Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data.We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high.We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models. Protein interaction  refers to physical interconnection between two or more proteins that occur in a cell, by which protein components can carry out most of cellular molecular processes . GeneticGiven a great deal of these interaction data collected, it is of challenges to elucidate biological meaning behind the data, especially to identify biological pathways underlying the data . A few mIn this paper, we present a Bayesian model that integrates high-throughput protein and genetic interaction data to reconstruct detailed biological pathway structures. The model can organize related genes into the corresponding pathways, arrange the order of genes within each pathway, and decide the orientation of each interconnection. Based on protein interaction network, the model predicts detailed pathway structures by using genetic interaction information to delete redundancy edges and reorient the kept edges in the network. Similar to APN , our modX, the fitness level is independent of the activity level of Y, if gene X is fully epistatic to gene Y. The constructed network can encode a linear pathway substructure between X and Y, in which Y must be the father node of X, that is, the direction of edge between is decided.We model a pathway network as a Bayesian network that is a directed acyclic graph. The activity of a gene is assigned to a node in the network . The edgFor a candidate pathway network Fig.  sampled D of genetic interaction and protein interaction, we can compute a local score for every pair of genes in a candidate pathway network N, and sum up all of the scores for all pairs to define the global score function f(N), to which the Bayesian network posterior probability distribution p(N|D) is proportional, shown as eq. , because the edge in our network represents protein interaction that insures its existence. Then, it avoids the dilemma how to adjust the balance between the two scores.Different from study of Ref. , we do np (N| D) ∝ f(N). The annealed importance sampling approach can assign weights to pathway networks sampled by simulated annealing schedules, then to evaluate that converge to the real network structure. The approach is appropriate for sampling N from multi-modal distributions p (N| D) or abbreviated to p(N), since its independent sampling method can overcome some problems of convergence and autocorrelation in general Markov chain Monte Carlo (MCMC) samplers. Figure We utilized annealed importance sampling , 28 to lK annealing runs, the sampler generates K pathway networks and their weights. Then we can compute the confidence for any given substructure s, shown asAfter I(∙) is the indicator function, Nk is the sample at the kth annealing run, and ωk is the important weight. Based on the theory of annealed importance sampling, we can compute confidences of all structure forms of an interesting gene subset, and choose the maximal one as the possible detailed pathway structure of the subset.Where Input: Matrix P: protein interaction networkVector S: signal mutation levelsMatrix D: double mutation levelsMatrix E: typical value for double mutation levelsVector T: temperatures for the annealing runInteger K: number of parallel annealing runsSome optional parametersOutput: Matrix of directed pathway networks and their weightsProcedure:Compute all scores for every possible gene pair by inputs of genetic interaction datap(N) by scores of gene pairs in NCompute m = length(TV)pj Design distributions i = 1 to K:nm − 1 from uniform distribution pm − 1Sample initial network j = m-2 to 0:nj from nj − 1 by uniform distribution over PGenerate nj according to Metropolis–Hastings algorithm by Tj and pjAccept Update importance weightFor Ni and its weight ωiSave network For Ni  and their importance weightsReturn networks Specify interesting pathways and compute their confidenceThe MATLAB codes of our algorithm can be freely downloaded at .Saccharomyces cerevisiae, and keeps available 86,396 raw double mutants from the 444 × 444 genetic interaction pairs [We applied our developed method to the genetic interaction measured by the protein folding in the endoplasmic reticulum  and the on pairs . Anotheron pairs , includiDue to the fact that the raw measurements of genetic interaction data are limited in publicly available databases, we applied our developed method to an available data set from Ref. . Though In our method, we modeled the pathway network as a Bayesian network. The sampling algorithm of annealed importance is applied to curate networks with the probability distribution defined by genetic interaction data, and simultaneously assign weights to them. And the corresponding protein interaction network of the genes in genetic interactions was used to represent underlying sample population, interpreting existence of potential edges in the sampled networks. Using these sampled networks and their assigned weights, we can estimate the detailed structure of the gene subset with high confidence (see Methods). Two substructures reconstructed by our method are shown in Fig. http://www.kegg.jp). One obvious difference is the place of OST3 that is incorrectly placed in APN . The edge from ALG3 to OST3 has a high confidence, 0.65, indicating that APN really cannot interpret some edges in its result. Moreover, the orders of genes from our model and APN may be reverse to the true one [ is not presented in our result, because the corresponding protein interaction is not found in currently used protein interaction databases. In future, we are planning to include more predicted protein interaction data from STRING, and design parallel computing in high-performance computers to improve the performance.We compared N-Glycan biosynthesis pathway substructure reconstructed by our model with the result of APN Fig. . The detAPN Fig. . It may true one  because We also applied our model to infer pathways from another available data set of a global genetic interaction profiles . From abIn this paper, we propose a Bayesian network model to identify pathway structures by integrating protein interaction with genetic interaction data. Our approach makes use of the complementarity between protein  and genetic  interaction data to refer the biological pathway structures. We define a scoring function by which the sampling algorithm of annealed importance can draw some pathway networks and their weights that are used to evaluate the candidate pathway structures. The results show that our model can predict the pathway structures more accurately.


One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes—whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call ‘Pathway Complexity’, that allows us not only to threshold the abiotic–biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive.This article is part of the themed issue ‘Reconceptualizing the origins of life’. These include searching for atmospheric gases such as methane , lookingpe ratio , and seape ratio , for fos in 1999 , Bullen  [et al.  publishe [et al. ; however [et al. .(b)Discussion of the concept of complexity can be difficult, as there is currently no consensus on a single unambiguous definition of complexity . In addiThere have been a number of suggestions for complexity measures on molecules  or cryst2.(a)We propose a measure of complexity based on the construction of an object through joining operations, starting with a set of basic substructures, where structures already built in the process can be used in subsequent joining operations. The sequence of joining operations that constructs the objects can be defined as a complexity pathway, and the number of associated joining operations is defined as the complexity of that pathway. The complexity of the object with respect to the set of substructures is defined as the lowest complexity of any pathway that builds the object. We call this complexity measure ‘Pathway Complexity’ and it is illustrated in without the influence of any biological system or biologically derived agent. An object of sufficient complexity, if formed naturally, would have its formation competing against a combinatorial explosion of many other possible structures. If that given object was found in abundance, it would be a clear indication that life-like processes were required to navigate in the state space to that particular structure, rather than diffusing the starting materials through the state space and ending up with a diverse mixture of structures that may or may not contain the structure in question. The ability to reliably produce specific non-trivial trajectories through state space is, we propose, a characteristic unique to living systems. Therefore, if we can use Pathway Complexity to place a lower bound on the threshold above which a trajectory becomes non-trivial, we can then establish whether an object is undoubtedly of biological origin.The motivation for the formulation of Pathway Complexity is to place a lower bound on the likelihood that a population of identical objects could have formed abiotically from an initial pool of starting materials, i.e. By following this reasoning, it can be proposed that living systems themselves are self-sustaining non-trivial trajectories in a state space. This means that the biosignatures produced by living systems are themselves non-trivial trajectories . As such(b)G. The vertices of G are grouped into equivalence classes, which, in the basic approach, would mean that subunits in the same equivalence class are identical. There may be multiple types of edges if there are different types of connection in the object. We then construct complexity pathways for G and establish their complexities using the following process. We start with a sequence containing only trivial ‘fundamental’ graphs representing each unique subunit in the object. A pair of graphs in the sequence is joined by adding one or more edges between vertices of one graph and the other The choice of the basic substructures depends on the context of the desired complexity. For example, if we are establishing the complexity of the word ‘banana’, then we could select the set of unique structures {b, a, n}, where the complexity is relative to all other words that can be made from those three letters. In fact, we could reasonably extend this set to include all letters of the English language plus punctuation, so we could then compare the complexities of any arbitrary phrase in any language using that alphabet. For a chessboard, natural units to choose would be {black square, white square}, and the complexity would then be relative to all patterns that can be made of black and white squares.In selecting the set of basic subunits, we need to consider the class of objects that we are comparing. For example, if comparing a polymer to all polymers made of the same types of monomer, then the monomers could be our basic subunits, but if being compared to all molecules in general, then we would be likely to select atom types or bond types instead. Although the choice of basic subunits is context-dependent, a natural choice for the subunit is normally apparent from the nature of the class of objects being examined, which is the minimum set of subunits that would be sufficient to construct any object of that class.