Caenorhabditis elegans and the pathogenic soil bacterium Serratia marcescens.Co-evolutionary arms races between parasites and hosts are considered to be of immense importance in the evolution of living organisms, potentially leading to highly dynamic life-history changes. The outcome of such arms races is in many cases thought to be determined by frequency dependent selection, which relies on genetic variation in host susceptibility and parasite virulence, and also genotype-specific interactions between host and parasite. Empirical evidence for these two prerequisites is scarce, however, especially for invertebrate hosts. We addressed this topic by analysing the interaction between natural isolates of the soil nematode Our analysis reveals the presence of i) significant variation in host susceptibility, ii) significant variation in pathogen virulence, and iii) significant strain- and genotype-specific interactions between the two species.C. elegans, the high specificity is observed among isolates from the same population, such that it may provide a basis for and/or represent the outcome of co-evolutionary adaptations under natural conditions. Since both C. elegans and S. marcescens permit comprehensive molecular analyses, these two species provide a promising model system for inference of the molecular basis of such highly specific interactions, which are as yet unexplored in invertebrate hosts.The results obtained support the previous notion that highly specific interactions between parasites and animal hosts are generally widespread. At least for  By definition, parasites have a negative effect on host fitness. Since parasites usually show a shorter generation time than their hosts, they are also able to adapt rapidly to newly arising host genotypes. Both characteristics together select for hosts with efficient counter-adaptations. Subsequently, parasites are favoured if they can circumvent these host countermeasures. Such interactions may result in a co-evolutionary arms race, consisting of repeated cycles of the emergence of new parasite offences and host countermeasures. Hence, parasite-host interactions can lead to extremely rapid evolutionary change ,2. As suPotamopyrgus antipodarum versus Microphallus [Bulinus globosus versus Schistosoma [Daphnia magna and its microparasite Pasteuria ramosa [Drosophila melanogaster and its parasitoid Asobara tabida [Bombus terrestris and the trypanosome Crithidia bombi [Macrocyclops albidus and the cestode Schistocephalus solidus [Co-evolutionary arms races between hosts and parasites  are in many cases assumed to be determined by negative frequency dependent selection. In particular, rare parasite and host genotypes should be at an advantage because commonness facilitates evolution of host or parasite counter-adaptations, respectively ,5. Such ophallus  or Bulinistosoma ), the asa ramosa , betweena tabida , betweenia bombi , or betw solidus . ClearlyCaenorhabditis elegans (Nematoda: Rhabditidae) by the Gram-negative bacterium Serratia marcescens (Enterobacteriaceae). C. elegans has recently been established as a model to study parasite-host interactions and in particular the genetics of host defence [Bacillus thuringiensis [C. elegans encounters under natural conditions have not yet been unambiguously identified. The ubiquitous soil-dwelling bacteria Pseudomonas aeruginosa, B. thuringiensis and S. marcescens are all likely candidates [S. marcescens, recently adopted as a model to study the genetic basis of virulence [In this study, we evaluated differences in host resistance and parasite virulence, both defined in a broad sense and reciprocally as the effect of an infection on host condition . In particular, we tested the presence of genetic variation and also the presence of strain- and genotype-specific interactions during the infection of the nematode  defence -17. It i defence ,19. Abou defence ,19. Theyngiensis . The parirulence ,21, as airulence , thus beC. elegans strains with 5 different S. marcescens strains plus one control (heat-killed bacteria of S. marcescens Db11, a strain for which the genome sequence is now complete). The C. elegans strains were isolated from Münster, in Northwest Germany, and belong to four different microsatellite genotypes [S. marcescens strains originate from different locations around the world. The interaction between the two species was examined with the help of a survival assay, in which the survival of individual worms was monitored in the presence of a defined concentration of bacteria [In our main experiment, we compared the consequences of infection of eight different natural enotypes . The S. bacteria . The surχ2 = 0.904, d.f. = 35, P > 0.999). In the control (worms with dead S. marcescens Db11), only 12 out of 625 were not found in the category "alive" (1.92%). Of these, 9 were morbid and 3 were dead. The recorded number of live worms per strain did not differ significantly from 100% (LRT, χ2 = 0.455, d.f. = 7, P > 0.999). It also did not differ significantly among the worm strains (LRT, χ2 = 0.416, d.f. = 7, P > 0.999). These results show that the experimental set-up itself does not cause significant levels of dead or morbid worms and that it does not have a different effect on different worm strains.80 out of a total 3840 cases (2.08%) had to be excluded because of errors during automated worm-transfer (either no worm or more than one worm per well), resulting in between 75 and 80 usable data points per combination of worm and bacterial strains. The number of valid cases did not differ significantly among these factor combinations , the observed differences are most likely determined genetically. Most interestingly, the interaction between specific worm and bacterial strains seems to differ across the table. For instance, C. elegans strain MY10 is more susceptible to S. marcescens strain Sma13 than to ATCC274, whereas the opposite is true for C. elegans strain MY20 . However, they are both significantly worse than the respective saturated models . The latter test examines whether the model employed considers a sufficient number of factors or factor combinations to explain the variation found in the data. The results suggest that the model is not sufficiently complex. We decided against employing more complex models (e.g. consideration of host genotype nested in host strain in a single model), because the response variable is ordinal with only three categories , such that a larger number of predictor variables in the model would most likely lead to highly increased random error in the regression analysis. Thus, as an alternative, we analysed the data using association tests.In general consistency with these observations, ordinal logistic regression (OLR) analysis indicates a significant effect of the factors bacterial strain, worm strain or genotype, the interaction between the two, and also experimental run on the treatment response Table . The twoC. elegans strains is due to differences in genotypic composition. The remaining results indicate that the significant effect from one of the factors on the treatment response is independent of the significant effect from one of the other factors. This finding is consistent with the presence of an interaction effect from the factors bacterial strain and nematode strain/genotype, as above suggested by OLR.Two-way associations were analysed with the LRT. The results show a significant effect of either of the different factors on worm condition Table . The relχ2 = 0.093, d.f. = 3, P = 0.996). In the control treatment of this experiment, all animals were alive.In the second experiment, we specifically addressed the presence of an interaction between two bacterial strains (Db11, ATCC274) and four host strains (MY8, MY10, MY14, MY15), the latter belonging to two different host genotypes. For this experiment, all factor combinations were included in each 96-well plate and in one experimental run. Only 7 out of 384 cases had to be excluded for the reasons given above (1.82%), resulting in 46 to 48 data points per factor combination. Again, the number of valid cases did not differ among factor combinations . Moreover, they were not significantly worse than the respective saturated models , suggesting that they contained sufficient details to explain the observed variation.The second experiment confirmed the presence of variation in host resistance and pathogen virulence, although the overall level of virulence was lower than in the main experiment Fig. . SubsequSubsequent performance of association tests generally corroborated the OLR analyses: The different predictor variables had a significant effect on the treatment response  genetic differences in virulence among natural S. marcescens strains, and also iii) strain- or genotype-specific interactions between the two. The first of these points is generally consistent with our previous results on the presence of strain-specific differences in resistance of C. elegans towards Bacillus thuringiensis [C. elegans strains from different locations across the world, whereas in the present study all strains derive from the same place (the town of Münster in Northwest Germany) [C. elegans, because the S. marcescens strains considered did not come from the same location.We here provide evidence for the presence of i) genetic differences in resistance among natural ngiensis . HoweverGermany) . PreviouGermany) . Our preC. elegans and S. marcescens in the wild must currently be considered unclear. To date, it is unknown whether the two species indeed co-exist under natural conditions, even though it is strongly suggested by the fact that both – especially S. marcescens – are common soil inhabitants [The observed strain- and genotype-specific interactions represent an important precondition for negative frequency dependent selection. As such, they may contribute to the emergence of co-evolutionary arms races ,4. The rabitants ,23. If tC. elegans-S. marcescens interactions as a side effect. For example, the C. elegans strains may have adapted differently towards environmental toxins. If the underlying detoxification mechanisms are also employed in the defence against pathogens, then this may result in the observed differences in resistance. Such mechanisms could indeed be of relevance in the interaction with S. marcescens, for which at least one toxin (hemolysin ShlA) was previously suggested to contribute to pathogenesis in C. elegans [The situation is clearly different if the two species do not share the same natural habitat. In this case, the observed specificity must be the result of independent adaptations of parasite and host strains to other environmental conditions. Pleiotropy of such adaptations should then have produced the specific  elegans . MoreoveC. elegans and Bt toxin, the main virulence factor of B. thuringiensis [M. albidus and the cestode S. solidus [D. magna and its microparasite P. ramosa [C. elegans in response to S. marcescens [Drechmeria coniospora [B. thuringiensis [C. elegans, this nematode may in the future provide a valuable model system to dissect the molecular basis of specificity in invertebrate-pathogen interactions.Whatever its origin, the finding of high specificity in the interaction has further implications. The molecular basis of highly specific resistance is currently unexplored in