Genetically-encoded biosensors based on fluorescence resonance energy transfer (FRET) have been widely applied to study the spatiotemporal regulation of molecular activity in live cells with high resolution. The efficient and accurate quantification of the large amount of imaging data from these single-cell FRET measurements demands robust and automated data analysis. However, the nonlinear movement of live cells presents tremendous challenge for this task. Based on image registration of the single-cell movement, we have developed automated image analysis methods to track and quantify the FRET signals within user-defined subcellular regions. In addition, the subcellular pixels were classified according to their associated FRET signals and the dynamics of the clusters analyzed. The results revealed that the EGF-induced reduction of RhoA activity in migratory HeLa cells is significantly less than that in stationary cells. Furthermore, the RhoA activity is polarized in the migratory cells, with the gradient of polarity oriented toward the opposite direction of cell migration. In contrast, there is a lack of consistent preference in RhoA polarity among stationary cells. Therefore, our image analysis methods can provide powerful tools for high-throughput and systematic investigation of the spatiotemporal molecular activities in regulating functions of live cells with their shapes and positions continuously changing in time. Cell migration plays important roles in embryonic development, tissue repair, cancer invasion and atherosclerosis FRET is a phenomenon of quantum mechanics. When two fluorescent proteins (FPs), a donor and an acceptor, are in proximity with favorable orientation, the excitation of donor FP can induce the emission from the acceptor While the development of the FRET biosensors has greatly advanced our knowledge of signaling transduction in live cells, the vast amount of imaging data produced by these biosensors requires automatic, intelligent, and objective image analysis tools to allow precise and efficient interpretation of biological information Image registration has been widely applied in engineering and science for automatically tracking moving objects in time and for finding the pixel-wise correspondence between two images of similar objects In this paper, we have developed automated image analysis methods to track the movement of single cells and quantify the FRET signals at subcellular regions utilizing the RhoA biosensor from Dr. Matsuda 2 incubator at 37°C. The culture medium was Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 1 unit/ml penicillin, 100 µg/ml streptomycin, and 1 mM sodium pyruvate. The cell culture reagents were obtained from Invitrogen .HeLa cells  were cultured in a humidified 95% air, 5% CO2-independent medium without serum (Invitrogen) at 37°C. The objective focus was aimed near the basal side of the cell. Images were collected by a Zeiss axiovert inverted microscope with a 420DF20 excitation filter, a 450DRLP dichroic mirror, two emission filters controlled by a filter changer (480DF30 for CFP and 535DF25 for YFP), and a cooled charge-coupled device camera . The intensity of excitation light was controlled by adjustable neutral density filters to ensure minimal photobleaching during imaging. The CFP and YFP fluorescence intensity images were background subtracted. The FRET ratio images were computed by calculating the pixel-wise CFP/YFP ratio to represent RhoA activity in space and time, using MetaFluor 6.2 software  or our customized programs developed in MATLAB (MathWorks).Since the Raichu-RhoA biosensor can be easily transfected and is well-characterized for HeLa cells, we used this biosensor to visualize the dynamics of RhoA activity in HeLa cells on fibronectin covered glass To automatically register the time-lapse images of a moving cell, we mapped these cell images into a unit disk, which served as a reference image both for the registration of the time-lapse image sequence of the same cell and for the comparison between different cells with varying shapes. It took four steps to map a cell to the unit disk. First, the cell body was determined by segmentation. Second, the control points on the cell were computed by Delaunay triangulation of the convex hull of the cell body. Third, the control points were mapped to the reference image (unit disk) by calculating the relative position of the points in the convex hull. Finally, the cells were mapped into the unit disk by a piecewise linear transformation Otsu's method To register the shape of a cell by a unit disk without ambiguity, it is required that the cell shape be convex. Therefore, the convex hull circumscribing the cell mask image was calculated  disk  . For any , i.e.  . ConversThe convex hull of the whole cell was mapped onto the unit disk by a piecewise linear transformation To track the regions of interest, the time-lapse images of a single cell were first transformed into a series of unit disks. One or multiple ROIs were then selected and defined by the user on the first cell image. Thereafter, a mask for the ROI was generated and mapped to the first unit disk by our image registration method. To track the average FRET signal in this region, the mask of ROI in the unit disk was assumed to remain at the same location in time . TherefoThe ROI(s) on the reference unit disk was mapped back to the original cell images to visually confirm that they can indeed track the actual region in migrating cells . To imprkmeans was used to classify the pixels into different clusters, by minimizing the within-cluster variation of the FRET values To examine the geometric features of subcellular regions with similar levels of RhoA activity, the pixels of the cell image were classified and divided into six clusters according to their RhoA FRET ratio values. The MATLAB function The MATLAB programs implementing these functions can be obtained by writing to the corresponding author of this paper.Two classes of HeLa cells with significantly distinctive migration patterns were observed during experiments: migratory and stationary. To determine whether a HeLa cell was migratory or stationary, the location of its centroid was monitored in time. The furthest distance traveled by the centroid from the initial position was divided by the sum of the distances traveled at each time step for about 60 minutes in each experiment. With a ratio greater or equal to 0.5, a cell was considered to be migratory. Otherwise, the cell was considered stationary. The results confirmed that there are clearly two classes of HeLa cells with the distance ratios at 0.71 and 0.1867 . The migWith our method of image registration, the intracellular RhoA activity under growth factor stimulation can be visualized within a uniform reference image frame . The RhoTo track and quantify the local RhoA activities at subcellular regions in HeLa cells, four ROIs were selected in both the migrating and stationary cells. The four ROIs in the migrating cells span the front, back and two sides of the cell . Since aIn the stationary cells, because the cells were not moving significantly, the positions of ROIs remained relatively unchanged in time . The RhoDue to the relatively stable RhoA activity at the tail of the migrating cells upon EGF stimulation, the global activity of RhoA in migrating cells was expected to decrease slower than that of the stationary cells. Hence we quantified the global activity of the RhoA biosensor in the migrating and stationary cells by taking the average of RhoA activities in all four ROIs and normalized according to their pre-EGF ratios. The results confirmed that the RhoA activity decreased in both the migrating and the stationary cells upon EGF, at a slower rate in migrating cells than that in stationary cells . The difWe further studied the geometric features of the subcellular regions with relatively high RhoA activity after EGF stimulation. The intracellular pixels in the FRET image was divided into six clusters according to their FRET ratio values. As shown in A similar cluster analysis was conducted on stationary cells. A representative stationary cell with six clusters is color-coded by the FRET ratio and depicted in The averaged polarity results from multiple migrating cells were calculated by aligning their direction of motion toward the same angle. At 52 minutes after EGF, the normalized number of pixels with high RhoA activity in each angular wedge clearly displayed a polarized pattern, with more pixels accumulating at the back of the migrating cells . In contThe recent advancement of live-cell imaging technologies has provided a variety of powerful tools and allowed the collection of a vast amount of image data/movies which requires significant time and effort on image analysis and quantification. However, there is a lack of user-friendly and automated image-analysis tools with high throughput capability. We have developed image analysis algorithms and methods based on the image registration for tracking the complex movement of migrating cells. These methods were applied to automatically track and quantify the time-lapse FRET ratio of Raichu-RhoA biosensor at subcellular regions in live HeLa cells, and to analyze the polarity of intracellular regions with high RhoA activities. The results suggest that the subcellular distribution of high RhoA activity is differentially regulated in migrating and stationary cells. These methods can also be applied to analyze the general fluorescence images of signaling transduction in live cells and enable an automated, efficient and objective quantification of a large quantity of imaging data.The image analysis methods presented in this paper are completely automated. Because the ROIs were automatically tracked in moving cells, users only need to define the initial regions of interest in the first cell image. Each frame with four ROIs requires several seconds to quantify the FRET ratio with a personal computer. The automation of this kind of analysis is crucial because, at the current stage, the majority of data analysis for FRET imaging in live cells was performed manually with the contours of ROIs moved by a biologist if the cell moves or changes the shape. This manual tracking of ROIs for live cells can result in low-efficiency, tediousness, and inaccuracy. With our analysis method, the tracking is very reliable in general if the ROIs were selected inside the cell body and away from the cell edge. Because the convex hull or contour of the cell, but not the actual cell edge, was mapped to the unit disk, this method would become less accurate if a ROI was close to some cell edges which change their shapes between concave and convex. Further improvement of the automated tracking method would require the development and implementation of more sophisticated image registration algorithm. Nevertheless, the conversion of different cells into a uniform scheme, such as a unit disk, should be useful and important to standardize and compare the signals from each individual cell.The application of the novel cluster analysis method is useful for analyzing and quantifying the subcellular distribution and the polarity of RhoA activity. The absolute value of RhoA activity did not present an obvious pattern of polarity. However, there was a significantly increase in the number of pixels with relatively higher RhoA activity at the tail of migrating cells upon EGF stimulation, evidenced by the quantification using the automated cluster analysis . This hiComparing the results from migrating and stationary cells revealed distinct spatial patterns of RhoA activity in these two kinds of cells. In contrast to the polarized RhoA activity in migrating cells, the global down-regulation of the RhoA activity in stationary cells upon EGF stimulation may promote the protrusion in all directions without discrimination, which prevents a persistent and directional migration. The global change of RhoA activity upon EGF stimulation in migrating cells appeared less significant as compared to that in stationary cells. This is largely due to the consistent maintenance of a high RhoA activity at the trailing edge of migrating cells, but not in stationary cells. Further studies are needed to elucidate the detailed molecular mechanism governing the differential RhoA responses of stationary and migrating cells.In summary, our image analysis method can allow a high-throughput and automated means for quantifying and analyzing the spatiotemporal molecular signals in live cells. This is particularly powerful in analyzing a large quantity of signals from cells with changing shapes, e.g. migrating cells. We have further demonstrated the significance of this method by applying it to quantify the dynamic RhoA activity at subcellular levels in migrating and stationary HeLa cells. The results revealed that EGF induced a down-regulation of RhoA in both migrating and stationary HeLa cells. However, a polarized distribution of RhoA activity can be consistently observed only in migrating, but not stationary cells.Movie S1The FRET images were mapped to a disk. The spatiotemporal dynamics of the FRET ratio representing RhoA activity upon EGF stimulation is shown in a typical migrating cell (left) and reference images mapped into a disk (right). Red and blue colors indicate high and low RhoA activity, respectively. The cell was shown to migrate in the view field, especially during the later part of the video (left), while the reference images of the cell remained within the static unit disk (right).(9.81 MB MOV)Click here for additional data file.Movie S2Four regions of interest were tracked in a migrating cell. The regions moved and changed shape as the cell migrated forward.(7.93 MB MOV)Click here for additional data file.Movie S3The geometric features of the six clusters (right) and three top clusters (left) in a migrating cell are shown in time. These regions are color-coded according to their RhoA activities, with cold and hot colors representing regions with low and high levels of RhoA activity, respectively.(10.26 MB MOV)Click here for additional data file.


