Microcystis aeruginosa. Cyanobacteria are a rich source of structurally diverse oligopeptides that are predominantly synthesized by NPRS. Several A-domains share amino acid identities lower than 20 % being a possible source of remote homologous. Therefore, A-domains cannot be easily retrieved by BLASTp searches using a single template. To cope with the sequence diversity of the A-domains we have combined homology-search methods with an alignment-free tool that uses protein four-color-maps. TI2BioP  version 2.0, available at http://ti2biop.sourceforge.net/ allowed the calculation of simple TIs from the protein sequences (four-color maps). Such TIs were used as input predictors for the statistical estimations required to build the alignment-free models. We concluded that the use of graphical/numerical approaches in cooperation with other sequence search methods, like multi-templates BLASTp and profile HMM, can give the most complete exploration of the repertoire of highly diverse protein families.The introduction of two-dimension (2D) graphs and their numerical characterization for comparative analyses of DNA/RNA and protein sequences without the need of sequence alignments is an active yet recent research topic in bioinformatics. Here, we used a 2D artificial representation (four-color maps) with a simple numerical characterization through topological indices (TIs) to aid the discovering of remote homologous of Adenylation domains (A-domains) from the Nonribosomal Peptide Synthetases (NRPS) class in the proteome of the cyanobacteria  Microcystis, a common cyanobacteria genus in eutrophic freshwaters, numerous bioactive peptides have been identified that can be mostly classified as aeruginosins, microginins, microcystins, cyanopeptolins, and anabaenopeptins Microcystis aeruginosa as a strategy to spot NRPS clusters. To handle the high sequence diversity of A-domains we used an ensemble of homology-search methods, including an alignment-free model that integrates the four-color-maps for proteins. TI2BioP  version 2.0, available at http://ti2biop.sourceforge.net/ allows the calculation of TIs from the four-color maps for protein sequences Microcystis aeruginosa matching 19 out of 20 hits previously annotated as A-domains. The multiple-template BLASTp found exactly the 20 A-domain signatures annotated in the proteome, while the profile HMM detected the same 20 hits plus three additional ones. DTM and profile HMM identified, respectively, two and three A-domain signatures not found by multi-template BLASTp among the hypothetical proteins. The consensus detection of additional hits by the two sequence search methods provides clues for the presence of further A-domains remote homologues. The new A-domain variants found in the proteome of Microcystis aeruginosa could unravel the presence of novel NRPS clusters.The Chemical Graph Theory (CGT) consists in the application of the graph theory to perform combinatorial and topological exploration of the chemical molecular structure. Currently, the CGT is being extended to bioinformatics through the introduction of two-dimensional (2D) graphs for comparative analyses of DNA/RNA and proteins without the use of sequence alignments. These 2D graphs or maps do not represent the “real structure” of the natural biopolymers but they have been very effective to inspect similarities/dissimilarities among them, either by direct visualization or by numerical characterization fcµ0-fcµ16) derived from four-color maps representing 8892 protein domains (138 A-domains and 8854 CATH domains) using TI2BioP (described in Methods and Database). The fcµ0-fcµ16 series were used as input predictors to build classification linear models as the simplest relation between the response variable and the predictors. General Discrimination Analysis (GDA) best subset implemented in the STATISTICA software was used for such purposes .We computed 17 TIs that consist in spectral moment series (see equation below) with four significant predictors  describing the topology of the four-color maps at short range  and at long range  interactions.N is the number of domain sequences used to train the classification model and the statistics parameters commonly used to evaluate linear functions  and Fisher ratio (F) with a probability of error (p-level)) We found a linear classification function  and Artificial Neural Networks (ANN) as can be seen below.fcµ1 and fcµ2 predictors as splitting variables to produce two decisions split at different values, respectively. The tree structure was very simple, two decision nodes (outlined in blue) and three terminal nodes (outlined in red) summing up a total of five nodes. The numbers of the nodes are labelled on its top-left corner and on the top-right corner are placed the label of the predicted class (A or CATH domain). The 6750 training sequences are assigned to the root node (first node) and tentatively classified as CATH domains or control set. CATH domains are chosen as the initial classification because they are numerically superior to A-domains.Although several alignment-free methods have been reported for improving classification accuracy in protein classes and super-families fcµ1 values higher than or equal to 3817 are sent to node number 3 and tentatively classified as A-domains, by contrary domain sequences with fcµ1 values lesser than this value are assigned to node number 2 and classified in the control set (CATH domains). Similarly, node 3 is subsequently split taking the decision that sequences with fcµ2 values lesser than or equal to 11.12 are sent to node number 4 to be classified as A domains (109 cases). The remaining domain sequence with fcµ2 value greater than 11.12 are sent to node number 5 to be classified as CATH domains reaching 6641 cases well classified (100%).The root node is split, forming two new nodes. The text below the root node describes the split. It indicates that protein sequences with . The information from the tree plot is also available in The tree graph presents all this information in a simple and straightforward way allowing processing the information easily. The histograms plotted within the tree's terminal nodes show the excellent performance of the DTM for the recognition of A- domain signatures . The infThe classification results from the DTM development to recognize A-domain signatures on training and test sets are shown in STATISTICA Neural Networks module for variable selection fcµ0 and fcµ1 predictors were selected by consensus from the three methods. Then, a good starting point to set the topology of the MLP is to use one hidden layer, with the number of units equal to half the sum of the number of input and output units.ANN is one of the most popular non-linear modelling techniques in use today and has been frequently applied into bioinformatics The  being the most robust model reported among them. Therefore, DTM was the selected model to perform A-domains search among the proteome of Microcystis aeruginosa.The classification results derived from the best MLP profile to classify A-domains are shown in http://www.csbio.sjtu.edu.cn/bioinf/PseAAC/) was used to generate alignment-free approaches based on amino acid composition (AAC) and pseudo amino acid composition (PseAAC) We carried out a comparatively analysis to evaluate the sensitivity of other different alignment-free approaches and homology-search methods in respect to our graphical/numerical model to detect A-domains among the overall dataset (138 A-domains and 8 854 CATH domains) included in study. Such comparison was addressed to inspect the ability of our alignment-free approach to detect distant A-domains members (A-domains placed in the twilight zone) in the selected dataset. The Webserver PseAAC .. Although A-domains share 10–40% of sequence identity with several members placed in the twilight zone, it was possible to retrieve all of them using four-color maps. In spite of the fact that the other two left alignment-free methods (AAC and PseAAC) showed lower sensitivity, they did not provide many false positives . It was also demonstrated the effect of the sequence order besides the AAC on the prediction quality; when λ was increased from 0 to 1, there was an improvement in all standard classification measures .Our alignment-free model (DTM) generated by four-color maps outperformed alignment-free models (DTM) supported by AAC and PseAAC . Althoug. Althoug. However, the BLASTp search using a single template provided false positives (significant matches) among CATH domains at both high  and relatively stringent cut-offs  –S5, which is considered statistically significant and useful for filtering easily identifiable homologs pairs Files S6). In addition, both the profile HMM described in the methods section (Files S7) and the DTM built up from four-color maps profiles reached the top in classifying the positive and negative sets. These facts support that profile-based methods are more effective to deal with remote protein homology unless a muli-template BLASTp strategy or PSI-BLAST is conducted. The easy and reliable identification of A-domains by multi-template BLASTp, profile HMM and four-color maps in contrast to a simple BLASTp search and other alignment-free methods provided real clues about the ability of the four-color maps to identify A-domain members in the twilight zone given the evaluated dataset.Regarding homology-based methods sensitivity, classification results agreed with the fact that multi-template BLASTp and profile HMM are more sensitive than simple BLASTp. Both multi-template BLASTp and profile HMM easily retrieved all A-domain members at expectation values  without reporting any false positive . HoweverMicrocystis aeruginosa.The potentialities of the four-color maps and its numerical characterization to detect A-domains in the twilight zone are promising, as we showed previously. Detecting A-domains remote homologues with reliability in a proteome that contains a large diversity of proteins is a challenge for any sequence search method. As several homology-search methods have been assembled into a certain annotation resource to retrieve accurately all members from highly diverse gene/protein families Microcystis aeruginosa NIES-843 (http://genome.kazusa.or.jp/cyanobase) is encoded from a 5.8Mbp genome with 6 311 annotated genes; some of them codifying NRPS proteins as hybrids with polyketide synthases (PKS) representing a good target to evaluate the detection of A-domains. DTM was selected among the alignment-free models due to its excellent performance at low sequence identity and its simple way to recognize A-domains. We just calculate the TIs for a proteome and select A-domain signatures according to the DTM rule (fcµ1≥3817 and fcµ2≤11.12) . DTM search detected 19 A-domain signatures that coincided with the previously annotation inferred for these genes in the proteome. Three additional cases were also detected as A-domains, but these cases have been previously predicted to be other protein signatures unrelated to NRPS A-domains in the proteome, namely a transketolase-like protein and the other two were hypothetical proteins. The putative hits with some remote relation to A-domains are probably found among the hypothetical proteins due to its unclear annotation. To increase the confidence and quality of the A-domains re-annotation, two sensitive homology-search methods were evaluated on the same proteome. We carried out multi-template BLASTp and profile HMM searches for A-domains in the proteome according to procedures described in the Methods section, respectively. Multi-template-BLASTp found 20 significant hits coinciding perfectly with the number of A-domains signatures in the annotated genome . The profile HMM detected 23 significant matches for the A-domain signature in the cyanobacteria proteome . Twenty out of these 23 matches agreed with the multi-template BLASTp results and therefore with the current proteome annotation. The remaining three detected hits by the profile HMM were found among the hypothetical proteins, similarly to the alignment-free search . These five hits retrieved by the use of two different sequence search methods among the hypothetical proteins could reveal the presence of additional A-domains remote homologues.The proteome of the et alTI2BioP tool to characterize graphically and numerically large sequences databases TI2BioPversion 1.0, especially to deal with functional