invertebrate hosts. It could be due to the presence of different alleles of a certain cell surface protein targeted by specific parasite effector molecules. Such cell surface proteins have been suggested to be important for the interaction between ngiensis ,25. As a solidus  or the w. ramosa . The prercescens , the funniospora , and alsngiensis . ConsideS. marcescens. This bacterium is considered to be an opportunistic pathogen with a broad host range [C. elegans also mediate virulence in other hosts , whereas other genes do not [S. marcescens in humans, where this bacterium has become a growing health problem, primarily in nosocomial infections [Similarly, the observed highly specific virulence was previously unknown for st range . Hence, s do not . This alfections .C. elegans and its potential microparasite S. marcescens, our study provides evidence for i) genetic variation in host susceptibility and parasite virulence, and also ii) strain- and genotype-specific interactions between the two. These two factors represent an important precondition for frequency dependent selection and as such for the emergence of co-evolutionary arms races. Such highly specific interactions were previously unknown for C. elegans or S. marcescens. Moreover, they have not as yet been reported for invertebrates other than molluscs and arthropods. At least for C. elegans, the observed variation was found among strains from the same population, such that it could indeed be of relevance for evolutionary changes under natural conditions. Taken together, these findings suggest that there is widespread potential for co-evolutionary interactions in animal hosts. Both C. elegans and S. marcescens represent important model organisms in biological research for which a diversity of manipulative techniques is available. Therefore, the association between these two species may in the future provide a valuable tool for the comprehensive analysis of such highly specific interactions.Based on the analysis of natural isolates of the nematode C. elegans strains with 5 different natural S. marcescens strains plus one control. The C. elegans strains were isolated by HS and co-workers in 2002 from Münster, North-West Germany [C. elegans strains had all been cryo-preserved within 5 generations after isolation [We compared eight different natural  Germany . They ar Germany . Some of Germany . Mainten Germany . These Csolation . They wesolation .S. marcescens strain Db11 was originally isolated by H. Boman [C. elegans strain N2 when tested on solid agar, whereby virulence varied in the following order (from high to low): Sm2170 > ATCC274 > Db11 > Sma3 = Sma13 [8 per ml. As a control, we used heat-killed bacteria of strain Db11 (incubation at 70°C for 15 min). These dead bacteria were previously shown to have lost their deleterious effects on C. elegans [The H. Boman ; the strH. Boman ; and strH. Boman , Sma3, SH. Boman . They we = Sma13 . Note, h elegans .C. elegans and S. marcescens was assessed using a simple survival assay [S. marcescens strains and the control, randomised across the plate. Only one C. elegans strain was examined per plate. Eight plates were analysed in parallel, each with one of the eight different C. elegans strains. Five runs of this set-up were performed, whereby the order in which strains were analysed during each run was randomised. This set-up resulted in a maximum of 80 data points per bacterial strain – worm strain combination.The interaction between al assay . For thi7 bacterial cells per well at the start of the survival assay. This concentration was found in a pilot study to permit detection of differences in survival among worm strains after 24 h. After this time period, the condition of the worms was recorded using the following three categories: i) alive , ii) morbid , iii) dead .For a specific run, 50 μl NGM solution (without any bacteria) were first added to each well of the 96-well plates using a multi-channel pipette. Thereafter, individual worms were transferred to each well using the COPAS automated worm-sorter (Union Biometrica Inc.). For worm-sorting, we used synchronised L4 stage worms. Success of worm transfer was monitored. If no worm or more than one worm was transferred to a particular well, then it was excluded from further analysis. After worm transfer was completed, 50 μl NGM solution with pathogens were added to each well with a multi-channel pipette. These 50 μl contained 45 μl NGM solution and 5 μl of bacteria in LB with an OD of 0.1, resulting in a total of approximately 1 × 10N = 40) using differential interference contrast microscopy and a fluorescent microscope (DMIRBE, Leica).After completion of the experiment, we re-assessed the interaction between two bacterial strains (Db11 and ATCC274) and four worm strains . The general set-up was the same as above. In this case, a total of four 96-well plates were studied at the same time. In contrast to the above experiment, each 96-well plate contained both the different worm and bacterial strains, randomised across the plate. This set-up results in a maximum of 48 data points per factor combination. In addition to the above, we included two 96-well plates as a control. These contained heat-killed Db11 bacteria and the four worm strains randomised across plates (48 data points per worm strain). After exposure to the pathogens, we confirmed that worms were indeed infected with the bacteria by analysis of some of the animals . Based on the hierarchical order of the categorical response variable , we used an ordinal logistic regression analysis (OLR) ,37. For For the main experiment, the lack of fit test was significant, indicating that the chosen model may not be sufficiently complex to explain the variation in the data (see results section). Therefore, we additionally employed association tests based on the inferred frequency counts for the different factor combinations. We specifically assessed the association between the treatment response (condition of the worms) and either of the following factors: bacterial strain, worm strain, worm microsatellite genotype, and run. The significance of the association was inferred using the likelihood ratio test (LRT) ,38. We fCMH, Cochran-Mantel-Haenszel test; LB, Luria broth; LRT, likelihood ratio test; NGM, nematode growth medium; OD, optical density; OLR, ordinal logistic regression.HS designed the study, carried out the experiments, analysed the data and wrote the first draft of the manuscript. JJE participated in the design of the study, the interpretation of the data and revised the manuscript.


The medical specialities chosen by doctors for their careers play an important part in the workforce planning of health-care services. However, there is little theoretical understanding of how different medical specialities are perceived or how choices are made, despite there being much work in general on this topic in occupational psychology, which is influenced by Holland's RIASEC  typology of careers, and Gottfredson's model of circumscription and compromise. In this study, we use three large-scale cohorts of medical students to produce maps of medical careers.Information on between 24 and 28 specialities was collected in three UK cohorts of medical students (1981, 1986 and 1991 entry), in applicants (1981 and 1986 cohorts, N = 1135 and 2032) or entrants (1991 cohort, N = 2973) and in final-year students (N = 330, 376, and 1437). Mapping used Individual Differences Scaling (INDSCAL) on sub-groups broken down by age and sex. The method was validated in a population sample using a full range of careers, and demonstrating that the RIASEC structure could be extracted.Medical specialities in each cohort, at application and in the final-year, were well represented by a two-dimensional space. The representations showed a close similarity to Holland's RIASEC typology, with the main orthogonal dimensions appearing similar to Prediger's derived orthogonal dimensions of 'Things-People' and 'Data-Ideas'.There are close parallels between Holland's general typology of careers, and the structure we have found in medical careers. Medical specialities typical of Holland's six RIASEC categories are Surgery , Hospital Medicine (Investigative), Psychiatry (Artistic), Public Health , Administrative Medicine (Enterprising), and Laboratory Medicine . The homology between medical careers and RIASEC may mean that the map can be used as the basis for understanding career choice, and for providing career counselling. Medical careers begin as undifferentiated, and postgraduate training ends with most doctors specialised for a specific area of practice. Relatively little is known about the transition from the medical student, who can be seen as a relatively undifferentiated, totipotent 'stem doctor' ,2, potenUK medical education requires undergraduates to study a wide range of medical specialities, and most students will have sampled many of the broad areas of practice by the time they qualify. As a result, it is often assumed that students do not make their career choices until after they have finished at medical school, remaining agnostic about their final speciality choice until that time. However, not only medical school entrants (e.g. ), but evMuch research into medical careers does not take into account the broader research literature on non-medical careers see -25), or , or 25])circumscription and compromise in career choice.The present study takes its origins in three separate sets of theoretical approaches, each of which examines different aspects of careers. None of these approaches, however, concerns medical careers specifically. Neither are they restricted to career choice in adulthood. Furthermore, at least one of them is specifically developmental, emphasising the processes by which career choice occurs and changes. The best place to begin this brief theoretical review is with the work of Gottfredson , who ideCareers differ in their demands, requiring different amounts of intellectual ability, manual skill, long-term commitment, or willingness to work in particular environments, and can be better suited to particular personalities, aptitudes, and physical dispositions. Individuals also differ, having different aptitudes, interests and abilities. Career choice therefore involves people considering the entire range of careers and then circumscribing those which they regard as broadly acceptable, making their eventual choices within that subset.negative, meaning that careers are rejected because they do not have attributes which are consonant with the person making the choice, rather than positively chosen for their special suitability.An important practical point highlighted by studies such as Gottfredson's is that choices tend to be compromise. Because of various practical constraints, certain careers are restricted in the number of people they can accommodate or they are unsuitable in other terms, such as their geographical location or the remuneration they can provide. The eventual career chosen is one that 'satisfices,' [Once circumscription has taken place, a number of possible careers still remain. The second stage of choice is sfices,'  being remap of careers. In her 1981 paper she provides an example a two-dimensional representation of 129 occupations which have been scored in terms of 'Prestige