Thiamine diphosphate (ThDP)-dependent enzymes form a vast and diverse class of proteins, catalyzing a wide variety of enzymatic reactions including the formation or cleavage of carbon-sulfur, carbon-oxygen, carbon-nitrogen, and especially carbon-carbon bonds. Although very diverse in sequence and domain organisation, they share two common protein domains, the pyrophosphate (PP) and the pyrimidine (PYR) domain. For the comprehensive and systematic comparison of protein sequences and structures the Thiamine diphosphate (ThDP)-dependent Enzyme Engineering Database (TEED) was established.http://www.teed.uni-stuttgart.de contains 12048 sequence entries which were assigned to 9443 different proteins and 379 structure entries. Proteins were assigned to 8 different superfamilies and 63 homologous protein families. For each family, the TEED offers multisequence alignments, phylogenetic trees, and family-specific HMM profiles. The conserved pyrophosphate (PP) and pyrimidine (PYR) domains have been annotated, which allows the analysis of sequence similarities for a broad variety of proteins. Human ThDP-dependent enzymes are known to be involved in many diseases. 20 different proteins and over 40 single nucleotide polymorphisms (SNPs) of human ThDP-dependent enzymes were identified in the TEED.The TEED The online accessible version of the TEED has been designed to serve as a navigation and analysis tool for the large and diverse family of ThDP-dependent enzymes. The for0N motif ,20-22. I0N motif ,18,23. T0N motif . A unifi0N motif .We established the Thiamine diphosphate dependent Enzyme Engineering Database (TEED) as a tool for a comprehensive and systematic comparison of ThDP-dependent enzymes from different protein families and annotated the conserved PP- and PYR domains. Thus, the TEED is the first data resource of ThDP-dependent enzymes which combines information on the individual protein families, sequence alignments and a consistent annotation of the conserved PYR and PP domains.The Thiamine diphosphate (ThDP)-dependent Enzyme Engineering Database (TEED) was established by utilising the data warehouse system DWARF . The DWAhttp://www.ncbi.nlm.nih.gov was carried out for each seed sequence with an E-value cut off of 10-5. New protein entries were assigned to a homologous protein family based on their sequence similarity to one of the seed sequences. If the sequence similarity was less than 60%, a protein was assigned to a new homologous family. The families were subsequently manually evaluated and adjusted: protein fragments were merged into the respective homologous family, and proteins with high sequence similarity but a different domain organization were separated into different protein families. This resulted in 63 different homologous families.The combination of previous classification schemes resulted in 8 different superfamilies, DC (decarboxylase), TK (transketolase), OR (oxidoreductase), and two subfamilies K1 and K2 of the KD (2-ketoacid dehydrogenase) family. In addition to these families, the SPDC (sulfopyruvate decarboxylase), the PPDC (phosphopyruvate decarboxylase), and the KDH (α-ketoglutarate dehydrogenase) family were included . Due to their medical importance they were systematically analysed. All human ThDP-dependent enzymes belong to only three superfamilies, the DC, TK, and K2 superfamily  has been designed to serve as a navigation and analysis tool for the large and diverse family of ThDP-dependent enzymes. The annotation of the conserved pyrophosphate (PP) and pyrimidine (PYR) domains allows for a direct comparison and analysis of these domains between different families. Thus the TEED is a valuable tool for the study of the protein families of ThDP-dependent enzymes.http://www.teed.uni-stuttgart.de. All information on families, sequence and structure data, as well as alignments and phylogenetic trees can be accessed by manual download.The Thiamine diphosphate dependent Enzyme Engineering Database (TEED) is online accessible at BLAST: Basic Local Alignment Search Tool; DSSP: Define Secondary Structure of Proteins; DWARF: Data warehouse system for analyzing protein families; HMM: Hidden Markov Model; SNP: Single-nucleotide polymorphism; TEED: Thiamine diphosphate dependent Enzyme Engineering Database; ThDP: Thiamine diphosphateMW established and annotated the database and wrote the manuscript. RR assisted in the implementation of the database and contributed to writing of the manuscript. JP supervised the project and finalized the manuscript. All authors read and approved the final manuscript.Microsoft Word 2003. Sequences of ThDP-dependent enzymes which were used to establish the TEEDClick here for fileMicrosoft Word 2003. Sequences of human ThDP-dependent enzymesClick here for file


The distribution of phosphoglycerate mutase , 2,3-bisphosphoglycerate phosphatase  and creatine kinase  activity and isoenzymes in various regions of adult human brain and in brain tumours  has been determined using electrophoresis. PGM and cytosolic CK exist in mammalian tissues as three isoenzymes that result from the homodimeric and heterodimeric combinations of two subunits [types M (muscle) and B (brain)] coded by separated genes. In addition, a dimeric form and an octameric form of mitochondrial CK exist in mammals. Type BB-PGM was the major PGM isoenzyme found in normal brain, although type MB-PGM and type MM-PGM were also detected. All brain tumours possessed lower PGM activity than normal brain, and meningiomas showed higher BPGP activity. In astrocytic tumours, the proportion of type MB- and type MM-PGM decreased, and in meningiomas these isoenzymes were not detected. Type BB-CK and mitochondrial CK were the only CK isoenzymes detected in normal brain. Astrocytomas possessed lower CK activity than anaplastic astrocytomas and glioblastomas and, in addition, tended to possess lower CK content than normal brain. No qualitative changes of the normal CK isoenzyme pattern were observed in the tumours.


Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases. DNA microarray experiments provide a powerful means to improve our understanding of diseases with a genetic basis or contribution. Commercial microarray chips for highly accurate diagnosis of several cancers are already available on the market ,2 and phThe fast accumulation of gene expression data in public online databases and the great variety of available analysis methods, however, also pose new challenges. Integrating data from different sources, choosing appropriate normalization, analysis and cross-validation methods and selecting suitable parameters requires substantial time and effort. Since different algorithms have different strengths and similar data from independent studies is often available, it is desirable to combine multiple methods and/or data sets to obtain more robust and accurate results. This creates ample opportunities for ensemble methods and cross-study normalization techniques.GEPAS [Expression Profiler [ASTERIAS [EzArray [CARMAweb [MAGMA [ArrayPipe [RACE [WebArray [MIDAW [Although statistical programming frameworks like R  and MatlGEPAS , ExpressProfiler , ASTERIAASTERIAS , EzArray[EzArray , CARMAweCARMAweb , MAGMA [b [MAGMA , ArrayPirrayPipe , RACE [1pe [RACE , WebArraWebArray  and MIDAy [MIDAW . These wAdditionally, numerous web-applications have been developed and optimized for single, specific analysis tasks, e.g. biclustering of genes and samples , co-clusSimilarly, it would be desirable not only to combine different algorithms but also different data sets for a common organism and phenotype. Although currently available cross-study normalization methods are based on simplified assumptions and limited in applicability and accuracy, various successful applications ,24 have For these reasons, we have developed a new web-application that provides access to multiple algorithms for each of the most common tasks in statistical microarray analysis, namely gene selection, sample clustering, sample classification and gene set analysis, based on a single, easy-to-use interface. In contrast to other web-tools, in which the results of individual methods are made available, here, ensemble feature selection, ensemble prediction and consensus clustering approaches are provided. Likewise, instead of using only data from a single study, different cross-study normalization methods are made available to integrate similar data from different studies and compare the results based on density and quantile-quantile plots.Apart from these combinations of data sets and methods within an analysis module, different modules have been interlinked, enabling for example the integration of gene set analysis with classification or cross-study analysis with gene selection or clustering. Other new features include access to an in-house developed rule-based evolutionary classification algorithm, automatic parameter selection mechanisms on all modules, the availability of specific cancer-related gene sets for enrichment analysis in addition to gene sets from KEGG and GO, and a 3D-VRML-visualization of clustering results using the authors' new R software package "vrmlgen" .Since the above methods and features are not available on other microarray-related web-tools, and similarly, other tool sets include methods distinct from our system, we see our service as a complement rather than an alternative to existing services.In the following we provide an overview of the workflow and describe all features in detail.Cross-Study Normalization, Gene selection, Class Discovery, Class Assignment and Gene Set Analysis. Each of these modules features multiple analysis methods accessible through a unified web-interface. The user can upload his own data in tab-delimited text-file format or as zip-compressed Affymetrix CEL-files which will be automatically extracted, normalized and summarized using the Robust Microarray Analysis (RMA) method [The ArrayMining.net tool set consists of five main modules for microarray analysis: ) method . Alterna) method , the larArrayMining.net is based on software written in the programming languages R  and C++ Current microarray studies often only contain a small number of samples, resulting in limited robustness and reliability of statistical analyses. To alleviate this problem five cross-study normalization methods have been made available on ArrayMining.net to combine samples from two different studies: An approach based on linked gene- and sample-clustering (XPN ), an empeBayes) [SAM) [CFS) [RF-MDA) [PLS-CV) [ENSEMBLE). The resulting outcome reports provide a ranked list of genes, in which known gene identifiers become clickable navigation items, referring the user to related entries in functional annotation databases and literature search engines. Additionally, box plots and heat maps  ,32, the