(d)The following is a mathematical formulation for establishing the complexity pathway of a graph, as described above.G can be constructed in one step from two graphs X and Y if and only if:X and Y are disjoint subgraphs of G,— G is in either X or Y, and— every vertex in G is either in X, in Y, or connects a vertex in X with a vertex in Y.— every edge in A graph Complexity Pathway of a graph G relative to a set of m single-vertex graphs is defined as a sequence of graphs n G= G,— i < 1, iG is a single-vertex graph, and— for i ≥ 1, iG can be constructed in one step from two graphs jG and kG, with j, k < i.— for A Pathway Complexity C of G is the length of the shortest complexity pathway of G, minus the number of single-vertex graphs in that pathway (i.e. n in Definition 2.2). In other words, C is the smallest number of construction steps, as defined in Definition 2.1, that will result in a set containing G.The (e)The Pathway Complexity of an object generally increases with size, but decreases with symmetry, so large objects with repeating substructures may have lower complexity than smaller objects with greater heterogeneity. In addition, the history dependence and recursive nature of the measure mean that internal symmetries are also accounted for if they lie on the shortest pathway. For example, an object may be asymmetric but have a symmetric feature in it that can be constructed through duplication prior to the asymmetric parts being added on. Those duplicated structures may themselves contain substructures with similar duplications, which are accounted for recursively. In this way, we can describe the construction of structures through repeated duplication and addition of subunits.Nv − 1, where Nv is the number of vertices on the graph. This represents joining two fundamental graphs in the first step, and then adding one more at a time until the object is constructed. One lower bound of Pathway Complexity is log2Nv, which represents the fact that the simplest way to increase the size of an object in a pathway is to take the largest object so far and join it to itself, e.g. we can make an object of size 2 with one join, 4 with 2 joins, 8 with 3 joins, etc. An illustration of the upper and lower bounds of Pathway Complexity can be seen in Pathway Complexity has an upper bound of 3.(1) A random sequence of letters: ‘anpncsaveuoaklkgobqfdfoqtyilrzausbcbsxfclanbipcwizlmajbualbs’Green Eggs and Ham [(2) Some text from  and Ham  by Dr SeDracula [(3) Some text from Dracula  by Bram (4) A highly repetitive sequence: ‘redrumredrumredrumredrumredrumredrumredrumredrumredrumredrum’Intuitively, one would expect the ascending order of complexity to be 4, 2, 3, 1 (with 2 simpler than 3 as Green Eggs and Ham is known for its simplicity and repetition). This ordering was confirmed by the algorithm, which found the Pathway Complexity of (4) to be 9, of (2) to be 25, of (3) to be 53 and of (1) to be 57.Pathway Complexity can be used to examine text strings, finding the shortest complexity pathway by leveraging internal regularities. In the following example, we used an algorithm to analyse four strings of text to establish their Pathway Complexity. The following text strings were used, each of them 60 characters long. For consistency, we have converted the strings to lower case without space or punctuation.The maximum possible Pathway Complexity for any 60-character sequence is 59, so we would expect a random string to have a value close to this. In our random string (1), there are two repetitions in the sequence that the pathway has leveraged to reduce the complexity to 57, which are repetitions of ‘an’ and ‘bs’.Dracula, the pathway found has used repetition of ‘ec’, ‘ia’, ‘an’, ‘th’ and ‘ousl’.In (3), the passage from In (2), the pathway constructs ‘am’ and then uses that in ‘sam’ and ‘iam’. It then constructs ‘samiam’ from that pair, and adds letters ‘t’, ‘h’, ‘a’, ‘t’, to make ‘thatsamiam’. These are then used in the final pathway where ‘samiam’ and ‘thatsamiam’ are repeated two and three times, respectively. The pair ‘do’ is also constructed and used twice in the final pathway.The pathway in (4) constructs the phrase ‘redrum’ from individual letters and then duplicates that to make ‘redrumredrum’, further duplicating that to make ‘redrumredrumredrumredrum’ and then ‘redrumredrumredrumredrumredrumredrumredrumredrum’. Finally, ‘redrumredrum’ is added to give the result.4.We can extend the basic complexity measure above to cope with assessing the complexity of a group of objects that contain identical connection motifs . In this5.S = {s1, s2, s3,…}, along with a set of equivalence classes for the subunits P = {p1, p2, p3,…}, and a function f : S → P. Here, f maps members of S into equivalence classes in P based on defining characteristics for each of the ip. For example, looking at bones in the human body, there could just be a single class for ‘bone’ with the characteristic ‘made of bone’, or different classes for different types of bone distinguished by some characteristic of that type of bone . There may be characteristics of members of S not considered by f and these can be thought of as ‘noise’. For example, the same type of bone will vary in shape and size across individuals, but we are only interested in the characteristics that define the mapping onto P. Connections are defined by mapping into another set of equivalence classes E = {0, e1, e2,…} by some function g : S × S→E. Here, E contains the 0 element to represent ‘not connected’, and the ie represent different types of connection. Here, a connection could be an actual physical connection, or it could be some more abstract relationship. We then define the archetypal graph G, in which vertices are members of S, with categories f(is), and an edge exists between is and js if g ≠ 0, with edges categorized by g. In the general case, we are looking at a class of objects to which the above rules can be applied to extract a graph isomorphic to G. In this case, members of S are not necessarily substructures that can rebuild an object in its entirety, but rather are shared connection motifs common to a number of objects that we consider to be similar/related.In the general case, we define an archetypal set of connected subunits G, it is important that, if a substructure is included in S, all equivalent substructures, as defined by f, are also included in S. This is to prevent overestimation of complexity by selecting a more complex subgraph of G through exclusion of some member of S. For example, one could remove some internal symmetries of a skeleton by selectively erasing some of the bones.In the construction of 6.G, and defining the Pathway Complexity of the object, then follows that of the basic approach, only now we are establishing the Pathway Complexity of the selected archetypal graph G that is contained within the whole class of objects. In this way, we can bound the complexity of sets of objects that are non-identical but that clearly share features in such a way that they have some relationship to each other, and establish if the relationship of those motifs exceeds the complexity threshold for a biological source. Greater specificity can provide a higher bound .The procedure for constructing complexity pathways on G using ‘any object’ as a vertex and ‘joined by string’ as an edge. If the objects were all joined in pairs, then G would be simple and indeed one could imagine plausible physical effects for such a phenomenon. However, if G were particularly complex and abundant, i.e. the same complex pattern were found in multiple locations, one would have to consider that some biological agency was involved. Note here that characteristics such as the lengths of the string or the shapes of the object are not considered—the connected structures could be entirely different sizes and made of completely different things, but the identical complex connectivity motif common to all of them would be enough to make a judgement on the probability of a naturally occurring origin from that perspective alone.With this approach, we can examine complex patterns within non-identical structures comprising non-identical parts. As an extreme example, if we were to find sets of entirely different objects  joined by lengths of string on a beach, we could then construct 7.a) that the number of repeated units drops off dramatically (note that the y-axis is logarithmic in plots (a) and (b)), with very few repeats above length 4 or 5. By splicing in the word ‘complex’ 1000 times at random positions (plot (b)), we dramatically increase the number of repeating units at larger sizes. The difference in the number of repeats can be seen in plot (c), with a large difference starting at size 4. From this we can tell that we would expect to find a rather large number of repeats of size 2 and 3, but finding any abundance of repeated strings of size 7 or 8 suggests some internal structure. We can then set a reasonable threshold at 3–5 sigma higher than the random data, suggesting that if we find an abundance of repeats of greater sizes, we have a biosignature.To assess a reasonable threshold for a given set of objects, we can examine the likelihood of objects of varying complexity being constructed randomly . For exaAlthough a useful indicator, this thresholding exercise does not capture the full details of the complexity