There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models.Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on aWeb site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license.Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include asmuchmolecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associatedWeb site. New content is added quarterly.The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway “signaling by interleukins.” The classification used is largely derived from Akdis et al.The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling  Reactome represents these processes as pathways, connected events termed reactions that define changes in the state of biological molecules. Reactions include binding; translocation; posttranslational modifications, such as phosphorylation; and biochemical reactions. The molecular events defined by reactions connect to represent ordered transformation networks. Reactome represents human biological events in graphic pathway diagrams supported by descriptions, references, and links to external resources. The result is an extension of the concept underlying classic metabolic maps, diagrams that represent a broad range of physiologic processes underpinned by data that are freely available in a formally defined and consistent structure that is computationally reusable.By placing human proteins in pathways, Reactome characterizes their molecular functions, providing a resource that is both an archive of biological processes and a tool for discovering functional relationships within user data sets. Reactome pathways include  10,996 human proteins. Reactome pathway descriptions, images, and underlying data can be downloaded easily in a variety of human-readable and computationally reusable formats. In addition to the Web site, Reactome provides programmatic access tools through content and analysis services.https://reactome.org), where content can be searched, viewed, and downloaded. Pathways are annotated manually by professional curators and peer reviewed by expert biologists. The aim is to represent the consensus view for each area of biology in a reliable and consistentmanner such that the underlying data have a structure amenable to use both as a reference knowledge base and as a computational resource for high-throughput querying and reuse. Pathways are represented in as much mechanistic detail as possible, detailing every established molecular event. Events are termed reactions but are not limited to classical biochemical events; binding, dissociation, and translocation between cellular compartments and all forms of posttranslational modification, such as phosphorylation, are also considered reactions.Reactome is a free database of human biological pathways available through the Reactome Web site ,3 are a family of secreted proteins that function as ligands for receptors that mediate signaling among cells of the immune system.2 Certain interleukins have chemokine- or interferon-like properties, but their most important role is as mediators of the physiologic response to infection. Interleukins initiate signaling by binding to receptors located on the cell surface. Their function is as a local signal (paracrine or autocrine) rather than as a circulatory long-range (endocrine) signal, which differentiates them from the steroids and peptide hormones. The response of a cell depends on the specific interleukin involved, the receptors it expresses on its surface, and the signaling cascades that become activated. This physiologic role, as well as their mechanism of action as ligands that bind to cell-surface receptors, makes them attractive drug targets, and they are the subject of much active pharmaceutical and clinical research.Reactome’s coverage of interleukins, their receptors, and signaling pathways has recently been extended to cover IL-1 to IL-38. The classification used is largely derived from Akdis et al.4 and subsequently, a common interleukin nomenclature system was approved by the International Union of Immunological Societies and the World Health Organization Nomenclature Subcommittee.6 A new nomenclature system based on peptide sequence conservation, common structural elements, gene architecture, and publication date was suggested for IL-1 family genes.7 More recently, interleukins have been classified by genomic architecture and characteristic protein structural features into 4 major groups, with a number of unclassifiable outliers. 8 The most recent comprehensive classification of interleukins from IL-1 to IL-38 uses a combination of sequence homology, receptor chain similarities, and functional properties.2Interleukins have been classified into families based on a variety of characteristics, including biological function, shared receptor components, sequence homology, structural motifs, and cellular origin. Alternative nomenclature systems and the frequent use of alternative names as new interleukins were discovered by more than 1 group has led to a great deal of confusion and ambiguity. A nomenclature for lymphokines was proposed in 1979,in vivo human biology. If an event has only data obtained by using model organisms, it is clearly annotated as an inferred event. Details of the event as it occurs in the model organism are included in the database, and links to the experimental data are provided, allowing the user to satisfy himself or herself that results can be extrapolated reliably to the equivalent human event. All such inferred events are checked by the expert reviewer to ensure that the inference from model organism to human is valid. Reviewers are asked to check the accuracy of molecular details and to ensure that events represent the consensus of knowledge in the field and not findings that are controversial or recent unconfirmed reports.Information in Reactome is captured from published scientific literature by using postgraduate curators. Every new event undergoes peer review by domain experts and cites reference publications that contain the original experimental data used to determine the event details. Publications that report experiments conducted with human reagents are given preference because these are most likely to reflect 9 Gene Ontology (GO),10 PubMed,11 Chemical Entities of Biological Interest (ChEBI),12 UniProt,13 Online Mendelian Inheritance in Man (OMIM),14 IntAct,15 and many others related to disease, genetic disorders, ontology, and literature, enriching the information that can be accessed.Reactome extensively cross-references external databases, such as Ensembl, 16 Although higher level pathways aggregate pathways within the same broader biological process, at lower levels of the hierarchy, pathways represent the molecular details of the pathway as a series of molecular events. In both cases the data are stored in a formal structured data model and made available as interactive diagrams. 