IIβ and β3 transmembrane domains reveals a specific heterodimerization interface in the resting receptor. This interface is lost rather than rearranged upon activation of the receptor by cytoplasmic mutations of the α subunit that mimic physiologic inside-out activation, demonstrating a link between activation of the extracellular domain and lateral separation of transmembrane helices. Introduction of disulfide bridges to prevent or reverse separation abolishes the activating effect of cytoplasmic mutations, confirming transmembrane domain separation but not hinging or piston-like motions as the mechanism of transmembrane signaling by integrins.Conformational communication across the plasma membrane between the extracellular and intracellular domains of integrins is beginning to be defined by structural work on both domains. However, the role of the α and β subunit transmembrane domains and the nature of signal transmission through these domains have been elusive. Disulfide bond scanning of the exofacial portions of the integrin α Integrin receptors mediate cell-matrix interactions by altering the conformation of their intra- and extra- cellular domains, a process mediated by lateral separation of the transmembrane helices Integrins are major metazoan cell adhesion receptors that have the distinctive property of transducing signals across the plasma membrane in both directions. Intracellular binding of cytoskeletal components to integrin cytoplasmic domains activates the ligand binding competency of the extracellular domain . Furthermore, ligand binding to integrin extracellular domains is coupled to alterations in cytoplasmic domains that are linked to downstream signaling . The three-dimensional architecture of integrin extracellular domains as well as their rearrangement in activation have been revealed by crystal, nuclear magnetic resonance (NMR), and electron microscopic methods , 2003. N3 subunit TM-cytoplasmic domain fragment in dodecylphosphocholine micelles predict that the TM segment comprising residues Ile693 to Ile720 is largely α-helical   Figure . HoweverIIb and Pro691 to Gly702 of β3  specific to the αIIbβ3 complex and subjected to sodium dodecyl sulphate polyacrylamide gel elecrophoresis (SDS-PAGE). Because of the extensive chase, only mature, cell-surface αIIbβ3 with complex N-linked glycan was isolated, which can readily be distinguished from the lower Mr αIIb and β3 precursors with high mannose N-linked glycans (data not shown). When the two cysteines on the α and β subunits are spatially close and are oxidized during biosynthesis, they form a disulfide bridge that can be detected by the appearance of a covalently attached αβ heterodimer band in nonreducing SDS-PAGE with a concomitant decrease in the intensity of α and β monomer bands (e.g., o-phenanthroline)3 (Cu-phenanthroline)  and the 12 β3 cysteine mutants (691C to 702C), i.e., a total of 120 different cysteine pairs. Transient transfection in 293T cells and CHO cells gave similar results. The disulfide bonding efficiency of all of these pairs is graphically summarized in IIb and Leu694 and Val695 in β3 (dashed line in 2), except that the efficiency of disulfide induction was slightly lower (data not shown). The third group, corresponding to the remaining 90 pairs, showed little or no intersubunit disulfide bond formation even after treatment with oxidant . Consistent with this, studies on dimerization of the glycophorin A TM domains have shown that cysteine substitutions are on average less disrupting than substitutions with any other hydrophobic residue (On both CHO-K1  and 293T residue . We concIIb band but not of the β3 band (IIb (data not shown). Furthermore, the same high-molecular-weight band was observed when α-W967C was cotransfected with any of the β3 cysteine mutants 2 tetramer. Notably, among the ten α and 12 β cysteine mutants used in this study, only α-W967C formed a homodimeric disulfide bond. This is consistent with the model of α–β TM domain association deduced here  motif in the αIIb subunit (3 subunit, αIIb truncated at Gly991 (denoted α*) formed a heterodimer on the cell membrane and appeared as an approximately 130-kDa band, slightly smaller than wild-type αIIb in nonreducing SDS-PAGE  No periodicity in disulfide formation was observed  Just as in αIIb*/β3, in αIIb"/β3, the helical periodicity of disulfide bonding was lost, as evidenced by the results with the β3 scanning mutants β-I693 to β-V700 (IIb*/β3 and not with αIIb/β3, none of the αIIb"/β3 mutants showed increased disulfide bond formation when treated with Cu-phenanthroline at 0 °C (We thought it important to confirm these results with an activated integrin that was not truncated and therefore used α mutants A. The sao β-V700 A. (2) As at 0 °C A and 5B.It is significant that a number of α*/β and α"/β cysteine-scanning mutants could form disulfide-bonded heterodimers during biosynthesis, but in contrast to α/β, none showed increased disulfide formation after oxidation at 0 °C. During biosynthesis at 37 °C, the membrane is fluid. Disulfide bond formation is catalyzed in the endoplasmic reticulum by disulfide isomerases, and because the redox balance is oxidizing in the endoplasmic reticulum, disulfide bond formation can covalently trap protein complexes that form only transiently. Therefore, a complex that would not be stable energetically by noncovalent interactions alone may nonetheless be stabilized by a covalent disulfide bond. This may particularly be the case for interactions between integrin TM domains, because the noncovalent association between the α and β subunits in the headpiece in the extracellular domain increases the probability of collision between the α and β subunit TM domains. If disulfide formation is the result of a stable noncovalent interaction between TM domains, it should occur at 0 °C when membranes are in a gel phase and proteins do not diffuse, as well as at 37 °C when membranes are liquid-crystalline and proteins diffuse. On the other hand, if disulfide formation is the result of transient interactions that are energetically unfavored, it should occur at 37 °C but not at 0 °C.To confirm the hypothesis that in α*/β and α"/β transient collision between TM helices can result in disulfide formation, Cu-phenanthroline oxidation was performed both at 0 °C and 37 °C. As described above, the α-G972C/β-L697C pair in the context of the wild-type receptor shows greatly increased disulfide bond formation upon oxidation by Cu-phenanthroline at 0 °C A, lane 5The above results were confirmed with the full-length α"/β receptor containing the Phe-Phe/Ala-Ala substitution see B and 5C.IIb which mediated α–α homodimerization in the wild-type receptor, underwent α–α homodimerization in the context of the activated α*/β receptor . The same reversal of basal ligand binding was also found for all constitutively disulfide-bonded α"/β pairs examined, including α"-I966C paired with either β-I693C, β-L694C, β-V695C, or β-V696C  and trimers (β3) in detergent micelles , respectively, as described by IIb cytoplasmic domain mutant receptors were made by introducing a stop codon at residue Gly991 to obtain αIIb1–990 (denoted α*), or by mutating G991/FFKR to GAAKR (denoted α"). Single amino acid substitutions to cysteine were made in αIIb, αIIb*, αIIb", and β3 in the positions indicated in the text. All mutants were made using site-directed mutagenesis with the QuikChange kit , and DNA sequences were confirmed before transfection of 293T cells using calcium phosphate precipitates, or CHO-K1 cells using Fugene transfection kit (Roche Diagnostics, Indianapolis, Indiana, United States).Plasmids coding for full-length human α35S]cysteine/methionine for 1.5 h before adding chase medium containing 500 μg/ml of cysteine and 100 μg/ml of methionine, and cells were cultured 17 h overnight (2+/1 mM Mg2+ (106 cells in 100 μl). After chilling on ice for 5 min, 20 μM CuSO4/100 μM o-phenanthroline was added by 10-fold dilution from stock solutions, and cells were incubated on ice for another 10 min. Oxidation at 37 °C was similar, except cells were suspended at room temperature and after Cu-phenanthroline addition were incubated at 37 °C for 10 min. Oxidation was stopped by adding an equal volume of TBS containing Ca2+/Mg2+ and 5 mM N-ethyl maleimide. Cells were centrifuged and resuspended in 100 μl of TBS containing 1 mM Mg2+, 1 mM Ca2+, and 5 mM N-ethylmaleimide, and lysed by addition of an equal volume of 2% Triton X-100 and 0.1% NP-40 in the same buffer for 10 min on ice. Cell lysate was immunoprecipitated with 10E5 (anti-αIIbβ3-complex-specific mAb) and protein G Sepharose at 4 °C for 1.5 h. After three washes with lysis buffer, precipitated integrin was dissolved into 0.5% SDS sample buffer and subjected to nonreducing 7.5% SDS-PAGE and fluorography , 150 mM NaCl, 5.5 mM glucose, and 1% bovine serum albumin, and incubated with 1 mM Ca2+/Mg2+ or a combination of 1 mM Mn2+ and 10 μg/ml of activating mAb PT25–2. Then cells were incubated with FITC-conjugated fibrinogen with a final concentration of 60 μg/ml at room temperature for 30 min, Cy3-conjugated AP3 was added to a final concentration of 10 μg/ml, and cells were incubated on ice for another 30 min before subjected to flow cytometry. Binding of soluble fibrinogen was determined and expressed as the percentage of mean fluorescence intensity relative to immunofluorescent staining with Cy3-labeled AP3 mAb.Binding of fluorescein-labeled human fibrinogen was performed as previously described . To dete2+ or 1 mM Mn2+ and 100 μM GRGDSP peptide at room temperature for 30 min. Anti-LIBS mAbs (AP5, D3, and LIBS6) was added to a final concentration of 10 μg/ml, and cells were incubated on ice for 30 min before staining with FITC-conjugated antimouse IgG and flow cytometry. LIBS epitope expression was determined and expressed as the percentage of mean fluorescence intensity of anti-LIBS mAbs relative to the conformation-independent mAb AP3  and thus was chosen as the final model. Models for clusters 11 and 12 were kindly provided by the authors of Model building was performed using the NMR structure of glycophorin A TM dimer (PDB code: 1AFO, model 1) as a template. The entire TM domains of αL server . For eac