measure. A stochastic model is currently in development to give a more accurate assessment of populations of larger structures being found among all the structures that could be created in the same time period. This will then be developed in tandem with an experimental system to investigate if a given dataset contains a biosignature or not.8.A variant on the concept of Pathway Complexity as described above is what we have called ‘Recursive Tree Complexity’. In this variant, we establish a complexity pathway by partitioning the object graph into a number of different subgraphs. Then the complexity of that pathway is established as the complexity of each unique subgraph, plus the number of times it is duplicated. If the subgraph is a single vertex, then it contributes 1 to the complexity. The procedure is repeated recursively on the unique subgraphs, while adding 1 to the complexity for each time they are duplicated, and the entire structure will eventually be broken down to single-vertex graphs. The partitioning which gives the lowest total complexity is defined as the Recursive Tree Complexity. The Recursive Tree variant provides a slightly different model of the natural construction of objects. In this variant, the parts that have come together to make a particular substructure cannot be leveraged to create multiple completely different structures. In the Recursive Tree variant, one can think of different structures developing separately and then being brought together, rather than all structures being available at all times in one pool. Any pathway in the Recursive Tree variant can also be made by the Pathway Complexity process, but it may not be the shortest pathway and may include redundant steps. Thus Recursive Tree Complexity is an upper bound for Pathway Complexity. Note also that because in Pathway Complexity the first step is a joining step, but in the Recursive Tree variant we effectively lay down a single fundamental structure first, the equivalent pathways in the Recursive Tree variant will be 1 greater than in the Pathway Complexity measure, and this must be accounted for when comparing the two.a), the substring ‘CO’ can be constructed and then used to make both ‘COA’ and ‘COR’. In the Recursive Tree variant (b), such sharing is not allowed, and the ‘CO’ that goes to make ‘COA’ is constructed separately from that which goes to make ‘COR’.A general mathematical formulation of the Recursive Tree variant is given below:single complexity measurePc of an object partitioned into a given multiset P isK is the set of unique objects in P, ith member of K and D(P) is a function of the multiplicity of the objects in P  is the total number of duplicated objects, or formally A Crecursive tree complexity  of an object is equal to 1 if the object cannot be partitioned further (it is atomic/its graph is a single vertex); otherwise it is equal to the minimum single complexity measureThe 9.It is clear that biological and biologically derived systems have an ability to create complex structures, whether proteins or iPhones, that is not found elsewhere in nature. Assessing the complexity of such artefacts will be instrumental in searching for undiscovered biospheres, either on Earth  or elsew


Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor.Multiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions.Each of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways.We interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors. Breast cancer is the most common cancer in women , with inDespite obtaining multiple specimens from percutaneous biopsies as well as analysis of surgical specimens, the temporal and spatial heterogeneity of tumor gene and protein expression cannot be adequately determined , 7, 8. Rl1 [L1-norm penalty of the LASSO [1/l2 penalty of elastic net [L1 and L2-norm penalty of elastic net [High-dimensional variable selection  is commol1 , horseshl1 , generall1 , L1-normhe LASSO , combinestic net , or combstic net . These rstic net . Severalstic net -26. In tstic net .Analyzing a cohort of 82 breast cancer patients included in the TCGA database, we built a model correlating MRI-derived imaging features with proteomics data using a high-dimensional regression approach. Though a previous study of 353 breast cancer patients assessed correlations between 21 imaging traits and mRNA transcript levels , to our Molecules were found to be significantly correlated to each imaging feature  with theIPA analysis was implemented to determine the functional implications of the molecules. The IPA software generated p-values and Z-scores for the IPA Canonical Pathways of each feature Figure , as wellIn order to determine which features were most strongly associated with functional alterations to signaling pathways, agglomerative unsupervised hierarchical clustering was performed on the p-values and Z-scores Figures  and 3. TThe strength of the associations between imaging features and protein expression, signaling pathways, and biological functions was computed using a sequential analysis of the protein expression data found through RPPA analysis of MRI scans of the TCGA patients. Correlation coefficients for each possible combination of imaging feature and protein were computed using a high-dimensional regression with a Bayesian selection of covariates. Corrected p-values were computed for each correlation coefficient in order to minimize the false discovery rate (FDR). Only the strongest ten percent of significantly-correlated molecules were analyzed using the standardized Core Analysis workflow in IPA, using correlation coefficients in lieu of gene expression values. The IPA analysis provides associations with pathway activity and pathobiology, allowing for hypotheses regarding the relationship between pathway activity at the cellular level and the manifestations of the alterations at the macroscopic, imaging levels. The activation Z-scores computed from the correlation coefficients indicate whether each pathway (or function) is up- or down-regulated by upstream transcription factor activity. A similar approach integrated breast cancer transcriptomics data with imaging features and extended the interpretation with gene set enrichment analysis to identify metagene signatures such as wound response and hypoxia . Our stuIn our study, we used a stringent two-step method to select the correlations least likely to result from chance association, overcoming a common issue with high dimensional regression analysis. Despite this, the approach we have described is essentially a hypothesis-generation pipeline, and should be interpreted carefully, following in-vivo perturbation experiments in appropriate model systems.We found that enhancing rim fraction score, a quantitative MRI feature, was shown to be significantly associated with the expression of the long, non-coding RNA HOTAIR . This exhttps://gdac.broadinstitute.org/).Eighty-two patients from multiple institutions with de-identified MRIs and reverse-phase protein array (RPPA) expression data were included in this study. All subject data was de-identified prior to the study through inclusion in The Cancer Genome Atlas (TCGA), and was thus exempt from requiring institutional review board approval, following the terms of the TCGA data use agreement. Imaging data was obtained through The Cancer Imaging Archive (TCIA) database. RPPA protein expression data was obtained from the TCGA through Firehose  by sequentially designating p-value thresholds . First, Pathway analysis was performed on each of the three data sets (based on the image feature subsets) using the “Core Analysis” feature in the IPA software . For theAgglomerative unsupervised hierarchical clustering of p-values and activation Z-scores was carried out the using the “Stats” package in R. Euclidean distance matrices were computed and Ward's method was minimized within-cluster variance .


Pathway analysis combining multiple types of high-throughput data, such as genomics and proteomics, has become the first choice to gain insights into the pathogenesis of complex diseases. Currently, several pathway analysis methods have been developed to study complex diseases. However, these methods did not take into account the interaction between internal and external genes of the pathway and between pathways. Hence, these approaches still face some challenges. Here, we propose a network-based pathway-expanding approach that takes the topological structures of biological networks into account.First, two weighted gene-gene interaction networks  are constructed integrating protein-protein interaction(PPI) information, gene expression data and pathway databases. Then, they are used to identify significant pathways through testing the difference of topological structures of expanded pathways in the two weighted networks. The proposed method is employed to analyze two breast cancer data. As a result, the top 15 pathways identified using the proposed method are supported by biological knowledge from the published literatures and other methods. In addition, the proposed method is also compared with other methods, such as GSEA and SPIA, and estimated using the classification performance of the top 15 expanded pathways.A novel network-based pathway-expanding approach is proposed to avoid the limitations of existing pathway analysis approaches. Experimental results indicate that the proposed method can accurately and reliably identify significant pathways which are related to the corresponding disease.The online version of this article (doi:10.1186/s12859-016-1333-x) contains supplementary material, which is available to authorized users. Complex diseases are likely to be associated with the effects of multiple genes, proteins and biological pathways . PathwayThe most commonly employed traditional pathway analysis methods use classical pathway databases  information, gene expression profile data and pathway databases into the pathway analysis and constructed two whole-genome level gene-gene interaction networks. Then, we expanded pathways based on the k-walks algorithm , 25 to tPPI network provides a valuable framework to elucidate the functional organization of the proteome. However, existing PPI networks cannot accurately describe the interactions between proteins in specific conditions and have different degrees of false positive and false negative