s) [SAM) , a correM) [CFS) , a ranki[RF-MDA)  and a Pa[PLS-CV)  using thsee Fig.  and 2 vik-Means, PAM [SOM [SOTA [Average Linkage Agglomerative Clustering, Divisive Analysis Clustering and a combination between the agglomerative and divisive approach, Hybrid Hierarchical Clustering [Clustering methods allow experimenters to identify natural groupings among microarray samples based on their expression patterns across the genes. To account for the great variety of existing scoring and search space exploration methods, our class discovery module includes both partition-based and hierarchical clustering algorithms, an evaluation based on multiple validity indices and a consensus clustering method. Currently, the partition-based clustering methods available are ans, PAM , SOM [39OM [SOTA , and theustering . To combustering . This meustering , which sustering . An alteustering , a 2D prustering  and the SVM [RF [PAM [kNN). We also provide access to an in-house developed rule-based machine learning approach, BioHEL [An important goal behind microarray analysis is to improve the diagnosis of diseases with genetic components by predicting the disease type based on labeled training data. The third module on our web-server is therefore dedicated to supervised learning methods, including various common methods for microarray sample classification , BioHEL , which l, BioHEL , while b, BioHEL , using aThe results for an analysis contain various performance measures for evaluation and Z-scores for the genes that were most frequently selected across different cross-validation cycles. To obtain more insights on these genes, similar analysis plots and annotation tools are available as for the gene selection module.Two common problems in microarray analysis are high noise levels for single genes and a high number of redundant or uninformative genes. Using gene set analysis (GSA) to aggregate functionally related genes into gene sets and summarizing their expression values to a robust "meta"-gene expression vector is a promising approach to overcome some of these limitations . MoreoveSummarized meta-gene expression vectors for a gene set are obtained by transforming the expression levels using Principal Component Analysis (PC-GSA) or Multidimensional Scaling (MDS-GSA).The outcome is presented as a ranked list of gene sets and additionally contains box plots and heat maps similar to those on the gene selection module. Meta-gene expression values derived from the gene sets can be downloaded or forwarded to other analysis modules, e.g. to be used as predictors in sample classification.Providing example results for all modules and algorithms on ArrayMining.net would exceed the scope of this paper. However, we have included some results obtained with the well-known three-class leukemia data set by Armstrong et al.  see Add. This inSince these examples cover only some of the available features, various well-known microarray cancer data sets are available on the different analysis modules to enable the user to more fully explore the capabilities of ArrayMining.net without needing to upload new data.We have developed a new web-application that provides a simple and fast way to analyze arbitrary DNA-chip data and other high-dimensional data sets. Ensemble, consensus and cross-study normalization methods help to increase the robustness and accuracy of the outcomes, and automatic parameter selection mechanisms and a direct linkage to functional annotation data bases  relieve the user of time-consuming routine tasks. For each of the major statistical analysis tasks - feature selection, clustering, prediction and gene set analysis - several analysis methods are available and can be compared, combined or interlinked in many ways. In contrast to other software products for microarray analysis, the user is neither tied to a particular methodology nor needs to understand in detail the inner working of the algorithms. New researchers in the field can use the web-tool without the risk of deviating from standard validation guidelines.For the next version of the server, we are planning to add a new module for co-expression network analysis and the possibility to integrate additional clinical or biological data..The web-application, video tutorials and an illustration of the features and workflow are freely accessible at EG participated in the conceptual design of the web-application, implemented the algorithms and PHP-interface and drafted the manuscript. NK took part in the conceptual design of the web-application and helped to draft the manuscript. JMG helped to draft the manuscript. JMG and NK wrote the grant application upon which this project was built. All authors read, made corrections and approved the final manuscript.Results for an example analysis with ArrayMining.net. The document provided contains the results and a discussion for an example analysis of microarray data with ArrayMining.net.Click here for fileExample VRML-visualization of an Independent Component Analysis. This file contains an example VRML-visualization of an Independent Component Analysis for the microarray data set by Armstrong et al. [g et al.  (a VRML Click here for file


Drosophila, antimicrobial peptides are key contributors to host defense. Antimicrobial peptide gene expression is regulated by the IMD and Toll pathways. Bacterial peptidoglycans trigger these pathways, through recognition by peptidoglycan recognition proteins (PGRPs). DAP-type peptidoglycan triggers the IMD pathway via PGRP-LC and PGRP-LE, while lysine-type peptidoglycan is an agonist for the Toll pathway through PGRP-SA and PGRP-SD. Recent work has shown that the intensity and duration of the immune responses initiating with these receptors is tightly regulated at multiple levels, by a series of negative regulators. Through two-hybrid screening with PGRP-LC, we identified Rudra, a new regulator of the IMD pathway, and demonstrate that it is a critical feedback inhibitor of peptidoglycan receptor signaling. Following stimulation of the IMD pathway, rudra expression was rapidly induced. In cells, RNAi targeting of rudra caused a marked up-regulation of antimicrobial peptide gene expression. rudra mutant flies also hyper-activated antimicrobial peptide genes and were more resistant to infection with the insect pathogen Erwinia carotovora carotovora. Molecularly, Rudra was found to bind and interfere with both PGRP-LC and PGRP-LE, disrupting their signaling complex. These results show that Rudra is a critical component in a negative feedback loop, whereby immune-induced gene expression rapidly produces a potent inhibitor that binds and inhibits pattern recognition receptors.Insects rely primarily on innate immune responses to fight pathogens. In  Drosophila (the fruit fly) immune response, which we call Rudra. Using various approaches, we show that in the absence of Rudra the innate immune system is hyper-activated. This elevated immune response leads to better protection against bacterial infection. On the other hand, when present in excess, Rudra prevents the activation of the immune response. Furthermore, we show that Rudra turns off the immune response by binding to the receptors that are responsible for detecting bacteria, thereby preventing downstream responses.The innate immune system controls the immediate response to infection. Innate immunity relies on germline encoded receptors, receptors that are present at birth, to recognize germs and trigger a protective response. Invertebrates  rely on innate immunity to survive in microbial-rich environments, such as rotting fruit. However, uncontrolled innate immune responses are dangerous, leading to severe pathologies like sepsis, inflammatory bowel diseases, and lupus. Therefore, the intensity and duration of the innate immune response is kept in-check by multiple regulatory mechanisms. Here, we have identified a new feedback regulator of the  Drosophila immune response has proven to be an experimentally powerful and conserved model system for the study of innate immunity Insects rely primarily on innate immune responses to fight pathogens. The Drosophila, two signaling pathways regulate the production of these antimicrobial peptides - the IMD and Toll pathways In imd protein functions immediately downstream of PGRP-LC and upstream of all other known components of the pathway Upon binding DAP-type PGN, both PGRP-LC and PGRP-LE multimerize and signal via a common motif in their N-terminal domains, known as the RHIM-like domain Drosophila. As in mammals, over-exuberant immune responses can be detrimental, and the proper down modulation of immunity is critical for health and fecundity Recent work has shown that the intensity and duration of the immune response is tightly regulated in rudra. Expression of rudra was rapidly induced following immune challenge. Moreover, in flies and cells, rudra is critical for controlling immune-induced gene expression. Following infection, rudra mutant flies hyper-activated antimicrobial peptide gene expression resulting in increased resistance to microbial infection. Using various biochemical and genetic techniques, Rudra was found to interact with the receptors PGRP-LC and PGRP-LE and disrupt the signaling complex assembled on these receptors. Due to its ability to destroy this receptor signaling complex and inhibit immune responses, rudra was named for Shiva, the Indian god of destruction, who in his Rudra phase of mind causes inhibition and destruction of all life on earth.In this study, we identify and characterize a negative feedback regulator of the IMD pathway, dubbed rudra from hereafter.In order to identify potential partners and regulators of the IMD pathway receptors, a yeast two-hybrid screen was performed with the cytoplasmic domain of PGRP-LC as bait Drosophila S2* cells, both PGRP-LE and PGRP-LC were found to associate with Rudra  of the receptors interact with Rudra, co-immunoprecipitation assays were performed with various mutant versions of PGRP-LC or PGRP-LE. Consistent with the yeast two-hybrid data, which indicated involvement of the RHIM-like domain for interaction, a mutant form of PGRP-LE lacking the RHIM motif (Δ98-113) showed little interaction with Rudra . Using arudra is a target of the IMD signaling pathway rudra was analyzed at various times after immune stimulation of S2* cells, by qRT-PCR. rudra transcript was rapidly induced, peaking in 30–60 minutes and returning to near baseline levels within 24 hours , Attacin (Att) and Cecropin (Cec) were induced to markedly higher levels in cells treated with rudra RNAi, compared to cells transfected with a control lacZ dsRNA  was analyzed rudra transcript in this strain was compared to an isogenic white strain, by qRT-PCR . This genomic rescue construct partially restored immune-inducible expression