B5, Sigma Chemical Co., St. Louis, MO) in 0.5 ml LPS-free saline; (3) an LPS-SP600125 group was injected with SP600125  1 hour before the IT instillation of 6 mg/kg body weight of LPS in 0.5 ml of LPS-free saline. (4) a saline-SP600125 group was injected with SP600125 1 hour before IT instillation of 0.5 ml of LPS-free saline (0.9 % NaCl); (5) an LPS-PD98059 group was injected with PD98059 (BIOMOL Research Laboratories, Plymouth, PA) 1 hour before IT instillation of 6 mg/kg body weight of LPS in 0.5 ml of LPS-free saline. (6) a saline-PD98059 group was injected with PD98059 1 hour before IT instillation of 0.5 ml of LPS-free saline (0.9 % NaCl). SP600125 or PD98059 was injected intraorally via a size 8 French feeding tube at a dose of 30 mg/kg body weight [Six groups of specific pathogen-free male Sprague-Dawley rats (280–300 g) were used: (1) controls received an intratracheal (IT) instillation of 0.5 ml of LPS-free saline (0.9 % NaCl); (2) an LPS-treated group received an IT instillation of 6 mg/kg body weight of LPS  for a total of 80 ml for each rat. The bronchoalveolar lavagate was centrifuged at 500 × g for 5 min at 4°C and cell pellets washed and resuspended in phosphate-buffered medium. Cell counts and differentials were determined using an electronic coulter counter with a cell sizing analyzer , as described by Lane and Mehta [Four hours after LPS treatment, the rats were sacrificed, and BAL was then performed through a tracheal cannula with aliquots of 8 ml each using ice-cold Cand Mehta . Red bloTo assess the permeability of the bronchoalveolar-capillary barrier, total protein was measured according to the method of Hartree , using bLung tissue homogenate samples (55 μg or 100 μg protein/lane for JNK, ERK, p38 MAP kinase, IκB-α and CINC) or aliquots of acellular BAL fluid (70 μl/lane for CINC and MMP-9) were separated on a 10% or 20% SDS-polyacrylamide gel. Separated proteins were electrophoretically transferred onto nitrocellulose paper and blocked for 1 hour at room temperature with Tris-buffered SAL containing 3% BSA. The membranes were then incubated with an anti-rabbit phospho-JNK/JNK antibody, anti-rabbit phospho-ERK/ERK, anti-rabbit phospho-p38 MAP kinase/p38 MAP kinase, antiserum against rat CINC, anti-human MMP-9 monoclonal antibody or anti-rabbit phospho-IκBα (Ser32)/IκBα at room temperature for 1 hour. Antibody labeling of protein bands was detected with enhanced chemiluminescence (ECL) reagents according to the supplier's protocol.6 alveolar macrophages, were added to 24-well plates (Costar, Cambridge, MA) and incubated at 37°C in a humidified atmosphere of 5% CO2 for 2 hours. The nonadherent cells were then removed, and adherent cells were counted and further incubated in 1 ml RPMI medium. After a 24 hour incubation, the supernatant was collected and filtered.The gelatinolytic activities in BAL fluid, or the supernatants of alveolar macrophage cultures, were determined using zymography with gelatin copolymerized with acrylamide in the gel according to previously published methods . To obta2 and 0.02% NaN3. The gels were then stained for 1 hour in 7.5% acetic acid/10% propanol-2 containing 0.5% Coomassie Brilliant Blue G250 and destained in same solution without dye. Positions of gelatinolytic activity are unstained on a darkly stained background. The clear bands on the zymograms were photographed on the negative (Polaroid's 665 film) and the signals were quantified by densitometric scanning using an UltroScan XL laser densitometer (LKB, Model 2222-020) to determine the intensity of MMP-9 activity as arbitrary densitometric units. To confirm MMP-9 activity, aliquots of BAL fluid were analyzed by Western blotting with anti-human MMP-9 monoclonal antibody, which was raised against MMP-9 secreted by human HT1080 fibrosarcoma cells [Aliquots of BAL fluid and the culture supernatants, normalized for equal volume (8 μl) or amount of protein (8 μg), were electrophoresed on a 10% SDS-PAGE gel with 0.1% gelatin as a substrate without boiling under non-reducing conditions. After removing SDS with 2.5% Triton X-100 for 2 hours, gels were incubated for 20 hours at 37°C in 50 mM Tris-Cl (pH 7.4) containing 10 mM CaClma cells  and crosma cells .in vitro and in vivo as an indicator of NO production [6 alveolar macrophages were added to 24-well plates (Costar, Cambridge, MA) and incubated at 37°C in a humidified atmosphere of 5% CO2 for 2 hours. The non-adherent cells were then removed by vigorous washing with two 1 ml of RPMI medium. After incubating the cells for 24 hours, the supernatant was collected and filtered.NO levels in the first aliquot of the acellular BAL fluid, and the supernatants of alveolar macrophage cultures, were measured using a nitrite assay. Direct measurement of NO is difficult due to the very short half-life . Howeveroduction . Briefly550) was measured using a microplate reader. Nitrite concentrations were calculated by comparison with OD550 of standard solutions of sodium nitrite prepared in cell culture medium. Data were presented as μM of nitrite.Nitrite was assayed after adding 100 μl Greiss reagent (1% sulfanilamide and 0.1% naphthylethylenediamide in 5% phosphoric acid) to 50 μl samples of BAL fluid and cell culture. Optical density at 550 nm  were harvested and then resuspended in hypotonic buffer A (100 mM HEPES, pH 7.9, 10 mM KCl, 0.1 M ethylenediaminetetraacetic acid [EDTA], 0.5 mM dithiothreitol [DTT], 1% Nonidet P-40, and 0.5 mM phenylmethylsulfonyl fluoride [PMSF]) for 10 min on ice, then vortexed for 10 s. Nuclei were pelleted by centrifugation at 12,000 rpm for 30 s. Nuclear extracts were also prepared from lung tissue by the modified method of Deryckere and Gannon [Nuclear extracts were prepared by a modified method of Sun et al. . Lavage d Gannon . Aliquot32P-labeled probe in binding buffer (4 mM HEPES, pH 7.9, 1 mM MgCl2, 0.5 mM DTT, 2% glycerol, and 20 mM NaCl), were incubated for 30 min at room temperature. The protein-DNA complexes were separated on 5% non-denaturing polyacrylamide gels in 1 × TBE buffer, and autoradiographed. Autoradiographic signals for activated NF-κB were quantitated by densitometric scanning using an UltroScan XL laser densitometer (LKB, Model 2222-020, Bromma, Sweden) to determine the intensity of each band.Binding reaction mixtures (10 μl), containing 5 μg (4 μl) nuclear extract protein, 2 μg poly (dI-dC)•poly (dI-dC) (Sigma Co., St. Louis. MO), and 40,000 cpm GGAATTTCCCTGGCC-3'), labeled with [α-32P]-dATP (Amersham, Buckinghamshire, UK), using DNA polymerase Klenow fragment (Life Technologies, Gaithersburg, MD). Cold competition was performed by adding 100 ng unlabeled double-stranded probe to the reaction mixture.The oligonucleotide used as a probe for EMSA was a double-stranded DNA fragment, containing the NF-κB consensus sequence . The BAL neutrophil counts in saline-kinase inhibitor animals were not significantly different from those of the saline control animals (p < 0.05).BAL cells were differentially analyzed, in order to evaluate the effects of these kinase inhibitors on LPS-induced neutrophil influx. As shown in Figure lane 2) and BAL fluid  from LPS animals was approximately 7- and 2.5-fold higher than in saline control animals, respectively. SP600125 or PD98059 significantly decreased the level of LPS-induced CINC expression, by 50 and 62%, respectively, in lung tissue  and, by 76 and 97%, respectively, in BAL fluid . These kinase inhibitors alone had little effect on CINC levels in the lung tissue and lavage fluid.CINC, MMP-9 and NO were chosen in our experiments as representative