Anesthesia for thoracoscopy is based on one lung ventilation. Lung separators in the airway are essential tools. An anatomical shunt as a result of the continued perfusion of a non-ventilated lung is the principal intraoperative concern. The combination of equipment, technique and process increase risks of hypoxia and dynamic hyperinflation, in turn, potential factors in the development of an unusual form of pulmonary edema. Analgesia management is modelled on that shown effective and therapeutic for thoracotomy. Perioperative management needs to reflect the concern for these complex, and complicating, processes to the morbidity of thoracoscopic surgery. Thoracoscopy nearly has a century long history (1912). It is only now that technological advance has enabled move beyond diagnostic and experimental to the therapeutic and even the norm. Most operations, considered as part of the repertoire of the thoracic surgeon and requiring access through a sternotomy or thoracotomy, are within the range of alternative and putatively less destructive thoracoscopic techniques. Initially, operations were conducted with local anesthetic blockade at the point of access, either local anesthetic infiltration or some form of single or extended intercostal nerve blockade . Hypoxia and accumulation of carbon dioxide occurred as a consequence of a pneumothorax. The mechanism was the phenomenon named ‘pendelluft’ - pendulum air. This described shunting of the physiological dead space between the two lungs during self-ventilation, resulting in a build-up of carbon dioxide and respiratory acidosis; and often was accelerated because of the poor health of patients suffering from infective conditions, notably tuberculosis. Today much of this history still influences the role of anesthesia: pain relief, facilitating instrument access, ensuring gas exchange and dealing with the effects of co-morbidities.In the background lies a perception, often pressing, that the less apparently invasive the surgery, the more ill the patients who are considered operable. There is no lessening in the complexity of the anesthetic process, the degree of ‘physiological trespass’ or the need for resources to cope with the pain experience. All benefits being, as for surgery, in reduced blood loss, wound pain and hospital stay.Almost uniquely in surgical practice, anesthetist and surgeon are very much rivals for the same anatomy. Given the proximity of the anesthetist and surgeon in the field, there are pinch points: almost inevitably, conjunction of anesthetic imperative - to oxygenate - will occur with that of surgery not being possible in the presence of lung employed to perform its function.Dynamic hyperinflation (‘breath-stacking’) during operation and non-cardiogenic pulmonary edema afterwards, rare in other surgical fields, are common enough to be high in the differential of any untoward situations developing perioperatively.A certain amount of interdisciplinary re-balancing is necessary as the surgical operative paradigm shifts from open to endoscopic methods. For instance, vascular and gastro-enterological surgeons have to get familiar with new directions of approach to the mediastinum, the oesophagus and the lung apex. They and their anesthetic partners, have to familiarise themselves with the culture, protocol, processes and problems of one lung ventilation (OLV). In turn, the practised need to anticipate that the basket of morbid conditions will expand.An example is the change in surgical management of myasthenia gravis. Intervention is passing from the traditional sternotomy for thymectomy to a suprasternal mediastinotomy or right video-assisted thoracoscopy. AlthoughIt is well within conventional practice to conduct thoracoscopy without formal OLV, but operating risks are increased and less than ideal operating conditions more frequent.–5 A requThere are lung ventilation, postoperative management and complication issues specific to OLV.Lung separators are inserted into the airway and stabilized in one or other bronchus. There are three categories of lung separator: the double-lumen tube, the bronchus blocker and, increasingly difficult to acquire, the endobronchial tube . For eac10The effects of stopping the ventilation of one lung can be categorized by the following: some or all of which may result in physiological trespass and can act independently or summate:Open pneumothorax and the additive effect of carbon dioxide insufflationIncrease in anatomical shunt (perfusion of a non-ventilated lung).Adoption of the lateral decubitus positionOnce the intrinisic negative pleural pressure dynamic is breached, the lung collapses and retracts toward the hilum due to intrinsic elasticity. So effective usually is the effect of this passive intervention that the additional insufflation of carbon dioxide rarely is required to separate the visceral from the parietal pleura. A lung that does not collapse is as likely to be due to the anchoring by pleural adhesions, as it is a fault of inadequate lung separation. But it is always worth checking the anesthetic side  before seeking and treating pleural adhesions and resorting to carbon dioxide insufflation.Carbon dioxide creates ‘tension’. The rise in intrathoracic pressure reduces venous return, pulmonary blood flow and increases right heart workload.12 In genStopping ventilation to a lung increases the proportion of the cardiac output that is not oxygenated (shunt fraction). Normally the shunt is in the region of 20-28%, irrespective of whether the anesthetic is volatile agent  or intravenous agent  based. Increasing the inspired oxygen realistically does not have any effect above this value. Some patients, notably the younger and fitter tend to the higher level and are harder to deal with. Attendants must resist temptation to raise the level of intervention beyond the safe. Even though it usually reflects a ventilation problem, surgery should not continue until oxygen saturations are consistently above 90%. Attempts to improve the oxygen flux by pharmacological manipulation to produce a ‘pneumonectomy circulation’ should be discouraged. TemporarThis increases gravitational influences by adding the weight of the mediastinum to other forces that physically reduce the functional residual capacity of the dependent (ventilated) lung. However there may be a beneficial effect as pulmonary blood flow is redistributed to the ventilated zone - in effect improving ventilation/perfusion ratios and reducing the physiological shunt. If bronchus blockers are used, the dependent main bronchus is unsupported and compressed by the weight of the mediastinum: hypoventilation and/or dynamic hyperinflation can result.It is essential to apply the same minute volume (to maintain normocarbia) to one lung. Given the switch to OLV and to reduce the inflation pressure, it is achieved by reducing the calculated tidal volume to a lower range of 5-10 ml per kg, adjusting the ventilation frequency rate to stabilize the end tidal carbon dioxide.OLV must never be assumed to be safe. No operaRecently the objectives of pain relief have been defined.HumanitarianReverse the effects of surgery Promote healingPrevent transition to chronic pain syndromes.Achieving standard for Objectives 1 and 2 is all that is required for much thoracoscopic practice. The painful experience relatively is short-lived; an aided ability to take deep breaths and cough can be anticipated and a rapid return to mobility is likely with early removal of chest drains, if present. Routine and standard patient controlled analgesia systems (PCAS) containing opioids generally are all that are required, in combination with oral supplementation with simple analgesics and non-steroidal anti-inflammatory drugs. Percutaneous paraverteral blockade is known to reduce the pressor reactions to the nociception of thoracic surgery: it is feasible to prolong this form of anti-nociception by inserting epidural-type catheters during surgery and infusion of local anesthetic postoperatively.19 Chest There is only speculation that Objective 4 realistically is achievable but a more proactive approach to the design, number and site of ports is likely to pay dividends in the amount of prolonged post-thoracotomy type discomfort and neuralgia.Special attention to this aspect is necessary because of a condition variously named postoperative pulmonary edema or post-pneumonectomy pulmonary edema. Characterized as a consequence of OLV and classically unilateral, it is part of a spectrum of acute respiratory distress syndrome and as such difficult to separate from more usual confounding diagnoses like pneumonia, bronchiolitis, aspiration and sepsis. Though premorbid dispositions , surgical (prolonged handling) and anesthetic (ventilator induced damage) have been identified, prevention policy and management of the condition are best viewed clinically on the basis of a failing or failed pulmonary lymphatic system. Attendan


E. coli among the prokaryotes.In the past years, several studies begun to unravel the structure, dynamical properties, and evolution of transcriptional regulatory networks. However, even those comparative studies that focus on a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms, such as In this paper we used the information annotated in Tractor_DB (a database of regulatory networks in gamma-proteobacteria) to calculate a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory link across thirty genomes of this subclass. Then we used this SOS to assess how regulatory connections have evolved in this group, and how the variation of basic regulatory connection is reflected on the structure of the chromosome. We found that individual regulatory interactions shift between different organisms, a process that may be described as rewiring the network. At this evolutionary scale (the gamma-proteobacteria subclass) this rewiring process may be an important source of variation of regulatory incoming interactions for individual networks. We also noticed that the regulatory links that form feed forward motifs are conserved in a better correlated manner than triads of random regulatory interactions or pairs of co-regulated genes. Furthermore, the rewiring process that takes place at the most basic level of the regulatory network may be linked to rearrangements of genetic material within bacterial chromosomes, which change the structure of Transcription Units and therefore the regulatory connections between Transcription Factors and structural genes.The rearrangements that occur in bacterial chromosomes-mostly inversion or horizontal gene transfer events – are important sources of variation of gene regulation at this evolutionary scale. E. coli and S. cerevisiae.The transcriptional regulatory network (TRN) defines the cellular response to environmental stimuli at the step of transcription initiation. It may be analyzed at four different levels of increasing complexity: individual regulatory interactions, network motifs, functional modules, and the entire network that may be represented as a directed graph . SeveralE. coli K12 [B. subtilis [E. coli) with orthology information between the genes of closely related organisms (based on the model organism) to identify putative orthologous regulatory interactions in organisms other than the model [Studies that focus on the evolution of the regulatory network, even within a group of closely related organisms are limited by the rather scarce knowledge on regulatory interactions outside a few model organisms. Among prokaryotes, coli K12  and B. ssubtilis  have thehe model ,10-18.E. coli and B. subtilis and assessed the conservation of their transcription factors (TFs) and regulated genes across a broad array of sequenced genomes. Both works showed that the set of regulatory genes – even global transcription factors – clearly varies from one group of organisms to another. On the other hand, several studies have shown that, within a group of closely related organisms, the conservation of a TF and a subset of the structural genes it regulates are not sufficient to assume that regulatory links between them are conserved between such two organisms [The problem is more challenging when one intends to extrapolate the information of known TRNs to phylogenetically distant organisms. Two recent studies ,19 took rganisms -18.et al. [et al. [Therefore, the question whether the interchange of regulatory links between TFs and structural genes could be a significant mechanism to "rewire" the regulatory network between closely related organisms remains open. This rewiring has been compared to changing the "software" of the transcriptional regulation machinery , as oppoet al.  and Loza [et al.  – betweeIn this paper we use the information stored in Tractor_DB to compute a normalized Site Orthology Score (SOS) that quantifies the conservation of a regulatory interaction across thirty genomes currently included in the database. We then use this SOS to assess the evolution of individual regulatory interactions and to find out whether the rearrangement of these connections affects the conservation of network motifs. We found that, within the evolutionary scale of the members of the gamma-proteobacteria subclass, the regulatory links that form feed forward motifs are more correlatedly conserved than triads of random regulatory interactions or pairs of co-regulated genes.et al. [Moreover, we used the information stored in Tractor_DB together with the information on the organization of gamma-proteobacterial genomes in Transcription Units (TUs)  to asseset al.  regardinet al. ,13. TherE. coli experimentally verified TRN, and the E. coli and S. typhimurium reconstructed TRNs with data from gene expression experiments; c) assessed the circularity of the populations of predicted sites, and; d) tested the possibility of a correlation between the SOS of E. coli regulatory sites and the scores of their orthologs. (See Discussion for details.) The starting dataset comprises all putative regulatory interactions found within the genomes of the thirty gamma-proteobacteria included in the database. Table E. coli K12 to 6 in X. axonopodis and M. capsulatus. Table As previously stated, given the scarcity of experimentally verified regulatory sites in gamma-proteobacterial genomes, our study is based entirely on computational predictions obtained from Tractor_DB. In order to validate the robustness of these predictions we a) calculated the sensitivity and specificity of the computational approaches taken to produce them; b) compared the et al. [et al. [et al. [Previous studies done in different groups of gamma-proteobacteria have shown that the gene content of a few individual regulons  changes from one organism to another -18. Thiset al.  and Loza [et al. . Neverth [et al.  as a comE. coli regulatory interactions  for which both an orthologous TF and an orthologous gene exist in another organism may be conserved (if an interaction links the orthologous TF and the orthologous gene); alternatively it may have changed to other regulon (if the orthologous gene is regulated by a different TF), or it may be lost (if no incoming interaction has been identified for the orthologous gene). This classification scheme, although based on E. coli , intends to illustrate a process of disappearance of interactions and appearance of new regulatory links occurred within the evolutionary divergence of the different gamma-proteobacteria from their last common ancestor. See Additional file To determine whether this variation in gene content is a general property of the regulatory network, we classified all orthologous regulatory interactions in our starting dataset (or their absence) into four groups depending on their conservation with respect to other regulon or lost link in other organisms ranges from approximately 18% (Hduc) to 55% (Ecoo). Figure E. coli. Regulatory interactions classified as other regulon indicate that a process of disappearance of interactions and appearance of new regulatory links – that may be described as a rewiring of the network – takes place at the most basic level of the network: the individual regulatory connections. A number of lost links may in fact be false negatives of the computational strategies that we used to rebuild the regulatory networks of the 30 organisms included in Tractor_DB.  However, given the results of the calculation of the rates of false negatives in the E. coli genome in the starting dataset  across the gamma-proteobacteria included in Tractor_DB. Additional file The SOS (see Methods) of E. coli regulatory interactions range between 0.1 and 0.69 in the FruR regulon, and between 0.1 and 0.71 in the NtrC and the PurR regulons. Since the SOS is the quotient of two weighed summations (see Methods), low SOS values may be interpreted as regulatory interactions that are preferentially conserved in organisms that are closer to E. coli, and lost in more distant ones, while higher SOS values correspond to interactions that are conserved both in close and in distant organisms. For instance, the glutamine synthetase gene (glnA) is regulated by NtrC in E. coli. Orthologs of both the TF and the gene exist in other 29 genomes analyzed. We identified orthologous regulatory connections in 19 organisms only but because this list includes both close and distant genomes, the SOS produced is rather high. Actually, only 2% of all E. coli regulatory interactions have SOS values higher than 0.7. In the other extreme, the codB and codA genes form a Transcription Unit with orthologs in 20 of the genomes included in Tractor_DB, where their regulator (PurR) is also conserved. However, the regulatory link between them is only conserved in S. typhi and S. typhimurium, producing an SOS value of only 0.1, which is below the mean value of the network.Table E. coli network and those of the CRP, FNR, and MetJ regulons separately) the fraction of regulatory interactions sampled decreases rapidly with the increase of the value of SOS used as cutoff for sampling. For instance, for the FNR regulon, the fraction drops to 0 at SOS 0.5, which means that no FNR outgoing interactions have associated SOS values higher than 0.5. In the entire E. coli network, only 8 regulatory interactions have SOS associated values higher than 0.8.We also analyzed the distribution of the SOS values of regulatory links within the network by computing what fraction of regulatory interactions have SOS equal to or greater than an increasing cutoff value. This means sampling the regulatory interactions with SOS higher than a given increasing value. As Figure E. coli motifs are conserved in other organisms to a higher extent than expected from the observed pattern of shifting of individual interactions.One question that arises concerning this rewiring of the network is whether local medium-level structures are affected by the appearance and disappearance of regulatory interactions. Regulatory interactions in the TRN form recurrent circuits that involve one, three or four nodes, and which are often referred to as network motifs ,24. NetwE. coli TRN reconstructed in Tractor_DB. For both types of motifs we used a metrics that is more elaborated than simply counting the number of conserved motifs in other organisms. We relied on the SOS of individual interactions to evaluate the conservation of motifs, because the SOS takes different values depending on which organisms bear orthologs to an E. coli regulatory site.To address this question, we extracted all the auto-regulatory loops  motifs in the -12). This result implies that natural selection acts at the level of motifs favoring the preservation of regulatory interactions that form auto-regulatory circuits – through which TFs control the transcription of their own genes – in this group of organisms.In the case of ALs, we calculated the mean of the associated SOS values of the interactions between the TF and its coding gene, and compared it with the average mean that resulted from randomly taking 1,000 samples composed of the same number of interactions than the set of ALs. As shown in Table E. coli interaction depends on which organisms have orthologous interactions, it may be seen as a phylogenetic profile measurement that takes similar values for two interactions when they are conserved in similar sets of organisms. Therefore, a positive correlation between the SOS values of two interactions implies that they appear in a similar set of organisms and are absent in a similar set. Table -8 and 3.98*10-7). These results mean that the profile of the conservation of two regulatory interactions that make part of an E. coli FF motif – across the genomes of the other 29 organisms in the dataset – tend to be more similar than the patterns of conservation of any two interactions sampled randomly from the network, which implies that the structure of E. coli FF motifs tends to be altogether conserved or lost in the same arrays of organisms, as defined by similar SOS. In other words, the regulatory links that form feed forward motifs [With regard to FF motifs, we assessed whether the regulatory interactions that form them are correlatedly conserved (see Methods). Again, since the SOS of a given d motifs ,27 are cE. coli co-regulated genes. Table -4), the pairs of co-regulated genes as a rule, tend to be less correlatedly conserved than pairs of interactions involved in FF motifs.In order to compare the correlation of the SOS of interactions involved in FFs to other non-random structures within the network, we calculated the correlation coefficient of the SOS of 4089 pairs of The connectivity distribution  in the transcriptional regulatory network may be approximated by a power law function with negative exponent -30. We c2 coefficients range from 0.85 to 0.88. These trend lines should not be taken as a golden standard because only a fraction of structural genes were included in the calculation. We use them to merely illustrate the similarity of their slopes between different organisms.Figure This result  suggestother regulon and lost link (see above) may have occurred by two main different events: either the gene was "relocated" to a different TU in one of the organisms resulting from a speciation event, or the orthologous regulatory regions diverged to an extent that they no longer bear sites recognized by the orthologous TFs (represented by orthologous weight matrices in the approach used to identify regulatory sites in Tractor_DB). In an attempt to assess the relative contribution of these two processes to changes in gene regulation, we computed the fraction of TUs in each organism whose structure is identical (see Methods) to their orthologous TUs in E. coli and bear genes with regulatory links either unknown (lost link) or that have switched to a different regulator (other regulon). We found that a fraction of the genes in these two categories ranging from 46% (P. aeruginosa) to 77% (E. coli O157H7) appear in TUs that are identical to their E. coli orthologs and therefore the change in regulation in their cases could only be explained