The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked.We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments.Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets.Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data. DNA microarray is a popular high-throughput technology for the monitoring of thousands of gene expression levels simultaneously under different conditions . The typThe analysis performed to meet the purposes of microarray studies mentioned above usually involves clustering genes according to their pattern of expression levels in various experimental conditions. In fact, cluster analysis means grouping samples (or genes) by similarity in expression patterns. To measure the similarity in cluster analysis, correlation distance and Euclidean distance are widely used. PrincipMissing values of microarray data commonly occur during data preparation mainly due to imperfections in the various steps in DNA microarray experiments. One of the yeast microarray data sets shows that the number of genes having at least one missing value was 2419 of 6198 rows (genes) (in other words, 39 %); and 174Recently, several methods have been applied to the imputation of microarray data, including row average , singulak was 10 for time-series data and mixed type data regardless of the missing rate, and the RMS errors of the non-time series data showed similarly low values when k values were between 10 and 20. For comparison of different imputation methods, we used 10 for k which showed a minimal RMS error in every data type with different missing rates.We evaluated the efficiency of our new SKNN method and the EM-SKNN method with three other imputation methods: KNN-based imputation, the MLE method, the MI method, by applying them to three different types of microarray data sets with different missing rates. The appropriate number (k) of nearest neighbors was dependent on the data types and missing rates. The RMS errors were minimal when k-nearest neighbors to be used for imputation. The overall similarity of the entire data set can affect, on average, the similarity of all possible k-nearest neighbors. The time-series data set, which has the narrowest distribution of Euclidean distances among genes, shows the least RMS error after imputation as we can see in Figure k-neighbors than the conventional KNN method as the missing rate grows. In the conventional KNN method, the selection pool and the dimension  of the distance measurement of neighbor genes are reduced according to the increase of missing rate. In this situation, the method inevitably selects less related (or less similar) neighbors for imputation. In addition, the size of data set can limit the maximum missing rate for stable imputation. In our data sets, the size of a mixed data set is about 20% larger than other data sets, which may affect the mixed data set in terms of having stable RMS error in a relatively larger (10%) range of missing rate.The performance of the KNN-based imputation method depends on the similarity of We tested the performance of other well known non-KNN-based methods such as maximum likelihood estimation (MLE) and multiple imputations (MI) methods. These methods are well known imputation methods but there has been no report on their application in microarray data analysis. The efficiency of the MLE method was much worse than the SKNN method for all tested data sets. The RMS errors in the MLE method were 0.11 to 0.33 in time-series data, 0.30 to 0.38 in mixed data, and 0.58 to 0.69 in non-time series data. The efficiency of the MI method was generally similar to SKNN but the former is more dependent on data types. The efficiency of the MI method was better at a lower missing rate, but slightly worse at a higher missing rate for the time-series data set. The MI method was worse than SKNN in terms of overall range of missing rate of non-time series data. However, the best imputation method for mixed data set proved to be the MI method. We can conclude that the MI method is as efficient as the SKNN method for the imputation of microarray data, even though the efficiency of the MI method experienced more fluctuations than the SKNN method depending on the data type.The result is similar in a comparison of overall RMS error after imputation of a data set having unequally distributed missing entries over the columns. We show a comparison of one of the data sets (time series data set) in Table For more careful estimation of imputation efficiency, we examined the structure of data after imputation. We calculated the Pearson correlation coefficients for each column (experiment) between original data and imputed data. The larger the correlation coefficient is, the better the relationship between original complete data and imputed data is preserved in a column. Figure O(m2n2) in the conventional KNN method and O(mn log m) in the SKNN method for a matrix with m rows (genes) and n columns (experiments). This is because the sequential KNN algorithm imputes all missing values in a gene simultaneously with given nearest neighbors, while the conventional KNN method must calculate neighbors for each missing entry. The application of Expectation Maximization (EM) to the sequential KNN method marginally improved the accuracy in compensation for the increase of computational time proportional to the number of iterations. For MI methods, the execution time increased as M times of single imputation method when MI used M multiple imputation. Using the SKNN imputation method, it took 28.3 seconds on a Pentium IV 2.4 GHz computer to estimate missing values for a data set with 4489 genes, 18 experiments and a 40% missing rate. The processing time using the EM-SKNN method was proportional to the number of iterations.The SKNN algorithm improves execution time for imputation. The computational complexities are approximately The SKNN method offers better performance than the previously developed KNN method for both time series and non-time series microarray data sets and for data sets having different missing patterns. As the missing rate increased, sequential reuse of imputed data did not propagate errors of imputation as in the conventional KNN method. It showed the best improvement of accuracy for the data set with a high missing rate.Notably, the SKNN method is also robust on the imputation of a data set with unequally distributed missing entries. A real microarray data set usually has non-random distribution of missing data. Furthermore, some systematic errors during the experiment can generate an abnormal increase in distribution of missing entries for the corresponding column of microarray data set. In this type of data, the SKNN method, which is especially efficient on the data set having heavy missing entries, can exert relatively more accurate imputation than other imputation methods as shown in our model data set. The MI method has not been well introduced in the field of microarray analysis, although it is a well known imputation method in other fields . In compThe computational complexity is reduced in the SKNN method for the dimension of both the number of genes and the experiments compared with the simple KNN method. Particularly, computation time can be saved substantially for microarray data with a large number of experiments. The SKNN method works efficiently in a wider range of missing rate with high speed. We want to emphasize that our results showed that the method using estimated values achieved even better accuracy than the method using only observed values in the case of the KNN-based imputation method. We suppose that this result could be applicable to other cluster-based analysis. It would be hardly acceptable for the experimentalist to use imputed data for further analysis. However, analysis could become more errorneous without imputation due to loss of information caused by missing values. The use of imputed data should definitely depend on the type of later process. If the next process is a cluster-based analysis, the genes with imputed values could be efficiently used, as we had good results for KNN-based imputation with the reuse of imputed values. For future works, it may be possible to integrate the imputation and gene clustering of microarray data for classification of genes with proper evaluation steps. This may offer more and better information sources of microarray data for the final decision of gene classification. All the procedures used in this paper are done by R-code and C++ and the programs are available upon request.The SKNN method is an especially efficient imputation method on data having high missing entries. It can be practically useful in saving data of some accidental microarray experiments having high missing entries. Our results also suggest that the imputed values generated by the SKNN method can be used reliably for further cluster-based analysis of microarray data.We developed and implemented SKNN and EM-SKNN methods for the imputation of microarray data, and we compared their accuracies with the previously developed KNN imputation method. Data sets used in this work were selected from publically available microarray data. The data sets were from a study of gene expression in yeast Saccharomyces cerevisiae cell-cycle regulation , calcineTo assess the relative efficiency of the imputation methods, we implemented known KNN imputation method developed by Troyanskaya et al. 2001)[01[20]. TThe matrix form of microarray data is composed of rows and columns that represent genes and experimental conditions respectively. Before any further process, the rows of original data sets containing missing values are removed to make complete matrices and test data sets for imputation methods were generated by random deletion of values in the complete matrices. The sizes of test data sets were 4489×18 for time series data set, 4380×24 for mixed data set, and 3779×22 for non-time series data set. The missing rates generated randomly in the test data sets were between 1% and 70% (1, 3, 5, 10, 20, 30, 40, 50, 60, 70). The occurrence of missing values can depend on the specific experiment in real miroarray data. Considering this case, we also generated test data set having missing values non-randomly along the columns which represent each experiment. The overall missing rate of data set was fixed to one of the value ranging from 10% to 60%. In a data set, the missing rates for two experiments (columns) were set to 80% and remaining columns have randomly generated missing entries.k-nearest neighbor genes are taken from the whole matrix of the test data set except any genes that has missing value at the same position with the gene to be imputed. Euclidean distance is used as the metric to estimate the similarity of neighboring genes. To compare the similarity by this metric, each gene should have the same dimension and missing positions of values inside. Missing value is imputed with weighted average of the corresponding column of the k-nearest genes.In KNN method, ith gene is calculated as equation (1), where k is the number of selected genes and Di is the distance between ith gene and a gene to be imputed.The weight of k-values for each data set and each method with different missing rates. Different k-values ranging from 1 to 500 were tested and we selected the k-values with the least error between imputed values and real values.For the performance comparison of the imputation methods, we selected appropriate SKNN method that we suggest in this report is distinguished in two main points from previously implemented KNN method described above. In SKNN method, genes are ordered by its missing rate and the imputation was executed sequentially from the gene that had least missing rate. In addition, these sequentially imputed genes are used for the later imputation of the other genes. The test data set was separated into incomplete and complete set that has or has not missing values respectively. The genes in incomplete set were imputed by the order of missing rate. Missing value was filled by the weighted mean value of corresponding column of the nearest neighbor genes in complete set. Once all missing values in a gene are imputed, the imputed gene was moved into the complete set and used for the imputation of the rest of genes in incomplete set. In this process, all missing values in one gene can be imputed simultaneously from the selected neighbor genes in complete set. This reduced execution time from previously developed KNN method that should select nearest neighbors for each imputation.k-nearest neighbors for the estimation of missing values. EM-style method executes this process repeatedly until the differences between newly updated values and previous values converge. Because all the imputed values were converged within less than 10 iterations, we did 10 iterations for the comparison of accuracy with the other methods.EM-style imputation algorithm was originally suggested by Rich Caruana (2001). EM-stylIn MLE method, data set with missing values are centered, scaled, and sorted by the patterns of missing through the preliminary manipulations. Missing entries in a data matrix are estimated under the multivariate normal model with user-supplied parameters and observed data (non-missing entries in the data set). The parameters are estimated using imputed and observed data. A vector of parameters representing the MLE, means and variance-covariance matrix are returned by using EM algorithm. Missing values are estimated through this iterative process until estimated parameters converge. We executed MLE method to estimate missing values by using 'norm' library of R  based onThe whole MI procedure is made of three steps. They are imputation, analysis, and pooling processes. We applied only the first step, imputation process, of the three steps because our interest is to fill in missing values with estimated values. We used Predictive Mean Matching (PMM) as a method for missing values estimation. It uses a linear regression on observed variables to impute missing values. The estimated coefficients provide the mean vector and the variance matrix to generate multiple sets of coefficients that leads M imputed sets. M plausible values for missing observations were created by above MI algorithm and then the mean of M imputed values was filled in the missing value. We implemented Multiple imputation method using 'mice' library of R  based onThe accuracy of imputation method was evaluated by calculating error between actual values and imputed values after missing values were estimated. The metric used to assess the accuracy of estimation was RMS error. RMS error was calculated as follows,Ri is the real value, Ii is the imputed value, and N is the number of missing values.where Besides RMS error, Pearson correlation coefficients were used to evaluate the sequential KNN method. Correlation coefficients were calculated between imputed data and complete data for each column. From this evaluation, we could find how the data structure of each column was preserved after imputation with different imputation methods.KNN: K Nearest Neighbor; SKNN: Sequential KNN; EM: Expectation Maximization; RMS: Root Mean Squared; PCA: Principal Component Analysis; MLE: Maximum Likelihood Estimation; MI: Multiple ImputationKK participated in the design of algorithms, performed statistical analysis and drafted the manuscript. BK participated in the design of algorithms and carried out C++ programming. GY conceived of the study, participated in its design and coordination, and finalized manuscript. All authors read and approved the final manuscript.