inflammatory mediators, because of their important roles in neutrophil influx and lung damage, and also because their gene regulation is dependent on NF-κB. Figure 4Cd Figure lane 2 frlane 2). In the supernatants from alveolar macrophage cultures of saline control animals, MMP-9 activity was also barely detectable, but was also markedly increased in the sample from LPS animals. SP600125 pretreatment significantly inhibited LPS-induced MMP-9 activity by 54% in BAL fluid, and by 30% in the supernatants from alveolar macrophage cultures . Similarly, PD98059 pretreatment significantly inhibited LPS-induced MMP-9 activity by approximately 67% in BAL fluid and the supernatants from alveolar macrophage cultures . MMP-9 activity was completely undetectable in the samples from saline-kinase inhibitor animals. The inhibitory effect of these kinase inhibitors on MMP-9 expression in BAL fluid was also observed .BAL fluid Figure  and 5D, 5Fin vivo exposure to LPS for 4 hours resulted in a 6.2-fold increase in NO level in BAL fluid, compared with the control animals. This increase was significantly inhibited by SP600125 or PD98059 . In LPS treated animals, nitrite production from alveolar macrophages cultured for 24 hours was increased 3.2-fold . This enhancement was significantly depressed  by a 1-hour pretreatment with SP600125 , whereas pretreatment with PD98059 did not inhibit LPS-induced activation of NF-κB (7C lane 3). Significant activation of NF-κB was also shown in alveolar macrophages from LPS-treated animals, compared to that seen in the saline control animals . SP600125 resulted in significant decreases (45%, p < 0.05) in LPS-induced NF-κB activation in alveolar macrophages . However, PD98059 did not inhibit LPS-induced NF-κB activation in alveolar macrophages . Saline or kinase inhibitors alone had little effect on NF-κB activation in lung tissue and alveolar macrophages.Figure 7C5 Figure lane 3, ws Figure lane 3. Hlane 5).The addition of the cold competitor eliminated the specific bands in the samples from LPS-treated animals, indicating that the band on the autoradiogram was specific for NF-κB binding Figure lane 5.lane 2), whereas these events were significantly inhibited by SP600125 (8A and 8C lane 3). PD98059, however, caused no significant changes in the LPS-induced phosphorylation and degradation of IκB-α . These kinase inhibitors, alone, had little effect on the phosphorylation and degradation of IκB-α.In order to investigate a possible mechanism underlying the actions of these kinase inhibitors on LPS induction of pathways leading to NF-κB activation, serine phosphorylation and degradation of IκB-α, in lung tissue from LPS and LPS-kinase inhibitor animals were analyzed by Western blotting with anti-phospho-IκB-α (serine 32), and anti-IκB-α Ab. As shown in Figure α Figure lane 3. Tin vivo relation between activation of JNK or ERK and LPS-induced acute lung injury; (2) the inhibition of JNK or ERK resulted in reductions of LPS-induced increases in lung injury parameters, such as total protein content and LDH activity in BAL fluid, neutrophil influx into the lungs, and proinflammatory gene products, such as CINC, MMP-9 and NO; and (3) the activation of JNK is involved in the LPS signaling pathway leading to NF-κB activation through phosphorylation of IκB-α and sequential degradation of IκB-α, whereas activation of ERK signaling is not associated with these NF-κB pathways.In the present study, we determined: (1) the in vitro experiments. Therefore, it is important to clarify the functions of these MAP kinases under pathological conditions. Furthermore, up to date, there have been no studies addressing the issues of the significance of JNK and ERK signaling in LPS-induced acute lung injury. Data from the present study clearly indicate that JNK and ERK were both activated after in vivo LPS exposure in models of acute lung injury. The degree of JNK and ERK activation from the baseline seems to be similar between these kinases, and to be generally higher than that of p38 MAP kinase (data not shown). The time course of JNK activation (progressive increase up to 24 hours after LPS treatment) appears to occur in parallel with those of the biochemical lung injury variables and neutrophil influx into the lungs after intratracheal treatment with LPS [in vivo data, Ishii et al. [JNK and ERK are known to play important roles as upstream regulators of the induced expression of inflammatory mediators in response to cytokines, stress, and cytoskeletal reorganization ,33. Howewith LPS . Consisti et al.  have repin vivo data with the specific JNK inhibitor are clearly consistent with an aggravating role of JNK in inflammation and tissue destruction. However, JNK1-/- mice have increased susceptibility to hyperoxia-induced lung injury [in vitro studies of primary monocytes and macrophage cell lines, ERK has been linked to production of MMP-9, and to IL-8 release in response to LPS [Helicobacter pylori [in vivo suppressed hyperoxia-induced cell death in lung tissues. ERK, however, also plays a protective role in myocardial ischemia/reperfusion injury [Since the increases in production or activity of CINC, MMP-9 and NO in BAL fluid were correlated with neutrophil influx and lung injury ,8,9,11, g injury . Throughe to LPS  or Helicr pylori . Consistr pylori  reportedn injury , and in n injury . Taken tin vivo activation of NF-κB in LPS-treated rats preceded the transcription of genes for proinflammatory mediators or lung inflammation and injury, i.e. the increases in neutrophil numbers, total protein, and LDH activity in BAL fluid [Helicobacter pylori [The activation of NF-κB has been associated with lung injury in LPS- and silica-treated rats ,40,41, aAL fluid . These rAL fluid ,44,45, wAL fluid  have repAL fluid ,47-51. Fr pylori . Consider pylori . FurtherData from this study suggest that JNK or ERK activation would play a detrimental role in LPS-induced acute lung injury. The inhibition of JNK or ERK should effectively block amplification of the LPS-induced catalytic cascade, through significant reductions in protein leakage, LDH release and neutrophil influx into the lung, and also in levels of CINC, MMP-9 and NO after LPS treatment. In addition, the inhibition of JNK activity, but not ERK activity, led to a decrease in NF-κB DNA binding activity through the suppression of phosphorylation and degradation of IκB-α. Based on these findings, we propose that inhibition of JNK or ERK may be an effective therapeutic strategy against the early stages of lung injury, via attenuation of the inflammatory cascade.2-terminal kinase, ERK; extracellular signal-regulated kinase, LPS; lipopolysaccharide, NF-κB; nuclear factor-kappa B, TNF; tumor necrosis factor, IL; interleukin, CINC; cytokine-induced neutrophil chemoattractant, MMP; matrix metalloproteinase, ECM; extracellular matrix NO; nitric oxide, ARDS; acute respiratory distress syndrome, IT; intratracheal, LDH; lactate dehydrogense, BAL; bronchoalveolar lavage, FBS; fetal bovine serum, DMEM; Dulbecco's modified Eagle's medium, EMSA; Electrophoretic Mobility Shift Assay, NEMO; NF-κB essential modulator (NEMO), IKK;IκB-α kinase, AP; activator protein, CREB; cyclic adenosine 5'-monophosphate response element-binding proteinMAP; mitogen-activated protein, JNK; c-Jun NHHSL, HJK and CSK carried out animal studies. HSL performed the statistical analysis. YHC participated in the zymographic analysis. JLK conceived of the study, participated in its design and coordination, and drafted and edited the manuscript. All authors read and approved the final manuscript.


Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold recognition. We propose a new automated method, EvDTree, based on a decision tree algorithm, for automatic derivation of amino acid substitution probabilities from a set of sequence-structure alignments. The main advantage over other approaches is an unbiased automatic selection of the most informative structural descriptors and associated values or thresholds. This feature allows automatic derivation of structure-dependent substitution scores for any specific set of structures, without the need to empirically determine best descriptors and parameters.Decision trees for residue substitutions were constructed for each residue type from sequence-structure alignments extracted from the HOMSTRAD database. For each tree cluster, environment-dependent substitution profiles were derived. The resulting structure-dependent substitution scores were assessed using a criterion based on the mean ranking of observed substitution among all possible substitutions and in sequence-structure alignments. The automatically built EvDTree substitution scores provide significantly better results than conventional matrices and similar or slightly better results than other structure-dependent matrices. EvDTree has been applied to small disulfide-rich proteins as a test case to automatically derive specific substitutions scores providing better results than non-specific substitution scores. Analyses of the decision tree classifications provide useful information on the relative importance of different structural descriptors.We propose a fully automatic method for the classification of structural environments and inference of structure-dependent substitution profiles. We show that this approach is more accurate than existing methods for various applications. The easy adaptation of EvDTree to any specific data set opens the way for class-specific structure-dependent substitution scores which can be used in threading-based remote homology searches. With the sequencing of entire genomes and the exponential growth of sequence databases on the one hand, and the significant number of known folds compared to the putative number of possible folds in the fold space on the other hand, sequence-structure comparison is currently one main challenge of the post-genomic era. To this goal, 3D-environments were used by Eisenberg and coll. in the early 90s to build statistical potentials indicating the probability of finding each amino acid in a given structural environment as described by the secondary structure, the solvent accessibility and the polarity of neighboring atoms . Such stStatistical potentials describing the propensity of a residue pair to be at a given spatial distance have proved successful as well -14, but In the first part of the work we focus on automatically building and evaluating structure-dependent substitution scores. The emphasis is given to the development of a method for automatic selection of the most informative classifications of 3D environments in order to set up a versatile method allowing easy compilation of structure-dependent substitution scores for any given set of proteins. In a second part, the method is applied to a specific protein class, the small disulfide-rich proteins.Decision trees have attracted our attention for several reasons. Knowledge acquisition and description language are optimized automatically and do not require human expertise during the learning process. Thanks to the hierarchical organization of the inferred trees, classifications are obtained quickly and the chain of decisions leading to the prediction is explicit and can be easily interpreted . Decision tree learning algorithms are also robust since they partition the data recursively. The first dichotomies near the tree root are then based on many examples and are therefore statistically reliable. To handle noise and uncertainty, the trees can be pruned during a post-processing step to remove possible misleading clusters at the bottom of the tree. The research field on this topic is well-established and the number of applications of decision trees to real world is huge.Standard substitution matrices are deduced from multiple sequence alignments of similar sequences -22. To dStandard structure-dependent substitution matrices report the probability of 20 × 20 or 21 × 21 possible substitutions in a given structural environment . In thisSeveral data sets of structure-structure superimpositions and the corresponding alignments are available -25. We h- Residues involved in a domain-domain or chain/chain interface, were excluded. Residues are considered to be involved in an interface when their solvent accessibility varies by more than 7 % when comparing the protein environment in the complex and in the isolated chain/domain. The cut-off value was taken from Mizuguchi et al , who use- Residues that are not correctly superimposed in the structural superimposition were also excluded. The superimposition was considered good enough when the deviation between the two corresponding alpha carbons is below 3.5 Å. We assume that larger deviations may correspond to incorrect structural superimposition for the particular residue even though other residues are correctly aligned. Large deviations may also imply significant modifications in the 3D-environment. Although this 3.5 Å criterion is sometimes too restrictive, it actually leaves enough data for robust statistical estimations while removing most of aligned amino acid pairs whose respective structural contexts are not superposable.Application of the two above filters excluded about 20% of the initial substitutions leaving about 2 million substitutions for the learning process. This data set was split into (i) a learning data set containing 950 000 substitutions similar to the learning set used by Mizuguchi et al. (ii) a pruning data set containing 325 000 substitutions, and (iii) a test data set containing 355 000 substitutions. The learning data set has been in some cases filtered further based on the percentage of sequence identity between superimposed proteins, resulting in smaller sets of 500 000 (0–40% id) or 700 000 (0–60% id) substitutions, respectively.- Since we only work with three-dimensional structures, the oxidation state of any cysteine (free or disulfide bridged) is known. The symbol 'C' refers to disulfide bridged cysteines , whereas the symbol 'J' was used for free cysteines.10 helices (G), π-helices (I), isolated bridges (B), extended conformations (E), turns (T) and coils (C), respectively. We also used a simpler 3-state description (ss2) deduced from the STRIDE assignment: ss2 = 1, 2 or 3 for helices (H or G), sheets (B or E) and coils (I, T, or C), respectively.Since the decision tree algorithm is able to automatically select the most discriminating structural descriptor at each classification step (see below) we do not need to empirically determine the 'best' descriptors. In this work, twenty-three structural descriptors were provided to the classification algorithm. The secondary structure (ss1) was assigned to each residue according to STRIDE  into sevHydrogen bonds were determined using the Hbond software . Four dii - Cαi+3, d4 = Cαi - Cαi+4, d5 = Cαi - Cαi+5, d6 = Cαi - Cαi+6, d7 = Cαi - Cαi+7. Other descriptors were the buried surface area (bur), percent of accessibility (pac), contact area with carbon atoms (C), nitrogen atoms (N), oxygen atoms (O), sulfur atoms (S), positively charged atoms (pp), negatively charged atoms (nn), or polar atoms (pol).Other structural parameters were obtained using the local program compilPDB [J.G.]. Beside the secondary structure, the local structure was also described by the Phi and Psi dihedral angles, and by Cα-Cα distances: d3 = Cαs1 to s23.For simplicity, these structural descriptors will now be called It should be noted that some structural descriptors are correlated . However, this descriptive redundancy is not a problem since it is eliminated during the tree construction where the most informative descriptors only are selected, as explained below.a(k), s(k)) the position k in a protein for which we want to score substitutions, a(k) the residue type and s(k) = (s1(k),...,s23(k)) the structural environment description at this position. After the learning phase explained below, each tree node will be associated to particular structural descriptor sj and threshold S and will be linked by edges to two subnodes whose structural environments will be constrained respectively by the tests sj≤S and sj>S. The classification of (a(k), s(k)) will be obtained by selecting the decision tree corresponding to residue type a(k) and then by running through the tree from its root node to an appropriate leaf following at each node the edge whose test, sj≤S or sj>S, is compatible with the value of the corresponding structural descriptor sj(k) .The native structural environments observed in the learning data set were classified for each of the twenty amino acids, plus the half-cystine, resulting in twenty-one independent decision trees Figures  and 2. TAccording to the standard data mining terminology, the predictive variables are therefore the native amino acid type and its associated structural descriptors and the dependent