classification problems at low sequence similarity The potential usefulness of several graphical/numerical approaches to characterize genes and proteins for comparative analyses without the use of alignments has been recently reported by Randić In this work we highlighted a practical utility of the four-color maps accompanied with sensitive alignment procedures to detect a functional signal among a highly diverse protein domains dataset including a proteome. The four-color maps construction was based on a similar procedure carried out to the building of 2D Cartesian maps for protein sequences, previously used with success to detect functional signatures at low homology level Proteins four-color maps were modified by clustering the amino acids according to their physicochemical properties in four groups  labeled in the map with four colors. The numerical characterization of the four-color maps can describe homologous sequences (replacement between amino acids of similar properties) and remote homologous . While small changes in the sequence do not affect the topology of the map, this kind of amino acid substitution produces implicit numerical changes in the calculation of the TIs making possible the differentiation of the sequences. When an amino acid exchange occurs between different physicochemical groups of amino acids, this change affects the topology of the map and consequently affects significantly the TIs values estimation.fcµ0-fcµ16) describing the protein four-color maps. The topology of the protein four-color maps is determined by the sequence order and its amino acid composition (amino acid content according to the above-mentioned four groups). These two sequence features define the number and composition of the clusters formed in the map. The spectral moments series codify a range of information about the protein four-color maps that comprise the number of formed clusters in the map (fcµ0) until the connectivity between the clusters in the map at different range (fcµ1-fcµ16). Our approach has a similar conceptual framework to the PseAAC introduced by Chou The TIs consist in the spectral moments series (fcµ0-fcµ16) were used to develop several alignment-free models with linear and non-linear statistical techniques. DTM and ANN showed a better performance in classifying A-domains in respect to linear models supporting that the identification of protein signatures are better assessed with non-linear models. DTM was the best-reported alignment-free model due to the reasons given in the previous section. Consequently, it was applied to get other alignment-free models based on AAC and PseAAC to inspect their sensitivity to retrieve all A-domains members. Such DTM displayed lower classification rates than those reached by the four-color maps based models . It seems that higher order patterns providing by the four-color maps are more effective in the detection of A-domains than linear sequence features driven by AAC and PseAAC. Therefore, the DTM based on four-color map patterns was selected to perform the alignment-free search for A-domains in the proteome of the cyanobacteria Microcystis aeruginosa.The spectral moments series  were able to detect more hits than the 20 A-domains already annotated in the proteome, which were also detected by the multi-template BLASTp. Hypothetical proteins are greatly expanded in cyanobacteria and have been placed into the diversity of the nuclease superfamily by homology inference. Probably the graphical and HMM profiles detected signals of the A-domain signature among the diversity of the hypothetical proteins leading us to new variants of A-domains.Interestingly, DTM detected in the proteome two putative hits of A-domain signatures among the hypothetical proteins and later another three hypothetical proteins were detected as A-domains by the profile HMM Microcystis aeruginosa. The use of an ensemble of methods provides more confidence to the predictions since each method exploits different features of the protein sequences. Four-color maps generate graphical patterns using the sequence order and the amino acid composition arranged into a 2D space. These graphical profiles are numerically described in a wide range of information by series of TIs, which characterize individually the sequences. Consequently, such TIs are flexible to be used for different classification problems (from high sequence identities until the twilight zone).Both methods detected different additional hits as A-domains but they were found among the hypothetical proteins, which is a good clue for the presence of further A-domains remote homologues in the proteome of On the other hand, the profile HMM is based on amino acid positions conserved at low range through multiple sequence alignments in linear sequences. HMM profiles are proved sensitive tools for remote protein homology detection even when the sequence conservation is restricted to short motifs, as is the case of A-domains The ensemble of the three sequence search algorithms  provided the best solution for the search of remote homologues among a highly diverse protein class.TI2BioP software version 2.0 was used for the calculation of spectral moments as TIs associated with the protein four-color maps depicted below . Protein four-color maps are inspired on the Randic's DNA/RNA .Brevibacillus brevis (pdb 1AMU). Each of the four colors is associated with each one of the amino acid groups: polar (green), non-polar (red), acid (yellow), basic (blue).http://www.nii.res.in/nrps-pks.html) to conform the training set. The test set was made up of 29 A-domain sequences independently gathered from the subset of the NRPS-PKS hybrids (http://www.nii.res.in/nrps-pks.html). The sequence diversity among A-domains was explored comparatively using the Needleman-Wunsch (NW) algorithm.109 A-domain sequences from NRPS were collected from the major NRPS–PKS database  domain database of protein structural families (version 3.2.0) (http://www.cathdb.info). We select the FASTA sequence database for all CATH domains sharing just the 35% of sequence similarity (<35% of sequence identity). The starting data was reduced to 8 854 CATH domains: 17 cases were removed because they showed the A-domain signature when an hmmsearch was performed against the AMP-binding profile HMM (PF00501). The members of the test set (2 213 sequences) were selected taking out at random the 20% from the 8 854 CATH domains; the rest 6641 CATH domains were used to train the models.The starting group was made up for 8 871 protein sequences downloaded from the Files S11).Each A-domain and CATH domain sequence retrieved was labeled respecting its original database ID code (B) between atoms for the small organic molecules. B is a square matrix of n x n row and column where its non-diagonal entries are ones or zeroes if the corresponding bonds or edges (n) share or not one atom. The different powers of B give the spectral moments of higher order to obtain the spectral moments series (µ0- µ15).kB = (B)k, which are the natural powers of BThe spectral moments are TIs calculated as the sum of the entries placed in the main diagonal of the bond adjacency matrix ) of all amino acids placed in the cluster). The main diagonal of B was weighted with the average of the electrostatic charge (Q) between two adjacent clusters. The q values were taken from Amber 95 force field ) using the first nine amino acids of pdb 1AMU (M1V2N3S4S5K6S7I8L9). The For the calculation of the spectral moments from the protein four-color maps, we consider each region of the map as a node made up for the amino acids clustering; two adjacent regions of the map sharing at least one edge (not a vertex) are connected. fcµ0, for k = 1 the fcµ1 and for k = 2 the fcµ2. The node adjacency matrix derived from this 2D map is described for each caseExpansion of expression (2) for k = 0 gives the TI2BioPversion 2.0 arranges automatically all domain sequences (positive and negative sets) into four-colour maps and allows the calculation of spectral moments series (fcµk). Files S12 shows the calculation of these indices to the positive and negative sets.p-level) p(F)<0.05.The General Discrimination Analysis (GDA) best subset was carried out for variable selection to build up the linear models Gini index was used as a measure of goodness of fit and the ¨FACT-style direct stopping¨ was set to 0.1 as stopping rule to select the right-sized classification tree.The development of the DTM was performed using the C&RT (Classification and Regression Trees)-style univariate split selection from the Classification Trees (CT) module of the STATISTICA 8.0 for Windows Feature and Variable Selection analysis of the ANN module from STATISTICA software We used the Multilayer Layer Perceptron (MLP) network architecture as the most popular network architecture in use today. The selection of the subset of predictors that is most strongly related to the response variable was supported on the The performance of the all alignment-free models was evaluated by several statistical measures commonly used for classification: accuracy, sensitivity, specificity and F-score . The robustness of the classification model was verified by a 10-fold cross-validation (CV) procedure on the training set. The CV statistics for each of the ten samples were averaged to give the 10-fold estimate for the accuracy, sensitivity and specificity Microcystis aeruginosa proteome considering its repertoire of A-domains signatures.We used an ensemble of three methods for the re-annotation of the Microcystis aeruginosa NIES-843 (6 311 annotated genes) and later a simple rule was applied to detect A-domain signatures (fcµ1≥3817 and fcµ2≤11.12).The graphical method represented by the alignment-free model (DTM) to perform the A-domain search in the proteome. Spectral moments series from the four-color maps were calculated for the proteome of hmmbuild release 2.3.2 Microcystis aeruginosa.A profile HMM for whole A-domain sequences was built as follows: (i) the 109 A-domain sequences used in training the alignment-free models were aligned by CLUSTALW The multiple-template BLASTp reported by the NRPS-PKS database developers for A-domain searches was used Microcystis aeruginosa. Since each sequence search method extract different features from the protein sequences, their integration allow a more exhaustive description of certain protein class and therefore provide a higher yield for the detection of remote protein homologous. The knowledge of the complete repertoire of A-domains in the proteome of cyanobacteria species may allow unraveling new NRPS clusters for the discovery of novel natural products with important biological activities.The utility of graphical approaches in bioinformatics has been demonstrated by the introduction of the four-color maps and the TIs as a cooperative tool for detecting remote homologous of A-domains in the proteome of File S1BLASTp  search for A-domains using a single template against the whole dataset.(TXT)Click here for additional data file.File S2BLASTp  search for A-domains using a single template against the whole dataset.(TXT)Click here for additional data file.File S3BLASTp  search for A-domains using a single template against the whole dataset.(TXT)Click here for additional data file.File S4BLASTp  search for A-domains using a single template against the whole dataset.(TXT)Click here for additional data file.File S5BLASTp  search for A-domains using a single template against the whole dataset.(TXT)Click here for additional data file.File S6BLASTp  search for A-domains using multiple templates against the whole data set.(XLS)Click here for additional data file.File S7Profile HMM  search for A-domains against the whole data set.(TXT)Click here for additional data file.File S8Alignment-free search for A-domain signatures in the proteome of Microcystis aeruginosa.(XLS)Click here for additional data file.File S9Multi-template BLASTp search for A-domains in the proteome of Microcystis aeruginosa.(XLS)Click here for additional data file.File S10HMM profile search for A-domain signatures in the proteome of Microcystis aeruginosa.(TXT)Click here for additional data file.File S11Database used in the study. Fasta files for training and test series of A and CATH domains.(ZIP)Click here for additional data file.File S12IDs, training and prediction series, values of the TIs for A and CATH domains.(XLS)Click here for additional data file.