Cancer invasion and metastasis have been likened to wound healing gone awry. Despite parallels in cellular behavior between cancer progression and wound healing, the molecular relationships between these two processes and their prognostic implications are unclear. In this study, based on gene expression profiles of fibroblasts from ten anatomic sites, we identify a stereotyped gene expression program in response to serum exposure that appears to reflect the multifaceted role of fibroblasts in wound healing. The genes comprising this fibroblast common serum response are coordinately regulated in many human tumors, allowing us to identify tumors with gene expression signatures suggestive of active wounds. Genes induced in the fibroblast serum-response program are expressed in tumors by the tumor cells themselves, by tumor-associated fibroblasts, or both. The molecular features that define this wound-like phenotype are evident at an early clinical stage, persist during treatment, and predict increased risk of metastasis and death in breast, lung, and gastric carcinomas. Thus, the transcriptional signature of the response of fibroblasts to serum provides a possible link between cancer progression and wound healing, as well as a powerful predictor of the clinical course in several common carcinomas. The transcriptional signature of the response of fibroblasts to serum provides a possible link between cancer progression and wound healing, as well as a predictor of the clinical course in several common cancers Since the classic observations of the many histologic similarities between the tumor microenvironment and normal wound healing, it has been proposed that tumor stroma is “normal wound healing gone awry” . During The complete sequence of the human genome and the advent of microarray technology have spurred a revolution in the classification and diagnosis of human cancers . By detaFibroblasts are ubiquitous mesenchymal cells in the stroma of all epithelial organs and play important roles in organ development, wound healing, inflammation, and fibrosis. Fibroblasts from each anatomic site of the body are differentiated in a site-specific fashion and thus may play a key role in establishing and maintaining positional identity in tissues and organs . Tumor-ahttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene). This common genomic response to serum includes induction of genes that represent entry into and progression through the cell cycle (e.g., E2F1, FOXM1, PTTG1), induction of cell motility (e.g., CORO1C, FLNC), extracellular matrix remodeling (LOXL2, PLOD2, PLAUR), cell–cell signaling (SDFR1, ESDN, MIF), and acquisition of a myofibroblast phenotype (e.g., TAGLN, TPM2, MYL6). Analysis of the public Gene Ontology (GO) annotation of the fibroblast serum response genes confirmed a significant enrichment of genes involved in cell proliferation, blood coagulation, complement activation, secretory protein synthesis, angiogenesis, and proteolysis, reflecting the diverse roles that fibroblasts may play during wound healing  . DespiteBecause serum  is encountered in vivo only at sites of tissue injury or remodeling and induces in fibroblasts a gene expression response suggestive of wound healing, we reasoned that expression of fibroblast CSR genes in tumors might gauge the extent to which the tumor microenvironment recapitulates normal wound healing. We examined the expression of genes comprising the fibroblast CSR in publicly available microarray data from a variety of human cancers and their corresponding normal tissues. To facilitate visualization and analysis, we organized the gene expression patterns and samples by hierarchical clustering . RemarkaIn each of the tumor types examined, the expression pattern of the fibroblast CSR genes in normal tissues closely approximated that seen in quiescent fibroblasts cultured in the absence of serum . In prosp = 0.013 and 0.041, respectively) (p = 0.13, 0.79, 0.05, respectively). A “basal-like” subtype of breast cancer, characterized by molecular similarities of the tumor cells to basal epithelial cells of the normal mammary duct and associated with a particularly unfavorable prognosis (p = 0.0075, Fisher's exact test). Thus, the presence or absence of the wound-like phenotype may be linked to intrinsic features of the tumor cells.To investigate the stability and consistency of the serum response signature in individual tumors and to explore its clinical implications, we examined CSR gene expression in a group of locally advanced breast cancers with extensive clinical and molecular data . As showctively) B. Using ctively) . A gene ctively) . Tumor srognosis , was sigp = 0.75; Worksheet 1 in p = 0.08; Worksheet 1 in We considered the possibility that the observed phenomenon may be simply a reflection of the number of fibroblasts in tumor samples. Perhaps tumors that are infiltrative or otherwise worrisome clinically would demand a wide margin of excision that would include more fibroblasts in the resultant samples. However, classification of breast cancers using the top 1% most highly expressed fibroblast genes (which include a number of extracellular matrix genes and have been previous observed as the “stroma signature” ) showed p = 0.00046) (p = 0.009); the serum-induced gene expression signature was associated with a 3.3-fold relative risk of breast cancer metastasis within 5 y of diagnosis. In the two breast cancer datasets examined, approximately 50% of the CSR genes demonstrated significant differences in expression between the activated and quiescent groups of samples, but permutation and 10-fold balanced leave-one-out analyses revealed that the correct classification can be accomplished using as few as 6% of CSR genes (Worksheets 10–12 in p = 0.021) (p = 0.0014) (p = 0.02)  A. Multiv= 0.021) B. These = 0.021) , tumors  0.0014) C. A sign 0.0014) , tumors  = 0.02) D. These For many other cancers, simple stratification based on expression of genes in the fibroblast CSR gene set is unlikely to be predictive of outcome. The dramatic differences in cellular composition and architecture among the tissues in which cancers can arise may influence the role that a wound-healing response can play in their progression. For example, lymphoma cells proliferate in the specialized microenvironment of lymph nodes and bone marrow, and the “stromal” cells in the central nervous system, predominantly astrocytes and microglia, are markedly different from those associated with most epithelial tissues. Indeed, in our initial analysis, the pattern of expression of the fibroblast CSR genes failed to stratify the outcomes in diffuse large B-cell lymphoma , medulloPLAUR, also known as urokinase-type plasminogen activator receptor, is a well-characterized receptor for matrix-degrading proteases that has been implicated in tumor cell invasion . In contrast, all five genes were induced in a significant fraction of invasive ductal carcinomas of the breast. As previously reported . ESDN protein was detected exclusively in breast carcinoma cells (69 out of 112, 62% positive). In contrast, LOXL2 mRNA was abundant in peritumoral fibroblasts around invasive carcinomas (45 out of 106, 42% positive). LOXL2 protein has been previously reported to be expressed in normal mammary ducts and increased in invasive breast carcinoma cells  and was a remarkably powerful predictor of metastasis and death in several different carcinomas.PLAUR, LOXL2, and MIF, have been previously shown to increase cancer invasiveness or angiogenesis in animal xenograft models; each of these three genes has also been shown to play an important role in wound healing , while The signals and regulatory systems that normally initiate, sustain, and eventually shut down the physiological response to a wound remain to be identified and understood. Identification of the molecular control mechanisms in this pathway may pave the way to new cancer therapies or chemopreventative agents. For example, cyclooxygenase 2 is strongly induced in the response of fibroblasts to serum , and plaHuman primary fibroblasts from ten anatomic sites were cultured in 0.1% versus 10% FBS, as previously described . For theConstruction of human cDNA microarrays containing approximately 43,000 elements, representing approximately 36,000 different genes, and array hybridizations were as previously described . mRNA wahttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene), was termed the common fibroblast serum response gene set. To identify the subset of these 677 genes whose variation in expression was directly related to cell cycle progression, we compared this set of genes to a published set of genes periodically expressed during the HeLa cell cycle . We used the Unigene unique identifier (build 158, release date January18, 2003) to match genes represented in different microarray platforms. For cDNA microarrays, genes with fluorescent hybridization signals at least 1.5-fold greater than the local background fluorescent signal in the reference channel (Cy3) were considered adequately measured and were selected for further analyses. For Affymetrix data, signal intensity values were first transformed into ratios, using for each gene the mean values of the normalized fluorescence signals across all the samples analyzed as the denominators , corresponding to the last 388 nucleotides of the 3′ end of the coding sequence and 39 nucleotides of the 3′ untranslated region. Sense and antisense riboprobes for LOXL2 were made from nucleotides 41–441 of IMAGE clone 882506 (ATCC #1139012), corresponding to the 3′ end of the coding sequence. In situ hybridization (ISH) results were considered to have appropriate specificity when we observed a strong, consistent pattern of hybridization of the antisense probe and little or no hybridization of the corresponding sense probe.Digoxigenin-labeled sense and antisense riboprobes for cription . Sense aImmunohistochemical (IHC) staining was performed using Dako (Glostrup, Denmark) Envision Plus following the manufacturer's instructions. Anti-PLAUR antibody against whole purified human uPA–receptor protein  was used at 1:200 dilution. Affinity-purified polyclonal antibody to PLOD2 was produced by immunizing rabbits with peptides EFDTVDLSAVDVHPN, coupled to keyhole limpet hemocyanin (KLH) ; affinity-purified antiserum was used for IHC at 1:25,000 dilution. Similarly, affinity-purified polyclonal antibody to ESDN was produced by immunizing rabbits with peptide DHTGQENSWKPKKARLKK coupled to KLH (Applied Genomics, Inc.) and used for IHC at 1:12,500 dilution. High-density tissue microarrays containing tumor samples were constructed as described in http://microarray-pubs.stanford.edu/wound/. Raw datasets and all supporting data are also available at http://microarray-pubs.stanford.edu/wound/.Dataset S1Provides a description of microarray datasets, cross-platform mapping and data normalization, classification of cancers by fibroblast CSR genes and correlated clinical outcomes (Worksheets 1–8 in (120 KB DOC).Click here for additional data file.Dataset S2Excel Worksheets of clinical and microarray data, as described in (736 KB XLS).Click here for additional data file.http://www.ncbi.nlm.nih.gov/LocusLink/) accession numbers for the genes discussed in this paper are CORO1C (Locus Link ID 23603), E2F1 (Locus Link ID 1869), ESDN (Locus Link ID 131566), FLNC (Locus Link ID 2318), FOXM1 (Locus Link ID 2305), LOXL2 (Locus Link ID 4017), MIF (Locus Link ID 4282), MYL6 (Locus Link ID 4637), PLAUR (Locus Link ID 5329), PLOD2 (Locus Link ID 5352), PTTG1 (Locus Link ID 9232), SDFR1 (Locus Link ID 27020), TAGLN (Locus Link ID 6876), and TPM2 (Locus Link ID 7169).The Locus Link (http://www.geneontology.org/) terms that appear in Dataset S1 are angiogensis (GO:0001525), blood coagulation (GO:0007596), complement activation (GO:0006956), immune response (GO:0006955), N-linked glycosylation (GO:0006487), protein translation (GO:0006445), and proteolysis and peptidolysis (GO:0006508).The accession numbers of the Gene Ontology (GO) (