17 Higher-level pathways are represented by enhanced high-level diagrams that are intended to visually guide users to the specific subpathway topics that interest them  and representation of expression data viewed as an overlay on Reactome pathways. Protein-protein and protein-compound interactors can be visualized as potential extensions to Reactome’s curated pathways. Interactors are available for more than 10 source databases or can be submitted as a custom interactions data set. This provides a useful approach for identifying candidate receptor-binding partners or pathway-signaling extensions, potential tool compounds that could be used to modulate the pathway, or novel lead compounds. Using the analytic tools on theWeb site or through the analysis services, users can easily discover whether the set of proteins they have found to be associated with a particular treatment or disease state is clustered in a particular biological process. They can take advantage of disease-associated pathway visualizations to determine whether a mutation that blocks the function of a protein of interest would directly perturb a biological process. Interpretation of these analyses is facilitated on the Web site by means of dynamic navigation and detail-level adjustments between views, features that enable visualization of the connections between pathways and domains.20 is used throughout the signaling by interleukins pathway to avoid the confusing plethora of alternative naming systems present in the historical literature. At the same time, the diverse names given various entities have been preserved as synonyms that are indexed by our search tools, so that this naming history is readily accessible.A protein-naming convention developed for Reactome2 and IUPHAR.21 Some of the most recent interleukins are not represented in the IUPHAR categorization . Reactome includes some interleukin-related cytokines that are not represented by Akdis et al2 .Interleukins have been classified into families by using a variety of characteristics, including biological function, shared receptor components, sequence homology, structural motifs, and cellular origin. The classification used in Reactome is derived largely from Akdis et alInterleukin signaling in Reactome is organized within the pathway signaling by interleukins. This pathway has subpathways that represent the molecular events of signaling of specific interleukins or interleukin families in an associated pathway diagram. Some such families contain several well-studied interleukins, each with an extensive signaling pathway. Family members are typically grouped together within the hierarchical organization and graphic views.2 who represent IL-4 and IL-13 as a branch of the IL-2 family.In a few cases the graphic representation of members of a family has been split into several pathway diagrams to allow complete representation of the molecular details in an uncluttered graphic. For example, IL-4 and IL-13 are typically considered part of the IL-2 family, but their signaling pathways are placed at the same level of Reactome’s pathway hierarchy and represented graphically as separate diagrams. This is consistent with Akdis et al,22 we follow the more recent classification of Akdis et al,2 which places IL-27 in the IL-12 family.Other interleukins with well-studied and consequently extensive signaling pathways that are represented in a dedicated pathway diagram include IL-1, IL-2, IL-6, IL-7, IL-9, IL-10, and IL-17. Interleukins grouped by shared receptor subunits include IL-4 and IL-13, which share a receptor that includes the IL-4 receptor, and IL-3, IL-5, and GM-CSF, which use receptors that share IL-3RB (the common β chain). Other interleukins are grouped into recognized families, such as the IL-6 family, which typically includes IL-11, leukemia inhibitory factor, CT-1, oncostatin M, ciliary neurotrophic factor, and IL-31. Although in some classifications IL-27 is placed in the IL-6 family,23 An online description of all the features of the Reactome Web site is available at: http://www.reactome.org/userguide/Usersguide.html.The formal data model embodied in Reactome’s representation of pathways makes its content consistent and computationally accessible. This content is available programmatically through a REST-based content service and as a Neo4J graph database. Reactome provides visualization widgets that can be embedded into Web pages, making Reactome content easily reusable in third-party Web sites, benefiting the systems biology and modeling communities. Reactome offers a pathway analysis service for enrichment and expression analysis.https://www.ebi.ac.uk/training/online/course-list?field_course_ subject_area_tid%5B%5D519).Reactome training is available in the form of a webinar and self-paced online courses through the EBI Train Online Web site – or CASP8- mediated processing of precursors, secretion of the active protein, receptor binding, and downstream signaling events. The event of CASP1-mediated IL-1 processing belongs to the IL-1 family signaling pathway and is linked to the downstream events as the preceding event. At the same time, this cleavage reaction is followed by CASP1 activation, which is in turn induced on inflammasome formation. Thus the events of the CASP1 activation bridge IL-1 family signaling pathway to the upstream intracellular signaling processes described in the pathway of inflammasomes.17 that contains glyphs representing interleukin subpathways . A diagram key is available within the Reactome Pathway Browser, as revealed by selecting the button with a compass icon . For events, the details include a description of the selected object or event and literature references containing experiments that provide evidence supporting the molecular details of the event.The level of detail visible within a pathway diagram adjusts depending on the size of the presentation area and the level of zoom. At the lowest level of zoom, any subpathways within the pathway diagram are indicated by enclosing shaded boxes A. As zooThe addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. Reactome content is dynamic and intended to expand and undergo revision as new discoveries are published and become accepted. A key ongoing aim is to increase coverage of gene expression changes induced by interleukin signaling as these data become confirmed and established.