level' (high vs low) and 'sextype rating' (masculine vs feminine). When careers are mapped into this space, the process of circumscription involves drawing an area within which careers are acceptable to a person, being neither too masculine nor too feminine, nor being too high in terms of their prestige and hence effort required, nor too low, and hence insufficiently rewarding. A primary concern of the present study is the nature of the map underlying medical careers, and on which circumscription eventually takes place.Implicit in Gottfredson's conceptualisation is the concept of a Journal of Vocational Behavior published in 2000  the structure of careers and career preferences, ii) the correlations of careers with ability and personality, and iii) the developmental processes by which career choices are made. The question for medical education is the extent to which these approaches are appropriate for understanding medical career choice. If they are valid, then that will allow the much broader research literature from career choice in general to inform the more specific area of medical career choice. Underpinning the models of both Ackerman and Gottfredson is Holland's picture of a relatively simple, two-dimensional career map, onto which ability and personality can project, and on the basis of which career choices can develop. We therefore have two main objectives in this paper; firstly, to use data on career preferences from three separate cohorts of medical students, both at the time of application and in their final year at medical school, in order to derive a map of medical careers. And second, to assess the extent to which this specific map of medical careers is homologous to Holland's more general map of a broad range of careers.career preference, but rather in career similarity. If a student has a preference for career A and career B, but has no interest in career C and D, it follows that career A is probably relatively close to career B on the map, and career C is relatively close to career D, whereas careers A and B are likely to be more distant from careers C and D. A matrix of similarities between all possible pairs of a large number of careers from a large number of students then allows one to construct the underlying map . Thearbitrary to rotation. Turning the map through any angle does not change any of the distances between pairs of objects, and therefore the axes of the map cannot be known – in the case of a geographical map, there is no indication of the north-south and east-west axes. The problem of the arbitrariness of dimensions can be circumvented by means of a variant of MDS known as INDSCAL  [Although conventional MDS can reconstruct the underlying map showing the relations between a number of objects, the map itself is Scaling) ,45. ThisTwo different types of data have been used in the present study. The bulk of the analysis looks at data collected during studies of medical student selection and training, and can be used to map medical careers. A subsidiary, but important, analysis looks at a large convenience sample of people of different ages who were taking part in a survey about careers in general, and were asked about their interest in a range of careers, of which only a few were medical. These latter data allowed us both to calibrate specific medical careers in the context of the general Holland typology, and to validate the INDSCAL methodology for deriving a map of careers.The data were collected during three longitudinal studies of medical student selection, the first of which began in the autumn of 1980, looking at students who had applied for entry to medical school in 1981 ,47, the For the present paper, the analysis of the 1981 and 1986 cohort data considers data on all applicants who replied to our questionnaires, whereas the 1991 cohort, which was very much larger, considers only those questionnaire respondents who entered medical school . In Students who entered medical schools in 1981, 1986 or 1991 (or in a few cases due to deferred or repeated entry, in 1982, 1987 or 1992) were followed up as final-year students in 1986 (or 1987), in 1991 (or 1992) and 1996 (or 1997). Students still in medical school were identified through their medical schools, and questionnaires sent to those medical schools. Response rates were 65%, 50%, and 56% in the follow-up of the 1981, 1986 and 1996 cohorts of students in their final year ,51.The questionnaires used in the study, both at application and in the final year, were detailed, typically covering 16 sides of A4, and the results reported here concern only one of the questions asked. Career preferences were assessed by a question which used the rubric,"Below is a detailed list of specialities in which a medical career can be pursued. Please indicate your attitude towards each speciality as a possible career. If you either know nothing about a speciality, or have no opinions about it at all, simply leave that answer blank".not to go into this".A list of specialities followed, each of which was rated on a five-point scale, for which the categories were, "Definite intention to go into this", "Very attractive", "Moderately attractive", "Not very attractive", and "Definite intention The list of specialities varied a little over the different surveys, becoming slightly more extensive as the years passed. The original list was based on the questionnaire distributed as part of the Royal Commission on Medical Education of 1968   and by sex. It asked about the suitability of twenty-four different careers for the person. Twenty careers were derived, as far as possible, from Holland's RIASEC classification, with at least three in each of the six categories. In addition there were four categories which were medical . The rubric was,"Below is a list of careers. Please indicate for each one how much you think it might have been suitable for you as a career".Each career was rated on a five-point scale, ranging from 'Extremely suitable', through 'Very suitable' and 'Quite suitable', to 'Not very suitable' and 'Completely unsuitable'. The subjects were a convenience sample obtained from amongst friends and relations by a first year lab class at University College London, each student being responsible for obtaining a group of twelve subjects, stratified by age and sex.INDSCAL analysis was carried out using the ALSCAL program within SPSS 10.1. Data in each subset were broken down into four groups by age and sex, age referring to mature vs non-mature students in the medical student samples  and to subjects aged <42 or ≥ 42 in the general population sample.The raw data, which were collected on a 5 point Likert scale, were transformed into Euclideanbased dissimilarities for all combinations of career pairs, using the PROXIMITIES program in SPSS. Four different dissimilarity matrices were produced , and these matrices provided the basis for the INDSCAL analysis that involved minimisation, in Euclidean space, of the discrepancies between the career dissimilarities and the corresponding interpoint distances on the map. The loadings of each career on the two extracted dimensions were then plotted onto the figures to provide the maps.The dimensionality of MDS/INDSCAL analyses can be assessed, in a manner analogous to that used in factor analysis in which eigenvalues are plotted against components. In MDS one plots a measure of 'stress' (in effect, the opposite of goodness-of-fit) against the number of dimensions which have been extracted. If too few dimensions have been extracted then the stress is high, the model not accounting adequately for the richness of the data. The optimal number of dimensions is typically indicated by a sudden 'dog-leg' in the stress plot.We consider firstly the general population sample since it both validates the method which we will subsequently use for the medical student samples, and also helps calibrate the axes.The questionnaire was completed by 1044 subjects, 49.2% of whom were male, and 46% aged over 42. Multidimensional scaling used the INDSCAL method, with the four groups comprising older and younger males and older and younger females. The stress plot indicated that there were two major underlying dimensions in the data, as Holland's typology would suggest . TheOf some importance, given the arbitrariness of the Holland hexagon to rotation in conventional MDS, is that the INDSCAL analysis clearly sets one axis as running from R to S, with the other axis orthogonal to that, running from I and A to C and E. These are similar to the Things-People and Ideas-Data dimensions shown in figure applicants in the 1981 and 1986 cohorts, and all entrants in the 1991 cohort), and were 330, 376 and 1437 for the final-year students in the 1981, 1986 and 1991 cohort studies. The INDSCAL analyses were restricted to those subjects for whom complete career information was available; this consisted of 538 applicants and 312 final-year students in the 1981 cohort, 1118 applicants and 301 final-year students in the 1986 cohort, and 1638 entrants and 1437 final-year students in the 1991 cohort. See Sample sizes for the medical student studies were 1135, 2032 and 2973 for the students in the 1981, 1986 and 1991 cohorts , separately for the combined applicant data and the combined final-year data from the three cohorts. The stress formula attempts to quantify the discrepancies between the fitted distances in the model and the observed dissimilarities among the career ratings, with larger values indicating poorer fit. It is obviously the case that the more dimensions are extracted, the better the fit of the model and, hence, the lower the stress value. However, it is also the case that a greater number of dimensions complicates interpretation and may lead to overfitted and unstable solutions. The stress levels with 1,2,3,4,5, and 6 dimensions were .352, .174, .112, .077, .059 and .048 for applicants, and .390, .174, .112, .082, .064 and .052 for final-year students. For the final-year students, it is clear that two dimensions are necessary, and that there is little advantage of adding extra dimensions. The applicant data are slightly less clear and although there is still no doubt that at least two dimensions are necessary there is a suggestion that a third dimension may be of value. Subsequent scrutiny of models with three dimensions suggested that the third dimension was contributed almost entirely by one or two specialities such as forensic medicine, which have a high public and media profile, but which form only a small proportion of medical personnel. It was, therefore, felt to be safe to extract two dimensions, particularly since Holland's typology provided an i.e. >21 yrs old; or typical post-school entry, at ≤ 21 years old). INDSCAL analyses can clarify the underlying dimensions within data as long as the sub-groups are likely to vary along those dimensions. It should be noted that many studies have found sex differences in medical career interest  and age , and yost . In each of the six analyses, the male subjects put more emphasis on the Things-People dimension whereas the female subjects put more emphasis upon the Data-Ideas dimension . In the 1981 cohort there is also a suggestion that younger subjects put more emphasis on the Things-People dimension, and older subjects on the Data-Ideas dimension for differentiating