APOBEC3G and Vif genes. This kind of conflict leads to rapid fixation of mutations that alter amino acids at the protein–protein interface, referred to as positive selection. We show that the APOBEC3G gene has been subject to strong positive selection throughout the history of primate evolution. Unexpectedly, this selection appears more ancient than, and is likely only partially caused by, modern lentiviruses. Furthermore, five additional APOBEC genes in the human genome appear to be engaged in similar genetic conflicts, displaying some of the highest signals for positive selection in the human genome. Despite being only recently discovered, editing of RNA and DNA may thus represent an ancient form of host defense in primate genomes.Host genomes have adopted several strategies to curb the proliferation of transposable elements and viruses. A recently discovered novel primate defense against retroviral infection involves a single-stranded DNA-editing enzyme, APOBEC3G, that causes hypermutation of HIV. The HIV-encoded virion infectivity factor (Vif) protein targets APOBEC3G for destruction, setting up a genetic conflict between the  APOBEC3G, a gene that edits retroviral DNA like HIV, is under positive selection that predates the origin of HIV, implying that RNA/DNA editing represents an ancient form of intragenomic host defense Neurospora crassa being a notable exception [Mobile genetic elements have been in conflict with host genomes for over a billion years. Our own genomes reveal the remarkable effects of retrotransposition, as about 45% of our genomic DNA results directly from this process . This pexception ) by evolapolipoprotein B–editing catalytic polypeptide 3G (APOBEC3G)  , whose pPOBEC3G) . APOBEC3POBEC3G)  and is pPOBEC3G) . How thiPOBEC3G) . Before APOBEC3G belongs to a family of nine primate genes that catalyze the deamination of cytosine to uracil in DNA and/or RNA , leading to a truncated form of the APOB lipid-transport protein , whose product counteracts the antiviral activity of APOBEC3G. Vif interacts with APOBEC3G and targets it for ubiquitination and proteasome-dependent degradation, thus preventing its incorporation into nascent virions  from ten primate species, including four hominids (other than human), four Old World monkeys (OWMs), and two New World monkeys (NWMs)  is congruent to the widely accepted primate phylogeny (To determine what selective pressures have shaped s (NWMs) . A phylohylogeny , indicatAPOBEC3G, however, we found that a majority of branches of the phylogeny  show evidence of positive selection . Maximum likelihood analysis using the PAML  suite of programs also finds strong evidence that the full-length APOBEC3G gene has been subject to positive selection (p < 10–13). Numbers in parenthesis in The hallmark of positive selection is an excess of non-synonymous substitutions  relative to synonymous substitutions (which retain the encoded amino acid). Because non-synonymous changes are more likely to be deleterious, they are typically culled out by selection   arguing that, although the positive selection of Vif may be explained in large part by that of APOBEC3G, the reverse is certainly not the case.The average Ks in  genomes . Thus, wd humans . HIV/SIVd humans . The preAPOBEC3G that were subject to positive selection, because this might suggest the driving evolutionary force. For instance, the positive selection in the major histocompatibility complex proteins is confined to only small segments of the protein that constitute the antigen-recognition site  comparisons of Ka/Ks ratios between two hominids, two OWMs, and two NWMs suggest that the same domain of volution A–3C. In volution A and 3C,volution B. When telection . This hiAPOBEC3G dataset (tree length = 1.59), PAML analyses were found to be highly accurate in identifying residues subject to positive selection  in addition to APOBEC3G. There is no expression evidence for APOBEC3E, and it is unclear whether it occurs as a stand-alone gene, but its ω ratio of 5.6 is among the highest seen for any human–chimp comparison and argues strongly that it is a functional gene and an active participant in some form of genetic conflict. Whole-gene analyses are notoriously poor at identifying specific domains of positive selection, especially when the rest of the gene is subject to purifying selection. We therefore performed a sliding window Ka/Ks test (APOBEC3F (amino acids [aa] 117–250).The discovery that inst HIV . An unbi/Ks test , which a(APOBEC1 and APOBEC3C), and there was insufficient chimpanzee sequence available in one case (APOBEC2). To gain further information about these genes, we sequenced them from either orangutan or gorilla . Although we might have expected APOBEC1 to be evolving only under purifying selection based on its important editing of APOB mRNA, our analysis suggests that APOBEC1 has also participated in some kind of genetic conflict involving its first active site, and suggests that the rapid evolution of APOBEC1 seen previously in mouse–rat comparisons may also be due to positive selection ((APOBEC2) and positive (APOBEC3E) selection. These findings greatly extend the current understanding of the APOBEC family, and implicate a majority of APOBEC genes as participants in host defense. They also raise the possibility of other editing systems being involved in genome defense; for instance, hepatitis delta virus is known to be edited by adenosine deaminase , especially in populations with a high incidence of HIV infection, since different alleles of APOBEC3G may have different susceptibility to various viral strains. The action of APOBEC3G on viral evolution could also be complex because, although it is ineffective as an antiviral mechanism in the presence of Vif, its action could also result in an increased likelihood of adaptive changes and viral diversity in the host due to the introduced G-to-A hypermutations. Polymorphisms in APOBEC3G may thus have direct impact on the progression time from initial HIV infection to AIDS, and should be investigated as such.The antiviral activity of APOBEC3G and the excess of non-synonymous changes specific to human EC3G see  implicatAPOBEC3G does not identify the biological step that exerts this selective pressure. Formally, this step could be the yet-undefined mechanism by which APOBEC3G is packaged into virions, the interaction of APOBEC3G with Vif-like destruction proteins encoded by other viruses, and/or its interaction with the proteasome machinery. APOBEC3G may indeed interact with other viruses, because G-to-A hypermutation—a hallmark of the single-stranded DNA–editing activity of APOBEC3G-like enzymes—has been observed in some nonlentivirus viruses  (NAO3448A), Pan paniscus (bonobo) (NGO5253), Gorilla gorilla (gorilla) (NG05251B), Pongo pygmaeus (orangutan) (NAO4272), Macaca nigra (Celebes crested macaque) (NG07101), Macaca fascicularis (crab-eating macaque) (NA03446), Erythrocebus patas (patas monkey) (NG06254), Lagothrix lagotricha (common woolly monkey) (NG05356), and Saguinus labiatus (red-chested mustached tamarin) (NG05308). Papio anubis (baboon) DNA was a personal gift from Dr. Trent Colbert. The APOBEC3G, APOBEC1, APOBEC2, and APOBEC3C genes were amplified exon-by-exon from genomic DNA with PCR Supermix High Fidelity , and PCR products were sequenced directly. PCR and sequencing primers are shown in APOBEC3G sequence was obtained from the Ensembl database of the human genome project (ENSG00000100289). The Chlorocebus aethiops (African green monkey) APOBEC3G sequence (GenBank AY331714.1) is missing the last 21 bp of the coding sequence because it was sequenced from mRNA (APOBEC3G from NWMs (woolly monkey and tamarin) where the “AG” directly 5′ of the eighth coding exon is missing. Sequences have been deposited in GenBank under the following accession numbers: APOBEC3G (AY622514–AY622593), APOBEC3C (AY622594–AY622597), APOBEC2 (AY622598–AY622599), APOBEC1 (AY622600–AY622604).Genomic DNA was obtained from Coriell (Camden, New Jersey, United States). Species and Coriell repository numbers are: rom mRNA  in a preAPOBEC1 (ENSG00000111701), APOBEC2 (ENSG00000124701), AID (ENSG00000111732), APOBEC3A (ENSG00000128383), APOBEC3B (NM_004900.3), APOBEC3C (ENSG00000179750), APOBEC3DE (ENSG00000179007), and APOBEC3F (ENSG00000128394). Transcripts for both APOBEC3D (NM_152426) and APOBEC3DE (BC017022.1) exist in the database. Chimp sequences were obtained from orthology to human genes assigned on the University of California at Santa Cruz Genome Bioinformatics Website (http://www.genome.ucsc.edu). All orthologous chimp exons were checked for AG and GT flanking the 5′ and 3′ boundaries, respectively, an indication that human splice sites are conserved. The mouse APOBEC3 protein sequence can be found in GenBank (NP_084531.1).Human sequences were obtained from the Ensembl or GenBank databases: DNA sequences were aligned using Clustal_X , with hap < 10−13) with a significant fraction of the sites (more than 30%) predicted to evolve at average ω ratios greater than 3.5  has undergone adaptive evolution in at least three distinct periods. Despite being only 29 codons long, this domain has accumulated ten non-synonymous changes and only two synonymous changes in the African green monkey since it and the patas monkey last shared a common ancestor. Similarly, the orangutan has retained eight non-synonymous changes and no synonymous changes since it split from the rest of the hominids. Finally, a ratio of 6:0 R:S changes is seen in the split between the NWMs and the common ancestor of OWMs and hominids. Surprisingly, even the two active site structures of APOBEC3G (B and E) show evidence for adaptive evolution , including along the branch leading to the common ancestor of all hominids. The first pseudoactive domain (D) acquired ten non-synonymous and no synonymous changes since the hominids split from the OWMs.