High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium  and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions. Antibodies are proteins that are key elements of the immune system and increasingly used as drugs. Antibodies bind tightly and specifically to antigens to block their activity or to mark them for destruction. Three-dimensional structures of the antibody-antigen complexes are useful for understanding their mechanism and for designing improved antibody drugs. Experimental determination of structures is laborious and not always possible, so we have developed tools to predict structures of antibody-antigen complexes computationally. Computer-predicted models of antibodies, or homology models, typically have errors which can frustrate algorithms for prediction of protein-protein interfaces (docking), and result in incorrect predictions. Here, we have created and tested a new docking algorithm which incorporates flexibility to overcome structural errors in the antibody structural model. The algorithm allows both intramolecular and interfacial flexibility in the antibody during docking, resulting in improved accuracy approaching that when using experimentally determined antibody structures. Structural analysis of the predicted binding region of the complex will enable the protein engineer to make rational choices for better antibody drug designs. To predict the structure of a protein-protein complex, computational docking requires the structures of the interacting partners. However, sometimes even the structures of the monomeric units are unavailable, forcing the use of a homology modeled structure for one or both partners High resolution structures of protein-protein complexes are necessary for understanding mechanisms of protein-protein interactions, analyzing mutations, and manipulating binding affinity L) and heavy (VH) chains et al found that the VL-VH relative orientation has a significant impact on the antigen binding properties of an antibody L-VH relative orientation and antibody-antigen relative orientation might capture some of the intramolecular changes undergone by an antibody upon antigen binding.Antibody-antigen (Ab-Ag) complexes provide a model system where much needed high-resolution computational docking predictions are challenged by inaccuracies in antibody homology models. The selection of antibodies for exploring homology model docking simplifies the problem by isolating the various degrees of freedom according to prior knowledge of the uncertainties in an antibody homology model, namely the conformation of the complementarity determining region (CDR) loops  et al., homology modeling played an important role in the development of 11 of the first 21 marketed antibodies including Zenapax (humanized anti-Tac or daclizumab), Herceptin (humanized anti-HER2 or trastuzumab), and Avastin (humanized anti-VEGF or bevacizumab) V) region make them ideal candidates for the development of novel flexible docking algorithms.An additional motivation for studying antibody-antigen complexes is that therapeutic antibodies are revolutionizing healthcare L and VH and antigen chains, has been explored in a few case studies et al.'s modifications to RosettaDock allow simultaneous gradient-based minimization of the backbone torsional angles and the protein-protein rigid-body orientation Although there are currently no flexible docking algorithms tailored for antibodies, there have been several efforts to incorporate some of the relevant modes of internal flexibility during docking. Early V region homology modeling protocol which incorporates refinement of VL-VH relative orientation, CDR H3 loop modeling and minimization of all the CDR loops. RosettaAntibody generates ten antibody homology models for each input sequence, and this set of models can be used simultaneously with EnsembleDock. However, errors in the CDRs of RosettaAntibody homology models (particularly H2 and H3) can still frustrate docking, and only the ten pre-generated backbone conformations are sampled during ensemble docking We also recently developed RosettaAntibody L-VH. SnugDock simulates induced fit by additional simultaneous optimization of the binding interface by allowing flexibility of CDR loops and interfacial side chains. Moreover, we combine SnugDock with EnsembleDock to synthesize a new docking algorithm that encompasses conformer selection, multi-body docking, and a flexible paratope. We test SnugDock using antibody homology models obtained from two accessible public servers, namely RosettaAntibody In this paper, we discuss the development and implementation of SnugDock, a new antibody docking algorithm built upon RosettaDock using the sampling components of RosettaAntibody. The new protocol performs multi-body docking by allowing simultaneous structural optimization of the relative orientations of antibody-antigen and VL-VH orientation; iii) gradient-based minimization of the CDRs L1, L2, L3 and H1 backbones; and iv,v) perturbation and minimization of the backbones of CDRs H2 or H3. The high-resolution iterations also include side-chain rotamer packing steps before each minimization and Monte Carlo Boltzmann acceptance decision  RosettaAntibody homology model, the ensemble of the ten lowest-energy RosettaAntibody homology models, or the WAM model. The antibody is docked to the unbound crystal structure of the antigen when available. In the following sections, we first detail the results of a case study as we build up the algorithm, then we summarize the performance of different algorithms on the whole set of antibodies. Next, we delve into the structural details of the sampling. Finally we summarize the results starting from WAM antibody models and the results of global docking.A docking algorithm can be evaluated by examining plots of a score (an approximation of free energy) versus a measure of distance from the native co-crystal structure for a set of candidate predicted structures. Since protein-protein complexes are presumed to be at equilibrium, the lowest-energy structures generated should match the native structure. Local docking runs, which are typically used to evaluate the ability of an algorithm to refine positions, refine a set of structures near the native complex conformation. In this case, starting positions were created with a local random translation (∼8 Å) and rotation (∼8°) and a spin around the axis of centers of the two proteins (0–360°). Local docking is useful when epitope information is known, as is common in many antibody applications α, C and O backbone atom coordinates in the predicted structure relative to that in the native crystal structure of the complex after superposition of the antibody N, Cα, C and O backbone atom coordinates. Candidate structures, or decoys, are rated according to the CAPRI assessment system as high quality, medium quality, acceptable or incorrect based on a combination of the ligand and interface rmsd and the fraction of correct residue-residue contacts (fnat) across the interface In L-VH orientation, and 1.4 Å rmsd-to-native considering the entire FV. We wish to compare the docking results when using an antibody homology model. In a blind prediction, RosettaAntibody When the lowest-energy RosettaAntibody homology model is docked using standard RosettaDock , medium L-VH and the antibody-antigen pairs. Although the lowest interface-energy decoy is still incorrect, the score difference between the lowest-interface-energy incorrect decoys and lowest-interface-energy acceptable quality decoys has been reduced. There are one medium and two acceptable quality decoys in the ten lowest interface-energy decoys. The interface scores produced by this run are higher than those in the standard RosettaDock run. The additional degrees of freedom allow lower total scores to be accessed, but those lower scores are achieved at the expense of the interface. That is, VH-VL interactions and some intra-chain energies are improved, but the Ab-Ag interaction suffers. Additional types of sampling are necessary to recover a low-energy interface.To test the relative importance of the various degrees of freedom in an antibody homology model, we built the SnugDock protocol progressively with increasing degrees of freedom. L-VH optimization, we added CDR minimization along with explicit perturbations to the CDR H3 loop conformation  is 39%, greater than the threshold of 30%. The fourth decoy is of medium quality, with ligand rmsd of 6.6 Å, interface rmsd of 3.5 Å and fnat of 55%, surpassing the stringent cutoff of 50%. There is one additional medium and acceptable quality prediction in the ten lowest interface-energy decoys.Based on findings in our previous work in antibody-antigen binding  quality . This stV models results in more accurate docking predictions than possible by standard RosettaDock Our previous work demonstrated that using EnsembleDock with ten RosettaAntibody homology FFinally, we have combined SnugDock and EnsembleDock by using all of the previous antibody sampling steps in addition to a conformer-selection step that samples from ten pre-generated RosettaAntibody models . With thWhile the 2BDN case demonstrates the type of analysis we use to evaluate differing algorithms, it is necessary to consider a broader set of targets to draw general conclusions. We identified 15 antibodies with known complex structures and H3 loop lengths between 7 and 11 residues, the range where loop structure prediction is of medium accuracy The gold standard of docking crystal structures with standard RosettaDock results in three and eight targets with lowest interface-energy structures of high and medium quality, respectively, and seven targets with funnels. However, when antibody homology models are used as inputs to standard RosettaDock, the successes fall to one medium and one acceptable quality result and no funnels. As the degrees of freedom are added towards building the SnugDock protocol, the number of successful predictions increases. Homology model docking using the combination EnsembleDock-plus-SnugDock protocol has similar number of targets with acceptable or better predictions as the use of crystal docking with standard RosettaDock, but the quality of docking predictions is still much better for simulations starting with the antibody crystal structure. Evaluation of docking protocols by a looser criterion involving analysis of the most native-like decoy in the ten lowest interface-energy predictions  shows thIn general, the results demonstrate that targeted flexibility in the antibody can overcome the inaccuracies inherent in a homology model and result in higher docking accuracy. The five energy funnels produced by the full SnugDock algorithm and the seven funnels produced by the EnsembleDock-plus-SnugDock protocol suggest more confident and robust predictions since more low-interface-energy decoys are native-like. Despite the general trends, results vary by individual target. For example, for 2BDN or 2B2X, accuracy improves as more degrees of freedom are used. But in other targets, such as 1MLC or 2AEP, some steps result in decreased performance.The improvements in the prediction accuracy of antibody-antigen complex structures are achieved at a computational cost. The penultimate row of L-VH rigid-body perturbation. With SnugDock alone, the CDR loops (other than H2 and H3) have small variations from the minimization steps. The structural diversity of the CDR H3 (grey) and H2 (cyan) is significantly broader, reflecting the additional sampling by explicit φ-ψ perturbations. Note the discrepancy between the native CDR H3 conformation (red) and the models (grey). With EnsembleDock-plus-SnugDock, CDR H3 structures (green) spread more broadly, thus the combined algorithm samples more diverse