Here advanced tractography was combined with quantitative indices to compare CPC to CTC pathways in healthy subjects. Differently to previous studies, our findings reveal that cerebellar cognitive areas are reached by the largest proportion of the reconstructed CPC, supporting the hypothesis that a CTC-CPC loop provides a substrate for cerebro-cerebellar communication during cognitive processing. Amongst the cerebral areas identified using in vivo tractography, in addition to the cerebral motor cortex, major portions of CPC streamlines leave the prefrontal and temporal cortices. These findings are useful since provide MRI-based indications of possible subtending connectivity and, if confirmed, they are going to be a milestone for instructing computational models of brain function. These results, together with further multi-modal investigations, are warranted to provide important cues on how the cerebro-cerebellar loops operate and on how pathologies involving cerebro-cerebellar connectivity are generated.Cerebellar involvement in cognition, as well as in sensorimotor control, is increasingly recognized and is thought to depend on connections with the cerebral cortex. Anatomical investigations in animals and post-mortem humans have established that cerebro-cerebellar connections are contralateral to each other and include the cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) pathways. CTC and CPC characterization in humans  The cerebellum exerts its functions in close communication with the cerebral cortex by exploiting two main pathways: the efferent cerebello-thalamo-cortical (CTC) pathway and the afferent cortico-ponto-cerebellar (CPC) pathway , synapses in the anterior pontine nuclei (APN) and passes through the contralateral middle cerebellar peduncle (MCP) before reaching the cerebellar cortex17.Several investigations 17, showing projections to the pons from several cerebral areas20. For example, Kelly and Strick17 reported trans-synaptic pathways traveling from primary motor (M1) and prefrontal cortices to distinct locations in the cerebellar cortex, mainly the lobules V-VI and Crus II respectively, in the primate brain, while Schmahmann and Pandya demonstrated connections between the superior temporal lobe and the pontine nuclei19. Furthermore the CTC and CPC pathways have been described in humans ex vivo using Nissl staining in post-mortem brains after leucotomy21 and using confocal laser and polarized light microscopy22. All these studies are focusing on specific aspects of the cerebro-cerebellar loop and support the knowledge that there is a complex interaction between cerebellum and cerebral cortex, involving both motor and non-motor areas.The CPC pathway has been examined by tracing techniques in experimental animals27 have indeed assessed the existence of a convincing relationship between the CPC pathway and cognition in humans in vivo. One approach has been to rely on functional MRI (fMRI) results to first identify cerebral and cerebellar regions that are functionally connected, then to examine structural connections between those pre-defined regions25. Tractography based on diffusion weighted imaging28, despite its limitations 30, is currently the only method to investigate important characteristics of specific neuronal pathways in humans in vivo. Previous studies using deterministic tractography based on the diffusion tensor model have assessed the feasibility of reconstructing these pathways in vivo. In particular, Kamali, et al.7 reconstructed different portions of the CPC pathway, e.g. fronto-ponto-cerebellar and temporo-ponto-cerebellar tracts, while Keser, et al.8 described both CTC and CPC pathways in terms of their volumes and diffusion parameters along them. The reported findings, though, were partially inconsistent with previous neuroanatomical investigations17. This can be explained due to their use of diffusion-tensor based tractography, which cannot resolve crossing fibres, meaning that the reconstructed tracts were ipsilateral with the exception of the fronto-ponto-cerebellar tract.Previous MRI studies(1) the contralateral CPC pathway can be reconstructed by simply constraining streamlines to pass through the MCP and the contralateral CP; (2) there is a correspondence between cerebral and cerebellar cognitive areas via the MCP, and how that supports the results found for the CTC pathway; (3) it is possible to hypothesize the existence of cerebro-cerebellar closed-loops by comparing the areas belonging to the CPC and the CTC pathways. Here, in order to achieve these aims, the reconstruction of the CPC pathway was accomplished by combining CSD, that models multiple fiber populations within a voxel resolving the decussation of trans-hemispheric connections, with TDI, that provides maps with higher resolution and contrast than with classical diffusion tensor imaging derived maps , which are useful to place seed and target regions of interest (ROIs)31. Moreover, to quantify the pattern of correspondence of cerebro-cerebellar cortical regions reached by the computed CPC pathway and to assess the relationship between areas associated to cognitive and motor functions, two previously defined metrics were used9: trGMcROI to assess the proportion of the tracts projecting to a particular ROI and cROItract to assess the proportion of a given ROI involved in the tract. For completeness, the raw total streamlines count (TSC) for each cortical region was also reported.The aim of this study is therefore to reconstruct the contralateral CPC pathway with the same pipeline used for the CTC pathway in order to gather critical information about the cerebro-cerebellar loops involving motor and non-motor areas. In particular, the study aims to assess whether: Both the left and right seeded contralateral CPC pathways were successfully reconstructed in all subjects by combining CSD and probabilistic tractography. Figure 18.In order to highlight the main skeleton of the CPC pathways, Fig. cROI, cROItract and TSC values), based on anatomical and functional parcellations34.Here are reported results  are the ones most reached by the tract.cROI values is similar between the lateral Crus I-II (62% ± 4%) and the temporal lobe (54% ± 6%).The range of trGMcROI involved the lobules I-VI (13% ± 4%) and VIIb/VIII (24% ± 5%) in the cerebellum and the frontal lobe (20% ± 6%) and prefrontal cortex (8% ± 5%) in the cerebrum.Lower percentages of trGMA careful inspection of Table cROI, cROItract and TSC values are reported for both cerebral and cerebellar parcellations. In the cerebrum, the highest trGMcROI and TSC are found in the associative areas, while cROItract has the highest value in the motor areas. In the cerebellum, the metrics have the highest values in the cognitive/sensory area.In Table cROI = 96% ± 1%).In the cerebellum almost all streamlines reach the cognitive/sensory area (trGMcROI = 69% ± 7%).The associative areas of the cerebrum encompassed the highest percentage of streamlines  previously proposed on the basis of comparative cortical surface measurement across vertebrates35, and their structural connectivity forming multiple closed loops involving not only motor but also cognitive areas. It is worth