Virtual reality (VR) technology is rapidly becoming a popular application for physical rehabilitation and motor control research. But questions remain about whether this technology really extends our ability to influence the nervous system or whether moving within a virtual environment just motivates the individual to perform. I served as guest editor of this month's issue of the Journal of NeuroEngineering and Rehabilitation (JNER) for a group of papers on augmented and virtual reality in rehabilitation. These papers demonstrate a variety of approaches taken for applying VR technology to physical rehabilitation. The papers by Kenyon et al. and Sparto et al. address critical questions about how this technology can be applied to physical rehabilitation and research. The papers by Sveistrup and Viau et al. explore whether action within a virtual environment is equivalent to motor performance within the physical environment. Finally, papers by Riva et al. and Weiss et al. discuss the important characteristics of a virtual environment that will be most effective for obtaining changes in the motor system. Virtual reality (VR) technology has been used for several decades for a variety of psychosocial interventions. But since the early 1990's there has been an explosion of laboratories and clinics promoting the use of virtual technology for physical rehabilitation . PresentVR describes a computer-generated scenario  with which the user can interact in 3 dimensions so that the user feels that he or she is part of the scene . CurrentMany people question why we don't just have subjects perform motor tasks in the real world. The answer to this question is that VR offers us the opportunity to bring the complexity of the physical world into the controlled environment of the laboratory. VR gives us the potential to move away from reductionism in science and towards the measurement of natural movement within natural complex environments. In general, VR allows us to create a synthetic environment with precise control over a large number of physical variables that influence behavior while recording physiological and kinematic responses . To thisThere are numerous strengths underlying the use of VR with rehabilitation ,12. AmonAnother question that has arisen at meetings and in the review of the papers for JNER is under what circumstances a computer generated environment should be considered virtual reality? Factors that differ among many of the laboratories claiming to use virtual reality and that also emerge amongst this group of papers include field of view, the presence of stereo vision, and real-time feedback of head position so that the scene can be updated to reflect natural movement of the visual world. There is evidence demonstrating that a transfer of training from the virtual to the physical environment is greater if the learner is immersed in the training environment . Perhaps


There is a large range of human skin color, yet we know very little about the underlying genetic architecture. Is the number of skin color genes close to five, 50, or 500? Diversity of human appearance and form has intrigued biologists for centuries, but nearly 100 years after the term “genetics” was coined by William Bateson in 1906, the genes that underlie this diversity are an unsolved mystery. One of the most obvious phenotypes that distinguish members of our species, differences in skin pigmentation, is also one of the most enigmatic. There is a tremendous range of human skin color in which variation can be correlated with climates, continents, and/or cultures, yet we know very little about the underlying genetic architecture. Is the number of common skin color genes closer to five, 50, or 500? Do gain- and loss-of-function alleles for a small set of genes give rise to phenotypes at opposite ends of the pigmentary spectrum? Has the effect of natural selection on similar pigmentation phenotypes proceeded independently via similar pathways? And, finally, should we care about the genetics of human pigmentation if it is only skin-deep?TP53 have helped to educate society about the risks of tobacco    However,e common , and itse common .More important than the ratio of melanin types is the total amount of melanin produced. In addition, histological characteristics of different-colored skin provide some clues as to cellular mechanisms that are likely to drive pigmentary variation . For theOf equal importance to what happens inside melanocytes is what happens outside. Each pigment cell actively transfers its melanosomes to about 40 basal keratinocytes; ultimately, skin reflectance is determined by the amount and distribution of pigment granules within keratinocytes rather than melanocytes. In general, melanosomes of African skin are larger and dispersed more widely than in Asian or European skin . RemarkaFor any quantitative trait with multiple contributing factors, the most important questions are the overall heritability, the number of genes likely to be involved, and the best strategies for identifying those genes. For skin color, the broad sense heritability  is very high , provideminimal , one of the more interesting findings was that skin reflectance appeared to be mainly additive. In other words, mean skin reflectance of “F1 hybrid” or “backcross hybrid” groups is intermediate between their respective parental groups.Statements regarding the number of human skin color genes are attributed to several studies; one of the most complete is by An alternative approach for considering the number of potential human pigmentation genes is based on mouse coat color genetics, one of the original models to define and study gene action and interaction, for which nearly 100 different genes have been recognized . SettingTYR, P, and MATP genes discussed earlier are well-known causes of albinism whose primary effects are limited to pigment cells  than synonymous (which do not change any amino acid) alterations. Data have been collected only for  regions . Thus, wCredit for describing the relationship between latitude and skin color in modern humans is usually ascribed to an Italian geographer, Renato Basutti, whose widely reproduced “skin color maps” illustrate the correlation of darker skin with equatorial proximity . More re3); when not otherwise provided by dietary supplements, deficiency for vitamin D causes rickets, a characteristic pattern of growth abnormalities and bony deformities. An oft-cited anecdote in support of the vitamin D hypothesis is that Arctic populations whose skin is relatively dark given their latitude, such as the Inuit and the Lapp, have had a diet that is historically rich in vitamin D. Sensitivity of modern humans to vitamin D deficiency is evident from the widespread occurrence of rickets in 19th-century industrial Europe, but whether dark-skinned humans migrating to polar latitudes tens or hundreds of thousands of years ago experienced similar problems is open to question. In any case, a risk for vitamin D deficiency can only explain selection for light skin. Among several mechanisms suggested to provide a selective advantage for dark skin in conditions of high UV irradiation  alleles. While specific answers will be different for every phenotype, there may be common themes, and some answers are better than none.


The rapid destruction of the planet's biodiversity has prompted the nations of the world to set a target of achieving a significant reduction in the rate of loss of biodiversity by 2010. However, we do not yet have an adequate way of monitoring progress towards achieving this target. Here we present a method for producing indices based on the IUCN Red List to chart the overall threat status (projected relative extinction risk) of all the world's bird species from 1988 to 2004. Red List Indices (RLIs) are based on the number of species in each Red List category, and on the number changing categories between assessments as a result of genuine improvement or deterioration in status. The RLI for all bird species shows that their overall threat status has continued to deteriorate since 1988. Disaggregated indices show that deteriorations have occurred worldwide and in all major ecosystems, but with particularly steep declines in the indices for Indo-Malayan birds (driven by intensifying deforestation of the Sundaic lowlands) and for albatrosses and petrels . RLIs complement indicators based on species population trends and habitat extent for quantifying global trends in the status of biodiversity. Their main weaknesses are that the resolution of status changes is fairly coarse and that delays may occur before some status changes are detected. Their greatest strength is that they are based on information from nearly all species in a taxonomic group worldwide, rather than a potentially biased subset. At present, suitable data are only available for birds, but indices for other taxonomic groups are in development, as is a sampled index based on a stratified sample from all major taxonomic groups. An index is developed from the IUCN Red List that measures the overall threat status of taxa and reveals that the status of the world's birds has deteriorated since 1988 The world's biodiversity is diminishing rapidly . At the The European Union has adopted the more ambitious target of halting the loss of biodiversity by 2010 . We do nHere we address the third of this suite of indicators, using changes in the threat status of species as measured by the categories of the World Conservation Union (IUCN) Red List. The IUCN Red List is widely recognised as the most objective and authoritative listing of species that are globally at risk of extinction . SpeciesOne of the goals of the programme is to provide a global index of the changing state of biodiversity . HoweverThe total number of extant threatened and Near Threatened birds listed on the IUCN Red List has changed relatively little over the four complete assessments of all the world's birds, increasing from 1,664 species in 1988 to 1,990 species in 2004. However, large numbers of species have moved between categories, particularly in the earlier assessments . Most ofCorvus hawaiiensis] and Spix's macaw [Cyanopsitta spixii]), and five Endangered species became Critically Endangered, but this was offset by seven species that improved in status as a result of conservation efforts , the index was calculated using weights for each category related to the relative extinction risk associated with them . This shThe RLI can be broken down by biogeographic realm , ecosystBird species are being driven Extinct by increasing human impacts on the planet. In total, 129 bird species have been classified as Extinct since 1500, with an additional four species listed as Extinct in the Wild, but surviving in captive populations , 2004c. By contrast, the RLIs presented here provide a robust, sensitive measure of the rate at which the world's birds are changing in relative projected extinction risk, as classified using the categories of the IUCN Red List. The indices show that the overall threat status of the world's birds has deteriorated steadily over the last 16 y. The RLI value has changed by −6.90% over this period. However, it should be noted that owing to the arbitrary nature of the weights applied to each category to calculate the score, this percentage decline is not directly comparable with percentage declines reported for population-based indices such as the Living Planet Index  or the UC. hawaiiensis]), and another is highly likely to have done so  and measures of the rate of change in this state . For indices based on proportional change in a measure , if the measure is one of state, a significant diminution in downward trend would show that the target has been met. If the measure is one of rate of change of state, however, the target is not met until we see a positive trend, not just a decelerating decline. Some of the Red List criteria are based on absolute population size or range size, while others are based on rates of decline in these values or combinations of absolute size and rates of decline. These criteria are used to assign species to Red List categories that can be ranked according to relative projected extinction risk, and the RLI is calculated from changes between these categories. Hence an RLI value relates to the rate at which species are slipping towards extinction at a particular time. To show that the 2010 target has been met, the RLI must therefore show a positive trend. A downward trend, even if diminishing, shows that the slide of species towards extinction is accelerating, not slowing down. The negative trends in the RLI values see  thus shoThe RLIs show some interesting regional variations. The index for birds in the Indo-Malayan realm shows a sharp decline during the 1990s see . This waRLIs can be calculated for particular species groups that have specific