While significant strides have been made in health research, the incorporation of research evidence into healthcare decision-making has been marginal. The purpose of this paper is to provide an overview of how the utility of health services research can be improved through the use of theory. Integrating theory into health services research can improve research methodology and encourage stronger collaboration with decision-makers.Recognizing the importance of theory calls for new expectations in the practice of health services research. These include: the formation of interdisciplinary research teams; broadening the training for those who will practice health services research; and supportive organizational conditions that promote collaboration between researchers and decision makers. Further, funding bodies can provide a significant role in guiding and supporting the use of theory in the practice of health services research.Institutions and researchers should incorporate the use of theory if health services research is to fulfill its potential for improving the delivery of health care. While significant strides have been made in medical research over the past several decades, many research results considered important by researchers and expert committees are not being used by health care practitioners. While the value of health services research must be judged by its validity, its utility cannot be taken for granted. There has been an assumption that when research information is available it will be accessed, appraised and then applied . HoweverThe gap between research evidence and its' incorporation into practice has led to an increase in research in how to bring new knowledge to bear on everyday health care. Factors influencing the adoption of research evidence have been studied extensively -5. PersoIn recent years a number of researchers have advocated a greater role for the use of theory in strengthening the practice of research -12. HoweTheory provides a systematic view of a phenomena by specifying the relations among variables and propositions with the purpose to explain or predict phenomena that occurs in the world ,17. In hTheory offers many advantages to the health services researcher. Theory helps to identify the appropriate study question and target group; clarify methods and measurement issues; provide more detailed and informative descriptions on characteristics of the intervention and supportive implementation conditions; uncover unintended effects; assist in analysis and interpretation of results; and, the successful application of an intervention to different settings ,12.Theory-driven studies are addressing the challenge of both decision-makers and funding agencies to move beyond simplistic explanations of significance in health services research. Decision-makers are seeking explanations about how an intervention works and whether it will work in a fashion similar to the intervention that was evaluated when applied to a different environment ,12,20.Despite these potential benefits, there are a number of reasons offered as to why there has been a failure to integrate theory into research. Ironically, clinical randomized control trials have discouraged the use of theory in health services research. Given the genesis of clinical trial methodology, this may derive, in part, from the very origins of epidemiology, whereby John Snow allegedly ended an epidemic of cholera by removing the handle from the Broad Street water pump, even though he had no concept of what actually caused cholera. By ignoring the need for theory, Snow was able to overcome the fact that the theories he would have needed had not yet been elucidated. Similarly, we know that lung cancer incidence can be reduced by elimination of cigarette smoking, even though we do not know exactly how cigarette smoke causes lung cancer. Experimental trials often determine intervention effects without considering how the component features of an intervention work together to bring about study outcomes ,15,21. TAdopting a theory-driven approach in health services research is not without its challenges. Given the typical training of researchers and the uni-disciplinary nature of the practice the first challenge is the capacity of researchers to engage in theory driven research. Second, a theory driven approach requires organizational conditions that support researchers and decision makers collaborating in the development and testing of theory. Finally, theory development and testing is cumulative in nature, encouraging researchers to pursue a programmatic approach in research. This approach has implications on how funding agencies support health services research. Despite the potential challenges, a theory based approach offers promise for a greater understanding on what happens when interventions work to address social/health problems.Collaborative research partnerships between academic researchers and decision-makers describe a relationship and process between individuals from different backgrounds, who together, develop an integrative cooperative approach to resolve a research problem . It has Collaborative practice has also been identified as a key strategy in facilitating a theory driven approach. Weiss  recommenViewing program stakeholders as a key source in developing theory in health services research demands stronger collaboration between researchers and program decision makers . In thisThe role and impact of the researcher and the research process in practice settings have received greater attention in other fields such as program evaluation, nursing, anthropology and community psychology. For example, core principles of community psychology practice include: a) consistency of goals and values between the researcher and the setting, and b) the notion that interventions should have the potential for being "institutionalized" or systematically established within the setting in such a way that strengthens the natural resources of the setting -27. RathRecognizing the importance of theory calls for new expectations in the practice of health services research. There are a number of challenges that must be met in order for these perspectives to gain acceptance in the health services research community.Evolving perspectives on the practice of health services research require recognition that few disciplines are able to span the breadth of responsibilities associated with the research process. To date there has been a tendency for health services research to be practiced as a uni-discipline where clinical disciplines tend to practice separately from the social science disciplines. A priority is to encourage the formation of research teams that are inter-disciplinary. Pursuing this agenda will promote the formation of research teams that may include: business, anthropology, sociology, psychology, education, engineering, nursing and medicine. Combined disciplinary skills would, in a complementary fashion, address the breadth of skills required in a more complex research environment that includes the development and testing of theory.A second point concerns broadening the training for those who will practice health services research. By and large, academic training has focused on methodological issues. While a focus on research methods has made an important contribution to the practice of health services research, relying on research methods as a core curriculum has led to limitations in the training of health services researchers such as inadequate attention to the value of theory driven research. As health services research expands its methodological repertoire beyond the classical randomized control trial, researchers face increased ambiguity in attributing the source of intervention impact. It is in this circumstance that theory can guide health services researchers in understanding the causal linkages within an intervention. Further, students are educated in separate departments with little planned, formal activity across disciplines, which discourages co-operative approaches to research and service -35. EducCollaboration between researchers and decision-makers are contingent upon supportive organizational conditions for both partners. Researchers have, and most likely will continue to operate from university-based settings where incentives for promotion and tenure can act as barriers to changes in the practice of health services research ,36. MostFunding bodies have the potential to play a significant role in guiding and integrating these considerations into health services research. Research sponsors can develop evaluation criteria that encourage the application of theory. As an example, the Agency for Healthcare Research and Quality (AHRQ) in the USA funded an initiative (Translating Research into Practice) to identify sustainable and reproducible strategies that will: 1) accelerate the impact of health services research on direct patient care; and 2) improve the outcomes, quality, effectiveness, efficiency, and/or cost effectiveness of care through partnerships between health care organizations and researchers . FurtherThis paper has examined the importance of theory in health services research. We have argued that by strengthening the role of theory encourages collaborative practice between researchers and decision-makers. It has been noted that a theory driven approach in health services research is not without its challenges. However, given the modest advances towards incorporating research evidence into healthcare decisions, a theory driven approach is well worth the effort. The implication of this approach for health services research is that it has impact on the training and practice of health services research. Institutions and researchers should consider this emerging model of practice if health services research is to fulfill its potential for improving the delivery of care.The authors declare that there are no competing interests. Disclaimer: The opinions expressed are the authors' and do not necessarily represent official policy of AHRQ or the Department of Health and Human ServicesKB drafted the manuscript, edited and revised the contents, EO edited and revised the manuscript, KB, EO, MC, RS, DS all contributed to the conceptual development, editing and review of the manuscript.The pre-publication history for this paper can be accessed here:


Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets. We have investigated the performance of Bayesian inference with empirical and simulated protein-sequence data under conditions of relative branch-length differences and model violation.With empirical protein-sequence data, Bayesian posterior probabilities provide more-generous estimates of subtree reliability than does the nonparametric bootstrap combined with maximum likelihood inference, reaching 100% posterior probability at bootstrap proportions around 80%. With simulated 7-taxon protein-sequence datasets, Bayesian posterior probabilities are somewhat more generous than bootstrap proportions, but do not saturate. Compared with likelihood, Bayesian phylogenetic inference can be as or more robust to relative branch-length differences for datasets of this size, particularly when among-sites rate variation is modeled using a gamma distribution. When the (known) correct model was used to infer trees, Bayesian inference recovered the (known) correct tree in 100% of instances in which one or two branches were up to 20-fold longer than the others. At ratios more extreme than 20-fold, topological accuracy of reconstruction degraded only slowly when only one branch was of relatively greater length, but more rapidly when there were two such branches. Under an incorrect model of sequence change, inaccurate trees were sometimes observed at less extreme branch-length ratios, and (particularly for trees with single long branches) such trees tended to be more inaccurate. The effect of model violation on accuracy of reconstruction for trees with two long branches was more variable, but gamma-corrected Bayesian inference nonetheless yielded more-accurate trees than did either maximum likelihood or uncorrected Bayesian inference across the range of conditions we examined. Assuming an exponential Bayesian prior on branch lengths did not improve, and under certain extreme conditions significantly diminished, performance. The two topology-comparison metrics we employed, edit distance and Robinson-Foulds symmetric distance, yielded different but highly complementary measures of performance.Our results demonstrate that Bayesian inference can be relatively robust against biologically reasonable levels of relative branch-length differences and model violation, and thus may provide a promising alternative to maximum likelihood for inference of phylogenetic trees from protein-sequence data. The inference of phylogenies from molecular sequence data, like most other quantitative problems in science, is most powerful within a model-based statistical framework. Sophisticated models are available to describe how sequences change along branches of a tree, and how the rate of sequence change varies among sites. Statistical measures describe both the quality of inferred trees, and the confidence that can be assigned to the existence and position of subtrees. Likelihood-based approaches have proven especially powerful for inferring phylogenetic trees ,2 but are.g. the relative contributions of vertical and lateral gene transfer to genomic diversity [At the same time, the ongoing success of genomic sequencing – new microbial genome sequences are now appearing at the rate of at least one per week – is yielding a wealth of ever-larger gene and protein datasets suitable for large-scale analysis of deep issues in comparative and evolutionary genomics, iversity ,8. Howeviversity  we have i.e. a tree) is associated with its probability of being correct, given the prior probability, model and data [Among the most interesting of these is Bayesian inference, in which the posterior probability of a hypothesis  that do not actually exist [Bayesian phylogenetic inference has been applied to simulated -18 as wely exist . Certaina priori reason that protein-sequence data should be more or less problematic than nucleotide data for Bayesian phylogenetics, gene and protein sequences have distinct statistical properties, and are subject to different selective constraints; so it is not inconceivable that, in practice, the corresponding models of sequence change might tend to fail in different ways, or to different extents. Bayesian inference has been applied to inference of phylogenetic trees for cytochrome b [et al. [Much less is known about the behaviour of Bayesian inference with protein-sequence data. While there is no chrome b , elongatchrome b , hydropechrome b , 3-hydrochrome b , membranchrome b , and conchrome b  and largchrome b  datasets [et al.  report aTo better characterize the behavior of Bayesian phylogenetic inference with protein-sequence data, we have applied MrBayes ,32 to boe.g. examining solution space, estimating support, and assessing stability to stochastic error. Only a subset of these broad bodies of theory and practice has so far been applied to phylogenetic inference, and even less implemented in platform-independent software. If we apply BIC to alternative trees and assume equal prior probabilities, it becomes possible to estimate Bayesian posteriors from their likelihood differences, linking the two approaches at this level [In this work we compare and contrast results obtained using two popular software programs, PROML  and MrBais level ,38. Stocis level  and the is level , can be is level ,18. The is level -43, the is level , and a mis level  lie fartEmpirical data under Methods) yielded the same topology. Interestingly, for these, the bootstrap consensus ML trees were topologically identical to the ML and Bayesian trees, indicating that the sequences in these datasets show a high degree of internal consistency across positions (i.e. bear few homoplasies). For another 10 datasets, one or more of these four approaches yielded a tree that differs slightly (edit distance ≤ 2) from the others. No pattern was obvious among these disagreements: the differences do not, for example, systematically separate ML from Bayesian trees. For these 10 datasets, the differences are simple edits, e.g. -(A(BC)) to -(B(AC)), or -((AB)(CD)) to -(A(B(CD))). For the remaining 4 datasets, one or more of the four approaches yielded a tree that differed more substantially (edit distance ≥ 3). Over these examples, the datasets that yield more-conflicted trees are slightly larger  than those yielding slightly conflicted  or identical trees , although the numbers of datasets involved are too few for this observation to be generalized.We inferred maximum likelihood (ML) and Bayesian (B) trees for the 21 empirical protein-sequence datasets. For 7 of these datasets, every combination of approach and model that we investigated  with Bayesian posterior probabilities (PPs) separately for all subtrees among the three groups of trees inferred from these 21 empirical datasets: the 7 trees for which all four sets of approaches and models  yielded the same topology, the 10 for which one or more approach yielded a slightly different tree, and the 4 for which one or more tree differed more substantially. In Figure e.g. involving minority subtrees (those not in the extended 50% majority-rule consensus) or higher-order  fit curves.From our data, it is not possible to reject the hypothesis that the relationship between BP and PP has the same slope whether the Bayesian inference is conducted using JTT, or EQ, as the model of sequence change. Analysis of covariation (ANCOVA) yields probabilities 0.579 (Panel A), 0.235 (Panel D) and 0.195 (Panel E) that the lines described in Table Because for these trees the true molecular phylogeny is unknown, these results do not speak to the accuracy of the inferred topologies. For this, it is necessary to examine inferences based data simulated on trees of known topology.We first examine cases where tree inference was carried out under the same model (JTT) as that used to generate the data, and where a single branch was progressively extended in length (see Methods). When trees were inferred using gamma-corrected ML, the correct tree was recovered in 100% (50/50) of the cases in which the relative branch-length difference was 5-, 10- or 20-fold . The similarities we observe in both magnitude and trend for the two approaches demonstrate that the comparison we are making between ML and Bayesian inference does not, in these cases at least, depend on whether or not the performance of ML is assessed using an approach that involves the nonparametric bootstrap.We investigated two ways of assessing the performance of ML inference. In panel A of Figures With Bayesian inference, inaccurately reconstructed trees were also first seen at the 30-fold branch-length ratio Figure , panel Di.e. the long branches were topologically adjacent in the reconstructed tree.In Figures i.e. identical with the known topology). By structuring the comparison in this way, we avoid cases where the ML consensus might be topologically different from the best component tree, and avoid dealing with the plethora of cases and sub-cases that arise in comparing topologically non-congruent trees.In Figure i.e. shows little or no "saturation". For both the single- and two-long-branches cases, the PP is greatest, compared to BP, for Bayesian trees inferred without correction for ASRV, and least generous for gamma-corrected trees where the prior distribution was assumed to be uniform. Unsurprisingly, the lower values of subtree support, as measured both by BP and by PP, arise from the trees with the most extreme relative branch length differences.For all three combinations of ASRV correction and prior , but inferring trees under the JTT model (see Methods). Performance of each of the four sets of approaches and methods was assessed by comparing four measures: the branch-length ratio at which inaccurate trees were first observed; the total number of steps (summed over the eight ratios) by which the 400 trees differ from the true topology; the weighted sum ("burden") of these steps; and the mean number of steps by which each inaccurate tree differs from the known tree. The latter two measures were each calculated using both Robinson-Foulds symmetric distance, and edit distance, yielding six comparisons in all. A more-complete description is provided at footnote 2 of Table For datasets in which a single branch was of relatively greater length, violating the model of sequence change degraded performance of the four approaches Table . In eachFor datasets containing two long branches, model violation affected performance of ML and Bayesian inference differently. With ML, inference under the wrong model produced a somewhat lower frequency of topologically inaccurate trees, although each inaccurate tree was more inaccurate as judged by symmetric distance Table . With BaUnlike the situation with established approaches based on pairwise distances, parsimony or maximum likelihood, relatively little experience has accumulated so far on the application of Bayesian approaches to phylogenetic inference, especially for protein-sequence datasets. In this work we (a) extend the comparison of Bayesian posterior probabilities with nonparametric bootstrap proportions as measures of confidence in subtrees, (b) systematically investigate the robustness of ML and Bayesian inference to branch-length differences, and (c) compare the behavior of these two approaches to one specific violation of the model of sequence change. We used two measures to compare topologies , and it is clear that they captured different facets of topological incongruence.Model violation, below). As it is unlikely that any existing model – certainly any that fails to account for lineage-specific processes and temporal variations along these lineages – fully represents the historical complexity of molecular evolution, the same criticism could be levelled, albeit perhaps in lesser degree, against all current applications of statistically based phylogenetic inference to empirical datasets.Using 21 empirical protein-sequence datasets, we compared Bayesian posterior probabilities with bootstrap proportions based on ML as measures of support for subtrees. To make this comparison as fair as possible, we restricted our analysis to a model of sequence change (JTT) and a correction for ASRV (discrete approximation to the gamma distribution) available in both PROML and MrBayes. We did not optimize models separately for each approach or for each dataset, as JTT+gamma represents the most-parameterized combination that these two programs support in common. It is therefore possible that some of the difference observed between the two measures results from differential sensitivity of ML and Bayesian inference, as implemented in these programs, to deviation of JTT and the discrete gamma distribution from an optimal description of the processes of sequence change that actually gave rise to these sequences , with our simulated data the relationship between BP and PP resembles a smooth curve reaching 100% PP only at BP greater than 99%. Further studies will be required to disentangle why little or no saturation was observed; possibilities include the structure of our simulated trees , the way that data were evolved on these trees , and/or the way we summarize the support data for ML (via extended majority-rule consensus trees).For sets of consensus trees inferred from simulated protein-sequence data Figure , Bayesiae.g. in selection of outgroups and resolution of topologically problematic regions. Parsimony analysis is particularly susceptible to "long branch attraction" (LBA) artefacts, in which two or more branches are resolved adjacent in a tree solely because they are highly divergent from the others [Dissimilar sequences (represented in phylogenetic trees as long branches) create difficulties in phylogenetic analysis. The issue has been most extensively explored in parsimony analysis, where branch length can be an important consideration, e others . ML infeOur results Figures , 3, 4, 5Both the mammalian mitochondrial (mtmam) and JTT models embody empirical probabilistic models of amino acid substitution. Codon usage is highly skewed in mitochondrial genomes compared with the cognate nucleocytoplasmic components , and theParticularly in simulations where a single branch was differentially lengthened Table , using tSupport for subtrees) that the relative performance of these approaches as reported herein might, in part, reflect their differential sensitivity to sub-optimality in the models used.The degree of insensitivity to model violation we observe for gamma-corrected ML and Bayesian inference goes some way toward mitigating possible concern  from each other. Our results also illustrate the difference in dynamic range offered by these metrics, while simulation studies [e.g. in distinguishing transformations that affect large numbers of termini from those that affect small numbers of termini, in robustness against displacement of particular termini, or in application to very large trees [Our results Figures  illustra studies ,55,56 inge trees -59.e.g. 100 or 1000 replicate runs required to estimate bootstrap proportions, ASRV-corrected Bayesian inference must be seen as an important alternative for statistically based phylogenetic analysis of protein-sequence data when computational resources are limiting. It appears that the interpretation of bootstrap proportions and posterior probabilities being developed for nucleotide sequences will apply as well to protein sequences.Bayesian inference can be as robust as ML against relative branch-length differences of 20-fold or greater in inference of correct topologies from protein-sequence data, although details depend on the number of relatively long branches, the presence or absence of an effective correction for ASRV, and (presumably) other factors. One might doubt that sequences so dissimilar as to produce a 20-fold (or more) difference in branch lengths could be believably recognised as homologous, or reliably aligned. Bayesian inference can also be as robust as ML to violation of the model of amino acid transition probability. For empirical protein-sequence data that might reasonably be encountered in biological research, then, both gamma-corrected ML and gamma-corrected Bayesian inference perform well in recovering the correct topology. As Bayesian inference is typically very much faster than even a single ML run, not to mention than Our interest in lateral genetic transfer (LGT) -9 led usSimulated data were evolved using the "evolver" program within PAML version 3.13a ,63. FirsProtein data sets were then generated on each of the 16 trees with differentially lengthened branches, using the "evolver" program in PAML. On each tree we evolved 100 replicate protein datasets under the JTT model of sequence change , with amvs simuated data. Here we present methods for the simulated data; methods specific to the empirical data are given below.All maximum-likelihood (ML) trees were inferred using PROML version 3.6a3 in Felsenstein's PHYLIP package  implemenN = 10) each of the 50 replicate datasets, as described in the preceding paragraph.For simulated data, we report results from both (1) single ML inference runs based on each of the 50 replicate protein datasets at each branch-length ratio increment, and (2) bootstrapping  among these sampled trees.Bayesian inference (B) was carried out using MRBAYES version 2.01  implemeni.e. did not merely serve to initialize estimation by MRBAYES). In runs where an exponential prior was used, the value of the exponent was estimated from the simulated data, and differed according to sequence-change model: under JTT, 0.10 for datasets with both one and two long branches, and under mtmam, 1.04 for one long branch, and 0.60 for two long branches.For simulated data, we examined three models of different complexities: (1) a uniform prior distribution over branch lengths, and a single rate category; (2) a uniform prior, and an 8-category gamma model of ASRV; and (3) an exponential prior, and an 8-category gamma. The gamma shape parameter was, as above, estimated using Tree-Puzzle, and was fixed  the Robinson-Foulds symmetric distance [For trees inferred from the 16 sets of simulated data , topologies were compared against that of the (known) tree on which the data had been evolved. For this we employed two metrics: (1) the minimum number of break-and-reanneal edits required to convert one tree into the other. This metric goes under various names, including distance ; we refedistance  as impledistance . The valdistance ) becauseMethods and procedures followed those for simulated data (above), except as described subsequently here.Aligned protein sequence datasets see  were obtFor empirical data, we inferred ML trees in two ways: (1) using a user-defined hidden Markov model (HMM) with 8 categories, each set to 12.5% of sites, and with rates in each category estimated using Tree-Puzzle version 5.0 ; and (2)N = 100 for the 21 empirical data sets, N = 10 for the 1600 simulated datasets) with preservation of rate-class information as described in the SEQBOOT documentation. Nonparametric bootstrap proportions (BPs) were computed under extended majority rule consensus using CONSENSE. Both SEQBOOT and CONSENSE are in PHYLIP [ML analyses were bootstrapped  were therefore compared amongst themselves, using edit distance  as the comparison metric. Topologies of the bootstrap ML consensus trees were compared as well, although as consensus trees they do not necessarily reflect most-likely topologies.The 21 empirical protein-sequence datasets from Dr Nick Goldman, and our simulated datasets with one or two long branches, are available for download at .JCM was responsible for data simulation and analysis. TJH was responsible for high-performance computing, and generated the figures. MAR initiated and supervised the project, and wrote the manuscript.Description of 21 empirical datasets This PDF file contains information on each of the 21 empirical datasets provided by Dr Nick Goldman, including: number of sequences, GenBank ID (gi number) of first sequence in dataset, key words from description line of first sequence, PAUP* parsimony score of dataset, number of internal nodes, and number of zero-length internal edges observed with PROML to have support in three non-overlapping intervals: P < 0.01, P < 0.05 but not P < 0.01, and worse than P < 0.05.Click here for file


Cancer specialists often talk about cancer as an umbrella term for over 200 different diseases, each having unique characteristics. But even these categories are too broad, as the same type of cancer can take very different paths in different people. It's not uncommon, for example, for a tumor to grow aggressively in one patient and stabilize or regress in another, even though their tumors are indistinguishable and are treated in the same way. Researchers have traditionally diagnosed and treated cancer based on microscopic analysis of cell size and shape, a method that's especially difficult for very closely related cancers, such as non-Hodgkin's lymphoma, which has 20 subtypes. As scientists learn more about the molecular alterations in cancer, they're beginning to establish cancer subtypes based on the underlying molecular footprint of a tumor. Four years ago, DNA microarray analysis revealed that the most common subtype of non-Hodgkin's lymphoma is in fact two separate diseases. Though the tumor cells of both cancers appear large and diffusely dispersed in a tissue sample under a microscope, each has a distinct genetic profile, possibly explaining why only 40% of patients with this subtype respond to the standard chemotherapy treatment.Such molecular pathology has led to the discovery of subtypes of several different tumor types and has successfully identified patients with different survival times. But such correlations work best when cancer subtypes based on genetic profiles are already known. If you know that different subtypes exist and which patients belong to which subtype, then you can build a statistical model to diagnose such cancers in future patients. But in most situations, clinicians don't know either of these variables—or even whether such a subtype exists—information that is crucial to developing effective diagnostic and treatment protocols. Statistical methods to identify such subtypes exist, but they can generate classifications that lack clinical relevance. Now Eric Bair and Robert Tibshirani describe a procedure that combines both gene expression data and the patients' clinical history to identify biologically significant cancer subtypes and show that this method is a powerful predictor of patient survival.Their approach uses clinical data to identify a list of genes that correspond to a particular clinical factor—such as survival time, tumor stage, or metastasis—in tandem with statistical analysis to look for additional patterns in the data to identify clinically relevant subsets of genes. In many retrospective studies, patient survival time is known, even though tumor subtypes are not; Bair and Tibshirani used that survival data to guide their analysis of the microarray data. They calculated the correlation of each gene in the microarray data with patient survival to generate a list of “significant” genes and then used these genes to identify tumor subtypes. Creating a list of candidate genes based on clinical data, the authors explain, reduces the chances of including genes unrelated to survival, increasing the probability of identifying gene clusters with clinical and thus predictive significance. Such “indicator gene lists” could identify subgroups of patients with similar gene expression profiles. The lists of subgroups, based on gene expression profiles and clinical outcomes of previous patients, could be used to assign future patients to the appropriate subgroup.An important goal of microarray research is to identify genetic profiles that can predict the risk of tumor metastasis. Being able to distinguish the subtle differences in cancer subtype will help doctors assess a patient's risk profile and to prescribe a course of treatment tailored to that profile. A patient with a particularly aggressive tumor, for example, would be a candidate for aggressive treatment, while a patient whose cancer seems unlikely to metastasize could be spared the debilitating side effects of aggressive anticancer therapies. By providing a method to cull the thousands of genes generated by a microarray to those most likely to have clinical relevance, Bair and Tibshirani have created a powerful tool to identify new cancer subtypes, predict expected patient survival, and, in some cases, help suggest the most appropriate course of treatment.


With the recent progress made in large-scale genome sequencing projects a vast amount of novel data is becoming available. A comparative sequence analysis, exploiting sequence information from various resources, can be used to uncover hidden information, such as genetic variation. Although there are enormous amounts of SNPs for a wide variety of organisms submitted to NCBI dbSNP and annotated in most genome assembly viewers like Ensembl and the UCSC Genome Browser, these platforms do not easily allow for extensive annotation and incorporation of experimental data supporting the polymorphism. However, such information is very important for selecting the most promising and useful candidate polymorphisms for use in experimental setups.in silico mining of high-throughput sequencing data. Currently, the database provides collections of laboratory rat (Rattus norvegicus) and zebrafish (Danio rerio) candidate SNPs. The database stores detailed information about raw data supporting the candidate, extensive annotation and links to external databases , verification information, and predictions of a potential effect for non-synonymous polymorphisms in coding regions. The CASCAD website allows search based on an arbitrary combination of 27 different parameters related to characteristics like candidate SNP quality, genomic localization, and sequence data source or strain. In addition, the database can be queried with any custom nucleotide sequences of interest. The interface is crosslinked to other public databases and tightly coupled with primer design and local genome assembly interfaces in order to facilitate experimental verification of candidates.The CASCAD database is designed for presentation and query of candidate SNPs that are retrieved by  allows universal access to the database content and allows various queries supporting many types of research utilizing single nucleotide polymorphisms.The CASCAD database discloses detailed information on rat and zebrafish candidate SNPs, including the raw data underlying its discovery. An advanced web-based search interface  Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation within species. As a result, SNPs are now becoming the most popular type of marker in genetic association and mapping studies. SNPs are also most likely to be the molecular basis for the majority of phenotypic variation in (outbred) populations. In particular, SNPs in regulatory and protein-coding regions can have an effect on gene expression levels and protein activity, respectively. The phenotypic differences observed between selected (sub) strains in model organisms may be the result of specific (combinations of) natural occurring polymorphisms. Hence, a comprehensive inventory of SNPs, including extensive annotation will be extremely valuable in the search for functional polymorphisms.in silico candidate SNP mining pipeline that uses all publicly available sequence data for a specific organism, and designed a database, CASCAD (CAscad SNP CAndidates Database), that allows storage of a wide variety of primary source data, cross-annotation to other databases, and analysis parameters for SNPs associated with expressed sequences.There is often a vast unexplored potential in large sequence datasets that have been collected for other purposes, for example, EST and whole genome sequencing (WGS) projects. In an effort to address these two issues, we have developed an We applied the SNP discovery pipeline to both rat  and zebrWe designed a web-based interface with Perl scripts communicating to a MySQL database, and displaying HTML pages through Apache server running on SuSE Linux. The interface provides simple, advanced Figure , and seqIn addition to primary sequence data analysis, the effect of all SNPs on protein coding capacity was evaluated and non-synonymous SNPs were categorized in classes reflecting the severity of the polymorphism using a BLOSUM-based score. The predicted missense SNPs were analyzed by SIFT  and PolyQuery results are summarized on the SNP details page Figure , listingin silico discovery pipelines [For many applications, it is important to be able to distinguish between SNP candidates by their characteristics, as they may be predictive for verification success rate or carry biologically relevant information. Non-confirmed candidate polymorphisms may represent variants uncommon for a given population, but also sequencing errors , RNA editing events and reverse transcriptase errors (EST reads). In order to minimize the contribution of false positives, one can exclude polymorphisms based on a single read for either allele, as is common for many ipelines . AlthougWe have developed our database to fulfill the needs of any particular SNP application by providing control over every parameter we used in the polymorphism discovery step.Applications of the CASCAD database include queries for potentially deleterious SNPs in a specific genomic region of interest, for example a QTL interval, design of SNP-based mapping panels using either RFLP or any other technology, and identification of informative SNPs for fine-mapping. Custom sequences can be provided to search for known SNPs in any sequence of interest. In addition, the CASCAD pipeline  can be uThe main purpose of CASCAD database is to provide flexible access to candidate single nucleotide polymorphisms, which were predicted using a computational approach from publicly available sequence data of the rat and zebrafish. The resulting database is crosslinked to most common public databases and can be queried for SNPs using accession numbers, sequence context, SNP characteristics, but also using parameters specific to the SNP discovery process, allowing stringent or relaxed conditions suitable for different types of applications.. Programs, scripts, MySQL database dumps, and instructions for setting up a species-specific SNP database can be obtained from the authors upon request.The database is freely accessible through the website VG designed and implemented the CASCAD database. EB tested database and interface. EC provided supervision and guidance for the project.


The circulating estrogen concentration elevated gradually along with time after ovariectomy in rats. To explore the source of the increased circulation estrogen, the extragonadal aromatization as well as the synthesis of androgen in the adrenal cortex of the ovariectomized rats was evaluated.Female rats were divided into twelve groups: 1 month after ovariectomy (OVX1M), OVX2M, OVX3M, OVX4M, OVX5M, OVX6M; intact 1 month (INT1M), INT2M, INT3M, INT4M, INT5M, INT6M. The blood concentration of testosterone (T) was measured by radioimmunoassay. The mRNA expressions of P450 aromatase in the liver and subcutaneous abdominal (SA) adipose as well as the adrenal cytochrome P450 17 alpha hydroxylase/lyase (P450c17) were semiquantified by RT-PCR. The P450 aromatase protein expressions in the liver and SA adipose were detected by Western blot.The blood E2 concentrations increased gradually along with time after ovariectomy in the rats. The 58-kDa aromatase protein and mRNA expressions normalized to β-actin in the OVX6M rats' SA adipose tissues showed higher levels than those from corresponding tissues in the INT6M (p < 0.05). And the ratios of aromatase mRNA and protein to β-actin in the OVX6M rats' liver tissues increased significantly compared with those in the OVX1M rats (p < 0.05). The ratio of adrenal P450c17 to beta-actin in the OVX6M increased markedly, and was higher than OVX1M (p < 0.05), though the blood concentration of T decreased significantly in all the ovariectomized rats (p < 0.05).Both the subcutaneous abdominal adipose tissues and the liver tissues contributed to the extragonadal aromatisation to promote the circulating E2 levels in the rats along with time after ovariectomy; the adrenal compensation might also be activated naturally. Ovaries are the primary source of estrogen. In ovariectomized rats, the production of estrogen is shifted from ovary to a number of extragonadal sites ,2. SimpsEstrogen production in extragonadal sites is dependent on an external source of C19 androgenic precursors . Circula2) and T as well as the aromatase expressions in adipose and liver tissues in the ovariectomized rats.We have observed that the release of corticotrophin-releasing hormone (CRH) in the hypothalamic paraventricular nucleus of the ovariectomized rats increased significantly compared with the intact rats . And morFemale Sprague-Dawley rats (180–200 g), with regular 4-day estrus cycles were purchased from Medical Experimental Animals Centre of Fudan University . The animals were housed under laminar flow in an isolated room with controlled temperature and at a 12 /12 (light /dark) schedule. Forty-eight of them underwent ovariectomy with ether anaesthesia, which were then divided randomly into six groups: 1 month after ovariectomy (OVX1M), OVX2M, OVX3M, OVX4M, OVX5M, OVX6M. The corresponding control groups were: intact 1 month (INT1M), INT2M, INT3M, INT4M, INT5M, INT6M. All experimental procedures involving the use of animals were conducted in accordance with NIH Guidelines and were reviewed and approved by the Animal Use and Care Committee for the Fudan University.At the time of sacrifice, the vaginal cytology of each rat was first examined. The tissues of the rats were collected respectively, and those of the intact control animals, during the period of diestrous. All the operations were carried out at 4°C. The liver tissues, subcutaneous abdominal (SA) adipose tissues and the adrenals were excised, and then snap-frozen in liquid nitrogen, and stored at -80°C. The preparation of the microsomal pellet was accordance with the report by Hiroshi . Total tTo compare the level of adrenal cytochrome P450 17α hydroxylase/lyase (P450c17), liver and SA adipose P450arom expressions after different treatments, PCR methologies were adapted to provide a semiquantitative measure of mRNA levels. Primers were synthesized based on published reports . Table 1A 50 μg sample of the microsomal protein was loaded into each lane along with a prestained protein size marker , electrophoresed on a 10% SDS-polyacrylamide gel at 18 V/cm, and electroblotted onto a polyvinylidene difluoride membrane  using a wet electroblotter. After blocking in fat-free milk, incubation was conducted with the antiaromatase antibody  and β-actin antibody (1:3000) at room temperature in 18°C for 4 h in TBS-T solution . After extensive washing, blots were incubated with AP-labelled goat antirabbit antiserum  for 60 min at room temperature in 18°C and developed using NBT/BCIP detection system (Amersham Pharmacia Biotech). The intensities of the bands were evaluated using the Image Master Software , and values were normalized to β-actin immunoreactivity in each sample and expressed as percent of the control. Specificity of the aromatase immuno-staining was determined by preincubation of antiserum for 24 h at 4°C with varying concentrations of aromatase, with the primary antibody omitted to identify non-specific staining as well.At the time of sacrifice, the blood samples (0.8 ml) of the rats were collected from tail veins respectively, and the corresponding intact controlling animals, during the period of diestrous. The plasma was separated by centrifugation and stored at -70°C until assayed. Concentration of blood hormones were determined by double-antibody RIA kits purchased from the National Atomic Energy Research Institute . The samples were assayed in duplicate, and all the subjects' samples were assayed together. The sensitivity of the kit was 0.8 pg/ml (testosterone) and 1.4 pg/ml (estrogen), the intra- and interassay coefficients of variation, 3.7–8.0% and 4.74–7.7%.All data are presented as means ± S.E.M. Statistical analysis was performed on raw data using two-way analysis of variance (ANOVA), with the significance set at p < 0.05 and p < 0.01 in two-tailed testing chosen.The epithelial cells were stained by haematoxylin-eosin (HE). The intact rats  showed regular 4-day estrus cycle change. The cyclic change disappeared in the ovariectomized  rats. A few of mature vaginal epithelia were observed in the smears of the OVX5M and OVX6M rats, and the percent of mature epithelia increased significantly in the OVX6M rats (p < 0.05)  compared with those in the INT1M, 2M and 3M. The concentrations in OVX4M, 5M and 6M groups increased significantly (p < 0.05) compared with OVX1M, though still lower than INT4M, 5M and 6M. There were no disparities between the INT1M, 2M, 3M, 4M, 5M and 6M groups . Densitometric analysis of the mRNA concentration using target product/β-actin was expressed as the mean with SEM. The ratios of liver P450arom to β-actin in the OVX1M, 2M, 3M, 4M and 5M groups were lower than the corresponding intact controls (p < 0.05). The ratio increased significantly in the OVX6M compared with OVX1M (p < 0.05), and no difference was detected between OVX6M and INT6M . But the ratio increased significantly in the OVX6M compared with OVX1M (p < 0.05)  . Its actThough it has been reported that the splanchnic tissue is a minor site for extragonadal aromatization of androgens , there i2 levels in the rats along with time after ovariectomy; the adrenal compensation might also be activated naturally.Both the subcutaneous abdominal adipose tissues and the liver tissues contributed to the extragonadal aromatization to promote the circulating EHong Zhao and Zhanzhuang Tian designed the study, performed the studies and the statistical analysis, and drafted the manuscript. Junwei Hao performed the animal experiment. Boying Chen conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.


Tuberculosis remains a major world-wide health threat which demands the discovery and characterisation of new drug targets in order to develop future antimycobacterials. The regeneration of methionine consumed during polyamine biosynthesis is an important pathway present in many microorganisms. The final step of this pathway, the conversion of ketomethiobutyrate to methionine, can be performed by aspartate, tyrosine, or branched-chain amino acid aminotransferases depending on the particular species examined.Mycobacterium tuberculosis H37Rv has been cloned, expressed, and characterised. The enzyme was found to be a member of the aminotransferase IIIa subfamily, and closely related to the corresponding aminotransferase in Bacillus subtilis, but not to that found in B. anthracis or B. cereus. The amino donor preference for the formation of methionine from ketomethiobutyrate was for isoleucine, leucine, valine, glutamate, and phenylalanine. The enzyme catalysed branched-chain amino acid and ketomethiobutyrate transamination with a Km of 1.77 – 7.44 mM and a Vmax of 2.17 – 5.70 μmol/min/mg protein, and transamination of ketoglutarate with a Km of 5.79 – 6.95 mM and a Vmax of 11.82 – 14.35 μmol/min/mg protein. Aminooxy compounds were examined as potential enzyme inhibitors, with O-benzylhydroxylamine, O-t-butylhydroxylamine, carboxymethoxylamine, and O-allylhydroxylamine yielding mixed-type inhibition with Ki values of 8.20 – 21.61 μM. These same compounds were examined as antimycobacterial agents against M. tuberculosis and a lower biohazard M. marinum model system, and were found to completely prevent cell growth. O-Allylhydroxylamine was the most effective growth inhibitor with an MIC of 78 μM against M. marinum and one of 156 μM against M. tuberculosis.The gene encoding for branched-chain amino acid aminotransferase in M. tuberculosis. This enzyme can be inhibited by selected aminooxy compounds, which also have effectiveness in preventing cell growth in culture. These compounds represent a starting point for the synthesis of branched-chain aminotransferase inhibitors with higher activity and lower toxicity.Methionine formation from ketomethiobutyrate is catalysed by a branched-chain amino acid aminotransferase in  Tuberculosis remains one of the leading causes of worldwide mortality and morbidity, infecting an estimated 8 million people annually with approximately 2 million deaths . The sitKlebsiella pneumoniae [Bacillus subtilis [Mycobacterium spp., only methionine adenosyltransferase has been cloned, expressed, and fully characterised [Polyamine synthesis and its associated methionine (Met) regeneration pathway Figure  are knoweumoniae -11 and tsubtilis -14 Selecsubtilis ,15-20. Fcterised .K. pneumoniae, B. subtilis, and B. anthracis [Plasmodium falciparum, Trypanosoma brucei brucei, Giardia intestinalis, and Crithidia fasciculata, this reaction is catalysed by the subfamily Ia enzyme aspartate aminotransferase [K. pneumoniae, however, the reaction was performed by the close homologue tyrosine aminotransferase, which is also a member of subfamily Ia [B. subtilis, B. cereus, and B. anthracis were recently found to catalyse Met regeneration via a branched-chain amino acid aminotransferase (BCAT) [B. subtilis and B. cereus/B. anthracis utilised BCAT enzymes from separate subfamilies (IIIa vs. IIIb respectively). As Mycobacterium spp. also appear to have no subfamily Ia aminotransferase sequences  by an aminotransferase. The specific aminotransferase responsible for the reaction has been identified and characterised in a number of microorganisms, including malaria, African trypanosomes, nthracis ,16,17. Ie (BCAT) . IntriguM. tuberculosis H37Rv was found to contain a single gene with a very high sequence homology to either B. subtilis YbgE or YwaA, which are both known to be subfamily IIIa BCATs [B. subtilis YheM, B. cereus BCAT, or B. anthracis BCAT, which are all subfamily IIIb aminotransferases [M. tuberculosis BCAT gene, Rv2210c, has not been previously cloned, expressed, or characterised. It is interesting to note that the M. tuberculosis genome contains a single BCAT homologue and no obvious DAAT homologue.The complete, published genome of Ia BCATs ,23. In csferases . This reMycobacterium spp. uncovered a single gene in M. leprae, M. bovis, M. marinum, M. ulcerans, M. avium, and M. smegmatis with an extremely high identity to Rv2110c. Together, with other subfamily IIIa aminotransferases, the putative mycobacterial sequences were aligned and a cladogram constructed  is the PLP binding site and would be expected to be invariant. If one excludes the only DAAT in Figure E. coli BCAT. Clearly, sequence conservation is very low across family III.The low level of sequence conservation outside of the genus can be seen in the alignment of selected BCAT sequences shown in Figure oli BCAT , only K2M. tuberculosis BCAT was cloned as a deca-histidine fusion protein for expression in E. coli. To prevent complete inclusion of the recombinant protein, it was necessary to induce expression with a relatively low concentration of IPTG (0.1 mM) at 20°C for 20 hr. Under these conditions, sufficient soluble material was produced and purified over Ni2+ affinity columns  and the human mitochondrial BCAT [M. tuberculosis BCAT. However, while the human mitochondrial BCAT is also a family IIIa aminotransferase, there are some clear differences when compared to the M. tuberculosis enzyme. The human enzyme will not accept aromatic amino acids, whereas the tuberculosis BCAT would use phenylalanine as an amino donor. In addition, the human enzyme contains the redox-active motif CXXC at positions 311–314  with a high similarity to the B. subtilis ykrV gene product. YkrV was found to be a subfamily If aminotransferase and could also catalyse the conversion of KMTB to Met using glutamine as the only effective amino donor [B. subtilis YkrV could transaminate KMTB with glutamine, B. subtilis cell homogenates did not produce Met from KMTB when supplemented with glutamine [M. smegmatis grown in Middlebrook 7H9 incomplete medium were only able to produce Met from KMTB when supplemented with valine, isoleucine, leucine, glutamate, or phenylalanine, as was seen for the recombinant M. tuberculosis BCAT in figure Several interesting findings arose during the course of this investigation. First, while no donor . Therefolutamine . Similarence Rv088c with aM. tuberculosis was found to contain no putative gene product with significant homology to a DAAT. In fact, the organism appeared to contain no subfamily IIIb aminotransferases. The physiological significance of a lack of a DAAT is unclear, but many organisms do not contain a homologue of this enzyme. With DAAT, there might be a diminished capacity to catabolise D-amino acids for energy, although the same reactions could be performed by a D-amino acid oxidase. M. tuberculosis is known to be reliant on carbohydrate catabolism during the active growth phase and lipid metabolism during the chronic, dormant phase [nt phase . TherefoM. tuberculosis was found to lack clearly identifiable homologues of several enzymes in the Met regeneration pathway. The most glaring omission is the lack of an S-adenosylmethionine decarboxylase (SAMdc) homologue  were cultured in liquid Middlebrook 7H9 complete medium or on Middlebrook 7H10 plates at 37°C for M. tuberculosis or 30°C for M. marinum. All substrates and inhibitors were obtained from Sigma-Aldrich .M. tuberculosis by vortexing packed cells in a minimal volume of 50 mM Tris-HCl pH 8.0/10 mM EDTA/100 mM NaCl containing 500 μm acid washed glass beads (Sigma). After allowing the glass beads to settle, the supernatant was added to an equal volume of 10 mM Tris-HCl pH 8.0/100 mM NaCl/25 mM EDTA/0.5% w/v sodium dodecyl sulfate/0.1 mg/ml proteinase K and incubated for 1 hr at 37°C with occasional gentle mixing. The mixture was then subjected to extraction with phenol:chloroform:isoamyl alcohol (25:24:1), and the DNA ethanol precipitated.Genomic DNA was isolated from M. tuberculosis BCAT gene was discovered by a BLAST search of the complete M. tuberculosis H37Rv genome using the B. subtilis YbgE, YwaA, or YheM gene products as the query proteins [Escherichia coli XL10 cells  and was subsequently recovered using the Qiaspin miniprep kit (Qiagen). Positive clones were determined by digesting the plasmid with NotI and XhoI to confirm the presence of the insert on a 1% agarose gel. The sequence of the insert was confirmed by using the Big-Dye cycle sequencing kit  and an ABI Prism 310 genetic analyser.The sequence of the putative proteins ,23,43. TE. coli BL21(DE3) CodonPlus RIL cells (Stratagene) for functional expression. Cells were grown in liquid LB medium containing 50 μg/ml ampicillin and 50 μg/ml chloramphenicol at 37°C and 250 rpm until the culture reached an A600 nm of 0.6–0.8. The culture was then cooled to 20°C for 30 min at 250 rpm before the addition of 0.1 mM isopropylthiogalactopyranoside (IPTG) and an additional 20 hr of incubation at 20°C and 250 rpm.The plasmid from positive clones was transformed into 4. The column was washed with 50 mM HEPES (pH 7.4)/750 mM NaCl and 50 mM HEPES (pH 7.4)/750 mM NaCl/80 mM imidazole, before elution with 50 mM HEPES (pH 7.4)/750 mM NaCl/800 mM imidazole. Fractions containing the recombinant protein were pooled and concentrated to less than 3.0 ml using a 30 kDa molecular mass cut-off filter . The concentrated enzyme was then dialysed against 50 mM HEPES (pH 7.4)/1 mM dithiothreitol/1 mM EDTA/trace pyridoxal-5-phosphate (PLP) overnight at 4°C. The concentrated enzymes were stored at 4°C for several days, or with 20% v/v glycerol at -20°C for several weeks, without appreciable loss of activity. Recombinant protein samples were examined by electrophoresis on 10% SDS polyacrylamide gels followed by Coomassie Brilliant Blue R250 staining. Protein concentration was measured using the Bio-Rad reagent .The culture was centrifuged at 3500 × g for 20 min at 4°C, and the cell pellet resuspended in 50 mM HEPES (pH 7.4)/750 mM NaCl and frozen at -20°C. The resuspended cells were then thawed, sonicated on ice, centrifuged at 3000 × g for 20 min at 4°C, and the supernatant loaded onto a 1.6 × 9.5 Chelating-Sepharose-FF column  charged with NiSO4 (pH 7.4)/50 μM PLP/various concentrations of amino acid/various concentrations of keto acid) and incubated for 30 min at 37°C. The samples were then stored at -20°C until analysis by HPLC. All samples were analysed by pre-column derivatisation and reverse-phase HPLC. 10 μl of sample was mixed with 50 μl of 400 mM borate pH 10.5 and then with 10 μl of 10 mg/ml o-phthalaldehyde/12 μl/ml mercaptopropionate/400 mM borate pH 10.5 prior to the injection of 7.0 μl onto a 2.1 × 200 mm ODS-AA column . The column was eluted using 2.72 mg/ml sodium acetate pH 7.2/0.018% v/v triethylamine/0.3% v/v tetrahydrofuran as Buffer A and 2.72 mg/ml sodium acetate pH 7.2/40% v/v methanol/40% v/v acetonitrile as Buffer B with a linear gradient of 0 – 17% B over 16 min followed by a linear gradient of 17–100% B over 1 min and 6.0 min at 100% B. The flow rate was 0.45 ml/min from 0 – 16 min and 0.80 ml/min from 17–30 min. The elution of derivatised amino acids was monitored at 338 nm and fluorometrically with an excitation of 338 nm and an emission of 450 nm. All separations were performed on an Agilent 1100 HPLC equipped with an autosampler, variable wavelength ultraviolet/visible spectrophotometric detector, fluorescence detector, and Chemstation operating system.Aminotransferase activities were assayed by an HPLC method . 5 or 10The amino donor range for Met regeneration was determined by incubating 2 mM of each individual amino acid and 1 mM KMTB, followed by HPLC for Met quantification. Amino acids which were effective amino donors were further studied at 0.1 – 10 mM amino acid and 10 mM KMTB to determine the kinetic constants. Similar assays were performed with 0.1 – 10 mM KMTB and 10 mM Leu. Replacement of KMTB with ketoglutarate (KG) in these experiments and subsequent HPLC analysis of Glu formation allowed for the determination of BCAT activity. The apparent Km and Vmax values for each substrate were assessed by non-linear curve fitting using the Scientist software programmed with the Michaelis-Menton equation .M. tuberculosis BCAT using 2.0 mM Leu/1.0 mM KMTB/0.1 or 1.0 mM inhibitor in the enzyme incubation. Inhibitors which demonstrated better than 50% reduction of activity at the 0.1 mM concentration were further studied for the determination of Ki values. These reactions involved 0.5, 1.0, 2.0, or 3.0 mM Leu and 1.0 mM KMTB in the reaction mixture together with 0, 25, 50, 75, 100, 150, 200 μM of inhibitor. The Ki values were determined by non-linear curve fitting with the Scientist software programmed with competitive, uncompetitive, and mixed inhibition equations [Initial inhibition studies screened 13 aminooxy compounds against M. tuberculosis and M. marinum using the most effective enzyme inhibitors. Cultures at mid-log growth in Middlebrook 7H9 complete medium was diluted to 2 × 105 cfu/ml and 100 μl added to 96 well microtitre plates containing 100 μl of doubling dilutions of each inhibitor. The final inhibitor concentration ranged from 10 mM – 298 pM. Positive and negative controls consisted of 100 μl Middlebrook 7H9 medium replacing the inhibitor or cells respectively. The plates were incubated at 30°C for 8 days (M. marinum) or 37°C for 14 days (M. tuberculosis) with no agitation before measurement of growth at A650 nm using a Molecular Devices 96-well spectrophotometer . The MIC was determined as the lowest dilution that completely prevented microbial growth and the IC50 was determined by non-linear curve fitting with the Scientist software programmed with the Chou equation [In vitro growth inhibition studies were performed on equation .[Mycobacterium spp. BCAT sequences from preliminary genome projects were made available from The Institute for Genomic Research  for M. smegmatis and M. avium, from The Sanger Centre  for M. marinum, and from The Institut Pasteur  for M. ulcerans. These sequences were aligned using the Clustal algorithm and the BLOSUM sequence substitution table in the ClustalX program [Additional BCAT and DAAT sequences were obtained from GenBank . Mycobac program . Aligned program  and were program  to const program . All tre program .M. marinum experiments. BJB conceived the study, performed the M. tuberculosis experiments, and wrote the manuscript.ESV performed the cloning, expression, and characterisation of the enzyme, and assisted in writing the manuscript. CLR assisted in the cloning and expression experiments. MHK performed the


As the presumed “seat of consciousness,” the cerebral cortex mediates the higher-level cognitive processing—such as abstract thought—that humans like to think distinguishes them from other animals. The cerebral cortex is, in fact, significantly larger in the human brain and has far more “columns” than it does in other mammals, particularly compared to the rat, a traditional model for brain study. Neurons in these cortical columns have similar response properties and form fundamental units of brain processing. It is the larger brain surface area, accommodating a greater number of cortical columns, that gives humans the computational edge.By studying the genetic and molecular agents of cortex development, scientists hope to understand the nature and extent of cortical cognitive function and identify effective therapies to repair brain injury and disease. Analyses of mutant mice have provided insights into the mechanisms controlling cerebral cortex development, but many details of cortical development remain to be revealed. Identifying individual molecules and genes involved in discrete brain processes is particularly difficult given the complexity of brain structure and function. Geneticists have traditionally linked genes to specific biological pathways by first screening large numbers of individuals of a species for unusual physical traits (phenotypes) and then determining the genetic makeup of these mutants to home in on the faulty gene. This approach, called forward genetics, typically requires large numbers of individuals to find unusual phenotypes and so has traditionally focused on fast-breeding organisms like zebrafish and fruitflies. But zebrafish and fruitflies are unlikely to reveal the secrets of higher consciousness.Now Andrew Peterson and colleagues have updated the forward genetic screen and added a new resource to the neuroscientist's “brain dissection” toolkit. Their approach, which labels specific populations of neurons with protein “reporters,” offers a novel way to find mutations in developing neurons and to identify mutations that interfere with cerebral cortex development. The reporter used here highlights mutations that disrupt interneuron migration into the cortex as well as those that affect cortex growth and morphology. Like most forward genetic approaches, the researchers started with a genetically well-characterized breed, then used a chemical mutagen to damage the organism's DNA. After two or three rounds of breeding, the researchers looked for cortical-related defects in the developing embryos. In this case, however, the region of the mouse brain that gives rise to developing interneurons was labeled in the original mice. Thus, when the researchers screened for mutants with defects associated with forebrain development and interneuron migration, they could easily find the cells and genes involved.The screen identified thirteen mutations affecting cortical development and interneuron migration.  Three mutations are variants of genes known to play a role in cortical development; nine mutations were in genes that had not been linked to cortical development before.The screen described here takes advantage of a chemical mutagen  that induces mutations at a single base pair, or nucleotide, in DNA. These mutations tend to have quite selective effects on protein function—by changing the composition of a single domain—which can provide information on how the protein should normally function and highlight its role in a particular process. The selective nature of ENU, the authors argue, offers new information about how the mutated genes identified here function in cortical development and how these putative roles might be tested.Altogether, the results suggest that this type of focused screening, so long a resource in fly genetics, can be a powerful tool in mammalian biology as well. That the strategy outlined here could identify novel mutations in a process as complicated as cerebral cortex development suggests that it could do the same for a broad range of biological processes. If the success of other model systems moving in this direction is any indication, this new strategy in the mouse offers researchers a powerful resource for identifying the genetic underpinnings of living systems.


Behavior results from the integration of ongoing sensory signals and contextual information in various forms, such as past experience, expectations, current goals, etc. Thus, the response to a specific stimulus, say the ringing of a doorbell, varies depending on whether you are at home or in someone else's house. What is the neural basis of this flexibility? What mechanism is capable of selecting, in a context-dependent way an adequate response to a given stimulus? One possibility is based on a nonlinear neural representation in which context information regulates the gain of stimulus-evoked responses. Here I explore the properties of this mechanism.By means of three hypothetical visuomotor tasks, I study a class of neural network models in which any one of several possible stimulus-response maps or rules can be selected according to context. The underlying mechanism based on gain modulation has three key features: (1) modulating the sensory responses is equivalent to switching on or off different subpopulations of neurons, (2) context does not need to be represented continuously, although this is advantageous for generalization, and (3) context-dependent selection is independent of the discriminability of the stimuli. In all cases, the contextual cues can quickly turn on or off a sensory-motor map, effectively changing the functional connectivity between inputs and outputs in the networks.The modulation of sensory-triggered activity by proprioceptive signals such as eye or head position is regarded as a general mechanism for performing coordinate transformations in vision. The present results generalize this mechanism to situations where the modulatory quantity and the input-output relationships that it selects are arbitrary. The model predicts that sensory responses that are nonlinearly modulated by arbitrary context signals should be found in behavioral situations that involve choosing or switching between multiple sensory-motor maps. Because any relevant circumstancial information can be part of the context, this mechanism may partly explain the complex and rich behavioral repertoire of higher organisms. The concept of a direct, one-to-one association between a sensory stimulus and a motor response has been strongly influential in neuroscience . Such asGain control is a common mechanism by which neurons integrate information from multiple modalities or sources ,9. Gain-Interestingly, all of these examples deal with the same problem – spatial localization – but the computations that can be effectively carried out through gain-modulated responses are much more general ,16,26. IUsing theoretical and computer-simulation methods, I show that this type of functional switching can be achieved through contextual modulation regardless of how the context is encoded – whether continuously or discontinuously – and independently of the discriminability of the stimuli. The results are presented using neural network models of hypothetical behavioral tasks similar to those used in experiments with awake monkeys. A report with a different example was published previously .All model networks discussed below have the same general, two-layer architecture -16. A fix along the horizontal and the subject responds by making an eye movement  may serve to indicate which condition applies in each trial + + B,     (16)x]+ = max{0, x}. The second one uses a sigmoid function,where [as and bs. The third nonlinear interaction is based on a power law,The sigmoid is widely used in artificial neural networks  and has and has two parameters too. This type of expression approximates some of the gain effects observed experimentally . The frex and y, (2) generating all GM responses (Equation 1), (3) calculating the driven, output responses (Equation 2), and (4) determining the encoded movement Mout by using the center of mass of the motor activity profile (Equation 19). Finally, the encoded movement is compared to the movement Mdesired that should have been performed given x and y – their difference is the error in that particular trial.Having specified a task, the tuning and gain curves of the GM neurons, and the network connections, the model is tested in a series of trials of the task. Each trial consists of the following steps: (1) specifying the stimulus and context, Mout is equated with the center of mass of the output population,The encoded movement ci is the preferred target location of output unit i. The root-mean-square average of the motor error is used to quantify performance over multiple trials,where Mout - Mdesired> ≈ 0. Thus, σCM is the standard deviation of the motor error, and measures the accuracy of the output population as a whole.where only go trials are included in the calculation. On average, the encoded movement is very near the desired one, <ci uniformly spaced between -25 and 25. Preferred stimulus values aj and preferred context values bj are first distributed uniformly and then jittered by small, random amounts.In all tasks, 25 output units are used, with All simulations were performed using Matlab . The source code is available on request.This section shows that, with a finite number of contexts, gain modulation is functionally equivalent to a switch. More precisely, for a discrete number of contexts and everything else being equal, a network of partially modulated neurons can generate the same mean downstream responses as a network of switching neurons.M populations or groups of sensory neurons with identical sets of tuning functions fj(x). There are N neurons in each population, so index j runs from 1 to N. These populations project to a postsynaptic neuron through synaptic connections , where the superscript indicates the presynaptic population of origin. Thus,  is the synaptic weight from neuron j in group p to the postsynaptic unit. The sensory neurons are gain modulated, so the mean response of unit j in population p is given byConsider x and y label the stimulus and the context, as before. Next, assume that there are M possible contexts, so y can take integer values from 1 to M. Therefore, the gain factors can be expressed as three-dimensional arrays, and the presynaptic firing rates can be rewritten aswhere  corresponds to the gain of unit j in population p during context k. With this notation, the response of the downstream neuron during context k becomesHere, j, the coefficient in front of the tuning function is given by the product of an M-dimensional vector of weights times an M × M matrix of gain factors.where the sums are over all populations and all units in each population. Note that, for each index The essential idea is to compare the response of the postsynaptic unit under two conditions: when only one input population is active in any particular context , and when the populations are only partially suppressed, with different combinations of gain factors for each context. For this, the hat symbol ^ is used to label all quantities obtained in the former case, with switching neurons; that is, the hat means 'obtained with full modulation'.j is equal to the identity matrix,Full modulation occurs when the matrix of gain factors for all the units with index y = 1 and is 0 otherwise; for population 2, the gain is 1 when y = 2 and is 0 otherwise, and so forth. Substituting this expression in equation 23 gives the firing rate of the downstream unit when driven by switching neurons,According to this expression, for population 1, the gain is 1 when  implements a different function of x for each context value, such that the function expressed in context 1 depends only on the weights from the first population of switching neurons, , the function expressed in context 2 depends only on the weights from the second population, , and so on. This is the situation depicted in Figs. Again, the hat simply indicates that the quantity was obtained with maximally modulated input neurons. In this case, R, equal to the response obtained with full modulation, ? Compare the right hand sides of Equations 23 and 25; for them to be the same, the coefficients in front of the tuning functions must be equal; that is,On the other hand, the postsynaptic response driven by partially modulated units is simply as in Equation 23, where the absence of a hat means 'obtained with partial modulation'. Under what conditions is the output response driven by partially modulated neurons, This condition is satisfied if the weights with partial modulation are set equal tohj is the inverse of the matrix of gain factors gj; that is, . Therefore, the key constraint here is that the gain factors in the partial modulation case must have linearly independent values across contexts, so that the inverses exist; in other words, the matrices gj must have full rank. An important consequence of this is that for M > 2, the gain of each neuron as a function of context ( in Equation 21) must be nonlinear.where fj(x)). For the recipe to apply, the gain factors in the latter must have the appropriate inverses, but otherwise they are arbitrary. Because each possible function that a network can generate corresponds to a different matrix of synaptic connections, this implies that all the possible functions of x that the output can implement with fully switching neurons can be replicated with partial gain modulation.Equation 27 is the key result. It provides a recipe for going from a network of switching neurons to a network of partially modulated neurons . These determine the possible functions of x that can be generated downstream – that is, the available sensory-motor maps – but have no effect on how these are switched or selected. Finally, the result is also valid if the postsynaptic response is equal, not simply to the weighted sum of GM responses, but to an arbitrary function of that sum.This statement is exact when there is no noise; with noise it applies to the average downstream responses. Notice that this result is independent of the tuning functions To illustrate the result in Appendix A, consider a simple case with two populations and two contexts, as in Figs. in context 1, andM = 2. Context turns one sensory population on and another off. Now, how can we obtain the same downstream responses, as functions of x, when the GM neurons are partially modulated? First, suppose that the modulation matrices arein context 2. This is simply Equation 25 for γ ≥ 0 for the non-preferred one; the full-modulation case is recovered when γ = 0. For simplicity, these factors are the same for all units in each population, so there is no variation across index j. This matrix was used in Fig. γ = 0 and γ = 0.5 for the left and middle columns, respectively, and in Figs. The gain factors can only take two values, 1 for the preferred context, and 1 >Next, substitute into the transformation rule found earlier, Equation 27; the result isp = 1, 2 and the gain factors in Equation 30) become identical to the rates driven by the switching neurons . This is the linear transformation used in Figs. With these synaptic weights, the downstream responses driven by the partially modulated neurons , but now it has a variance, which is equal towhere .Here, the angle brackets indicate an average over trials, which affects the noise terms only. To go from the second to the third line above, the key is to assume that the fluctuations are independent across neurons, such that γ. The variance  of the postsynaptic response driven by switching neurons is exactly as in Equation 35, but with  and p = 1,2. This must be compared to the variance obtained with partial modulation for the same mean postsynaptic responses. The synaptic weights that achieve this are given by Equations 32 and 33; substituting those into Equation 35 givesThe next step is to compare the variance of the postsynaptic unit when driven by the switching neurons and by the regular, partially modulated GM neurons. For simplicity, consider the same 2 × 2 case as in Appendix B, where the modulation is parameterized by  obtained when the response is driven by fully modulated, switching neurons. Here, a depends on the weights , but is not a function of γ,This is the variance of the postsynaptic response driven by an array of partially modulated GM neurons as a function of the variance b is a constant. Note that a is a measure of the overlap between the sets of connections from the two populations. The dependence of  on the weights is such that a ≤ 2.where x and y for all γ, its variability changes with γ. A similar result is obtained when the variance of the input firing rates is proportional to their mean. In that case,Equation 36 shows that, although the average postsynaptic response is the same function of  = 1. A calculation analogous to the one just described leads towith a is the same as in Equation 37, except with a different proportionality constant. In this case, it is still true that a ≤ 2. This expression was used to generate the continuous lines in Fig.  was simply the variance in the postsynaptic firing rate found from the simulations with γ = 0, and b was chosen to generate the best fit to the rest of the simulation data points.where γ. This depends on a, which is a measure of the similarity between the sensory-motor maps established in the two contexts. For instance, when the two maps are the same,  for all j, and a attains its maximum value, 2. In that case, the variance with partially modulated neurons is always smaller than with switching neurons. This makes sense: if the maps in the two contexts are the same, it is always better to have the two populations active at the same time, as this reduces the noise. According to the analysis, when a = 2 and γ = 1, Equation 36 gives . The variance is divided by 2 because noise is additive and there are two active populations doing the very same thing. In contrast, when the two maps are different, their respective synaptic weights are also different, and a is either positive but much smaller than 2, or negative. Then,  might have a minimum for some intermediate value of γ, or may increase monotonically, which is what happens in Figs. Equations 36 and 39 do not always increase monotonically with GM, gain-modulated.


There is evidence of a contribution of early life socioeconomic exposures to the risk of chronic diseases in adulthood. However, extant studies investigating the impact of the neighborhood social environment on health tend to characterize only the current social environment. This in part may be due to complexities involved in obtaining and geocoding historical addresses. The Life Course Socioeconomic Status, Social Context, and Cardiovascular Disease Study collected information on childhood (1930–1950) and early adulthood (1960–1980) place of residence from 12,681 black and white middle-aged and older men and women from four U.S. communities to link participants with census-based socioeconomic indicators over the life course.Most (99%) participants were linked to 1930–50 county level socioeconomic census data  corresponding to childhood place of residence. Linkage did not vary by race, gender, birth cohort, or level of educational attainment. A commercial geocoding vendor processed participants' self-reported street addresses for ages 30, 40, and 50. For 1970 and 1980 censuses, spatial coordinates were overlaid onto shape files containing census tract boundaries; for 1960 no shape files existed and comparability files were used. Several methods were tested for accuracy and to increase linkage. Successful linkage to historical census tracts varied by census . This compares to linkage rates of 94% for current addresses provided by participants over the course of the ARIC examinations.There are complexities and limitations in characterizing the past social context. However, our results suggest that it is feasible to characterize the earlier social environment with known levels of measurement error and that such an approach should be considered in future studies. Notwithstanding concerns about the accuracy in the assignment of statistical tabulation areas by commercial geocoders  modify the association of individual-level SES exposures and CVD. Trained interviewers administered a telephone questionnaire including 44 questions about parental and early adulthood occupational and educational exposures, current sociodemographic characteristics and childhood and earlier adulthood places of residence. Participants responding to the questionnaire (N = 12681), represent 81% of the ARIC baseline cohort and approximately 94% of cohort survivors. Additional details about the LC-SES Study can be found in the manual of procedures  and otheParticipants were asked "Where did you mostly live when you were a child? If possible, give me the city/town, county, and state of residence." Participants were also asked to provide their address  at various points during adulthood. Everyone was asked to provide addresses for age 30 , and those who had first participated in the ARIC study after age 49  or 59  were also asked to provide addresses for ages 40/50 and 50, respectively. Those unable to provide an exact address were asked to provide the street name and the closest cross-street.The year at which participants were aged ten years, which represented the approximate midpoint of childhood, was determined in order to link with the county-level socioeconomic data from the closest census year . When a city, but not a county was provided, we used a publicly available website to attempt to identify the correct county . County Prior to geocoding, all state data was standardized to conform to the two-digit U.S. Postal Services state coding system. Within each state the accuracy of the spellings of cities were verified. Street addresses were reviewed and computer programs written to correct obvious misspellings and to standardize formats. We did not submit zip codes because those accompanying the historical address could have changed over time. Because our goal was to classify the social environment where participants lived, we excluded post office box addresses as they do not necessarily correspond to actual residences. These along with other incomplete and unusable addresses  were not sent for geocoding. After editing, addresses and an encrypted study ID number were sent to a commercial vendor under contractual terms of confidentiality negotiated by university counsel and approved by the IRB.The vender assigned to each address: spatial coordinates, Federal Information Processing Standards (FIPS) codes for statistical tabulation areas corresponding to 1990 census boundaries, and a match code describing the degree of accuracy of the geocoding. The accuracy rating assigned by the vendor ranged from" house range address matches" (best) to the "centroid of county" (worst). As we were interested in accurately classifying each participant's place of residence at the level of the census tract , we accepted only house range address matches  or matches to centroids of zip code areas where everyone lived within a census block group, census tract or where more than 80% of addresses in area were located in the same tract. Rural routes were sent to the vendor but these addresses were not successfully geocoded.Two methods were considered to link the spatial coordinates obtained from the vendor with the appropriate historical census tract. The overlay method uses the spatial coordinates assigned to exact address matches in conjunction with historical boundary maps to place addresses into historical tracts. The comparability file method uses current US Bureau of the Census tract assignments that are traced back in time stepwise to 1980 tracts, then from 1980 to 1970 tracts using files that describe tract changes from decade to decade. As a test, we compared the 1970 tract assignments by the two methods for 13,044 addresses that were successfully geocoded to the 1990 census by the geocoding vendor. While all addresses were assigned tracts using the overlay method, 36% could not be assigned a 1970 tract using the comparability files due to census tract merges (a tract contains parts of more then one tract from the previous decade). Of the remaining addresses (n = 8348), 97% were assigned the same tract by both methods. Because there are known errors in the assignment of spatial coordinates by commercial geocoding vendors ,12,14, wWe determined the census year  that corresponded most closely to when the participant resided at each address. Arcview GIS Version 3.3 software was used to overlay the spatial coordinates assigned to addresses by the vendor onto Geolytics, Inc. shape files of census tract and block numbering area boundaries of the appropriate census year . The spatial coordinates falling within the historical tracts were assigned the corresponding tract number. Figure Electronic shape files were not available for the 1960 census. Thus, 1960 addresses were placed into 1970 tracts using the overlay method and then mapped to the appropriate 1960 tracts using files providing data on the correspondence between 1970 and 1960 tracts. These were obtained from print volumes of comparability files published by the US Bureau of the Census  and keye® tools and the street map legends, we attempted to locate each address. If a street was contained within the boundary of a census tract, we assigned it the corresponding tract number. If a street crossed a census tract boundary or was the boundary for two or more tracts, we did not assign a census tract.When a 1960 address fell into a 1970 tract made up of merged 1960 tracts or when addresses were not geocoded by the commercial vendor , we attempted to manually place addresses into historical census tracts. Because this process is labor intensive, we undertook this effort only for addresses which were located within the four ARIC study communities (as a large number of addresses were not clustered in other areas). First, we obtained detailed street maps for the four study areas and overlaid them with census tract boundaries and numbers from the three historical censuses. Then, using web-based MapquestA large number of historical addresses in Washington County, MD were obsolete, because in the early 1990s the state changed to a grid address system to improve emergency response systems. Thus, we obtained historical street maps from the Hagerstown, MD Public Library and tried to locate the original street names in an attempt to manually assign a census tract using the procedure described above.For addresses placed within a 1960–1980 census tract, we linked with tract level socioeconomic data. For 1970 and 1980 we used data from Geolytics, Inc. . The ODUM Institute at the University of North Carolina, USA provided electronic 1960 census tract data. Jackson MS and Washington County MD had not been assigned census tracts in 1960. For Jackson, MS and the portion of Washington County, MD falling within the Hagerstown city limits, we obtained 1960 census housing data at the level of city blocks from print volumes , and aggKMR conceived of and led the writing of the manuscript. JLW analyzed the data on early adulthood and developed the methods for assigning census tracts to historic addresses. GH, the principal investigator of the LC-SES Study, contributed to the conceptualization and writing of this manuscript. EAW assessed the accuracy of the commercial geocoding and developed standardized procedures for manual geocoding. SK developed standardized procedures for editing the recalled address data. RP analyzed data pertaining to childhood place of residence. DY was instrumental in reviewing methods for placing participants into their historical tracts. AVDR provided expert input on methods of geocoding. All authors helped to frame the ideas, interpret findings, and review drafts of the manuscript.