of rudra, but it did not completely return to wild-type levels . These results, together with the data from ectopic expression, demonstrate that rudra is a potent negative regulator of the IMD pathway in flies, as well as in cultured cell lines.Next, the immune response of wild-type, e strain . The rudEY00723rudra flies were infected with the Gram-negative pathogen Erwinia carotovora carotovora (Ecc). As reported previously, Ecc is a mildly pathogenic infection in wild-type animals, such that most flies succumb over the course ∼10 days . On the other hand, rudra mutants showed significantly improved survival compared to wild-type flies (P = 0.0052). These results show that loss of rudra, and the ensuing increase in AMP levels, enhances resistance to this Gram-negative pathogen.We then asked what consequence these elevated AMP levels might have during an infection. To this end, wild-type and ∼10 days  [27],[40imd protein is rapidly cleaved following immune stimulation  and expression of rudra potently inhibits this cleavage (We next sought to determine the molecular mechanism(s) used by Rudra to control signal transduction. Relish, the NF-κB precursor protein essential for IMD triggered gene expression, is regulated by immune-induced cleavage and phosphorylation . Recentlcleavage . These rPGRP-LC or PGRP-LE, or imd is sufficient to drive AMP gene expression. Likewise, over-expression of the caspase Dredd is sufficient to drive Relish cleavage. To further analyze the position that Rudra acts in the IMD pathway, it was over-expressed with these signaling components in doubly selected stable cell lines. Rudra potently inhibited signaling induced by over-expression of the receptors PGRP-LC or PGRP-LE, but had no effect on the induction of Diptericin expression caused by IMD over-expression  or HEK cells . On the PGRP-LC (T7 tag), imd (FLAG tagged) and/or rudra . In assays with just the receptor and either IMD or Rudra, PGRP-LC interacted with either the adaptor or the inhibitor, in both Drosophila and human cells  Rudra may associate with both the receptor and its signaling adaptor (IMD), holding them together in an inactive confirmation; or (2) Rudra may interact with both PGRP-LC and IMD separately, disrupting the association between the receptor and its adaptor. To probe these possibilities, co-immunoprecipitation experiments were performed with lysates from cells co-transfected with an cells . HoweverDrosophilarudra transcript is rapidly induced following septic infection, and rudra mutant flies or rudra knockdown cells over-express antimicrobial peptides. In the case of Erwinia carotovora carotovora infection, this elevated level of AMP production leads to increased survival. A similar phenotype was reported for mutants lacking Caspar, which is thought to inhibit downstream signaling events rudra is a key component in a negative feedback loop that keeps the IMD pathway in check.Recent work has shown that the intensity and duration of the immune response is tightly regulated in rudra potently blocked signaling through the IMD pathway, both in cells and in flies. Moreover, we exploited this activity to analyze which steps in the IMD pathway are inhibited by Rudra. Using various molecular assays to monitor different PGN-induced events in the IMD pathway, we found that Rudra interfered with cleavage of IMD. Signaling mediated by receptor over-expression was also inhibited by Rudra, but this was not the case for signaling induced by over-expression of downstream components. Together, these data strongly support the notion that Rudra interferes with receptor function and is consistent with the association between Rudra and the receptors PGRP-LC or PGRP-LE.In addition to these loss-of-function results, over-expression of Drosophila, human cells and in vitro, Rudra was shown to interact directly with PGRP-LC and PGRP-LE. The interaction between PGRP-LE and Rudra required the RHIM-like domain of PGRP-LE, which is also critical for signaling by this receptor. However, the region through which PGRP-LC interacts with Rudra is less clear and likely involves multiple, partly redundant interfaces. Rudra also interacted with the imd protein. Moreover, Rudra interfered with the interaction between the receptor PGRP-LC and IMD, destabilizing the receptor signaling complex. From these results, we propose that Rudra is a negative feedback regulator that down modulates the IMD pathway by binding the receptors and interrupting the associations with their cognate signaling adaptor IMD. This regulatory loop is critical to properly regulate the immune response.Using assays in yeast, rudra/CG15678 is a negative regulator of the IMD pathway, although they refer to this gene as poor Imd response upon knock-in (pirk). They showed that rudra/pirk is rapidly induced following infection, similar to the data presented here, and further demonstrated that rudra induction is dependent on Relish, both in cells and in flies. Using reporter assays in S2 cells, they found that Pirk inhibits IMD signaling but not the Toll pathway. With transgenic RNAi fly lines, they also found that knockdown of pirk caused the hyper-expression of the antimicrobial peptide genes. Also, flies over-expressing Pirk blocked the activation of the IMD pathway and were more susceptible infection. These results are consistent with the data presented here, although we have characterized a mutant allele of rudra and additionally show that this mutant exhibits enhanced protection against Erwinia infection. The data presented here also expand on the findings of Kleino et al. (2008) by showing that Rudra not only interacts with both PGRP-LC and IMD, but also that these interactions with Rudra disrupt the direct association between PGRP-LC and IMD. Kleino et al. (2008) reported that central portion of Rudra consists of two repetitive amino acid elements of unknown function and structure, which they named the Pirk domain. The Pirk domain is required for the interaction with IMD, but not with PGRP-LC. Rudra does not contain obvious homology to any other protein motifs, and no mammalian homologs are readily detected. In agreement with the data presented here, Kleino et al. (2008) recently reported that Drosophila immune response have come to light. Given that it is well-established that immune activation in flies has a cost, such as reduced fecundity rudra mutant animals.Recently, multiple mechanisms involved in regulating the E. coli was purchased from Invivogen.Insoluble PGN from rudra mutant line, EY00723, was originally isolated by the Drosophila Genome Project gene disruption consortium and provided by the Bloomington Drosophila Stock Center. The flies were backcrossed for six generations to a 1118w strain in order to isogenize. In all experiments, EY00723rudra mutants were compared to isogenic 1118w animals. 112;;PGRP-LCPGRP-LEEΔ, double mutant flies were reported previously of Erwinia carotovora carotovora 15Diptericin, Attacin, Cecropin and the control rp49  was analyzed by Northern blotting rp49 levels. For qRT-PCR, RNA was DNase treated and re-extracted with phenol-chloroform. cDNA was synthesized using Superscript II (Invitrogen) and quantitative PCR analysis was performed on a DNA engine Opticon 2 cycler  using SYBR Green (Biorad). The specificity of amplification was assessed for each sample by melting curve analysis and relative quantification was performed using a standard curve with dilutions of a standard. The quantified data was normalized to rp49 levels. In all S2*-based cell experiments, cells were pre-treated with 1 mM 20-hydroxyecdysone for 24 to 40 hr before treatment with 500 mM CuSO4 and/ or PGN (100 ng/ml).Total RNA from flies or cultured cells was isolated with the TRIzol reagent (Invitrogen) as described previously 6/mL and then were treated with 1 mM 20-hydroxyecdysone. After 24 hours, cells were treated (or left untreated) with PGN (100 ng/ml) for various time, as indicated.dsRNA was generated and purified as reported previously In vitro translation was performed following the protocol of the manufacturer (Promega). Immunoprecipitations were carried out with rabbit anti-T7 (Bethyl labs) in lysis buffer . For immunoprecipitation from cells, Schneider S2* cells were first transfected by calcium phosphate method with appropriate expression plasmids. Cells were split 24 hours after transfection to 1.0×106/mL and 24 hours later, were treated with 500 µM copper sulphate for 5 hr, when necessary, for expression from the metallothionein promoter. Immunoprecipitations were performed in lysis buffer and analyzed by SDS-PAGE followed by immunoblot analysis with anti-T7 MAb (Novagen), anti-V5 (Sigma), anti-IMD (gift of J.-M. Reichhardt) or anti-Flag (Sigma) antibodies. Stable cell lines and immunoblotting were performed as described previously rudra ORF was amplified by PCR and subcloned into the EcoRI and BglII sites of pUAST. For genomic rescue, a BAC clone , RNA was extracted 8 h later, and assayed by Northern blotting.For the UAS transgenic, the rudra gene was cloned into pRmHa3 vector by standard methods to create constructs expressed from the metallothionein promoter. The constructs were then transfected into S2* cells in conjunction with pHs-Neo at a ratio of 50∶1; stable transfectants were then selected with G418 (1 mg/ml). For double stable cell lines, the rudra expression plasmid was transfected into S2* cell lines that were previously selected to carry plasmids expressing either PGRP-LC, PGRP-LE, IMD or Dredd. The rudra plasmid was selected with a second selectable marker, either G418 (1 mg/ml) or hygromycin (20 U/ml), as appropriate.The Figure S1in vitro Co-immunoprecipitation of in vitro co-translated PGRP-LE and Rudra. Co-immunoprecipitation was performed using anti-FLAG antibodies with 35S-methionine labeled in vitro translated T7-Rudra and FLAG-PGRP-LE.PGRP-LE and Rudra interact (3.77 MB TIF)Click here for additional data file.Figure S2PGRP-LCx (wild-type and deletion mutants) and FLAG-tagged rudra with or without CuSO4 treatment, as indicated. Lower diagram indicates the regions deleted in each mutant form of PGRP-LC.Rudra interacts with all the deletion mutants spanning the cytoplasmic domain of PGRP-LCx. IP-IB analysis of lysates from S2* cells transiently transfected with metallothionein promoter expression plasmids encoding T7-tagged (0.87 MB TIF)Click here for additional data file.Figure S3Drosomycin and Diptericin expression in S2* cells stimulated with SPZ-C106 or PGN, respectively, with rp49 as a loading control. Cells expressing rudra, from the actin promoter, failed to respond to PGN but displayed robust SPZ-induced Drosomycin expression. Stimulation time as indicated.Rudra inhibits IMD signaling but not the Toll pathway. Northern blot of (4.55 MB TIF)Click here for additional data file.