(343 KB PDF).Click here for additional data file.Figure S2p <10−13) (C) with a significant fraction of the sites (more than 30%) predicted to evolve at average ω ratios greater than 3.5. These analyses also identified certain amino acid residues with high posterior probabilities (greater than 0.95) of having evolved under positive selection (A and B).Maximum likelihood analysis was performed on APOBEC3G sequences using the PAML software package. To detect selection, the multiple alignments were fitted to either the F3×4 (A) or F61 (B) models of codon frequencies. We compared the log-likelihood ratios of the data using comparisons of different NSsites models: model 1  versus model 2  and model 7  versus model 8 . In both cases, permitting sites to evolve under positive selection gave a much better fit to the data ((60KB PDF).Click here for additional data file.Figure S3The individual domains of the APOBEC3G protein are demarcated. Catalytically important residues are highlighted in bold, and those residues identified by PAML analysis as being under positive selection are indicated with gray shading. Blue shading highlights the single amino acid residue that can switch specificity of Vif interaction with APOBEC3G. AGM, African green monkey.(46 KB PDF).Click here for additional data file.Table S1(39 KB PDF).Click here for additional data file.http://www.ncbi.nlm.nih.gov/) and Ensembl (http://www.ensembl.org/) accession numbers for the genes and gene products discussed in this paper are as follows. GenBank: APOBEC1 (AY622600–AY622604), APOBEC2 (AY622598–AY622599), mouse APOBEC3 (NP_084531.1), human APOBEC3B (NM_004900.3), APOBEC3C (AY622594–AY622597), APOBEC3D (NM_152426), APOBEC3DE (BC017022.1), APOBEC3G (AY622514–AY622593), and African green monkey APOBEC3G (AY331714.1). Ensembl : APOBEC1 (ENSG00000111701), APOBEC2 (ENSG00000124701), AID (ENSG00000111732), APOBEC3A (ENSG00000128383), APOBEC3C (ENSG00000179750), APOBEC3DE (ENSG00000179007), APOBEC3F (ENSG00000128394), and APOBEC3G (ENSG00000100289).The GenBank (http://www.coriell.undmj.edu/) repository numbers for primate genomic DNAs are Pan troglodytes (chimpanzee) (NAO3448A), Pan paniscus (bonobo) (NGO5253), Gorilla gorilla (gorilla) (NG05251B), Pongo pygmaeus (orangutan) (NAO4272), Macaca nigra (Celebes crested macaque) (NG07101), Macaca fascicularis (long-tailed macaque) (NA03446), Erythrocebus patas (patas monkey) (NG06254), Lagothrix lagotricha (common woolly monkey) (NG05356), and Saguinus labiatus (red-chested mustached tamarin) (NG05308).Coriell (


Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials.We used a sample of cluster randomised trials from a recent review and from a systematic review of hip protectors. We compared the mean age of participants between intervention groups in a sample of 'good' cluster trials with a sample of potentially biased trials. We also compared the effect sizes, in a funnel plot, between hip protector trials that used individual randomisation compared with those that used cluster randomisation.There is a tendency for cluster trials, with evidence methodological biases, to also show an age imbalance between treatment groups. In a funnel plot we show that all cluster trials show a large positive effect of hip protectors whilst individually randomised trials show a range of positive and negative effects, suggesting that cluster trials may be producing a biased estimate of effect.Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants. The randomised controlled trial (RCT) has a number of important features that make it the 'gold-standard' evaluation method. One of the most important aspects of random allocation is that it eliminates selection bias. Randomisation ensures that the two or more groups formed are similar, except for chance differences, in all aspects. Nevertheless unless trials are undertaken in a rigorous manner biases can be introduced that negate the effect of random allocation. Indeed, a poorly conducted RCT can be worse than a good observational study as the latter is interpreted in the light of possible confounding whereas the results of an RCT might be uncritically accepted.Random allocation can take place either at the level of the individual level or at a higher group or cluster level. In a cluster randomised trial groups of people are allocated to receive an intervention or not. In some areas of evaluation (e.g., education) the cluster is the natural method of allocation. For example, a trial among school children may well randomise by class or by school rather than by individual child. Allocation by cluster may be preferable for a number of reasons. There may be practical reasons: for instance, teaching a novel curriculum will be easier to use existing classes than form new ones through randomisation. There may be contamination issues. Individuals allocated to a control treatment may inadvertently receive some aspects of the intervention if they are in proximity to the treated group.Allocation by cluster has some important statistical issues that have been addressed 65 years ago in the educational trial literature  and subsRandomisation should eliminate selection bias. Selection bias can be reintroduced within any trial if there is high loss to follow-up or failure to use intention to treat analysis. In cluster trials selection bias can also be introduced through participant recruitment. Because cluster trials often recruit their participants after the clusters have been randomly allocated this can lead to selection effects,4. ThereIf the person recruiting participants has both knowledge of the clinical characteristics of the participants and of the allocation schedule biased recruitment can occur. Subversion, within individually randomised trials, can occur by recruiting participants with poor prognostic characteristics so that they are more likely to enter the 'unfavoured' group ,6. EvideCluster randomised trials often do not, or cannot, conceal treatment allocation. For example, a trial was undertaken to reduce violence among children randomised by school . After aRecruitment of participants with different clinical characteristics is not necessarily a sign of subversion it could be simply a consequence of the cluster level intervention. For example, in an evaluation of an educational package for the treatment of back pain primary care physicians were trained in 'evidence based' management of back pain . This trAs well as having more potential, than individually randomised trials, for the introduction of selection bias. Cluster randomised trials also may be at more risk of dilution bias. Because consent for treatment is often not obtained until after randomisation more participants, than in an individually randomised study, may refuse treatment and this will consequently dilute any treatment effects. For example, Kendrick and colleagues in a cluster randomised trial to prevent accidental injuries among young children found that only 75% of the group allocated to the experimental group actually received the intervention .Whatever the underlying reasons for differences in recruitment the consequences are potentially the same: selection bias has been introduced. Figure A recent review identified a sample of 36 cluster randomised trials from three major general medical journals, between 1997 and 2002 . This reOn the other hand a review of 152 cluster trials undertaken in primary care found that only 8 (5%) were found were the authors reported differential recruitment . Howeveret al considered there was evidence for differential recruitment. Eight out of the 14 trials had been regarded as potentially biased. In Figure Although Puffer and colleagues noted that some trials had significant differences in recruitment and retention rates between groups they did not investigate whether or not this had an impact on important treatment covariates. To assess whether observed differences in recruitment could have had an effect on important predictors of outcome we examined the age differences between treatment groups. We chose age for two reasons: first, it is a commonly reported baseline characteristic and, second, is the most likely common confounder across different disease groups. Nevertheless, we acknowledge that biased recruitment may not manifest itself in terms of age differences . From thpotentially biased group. The mean age difference was greater than 10% of their standard deviation in 3 out of the 8 potentially biased trials. The pooled standardised mean difference in the biased group was also twice as large as that in the non-biased group. A test for the difference using a meta-regression resulted in a non-significant p-value of 0.15; therefore, the difference observed in this instance was not conclusive. Age imbalances for any single trial could be due to chance as it is more difficult to achieve balance in cluster randomised trials compared with individually randomised trials due to the smaller number of allocated units. On the other hand, cluster trials, like individually randomised trials should be balanced across all cluster trials if there was no bias present.As can be seen in Figure In Figure Cluster randomised trials often answer different questions to individually randomised trials or cannot use individual allocation. Therefore, it is difficult to make a direct comparison between individual and cluster randomised trials in terms of the likely differences in effect sizes within the same subject area. However, within the area of hip protection for fracture prevention there are trials using both individual and cluster allocation. The most recent Cochrane review of hip protectors has identified 13 RCTs of hip protectors with hip fracture outcomes . In addiIn figure There is a tendency for the cluster trials to be largely undertaken among residents of nursing homes compared with individually randomised trials – although one of the largest individually randomised trial was in a nursing home setting. It is possible that compliance might have been better in a nursing home setting and this could account for the difference in effect. However, the compliance rates were not that different from those trials using individually randomisation.The second reason that hip protection might work in a clustered design might be that the intervention is delivered as a 'package' of care and their use alerts the clinical staff responsible (e.g., nursing home staff) to the dangers of falls and this encourages other anti-fracture interventions.It is possible that in the individually randomised trials the control group could have been 'contaminated' by accessing hip protection by, for example, buying the product themselves. In a large individually randomised trial we undertook of hip protectors  some parAn alternative explanation of the difference was poor implementation of the cluster trial methodology: including selective recruitment, differential loss to follow-up and failure to use