by divergence of the orthologous regulatory regions.Changes in gene regulation, that we detect as regulatory links that fall within the categories E. coli orthologs (from 23 to 54% depending on the organism). To test this hypothesis, we searched for any discernible relationship between the degree to which a regulatory site is conserved across the genomes of the organisms included in Tractor_DB, and the conservation of the structure of the regulated TUs. To assess this relationship, we classified the structure of each TU in one of four categories, according to its similarity to the structure of its E. coli orthologous TU(s). Sorted in order of decreasing conservation, the categories are Identical, Similar, Destroyed, and Lost , and those with Destroyed or Lost structure (D+L). Figure E. coli. On the other hand, sites with SOS between 0.6 and 1 are located upstream Identical or Similar TUs with a probability of 0.6. These results are very similar to those obtained by a previous study that used a simpler equation and less data to calculate the SOS [-69 to 1.35*10-44.We calculated an I+S/D+L ratio for each SOS interval, and assessed the statistical significance of the observed ratios with respect to random associations between TUs and regulatory sites. The results  and calculated a ratio of the two aforementioned groups of sites for each SOS used as cutoff for sampling. Figure E. coli and either Sty, Sfle, Sfl2 and Ypes; Stm has been excluded from the graph for clarity, since its behavior is almost identical to the one observed in Sty; similar trends to the ones shown are observed in all other organisms, but are less clear due to lower number of conserved sites). The graph shows that the number of conserved sites within conserved syntenic groups between E. coli and each genome tends to decrease slower than the number of conserved sites outside conserved syntenic regions as higher SOS values are used for sampling the sites. Therefore, regions of conserved synteny between genomes are enriched of sites that are more conserved across genomes. Additional file We also looked at the conservation of regulatory sites in connection to the conservation of gene order between genomes. We counted the number of E. coli TRN to other organisms to investigate the evolution of prokaryotic TRNs as a whole [E. coli (and B. subtilis) TFs and regulated genes across more than 100 genomes, showing that TFs are flexibly substituted in the course of evolution. Babu et al. [To our knowledge, two studies have extrapolated the information on the  a whole ,19. Bothu et al.  found thu et al.  have madu et al. .E. coli regulon and which had apparently left that regulon in another organism but if possible, we wanted to identify the TF(s) that appear to regulate the orthologous genes in that second species. Furthermore, we intended to assess the influence of this change in regulatory connections on structures of higher complexity within the network, i.e., network motifs and the free scale architecture.In this study, we aimed at characterizing this process of rewiring the regulatory connections within the TRN that had been noticed for a few isolated regulons. We used the data on putative regulatory sites in 30 gamma-proteobacterial genomes that we had produced using two complementary computational strategies and annotated in Tractor_DB. We intended to investigate not only the genes that belonged to a given Moreover, we wanted to evaluate how this process of rearranging regulatory interactions is reflected on the physical structure of the bacterial chromosome. Since the TU is the basic structure responsible for the spatial proximity of all the sequences necessary to accomplish the correct control of the transcription of a group of genes, we decided to search for changes in the structure of orthologous TUs that might correlate with changes on regulatory input interactions of the genes that form them.E. coli K12 [B. subtilis [The scarcity of experimental data on regulatory interactions has been a limitation for comparative analyses that aim at understanding the evolution of TRNs. Manually curated experimental data are available for very few model organisms. Among the prokaryotes coli K12  and B. ssubtilis  have thelost link and other regulon may indeed be artefacts of the computational approaches taken to predict orthologous regulatory sites. The fact that such methodologies fail to recognize an ortholog of an E. coli regulatory site in another organism (or assign the site to another TF) may be caused by genetic drift (divergence between sequence motifs recognized by orthologous TFs). Also, since the recognition of regulatory sites by TFs is aided by protein-protein interactions, the absence of a sequence strong enough to be identified by a weight matrix does not guarantee that the TF will fail to bind to the regulatory region.In the case of our study, for example, regulatory sites labeled as E. coli with experimentally known regulatory sites in this organism, obtained from RegulonDB [et al. [To evaluate the robustness of the data stored in Tractor_DB we compared the results obtained by the methodology of statistical models  E. coli and S. typhimurium gene pairs. We found that the strong trend of co-expression observed for experimentally verified co-regulated E. coli gene pairs may also be recognized for E. coli and S. typhimurium computationally predicted co-regulated gene pairs , we compared the egulonDB ), and thE. coli could in principle reproduce these starting data and create the false impression that gamma-proteobacterial TRNs are very similar. We have used the term circularity to denote this behavior of computationally predicted regulatory binding sites. We have previously tested the data stored in Tractor_DB for this bias [et al. [The identification of putative regulatory sites applying a comparative genomics approach based on regulatory information extracted from his bias , and fou [et al.  have chaE. coli regulatory sites and the scores of their orthologs  that may have arisen as the result of the aforesaid problem. The results in Additional file Different TFs present different binding affinity distributions ,5, whichet al. [et al. [E. coli and B subtilis TFs and (to a lesser extent) structural genes is probably crucial for the adaptation of different evolutionary groups to diverse environmental conditions. This process may be described as the substitution of the TRN "hardware" [Babu et al.  and Loza [et al. found thaardware" . In thiset al. [E. coli do not tend to appear associated in the networks reconstructed in other organisms with a frequency that is higher than that expected by chance. However, our results suggest that if the three genes that form the FF motif in E. coli co-occur in another genome the probability that the regulatory links between them are conserved  will be greater than the probability of correlated conservation of a triad of randomly chosen regulatory links. And this trend is also stronger than the one observed for pairs of co-regualted genes, although the latter is also statistically significant when compared to randomly sampled nodes. This observed trend may be a consequence of the dynamical properties that motifs confer to the regulation of individual structural genes [E. coli its ortholog in another gamma-proteobacteria it will probably be autorregulated as well.Furthermore, Babu et al.  showed tal genes ,25. InteThe fact that small regulatory circuits as ALs are conserved more often than would be expected from the conservation of individual regulatory interactions – and that regulatory interactions involved in FF motifs are more correlatedly conserved than triads of randomly chosen interactions or pairs of co-regulated genes – suggests that some of these features might be favored by natural selection within a group of closely related organisms once they appear in their common ancestor. In other words, even though the free-scale architecture of TRNs may show converging evolution in different groups of organisms, some of these elements that are present in the TRN of the common ancestor of the group might be preserved by natural selection, while the adaptation of various members of the group to slightly changing lifestyles may be accomplished through changes in regulatory connections, a process that may occur in a short period of time, compared to the substitution of a subset of regulatory proteins.et al. [et al. [Analysis of the conservation of the structure of TUs showed a trend for conserved sites to be located upstream conserved TUs. On the other hand, regulatory interactions conserved in few genomes involve genes within TUs whose structure is less conserved. This bias suggests that rearrangements of the bacterial chromosome that affect the structure of TUs may be related to the rewiring process observed in the regulatory network. Itoh et al.  pointed et al.   to 77% (Ecoo) of all genes that receive interactions from TFs that differ from those that regulate their orthologs in While at large evolutionary scales, the main source of variation of the regulation of individual genes may be changes in the repertoire of TFs (by changing in gene content), at smaller scales, the shift of individual regulatory interactions – or rewiring of the network – between existing TFs and structural genes may be also an important source for this modification. The rewiring observed at the most basic level of the regulatory network occurs mainly by two processes: rearrangements of genetic material and divergence of orthologous regulatory regions. The rearrangements that occur in bacterial chromosomes at this evolutionary scale-mostly inversion or horizontal gene transfer events – alter the structure of some TUs but leave the order of genes in certain pieces of the chromosome unchanged. At this evolutionary scale, the connections that are part of motifs tend to be more correlatedly conserved than triads of randomly chosen regulatory links or pairs of co-regulated genes. Moreover, the rewiring process does not affect the global properties of the graph, such as the connectivity distribution. This preservation of connectivity distribution takes place without modifying the set of global regulators, suggesting that only fine-tuning of the regulatory network occurs within this group of closely related organisms, in the process of divergence from a common ancestor.