conservation or policy significance. For example, there are particularly active conservation networks for game birds (e.g., World Pheasant Association), raptors (e.g., World Working Group on Birds of Prey), and parrots (e.g., Loro Parque and World Parrot Trust), and the threat status of all three of these species groups is deteriorating, with steeper declines in the index value for parrots in the earlier part of the period see . In addiIt should be noted that setting all disaggregated index values to a common baseline in 1988 obscures any changes prior to this period  to reflect the ordinal ranks of the categories, whereby each step from Least Concern to Extinct indicates that at least one measure of extinction risk has become worse. The advantage of this approach is that it is simple, and the trends in the resulting index are driven by a relatively large number of species (hence producing a more robust and representative index). This is because a species moving from Least Concern to Near Threatened contributes just as much to the changing score as a Critically Endangered species going Extinct, and the numbers of species in each category (and the number moving in and out of each category) increases disproportionately from Critically Endangered to Least Concern see .However, steps between lower categories of threat represent smaller increases in extinction risk than steps between higher categories. Therefore we also tested an ‘extinction risk' approach, with each category weighted according to its relative extinction risk based on the quantitative thresholds for each of its criteria . AlthougThe most important difference between the two approaches is the effect of status changes in less-threatened or nonthreatened species. The equal-steps approach gives an index that is heavily influenced by movements of species among the lower categories of threat. The extinction risk approach gives an index that is largely influenced by movements of species among the higher threat categories. For example, if a Vulnerable species improves in status and becomes Near Threatened, and at the same time, a Critically Endangered species goes Extinct, the RLI based on equal-steps weights registers no change, but the index based on extinction risk weights shows a substantial decrease. Downlisting of a Vulnerable species to Near Threatened might represent a very substantial population increase, whereas extinction of a Critically Endangered species might represent the loss of very few individuals. The latter is arguably more significant in terms of genetic diversity, but the former might be more important as an indicator of wider biodiversity trends. Thus, the extinction risk weights emphasise the loss of biodiversity owing to imminent or potential extinctions of species, whereas the equal-steps weights allow the index to capture large changes in the populations of less-threatened species.For the RLI for complete taxonomic groups, and for disaggregating the index to show trends for subsets of species, for example, in particular realms or ecosystems we used the equal-steps approach because the number of species moving between the higher threat categories (those effectively driving trends when an ‘extinction risk' weighting is used) was too small to be meaningful in disaggregated indices. Only 23% of all genuine status changes  involved moves in or out of the highest threat categories. However, for examining trends in the species closest to extinction, we used the weights based on relative extinction risk.The usefulness of the IUCN Red List as an indicator of trends in the status of biodiversity e.g.,  has beenThe first of these problems has been addressed since 1994, when quantitative and objective categories and criteria for the IUCN Red List were introduced , 2001. TRLIs have a fairly coarse level of resolution of status changes because of the broad nature of Red List categories. Populations in the wild may have to undergo quite significant changes in size, trend, or distribution before crossing the thresholds to qualify for a higher or lower Red List category and, hence, before changing the RLI value. This is inherent in using the Red List categories rather than more precise parameters such as estimates of population size. It is not always true, however: The Red List criteria allow for species to be assessed as threatened on the basis of projected declines, and thus changes in status can reflect new or emerging threats in anticipation of population or range changes. We suggest that the disadvantage of coarse resolution is outweighed by the advantage of using a system that allows all the world's species in a taxonomic group to be assessed, rather than just a  subset for which adequately detailed information is available.Insensitivity of the index to status changes may also arise from time lags between changes in a species' population or range and changes in the RLI value, because of delays before detection of the status change, and/or before this knowledge becomes available to assessors. This is potentially more problematic, but several factors act to mitigate it. The Red List Programme partners have a large and expanding network of scientists across the world providing detailed and up-to-date information for an increasing number of species. Furthermore, with improving channels of communication , these status changes are the most important among the world's birds in terms of changes in projected extinction risk. We therefore suggest that the declines shown by the RLI since 1988 represent very significant losses to global biodiversity.Relatively large numbers of species changed categories in 1994 and 2000 owing to improvements in knowledge and improved consistency of interpretation of information against the Red List criteria see . This waThe greatest strength of the RLIs presented here is that they are based on comprehensive and complete assessments of nearly all species in a taxonomic group across the world . Most other global indicators based on, for example, population estimates, are derived from data biased towards common, well-studied species in the developed world, particularly Europe and North America. For example, the Living Planet Index  is basedAt present, indicators based on more representative suites of species are only available for particular countries or regions, such as the United Kingdom headline indicator for birds  and the Species-based indicators such as the RLI arguably provide far more powerful measures of biodiversity loss than other indicators proposed for measuring progress towards the 2010 target . Trends At present, data are only available for birds to produce the sorts of indices shown here. By 2010 at least two complete global assessments will also be available, and RLIs calculated for all the world's mammals (about 5,000 species), amphibians (about 5,700 species), and hopefully some plant and marine groups. Additional indices, and an aggregation of RLI trends in multiple groups, will provide a more representative picture of the changing state of biodiversity. In recognition that this will take some time to implement, the IUCN Red List Programme is also developing a sampled RLI based on a stratified sample of about 3,000 species from all major taxonomic groups, biogeographic realms, ecosystems, and Red List categories. This will provide an index that may be more representative of trends in the threat status of all biodiversity. We suggest that RLIs will have a key role to play alongside other types of indicators in assessing progress towards reducing the rate of, or halting, the loss of biodiversity.BirdLife International  has been responsible for providing the assessments of the world's 10,000 or so species of birds for the IUCN Red List since 1963. Since 1988, BirdLife has assessed every species of bird on a regular basis, and birds are regarded as the most comprehensively documented class of organisms on the Red List. BirdLife is the official Red Listing Authority for birds, and assessments are based on data gathered from the BirdLife Partnership of organisations in over 100 countries around the world, from published and unpublished literature, and from information provided by a worldwide network of over 1,000 species experts , 2004c.The principal categories on the IUCN Red List are: Extinct, Extinct in the Wild, Critically Endangered, Endangered, Vulnerable, Near Threatened, and Least Concern . Since aChlamydotis undulata] and Saker falcon [Falco cherrug]) whose categories were revised for the 2004 IUCN Red List too late to be included in Data from which to calculate the indices were derived from four complete assessments of the status of the world's birds by Published lists of numbers of species in different Red List categories cannot simply be used to calculate the index, for several different reasons. For example, changing knowledge and taxonomy result in many category changes, but such revisions are not indicative of changes in the conservation status of species' populations. Hence, to identify those species changing categories between assessments for relevant reasons, a ‘reason for change' code was assigned for each recategorisation. These mutually exclusive codes were (1) recent genuine status change, (2) genuine status change since first assessment, (3) knowledge, (4) criteria revision, and (5) taxonomy. These last three codes were used for changes not relevant for calculating the indices.‘Recent genuine status change' was applied to species that had undergone a genuine improvement or deterioration in status in the period since the previous assessment. This included species qualifying because of population declines (under IUCN Red List criterion A), particularly those with long generation times, for which more than half of the period during which the change occurred was subsequent to the previous assessment. This code was also applied in a few cases where species entered the Red List as a result of their elevation from subspecies to species level, but for which there had been a genuine status change when the taxon was compared to the equivalent population of the original species. For species categorised as threatened in 1988 and Critically Endangered, Endangered, or Vulnerable in 1994, genuine changes in status between these two assessments were identified by searching the account in Serinus syriacus) was uplisted from Near Threatened to Vulnerable in 2004 because of the discovery that populations had declined during a drought in 1998–1999. This information was unavailable during the 2000 assessment, so the species was recategorised in 2004 and given this ‘reason for change' code (‘Genuine status change since first assessment' was applied to species that had undergone a genuine improvement or deterioration in status in the period since the first complete assessment, but prior to the last assessment. This code denoted genuine changes in status that were not detected at the time they occurred. For example, Syrian serin (ge' code . For casge' code .Charadrius thoracicus) was uplisted from Near Threatened to Vulnerable in 2004 because its population was estimated to number as few as 750 individuals. However, the evidence suggests that the population may have been this small since before 1988 . (2) Over each period between complete assessments (1988–1994, 1994–2000, and 2000–2004) the net number of genuine changes to the total in each category was calculated, multiplied by the weights above (with Possibly Extinct and Extinct = 5), and summed to calculate the proportional change in the total