variable to be predicted is the substituted residue at this position. During the learning phase which we will now describe, the goal of the decision tree construction is to optimize the predictive power of the structural descriptor test chosen at each node and therefore to maximize the bias of the statistical distributions of the substituted residues associated to each subnode towards a few types of amino acids. Ideally, tree leaves should be associated to only one type of substituted amino acid, but this never happens in practice because of the tree depth limitation and the data set noise.a(i), s(i), b(i)) be the i-th example of the whole learning data set where a(i) is a native residue, s(i) = (s1(i),...,s23(i)) is its structural environment description and b(i) is the substituted residue as observed in a structurally similar protein at the same position as a(i). The main steps of the decision tree construction from the learning data set are as follows  = A } grouping all examples with native residue type A.1. The decision tree for a given residue type c do :2. For each tree cluster sj and each associated threshold S that creates possible dichotomies of c into two subclusters c1 and c2. If sj has continuous values, 9 possible thresholds S are chosen to create dichotomies c1 = {i∈c/sj(i)≤S} and c2 = {i∈c/sj(i)>S} corresponding to the 10th, 20th, ..., and 90th percentiles of the statistical distribution of the considered descriptor. If sj is restricted to a few discrete categories, all possible dichotomies c1 = {i∈c/sj(i)==S} and c2 = {i∈c/ sj(i)! = S} are created, where S is one of each possible value of sj.a. Test in turn each descriptor b. Select the optimal dichotomy from previous step which satisfies the tree constraints (see section (i) below) and minimizes the chosen splitting criterion (see section (ii) below).c1 and c2 as nodes in the tree by linking them to cluster c with respective edges labeled {sj(i)≤S} and {sj(i)>S} or {sj(i) == S} and {sj(i)! = S}. The structural environment associated to a particular cluster will be defined by all edge labels from the tree root to the considered tree node or leaf  below).It should be noted the choice of the optimal descriptor at a given tree level will depend on both the amino acid identity of the native residue and each structural descriptor previously chosen as splitting criteria along the tree path that leads to the considered node.Main parameters in the classification are (i) the tree constraints, (ii) the splitting criterion, and (iii) the tree pruning method.- Tree depth: as the learning process goes deeper in the tree, more and more specific clusters are created. Beyond a certain depth, the chance that the corresponding rules can be applied to new examples outside the learning set drops significantly, resulting in an overfitting of the available data since deep clusters won't have enough associated examples to derive statistically significant distributions. Therefore, to avoid wasting time to partition the data into smaller and smaller clusters, maximum tree depths of 2 to 6 were tested.- Cluster cardinal: For the same reason as above, a minimum cardinal of examples was required for each cluster. We tested values between 200 and 1200 with increments of 200.simcc measuring the cluster cardinal similarity between two subclusters obtained by splitting :- Tree balancing: A restriction on uneven distributions of samples among two clusters from the same parent was applied to prevent the creation of unbalanced trees which would require higher depth to fully partition the data. This restriction is achieved by the parameter n1 is the cardinal of the subcluster 1 and n2 the cardinal of the subcluster 2.where, - The Gini criterion evaluates the probability that two randomly selected elements in a cluster correspond to two different types of residues :P(a|c) is the relative frequency of residue type a in cluster c.where To evaluate the quality of a given segmentation into several clusters, the splitting criterion is given bync is the number of elements in the cluster c and n is the total number of elements in all clusters.where - The Shannon entropy  tries toP(a|c) is the relative frequency of residue type a in cluster c.where MR. Each class (residue type) in a cluster is ranked according to the number of elements of this class in the cluster (rank 1 is assigned to the most frequent residue type and rank 21 to the least frequent one). The mean rank MR evaluates the mean rank for a randomly selected element in the cluster. Low MR indicates clusters with only few well represented classes. Such clusters would correspond to structural environments that induce significant bias in the sequence and therefore strong structural constraints.- We also used a specifically developed splitting criterion called the "mean rank" R(a|c) and P(a|c) are the frequency rank and probability of the residue type a in the cluster c.where - The Pessimistic Error Pruning (PEP)  consistsN(c) is the number of examples assigned to the cluster c and n(c) is the number of occurrences of the most frequent amino acid in the cluster c. Let C be the father cluster from which c is derived in the tree, then c and its subtree will be removed if E(c)≥E(C).where c will be removed if R(c)<R(C) where R(c) and R(C) are respectively the mean ranks of the current cluster c and of its father cluster C averaged over the pruning test set.- The Mean Rank Pruning (MRP) has a principle similar to PEP, except that c will now be removed if R(c)+ σ t80<R(C), where σ is the mean rank standard deviation over the pruning test set and the scaling factor t80 = 1.82 corresponds to a 80% confidence level for a Gaussian distribution.- The pessimistic Mean Rank Pruning (PRP) is a more stringent version of MRP using a confidence margin to prevent statistically biased clusters to be kept in the tree. The current cluster Nf inversely proportional to the total number of residues Nf in each protein family f of the learning data set. This insures that all structural families have similar importance in the derivation of the substitution probabilities.- A mutation weight α = 1/ide inversely proportional to the percentage of identity ide between the two considered proteins. If ide<25%, then the mutation weight is decreased to 1. This reduces the importance of substitutions observed in similar sequences and could be used later to specialize EvDTree on different kinds of applications involving different sequence similarities.- A mutation weight β = 25/Once trees have been constructed, statistical distributions of observed substitutions in each cluster are used to compute cluster-specific environment-dependent substitution profiles. As explained previously, the structural environment associated to a particular cluster will be defined by all edge labels from the tree root to the considered tree node or leaf.a in the 3D environment s to be substituted by amino acid b isThe probability for the amino acid  is the number of observed substitutions of amino acid a by amino acid x in the 3D environment s.where Q(b|a,s) are then calculated asSmoothed probabilities A(b|a, s) is the a priori distribution of Topham et al. [where m et al. . Relativ is the total number of occurrences of amino acid a in 3D environment s, n is the number of classes (21 in this case), and σ is a normalization constant. We used the value of 5 previously used by Topham et al. [ and is therefore maximum for undersampled substitutions.where m et al. . It shouThen the log odds scores are calculated asP(b) is the background probability of occurrence of amino acid b in the whole database.where These log-odds are calculated for each node cluster of each native amino acid tree.a(k), s(k), b(k)) from the test data set is classified by the tree corresponding to residue type a(k). Then the tree leaf corresponding to the structural environment s(k) is searched and its associated log-odds substitution scores are finally used to score the substituted residue b(k).To evaluate the EvDTree scoring function, each example of associated native residue, structural environment, substituted residue (MR) as the criterion to evaluate the quality of scoring functions. For each example in the test data set, the 20 possible substitutions are scored as indicated above, and the observed  substitution b(k) is ranked according to its score among all other possible substitutions. The mean rank over all examples in the test data set is indicative of how well the scoring function is able to recognize as probable the "real" substitutions. A MR of 1 would mean that the scoring function always gave the better score to the observed substitution. At the opposite, a MR of 10.5 would indicate that observed substitutions are scored randomly. The main advantage of the mean rank criterion is that it is fast to calculate and it is independent from the absolute values of the scores, therefore allowing comparisons between very different scoring functions. Similar criteria based on ranking were previously used to evaluate 1D-3D scoring functions [To compare the EvDTree substitution scores with other scoring methods, we have used the mean rank  and gap extension (Ge) penalties were optimized by comparing the alignments for several penalty combinations .To complement the MR) criterion as explained in Methods. Due to the amount of data, CPU time limitations did not allow systematic examination of all parameter combinations, and the best parameters were determined through a limited trial and error protocol.Several learning data sets were compiled by filtering out observed protein substitutions Figure  with seqMR < 6.89) showing that the method is robust and is not critically affected by slight modifications of the parameters. The lowest MR (6.74) was obtained with the following protocol:The variation of the mean rank over sixteen different runs remained limited - No weighting is done in relation to the sequence identity .For each residue type, the maximal tree depth used was 6, leading to a maximal number of leaves 21 × 64 = 1344, each of them potentially leading to a substitution profile that corresponds to one line in a classical substitution matrix. In other words, the total amount of data corresponds to 64 distinct substitution matrices, although it is not possible to associate substitution profiles to matrices since each profile corresponds to a different structural environment. The overall amount of data is nevertheless, in principle, comparable to the 64 environment-dependent substitution matrices used in the FUGUE system. However, the tree pruning step removed a significant number of clusters that do not afford improved information, leaving only 111 environments each associated to a specific substitution profile  are displayed in Figure On the other hand, the substitution profiles for leucines in different structural contexts also display significant differences Figure . Thus, sThe detailed impact of structural information for correct prediction of substitution probabilities can be approached by comparing the EvDTree substitution profiles with the evolutionary substitutions matrices GONNET  and BLOSThe results in Table More surprisingly, five residues (Gly, Met, Pro, Gln, and Thr) and the free cysteine (J) do not display evaluation improvement by using structural information (compare EvDTree0 and EvDTree). The latter remark means that for these five residues no structural descriptor permitted efficient splitting of the data. It is likely that for these residues new descriptors or descriptor combinations remain to be discovered. Nevertheless, on average, the structural information significantly improves the performance and EvDTree appears as a clearly better scoring function than evolutionary matrices in evaluation of sequence-structure alignments.Comparison with structure-dependent substitution matrices obtained by other groups is not as simple as for standard substitution matrices, because we must make sure that we compute the structural environment exactly the same way that was used for generating the matrices. We were able to compare EvDTree with FUGUE, thanks to the FUGUE accessory programs kindly provided by Dr K. Mizuguchi.As shown in Table Go = 2, 5, 10, 15, 20; Ge = 2, 5, 10, 15, 20). The results shown in Figure Although the Mean Rank test determines the ability of a scoring function to correctly evaluate structure-compatible sequences, it does not determine how well a scoring function would actually perform in a particular application, e.g. fold recognition. In this paper, we do not focus on a particular application, but rather on the method to automatically derivate structure-dependent substitution profiles, and the Mean Rank appears as a simple and efficient criterion to rapidly compare different learning parameterizations or scoring functions. To verify that the theoretical evaluations using the Mean Rank have some significance for future real applications, we compared EvDTree with other methods used as scoring functions in sequence-structure alignments. The percent of correctly aligned positions for different methods and for different sequence identity ranges is displayed in Figure The strongest potential of EvDTree is its ability to adapt itself to any particular set of structures. As a test case, we have applied EvDTree to small disulfide-rich proteins. Small disulfide-rich proteins display several peculiar structural features: (i) due to their small size, a larger number of residues than usual are solvent-exposed, (ii) regular secondary structures are limited and the content in turns and loops is high, (iii) the hydrophobic core is largely constituted by the disulfide bridges that are responsible for the high stability despite the small size, and (iv) glycine, proline, and, of course, cysteine residues are more frequent than usual. With all these peculiarities, small disulfide-rich proteins are not well-suited to standard prediction methods and it has been shown that these proteins score poorly using the standard PROCHECK database . EvaluatThus, to test the ability of EvDTree to automatically adapt itself to a class of proteins with specific structural features, we have computed disulfide-rich specific substitution profiles (EvDTreeDS). For this, we have compiled a specific data set from the structural class "small disulfide" in the HOMSTRAD database. These data were complemented by data extracted from the KNOTTIN database . The numMean rank evaluations of the new, specific, EvDTreeDS substitution scores on small disulfide-rich proteins are reported in Table Furthermore, the comparison of the structural descriptors used in the first partition of root clusters in EvDTree and EvDTreeDS classifications highlights interesting differences Table . First, Table More surprisingly, we also notice that charged and polar residues (Asn, Asp, Glu, Lys, Arg) display the opposite switch, i.e. a secondary structure descriptor is used in EvDTree but a solvent accessibility descriptor is used in EvDTreeDS. Analysis of the EvDTreeDS classification suggests that these residues, when in more buried positions, are far more conserved than when in more exposed positions. We think that beside the disulfide bridge core, additional elements of stability often occur through specific hydrogen bonding networks between charged or polar residues and the backbone, rather than through hydrophobic packing. Typical examples of this are the conserved glutamic acid in position 3 of cyclotides , or the All these subtle modifications revealed by the EvDTree and EvDTreeDS classifications suggest that there is probably no universally optimal description language and that the choices and partitions of structural descriptors should be adapted to the class of proteins considered. Our new decision tree learning algorithm makes this fine tuning automatically from scratch whereas classical potentials are based on globally optimized description languages which may become suboptimal in specific contexts.et al., 2001). Interestingly, the tree-pruning step removed a significant number of structural clusters yielding an average tree depth of 4 instead of the six allowed levels. This is an indication that clusters at higher levels corresponded to the learning of specific sequence-structure relationships that could not be generalized (Figure We have described a new method, EvDTree, based on decision tree classification of structural environments to automatically construct structure-dependent substitution profiles from a set of sequence-structure alignments. The EvDTree method was shown to perform similarly to the successful environment-dependent substitution matrices used in FUGUE (Shi d Figure . It may In this work, we were interested in the development of a fully automatic method for the classification of structural environments and inference of structure-dependent substitution profiles. The evaluation of the intrinsic performance of the substitution profiles was primarily done on known sequence-structure alignments, using the mean rank of observed substitutions. We have shown that in this context, the EvDTree substitution profiles perform slightly better than other successful substitution matrices, and as such, the EvDTree matrices constitutes interesting elementary data for various applications. Moreover, comparison of the EvDTree substitution scores with other scoring functions for sequence-structure alignments led to results similar to the mean rank evaluation supporting the usefulness of the latter criterion.One specific strength of the EvDTree method is its easy automatic adaptation to any specific data set. Here we have shown that it is possible to obtain structure-dependent substitution profiles specific of small disulfide-rich proteins with better predictive power than standard substitution scores. This approach could be easily extended to other specific protein classes such as coil-coils, membrane proteins, etc. as soon as enough structures are available for learning. Fold-specific substitution matrices have recently been proposed for protein classification . The EvDThe fact that, as stated above, the structural information did not yield better prediction for several residues in the EvDTree approach Table  suggestsAfter an initial program from JG, JCG coded a new version of the software to incorporate a novel algorithm. All methods were implemented and tested by JCG. The whole work was conceived by JCG, JG and LC and was supervised by LC and JG. All authors read and approved the final manuscript.