conformations than possible by SnugDock alone. The gap between the native CDR H3 conformation and the SnugDock generated CDR H3 conformations is partially bridged by the EnsembleDock-plus-SnugDock generated CDR H3 conformations.A major objective of this study was to generate structural diversity in the backbone to compensate for errors in the antibody homology model. viz. CDRs L1, L2, L3 and H1, exhibit a mean divergence of less than 0.1 Å rmsd from the starting structure. CDRs H2 and H3, which are subjected to explicit perturbation, sample a larger conformational space and show a mean fluctuation of about 0.3 Å rmsd from the starting structure. The relative VL-VH orientation, which is subjected to rigid body moves followed by minimization, also exhibits a similar divergence. The paratope as a whole, influenced by both the loop conformations and the relative orientation of the heavy and the light chains, has a mean rmsd of 0.3 Å to the starting structure. These deviations enable the antibody to sample lower energy conformations, but are not typically large enough to capture the full transition from the homology model to the bound conformation. Homology modeled CDR H3s, for example are typically 1–3 Å away from the bound conformation, and this range is similar to the diversity of conformations of low-energy antibody models used in EnsembleDock.The generated diversity is summarized quantitatively in V homology model with the bound crystal conformation of the antibody in the crystal complex gives rise to clashes with the bound conformation of the antigen  created by Whitelegg and Rees Local docking is often of interest in antibody applications since epitope information can be obtained by a variety of other methods. However, global docking is a computational alternative for producing epitope information when it is unknown. Global docking can be significantly more challenging because of the larger conformational space to search. Further, flexible docking creates additional danger of creating an unrealistic induced fit at a non-native docking location, resulting in a false positive prediction Global docking using standard rigid-body RosettaDock generated no acceptable quality lowest-interface-energy decoys for any targets, and a top-ten ranked acceptable decoy for only one target . Using EH-VL) adjustments. The introduction of flexibility during docking is critical to overcome the inaccuracies inherent in a homology modeled antibody structure. Comparison of algorithms shows that increasing the degrees of freedom in local docking gradually increases the quality of predictions. Ultimately, EnsembleDock-plus-SnugDock with homology models achieves accuracy comparable to docking crystal structures with standard RosettaDock. While the algorithm is limited to antibody-antigen interactions, the results suggest that it is possible for computational predictions to use homology models to bridge the gap between the number of experimentally determined complex structures and the available sequences of pairs of interacting proteins.SnugDock is the first docking algorithm with targeted antibody flexibility. SnugDock models flexible loop conformations, backbone motions, and inter-chain  The CDR H3 loop of antibodies provides the most diversity and is thus a focus of the conformational sampling in the SnugDock algorithm. In our experience with the RosettaAntibody Server The flexible docking methods help in identifying the correct docked complex structure, but unfortunately they do not yet help in refining the monomer homology structures themselves closer to the crystal backbone conformations. This limitation likely arises from the vast conformational space of the backbone and the difficulties with high-resolution refinement of protein structures Experimental techniques for epitope mapping like hydrogen-deuterium mass spectroscopy One could envision a complete computational antibody engineering pipeline starting from the antibody sequence and ending with accurate predictions for optimized antibody-antigen interactions. In this paper we have been successful in reaching the second step by computational docking using computational models of the monomer antibody. The next stages may be additionally challenging. High-resolution complex structures might next be used for computational alanine scanning To compare with prior work, we use the set of fifteen antibody-antigen complexes as tested in the original RosettaAntibody publication L-VH and antibody-antigen pairs relative to each other. A second fold tree for CDR loop relaxation had fixed jumps joining the loop stems, and cuts at the middle of the loops. Move map objects are used to select particular sets of residues for backbone and/or side-chain torsion angle flexibility.SnugDock is implemented in the Rosetta biomolecular modeling suite. Fold trees objects Local perturbations: From a superposition of the antibody in the bound orientation, spin antigen randomly around the axis connecting the center of masses of the antibody and the antigen, tilt (8°) away from the same axis and translate (8 Å), similar to earlier treatments Global perturbations: Randomly orient the antigen without using any information of the antigen's orientation in the crystal structure. Point antibody paratope towards antigen.Orient the antigen randomly: Slide into glancing contact as defined by at least one antibody-antigen atomic contact within 1 Å of van der Waals contact distances.Perturb the antibody by rigid-body transformations following the low resolution phase in RosettaDock Optional for input comprising of an ensemble of antibody structures: Select a structure from the ensemble of input antibody structures by Monte Carlo swapping as described previously Repeat steps 3 and 4, fifty times.L-VH movement at coarse resolution.Optimize the CDR H2 & H3 loop conformations by small Change representation of the protein from the low resolution to full atomistic detail by using the side-chain conformations from the starting antibody homology model and the unbound antigen crystal. If the unbound crystal structure of the antigen is not available, the antigen is packed with side-chains from a rotamer library.L-VH interface, antibody-antigen interface and all CDR loops and neighboring residues (within 8 Å) by rotamer packing and minimization including the unbound side chain conformations as described previously Optimize the side-chain conformations of the residues at the VRigid body perturbation of the antigen relative to the antibody using the parameters from rigid-body perturbation in the standard RosettaDock algorithm. (40%)L-VH orientation using the parameters from rigid-body perturbation in the standard RosettaDock algorithm. (40%)Rigid body perturbation of the relative VMinimization of the backbone residues of all the CDR loops. (10%)L-VH movement. (5%)Relaxation of CDR H3 by small, shear and CCD moves followed by minimization as detailed previously Relaxation of CDR H2 by small, shear and CCD moves followed by minimization as in Step 9iv. (5%)Choose and apply a move from the move set. The move set consists of five kinds of moves (the probability of each move is indicated in percentages): Optimize selected side-chains as described in Step 8: For rigid body perturbations (following moves 9i and 9ii), the relevant interfacial side chains are selected for optimization. For loop optimizations (following move 9iii), side chains of the loop and neighboring residues are selected for optimization.Minimize selected region: Rigid body positions (following move 9i or 9ii) or all the CDRs (following move 9iii). CDRs H3 (move 9iv) or H2 (move 9v) are not subjected to additional minimization.Steps 9–11 are repeated fifty times and each iteration is accepted or rejected based on a Monte Carlo criterion .The lowest interface-energy structure observed during the course of the simulation is selected as the output of the simulation.L-VH interface, and (ii) chain break penalties for six CDR loops. Interface energy Each decoy of an independent docking simulation begins from a different random starting position. In local docking, the set of starting positions comprises a diffuse cloud that covers a reasonable area (∼20 Å rmsd) with moderate density around the native ligand position. In local and global docking, 1,000 and 5,000 candidate docking structures are generated for each target respectively. In our empirical tests, 5,000 decoys were sufficient and results were similar for test runs of 10,000 decoys in a subset of targets. The energy function used during the course of the simulation is as described previously L-VH, non H2 & non H3 CDRs, CDR H2, CDR H3) resulted in only small perturbations to the relative orientation of the antibody and the antigen, while all the other degrees of freedom remained unaltered. Better sampling was achieved by isolating the various degrees of freedom and carrying out multiple rounds of minimization over one randomly selected degree of freedom while fixing the other degrees. Isolating the degrees of freedom increased computational efficiency, and required about one-third the time required for simultaneous minimization.Two algorithm variations we tested require brief mention. First, incorporation of CDR H2 and H3 relaxation in low resolution generated a threefold increase in the diversity of loop conformations than that generated by the relaxations in full atom representation of the high resolution alone, and improved docking accuracy. Second, early attempts at simultaneous gradient-based minimization over all degrees of freedom  that are recovered in the docked model Ligand rmsd is the deviation of the N, Cwww.rosettacommons.org. The distribution includes documentation and full source code. The Rosetta version numbers and command lines used to generate the data are provided in Supporting Information The SnugDock method presented here is freely available for academic and non-profit use as part of the Rosetta structure prediction suite at Figure S1Docking perturbation plots. Each row shows the simulation for one target denoted by the four letter PDB code at the top of the first plot in the respective row. The columns correspond to the different docking algorithms used: 1) Standard rigid-body docking using RosettaDock starting with the antibody crystal structure. 2) Standard rigid-body docking using RosettaDock. 3) Docking with VL-VH optimization. 4) Docking with VL-VH optimization with CDR minimization and CDR H3 perturbation. 5) Docking with SnugDock (VL-VH optimization with CDR minimization and CDR H3+H2 perturbations). 6) Rigid-body docking using EnsembleDock with the ten lowest-energy RosettaAntibody models. 7) Docking using a combined protocol incorporating EnsembleDock and SnugDock with the ten lowest-energy RosettaAntibody models. Refer to (6.97 MB EPS)Click here for additional data file.Figure S2Global docking plots. The four letter PDB code at the top of each column indicates the target for which simulations were executed for the respective column. The rows correspond to the different docking algorithms used: 1) Standard rigid body docking using RosettaDock. 2) EnsembleDock 3) SnugDock 4) EnsembleDock-plus-SnugDock. Refer to (1.35 MB TIF)Click here for additional data file.Table S1Quantitative accuracy measures of lowest-energy docking decoy for different docking protocols. Refer to (0.03 MB XLS)Click here for additional data file.Table S2Quantitative accuracy measures of lowest-energy docking decoy, using WAM homology models. Refer to (0.03 MB XLS)Click here for additional data file.Table S3Quantitative accuracy measures of global docking decoys. Refer to (0.03 MB XLS)Click here for additional data file.Text S1Rosetta Version Numbers and Command Lines(0.03 MB DOC)Click here for additional data file.