careers, but the effect is smaller in the 1986 cohort, and barely visible in the 1991 cohort, suggesting a possible change in the way these groups perceive medical careers. In interpreting these analyses it should be noted that although the absolute size of the various groups was more than adequate for the INDSCAL analyses, the group weights for the mature candidates  in the 1981 and 1986 finalyear data were based on very small samples, ranging between 5 and 17 participants, and may, therefore, be somewhat unstable.The maps shown in figures The primary objectives of this study were to use the empirical method of individual differences scaling to derive maps of the underlying perceived structure of medical career specialities, and to assess the extent to which those maps are similar to those described by Holland in his hexagonal representation of the RIASEC groups of careers. That this method is a valid way of deriving Holland's structure in general is seen in figure all of the four medical specialities are seen by the general public as being primarily concerned with Ideas rather than with Data, as surely befits medical careers.The general population sample also rated four medical specialities, with Surgery and Anaesthetics at one extreme, and Psychiatry at the other, and these medical specialities differed principally along the R-S dimension. That Surgery and Anaesthetics are more concerned with Things, and Psychiatry is more concerned with People fits well with the reduction of Holland's hexagon to the two dimensions of Things-People and Ideas-Data. It is also worth noting that The MDS analyses demonstrate that the representation of the various medical specialities by the medical student samples can be captured within a two-dimensional space, as Holland had suggested. The maps shown in figures From scrutinising figures Surgeons can be seen as the engineers of medicine, solving problems at high levels of mechanical and technical proficiency, with an emphasis upon practical skills, craftsmanship, and immediate and effective results.The core of Hospital Medicine  is diagnosis, achieved by carrying out appropriate investigations. Physicians typify the model of the 'scientist-practitioner', investigating symptoms and signs and relating them to the underlying pathophysiology of the patient.Psychiatrists, and also General Practitioners, have a more artistic approach to medicine, seeing, interpreting and responding imaginatively to a range of medical, social, ethical and other problems. The emphasis in many ways is on the uniqueness of the patient, the ideas that they are expressing, and the psycho-social theories and concepts which are necessary for interpreting the individual.Although most medicine is concerned with individual patients, the remit of Public Health is primarily social in the sense of applying medicine to society as a whole, treating the 'body politic'. It is noteworthy that in the maps, Public Health is not only at the Social end, but also closer to Data than to Ideas. Public Health manages social and community health by the appropriate analysis of data.The management of hospitals and health-care requires the creative skills of the business executive, the lawyer and the personnel director to achieve a smoothly running system. People, both patients and carers, are at the heart of any health-care system, and therefore administrative medicine is at the People end of the dimension.The running of efficient systems in haematology, histopathology or chemical pathology requires many of the attributes shared with the accountant or the banker, including the willingness to develop, implement and follow standard procedures within a complex system. The emphasis is inevitably upon the things that do the measurements, and upon the data collected, rather than the ideas or people behind the data and the technology.The analyses in this paper suggest that in our groups of students there is a broad similarity between preferences for medical careers and the typology found by Holland in careers in general, suggesting that the structures are homologous. Although our study has been restricted to medical students in the UK, our findings are likely to be generalisable, given that the patterns are found in three separate cohorts studied over a decade, and across medical school applicants and final-year students. Just as Holland's typology is found in most studies of careers, over a period of three decades and in many countries, despite a wide range of changes in society, in education, and in the nature of jobs and careers themselves, so we would predict that our typology of medical careers will be robust to such changes. To put it more strongly, we would predict that despite enormous changes in every aspect of medicine over two and a half millennia, just as Hippocrates recognised that surgery is different in many ways from other branches of medicine, and that not every doctor wishes or is able to be a surgeon, so the same applies today and will probably continue to apply as long as medicine is practised. That is likely to be so primarily, as Ackerman has suggested, because Holland's typology is underpinned by wide-ranging, broadly defined individual differences in aptitude and personality  which arIt may at first be felt that our approach to mapping careers is fundamentally different to that of Gale and Grant ,59, who INDSCAL Individual differences scalingMDS Multidimensional scalingRIASEC Realistic-Investigative-Artistic-Social-Enterprising-ConventionalSpeciality abbreviations in figures ADM Administrative MedicineANS AnaestheticsARM Armed ForcesBMS Basic Medical SciencesDRM DermatologyENT Ear, Nose & ThroatFRN ForensicGER GeriatricsGPlrg GP Large Group practiceGPsml GP Small practiceGPsng GP Single handedGUM Genito-urinary medicineIND Industrial MedicineINF Infectious diseasesLAB Laboratory MED Internal MedicineO&G Obstetrics & GynaecologyO&T Orthopaedics & TraumaOPH OphthalmologyPED PaediatricsPHM Pharmaceutical MedicinePSY PsychiatryPTH PathologyPUB Public HealthRAD Radiology/ RadiotherapyRDL RadiologyRDT RadiotherapyRES ResearchSRG SurgeryThe authors declare that they have no competing interests.ICM had collected the data in the various surveys over a number of years. ICM and KVP jointly decided how to do the statistical analysis, and KVP was responsible for the programming and data analysis. ICM wrote the first draft of the paper, which was revised by KVP, with both authors being responsible for the final draft.The pre-publication history for this paper can be accessed here:Additional analyses of dataClick here for file


While studies of non-model organisms are critical for many research areas, such as evolution, development, and environmental biology, they present particular challenges for both experimental and computational genomic level research. Resources such as mass-produced microarrays and the computational tools linking these data to functional annotation at the system and pathway level are rarely available for non-model species. This type of "systems-level" analysis is critical to the understanding of patterns of gene expression that underlie biological processes.FunnyBase that has been used to store, annotate, and analyze 40,363 expressed sequence tags (ESTs) from the heart and liver of the fish, Fundulus heteroclitus. Primary annotations based on sequence similarity are linked to networks of systematic annotation in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and can be queried and computationally utilized in downstream analyses. Steps are taken to ensure that the annotation is self-consistent and that the structure of GO is used to identify higher level functions that may not be annotated directly. An integrated framework for cDNA library production, sequencing, quality control, expression data generation, and systems-level analysis is presented and utilized. In a case study, a set of genes, that had statistically significant regression between gene expression levels and environmental temperature along the Atlantic Coast, shows a statistically significant (P < 0.001) enrichment in genes associated with amine metabolism.We describe a bioinformatics pipeline known as FunnyBase can be accessed at . Data and source code are available by request at jpaschall@bioinfobase.umkc.edu.The methods described have application for functional genomics studies, particularly among non-model organisms. The web interface for  Fundulus heteroclitus (killifish) [Investigating patterns of gene expression using mouse and human microarrays has produced insights into cancer ,2, cardillifish) ,29.Fundulus heteroclitus are distributed along the eastern coast of North America which has one of the steepest thermal clines in the world: northern populations have environmental temperatures more than 12°C below southern populations across 12 degrees of latitude. Migration among populations is sufficient to minimize random genetic drift [Fundulus species can be used to discern adaptive changes [F. heteroclitus an ideal species to investigate adaptive variation in gene expression.The killifish ic drift  but not ic drift ,32. Popu changes ,33,34. TMicroarrays from diverse EST collections offer opportunities to address many biological problems, but to effectively use this information often requires a locally generated bioinformatics approach. Tools like the TIGR Gene index  and UnigFunnyBase to meet these functional genomics needs. FunnyBase provides functional information for >40,000 ESTs from the teleost fish Fundulus heteroclitus, provides the means to quickly process, evaluate, and store annotation based on similarity searches of public resources, and integrates these data with species-specific clustering and microarray analysis. Perhaps ironically, the greatest challenge for functional annotation based on similarity searches is an overabundance of data. There are a number of databases to chose from, and often the single best hit from a given database search is not the most informative. FunnyBase implements a strategy to make maximum use of systems-level functional information from Gene Ontology (GO) [We have developed ogy (GO)  assignmeogy (GO) . SpecifiFunnyBase can be systematically analyzed in the context of biological functions. We present a case study to illustrate how assessment of systems-level annotation can identify statistically significant functional differences among sets of genes.Microarray data using genes annotated in FunnyBase , metabolic or signal pathway (e.g., oxidative phosphorylation), or biological function (e.g., protein translation). Sequence data from the first module and functional annotation from the second are matched using database similarity searches (BLASTX and BLASTN). E-values, bit scores and local alignments are stored in the 'Similarity Data' table for all significant matches. One of the strengths of wissProt , NCBI UnwissProt , and NCBwissProt ) to provFunnyBase annotation is accessible through the web or through local SQL queries and data-mining scripts.The third module, Microarray Production & Analysis provides a list of unique genes to be printed and integrates expression data from microarray experiments with the Hierarchical Annotation module. This provides functional annotation for expression data. Fundulus cDNAs was (1) generate a high quality unidirectional cDNA