The 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak infected 8,422 individuals leading to 916 deaths around the world. However, there have been few epidemiological studies of SARS comparing epidemiologic features across regions. The aim of this study is to identify similarities and differences in SARS epidemiology in three populations with similar host and viral genotype.We present a comparative epidemiologic analysis of SARS, based on an integrated dataset with 3,336 SARS patients from Hong Kong, Beijing and Taiwan, epidemiological and clinical characteristics such as incubation, onset-to-admission, onset-to-discharge and onset-to-death periods, case fatality ratios (CFRs) and presenting symptoms are described and compared between regions. We further explored the influence of demographic and clinical variables on the apparently large differences in CFRs between the three regions.All three regions showed similar incubation periods and progressive shortening of the onset-to-admission interval through the epidemic. Adjusted for sex, health care worker status and nosocomial setting, older age was associated with a higher fatality, with adjusted odds ratio (AOR): 2.10  for those aged 51-60; AOR: 4.57  for those aged above 60 compared to those aged 41-50 years. Presence of pre-existing comorbid conditions was also associated with greater mortality .The large discrepancy in crude fatality ratios across the three regions can only be partly explained by epidemiological and clinical heterogeneities. Our findings underline the importance of a common data collection platform, especially in an emerging epidemic, in order to identify and explain consistencies and differences in the eventual clinical and public health outcomes of infectious disease outbreaks, which is becoming increasingly important in our highly interconnected world. The 2002-2003 SARS outbreak infected 8,422 individuals leading to 916 deaths in eight affected areas [Except for a few sporadic case reports mostly involving laboratory mishaps -7, thereWe analyzed a combined database of SARS patients from Hong Kong (n = 1755), Beijing (n = 917) and Taiwan (n = 664). Clinical, demographic and epidemiologic details for SARS patients from each region were collected, coded and anonymized for analysis. All analyses were based on patients who satisfied the WHO definition for 'probable cases' . In BeijOur database on all 1755 probable cases in Hong Kong was derived from the Department of Health master list and the Hospital Authority eSARS system as previously described .Data on 917 of the 2521 probable cases in Beijing , who werSimilarly, we captured the corresponding data from all 664 probable cases in Taiwan who provided the information through standardized screening questionnaires at emergency rooms and on admission. Data were collected from all hospitals and integrated into a dataset officially maintained by the Centers for Disease Control, Taiwan.We compared the characteristics of SARS patients by region in terms of demographic and clinical variables such as age, sex and health care worker (HCW) status, presence of pre-existing comorbid conditions  and whether the patients were admitted before symptom onset. We estimated the case fatality ratios (CFRs) within subgroups of each of these characteristics. We calculated the age- and sex-standardized CFRs and associated exact binomial 95% confidence intervals based on the World Standard Population . We fittIncubation period was estimated on patients with dates of exposure to a potentially infected individual and a symptom onset date, using both non-parametric and parametric methods allowing for interval censoring . We plotWe identified factors which might have affected the onset-to-death and onset-to-discharge distribution and compared the differences across the regions. The onset-to-death and onset-to-discharge periods were firstly log-transformed and then fitted by linear regression separately to the same variables as in the previous model.Finally, we examined regional differences in symptoms at presentation. All analyses were conducted in R version 2.3.1 .Additional file Overall, the crude CFRs differed substantially across regions , while those with pre-existing comorbid conditions in Taiwan had shorter periods . After adjustment for the above factors, Beijing had a significantly longer onset-to-discharge period comparing with Hong Kong, while Taiwan had a significantly shorter period.Figure This set of analyses is the most comprehensive to date, based on data accounting for 40% of the global case load. Simultaneously analysing data from various affected areas in Greater China together revealed consistencies and explained heterogeneities that would have otherwise remained unexplored. Our combined database is unique in that of the three regions, only mainland China is a member of the WHO whereas Hong Kong is an observer and Taiwan is a non-member. Such geo-political influences have posed an almost insurmountable challenge to official supranational agencies in pulling together the respective databases . DatabasOur findings confirm the widely reported heterogeneous CFRs in the three regions ,21,22. HAn interesting related finding is that the relatively higher CFR also applied consistently to each subgroup including low risk groups such as younger patients and HCWs, which suggests that the presence of some common factors not included in our models  may have independently increased CFRs in Taiwan. However, the treatment of choice in Hong Kong and Taiwan was the combined use of ribavirin and corticosteroids ,23, and th percentile of 12.4, 19.7 and 17.9 days in Hong Kong, Beijing and Taiwan respectively, with some degree of variation between regions [In terms of epidemiologic parameters, the incubation period had mean 4.4, 5.7 and 6.9 days and a 95 regions . Togethe regions , our fin regions . HoweverAs would be expected, our results show that onset-to-admission periods shortened throughout the epidemic Figure  as publiTaiwan's shorter onset-to-death distributions, indicating quicker deterioration, coupled with the consistently higher fatality ratios for most subgroups raises the possibility that the management of these patients did not compare favorably with those in the other two regions, especially if we assume the viral agent and host genetic susceptibility were similar throughout. However without more detailed clinical data, for example levels of viral load or as a proxy lactate dehydrogenase at admission reflecting initial illness severity ,29, it iOur description of the variability between regions in symptoms at presentation Figure  is the fFinally, a few limitations bear mention. As in most previous studies ,29,41,42The authors declare that they have no competing interests.BJC and GML conceived and designed the study. EHYL, CAH and BJC performed the statistical analysis. EHYL, CAH, BJC and GML analyzed and interpreted the data. EHYL and BJC drafted the manuscript. CHC, TT and CWC provided the study materials or patient data. CAH, CAD and GML obtained the funding. CHC, LMH and CWC collected and collated the data. All authors critically reviewed the manuscript, read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/10/50/prepubCharacteristics of SARS patients in Hong Kong, Beijing and Taiwan. The associated case-fatality ratios and adjusted odds ratios  are also reported. CFR, case fatality ratio; AOR, adjusted odds ratio; CI, confidence interval. * Patients with unknown age, pre-existing comorbid conditions or admission date were excluded. † Adjusted for sex, age, health care worker status, preexisting comorbid conditions and nosocomial infection. ‡ Data on final outcome were not available for 12 patients in Taiwan and were excluded for analysis. § The estimates were not shown as there was not more than 2 deaths in these age groups out of a relatively large number of patients. || Based on the WHO World Standard Population distribution [ribution .Click here for fileCharacteristics of SARS patients in Hong Kong, Beijing and Taiwan (pooled data). The associated case-fatality ratios and adjusted odds ratios  are also reported. CFR, case fatality ratio; AOR, adjusted odds ratio; CI, confidence interval. * Data on final outcome were not available for 12 patients in Taiwan and were excluded for analysis. Patients with unknown age, pre-existing comorbid conditions or admission date were excluded from multivariable logistic regression models. † Adjusted for sex, age, health care worker status, preexisting comorbid conditions, nosocomial infection and region. ‡ Based on the WHO World Standard Population distribution [ribution .Click here for fileCase fatality ratio by different onset-to-admission periods, Hong Kong, Beijing and Taiwan. CFR, case fatality ratio; CI, confidence interval. * Excluding 16 patients with unknown admission dates or discharge outcome.Click here for fileCase fatality ratio by different onset-to-admission periods in Beijing, XTS Hospital, Hospital 302 and Hospital 309. CFR, case fatality ratio; CI, confidence interval.Click here for fileFactors affecting the onset-to-death and onset-to-discharge period of SARS patients in Hong Kong, Beijing and Taiwan. CI, confidence interval. * The acceleration factor is computed as exp(β). It indicates the relative increase (>1) or decrease (<1) in the median time from onset of symptoms to death or discharge. † also adjusted for interaction between location with admission before symptom onset. ‡ also adjusted for interaction between location with health care worker and pre-existing comorbid conditions.Click here for fileFactors affecting the onset-to-death and onset-to-discharge period of SARS patients in Hong Kong, Beijing . It indicates the relative increase (>1) or decrease (<1) in the median time from onset of symptoms to death or discharge. † also adjusted for interaction between location with admission before symptom onset. ‡ also adjusted for interaction between location with health care worker and pre-existing comorbid conditions.Click here for file