One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers.We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Our specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown. Metabolomics is a fundamental approach in basic research to detect and quantify the low molecular weight molecules (metabolites) in a biological sample. Besides the other so-called "omics" technologies , metabolomics is becoming a key technology that facilitates the measurement of the ultimate phenotype of an organism . In partClustering of intensity profiles from mass spectrometry measurements is an unsupervised approach to analyze metabolic data. In analogy to clustering of gene expression data , one mayRegarding the scope of application, sample-based clustering for unbiased, comprehensive metabolite analysis is often applied in order to identify different phenotypes . In otheHere we introduce an approach to metabolite-based clustering and visualization of large sets of metabolic marker candidates based on self-organizing maps (SOMs). Unlike applications of the classical two-dimensional SOMs, we are proposing one-dimensional linear array SOMs (1D-SOMs). The 1D-SOM supports the search for relevant metabolites in two aspects: First, according to the assignment of data vectors to certain array positions, a "pre-clustering" of the data facilitates the analysis of large and noisy data sets. The resulting clusters provide building blocks for biologically meaningful groups of markers. In general, the determination of relevant groups requires task-specific knowledge in order to aggregate related clusters or to discard "spurious" clusters which cannot be associated with any biological meaning. This second step is supported by the dimensionality-reduced representation which results from the mapping to the linear array. By means of this mapping, 1D-SOMs allow to visualize the variation of intensity patterns along the array axis. This visualization provides a quick overview on relevant patterns in large data sets and facilitates the aggregation of related neighboring clusters. In particular, this kind of visual partitioning provides a powerful means to cope with the problem of an unknown number of "true" clusters which in general cannot be solved without task-specific constraints . In the d-dimensional vector x which contains the condition-specific quantities as inferred from mass spectrometry intensities. Besides the intensity profile vector xi, also a particular retention time (rt) index and mass-to-charge ratio (m/z) is associated with each marker candidate i in a given sample. While the intensity profiles are used in the clustering algorithm as shown below, the rt and m/z indices are only used for interpretation of the resulting groups .The objective of our approach is to provide a convenient visual overview on potential metabolite clusters across a sample set of marker candidates. A marker candidate is characterized by its intensity profile under certain conditions. Thus, the marker can be represented by some d dimensions corresponding to d experiment conditions. The averaging is important in order to compensate for random variations between different measurements and can be viewed as a noise reduction step. In principle, repeated measurements for averaging are not strictly necessary for application of our clustering approach. In practice, however, the noise reduction will help to achieve reproducible results. Furthermore, repeated measurements allow to evaluate the robustness of the clustering: single replica samples may be left out to analyze the variation induced by this kind of "leave-one-out" disturbance. In other words, it becomes possible to measure clustering or prototype stability with respect to a reduced quality of the training data. As compared with a marker-based cross-validation which reduces the size of the training set due to left out markers, the sample-based cross-validation allows to detect the same groups of markers across all leave-one-out folds.In general, mass spectrometry-based metabolite profiling is performed for each condition with multiple samples. For clustering, we use average intensity values of replicas for each marker candidate and treatment condition. After the averaging step, each marker candidate is represented by a vector with In order to improve the comparability between putative metabolites of different abundance, the vector of intensity values for each marker candidate is normalized to Euclidean unit length. The normalization step ensures that marker clustering only depends on relative intensities and not on the usually large differences of absolute intensities. Therefore, the normalization allows to detect related metabolites irrespective of their abundancies. Without normalization, the clustering would mainly reflect the length variation within the set of marker candidate vectors.wk ∈ ℝd for k = 1,..., K. A prototype vector corresponds to an average intensity profile and can be viewed as a noise-reduced representation of the associated marker candidates in that group. The clustering algorithm imposes a topological order on the prototypes according to a one-dimensional linear array. In that way, the projection onto an ordered set of prototypes also provides a dimensionality-reduced representation of the data in terms of a one-dimensional array index. The objective of the ordering is that prototypes adjacent in the array should provide more similarity than prototypes with distant array positions. The algorithm for optimization of prototypes is based on topographic clustering, which is a well-known technique in bioinformatics, usually applied by means of two-dimensional SOMs [In our 1D-SOM algorithm, a particular cluster arises from a group of marker candidates assigned to one of K "prototype" vectors nal SOMs . Unlike hjk which model the similarity of two prototypes wj, wk. The indices j, k ∈ {1,..., K} of the prototypes correspond to positions in a linear array where nearby positions (indices) imply high similarity. The confusion probabilities are computed from normalized Gaussian functions depending on the bandwidth parameter σ as follows:For optimization of prototypes we utilize the principle of topographic vector quantization , which cK and the smoothing parameter σ. This means that for a large amount of smoothing  the actual number of clusters can be much smaller than the number K of available prototypes. In particular for a sufficiently high degree of smoothing, some prototypes may associate with zero-size clusters, i.e. they do not represent actual clusters. These prototypes are merely influenced by neighboring prototypes, without assignment to marker data.It is important to note that the final number of clusters depends on both, the maximal number of prototypes s is decreased from a large initial value with a small number of resulting clusters towards a minimal final value with an increased number of groups. With this kind of "annealing" process one tries to avoid bad local minima of the objective function which may result in a disrupted order of prototypes. For each annealing step with a particular (fixed) σ the optimization is realized by minimization of an objective function which measures the squared distances between prototypes and intensity data vectors. The objective function depends on the matrix X of N intensity column vectors xi, a matrix W of K prototype column vectors wj and an N × K matrix A of binary assignment variables aij ∈ {0, 1}. If aij = 1, then data vector xi is exclusively assigned to the j-th prototype. For a fixed σ the following objective function is minimized in an iterative manner:During optimization, the smoothing parameter The minimization iterates two optimization steps until convergence: first for given prototypes all assignment variables are (re)computed according to:Then the prototype vectors are (re)computed according to:W of prototypes and an annealing schedule for the smoothing parameter s. For initialization, all prototypes (columns of W) are placed along the first principal component axis within a small interval around the global mean vector. The annealing schedule is chosen to realize an exponential decrease of σ over 100 steps, starting with a maximum value σmax = 100 and ending with an adjustable minimum value which we set to σmin = 0.1. In supplementary material  as described in the previous section. Each prototype represents a group of marker candidates and corresponds to an average intensity profile of that group. Therefore, the prototype-specific intensity profile can be viewed as a noise-reduced representation of all marker candidates assigned to this prototype. The order of prototypes in the array implies that similar intensity profiles are closer to each other than unrelated intensity profiles.1D-SOMs are well-suitable for visualization and interpretation of multivariate data. Figure The 1D-SOM matrix in figure Figure m/z plot). Big red dots correspond to marker candidates associated with the particular prototype and small black dots correspond to the remaining marker candidates of the same data set. The rt-m/z plot complements the 1D-SOM visualization of intensity profiles and shows an overview of those prototype-specific marker properties that are not used for the intensity-based clustering. In this plot, the distribution of marker candidates of a particular group within the rt-m/z plane can be analyzed. For example, vertical stacks of marker candidates may indicate adducts of particular compounds since the corresponding markers do not differ in retention time.Figures The objective of our experimental evaluation is not to provide "hard" performance indices, e.g. in terms of detection rates, but rather to show how our 1D-SOM approach can support scientists in the interpretation of large metabolic data sets, especially for the identification of interesting groups of markers. On one hand there is no "benchmark" data set with known markers available which provides a complex experimental setup with a sufficiently large number of conditions. On the other hand our 1D-SOM approach is designed for visual exploration of multivariate marker data which is difficult to evaluate in terms of a simple performance criterion. Therefore, we here provide a case study in order to illustrate the practical utility of our method. For that purpose we chose a well-established experimental setup for analyzing the wound response of plants.Arabidopsis thaliana during a wounding time course. The genome of this model plant has been sequenced and its wound response is well characterized [A. thaliana in a broad functional context we compared a wounding time course of wild type (wt) plants with that of dde 2–2 mutant plants. The dde 2–2 plants are deficient in JA biosynthesis due to the mutation of the allene oxide synthase (AOS) gene  ecotype Columbia-0 (Col-0) and the JA-deficient mutant plants dde 2–2 [Two plant lines were used: wt plants of  dde 2–2 . Plants  dde 2–2 . Whole rdde 2–2.The data set resulting from the wounding experiment consists of eight conditions  as described in , but witm/z