-15 and sequence coverage was at least 60%. These orthology relationships were then extended to other components of the regulatory machinery. We defined orthologous TUs as those that share at least one pair of orthologous genes. Orthologous regulatory regions/sequences are defined as those extending from -400 to +50 with respect to the first translated nucleotide of orthologous TUs . Putative regulatory sites that occur at ortholgous regulatory regions, and are predicted to be recognized by the same TF were labeled as orthologous regulatory sites. As mentioned above, we worked with regulatory sites stored in Tractor_DB that were identified using either statistical models or pattern matching [We determined orthology relationships between gene pairs using the BLAST Bi-Directional Best Hits (BBH) approach . Brieflymatching ,36,37. AX. axonopodis and M. capsulatus to 2118 in E. coli K12), with a mean of 446 interactions per genome.In this work, regulatory site conservation is used as synonymous to conservation of a regulatory interaction, although two conserved sites may in practice correspond to a single conserved interaction (when multiple binding sites for the same TF affecting the same gene are conserved). As Table E. coli as a template to assess the variability of regulatory interactions of each individual regulon across all the organisms included in the database. Putative orthologous interactions – or their absence – in each of the other twenty-nine genomes were classified into four categories. Interactions established between a pair of orthologous TFs and a pair of orthologous structural genes were labeled as conserved; a pair of orthologous genes regulated by non-orthologous TFs were included in the other regulon category; when an ortholog of an E. coli gene with a known input regulatory interaction has no identified regulatory site, we considered this a lost link; an E. coli gene without an identified ortholog in the other organism was classified as lost gene. Figure We used regulatory interactions identified in E. coli regulatory site across the other 29 genomes. Let ai be a binary coefficient that takes value 1 if an orthologous site exists (a putative regulatory site predicted to be recognized by the orthologous TF) in the organism i and 0 otherwise . Let fi-Eco be the fraction of genes of the organism i with orthologs in E. coli.  Let then bi be a binary coefficient that takes value 1 if an orthologous site is expected to appear in the organism i and 0 otherwise. (Orthologous sites are expected to appear in the genome of i if and only if an orthologous TF and at least an orthologous TU of the E. coli TU upstream which the site is located exist in the genome of organism i.) The SOS may be expressed as:The orthology score of a site (SOS) assesses the conservation of an i and E. coli (one minus the fraction of genes from i that have orthologs in E. coli). Therefore, the contribution of an organism to the SOS is greater the smaller the fraction of its genes with orthologs in E. coli. On the other hand, the denominator computes the highest number of organisms where orthologous sites are expected to occur under the conditions of the strategies used to identify putative regulatory interactions – i.e., an orthologous site may only be found in organisms where an orthologous TF and at least one orthologous TU exist – and normalizes the SOS. The SOS of an E. coli site is thus the fraction that results from counting the genomes with an orthologous site (weighed by a measure of their phylogenetic distance to E. coli) and dividing them by the sum of all genomes with an expected orthologous site (weighed in the same manner). It is thus a normalized score that reflects the conservation of a site within the set of the 29 genomes included in this study. The SOS does not include the score of putative regulatory sites . Nevertheless, by considering only sites scoring above estimated cutoffs by the predictive approach  with those calculated using the more rigorous measure described in the cited paper. Therefore, a study carried out using a more rigorous measure of phylogenetic distance would most likely arrive to very similar results  that could in principle distort the fractions of orthologous genes calculated, we tested the influence of the chosen measure of the evolutionary distance on the distribution of the SOS. We recalculated all the SOS of ez et al , based oThe SOS takes values between 0 and 1. A site with SOS 0 has no orthologous sites, and a site with SOS 1 has orthologs in all the organisms where an ortholog is expected given the aforementioned conditions.E. coli network. The current improvement produces a more realistic SOS for each site by constraining the maximum expected SOS to the contribution of the organisms where orthologous sites may actually occur.We presented a previous version of the SOS in a study that included seven organisms . Instead and the Feed Forward (FF) motifs from coding Y ,21. In o network , becauseE. coli network (MAL). Then, we sampled 1000 random groups of 28 interactions from the E. coli network and calculated the mean SOS for each group. The mean and standard deviation of the 1000 random means were subsequently calculated. These measures were then used to assess the statistical significance of the MAL, through the calculation of the corresponding Z score and p-value.We calculated the mean SOS of the interactions involved in the 28 AL motifs in the E. coli network and calculated the Pearson Correlation Coefficient of the three pairs of 111 sets of SOS of homologous interactions.) First, we calculated the mean SOS values corresponding to the TFX-TFY, the TFX-A, and the TFY-A interactions from all the FF motifs in the network. Then, we calculated the standard deviation value of these three interactions and the covariance of the three possible pairs of interactions  in the set of 111 FF motifs. Finally, we calculated the correlations of the mean SOS values of the three interaction pairs. For instance, we calculated the correlation between the SOS of all the TFX-TFY interactions and all the TFX-A interactions (cor1) using the equationWe calculated correlations for the three pairs of regulatory interactions (their SOS) that form FF motifs.  regulatory interactions, i.e., 111; SOSXYi and SOSXAi are the SOS of the i-th TFX-TFY interaction and the i-th TFX-A interaction, respectively; and MXY and MXA are the mean SOS of the TFX-TFY interactions and the TFX-A interactions, respectively. where E. coli original site appears  this correlation does not assess whether a trio of E. coli genes involved in a FF motif co-occur in other organisms. Instead, it assesses whether a trio of E. coli regulatory links appear together in other organisms that bear orthologs for the three genes.Since the denominator of the SOS computes the number of organisms for which a site orthologous to the E. coli network, and calculated the corresponding 1000 triplets of correlations. Then, we calculated the mean and standard deviation of each of these three random sets of correlations and the Z score and p-value corresponding to the three true correlation values.To assess the statistical significance of the correlations calculated for each pair of interactions, we randomly sampled 1000 groups of 111 triads of interactions from the E. coli co-regulated genes from Tractor_DB . We then calculated the Pearson correlation coefficient of the SOS corresponding to the regulatory links that connect the TF with the genes that form each pair. The calculation of the Pearson correlation coefficient and the assessment of its statistical significance were carried out as described above for the FF motif .We also extracted the 4089 pairs of computationally predicted E. coli TU was assessed using the qualitative classification proposed by Itoh et al. [E. coli TU at the center of the analysis; as Similar if the structure is only partly conserved ; as Destroyed if at least two orthologous genes of the E. coli TU are found, but they belong to different orthologous TUs; and finally, as Lost when at most one ortholog to the genes of the E. coli TU is found.The conservation of the structure of each h et al. . Brieflyet al. (2005) [We labeled each orthologous TU using this classification system, and associated the label of each TU to the SOS of the regulatory site(s) found at their regulatory regions. TUs were clustered into four groups according to the SOS of their regulatory sites: TUs with SOS between 0 and 0.13, TUs with SOS between 0.14 and 0.36, TUs with SOS between 0.37 and 0.59 and TUs with SOS between 0.6 and 1 as in Espinosa To find out whether the resulting association is statistically significant with respect to all possible associations of SOS and TU structure conservation we randomized the association between TUs and regulatory sites, keeping both SOS frequencies and the frequencies of the four categories of TUs structure conservation. This randomization was iterated 1000 times and for each randomization we calculated the resulting ratios of Identical and Similar TUs to Destroyed and Lost TUs in each group of SOS. Finally, the mean and standard deviation of each SOS group ratio was calculated, as well as the corresponding Z scores and p-values.SOS: Site orthology score; TU: Transcription unit; TF: Transcription factor; TRN: Transcriptional regulatory network.AGP carried out SOS calculations and motifs conservation assessment, participated in the conception and design of the study, in TUs conservation assessment, contributed to the discussion, and drafted the manuscript. EGG participated in the design of the study, and in TUs conservation assessment. VEA participated in the conception of the study, and in TUs conservation assessment, and helped drafting the manuscript. ATRV and JC-V coordinated the study, contributed to results, discussion, and helped drafting and revising the manuscript. All authors read and approved the final manuscript.M1: Approach to prediction of putative TF binding sitesClick here for fileR6: The process of disappearance/appearance of regulatory linksClick here for fileR1: Specificity and sensitivity of the identification of putative regulatory sites stored in Tractor_DBClick here for fileT1: Distribution of the SOS  for each regulon and globally within the networkClick here for fileR4: Conservation of regulatory sites and chromosome vicinity of their target genesClick here for fileR5: Assessing the quality of reconstructed TRNs using expression dataClick here for fileR3: SOS values of E. coli regulatory sites against mean scores of their orthologous sitesClick here for fileR7: Recalculation of the SOS using a different measure of phylogenetic distanceClick here for fileR2: Calculation of the SOS of a regulatory linkClick here for file


The technological evolution of platforms for detecting genome-wide copy number imbalances has allowed the discovery of an unexpected amount of human sequence that is variable in copy number among individuals. This type of human variation can make an important contribution to human diversity and disease susceptibility. Multiplex Ligation-dependent Probe Amplification (MLPA) is a targeted method to assess copy number differences for up to 40 genomic loci in one single experiment. Although specific MLPA assays can be ordered from MRC-Holland (the proprietary company of the MLPA technology), custom designs are also developed in many laboratories worldwide. After our own experience, an important drawback of custom MLPA assays is the time spent during the design of the specific oligonucleotides that are used as probes. Due to the large number of probes included in a single assay, a number of restrictions need to be met in order to maximize specificity and to increase success likelihood.We have developed a web tool for facilitating and optimising custom probe design for MLPA experiments. The algorithm only requires the target sequence in FASTA format and a set of parameters, that are provided by the user according to each specific MLPA assay, to identify the best probes inside the given region.To our knowledge, this is the first available tool for optimizing custom probe design of MLPA assays. The ease-of-use and speed of the algorithm dramatically reduces the turn around time of probe design. ProSeeK will become a useful tool for all laboratories that are currently using MLPA in their research projects for CNV studies. The technological evolution of platforms for assessing genome-wide copy number imbalances  has alloMLPA , MultiplAlthough specific MLPA assays can be ordered from MRC-Holland (the proprietary company of the MLPA technology), custom designs are also developed in many laboratories