intention to treat analysis. For example, the largest cluster randomised trial had a 30% difference in the population that were included in the trial after random allocation .In order for cluster randomised trials to provide unbiased evidence for treatments we must design out any sources of recruitment bias. In this section we will consider design suggestions that should minimise this threat.perceived threat of contamination between the treatment groups. Although in many instances this threat is real in some cases there may be little contamination. Indeed, even if there are quite high contamination rates (e.g., 20%) it may still be more efficient in sample size terms to randomise more patients in an individual trial and accept a diluted effect size [Often cluster randomisation is used to overcome the ect size . TherefoIn some instances it may be possible to identify participants before cluster allocation. For example, if we consider a school based evaluation of a health promotion curriculum. Children within schools or intact classes can be identified before the cluster allocation. Children and their parents can be asked to participate in the study and are presented with the alternatives under consideration. Once consent has been obtained to take part in the study then the schools or classes are randomised to the different curricula.Evaluation of an intervention for incidence disease cases means prior identification is not possible. For example, consider a trial of educating primary care physicians for the treatment of acute shoulder pain. Because the condition has an incident nature it is necessary to recruit participants in a prospective manner. Should the primary care physician undertake this then selection bias is likely to ensue. Therefore, to reduce this possibility an 'independent' person needs to recruit participants. Consider a recent example of such an approach. In a trial of educating GPs for the identification and treatment of depression in primary care trial participants were recruited by practice receptionists. Because the receptionists from both intervention and control practices had been exposed to the same amount of trial training then the potential for selection bias is reduced, although never eliminated .The use of cluster randomised trials has significantly increased in medical research in recent years . DespiteCluster randomised trials are potentially more susceptible to some forms of bias than individually randomised trials. Biased recruitment can be a problem in some cluster randomised trials. One symptom of biased recruitment is differential recruitment rates. However, one trial noted significant selection bias even when there were similar recruitment rates . TherefoIf there are important confounding variables, stratification, matching or regression models for clustered data are required. Studies with evidence of biased recruitment might try methods of analysis that allow for observed confounding. For example, if there were imbalances in patient or cluster level covariates between the randomised groups multi-level or hierarchical models explicitly model the treatment effect adjusting for the confounding, provided that there is a fairly large number of clusters. However, even the most sophisticated statistical analysis cannot adjust for the unmeasured or unknown confounder, which is one of the main reasons we undertake random allocation. Therefore, it is crucial that we avoid the introduction of bias into our cluster designs.Future cluster randomised trials should endeavour to either identify participants before randomisation or use an independent person, preferably blind to allocation, to recruit participants. Furthermore, cluster randomised trials ought to be undertaken such that loss to follow-up is similar between groups and intention to treat is always used.The author(s) declare that they have no competing interests.SH undertook the statistical analysis and wrote sections about statistical methods and implications. SP wrote the first draft and contributed to the original data collection. DT had the original idea of the paper and undertook revisions to the original draft. SW contributed to the original review and collected additional data for the paper. All authors contributed to commenting on drafts of the manuscript.The pre-publication history for this paper can be accessed here:


The heat shock proteins (Hsps) are induced by stresses such as allergic factors and inflammatory responses in bronchi epithelial cells and therefore may be detectable in patients with asthma. However, the etiologic link between anti-Hsps and asthma (its severity and related inflammatory responses such as interleukin-4 and immunoglobulin E) has not been established. We determined whether antibodies against Hsp60 and Hsp70 were present in patients with asthma and evaluated their associations with risk and severity of asthma.We determined the levels of anti-Hsp60 and anti-Hsp70 by immunoblot and their associations with risk and symptom severity of asthma in 95 patients with asthma and 99 matched non-symptomatic controls using multivariate logistic regression analysis.Compared to the controls, asthma patients were more likely to have detectable anti-Hsp60 (17.2% vs 5.1%) and anti-Hsp70 (33.7% vs 8.1%) (p ≤ 0.001). In particular, the presence of anti-Hsp70 was associated with a greater than 2 fold risk for asthma . Furthermore, both anti-Hsp60 and anti-Hsp70 levels were positively correlated with symptom severity (p < 0.05) as well as interleukin-4 and immunoglobulin E (p < 0.05). Individuals with antibodies against anti-Hsp60 and anti-Hsp70 were more likely to have a family history of asthma (p < 0.001) and higher plasma concentrations of total immunoglobulin E (p = 0.001) and interleukin-4 (p < 0.05) than those without antibodies.These data suggest that anti-Hsp60 and especially anti-Hsp70 correlate with the attacks and severity of asthma. The underlying molecular mechanisms linking antibodies to heat shock proteins and asthma remain to be investigated. Heat shock proteins (Hsps) are highly conserved proteins inducible in response to a wide variety of stresses (such as exposure to heat) and pathological  or physiological  stimuli ,2. ThereAsthma is a multifactorial and likely multigenic immune inflammatory disease of the upper airways, arising from complex interactions among environmental and genetic factors ,15. ThesUp to now, few studies investigated possible associations of autoantibodies to human Hsps with the severity of asthma. In the present study, we determined the presence of autoantibodies to human Hsp60 and Hsp70 in 193 subjects with (n = 95) and without (n = 99) asthma by immunoblot analysis, and evaluated the associations of these autoantibodies with asthma severity and their correlation with interleukin-4 (IL-4) and immunoglobulin E (IgE) both involved in the development of asthma, by using multivariate logistic regression analyses.This 95 patients with asthma  and 99 healthy, age-matched non-asthmatic controls  were residents living in the same geographic area. Patients and controls were from Wuchang, one of the three cities of Wuhan and were all of Han nationality. Their age ranged from 10 to 45 years old (Table E. coli GJ1168 cells using pET30 (Novegen) as the expression vector [Recombinant human Hsp60 and inducible Hsp70 were obtained through the expression of corresponding cDNA in NaCl-induced n vector . Approxin vector ,22. An eTotal IgE was measured in plasma by using a fluorescence enzyme immunoassay kit from Bayer Company (Leverkusen, Germany). IL-4 was determined using a commercial enzyme-linked immunosorbent assay kit from OptEIA . Each sample was tested in duplicate by a series of dilutions using a standard provided with the kit.t test. Frequency data (e.g., sex) were analysed by the Chi-square test. The associations were estimated by fitting univariate and multivariate logistic regression models. Statistical inferences were based on a significance level of 0.05. All analyses were two-sided and performed by using the Statistical Package for Social Sciences (SPSS) software (Version, Chicago).All the continuous data (e.g., age, FEV1, IGE, IL4) were presented as the mean ± standard deviation (SD) and analysed by the Student's The patient and control groups comprised 56.8% and 64.6% of males, respectively, and had mean ages of 28.2 and 29.1 years, respectively (Table We first looked for the presence of anti-Hsp60 and anti-Hsp70 in plasma at dilutions of 1:10 to 1:80 in the patients with asthma and in the matched controls. At a dilution of 1:10, asthma patients had a significantly higher positive rate of anti-Hsp60 than the controls (17.9% vs 5.1%, p = 0.001). In the case of Hsp70, antibodies were observed in 33.7% of patients as compared to 8.1% in the controls at a dilution of 1:10 (p < 0.001). At plasma dilutions between 1:20 to 1:80, the difference in the detection rates of both anti-Hsp60 and anti-Hsp70 between the patients and controls remained highly significant  and anti-Hsp60 and anti-Hsp70 was carried out by using a multivariate logistic regression model built with a forward stepwise selection procedure  and also based on clinical experience. The results in Table To understand the possible significance of the anti-Hsp60 and anti-Hsp70 in asthma, we analyzed the correlation of anti-Hsp60 and anti-Hsp70 with the severity of asthma. Table P = 0.001) and IL-4 (36.9 Vs 28.5, p = 0.019)  or negative (58 patients) for the presence of anti-Hsp60 and anti-Hsp70. The patients positive for anti-Hsps were more likely than the antibody-negative group of patients to report a family history of asthma (48.6% Vs 13.8%, p < 0.001) and had higher concentrations of total IgE , whereas the development of allergic diseases requires IL-4 and IL-5, both of which are produced by type 2 helper T cells (Th2). The reciprocal down-regulation of Th1 cells by Th2 cytokines raises the possibility that these cytokines are involved in allergy or immunity . IL-4 isThe present study showed that there was a significant increase in positive rates of antibodies against Hsp60 and Hsp70 in patients with asthma and that the presence of autoantibodies against Hsp70 was associated with the severity of asthma. The presence of anti-Hsp70 associated with a high risk of asthma was also correlated with the family history of asthma and higher levels of total IgE and IL-4 in the patients. These results suggest that anti-Hsp70 correlate with the pathogenesis of asthma, but the precise underlying molecular mechanisms for these interactions remain to be established.MY and LC performed the immunoblot assays, the acquisition of data and wrote the first draft of the manuscript. FW carried out the collection and statistical analysis of data. QW was responsible for the analysis and interpretation of data, and critical revision of the manuscript. TW and RMT initiated the project, designed the experiments, and wrote the manuscript.