results because most large-scale PPI networks are obtained in different experimental conditions, predicted/extracted using different algorithms , 27. AddThe gene co-expression network (GCN) is an undirected graph where each node corresponds to a gene and a pair of nodes is connected with an edge if there is a significant co-expression relationship between them . Using gTo accurately describe the change in gene interactions for several samples or experimental conditions, here we constructed two weighted gene-gene interaction networks  with PPI and GCN see Fig. .Fig. 2CThe gene-gene interaction of pathway is different in different tissues or samples. These differences may be caused by changes in the interactions between internal genes of the pathway or between pathway and neighbor genes. To assess the significance of the pathway in different phenotypic data, we expanded the pathway based on the k-walk algorithm  by consiG= comprise a set V of genes and a set E of edges denote the weighted gene-gene interaction network with E⊆V×V. Let n=∣V∣reflect the number of genes. Symmetric matrix A represents the weighted n×n adjacency matrix of G, where aijdenotes the weight of the edge connecting gene i to gene j. Let di(i∈1⋯n) represent the weighted degree of each gene node i where aij is calculated by Pearson’s correlation coefficient as: Let β=1.where S (|S|≥2) of a pathway belonging to a subset of G, we formally define an edge relevance function ER:i to gene j is calculated as: Given a gene set S is a set of absorbing states of the Markov chain. If the random walk starts from gene x, the modified transition will be: Here, a gene set Then, the transition matrix is described as follow: xQ is a matrix that denotes transient states, xR is a matrix that denotes the transition probability from transient states to absorbing states, and I is the identity matrix. After k steps, the transition matrix becomes (xQ)k.where x, the joint probability of visiting the edge E between step k and k+1 is calculated as follow: Given that the walk started in state L is a total walk length, xQ)k]xi is the probability of transiting from x to i in k steps.where L starting in x is calculated as follow: The probability of a walk of length e is defined as the number of times a random walk starts in x using the transition from i to j. Given that the walk length is L, the conditional expectations of e is given by: The Lmax denote a maximal walk length. Then: Let ER is given by: Finally, the edge relevance lx represents an initial probability distribution. Here, the maximal relevance score that can lead to a connected subgraph is chose as the threshold θ. Finally, a subnetwork is obtained by keeping only edges with relevance scores ER above a threshold value θ for two phenotypic datasets separately. The union genes of two expanded pathway play a role in performing a similar function in normal or tumor tissues. Moreover, genes in expanded pathway from tumor and normal tissues are almost different. The union genes construct sub-networks in two weighted networks. These sub-networks weights’ differences can describe the change of pathways between different phenotypes. Accordingly, we calculate the difference between the two pathway-based sub-networks reflects the change of the given pathway for the two phenotypic datasets through the union of the two sub-networks.For a given pathway i] denote the union of two sub-networks (T_Ex_Pathway [i] and N_Ex_Pathway [i]) that are the expansion of pathway i in two weighted gene-gene interaction networks . Then, we mapped Union_Pathway [i] into the two weighted gene-gene interaction networks (T_subnet [i] and N_subnet [i]) and obtained two edge weight vectors T_w [i] and N_w [i]. Pearson’s correlation coefficient was calculated as: Let Union_Pathway [n is the dimension of the vector and i], which depicts the difference in pathway i for two phenotypic datasets as follow: where i] is a measure depicting the relevance degree between pathway i and the corresponding disease (for the pseudo-code see Algorithm 1).Here, Score [http://cancergenome.nih.gov/). The BRCA dataset consists of 590 samples obtained from comparing 529 breast cancer samples with 61 normal samples using the Agilent platform. The second dataset was available via the Gene Expression Omnibus (ID= GSE25066). This dataset compared 99 pathologic complete response (PCR) samples and 389 residual disease (RD) samples [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25066). PPI network (version 2.9) was obtained from the Interologous Interaction Database (I2D) website [http://ophid.utoronto.ca/ophidv2.204/downloads.jsp). PPI was mapped into the gene-gene interaction (GGI) data through the UniProt website (www.uniprot.org/). Finally, 234,524 unique gene pairs were selected for BRCA, and 204,772 unique gene pairs were selected for GSE25066 by data pretreatment. The KEGG pathways were downloaded from the ConsensusPathDB website (http://consensuspathdb.org/). We selected 280 pathways related to humans by screening; only genes of pathways belonging to the BRCA gene set were used in the downstream analysis. The breast cancer gene set was downloaded from website(http://mlg.hit.edu.cn/SIDD/).The breast invasive carcinoma (BRCA) dataset was downloaded from the TCGA (The Cancer Genome Atlas) website  network in which the weight of each pair of genes was calculated using high-throughput gene expression profiling data. Finally, we obtained two weighted gene-gene interaction networks for the two phenotypic datasets. The weighted gene-gene interaction network has 15,129 vertices and 234,524 edges for BRCA.Based on the above algorithm, we expanded a gene set of the given pathway based on the k-walks algorithm into two sub-networks in two weighted networks . Then, we compared the number of the genes in the original pathway and the expanded pathway see Fig. . FinallyNext, we ran the proposed approach using the BRCA dataset.To provide a more comprehensive understanding of the proposed method, we discuss the method from the following aspects separately.Based on the background mentioned above, each pathway score depicts the degree of relevance between the given pathway and the corresponding disease. All scores were calculated using Algorithm 1 . Growing evidence suggests that the lack of several trace elements, such as vitamin B6 and folate, can induce DNA damage , eventually leading to tumors, cancers and a variety of degenerative diseases . There iThe number 2 ranked significant pathway identified by the proposed method was Synthesis and degradation of ketone bodies (hsa00072). Ketone bodies  are sufficient to drive mitochondrial biogenesis in human breast cancer cells , 35. CarThe proposed method ranked the Sulphur relay system (hsa04122) in 3rd place. Sulphur enables the transport of oxygen across cell membranes. Oxygen is necessary for healthy cellular regeneration in mammals. Therefore, sulphur deficiencies may promote sickness and disease. Sulphur is commonly used as an herbal medicine to treat inflammation and cancer and organic sulphur has been studied in several types of cancers and found to have remarkable anti-cancer benefits. Methylsulfonylmethane (MSM) is an organic sulphur-containing natural compound without any toxicity. It was found that MSM substantially decreased the viability of human breast cancer cells in a dose-dependent manner and recommended the use of MSM as a trial drug for the treatment of all types of breast cancers . LeimkühPhenylalanine, tyrosine and tryptophan biosynthesis hsa00400) was ranked 4th in the list of proposed methods. ENO1 in phenylalanine tyrosine and tryptophan biosynthesis was significantly overexpressed in HER-2/neu positive breast tumors [0 was ranThe 5th ranked pathway was Glycosaminoglycan biosynthesis (hsa00533). Abnormal glycosaminoglycan (GAG) concentrations have been reported for various types of tumors, suggesting that they may play a role in neoplasia. Recently, cell biology studies revealed that glycosaminoglycans were among the key macromolecules that affected cell properties and functions by acting directly on cell receptors or via interactions with growth factors. The interactions of GAGs with growth factors, cytokines and growth factor receptors have been implicated in cancer growth and progression. GAGs are involved in signalling cascades that regulate the angiogenesis, invasion and metastasis of malignant cells. Investigations of the fine structures and specific biological roles of GAGs has led to novel therapeutic approaches –43. The The top 6–15 pathways are also associated with human breast cancer  . FinallyThe significantly impacted pathways identified by the proposed method in the corresponding conditions were mostly consistent with the known biological processes. Accordingly, the novel proposed method is of methodological and biological significance for future research.Pathway analysis not only reduces data involving thousands of altered genes and proteins to a smaller and more interpretable set of altered processes but can also combine multiple types of high-throughput data. The analysis results play an important role in elucidating the mechanisms of complex diseases, improving clinical treatment, and discovering drug targets and biomarkers. Therefore, pathway-based analysis of complex diseases has become a research hotspot. To date, these methods have entered the third stage : 1) PathUnlike existing pathway analysis approaches that do not take into account the interaction between internal and external genes of the pathway and between pathways, we propose a novel approach that addresses the above-mentioned limitations by expanding a pathway based on the k-walk algorithm to two small networks in two weighted networks . Finally, our approach effectively identified significant pathways that corresponded to a complex disease through a series of verification steps. It is undeniable that the pathways identified by GSEA and SPIA but not by our method are mostly significantly associated with the breast cancer risk. Based on the above analysis, our method combined with GSEA may produce better results. Hence, we will combine our method with GSEA in future studies. This study provides a new research direction for the pathway-based analysis of complex diseases. We will employ more datasets to assess the validity of our approach in future research.


Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related mortality in China with increasing incidence. This study is designed to explore early genetic changes implicated in HCC tumorigenesis and progression by whole-exome sequencing.  We firstly sequenced the whole exomes of 5 paired hepatitis B virus-related early-stage HCC and peripheral blood samples, followed by gene ontological analysis and pathway analysis of the single-nucleotide variants discovered. Then, the mutations of high frequency were further confirmed by Sanger sequencing.  We identified a mutational signature of dominant T:A>A:T transversion in early HCC and significantly enriched pathways including ECM-receptor interaction, axon guidance, and focal adhesion and enriched biological processes containing cell adhesion, axon guidance, and regulation of pH. Eight genes, including MUC16, UNC79, USH2A, DNAH17, PTPN13, TENM4, PCLO, and PDE1C, were frequently mutated.  This study reveals a mutational profile and a distinct mutation signature of T:A>A:T transversion in early-stage HCC with HBV infection, which will enrich our understanding of genetic characteristics of the early-stage HCC. Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related mortality in China . In the TP53, ARID2, ARID1A, CTNNB1, AXIN1, RPS6KA3, and IRF2 [A multistep process of sequential genetic changes is a core driving force behind the development and progression of cancer . In thisand IRF2 –10, and and IRF2 , 9. Howeand IRF2 . The onland IRF2 . TherefoWith Over 240 million people chronically infected worldwide , hepatitFive paired HCC specimens and peripheral blood samples were collected at the Liver Cancer Institute of Zhongshan Hospital  from patients receiving curative resection, and the samples were immediately frozen at −80°C, followed by pathological examination and confirmed to be HCC by experienced pathologists. These patients received no prior therapies, such as transarterial chemoembolization or radiofrequency ablation. Zhongshan Hospital Ethics Committee approved this study, and each patient signed informed consent following institutional review board protocols.Figure S1. The somatic variants from each paired sample were justified by the following criteria: (1) phred-scaled consensus scores > 20 and mapping qualities > 30; (2) variant reads ≥ 10% of the total reads; and (3) number of variant reads ≥ 5 and total reads ≥ 10. We removed common polymorphisms by comparison to dbSNP135 and the 1000 Genomes Project database (http://www.1000genomes.org), as well as synonymous mutations and variants found in normal exomes. The Integrated Genomics Viewer (IGV) software was used to examine all the candidate mutations, and the selected SNVs were confirmed by PCR-based Sanger sequencing in each tumour tissue and paired blood sample. The mutational signatures of each tumour were extracted by the application of deconstructSigs, as described before [Using a DNeasy tissue kit (Qiagen), paired tumour/normal genomic DNA from 5 patients were extracted and randomly sonicated to generate 150–200 bp products. After end repair with 3′A overhang and NimbleGen linker adaptation, the AMPure XP bead-purified DNA fragments were subjected to LM-PCR. In accordance with the manufacturer's protocol, the prepared DNA libraries were hybridized to Agilent SureSelectXT Human All Exon kit to capture the target exome. After another round of LM-PCR, a minimum of 80-fold enrichment was confirmed by qPCR for all of the captured sequences. The exonic DNA library was then sequenced on the Illumina HiSeq2000 platform. We aligned the sequenced reads to the human reference genome (GRCh37/hg 19) from the UCSC Genome Browser by Burrows-Wheeler Aligner  software and removed the PCR duplicates by SAMtools software. To detect potential variants, we employed the Genome Analysis Toolkit (GATK) to perform consensus calling, and the discovery pipeline is detailed in http://david.abcc.ncifcrf.gov) v6.7 using GO to reveal the molecular function denoted in the gene profile. Simultaneously, these genes were input into the Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the pathway associated [P value of 0.05 is recommended.The identified genes with SNVs were further input into Database for Annotation, Visualization and Integrated Discovery . Somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) were predicted using algorithms described in Figure S1. Briefly, to remove common germline variants, we removed candidate mutations that already existed in dbSNP135 or in the 1000 Genomes Project dataset, and tumour-specific mutations were confirmed by comparing the tumour data with those of matched peripheral blood samples. On the whole, we identified 736 potential protein-altering somatic mutations within 708 genes, with 614 missense mutations, 43 nonsense mutations, and 79 small indels (Table S2 and S3); this pattern is analogous to the distribution found in solid tumours [To obtain insight into the genetic basis of HBV-related eHCC, exome sequencing was performed on paired tumour and peripheral blood DNA from 5 HCC patients , consisting of dominated T:A>A:T transversions, was found overrepresented in 4 out of 5 HBV-related eHCC and when all the SNVs were pooled together . Additionally, an overrepresentation of transversions over transitions  was found, in agreement with the results of previous HCC sequencing studies [Notably, a predominance of T:A>A:T transversions with a strong transcriptional strand bias was identified, which affected 4 out of 5 individuals in our cohort . In case studies , 10.Table S4 and S5) and found that pathways associated with extracellular matrix- (ECM-) receptor interaction , focal adhesion , and amoebiasis  were the top 3 significantly enriched pathways and that cell adhesion , axon guidance , and regulation of pH  were the top 3 significantly enriched biological processes  and pathway analysis based on the identified SNVs . Each mutation was heterozygous, leading to sequence changes of the corresponding amino acid in the encoded protein.In total, 8 genes, including MUC16, UNC79, USH2A, DNAH17, PTPN13, TENM4, PCLO, and PDE1C, were found to be mutated in at least 2 HCC samples ; 17 candCancer is a disease of the genome . GeneticMutational signatures differ vastly among cancers . VariousUsing mutated genes affected by SNVs in 5 cases, we carried out GO and pathway analysis and identified ECM-receptor interaction, axon guidance, and focal adhesion as significantly mutated pathways and/or biological processes. Notably, axon guidance was identified as both significantly mutated pathway and biological process. 12 mutated genes involved in this pathway affected 4 out of 5 cases in this cohort. The axon guidance genes were originally characterized by their roles in guiding axon pathfinding to its synaptic targets, thereby forming neural connections and wiring the nervous system during embryogenesis . Recent TENM4) were annotated in the Catalogue of Somatic Mutations in Cancer (COSMIC) and also reported in recent sequencing studies of HCC [MUC16 and USH2A were mutated with over 5% frequency in documented tissue samples. Elevated expression of MUC16 has been found in breast cancer and late stage and metastatic sites of pancreatic cancer [USH2A could possibly facilitate the process of EMT. The nonsense mutation (Tyr1758∗∗∗) in PTPN13 leads to loss of function in accord with the prior study reasoning PTPN13 as a candidate tumour suppressor gene in HCC [Of the 8 frequent mutated genes, 7 (except s of HCC , 30, as c cancer , 32. Eudc cancer , the mute in HCC . These oIn conclusion, our work documents the mutational profile, affected pathways, a distinct T:A>A:T transversion signature, and frequent mutated genes in HBV-related eHCC, which enriched our understanding of the biology underlying the tumorigenesis of HCC.