S159 (p-Cdk5S159) in the spinal cord dorsal horn (SCDH). CFA-induced p-ERK primarily colocalized with p-Cdk5S159 in superficial dorsal horn neurons. Levels in p-ERK and p-Cdk5 were also increased in the 2nd phase of hyperalgesia induced by formalin injection, which can produce acute and tonic inflammatory pain. MAP kinase kinase inhibitor U0126 intrathecal delivery significantly suppressed the elevation of p-Cdk5S159, Cdk5 activity and pain response behavior  induced by CFA or formalin injection. Cdk5 inhibitor roscovitine intrathecal administration also suppressed CFA-induced heat hyperalgesia and Cdk5 phosphorylation, but did not attenuate ERK activation. All these findings suggested that p-Cdk5S159 regulated by ERK pathway activity may be a critical mechanism involved in the activation of Cdk5 in nociceptive spinal neurons contributes to peripheral inflammatory pain hypersensitivity.Cyclin-dependent kinase 5 is a proline-directed serine/threonine kinase and its activity participates in the regulation of nociceptive signaling. Like binding with the activators (P35 or P25), the phosphorylation of Cdk5 plays a critical role in Cdk5 activation. However, it is still unclear whether Cdk5 phosphorylation (p-Cdk5) contributes to pain hyperalgesia. The aim of our current study was to identify the roles of p-Cdk5 and its upstream regulator in response to peripheral inflammation. Complete Freund's adjuvant (CFA) injection induced acute peripheral inflammation and heat hyperalgesia, which was accompanied by sustained increases in phospho-ERK1/2 (p-ERK1/2) and phospho-Cdk5 S159) in the spinal cord dorsal horn (SCDH) may contribute to pain sensitization.Cyclin-dependent kinase 5 (Cdk5) is a member of the cyclin-dependent kinase family S159 phosphorylation after peripheral inflammatory stimulation. Interestingly, it has reported that ERK1/2 activity through phosphorylation is also regulated by Cdk5 activity S159 in nociceptive transmission in a CFA or formalin induced inflammation model. Furthermore, to explore the link between the activation of ERK1/2 and p-Cdk5S159 in pain signaling, we delivered the MEK (MAP kinase kinase) inhibitor U0126 and Cdk5 inhibitor roscovitine respectively, and then determined the activity of ERK1/2 and p-Cdk5S159 expression in the dorsal horn of spinal cord enlargement after CFA injection.Cdk5 activity can regulate many proteins associated with cell morphology, synaptic activity, neuronal survival, and apoptosis Male Sprague-Dawley rats (220∼250 g) were obtained from Shanghai laboratory animal center . All experimental procedures were approved by the Committee of Animal Use for Research and Education of Shanghai Jiao Tong University School of Medicine. The numbers of animals used in all experiments were minimized according to the guidelines established by the Ethical Issues of the International Association for the study of pain. Rats were housed in a climate-controlled environment on a12 h light/dark cycles and free access to food and water. CFA  or 5% formalin (100 µl) was injected into the plantar surface of the left hind paw of rats. For intrathecal drug delivery, a polyethylene-10 catheter was implanted into the intrathecal space of the spinal cord at the lumbar enlargement, and 5 µl of the MEK inhibitor U0126  dissolved in 4% dimethyl sulfoxide (DMSO), was administered 30 min before treatment and once per day (for 7 days) after treatment, DMSO (4%) was injected as vehicle control. 5 µl of the Cdk5 inhibitor roscovitine  dissolved in 10% DMSO, was administered 30 min before treatment and once per day (for 7 days) after treatment, DMSO (10%) was injected as vehicle control.Thermal escape latencies were evaluated using a radiant heat stimulation apparatus as previously described To measure spontaneous flinches for formalin injection, rat was acclimated in behavior chamber (30 cm×30 cm) with a plexiglass floor for at least 1 hour. 30 minutes after 2 µg MEK inhibitor U0126 or vehicle (DMSO) injection, 100 µl of 5% formalin solution was then injected subcutaneously into the plantar surface of the left hind paw of rat. Animal was immediately returned to the chamber for observation. Video recorded from below with a webcam (Logitech) for 1 hour. Flinching was characterized as a rapid and brief withdrawing or flexing of the injected paw and quantified by counting the number of flinches in five-minute bins for one hour.Motor function was assessed by the inclined plane test as described in a previous study 2, 10% glycerol, 1% Triton X-100, 5 mM EGTA, 0.5 mg/ml leupeptin, 1 mM PMSF, 1 mM Na3VO4, 10 mM NaF, and a protease inhibitor cocktail from Roche). Homogenates were centrifuged at 13,000 g for 15 min at 4°C and protein concentrations determined using the BCA assay kit . Equal amounts of samples (40 µg) of protein were separated by SDS–PAGE using 10% running gels and transferred to nitrocellulose membranes (Bio-Rad Laboratories). After blocking with 5% non-fat milk in TBST  for 1 h at room temperature, the membranes were incubated overnight at 4°C with primary antibody, P-Cdk5S159 ; Cdk5 antibody ; p-EPK1/2 ; ERK1/2 ; β-actin . The membranes were washed in TBST 10 min for three times, and then incubated with HRP-conjugated secondary antibody  for 1 h at room temperature. Finally, the blots were developed with the lightening chemiluminescence kit .Rats were deeply anesthetized with sodium pentobarbital , then the dorsal horns of the L4/5 spinal cords were removed and immediately homogenized in ice-chilled lysis buffer ; mouse polyclonal anti-p-ERK1/2, , then sections were washed and incubated for 1 h with fluorescence-labeled secondary antibodies  at room temperature. The double-stained images were examined using Leica DFC500 (Bioscience).Rats were anesthetized with sodium pentobarbital  and then perfused with 200 ml 0.9% saline followed by 4% ice-cold paraformaldehyde. L4/L5 ipsilateral spinal cords were immediately removed and post-fixed in 4% paraformaldehyde overnight at 4°C, then immersed in 30% sucrose until the samples sank to the bottom of the container. The tissues were embedded with Tissue-Tek  and frozen in −80°C. Frozen tissue sections were cut coronally in a cryostat at a thickness of 8 µm. For staining, sections were blocked for 1 h at room temperature with 0.35% Triton X-100/1% BSA. Subsequently, sections were incubated overnight at 4°C with rabbit polyclonal anti-P-Cdk52, 1 mM EDTA, 1 mM EGTA and 0.1 mM dithiothreitol). To determine kinase activity of Cdk5, the corresponding immunoprecipitates were incubated with 6 ml assay dilution buffer containing 10 µg histone H1  and 12 ml magnesium/ATP mixture  in a final volume of 30 ml at 30°C for 30 min. The reaction was terminated by the addition of SDS sample buffer, followed by boiled for 5 min. Proteins were separated by 12% SDS-PAGE gels and transferred to PVDF membranes. Histone H1 and p-Histone H1 levels were detected, and the phosphorylation level of Histone H1 was indicated as the Cdk5 activity.Cdk5 kinase assay was performed as described previously We quantified the Western blots in two steps, first, using β-actin levels to normalize  protein levels and control for loading differences in the total protein amount. Second, we presented protein level changes in treatment groups as a percentage of those naive groups. 100% of protein level changes refer to control levels for the purpose of comparison to experimental conditions. Data are expressed as means ± SEM. Differences between groups were analyzed using either the Student's t test or one-way ANOVA. All the data of rat behavioral experiments are expressed as means ± SD. Statistical analyses were performed by two-way repeated-measures ANOVA followed by Bonferroni's multiple comparison tests. The standard for statistical significance was p<0.05.S159) increases the enzymatic activity of Cdk5 activating kinases S159 levels. Immunoblotting of p-Cdk5S159 showed a significantly time-dependent change, being elevated visibly at 6 h (3-fold), reaching a maximal level at 1 day (5.8-fold) and retaining a high level 1 week after CFA injection (S159 expression was similar to the Cdk5 activity (4-fold at 6 h and 5-fold at 1 day) induced by CFA injection according to a previous report S159 may be closely associated with Cdk5 activity.To investigate the changes in protein phosphorylation levels of ERK and Cdk5 in the SCDH after peripheral persistent inflammation, we injected 100 µl CFA into the plantar surface of a hindpaw. ERK activation by CFA was confirmed by Western blot analysis. In agreement with previous studies njection . The aboIt is well known that nociceptive behavior associated with inflammatory pain is linked to heat hyperalgesia. We observed the changes in nociceptive behavior following CFA injection by measuring thermal PWLs. The average thermal PWL threshold (seconds) of ipsilateral hind paw was significantly reduced 2 h to 7 d post CFA injection  compared to normal rats during the same time spans  was mediated by an activation of ERK, MEK inhibitor U0126 2 µg was intrathecally administered twice (30 min before and 1 d after intraplantar CFA injection). As shown in S159 was remarkably increased to 842.62±38.66%  of naïve following CFA (1 d), and then declined to 363.37±40.83%  of naïve following U0126 injection  of naïve following CFA (1 d), and then reduced to 318.60±33.55%  of naïve following roscovitine injection  was intrathecally injected in rats (30 min before and once per day (for 7 days) after CFA injection). At the dose we used, we did not observe obvious toxicity of this inhibitor, animals behaved normally, and locomotion was unaffected before CFA injection . Heat hynjection , suggestreatment , indicatS159  Most p-Cdk5S159 colocalized with p-ERK in spinal cord dorsal horn 1 d after CFA injection, and Intrathecal pretreatment with ERK inhibitor U0126 significant decrease p-Cdk5S159 expression, meanwhile, U0126 also inhibit Cdk5 activity after CFA or formalin injection. The focus of our work was on the phosphorylation of Cdk5 itself in inflammatory pain. Previous studies reported that the acute stimulation of opioid receptors leads to upregulation of Cdk5 activation through a MEK-ERK -dependent pathway, and to disruption of MEK-ERK signalling by a cdk5/p35-induced MEK1 inhibition Yang et al have reported that Cdk5 activity was upregulated in dorsal horn neurons, which was detected by an in vitro phosphorylation assay and Histone H1 was used as a substrate. Their studies have shown that intrathecal pretreatment with Roscovitine, a specific inhibitor of Cdk5 activity, and intrathecal delivery of the DN-Cdk5 (N144) gene or overexpression of Cdk5, p35 or p25 in dorsal horn neurons significantly alleviated or enhanced heat hyperalgesia, but not mechanical allodynia. Pretreatment with Roscovitine have no effect on p-ERK after CFA injection 15 min and 6 h S159 would be an advantageous target for the control of chronic pain development to prevent or treat pain.In this study, MEK inhibitor U0126 attenuated the inflammatory nociceptive response and the activation of spinal ERK 1/2 and Cdk5 phosphorylation in the CFA or formalin-induced inflammatory pain. These results strongly suggest that Cdk5 phosphorylation at ser159 is an important downstream of ERK activity, contributing to Cdk5 activity in the early stage of peripheral inflammatory pain. Thus, in the clinic, p-Cdk5