noting that our findings are the first CSD-based tractography reconstructions that give evidence of a complex system of cerebro-cerebellar closed loops going beyond the pure motor system.This work has successfully reconstructed the contralateral CPC pathway and has actively contributed to the open discussion on et al.7 and Keser, et al.8, is to employ advanced probabilistic tractography and track-density imaging (TDI) combined with constrained spherical deconvolution (CSD)36, which models multiple fiber populations within a voxel and resolves crossing fibres issues. Moreover, in this work we do not impose cortical parcellations prior to running the tractography, differently from previous studies8, hence our analysis is not limited to specific cortical regions (e.g. motor), while at the same time ensuring a high degree of fidelity to known anatomy by imposing some a priori key conditions. Indeed, according to anatomical knowledge by using the superior cerebellar peduncle as tractography seed region and the red nucleus as waypoint to constrain the pathway of the streamlines, it was possible to reconstruct an unconstrained distribution of target cortical regions reached by efferent streamlines originating from the cerebellum.One key novel aspect of our work, compared to the previous contributions of Kamali, 9: trGMcROI to assess the proportion of the tracts projecting to a particular ROI and cROItract to assess the proportion of a given ROI involved in the tract. For completeness, the raw total streamline count (TSC) for each cortical region has also been reported for the CPC as for the CTC pathways. These metrics helped us to assess the potential correspondence between the cerebral cortex and the cerebellum, compare results of the cerebellar CPC afferent pathway with those of the efferent CTC one, in an attempt to define the complexity of the cerebro-cerebellar loops. Furthermore, it is worth noticing that the reconstructed pathways are more extended than those provided in previous diffusion-tensor based works8, as CSD facilitates tracts to reach within cortical areas.To quantify the pattern of correspondence of cerebro-cerebellar cortical regions reached by the computed CPC pathway and to assess the relationship between areas associated to cognitive and motor functions, two previously defined metrics were used4. This organization poses a key problem for diffusion-tensor-based tractography, that of following crossing fibers. Thus, in this work a non-tensor model combining CSD algorithm with probabilistic tractography37 was adopted, as already successfully done for the CTC pathway reconstruction9. Some fidelity to the known anatomical characteristics of the CPC pathway was imposed by using a relatively unconstrained approach , and the resulting cerebral and cerebellar areas involved in the reconstructed pathway were in agreement with the anatomical findings of ex vivo experiments19. Moreover, three previously reported indices were used to quantify the results9, and thus allow direct comparison to previous findings: trGMcROI to assess the proportion of the tracts projecting to a particular ROI, cROItract to assess the proportion of a given ROI involved in the tract, and TSC to reflect the raw number of the streamlines reaching a specific cortex. Using these indices, it was possible to infer the correspondence of involvement between specific cerebral and cerebellar areas. Of course, the specific contribution of different cortical regions to the cerebro-cerebellar loop remains to be carefully evaluated considering the implications linked to the chosen cortical parcellation atlas and the effective ability of diffusion tractography to provide quantitative comparative information on long-range transynaptic pathways, as further discussed below.Current neuroanatomical knowledge anticipates that the CPC pathway starts from cerebral cortical areas, passes through the CP, reaches the contralateral MCP as a coherent bundle, and finally spreads towards the cerebellar cortexex vivo and in vivo studies have reported that the majority of frontal lobe connections involves lobules V-VI while fewer connections involve lobules VIIb/VIII27. In the present study the frontal lobe, which comprises the motor areas  as well as the frontal eye field (BA 8) and Broca’s areas (BA 44-45), accounts for 20% of the CPC pathway, showing the second highest trGMcROI value in the cerebrum, while lobules I-VI account for 13% of the CPC pathway. Thus, we hypothesize that trGMcROI of the frontal lobe (20% ± 6%) finds cerebellar correlate in lobules I-VI (13% ± 4%) and only partially in lobules VIIb/VIII (24% ± 5%). Moreover, this hypothesis could explain the discrepancy between trGMcROI values of the cerebral motor (comprising both premotor cortex and M1) and the cerebellar primary motor (lobules I-V) areas (19% ± 6% and 2% ± 6% respectively). The high number of streamlines involving the frontal lobe is in agreement with viral tracing experiments where BA 4, 6, 8 project a large amount of fibers to the pons18 and then to the cerebellum17, but also find correspondence in several fMRI studies which found coherent activity between the primary motor cortex and lobules V, VI and VIII39. Furthermore, cerebellar lobule VII has been shown to activate during cognitive tasks and coherent functional connectivity has been found between lobules VIIb/VIII and frontal lobe in observation learning40. Altogether these considerations support our findings of streamlines between the frontal lobe and the cerebellum and, therefore, the cerebellar involvement in sensorimotor control and related cognitive functions.Despite the intrinsic limitations of diffusion tractography, the results of this study are in line with previous findings using different techniques. Indeed, both 42 using viral tracing techniques in animals while fMRI studies have revealed coherent functional connectivity between these regions43. Here is reported that the parietal lobe encompasses 9% of the CPC pathway. Since the parietal lobe is known to be involved in response to the sight of an object, as well as to the act of grasping it44, and in the creation of cross-modal sensorial representations of objects45, results of the present study support the cerebellar involvement in spatial orientation and representation functions. Furthermore, tracing experiments in non-human primates20 have identified a “prefronto-cerebellar bundle” connecting the prefrontal cortex mainly with the Crus I-II, which has also been found by functional and structural studies in humans in vivo47. In line with such findings, the current results confirm that about 8% of the CPC streamlines leaves the prefrontal cortex , supporting the cerebellar role in cognitive functions, such as working memory and mental preparation for imminent actions. Lastly, the contribution of temporal fibers to the CPC pathway has been demonstrated with experiments on rhesus monkeys19, whereas recent studies using fMRI49, tractography24 and dynamic causal modelling50 have reported a strong bidirectional connection between the superior temporal sulcus and the Crus I-II in humans. The results here show a coincidence of trGMcROI values between the temporal lobe 34 and