A study of the genetic and regulatory factors in several biosynthesis, metal ion homeostasis, stress response, and energy metabolism pathways suggests that phylogenetically diverse δ-proteobacteria have homologous regulatory components. Relatively little is known about the genetic basis for the unique physiology of metal-reducing genera in the delta subgroup of the proteobacteria. The recent availability of complete finished or draft-quality genome sequences for seven representatives allowed us to investigate the genetic and regulatory factors in a number of key pathways involved in the biosynthesis of building blocks and cofactors, metal-ion homeostasis, stress response, and energy metabolism using a combination of regulatory sequence detection and analysis of genomic context.RFN-, THI-, B12-elements and S-box), and new binding sites for the FUR, ModE, NikR, PerR, and ZUR transcription factors, as well as for the previously uncharacterized factors HcpR and LysX. After reconstruction of the corresponding metabolic pathways and regulatory interactions, we identified possible functions for a large number of previously uncharacterized genes covering a wide range of cellular functions.In the genomes of δ-proteobacteria, we identified candidate binding sites for four regulators of known specificity (BirA, CooA, HrcA, sigma-32), four types of metabolite-binding riboswitches (de novo reconstruction of a regulatory network in a poorly studied taxonomic group of bacteria. Recent efforts in large-scale functional genomic characterization of Desulfovibrio species will provide a unique opportunity to test and expand our predictions.Phylogenetically diverse δ-proteobacteria appear to have homologous regulatory components. This study for the first time demonstrates the adaptability of the comparative genomic approach to  Myxococcus with complex developmental lifestyles and Bdellovibrio, which prey on other bacteria [Desulfovibrio and Desulfotalea species, are metabolically and ecologically versatile prokaryotes often characterized by their ability to reduce sulfate to sulfide [Desulfotalea psychrophila has been isolated from permanently cold arctic marine sediments [Geobacter and Desulfuromonas comprise dissimilative metal-reducing bacteria, which cannot reduce sulfate, but include representatives that require sulfur as a respiratory electron acceptor for oxidation of acetate to carbon dioxide [The delta subdivision of proteobacteria is a very diverse group of Gram-negative microorganisms that include aerobic genera bacteria . In this sulfide . They ca sulfide . Industrediments . In cont dioxide . These b dioxide , and havKnowledge of transcriptional regulatory networks is essential for understanding cellular processes in bacteria. However, experimental data about regulation of gene expression in δ-proteobacteria are very limited. Different approaches could be used for identification of co-regulated genes (regulons). Transcriptional profiling using DNA microarrays allows one to compare the expression levels of thousands of genes in different experimental conditions, and is a valuable tool for dissecting bacterial adaptation to various environments. Computational approaches, on the other hand, provide an opportunity to describe regulons in poorly characterized genomes. Comparison of upstream sequences of genes can, in principle, identify co-regulated genes. From large-scale studies -9 and anDesulfovibrio vulgaris [D. desulfuricans G20, and Desulfotalea psychrophila, as well as the sulfur-reducing G. sulfurreducens [G. metallireducens genome has been completed to draft quality. A mixture of Desulfuromonas acetoxidans and Desulfuromonas palmitatis has been sequenced, resulting in a large number of small scaffolds, the identity of which  has not been determined, and we refer to this sequence set simply as Desulfuromonas. Though draft-quality sequence can make it difficult to assert with confidence the absence of any particular gene, we have included these genomes in our study because they do provide insight as to the presence or absence of entire pathways, they can be compared to the related finished genome of G. sulfurreducens, and because complete genome sequence is not necessary for the methodology we use to detect regulatory sequences.Recent completion of finished and draft quality genome sequences for δ-proteobacteria provides an opportunity for comparative analysis of transcriptional regulation and metabolic pathways in these bacteria. The finished genomes include sulfate-reducing vulgaris , D. desureducens , while tIn this comprehensive study, we identify a large number of regulatory elements in these δ-proteobacteria. Some of the corresponding regulons are highly conserved among various bacteria (for example, riboswitches, BirA, CIRCE), whereas others are specific only for δ-proteobacteria. We also present the reconstruction of a number of biosynthetic pathways and systems for metal-ion homeostasis and stress response in these bacteria. The most important result of this study is identification of a novel regulon involved in sulfate reduction and energy metabolism in sulfate-reducing bacteria, which is most probably controlled by a regulator from the CRP/FNR family.Escherichia coli, but are predicted to recognize entirely different DNA signals. We then describe two stress-response regulons: heat-shock regulons (σ32 and HrcA/CIRCE), which operate by regulatory elements conserved in diverse bacteria, and newly identified peroxide stress response regulons that are quite diverse and conserved only in closely related species. Finally, we present a completely new global regulon in metal-reducing δ-proteobacteria, which includes various genes involved in energy metabolism and sulfate reduction.The results are organized under four main headings for convenience. In the first, we analyze a number of specific regulons for biosynthesis of various amino acids and cofactors in δ-proteobacteria. Most of them are controlled by RNA regulatory elements, or riboswitches, that are highly conserved across bacteria . In the 14/15]-GGTTTACAA [Desulfovibrio species, the predicted BirA site is located between the divergently transcribed biotin operon and the birA gene. In other genomes, candidate binding sites for BirA precede one or two separate biotin biosynthetic loci, whereas the birA gene stands apart and is not regulated.Biotin (vitamin H) is an essential cofactor for numerous biotin-dependent carboxylases in a variety of microorganisms . The strGTTTACAA . Consistde novo biotin synthesis from pimeloyl-CoA precursor (bioF, bioA, bioD, bioB) and the bifunctional gene birA, but the initial steps of the biotin pathway are variable in these species , 3-oxoacyl-(ACP) synthase, 3-oxoacyl-(ACP) reductase and hydroxymyristol-(ACP) dehydratase. From positional and regulatory characteristics we conclude that these genes are functionally related to the biotin pathway. The most plausible hypothesis is that they encode a novel pathway for pimeloyl-CoA synthesis, as the known genes for this pathway, bioC, bioH, bioG and bioW, are missing in the Desulfovibrio species.All δ-proteobacteria studied possess genes for s Figure . The Geo pathway . Both De2) is an essential component of basic metabolism, being a precursor to the coenzymes flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN). The only known mechanism of regulation of riboflavin biosynthesis is mediated by a conserved RNA structure, the RFN-element, which is widely distributed in diverse bacterial species [de novo synthesis of riboflavin (ribD-ribE-ribBA-ribH), but lack this regulatory element. The only exception is D. psychrophila, which has an additional gene for 3,4-dihydroxy-2-butanone-4-phosphate synthase (ribB2) with an upstream regulatory RFN element.Riboflavin  and thiazole (HET) moieties in bacteria. The only known mechanism of regulation of thiamine biosynthesis in bacteria is mediated by a conserved RNA structure, the THI-element [THI-elements per genome that are located upstream of thiamine biosynthetic operons . The δ-proteobacteria possess all the genes required for the de novo synthesis of thiamine , and D. psychrophila, which has no thiF. In most δ-proteobacteria there are two paralogs of the thiamine phosphate synthase thiE, and Geobacter and Desulfuromonas species have fused genes thiED. In D. psychrophila, the only THI-regulated operon includes HET kinase thiM and previously predicted HMP transporter thiXYZ [THI-element , a derivative of vitamin BL Figure . The onlhimurium .12 biosynthesis and transport via a co- or post-transcriptional regulatory mechanism, which involves direct binding of Ado-CBL to the riboswitch called the B12-element [B12-elements in the genomes of δ-proteobacteria produced one B12-element in D. desulfuricans, D. psychrophila and G. metallireducens, two in D. vulgaris and G. sulfurreducens, and four in Desulfuromonas . In Geobacter species these riboswitches regulate a large locus containing almost all the genes for the synthesis of Ado-CBL  are important in protein synthesis and cellular metabolism. There are two alternative pathways for methionine synthesis in microorganisms, which differ in the source of sulfur. The trans-sulfuration pathway (metI-metC) utilizes cysteine, whereas the direct sulfhydrylation pathway (metY) uses inorganic sulfur instead. All δ-proteobacteria in this study except the Desulfovibrio species possess a complete set of genes required for the de novo synthesis of methionine . A strong hairpin with a poly(T) region follows all these S-boxes, implying involvement of these S-boxes in a transcriptional termination/antitermination mechanism.In Gram-positive bacteria, SAM is known to repress expression of genes for methionine biosynthesis and transport via direct binding to the S-box riboswitch . In contDesulfovibrio species have genes involved in the conversion of homocysteine into methionine (metE, metH and metF), which could be involved in the SAM recycling pathway, but not those genes required for de novo methionine biosynthesis. The ABC-type methionine transport system (metNIQ), which is widely distributed among bacteria, was also not found in these δ-proteobacteria. The Desulfovibrio species appear to have the single-component methionine transporter metT [Both ter metT .dapA, dapB, dapF and lysA) were further identified in δ-proteobacteria, whereas we did not find orthologs for three other genes (dapC, dapE and dapD), which vary in bacteria using different meso-DAP synthesis pathways. The lysine synthesis genes are mostly scattered along the chromosome, and in only some cases are dapA and either dapB, dapF or lysA clustered. All δ-proteobacteria studied lack the previously known lysine transporter LysP. However, in D. desulfuricans and D. psychrophila we found a gene for another candidate lysine transporter, named lysW, which was predicted in our previous genomic survey [The amino acid lysine is produced from aspartate through the diaminopimelate (DAP) pathway in most bacteria. The first two stages of the DAP pathway, catalyzed by aspartokinase and aspartate semialdehyde dehydrogenase, are common for the biosynthesis of lysine, threonine, and methionine. The corresponding genes were found in δ-proteobacteria where they form parts of different metabolic operons. Four genes for the conserved stages of the lysine synthesis pathway  and is the most likely candidate for the lysine-specific regulator role in Desulfovibrio. To find new members of the regulon, the derived profile (named LYS-box) was used to scan the Desulfovibrio genomes. The lysine regulon in these genomes appears to include an additional gene (206613 in D. vulgaris, and 394397 in D. desulfuricans), which encodes an uncharacterized membrane protein with 14 predicted transmembrane segments. We predict that this new member of the lysine regulon might be involved in the uptake of lysine or some lysine precursor.In various bacterial species, lysine is known to repress expression of genes for lysine biosynthesis and transport via the L-box riboswitch . In addirocedure . The strns Table . The firfeoAB genes, and lack ABC