score, P. (3) The value of the index (I) was set to 100 in 1988. For subsequent assessments I was calculated by multiplying −P for the previous period by the previous index value (see T, P, and I for each index).The number of species in each Red List category for each complete assessment and the number of species that changed categories as a result of genuine status changes were used to determine the index value in the following way: (1) For species assessed in two consecutive assessments , the total numbers of species in each category in the earlier assessment  were multiplied by a weight, and these were summed to give a total score, T is total score; c (ti)N is the number of species in category c at time i,t where it is the year of the ith assessment (assessments are not necessarily made every year); cW is the weight for category c; P is proportional genuine change; tiI is the value of the index at time i; Cat(ti, s)t is the category of species s at time i; Wct is the weight for category sc; G = 1 if change (from ti−1)( to it) in category of species s is genuine (otherwise sG = 0).Mathematically, the method can be described as follows, where ti−1I = 100 for the first year of assessment.where relative extinction risk associated with each category. Several of the quantitative thresholds in the Red List criteria can be used to obtain approximate values for the relative risk of extinction (for species at the lower boundary of that category). The most obvious is criterion E, which is based on quantitative analysis of extinction probability. The quantitative thresholds in criterion E change for both extinction probability and time frame for the three categories, and depend on generation length (e.g., the threshold for Endangered is a probability of extinction in the wild greater than 20% within 20 y or 5 generations). Taking a 3-generation time frame, a generation length set arbitrarily at 5 y, and assuming a constant annual risk of extinction, the 3-generation probabilities are approximately 0.5, 0.13, and 0.016 for Critically Endangered, Endangered, and Vulnerable, respectively (H. R. Akçakaya, unpublished data). However, most extinctions do not occur as a result of random catastrophes, as implied by the assumption of the constant annual risk of extinction. Many are preceded by declines, resulting in sigmoid extinction risk curves (with probability of extinction as a function of time). For such cases, the 3-generation probabilities are approximately 0.5, 0.1, and 0.025 for Critically Endangered, Endangered, and Vulnerable, respectively  translate to smaller increases in extinction risk than steps between higher categories . Therefore we also calculated the aggregated RLI using weights based on the vely see . Comparavely see . Based ovely see . The weiP  from these simulations gave the lower error bar for the 2004 RLI value. Similarly, we ran 10,000 simulations of six species moving to categories of lower extinction risk (with probabilities for each number of category steps set by the distribution of category changes for ten ‘downlisted' species in 2000–2004), and took the minimum value for P to give the upper error bar. These are very similar to the minimum and maximum values for P derived by simulating an additional six species moving in a direction (and by a number of categories) in proportion to the distribution of these values for all 45 species that underwent genuine status changes in 2000–2004  RLI value owing to time lags before genuine status changes are detected . We esti2004 see .(T) for each of those realms. This is because a species could potentially undergo genuine changes in status in any or all realms in which it occurs. However, so that trends in indices for particular realms reflect changes in the threatening processes operating within each particular realm (rather than threats operating elsewhere in the range of the species), species were only included in the calculation of P for a particular realm if the genuine status change had been driven by factors operating in that realm. For example, Saker falcon (F. cherrug) occurs in the Palearctic, Indo-Malayan, and Afrotropical realms and was included in the score calculations for each of these. However, recent declines have been driven by factors operating on the breeding grounds in Central Asia  has declined as a result of incidental capture in commercial longline fisheries in oceans in the Afrotropical, Neotropical, and Australasian/Oceanic realms  to one or more biogeographic realms  following the boundaries mapped by c realms , and so T for all ecosystems and habitats for which they were scored, but only included in the calculation of P for a particular ecosystem or habitat if the genuine status change had been driven by threatening processes operating in that ecosystem or habitat. Species were only assigned to a habitat type if this was of critical or major importance .The index was disaggregated for ecosystem  and for two terrestrial habitat types  in a similar way. Species were included in the calculation of Macronectes spp. and Procellaria spp.) covered by the ACAP under the CMS.To exemplify how the approach can be used for taxonomic subsets of species, RLIs were also calculated for several high-profile species groups with specific conservation interest groups: raptors , game birds , and parrots (Psittaciformes); and for species groups relevant to particular international conservation treaties: waterbirds  and the total number of species in categories Extinct in the Wild, Critically Endangered, Endangered, Vulnerable, and Near Threatened in at least one assessment during the period .Table S1(66 KB DOC).Click here for additional data file.Table S2p-values  based on the assumption that an additional six genuine changes occurred from 2000 to 2004 but have not yet been identified owing to time lags in knowledge (see n = 9,453 species), with a maximum of two species from each category. For each species, the number and direction of category changes were randomly assigned with probabilities (1) based on the change in categories for the ten species that underwent genuine status changes and were downlisted to a lower category of threat during 2000–2004 (‘only down'); (2) based only on the 35 species that were uplisted to a higher category of threat (‘only up'); (3) based on all 45 species (‘both up and down'); and (4) based on all 45 species with probabilities that were set individually for each threat category (‘category dependent,' so that, e.g., the probability of an Least Concern species being downlisted to a lower category of threat was zero). In each case, the procedure was repeated 10,000 times to calculate the minimum, maximum, mean, and standard deviation of the simulated p-value.Lists the simulated edge see . Six spep-value for cases when all six species were downlisted to lower categories of threat (shown in red in the table). The lower error bars were determined by the maximum simulated p-value for cases when all six species were uplisted to higher categories of threat (shown in blue in the table). In both cases the values are close to those calculated if the six species changed category, with probabilities based on the direction and number of category changes for all 45 species, and encompass those derived using the method based on category-dependent probabilities.The upper error bars for the RLI were determined by the minimum simulated (35 KB DOC).Click here for additional data file.


Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks  in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information. Analysis of characteristic patterns of connectivity in neuroanatomical datasets suggests that nervous systems evolved to maximize functional repertoires and support highly efficient integration of information The complex vertebrate brain has evolved from simpler networks of neurons over a time span of many millions of years. Brain networks have increased in size and complexity , as haveCaenorhabditis elegans  of the motif, equal to the number of nodes (vertices), and the number and pattern of interconnections. For a more formal definition of motifs and related concepts, see What rules underlie the organization of the particular types of networks that we see in complex brains? It is likely that, as networks become more complex, already existing simpler networks are largely preserved, extended, and combined, while it is less likely that complex structures are generated entirely de novo. One hypothesis states that complex and highly evolved networks arise from the addition of network elements in positions where they maximize the overall processing power of the neural architecture. This could be achieved by increasing the number of existing processing configurations or by introducing new processing configurations that add to the robustness or range of cognitive and behavioral repertoires. We may gain insight into the rules governing the structure of complex networks by investigating their composition from smaller network building blocks. Those building blocks are called “motifs” , and they have been examined in the context of gene regulatory, metabolic, and other biological and artificial networks . Motifs M, and the motif diversity, defined as the number of classes that are represented within the network by at least one example.While the most common definition of network motifs is based on their structural characteristics , structu(N) and edges (K) within a large network has a strong effect on the motif number and diversity of its constituent structural and functional motifs. But even if N and K are held constant, different connection patterns will result in different repertoires of such network motifs, expressed in terms of both number and diversity. These considerations lead us to formulate hypotheses concerning the rules for brain network organization in terms of network motifs. We hypothesize that neuronal networks have evolved such that their repertoire of potential functional interactions  is both large and highly diverse, while their physical architecture is constructed from structural motifs that are less numerous and less diverse. A large functional repertoire facilitates flexible and dynamic processing, while a small structural repertoire promotes efficient encoding and assembly.Clearly, the number of vertices We investigate this hypothesis first by performing an analysis of structural and functional motifs in various brain networks. We compare the motif properties of real brain networks with random networks and with networks that follow specific connection rules such as neighborhood connectivity (lattice networks). We identify some motif classes that occur more frequently in real brain networks, as compared to random or lattice topologies. Second, by rewiring random networks and imposing a cost function that maximizes functional motif number, network topologies are generated that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. The results of our analyses are consistent with the hypothesis that complex brain networks maximize functional motif number and diversity while maintaining relatively low structural motif number and diversity.M = 2, 3, 4, and 5 (estimations). In addition, we obtained motif frequency spectra for the matrix of interneuronal connections  of C. elegans, for motif sizes M = 2, 3, and 4 (estimations). For each neural connectivity matrix we generated equivalent (N, K) random and lattice matrices, preserving degree distributions . All neuronal networks  showed maximal functional motif diversity