The purpose of this paper is to utilize the signaling pathway polymerase chain reaction (PCR) arrays to investigate the activation of two important biological signaling pathways in endothelial cell adhesion and growth mediated by adsorbed serum protein on the surface of bare and titanium nitride (TiN)-coated nickel titanium (NiTi) alloys. First, the endothelial cells were cultured on the bare and TiN-coated NiTi alloys and chitosan films as control for 4 h and 24 h, respectively. Then, the total RNA of the cells was collected and the PCR arrays were performed. After that, the differentially expressed genes in the transforming growth factor beta (TGF-β) signaling pathway and the regulation of actin cytoskeleton pathway were screened out; and the further bioinformatics analyses were performed. The results showed that both TGF-β signaling pathway and regulation of actin cytoskeleton pathway were activated in the cells after 4 h and 24 h culturing on the surface of bare and TiN-coated NiTi alloys compared to the chitosan group. The activated TGF-β signaling pathway promoted cell adhesion; the activated regulation of actin cytoskeleton pathway promoted cell adhesion, spreading, growth and motility. In addition, the activation of both pathways was much stronger in the cells cultured for 24 h versus 4 h, which indicated that cell adhesion and growth became more favorable with longer time on the surface of two NiTi alloy materials. The developments of high-throughput biomics technologies provide powerful means to comprehensively understand the molecular mechanisms of how biomaterials impact organism from an overall genome/protein level. Bioinformatics analysis, an indispensable part of nowadays biomics research, extracts and interprets the biological significance of biomics data using some common techniques such as cluster analysis , 2, funcBiological pathways have many classifications, and the most well-known pathways are metabolism pathways, gene-regulation pathways and signal transmission pathways . Biological signaling pathways are an important mechanism to maintain biological viability. A signaling pathway transmits a physical or chemical signal from a cell's exterior to its interior by receptors and then by specialized proteins that trigger a cascade of biochemical events along the signaling pathway , to mainBesides using bioinformatics analysis of biomics data to identify important signaling pathways, they can be further filtered according to the effects of biomaterials corresponding to specific cell functions based on the cytology experiments and then be verified . There aPCR array was developed as a class of functional classification arrays following the gene expression profile arrays. First proposed in 2002 based on PCR technique , it is aCol1 and Gpnmb were inhibited in bone marrow mesenchymal stem cells growing on natural hydroxyapatite (NHA) after blocking the ERK1/2 and the JNK mitogen-activated protein kinase (MAPK) pathways, therefore validating the significance of these two pathways in NHA-induced osteogenic differentiation . BMP6 binds to TGFB1, inhibiting SMAD2/3 activation, promoting SMAD1/5/8 activation, and thereby promoting the enhancement of TGFB1-mediated adhesion . NODAL iSMAD7 is an inhibitory SMAD that acts as a negative regulator in the TGF-β signaling pathway. SMAD7 is induced by TGF-β, interacts with activated TGFBR1 and interferes the phosphorylation of the receptor-regulated SMADs . SMURF1 According to the above analysis, among these five genes, three genes  were facilitative and two were inhibitory (SMAD7 and SMURF1) to TGF-β signaling pathway when they were up-regulated. The expression patterns of these five genes and their effects on the pathway were summarized in Cytoskeleton as an important structure in the cell is a network made up of proteins; it maintains cell morphology, offers material transfer, facilitates cell movement, and supports cell differentiation. Cell signaling is a critical regulatory component of cytoskeletal involvement in cellular processes. Many related proteins such as the Rho family, MAPK, etc., interact with each other to affect cell morphology and functions through signal transduction associated with certain cytoskeleton regulatory pathways .There were five genes that were differentially expressed on both the bare and the TiN-coated NiTi alloys . Their lThe protein encoded by PAK4 is a member of the PAK family. PAK proteins are critical effectors link Rho GTPases to cytoskeleton reorganization and nuclear signaling. They serve as target for the small GTP binding protein Cdc42 and Rac, and have been implicated in a wide variety of biological activities. PAK4 plays a role in the reorganization of actin cytoskeleton as a mediator of filopodia formation . PAK4 phAccording to the above analysis, these five genes all activated the regulation of actin cytoskeleton pathway and were all up-regulated in each experimental group . The sumThe results of the above-mentioned TGF-β signaling pathway and regulation of actin cytoskeleton pathway were compared with the previous proteomics results of the adsorbed proteins on the bare and the TiN-coated NiTi alloys , as showTGFBR1, TGFBR2, TGFBR3) in the TGF-β signaling pathway and three genes  in the regulation of actin cytoskeleton pathway have also been detected by using RT-PCR; these six genes were significantly (P < 0.05) or highly significantly (P < 0.01) expressed in the bare and the TiN-coated NiTi alloy groups compared to the chitosan group. This indicated that both alloys enhanced the activation of the TGF-β signaling pathway and the regulation of actin cytoskeleton pathway [In our previous study, the expression levels of three genes , 973 Project (No. 2009CB930000), and the Natural Science Foundation of Jiangsu Province (BK20150599).REGBIO online.Conflict of interest statement. None declared.Supporting InformationClick here for additional data file.


Although shape perception is considered a function of the ventral visual pathway, evidence suggests that the dorsal pathway also derives shape-based representations. In two psychophysics and neuroimaging experiments, we characterized the response properties, topographical organization and perceptual relevance of these representations. In both pathways, shape sensitivity increased from early visual cortex to extrastriate cortex but then decreased in anterior regions. Moreover, the lateral aspect of the ventral pathway and posterior regions of the dorsal pathway were sensitive to the availability of fundamental shape properties, even for unrecognizable images. This apparent representational similarity between the posterior-dorsal and lateral-ventral regions was corroborated by a multivariate analysis. Finally, as with ventral pathway, the activation profile of posterior dorsal regions was correlated with recognition performance, suggesting a possible contribution to perception. These findings challenge a strict functional dichotomy between the pathways and suggest a more distributed model of shape processing. We rely on our sense of vision to perceive the world around us and the objects within it. We also use vision to guide our interactions with objects. One of the most influential theories in cognitive neuroscience is the idea that separate pathways within the brain support these two processes. The ventral pathway is in charge of vision-for-perception. It analyses the features that help us recognize objects, such as their color, size or shape, enabling us to identify the hammer in a toolbox, for example. The dorsal pathway is responsible for vision-for-action. It processes features that help us interact with objects, such as their movement and location, enabling us to use the hammer to strike a nail.However, recent studies have suggested that the ventral and dorsal pathways may not be as independent as originally thought. Freud et al. now test this idea by examining if the dorsal vision-for-action pathway can also perceive and process objects.Healthy volunteers viewed pictures of objects while lying inside a brain scanner. Some of the objects in the pictures were intact, whereas others had been distorted. If a brain region shows greater activation when viewing intact objects than distorted ones, it implies that that region is sensitive to the normal shapes of objects. Freud et al. found that both the ventral and dorsal pathways were sensitive to shape, with some areas in the two pathways showing highly similar responses. Furthermore, the shape sensitivity of certain regions within the dorsal pathway correlated with the volunteers’ ability to recognize the objects. This suggests that regions distributed across both pathways – and not just the ventral one – may contribute to object recognition.The two-pathways hypothesis has governed our understanding of vision and of other sensory systems including hearing for several decades. By challenging the binary distinction between the two pathways, the results of Freud et al. suggest that models of sensory processing may require updating. This improved understanding may ultimately improve diagnosis and treatment of perceptual disorders such as agnosia, in which patients struggle to recognize objects. Shape is the most fundamental perceived property of objects, and, accordingly, shape processing is crucial for successful visual object recognition . DerivinIt appears, however, that shape perception is not solely a product of the computations mediated by the ventral pathway. Human neuroimaging studies , primateIn the current study, we addressed these questions in two studies, each combining functional magnetic resonance imaging (fMRI) and psychophysical measures. We hypothesized that the large-scale topographical organization of dorsal object representations obeys a spatial gradient in which posterior regions of the pathway derive shape representations and contribute to visual perception, whereas more anterior regions derive representations that are better tuned to subserve visuomotor behaviors . Our motWe adopt an approach that has been used previously to characterize the neural basis of shape processing in ventral cortex in response to stimuli in which shape information is parametrically eliminated by increasingly distorting images of objects  see Fig.Following these previous studies, in the first experiment, we adopted a parametric box scrambling procedure to map the large-scale organization of shape processing along the ventral and dorsal visual pathways and the contribution of these representations to shape perception. In addition, we employed more advanced multivariate analyses to further elucidate the nature of representations and the similarities or differences in these representations in the two pathways. However, the box-scrambling method has some inherent limitations. First, it increases the number of edges, and early visual cortex is especially sensitive to this type of information . Second,To circumvent these limitations and verify the results, in the second experiment we used a diffeomorphic scrambling manipulation that increasingly precludes the identification of the object but preserves some fundamental shape properties by repeatedly applying a flow field generated from a set of two-dimensional cosine components with random phase and amplitude . ConsequThe large-scale organization of shape processing along the dorsal and ventral visual pathways was assessed in two imaging experiments, by computing the level of the BOLD signal as a function of the decrease in shape information across five levels. Three analytical approaches were employed: a novel voxel-wise approach that provides a continuous voxel-wise mapping of shape processing along the two pathways, a more traditional ROI analysis, and a multivariate representational similarity analysis (RSA) to uncover