range of 50 to 1200. For chromatographic separation an ACQUITY UPLC™ BEH SHIELD RP18 column  was used with a methanol/acetonitrile/water gradient, containing 0.1% (v/v) formic acid. The LC/MS analysis was performed at least twice for each extract resulting in nine replicas for each condition. The identification of metabolites was verified by exact mass measurement and coelution with authentic standards.The chloroform phase containing lipophilic metabolites was analyzed by Ultra Performance Liquid Chromatography  coupled with an orthogonal time-of-flight mass spectrometer  working with negative electrospray ionization (ESI) in an The raw mass spectrometry data of all samples were processed  using the MarkerLynx™ Application Manager for MassLynx™ software  with parameter settings as shown in the supplementary table "MarkerLynx parameters"  in combination with the K-means algorithm  and finaK = 33 prototypes for the analysis in our case study. For higher numbers of prototypes we observed an increasing number of singleton clusters as well as the occurrence of "empty" clusters without any assigned marker candidates.Because the true number of biologically meaningful groups is unknown, we had to choose a sufficiently high number of prototypes for clustering. In accordance with a prior robustness study (see section "Accessing Robustness") we chose m/z scatter plots . Within block A a remarkable shift of late enriched marker candidates (prototype 1) over time stable candidates (prototypes 5–7) towards very early enhanced and transient marker candidates (prototype 9) can be observed. Thus, block A represents candidates that are characteristic for the wound response of wt plants and which clearly show a trend along the first 10 prototypes of the 1D-SOM.A first dominant block  over very early time-stable patterns (prototype 21 and 22) towards late marker patterns of the wound response (prototype 24) is obvious.Prototypes 20–24 can be grouped in a block E  and block C showing mainly indifferent intensity patterns (prototype 13–17). After the initial assignment of prototypes, blocks were analyzed in more detail at the level of individual metabolites. For this purpose we searched the data set for well known metabolic constituents of the wound response, such as JA, its immediate precursors 12-oxo-phytodienoic acid (OPDA), 3-oxo-2-(pent-2'-enyl)-cyclopentane-1-octanoic acid (OPC-8), 3-oxo-2-(pent-2'-enyl)-cyclopentane-1-hexanoic acid (OPC-6) and 3-oxo-2-(pent-2'-enyl)-cyclopentane-1-butanoic acid (OPC-4), as well as JA derivatives and the roughanic acid-derived homolog of OPDA, dn-OPDA  and OPC-4  were detected in cluster 5 . Due to the high similarity of intensity profiles between a metabolite and its adducts, metabolites and their adducts are likely to be assigned to the same prototype. Thus, adducts are easy to detect within the same cluster by means of stack formation which results from identical retention times.The wound markers JA m/z 09 and OPm/z plane in figure m/z range from 800 to 1200) not identified up to now. However, the arrangement of these metabolites in the JA-containing cluster suggests them to play a role in wound response of wt plants. The wound markers dn-OPDA (m/z 263) and jasmonoyl-isoleucine  were detected in cluster 8 and 9, respectively  and stronger lipophilic properties (rt range from 2.5 to 4 min) not identified with certainty up to now. Interestingly, the time-dependent order of prototypes in the 1D-SOM allows the prediction that JA-Ile and the associated group of marker candidates of high molecular weight in cluster 9 are more transiently regulated than the main wound marker JA located in cluster 5. Therefore, the group of compounds associated with JA-Ile appears to represent valuable candidates for further investigations into the network of wound signaling in A. thaliana.Similar to prototype 5, prototype 9 also associates the intensity profile of a small, rather polar wound signal substance (JA-Ile) with the profile of a group of markers of high molecular weight (m/z 225) and the JA-Ile derivatives hydroxy-jasmonoyl-isoleucine  and carboxy-jasmonoyl-isoleucine  are assigned to prototype 1. All three substances show an intensity profile typical for late-occurring wound responsive metabolites. OH-JA is a product of JA modification with the capability to counteract the JA signaling pathway [m/z 291, see figure Hydroxy-JA  in cluster 21 and hydroxy octadecatrienoic acid  as well as keto octadecatrienoic acid  in cluster 22, respectively , hydroxy-docosanoic acid , hydroxy-tetracosanoic acid , hydroxy-hexacosanoic acid  and the formate adducts of the latter three hydroxy fatty acids. These formate adducts are characterized by identical retention times and a mass shift of m/z 46 regarding the molecular ion. The formation of strong formate adducts for the hydroxy fatty acids but not for the dicarboxylic fatty acid could be confirmed by LC/MS analysis of the corresponding standards. The analysis shows the potential of adduct formation occurring in ESI-MS analysis for the further identification of markers. Here the visualization by means of rt-m/z scatter plots makes it possible to recover specific adduct formation  in combination with K-means. The HCA/K-means scheme combines hierarchical clustering for prototype initialization with a K-means algorithm for iterative improvement of prototypes. For this purpose the resulting HCA dendrogram is cut at a particular distance to obtain a predefined number of ordered clusters. In the next step K-means is applied using the HCA partition means as initial prototypes.dde 2–2 mutant plants, for example, are mainly associated with prototypes 10, 12, 16 and 31, while the marker candidates which show accumulation in mutant control plants are distributed among cluster 18 and 32. Furthermore, eight clusters only contain a single marker candidate. These singleton clusters do not provide information about groups of related candidates sharing the same distinct intensity profile. Due to the weak prototype ordering it usually makes no sense to merge these singletons with neighboring clusters.For direct comparison with the previous 1D-SOM results we performed an average linkage HCA/K-means clustering with 33 prototypes using Euclidean distances. Figure K = 2, 3,..., 50. The resulting curve plots in figure K from the set of local maxima. Considering these curves we chose K = 33 prototypes for the more detailed analysis described in the two previous sections.To investigate the robustness of the cluster-based visualization approaches we applied the leave-one-sample-out strategy as motivated in section "Normalization". In that way we measured the robustness with respect to a reduced number of replicas: we removed one sample for each condition from the data and compared the resulting prototypes with the original array of prototypes obtained with the full data set with all nine samples per condition. In particular, we measured the Pearson correlation between the ordered prototype intensities of both arrays. We chose the reversed order of the original array if it yielded a higher correlation. As a measure of reproducibility, we took the mean correlation over the nine folds of the leave-one-out procedure. The mean leave-one-out correlation was computed for a varying number of prototypes, according to dde 2–2 mutant plants in the "north west" quadrant, respectively. However, there is no evidence for a more detailed cluster structure which could be inferred from the plot. The dicarboxylic and hydroxy fatty acid markers COOH-22:0, OH-22:0, OH-24:0 and OH-26:0 for example, share the same distinct intensity profile  serve as projection weights for the original data vectors. The reduced data coordinates  can be plotted in order to identify outliers or groups of correlated data samples. The corresponding eigenvector coordinates (loadings) can be used to identify clusters of correlated variables (marker candidates). The eigenvalues represent the amount of variance captured by the corresponding principal components. As a common preprocessing step, the marker-specific intensities (sample dimensions) were normalized to unit standard deviation before applying PCA. The eigenvalue spectrum . In a case study about the wound response in The authors declare that they have no competing interests.PM implemented the clustering algorithm, drafted parts of the manuscript and contributed machine learning expertise, TL contributed conceptually and drafted parts of the manuscript, AK implemented the visualization and drafted parts of the manuscript, KF, CG and IF planned and generated the plant wound data set, analyzed the clustering results and drafted parts of the manuscript, PK contributed biological expertise and input to the concept of marker clustering, BM contributed conceptually. All authors read and approved the final manuscript.σ over 100 steps. The initial value is σmax = 100 and the final value is σmin = 0.1.Movie of the annealing process during clustering. The file cluster_process_33nodes.mpg contains a movie that shows the annealing process during clustering of the experimental data used in our case study. The annealing schedule realizes an exponential decrease of the smoothing parameter Click here for file® Excel table with parameters that were used for data preprocessing with MarkerLynx™.List of MarkerLynx™ parameters. The data file MarkerLynxParameters.xls contains an MicrosoftClick here for filem/z) and retention time (minutes), respectively. Columns 6 to 77 contain intensity values from mass spectrometry measurements. Here, nine successive values correspond to replicas of a particular experimental condition . The intensity values are ordered according to successive replicas for each condition (order of conditions according to table Table of marker candidates used in the case study. The data file dataset837.csv contains the marker candidates used for clustering and visualization. Rows correspond to particular marker candidates. The first column corresponds to marker candidate ID, the second and third column represent cluster ID and block ID according to table Click here for file


Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters . Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline.https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.MetabolomeExpress  The rapidly growing field of metabolomics aims to monitor the levels of as many metabolites as possible in living systems as they respond to genetic and/or environmental perturbations. A variety of analytical technologies have been applied to metabolomics studies. However, GC/MS is by far the most commonly employed technology. Reasons for this include its relative affordability, sensitivity and reproducibility. GC/MS is therefore a particularly attractive target platform for development of web-based tools to support community-scale comparisons and mining of raw and processed metabolomics data.Before biological insights derived from metabolomics-based studies can be communicated, they must first be extracted from raw instrumental datasets . Raw GC/1. Detection of analytically useful signal features (ie. 'peaks')2. Identification of biologically relevant signal features by matching against a reference library of known signal characteristics for biological analytes.3. Assignment of some quantitative signal measurement to each identified biological analyte.4. Construction of a [Metabolite x Sample] data matrix, usually with some form of data normalization against internal standard(s) and/or biological sample mass/volume/amount.5. Use of statistical and exploratory data analysis tools to determine the effect(s) of experimental factors on metabolite levels.6. Interpretation of observed metabolite-level changes in the context of prior knowledge about the metabolic system under investigation .A general lack of software for carrying out the above data analysis steps quickly and easily has been one of the greatest challenges hampering the establishment of metabolomics as a mainstream technique. This challenge has triggered widespread efforts to develop data processing software tailored to the needs of metabolomics researchers, resulting in a variety of specialised open-source and proprietary tools now being available. In most cases, these packages do not perform every step of the metabolomics data processing workflow and therefore require transfer of data to and from third party software. For example, some tools such as AMDIS , XCMS anIt has been demonstrated in the microarray field that standardization of analytical approaches and reporting methods via community-wide collaboration and enforcement by scientific journals can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. While GC/MS provides the reproducibility required to support a similar community effort in metabolomics, there have only been a few reports on systems designed to undertake this  in a way that makes them highly accessible and useful to the public without requiring them to download and locally reprocess the enormous amounts of data (frequently > > 1 GB) that constitute the typical GC/MS metabolomics dataset. The second function was to provide researchers with cost-free online access to a powerful raw data processing pipeline that extracts biological information from raw GC/MS data with minimal effort. The third function was to store metabolite response statistics in a central database that provides tools enabling the searching, comparison and verification of results. Registration is free for non-commercial use.Here, we introduce MetabolomeExpress, a new web-tool for the online storage, processing, visualization and re-analysis of publicly-submitted raw and processed GC/MS metabolomics datasets. The MetabolomeExpress web-server application http://www.hmdb.ca/public/downloads/current/metabocards.zip representing Version 2.5 of the publicly available Human Metabolome Database [http://www.php.net and R http://www.r-project.org. The fourth structural layer is a JavaScript-based web interface that provides integrated access to all the data-processing, -visualisation and -analysis tools. See Figure In a structural sense, MetabolomeExpress is comprised of four interacting layers. The first is an FTP-accessible repository system on the server file system that stores raw and processed GC/MS datasets. The second layer is a simple MySQL database containing three core tables: i) a table of highly annotated pre-processed metabolite response statistics with references to underlying data including raw data signals contained in files in the file system; ii) a table linking ~100,000 commonly used metabolite names to their unique InChI structural identifier strings; and iii) a table linking InChI-encoded chemical structures to a variety of biochemical, bioinformatic and physicochemical attributes. Most of the information in the latter two database tables was obtained by parsing the file 'metabocards.txt' Database . A numbeOne of the central purposes of a metabolomics database is to store treatment:control metabolite signal intensity ratio statistics in a central database and provide tools to: i) search for metabolic phenotypes of interest; ii) compare the results of different experiments; and iii) manually verify original interpretations of raw data. For this purpose, MetabolomeExpress uses a simple database table containing columns for statistical information and a wide variety of administrative, biological and technical metadata. The database currently contains > 9500 metabolite ratio statistics derived from 11 experiments published in 8 articles in high-ranking plant science journals -25 and aArabidopsis thaliana, rice, human, mouse, Escherichia coli and Saccharomyces cerevisiae). These templates have been designed to facilitate reporting according to recommendations of the Metabolomics Standards Initiative . Currently implemented templates are available from the MetabolomeExpress website while instructions for their interpretation are provided in the MetabolomeExpress User's Guide  and serves only to ensure that the dataset provided is complete . The validation script uses human-readable 'validation template' files defining reporting requirements and controlled vocabularies for major metabolomics research areas  and model systems with highly-developed bioinformatics resources . A key goal of the project was to provide raw data interaction capability comparable to that offered by modern desktop chromatogram analysis programs. The current version of the viewer has two windows: one for displaying chromatograms and one for displaying arbitrary mass-spectral scans. One or more chromatograms may be simultaneously overlaid in the viewer and two 'colour-channels' are available so that two sets of chromatograms may be compared. Peak detection and/or library matching results may also be overlaid on chromatograms.One of the distinguishing features of MetabolomeExpress compared to other available GC/MS metabolomics web-tools is its To our knowledge, MeltDB  is the oAs a GC/MS data processing and analysis pipeline, MetabolomeExpress provides tools for chromatographic peak detection, peak identification (library matching), data matrix construction and a range of statistical and multivariate analysis tools including t-test, principal components analysis (PCA) and hierarchical clustering analysis (HCA). The basic mechanics and validation of each of these tools is described briefly in the sections below while detailed algorithm descriptions and a demonstration of the use of each tool are provided in the MetabolomeExpress User's Guide  to detect chromatographic peaks in all extracted ion chromatograms and generate, for each raw data file, a corresponding tab-delimited peak list report file. The algorithm may be passed a number of settings that tailor the algorithm to suit particular chromatographic and mass spectral conditions. These include: i) a minimum 'slope' threshold that a signal must exceed in order to be considered as possibly being part of a peak; ii) minimum peak height; iii) minimum peak width; iv) minimum peak area; and v) a minimum peak purity factor . In our experience, once the optimal settings for a given type of data have been set, no further adjustment is required as long as no major changes to mass spectral or chromatographic protocols have been made. A detailed description of the algorithm's mechanics is presented in the MetabolomeExpress User's Guide  while discarding signals devoid of useful analytical information (such as baseline noise). For this purpose, MetabolomeExpress uses a simple yet highly effective slope-based peak detection algorithm  of the 77 EIC peaks detected by the ChemStation algorithm plus an additional 55 peaks not reported by ChemStation  were partial but otherwise valid integrations of very small and noisy peaks, 4 (3%) were detections of very small signal features that were difficult to discern from baseline noise and 3 (2%) were clear false positive detections in regions of baseline noise. Increasing the minimum peak purity factor from 2 to 6 removed all but one false-integration. However, this increased stringency came at the expense of 33 valid integrations. Importantly, PeakFinder still outperformed ChemStation by 11 valid integrations under these settings.To validate the PeakFinder, we performed linear regressions to determine the correlations  between PeakFinder- and ChemStation-reported retention times and peak areas. Regression of retention times yielded an R2 of 1.0000 while regression of integrated peak areas gave an R2 of 0.997 indicating excellent correlation between the two algorithms. Detailed tables of integration results and regression analyses are presented in additional file Given that MetabolomeExpress library matching (subsequent to EIC peak detection) requires both correct retention index and multiple ions with correct intensity ratios and that analyte quantification is based on carefully selected specific quantifier ions (which are likely to be relatively abundant in positively matched spectra), it is highly unlikely that the occasional false integration of near-baseline signal features will end up affecting final results. We therefore recommend accepting the above 'false-positive' EIC integration rate of ≤ 10% for the sake of gaining a 40% increase in the number of 'true-positive' integrations. To further validate Any detailed biological interpretation of a GC/MS metabolomics data set requires that signals corresponding to analytes of biological origin are correctly identified and distinguished from those corresponding to artefacts or internal standard compounds. Compliance with Metabolomics Standards Initiative (MSI) standards for metabolite identification requires that at least two orthogonal analytical parameters are used to match analytical signals to particular metabolites . For thiPeakFinder algorithm), a mass-spectral and retention index (MSRI) GC/MS reference library  and a simple RI calibration file  as input and generates a match report file as output. A number of matching criteria may be set.The MetabolomeExpress MSRI library matching algorithm accepts EIC peak lists . Users may provide their own reference library or use one of the public libraries provided by MetabolomeExpress or one of its members. The MetabolomeExpress MSRI library format is a simple tab-delimited structure that should be easy to generate from other formats. Alternatively, libraries may be constructed in AMDIS format (*.MSL) using the freely available AMDIS software package http://chemdata.nist.gov/mass-spc/amdis/ is widely regarded as one of the best freely available qualitative analysis tools for the deconvolution of complex GC/MS datasets and matching of GC/MS signals to reference libraries on the basis of mass spectra and retention indices. Notable metabolomics applications of AMDIS include creation and implementation of the popular Golm Metabolome Database 'Q_MSRI_ID' GC-Quadrupole-MS MSRI library [http://csbdb.mpimp-golm.mpg.de) and integration with quantitative data processing tools such as the freely available MET-IDEA [http://www.chem.agilent.com) to create metabolomics data processing