Pre-recorded register data from dairy herds are available in almost all Nordic countries. These databases can be used for research purposes, and one of the research areas is animal welfare. The aim of this study was to investigate if pre-recorded register data could be used to identify herds with good welfare, and to investigate if a combination of register data sets could be used to be able to more correctly distinguish between herds with good welfare and herds with welfare deficiencies.As a first step, nine animal-based measurements on calves, young stock and cows in 55 randomly selected herds were performed on-farm as the basis for a classification of welfare at the herd level. The definition for being a case herd with “good welfare” was no score lying among the 10% worst in any of the nine welfare measurements. Twenty-eight of the 55 herds were cases according to this definition. As a second step, 65 potential welfare indicators, based on register data in a national dairy database, were identified by expert opinion. In the final step, the extent to which the suggested welfare indicators predicted farms’ as having good welfare according to the stated definition was assessed. Moreover, the effect of combining in sequence a previously developed model that identified herds with poor welfare with the present model identifying herds with good welfare was investigated.The final set of welfare indicators used to identify herds with good animal welfare included two fertility measures, cow mortality, stillbirth rate, mastitis incidence and incidence of feed-related diseases . This set had a test sensitivity of correctly classifying herds with no score lying among the 10% worst of the nine welfare measurements of 96 %. However, the specificity of the test was only 56% indicating difficulties for the test to correctly classifying herds with one or more scores lying among the 10% worst. Combining the previously developed model with the present model, improved the welfare classification.This study shows that pre-collected register data may be used to give approval to dairy farms with “good welfare” and that combining different sets of register data can improve the classification of herd welfare. All Nordic countries except Iceland have national dairy disease recoding systems that rely on the reporting of veterinary-treated disease events -3. In SwToday there is considerable agreement regarding the use of animal-based measures such as lameness, body condition and cleanliness in assessments of animal welfare in dairy herds ,6. HowevThe aim of this study was to investigate if pre-recorded register data could be used to identify herds with good welfare, and to investigate if a combination of register data sets could be used to distinguish more correctly between herds with good welfare and herds with welfare deficiencies.Herds eligible for inclusion in the study were Swedish dairy herds enrolled in the Swedish official milk recording and animal disease recording schemes (SOMRS and SADRS) in 2004. The study population was selected to reflect expectations regarding the herd size in 2010. Based on data from 1995 – 2004 an expected median herd size was estimated for 2010. To establish a similar distribution for herd size for the estimation of 2010 as of 1995 – 2004 stratification was used. The distribution of 1995 – 2004 was stratified in 10 strata making the proportion of herds in each stratum equal. These strata and proportions were then used to establish the expected distribution in 2010. The estimated median herd size and herd size in the 10 strata of 2010 were 65 cows, and 15-23, 24-30, 31-35, 36-40, 41-46, 47-52, 53-60, 61-72, 73-93, >93, respectively. The participating herds were then randomly selected from all dairy herds delivering milk in two selected areas, one in southern and one in northern Sweden, ensuring equal proportion in the set strata. The randomly selected herds were contacted and ask if they were willing to participate. Out of 64 contacted herds 62 accepted the invitation to participate and were visited twice during 2005, the first visit occurring in March to mid-June and the second in October to December. Of the 62 herds 55 had complete records in the SOMRS and in the SADRS, and only these were included in the final statistical analyses.The methodology used to assess animal welfare within the study herds has previously been described , but briHerd-level estimates of the animal-based measurements were obtained by applying cut-off levels and calculating the proportion of animals within each age group that exceeded the cut-off. The welfare “gold standard” i.e. the definition of welfare status (no welfare remarks vs. one or more welfare remarks) against which the performance of potential welfare indicators in the database was to be evaluated, was based on the number of animal-based measurements where a herd did not score among the 10% worst. A score among the 10% worst gave the herd a ‘remark’ for that measure, and a herd with zero scores among the 10% worst was defined as being a herd with good welfare.The database of the Swedish national dairy recording system  in which data from the SOMRS and SADRS are merged was used as the main source of the potential welfare indicators for this study. The SCD includes information on e.g. fertility, genetics, diseases, mortality including culling reasons, production and slaughterhouse registrations as well as demographic data ,8. The cThe potential welfare indicators were calculated for the period from January to December 2005, i.e. over the same period of time as the on-farm data were collected, for the 55 farms with complete records. The selection of potential welfare indicators has also been described earlier , but wilThe goal of the analyses was not to study causality, nor to determine exact relationships between animal-based measurements and potential welfare indicators. Rather, the aim was to identify a limited set of pre-recorded welfare indicators that could, in combination, be used to identify herds with possible good animal welfare. Consequently, it was necessary to reduce the initial set of potential welfare indicators suggested by the experts. Therefore, as a first step, univariable associations between all 65 potential welfare indicators and each of nine animal-based measurements were screened using linear regression. Welfare indicators with an association significant at P < 0.05 were then taken forward to a multivariable reduction step, using the same methodology. By including the second step we added a stronger requirement that the potential indicator should show a significant multivariable association with one or more of the animal based measurements, in order to be taken further. Consequently, only indicators that were significantly (P < 0.05) associated with one or more animal-based measures in this multivariable context were considered to be candidates for the final set of welfare indicators. All statistical analyses were performed using the software Stata® version 10 .th, 10th and 5th percentile for welfare indicators that were associated with the animal-based measurements. In this way, all potential welfare indicators were dichotomised (positive or not) into three different “tests” and the sensitivity and specificity for each “test” in identifying a herd “positive” for good welfare were then estimated. By this non-statistical, but systematic, selection procedure, the set of potential welfare indicators was then applied to the study herds, and a herd was regarded as being a case (herd with good welfare) if it was positive on this test. The overall performance of the identified set of welfare indicators was evaluated in terms of sensitivity, specificity and percentage of all herds classified correctly with respect to the field assessed welfare status (good vs. not good). Finally, in an attempt to increase the specificity of the previously published test tool for identification of herds with poor welfare [Each potential welfare indicator was treated as a “diagnostic test”, i.e. a tool that distinguishes between two different statuses; e.g. sick vs. healthy; good vs. poor welfare etc. Like in any diagnostic test measured on a continuous scale, cut-off levels needed to be identified as these are the points at which the “test” would be regarded as “positive”. Because the choice of cut-off level affects “test” performance, measured as sensitivity (the probability of correctly identifying case herds) and specificity (the probability of correctly identifying herds that are not cases), we evaluated each potential welfare indicator at three different levels. The cut-off levels evaluated were the 20 welfare , this waEighteen of the 55 participating herds were housed in loose housing with cubicle stalls and 37 in tie stalls; six had Swedish Red and White (SRB) cattle, 14 had Swedish Holstein (SHF) breed and 35 had mixed breeds (SRB and SHF). The arithmetic mean annual herd size was 70 cows .th and the 100th percentile constituted (10% worst), on average, 38% (23 - 55%) of the total range in herd prevalence of the nine animal-based measurements. Of the 55 herds in the study population, 28 met the criteria for being classified as a herd with good welfare (no remarks). The distribution of number of welfare remarks is shown in figure Descriptive statistics for the animal-based measurements have previously been described . The ranP < 0.05) with one or more of the nine animal-based welfare measurements. They represented all focus areas except drug use. Following variable reduction using multivariable regression, another 10 indicators were excluded leaving 18 potential welfare indicators for the systematic selection procedure [Twenty-eight of the initial 65 potential welfare indicators showed a significant univariable association (rocedure .th percentile), percentage heifers without mating/artificial insemination by 17 months of age (cut-off: 10th percentile), stillbirth rate (cut-off: 5th percentile), cow mortality (cut-off: 10th percentile), mastitis incidence (cut-off: 10th percentile), and incidence of feed-related diseases . Herds considered to have good welfare had to have values below the cut-offs in at least one of the welfare indicators. Table After the systematic selection procedure, six welfare indicators that were jointly able to identify 27 of the 28 case herds (with good welfare according to gold standard definition) were identified. These were; percentage cows with late ongoing artificial inseminations >120 days) (with a cut-off at the 2020 days (The combination of the model to identify herds with poor welfare with the model to identify herds with good welfare in sequent resulted in herds being classified in to three groups Table ; good weTheoretically this study together with our previous work show that register data can be used to identify herds according to their welfare status. This is very valuable since usage of register data is quick and requires less financial resources than on-farm welfare assessments. However, the methods to identify herds with poor or good welfare have to be validated in field studies to obtain a true measure on how well they actually distinguish between herds with different levels of welfare. For instance, there is a potential risk that smaller dairy herds more often will be identified as herds with poor welfare since the impact of one animal with a deficiency is proportional higher in a small herd than in a larger herd, and this has to be addressed if a national screening is performed. Another concern is the quality of the data in the SADRS and the SOMRS. Studies have shown discrepancies between farmer recordings of disease treatments done by the veterinarians and the veterinary treatments actually registered in the databases ,12. ThusTo identify herds with poor welfare and then herds with good welfare in sequence improved the welfare classification but still resulted in some misclassifications. If only the model to identify herds with < 2 or ≥ 2 welfare remarks were used we could only harshly classify the herds in to two groups, those with presumed poor welfare and those with presumed not poor welfare. Conversely, using only the model which identified herds either with no welfare remarks or with one or more welfare remarks would give an equally harsh classification. Combining the two models resulted in a more refined classification of the herds. Extrapolated to a national level a national screening of herds with these methods would result in approximately 1,500 dairy herds being classified as having poor welfare in Sweden. However, 3,200 herds would be classified as having good welfare, and could get approval for this.Register data might be used to identify dairy herds welfare status. However, the developed tools to identify herds with good or poor welfare have to be validated in a field study before nationwide use.The authors declare that they have no competing interests.