Crus I-II (54% ± 6% and 62% ± 4% respectively), suggesting that the two areas could be interconnected trough the CPC pathway11 and supporting the involvement of Crus I-II and the temporal lobe in multimodal cognitive functions39. This study reports a greater amount of streamlines leaving the temporal lobe compared to those expected from previous studies that employed probabilistic or deterministic tractography based on in vivo diffusion tensor imaging in humans and macaques26. Other than these, there are no other reported quantifications of the temporo-cerebellar connectivity in vivo to compare results with. The greater amount of temporo-ponto-cerebellar connections found in our study can be explained by the use of a non-tensor model for tractography and a different cerebral cortical parcellation. To support our findings, we have noticed and reported that previous tract tracing studies have given evidence of temporo-ponto-cerebellar tracts from the superior temporal sulcus (e.g. Schmahmann 1997), but literature about which tract is greater among those from temporal, parietal and occipital lobes is lacking and controversial26.Moreover, several tracing and MRI studies have provided evidence of connections between other cerebral areas, such as parietal, prefrontal and temporal cortex, and the cerebellum. In particular, a number of studies mapped the bidirectional connection between the parietal lobe and the cerebellum47. As far as sensorimotor areas are concerned with, the cerebral motor area  and the anterior part of the cerebellum (lobules I–VI) show a high involvement (trGMcROI is 19% ± 6%), in agreement with the cerebellar role in motor functions. In contrast, the primary sensory cortices account for just a minor fraction of the CPC streamlines . These values are in accordance with fMRI studies showing that primary auditory and visual cortices do not appear functionally coupled with the cerebellum43. However, a functional relationship of these cortices with the cerebellum should exists, since connections from the fastigial and vestibular nuclei passing through the inferior and the middle cerebellar peduncles play an important role in controlling the execution of saccades and in elaborating the visuospatial information concerning the eye target51. An underestimation by tractography methods of these connections has to be considered: it could be caused by the high curvature of fibers connecting the cerebellum with visual and auditory areas . The cerebrum somato-sensory area constitutes only 4% of the CPC pathway. Since works based on functional connectivity reported an existing network between the cerebellum and this cerebral area43, further studies with new analysis methods53 could help to clarify the issue.Overall, all these findings support that the cerebellum has indeed an important role in cognition as the prefrontal cortex , language areas (BA 44–45), parietal (except BA 1–3), temporal (except BA 41–42) and the limbic lobes all together encompass 69% of the tract in the cerebrum, while cerebellar cognitive hemispheres encompass 96% of the tract9 to improve knowledge about the cerebro-cerebellar closed-loops. Indeed, the cerebro-cerebellar connections are thought to form closed-loops, with the feedforward and feedback branches passing mainly through the CPC and CTC pathways, respectively5. Thanks to these loops, the cerebellum is thought to operate as an information processing system that modulates cerebral activity4. For example, the error-correction model posits that the cerebellum behaves as an internal simulator, which receives inputs from the cortex via the CPC pathway and modulates cerebral processing via the CTC pathway55. These loops might be closed either by direct connections of the same cerebral areas or pass through intra-cerebral connections both concerning sensorimotor and associative intermediate loops56. As far as the sensorimotor areas are concerned, in the frontal lobe  and in the lobules I–VI, CTC and CPC pathways showed a similar range of trGMcROI values. Therefore, despite all tractography caveats57 (discussed further on), the symmetry of CTC-CPC pathways in motor areas would support a cerebro-cerebellar closed-loop for controlling motor programming and execution1. Conversely, about 70% of the CPC pathways were described as connecting associative areas of the cerebral cortex to the contralateral cerebellar cognitive areas, in a proportion almost equating the ratio reported for the CTC pathways (80%)9. Despite this analogy, our results showed that the CTC pathways reached mainly the prefrontal cortex (trGMcROI 38% ± 11%) and the temporal lobe (trGMcROI 35% ± 5%), while the CPC pathways originated preferentially from the temporal lobe (trGMcROI 54% ± 6%) rather than from prefrontal cortex (trGMcROI 8% ± 5%). The discrepancy found in the amount of CTC streamlines reaching the prefrontal cortex9, compared with CPC streamlines leaving it, may reflect a complex network structure, rather than being simply due to tractography limitations. Indeed, it is well known that several tracts exist, which are connecting sensory areas in the occipito-parietal, temporal lobes and the frontal lobe, including the arcuate fasciculus connecting the Wernicke to the Broca areas58. As far as the prefrontal cortex and temporal lobe are concerned with, histological and MRI studies have shown that the uncinate fasciculus bidirectionally connects the prefrontal cortex and the temporal lobe59, whereas these areas are also probably connected through the fornix and the parahippocampal cingulum61. Therefore, current results provide the first quantitative MRI-based evidence of in vivo cerebro-cerebellar connectivity that, if confirmed, would be useful for informing closed-loop models for both sensorimotor and cognitive control functions by providing the proportions of CTC and CPC streamlines that are reaching different cerebellar and cerebral cortices62. CTC and CPC pathways of a representative subject are reported in Fig. Beyond the characterisation of the CPC pathway itself, it is fundamental to combine the present results with those of previous work on the CTC pathway performed with identical tractography pipeline (apart from seed and target regions)et al.9 for further discussion); nevertheless, tractography is the only available method for reconstructing axonal bundles in humans in vivo. In general, MRI tractography cannot distinguish between efferent and afferent tracts, cannot discriminate between mono-synaptic and trans-synaptic connections, cannot resolve single axonal fibers, and is known to produce large number of false-positive streamlines57. Nevertheless, the common issue of resolving crossing fibres was addressed here by employing an algorithm that models multiple fibre populations within a voxel37. Here below, technical considerations with respect to CPC reconstruction and the adopted pipeline are discussed with further detail.The intrinsic limitations typical of MRI tractography also apply to the present study  was able to follow the fibers curvature and their trans-synaptic collaterals in the pons.in vivo tissue remains unknown. Quantifying the false positives of fiber tracking using in silico phantoms have