workflows capable of both identification and quantification of metabolite signals in complex GC/MS metabolomics datasets.The Automated Mass-spectral Deconvolution and Identification System (AMDIS) software package  library   using the same MSRI library and compared the results  of the total set of 197 peak identifications being reported by both programs. However, MetabolomeExpress outperformed AMDIS in all regards by reporting more true positives, fewer false/ambiguous positives and fewer false/ambiguous negatives  regular re-adjustment of target RT settings to account for changes in RT associated with drift or column maintenance; or b) regular time-consuming 'retention time locking' of the instrument by fine tuning of carrier gas flow rates based on flow:RT calibration runs.A common data-processing approach employed by metabolomics researchers is to use AMDIS-based peak deconvolution and identification to assist creation of target compound quantification libraries for specific quantifier ion-based quantification in their GC/MS manufacturer's proprietary data-analysis software (eg. Agilent's ChemStation). To demonstrate that the MetabolomeExpress workflow can achieve results of at least the same level of quality, we used the results generated in the above analysis to build an RT and quantifier ion-based ChemStation compound quantitation library for the 80 identified compounds of known structure and quantitatively processed the representative plant cell extract data file using ChemStation with an optimised sensitivity setting. Linear regression of ChemStation- and MetabolomeExpress-reported RTs and peak areas for the 78 target peaks successfully integrated by ChemStation  gave near perfect RIn order to further quantitatively validate the MetabolomeExpress data processing workflow, we generated a challenging dataset through the GC/MS analysis of a set of complex standard mixtures carefully designed to introduce large known variations in the relative concentrations of co-eluting analytes and hence the relative intensities of overlapping or closely neighbouring signals. The procedure used to prepare these complex mixtures is detailed in Figure 2) between concentration and reported signal intensity for 81 metabolite peaks representing a wide range of retention times and chemical classes . Indeed, a strong average concentration:signal intensity R2 value of 0.88 was observed when the best performing library spectrum and quantifier ion was selected for each of the 81 metabolite peaks  of the 81 metabolite peaks showing an R2 value > 0.8 , then a strong relationship between the signal intensity ascribed to a particular metabolite and the known concentration of that metabolite should be observed. Therefore, to validate peak integration, library matching and data matrix construction, we calculated the coefficient of determination (RTo determine whether the poor performance of Glutamine (3TMS) was due to some kind of processing error, we manually inspected the Glutamine (3TMS) signals and found their sizes were consistent with reported intensity values. Upon further investigation, we found that, for samples originally containing the same amount of glutamine, the observed Glutamine (3TMS) signal was negatively correlated with the length of time between derivatisation and GC/MS analysis Figure . This inStatistics and Data Exploration panel of the Experiment Explorer module currently provides tools to carry out data matrix construction, data matrix renormalisation, data matrix heatmap visualisation, Welch's t-tests, principal components analysis (PCA) and hierarchical clustering analysis (HCA). Statistical results are displayed in the web interface but may also be downloaded for offline analysis. Colour is used to aid data interpretation wherever possible. T-test results are presented as a red/blue heatmap table that allows results to be sorted by metabolite name, chemical class, retention time, retention index, signal intensity ratio or p-value. Wherever possible, displayed results are linked to their underlying raw GC/MS signals by point-and-click access - thus aiding manual verification of processed results. PCA plots are provided in 2D and 3D formats. PCA plots and HCA heatmap clustergrams are provided in vector formats for creation of publication quality figures.The To further demonstrate the validity of MetabolomeExpress data processing, we performed hierarchical clustering analysis on the data matrix generated from the challenge dataset to show that we could resolve three major clusters of highly correlated metabolites Figure  that corDatabase Explorer module of MetabolomeExpress provides a number of tools with which to explore the contents of the MetabolomeExpress database of metabolite signal intensity ratio statistics. The first of these, Database Statistics, provides an overview of the current database contents and buttons to load experiments into the Experiment Explorer module for more detailed analysis. The second tool, ResponseFinder, is a simple query tool that allows users to search for metabolite responses of interest. Results are returned with links to experimental metadata and underlying raw GC/MS signals in the raw data viewer. The third tool is MetaAnalyser. This allows the results of multiple experiments to be aligned, clustered and compared in heatmap form.The Development of the MetabolomeExpress platform will be an ongoing process. Developments planned for MetabolomeExpress include: i) support for data exchange formats developed or endorsed by the Metabolomics Standards Initiative and similar initiatives; ii) enhanced capability for processing LC/MS and CE/MS metabolomic and quantitative proteomic data ; iii) expansion of the suite of statistical data mining tools to include multivariate pattern matching tools to identify relationships between different metabolome response patterns and hence different biological processes; iv) enhanced facilities for capturing experimental metadata during experimental workflows; v) increased variety of import/export options for easier integration with external data processing workflows; vi) support for integrated analysis of parallel metabolite-, gene- and protein-expression data; and vii) a capacity to accept GET requests for information about experiments or statistical results so that results presented in electronic articles and other databases may be directly linked to MetabolomeExpress database content.As GC/MS metabolomics becomes an increasingly mainstream bio-analytical technique, it is crucial that steps are taken to ensure published metabolomics data are accessible, reliable, reusable and transparent. This is largely the case in the microarray field where journals have enforced compliance to metadata reporting standards and submission of raw microarray data to online public repositories. However, the development of similar repositories for raw and processed metabolomics datasets is a far greater challenge due to the sheer size, complexity and heterogeneity of these datasets. Precisely for these reasons, it is essential that online metabolomics databases do not act merely as warehouses for raw and/or processed datasets that must be painstakingly downloaded and pieced back together manually using local desktop programs. Rather, online metabolomics databases should provide easy, in-situ access to the various levels of metabolomics datasets in an integrated, interactive manner. This is the philosophy we have adopted in the development of MetabolomeExpress. For an overview of the features that distinguish MetabolomeExpress from existing comparable GC/MS metabolomics web-tools, see Table https://www.metabolome-express.org provides a new opportunity for the metabolomics community to transparently and interactively present online the raw and processed GC/MS metabolomics data underlying their research findings. Transparent sharing of these data has the potential to increase the value of metabolomics publications by allowing the broader research community to assess data quality and draw their own insights from disseminated datasets. Moreover, the centralised storage and systematic annotation of metabolite response information from diverse biological systems and experimental perturbations, together with meta-analysis tools, will reveal previously unrecognised patterns and thus facilitate mechanistic and evolutionary discoveries that would have otherwise been far more difficult to make.MetabolomeExpress https://www.metabolome-express.org. Registered users may access the FTP repository at ftp://www.metabolome-express.org.The MetabolomeExpress web application is freely accessible for non-commercial, academic use at The major client hardware and software requirements to use the MetabolomeExpress web interface and FTP server are:• A modern version of one of the major web browsers • Minimum of 512 MB of RAM (1GB+ recommended)• A relatively fast internet connection• A two-button mouse• An FTP client program .AJC carried out project conception, programming, testing, wet experimental work and wrote the manuscript. AHM and MRB critically revised the manuscript. All authors read and approved of the final manuscript.MetabolomeExpress v1 User's Guide. The current MetabolomeExpress user's manual including detailed instructions and examples on how to upload and manage datasets via FTP and how to use all current features of the MetabolomeExpress web interface.Click here for fileNotes on FTP repository and data formats. Detailed comments about the MetabolomeExpress FTP repository and data formats supported by MetabolomeExpress and rationales behind their designs.Click here for fileMetabolomeExpress peak detection validation results. Comparison of peak integration results from Agilent ChemStation and MetabolomeExpress PeakFinder algorithm. Includes detailed peak detection information.Click here for fileComparison of MetabolomeExpress Library Matching with AMDIS and ChemStation. Detailed results of the comparison of AMDIS-based analyte signal identification (and subsequent ChemStation-based quantitation) with the integrated identification and quantitation approach of MetabolomeExpress (described in the section entitled 'Comparison of MetabolomeExpress- and AMDIS-based MSRI library matching' with results summarised in Table Click here for fileMetabolomeExpress validation - standard curves. The data matrix obtained by MetabolomeExpress processing of the dataset described in Figure 2 values describing the relationships between metabolite concentration and reported signal intensity, as determined by MetabolomeExpress.Click here for file


The sequencing of the black 6 mouse (strain C57Bl/6) has reached an importantjuncture. The BAC fingerprint map is almost complete, the BACs have been endsequencedand a seven-fold coverage whole-genome shotgun has been assembled. Nowthe BAC-by-BAC sequencing phase is under way and in-depth comparative analysiscan be carried out on regions that have been the subject of targeted sequencing.This paper reviews the progress so far and looks forward to the promises of finishedsequence.