worldwide. After our own experience, an important drawback of custom MLPA assays is the time spent during the design of the specific oligonucleotides. Due to the large number of probes included in a single assay, a number of restrictions need to be met in order to maximize specificity and to increase success likelihood. Given the tedious stepwise procedure that is followed, the goal of ProSeeK is to automate the process of probe design and to obtain the best candidate probes for a given region.) with IE5.0 and Netscape 7 or higher, from any platform. By making use of universally available web GUIs, the system solves the problem of portability of this software. No client-side software installation is required.ProSeeK is presented as an easy-to-use and point-and-click web interface. Is implemented in CGI (Common Gateway Interface) Perl scripts and made accessible to the user using PHP on top of an APACHE server with MYSQL database support. It is accessible through the Internet  maximum GC content, (2) maximum melting temperature (Tm) of the hybridizing sequence, (3) Blat e-value , (4) hybridizing sequences (HS) length, (5) stuffer sequence, (6) sequence of the universal primers to flank the HS, and (7) desired probe length. , and liA number of high-throughput technologies have become available to address the genome-wide detection of structural variations in humans. An important drawback of these new methods is that a huge amount of false positive results typically arise after analysis, thus it is mandatory to validate observations made with these technologies using alternative and more reliable approaches. Among others, due to its simplicity, robustness and relative low price, the MLPA is often used as a targeted method to assess copy-number differences. One important inconvenience is the required time for designing the probe-mixes to target the desired regions, since a lot of restriction should be fulfilled to get a sensitive, specific and reproducible experiment. To overcome this aspect, we developed ProSeek that produces the optimal probes for the regions of interest. ProSeeK is, to our knowledge, the first algorithm for the design of MLPA probes, that allows saving time and improving accuracy of MLPA assays.• Project name: ProSeeK• Project home page: • Programming language: Perl• License: GNU General Public LicenseLP initiated this web server project, wrote the original source code, constructed the web interface, implemented it on the server and wrote the manuscript. LA conceived the server and participated in manuscript writing. XE revised and helped to write the manuscript. SV helped to design the web interface particularly from the viewpoint of an experimental research field. All authors contributed to the final manuscript and agreed the final version.Tutorial. A complete tutorial explaining step by step the ProSeeK procedure.Click here for file


The commissure of the inferior colliculus interconnects the left and right sides of the auditory midbrain and provides the final opportunity for interaction between the two sides of the auditory pathway at the subcortical level. Although the functional properties of the commissure are beginning to be revealed, the topographical organization of its connections is unknown. A combination of neuroanatomical tracing studies, 3D reconstruction, and neuronal density maps was used to study the commissural connections in rat. The results demonstrate that commissural neurons in the central nucleus of the inferior colliculus send a divergent projection to the equivalent frequency-band laminae in the central nucleus and dorsal and lateral cortices on the opposite side. The density of this projection, however, is weighted toward a point that matches the position of the tracer injection; consistent with a point-to-point emphasis in the wiring pattern. In the dorsal cortex of the inferior colliculus there may be two populations of neurons that project across the commissure, one projecting exclusively to the frequency-band laminae in the central nucleus and the other projecting diffusely to the dorsal cortex. Neurons in the lateral cortex of the inferior colliculus make only a very weak contribution to the commissural pathway. The point-to-point pattern of connections permits interactions between specific regions of corresponding frequency-band laminae, whereas the divergent projection pattern could subserve integration across the lamina. J. Comp. Neurol. 514:226–239, 2009. © 2009 Wiley-Liss, Inc. The commissure of the inferior colliculus (CoIC) is a prominent fiber tract interconnecting the two inferior colliculi and provides the last opportunity for interaction between the left and right sides of the auditory pathway at the subcortical level Adams,,1996. ThA wealth of studies has described how ascending projections from the brainstem and descending projections from the auditory cortex terminate in the three main subdivisions of the IC Adams,. In contStudies using anterograde tracers demonstrated connections between the frequency-band laminae in one IC and the equivalent laminae in the contralateral IC  distinct patterns of commissural connections occur between different subdivisions of the IC and 2) a point-to-point pattern of connections exists between frequency-band laminae.The distribution of commissural neurons labeled by a single small injection in a frequency-band lamina is consistent with the notion that a point on the lamina makes divergent connections throughout the lamina in the opposite IC but that the density of connections is greatest between corresponding points. Although the projections of some commissural neurons in the dorsal cortex connect to the contralateral dorsal cortex, another population projects only to the central nucleus, connecting these two subdivisions. We conclude that the connections of the CoIC mediate integration both within frequency-band laminae and across different subdivisions of the IC.Rattus norvergicus, Rj: Long Evans) of either sex weighing between 180 and 420 g (n = 10). All procedures were approved by and conformed to the standards of the University of Salamanca Animal Care Committee.Experiments were performed on adult pigmented rats (−1) and xylazine  and supplemented with the same mixture to maintain suppression of the pedal withdrawal reflex. Atropine sulfate  was administered subcutaneously to suppress bronchial secretions. Body temperature was maintained at 38°C with a thermostatically controlled blanket. The animal was placed in a stereotaxic frame in which the ear bars were replaced by hollow speculi that accommodated a sound delivery system. A craniotomy was performed to expose the cerebral cortex overlying the inferior colliculi on one side, and the animal was situated inside a double-walled sound-attenuating enclosure.An areflexive anesthetic state was induced via an intramuscular injection of a mixture of ketamine hydrochloride (60 mg · kgTracer injections were made with glass micropipettes (tip diameter 10–40 μm) filled with either 10% tetramethyl-rhodamine-dextran (TRD) dissolved in 0.9% saline or a mixture of 10% fluorescein-dextran (FD) and 10% biotinylated dextran amine (BDA) in 0.9% saline , followed by a 4% paraformaldehyde fixative solution. After fixation, decapitation, and dissection to remove the brain, it was cryoprotected in 30% sucrose and sectioned in the transverse plane at 35 μm or 50 μm on a freezing microtome. Single sections underwent avidin-biotin complex histochemistry for FD-BDA  Diaplan microscope at a total magnification of ×52. Special care was taken to ensure that neurons at the section interface were drawn only once. Six cases were reconstructed in three dimensions  as described in detail elsewhere . In six rats Table , a pair To discover the relationship between the patterns of labeling that originate from injections into different parts of the same lamina, we made 3D reconstructions of the injection sites in one IC and of the labeled neurons on the contralateral side . This difference may be explained by the different location of the injections. While the FD-BDA was placed in rostral cortex (RCIC), the TRD injection was sited at the dorsolateral corner of the IC, in the lateral cortex Fig. B. In thiIn case 35, the FD-BDA injection was made at BF 2 kHz in the dorsocaudal region near the DCIC-LCIC border, but outside the CNIC. The TRD injection (which was larger than the FD-BDA injection) was made in the equivalent-frequency region of the dorsal cortex Fig. , as doesBy combining tract-tracing and 3D reconstructions of the locations of retrogradely labeled neurons, we have provided anatomical evidence for the topography of the commissural neurons interconnecting the ICs in rat. Our results suggest that 1) a focal injection into the CNIC labels neurons throughout the equivalent frequency-band laminae in the central nucleus, and in the dorsal cortex on the opposite side, but that 2) the density of the labeled commissurally projecting neurons is weighted toward a point that matches the position of the tracer injection. 3). The results are consistent with there being two populations of commissural neurons in the DCIC Fig. B; one thThe combination of tract-tracing with 3D reconstruction has enabled us to reveal aspects of the commissural connections that were hitherto unknown. Our injections are smaller than those used in previous studies Adams, and permPrevious studies of the CoIC described the overall distribution of commissural neurons but not the fine grain pattern of their projections. Aitkin and Phillips  retrograThe retrograde labeling of neurons throughout a frequency-band lamina following an injection at a point in the CNIC shows that neurons over the whole plane of the lamina project to each point within the lamina and is consistent with commissural neurons in the CNIC making a divergent pattern of connections to the equivalent contralateral laminae Fig. . ImposedIt is not possible to tell from the current data whether there are two types of commissural neurons in the CNIC, one type that is solely responsible for the focal, point-to-point projection (Fig. The coexistence of divergent and point-to-point projections suggests that two functional systems operate across the commissure. One would allow corresponding points on the lamina to interact, whereas the other, more diffuse network would facilitate interactions between neurons over the plane of the lamina. If the IC plays a role in integrating inputs from the lower brainstem, one might expect to find connections that provide the means whereby different input sources can interact.Our results are therefore consistent with the possibility that, whereas commissural projections emphasize connections between corresponding points on a lamina, they also allow for the integration of information both within a lamina and between the DCIC and the CNIC. An important challenge in understanding the organization of the IC is discovering what is represented over the surface of a frequency-band lamina, i.e., in the plane orthogonal to the tonotopic axis. Evidence exists for a shallow gradient in best frequency across the laminae (Langner and Schreiner,A second important feature of our results is the differential labeling following injections made in the dorsal cortex and the central nucleus where they occur in a single lamina. A point injection in the central nucleus labels neurons in a laminar pattern in the opposite colliculus. This extends throughout the central nucleus and into the deep dorsal cortex, with only a few labeled cells in the LCIC. In contrast, an injection into dorsal cortex in the same lamina labeled cells predominantly in the dorsal cortex. An alternative explanation for these results is that the commissural connections between the central nuclei and the dorsal cortices are distinct but that the TRD injection into the central nucleus diffused into the dorsal cortex, resulting in labeling in both divisions. However, if this were so, one would expect a similar dispersion of tracer to occur with the FD-BDA injection in the dorsal cortex injection, leading to labeling in the central nucleus, but this is not the case. Furthermore, it is important to emphasize that our injections are smaller than those in previous studies in which larger injections were used to facilitate as much labeling as possible. Such caveats notwithstanding, these results suggest the possibility that there are separate zones within the IC with respect to the distribution of commissural connections. Recent studies by Cant and Benson , 2008 haThe different patterns of labeling observed in the DCIC following injections into the DCIC or the CNIC support the possibility that the DCIC contains two distinct populations of neurons with respect to their commissural connections. One population makes connections along the length of the lamina extending from the DCIC into the CNIC, whereas the other appears to send connections only to the DCIC on the opposite side in a more diffuse manner Fig. B. Defini