additional information about the similarity of spatial patterns of activation as a function of scrambling.2 when compared with a simple linear regression (ps <0.05), thereby providing a better fit to the data than a one-component linear model .To map the topographical organization of shape processing along the two pathways in a continuous fashion, we generated a voxel-wise map of shape sensitivity for all visually selective voxels. In each voxel, beta weights were extracted for each of the five stimulus conditions from most scrambled to intact , and the linear slope between these conditions was used as an index of shape sensitivity. Voxels that are shape sensitive were those in which activation, as a function of object coherence, had a positive slope. Conversely, voxels that showed greater sensitivity for scrambled than intact images had a negative slope. To map the profile of shape sensitivity across all voxels, we then applied a piecewise linear regression across all the voxels using two (rather than one) linear components (see Methods for details). In each pathway, the piecewise linear regression increased the R(10)=8.21, q < 0.00001 CI {0.39, 0.69}; RH: t(10)=12.1 q < 0.00001, CI {0.43, 0.62}]. The second component was characterized by a decrease in shape sensitivity, as evident from the negative correlation between location on the posterior-anterior axis and slope , and a robust negative correlation for the second component, which reflects the decrease in shape sensitivity in the anterior parts of the dorsal pathway .Despite the apparent qualitative similarity in the large-scale topographical organization of shape processing along the two pathways, further analyses also revealed some differences between the ventral and dorsal pathways. In particular, the maximal shape sensitivity (the apex or inflection point of the regression line) was more posterior in the dorsal relative to the ventral pathway  with greater difference between the pathways in the left than the right hemisphere. The greater difference between the pathways in shape sensitivity in the left hemisphere might reflect hemispheric specialization, such that the left dorsal pathway is more tuned to visuomotor aspects than the right dorsal pathway.Additionally, the average sensitivity of all shape-selective voxels (slope > 0) was significantly greater in the ventral than dorsal pathway ; RH: t(10)=13.2, q = 0.000004, CI {44, 0.63}], and a negative correlation for the second component, which reflects the decrease in shape sensitivity in the most anterior parts of the temporal lobe .In the ventral pathway, the piecewise regression revealed a positive correlation between slope and location on the posterior-anterior axis (first component), which reflects the emergence of shape sensitivity in the transition from early visual cortex to object selective cortex . In contrast, the second component, which reflects the representational gradient along the IPS, was characterized by a negative correlation between slope and location of the voxels on the posterior-anterior axis .The piecewise regression of the dorsal pathway revealed two opposite linear components. The first component was characterized by a positive correlation between slope and location on the y-axis, which reflects the transition from early visual cortex to object-selective cortex . This result indicates that, despite the difference in the overall shape sensitivity between the two experiments, they provided consistent information on the overall large-scale organization of shape sensitivity.=18.78, p=0.001, ηp2 = 0.65], and the effect held when distance based on the Y and Z axes served as the dependent variable . The similarity between the two experiments was confirmed by an ANOVA that included Experiment as a between-subjects variable. In particular, a main effect of pathway was found  and was not modulated by experiment [F  < 1]. Interestingly, the inflection point of the regression was more anterior in the diffeomorphic scrambling experiment compared with box scrambling experiment , and this might reflect the increased high-level shape or identity processing associated with the diffeomorphic scrambling stimuli.As in box scrambling experiment, the maximal shape sensitivity emerged in more posterior regions of the dorsal pathway compared with the ventral pathway . The comparison between the two experiments revealed two main effects, with no interaction. As expected, there was a main effect of pathway: shape sensitivity was greater overall in the ventral than dorsal pathway across the two experiments . There was also an additional main effect of Experiment, with greater average positive slope in the box scrambling experiment than the diffeomorphic scrambling experiment, reflecting the different nature of the two manipulations  was found and was not modulated by experiment [F <1]. This interaction was the result of a greater difference between the two pathways in the left than right hemisphere, and therefore further implicates hemispheric differences in shape processing.In addition, we compared the overall neural sensitivity to shape information in all shape-selective voxels (slope > 0). The average shape sensitivity was greater in the ventral than the dorsal pathway  and dorsal  pathways, further challenging a binary distinction between the two pathways. Notably, this effect is not the result of the assignment of a particular ROI to the posterior/anterior dorsal pathway, and was replicated even when aIPS was excluded from the analysis, or when IPS2 was included in the posterior (rather than anterior) dorsal pathway.To quantify the representational similarity within and between pathways, we compared the correlations of the object-selective ROIs within each pathway  to the correlations of the object-selective ROIs between the two pathways . Interestingly, this analysis revealed that the between-pathways correlations were higher than the within-pathway correlations both in the ventral . The robust decrease of correlation to the shape model, further suggests that the LOC codes shape information rather than a high-level abstraction of the object’s identity.A robust reduction in the correlation to the shape model was observed along the ventral pathway, particularly along its lateral surface. This was confirmed by an ANOVA that revealed an interaction between experiment and ROI . Notably, as in the box scrambling experiment, this effect was replicated even when aIPS was excluded from the analysis, or when IPS2 was included in the posterior (rather than anterior) dorsal pathway.The second-level RSA was compatible with that observed for the box scrambling experiment, reinforcing the notion that the large-scale organization of the two pathways was reliable and stable across the two experiments. The MDS plot shows that the patterns of activation in the posterior parts of the dorsal pathway (i.e. V3a-IPS1) were highly correlated with regions of the ventral pathway , while more anterior parts of the two pathways had distinctive representational structures. The statistical quantification replicated the box scrambling experiment findings, with higher correlations of the between-pathways ROIs  compared with the correlations of the within-pathways ROIs , and a robust simple effect of the linear contrast  =929, p=0.0000001, ηp2 = 0.99], and a robust simple effect of the linear contrast  =71, p=0.0000001, ηp2 = 0.78] and the planned comparisons . Hence, the lower fMRI sensitivity to object shape (i.e. flatter slope) in the diffeomorphic scrambling experiment could not be related to superior recognizability of the distorted images in this experiment. The comparison between the behavioral performances in the two experiments should be interpreted with caution, however, since participants in the box scrambling experiment were exposed to each object in all levels of scrambling whereas, in the diffeomorphic scrambling experiment, participants were exposed to a particular object only in one level of distortion.In the diffeomorphic scrambling experiment, object recognition abilities decreased as a function of scrambling, as revealed by a main effect of level of scrambling in the repeated measures ANOVA eLife. Your revised article has been favorably evaluated by David Van Essen (Senior Editor), Nick Turk-Browne (Reviewing Editor), and one of the original reviewers.Thank you for resubmitting your work entitled "The large-scale organization of shape processing in the ventral and dorsal pathways" for further consideration at The manuscript has been improved considerably and we plan to accept it if you can address a couple of remaining issues:1) In light of the block design and mixing of exemplars, the added multivariate analysis is non-standard. Please acknowledge this more thoroughly before presenting the results, including by explaining how this differs from standard RSA over exemplars (which will be more familiar to readers).2) Please also clarify your interpretation of the multivariate findings. Insofar as these regions represent exemplars and scrambling gradually destroys exemplar identity, why does collapsing over exemplars work at all? What aspects of the exemplar-general representations at each level of scrambling are similar? There are potentially uninteresting explanations, such as that scrambling changes the spatial extent of the image . If you cannot rule out such accounts or provide a more parsimonious explanation, you might consider re-calibrating how much emphasis these findings receive and/or moving them to supplement. At a minimum, this analysis needs more motivation and interpretation.3) It would be more informative to use Box Scrambling and Diffeomorphic Scrambling as subtitles rather than Experiment 1 and Experiment 2. Essential revisions:1) Many of the claims relate to what has been learned about the nature of object representations in the dorsal stream. However, the reviewers question whether the approach taken here is adequate to assess representational content. In particular, the use of univariate methods and image scrambling to assess shape information was considered indirect and outdated, compared to the multivariate (or even adaptation) methods used in prior studies. Admittedly, the difference between scrambling techniques across streams  speaks to what is being coded, but this was not viewed as sufficient. In particular, this can speak to selectivity for shape and/or identity, but does not address what features are being represented. Although adaptation would not be possible to consider with the current design, I see no reason that a representational similarity analysis couldn't be performed. There were 2 and 4 (Experiments 1 and 2) repetitions of each stimulus, which would allow a cross-correlation of the raw pattern of BOLD activity over searchlights or ROIs for each stimulus with its own repetition(s) and with the other stimuli. Average diagonal vs. off-diagonal correlations would provide an index of shape coding, and this could be examined at different levels of scrambling. Moreover, how this index changes over the longitudinal axis could provide confirmation of the piecewise regression results, and also about differences between scrambling techniques across streams. Finally, second-order correlations of the correlation matrices would speak directly to the question of the similarity between ventral and dorsal stream representations. Other multivariate approaches could achieve the same goals, and the authors are experts in this class of techniques, but something along these lines is necessary.We thank the reviewers for this helpful comment. Note that, because we used a block design to increase the statistical robustness of our experiment, we cannot employ a multivariate analysis of the responses to the different exemplars. Nevertheless, following