library, (2) normalize the library, (3) randomly pick colonies and amplify by PCR the cDNA within the vector, (4) sequence and identify PCR products, and (5) after approximately every 1,000 clones, subtract these from the normalized library and repeat steps 3–5. Details for all protocols are provided at  and were used in the Comparative Functional Genomic course at Mount Desert Island, ME 2000.The overall strategy used to isolate and sequence thousands of FunnyBase are identified by a number series: unique sequence number, array number, plate number and well identifier (example: 23434_125_001_H04). The remaining 6,966 un-submitted sequences failed to meet one of the sequence quality parameters. Two criteria are used for defining "good" sequences: 1) >100 bp of sequence with Phred score >20 or 2) form an overlapping cluster with other sequences. Of the 19,937 sequences processed with the current version of FunnyBase, 17,893 (90%) passed one of these quality parameters. In earlier iterations of FunnyBase, visual inspection and later a sequencer-specific quality measure equivalent to a Phred score of 15 were used as filters resulting in 3,603 of the first 5,760 sequences (63%) and 13,922 of the next 15,168 sequences (92%) meeting quality standards, respectively. Re-sequences account for 5,668 sequences, and 4,625 of these were submitted.We sequenced 46,433 ESTs and 40,043 of these are available in the dbEST database at NCBI  as of June 26, 2004. Sequences in FunnyBase system has a number of integrated quality control steps. First, a Ctenophore cDNA (NCBI: accession number: CN992733) that is unlike anything else in GenBank is used as a control. Controls are placed in 96-well plates in wells corresponding to the plate number and two orientation wells (A5 and F9). Sequences from 96-well plates are automatically scanned for these controls so that the identity and orientation are confirmed and a report is generated for manual review. Secondly, all clones used for microarray production are re-sequenced. This is necessary because individual cDNAs are cherry picked, re-grown and re-amplified, and each of these steps has the potential to introduce or magnify an error. For example, for a 6,000 gene array, a 5% error rate would result in 300 incorrect clones. Re-sequenced array plates are compared using pair-wise BLAST [One of the most important steps for producing microarrays from cDNA libraries is being able to associate the bacteria containing the cDNAs of interest with the EST annotation. High-throughput procedures are highly prone to tracking errors including: loading plates into an automatic sequencer in the wrong order, orienting symmetric plates in the wrong direction, or mislabeling of plates. The ability to identify these types of mistakes requires controls for identifying plates and plate orientation. The se BLAST  against EST projects generate a number of redundant sequences due to the random selection of cDNAs from tissue libraries Table . ClusterFunnyBase contains a total of 40,043 EST sequences from F. heteroclitus heart and liver. Clustering with CAP3 yields 3,776 clusters that contain 30,688 ESTs (77%). The remaining 8,991 ESTs (23%) are singletons. By storing the results of clustering with annotation, FunnyBase easily identifies these genes and aids in the selection of genes to be used for library subtraction with the goal of picking less common transcripts.The 10 annotated clusters with the most sequences are listed in Table FunnyBase have two or three-to-four sequences  were annotated as compared to 333 of 1,131 (30%) of confirmed high quality singletons. The lower percent of annotated singletons suggests that these are either rare fish-specific genes, or represent otherwise divergent, likely non-coding (5' or 3' UTR), regions.Of the 12,776 unique ESTs (3,776 clusters and 8,991 singletons), 3,877 (30%) were annotated. The distribution of e-values for these annotations is shown in Figure Danio rerio UniGene, Oncorhynchus mykiss Unigene, Oryzias latipes Unigene, or GenBank NR. FunnyBase includes locally parsed copies of these databases in a relational format. Thus, all searches are done locally and annotation features beyond the FASTA description can be queried. Consensus sequences from the Fundulus EST clusters as well as high quality single unique sequences (singletons) are used as query sequences for BLAST searches against these public databases. The use of consensus sequences, when available, allows sequences that do not contain regions of significant similarity with known protein (e.g., 5' or 3' noncoding regions) to be annotated if they are members of an annotated cluster. All hits with e-value less than 0.001 and their associated alignments are stored in the database and tracked with any associated functional annotation. Users can specify a custom level of significance when assessing the validity of homology based annotation. This record, which goes beyond storing a certain number of 'best hits', is critical because in many cases additional results may have a negligibly lower alignment score, but provide much more useful functional data.Annotations for ESTs are based on similarity using BLASTX or BLASTN  to sequesystematic functional annotations below). Human Unigene provided an additional 311  annotations. SwissProt provided an additional 32 annotations  with 743 fewer annotated sequences than the NR. However, SwissProt is uniformly well annotated as compared to NR where informative functional annotation can easily be buried by numerous uninformative hits at similar e-values. Besides increasing the number of annotations, comparing the annotations from multiple databases ensures that mistakes in the curation can be detected and information such as alternative gene names can be compiled from multiple sources.The use of multiple databases increases the total number of annotated ESTs Fig.  as compaFunnyBase includes locally parsed representations of public databases such as SWISS-PROT in a relational database format. These databases provide additional information that cross-references other public resources such as GO, KEGG or OMIM [In conjunction with performing similarity searches by BLAST,  or OMIM  that areFunnyBase links annotated genes to enzymes in KEGG pathways based on enzyme commission (EC) numbers. These pathway associations are stored and queries can readily identify genes from a given pathway that show specific patterns of expression. For visual inspection of the pathway, the web interface  links directly to the graphical KEGG pathways in which a gene occurs.KEGG  is a uniFunnyBase, 588 (14%) participate in one or more pathways defined by KEGG. These 588 ESTs represent 105 different pathways. Table FunnyBase for the 10 pathways associated with the largest number of distinct sequences (contigs or singletons). The extent that a given pathway is represented in FunnyBase can be used to identify metabolic differences among tissues [Of the 3,877 annotated ESTs in  tissues  or in diFunnyBase includes two non-trivial steps to make the best possible use of GO terms. First, many GO annotations are lost if only the single 'best hit' from a homology search is considered because GO annotation is applied most often to a few model species such as human that may not appear as the single 'best hit' in a list of BLAST results. FunnyBase identifies the gene name associated with the 'best hit' BLAST result and then uses all GO annotation associated with hits from the complete BLAST results that have the same gene name as the 'best hit' and an e-value of e < 10-12. The goal of this approach is to identify annotation associated with a single 'best hit' gene based on results that may come from multiple species (orthologous genes) and therefore may have varying degrees of sequence similarity due to phylogenetic distance, but to avoid the problem of selecting an inconsistent set of GO terms arising from gene families that share regions of sequence similarity but may have different functions.The Gene Ontology project (GO) has produced a structured vocabulary in the form of an acyclic directed graph that biologists can use to annotate genes in a systematic manner . FunnyBaRNA polymerase II transcription factor activity, enhancer binding (GO:0003705) rather than the more general parent term transcription regulator activity (GO:0030528). However, in functional genomic analysis, significant patterns of expression may exist at the more general level of functional description. FunnyBase takes advantage of the connected parent-child relationship of GO terms provided by using the relational database version of GO available for download at  to identify such relationships. These data are used to extract the tree of more general GO terms related to those provided by public databases. A FunnyBase script then re-annotates genes with this more complete set of GO terms.Secondly, GO annotation in public databases tends to annotate sequences with only the most specific GO term available, for example Of the 3,877 annotated genes, 1,912 (54%) are assigned one or more GO terms with a total 6,728 GO assignments being made directly based on information in public databases such as SwissProt. Using parent-child GO term relationship backtracking, an additional 34,112 GO term assignments were made, resulting in a final count of GO assignments of 36,024 excluding the most general terms that divide GO into three categories. Thus, on average, 19 GO terms are assigned to each of 1,912 annotated genes.Fundulus clusters that were similar to a sequence in Human RefSeq , 1,818 (76%) had distinct gene annotations. This method of clustering clusters by similarity to well-annotated reference sequences provides a method to more accurately define the number of unique genes represented by an EST set.Humans have approximately 30,000 expressed genes, yet there are over 1,000,000 human UniGenes (NCBI). Clearly, these clusters of cDNAs greatly overestimate the number of unique genes. Similarly, FunnyBase has multiple clusters for the same gene: 15 apolipoprotein I, 10 cytochrome oxidase I, and 53 vitellogenin clusters. To provide a more precise estimate of the number of unique genes, consensus sequences were queried against the 27,695 sequences from the Human RefSeq  databaseFunnyBase can be integrated with microarray data in a rigorous manner, we used a data set based on a microarray of metabolic genes printed from ESTs annotated in FunnyBase [Amine Metabolism (GO:0009308) is assigned to 14% of the 62 statistically significant genes but only 3% of the non-significant genes (those that do not show significant regression with temperature). A Fisher-exact test indicates these frequencies (14% vs. 3%) represent different underlying distributions (p < 0.001). Specifically, genes involved in amine metabolism are overrepresented in the set of genes that show regression with temperature as compared with the remaining sequences. This significant increase is found for two other non-mutually exclusive GO terms: amino acid and derivative metabolism, and amino acid metabolism (p < 0.05). Other GO terms show a reverse trend although none were statistically significant. For example, ion transport (p = 0.08) and cell growth (p = 0.16) had few genes with a clinal variation in expression. These data suggest that the functions of genes influence whether