for all motif sizes examined . Their structural motif diversity tended to be submaximal. For example, the structural motif diversity of macaque cortex was significantly reduced in comparison to random matrices (168, compared to 198 ± 1 for random networks at M = 4). This tendency was especially pronounced for higher values of M .M = 3, M = 4) found within the network of the macaque visual cortex and C. elegans and their corresponding reference cases. Spectra of macaque and C. elegans networks are both less similar to random networks than to lattice networks. For M = 3, in the case of the macaque visual cortex, some motif counts appear decreased over random networks (e.g., motif identity number [ID] = 1,…,6) while other motif counts appear increased (e.g., ID = 9) over both random and lattice networks. M = 2, 3, and 4. Given motif frequencies from samples of n = 100 random or lattice networks, we calculated z-scores for the corresponding motifs in neuronal networks. Only structural motifs that were significantly increased (z > 5.0, p < 0.0001) in real networks as compared to both random and lattice networks are tabulated. Despite variations in size, areal composition, species, and collating authors, specific motif classes consistently emerged across several different cortical networks. M = 3) in all cortical matrices examined in this study. The appearance of this motif cannot be explained by a higher proportion of reciprocal  edges (a motif of size M = 2): While random networks contain fewer such edges, lattice networks contain an equally high proportion of such edges . No motif of size M = 2 was significantly increased in frequency for any of the connection matrices in this study (C. elegans. Motif ID = 9 was not significantly increased in frequency, while two other non-connected motifs (ID = 4 and 6) occurred more frequently than expected.is study . FurtherM that the area participates in. Motif fingerprints characterize brain areas, as do other structural and functional features (Each vertex (brain area) participates in a subset of the structural motifs that compose the entire network. We asked whether individual brain areas participate in similar or different sets of motifs and whether motif participation might reveal functional relationships. We define the motif fingerprint of a brain area as the number of distinct structural motifs of size features , and theM = 3) for several visual areas of macaque visual cortex. Motif ID = 9 was the only motif found to be significantly increased over both random and lattice networks, but it was increased for only five visual areas (V1, V3, V4, MSTd, and DP). All of these areas showed highly similar motif fingerprints characterized by a specific ratio of motif classes 9, 12, and 13 , in generations of 10 individuals, with a low rewiring rate of one connection per generation and a survivor rate of one network per generation, over 2,000 generations. Convergence was robust and consistent structural features of optimized connection matrices were observed.We hypothesized that high functional motif number and diversity represent important ingredients in the global organization of cortical networks, and that a selective advantage for these two properties might contribute to the generation of other significant structural properties. To test this hypothesis we applied an evolutionary algorithm  that selM = 3) as well as at lower and higher motif sizes . These motifs essentially form of a chain of reciprocally connected units, while pairs of connections linking the ends of the chain are absent. In functional terms, units in these motifs are highly integrated with their neighbors, while some pairs of units remain more segregated from each other and do not communicate directly. Thus, this motif type combines two major principles of cortical functional organization, integration and segregation (C. elegans exhibits very different patterns that are less indicative of high integration and segregation. At M = 3, motif ID = 9 does not occur in higher-than-expected numbers, while other motifs (ID = 4 and ID = 6) are increased. Our results suggest that large-scale cortical connection matrices form a distinct family ; rather, optimized networks routinely exhibit functional motif numbers that exceed those of ideal lattices, and they belong to a general class of networks that maintain a mixture of “local” and “long-range” connectivity. Importantly, even though structural and functional motifs are directly related , optimizing structural and functional motif number yielded strikingly different connection topologies.M = 3; unpublished data). In turn, networks optimized for functional motif number have significantly higher complexity than random networks, while those optimized for structural motif number are much less complex. Thus, it appears that several criteria for optimality  favor similar global network architectures that are all characterized by two coexisting organizational principles, functional segregation and functional integration. The functional motif frequency spectrum provides a sophisticated way of characterizing subtypes of such networks geared at more specific functional modes of information processing.Optimizing functional  motif number produced a tendency toward the emergence of small-world attributes (high clustering coefficient and short path length), a mode of connectivity that promotes functional cooperation, recurrent processing, and efficient information exchange . High clAll networks and network motifs in this paper are described as graphs of units  with directed  connections .M vertices and a set of edges  forming a subgraph of a larger network. For each M there is a limited set of distinct motif classes. For M = 2, 3, 4, and 5, the corresponding numbers of motif classes are 2, 13, 199, and 9,364 . Thus, the functional motif frequency spectrum is easily obtained from the structural motif frequency spectrum, without the need for additional motif detection.A structural motif provides the complete anatomical substrate for possible functional interactions among its constituent vertices. However, in real neuronal networks, not all structural connections participate in functional interactions at all times. As different edges or connections become functionally engaged, different “functional motifs” emerge within a single structural motif. The former  refers to “processing modes” or “effective circuits,” while the latter  refers to “anatomical elements” or “building blocks.” The existence of different functional motifs greatly enhances the processing power of any neuronal architecture. We then distinguish structural motifs from functional motifs that form a set of subgraphs of the structural motif. All such functional motifs consist of the original C. elegans are provided in This definition implies that functional motifs are more naturally applied to networks with vertices that contain multiple neurons or neuronal populations. In the present study, our main focus is on motifs of large-scale connection matrices; data for the single neuron network of M = 3, motifs with ID = 7, 9, 10, 12, and 13 are connected motifs.A “connected motif” is a structural motif that forms a strongly connected graph. In a connected motif, all constituent vertices can be reached from all other constituent vertices. Such a motif, in principle, allows all vertices to exert causal effects on each other. For M. The motif frequency spectrum for structural motifs is obtained by motif detection. The motif frequency spectrum for functional motifs can be obtained from the structural spectrum by simple multiplication with the characteristic number of functional motifs for the respective structural motif.A “motif frequency spectrum” records the number of occurrences of each motif of a given class for a size M) encountered in a network. The motif number is obtained as the sum over the motif frequency spectrum, either structural or functional.“Motif number” is the total number of all motifs of all classes (for a given size M) encountered in a network. The motif diversity is obtained as the number of all motif classes for which the frequency spectrum is greater than zero.“Motif diversity” is the total number of all motif classes . We used data of all chemical synapses from both individuals, discarding data on gap junctions , resulting in a matrix of N = 197 neurons and K = 1,974 directed connections. Other studies used matrices with N = 282 (Watts and Strogatz, 1998), N = 280  and by imposing equal degree distributions on all comparison networks . see also . We notesee also .i1,j1) and (i2,j2) is selected for which cij11 = 1, cij22 = 1, cij21 = 0, and cij12 = 0. Then we set cij11 = 0, cij22 = 0, cij21 = 1, and cij12 = 1. This is repeated until the connection topology of the original matrix is randomized.Random and lattice matrices that preserve the in-degree and out-degree for each vertex are generated from the original anatomical connection matrices by a Markov-chain algorithm . For ranFor lattice matrices, the same Markov procedure is employed but swaps are only carried out if the resulting matrix has nonzero entries that are located closer to the main diagonal (thus approximating a lattice or ring topology). This algorithm is implemented as a probabilistic optimization using a weighted cost function.http://www.indiana.edu/~cortex/CCNL.html. In some cases, for large networks or high values of M, we employed random sampling to estimate motif frequency spectra and their associated values for motif number and diversity. We selected different sample sizes to ensure convergence of these estimates and performed up to ten separate runs to generate good estimates. The evolutionary algorithms used in this study for optimizing structural and functional motif numbers of networks were similar to the algorithm described in N, K, and the original degree distribution were conserved.All graph theory methods used in this paper—including those for calculating clustering coefficients and path lengths —as well