transporters for siderophores. Regulation of iron metabolism in bacteria is mediated by the ferric-uptake regulator protein (FUR), which represses transcription upon interaction with ferrous ions. FUR can be divided into two domains, an amino-terminal DNA-binding domain and a carboxy-terminal Fe(II)-binding domain. The consensus binding site of E. coli FUR is a palindromic sequence GATAATGATNATCATTATC [Iron is necessary for the growth of most bacteria as it participates in many major biological processes . In aeroTCATTATC .D. psychrophila, we identified one to three FUR orthologs that form a distinct branch (FUR_Delta) in the phylogenetic tree of the FUR/ZUR/PerR protein family (see below). One protein, FUR2 in D. desulfuricans, lacks an amino-terminal DNA-binding domain and is either non-functional or is involved in indirect regulation by forming inactive heterodimers with two other FUR proteins. Scanning the genomes with the FUR-box profile of E. coli did not result in identification of candidate FUR-boxes in δ-proteobacteria. In an attempt to analyze potential iron regulons in this phylogenetic group, we collected upstream regions of the iron-transporter genes feoAB and applied SignalX to detect regulatory signals. The strongest signal, a 17-bp palindrome with consensus WTGAAAATNATTTTCAW (where W indicates A or T), was observed upstream of the multiple feoAB operons and fur genes in all δ-proteobacteria except D. psychrophila , the fur genes themselves (one copy in the Geobacter species and two copies in Desulfuromonas), and two hypothetical porins. The first one, named psp, was found only in G. metallireducens and Desulfuromonas genomes, where it is preceded by two tandem FUR-boxes. The psp gene has homologs only in Aquifex aeolicus and in various uncultured bacteria, and in one of them (a β-proteobacterium) it is also preceded by two FUR-boxes (GenBank entry AAR38161.1). This gene is weakly similar to the family of phosphate-selective porins (PFAM: PF07396) from various Gram-negative bacteria. The second hypothetical porin was found only in G. sulfurreducens (383590), where it is preceded by a FUR-box and followed by feoAB transporter. This gene, absent in other δ-proteobacteria, has only weak homologs in some Gram-negative bacteria and belongs to the carbohydrate-selective porin OprB family (PFAM: PF04966). Thus, two novel genes predicted to fall under FUR control encode hypothetical porins that could be involved in ferrous iron transport.The smallest FUR regulons were observed in the G. sulfurreducens genome precedes a cluster of two hypothetical genes located immediately upstream of the feoAB-containing operon. The first gene in this operon, named genX (383594), has no orthologs in other bacteria and the encoded protein has a heme-binding site signature of the cytochrome c family (PFAM: PF00034). The second gene, named genY (383592), encodes a two-domain protein that is not similar to any known protein. In Desulfuromonas, an ortholog of the genY amino-terminal domain (391875) is divergently transcribed from a predicted ferric reductase (391874), and their common upstream region contains a strong FUR-box. Moreover, orthologs of the genY C-terminal domain were identified in Desulfovibrio species, where they are again preceded by two tandem FUR-boxes and form a cluster with the hypothetical gene, genZ, encoding a protein of 100 amino acids with two tetratricopeptide repeat domains that are usually involved in protein-protein interactions (PFAM: PF00515). From genomic analysis alone it is difficult to predict possible functions of these new members of the FUR regulon in δ-proteobacteria.Another strong FUR-box in the Desulfovibrio species have significantly extended FUR regulons that are largely conserved in these genomes and include ferrous iron transporter genes feoAB and many hypothetical genes. Another distinctive feature of the FUR regulon in Desulfovibrio species is a structure of two partially overlapping FUR-boxes shifted by 6 bp. Interestingly, the flavodoxin gene, fld, is predicted to be regulated by FUR in both Desulfovibrio species. In addition to this iron-repressed flavodoxin (a flavin-containing electron carrier), the Desulfovibrio species have numerous ferredoxins (an iron-sulfur-containing electron carrier). One possible explanation is that in iron-restricted conditions these microorganisms can replace ferredoxins with less-efficient, but iron-independent alternatives. A similar regulatory strategy has been previously described for superoxide dismutases in E. coli, Bordetella pertusis and Pseudomonas aeruginosa [12-dependent and B12-independent enzymes [Two ruginosa -36 and p enzymes ; see theDesulfovibrio species are the hypothetical genes pep (Zn-dependent peptidase), gdp (GGDEF domain protein, PF00990), hdd , and a hypothetical P-type ATPase (392971) that could be involved in cation transport, and a long gene cluster starting from the pqqL gene (Zn-dependent peptidase). The latter cluster contains at least 10 hypothetical genes encoding components of ABC transporters and biopolymer transport proteins (exbB, exbD and tonB). In D. vulgaris, the first gene in this FUR-regulated cluster is an AraC-type regulator named foxR, since it is homologous to numerous FUR-controlled regulators from other genomes , which usually regulate iron-siderophore biosynthesis/transport operons [foxR, a single FUR-regulated gene, was identified in D. desulfuricans located about 30 kb away from the FUR-regulated pqqL gene cluster. Given these observations, we propose that this gene cluster is involved in siderophore transport and is regulated by FoxR.Other predicted regulon members with conserved FUR-boxes in both  operons . An orthD. vulgaris (209207) has the strongest FUR-box in this genome; however, its orthologs in D. desulfuricans are not predicted to belong to the FUR regulon. Another operon in D. desulfuricans (392971-392970-392969), encoding three hypothetical proteins, is preceded by two candidate FUR-boxes, but these genes have no orthologs in other δ-proteobacteria. Thus, FUR-dependent regulation of these hypothetical genes is not confirmed in other species, and their possible role in the iron homeostasis is not clear.A hypothetical gene in E. coli (the nickel/peptide ABC transporter family) and the HoxN of Ralstonia eutropha . Nickel uptake must be tightly regulated because excessive nickel is toxic. In E. coli and some other proteobacteria, nickel concentrations are controlled by transcriptional repression of the nikABCD operon by the Ni-dependent regulator NikR [The transition metal nickel (Ni) is an essential cofactor for a number of prokaryotic enzymes, such as [NiFe]-hydrogenase, urease, and carbon monoxide dehydrogenase (CODH). Two major types of nickel-specific bacterial transporters are represented by the NikABCD system of tor NikR .nikR. In an attempt to identify potential nickel transporters in this taxonomic group, we analyzed the genome context of the nikR genes. The nikR gene in Desulfuromonas is co-localized with a hypothetical ABC transport system, which is weakly homologous to the cobalt ABC-transporter cbiMNQO from various bacteria. Orthologs of this system, named here nikMNQO, are often localized in proximity to Ni-dependent hydrogenase or urease gene clusters in various proteobacteria (data not shown). Among δ-proteobacteria, the Geobacter species have a complete nikMNQO operon, whereas operons in D. desulfuricans and D. psychrophila lack the nikN component but include two additional genes, named nikK and nikL, which both encode hypothetical proteins with amino-terminal transmembrane segments , we propose that these two genes encode additional periplasmic components of the NikMQO ABC transporter, possibly involved in the nickel binding.The genomes of δ-proteobacteria studied so far contain multiple operons encoding [NiFe] and [Fe] hydrogenases and Ni-dependent CODH, but lack urease genes. Both known types of nickel-specific transporters are absent in δ-proteobacteria, but these genomes contain orthologs of the nickel repressor s Figure . DesulfonikMQO operons, we identified de novo the NikR binding signal in all δ-proteobacteria except D. psychrophila (Table 13/14]-GTAACAC) differs significantly from the consensus of NikR binding signal of enterobacteria (GTATGAT-[N13/14]-ATCATAC) [D. desulfuricans. Thus the nickel regulon in this bacterium includes the hydAB2 operon, encoding periplasmic iron-only hydrogenase. Altogether, D. desulfuricas has three paralogs of [NiFe] hydrogenase and two paralogs of [Fe] hydrogenase. We predict that an excess of nickel represses a nickel-independent hydrogenase isozyme using the Ni-responsive repressor NikR. Regulation of hydrogenase enzymes by NikR has not been described previously. A closer look at the upstream region of the putative nickel transport operon in D. psychrophila revealed similar NikR consensus half-sites but in the opposite orientation to each other (GTAACAC-[N13/14]-GTGTTAC). Searching the genomes with this reversed NikR signal, we observed one more hypothetical gene cluster in D. psychrophila which has two high-scoring NikR-sites in the upstream region, and a NikR-site upstream of the single nikK gene in D. vulgaris  . Using ts Figure .znuABC in various bacteria [G. sulfurreducens and the Desulfovibrio species, this cluster also includes a hypothetical regulatory gene from the FUR/ZUR/PerR family, named zur_Gs and zur_D, respectively. Phylogenetic analysis of this protein family demonstrated that ZUR_Gs and ZUR_D are not close relatives and are only weakly similar to known FUR, ZUR, and PerR regulators from other bacteria (see below). The predicted ZUR-binding site located just upstream of the zur-znuABC operon in G. sulfurreducens is highly similar to the ZUR consensus of Gram-positive bacteria (TAAATCGTAATNATTACGATTTA). Another strong signal, a 17-bp palindrome with consensus ATGCAACNNNGTTGCAT, was identified upstream of the znuABC-zur operons in two Desulfovibrio genomes . In contrast, other δ-proteobacteria, possessing a different cobaltochelatase (cbiK), lack homologs of any known cobalt transporter. It was previously suggested by global analysis of the B12 metabolism that different types of cobalt transporters are interchangeable in various bacterial species [cbiK gene, we predicted a novel candidate cobalt transporter in δ-proteobacteria, named cbtX . In contrast, we could not identify E. coli-type ModE-binding sites upstream of the mod operons in Desulfuromonas, indicating that these operons may be regulated by a different, unidentified signal.Molybdenum (Mo) is another transition metal essential for bacterial metabolism. Bacteria take up molybdate ions via a specific ABC transport system encoded by iquitous . All δ-pr Figure . Howeverte Table , which iDesulfovibrio species and D. psychrophila) have genes encoding a single DNA-binding domain of ModE  and one or two molybdate-binding domains (MOP). The functions of these chimeric molybdate-binding proteins, and the mechanism of Mo-sensing by DNA-binding ModE domains in the Desulfovibrio species, are not clear.Three other δ-proteobacteria , which has superoxide reductase activity, rubrerythrin (Rbr) with NADH peroxidase activity, and rubredoxin-like proteins (Rub, Rdl), which are used as common intermediary electron donors [Under aerobic conditions, generation of highly toxic and reactive oxygen species such as superoxide anion, hydrogen peroxide and the hydroxyl radical leads to oxidative stress with deleterious effects . Strictln donors .E. coli OxyR and SoxR/SoxS regulators, the δ-proteobacteria studied have instead multiple homologs of the peroxide-sensing regulator PerR of B. subtilis [D. psychrophila, the perR genes are co-localized on the chromosome with various peroxide stress-responsive genes , and a hypothetical gene of unknown function in both Desulfovibrio species (206199 in D. vulgaris and 395549 in D. desulfuricans).In the dl genes . SearchiperR-rbr-roo operon in both Geobacter species is preceded by a conserved palindromic region , which is followed by a gene cluster containing two cytochrome peroxidase homologs (hsc and