Medullary thyroid carcinomas (MTC) are slow-growing tumors originating from neuroendocrine, parafollicular cells (C-cells) in the thyroid gland. Despite its low prevalence, MTC is one of the most deadly forms of thyroid cancers. Like other neuroendocrine cancers (e.g. pancreatic), MTC is often diagnosed at advanced stages at which surgical removal of the thyroid is no longer an effective treatment. MTC affects both men and women equally, is either sporadic (75% MTC cases) or hereditary (25% MTC cases) and associated to other neuroendocrine cancers .Rearranged-during-transfection (RET) protooncogene are responsible for 100% hereditary forms of the disease and about 40% sporadic forms of MTC [RET, which encodes a receptor tyrosine kinase, causes MTC. Furthermore, molecular therapies targeting RET and other receptors tyrosine kinase have been developed which have led to the recent approval of two new drugs by the FDA. However these drugs have limited benefits and a large proportion of MTC patients still have no access to an efficient treatment because the causes of sporadic MTC are not well understood [About 20 years ago, a breakthrough discovery revealed that activating germline mutations in the derstood .in vivo in a transgenic mouse line. Overexpression of the Cdk5 activator, p25, in mouse thyroid C cells leads to the development of MTC, while preventing p25 overexpression arrests tumor growth. These findings demonstrate clearly the importance of Cdk5 activity for MTC progression. This was at first intriguing because Cdk5 is better known as a neuronal protein. Indeed, Cdk5 and its activators are expressed at high levels in brain compared to other tissues. Cdk5 regulates brain development and function and deregulation of Cdk5 activity by binding to its cleaved activator, p25, has been linked to neurodegenerative diseases such as Alzheimer's disease. Considering that neuroendocrine cells and neurones have a common ontological origin  and that they share similar properties , this study raises the possibility that common signalling pathways may underlie cancer and neurodegenerative diseases.In our study , we showMechanistically we found that Cdk5 regulates the phosphorylation state of the tumor suppressor, retinoblastoma protein (Rb), and the subsequent expression of Cdk2 and cyclin A Figure . It is wIn this study, we report a novel animal model for MTC based on a tetracycline-controlled transactivator system (tTA) in which we can induce or arrest MTC development by activating or repressing p25 overexpression in the mouse thyroid C cells Figure . We showIn conclusion, our study unravels a major role for Cdk5 in neuroendocrine thyroid cancer and suggests that targeting Cdk5 or downstream effectors is a valid strategy to cure MTC. Furthermore our animal model provides new routes to discover molecular pathways important for MTC and test potential therapies. Our findings represent an important advance in a field in which molecular tools are limited and the discovery of new putative drug targets difficult.


NTR conveys multiple signals via its intracellular death domain. However, how the death domain is activated and interacts with downstream adaptors remains unclear. Here, we report two crystal structures of the p75NTR death domain in the form of a non-covalent asymmetric dimer and a Cys379-Cys379 disulfide bond linked symmetric dimer, respectively. These two dimer arrangements have not previously been observed in other death domain-containing proteins. Further analysis shows that both the Cys379-Cys379 disulfide linked and non-covalent full-length p75NTR dimers are present on the cell surface. These observations suggest that various oligomers may exist simultaneously on the cell surface, and that p75NTR activation and signalling may be modulated by neurotrophins or other factors via inducing a shift of the equilibrium between different oligomeric states.The neurotrophin receptor p75 NTR, which binds to all neurotrophins with equal affinity NTR lacks intrinsic kinase activity. Glycosylated extracellular cysteine-rich domains serve as the ligand-binding site of p75NTR, while its intracellular region consists of a flexible juxtamembrane segment (residues 280–333), a globular death domain (residues 334–418), and a short tail (residues 419–425) NTR is also widely expressed in non-neuronal cells, including immune cells, smooth muscle and epithelial cells, and fibroblasts NTR can recognise a number of novel ligands such as β-amyloid peptides, prion peptides and myelin-associated inhibitors, in cooperation with their related co-receptors NTR are conveyed by the recruitment and release of multiple cytoplasmic adaptor proteins such as neurotrophin receptor interacting factor (NRIF), neurotrophin receptor interacting MAGE homolog (NRAGE) and tumor necrosis factor receptor (TNFR) associated factor 6 (TRAF6), and result in the activation of a wide array of downstream signalling molecules , c-jun N-terminal kinase (JNK) or caspases) NTR regulates various cellular functions including cell apoptosis, survival, neurite outgrowth and synaptic plasticity, making it an important target for the treatment of neurodegenerative and other diseases Neurotrophins are a family of secreted growth factors essential for early stage innervation and maintenance of the adult vertebrate nervous system. They interact with two different types of cell surface receptors, the selective Trk receptor tyrosine kinases and p75NTR, solved by NMR, displays a folding module that is similar to death domain superfamily members such as Fas and TNFR, in spite of low sequence identity NTR ectodomain complex with NT3 NTR binds dimeric neurotrophins and that dimerization of the p75NTR intracellular region is functionally significant NTR is likely to be different from that of Fas and TNFR. Recently, Vilar and colleagues reported that p75NTR could exist as a disulfide bond-linked dimer before ligand binding via a highly conserved Cys257 residue in its transmembrane region, and the dimer proportion in transfected cells didn't change upon NGF binding NTR intracellular domains was transiently disrupted by conformational changes that occured upon ligand binding. These results suggest that neurotrophins activate p75NTR by a mechanism involving the rearrangement of intracellular domains, in which Cys257 acts as a fulcrum, mediating the propagation of conformational changes , p75CTD (334–425), p75ICD (280–425), and p75DD-Fusion (a chimera of the p75NTR death domain and a p75-interacting peptide from NRIF)  . By intrNTR constructs in solution is shown in NTR is important for dimer formation. We failed to obtain crystals until the proteins were treated with DTNB, a cysteine-modifying reagent that generates a homogeneous oxidised state and stabilises local fluctuations The oligomeric behavior of the four p75NTR with other proteins The structure of p75DD was solved by molecular replacement using the NMR structure (PDB 1NGR) as the search model. After model building and refinement, we were able to trace amino acids 334–417 of the death domain. The overall fold of the p75DD monomer is similar to the NMR structure with a small RMSD value (1.44 Å) of Cα traces . Briefly2The death domain, a versatile protein interaction module, plays an essential role in the assembly and activation of apoptotic and inflammatory complexes such as the DISC, PIDDosome and TLR/IL-1 receptor complex NTR death domain and a p75NTR-interacting peptide from NRIF, connected by an 18-amino-acid linker  co-eluted with the p75DD protein during size exclusion chromatography. We then generated the p75DD-Fusion protein and successfully crystallised the dimer fraction (NTR death domain and a portion of the linker residues (GSGSGSGS) were retained in p75DD-Fusion crystals  shows that an inter-chain disulfide bond is formed between the Cys379 residues from each monomer , showing a large conformational change relative to the typical globular structure of its native state. Second, the p75NTR symmetric death domain dimer is linked by an inter-chain disulfide bond, whereas the Fas death domain dimer is associated by non-covalent interactions between the stem helices (residues 287–318) and C-helices . It is noteworthy that a later NMR study suggests a different 5∶4 or 5∶5 Fas/FADD complex in solution under near physiological condition NTR symmetric death domain dimer is likely in a self-inhibitory state, according to the Snail-Tong model NTR symmetric dimer and Fas-Fas dimer  completely abolishes the dimerization of p75DD-Fusion . Interesrization . We furtrization . Furtherrization , suggestNTR without ligand treatment. The single mutation of Cys257 or Cys379 only slightly reduces the dimer fraction, while double mutation of Cys257 (C257A) and Cys379 (C379S) nearly abolishes p75NTR dimerization  , and other potential non-covalent dimers may simultaneously exist on the cell surface prior to ligand binding  kinase Here we described two oligomeric arrangements of the p75tigation . In addi binding . These mNTR regulation may be conferred by oxidative stress. Cysteine residues are believed to mediate cellular responses to redox conditions via both detecting oxidative changes and transducing conformational alterations in protein structure and function. A common mechanism underlying the sensitivity of cysteines to redox status is the formation of disulfide bonds. Recent studies have shown that the intracellular domain confers protection on p75NTR against oxidative stress-induced apoptosis, independent of its extracellular domain or ligand binding NTR death domains suggests that the Cys379 residue may function as a redox sensor. Although the Cys379 residue of p75NTR is not conserved in other death domain-containing proteins , p75CTD (334–425) and p75ICD (280–425), were amplified respectively from a rat p75NTR cDNA and constructed into pET24a vector with C-terminal His6 tag. Proteins were over-expressed in the E. coli BL21 strain and purified with Ni-NTA column and size-exclusion chromatography using a Hepes-KOH buffer (pH 7.0) containing 100 mM KCl, 5% glycerol and 1 mM EDTA. DTNB powder was dissolved in the same buffer to 2 mM concentration, and was added to protein solution at a molar ratio of 5∶1 between DTNB and proteins. After modification with DTNB, proteins were concentrated to 40 mg/mL to screen crystal growth conditions.Several constructs of the p75NTR death domain (residues 334–418) and a 26-amino-acid sequence (residues 552–577) of the NRIF protein were fused with an 18-amino-acid linker coding for the sequence GSEFGSGSLVPRGSGSGS. These proteins were also overexpressed in the E.coli BL21 strain and purified in a Hepes-NaOH buffer (pH 7.5) with 100 mM NaCl, 5% glycerol and 1 mM EDTA. Proteins used for crystallization were concentrated to 20 mg/mL after DTNB treatment.The p75DD-Fusion protein constructed from p756 tag and overexpressed in E. coli. Purification of RIP2 CARD domain was similar to that of p75DD.For binding tests, NRIF peptide (residues 552–577) was chemically synthesized. Human RIP2 CARD domain (residues 438–524) was constructed into pET24a vector with C-terminal HisP75DD and p75DD-Fusion protein crystals were grown at 4°C using the sitting-drop vapour diffusion method. Crystals of p75DD were obtained using 0.1 M Bis-Tris propane pH 7.0 and 1.2 M sodium citrate, while p75DD-Fusion crystals were grown in 0.1 M Bis-Tris propane pH 7.0 and 1.4 M sodium malonate pH 7.0. All crystals were harvested directly from the crystallization drop and flash-frozen in liquid nitrogen. Diffraction data for p75DD crystals were collected at 100K in-house and processed with iMosflm NTR death domain (PDB 1NGR) as the initial search model. The residual group of the DTNB reagent (MNB) was sculpted with Sketcher The structures of p75DD and p75DD-Fusion were solved by molecular replacement via MOLREP NTR construct in a pCDNA3 vector with an N-terminal hemagglutinin (HA) epitope tag was a generous gift from Prof. Bruce Carter and Carlos Ibanez. Mutations were introduced by site-directed mutagenesis and verified by DNA-sequencing. 