Biphasic neural response properties, where the optimal stimulus for driving aneural response changes from one stimulus pattern to the opposite stimuluspattern over short periods of time, have been described in several visual areas,including lateral geniculate nucleus (LGN), primary visual cortex (V1), andmiddle temporal area (MT). We describe a hierarchical model of predictive codingand simulations that capture these temporal variations in neuronal responseproperties. We focus on the LGN-V1 circuit and find that after training onnatural images the model exhibits the brain's LGN-V1 connectivitystructure, in which the structure of V1 receptive fields is linked to thespatial alignment and properties of center-surround cells in the LGN. Inaddition, the spatio-temporal response profile of LGN model neurons is biphasicin structure, resembling the biphasic response structure of neurons in cat LGN.Moreover, the model displays a specific pattern of influence of feedback, whereLGN receptive fields that are aligned over a simple cell receptive field zone ofthe same polarity decrease their responses while neurons of opposite polarityincrease their responses with feedback. This phase-reversed pattern of influencewas recently observed in neurophysiology. These results corroborate the ideathat predictive feedback is a general coding strategy in the brain. For many neurons in the early visual brain the optimal stimulation for driving aresponse changes from one stimulus pattern to the opposite stimulus pattern overshort periods of time. For example, many neurons in the lateral geniculatenucleus (LGN) respond to a bright stimulus initially but prefer a dark stimulusonly 20 milliseconds later in time, and similar changes in response preferencehave been found for neurons in other areas. What would be the computationalreason for these biphasic response dynamics? We describe a hierarchical model ofpredictive coding that explains these response properties. In the model,higher-level neurons attempt to predict their lower-level input, whilelower-level neurons signal the difference between actual input and thehigher-level predictions. In our simulations we focus on the LGN and area V1 andfind that after training on natural images the layout of model connectionsresembles the brain's LGN-V1 connectivity structure. In addition, theresponses of model LGN neurons are biphasic in time, resembling biphasicresponses as found in neurophysiology. Moreover, the model displays a specificpattern of influence of feedback from higher-level areas that was recentlyobserved in neurophysiology. These results corroborate the idea that predictivefeedback is a general coding strategy in the brain. Cells in the LGN exhibit striking receptive field dynamics. Besides their well-knowncenter-surround organization, LGN receptive fields are characterized bybright-excitatory (dark-excitatory) regions that become dark-excitatory(bright-excitatory) over time intervals that may be as short as 20 millisecondsA longstanding approach to understanding early-level processing has been to considerit in terms of efficient coding of natural images Building along these lines, it has been suggested that early visual areas removecorrelations by removing the predictable, and hence redundant, components in theirinput. For example, the center-surround structure of LGN receptive fields can beexplained using predictive coding mechanisms Predictive coding may have further value as a general principle that works throughinteractions between all lower-order and higher-order visual areas Here, we show by simulation that biphasic responses may result from similarinteractions with higher-order areas, which remove redundancy by removing thepredictable components in their input. We focus on the LGN and V1, for which thefeedforward-feedback connectivity structure and bottom-up inputs are fairlywell-known. Although responses of LGN cells tend to follow many of thecharacteristics of their retinal input The model consists of two layers . The firConnection weights of the model are adapted to the input by minimizing thedescription length or entropy of the joint distribution of inputs and neuralresponses .This miTo characterize V1 model receptive fields, feedforward connection weights fromon-center type and off-center type LGN cells coding for the same spatiallocation are summed for each of the model's 128 V1 cells. These summedweights are shown in In To ascertain whether biphasic responses can be interpreted as the result ofpredictive feedback, we first consider a model with non-biphasic inputs . ThespaWhy do biphasic responses appear in the mapped model LGN receptive fields? Recallthat reverse correlation uses a large number of white noise stimuli presented inrapid succession, resulting in visual changes much faster than most naturalinput the system would encode. Consider when a stimulus consisting primarily ofbright regions is presented to the model . On-centReversals in polarity of model LGN cells are most profound in a small time windowafter presentation of the reference stimulus but disappear gradually later on.This happens because the initial prediction is dynamically updated to includepredictions of stimuli presented after the reference stimulus, bringing newpredictions closer to the average white-noise stimulation. Note that reversalsin polarity will appear as long as predictions deviate from the averagewhite-noise stimulation; the precise amount of overlap between prediction andstimulus is not critical.These findings suggest a specific pattern of influence of feedback on LGN cells,in which the simple cell off-zones mediate inhibitory influences to off-centerLGN cells and excitatory influences to on-center LGN cells. This effect isfurther investigated and quantified in We have shown that a model that encodes an image using predictivefeedforward-feedback cycles can learn the brain's LGN-V1 connectivitystructure, in which the structure of V1 receptive fields is linked to the spatialalignment and properties of center-surround cells in the LGN The natural visual world is dominated by low temporal frequencies Geniculate cells receive many more feedback connections (around 30%) thanfeedforward connections (around 10%) A previously published model has successfully captured end-stopping and some othermodulations due to surround inhibition in terms of predictive feedback Previous authors have suggested mechanisms that could account for the strongerbiphasic responses in the LGN, such as higher LGN thresholds While predictive coding could work through local computations between neighboringneurons on-center type cells and 8 by 8 (64) off-center type cells. This LGN‘patch’ is randomly selected from the filtered image andrepresented as a single vector , unless described otherwise. At anygiven time step in the model, either the on-center cell or its off-centercounterpart coding for the same spatial location is active.The second layer, which would correspond to an orientation column in corticalarea V1, is represented by 128 units. In the language of the model, the synapticweights between LGN and V1 units form basis vectors that represent the preferredstimulus of the model V1 neurons . Model VAt the next time step, an additional vector is chosen that minimizesThus, the probability with which a unit is selected increases when its receptivefield structure better predicts the lower level input and its response ishigher. To guarantee optimality, the response of a selected unit In words, the number of active V1 neurons increases at each time step in themodel, and their combined prediction is subtracted from the actual LGN input. Tosee how combining active V1 units results in better predictions of the input,consider when a bar of a certain orientation is presented to the model, togetherwith a small dot next to the bar. A likely first prediction could capture thebar, but not necessarily also the dot. However, this initial prediction will beupdated in later feedforward-feedback cycles when other V1 neurons becomeactive, so that the neurons will together represent both the bar and the dot.This is what combining V1 neurons represents, a prediction that is updated ineach cycle to better capture the V1 input see also . We assuTo enhance the sparseness of the neural code and better capture the inputstatistics, basis vectors are updated in each feedforward-feedback cycle. Thisis done by minimizing the description length (MDL) or entropy of the jointdistribution of inputs and neural responses To model the spatiotemporal response of LGN neurons, we have to relate modelscalar activation values directly to biology. Several possibilities exist; weemphasize, however, that the model does not explicitly implement any of theseinterpretations and is, in fact, very general. One possibility is thatactivation values of model units stand for the mean firing rate of a group offunctionally similar neurons in physiology The spatio-temporal response of on-center type units in model LGN  was calcIn the modified model, the initial feedforward input into on-center type LGNcells is obtained using equation 2, and then this bottom-up input is updated ineach feedforward-feedback cycle as follows:tructure .


The present study was conducted to define the mechanisms underlying the variable intrinsic susceptibility of rhesus monkey PBMC to SIVsmE660 infection. In a cohort of 15 unrelated Indian-origin rhesus monkeys, infectability of PBMC of individual animals with SIVsmE660, as defined by tissue culture infectious dose (TCID50), varied by more than 3 logs and was a stable phenotype over time. Susceptibility of a monkey's PBMC to wild type SIVsmE660 infection correlated with the susceptibility of that monkey's PBMC to infection with VSV-G pseudotyped SIVsm543-GFP. Moreover, the permissivity of an individual monkey's PBMC for infection with this construct correlated with the permissivity of a B-lymphoblastoid cell line (B-LCL) generated from PBMC of the same animal. We found that the degree of intrinsic resistance of monkey B-LCL correlated with the copy number of early reverse transcription (ERT) SIV DNA. The resistance of monkey B-LCL to SIVsmE660 replication could be abrogated by preincubation of cells with the SIV virus-like particles (VLPs) and SIV resistance phenotype could be transferred to a SIV susceptible B-LCL through cell fusion. Finally, we observed a positive correlation between susceptibility of monkey B-LCL to SIV infection with a VSV-G pseudotyped SIV-GFP construct in vitro and both the peak plasma virus RNA levels in vivo and time to death following wild type SIV infection. These findings suggest that a dominant early RT restricting factor that can be saturated by SIV capsid may contribute to the variable resistance to SIV infection in rhesus monkey B-LCL and that this differential intrinsic susceptibility contributes to the clinical outcome of an SIV infection.While it has long been appreciated that there is considerable variability in host containment of HIV/SIV replication, the determinants of that variability are not fully understood. Previous studies demonstrated that the degree of permissivity of a macaque's peripheral blood mononuclear cells (PBMC) for infection with simian immunodeficiency virus (SIV)  Humans vary in their susceptibility to human immunodeficiency virus type 1 (HIV-1) acquisition and in the level of HIV-1 replication following infection . Virus pin vivo viral set point [Some of the undefined host factors contributing to HIV-1 containment may be responsible for the variable ability of cells to be infected by and sustain replication of these viruses. It has been shown that permissiveness for HIV-1 varies substantially between isolated primary cells Permissiveness, the ability of cells to be infected and sustain the replication of HIV-1, also varies substantially between isolated primary cells of individuals . It has et point .The variability in permissiveness observed in rhesus monkey PBMC for SIV replication can be shaped by dominant and nondominant mechanisms: through the altered expression of required host factors and/or virus restricting molecules . The abiin vitro susceptibility of PBMC of rhesus monkeys to infection (TCID50) with this virus isolate and the extent of early virus replication in these animals following experimental infection [in vivo.We set out to identify genetic mechanisms underlying the variable susceptibility to lentivirus replication in a primate species through an analysis of the differential rhesus monkey permissivity of lymphocytes for SIV replication. The SIVsmE660-infected rhesus monkey provides a powerful model system for elucidating the genetic contribution to virus control. Studies can be done with a single, defined challenge stock of virus, and