The diversity of placental architectures within and among mammalian orders is believed to be the result of adaptive evolution. Although, the genetic basis for these differences is unknown, some may arise from rapidly diverging and lineage-specific genes. Previously, we identified 91 novel lineage-specific transcripts (LSTs) from a cow term-placenta cDNA library, which are excellent candidates for adaptive placental functions acquired by the ruminant lineage. The aim of the present study was to infer functions of previously uncharacterized lineage-specific genes (LSGs) using co-expression, promoter, pathway and network analysis.Clusters of co-expressed genes preferentially expressed in liver, placenta and thymus were found using 49 previously uncharacterized LSTs as seeds. Over-represented composite transcription factor binding sites (TFBS) in promoters of clustered LSGs and known genes were then identified computationally. Functions were inferred for nine previously uncharacterized LSGs using co-expression analysis and pathway analysis tools. Our results predict that these LSGs may function in cell signaling, glycerophospholipid/fatty acid metabolism, protein trafficking, regulatory processes in the nucleus, and processes that initiate parturition and immune system development.The placenta is a rich source of lineage-specific genes that function in the adaptive evolution of placental architecture and functions. We have shown that co-expression, promoter, and gene network analyses are useful methods to infer functions of LSGs with heretofore unknown functions. Our results indicate that many LSGs are involved in cellular recognition and developmental processes. Furthermore, they provide guidance for experimental approaches to validate the functions of LSGs and to study their evolution. Placentae exhibit remarkable variation in tissue structure and morphology within and between mammalian clades, and even within a single mammalian order . The divc-myc gene are transcribed in an overlapping fashion and that transcription of the coding and non-coding strands is regulated independently. Yu and coauthors  was generated by temporal gene expression profiling of liver RNA during the peripartal period in Holstein cows fed with a moderate energy ad-libitum, or restricted diet in which the animals were fed to consume ca. 80% of their calculated energy requirements from -65 days until parturition  were extracted for gene clusters from Btau_3.1 using the UCSC Genome Table browser. To identify TFBSs that are over-represented in the gene clusters, we used promoters of unique cattle RefSeq genes as the background set. The coordinates for a non-redundant set of Btau_3.1 RefSeq genes were downloaded from UCSC Genome Table Browser, and the proximal promoters  were extracted as described for the clusters.Match program [Vertebrate-specific TFBSs were predicted by scanning the repeat-masked promoters using the  program  with a c program . This da program .Match program. The Match output lists the PWM matches in their positional order on the promoter. Both ordered (A-B ≠ B-A relative to the TSS) and unordered (A-B = B-A) TFBS pairs and triplets were predicted separately, and the orientation of the TFBSs was ignored. To prevent double-counting, only a single occurrence of a combination was counted per gene. For the unordered combinations, non-redundancy was ensured by collapsing each identified combination in its sorted order (A-B-A or A-C-B or B-A-C = A*B*C), and then counting only a unique occurrence of the unordered combination within a gene promoter. Unordered TFBS composites were denoted with a comma separating the sites. Composite ordered TFBSs were denoted with an asterisk between the sites indicating that they were predicted to be co-occurring in that order in the promoter relative to the TSS. TFBS pairs and triplets were predicted for three different minimum threshold distances of 20 bp, 50 bp, and 100 bp between the TFBSs to identify all adjacent non-overlapping TFBS combinations [Two TFBSs were defined as co-occurring if they were distinct, non-overlapping, and their PWMs had a core similarity threshold of 0.9 and matrix similarity threshold of 0.85 in the output from the inations . The maxThe counts of the ordered pair and triplet TFBSs were computed for each cluster of genes and compared to the counts of the respective pairs and triplets in the background RefSeq promoters using Fisher's exact test. A minimum cell count of five was necessary for comparisons. The computed P-values were adjusted for multiple comparisons using the FDR correction as before, and comparisons were deemed significant if the adjusted P-value was ≤ 0.1. The entire analysis was carried out for both repeat-masked and unmasked promoters and significant predictions in the repeat-masked promoters had to be predicted in the unmasked cluster promoters to be selected. This precautionary measure ensured that no predictions were within repeats. TFBS prediction results were manually checked for the presence of over-represented composites.ANN-Spec [ab initio prediction of motifs that were common to an entire cluster. For each cluster of genes, the motif predictions were made on unmasked promoters, using the unmasked Btau_3.1 RefSeq promoters from which the cluster genes were subtracted as background. ANN-Spec was run iteratively by varying the predicted motif length from 6 to 16 bp and setting the run cycle (parameter m) to 100. The PWMs of predicted motifs were parsed from the ANN-Spec output. Tomtom [ANN-Spec-predicted and corresponding matching Transfac PWMs using the EnoLOGO web server [ANN-Spec  was used. Tomtom  was usedb server .Ingenuity Pathway Analysis (version 5.5)  was used-03. In addition, we used the Predicted Regulatory Module (PREMOD) database [To identify known pathways in which the TFs were involved we queried the CRSD, which consists of miRNA, TF and gene expression regulatory signatures assigned to specific BioCarta and KEGG pathways using genome-wide enrichment analysis . A Perl database  to identTo identify sub-networks of co-expressed genes in the LIVR cluster and "hub genes" we used WGCNA (Weighted Gene Co-Expression Network) . Expressij is the adjacency between two genes i and j, x is the expression of a gene, and β is the power factor for a scale-free network. For the LIVR cluster, this power was 8 as determined by the scale-free network criterion provided by the authors [where a authors . DefaultGeneGO MetaCore [MetaCore  was usedLST: Lineage-specific transcript; LSG: Lineage-specific gene; ncRNA: noncoding RNA; TFBS: Transcription factor binding site; TF: Transcription factor; PWM: position weight matrix; LIVR: cluster of genes expressed in liver and showing effect of diet; PLAC: cluster of genes preferentially expressed in cattle placenta; THYM: cluster of genes preferentially expressed in cattle thymus; SKIN: cluster of genes preferentially expressed in cattle skin; ADRBRN: cluster of genes preferentially expressed in cattle adrenal gland, thalamus, and cerebellum; FET: Fisher's exact test; FDR: Benjamini-Hochberg false discovery rate.CGK and HAL conducted the research, analyzed the results, and wrote the manuscript, REE contributed to data analysis, and JJL provided the liver time-series data and participated in the analysis. All authors have read and approved the manuscript.Tissue expression profile of the LIVR cluster of genes. Tissue expression profile of the LIVR cluster of genes includes two of the LSTs  and 104/208 other genes in the cluster. L_Intestine, large intestine; M_L_Node, mesenteric lymph node; S_Intestine, small intestine.Click here for fileab initio predicted motif PWMs with known Transfac binding site PWMsComparison of ANN-Spec. . This table displays the frequency logos of PWMs that were predicted using ANN-Spec and for known Transfac binding sites with which the predicted PWMs were significantly matched.Click here for fileMultiple alignment of 34FL with mouse SSLP-1. Multiple alignment of 34FL with mouse SSLP-1 and other secreted Ly6 domain containing proteins having 10 conserved cysteine residues. Dashes in the alignment indicate gaps, and gray shaded areas indicate conserved cysteines.Click here for fileFunctions of gene products. Text file containing glossary of gene functions.Click here for fileLSTs used for clustering with tissue expression data. Initial set of 49 LSTs used as seeds to cluster the tissue expression data.Click here for file


Intraepithelial lymphocytes (IEL) are specialized T cells found between the epithelial cells ofthe small intestine. Because of their location, IEL are the first lymphocytes to contact intestinalbacteria and food antigens. In the neonate, IEL may be the first cells of the immune systemto interact with milk-borne hormones including prolactin (PRL). PRL, an endocrinehormone abundant in breast milk, interacts with cells through surface receptors. PRL hasbeen shown to function as an immunoregulator and may affect the development of the newborn'simmune system. To determine if PRL plays a role in IEL development, small intestineIEL from rats of various ages were examined for the presence of surface prolactin receptor(PRL-R) and several lymphoid markers by flow cytometry. Between birth and 96 days of ageabout 80% of IEL were found to express PRL-R. These same cells also expressed the mRNAfor PRL. Additionally, all of the IEL subpopulations examined were found to express PRL-R.Analysis of the normal development of rat IEL revealed an age related increase in total IEL,CD4 positive cells as well as a peak in interleukin-2 receptor (IL-2R) expression at weaning.In summary, the results indicate that IEL express PRL and PRL-R. In addition, an activationmarker, IL-2R, changes in expression during neonatal development.


DSM-IV-compatible interview on suicidal-behavior/ideation, trauma, and morning cortisol, we found no association between these factors and morning cortisol. A significant gender interaction was found for those threatened with a weapon—men showing a negative and women a positive association, suggesting that any cortisol/trauma association may be partially explained by coexisting behavioral problems and gender.Individuals exposed to trauma or who have attempted suicide may show abnormal cortisol profiles; those exposed to significant trauma show reduced, while those who attempt suicide show increased cortisol output, although the evidence is inconsistent. This study explores the associations between morning cortisol, trauma, and suicide attempts or ideation among young people. In a community-based sample of 501 15-year-olds, using data from a  The hormone Cortisol is part of the hypothalamic–pituitary–adrenocortical axis (HPA-axis), a key biological system involved in the regulation of the stress response. Dysfunction of this system has been (inconsistently) linked with stress-related psychiatric conditions such as depression  and postA recent meta-analysis of PTSD and basal cortisol levels concluded there was no systematic difference between adults with PTSD and controls, but that individuals exposed to trauma, with or without PTSD symptoms, demonstrated a lower or "flattened" cortisol profile . A complDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition- . In addition to survey measures, pupils in the subsample completed a computerized,  DSM-IV;  compatib DSM-IV; , the Voi DSM-IV; . We repo DSM-IV; . Briefly DSM-IV; . ProbabiDSM-IV diagnosis for mood disorder and two for PTSD, neither diagnosis was associated with cortisol  sampling device 5 minutes into questionnaire completion using standard methodology .Analysis used a series of separate regressions with morning cortisol predicted by either trauma or suicide-related variables, adjusted for cortisol confounds and analyzed separately by gender. One multivariate outlier was identified and excluded. Tests for gender interactions were conducted. Gender differences in trauma exposure were explored using the chi-square test. Power calculations suggest we have over 80% power to detect a small association (n and %) to each form of trauma is shown in p < .05).Exposure (Despite finding that exposure to different forms of trauma, attempted suicide, and ideation varied considerably by gender, contrary to expectations we found no evidence that these were related to morning cortisol levels. Exposure to different forms of trauma patterned according to gender, i.e., female adolescents were exposed to more sexually related and male adolescents more violence-related trauma. Our findings confirm that exposure to various forms of trauma appears relatively common during childhood and adolescence .Our results suggest a lack of association between exposure to trauma and basal cortisol levels; however, there are at least two additional factors to consider. First, some individuals may be more vulnerable to exposure to certain types of trauma. Previously, we found several traumatic and life events not specifically assessed by the PTSD module were associated with elevated cortisol, notably, recent death of a friend, or new step-parent .The desiSecond, the degree of trauma experienced by participants could be of greater severity or duration than is assessed by the Voice-DISC. Potentially, different results may have been obtained either by the use of more extensive measures; from adult, children, or clinical samples; from those exposed to recent or more severe trauma ; or thos