293T human embryonic kidney fibroblasts were cultured in DMEM. Wildtype and mutants plasmids were transiently transfected into 293T cells. After 48 h, cells were harvested and lysed in 40 mM Tris (pH 7.6) buffer containing 150 mM NaCl, 0.5% NP-40, 10% glycerol and a protease inhibitor cocktail. Cell lysates were subjected to immunoprecipitation in the lysis buffer using an anti-HA monoclonal antibody. The precipitated proteins were boiled with reduced (DTT+) or non-reduced (DTT−) SDS loading buffer before being detected by immunoblotting with anti-HA monoclonal antibody.A full-length rat p75on and koff) and affinities (kD) were calculated from a non-linear fit of the data using the Octet instrument.The binding kinetics of the p75CTD monomer or dimer proteins to the CARD domain of RIP2 were measured by bio-layer interferometry on an Octet (ForteBio). A double cysteine residue mutant (C379S/C416S) was used as the p75CTD monomer, while the p75CTD dimer used in these experiments was the dimer of the C416S mutant. Streptavidin High Binding FA biosensors were loaded with biotinylated-p75CTD proteins in 10 mM PBS (pH 7.4) containing 0.1 mg/ml BSA and 0.002% (v/v) Tween. Loaded biosensors were first washed and transferred to wells containing CARD proteins at concentrations of 5 µM in kinetic buffer , and initial results showed no affinity for dimer proteins. We then transferred the biosensors with monomer proteins to wells containing the CARD protein at concentrations of 20 µM, 10 µM, 5 µM and 2 µM in kinetic buffer. Association and dissociation kinetics were recorded for 5 min and 10 min, respectively. Kinetic parameters (kExperiments to characterize the binding of p75CTD proteins and the NRIF 26-amino-acid peptide were conducted at 25°C using a Biacore 3000 SPR instrument (Biacore Life Sciences), equilibrated in running buffer containing 50 mM Hepes-KOH (pH 7.5), 100 mM KCl, 5% glycerol and 0.005% (v/v) Tween-20. P75CTD monomer and dimer proteins were coupled onto a CM-5 chip at equal molarities, and analytes were serially diluted in running buffer.Figure S1NTR death domain.DTNB stabilizes the p75 (A) Superimposition of different structural models of the p75NTR death domain. The NMR model is shown in grey, one monomer of the p75DD crystal in green and one monomer of the p75DD-Fusion crystal in purple. (B) The MNB groups, produced by reduction of proteins with DTNB, covalently bind to the free cysteine residues of p75DD and p75DD-Fusion. (C) The polar moieties (−NO2 and −CO2) and the benzene ring of MNB interact with the surrounding residues of p75DD.(TIF)Click here for additional data file.Figure S2New asymmetric interface. The interface of p75NTR death domain asymmetric dimer is different from three classic types of asymmetric interaction of death domain family, which are exemplified by Tube/Pelle complex (PDB 1D2Z) and Fas/FADD oligomer (PDB 3OQ9). The regions involved in classic asymmetric interface are indicated with blue dash circles. Residues important for the asymmetric interaction of these complexes exhibit limited conservation, as shown in (TIF)Click here for additional data file.Figure S3Biochemical features of the p75DD-Fusion protein. (A) The eluted dimer portion of p75DD-Fusion was influenced by β-ME, not DTNB. (B) p75DD-Fusion proteins form dimer via disulfide bond, as determined by SDS-PAGE in the presence/absence DTT condition. (C) Mass analysis shows degradation of p75DD-Fusion (MW≈10.7 kD) observed in the crystal, but not in solution. Mutations of the symmetrical dimer interface residues of p75DD-Fusion profiled via size exclusion chromatography (D). Mutation of C379S completely abolished the dimer fraction, and the R382A mutation also decreased the dimer peak. Notably, the Pro380 residue of the H3–H4 loop region might be important to the structural stability of the entire p75DD-Fusion protein because the P380V mutant is prone to aggregation.(TIF)Click here for additional data file.Figure S4Distinct surface properties of symmetric dimer and asymmetric dimer. The N termini of the symmetric dimer (A) and asymmetric dimer (B) are colored in blue and point together towards the membrane . The distribution of the surface electrostatic potential for the symmetric dimer is different from that of the asymmetric dimer, viewed from near membrane side (top-view) and off membrane side (bottom-view).(TIF)Click here for additional data file.Figure S5NTR intracellular domain and C-terminal domain.Features of the p75 (A) Monomer/dimer transition of p75ICD at various concentrations of β-ME. (B) The p75CTD protein shows similar elution profile to p75ICD as the β-ME changes. (C) The cysteine mutants of p75CTD exhibit different oligomerization. Evidently, the Cys379 residue is important for p75CTD dimerization in solution.  Similar binding affinities of p75CTD dimer and monomer with NRIF peptide, measured by SPR. Almost equal molar concentrations of p75CTD monomer and dimer proteins were captured on a CM5 chip, and various concentrations of NRIF peptide  flowed over the chip in a BIACore 3000 machine.(TIF)Click here for additional data file.


Human Tubulin Binding Cofactor C (TBCC) is a post-chaperonin involved in the folding and assembly of α- and β-tubulin monomers leading to the release of productive tubulin heterodimers ready to polymerize into microtubules. In this process it collaborates with other cofactors  and forms a supercomplex with TBCD, β-tubulin, TBCE and α-tubulin. Here, we demonstrate that TBCC depletion results in multipolar spindles and mitotic failure. Accordingly, TBCC is found at the centrosome and is implicated in bipolar spindle formation. We also determine by NMR the structure of the N-terminal domain of TBCC. The TBCC N-terminal domain adopts a spectrin-like fold topology composed of a left-handed 3-stranded α-helix bundle. Remarkably, the 30-residue N-terminal segment of the TBCC N-terminal domain is flexible and disordered in solution. This unstructured region is involved in the interaction with tubulin. Our data lead us to propose a testable model for TBCC N-terminal domain/tubulin recognition in which the highly charged N-terminus as well as residues from the three helices and the loops interact with the acidic hypervariable regions of tubulin monomers. In recent years, a great effort has been made to elucidate the complex series of events occurring during the α- and β- tubulin folding pathways that lead to the final release of αβ native heterodimers incorporated in microtubules in vivo have been published.Recent results have shown that tubulin binding cofactors also participate in the proteostasis of the tubulin dimer through their intrinsic ability to dissociate the tubulin heterodimer in vivoTBCC is organized into three different domains  . The C-tAlthough the N-terminal domain is expected to interact with other spectrin-like domains In this work we have demonstrated that TBCC is found at the centrosome and we have used NMR spectroscopy to determine the solution structure and the interactions with the αβ-tubulin dimer of its N-terminal domain .To study TBCC function, we investigated the subcellular distribution of the endogenous protein in HeLa cells with a novel affinity antiserum purified against the human recombinant protein. The primary antibody recognizing human TBCC used was affinity purified as previously described We next overexpressed TBCC in order to investigate TBCC subcellular localization. We observed accumulates of this cofactor at spindle pole bodies and occasional multipolar spindles . These rBased on these findings, we hypothesize that TBCC is localised at the centrosome. We compared TBCC colocalization with classical centrosomal markers, such as γ-tubulin (not shown) or Nedd1, and as Subsequently, we silenced TBCC gene expression with a pool of four synthetic RNAs recognizing different segments of the TBCC mRNA and specifically designed to knockdown the human TBCC gene with no off-target effect see . As Fig.As part of the original study, we also affinity purified the same rabbit polyclonal antiserum against the N-terminal domain of TBCC. Unexpectedly, the same antisera, when purified against the TBCC N-terminal domain, produced a similar cytoplasmic immunostaining pattern but did not label the centrosome , arrows.In the view of the above results, we decided to study a TBCC truncation mutant containing the N-terminal domain overexpressed in HeLa cells. In contrast to the cytoplasmic pattern observed for the full-length polypeptide, the TBCC N-terminal domain produced a dot-like pattern, distributed at the perinuclear-centrosomal region  left. As3 and a global accessible surface area of 6400 Å2The side chains of a significant number of hydrophobic residues are deeply buried in the protein core, pointing to the interior of the helix bundle . Among tThe surface of the TBCC N-terminal domain is highly charged . InteresWe have measured the heteronuclear NOEs to get information on the local backbone flexibility of the TBCC N-terminal domain in the ns-ps time-scale . The dyn15N-HSQC spectra of the labelled TBCC N-terminal domain in the presence and absence of unlabelled αβ-tubulin and β-tubulin peptides  and 20  residues derived from the C-terminus of the β6-tubulin subunit , is predicted by the program COILS In summary, we show that TBCC is a protein implicated in centrosomal stability particularly at mitosis. TBCC expression changes in human cells produce several mitotic spindle defects leading to mitotic failure and apoptosis. These results demonstrate that TBCC is a crucial protein in the control of the eukaryotic cell cycle, and support the hypothesis that this tubulin binding cofactor could be implicated in genomic instability and cancer. Our data show how TBCC interacts with components of the centrosome by its N-terminal domain, which is masked within this organelle. We have also shown that the structure of the TBCC N-terminal domain solved by NMR adopts a spectrin-like fold and with a flexible and disordered N-terminal segment. This segment is highly charged and participates in tubulin interaction. The tubulin binding region of the structured coiled coil region resembles those proposed for TBCA and BAG1 proteins.E. coli BL21(DE3)/pLysS strain  using the T7 expression system 15NH4Cl alone or with 13C-glucose and was incubated to allow the recovery of growth. Protein expression was induced after 1 h by addition of isopropylthio-β-D-galactoside to a concentration of 1 mM. After a 3 h incubation period the cells were harvested. Cells were pelleted by centrifugation, washed, and stored frozen at −70°C. Pellets were resuspended in 10–20 mL of 50 mM Tris pH 8.0 with protease inhibitors. Cells were then ruptured by sonication (3 bursts of 10 seconds). After centrifugation at 25,000× g in a JA-25.50 rotor for 30 min at 4°C, the supernatant was loaded onto a HiTrap Q  equilibrated with Tris-HCl pH 8 containing 10 mM KCl. The flow-through was passed through the same column for a second time, and then applied to a high-resolution Mono-S column (5/50 GL). The TBCC N-terminal domain was eluted with a linear gradient of 10–500 mM KCl in Tris-HCl pH 8. Fractions containing the TBCC N-terminal domain were pooled, diluted 10 fold with 20 mM phosphate buffer pH 6 containing 1 mM TCEP and concentrated by ultrafiltration with Amicon Ultra 10 filters. Protein purity was determined by SDS-PAGE.Human TBCC cDNA (accession number U61234) was obtained from Dr N. Cowan . The TBCC N-terminal domain was generated by PCR  and cloned into pET3a vector (Invitrogen). The TBCC N-terminal domain was expressed in the Passage 10 human cervical carcinoma HeLa cells cultures  were fixed in chilled (−20°C) methanol