they are affected by ecologically interesting patterns of expression . These PCR products (PCR-cDNAs) were denatured and hybridized to single stranded plasmids from the cardiac cDNA library. Taking advantage of Cot values, the most abundant cDNAs were annealed to the more abundant PCR products and were removed selectively by hydroxyapatite-column chromatography. The single-stranded plasmids in the flow-through were converted to double strands using the Sequenase DNA polymerase (Amersham, Piscataway, NJ, USA). DH10s E. coli (BRL) were transformed with these double-stranded plasmids by electroporation. The number of recovered plasmids and the resulting complexity of the normalized library depended on the duration of hybridization or Cot values. Two normalized libraries were made using either a 12 or 24 hour hybridization. The library from the 12-hour hybridization yielded 250,000 plasmids. The library from the 24 hour hybridization yielded 3,000 plasmids and had a greater representation of rare mRNAs and greater frequency of non-recombinants.Normalization of cDNA libraries reduces the differences among expressed genes to less than 10-fold among rare and abundant mRNAs ,45. Norm4)2SO4, 2.25 mM MgCl2, 2% (v/v) DMSO, 0.1% (v/v) Tween 20). Two-step thermal cycle conditions were used . PCR products were purified manually in 96 well format using Sephadex G-50 in a deep well plate with a 0.2 microfilter (Millipore, Billerica, USA) or robotically using AmPure (Agencourt, Beverly, MA, USA) and EvolP3 96 pipetting liquid handling system (PerkinElmer Life Sciences Inc., Boston, MA, USA).Characterization of cDNAs  used 96 well plates and octopipettes. To characterize cDNAs, 96 individual bacterial colonies from the normalized library were randomly chosen, and each was grown in 1.25 ml of Superbroth in 2 ml-96 well plates. After 18 hours of growth, two 250 ul bacterial glycerol stocks were made and stored in 96 well plates at -80°C. One microliter of these bacterial growths was used for PCR reactions using forward and reverse plasmid specific primers: (PucF = CGCCAGGGTTTTCCCAGTCACG, PucR = GAGCGGATAACAATTTCACACAGGAAA). PCR reactions had 0.2 mM dNTPs, 10 pmoles of each primer, 1 unit of Promega Taq (0.2 ul), and reaction buffer with detergents and DMSO , 16 mM (NHPCR products were sequenced from the 5' end (relative to the mRNA) on an ABI 373 or ABI 3730 sequencer using ABI "Big Dye" reaction mix. We typically used 1/16 the amount of reaction mix, yielding 300 to 400 unambiguous bases. Sequences were purified using biotin primers and streptavidin coated magnetic beads (for the ABI 373) or Agencort CleanSeq (for the ABI 3730).Ctenophore cDNA #5, a random cDNA with no similarity to any sequence in GenBank). Two wells (#40 and #67) always contained the marker cDNA, and thus any misloading or mislabeling of sequencing lanes was identifiable. The third marker cDNA was placed in a well that corresponds to the plate number (e.g., plate 2 had the marker in well 2). 2) After the production of 12 plates, one row (8 wells) from each plate was re-sequenced. Thus, 8/96 or ~8% of all sequences and their locations were confirmed. 3) cDNAs used for microarrays were re-sequenced. These measures are important to ensure that the correct and known cDNAs are printed.We used three procedures to verify that the correct sequence was associated with each cDNA. 1) Each 96-well plate had three wells with a "marker cDNA" . After a 24 hour hybridization, genes in the library that bound to these biotin-labeled PCR products were removed with the use of magnetized, streptavidin coated beads. DH10s Computational work was done on an Apple G5 dual 2 GHz processor system with 4 GB of RAM. Data are stored in a MySQL database, perl scripts were used extensively for parsing and loading data, and PHP was used on an APACHE web server to construct the user interface. Additional programs available from their authors are mentioned within context. Software and databases are described in Table F. heteroclitus cardiac library encoding essential proteins for cellular metabolism isolated from over 40,000 expressed sequences . These 384 cDNAs were amplified with amine-linked primers and printed on 3-D Link Activated slides (Surmodics Inc., Eden Prairie, MN, USA) using GeneMachine OminGrider, and blocked following slide manufacturer protocols. The suite of 384 amplified cDNAs was printed as a group in four spatially separated replicates. Four hybridization zones of these four replicate arrays were printed per slide, with each zone set separated by a hydrophobic barrier. Samples were hybridized twice; once with Cy3 and once with Cy5 resulting in overall technical replication of 8-fold per sample.Microarrays were printed using a select 384 cDNAs from RNA was extracted from tissue homogenate in a chaotropic buffer using phenol/cholorform/isoamyl alcohol and RNA quality was assessed using the Agilent 2100 Bioanalyzer . RNA for hybridization was prepared by amplification using a modified Eberwine protocol  using thFunnyBase and provided statistical analyses of database. MFO initiated, designed protocols and provided sequences for F. heteroclitus' EST project. JDV optimized robotic interfaces for sequencing and sequenced ESTs. JLR and KJK sequenced ESTs. GJW collaborated on bioinformatics and database development. JAW provided microarray data and analyses. DLC initiated F. heteroclitus' EST project and developed the database and annotation schemes for FunnyBase. All authors read and approved the final manuscript.JP designed, scripted and implemented


Arabidopsis splicing related genes includes classification of genes encoding snRNAs and other splicing related proteins, together with information on gene structure, alternative splicing, gene duplications and phylogenetic relationships.The database of  Arabidopsis genome by sequence comparison and motif searches, including the previously elusive U4atac snRNA gene. Most of the genes have not been studied experimentally. Classification of these genes and detailed information on gene structure, alternative splicing, gene duplications and phylogenetic relationships are made accessible as a comprehensive database of Arabidopsis Splicing Related Genes (ASRG) on our website.A total of 74 small nuclear RNA (snRNA) genes and 395 genes encoding splicing-related proteins were identified in the  Although the splicing mechanisms in plants and animals appear to be similar overall, incorrect splicing of plant pre-mRNAs in mammalian systems (and vice versa) suggests that there are plant-specific characteristics, resulting from coevolution of splicing factors with the signals they recognize or from the requirement for additional splicing factors (reviewed in [Most eukaryotic genes contain introns that are spliced from the precursor mRNA (pre-mRNA). The correct interpretation of splicing signals is essential to generate authentic mature mRNAs that yield correct translation products. As an important post-transcriptional mechanism, gene function can be controlled at the level of splicing through the production of different mRNAs from a single pre-mRNA (reviewed in ). The geiewed in ,3).Drosophila and fission yeast genomes in an effort to identify potential homologs [Drosophila and fission yeast. The availability of the near-complete genome of Arabidopsis thaliana [et al. [et al. [et al. [Arabidopsis protein kinases and computationally compared derived protein clusters with established gene families in budding yeast. Previous surveys of Arabidopsis gene families that contain some splicing-related genes include the DEAD box RNA helicase family [Arabidopsis Information Resource (TAIR) links to more than 850 such expert-maintained collections of gene families [Genome projects are accelerating research on splicing. For example, with the majority of splicing-related genes already known in human and budding yeast, these gene sequences were used to query the homologs ,5. Most thaliana  provides [et al.  identifi [et al.  catalogu [et al.  surveyede family  and RNA-e family . At presfamilies .Arabidopsis genes involved in pre-mRNA splicing. Recent mass spectrometry analyses revealed more than 200 proteins associated with human spliceosomes , which should facilitate genome-wide studies of pre-mRNA splicing in plants.Here we present the results of computational identification of potentially all or nearly all eosomes  and TATA box -27. All 3-10AGTNNAA in U snRNAs (U1, U2, U4 and U5) transcribed by RNA polymerase II (Pol II) [Nicotiana plumbaginifolia protoplasts [Arabidopsis.Other conserved transcription signals were also identified in most active snRNAs, including the sequence element CAANTC (where N is either A, C, G or T) in U2 snRNAs (located at -6 to -1) , and the(Pol II) ,24,32. T(Pol II)  is also toplasts , the extArabidopsis [U6atac gene (atU6atac-2) by sequence mapping. This gene has a USE and a TATA box in the promoter region. The atU6atac-2 gene is more than 90% similar to atU6atac in both its 5' and 3' ends, with a 10-nucletotide deletion in the central region. The putative U4atac-U6atac interaction region in atU6atac-2 is 100% conserved with the interaction region previously identified in atU6atac [The minor snRNAs are functional in the splicing of U12-type (AT-AC) introns. Four types of minor snRNAs, which correspond to four types of major snRNAs, exist in mammals. U11 is the analog of U1, U12 is the analog of U2, U4atac is the analog of U4, and U6atac is the analog of U6. The U5 snRNA seems to function in both the major and minor spliceosome . Two minbidopsis . Both haatU6atac ,35.Arabidopsis. BLAST searches using the human U11 and U4atac homologs as queries against the Arabidopsis genome failed to find any significant hits, indicating divergence of the minor snRNAs in plants and mammals. Using the strategy described below, we successfully identified a putative Arabidopsis U4atac gene. It is a single-copy gene containing all conserved functional domains. We also found a single candidate U11 snRNA gene (chromosome 5, from 17,492,101 to 17,492,600) that has the USE and TATA box in the promoter region. This gene also contains a putative binding site fr Sm protein and a region that could pair with the 5' splice site of the U12-type intron.U11 and U4atac have not been experimentally identified in Arabidopsis homologs. Therefore, we deduced the tentative AtU4atac stem II sequence (CCCGTCTCTGTCAGAGGAG) from AtU6atac snRNA and searched for matching sequences in the Arabidopsis genome. Hit regions together with flanking regions 500 base-pairs (bp) upstream and 500 bp downstream were retrieved and screened for transcription signals (USE and TATA box). One sequence was identified that contains both the USE and TATA box in appropriate positions, as shown in Figure Like U4 snRNA and U6 snRNA, human U4atac and U6atac snRNAs interact with each other through base pairing . The samU4atac snRNA gene contains not only the stem II sequence, but also the stem I sequence that presumably base-pairs with U6atac snRNA stem I. Furthermore, a highly conserved Sm-protein-binding region exists at the 3' end. The predicted secondary structure is nearly identical to hsU4atac, with a relative longer single-stranded region (data not shown). With the highly conserved transcriptional signals, functional domains and secondary structure, this candidate gene