Because deleterious alleles arising from mutation are filtered by natural selection, mutations that create such alleles will be underrepresented in the set of common genetic variation existing in a population at any given time. Here, we describe an approach based on this idea called VERIFY , which can be used to assess the extent of natural selection acting on an oligonucleotide motif or set of motifs predicted to have biological activity. As an application of this approach, we analyzed a set of 238 hexanucleotides previously predicted to have exonic splicing enhancer (ESE) activity in human exons using the relative enhancer and silencer classification by unanimous enrichment (RESCUE)-ESE method. Aligning the single nucleotide polymorphisms (SNPs) from the public human SNP database to the chimpanzee genome allowed inference of the direction of the mutations that created present-day SNPs. Analyzing the set of SNPs that overlap RESCUE-ESE hexamers, we conclude that nearly one-fifth of the mutations that disrupt predicted ESEs have been eliminated by natural selection (odds ratio = 0.82 ± 0.05). This selection is strongest for the predicted ESEs that are located near splice sites. Our results demonstrate a novel approach for quantifying the extent of natural selection acting on candidate functional motifs and also suggest certain features of mutations/SNPs, such as proximity to the splice site and disruption or alteration of predicted ESEs, that should be useful in identifying variants that might cause a biological phenotype. A computational analysis of single nucleotide polymorphisms showed that one-fifth of mutations that disrupt predicted exonic splicing enhancers have been eliminated by natural selection Exonic splicing enhancers (ESEs) were identified about a decade ago as short oligonucleotide sequences that enhance exon recognition by the splicing machinery -ESE which identifies ESEs in human genomic sequences using statistical properties of the oligonucleotide composition and splice site strengths of large datasets of exons and introns . This meA variety of other selection-based methods have been used to identify sets of sequences that are capable of functioning as ESEs. These SELEX methods isolate ESEs from a complex pool of random sequence by iteratively selecting and amplifying the fraction of molecules that can function as ESEs in a reporter assay . These mDespite this, a few previous studies have identified several disease alleles where the disruption of a conserved splicing enhancer corresponds to observed splicing defects, a noteworthy example being splicing mutations in the breast cancer gene BRCA1 . To dateHere we test the hypothesis that, in addition to protein-coding requirements, human exon sequences are also significantly constrained by the requirement to encode ESEs. We have developed a population genetic approach  which exploits the simple principle that, because they are selected against, deleterious alleles will tend to be underrepresented in the pool of sequence variants that are common in a population . Taking To assess the relationship between the locations of common genetic variation in human genes and ESEs, we screened the public SNP database  for SNPs that mapped to human exons. A set of biallelic reference SNPs was used, excluding entries that (1) mapped to multiple regions in the human genome, (2) mapped to repetitive elements, or (3) were derived from transcript sequence data, e.g., through comparison of expressed sequence tags (ESTs) see . This seRecording the position of each SNP within the corresponding exon revealed that SNP density is not uniform along exons . ConsistThe frequency of ESE disruption for simulated (randomly generated, unselected) mutations was compared to the frequency of ESE disruption observed in SNPs, which represent mutations that have survived selection to become reasonably frequent in the human population. SNPs are shaped by the interplay between the mutation process and the process of natural selection. Comparing simulated mutations to natural variations is an effective way to decouple these processes, and this approach has been used by others to study selective pressure in protein-coding genes . Here, wWhen considering SNPs, in order to distinguish mutations that disrupt predicted ESEs from those that create predicted ESEs, the identity of the ancestral allele must be established. Since the mutations that created most human polymorphisms occurred less than 1 million years ago , long afPan troglodytes, accessed through the NCBI trace archives (ftp://ftp.ncbi.nih.gov/pub/TraceDB/pan_troglodytes/). As the trace archives represented several-fold coverage of the chimp genomic sequence, most SNPs matched to several sequence reads. Whenever one allele of a human SNP consistently matched the chimpanzee sequence in all high-quality alignments, that allele was designated as the ancestral allele, and the other allele at that position was designated as a variant allele. The 8,408 SNPs that satisfied this criterion were then annotated for predicted ESEs in both the ancestral and variant sequence, simply by comparing the six overlapping hexanucleotides that differed between the two alleles to the set of RESCUE-ESE hexamers and recording the number of matching hexamers.Each of the 9,862 mapped human SNPs described previously was aligned (using a 33-nucleotide sequence window centered around the polymorphic position) to unassembled reads from the genome of the chimpanzee It is well known that current SNP databases contain a certain rate of error. The SNP consortium estimated that about five percent of their submissions were false positives attributed to base calling errors . Incorretrans-factor(s). The relative frequencies of these four outcomes are listed in The annotation of RESCUE-ESE hexamers in a biallelic SNP results in one of four possible outcomes: no ESE hexamers in either allele , one or more ESE hexamers only in the ancestral allele (ESE disruption, + −), one or more ESE hexamers only in the variant allele (ESE creation, − +), or one or more ESE hexamers in both alleles . The latter category is referred to as ESE alteration because the sets of RESCUE-ESE hexamers in the ancestral and variant alleles are, of course, different, and therefore may not necessarily be recognized with the same affinity by the same Mutations were simulated at random in human exons using nucleotide substitution frequencies that reflect the mutational biases observed in unselected regions of the genome with similar nucleotide composition. In vertebrates, the greatest biases in nucleotide-to-nucleotide substitution frequencies are related to the hypermutability of C residues  and the Using the substitution frequencies derived from a recent large-scale study of nucleotide substitution patterns in processed pseudogenes , mutatioTo assess the degree of selection on ESEs, one standard measure is the relative risk (RR), defined in this instance as the ratio of the frequency of ESE disruption in SNPs to the frequency of ESE disruption for unselected mutations (e.g., RR = 10.9%/13.6% = 0.80, using the pooled data from 2 < 0.5). This observation confirms our previous result and alleviates the concern that the analysis might have been confounded by the effects of selection acting at the protein level. The summary OR  for ESE disruption was 0.82 ± 0.05 (p < 0.001).As expected, the SNP dataset was greatly enriched for synonymous variation relative to the simulated mutation dataset. There is a 1.3:1 ratio of synonymous:nonsynonymous changes in the SNP dataset compared to a 0.5:1 ratio of synonymous:nonsynonymous changes in the simulated dataset. This difference, which has been observed many times, suggests that more than 60% of mutations that change the amino acid sequence are eliminated by natural selection . In orde2 ± 0.05 B, which Base changes that alter one or more predicted ESE hexamers but result in creation of other RESCUE-ESE hexamers  are also selected against, but to a somewhat lesser degree . SR protAt first glance, the overrepresentation of some categories in this analysis may seem surprising. However, this is simply a consequence of the underrepresentation of disruption and alteration mutations in the SNP pool causing the remaining two categories of variation, ESE neutrality and ESE creation, to appear slightly overrepresented in SNPs relative to unselected mutations B. As mutp < 0.05) relative to predicted ESEs located further from splice sites (Experiments measuring the splicing activity of substrates with variations in the distance between a well-characterized ESE and the 3′ss have demonstrated a strong proximity effect, with ESE activity decreasing as the distance from the splice site increases . Althougce sites .Our observations that SNPs tend to avoid disrupting RESCUE-ESE hexamers see  and thatSNP/frequencysimulated) was calculated for each hexamer and provided a means of assessing the selective pressure on each hexamer. The RR for a hexamer that was under no additional selective pressure would therefore be equal to 1.0, and a hexamer under increased selection would have an RR of less than 1. Consistent with our previous analysis  and compared this frequency to the value we would expect in the absence of selection (simulated mutations). For this analysis we used a simple RR measure, defined as the frequency with which a hexamer overlaps with SNPs relative to the frequency with which the same hexamer overlaps with simulated mutations. This ratio , about half of the hexamers predicted to be ESEs in all four vertebrates examined (human, mouse, zebrafish, and pufferfish) were significantly conserved in the SNP analysis. In the future, with a larger SNP database and more genomes available, it should be possible to use these methods to analyze more individual hexamers for evidence of selective pressure.A comparison of RESCUE-ESE predictions for four different vertebrates (G. Yeo, S. Hoon, B. Venketesh, and C. B. B., unpublished data) revealed a strong correspondence between within-species conservation  and cross-species conservation B. In othAs mentioned previously, the public SNP database used in this study contains a significant fraction of entries that could not be validated by resequencing. If these unvalidated entries in dbSNP were errors, they would not be expected to specifically avoid ESEs, or particular exon positions. Therefore, SNP artifacts are likely to reduce, rather than increase, the apparent significance of the biases in the nature and distribution of SNPs that we have observed.Here we have shown, using polymorphism data, that RESCUE-ESE hexamers have been preferentially conserved in the recent evolutionary history of the human lineage and that the strength of this conservation increases with increasing proximity to the splice sites. These results imply that splicing imposes important constraints on the evolution of human exons.As the size and quality of the SNP database is rapidly increasing, measures of selection deriving from SNP data are likely to become increasingly useful for evaluating the function of short, degenerate sequence elements like ESEs. For example, it should be possible to use the VERIFY method to analyze selection on motifs that are postulated to control transcription, polyadenylation, messenger RNA (mRNA) stability, or translation. The current build of dbSNP contains 360,000 SNPs that are located in a “locus region” within 2 kb upstream of the transcript start site or 500 bp downstream of the polyadenylation site . There aIn addition, this work identifies two new categories of mutations that are under selective pressure: mutations that disrupt or alter RESCUE-ESE hexamers. This increased selective pressure presumably reflects the increased likelihood that such variations will alter splicing and, therefore, gene activity. Several cases of polymorphisms which result in allele-specific differences in splicing have been reported . Here, wftp.ncbi.nih.gov) and parsed from the XML format, which integrates data (e.g., annotation, sequence, transcript) from several sources (http://ncbi.nih.gov/). To limit the contribution of erroneous SNPs, several filters were applied to produce the set of SNPs used in this analysis. First, SNPs located within a coding region in the assembly annotation were required to align to an internal exon in the GENOA exon dataset. GENOA is a genome annotation script that annotates exons by spliced alignment of mRNA/cDNA sequence to an assembled genome, applying a number of checks on the quality of the resulting alignments (see below). SNPs that mapped to multiple genomic regions, to known repetitive elements, to regions where the reading frame differed in the genomic/transcript alignment, or to an entry that had been contributed in the context of spliced transcripts (e.g., through comparison of ESTs) were excluded. We also removed entries where both versions of a SNP contained a match to either the human exon database or the chimpanzee (Pan troglodytes) genome (ftp://ftp.ncbi.nih.gov/pub/TraceDB/pan_troglodytes/). From the resulting pool of reference SNPs, entries associated with genotype data (population frequency) were used to build a validated SNP set. Unless otherwise indicated, SNPs were aligned to other sequences only when a perfect (33/33) base match was obtained using BLAST (Build 112 of dbSNP was downloaded from the NCBI ftp server ( sources . A curreng BLAST  with onehttp://genome.ucsc.edu) using the genome annotation script GENOA (D. H., Lee P. Lim, R.