ccpA), glutaredoxin (grx) and rubrerythrin (rbr), has a close ortholog in the Desulfuromonas species, where it precedes the rbr gene  probably result from a recent gene duplication  and proteases (ClpA, ClpP, ClpX, Lon)  Table . Thus, iRhodospirillum rubrum - CO dehydrogenase (CODH) and an associated hydrogenase - which are encoded in the coo operons and induced by the CO-sensing transcriptional activator CooA [Desulfovibrio species have coo operons and the CooA regulator. D. vulgaris has two separate operons encoding CODH and the associated hydrogenase, whereas D. desulfuricans has only one operon encoding CODH  as the sole energy source involves two key enzymes in the γ-proteobacterium tor CooA . Among tH Figure . The strum Table . This coJ operon .Desulfovibrio species. A conserved palindromic signal with consensus sequence TTGTGANNNNNNTCACAA was detected upstream of the sat and apsAB operons, which encode ATP sulfurylase and APS reductase, respectively. This novel signal is identical to the E. coli CRP consensus, and we hypothesized that a CRP-like regulator might control the sulfate-reduction regulon in Desulfovibrio. Scanning the Desulfovibrio genomes resulted in identification of similar sites upstream of many hypothetical genes encoding various enzymes and regulatory systems  of unknown function [frdX*: new gene names introduced in this study are marked by asterisk. In both Desulfovibrio species, the hcp-frdX* genes are co-localized with a hypothetical regulatory gene from the CRP/FNR family of transcriptional regulators, named HcpR* for the Hcp regulator  in the helix-turn-helix motif involved in DNA recognition, which is changed from arginine (for example, in E. coli CRP and Desulfovibrio HcpR*) to serine and proline in these two δ-proteobacteria (data not shown). As both these species have multiple hcp and frdX paralogs, we applied SignalX to a set of corresponding upstream regions and obtained another FNR-like palindromic signal with consensus at ATTTGACCNNGGTCAAAT, which is notably distinct from the CRP-like signal in the third position (which has T instead of G). Such candidate sites were observed upstream of all hcp and frdX paralogs identified in D. psychrophila and Desulfuromonas, as well as upstream of some additional genes in Desulfuromonas, for example those encoding polyferredoxin and cytochrome c heme-binding protein . Moreover, the hcpR gene is often co-localized with hcp on the chromosome. In clostridia, frdX orthologs are also preceded by candidate HcpR sites. These data indicate that the main role of HcpR is control of expression of two hypothetical proteins - hybrid-cluster protein and ferredoxin - which are most probably involved in electron transport. However, the HcpR regulon is significantly extended in some organisms. Additional members of this regulon that are conserved between the two Desulfovibrio species include two operons involved in sulfate reduction (apsAB and sat), a hypothetical cluster of genes (206515-206516) with similarity to dissimilative sulfite and nitrite reductases, polyferredoxin, a hypothetical gene conserved in Archaea (209119), and the putative thiosulfate reductase operon phcAB (209106-209105). Notably, both CooA and HcpR candidate sites precede the cooMKLXUHF operon for CODH-associated hydrogenase, which is present only in D. vulgaris.The HcpR regulon was also identified in other taxonomic groups, including Desulfovibrio species. These two closely related genomes are diverse enough to identify regulatory elements as conserved islands in alignments of intergenic regions. For the sat and apsAB operons, the HcpR sites were found within highly conserved parts of alignments and in both cases the site overlaps the -10 box of a site strongly resembling a promoter . While HcpR is predicted to activate these two hypothetical operons, as well as the CODH-associated hydrogenase operon, it most probably represses two enzymes from the sulfate reduction pathway, APS reductase and ATP sulfurylase. We hypothesize that HcpR is a key regulator of the energy metabolism in anaerobic bacteria, possibly controlling the transition between utilization of alternative electron acceptors, such as sulfate and nitrate. The absence of the dissimilatory sulfite reductase DsrAB in the predicted HcpR regulon of Desulfovibrio could be explained by its experimentally defined ability to reduce both sulfite and nitrite [By analysis of the functions of genes co-regulated by HcpR, it is difficult to predict the effector for this novel regulon. The physiological role of the hybrid iron-sulfur cluster protein Hcp, the most conserved member of the HcpR regulon, is not yet characterized despite its known three-dimensional structure and expression profiling in various organisms. In two facultative anaerobic bacteria, cceptors ,51. More nitrite .THI-element riboswitch in Desulfovibrio species. Multiple protein-binding sites are a common regulatory feature and often imply cooperative binding of multiple protein factors. Although true riboswitch units do not interact with trans-acting factors, it is theoretically possible for independently acting sites to yield a cooperative effect when ligand binding derepresses transcription. For switches that are repressed by ligand binding, however, tandem sites would simply lower the concentration threshold at which a response is seen, but not affect cooperativity unless some more complicated interaction of the sites were allowed. On the one hand, independently acting sites is a simpler mechanism to explain, while on the other hand, it seems unusual that duplicate sites would have evolved to adjust the concentration response instead of simply changing the binding affinity for the ligand at the sequence level. Moreover, it seems unlikely that a tandem switch would be preserved across a large evolutionary distance without offering some other advantage such as cooperativity. It would be interesting to investigate the biochemical behavior of these tandem THI-elements in the laboratory to resolve whether their genomic organization reflects a more sophisticated mode of regulation, or is simply an evolutionarily convenient way to adjust the concentration response, or is perhaps just a recombination remnant that has persisted in these genomes by chance.Because the organisms considered in this study are commonly identified on the basis of their catabolic capabilities, comparatively little is known about the regulation of their biosynthetic pathways. In this study, we identified a number of previously characterized regulatory mechanisms , all of which, excluding the biotin regulon, are mediated by direct interaction of a metabolic product with a riboswitch control element /catalase-type oxidative response as well as the anaerobic Sor/rubrerythrin-type response as previously noted for vulgaris . Analysivulgaris . While tss Table . Clusterinvolved .32 regulon was identified, with a consensus signal similar to that characterized for E. coli. The second observed regulon was the HrcA/CIRCE regulon known in B. subtilis and other bacteria, but not present in E. coli. These two regulons include a partially overlapping set of genes. Notably, CIRCE elements were identified in all of the genomes used in this study with the exception of D. psychrophila. It is tempting to speculate that the constant and cold temperatures encountered by this species in its environmental niche have removed the need for this particular heat-shock response.The heat-shock response of these bacteria was found to be mediated by two regulons previously described in other species Table . First, E. coli-like regulation. However, the presence of PerR, CIRCE elements and S-box motifs is reminiscent of B. subtilis-like regulation. Moreover, in the case of FUR, although the regulon structure showed overlap with known downstream targets in model organisms, the sequence of the FUR box, which is conserved in both E. coli and B. subtilis, was observed to be different in the metal-reducing δ-proteobacteria.Comparison with well studied bacterial model organisms has shown that δ-proteobacteria share regulatory components with both Gram-positive and Gram-negative microorganisms Table . For exaD. vulgaris or two Geobacter species, used the AlignACE signal detection program, which is based on a Gibbs-sampling algorithm, to derive large sets of conserved DNA motifs without linking them to specific regulatory systems [et al. [D. vulgaris under various conditions. In support of our findings, all operons predicted to be co-regulated by the peroxide-responsive regulator PerR in D. vulgaris are significantly downregulated by oxygen stress . Furthermore, recent microarray data obtained for G. sulfurreducens in iron-limiting conditions confirm our prediction of the FUR regulon in this genome .We recognize that this is one of the first direct studies comparing entire regulons in δ-proteobacteria. Two recent computational works, considering either a single  systems ,57. Unfo [et al. ,57. Prev32 and BirA) seem to be conserved across vast spans of evolutionary time, in many cases we observe divergence in binding signals even when the core components of a regulon are conserved (NikR, FUR, PerR, ModE). These findings raise, but do not answer, questions such as what circumstances cause transcription factor binding specificities to change or remain conserved, and whether those changes reflect genetic drift, or active selection to alter the regulatory action of the factor.It is interesting to observe the extent to which regulatory motifs are conserved between δ-proteobacteria. Although riboswitches and some DNA signals ; and Fe(II) was predicted to repress a flavodoxin gene which we suspect may be used as an alternative to ferredoxins present in the genome. This mode of regulation for B12-independent isozymes of ribonucleotide reductase and methionine synthetase has been previously described [In the course of this study we identified several cases in which different variants of genes were predicted to be regulated according to the availability of required cofactors or nutrients. Three examples were observed in which an alternative enzyme, not requiring a given cofactor, was repressed by the availability of that cofactor: Bescribed . Moreoveescribed -36,38,59Desulfovibrio vulgaris Hildenborough (DV); Desulfovibrio desulfuricans G20 (DD); Geobacter metallireducens (GM); Geobacter sulfurreducens PCA (GS); Desulfuromonas species (DA); and Desulfotalea psychrophila (DP). Complete genomic sequences of DV and GS were downloaded from GenBank [The genomes of δ-proteobacteria that were analyzed in this study are  GenBank . Draft s GenBank . Draft s GenBank . Numeric GenBank  are usedde novo definition of a common transcription factor-binding signal in a set of upstream gene fragments, a simple iterative procedure implemented in the program SignalX was used [For was used . Weak paW(b,k) = log[N(b,k) + 0.5] - 0.25Σi = A,C,G,Tlog[N(i,k) + 0.5],N(b,k) is the count of nucleotide b in position k [Z is defined as the sum of the respective positional nucleotide weightswhere sition k . The canZ(b1...bL) = Σk = 1...LW(bk,k),k is the length of the site.where These profiles were used to scan the set of palindromes again, and the procedure was iterated until convergence. Thus a set of profiles was constructed. The profile with the greatest information content  was seleEach genome was scanned with the profile using the GenomeExplorer software , and genThe RNApattern program  was usedOrthologous proteins were identified as bidirectional best hits  by compain vitro experiment that the glycine-specific riboswitch consists of two tandem aptamer sequences that appear to bind target molecules cooperatively [THI-elements in Desulfovibrio spp. Also we have recently shown that Geobacter spp. have a modified HcpR regulon, which uses a signal similar to that found in DA and DP, but contains multiple nitrate/nitrite reductase genes.Recently it has been demonstrated by ratively . This inAn additional data file (Additional data Three figures with detailed description of DNA- and RNA-type regulatory sitesClick here for additional data file