this comment, we have conducted a multivariate analysis (see below) and utilized RSA to uncover the sensitivity of the different ROIs to object scrambling and to compare the representational structure of the different ROIs.The first-level RSA correlated the representational structure of the different ROIs with that of a shape-sensitivity model. Consistent with the results of the univariate analysis, we were able to demonstrate that the two- components organization along the longitudinal axis is not a unique property of the fMRI amplitude, but also emerges from the similarity of response patterns in the different ROIs.Even more importantly, the second-level RSA allowed us to compare directly between the representational structures of the different ROIs. We used multidimensional scaling (MDS) plots  and 5 toThe new analysis has been included in the revised manuscript  and 2) Related to questions about representation, it was unclear which exact properties of the objects were affected by scrambling. For example, reduced sensitivity to diffeomorphic scrambling in the dorsal stream is interpreted as evidence of coding for the presence of a single shape rather than identity. However, this kind of scrambling affects many other features , all of which could be potential explanations. The convex hull analysis is a good start, but the authors should provide a more comprehensive, perhaps computational analysis of the features present in the stimuli from both experiments at different levels of scrambling.We have conducted three additional image analyses to further quantify the differences between the diffeomorphic and the scrambling manipulations. These analyses enabled us to examine how the different distortion manipulations modulated low, mid and high-level shape attributes. Low-level attributes were examined using a pixel-similarity analysis. Mid-level attributes were mapped using image entropy (the amount of the disorder in an image) and image homogeneity (texture). High-level attributes were assessed using the Convex Hull algorithms. The results of these analyses revealed that object distortion was greater in box scrambling experiment compared with diffeomorphic experiment across all levels of shape information.The continuity of object and image texture are essential to the existence of a “shape” and therefore it is hard to conclude whether the relative reduction of dorsal sensitivity to the diffeomorphic manipulation reflects sensitivity to “shape” per-se, or to shape elements such as continuous texture and a clear figure-ground segregation. We address this limitation in the Discussion section of the revised manuscript 3) This paper builds on a long history of related studies. The reviewers felt that this history and its implications were not adequately presented. This includes the classic work by Grill-Spector and others – and whether the current results would (or would not) have been predicted from it – as well as the more recent work using multivariate methods . Many of these papers are cited, but a more in-depth weighing of the prior literature, discussion of the new findings and why they are novel in this historical context, and consideration of theoretical implications (including for previous conclusions) is needed.We have now considered previous investigations and the interpretation of the present findings in light of these previous findings. These discussions have been included in different sections of the revised manuscript. Thanks for drawing our attention to this inadvertent neglect of related work.4) The conclusion about differences in the representational gradient of ventral and dorsal streams depends on how the streams are defined. For example, the authors seem to go as anterior as possible in the dorsal stream and obtain a decrease in shape sensitivity. Would a decrease not be observed in the ventral stream if its end was likewise extended further, say into the hippocampus or anterior temporal lobe? Alternatively, if the dorsal stream was defined more conservatively, wouldn't the gradient match the ventral stream? The root concern is that it is unclear whether the gradients are fundamental, organizing properties of these streams, or at least partially artifacts of how the analysis was conducted. The authors need to better justify their definition of the streams, and address this limitation.Following this comment, we redefined the ventral pathway mask to include the anterior parts of the temporal lobe. This new definition extended the previous results observed for the ventral pathway. In particular, we have found that the ventral pathway, similar to the dorsal pathway, has a first positive component and a negative second component. Hence, the results obtained from the new, extended mask, may suggest that the topographical organization of the two pathways is shaped by similar factors, namely cortical distance from the visual cortex and connectivity to other cortical systems .Notably, even though the gradients are qualitatively similar , there is also evidence for differences in shape processing in the two pathways. First, in both experiments, the maximal shape sensitivity (the inflection point of the regression line) emerged in more posterior regions of the dorsal pathway compared with ventral pathway (and this effect holds even when we have used a different distance measurement that was computed based on both the Y and Z axes see ). In addThe results of the new analyses have been included in the revised manuscript  and on 5) Relatedly, the anterior-posterior axis is defined simply as Y coordinates. Given the brain's curvature, especially in the dorsal stream, these coordinates do not reflect neural distance. The authors should calculate alternative distance metrics, for example tracing along the surface of inflated cortex from calcarine to central sulci, re-performing their piecewise regressions, and either replacing the existing analyses or reporting one in the supplement.It is true that we focus our analysis on the Y (posterior-anterior) axis. This focus was theoretically driven and followed a previous related investigation . However, in light of the reviewers’ concerns, we have reanalyzed the data, taking into account both the Z (superior-inferior) and Y axes. To this end, we have calculated the distance between each voxels to the most posterior voxel (using both Y and Z coordinate). This approach replicates the main organization revealed using only the Y-axis  was correlated with the slope values and revealed similar organization to this described above.6) Object recognition performance is almost perfectly correlated with scrambling. As a result, the neural correlations with object recognition almost perfectly mirror the baseline relationships to scrambling. Thus, it is unclear what these results add to the story. More problematically, it is unclear whether neural activity is driven by scrambling or recognition. These can be dissociated, as Grill-Spector did with training, but that hasn't been done in the present manuscript. At a minimum, this issue should be highlighted in the Discussion; better would be an analytical solution to dissociating these factors.We thank the reviewers for this important comment. To address this concern, we have calculated the partial correlations between behavioral performance and fMRI activation controlling for the correlation with the level of scrambling. Importantly, the partial correlation treats the level of scrambling as a covariate, and therefore allows us to reveal the unique correlation between fMRI activation and behavioral performance. The partial correlations were weaker  but were nevertheless, still evident and significant in the posterior parietal ROIs and in regions along the ventral pathway, pointing to a plausible contribution of dorsal regions to perception. The revised analysis is included in the subsection “iii. Correlation between fMRI activation and object recognition performance” and The manuscript has been improved considerably and we plan to accept it if you can address a couple of remaining issues:1) In light of the block design and mixing of exemplars, the added multivariate analysis is non-standard. Please acknowledge this more thoroughly before presenting the results, including by explaining how this differs from standard RSA over exemplars (which will be more familiar to readers).We have addressed this issue in the revised manuscript: “Notably, most previous investigations utilize the RSA approach to uncover the representational space of individual exemplars. […] This procedure still allows us to explore how the availability of shape information modulated the representational content of different ROIs.”2) Please also clarify your interpretation of the multivariate findings. Insofar as these regions represent exemplars and scrambling gradually destroys exemplar identity, why does collapsing over exemplars work at all? What aspects of the exemplar-general representations at each level of scrambling are similar? There are potentially uninteresting explanations, such as that scrambling changes the spatial extent of the image . If you cannot rule out such accounts or provide a more parsimonious explanation, you might consider re-calibrating how much emphasis these findings receive and/or moving them to supplement. At a minimum, this analysis needs more motivation and interpretation.availability of shape information modulates the representational content in different ROIs . You will recall that the experiment is blocked by level of scrambling: intact through 256 scrambled pieces. The RSA allows us to explore the extent to which each ROI represents information as a function of the coherence of the shape, and it is this parameter that enables us to reach a conclusion regarding the sensitivity to shape.We agree with the reviewers that the interpretation of the RSA results needs to be thoughtfully considered, given the non-conventional approach we have adopted. In particular, since we have utilized a block design, we cannot reach any conclusions about the representational structure of any particular exemplar. Therefore, the RSA focuses on the extent to which the Another concern raised by the reviewers is that the similarities or dissimilarities between the representational structures of different ROIs might be accounted for by changes of low-level visual attributes, which are being distorted by the scrambling manipulation. It is for this very reason that we conducted the second experiment with the diffeomorphic manipulation and we conducted the RSA on these data as described above for the box scrambling manipulation i.e. we have applied the same analyses on fundamentally different scrambling manipulations (box scrambling and diffeomorphic scrambling) that distort object shape, but preserve different attributes of the original stimuli. Importantly, similar patterns of results for the first-order and second-order RSA were observed across the two experiments, reinforcing the notion that our results reflect the sensitivity of the visual system to shape information, rather than to low level attributes. This conclusion is also supported by the differences observed between the two experiments. In particular, the diffeomorphic scrambling preserves more shape information than the box scrambling manipulation and accordingly, the RSA for this experiment uncovered overall lower correlations with the shape model.We have incorporated this discussion in the subsection “ii. Representational Similarity Analysis” and in the last paragraph of the subsection “What aspects of object properties are encoded by the dorsal pathway?”3) It would be more informative to use Box Scrambling and Diffeomorphic Scrambling as subtitles rather than Experiment 1 and Experiment 2.The paper has been revised accordingly.