The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular 'reactive oxygen species' (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation.combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause .We review the considerable and wide-ranging evidence for the involvement of this simultaneous combination of multiple factors that is responsible.The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological  importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, since in some circumstances  molecules that are nominally antioxidants can actually act as pro-oxidants. The reduction of redox stress thus requires suitable levels of both antioxidants and effective iron chelators. Some polyphenolic antioxidants may serve both roles.Understanding the exact speciation and liganding of iron in all its states is thus crucial to separating its various pro- and anti-inflammatory activities. Redox stress, innate immunity and pro- (and some anti-)inflammatory cytokines are linked in particular via signalling pathways involving NF-kappaB and p38, with the oxidative roles of iron here seemingly involved upstream of the IkappaB kinase (IKK) reaction. In a number of cases it is possible to identify mechanisms by which ROSs and poorly liganded iron act synergistically and autocatalytically, leading to 'runaway' reactions that are hard to control unless one tackles multiple sites of action simultaneously. Some molecules such as statins and erythropoietin, not traditionally associated with anti-inflammatory activity, do indeed have 'pleiotropic' anti-inflammatory effects that may be of benefit here.Overall we argue, by synthesising a widely dispersed literature, that the role of poorly liganded iron has been rather underappreciated in the past, and that in combination with peroxide and superoxide its activity underpins the behaviour of a great many physiological processes that degrade over time. Understanding these requires an integrative, systems-level approach that may lead to novel therapeutic targets. Most imlevated) ,613. Lowlevated) ,614, andlevated) -619. Thelevated) .Non-transferrin-bound iron is also considerably elevated in type 2 diabetes , and thiAlthough some of its origins may be pre-natal , many ofet al. )strongly implicated in a variety of neurodegenerative diseases )H. pylori ). pylori -1430 and. pylori -1442. Co. pylori ,411,1443. pylori  . Note ils , afixation  or NGAL fixation . Iron chfixation ,1535, anfixation .It is well known that one consequence of bacterial infection (sepsis) can be septic shock, that this can be mimicked by the Gram-negative bacterial outer membrane component LPS (lipopolysaccharide), and that in the worst cases this leads via multiple organ failure to death. However, whatever LPS does it is quite independent of the present of viable  baMany circulating inflammatory factors have been identified as important in the development of septic shock, including cytokines such as Tissue Necrosis Factor (TNF) , and celIn this regard, it is especially interesting that the antioxidant melatonin is particularly effective in preventing septic shock -1587, anreduction that needs to be the focus, with the 'iron'-catalysed production of hydroxyl radical being the nexus, with the standard redox potential of a redox couple per se being less significant in absolute terms, and the redox potential that a particular redox couple 'feels' being dependent in a complex manner on a variety of thermodynamic and kinetic factors )per se may not be relevant, as drugs require carriers to cross membranes )Clioquinol (CQ) ,1711 . F. F1714])in vivo and in vitro )per se seems not to be important for the biological activity of 8-hydroxyquinoline chelators )The induction of NF-κB by ROSs appears to involve a coupling via the glutathione system -2078 . T. T2093])their concentration and activity, and so on. This is of course well established in biology in the formalism of metabolic control analysis (MCA) , asee e.g. . Modern see e.g.  and abovps e.g. -2127). I. Itheir tructure ), and thtructure -2132. Intructure -2140).• (or any other) radicals we need both to decrease their production and increase their removal to harmless substances )iron Fig , of the iron Fig , the autsee e.g. -2291).• )• ,2295: 8-• )"Actually, the orgy of fact extraction in which everybody is currently engaged has, like most consumer economies, accumulated a vast debt. This is a debt of theory and some of us are soon going to have an exciting time paying it back – with interest, I hope." ."But one thing is certain: to understand the whole you must look at the whole" "If you are not thinking about your experiments on a whole-genome level you are going to be a dinosaur". J. Stamatoyannopoulos, quoted in . and the myExperiment [ environments, represent an intellectually important approach. Important too to this endeavour will be Open Access initiatives [While it is less common for scientists to publish 'negative' results ('there was no effect of some agent on some process'), and there has been a tendency to seek to falsify specific hypotheses rather than to paint a big picture , there iperiment  environmtiatives ,2440 andtiatives ,2442 of tiatives ), prefertiatives , and hentiatives , where btiatives -2452), atiatives  as they tiatives -2457) artiatives , and inntiatives ). In partiatives ,2460. Astiatives ) in a fitiatives ,2462. Fetiatives ) allow dtiatives  and Utoptiatives -2468 areGiven the widely dispersed communities that have been referenced herein, and the future requirement for integrating knowledge generated throughput the world, a programme for understanding the combinatorial roles of poorly liganded iron and reactive oxygen species in the aetiology of many diseases, as set out in the above, appears to be prototypical for the kinds of new approaches to doing science that we may anticipate in the eras of Web 2.0 and the Semantic Web.AECOPD: Acute Exacerbation of Chronic Obstructive Pulmonary Disorder; AKI: Acute Kidney Injury; ALS: Amyotrophic Lateral Sclerosis; AMD: Age-related Macular Degeneration; BBB: Blood-brain barrier; ChEBI: Chemical Entities of Biological Interest; CQ: Clioquinol; COPD: Chronic Obstructive Pulmonary Disorder; DFO: Desferrioxamine; Dp44mT: di-2-pyridylketone-4,4,-dimethyl-3-thiosemicarbazone; EDTA: Ethylenediamine-tetraacetic Acid; FA: Friedrich's ataxia; FPN1: Ferroportin-1; HBED: N,N'-bis-(2-hydroxybenzyl)ethylenediamine-N,N'-diacetic acid; HCP1: Heme Carrier Protein-1; HIF: Hypoxia-Inducible Factor; HO1: Heme Oxygenase-1; HMG-CoA: Hydroxymethyl glutaryl Coenzyme A; Hp: Hephaestin; 8HQs: A variety of 8-hydroxyquinolines; IBD: Inflammatory Bowel Disease; InChI: International Chemical Identifier; IKK: IκB kinase; KEGG: Kyoto Encyclopedia of Genes and Genomes; L1: Deferriprone; LFA-1: Leukocyte Function Antigen-1; LIP: Labile Iron Pool; LIPID: Long-Term Intervention with Pravastatin in Ischaemic Disease; LPS: Lipopolysaccharide; MOF: Multiple organ failure; NF-κB: Nuclear Factor κB; NGAL: Neutrophil Gelatinase-Associated Lipocalin ; NIH: 2-hydroxy-1-napthylaldehyde isonicotinyl hydrazone; PE: Pre-eclampsia; PIH: Pyridoxal isonicotinyl hydrazone; PPAR: Peroxisome Proliferator Activated Receptor; RA: Rheumatoid Arthritis; ROS: Reactive Oxygen Species; SBML: Systems Biology Markup Language; SIH: Salicylaldehyde isonicotinyl hydrazone; SIRS: Systemic Inflammatory Response Syndrome; SLE: Systemic Lupus Erythematosus; SMILES: Simplified Molecular Input Line Entry Specification; SOD: Superoxide Dismutase; Tf: Transferrin; TNF: Tissue Necrosis Factor; VEGF: Vascular Endothelial Growth Factor.The author declares that he has no competing interests.The pre-publication history for this paper can be accessed here:


No correlations between cognitive impairment and pain, fatigue, or perceived cognitive failures were observed in the Depressed-SLE participants. Moderate agreement (86.4%) was found between a comprehensive neuropsychology battery cognitive impairment index and the ACR-SLE impairment index in the Depressed-SLE patients. Overall, the magnitude and pattern of cognitive impairment in Depressed-SLE patients cannot be explained by depression alone.Thirteen patients with systemic lupus erythematosus and depression (Depressed-SLE), 10 Depressed-Control subjects, and 25 Healthy Control subjects completed cognitive testing and self-report questionnaires of pain, depression, and fatigue. The Depressed-SLE group scored higher on the American College of Rheumatology Neuropsychological Battery for systemic lupus erythematosus cognitive impairment index compared to Depressed-Control and Healthy Control subjects ( More than 50% of patients with systemic lupus erythematosus (SLE) demonstrate major psychiatric and neurological disorders indicating central nervous system (CNS) involvement ,2. NeuroThere is some discrepancy in the literature regarding the association between psychological factors and cognitive functions in patients with SLE. Although some studies have demonstrated that patients with SLE with overt neuropsychiatric disorders (NPSLE) have strong correlations between psychological and cognitive distress -13, otheSeveral studies have demonstrated that patients with NPSLE have stronger correlations between psychological and cognitive distress -12,20. IDenburg and colleagues  used queOur brief review of investigations suggests that several approaches have been used to study cognition and psychological function in SLE. A majority of studies to date have used self-report questionnaires to classify distress, typically including brief questionnaires that measure depression, anxiety, or general psychological well-being ,11,12,20Some of the discrepancies in relationships between cognition and depression in SLE may be related to the research methods used to diagnose depression. For example, most studies relating cognition and SLE use standardized measures of depression as noted above. Previously, we reported significant differences between data obtained from participant self-report on standardized questionnaires, physician ratings of depression, and structured psychiatric interviews . A cliniOnly one such study of depressed SLE and depressed outpatients has been published to date. Denburg and Denburg  reportedOur present study compared the performance of Depressed-SLE subjects to Depressed-Controls (history or presence of major depressive disorder only) as well as Healthy Controls on the brief American College of Rheumatology Neuropsychological Battery for SLE (ACR-SLE battery). Additionally, the validity of the ACR-SLE battery was compared to a comprehensive battery in Depressed-SLE participants. Finally, associations between performance on the ACR-SLE battery and measures of depression, fatigue, pain, and perceived cognitive failures were investigated.Participants in this study included 13 Depressed-SLE participants, 10 subjects with the presence of major depressive disorder (Depressed-Controls), and 25 Healthy Controls. All subjects signed an approved consent form authorized by the Institutional Review Board at the National Jewish Medical and Research Center. The SLE participants were obtained from a pool of SLE outpatients seen at the National Jewish Medical and Research Center, the University of Colorado Hospital, and local rheumatology clinics. The primary physician/rheumatologist also completed a neuropsychiatric checklist indicating the presence or absence of neurological and psychiatric symptoms. SLE subjects with possible neurological damage , major substance abuse, or major psychopathology prior to their