Listeria monocytogenes. The data generated by the consortium members isstored under the RIBDB database, a system built using SRS which integrates consortiumdata, public databases, and applications for analysis. RIBDB is available to all consortiummembers through a web server, with the option of installing a local mirror of the mainserver for local analysis.The REALIS project is an EU-funded consortium for the post genomic analysis of the foodpathogen


Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.Saccharomyces cerevisiae (budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells. Saccharomyces cerevisiae  that contain this gram and the number of hits of this gram in each domain;Step A. For each 4-gram Gj appearing in query protein B, we also generated a list of Pfam annotated domains dlistG[j] that contain this gram and the number of hits of this gram in each domain;Step B. For each 4-gram Gi, Gj) between the query proteins A and B, we calculated the frequency of hits freq[i][j] for this gram-pair represented in interacting domain-domain pairs previously established in Pfam [score[i][j] for this gram-pair was weighted by weightScore[i][j] to determine if the number of its occurrences in the interacting domain pairs is statistically significant. The hit scores and weight scores are calculated by the following formulas:Step C. For each gram-pair  [i] [j] is the probability of the number of occurrences of Gram[i][j] in the interacting domain pairs is expected at random. Comparable control domain pairs were randomly generated by pairing domains from the DDI data set.Here, Pc and this gram-pair was located in a domain region, then this gram-pair and their corresponding domain pair was predicted to interact with each other. A profile containing the number of hits and the positions of the gram-pairs in the input query protein pair was simultaneously generated. This profile is important because it provides information on the amino acid hotspots that are potentially contributing to the physical interaction between the pair of query proteins  curve and the Area Under the Curve (AUC). The area under the curve was calculated by the Wilcoxon rank sum test. ROC curve provides us an indicator of the sensitivity and how it is affected by the specificity. The area under the curve highlights discrimination . The ROC curve was generated by calculating the true positive rate (sensitivity) and the false positive rate (1-specificity) at the different thresholds on scores derived from PPIs and DDIs in the network, and combined scores from both kinds of interactions against the "gold-standard" data set. If the number of hits of any domain pair in a protein pair was above the threshold and it was in the DDIs of positive portion of the "gold-standard" data set, then it was regarded as a true positive. Alternatively, if it was not in the positive portion of the "gold-standard" dataset, then it was a false positive. If the number of hits of a domain pair in a protein pair was below the threshold and it was in the negative portion of the "gold-standard" data set, then it was regarded as a true negative. Alternatively, if it was not in the negative portion of the "gold-standard" data set, then it was a false negative.The sensitivity, specificity and positive prediction value (PPV) were calculated as follows:The related data sets and scripts, source code, and binaries are available for download from . All scrThe authors declare that they have no competing interests.KXZ and BFFO conceived and designed the experiments. KXZ performed the experiments. KXZ analyzed the data. KXZ performed analysis and wrote the tools. KXZ and BFFO wrote the paper. All authors read and approved the final manuscript.


Hip replacement (arthroplasty) surgery is a highly successful treatment for patients with severe symptomatic arthritis of the hip joint. For older patients, several designs of Total Hip Arthroplasty have shown excellent results in terms of both function and value for money. However, in younger more active patients, there is approximately a 50% failure rate at 25 years for traditional implants. Hip resurfacing is a relatively new arthroplasty technique. In a recent review of the literature on resurfacing arthroplasty it was concluded that the short-term functional results appear promising but some potential early disadvantages were identified, including the risk of femoral neck fracture and collapse of the head of the femur.The aim of the current study is to assess whether there is a difference in functional hip scores at one year post-operation between Total Hip Arthroplasty and Resurfacing Arthroplasty. Secondary aims include assessment of complication rates for both procedures as well cost effectiveness.All patients medically fit for surgery and deemed suitable for a resurfacing arthroplasty are eligible to take part in this study. A randomisation sequence will be produced and administered independently. After consenting, all patients will be clinically reviewed and hip function, quality of life and physical activity level will be assessed through questionnaires. The allocated surgery will then be performed with the preferred technique of the surgeon. Six weeks post-operation hip function will be assessed and complications recorded. Three, six and 12 months post-operation hip function, quality of life and physical activity level will be assessed. Additional information about patients' out-of-pocket expenses will also be collected.Current Controlled Trials ISRCTN33354155UKCLRN portfolio ID 4093 Hip arthroplasty is a procedure which has been performed by orthopaedic surgeons for decades, with improvements to the procedure and the implants being made almost continuously over that time. For older patients, several designs of Total Hip Arthroplasty (THA) have shown excellent results in terms of both function and value for money . HoweverAt present, only a few randomised trials have been performed comparing the traditional techniques of THA with the resurfacing technique. These studies have focused on the technical aspects of the procedure, such as the position of the implants or the amount of bone removed during the resurfacing procedure ,7. ThereThe null hypothesis for this trial is that there is no difference in functional hip scores (Harris and Oxford Hip score) at one year post-operation between Total Hip Arthroplasty and Resurfacing Arthroplasty.th of May 2007. The research carried out is in compliance with the Helsinki Declaration.This is a single-blind randomised controlled trial. This study has been reviewed by the Coventry Research Ethics Committee under reference number 07/Q2802/26. The study was approved on the 9The only eligibility criterion for participation in this study is that the patient is medically fit for an operation and suitable for a RSA . These broad eligibility criteria should ensure that the results of the study can readily be generalised to the wider population.Contra-indications to surgery, defined as: (i) severe cardiac impairment, e.g. heart or valve replacement, arrhythmia, previous myocardial infarction, (ii) severe respiratory impairment, e.g. chronic obstructive pulmonary disease, asthma that has required hospital admission, or (iii) any other systemic medical condition that would produce a specific contraindication to a general anaesthetic.Evidence that the patient would be unable to adhere to trial procedures or complete questionnaires, such as dementia or intravenous drug abuseIf a recruited patient requires a contra-lateral hip replacement during the trial period, this second hip cannot be included in the study (i.e cannot be randomised) since the result of this intervention would not be independent from the first intervention.Participants may withdraw from the trial at any time without prejudice. If patients decide to have the treatment to which they were not randomised, participants will be followed-up wherever possible and data collected as per the protocol until the end of the trial. The primary analysis will be on an intention-to-treat basis with a secondary per-protocol analysis. If patients decide to have neither treatment, they will be withdrawn from the trial and not included in the analysis.The two treatments in this study are THA and RSA. Each patient will undergo the allocated surgery according to the preferred technique of the operating surgeon. The surgeons involved in the study use a variety of surgical approaches to the hip joint: anterolateral, posterior and, for the resurfacing patients, the Ganz approach. Some surgeons use an uncemented THA for younger more active patients whilst others prefer a more traditional cemented design or a 'hybrid' of the two. In the same way, some surgeons cement the femoral component of a RSA while others prefer an uncemented component.In summary, the details of the surgery will be left entirely to the discretion of the supervising surgical consultant to ensure that the results of the trial can be generalised to as wide a group of patients as possible.In a THA, the femoral head is removed along with the majority of the femoral neck. The femoral shaft is 'reamed' to open up the femoral canal. The femoral component is then inserted into the canal and the articulating femoral head is placed onto the neck of the femoral component.In a RSA, the articular surfaces of the femoral head are removed but the neck is left in-situ. The femoral component is then impacted onto the patient's own femoral neck.In both forms of Arthroplasty, the acetabulum is reamed and the acetabular component inserted into the 'socket'. The radiograph Figure  shows a Standardised rehabilitation plans will be implemented for all of the patients; as outlined in the University Hospitals Coventry and Warwickshire NHS Trust 'Hip Replacement: A Guide for Patients' booklet. This includes information on early exercises, precautions to be followed for the first three months, functional activity and later stage exercises. A photocopy of the 'joint rehabilitation discharge letter' will be made as proof that all of the patients