or 4% paraformaldehyde, and further permeabilized in phosphate-buffered saline (PBS)–0.1% Triton X-100. For centrosomal immunostaining, microtubules were depolymerized with 2 µM nocodazole and 4°C treatments for 30 min. Anti-α-tubulin (B512) and anti-acetylated tubulin antibodies were both from Sigma (Aldrich). Human TBCC N-terminal domain was generated by PCR and inserted into the pcDNA3 vector . RNA interference was performed with a pool of four siRNA fragments targeting human TBCC . These siRNAs have been designed and tested to have no off-target effects. TBCC silencing was confirmed 24, 48, and 72 h after RNAi treatment by western blotting on quantified total cell extracts compared to and non target RNAi control. Morphological cell quantification  was perf13C, 15N-labelled TBCC N-terminal domain sample was prepared in 90∶10 H2O∶D2O and D2O solutions of KH2PO4/K2HPO4 buffer 20 mM, 1 mM TCEP, 20 mM KCl, 1 mM EDTA, with protease inhibitors, pH 6.0 at final concentrations in the range 0.5–1 mM. DSS was used for spectra referencing.For NMR experiments, the 2, pH 6.7. The concentration of the stock solution was ∼30 µM. Two concentrated solutions of peptides derived from the C-terminal end of the β6-tubulin (Class III) monomer (412ESNMNDLVSEYQQYQDATAD431 and 435EMYEDDEEESESQGPK450) Microtubule proteins were prepared from calf brains by repeated cycles of assembly– disassembly using a temperature-dependent procedure. Native tubulin heterodimers were purified following our published protocols and their viability was checked by non-denaturing electrophoresis, as described 15N-1H NOE measurement, experiments with and without proton saturation were acquired simultaneously in an interleaved manner and split during processing into separate spectra for analysis. A relaxation delay of 7.5 s was used. The NOE values were obtained from the ratio intensities of the resonances in both spectra.Spectral assignment was done using sets of standard 2D and 3D experiments as reported 15N-HSQC spectrum of the free TBCC N-terminal domain, with that obtained after the addition of ∼100 µL of the TBCC N-terminal domain to the tubulin stock solution. Both the chemical shift and line width changes were analysed. To test the interaction with the β-tubulin peptides, the appropriate volumes of the concentrated solutions were added to the TBCC N-terminal domain sample to get approximately 1∶1 protein∶peptide stoichiometries. Changes of peak intensity and position were monitored. In all cases the pH was checked at the final points of the titrations.The TBCC N-terminal domain interaction with αβ-tubulin was followed by comparing the All the experiments were recorded at 25°C on a Bruker AV 800 NMR spectrometer equipped with a cryoprobe. The spectra were processed with Bruker Topspin  and spectral analysis was performed with Sparky3 The structure calculation of the TBCC N-terminal domain was performed with CYANA


BMC Biology reveals the first structure of the archaeal GINS complex. The structure reveals the anticipated similarity to the previously determined eukaryotic complex but also has some intriguing differences in the relative disposition of subunit domains.GINS is an essential eukaryotic DNA replication factor that is found in a simplified form in Archaea. A new study in this issue of See research article: http://www.biomedcentral.com/1741-7007/9/28 Saccharomyces cerevisiae. It is a DNA polymerase accessory factor that, with Cdc45, binds to and activates MCM helicase. The factor is composed of four distinct but related subunits, Sld5, Psf1, Psf2 and Psf3, that are conserved across the eukaryotic domain of life. The name GINS arises from the first letters of the Japanese names for the numerals in the subunits: Go, Ichi, Ni and San. The sequence relationship of the four subunits suggests that they arose from a common ancestor  at replication origins prior to the initiation of DNA replication. When MCM(2-7) leaves the replication origin and drives replication fork movement, GINS and Cdc45 travel with it, along with an array of additional factors, in what has been termed the replisome progression complex . HoweverThermococcus kodakaraensis have an analogous permutation, with Gins15 being AB and Gins23 being BA.The GINS subunits show a permutation of their domain organization - structural studies of Psf1 and Sld5, the human homologs of archaeal Gins15, show that they have an amino-terminal α-helix domain (A) and a carboxy-terminal β-strand-rich domain (B). Homologous domains are found in Psf2 and Psf3, the human homologs of archaeal Gins23, but in a permuted, BA, order, with the β-sheet preceding the α-helical domain [Overall, the archaeal complex, despite having a simplified subunit composition relative to humans, has striking similarity to the human GINS assembly, although there are some differences in the contacts observed between Gins15 and Gins23 from those seen between their eukaryotic counterparts. Probably the most significant difference lies in the positioning of the carboxy-terminal β-strand-rich B-domain of Gins15 when compared with the analogous features of its human counterparts Psf1 and Sld5. Despite the conservation of Sld5 and Psf1, the B-domain of human Sld5 is involved in contacts with Psf2, while the B-domain of Psf1 is highly mobile and dispensable for GINS complex formation. In this regard, Gins15 is more reminiscent of Psf1 than Sld5, as the B-domain is not required for formation of the archaeal GINS complex [Sulfolobus solfataricus. The first study is the identification by Kelman and colleagues of a RecJ family nuclease, GAN, that interacts specifically with Gins15 in Thermococcus kodakaraensis [Sulfolobus solfataricus, where the GINS complex co-purifies with a further protein, RecJdbh, named for its significant homology to the single strand DNA binding domain of RecJ [Sulfolobus revealed that this interaction is mediated by Gins23. Furthermore, in Sulfolobus, Gins23 also interacts with the DNA primase [What then are the implications of this new archaeal structure for the formation and organization of the higher order macromolecular assembly at the replication fork? To address this, the new structure must be viewed in the context of two further recent papers and some older data from the archaeon araensis . This is of RecJ . Binding primase . Perhaps primase . Thus, tA recent single particle EM reconstruction study by Berger, Botchan and colleagues has revealed the architecture of the eukaryotic Cdc45-MCM(2-7)-GINS complex . The MCMWhat is the significance of this dual pore structure of eukaryotic CMG? All available data in the eukaryotic system indicate that MCM is loaded onto double stranded DNA at replication origins as a head-to head double hexamer . If the The structure of archaeal GINS complex represents a first step towards understanding the architecture of the replication fork assembly - clear future goals lie in structural analyses of higher order assemblies coupled with detailed biochemical investigations of the interactions between replisome components and their consequence for the coordination of DNA unwinding, priming events and subsequent DNA synthesis at the replication fork.


The V proteins of paramyxoviruses are composed of two evolutionarily distinct domains, the N-terminal 75 % being common to the viral P, V and W proteins, and not highly conserved between viruses, whilst the remaining 25 % consists of a cysteine-rich V-specific domain, which is conserved across almost all paramyxoviruses. There is evidence supporting a number of different functions of the V proteins of morbilliviruses in blocking the signalling pathways of type I and II IFNs, but it is not clear which domains of V are responsible for which activities and whether all these activities are required for effective blockade of IFN signalling. We have shown here that the two domains of rinderpest virus V protein have distinct functions: the N-terminal domain acted to bind STAT1, whilst the C-terminal V-specific domain interacted with the IFN receptor-associated kinases Jak1 and Tyk2. Effective blockade of IFN signalling required the intact V protein. Paramyxovirinae mediate their IFN evasion activities through the protein products of the P gene, primarily the non-structural proteins V, W and C. The P, V and W proteins are all produced from the P gene by a co-transcriptional editing mechanism, as originally described for measles virus (MV)  , althougMorbillivirus, a genus containing several important pathogens, including RPV itself, MV, peste des petits ruminants virus and canine distemper virus (CDV). Each of these viruses is pathogenic in a restricted range of hosts, and studies on the viral proteins of RPV and related morbilliviruses have given insights into the mechanisms that underlie pathogenesis, including the mechanism(s) of host innate immune evasion by these viruses (e.g. Rinderpest virus (RPV) is a paramyxovirus belonging to the genus ses e.g. . In our ses e.g. , we showses e.g. . SubsequTo dissect out the relative importance of the P/V/W shared domain (231 aa) and the Vs domain (68 aa) in blocking type I and type II IFN actions, we made plasmid constructs encoding them separately. We based all our P gene derivatives on the virulent Saudi/81 strain of RPV; as the RPV W ORF terminates 2 aa after the editing site, we refer to the P/V/W shared domain as W for brevity. The P, V and W constructs were engineered to prevent C protein expression  and alsoAs our recent studies suggested that the ability of a morbillivirus V protein to block type II IFN signalling was related to its ability to bind STAT1 , the P, To check whether the Vs domain was folding properly in the GFP–Vs construct, we assayed its ability to bind the cellular pattern recognition receptor protein MDA-5. It has been shown previously for the V proteins of several paramyxoviruses that they bind MDA-5 through their Vs domain . These dY110H), as this mutation has been shown to abolish binding to STAT1 in MV V protein . The development of the antiviral state in the different cell lines is shown by the prevention of VSV-induced cytopathic effect and cell death. A cell line transduced stably with the empty vector (A549-blank) was generated to act as a negative control. The physical separation of the RPV V protein into its N-terminal (W) and C-terminal (Vs) domains has shown that the V protein has at least two quite distinct methods of interfering with type I IFN action. We have also shown that neither functionality is sufficient on its own to enable the RPV V protein to show maximally efficient blocking of IFN signalling activity. The P/V/W shared domain, either on its own (as in the case of the W protein) or when present in the P protein along with the P-specific domain, was not sufficient to prevent cells entering the antiviral state in the functional assay, despite its ability to block IFN action when overexpressed in the reporter gene assay. Similarly, the Vs domain on its own showed a partial ability to block type I IFN action in the reporter gene assay, presumably due to its ability to bind to and inhibit the Jak1 and Tyk2 kinases, but could not prevent the function of type I IFN in inducing the antiviral state.The data from our study on the RPV V protein are in agreement with previous studies with MV V protein, where both the N-terminal and C-terminal regions of the V protein were required for its efficient IFN signalling evasion activities, and the P/V/W shared domain and Vs domain on their own were shown to have only partial inhibitory effects on type I IFN signalling compared with the full-length MV V protein , the binding of STAT2 (requiring the Vs domain and part of the W domain), and the association with IFN receptor-associated kinases (requiring the Vs domain). All three activities combined form an extremely effective blockade of type I IFN signalling, rendering the