monkeys can be selected for evaluation with specified genetic characteristics. There is a well-documented variability in both peak and set point replication of this virus in genetically disparate rhesus monkeys. Moreover, there is a clear correlation between the nfection . In the 6 PBMC per ml. Three days after initial culture, aliquots of 40,000 PBMC were incubated for 4 hours with 150 ul of serial 10-fold dilutions of a cell-free virus stock of SIVsmE660. PBMC were washed with Hanks balanced salt solution to remove residual virus, resuspended in RPMI 1640 with 10% FCS and 10% IL-2, and cultured in 96-well plates. Cultural supernatant were collected at days 0,3,7,10, and 13 after infection and monitored for the presence of virus by antigen capture assay for SIV p27 antigen (Coulter Corp.) The minimal TCID or endpoint titer of virus required to infect the cells of each donor was determined as the last dilution in which virus was detected by 13 days after infection.PBMC were separated by centrifugation through Ficoll and CD8+ cells were depleted from PBMC cultures using CD8 Dynabeads . CD8+- depleted PBMC were resuspended in RPMI 1640 with 6.25 ug of ConA per ml, 10% fetal calf serum (FCS), and 10% interleukin-1 (IL-2) at a density of 2 × 10The majority of studies were conducted using SIVsmE660. The molThe quantification of early reverse transcription, late reverse transcription, and integrated SIV DNA copy number was modified from a previously described method . At 2, 3B-LCL were stained with 1 ul of Oregon Green or 2 ul of Vybrant DiD (Invitrogen) for 1 hour. Cells were washed with RPMI 1640 and allowed to rest for 30 minutes. B-LCL populations were mixed together and added to 2 ml of 50% PEG for 1 minute. B-LCL were washed and allow to rest in RPMI 1640 with 10% FCS for twelve hours. Double positive fusion events were sorted using flow cytometry and infected with VSV-G pseudotyped SIVsm543-GFP.in vivo viral replication. CD8+ T lymphocyte-depleted PBMC from each monkey were evaluated for their relative ability to sustain SIVsmE660 replication in vitro. Lectin-stimulated CD8+ lymphocyte-depleted PBMC were exposed to serial dilutions of SIVsmE660. Replication of virus was assessed by measuring SIV p27 antigen in culture supernatants and a minimal tissue culture infectious dose (TCID50) of virus was defined for each population of PBMC. We observed a broad range of permissiveness of these PBMC for SIVsmE660 replication, with rhesus PBMC populations differing by as much as 4 logs of the virus needed to initiate infection  to allow the expression of GFP following SIV integration, transcription and translation of viral proteins. This construct allowed a simple and rapid quantification of SIV infected cells by flow cytometric analysis. Moreover, since the VSV-G protein mediated viral entry into a broad range of cell types, variability in infection by this construct must reflect a gp120-CD4 independent phenomenon.Since genetic polymorphisms have been described that impact HIV-1 replication through modulation of viral entry, we sought to determine whether this variable susceptibility to SIV replication was a consequence of differential SIV entry or represented a post-entry phenomenon. To explore these issues, we created a VSV-G-pseudotyped SIVsmE660 construct that expressed GFP. The molecular clone of SIVsmE660, SIVsmE543, was engineered to produce VSV-G pseudotyped SIVsme543 through the deletion of the SIV 50, while susceptibility to the single cycle SIVsm543-GFP construct was defined as the % GFP+ PBMC 48 hrs post-infection. Rhesus monkey PBMC exhibited varied susceptibility to the single cycle VSV-G pseudotyped SIVSsm543-GFP, and a significant correlation was observed for each cell population between % SIVsm543-GFP+ cells and the TCID50 of these cells for wild type SIVsmE660 . Additionally, the relative representation of CD4+ T cells in a PBMC population could impact the quantitation of the relative susceptibility of PBMC to SIV infection. To rule out a contribution of these well-defined factors to the variability of rhesus monkey lymphocyte susceptibility to SIV replication, we assessed the permissivity of rhesus monkey B-lymphoblastoid cell lines (B-LCL) to VSV-G pseudotyped SIVsm543-GFP infection. In addition to facilitating rapid screening of large numbers of cell populations, analysis of B-LCL would establish if this differential permissivity for SIV replication is T cell-specific or is manifested in other cell types.We generated B-LCL from 15 unrelated Indian-origin rhesus monkeys and analyzed B-LCL and PBMC from the same animals for susceptibility to infection by VSV-G pseudotyped SIVsm543-GFP. Following infection of rhesus monkey B-LCL with this construct, we observed a broad range in B-LCL susceptibility to SIV infection, varying from 1% to 30% of target cells infected Fig. . To deteThe differential susceptibility of B-LCL populations from different monkeys for SIV replication could be a consequence of two possible mechanisms: a subpopulation of variable size in a B-LCL population could be permissive or the total B-LCL population could be uniform in its relative resistance or susceptibility to SIV infection. To examine these two possibilities, B-LCL were infected with the VSV-G pseudotyped SIVsm543-GFP, GFP negative cells were sorted, and the GFP negative cells were reinfected with the same viral construct. No significant change in relative susceptibility to SIV infection was observed following the second round of infection Fig. . This ob50. To represent SIV permissivity of each cell line accurately over a range of multiplicities of infection (MOIs), B-LCL susceptibility was defined as the area under the curve (AUC) of % GFP+ cells over 4 serial viral dilutions following infection with the single cycle VSV-G pseudotyped SIVsm543-GFP. We observed a clear positive correlation between PBMC susceptibility to wild type SIVsmE660 infection and the permissivity of B-LCL from the same monkey to single cycle VSV-G pseudotyped SIVsm543-GFP , late reverse transcription (late RT), and integrated viral DNA copy number using quantitative real time PCR. Spearman correlations were performed to evaluate the relationship between copy number/cell of each viral DNA species and the SIVsmE660 permissivity of that cell. A significant positive correlation was observed between copy number of early RT, late RT, and integrated copy number and SIV permissivity  which contain intact viral capsid. In doing so we should saturate a capsid binding factor and reverse the dominant block of early reverse transcription and SIV replication. We engineered an SIVsmE660 construct with a deletion in reverse transcriptase and produced VLPs through transfection of the plasmid into 293T cells. We preincubated rhesus monkey B-LCL with SIVsmE660 VLPs and then infected the cells with SIVsm543-GFP Fig. . We obsein vivo viral replication and clinical outcome in SIV-infected rhesus monkeys, we evaluated the permissivity of B-LCL generated from 14 monkeys for VSV-G pseudotyped SIVsm543-GFP infection. Then, following infection of the monkeys with SHIV89.6P, we assessed the correlation between the in vitro permissiveness of these B-LCL for replication of this virus and the in vivo peak plasma virus RNA levels and time to death following infection of rhesus monkeys with wild type virus (Fig. in vitro B-LCL susceptibility to infection by these VSV-G pseudotyped SIV constructs and both peak viremia and time to death in the wild type SIV-infected monkeys, respectively (Fig. in vivo with SIVmac251. These data indicate that in vitro permissivity of B-LCL for SIV replication is a reliable predictor of in vivo viral replication and clinical outcome.To determine if this variable intracellular blockade of primate immunodeficiency virus replication impacts rus Fig. . Additioely Fig. . Monkeysin vitro CD4+ T cell susceptibility to SIV infection is correlated with in vivo peak virus load [in vivo virus set point and time to death in rhesus monkeys infected with SIVmac251. The fact that B-LCL susceptibility can predict the variability observed in PBMC permissiveness for lentivirus replication indicates that the intracellular control of retrovirus replication is of central importance in determining the infectability of CD4+ T cells and the clinical outcome of SIV infections. This positive correlation between PBMC and B-LCL permissiveness for SIV replication also indicates that the variability in virus replication between rhesus monkeys results from a post-entry block of SIV replication that is manifested in diverse lymphocyte populations.The variability in the clinical course of HIV-infected humans and SIV-infected monkeys is likely a consequence of both host and viral factors. The present study was initiated to begin an exploration of the impact of the intrinsic immune response to lentiviral infection on clinical course in rhesus monkeys. We attempted to determine the importance of CD4+ T cell permissiveness for SIV replication on clinical outcome in monkeys and define a mechanism for the variability of this permissivity. The present experiments built upon earlier studies of this phenomenon which showed that rus load . We obsein vivo virus challenge. This should facilitate the preselection of animals for a study with similar permissivities, improving the power of the study and clarifying the impact of the evaluated intervention.The infection of rhesus monkeys with SIV is a powerful model for HIV infection in humans and is of critical importance for drug and vaccine development. The variability in the level of virus replication in monkeys following SIV infection necessitates the use of large numbers of animals to appropriately power vaccine trials. Prescreening monkeys' B-LCL for susceptibility to the replication of VSV-G-pseudotyped SIV-GFP should allow a prediction of the relative susceptibility of monkeys to SIV replication following Following the identification of this phenotype of variable permissiveness for SIV replication in lymphocytes of rhesus monkeys, we conducted a series of studies to begin defining the mechanism underlying this observed intraspecies variability. We established that the relative block in SIV replication was not dependent on multiple cycles of SIV replication and was due to a differential ability of monkey lymphoctyes to block early reverse transcription of SIV. Moreover, we demonstrated that the relative block in early RT was a dominant phenotype. This dominant block to SIV replication could be transferred to a highly susceptible monkey B-LCL population by cell fusion. Additionally, the block to early RT could be overcome by preincubation of SIV resistant cell lines with virus-like particles. These data suggest that the block to early RT may involve the binding of capsid of incoming virions.in vitro. APOBEC-3G, a cellular cytidine deaminase, induces C to U mutations in the negative strand of the HIV DNA, which results in a reduced number of infectious HIV progeny virions[trim gene family have the capacity to inhibit virus replication. Transfection of rhesus monkey TRIM5α into feline fibroblast cells potently blocks early reverse transcription of HIV-1, but only modestly alters SIV replication kinetics[in vitro nor in vivo [in vivo viral set point, CD4+ T cell decline, and a delay in the time to death following SIV infection. Whether differences in lymphocyte susceptibility to SIV represent consequences of allelic forms or variable expression levels of an SIV restricting molecule, our findings underscore that an innate antiviral response, which is capable of inhibiting early RT, can impact the in vivo clinical outcome of the animals infected with SIV. A complete understanding of the host immune mechanisms that have a significant impact on in vivo viral replication is critically important to aid in our design and implementation of preventative and therapeutic interventions to reduce HIV acquisition and viral burden.Several gene products have been implicated to date in the control of HIV/SIV entry and cellular immunity, including CCR5, MHC, and KIR. The present study demonstrates another mechanism that contributes to SIV control in monkeys. Several intrinsic anti-viral immune molecules have been shown to inhibit retroviral replication by preventing early reverse transcription y virions. Product kinetics. Althoug in vivo ,15. Our The authors declare that they have no competing interests.in vivo correlation studies. All authors have read and approved the manuscript.TR conceived of and designed the study as well as participated in all assays. SL participated in B-LCL phenotyping and staging assay. TS participated in B-LCL phenotyping, staging, and fusion assay. TC participated in staging and fusion assay. AH participated in