diagnoses of SLE were excluded from the study. Any patients with neurological damage or substance abuse following their SLE diagnosis were also excluded. All SLE participants fulfilled the revised criteria for SLE as defined by the ACR  as documDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition) Non-Patient Version [All of the subjects were screened by means of a detailed neuromedical interview . This in Version  to deterSubject demographics and health characteristics are presented in Table The analyses for this study used a comprehensive cognitive battery (CB), the previously described ACR-SLE battery , and quet scores using demographically corrected normative data, and t scores below 40 were considered impaired. The CII has a range of 0 to 12, with a higher number representing greater cognitive impairment [t scores below 40 were considered cognitively impaired.A test battery proposed by the ACR for SLE  was admipairment . Subjectt score of 40 were classified as cognitively impaired. In our initial study, approximately 33% of the non-NPSLE participants and 11% of the controls were classified as impaired based on this criterion [The Comprehensive Neuropsychological Battery (CB), a comprehensive battery previously established as reliable with SLE participants, was administered . The neuriterion .The Center for Epidemiological Studies Depression Scale (CES-D)  is a selThe Multidimensional Assessment of Fatigue Questionnaire-Modified (MAF)  is a selThe Short-Form McGill Pain Questionnaire (MPQ)  providesThe Cognitive Failures Questionnaire (CFQ)  is a selp values less than 0.05 were designated to be statistically significant.All statistical analyses were conducted with the SAS statistical analysis package . Data are presented as means ± SDs for continuous variables and as numbers of subjects for categorical variables. An analysis of variance (ANOVA) model and Fisher exact test were used to evaluate the overall group differences in demographic variables. An ANOVA model was also used to compare neuropsychological tests and measures of depression, fatigue, pain, and perceived cognitive deficits between the three groups. In this ANOVA model, each test score entered the model separately as the outcome variable and group was used as the predictor variable. Post hoc analyses were performed using the Tukey-Kramer multiple comparisons procedure. Intra-class correlation coefficients (r) were calculated via a one-way random-effects ANOVA model. A simple linear regression model was used to assess the association between CII and measures of fatigue, depression, pain, and perceived cognitive deficits; A cognitive impairment index (ACR-SLE-CII or CB-CII) was used as the outcome variable and one of the above measures was used as the predictor variable. For all of the analyses, t score [t score and SD for each test by group are presented in Table t scores were compared between groups for each of the 12 scaled scores using a one-way ANOVA model. As indicated, scores were significantly different across groups for three tests: the Digit Symbol Test, the Stroop Color-Word Score, and the Trail Making Test-Part B. Post hoc analyses were performed using the Tukey-Kramer multiple comparisons procedure, with the experiment-wise type I error rate at the 5% level. Results illustrated in Table p = 0.049).The ACR-SLE battery includes 10 tests with a total of 12 scales. Each scale was demographically corrected using available normative data for each test to calculate a t score . The meat score of 40 were designated as impaired for all the test scores on the ACR-SLE battery. The frequency of impairment on these tests for the groups is presented in Figure Scores below a p = 0.056). In terms of specific subtests with overall group differences, the Depressed-SLE group performed worse than Healthy Controls on Performance Intelligence Quotient, Trail Making Test-Part A, PASAT , Story Learning, Story Recall, and Block Design. The Depressed-Controls performed worse than Healthy Controls on Trail Making Test-Part A. The Depressed-SLE participants performed worse than Depressed-Controls on Story Learning and Object Assembly. Finally, a trend for lower scores on Story Recall was noted in the Depressed-SLE group compared to Depressed-Controls (p = 0.08)As indicated in Table p = 0.015). The Depressed-SLE group had a mean ACR-SLE-CII of 3.4 (SD 2.4), Depressed-Controls had a mean of 1.5 (SD 1.5), and Healthy Controls had a mean of 1.6 (SD 1.6). Post hoc analyses using the Tukey-Kramer method adjusting for multiple comparisons indicated that the Depressed-SLE participants had a higher ACR-SLE-CII compared to the Depressed-Controls (p = 0.048) and Healthy Controls (p = 0.018). A significant group difference was also noted across the CB-CII (p = 0.01); post hoc analyses indicated that the Depressed-SLE participants had a greater CB-CII compared to the Depressed-Controls (p = 0.048) and Healthy Controls (p = 0.012).We compared the CII from the ACR-SLE battery (ACR-SLE-CII), and found significant differences between groups  on all self-reported scales of depressive symptoms (CES-D), pain (MPQ), fatigue (MAF), and cognitive failures (CFQ). The Depressed-SLE group and Depressed-Controls did not differ in terms of CES-D total (29.6 versus 30.7), MAF Global Fatigue (38.0 versus 32.2), or CFQ (60.0 versus 55.8). The Depressed-SLE patients did have higher overall pain on the MPQ  compared to the Depressed-Controls  (p = 0.03).Both the Depressed-SLE group and the Depressed-Controls had higher scores compared to Healthy Controls  ACR-SLE-CII and CB-CII and (b) measures of fatigue (MAF), depression (CES-D), pain (MPQ), and perceived cognitive deficits (CFQ). When the groups were analyzed separately, we found no associations between (a) ACR-SLE-CII and CB-CII and (b) CES-D, MPQ, MAF, and CFQ.p = 0.02); however, none of the other 11 test scores showed an association.For the Depressed-SLE group, no associations were reported between the individual ACR-SLE battery tests and SLEDAI score total and length of diagnosis. Only one of the measures (category fluency) was associated with prednisone use, indicating that those on prednisone had a lower fluency score   had poor performance on measures of immediate memory, fine motor function, and psychological functioning [In this study, Depressed-SLE participants showed specific deficits in learning and recall for verbal material when compared to Depressed-Controls. Specific deficits of verbal and nonverbal learning and memory have frequently been reported in SLE ,16,18,48ctioning . This obThis study's Depressed-SLE participants had a greater frequency of attentional deficits compared to Healthy Controls and Depressed-Controls. Frontal white matter abnormalities are another region of interest in SLE given this neuroanatomic region's relationship to attention and efficiency of information processing . In our Correlations between the cognitive impairment in Depressed-SLE participants and measures of fatigue, pain, and perceived deficits were not found. Interestingly, in a prior study with a diverse group of NPSLE participants, we found strong correlations between cognitive dysfunction and fatigue, pain, depression, and perceived dysfunction . The lacPsychometric properties of the brief ACR-SLE battery and the CB were evaluated and indicate that the ACR-SLE battery is valid for research in depressed participants with SLE. In our prior study, we noted that associations between the brief ACR-SLE battery and the CB are 95% for non-NPSLE and 81% for patients with a previous history of NPSLE . This stSome methodological issues limit conclusions from this study. For example, the small sample size of our depressed groups reduced the overall conclusions and general applications of our findings. It also limited our statistical capabilities and, as noted earlier, may have suppressed some potential findings. Notably, of the 92 SLE participants screened, only 14% had major depression without other neuropsychiatric disorders. This suggests that this subgroup of patients with SLE is a very select group of patients, and that continued investigations in such a group may improve scientific understanding of depression in SLE.Despite methodological limitations, our findings indicate that Depressed-SLE participants have greater cognitive deficits compared to Depressed-Controls and Healthy Controls. Specific deficits in learning and attentional skills were more frequent in the Depressed-SLE group compared to Depressed-Controls. Notably, there were several tests impaired in both of our depressed groups, indicating that depression is a common contributing factor. This study does not allow investigation into the processes by which Depressed-SLE patients had greater deficits, and it remains unclear if these processes are biologically mediated and specific to SLE or if they are related to chronic disease in general. Continued studies investigating immunological  and neuropathological  abnormalities in this type of SLE patients are necessary to understand biological mechanisms related to neurobehavioral changes in this population. Clinically, this suggests that cognitive concerns in patients with SLE and depression may require additional evaluation by neurology, neuropsychiatry, and/or neuropsychology specialists.N-methyl-D-aspartate; non-NPSLE = systemic lupus erythematosus with inactive disease and without overt neuropsychiatric disorders; NPSLE = systemic lupus erythematosus with overt neuropsychiatric disorders; SD = standard deviation; se = sensitivity; SLE = systemic lupus erythematosus; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; sp = specificity; WAIS = Wechsler Adult Intelligence Scale.ACR = American College of Rheumatology; ACR-SLE battery = American College of Rheumatology Neuropsychological Battery for systemic lupus erythematosus; ACR-SLE-CII = American College of Rheumatology Neuropsychological Battery for systemic lupus erythematosus cognitive impairment index; ANOVA = analysis of variance; CB = Comprehensive Neuropsychological Battery; CB-CII = Comprehensive Battery cognitive impairment index; CES-D = Center for Epidemiological Studies Depression Scale; CFQ = Cognitive Failures Questionnaire; CII = cognitive impairment index; CNS = central nervous system; Depressed-Control = control subject with only a history or presence of major depressive disorder; Depressed-SLE = patient with a diagnosis of systemic lupus erythematosus and the presence of major depressive disorder; κ = kappa statistic; MAF = Multidimensional Assessment of Fatigue Questionnaire-Modified; MPQ = McGill Pain Questionnaire; NMDA = The authors declare that they have no competing interests.EK was the principal investigator of the study and the primary author of the manuscript. She was responsible for writing the initial research design, supervising subject enrollment, protocol administration, data collection, and interpretation of data. DBA was a co-investigator on the study and contributed to the initial study design, recruitment of depressed control subjects, and inclusion/exclusion of depressed SLE patients. He made a major contribution to manuscript preparation and to the discussion of results. LZ was the biostatistician responsible for all the statistical analyses presented in the paper. She also made major contributions to the manuscript with regard to statistical procedures. SW was a co-investigator on the study, was responsible for the accurate inclusion/exclusion of SLE patients, and reviewed neuropsychiatric criteria and SLEDAI activity. He also contributed to the manuscript preparation and discussion of results. All authors read and approved the final manuscript.