is likely to be a real U4atac snRNA homolog. We named it AtU4atac and assigned At4g16065 as its tentative gene model because it is located between gene models At4g16060 and At4g16070 on chromosome 4.The tentative Arabidopsis chromosomes [Arabidopsis U1-U4 clusters have the U1 snRNA gene located upstream of the U4 snRNA gene, with a 180-300-nucleotide intergenic region. Five of the U1-U4 arrays are located on chromosome 5 (U1a/U4.1, U1-4/U4-5, U1-8/U4-7, U1-9/U4-8, and U1-13p/U4.3p), and the remaining two on chromosome 1 (U1-10/U4-6 and U1-14p/U4-10). The U2-17 and U5-10 occur in tandem array on chromosome 5, separated by fewer than 200 nucleotides.Some snRNAs genes exist as small groups on the omosomes . We idenDrosophila are known [Arabidopsis. Sequence-similarity scores and comparison of gene structure and protein domain structure were used to assign the genes to families. Each gene was assigned a tentative name based on the name of its respective animal homolog. Different homologs within a gene family were labeled by adding an Arabic number (1, 2, and so on) to the name. Close family members with similar gene structure were indicated by adding -a, -b, and -c to the name. The 395 genes were classified into five different categories according to the presumed function of their products. Ninety-one encode small nuclear ribonucleoprotein particle (snRNP) proteins, 109 encode splicing factors, and 60 encode potential splicing regulators. Details of EST evidence, alternative splicing patterns, duplication sources and domain structure of these genes are listed in Table Arabidopsis proteins corresponding to 54 human spliceosome-associated proteins. The remaining 51 genes encode proteins with domains or sequences similar to known splicing factors, but without enough similarity to allow unambiguous classification. These two categories are not discussed in detail here, but information about these genes is available at our ASRG site [Most of the proteins involved in splicing in mammals and re known ,37,38. Ire known ). Using Arabidopsis. All U snRNPs except U6 snRNP contain seven common core proteins bound to snRNAs. These core proteins all have an Sm domain and have been called Sm proteins. The U6 snRNP contains seven LSM proteins ('like Sm' proteins). Another LSM protein (LSM1) is not involved in binding snRNA (reviewed in [There are five snRNPs (U1, U2, U4, U5 and U6) involved in the formation of the major spliceosome, corresponding to five snRNAs. Five snRNPs (U1 snRNP, U2 snRNP, U5 snRNP, U4/U6 snRNP and U4.U6/U5 tri-snRNP) have been isolated experimentally in yeast or human -45. Eachiewed in ).Arabidopsis, and eight of them are duplicated. It is likely that these genes existed as single copies in the ancestor of animals and plants, but duplicated within the plant lineage. Only one of the 24 genes (LSM5, At5g48870) has been characterized experimentally in Arabidopsis. The LSM5 gene was cloned from a mutant supersensitive to ABA (abscisic acid) and drought . LSM5 it stress . cDNA anArabidopsis proteins corresponding to the 35 snRNP-specific proteins used as queries in our genome mapping. Very few of them have been characterized experimentally, including U1-70K, U1A and a tandem duplication pair of SAP130 [U1-70K was reported as a single-copy essential gene. Expression of U1-70K antisense transcript under the APETALA3 promoter suppressed the development of sepals and petals [U1-70K (At2g43370) and named it U1-70K2. The U1-70K2 proteins showed 48% similarity to the U1-70K protein according to Blast2 results. Both genes retain the sixth intron in some transcripts, a situation which would produce truncated proteins [Arabidopsis U1 snRNP proteins, including the U1-70K-coding genes, may undergo alternative splicing.There are 63 f SAP130 -50. U1-7d petals . We idenArabidopsis genome. The atSAP114 family has five members, including two that occur in tandem (atSAP114-1a and atSAP114-1b). Three members have EST/cDNA evidence  subunit, which are clustered on chromosome 4. Both the U4/U6 90 kDa protein and the U4/U6 15.5 kDa protein also have three gene copies, and the 116 kDa and 200 kDa subunits in U5 snRNP have four copies apiece.Several genes in U2, U5, U4/U6 and U4.U6/U5 snRNPs, but none in U1 snRNP, occur in more than three copies in the Arabidopsis also lacks homologs of Prp42, a component of U1 snRNP in yeast [Arabidopsis has two copies of the gene for Prp39, which are similar to Prp42. Furthermore, atPrp39a can produce a shorter protein isoform with a novel amino-terminal sequence by exon skipping. It is possible that the duplicates and alternative isoforms of plant U1 snRNP proteins are functional homologs of the yeast-specific proteins.The yeast U1 snRNP contains several specific proteins that are not present in mammalian U1 snRNPs . As in min yeast . HoweverArabidopsis. The human 18S U11/U12 snRNP contains several proteins found in U2 snRNP as well as seven novel proteins [Arabidopsis, while the remaining three (59K, 48K and 20K) have no clear homologs. Interestingly, all four Arabidopsis genes are single copy in the genome, and three of them are apparently alternatively spliced  is not lethal and is non-pleiotropic. The abh1 plants show ABA-hypersensitive closure of stomata and reduced wilting during drought [We divided the splicing factors into eight subgroups according to recent human spliceosome studies ,14,16,18on yeast ) and capon yeast ), deriveon yeast . These s drought .Arabidopsis, including two U2AF65, two U2AF35, and 18 SR proteins [Arabidopsis genome. Although the Urp gene is expressed ubiquitously in human tissues, no ESTs from this gene were found in Arabidopsis. Three copies of PTB/hnRNP-I genes were identified in Arabidopsis. The PTB protein competes for the poly-pyrimidine tract with the U2AF large subunit, thus negatively regulating splicing [Many splicing factors have been identified previously in proteins -67. The proteins , is alsosplicing .65 (At2g33440) and an additional SR protein (At2g46610). The U2AF65-related protein (atULrp, At2g33440) has 247 amino acids and shares over 40% similarity with the carboxy terminal region of the two atU2AF65 homologs. Only one RRM can be identified in atULrp, in contrast to three RRMs and one amino-terminal RS domain in atU2AF65 proteins, and there is no apparent RS domain in atULrp. No animal homolog of atULrp could be identified. The function of this one-RRM U2AF65-related protein is not clear. As it lacks other functional motifs, it might act as a competitor of U2AF65. A two-RRM U2AF65 protein can be produced through alternative splicing. The 11th intron of atU2AF65a can be retained (see RAFL full-length cDNA, gi:19310596) to produce a truncated protein with only the first two RRMs. Interestingly, the last RRM in atU2AF65a contains several amino-acid variations from the consensus pattern such that it could not be detected by InterPro and NCBI-CDD searches using default values, also suggesting that perhaps only the first two RRMs are essential.We also identified a homolog related to atU2AFatRSp32 is similar to that of atRSp31. Two other genes (atRSp40 and atRSp41) are in the same family and also have similar exon and intron sizes . It shares 70% identity and 78% similarity with atRSp31. The protein is 250 amino acids in length and contains two RRMs and some RS dipeptides in the carboxy-terminal region. The gene structure of 0. It shaArabidopsis has a single copy of the SC35 ortholog and four SC35-like proteins (atSR33, atSCL30a, atSCL30 and atSCL28), which appear to have diverged significantly from SC35. It seems that this divergence predates the split of plants and animals because a similar SC35-like gene family exists in the human genome (SRrp35 and SRrp40). The SRrp35 and SRrp40 were found to antagonize other SR proteins in vitro and function in 5' splice-site selection [Arabidopsis, with three (atRSZp21, atRSZp22 and atRSZp22a) containing one CCHC-type zinc finger and two (atRSZ33, atRSZ32) containing two CCHC-type zinc fingers in addition to an RRM and an RS domain. Interestingly, several SR proteins (atRSZp21, atRSZp22, SR45 and SCL33) were found to interact with atU1-70K, and some SR proteins can interact with each other, thus forming a complicated interaction network to facilitate splice-site selection and spliceosome assembly [RNPS1 gene, which encodes an EJC protein. Other human SR proteins (SRp20, SRp30c, SRp40, SRp54, SRp55 and SRp75) lack clear orthologs in Arabidopsis. We conclude that SR protein families evolved differently in animals and plants from three to four common ancestors, including SC35, SF2/ASF and 9G8/RSZ. The SRrp (SC35-like in plants) family may not be classical SR proteins but they play important roles in splice-site selection.The atRSp31 family (atRSp31, atRSp32, atRSp40 and atRSp41) belongs to a novel plant SR family and has no clear animal ortholog. Other families include the SC35 (or SRrp/TASR2) family, SF2/ASF family, and the 9G8 family. election . SF2/ASFassembly -63. atSRArabidopsis. The BΔU1 complex is the spliceosome complex captured immediately before catalytic activation. Most proteins in the 35S U5 snRNP are absent in the BΔU1 complex but present in the active B complex, indicating the important roles of 35S U5 snRNP-associated proteins in spliceosome activation [Arabidopsis revealed the same pathway of spliceosome activation in plants. A subcomplex named Prp19 complex in 35S U5 snRNP has a critical role in spliceosome activation [Arabidopsis, including a chromosomal duplication pair of Prp19 genes and a single copy of the CDC5 gene. For the BΔU1 complex, six human genes have homologs, and five of them are single copy in Arabidopsis. Two genes (NPW38BP/SNP70 and p220(NPAT)) in the human BΔU1 complex have no apparent Arabidopsis homologs.Proteins in other subgroups, such as 17S U2 snRNP-associated proteins, 35S U5 snRNP-associated proteins, and protein specific to the BΔU1 complex, are also conserved in tivation . Conservtivation ,72. All Arabidopsis also lacks an SMN protein complex. In human, the SMN protein  can interact with a series of proteins including Gemin2, Gemin3 (a helicase), Gemin4, Gemin5 and Gemin6 to form an SMN complex, which has important roles in the biogenesis of snRNPs and the assembly of the spliceosome through direct interactions with Sm proteins and snRNA [Arabidopsis genome.nd snRNA . AlthougArabidopsis. A total of eight SR protein kinases were identified in Arabidopsis, including three Lammer/CLK kinases (AFC1, AFC2 and AFC3), two SRPK1 homologs, and three SPRK2 homologs. The three Lammer/CLK kinases were identified previously, and AFC2 was shown to phosphorylate SR protein in vitro [Arabidopsis changed the alternative splice patterns of several genes, including atSRp30, atSR1/atSRp34 and U1-70K [Splicing regulators are proteins that can either modify splicing factors or compete with splicing factors for their binding site. Important splicing regulators are hnRNP proteins and SR protein kinases. The exact role of phosphorylation of SR proteins in splicing is not yet clear, but SR protein kinases are well conserved and exist as multiple copies in in vitro ,74. Overd U1-70K , indicatArabidopsis by sequence-similarity searches, including a superfamily of glycine-rich RNA-binding proteins. This family contains 21 members similar to human hnRNP A1 and hnRNP A2/B1. It can be further divided into two subfamilies. One includes eight proteins containing one RRM, and another has 13 members with two RRMs. 12 of these proteins were identified previously, including AtGRP7, AtGRP8, UBA2a, UBA2b, UBA2c and AtRNPA/B1-6 [AtGRP8 transcripts [The heterogeneous nuclear ribonucleoproteins (hnRNPs) bind to splice sites and to binding sites for splicing factors on nascent pre-mRNAs, thus competing with splicing factors to negatively control splicing (reviewed in ). HumansNPA/B1-6 ,77,78. Anscripts . UBA2 prArabidopsis. BLAST searches of the human (CUG)n triplet repeat RNA-binding protein (CUG-BP) against all Arabidopsis proteins revealed three putative homologs, including atFCA. atFCA and CUG-BP share similarity within the RRMs and a region approximately 40 amino acids in length. An additional protein (At2g47310) related to FCA was identified and named FCA2, as it shares about 50% similarity with FCA. The FCA proteins have two RRMs and a WW domain, which interact with the FY protein, a homolog of yeast polyadenylation factor Psf2p [Other human hnRNPs related to splicing also have homologs in or Psf2p ,81. The or Psf2p ,82. FCA or Psf2p . We alsoor Psf2p , whereasor Psf2p ,85.Arabidopsis proteins corresponding to human spliceosome-associated proteins identified in recent proteomic studies [Arabidopsis. We also identified a total of 51 Arabidopsis protein-coding genes similar to known splicing proteins. They have conserved domains and some level of sequence similarity to known splicing factors. We did not include these two categories in Table In addition to the 260 proteins in the above three categories, there are also 84  studies -18. SomeArabidopsis snRNA and splicing-related proteins across the genome is shown in Figure atU2A-atCdc5 and atCUG-BP1-atU1C, include genes from different functional categories. No clear clusters of genes for snRNA-splicing-related proteins were identified. Although about one third of snRNA genes are located near other protein-coding genes, none of their neighboring genes is related to splicing. As a caveat, we should point out that our snRNA gene determination strongly suggests annotation errors in overlapping protein-coding gene models. Thus, atU2-1, atU2.3, atU4.2, atU4-11p, atU5-13 and atU6.26 overlap gene models At1g16820, At3g57770, At3g06895, At1g68390, At5g53740 and At3g13857, respectively, but none of these models is well supported by cDNA or EST evidence (see displays linked at ASRG [The distribution of  at ASRG ).Arabidopsis transcription factors [The 260 proteins in the first three categories could be grouped into 130 families, 66 of which consist of multiple members. The duplication rate is over 50%, which is higher than the 44% duplication rate of  factors . As showU2-14/U2-10, U5-3/U5-5 and (U6.1 U6.26)/(U6-8p U6-9p), may also have been produced by chromosome duplication. The C.D.2-3 region (chromosome duplication region between chromosomes 2 and 3, see [U5.1, U5.1b and U5-4) and four U2 snRNA genes (U2.2, U2.3, U2.4 and U2.6) also are located in the same region on chromosome 3. No U5 and U2 homologs exist in the corresponding region on chromosome 2, suggesting that the snRNA duplication events in that region may have happened after the chromosome duplication event, or that the snRNA duplicates were lost subsequent to chromosome duplication.At least 130 duplication events are required to yield the 260 proteins from 130 families given one single-copy ancestor per family. Thirty-three duplication events  are likely to be the result of chromosome duplications. The chromosomal duplication ratio is 18.9-27.5% among the three groups (see Table d 3, see ) has theChromosomal duplication rather than individual gene duplication appears to be the predominant mode of amplification for some types of genes. As shown in Table Arabidopsis, which is about 13% using the same criteria . As shown in Table According to EST/cDNA alignments, 80 of the 260 protein coding genes show 66 alternative splicing events. This rate (30.8%) is much higher than the overall frequency of alternative splicing in Arabidopsis alternative splicing events , the ratio of intron retention in splicing-related genes is similar and the ratio of exon skipping is higher. Interestingly, only one of the 20 splicing regulator genes processed by alternative splicing (about 5%) shows exon skipping, indicating that exon skipping is an important post-transcriptional method for controlling the expression of splicing factor coding genes but not the splicing regulator genes.Among different types of alternative splicing, intron retention is the most abundant of the alternative transcripts identified for the 260 classified splicing-related genes. As shown in Table Arabidopsis , BAD09319 (osSC35b), AAP46199 (osSR33-1), BAC799901 (osSCL30a/osSR33-2), and BAD19168 (osSCL30-1). As shown in the phylogenetic tree displayed in Figure Arabidopsis chromosomal duplication pair atSR33 - atSCL30a forms a clade, while their rice copies (osSR33-1 and osSCL30a) form another clade. Also there are additional duplications for the rice SC35 and SCL30 genes. We are currently working to identify all rice splicing related genes. The complete sets of these genes in two plant species should provide a good foundation for assessing similarities and differences in splicing mechanisms used by monocot and dicot plants.To confirm the above hypothesis, we searched the recently sequenced rice genome using the five Arabidopsis indicates that other organisms also have integrated new proteins or pathways into the splicing mechanism over the course of evolution relative to other eukaryotes. Other evidence supporting the conserved splicing but flexible regulating mechanism include differential conservation among U snRNAs (U1 snRNAs are less conserved than U6 snRNAs) and high alternative splicing frequency in U1 snRNP proteins, SR proteins and hnRNP proteins. The SR proteins and U1 snRNP proteins are involved in early steps of splicing and 5' and 3' splice-site selection; multiple isoforms of these proteins may be functionally significant in the control of splicing.As introns evolve rapidly, the mechanism to recognize and splice them should either evolve correspondingly or be flexible enough to accommodate the changes. It seems that plants deploy the most economic and practical way by keeping a largely conserved splicing mechanism and a very flexible recognition and control mechanism. Direct evidence comes from the presence of plant-specific splicing proteins, such as the novel SR protein family and the superfamily of hnRNP A/B. The absence of SMN complex and some yeast U1 snRNP proteins in Arabidopsis genes. More than half of SR proteins and U1 snRNP proteins show alternative splicing. Alternative splicing might increase protein diversity derived from splicing-related genes, which would further add flexibility to the splicing mechanism. The high frequency of alternative transcripts from splicing related genes raises another interesting question - how is splicing regulated in these splicing-related genes? One possible answer is that some splicing-related genes may be autoregulated. Accumulation of one transcript would feed back to inhibit/promote other isoforms. Several splicing-related genes have been reported to be regulated in this way. For example, AtGRP7 (hnRNP A/B superfamily) is a circadian clock-regulated protein which negatively autoregulates its expression [AtGRP7 and atSRp30 is known to affect the splicing of AtGRP8 and atSR1, respectively [SR1 gene. The ratio of two transcripts from the SR1 gene (SR1B/SR1) increases in a temperature-dependent manner [It is interesting to note that the overall alternative splicing frequency in splicing related genes is much higher than the frequency averaged over all pression . When thpression . atSRp30pression . Anotherectively ,79. A tht manner . GeneralArabidopsis splicing-related genes were computationally identified in this study by means of sequence comparisons and motif searches, including a tentative U4atac snRNA gene containing all conserved motifs, a new SR protein-coding gene (atRSp32) belonging to the atRSp31 family, and several genes related to genes encoding known splicing-related proteins (atULrp and atFCA2). A web-accessible database containing all the Arabidopsis splicing related genes has been constructed and will be expanded to other organisms in the near future. This compilation should provide a good foundation to study the splicing process in more detail and to determine to what extent these genes are conserved across the entire plant kingdom. Our data show that about 50% of the splicing-related genes are duplicated in Arabidopsis. The duplication ratios for splicing regulators are even higher, indicating that the splicing mechanism is generally conserved among plants, but that the regulation of splicing may be more variable and flexible, thus enabling plants to respond to their specific environments.A large number of Arabidopsis genome at the AtGDB BLAST server [Arabidopsis genome (GenBank accession numbers NC_003070.4, NC_003071.3, NC_003074.4, NC_003075.3, and NC_003076.4). Local BLAST [-5 were regarded as possible homologs. A total of 70 major snRNAs and three minor snRNAs were identified by this method. Each major snRNA type has 10-18 copies in the genome. A tentative gene name and gene model were assigned to each snRNA gene after comparison with the snRNAs identified in MATDB [Sequences of the 15 experimentally identified major snRNAs were downloaded from GenBank. The two minor snRNAs sequences were compiled from the literature . These gT server  and at tT server . Our inial BLAST  was usedin MATDB . SequencArabidopsis splicing related protein-coding genes. First, sequences of splicing-related proteins from human and Drosophila were downloaded from GenBank according to several recent proteomic studies [Arabidopsis annotated proteins . These candidates were regarded as true homologs.A three-round BLAST search strategy was used to identify  studies -18 and t studies . Human h studies  were dow TIGR at ). All hiArabidopsis protein set again. An e-value of 10-20 was used as a cutoff value to find possible paralogs of the true homologs. Sequences identified in both rounds of BLAST hits were regarded as main candidates for splicing related proteins.In the second step, all identified true homologs were used to query the Finally, the main candidates were queried against GenPept and all annotated human proteins (obtained from Ensembl ). All caThe gene structure and chromosomal locations for the genes encoding splicing-related proteins were retrieved from AtGDB . The chrInterProScan 3.3 was downloaded from  and was The gene families with multiple copies were inspected to determine whether they were likely to have derived from chromosome-duplication events. Gene models of the duplicated gene were searched against the gene list of each chromosome redundancy region at MATDB . If the Arabidopsis ESTs and cDNAs were aligned against the genome using the spliced alignment program GeneSeqer as made available through AtGDB [All gh AtGDB . We retrDetails about each splicing-related gene were saved in a MySQL database. PHP scripts were written to interact with the database and generate the interface web pages. Text and BLAST searches were implemented by Perl-cgi scripts.