claimed a high percentage of spurious streamlines30. How this proportion can be reduced by imposing a priori knowledge, like in the present study, has to be verified. Thus, in general, neither the absolute fiber number or density, nor the strength of connections can be precisely determined with tractography57.Thirdly, diffusion MRI data have poor resolution  compared to the scale of single axonal fiber  and may not be able to robustly follow axonal bundles at crossing points. Moreover, the extent to which specific tracts might be lost depending on the presence of synapses or axonal bending or fiber crossing in the complex situation of 4. It is worth noting that these discrepancies between present results and those reported by previous studies27 may strongly depend upon the different cortical parcellation that has been chosen  and upon the mathematical model and tractography algorithm that have been used. Although no reliable conclusion on tract size can be drawn at present, we tried to limit this problem by using the same MRI scans and the same analysis pipeline in both the CTC and CPC pathways reconstructions, so that direct comparisons of the CPC and CTC quantitative metrics would be performed10. Further improvements may be achieved by exploiting recent developments in diffusion MRI tractography methods53 and by repeating the analysis using higher resolution data, such as those available through the Human Connectome Project (www.humanconnectomeproject.org). Ultimately, unless moving to tracers studies in animals or post mortem studies in humans, tractography is currently the only available technique for the investigation of complex pathways in humans in vivo, therefore it is important that studies such as this one are performed and discussed, taking into consideration a comprehensive knowledge gathered from multiple modalities, anatomical studies and non-human primate and animal investigations.Finally, beyond a reliable identification of the CPC pathways bilaterally, there is the problem of evaluating to what extent the present analysis can provide a quantitative description of the involved tracts. The issue is particularly relevant since the proportion of CPC and CTC tracts connected to the same cortical areas were asymmetric in some cases, and since some connections  seemed to be over-represented compared to results reported by previous worksin vivo. Evidence of congruent streamline metrics of the cerebral and cerebellar cortices in cognitive areas bears relevant functional implications supporting the existence of a complex network with multiple closed-loops, which comprises also intra-cortical cerebral connectivity. This result confirms the coevolution of the two cortices proposed on the basis of comparative cortical surface measurement across vertebrates35. Since the cerebellar cortex has almost an identical structure in all its sections and is organized in parallel poorly-interacting modules3, it is possible that a similar computational algorithm is applied to different cortical functions, ranging from motor control to sensory perception and cognition11, supported by the presence of pathways connecting different cerebral and cerebellar cortices (CTC and CPC). Quantification of the involvement of different cerebral areas, depending on their motor or cognitive function, would provide very relevant information for the development of computational models of cerebro-cerebellar loops55. Since the analysis was performed on high-angular resolution diffusion imaging (HARDI) data acquired on a standard clinical scanner, this method could be applied in the assessment of the integrity of the cerebro-cerebellar connectivity in pathologies  for which a cerebellar involvement has been proposed14.The work reported in this study led to the characterization of the descending pathway connecting the cerebral cortex with the contralateral cerebellar hemisphere, passing through the CP and the contralateral MCP in humans et al.10 was analyzed: 15 right-handed healthy adults  with no previous history of neurological symptoms. All participants gave written informed consent for the study and for the publication of research data and images. The study protocol was approved by the local institutional research ethics committee (NRES Committee London-Queen Square) and all experiments were performed in accordance with relevant guidelines and regulations.To directly compare CTC and CPC pathways results, the same cohort of subjects as in Palesi, 63. For each set of diffusion-weighted data, 7 volumes with no diffusion weighting (b = 0) were acquired. For anatomical reference a whole brain high-resolution 3D sagittal T1-weighted (3DT1w) fast field echo (FFE) scan was acquired using the following parameters: TR = 6.9 ms, TE = 3.1 ms, TI = 824 ms, acquisition matrix = 256 × 256, 1 mm isotropic voxel, 180 sagittal slices, acquisition time 6 min 31 s.All data were acquired on a Philips Achieva 3 T MRI scanner  using a 32-channel head coil. The HARDI scan consisted of a cardiac-gated SE echo-planar imaging (EPI) sequence acquired axial-oblique and aligned with the anterior commissure/posterior commissure line, for a total scan time of approximately 20 min. The imaging parameters were TR ≈ 24 s (depending on the cardiac rate), TE = 68 ms, SENSE factor = 3.1, acquisition matrix = 96 × 112, 2 mm isotropic voxel and 72 axial slices with no gap. The diffusion weighting was distributed along 61 optimized non-collinear directions with a b value of 1,200 s/mmhttp://fsl.fmrib.ox.ac.uk/fsl/fslwiki/)64 and MRtrix (http://www.brain.org.au/software/mrtrix/)37 software packages. Both pipeline and parameters used for the analysis were described in detail in Palesi, et al.9; a brief summary of the main steps will follow .Pre processing and data alignment: FSL was used to perform eddy current correction and brain extraction. 3D-T1w volumes were realigned to the diffusion data with FLIRT.Whole brain tractography and TDI maps: To obtain TDI maps, whole brain tractography was performed using MRtrix and combining CSD with probabilistic tractography . Streamlines were randomly seeded throughout the whole brain until 2.5 millions of them were selected37. A TDI map was computed assigning to each element of a user-defined 1-mm resolution grid31 an intensity proportional to the total number of streamlines passing within it31.Cortico-ponto-cerebellar pathways: the CPC pathway was reconstructed using the MRtrix algorithm mentioned above. Two hand-drawn ROIs were used as seed and target, as previously described27: the MCP13 and the whole contralateral CP for each hemisphere27. A total of 3,000 streamlines for each subject were selected.Seed/target ROIs: High-resolution TDI images were used to define both seed and target ROIs. The seed ROI was defined as a sphere with 2.5 mm radius centered on the MCP in each cerebellar hemisphere and was identified in the coronal plane, as described by Calamante et al.31  on the axial plane of colour coded TDI maps 34 and of the cerebellum (SUIT)33 were aligned to the native space of each subject in order to parcellate cerebral and cerebellar cortices based both on anatomical grounds and on a functional basis.HARDI data were analysed using FSL  