-F. Yeh, U. Ohler, and CBB, unpublished data). The exon/intron structures were inferred from at least two cDNA/genomic alignments, and the reading frame was determined from the CDS annotation of the GenBank cDNAs. As RESCUE-ESE hexamers were identified in constitutively spliced exons and alternative splicing sometimes results in ambiguous splice site positions, we confined our analysis to exons which showed no (cDNA) evidence of alternative splicing. Therefore, exons present in some, but not all, cDNA alignments overlapping a genomic region were excluded. In addition, it was required that each exon (1) be flanked by introns whose ends matched the consensus terminal dinucleotides of U2-type or U12-type introns (GT-AG, GC-AG, or AT-AC) and (2) be in an open reading frame that spanned at least three exons (the exon being considered and at least one flanking exon on either side).Datasets of internal human exons were generated by spliced alignment of cDNA sequences from GenBank (release no. 134) to the assembled, masked human genome sequence  whether a mutation would occur in an exon, (2) the position where a mutation would occur, and (3) the identity of the variant allele. Mutations were simulated in present-day internal coding exons with a probability of occurrence proportional to the length of the exon and no a priori strand or position bias. The output mutations were stored in reference SNP format and annotated with predicted ESEs as described in the text.The mutation probabilities and the pattern of substitution are functions of the input sequence (i.e., the base to be mutated and the 3′ neighboring base). The values of all 12 nucleotide-to-nucleotide substitution probabilities used here were considered for all four possible 3′ neighboring-base contexts. The distribution of dinucleotide substitution probabilities (48 possible) used in this work was derived from a large study of mutation in ribosomal protein pseudogenes , ESE creation (− +), ESE alteration (+ +), or ESE neutrality (− −). In the annotations, the first position refers to the wild-type or ancestral allele, where “+” indicates one or more ESE hexamers and “−” indicates no ESE hexamers. The ESE alteration category can include mutations in which a net gain or a net loss of ESE hexamers is annotated. All annotations used the set of 238 RESCUE-ESE hexamers described previously .ORs (rather than RRs) were used to quantify the difference between the set of experimentally validated SNPs and simulated mutations in where P(outcome|selection) represents the probability that SNP (a mutation under selection) will have a particular ESE annotation outcome (+ +, + −, − −, or − +). The extent of selection was measured separately for synonymous and nonsynonymous mutations and standard methods were used to calculate 95% confidence intervals . The MH Experimentally validated SNPs that were located in the first or last 70 nucleotides of an exon were evaluated for ESE disruption as a function of position. As no restrictions were placed on exon size, it was possible for SNPs in small exons to be placed in multiple categories . ORs were used to measure the extent of selection, and significance was assessed at an α level of 0.05 (one-tailed) for ESE disruption mutations in the four different regions of an exon.p value for each of the 238 hexamers.Bootstrap sampling was used to determine hexamers that were significantly avoided by SNPs. RR was used to compare the selection on individual hexamers over the course of 5,000 trials. For each trial, 8,408 SNPs were sampled with replacement from the set of 8,408 SNPs described in the text. The frequency of cases where an ESE coincided with an SNP was calculated for each of the 238 RESCUE-ESE hexamers and divided by the expected frequency for that hexamer (determined through simulation). This ratio of frequencies was used to estimate RR for each hexamer. Dividing the instances where RR was greater than 1 by the number of trials (5,000) provided a bootstrap The interspecies comparisons considered the output of RESCUE-ESE on four vertebrate genomes: human, mouse, zebrafish, and pufferfish. The set of human hexamers was divided into subsets according to the degree of conservation across vertebrates in the following manner: Hexamers that were only present in humans defined set 1; hexamers present in human and mouse, set 2; hexamers present in human, mouse, and one of the fish species, set 3; hexamers present in human, mouse, zebrafish, and pufferfish, set 4. The degree of overlap between these hexamer sets and the RESCUE-ESE hexamers with an RR of significantly less than 1 was recorded and displayed in −4 rather than 2.4 × 10−4), and so a particular ESE would be expected to be interrupted by a SNP at any one of six positions with a frequency of 6 × 3.7 × 10−4 = 2.2 × 10−3. This probability was used to estimate the number of variations that would interrupt  an ESE hexamer with and without selection for the different SNP database sizes and degrees of selection (ORs) described in the text.Power calculations for single hexamer analysis were performed as described previously (http://genes.mit.edu/burgelab/rescue-ese/. An online tool to annotate RESCUE-ESE hexamers in exons.http://genes.mit.edu/burgelab/Supplementary/fairbrother04/. Contains exon, SNP, RR, and ancestral allele databases used and/or generated in this study.


Methylphenidate (MPH) is commonly prescribed in the treatment of Attention-Deficit/Hyperactivity Disorder or ADHD. Concerta and Metadate CD are once-daily formulations of MPH using different delivery mechanisms resulting in different pharmacokinetic profiles. A recent study (COMACS) showed that for near-milligram (mg) equivalent daily doses, Metadate CD provides greater symptom control in the morning (1.5 through 4.5 hours post-dose), while Concerta provides greater control in the early evening (12 hours post-dose). Non-inferential comparison of effects for different dose levels of the two formulations suggested that equivalent levels of morning symptom control could be obtained with lower daily doses of Metadate CD than Concerta; the situation being reversed in the evening. The current paper presents a secondary analysis that provides a statistical test of these observations.post-hoc comparisons across dose levels presented here, total SKAMP scores with the active treatments (adjusted for placebo response) were analyzed using an analysis of covariance, with a combined measure modeling placebo response across all time period as the covariate.The COMACS study was a multi-center, double-blind crossover study of Metadate CD, Concerta and placebo with each treatment administered for 1 week. Children were assigned on the basis of their pre-trial dosage to either high , medium  or low doses  of MPH, and attended a laboratory school on the 7th day for assessment at 7 sessions across the day. For the Symptom control from 1.5 through 6.0 hours post-dose was as good with lower doses of Metadate CD (20 and 40 mg) as with higher doses of Concerta (36 and 54 mg, respectively). Lower daily doses of Concerta (18 and 36 mg) and higher doses of Metadate CD (40 and 60 mg, respectively) gave equivalent control at 7.5 and 12 hours with Metadate CD giving better control from1.5 through 6.0 hours post-dose.Different delivery profiles of Metadate CD and Concerta can be exploited to limit total daily exposure to MPH while at the same targeting a specific, especially clinically significant, period of the day. These results need to be confirmed in a study in which children are randomly allocated to different dose levels of the two formulations and plasma MPH concentrations are assessed simultaneously. Attention Deficit /Hyperactivity Disorder (ADHD) is a relatively common early onset developmental condition characterised by a pervasive and persistent pattern of age inappropriate and debilitating inattention, impulsiveness and overactivity. It is reported to affect between 3 and 6 percent of the childhood population and, if untreated, to be associated with a poor prognosis in adolescence and adulthood ,2. Methymax). Recently a 10 and 30 mg dose of MCD and a 27 mg dose of CON were licensed, but these were not available during the COMACS study. As a result of the design differences, MCD releases 50% more IR MPH in the initial bolus delivery process than CON for a near mg-equivalent total daily doses. Furthermore although the amount of ER MPH is the same for near mg-equivalent doses the pattern of delivery differs with MPH release front-loaded with MCD (starting at 1 hour) and more back-loaded with CON.We recently reported the results of a randomised, placebo-controlled, head-to-head comparison of the pharmacodynamic (PD) properties of near mg-equivalent daily doses of two safe and effective ,11 ER foThe different IR/ER ratios and time-course for dissolution of the two extended-release portions of the formulations result in distinctly different plasma concentration vs. time profiles in adults when compared at near-mg equivalent total doses . ConcentThe publication of the main analysis of the COMACS study data has led to a number of suggestions for alternative analyses that would usefully address questions of the clinical utility of the two formulations. Although from a scientific point of view the aim of the COMACS study – to compare across-the-day PD profiles of the two formulations – was best served by a comparison of bio-equivalent  total daily doses, other strategies for selecting comparator doses could have been legitimately followed. For instance, it has been suggested that comparators could have been matched on the basis of the size of the IR component dose rather than the total daily dose  of the formulations. Thus this primary analysis of the data did not include a cross-dose analysis, which would allow the relative efficacy of different daily doses of MCD and CON to be directly tested at different times of the day. The present paper presents a statistical cross-dose analysis of the efficacy of MCD and CON at different times of the day in order to test these observations directly.post-hoc exploration of the PD profiles of different doses of MCD and CON. Furthermore, the inclusion of the placebo arm in the COMACS design enabled us to directly address one of the possible major confounds associated with across dose comparisons in this sort of stratified design. An inspection of the published COMACS data revealed that the placebo scores measured at different sessions across the day were higher in the high dose group, than the medium or low dose groups. This suggests that children on higher doses may have had a more severe expression of the disorder making cross-group comparisons complicated. In the analysis reported in the current paper these placebo scores were used to adjust the treatment outcome scores to take account of this.The ideal way to test across-dose comparisons of different medications is to randomise patients to different dosing strata. In the COMACS study children were assigned to dosing strata on the basis of pre-trial dose levels. However, because the COMACS study was designed to have similar and substantial numbers of children in each dosing strata the data set still offers the opportunity for a preliminary A number of specific across-dose comparisons were made. First, the lower daily doses (20 mg and 40 mg) of MCD were compared to the higher daily doses of CON (36 mg and 54 mg respectively). The comparison between MCD 20 mg and CON 36 mg is of particular interest to clinicians as these are the suggested dose substitutions for IR MPH 10 mg BID and 10 mg TID, respectively. On the basis of the initial observations of the COMACS study it was predicted that MCD 20 and CON 36 mg, and MCD 40 and CON 54 mg would produce equivalent control in the morning (from 1.5 through 4.5 hours post-dose). In contrast, at these dose levels it was predicted that CON would produce significantly greater effect as the day wore on (from 6 hours onwards). Second, lower daily doses (18 mg and 36 mg) of CON were compared with higher doses of MCD (40 mg and 60 mg respectively). We tested the prediction that at these dosing levels MCD would demonstrate greater efficacy only in the morning and early afternoon (from 1.5 through 6 hours); so that despite the lower total daily dosing levels, CON, designed to be effective over a 12 hour period, would still be more effective than MCD, designed to be effective over an 8 hour period, at reducing symptoms in the early evening (i.e., at 12 hours).