By the dawn of the third millennium, non communicable diseases are sweeping the entire globe, with an increasing trend in developing countries where, the transition imposes more constraints to deal with the double burden of infective and non-infective diseases in a poor environment characterised by ill-health systems. By 2020, it is predicted that these diseases will be causing seven out of every 10 deaths in developing countries. Many of the non communicable diseases can be prevented by tackling associated risk factors.Data from national registries and international organisms are collected, compared and analyzed. The focus is made on the growing burden of non communicable diseases in developing countries.Among non communicable diseases, special attention is devoted to cardiovascular diseases, diabetes, cancer and chronic pulmonary diseases. Their burden is affecting countries worldwide but with a growing trend in developing countries. Preventive strategies must take into account the growing trend of risk factors correlated to these diseases.Non communicable diseases are more and more prevalent in developing countries where they double the burden of infective diseases. If the present trend is maintained, the health systems in low-and middle-income countries will be unable to support the burden of disease. Prominent causes for heart disease, diabetes, cancer and pulmonary diseases can be prevented but urgent (preventive) actions are needed and efficient strategies should deal seriously with risk factors like smoking, alcohol, physical inactivity and western diet. For centuries, communicable diseases were the main causes of death around the world. Life expectancy was often limited by uncontrolled epidemics. After the second World War, with medical research achievements in terms of vaccination, antibiotics and improvement of life conditions, non communicable diseases(NCDs) started causing major problems in industrialized countries. Heart diseases, cancer, diabetes, chronic pulmonary and mental diseases became a real burden for health systems in developed countries. For a while, these diseases were associated with economic development and so called diseases of the rich. Then, by the dawn of the third millennium, NCDs appeared sweeping the entire globe, with an increasing trend in developing countries Table  where, tEfficient (preventive)strategies are needed and urgent measures should be taken to control risk factors like tobacco, alcohol, obesity, blood pressure diet and inactivity. Otherwise, developing countries will be unable to provide their people with standard health care.The costly and prolonged treatment of NCDs raises the equity problem between and within countries. As expressed by the WHO Director-General in his overview to the annual report, If a JaIn previous papers, the authors proposed mathematical models dealing with the burden of diabetes and its complications, DynamicDifferent methods can be considered to quantify the burden of NCDs. In order to overcome the specific problems of each country, the most used method is the approach that measures the global burden of NCDs in terms of Disability Adjusted Life Years (DALYs) which is a combination of Years of Life Lost(YLL) through premature death, and Years Lived with Disability(YLD). Thus, DALY is thought of as one lost year of healthy life -9]. For Data from national registries and international sources are collected, compared and analyzed in order to show the trend of NCDs. Four diseases or cluster of diseases(Cardio-Vascular Diseases(CVDs), diabetes, cancers and chronic respiratory diseases) are considered to illustrate the growing burden of NCDs in developing countries. The main sources of data are the annual reports and regular publications released by the World Health Organization(WHO), World Heart Federation(WHF), Pan American Health Organization(PAHO), International Diabetes Federation (IDF), International Agency for Research on Cancer(IARC), Centre for Chronic Disease Prevention and Control(CCDPC), International Task Force for Prevention of Coronary Heart Disease and a multitude of websites and papers dealing with NCDs. The literature associated with these diseases in developed countries is abundant. However, despite the encouraging programmes and joint projects proposed by WHO and other organisms in the form of collaborative research agreements to developing countries, in order to support national registries, unreliable and insufficient data are still prevailing in most of these countries. Moreover, the release of health data is shadowed by the security vision in some countries. Extrapolations are needed in the case of missing or incomplete data. Consequently, more efforts are needed to convince health decision makers in low- and middle-income countries of the necessity to develop epidemiological studies that allow for preventive strategies making health policy at the centre of sustainable development.According the World Health Organization's statistics, chronic NCDs such CVDs, diabetes, cancers, obesity and respiratory diseases, account for about 60% of the 56.5 million deaths each year and almost half of the global burden of disease. In 1990, 47% of all mortality related to NCDs was in developing countries, as was 85% of the global burden of disease and 86% of the DALYs attributable to CVDs. An increasing burden will be born mostly by these countries in the next two decades. The socio-economic transition and the ageing trend of population in developing countries will induce further demands and exacerbate the burden of NCDs in these countries. If the present trend is maintained, it is predicted that, by 2020, NCDs will account for about 70 percent of the global burden of disease, causing seven out of every 10 deaths in developing countries, compared with less than half today.In 1990, approximately 1.3 billion DALYs were lost as a result of new cases of disease and injury, with the major part in developing countries. In 2002, these countries supported 80% of the global YLDs due to the double burden of communicable and non communicable diseases. Consequently, their people are not only facing higher risk of premature life(lower life expectancy) but also living a higher part of their life in poor health[CVD is the name for the group of disorders of the heart and blood vessels and include hypertension (high blood pressure), coronary heart disease (heart attack), cerebrovascular disease (stroke), peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease and cardiomyopathies. These diseases constitute the major contributor among NCDs Table .Worldwide, an estimated 17 million people die of these diseases, particularly heart attacks and strokes, every year. Once associated with industrialized countries, CVDs are now emerging or rapidely increasing in developing countries. Indeed, in 1998, 86% of the DALYs caused by CVDs were attributed to developing countries and in 1999 CVDs contributed to a third of global deaths with 78% in low- and middle-income countries. The trend is increasing, indicating that by the year 2010 CVDs will be the leading cause of death in developing countries as a consequence of lifestyle changes brought about by industrialization and urbanization in developing countries engaged in the socio-economic transition. CVDs are promoted by risk factors like tobacco use, alcohol, physical inactivity and unhealthy diet. Unfortunately, the harm caused by these risk factors affects the rise of life expectancy in developing countries,12.The costly and prolonged care of CVDs in low-and middle-income countries often divert the scarce family and societal resources to medical care. Consequently, the lower socio-economic groups have greater prevalence of risk factors, higher incidence of disease and higher mortality.The recent statistics released by the World Health Organization and the International Diabetes Federation are alarming,12Table. The numStudies in different countries have shown that diabetes is a costly disease accounting for between 2.5 and 15% of the total healthcare expenditure. For theAccording to the National Institute of Diabetes and Digestive Kidney Disease(NIDDK) and the American Diabetes Association, diabetes was the sixth leading cause of death in 1999 with a direct cost of $44 billion and an indirect cost of $54 billion annually. In 2002, the direct and indirect cost totalled $132 billion.In France, an estimation of $5.7 billion was given for the direct cost of diabetes, whereas, an equivalent cost of 5.2 billion, representing approximately 9% of the annual NHS budget, was given for UK in 2000.The burden affects more and more developing countries as stressed by the different authors who attended the seventh congress of the Pan-African diabetes study group in 2001 and the Cancer is now a major cause of mortality throughout the world Table . In the Between 2000 and 2020, the total number of cases of cancer in the developed world is predicted to increase by 29% whereas, in developing countries an increase by 73% is expected (largely as a result of an increase in the number of old people and as a result of urbanization and change in dietary habits).The incidence of cancers of the lung, colon and rectum, breast and prostate generally increases in parallel with economic development, while the incidence of stomach cancer usually declines with development.This is currently the most common cancer in the world. In developed countries, smoking causes over 80% of such cancers and generally, heavy smoking increases the risk by around 30-fold making lung cancer a major problem in developing countries where the consumption of tobacco is flourishing.According to the International Agency for Research on Cancer (IARC), there were over a million new cases in the world in the year 2000, making it the second most common in the world and the most common among women with 47% in developing countries. Although rates are five times higher in industrialized countries, the burden of disease is heavier in poorer countries because breast cancer is highly curable if detected early and, unfortunately, about 80% of the cases are detected at advanced stages in developing