belonging to the tract (trGM) in one cortical parcellation, divided by the total trGM across all cortical parcellations. The cROItract index represents the proportion of a given ROI that is involved in the tract, and was obtained by dividing trGM in one cortical parcellation by the number of voxels within the parcellation itself. The total streamline count (TSC) was used to reflect the number of the streamlines reaching the cortex. TSC was calculated with MRtrix filtering from the tract only the streamlines related to each specific cortical area.Mean cortico-ponto-cerebellar pathway: To assess the consistency of the pathways and for display purposes, the output from all subjects were normalized to MNI space using a non-linear registration algorithm with nearest neighbor interpolation from the FSL library (FNIRT). A mean image was calculated from the binarized pathways  for each subject65. Voxels were assigned the count of the number of subjects with that specific voxel included in the mask. The mean image was thresholded to include voxels common to at least 20% of subjects10.Functional parcellation consisted of the following areas:


Epigenetic modifications change expression levels of components of the Wnt pathway, underlying oncogenic transformation. However, no clear Wnt component increasing expression universally across breast cancer (BC) or its most Wnt-dependent triple-negative BC (TNBC) subgroup has been identified, delaying development of targeted therapies. Here we perform network correlation analysis of expression of >100 Wnt pathway components in hundreds of healthy and cancerous breast tissues. Varying in expression levels among people, Wnt components remarkably coordinate their production; this coordination is dramatically decreased in BC. Clusters with coordinated gene expression exist within the healthy cohort, highlighting Wnt signaling subtypes. Different BC subgroups are identified, characterized by different remaining Wnt signaling signatures, providing the rational for patient stratification for personalizing the therapeutic applications. Key pairwise interactions within the Wnt pathway (some inherited and some established  Nineteen members of the Wnt family of secreted lipoglycoproteins and ten members of the FZD family of seven transmembrane-helix receptors are encoded by the human genome. Signaling in the canonical pathway promoting cell proliferation relies on reorganization of the Axin/APC/GSK3β destruction complex, functioning to promote degradation of the cytoplasmic pool of β-catenin. In the presence of Wnt ligands, FZD and the co-receptor LRP5/6, through the joint action of the scaffolding protein Dishevelled and heterotrimeric G proteins, induce reorganization of the complex resulting in stabilization of the cytoplasmic β-catenin, which can enter the nucleus and activate transcription of the Wnt target genes through interactions with TCF and other co-factors6. Stabilization of β-catenin can be seen in BC and a variety of other cancers7. In addition to the canonical pathway, β-catenin-independent Wnt pathways have been described to control cytoskeleton, and through this – cell motility and cancer metastasis8.Aberrant activation of the Wnt-FZD signaling pathway leads to tumorigenesis in many tissues4. Of the positive canonical components, examples of proposed over-expressions include: canonical Wnt ligand proteins10 (e.g. Wnt1 and Wnt10b), receptors12 , and transducers14 . Some proposed under-expressions include non-canonical Wnts16 , secreted antagonist proteins19 , and negative transducers21 . These works painted the picture of general overactivation of the canonical Wnt pathway in BC and proposed certain Wnt pathway components as potential targets for targeted drug discovery.The generally assumed picture concerning the alteration of the Wnt pathway during breast transformation is that, through epigenetic changes, expression of positive components of the canonical Wnt pathway is increased, while expression of negative components or antagonists of this pathway is decreased22 to 35% in another21). The lack of potency is illustrated by the fact that overexpression of e.g. FZD7 was only found to be two-fold in TNBC patients as compared with non-TNBC patients11  in hundreds of healthy and BC samples provided by the RNA-seq data of the TCGA databaseThese changes in expressions of the Wnt pathway components are translated into a consistent picture of the pathway overactivation emerging in BC as judged by the target genes’ expression levels . As expected from prior works35, random sets of genes show high level of correlation of their expressions in the healthy tissues, which strongly drops in cancer tissues  pathway, known to be important in the breast and HER2-positive BC38, and to a lower extent – for the related Ras, Rap, PtdIns, and Sphingolipid pathways, but not so much for the other KEGG pathways  in the healthy breast analyzed over 113 patients, we find >800 correlations of the Wnt component genes, or close to 20% of total possible correlations. This number drops to 69 pairs in non-TNBC and 28 pairs in TNBC patients. Since the number of patient groups was unequal (850 in non-TNBC and 118 in TNBC), we randomly selected 5000 subsamples, 118 patients each, of the non-TNBC cohort and rerun the analysis, producing on average 86 ± 19 (mean ± SD) strongly correlating (│r│ ≥ 0.5) pairs in the non-TNBC patients. These findings indicate that a hallmark of BC is the loss of coherence in expression (and hence activity) levels of the Wnt components observed in the healthy breast, potentially resulting in the dysbalancing of the Wnt pathway.If we take the strong positive and negative correlations . However, it is clear that the ability of a given Wnt to interact with a given FZD, measured e.g. in the binding assay, does not necessarily translate into the functional activation of the intracellular signaling, nor proper physiological response in the context of a given tissue. Our network correlation analysis provides an interesting alternative to these prior attempts to comprehend the ligand-receptor specificity/promiscuity in the Wnt pathway. We propose that, at least in the context of the healthy breast tissue, physiologically relevant and distinct large subgroups of Wnt/FZD interactions can be identified  and are therefore likely to be the relevant Wnt pathway target genes in the breast , revealing two subgroups (38 and 39 patients each) with 6- to 10-fold higher numbers of strong correlations Fig. . The Wnt43), but their potential clinical importance in the Wnt pathway has not emerged previously. Future investigations will show whether specific targeting of these interacting pairs may indeed provide the clinical significance combined with the required drug selectivity.We suggest that the TNBC subgroups we have identified will have common vulnerability and therefore should respond differently to the anti-Wnt treatments  tailored against their corresponding essential components. Supplementary Table We assessed 110 clinical characteristics of the patients available in TCGA to see if they can be associated with the TNBC subgroups identified, finding the patient race and menopausal status to be a