Apodemus ponticus, are emerging.These lineages, including a new lineage associated with the novel rodent host  Apodemus agrarius. In southern Russia (Sochi district), where HFRS is endemic, HFRS cases were caused by a new DOBV variant (DOBV-Ap), found in A. ponticus, a novel hantavirus natural host. Both viruses, DOBV-Aa/Lipetsk and DOBV-Ap/Sochi, were isolated through Vero E6 cells, genetically characterized, and used for serotyping of the HFRS patients’ serum. The clinical severity of HFRS caused by DOBV-Aa resembles that of HFRS caused by Puumala virus (mild to moderate); clinical severity of disease caused by DOBV-Ap infections is more often moderate to severe.Dobrava-Belgrade virus (DOBV) is a European hantavirus that causes hemorrhagic fever with renal syndrome (HFRS); case-fatality rates in Balkan countries are as high as 12%. To determine causative agents, we examined 126 cases of DOBV-associated HFRS in central and southern European Russia. In central Russia , outbreaks were caused by a DOBV variant (DOBV-Aa) carried by  In Russia, the zoonotic virus infection with the highest morbidity rate is hemorrhagic fever with renal syndrome (HFRS). This disease was first described in the 1930s as hemorrhagic nephroso-nephritis in far eastern Russia and Tula fever in European Russia  and hantavirus (cardio)pulmonary syndrome in the Americas (caused by Sin Nombre and Andes viruses)  but also shrews (family Soricidae). Particular hantavirus species are usually harbored each by a single or a few closely related rodent species. The virus genome contains 3 segments of negative-stranded RNA; the large (L) segment encodes viral RNA-dependent RNA polymerase, the medium (M) segment encodes glycoprotein precursor, and the small (S) segment encodes nucleocapsid protein (Apodemus flavicollis) captured in a natural focus of HFRS in Dobrava village, Slovenia (A. agrarius (striped field mouse), but not A. flavicollis, trapped in Kurkino village, a few kilometers from Tula city  and that strains from A. agrarius show higher diversity. Strains from central Europe and European central Russia form the DOBV-Aa lineage, which is distinct from Saaremaa strains from northeastern Europe (A. ponticus (Caucasian wood mouse), captured in Sochi district, in the southern part of European Russia . Of these, 26 patients were found to be hantavirus antibody–positive. During the HFRS outbreak in the Lipetsk region in the winter of 2001–02, hantavirus infection of >100 patients was serologically confirmed by IFA.Small mammals were trapped in the Sochi region during the 3 summer and autumn seasons of 2000–2002 and in the Lipetsk region during the winter of 2001–02. Lung tissues of the mammals were screened for the presence of hantavirus antigens by an antigen-capture ELISA as described (A. agrarius mouse from the Lipetsk region (Aa1854/Lipetsk-02 strain [Aa/Lipetsk]) and on the 70th day in another flask with cells from an A. ponticus mouse from Sochi (Ap1584/Sochi-01 [Ap/Sochi]).Suspensions (10%) of ELISA-antigen–positive lungs were added to Vero E6 cells as described . The viral stocks, prepared from cell-culture supernatants of infected Vero E6 cells, were titrated with the chemiluminescence focus assay . The standard QIAamp viral RNA mini spin protocol was performed. Amplification and sequencing of the entire S and M segments and partial L segment sequences were performed as described for the DOBV SK/Aa isolate  that encoded a putative nucleocapsid protein (N) of 429 amino acids. The complete S segment of Lipetsk/Aa was 24 nt longer  due to a longer 3′ noncoding region. The Sochi/Ap M segment consisted of 3,616 nt that encoded a single ORF (nt 47–3448) of putative 1,133-aa glycoprotein precursor. The first putative start codon at positions 41–43, present in all other DOBV as well as HTNV M segment sequences, was missing, but the next one was located just 6 nt downstream in the same frame . The M-segment sequence of Lipetsk/Aa was 3,643 nt long ; the difference in length is again the result of insertions/deletions in the 3′ noncoding region. In addition, a partial L-segment sequence of 541 nt  was determined for the Sochi/Ap and Lipetsk/Aa strains.A. ponticus. (Krasnodar and Goryachiy Klyuch are places not far from Sochi.) As expected, the Lipetsk/Aa strain was most similar to Kurkino, another A. agrarius–associated strain from Russia  and formed a distinct lineage, which we named DOBV-Ap (panel A). Whereas DOBV-Ap and DOBV-Af share a common ancestor in the S-segment phylogenetic tree, in M and L segment analysis Sochi/Ap formed an outgroup from all other DOBV sequences and did not directly cluster with DOBV-Af sequences . Besides putative genetic reassortment processes, incomplete and unequal sequence datasets (fewer sequences for M- and L-segment datasets are available) could be the reason for these conflicting results. More sequence data are necessary to confirm these findings.In the S-segment analysis, the Sochi/Ap sequence clustered with high statistical support with the patient-associated sequence from Krasnodar, Russia (AF442623), and the  DOBV-Ap , panel Apanels A–C). As expected, it formed a well-supported monophyletic group with DOBV-Aa strains from Kurkino, Russia. In M and L segment analysis, for which the number of available sequences is rather limited, DOBV-Aa strains from Slovakia were most closely related, although the statistical support for this clustering was below the cutoff value of 70%.Lipetsk/Aa sequences unambiguously clustered within the DOBV-Aa lineage in the analyses of all 3 segments .>4-fold differences in titers (considered to be significant) were found in serum samples of only 10 patients. For 8 patients, such significant difference was not found . The availability of the 2 novel DOBV strains as cell culture isolates enabled us to use them in chemiluminescence FRNT (c-FRNT) and characterize serum from selected patients also by neutralizing antibody titers. The DOBV prototype strain  and both novel isolates were used in c-FRNT to serotype all 18 patients from the Sochi region . For 15 of these 18 patients, at least 1 serum sample showed the highest neutralizing antibody titer against the local Sochi virus. However, From patients in the Lipetsk region, 6 serum samples were characterized by c-FRNT . To veriIn addition, 2 DOBV convalescent-phase serum samples from Slovakia, previously serotyped as anti–DOBV-Af reactive .A. ponticus as a novel natural hantavirus host, was a causative agent of the human infection. Second, from an outbreak occurring in the Lipetsk region, central European Russia, >100 HRFS patients were characterized. This outbreak was found to be caused by DOBV-Aa infections. Both viruses, DOBV-Ap/Sochi and DOBV-Aa/Lipetsk, were isolated through Vero E6 cells, genetically characterized, and used for HFRS patient serotyping.DOBV circulation was found in the Sochi region, southern part of Russia. We demonstrated that a new DOBV lineage (DOBV-Ap), associated with A. ponticus animals  and 2001–2002 . During the winter of 2006–2007, this area faced a new large outbreak, which had ≈600 registered HFRS cases (authors’ unpub. data).Serotyping of neutralizing antibodies confirmed as reasonable the assumptions that the HFRS cases in the Sochi region were caused by DOBV-Ap and that the Lipetsk outbreak was caused by DOBV-Aa strains. In this respect, the differences in clinical courses of infection for the Sochi and Lipetsk patients could be then also assigned as differences in the virulence of DOBV-Ap and DOBV-Aa lineages, respectively. Overall, the clinical course of DOBV-Aa infections in Lipetsk resembles that of PUUV infections observed in Russia (authors’ unpub. data), and the DOBV-Ap infections seem more often to be moderate to severe.However, these differences should not be overestimated. Cases in the Lipetsk region occurred in an outbreak situation in a region where HFRS is a well-known disease. It is therefore possible that physicians and local authorities were much more aware of hantavirus infections and, therefore, also recognized those infections with mild clinical courses. In contrast, the sporadic cases in the Sochi region might be recognized only if the clinical course was severe. Differences in physician awareness in the 2 regions may result in a bias giving the impression that DOBV-Ap infections have a higher clinical severity. Alternatively, the higher virulence of DOBV-Ap might correspond with its close genetic relatedness with the DOBV-Af lineage, which causes rather severe disease in southeastern Europe. At the current stage of knowledge, the order of virulence of DOBV-like viruses in humans seems to be as follows: Saaremaa <DOBV-Aa <DOBV-Ap <DOBV-Af.A. flavicollis animals as well as in human patients  and the first originating from Russia and the first isolated in an outbreak region.In addition to the public health aspect, our findings add another stone into the very complex mosaic of DOBV ecology and evolution. The Sochi/Ap virus is the first cell culture isolate of novel evolutionary lineage DOBV-Ap. Apodemus species. Nevertheless, other hantaviruses are harbored by >1 (related) host species, e.g., Tula virus has been found in Microtus arvalis, M. rossiaemeridionalis, and M. agrestis (Rattus rattus and R. norvegicus (Apodemus hosts share high amino acid sequence similarity, they can be distinguished in phylogenetic analyses as distinct lineages and seem to possess different virulence in humans as well as in an animal model (A. ponticus emerging in an area south of European Russia confirms the reputation of DOBV being the most virulent of the European hantaviruses.Rather unusual for hantaviruses, DOBV has already been found in 3