infected cell essentially immune to the antiviral state.Escherichia coli (DH5α strain) and purified on CsCl gradients. The pcDNA constructs containing the P, C, W and V ORFs from the Saudi/81 strain of RPV, and the constructs encoding FLAG-tagged Jak1 and Tyk2, have been described previously (Y110H plasmid was created by site-directed mutagenesis using Pfu Turbo polymerase (Stratagene). The GFP–V–INS–Vs construct was based on the GFP–Vs construct, which was extended by an additional 12 aa (RSIASEKPIKKG) of the P/V/W shared region upstream of the Vs domain by site-directed mutagenesis. As a control, 12 non-V protein amino acids  were added at the same position to create pcDNA-GFP-HA-INS-Vs. pCMYC-MDA5, pJAT-lacZ, pGAS-luc and the plasmid components of the lentivirus transduction system  were the kind gifts of Professor S. Goodbourn . pGL3-MX1P-luc was the kind gift of Professor Georg Kochs . The plasmid encoding the PIV5 V protein (pEF-SV5V) was the kind gift of Professor R. E. Randall .Plasmids were cloned and grown in eviously . For allRecombinant human IFN-αA was purchased from Calbiochem and human IFN-γ was purchased from Millipore. Sources of antibodies were: mouse anti-V5 (AbD Serotec), mouse anti-FLAG , mouse anti-STAT1P , rabbit anti-STAT1 (Upstate), rabbit anti-STAT2 (Upstate), rabbit anti-Tyk2P , rabbit anti-Jak1P , rabbit anti-Tyk2 (Upstate), rabbit anti-Jak1 (Upstate) and mouse anti-PCNA . HRP-coupled anti-mouse or anti-rabbit antibodies used in Western blots were from GE Healthcare Life Sciences.Derivation of stable cell lines expressing virus proteins was carried out using a pseudo-typed lentivirus-based vector system described previously (Jak1/Tyk2 overexpression and co-immunoprecipitation assays were carried out using modifications of previously published methods (


Protein domains are the structural, functional and evolutionary units of the protein. Protein domain architectures are the linear arrangements of domain(s) in individual proteins. Although the evolutionary history of protein domain architecture has been extensively studied in microorganisms, the evolutionary dynamics of domain architecture in the plant kingdom remains largely undefined. To address this question, we analyzed the lineage-based protein domain architecture content in 14 completed green plant genomes.Our analyses show that all 14 plant genomes maintain similar distributions of species-specific, single-domain, and multi-domain architectures. Approximately 65% of plant domain architectures are universally present in all plant lineages, while the remaining architectures are lineage-specific. Clear examples are seen of both the loss and gain of specific protein architectures in higher plants. There has been a dynamic, lineage-wise expansion of domain architectures during plant evolution. The data suggest that this expansion can be largely explained by changes in nuclear ploidy resulting from rounds of whole genome duplications. Indeed, there has been a decrease in the number of unique domain architectures when the genomes were normalized into a presumed ancestral genome that has not undergone whole genome duplications.Our data show the conservation of universal domain architectures in all available plant genomes, indicating the presence of an evolutionarily conserved, core set of protein components. However, the occurrence of lineage-specific domain architectures indicates that domain architecture diversity has been maintained beyond these core components in plant genomes. Although several features of genome-wide domain architecture content are conserved in plants, the data clearly demonstrate lineage-wise, progressive changes and expansions of individual protein domain architectures, reinforcing the notion that plant genomes have undergone dynamic evolution. Protein domains, usually segments of continuous amino acids within a protein, are the structural, functional and evolutionary units of the protein. Most proteins are composed of one or more domains that can fold independently into a stable core structure -3. Each de novo created [Simple domain architectures are composed of a single domain per protein and most have been  created . Complex created -8. Fong  created .Protein domain content, the overall collection of domains in a given proteome, was successfully used to reconstruct a phylogenetic tree of life that was equivalent to standard phylogenies derived from molecular sequences and phylogenomic trees based on gene content and gene order . PresumaNumerous studies have sought to globally reconstruct the evolutionary history of protein domains and domain architectures by including hundreds of completed genomes ,6,10,11.Numerous studies examined domain architecture evolution in the plant kingdom -16. HowePhyscomitrella patens) to 51%  depending on species . The percentages of Pfam-predictable proteins in these 14 algal and plant genomes range from 42% to 75% with median 62%. Pfam-predicted architectures were further categorized into single-domain, double-domain, triple-domain and greater-than-four-domain architectures. The predominant category is single-domain architectures and the proportions of single-domain architectures per genome range from 30%   Figure . However) Figure . NeverthChlamydomonas reinhardtii , Ostreococcus lucimarinus, O. tauri, Chlorella vulgaris, which are unicellular flagellates, and Volvox carteri, which is a multi-cellular algae. The green algae are generally considered the sister to ancestors from which land plants evolved. Physcomitrella patens (moss) is a bryophyte and non-vascular plant. It diverged from the lineages leading to higher plants approximately 443-490 million years ago [Selaginella moellendorffii (spikemoss) is a lycophyte and was placed between the bryophytes and the euphyllophytes, which include ferns, gymnosperms and flowering plants. Given that both P. patens and S. moellendorffii are among the early diverging land plants and have many characteristics in common and that no other bryophyte and lycophyte genome sequences available, we grouped P. patens and S. moellendorffii together into a virtual lineage and termed this group the "early diverging lineage" in this study. The early diverging lineage is followed by the monocot lineage, which includes rice, maize and sorghum, and then the dicot lineage including grape, Arabidopsis, cottonwood, and soybean.Since species-to-species comparison does not necessarily reveal the general trends of domain architecture changes, we grouped the 14 green plants species into lineages and analyzed lineage-wise, dynamic changes of domain architecture content in plants. The algal lineage includes ears ago . SelaginWe identified a total of 11545 distinct protein architectures collectively from these 14 genomes , algal+early diverging+dicot lineages , algal+monocot lineages , and algal+dicot lineages  , and early diverging+dicot lineages  occurred at a higher frequency  and HTH_AraC PF00165), which are involved in the bacterial two-component signal transduction system and bacterial regulation of transcription by the arabinose operon regulatory protein AraC, respectively. To investigate the origins of these proteins, we extracted the protein sequences and BLASTed these against the NCBI non-redundant protein database. It appears that a large proportion of these proteins are highly homologous, but not perfectly matched, to known bacterial, fungal and primitive marine animal proteins, which rules out the possibility of contamination of known microbial sequences during genome sequence assembly. From 640 architectures specific for the early diverging lineage, we identified a list of 243 domain architectures whose protein sequences matched to non-plant protein sequences  -23, whicCompared to the massive domain architecture expansion from algal species to higher plants  in re-constructed TAIR7 dataset  and that of multi-domain architectures range from 13% to 25% (19% ± 1%)  ; Ostreococcus lucimarinus (http://genome.jgi-psf.org/Ost9901_3/Ost9901_3.home.html); O. tauri (http://genome.jgi-psf.org/Ostta4/Ostta4.home.html); Chlorella vulgaris (http://genome.jgi-psf.org/Chlvu1/Chlvu1.home.html); Volvox carteri (http://genome.jgi-psf.org/Volca1/Volca1.home.html); moss ; spikemoss ; rice ; maize ; sorghum ; grape ; Arabidopsis ; poplar ; soybean .The protein sequences were retrieved from the following databases: green algae  PKinase (1)". All domain architecture analyses were automatically performed by a pipeline using PERL programming language. The final domain architecture dataset was input into MS Office Excel and lineage-based domain architecture content was manually sorted based on the lineages and the copy numbers of domain architectures.In order to predict domains, we ran Pfam prediction locally . We alsoThe protein sequences corresponding to domain architectures in early diverging lineage were extracted and used as query sequences to BLAST against the NCBI non-redundant protein database at an E-value cutoff of E-10. The protein sequences of candidate proteins were extracted and aligned using MUSCLE3.6  with defp-value higher than the chosen α-level of 0.05. Pearson's chi-square tests were performed following standard procedures. For lineage-based domain architecture expansion, we normalized the architecture content of maize genome and soybean genome by dividing with factors of 3 and 2, respectively, because the annotation of maize genome was not masked and the soybean genome underwent a nascent WGD around 13 million years ago [p-values were adjusted by BOOTSTRAP.The histograms and probability plots of the proportions of Pfam-predictable and species-unique domain architectures were performed using MINITAB 15. The fitness of distributions was judged by the smallest value Anderson-Darling goodness-of-fit statistic and the associated ears ago . LineageXCZ, DX, JC, and GS conceived this study. XCZ, ZW, and MHL collected data, carried out analyses. JS provided statistical advice. XZ carried out statistical analyses. XCZ wrote the draft manuscript. DX, JC, and GS discussed the results and contributed to manuscript revisions. All authors read and approved the final manuscript.Overall and species-specific domain architectures in green plants. This file contains all Pfam-predicted domain architectures. The "overall" tab lists 11545 domain architectures predicted from all 14 plant genomes included in this study. The remaining tabs list the species-specific domain architectures.Click here for fileDistributions of overall domain architectures in plant lineages. Pfam-predicted domain architectures  and by a majority- ruled parsimony tree with maximum- likelihood branch length 9 the P. patens WD40 protein cluster together with bacterial sequences).Click here for fileExample domain architectures illustrating architecture expansion in green plants. Expansion of domain architectures in plants illustrated by representative architectures, including Myb_DNA-binding (2), F-box(1), as well as TIR (1). The right six columns indicate the pairwise comparison between lineages of the probability of domain architecture expansion during the plant genome evolution.Click here for fileExpansion of universal and land domain architectures. Expansion of universal and land domain architectures (BCD) was shown in individual tabs. Domain architectures that have not undergone expansion are also shown.Click here for fileCategorical distribution of domain architectures in TAIR8 and presumed TAIR6, 7, and 9 annotations. The effect of genome annotation errors on the genome-wide domain architecture content was examined by analyzing genome-wide domain architecture content built on different version of Arabidopsis annotations, which are thought to be well annotated. In general, we did not observe significant difference of domain architecture content between TAIR6, 7, 8 and 9 annotations.Click here for file