® CD (methylphenidate HCl, USP) Extended-Release Capsules (MCD) were obtained from Eurand Americas, Inc , while Concerta® (methylphenidate HCl) Extended-release Tablets (CON) were obtained from Alza Corporation (Mountain View, CA). For a detailed description of the preparation of clinical materials see Swanson et al., 2004 [Metadatel., 2004 .Six to 12 year old children, with interview-confirmed-diagnoses of ADHD who were being treated with MPH in doses of between 10 to 60 mg/day (5 mg to 20 mg per administration, one to three times a day) were recruited for the trial. Children were deemed otherwise healthy on the basis of an extensive medical history and physical examination. Children were excluded if they had an IQ below 80 or the inability to follow or understand study instructions. Other standard exclusion criteria for MPH drug trials applied [This was a ten-site, double-blind, placebo-controlled crossover study comparing three treatment conditions: MCD, CON, and placebo (PLA). The study was conducted in accordance with the principle of the Declaration of Helsinki and its amendments and the International Committee on Harmonization Guidelines on Good Clinical Practice. Dose-level assignment was made according to pre-existing daily dosing requirement for MPH and children remained at the dose-level assigned for the entire study duration. Children treated with low doses (≤ 15 mg/day IR or 20 mg/day ER) of MPH were randomized to receive a daily dose of MCD 20 mg, CON 18 mg or PLA; those treated with medium doses (>10 to ≤ 30 mg/day IR or >20 to ≤ 40 mg/day ER) of MPH were randomized to receive MCD 40 mg, CON 36 mg or PLA; and children treated with high doses (> 30 mg/day IR or >40 mg/day ER) of MPH were randomized to receive MCD 60 mg, CON 54 mg, or PLA. Each of the three treatments was administered for 7 days (in randomized order) without an intervening washout period. Assessments took place in the laboratory school on Days 7, 14, and 21 . Two trA preliminary factorial Analysis of Variance (ANOVA) using the SPSS General Linear Model was conducted in order to confirm that the pattern of effects of Treatment (CON, MCD, PLA), Dose (low, med, high) and Session (0, 1.5, 3.0, 4.5, 6, 7.5 and 12 hours) found in the Swanson et al. (2004) paper [Table The PLA adjusted scores for the total SKAMP score relating to the specific cross-dose comparisons are presented in Figures The comparison of MCD 20 mg and CON 36 mg revealed no overall difference between treatments, F (1,108) = 0.001; ns. There was an effect of session, F (6,648) = 15.08; p < 0.001 and an interaction between treatment and session F (6,648) = 3.98; p < 0.01. The two treatments did not differ at the time of dose administration or 3, 4.5, 6 and 7.5 hours after it (Fs < 2.5). MCD was superior to CON at 1.5 hours, F (2, 110) = 4.48; p < 0.05, while CON was superior to MCD at 12 hours, F (2,109) = 3.94; p < 0.05. The comparison of MCD 40 mg and CON 54 mg revealed no overall difference between treatments, F (1,99) = 1.05; ns. There was an effect of session F (6,594) = 36.20; p < 0.001 and an interaction between treatment and session, F (6,594) = 3.72; p < 0.01. There was no difference between treatments at the time of administration or at 1.5, 3, 4.5 and 6 hours . However, CON was superior to MCD at 7.5, F (1,100) = 6.26; p < 0.05, and 12 hours, F (1,99) = 4.55; p < 0.05.For the comparison of MCD 40 mg and CON 18 mg there was a main effect of treatment with MCD being associated with lower (i.e., better) SKAMP scores than CON, F(1,109) = 6.50, p < 0.05. There was also an effect of session, F(6,654) = 18.61, p < 0.001 and an interaction between treatment and session, F(6,654) = 8.32; p < 0.001. MCD gave lower (better) scores at 1.5 (F(1,111) = 10.02; p < 0.005), 3 (F(1,110) = 18.36; p < 0.001), 4.5 (F(1,111) = 16.16; p < 0.001), and 6 (F(2,111) = 5.94; p < 0.05) hours. However, there was no difference between the two formulations at 7.5 and 12 hours, Fs < 2.75. MCD 60 mg also gave significantly lower scores than CON 36, F (1,99) = 14.03; p < 0.001. Once again there was an effect of session, F (6,594) = 35.38; p < 0.001, and an interaction between treatment and session, F (6,594) = 10.55; p < 0.001. MCD gave significantly lower scores at 1.5 (F (1,100) = 35.01; p < 0.001), 3 (F (1,100) = 30.99; p < 0.001), 4.5 (F (1,99) = 18.23; p < 0.001) and 6 (F (1,100) = 9.66; p < 0.005) hours but not at 7.5 and 12 hours, Fs < 0.55.CON and MCD are once-daily formulations of MPH using different drug delivery mechanisms resulting in different plasma concentration vs. time profiles in both adults  and chilThere was only partial support for these predictions. Not only was there no significant difference between placebo adjusted total SKAMP scores for MCD 40 mg and CON 54 mg from 1.5 through 4.5 hours, but also at 6.0 hours post-dose. In addition, comparison at the lower dose levels (MCD 20 mg and CON 36 mg) gave a surprising result: MCD appeared to be associated with a stronger effect and a more rapid onset of action than CON during the very early period post-dose. This was indicated both by the absolute values (i.e., the intercept) for the two formulations at 1.5 hours post-dose and the steeper slopes for MCD than CON between 0 and 1.5 hours. This was a particularly unexpected finding because at the dose levels being compared, MPH available from the IR component of MCD (6 mg) was less than that available for CON (8 mg). Given that the active drug is the same in the two formulations and assuming that clinical efficacy reflects MPH serum concentrations as has been proposed by Swanson  this wouThe predictions relating to the efficacy of these different dose comparisons during the latter part of the day were confirmed only at the 12 hour testing session for the lower doses (CON 36 mg vs. MCD 20 mg) and the 7.5 and 12 hours sessions for the higher doses (CON 54 mg vs. MCD 40 mg). Given their expected plasma concentration vs. time profiles in children, one would expect greater efficacy from CON than MCD whatever the dose comparison being made. The 7.5 hour post-dose period is less clear-cut in terms of the relative benefit of the two formulations.The second set of predictions related to the value of lower daily doses of CON (18 mg and 36 mg) to provide equivalent symptom coverage compared to higher doses of MCD (40 mg and 60 mg, respectively) in the late afternoon and superior coverage in the evening. The predictions for late afternoon were supported at both the higher and lower cross-dose comparisons with lower doses of CON giving equivalent control at 7.5 hours. However, interestingly, at 12 hours the higher doses of MCD remained equivalent to CON. This result is in keeping with the idea, supported by the expected plasma concentration and known dissolution data that MCD continues to release MPH up to 12 hours post-dose. As expected, the higher doses of MCD had a greater effect between dosing through 6 hours post dose.Given design limitations it is possible that these effects are not 'real' effects related to dose and treatment type but are related to differences between the types of children assigned to different dose levels. The way in which the children were assigned to dose level meant that across-dose comparisons of different treatments could be subject to a number of confounds. First, it could be that children were placed on higher doses because they had a more severe form of ADHD. In the present study we attempted to deal with the possible confounds that such an approach to dose assignment might bring by using the placebo SKAMP score  as a covariate. This score was included as a proxy for severity of ADHD in order to control for the possibility that children assigned to the higher-dose level had a more severe form of the condition.A second possible confound, not corrected for by controlling for placebo scores, relates to the child's sensitivity to MPH: Children may have been prescribed higher pre-study doses of MPH because they were less sensitive to MPH and did not respond to the lower dose. Such variations in sensitivity could be independent of overall severity of the disorder and therefore constitute a second possible confound. If this were the case then an across-dose comparison would be between more- and less-sensitive children. There are two pieces of evidence that argue against this as an explanation for the current results. First, Swanson et al.  reportedTaken together, the existence of these confounding factors and complications means that while these data provide an initial indication of the relative efficacy of different doses of CON and MCD at different points across the day they should be treated with a certain degree of caution, and the results should be confirmed in a study where subjects are randomized to dose level rather than being assigned to it on the basis of their pre-study MPH daily dose and plasma levels of MPH are assessed simultaneously.From the point of view of the practicing clinician the results of this study further highlight the value of having available different ER MPH formulations with different expected plasma concentration vs. time profiles offering different patterns of efficacy over different periods of the day. Given the established dose-response relationship between MPH and side effects, clinicians and parents may wish to limit total daily MPH intake as far as possible while maintaining a tolerable level of efficacy over the day as a whole and/or targeting a particularly important period of the day for a particular patient. This head-to-head study makes a first step towards providing the systematic evidence base on which to make such decisions.EJB and DC are consultant for and/or have received support from Celltech, McNeil, Jannsen Cilag and Eli Lilly. JMS is a consultant for Celltech, McNeil and Eli Lilly. SJH and HHD are employees of Celltech Americas, Inc.JMS, DC, SJH and EJB participated in the design of this post-hoc analysis while EJB performed the statistical analysis. HHD and EJB prepared the manuscript. All authors contributed discussion and read and approved the final manuscript.The pre-publication history for this paper can be accessed here: