Functional but silent peroxisomal targeting signals have been found in non- peroxisomal proteins. This discovery has important implications for sequence-based signal prediction and for evolution. Can sequence segments coding for subcellular targeting or for posttranslational modifications occur in proteins that are not substrates in either of these processes? Although considerable effort has been invested in achieving low false-positive prediction rates, even accurate sequence-analysis tools for the recognition of these motifs generate a small but noticeable number of protein hits that lack the appropriate biological context but cannot be rationalized as false positives.MRP7) - are imported into peroxisomes in vivo if their original sorting signals are disguised. We also show that even prokaryotic proteins can contain peroxisomal targeting sequences.We show that the carboxyl termini of a set of definitely non-peroxisomal proteins with predicted peroxisomal targeting signals interact with the peroxisomal matrix protein receptor peroxin 5 (PEX5) in a yeast two-hybrid test. Moreover, we show that examples of these proteins - chicken lysozyme, human tyrosinase and the yeast mitochondrial ribosomal protein L2 (encoded by Thus, functional localization signals can evolve in unrelated protein sequences as a result of neutral mutations, and subcellular targeting is hierarchically organized, with signal accessibility playing a decisive role. The occurrence of silent functional motifs in unrelated proteins is important for the development of sequence-based function prediction tools and the interpretation of their results. Silent functional signals have the potential to acquire importance in future evolutionary scenarios and in pathological conditions. In addition to sequence homology and annotation transfer considerations [For an increasing number of otherwise uncharacterized protein sequences from genome-sequencing projects, function assignment is attempted solely with erations , these fAlthough considerable effort has been invested in achieving low false-positive prediction rates, our experience with tools for recognizing glycosyl phosphatidylinositol (GPI) lipid ,3 and myin vivo. Only experimental tests can resolve this contradiction. As a case study, we report the results of an experimental analysis that demonstrates the existence of naturally occurring peroxisomal targeting signals in several known non-peroxisomal proteins. We also discuss the evolutionary perspective of functional localization signals in unrelated proteins as well as the consequences for experimental localization determination and function prediction from sequence.Are these false-positive predictions? All three of the sequence-analysis tools mentioned above check query sequences for a recognition pattern that is explicitly described in terms of its physical properties and it is possible to check the concordance between pattern descriptions and query sequence individually. Nevertheless, this visual inspection is frequently unable to rationalize the findings as false-positive predictions, as all known components of the pattern appear to be present. Even in the case of high accuracy of the prediction tool, an erroneous prediction cannot be excluded. Alternatively, these predicted sequence motifs may occur by chance and be functional in an appropriate test system, but still have no biological meaning because the necessary cellular context is absent The major mechanism for targeting proteins to the matrix of peroxisomes, which are membrane-bounded organelles  of eukarDrosophila sevenless (P13368, a large transmembrane protein required for photoreceptor development); precursor of lysosomal bovine cathepsin D (P80209); and a mitochondrial ribosomal protein from yeast (P12687). We also examined the carboxyl terminus of a mouse dihydrofolate reductase construct with an added SKL peptide, which has been shown not to be imported into yeast peroxisomes [Screening of SWISS-PROT  entries oxisomes ,17.Depending on their taxonomic origin, the carboxyl termini of the eukaryotic sequences were assayed for interaction with the tetratricopeptide repeat domains of either human or yeast PEX5 using published methodologies . The quein vivo could be explained by the non-accessibility of their carboxyl termini. These could either be hidden in the native structure of the mature protein or of its functional complexes, or competing translocation machineries could lead to a removal of the respective proteins from the cytosol before their recognition by PEX5.The fact that the peroxisomal translocation machinery fails to import naturally occurring mature proteins carrying PTS1 signals into peroxisomes SKL, sequences appended to DHFR are in bold type, see results in Table in vivo the complete construct is not imported into peroxisomes, thus confirming the prediction [GGKSKL and IKYKFEVYEKKNIESKL) are predicted to be peroxisomally targeted. Their scores calculated with the PTS1 predictor [The first possibility is exemplified by DHFR-SKL. The carboxy-terminal 16 residues of the DHFR-SKL construct  was appended to the amino terminus of each of the selected proteins. It can be assumed that translocation into the endoplasmic reticulum (ER) or mitochondria is disrupted by the resulting shift of the signal peptide from the amino terminus to the center of the protein. The resulting molecules are expected to be redirected into peroxisomes if their carboxyl termini can act as PTS1 signals. Targeting of the GFP-constructs Escherichia coli glutamate-1-semialdehyde 2,1-aminomutase (P23893), E. coli transaldolase A (P78258), Methanopyrus kandleri riboflavin synthase (NCBI-Refseq accession NP_613646) and Archaeoglobus fulgidus 2-nitropropane dioxygenase (NCBI-Refseq accession NP_070998). Indeed, these proteins harbor carboxyl termini that qualify as PTS1 signals  could not be found in peroxisomes [In families of orthologous proteins, peroxisomal location and its targeting signal in the amino-acid sequence are not necessarily conserved. For example, in plants the five enzymes of the glyoxylate cycle are localized to peroxisomes, but in oxisomes . Thus, iIn dually localized proteins such as AGT , the PTSThe case of lysozyme is particularly noteworthy because a large number of homologous proteins were systematically hit when performing a SWISS-PROT screen using the prediction tool  of the carboxy-terminal exons for human tyrosinase (GenBank accession AP000720.4), fly sevenless (GenBank accession AE003484.2) and chicken lysozyme (GenBank accession AF410481.1) reach far into the functional domains of their proteins, rather supports an evolutionary mechanism of several point substitutions. The occurrence of functional PTS1 sequences in non-eukaryotic species further supports a stochastic model for the evolution of PEX5-interacting protein carboxyl termini.N-myristoylation of glycines behaves as an exchangeable functional module, as protein families do exist where it has been substituted by alternative sequence determinants that facilitate membrane association [Arabidopsis thaliana Rab5 ortholog Ara7 and its paralog Ara6. Ara7 is geranylgeranylated on carboxy-terminal cysteines just as Rab5 is in other species. However, the closely related paralog Ara6 lacks the carboxy-terminal cysteines and has an experimentally verified amino-terminal myristoylation motif [In non-globular regions of proteins, sequences that code for targeting to other subcellular compartments, or for posttranslational modifications, might appear in similar ways during evolution. For example, the sequence motif coding for amino-terminal ociation . This ison motif .Many of these signals seem to remain silent under normal physiological conditions  but have the potential to become important in some future evolutionary scenarios or in pathological situations. Alternatively, the PTS1 signal might have become obsolete and the corresponding sequence segment is now subject to evolutionary alterations. Apparently, the cell exploits only a fraction of the potential molecular capabilities of its proteins.Futhermore, subcellular targeting is organized in a hierarchy of cellular recognition mechanisms. The co-translational sorting into the ER serves as a first decision node. Posttranslational processes such as interaction with chaperones, folding, and covalent modifications are concomitant with the appropriate exposure of targeting signals. The amino-terminal signals are made first and are therefore favored when it comes to recognition by receptors. PEX5 needs only to categorize the remaining unsorted proteins with accessible carboxyl termini into 'stay here' or 'let's go into peroxisomes'. This might also explain why the PTS1 signal is comparatively short and permissive for a wide range of residues.Clearly, the fact that functional sequences for subcellular targeting occur in unrelated proteins needs to be considered for prediction-tool development. The construction of a negative learning set  on the basis of proteins with differing cellular localization is problematic. For example, a set of non-peroxisomal but organellar localized , viral  or bacteN-myristoyltransferases (NMT) in bacteria. Are these false-positive predictions? A literature search revealed that myristoylation by host NMTs has physiological relevance for several secreted proteins of intracellular bacterial parasites [N-myristoylation is typical for eukaryotes but occurs also in bacteria. In many cases, it remains without phenotypic effect for bacteria but may become evolutionarily important in the case of host-parasite interactions.Surprisingly, when Maurer-Stroh and Eisenhaber applied their myristoylation site predictor for eukaryotic proteins to bacterial proteomes , systemaarasites . Thus, tIn the case of the endothelin-converting enzyme 1 and the neprilysin-like zinc metallopeptidase family, the carboxy-terminal CXAW motif is a valid prenylation motif. This carboxy-terminus is functionally hidden because the protein is exported to the extracellular side of the cytomembrane and the carboxy-terminal residues are apparently involved in folding and enzyme function .Clearly, the accessibility of the recognition motif in the substrate protein to the respective receptor or protein-modifying enzyme is a major issue. For PTS1 signal prediction from the amino-acid sequence, carboxy-terminal exposure needs to be assessed both from the steric point of view as well as in the context of competing translocation mechanisms. Analyzing only the carboxy-terminal dodecamer peptide ,13 mightIn databases, it should also be routine to flag proteins that contain several competing targeting signals with differing priority. Finally, silent localization signals might become active in mutant protein constructs and lead to non-native localizations, an issue that needs to be assessed especially in localization screens of proteins with uniformly incorporated fluorescent dyes such as GFP. It cannot be excluded that the subcellular location of a considerable number of proteins has not been correctly determined in published large-scale studies that rely on this methodology ,31.To conclude, sequence segments coding for subcellular targeting or for posttranslational modifications can occur in proteins that are not substrates in either of these processes. Accurate prediction techniques reveal candidate proteins carrying hidden sequence signals. Many of these can be experimentally confirmed. In the case of the PTS1 predictor program, there is no reasonable argument to assume a difference in prediction accuracies for real and hidden PTS1s as, in both cases, productive interaction of the carboxyl terminus with PEX5 is the criterion for a functional PTS1.E. coli strain DH5α, Bethesda Research Laboratories) was used for all transformations and plasmid isolations. For the yeast two-hybrid-assay, the hybridized oligonucleotide pairs coded for the carboxy-terminal 16-mers of the selected proteins flanked by BamHI (5') and EcoRI (3') restriction sites. Each oligonucleotide pair was introduced into a BamHI-EcoRI-digested pGAD.GH fragment, generating plasmids containing the Gal4p activation domain in addition to the desired carboxy-terminal 16-mer extension . All pGAD.GH constructs were sequenced (VBC Genomics, Vienna, Austria). The plasmids pAH987 and hP87 contain the binding domain of Gal4p fused to the TPR domain of S. cerevisiae or Homo sapiens PEX5, respectively  [Oligonucleotides were purchased from MWG Biotech (Munich, Germany). The pBD-TPR) .EcoRI/BamHI and ligated into pEGFP-C1 (Clontech). The DsRed2-SKL construct was obtained by PCR using Pfu-polymerase (Promega) and the plasmid pDsRed2-C1 (Clontech) as template  according to the manufacturer's instructions. Reverse transcription was performed using RNA-PCR Core Kit (Applied Biosystems) following the manufacturer's instructions. For the amplification of tyrosinase, we used cDNA from the melanoma cell line 29 WUBI . The coding regions of lysozyme and tyrosinase were gained by PCR  after the MLS1 promoter in the vector YEplac181. DNA fragments coding for MRP7 and GSA were obtained by PCR (see Table BamHI-SphI) in-frame with GFP to give rise to the expression of GFP-MRP7 and GFP-GSA, respectively, all of them under the control of the MLS1 promoter.Standard procedures were used for cloning of the GFP-MRP7 and GFP-GSA constructs including control sequencing (VBC Genomics). The plasmids expressing GFP and GFP-SKL under control of the eviously . The DNAMATα, his3Δ200, ade2-101, trp1Δ63, leu2, gal4Δ, gal80Δ, lys2::GAL1-HIS3, ura3::GAL1-lacZ) [A600 = 0.3 into the same medium and finally harvested at absorptions of A600 between 0.9 and 1.1.According to the Matchmaker two-hybrid protocol, yeast strain PCY3 (L1-lacZ)  was tranL1-lacZ) , yeast cCOS7 cells were transfected with the pEGFP-C1-constructs and DsRed2-SKL by electroporation using 920 μF and 220 mV (Gene pulser II, Bio-Rad), grown on coverslips for 36 h, washed, fixed with 0.5% formaldehyde in PBS for 15 min and covered with geltol. Cells were analyzed using the Olympus BX51 fluorescence microscope (60 × enlargement).S. cerevisiae CB80 (MATa, ura3-52, leu2-1, trp1-63, his3-200). Yeast transformants were selected and grown on minimum medium containing 0.67% yeast nitrogen bases without amino acids (Difco Laboratories), 2% glucose and amino acids (20-150 μg/ml) as required (SC-leu-ura). For fluorescence microscopy, yeast cells were grown at 30°C with shaking in selective media with 0.5% glucose as sole carbon source until the glucose concentration was very low , harvested by centrifugation and resuspended in the original volume of induction medium containing 0.67% yeast nitrogen bases without amino acids, 0.1% yeast extract, 30 mM potassium phosphate pH 6.0, 0.125% oleate, 0.2% Tween-80 and amino acids as required. Cells were grown for 16 h in induction medium and observed live for fluorescence. Briefly, cells were collected by centrifugation and washed twice in water. Cell pellets were resuspended in induction medium without oleate and aliquots were spotted onto multitest slides  previously coated with concanavalin A (6 mg/ml, Sigma). Cells were allowed to attach for 5 min at room temperature and the slides were washed twice with induction medium and a coverslip applied for observation. Fluorescence was viewed with a Zeiss Axioplan 2 fluorescence microscope using a 63 × (1.4 NA) lens. Digital images were captured with a Quantix CCD camera using Lightview software without further modification. The pictures were mounted and false-color overlays were made in Adobe Photoshop.The yeast strain used in this study is


The enormous amount of genome sequence data asks for user-oriented databases to manage sequences and annotations. Queries must include search tools permitting function identification through exploration of related objects.The GenoList package for collecting and mining microbial genome databases has been rewritten using MySQL as the database management system. Functions that were not available in MySQL, such as nested subquery, have been implemented., a set of 17 specialized end-user-oriented microbial databases  has been made publicly available. These databases allow the user to browse genome sequence and annotation data using standard queries. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation updates on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences, taking into account a clustering of genes into formal operons, as well as providing extra facilities to query sequences using predefined sequence patterns.Inductive reasoning in the study of genomes starts from "islands of knowledge", centered around genes with some known background. With this concept of "neighborhood" in mind, a modified version of the GenoList structure has been used for organizing sequence data from prokaryotic genomes of particular interest in China. GenoChore Thermoanaerobacter tencongensis, LeptoList, with two different genomes of Leptospira interrogans and SepiList, Staphylococcus epidermidis) associated to related organisms for comparison.This growing set of specialized microbial databases organize data created by the first Chinese bacterial genome programs (ThermaList,  While this shows that we implicitely possess an enormous wealth of information about the functions carried out by genes and genomes, the very fact that this amount is enormous makes it extremely difficult to mine that information easily. The role of specialized databases is to make this task easier for end-users. Many types of microbial genome databases exist. Most of them have been developed in a context of bioinformatics centres or laboratories purely favoring in silico research rather than the coupling between experiments using computers and experiments at the bench, and this is reflected in the structure and aims of the databases [Escherichia coli genome data, well before the whole genome sequence was known [Bacillus subtilis genome program data access [, [We are facing a deluge of genome sequences. As of January 14th, 2005, the GOLD site identified 1248 completed or ongoing genome programs as known . Later oa access . Many daccess [, ). Howeveccess [,  and we dBecause we wished to shift from a commercial DBMS to an open-source one, there were some applications that could not be implemented readily due to the lack of certain advantages possessed by the commercial DBMSs. Hence, we had to alter the data structure in order to cope with this situation. The core data schema used in this work was that of GenoList version 3.1 , with sl. Most function transfers were straightforward. However some functions such as nested subquery that were not available in MySQL had to be dealt with indirectly. The nested subquery has been entirely circumvented in the PERL code and is dealt with in the Extended Search algorithm by concatenating different SQL queries simply using the "AND" or "OR"connector.In the present GenoList databases, the DBMS used is Sybase™. While this is convenient because of excellent stability and maintenance, this may pose problems in terms of commercial policies, especially if the structure has to be exported. We therefore decided to rewrite the management of the GenoList structure using MySQL . The current version is Version 6.2 Oct 15, 2004. While this specification is rigid, there is still a significant degree of freedom in annotation, so that a large number of individual situations have to be taken care of semi-automatically. The basic idea of the parser is firstly to read through the input file at the INSD and check file formats. Subsequently, the information is collected and distributed into several temporary files using a set of predefined keywords and their qualifiers (i.e. those characterizing the data schema). Subsequently, a check process is initiated to identify all situations that do not fit the specifications, so that they can be corrected manually. Usually, most of the process of creating tables is automatic and only a few exceptions have to be corrected individually. A second type of input is also provided as an interactive interface to tell the database curator what information has to be collected: once collected this information can be loaded into the databases directly  withth Dimension™). The modification of the database schema needed for using MySQL required additional adaptations of the application interfaces. Using the original package would have required iterative work that was systematically adding complexity into the system. Current best Web interfaces and application interfaces (i.e. friendly for sharing parties) are often based on Perl scripts. For this reason a new core management script was recreated, written in Perl, while keeping the package architecture and the Web interfaces. Among other languages that have comparable functionalities, the choice of Perl to create the system was motivated by its powerful capability to glue different programs or scripts together. In addition it is widely used by the INSD, and at the European Bioinformatics Institute in particular within the BioSapiens program . Furthermore, this choice allowed us to keep the optimized fast C code that has been constructed for searching pattern (strings of symbols) inside the DNA or amino acids sequences. The GenoList C/C++ package chose to use the GD library  for generating graphic representations of genome regions. The GD graphics library is an open source library which allows programmers to easily generate PNG, JPEG, and WBMP images from many different programming languages. We used here a newer version of the same library (perl module perl-GD version 2.11) to make use of its improvements in creating dynamic pictures. In rewriting the core of the program we used the Perl module DBI . A DBI is a middle layer between the outside applications and the communicator (DBD). Different DBMSs have their own communication mechanism to talk with outside applications, and in the present version the choice of the DBI module has been implemented in such a way that we could change the DBMS if necessary with minimal work. In this way, when changing the DBMS, it will only be necessary to tell the DBI about the specifications of the new DBMS without having to modify any other code. Finally, we used the Perl module CGI to facilitate the production of the WebPage interfaces. As a consequence further developments of the GenoChore package should be performed with minimal effort.We kept the interface of GenoList as published, except that a box providing access to protected curation of annotations is now provided, aiming initially at helping the first party (sequencing teams) annotators. The front page is made of three frames. Briefly, the vertical frame on the left contains the controls necessary to get access to the content of the database. The upper part of this frame contains text fields for querying the database according to five types of queries: gene name(s), chromosome region around a gene, chromosome region defined by positions, free text, functional classification (more detailed information about each type of query can be obtained by clicking on the question mark near the query title). The "Extended Search" button gives access to a search form allowing the user to perform multicriteria searches on all the database fields. The lower part of this frame allows one to launch sequence analysis tools: BLAST and FASTA database searches (on the sequence data), and DNA or protein pattern searches. In the former case, the user can choose to explore sequences located upstream of putative operons. In the latter case, the user can search for patterns anywhere in proteins, but also restrict the search to the beginning or end of the protein. The upper frame on the right can contain various types of information, depending on the genome and on the query. It can contain a graphical representation of a chromosome region, that can be obtained in several ways: usually from a gene in the bottom frame. This frame may contain launch forms and result lists from the sequence analysis tools available (pattern search, BLAST or FASTA scanning). The bottom frame on the right always contains detailed information about one given gene, including regularly updated BLAST searches and Teraprot reports as well as related bibliographic references. The original package managing the interface of GenoList databases was written in C/C++, following the first database schema , that haIn the original GenoList structure, the central table corresponding to genomic objects carried all relevant features that are associated to genes and gene products. For the sake of future developments and to accommodate new feature annotation present in genome flatfiles, we separated this table into several gene product tables, specific for RNAs and proteins. The current data structure remains open to include tables for other types of data, such as regulation properties annotations when they will become available. Figure Streptomyces coelicolor database, CoeliList).It is important here to notice that, in contrast to a rather ubiquitous practice, we explicitely separate between Open Reading Frames (ORFs) that are simply sequences multiple of 3 between two termination codons (TAA, TAG and TGA) and CDSs, that begin with a specific codon, usually ATG (in the DNA text), preceded by a ribosome binding site (RBS), typically AAGGAGGT in many bacterial genomes. One must remember that in most genomes the beginning of CDSs has not been experimentally identified. Identification of CDS starts is however much easier in low G+C Firmicutes that do not possess a counterpart of ribosomal protein S1 found in gamma proteobacteria . In the PA3004 for a gene found in the genome of Pseudomonas aeruginosa). In the absence of knowledge of a gene name it takes some time to identify it (often using BlastP), for example when aiming at the study of its neighborhood . Orthologues were identified as reciprocal best hits [B. subtilis for Firmicutes and another organism of the same family, such as Listeria monocytogenes, and E. coli for gamma-proteobacteria, with another one of the same family, such as Photorhabdus luminescens). Finding putative orthologues in the three organisms was considered as substantiating evidence for the use of a gene name. Then, in each triplet, we did not transfer the model organism gene name to all orthologues that were not simultaneously present in the three genomes or that gave different correspondences in different comparisons. In another comparison where the orthologues were found with at least 50% similarity, the model organism gene names to be transferred were preceded by the letter 'y'. In order to help users recognize gene names  we used as reference names those in the model bacteria, trying to comply as much as possible with the names used at SwissProt in the HAMAP project [Users know that the system used for naming genes in genome databases is extremely unwieldy and completely lacks standardization. This is usually because genes are simply labelled in databases by access numbers corresponding to the annotation phase of the relevant genome program (e.g.  article ). Natura project .rpsA can be accessed directly or using its synonym PA3162: it is then found downstream of cmk (a context similar to that found in many Gram negative bacteria) and upstream of himD. We are aware that some erroneous identification (or propagation of erroneous identifications) must have occurred in some cases, but we think that this is a trade-off (which will be continuously corrected) for a much more user-friendly usage of the databases. A 'y' letter starting a gene name indicates that it has not been experimentally identified, nor convincingly identified after in silico analysis yet. We provide curation pages (see below) to help users to correct annotation errors and improve annotation in a continuous way.This allows the users to have "anchor" points to start to use the databases in a more efficient way. Naturally, the names previously used in the corresponding genome programs are kept as synonyms, so that access to the sequences with these names is still allowed. For example, in AeruList, gene Escherichia coli created by Monica Riley and her collaborators [An important feature for allowing users to explore biological functions is to investigate the genes neighborhoods . Relatedborators  (Additio12–25C). This facility has already, in a quite unobtrusive but efficient way, permitted discovery of many unexpected functions. We have also provided means to explore the beginning and the end of protein sequences, as well as DNA regions upstream of putative operons, computed as strings of genes transcribed in the same orientation and separated by a maximum number of nucleotides (60 nt by default).In addition to using keyword queries or sequence tags (such as molecular mass or isoelectric point of a protein) the database provides a versatile way to identify sequences from the biological knowledge viewpoint. In particular, as in many other databases, it allows the user to use Fasta, BlastP and BlastN to compare a sequence of interest to that of those present in the database. Furthermore, in contrast to most cases, it allows the user to extract information using motifs, that can be either continuous or discontinuous  are provided, allowing the user to look for remote kinships.Genome annotation is continuously updated by scientists all over the world, at a time when new genome sequences appear every three days or so. In order to cope with this enormous flux of information, a facility for browsing automatically new entries in major data libraries has been implemented. In the gene information panel, where each gene of interest is described after being identified as the result of a query (including resulting from a Blast or Pattern search), an "Automatic Blast" link provides a list of updated blast searches against the UniProt library (SWISSPROT+TREMBL). In addition, when the genome belongs to the 'Teraprot' Smith and Waterman Z-score family To discuss the use of the databases we shall restrict our exploration to two databases from the package. LeptoList, that comprises two genomes (each one having two chromosomes) for Bacteria, and CunicuList, that describes the genome sequence and annotation of a small eukaryote.Leptospira interrogans serovar Lai, the paradigm of leptospirosis causative agents [L. interrogans serovar Copenhageni in order to allow easy comparison [L. interrogans has two chromosomes (this feature was not yet displayed in GenoList databases). Using the regular comparison to the CDS to the non-redundant INSD protein database allowed us to suspect that a significant proportion of the short putative CDSs in the genome are likely ORFs and not authentic CDSs. This fits with the recent sequencing of the second Leptospira genome [LeptoList is the reference database dedicated to the genome of e agents . It is pmparison . The WWWa genome ,21.B. subtilis (frr gene product) and compared it using BLAST with the functionality implemented in LeptoList. This search led to a single gene, LA3295, located downstream of gene pyrH . This synteny is obviously highly significant. In the same way, the gene immediately upstream from pyrH (LA3297), as in other bacteria, is likely to be coding for elongation factor EFTs (tsf). When curating the database, we suggest to the curator that it would be of excellent policy to replace the gene numbers by the corresponding gene name. In another type of investigation, looking for patterns of the type TTGACA (1 ambiguity) – 17 nt – TATAAT# (1 ambiguity) (consensus sequence of the σ70-type promoter) in the 300 nt region upstream of genes revealed 70 sequences in chromosome I, many of which are likely to be promoters (at least they would be good guesses to start investigating promoters in L. interrogans). In the same way, the putative DNA binding site located in the 300 nt nucleotide region upstream of genes, TGTGA (1 ambiguity) – 2 nt – KK – 2 nt – TCACA (1 ambiguity) , yielded 130 matches in chromosome I of serovar Lai and 72 matches in serovar Copenhageni and 2 in chromosome II of serovar Lai and 0 in serovar Copenhageni, allowing one to start investigating possible regulatory elements.A couple of examples of its use are given here. We looked for counterparts of RRF, the ribosome release factor. In order to find the gene we used a known sequence, from fadH or prfC), accounting for the higher total number of putative binding sites in that organism. It seems most interesting that genes involved in the control of respiration (cytochrome c biosynthesis), control of the TCA cycle (pyruvate dehydrogenase synthesis), control of the coupling between translation and transcription (stringent control) or translation itself (release factor 3 synthesis) are present in the list. While there are several putative adenylyl cyclase genes present in the organism, as well as several homologs of crp, it is plausible to propose that cAMP plays an important role in the life cycle of L. interrogans, perhaps suggesting ways to allow multiplication on plates of this elusive organism.This result is interesting as it suggests that chromosome I genes are submitted to a regulation recognizing that particular DNA-protein binding site. Furthermore, most genes found with the site in serovar Copenhageni are also found in serovar Lai, with sometimes several repeats in the latter, occuring upstream of some genes . Using MesJ (TilS) [yacF B. subtilis gene. Looking for counterparts of genes in the methionine salvage pathway [The GenoList structure has been initially constructed for organizing sequence data from prokaryotic genomes. However it may be extended to other organisms as well (the "genomic object" type must be extended accordingly). We have therefore tested the implementation of the structure for the genome of J (TilS)  as well J (TilS)  we found pathway , we fail pathway  are appa. Table Several other similar bacterial databases are accessible at URL Despite of – or because of – the large number of genome programs, once a sequence has been deposited at the INSD, its annotation is seldom updated. This is because the cost of curating annotations is extremely high, and usually not considered, despite its enormous importance. One of our aims was therefore to allow curation by selected teams by creating a curator page where such teams would input their annotations, that would then be propagated to the databases. The basic schema of the curator interface is shown in Figure A set of 17 specialized end-user-oriented microbial databases (including one instance of Microsporidia) has been implemented in Hong Kong. They allow one to browse genome sequence and annotation data using the most frequent queries that end-users would like to ask. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences present in the databases. All comments, bug reports and suggestions for improvement are more than welcome: this work is meant to be useful for the community of microbiologists interested in genomics.The author(s) declare that they have no competing interests.Bacillus subtilis and Escherichia coli), created the link to Teraprot for identification of gene functions, and implemented parts of the PERL package; YQ implemented parts of the Perl package; CC and VC implemented most of the databases into the core structure; ZY wrote part of the new parsers, implemented the two chromosomes of LeptoList by changing the data structure in the database and set up with CC the first LeptoList database; FC set up and administered the Apache web server and MySQL database; IM over the years designed most of the GenoList data schema and user interface; AD was at the origin of the project, participated in the design and evolution of the data schema, was the systematic tester and end-user and wrote the core of the article.GF wrote the parsers used to create the preliminary C/C++ MySQL package, and created important sections of the Perl package; CH created the procedure for renaming orthologs with reference to accepted names for model bacteria (Functional categories. The genes' roles are listed into six major categories. The three first ones are directly linked to biological roles, while the remaining categories are created ad hoc: adaptation to atypical conditions correspond to miscellaneous roles, while the two last categories correspond to roles that have not yet been ascribed to genes because of lack of in vivo or in silico dataClick here for file


A small rodent rustles through a field in the still night, making just enough noise to betray its location to a circling barn owl. A female frog sits on the bank of a pond amid a cacophony of courting bullfrogs, immune to the mating calls of all but her own species. Thanks to a sophisticated sensory processing system, animals can cut through a vast array of ambient auditory stimuli to extract meaningful information that allows them to tell where a sound came from, for example, or whether they should respond to a particular mating call.An acoustic stimulus arrives at the ear as sound energy in the form of air pressure fluctuations. The sound signal triggers oscillations in mechanical resonators such as the eardrum and hair sensilla. These oscillations convert sound energy into mechanical energy, opening ion channels in auditory receptor cells and producing electrical currents that change the neuron's membrane potential. This, in turn, produces the action potential that carries the sound signal to the brain. This multistep signal transduction process takes less than a millisecond, but exactly how it occurs at this time scale remains obscure. Direct measurements of the individual steps can't be made without destroying the mechanical structure; consequently, most measurements are taken downstream of the mechanical oscillations at locations like the auditory nerve. Likewise, the temporal resolution of most stimulus–response trials is far too imprecise to analyze processing at the sub-millisecond level.Given these experimental limitations, Tim Gollisch and Andreas Herz turned to computational methods and showed that it's possible to reveal the individual steps of complex signal processing by analyzing the output activity alone. Using grasshopper auditory receptors as models, the authors identified the individual signal-processing steps from eardrum vibrations to electrical potential within a sub-millisecond time frame and propose a model for auditory signaling.fixed output (response). To get the parameters to describe the final output, the authors generated a sound stimulus (two short clicks) and recorded axon responses of receptor neurons in a grasshopper auditory nerve. From these recordings, they defined the fixed output as the probability of a receptor neuron firing a single action potential. They then asked how the various parameters, which were associated with different time scales, could produce the same predefined firing probability.The crucial step in their study is the search for those sets of inputs (stimuli) that would yield a given By varying the stimulus parameters and comparing the obtained values within their mathematical framework—and making certain assumptions, for example, that the steps signal through a “feedforward” process—they could then tease out the individual processing steps that contribute to the desired output within the required time frame. With this approach, Gollisch and Herz disentangled individual steps of two consecutive integration processes—which they conclude are the mechanical resonance of the eardrum and the electrical integration of the receptor neuron—down to the microsecond level. Surprisingly, this fine temporal resolution is achieved even though the neuron's action potentials jitter by about one millisecond.Thus, using just the final output, this approach can extract the temporal details of the individual processes that contribute to the chain of auditory transduction events. While this method is best-suited for deconstructing unidirectional pathways, the authors suggest it could also help separate “feedforward” from feedback signaling components, especially when feedback is triggered by the final steps. But since many sensory systems share the same basic signal-processing steps, this method is likely applicable to a broad range of problems.


Researchers who use MEDLINE for text mining, information extraction, or natural language processing may benefit from having a copy of MEDLINE that they can manage locally. The National Library of Medicine (NLM) distributes MEDLINE in eXtensible Markup Language (XML)-formatted text files, but it is difficult to query MEDLINE in that format. We have developed software tools to parse the MEDLINE data files and load their contents into a relational database. Although the task is conceptually straightforward, the size and scope of MEDLINE make the task nontrivial. Given the increasing importance of text analysis in biology and medicine, we believe a local installation of MEDLINE will provide helpful computing infrastructure for researchers.We developed three software packages that parse and load MEDLINE, and ran each package to install separate instances of the MEDLINE database. For each installation, we collected data on loading time and disk-space utilization to provide examples of the process in different settings. Settings differed in terms of commercial database-management system (IBM DB2 or Oracle 9i), processor (Intel or Sun), programming language of installation software (Java or Perl), and methods employed in different versions of the software. The loading times for the three installations were 76 hours, 196 hours, and 132 hours, and disk-space utilization was 46.3 GB, 37.7 GB, and 31.6 GB, respectively. Loading times varied due to a variety of differences among the systems. Loading time also depended on whether data were written to intermediate files or not, and on whether input files were processed in sequence or in parallel. Disk-space utilization depended on the number of MEDLINE files processed, amount of indexing, and whether abstracts were stored as character large objects or truncated..Relational database (RDBMS) technology supports indexing and querying of very large datasets, and can accommodate a locally stored version of MEDLINE. RDBMS systems support a wide range of queries and facilitate certain tasks that are not directly supported by the application programming interface to PubMed. Because there is variation in hardware, software, and network infrastructures across sites, we cannot predict the exact time required for a user to load MEDLINE, but our results suggest that performance of the software is reasonable. Our database schemas and conversion software are publicly available at  MEDLINE is a large biomedical bibliographic database that is well known to users around the globe. It contains over 12 million citations from over 4,600 journals. MEDLINE is a rich source of biomedical text that lends itself well to research on text mining, information extraction, and natural language processing in biomedical domains. The usual way in which users query MEDLINE is through PubMed, the web-based interface and search engine provided by the National Library of Medicine (NLM) . By contThe entire content of MEDLINE is available as a set of text files formatted in XML (eXtensible Markup Language) . The NLMAlternatives to relational databases are XML-based databases, which have recently emerged as another option for storing information transmitted in XML format.XML databases may exist as standalone databases or as add-ons to relational systems. The MEDLINE data set would be an excellent test of the capabilities of these databases because of its size and complexity. We focused on relational databases because they are currently more ubiquitous and standardized, and interested users are more likely to be comfortable with relational database technology.In the remainder of this report, we describe the software tools we developed for converting MEDLINE in XML files to MEDLINE in a relational database, and provide a few sample queries that demonstrate the flexibility of the resulting system.The NLM provides a DTD (Document Type Definition) that defines the structure of data in the MEDLINE XML files -7. From There are multiple ways in which one can design a schema from the same DTD, because DTD elements and attributes can be mapped to tables and fields in different ways. Certain design decisions may favor speed at loading time, and others may favor speed and ease of use at query time. Loading records associated with 12 million citations into a database is very time consuming, and the time can be minimized if lookups to the database are minimized during loading. In general, we aimed to minimize lookups even if that meant repeating information in the database. As developers often do in the design of data warehouses, we chose to de-normalize the schema in order to improve read-only query performance, which is the typical data access pattern in our workload.The typical table contains a PubMed identifier (PMID) in one column, and data related to that PMID in the remaining columns. Figures Our development team included a group of researchers from the University of California at Berkeley and another group from Stanford University. We shared similar goals in that we all wanted to load MEDLINE into a relational database, but because we were in two different departments at two different institutions, we had different project constraints and timelines. Thus, our groups were loosely associated in the software development process, but not closely integrated, and therefore, the original schema that we shared diverged.The result was three MEDLINE schemas and three software variants: One schema was used with Java code developed at Berkeley, another schema was used with Berkeley's code modified to run at Stanford, and the third schema was used with Perl code developed at Stanford. Here we describe the underlying design that influenced all three of the schemas. (The schema used for the Java program did not include information from DTD elements DataBankList and AccessionNumberList. This has been corrected in the most recent version of the software available on the Berkeley website.)medline_citation. The medline_citation table contains the PMID as the primary key and has additional columns that correspond to single-valued elements in the DTD, where the values of those elements depend on the PMID. The medline_abstract table is similar in that it has a PMID as the primary key and columns of data that depend on the PMID. Since document abstracts are larger than the other data types, we placed them in a separate table. However, since abstracts are stored as CLOBs (Character Large Objects), they are not stored in the same pages as the rest of the fields in the medline_abstract table. Therefore, in a more recent implementation, we removed the medline_abstract table from the schema, and added the abstract_text field as a CLOB in the medline_citation table. This change reduces the number of tables by one, and eliminates the need for a join between the medline_citation and medline_abstract tables.The main table in the schema is medline_keyword_list, which stores multiple values of keyword for a given PMID, and medline_gene_symbol_list, which stores multiple values of gene_symbol for a given PMID.Some tables in the schema have more than one row corresponding to the same PMID. Columns in these tables map to multi-valued elements in the DTD. Examples are the table Article in the DTD has a one-to-one relationship between an article and a PubMed identifier. Rather than giving Article its own table, we put single-valued data from Article into the table medline_citation.The element xml_file_name to the medline_citation table. This field does not correspond to any element in the DTD structure, but allows the database administrator to go back to the original XML file if necessary to find the original source of the data.To keep track of the name of the file from which data are read for a given citation, we added the field We could have stored each author only once in a table of its own, and assigned each author a unique integer primary key to serve as an author identifier. An author is represented by a combination of values in fields for last name, forename, first name, middle name, initials, suffix, affiliation, and collective name. Another table would have stored the set of author identifiers associated with each PMID, and because integer joins are fast, this design would have facilitated rapid search for all PMIDs associated with a given author, by joining the author table with the table of author identifiers and citations. However, there are several drawbacks to this approach. Generating integer primary keys during loading requires that either a lookup be done to see if each author of each citation already exists or not (35 million lookups), or all authors and primary keys must be kept in memory. The former approach is very time consuming during loading; the latter approach strains memory resources. In addition, regardless of how primary keys are managed during loading, it is not possible to determine algorithmically if two different representations of one author are actually the same author, or if one representation is actually two different authors. We therefore avoided generating unique primary keys and repeated all eight fields representing the author for every citation occurrence of that author.medline_journal is a parent of thirteen other tables (it contains the primary key pmid, which is used as a foreign key by the other tables). One of the other tables, medline_mesh_heading, is a parent of medline_mesh_heading_qualifier. Multiple qualifiers can be associated with each MeSH heading for a given citation.Figure We implemented three versions of software that parses and loads MEDLINE. The first was Java MedlineParser, which was developed at Berkeley [see additional file 1]. The second was the same Java code, modified to run at Stanford. The third was Perl ParseMedline, which was developed at Stanford.All versions of the software perform two basic tasks: (1) they parse the XML files to collect data, and (2) they load the data into the database. Figure There are two types of application programming interfaces (APIs) for parsing XML files – the tree-based DOM (Document Object Model) and the event-based SAX (Simple API to XML) . We chosThe Java version uses the Java SAX parser to parse the XML files, and JDBC (Java Database Connectivity) to communicate with the database. The Perl version uses the Perl SAX parser, and Perl DBI (Database Interface) to communicate with the database. We provide additional detail about the Java implementation here.The SAX parser requires the developer to write code that specifies the data model for objects in the domain. The data model is an object model that represents tables in the schema. The SAX parser also requires code that listens for SAX events and that maps elements – or nodes in the XML tree – to the object model. We created two main classes upon which our code is based: GenericXMLParser and NodeHandler. GenericXMLParser is responsible for generating events when nodes that correspond to objects in the object model are encountered in the document, and NodeHandler provides the event listener. Together, these two classes form a generic approach to reading in XML data and writing out those data to tables; they are independent of the DTD or table structure.As the parser processes the document, it decides how to handle the semantics of data at each node and determines whether to store parsed data at that node or to delegate the event to a child handler. For each node that corresponds to a table, there is a handler class that extends NodeHandler. A handler defines metadata for the node, and encodes any non-standard behavior at that node. An example of metadata is shown in Table Since NodeHandler and GenericXMLParser are generic, they can be used to write similar parsers for other XML documents. We have, for example, used these classes to write a parser for MeSH  XML files, which are also distributed by the NLM. The MeSH files are small compared to MEDLINE. MeSH 2003 comes in three XML files that total less than 600 MB.An optional feature is validation. XML files provided by the NLM are validly formatted, but we provide additional checks to ensure that all element tags in the XML data file have been handled by the parser and that all data have been inserted into the database. A developer who is extending the software to cover new tables can use this feature to ensure that metadata definitions are correct in classes that extend NodeHandler.In the course of our work, we applied the software tools we were developing to three different relational database products. Our Berkeley team initially experimented with PostgreSQL, since PostgreSQL is an open-source relational database and is freely available and modifiable. For the final implementation, however, we chose IBM's DB2 8.1 over PostgreSQL because we found that it could load our data more efficiently and because DB2 has a text-search extender. Our Stanford team used Oracle 9i, which like DB2, offers word-based indexing of text fields. Word-based indexing is essential to support keyword search of MEDLINE titles and abstracts.At Berkeley, we used a Pentium IV Intel Xeon 2.00-GHz dual-processor system, with 1 GB of random access memory (RAM). It had an Integrated Drive Electronics (IDE) hard disk with a rotational speed of 7200 revolutions per minute (RPM). At Stanford, we used a Sun Fire V880 server configured with four 750-MHz processors, 8 GB of RAM, and storage-area-network (SAN) storage for the relational database. We also used a Sun Enterprise 3500 server with eight 400-MHz processors and 4 GB of RAM for reading input files and writing intermediate output files in the Perl version.In this section, we describe loading time and disk-space utilization for the three implementations, followed by examples of queries, emphasizing differences between our system and PubMed.The first implementation used the Java software, run on an Intel system (Linux), using IBM's DB2 database management system. The second implementation also used the Java software, run on a Sun server (SunOS), using Oracle's 9i database-management system. The third implementation used the Perl program, run on networked Sun servers, also using Oracle 9i. Table It took 76 hours (3 days and 4 hours) for the Berkeley group to run Java MedlineParser to load MEDLINE, and 196 hours (8 days and 4 hours) for the Stanford group to do so in Oracle. There were numerous differences between the two systems, and it was not possible to test each variable independently. Therefore, we present our data as a range of possibilities, and recognize that other users will have systems that are not the same as either of ours. We believe that differences in processor speed, memory, disk read-write efficiency, and optimization methods employed in commercial database-management systems may have affected loading times. In addition, the code diverged slightly after the initial transfer of code from Berkeley to Stanford, with the main difference being that the Berkeley version used CLOBs for abstracts, whereas the Stanford version used text fields truncated to 4000 characters (size limit imposed on VARCHAR datatype). The Stanford run was also slower because a log file was generated, whereas this feature was turned off in the Berkeley run.The space requirement for the DB2 instance of MEDLINE at Berkeley was 46.3 GB, of which 10.4 GB are consumed by the abstract text CLOBs, 18.1 GB by the other tables, and 17.8 GB by indexes. The space requirement for the Oracle instance of MEDLINE at Stanford was 37.7 GB. The difference in size is primarily due to differences in the number of records that were loaded. The Berkeley group loaded data from XML files that included all of 2002 (early 2003 release) but also included additional files through April 2003. The Stanford group loaded data from 2002 XML files only. Berkeley parsed and loaded 500 input files (44.4 GB); Stanford parsed and loaded 396 input files (40.8 GB).The Stanford group used Perl ParseMedline to load an additional instance of MEDLINE. Parsing and loading of this instance of the database took place in a two-stage process. In the first stage, Perl ParseMedline parsed the XML files and wrote the data to disk in comma-separated-value files. To reduce processing time, the 396 XML input files were divided into 8 sets of about 50 files each, and sets were processed in parallel. The maximum time required for processing one set was 31 hours (1 day and 7 hours). The output comma-separated-value files required 25.6 GB of disk space. In the second stage, the Stanford team loaded data from the comma-separated-value files into the Oracle database using SQL*Loader, a data loading tool provided by Oracle. This stage took 33 hours (1 day and 9 hours) and used 31.6 GB of space in the Oracle database. This version used less space than the other two primarily because it had less indexing and fewer key constraints. Relaxation of constraints is reasonable because the data are well curated by the NLM, and we can count on data in the XML files released by the NLM to be of high quality.The total time required to parse and load the files in this two-stage process is the sum of the time required to parse the largest file if all files are processed in parallel (first stage) and the time required to load the resulting comma-separated-value files into the database (second stage). Alternatively, if the input files are parsed in series, the time for the first stage would be the sum of the input-file processing times. In our case, we overlapped the runs in a way that was convenient for us, given space and user constraints, and therefore mixed the parallel and serial approach. The overall time for our first stage was 99 hours (4 days and 3 hours); adding this time to the second stage gave a total time of 132 hours (5 days and 12 hours). Given the length of time to process each of our eight batches, we can estimate a lower limit of 64 hours (2 days and 16 hours), and an upper limit of 253 hours (10 days and 13 hours), if we had run the files completely in parallel or in series, respectively.Certain queries that cannot be done easily through the PubMed application programming interface (API) can be done in a single SQL query to our relational database. In this section, we show the results of a several sample queries, run on a version of MEDLINE that contains citations through April 2003.Timing data is presented in terms of "cold" caches and "warm" cache. The cold cache represents the worst case for timing, assuming the database server has just been restarted and the buffer pool is empty. The warm cache represents the best possible performance: running the same query a second time. A typical timing number should fall somewhere between the two; hence these times represent the range of expected times to run the sample queries.pmid is a column in table medline_citation  "Leukemia" is shown for each journal .Many articles in MEDLINE are assigned terms from MeSH. Another capability of this system that distinguishes it from PubMed is the ability to rank order journals according to how many articles those journals have published that have been assigned a particular MeSH term. In the query shown below Table , the numThe result of this query is a table consisting of journals paired with number of publications Table ; note thbegin with "Leukemia". The query would thus include terms such as "Leukemia, Subleukemic" and "Leukemia, Feline." This results in dramatically more results, although the rank ordering is not all that different , and adThe results of this query are shown in Table This query ran in 4 minutes with a cold cache, and in 41 seconds with a warm cache.The DB2 version of the system implementation makes use of the text index that is incorporated into the RDBMS system, using the operator "CONTAINS" which is not part of standard SQL. The following query asks how many papers in the last three years of MEDLINE have been published by authors with affiliations at Berkeley or Stanford Table .This yields the results in Table This query ran in 2.5 minutes with a cold cache, and in 7 seconds with a warm cache.When we ran a similar query to determine the number of articles published by Berkeley, Stanford, MIT, Yale, and Harvard, the increase in time to run the query was minimal. This modified query ran in 3 minutes and 35 seconds with cold cache, and in 15 seconds with warm cache.Thus, SQL makes it easy to quantify and rank order results, and does not require a post-processing step as would be necessary with queries to the PubMed API. Similarly, results retrieved from previous queries can be stored directly in the same database, and reused in later queries by simply joining MEDLINE tables with user-created tables. Again, the power of SQL may alleviate the need for a post-processing step. Instead of writing custom code to integrate results from the current query to PubMed with results from previous queries, the user could use SQL joins to integrate current and previous results.Although our system offers capabilities that the PubMed API does not, we point out that PubMed offers functionality that is not available in our system. For example, the "Related Articles" feature found in PubMed is not available, and links to full text are not available. Also, PubMed provides a user interface that is more intuitive than SQL for an end user who is not a database expert, and will be preferred by users who simply want to look up an article.The value of our system is that it offers greater flexibility for innovative software developers who want to experiment with novel techniques for searching biomedical text, and for system developers who want to build systems of which MEDLINE is a component. If such developers want to offer their systems to end users (e.g., biologists, clinicians, or the lay public), they will need to create more intuitive user interfaces. With direct access to the underlying database, developers can create interfaces that are specifically designed to serve the needs of their particular users.In this work, we developed highly customizable Java parsing code and a relational database schema that others may be interested in using or modifying. We developed software that uses the Java programming language and the SAX parser to parse XML-formatted MEDLINE files and load the data into a relational database. We loaded one copy into DB2 and another into Oracle, using our Java tool.We also created a similar tool in Perl. The Perl code is less flexible and not as readily extensible as the object-oriented code of our Java software, but the functionality offered by the resulting database implementations is very similar.Differences in loading time among the three installations of MEDLINE were due to a multiple factors, including differences in processors, disk storage devices, memory, operating systems, database-management systems, methods implemented in the software, and choices made by the user. Factors that affected disk-space utilization included the fact that one group loaded more data than the other, abstracts were stored as CLOBs at one site and as truncated text at the other, and indexes differed. Other groups will have system setups that differ from ours, and may make their own modifications to the code that affect their loading times. By presenting data on three examples, we have demonstrated a range of performance results as a guide to what other users might expect at their sites.Future work includes adding functionality to update the system to new versions of MEDLINE, and to accommodate MEDLINE update files. The Stanford group has begun to use MEDLINE to extract drug-gene relationships from the literature, and the Berkeley group used the system, augmented with data from MeSH and LocusLink, to compete in the TREC 2003 genomics track competition . As we c.An updated version of our Java code accepts MEDLINE XML input files released in early 2004 that conform to the latest DTD (November 2003). The open-source code for this most current version of MedlineParser is available at Project name: Java MedlineParserProject web page: Operating system: Platform independentProgramming language: JavaOther requirements: Java 1.4.1 or higher, JAXP, relational database, and JDBC driver appropriate for the particular target databaseLicense: NoneAny restrictions on use by non-academics: NoneProject name: Perl ParseMEDLINEProject web page: Operating system: Platform independentProgramming language: PerlOther requirements: Perl 5.8 or higher to handle MEDLINE Unicode data (if writing directly to database), or earlier version of Perl , Perl modules DBI and XML::Parser::PerlSAX, relational database, and Perl database driver appropriate for the particular database (e.g., DBD::Oracle)License: NoneAny restrictions on use by non-academics: NoneDO, GB, and AS, developed the MEDLINE database schemas. GB and AS designed and implemented the Java MedlineParser. GB and AS ran MedlineParser to install MEDLINE in DB2 at Berkeley. DO ran MedlineParser to install MEDLINE in Oracle 9i at Stanford. DO developed Perl ParseMedline and ran it to install the second version of MEDLINE at Stanford. DO and GB were primary authors of the article, and the remaining authors added their contributions to the manuscript. MH supervised the work at Berkeley; RB supervised the work at Stanford.


The mannose receptor (MR) is an endocytic receptor of Mφ and endothelial cell subsets whose natural ligands include both self glycoproteins and microbial glycans. It is also expressed by immature cultured dendritic cells (DC), where it mediates high efficiency uptake of glycosylated antigens, yet its role in antigen handling in vivo is unknown. Knowledge of which APC subsets express MR will assist the design of experiments to address its immunological functions. Here the expression of MR by MHC class II positive APC in non-lymphoid organs of the mouse is described.MR positive APC were identified in several peripheral organs: skin, liver, cardiac and skeletal muscle and tongue. MR positive cells in salivary gland, thyroid and pancreas coexpressed MHC class II and the myeloid markers macrosialin and sialoadhesin, but not the dendritic cell markers CD11c or DEC-205. MR and MHC class II colocalised in confocal microscope images, implying that antigen capture may be the primary role of MR in these cells. Distinct ligands of MR were found in salivary gland and pancreas tissue lysates that are candidate physiological ligands of MR positive APC in these organs.The tissue and subcellular distribution of MR suggest it is appropriately located to serve as a high efficiency antigen uptake receptor of APC. Dendritic cells (DC), APC specialised for the efficient stimulation of naïve T cells, are of fundamental importance in the control of antigen-specific immune responses (reviewed in ). ImmatuImmature DC capture antigens by receptor-mediated endocytosis, in addition to macropinocytosis and phagocytosis (reviewed in ). DC areNotwithstanding their role in stimulating immune responses, it is becoming increasingly apparent that DC play a role in maintaining T cell tolerance to self antigens in the periphery (reviewed in ,11). DirWe recently found MR positive interstitial cells in the thyroid, pancreas and salivary gland: secretory organs which bear endogenous ligands of the CRDs of MR . ThyroglWe previously characterised the expression pattern of MR in the adult mouse by immunohistochemistry using a polyclonal Ab raised against MR, and by in situ hybridisation . MR posiA488, was verified by observation of the expected expression pattern of MR in the spleen. Expression was restricted to the red pulp and did not overlap in dual-labeled specimens with the CD11c positive immature DC located at the border of the white and red pulps .The specificity of MR6F3To detect the scattered interstitial DC of non-lymphoid organs, sections were labeled for MHC class II figure . AlthougWe previously showed that secretory cells in the salivary gland, thyroid and exocrine pancreas were rich in ligands of the CRDs of MR, and expression of MR by adjacent interstitial cells was particularly intense . In adjaSince the MR positive cells of secretory organs exhibited a strongly myeloid phenotype but lacked expression of traditional DC markers, the possibility that they may be activated Mφ was considered. IFNγ is the most potent activator of Mφ, leading to expression of MHC class II, so the phenotype of interstitial cells in the secretory organs of IFNγ -/- mice was examined. Constitutive expression of MR and MHC class II was retained (not shown). In summary, MR positive interstitial cells displayed a novel APC phenotype, and constitutively expressed MHC class II in the absence of stimulation with IFNγ.The coexpression of MR and MHC class II in secretory organ APC implied that these cells may have immunologic functions. The distribution of these markers were examined by confocal immunofluorescence microscopy, in cells that expressed comparable levels of the two proteins figure . In someWe previously identified thyroglobulin as an abundant ligand of MR present in the thyroid, and showed that CRD4-7Fc, an Fc-fusion protein bearing four of the CRDs of MR, which retains the same ligand specificity as MR, bound specifically to sections of salivary gland and pancreas . Here, fAs a control, equivalent quantities of lung, thymus and spleen tissue lysates were subjected to lectin blotting with CRD4-7Fc as described above, but defined bands were not detected (not shown). We previously showed that Mφ within lung and thymus tissue sections contained ligands of CRD4-7Fc, as did Mφ and other cells within spleen . The absThe MR was the first non-opsonic C-type lectin receptor shown to be expressed and mediate adsorptive endocytosis in DC ,20, propIn contrast to our earlier observation that MR is not expressed by well-characterised subsets of DC in lymphoid organs , many AP+ and CD8+ T cells by DC has recently been shown using Ag fused to mAb specific for MR [The function of MR in relation to antigen handling in vivo is poorly understood, in part because most studies have used glycosylated Ag that are now known to be ligands of other receptors in addition to MR (reviewed in ). Howevec for MR ,30. Surpc for MR ), MR mayc for MR . Therefoc for MR . A link c for MR .K were compared [Antigen uptake by MR on cultured DC promotes presentation to T cells of antigen-derived peptides on MHC class II . The colcompared . These mcompared . Therefocompared .If MR is poised to direct Ag to the MHC class II compartments of secretory organ APC, the question of which Ag are captured in these environments must be raised. We previously showed that thyroglobulin, a major auto-antigen in murine autoimmune thyroid diseases, is a ligand of MR, and suggested that MR expressed by interstitial cells in the thyroid may be involved in maintaining tolerance to this Ag in normal mice . Here, fIn light of the location of MR positive APC in secretory organs, the abundance of endogenous ligands, and the positive identification of one ligand as thyroglobulin, the available data could suggest a tolerogenic role of MR is more likely than an immunostimulatory role in these organs in the steady-state. Such a mechanism may represent a physiological correlate of the experimentally induced immune tolerance achieved by targeting Ag to DEC-205 in naïve mice ,14,15. FMR positive APC are present in non-lymphoid organs of the naïve mouse, but in most organs examined, only a subset of MHC class II positive cells express MR, in contrast to the ubiquitous expression of MR described in cultured DC. This information will inform the design of experiments to test the function of MR in antigen handling in vivo. An immuno-regulatory role for MR in relation to its endogenous ligands in salivary gland, pancreas and thyroid is suggested by the finding that MR positive APC are abundant in these organs, they co-express MHC class II and appear to have an overlapping subcellular distribution of MR and MHC class II.IFNγ -/- mice  were bac488 (Molecular Probes, Leiden, The Netherlands) according to the manufacturer's instructions. Goat anti-amylase was from Santa Cruz Biotechnology Inc (Santa Cruz, CA). CRD4-7Fc was prepared as described [2 anti-rat IgG, and goat F(ab')2 anti-Armenian hamster IgG, both cy3 conjugated, and horseradish peroxidase conjugated mouse F(ab')2 anti-human IgG Fc . Horseradish peroxidase conjugated donkey F(ab')2 anti-goat IgG was from Chemicon (Harrow, UK).Rat mAb were prepared in our laboratory and used at optimal concentrations for immunolabelling. They were directed against MHC class II ; macrosialin  and siaescribed . Seconda488 conjugated MR6F3 mAb or isotype matched control. Nuclei were labeled with Hoechst 33342 dye (Molecular Probes). Sections were mounted in Aqua Polymount (Polysciences, Inc., Warrington, PA). Slides were examined by fluorescence microscopy and 12-bit digital images captured using a CCD camera attached to a Zeiss Axioplan photomicroscope. Slides were prepared in the same way for confocal microscopy, except that cryosections were cut at 20 μm. Confocal microscope z-stack images were collected using a Bio-Rad Radiance 2000 MP laser scanning confocal microscope, with lasers exciting at 367, 488 and 543 nm. Images corresponding to each fluorophore were collected using individual lasers sequentially to eliminate bleed-through. Immunofluorescence and confocal images were processed using MetaMorph version 4.5 software.For immunofluorescence microscopy, 6 μm cryosections were prepared from unfixed frozen tissues. They were permeabilised in an incubation buffer consisting of 0.5% BSA and 0.05% saponin in PBS, then blocked with 5% normal rabbit serum in incubation buffer. Sections were subsequently incubated with rat or hamster mAb specific for APC antigens for 1 hr, then the appropriate cy3-conjugated secondary Ab for 30 min. Sections were blocked with 100 μg/ml rat IgG for 30 min, before being probed with Alexa3, 150 mM NaCl, 10 mM EDTA, 5 mM iodoacetamide, 1 mM PMSF, 1 mg/ml pepstatin, 1μM leupeptin). Proteins were quantified in duplicate using Bicinchoninic acid protein assay kit . Samples were electrophoresed by SDS-PAGE, transferred to nitrocellulose membranes and subjected to western or lectin blotting using standard methods [2) and mannose-BSA (Sigma, St. Louis, MO) was used as positive control. Gels were also stained with Coomassie R-250 to detect protein.Mouse tissue lysates were prepared in lysis buffer (2% v/v Triton X-100, 10 mM Tris pH 8.0, 10 mM NaN methods . CRD4-7FCRD, carbohydrate recognition domain; DC, dendritic cell; MR, mannose receptor


To examine the prevalence of major chronic diseases and their risk factors in different socioeconomic groups in the Australian population, in order to highlight the need for public policy initiatives to reduce socioeconomic inequalities in health.Data were provided by the Australian Bureau of Statistics (ABS) from the 2001 National Health Survey (NHS) for selected chronic diseases and associated risk factors. Conditions selected were those, which form the National Health Priority Area (NHPA) conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers; and for which sufficient cases were available for analysis by socioeconomic status. Indirectly age-standardised prevalence rates were calculated by broad age group for Australia and for five groups of socioeconomic status; rate ratios were calculated to show variations in prevalence between these groups.Significant socioeconomic inequalities were evident for many of the major chronic diseases; the largest was for diabetes mellitus (at ages 25 to 64 years); and for many diseases, there was also a strong, continuous socioeconomic gradient in the rates.Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibited a strong differential in the 25 to 64 year age group.In the 65 years and over age group, the strongest inequalities were evident for mental and behavioural problems, diabetes (with a continuous socioeconomic gradient in rates) and respiratory system diseases.A number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight showed a striking association with socioeconomic status, in particular for people who were smokers and those who did not exercise.This analysis shows that the prevalence of chronic disease varies across the socioeconomic gradient for a number of specific diseases, as well as for important disease risk factors. Therefore, any policy interventions to address the impact of chronic disease, at a population level, need to take into account these socioeconomic inequalities. As in other developed countries, chronic diseases in Australia are major contributors to the extent of illness, disability and premature mortality in the population. They are estimated to make up the greatest proportion of the burden of disease, mental problems and injury for the population as a whole (about 80%), and for particular sub-population groups .Chronic diseases are exemplified by having multifactorial aetiologies, including common disease risk factors and determinants; significant latency periods and protracted clinical courses; and are seldom cured completely ,3. CausaThe inequalities in health observed across populations are many – some of them are inevitable and others, unnecessary and unfair. Those inequalities that are potentially avoidable are deemed 'inequitable' . DespiteThere have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate -10. An aHowever, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living  . One of The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) . MathersCurrent information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population.Information for a selection of chronic diseases is shown in Table The main findings are:The largest differential between those in the most well off and those in the most disadvantaged areas was for diabetes mellitus at ages 25 to 64 years, with the prevalence in the most disadvantaged areas being just over two and a quarter times (a rate ratio of 2.28) the prevalence for the least disadvantaged; there is also a strong, continuous gradient in the rates, with the rate ratios in each of the third to fifth quintiles statistically significant.There was a statistically significant differential of 67% at ages 25 to 64 years, with a strong, continuous gradient, in the prevalence of self-reported mental and behavioural problems across the socioeconomic gradient; differentials  in the 0 to 14 year and 65 years and over age groups were 52% and 56%, respectively.Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibit a strong differential in the 25 to 64 year age group .In the 65 years and over age group, the strongest differentials were evident for mental and behavioural problems , diabetes  and respiratory system disease .Asthma accounted for almost two thirds of the rate of reporting of respiratory system disease in the 0 to 14 year age group, almost half in the 15 to 24 year age group, and for about a third of the rate in the 25 to 64 year age group.The NHS also included data on a number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight. A number of these risk factors show a striking association with socioeconomic status, in particular for people who are smokers and those who did not exercise, with continuous gradients and highly elevated rates of statistical significance Table . The difIt is important to note that the inequalities reported above relate to the health of those people living in a geographic area and to the overall level of socioeconomic disadvantage of that area. Most areas will contain varying levels of individual socioeconomic disadvantage and, to the extent that the poorer health is associated with individual economic circumstances and living conditions rather than communal environment, the inequalities will understate the true differences in health status according to socioeconomic disadvantage .Furthermore, there are limitations to the use of area-based measures of SES. Due to misclassification error , estimates of difference across the quintiles will be smaller than if data on individual-level measures of SES were used . Thus, cOur analysis indicates that socioeconomic inequalities in the prevalence of chronic diseases and their concomitant risk factors are evident across the Australian population. However, the diseases with substantial disparities across the socioeconomic quintiles are different, for different stages in the life course. Although these results cannot be directly compared with those of previous studies, because of definitional and methodological differences, the recurring finding of inequalities for chronic disease morbidity and risk factor prevalence across the socioeconomic gradient remains a significant concern.The burden in the Australian population attributable to socioeconomic inequality is large, and has far-reaching implications in terms of unnecessary disability and suffering, the loss of potentially economically productive members of society, and increased costs for the health and social care systems . DespiteWhat should we be doing differently? There is a growing body of knowledge that will help to provide direction for developing policies to reduce inequities across the population. The socioeconomic environment is a powerful and potentially modifiable factor, and public policy is a key instrument to improve this environment, particularly in areas such as housing, taxation and social security, work environments, urban design, pollution control, educational achievement, and early childhood development .However, attention must be paid to the nature of any action that is taken, to ensure that social and economic inequalities are not increased. Some programs, by their very success, can increase inequality by widening the gap between groups in the population; for example, such programs may be more attractive to those who are already healthier, or not as effective for certain groups with poorer health, less education or more stressful lives. In one smoking cessation initiative, it was found that the prevalence of smoking decreased predominately in those adults with higher education, thus increasing the existing difference with those who were more disadvantaged . While sIt is also evident that the ways in which systems such as education and health are delivered and structured can increase existing inequality. For example, schooling can be a way of addressing inequality and also a way of reproducing it. It has been suggested that there are two goals for a social justice program in education: to work to eliminate the contribution that the education system makes to the production over time of social inequality in general; and to maximise the positive contributions that the education system makes to reducing social inequality . TherefoPolicy-makers who wish to address socioeconomic inequalities in health may favour one of the following approaches. Some view the impact of socioeconomic disadvantage on those groups with the poorest health in the population, such as Aboriginal people and Torres Strait Islanders, as the priority policy goal. Others identify the gap between the health of those groups at the outer ends of the socioeconomic hierarchy , and see the narrowing of the gap as the goal. Others prefer to focus on the socioeconomic gradient in health that runs across the whole population .Graham has identified that the last approach widens the policy debate in three ways . FirstlyShe also observes that, "improving the health of poor groups and improving their position relative to other groups are necessary elements in a strategy to reduce the socioeconomic gradient. However, neither is sufficient on its own. To reduce the socioeconomic gradient, health in other socioeconomic groups also needs to improve at a faster rate than in the highest socioeconomic group. Thus, policies to ameliorate health disadvantage, to close health gaps and to reduce health gradients need to be pursued together, and not at the expense of each other" .There is also an urgent need to make health inequalities a research priority for each stage of the life course – not just to monitor the size and extent of the disparities but also to undertake research that will find preventive approaches and further policy interventions that will be effective in reducing them, and that are likely to be implemented by governments and communities.Clearly, any moves to address the impact of chronic disease at a population level must take into account socioeconomic inequalities in prevalence. More research is needed to determine which approaches are effective and why others have failed to have the desired impact, particularly for those who are from socioeconomically disadvantaged areas. Finally, although rates are generally highest at the oldest ages, the development of risk factors for many chronic diseases occurs early in life, and thus, it is essential those health inequities are addressed right across the life course.The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 . The NHSThe survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs . HoweverThe particular conditions for which data were requested from the ABS for this analysis were:the NHPA conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers ; and forThe risk factors used by the ABS were those identified for the NHPA conditions .The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases . The risGiven the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions (e.g., diabetes) appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings  the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data.The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population.The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores (highest socioeconomic status, or most advantaged, areas) and Quintile 5 comprises the CDs with the lowest IRSD scores (lowest socioeconomic status, or most disadvantaged areas). Each quintile comprises approximately 20% of CDs.The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 (and 708,980 fewer in Quintile 5 than in Quintile 1). The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used (that is, that the quintiles are based on the Census population, and applied to the NHS population which has a different profile), it may also reflect different response rates to the survey from different socioeconomic groups.The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health . The ageChronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups (0–14 years, 15–24 years, 25–64 years and 65 years and over) were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00).The author(s) declare that they have no competing interests.JG conceived the study, and was responsible for its design and coordination. ST participated in the design of the study and performed the statistical analysis. DH participated in the drafting of the manuscript. All authors read and approved the final manuscript.


Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer.A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively.A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival . However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival.The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis. In general, colorectal cancer patients are three times more likely to be in atrial fibrillation preoperatively than matched controls undergoing non-colorectal cancer surgery ,2. It mast January 2000 and 31st December 2001 in a 600-bed district general hospital in the United Kingdom National Health Service were identified. The hospital serves a population of approximately 230,000. About 90 elective and emergency laparotomies are performed each year for colorectal cancer. Patients were identified from the prospectively maintained colorectal cancer database maintained by the colorectal surgical department. Patients with radiological, endoscopic or clinical examinations suspicious of colorectal cancer are referred to the weekly colorectal multi-disciplinary team (MDT) meeting. In the case of suspicious radiology, the referral to the MDT is made automatically by the radiology department. This avoids the possibility of the responsible clinical firm failing to refer a patient for consideration. Similarly, the pathology department automatically refers any patient in whom histology shows colorectal malignancy. In addition, patients who undergo surgery where a suspicion of colorectal cancer is raised are referred for consideration. The colorectal meeting is attended by the colorectal surgeons, radiologists, pathologists, palliative care physicians and nursing staff. Patients determined to have colorectal cancer by the MDT are entered into the database. The colorectal department periodically compares the database to clinical coding data for patients with colorectal cancer in order to ensure complete data capture. All patients are followed-up regularly by a team of colorectal nurse specialists in a dedicated clinic.Patients who underwent a resection for colorectal cancer between 1® version 2 (Statsdirect Ltd., UK).Age, sex, mode of presentation (emergency or elective), Dukes stage, postoperative anastomotic leakage and adjuvant therapy were recorded for all patients. The colorectal cancer database was cross-referenced with the hospital clinical-coding database to identify those patients who were in atrial fibrillation at any time before or after their surgery. Patients with colorectal cancer who did not undergo surgery or who only underwent palliative stoma formation were excluded. All patients were followed up for at least two years postoperatively. Overall survival and recurrence-free survival were recorded. Recurrence-free survival was defined as the time interval between operation and first diagnosis of local or distant recurrence. Patients with no recurrence were censored at the time of death from any cause other than cancer or at the time they were last seen by the colorectal team. Characteristics between those with and without AF were compared using the Student t-test and Fisher Exact test for continuous and categorical data respectively. Potential prognostic factors were compared by the log-rank test. Significant prognostic factors identified from the univariate analysis were entered into a multivariate Cox regression model of survival to test for independence. The 5% level was considered significant in the multivariate analysis. Statistical analysis was performed using StatsdirectOne hundred and seventy-five patients M:F = 111:64) who underwent bowel resection for colorectal cancer were identified from the database. Their median age was 74 years (interquartile range 66 to 80 years). Tumour site, Dukes stage and mode of operation (emergency or elective) are summarised in Table 11:64 whoCross-referencing with the clinical coding database identified thirteen patients with a history of atrial fibrillation. Five patients were in AF preoperatively. The remaining eight patients developed postoperative AF. A comparison of baseline characteristics between patients with and without AF is shown in table There was no correlation between overall survival and the following variables in the univariate survival analysis: gender, postoperative anastomotic leak, site of tumour  or preoperative radiotherapy. Mode of surgery (emergency or elective), age (<72 years or ≥ 72 years) and Dukes' stage had a significant effect on survival. When patients with atrial fibrillation were compared to those without Figure , atrial Tumours stimulate an inflammatory response when they invade or metastasise . This inIn our cohort, atrial fibrillation was not an independent predictor of survival. Dukes' stage was the strongest predictor of survival. Previous data from colorectal cancer patients suggest the degree of inflammatory response may reflect the degree of disease progression . The incThere are several other limitations to our study. The quality of the clinical coding has not been assessed. Thus, the sensitivity and specificity of our clinical coding department in coding AF is unknown. Only 13 patients (7.4%) developed AF. Previous work suggests that 13% of elective colorectal patients develop postoperative AF . Thus, iThe author(s) declare that they have no competing interests.SRW conceived the study, performed the statistical analysis and drafted the paper.KMG and NJW performed the literature search and collected the data.TAJ and NJK co-wrote the paper with SRW.All authors approved the manuscript.None declared