countries.Ranking at the third place, with incidence rates tenfold higher in developed than in developing countries, this type of cancer is assumed to be mainly related to dietary factors which account to up to 80% of the between-country differences in rates.20 years ago, this cancer used to be the most common in the world. At the moment, it is the fourth most common in the world but the second most common in developing countries. Substantial evidence suggests that risk is increased by high intakes of some traditionally preserved salted food and that risk is decreased by high intakes of fruit and vegetables.Approximately 75% of cases occur in developed countries, the rate vary over 20fold between countries. In developing countries, ingestion of contaminated food is an important risk factor together with active hepatitis virus infection whereas, alcohol consumption is the main diet-related risk factor in the world.80% of the new cases and deaths are occurring in developing countries where it constitutes a major health problem. In developed countries, screening programmes and early detection have led to a noticeable decline in cervical cancer incidence and mortality, whereas, the trend is stable or increasing in low- and middle-income countries owing to their limited health care resources but also to their ill-health systems generating inefficient (or no)strategies.In developed countries these types of cancer are mainly correlated to alcohol and tobacco(up to 75% of such cancers are attributable to these two lifestyle factors).In developing countries, around 60% of such cancers are thought to be a result of micronutrient deficiencies related to a restricted diet that is low in fruit and vegetables and animal products. There is also consistent evidence that consuming drinks and foods at a very high temperature increases the risk for these cancers.These types of cancer are more common in industrialized countries. However, the fact that overweight/obesity is an established risk factor, their incidence is expected to increase in developing countries engaged in the socio-economic transition.Chronic respiratory diseases represent a major burden for the health systems worldwide. Most developing countries have no standard protocols for assessing and managing chronic non communicable respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and Asthma. In these countries, the population afflicted by poverty and illiteracy, having very little (or no) access to health services, will die before the age of 40 years. They comprise 15% of the population in Latin America, 34% in Arab world, 45% in Sub-Saharan Africa and south-east Asia,22.Respiratory diseases cause 15% of the global burden of disease. Worldwide, it is estimated that 600 million people suffer from COPD and 2.5 million deaths were attributed to these diseases in 2000. By 2020, COPD is expected to become the third most common cause of mortality in the world.In the previous sections, we considered four classes of non communicable diseases, namely, CVDs, diabetes, cancer and chronic respiratory diseases. Despite some differences between these classes and into each class, they do have a common denominator which is the risk factors. Indeed, Tobacco, alcohol, high blood pressure, diet and physical inactivity were indicated, at different levels, as risk factors in the four classes of NCDs. Moreover, these risk factors are seen to affect people worldwide with an increasing tendency. Table Globally, many of the risk factors for heart disease, diabetes, cancer and pulmonary diseases are due to lifestyle and can be prevented. Physical inactivity, western diet and smoking are prominent causes. TobaccoIn the 20 the century, approximately 100 million people died worldwide from tobacco-associated diseases such as cancers, chronic lung disease, diabetes and CVDs.While tobacco consumption is falling in most developed countries, it is increasing in developing countries by about 3.4% per annum. Today, 80% of the 1.2 billion smokers in the world live in poorer countries where smoking prevalence among men is nearly 50% (48%) and 50% of the 5 million deaths attributed to smoking in 2000 occurred in developing countries, also responsible for the increase in deaths by more than one million during the last decade.Tobacco remains the most important avoidable risk for the four classes of NCDs. It increases the risk of dying from coronary heart disease and cerebrovascular disease 2–3 fold. It increases the risk of many types of cancer, for lung cancer the risk is increased by 20–30fold. According to studies conducted in Europe, Japan and North America, 83–90% of lung cancers in men and 57–80 in women, are imputable to tobacco. Between 80 and 90 % of oesophagus, larynx and oral cavity are caused by tobacco and alcohol . In deveFinally, tobacco exacerbates the conditions of people living with COPD and asthma.Up to 80% of cases of coronary heart disease, and up to 90% of cases of types 2 diabetes, could potentially be avoided through changing lifestyle factors.One-third of cancers could be avoided by eating healthily, maintaining normal weight, and exercising throughout life.It was estimated that in high-risk populations, an optimum fish consumption of 40–60 grams per day would lead to approximately a 50% reduction in death from coronary heart disease. A recent study based on data from 36 countries, reported that fish consumption is associated with a reduced risk of death from all causes as well as CVD mortality. Unfortunately, the fish consumption is very low even in some countries known for their large fish stock like the north African region.Daily intake of fresh fruit and vegetables in adequate quantity (400–500 grams per day), is recommended to reduce the risk of coronary heart disease, stroke and high blood pressure. But, once more, this is thwarted by the western lifestyle invading developing countries.Overweight and Obesity lead to adverse metabolic changes such as insulin resistance, increasing blood pressure and cholesterol. Consequently, they promote CVDs, diabetes and many types of cancer. Worldwide, overweight affects 1.2 billion of which 300 million are clinically obese. In some developed countries like USA, the prevalence reaches 60% but developing countries like Kuwait have also a very high prevalence. More and more children are suffering from overweight and obesity. However, the most contrasting phenomenon is to find Overweight/Obesity and malnutrition side by side in low- and middle-income countries and hence contributing to the growing burden afflicting these countries. According to the International Obesity Task Force (IOTF) and the WHO World Health report 2002, about 60% of diabetes globally can be attributable to overweight and obesity. In other respects, it is estimated that 60% of world's population do not do enough physical activity.Alcohol consumption has also increased in the last decades, with the major part of this increase imputable to developing countries. In 2000, Alcohol was responsible for nearly 2 million deaths in the world, representing 4% of the global disease burden. Moreover, alcohol was estimated to cause 20 to 30 % of oesophagus cancer, liver disease, epilepsy, motor vehicle accidents and other hazards.Non communicable diseases are more and more prevalent in developing countries. These diseases are highly correlated to risk factors like smoking, alcohol, obesity, diet and inactivity. The World Health Organization and many other organisms and associations are urging health decision makers to develop efficient preventive strategies to halt the growing trend of NCDs through the control of risk factors. However, although most of developed countries have reacted by pragmatic measures, the trend remain globally passive mainly because developing countries have been, so far, satisfied with adopting national conventions and adhering to international recommendations instead of pragmatic decisions such as prohibiting smoking in public areas, controlling alcohol abusers, encouraging physical activity, promoting healthy diet and improving primary health care for screening and early detection of chronic diseases. In these countries, 2.8 billion people live with less than 2 dollars, 1.2 billion live with less than one dollar and 1.3 billion live on fragile and often remote rural ecosystems. So, theThe author(s) declare that they have no competing interests.AB contributed by the collection of data concerning CVDs and diabetes and to English writing SB contributed by the collection of data concerning cancer and chronic pulmonary diseases.The two authors contributed equally to the final version of the paper.