completed these tasks to a standard level before discharge.Patients will continue in routine clinical follow-up, as per their surgeon's practice. For this trial, the primary outcome point will be at one year, with other planned assessments at six weeks, 3 months and six months after operation. At all these occasions patients will come to the clinic to be reviewed by the research associate and if necessary a doctor.There are three main objectives of this randomised controlled trial:1. To quantify and draw inferences on observed differences in primary and secondary outcomes measures between the trial treatment groups at one year post-operatively.2. To detemine the complication rate of Resurfacing Hip Arthroplasty versus Total Hip Arthroplasty at one year post-operatively.3. To investigate, using appropriate statistical and economic analysis methods, the resource use, and thereby the cost effectiveness, of Resurfacing Hip Arthroplasty versus Total Hip Arthroplasty.Oxford Hip Score [Harris Hip Score [This study has two primary outcome measures; the ip Score  and the ip Score . The OxfEuroQol 5D (EQ-5D): A validated quality of life questionnaire consisting of 5 questions related to daily activities scored on a 3-point ordinal score scale. Answers can be combined using published algorithms to obtain health-related quality of life values (ii) Disability Rating Index, a self-administered, 12-item VAS questionnaire assessing the patient's own rating of disability and (iii) Paffenbarger Physical Activity Questionnaire, this questionnaire assesses the number of calories a patient expends on leisure and physical activities built on an assessment of energy spent on average week and weekend days, as well as additional energy spent during sporting activities throughout the year. All complications will be recorded during the course of the trial.Three secondary outcome measures will be used in this trial; (i) Resource use will be monitored for the economic analysis. NHS costs associated with each arm of the trial will be estimated using data from both national sources  and theIn this study, we will use techniques common in long term cohort studies to ensure minimum loss at recruitment, such as collection of multiple contact addresses and telephone numbers, mobile telephone numbers and email addresses. Every possible effort will be made to minimise loss to follow-up, with a target of complete follow-up. Using these techniques, we are confident that loss will not exceed 10%. In the event of a participant being lost to the final follow-up at one year, we will consider, on the advice of the trial statistician, imputing missing primary outcome data from interim scores.A system of reminders will be instituted to ensure that return to clinic at three, six and twelve months is as complete as possible. The Research Associate will phone participants to make an appointment, after two weeks non-response a letter will be send out to the patient. The letter will be followed up by phone call after 1-2 weeks. If there is no response from the participant, they will be classed as a 'non-responder' and the case closed.Previous work has shown the Oxford Hip Score (OHS) and the Harris Hip Score (HHS) to be reliable measures of hip function after surgery, as measured by Cronbach's alpha statistic for internal consistency ,14. Corrhttp://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSizeHarris Hip Score. There is less available specific information on previously observed variability for the HHS, than for the OHS. Assuming Normally distributed scores, the required numbers of patients in each arm of the trial are shown in Table The minimum clinically important differences used for this sample size calculation give effect sizes of around 0.6; a medium to large effect size using the conventional descriptions of these terms . More reOxford Hip Score. The best estimate for the standard deviation for the OHS is 9 and the MCID is between 5 and 7 points on the OHS scale, calculated from a previous comprehensively reported study . Using tThe results for the OHS and the HHS at the 5% significance level, based on the best available estimates of MCID (5 and 7 score points respectively) are very similar. Both scores are intended to measure patient hip function, so it is not unexpected that the sample sizes obtained using these scores give very similar results. An adjustment to the significance level for multiple comparisons resulted in only a small increase (2-5 patients) in the required numbers.172. If recruitment proves to be problematic during the course of the trial, then with the agreement of the trial steering committee the target will be lowered and the more usual 80% power level will be considered sufficient. For this scenario, the total number of patients required will be 120 (including 10% for drop-out).In summary, this study will use two primary outcome measures the Oxford Hip Score and the Harris Hip Score. The aim will be to recruit 78 patients in each group as this will provide sufficient participants to obtain a power of 90% for both primary outcome measures. With an allowance for 10% drop-out, the total number of patients required will be A randomisation sequence has been independently generated on a computer. The sequence is stratified by supervising consultant orthopaedic surgeon. The randomisation sequence will be held by the Clinical Trials Unit of the University of Warwick. After patients have consented to take part in the trial, pre-operation outcome scores will be collected. Thereafter, the randomisation officer will be alerted by telephone of a new enrolment. The randomisation officer will provide the surgeons secretary with the treatment allocation for the patient which will then be entered onto the hospital system.Before this trial was designed, patients undergoing resurfacing arthroplasty of the hip in our department were given a different pre-operative information sheet from those having a total hip arthroplasty; this reflected the existing evidence regarding the different risk/benefit profile of the two procedures. Therefore, we do not consider it ethical to blind the patients to their treatment allocation within the trial. The patients will be informed, by letter, of their treatment allocation in the week after they have given their consent. However, we will collect pre-operative information about the patients' preference for one or other treatment.The consultants will, of course, not be blind to the treatment but will take no part in the post-operative assessment of the patients.The research associate who performs the pre- and post- treatment outcome measurements will be blind to treatment allocation throughout the entire study. Patients will be asked not to discuss their allocated treatment with the research associate.The main analysis will investigate differences in the primary outcome measures, the Harris Hip Score and the patient-reported Oxford Hip Score, between the two treatment groups on an intention-to-treat basis. The differences between treatment groups will be assessed using an independent samples t-test at 12 months post-operatively at the 5% level. Test levels, for the two primary outcome measures will be adjusted using the methods of Holm-Bonferroni  to allowThe second main statistical objective of the trial is to determine, and compare, the complication rate of RSA and THA at one year post-operatively. All complications will be recorded during the trial, and rates will be determined at 12 months post-operatively and compared between treatment groups using a chi-squared (at the 5% level).The economic evaluation will estimate the incremental cost effectiveness of RSA compared to THA from the perspective of the UK NHS. A within trial (12 month) analysis will be conducted, with lifetime results extrapolated from trial data, registry data, and the literature using a patient level simulation model. These analyses will have common features: both will use a quality-adjusted life-year (QALY) outcome measure based on EQ-5D data and the 10 year MVH TTO tariff ; both wiThe within trial analysis will consider cost-effectiveness within the first 12 months of treatment. This analysis will be based on quality of life data collected at discharge (baseline) and at 3, 6, and 12 months post operatively, with QALYs obtained by calculating the area under the curve. Resource usage will be estimated from trial forms plus patient questionnaires detailing other NHS contacts and personal expenditures within the trial period, with costs assessed as above.As hip replacement and failure of hip replacements may have an impact upon mortality as well as morbidity, it will be necessary to adopt a lifetime time horizon to estimate the full incremental value of RSA compared to THA. Therefore we will construct a patient-level decision analytic cost effectiveness model to estimate the expected incremental cost per life year gained for RSA compared to THA. Whilst the trial dataset will be used to provide parameters for several states, the main data sources for the extrapolation will come from existing literature and registries as the model horizon greatly exceeds the trial length. The model will comprise states specific to both RSA and THA , with adjustments for THA revision surgeries (as feasible). Quality of life figures will be obtained from a combination of trial data and literature/registries. Mortality will be represented using age/gender specific rates, with additional mortality assigned in surgical, recovery and failure states (as feasible). Failure rates for devices will be based on survival analysis, with the type and complexity of relationships determined by Bayesian decision criteria. Costs will be obtained from the within-trial analysis by status and used to inform state-specific costs, with existing literature and expert judgement used where necessary. The model will use a standard discount rate of 3.5 and parameter uncertainty will be addressed through probabilistic sensitivity analysis, with any inflation-adjustments based on the HCHS Pay and Prices Index. Wherever possible, the results of all analyses will be presented in a simple, easy to follow manner using standard techniques within economic evaluation. Outputs of each analysis will be presented as Cost Effectiveness Acceptability Frontiers and Expected Net Benefit figures assuming that the cost-effectiveness threshold lies at £30,000 per QALY.THA: Total Hip Arthroplasty; RSA: Resurfacing Arthroplasty; QALY: Quality adjusted life year; OHS: Oxford Hip Score; HHS: Harris Hip Score.The authors declare that they have no competing interests. The department of trauma and orthopaedics at UHCW receives funding from manufacturers of both total hip arthroplasty and resurfacing arthroplasty for routine post-market surveillance of implants. However, no funding has been received in relation to this trial.JA developed the protocol, assisted in securing grant funding and manages the running of the trial. NP developed the protocol, assisted in securing grant funding and is responsible for the statistical analysis of the trial. RE has been responsible for developing the health economics analysis part of the protocol. DG developed the protocol and is responsible for the recruitment of trial patients. MC developed the protocol, secured the grant funding, is responsible for the recruitment of trial patients, management of trial and has overall clinical responsibility for the conduct of the trial. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2474/11/8/prepub