The propensity of small amino-terminal fragments with mutant, but not wild-type, glutamine tracts to self-aggregate is consistent with an altered conformation but such fragments occur relatively late in the disease process in human patients and mouse models expressing full-length mutant protein. This suggests that the altered conformational property may act within the full-length mutant huntingtin to initially trigger pathogenesis. Indeed, genotype-phenotype studies in HD have defined genetic criteria for the disease initiating mechanism, and these are all fulfilled by phenotypes associated with expression of full-length mutant huntingtin, but not amino-terminal fragment, in mouse models. As the 50 < 15 μM, including gossypol, gambogic acid, juglone, celastrol, sanguinarine and anthralin. Of these, both juglone and celastrol were effective in reversing the abnormal cellular localization of full-length mutant huntingtin observed in mutant HdhQ111/Q111 striatal cells.We screened the NINDS Custom Collection of 1,040 FDA approved drugs and bioactive compounds for their ability to prevent in vitro aggregation of Q58-htn 1–171 amino terminal fragment. Ten compounds were identified that inhibited aggregation with ICin vitro fragment aggregation can act as a proxy for monitoring the disease-producing conformational property in HD. Thus, identification and testing of compounds that alter in vitro aggregation is a viable approach for defining potential therapeutic compounds that may act on the deleterious conformational property of full-length mutant huntingtin.At least some compounds identified as aggregation inhibitors also prevent a neuronal cellular phenotype caused by full-length mutant huntingtin, suggesting that  Huntington's disease (HD) is a severe, dominantly inherited neurodegenerative disorder that typically has its onset in mid-life, though it may occur in the juvenile years or in the elderly, and that produces an inexorable decline to death 10–20 years later . Huntingin vitro studies of a small amino-terminal huntingtin fragment, where an expanded polyglutamine tract promotes self-aggregation in a manner that conforms to the first four genetic criteria [in vitro aggregation involves a conformational change of the polyglutamine segment from a random coil to an amyloid structure and is paralleled in cell culture in some ways by the formation of cytoplasmic and nuclear inclusions that also incorporate other proteins [The "gain-of-function" due to the HD mutation is thought to lie in a novel conformational property conferred on mutant huntingtin by the expanded polyglutamine tract . This hacriteria -12. The proteins . Neuronaproteins .in vivo, the "gain-of-function" property conferred by the expanded polyglutamine acts within full-length huntingtin to cause abnormalities that do not initially involve formation of an insoluble aggregate [Hdh, the mouse orthologue, display early biochemical and histological phenotypes that are associated with expression of full-length mutant huntingtin at normal physiological levels and in a normal developmental pattern [Precise genetic modeling of HD in the mouse supports the view that ggregate ,16. Knoc pattern ,15-20. I pattern  One of tin vitro aggregation of amino-terminal mutant huntingtin [Hdh knock-in mice. Our findings indicate that some of these compounds reverse the effects of the expanded polyglutamine in both assays and support the view that some inhibitors of polyglutamine aggregation may lead to viable therapeutics targeted at full-length mutant huntingtin, early in the disease process.We have postulated that the same conformational property that promotes aggregation in the context of a small fragment may also act with the context of full-length huntingtin to trigger pathogenesis, possibly by altering huntingtin's interaction with another cellular element. Consequently, we have identified small molecules from the NINDS Custom Collection of bioactive compounds that inhibit ntingtin . These hWe have previously demonstrated that, when released from the protection of a GST fusion protein, the amino terminal fragment 1–171 of mutant huntingtin, forms aggregates in a manner consistent with the genetic criteria for the mechanism of HD pathogenesis . We usedIn our primary screen Figure , GST-Q58Potential inhibitors were distributed evenly cross the whole NCC library Figure . Sixty c50  values for the most potent 10 compounds, which range from 0.7 to 15 μM, were obtained from at least two independent experiments each  is a compound approved for internal use in humans. The most effective inhibitor of aggregation was gossypol-acetic acid complex. Gossypol, a polyphenol found in cottonseed, has been studied extensively as a male contraceptive in China but the World Health Organization has argued against its use because of induced hypokalemia, high toxicity and the risk of permanent sterility [Garcinia hanburyi tree. It has long been used as a pigment for painting and in traditional medicine as a potent purgative. Recently, it was identified in a high-throughput screen as an apoptosis inducer with potential for development as an anti-cancer agent [Juglans nigra), which has been used as an herbal medicine for its antihaemorrhagic and antifungal properties [Tripterygium wilfordii used as an alternative medicine for rheumatoid arthritis that has anti-oxidant, anti-inflammatory activity, immunosuppressive and anti-angiogenic activities. It has been proposed as worthy of exploration as a therapeutic in Alzheimer disease [Sanguinaria canadensis) has broad antimicrobial and anti-inflammatory, as well as potential anti-angiogenic activity [Andira araroba, that has been widely used as topical treatment for psoriasis and alopecia areata [Unfortunately, despite the preponderance of FDA approved drugs in the collection, none of our top hits . This is consistent with the inhibitory effect on huntingtin exon 1 aggregation reported previously at 30 μM in long-term hippocampal slice cultures from the R6/2 mouse [The remaining compounds identified as weaker aggregation blockers in our primary screen include selamectin, a veterinary anti-parasitic , pararos/2 mouse . Two saf/2 mouse ,46 and cin vitro aggregation assay. Among a secondary class of partially protective compounds in the PC12 assay, only celastrol overlapped with our hits. The NCC compounds were also screened in a cellular assay in HEK 293T cells expressing androgen receptor with 112 glutamines [Interestingly, the same set of 1040 NCC compounds were screened for their ability to block toxicity in a PC12 cellular assay where induced expression of huntingtin exon 1 encoding 103 glutamines leads to the accumulation of aggregates and rapid cell death . Althougutamines . In thisin vitro aggregation of an amino-terminal fragment of mutant huntingtin offer promise for the development of potentially therapeutic compounds that also target the deleterious conformational property of full-length mutant huntingtin.The identification and further characterization of chemical inhibitors of A library containing 1040 small chemical compounds consisting of FDA-approved drugs and bioactive natural products, the National Institute of Neurodegenerative Diseases and Stroke Custom Collection (NCC), was provided in thirteen 96-well plates by MicroSource Discovery Systems, Inc (Gaylordsville CT). The complete list of NCC compounds is available . All comA recombinant pGEX-2TK expression vector with cDNA fragment encoding amino terminal 171 amino acids of human huntingtin with a polyglutamine tract of 58 was used to prepare the GST-Q58-htn fusion protein. The GST-Q58-htn was overexpressed in BL21 cells and purified by affinity chromatography over glutathione-sepharose 4B beads (Amersham Pharmacia Biotech). The purified proteins were stored at concentration of 2.0 mg/ml at -80°C.2, 1.0 mM EDTA. The mixture was immediately distributed into the 96-well plates containing diluted compounds at 40 μl/well and mixed well. The final concentration of the small compounds was 100 μM. After 24 hours incubation at room temperature, the reaction was stopped by adding 10 μl of 10% SDS/50 mM 2-mercaptoethanol to each well followed by boiling in a PCR machine for 5 minutes. The mixture from each well was filtered through a cellulose acetate membrane ((0.2 μm, GE Osmonics labstore, Minnetonka MN) by using a 96-well ELIFA (Pierce Biotech). The aggregates retained on the membrane were detected by a specific anti-huntingtin antibody, HP1 (diluted 1:1000), followed by incubation with peroxidase conjugated anti-rabbit antibody (diluted 1:10,000, Sigma). Signals from SDS insoluble aggregates were scanned and quantified by using ImageMaster Totalab image analysis software (Amersham Pharmacia Biotech). In the secondary screening, all steps were the same except the final concentration of compound in each well was reduced to 10 μM and the GST-Q58-Htn/thrombin mixture was preincubated for 45-minutes at room temperature and clarified by centrifugation at 28,000 × g for 30 minutes before being added to the test wells. For IC50 determinations, the in vitro aggregation assay and signal quantification were performed as in the second screening but varying the final concentration of input drug. The data for each inhibitor were obtained from at least two independent experiments in which every sample was analyzed in triplicate with Prism 3.0 software (Graphpad Software, Inc., San Diego, CA).For primary screening of the chemical library, 1 μl of 4.0 mM small compound stock, diluted from the original plates, was placed in wells of 96-well plates. The fusion protein, GST-Q58-Htn was mixed with thrombin (0.5 unit/1 μg protein) at a concentration of 20 μg/ml in a buffer of 50 mM Tris-HCl, pH 8.0, 100 mM NaCl, 2.5 mM CaClHdhQ7/Q7 striatal neuronal progenitor cells (ST7/7) expressing endogenous normal huntingtin, and homozygous HdhQ111/Q111 striatal neuronal progenitor cells (ST111/111), expressing endogenous mutant huntingtin with 111-glutamines, have been described previously [Conditionally immortalized wild-type eviously . The strIn the immunofluorescence and confocal analysis experiment, wild-type ST7/7 and homozygous mutant ST111/111 cells, grown to confluence on glass coverslips, were treated with 6 different drugs at four different concentrations (0.5, 5, 10 or 50 μM) for 30 min at 33°C. After the treatment the drugs were removed and the cells washed twice in PBS. The cells were then fixed by incubation in 4% formaldehyde for 15 min, permeabilized by 0.1% Triton X-100 for 5 min and incubated for 30 min with blocking solution . Coverslips were then incubated in primary antibody (AP229 1:500 dilution) for 2 h at room temperature and washed three times in PBS before a further 1 h in blocking solution containing the secondary antibody (goat anti-rabbit Cy3, Jackson ImmunoResearch, West Grove, PA. USA). After three washes in PBS coverslips were mounted onto glass slides with Vectashield (Burlingame, CA. USA) and the images were analyzed with a laser confocal microscope (Bio-Rad, Hercules, CA. USA) using 20 × objective. Cell death was quantified by scoring the percentage of cells with apoptotic nuclear morphology, i.e. condensed or fragmented nuclei, under the confocal microscope. In each case five to ten randomly selected fields were counted, comprising at least 200 cells, and each experiment was repeated 3 times.JW carried out the library screen and aggregation assays. SG carried out the cell-based assays. MEM and JFG contributed to the conception of these studies. JW and JFG drafted the manuscript and MEM and SG contributed to its final version. All authors read and approved the final manuscript.