distinguishing factor Fig. . Subgrou44. When performing the network correlation analysis on breast GTEX samples, we find a strong similarity with the TCGA data reflected by the preservation of Wnt pathway clusters #2 and #4. At the same time, we see a decrease in the connectivity within cluster #1, although their strong antagonistic interactions with cluster #4 remain  are present at similar proportions in any healthy breast sample; stromal and immune cells will also ‘contaminate’ the tumor. If one gene is stably expressed in one cell type, and another gene – in another cell type, a cohort of tissue samples with varying levels of heterogeneity in the proportions of these two cell types will produce a false correlation in expression of these two genes. To address the question of how many correlations reported in this paper might be such false correlations, we created a stochastic model comprising a set of patient samples composed of two cell types in different proportions, with different gene expression levels in these cell types : Firehose 2016_01_28 run. Broad Institute of MIT and Harvard. doi:10.7908/C11G0KM9). Most recent version of GTEX RNA-seq database (v6) has been downloaded from GTEX website44.TCGA datasets with pre-calculated RSEM for RNA-seq quantification of gene expression, normalized RNA chip data and patient’s clinical data have been downloaded from Broad institute Firebrowse website . The list of 138 validated Wnt target genes was assembled using information provided on website of Nusse laboratory (http://web.stanford.edu/group/nusselab/cgi-bin/wnt/), NetPath47 and literature4. Several Wnt component genes are also well-proven target genes of the pathway, such as FZD7, FZD4, WNT3A49.The gene list for the core 101 Wnt component genes  : ER+/PR+ and HER+ (or both) and TNBC (118) with the negative status for all three receptors. Patients with healthy breast tissue analyzed (113) were treated as a single cohort, though tumors of 12 of them belonged to the TNBC cohort and the others had non-TNBC tumors.50, which provides genomic data analysis for the tumors available in TCGA. For each of the 101 Wnt component genes, the mutation rates in BC rarely exceeded 1% (most of them <0.2%)  and export pairwise correlation data in Cytoscape54 for building the networks. The complete linkage algorithm in the built-in R function hclust has been used to create dendrogram and identify major clusters of the genes in all cases.Tables containing log levels of RSEM (for TCGA RNA-seq data), RPKM (GTEX data) or chip median expression levels (for TCGA RNA chip data) for component or target genes were used to build corresponding figures using scripts developed in RTo confirm the validity of the correlation networks we have identified, Supplementary Fig. For search of the subgroups within TNBC cohort, the following algorithm was implemented: the patients were grouped in blocks of 5. For all possible groups formed by combinations of five such blocks (53130) a number of strong (|r| > 0.5) and highly significant (p < 0.001) correlations was calculated. The five blocks of patients most frequently appearing in the 100 top-scoring combinations were picked. For this group , enriched in patients belonging to a certain subgroup, we removed the “wrong” patients by iteratively deleting two patients, removal of which resulted in the maximal gain of correlations until the removal of the next pair resulted in decreased or unchanged number of correlation. The group containing the maximal number of correlations was used as a “core group” to extract the patients belonging to the subgroup from the remainder using the inverse approach: two random patients were iteratively added to the “core group” and those, which provided the maximal increase in the number of strong correlations, were added. These iterations continued until the number of correlations started to decrease with addition of the next pair. The last pair was considered a cut-off for the first subgroup (containing 246 strong correlations) and the remainder of the patients was treated the same way resulting in identification of the second subgroup (133 correlations). Finally, the “residual” group was treated the same way, however it did not contain any combinations with significantly increased number of correlations over the parental subgroup (fluctuating within a window of 30–40 strong correlations) and therefore was considered to not contain any smaller subgroups.https://www.unil.ch/dpt/en/home/menuinst/recherche/groupe-katanaev/files.html).The R scripts and data tables which has been used to produce the figures and for TNBC subgrouping are available in corresponding section on the public website of Katanaev lab (Supplementary figures and tables


When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.The online version of this article (doi:10.1186/s12859-016-1394-x) contains supplementary material, which is available to authorized users. When modeling in Systems Biology and in Systems Medicine, the resulting data is often extensive, complex and heterogeneous. A visual representation can support users in data analysis and interpretation . HoweverSBGN is composed of a set of three complementary languages: Process Description (PD), Activity Flow (AF) and Entity Relationship (ER). PD shows “all the molecular processes and interactions taking place between biochemical entities, and their results” . It creaSeveral studies show that graphs are realistic and well-suited for the representation of biological networks , 10. Empnodes connected by edges (denoted in Neo4j as relationships). The Neo4j nodes and relationships can be categorized using the “Label” and “Type” features, respectively. Additional information about entities can be stored as attributes (denoted as properties). An example is given in Fig. The Neo4j graph database  was chosSBGN TO Neo4j (STON) framework is a graph-based tool to extend the existing infrastructure for storing and exploring biological pathways in SBGN-ML format. Our work provides a transformation from SBGN maps into a graph representation, thereby enabling: i) efficient management and querying of networks; ii) identification of subgraphs in networks; iii) merging of existing pathways into larger networks.The STON is a standalone, Java-based framework that uses two sets of external libraries: LibSBGN  and a coLibSBGN provides access to SBGN through reading, writing and validation of SBGN-ML files and supports both, C++ and Java programming languages. The library offers several test files in the three SBGN languages. Test files contain small SBGN networks and are available for tool developers to check for SBGN compliance. The files are created by the developers of LibSBGN whenever new functionality is added to the languages. The LibSBGN project also provides example pathways to showcase the features of the SBGN specifications . We use The Neo4j libraries encode functionality for the Neo4j graph development , and provide access to the Neo4j database environment, including a web interface to query the data. STON converts existing SBGN PD and SBGN AF files into a Neo4j representation. As input, it takes a SBGN PD or a SBGN AF file, and a path to the Neo4j database location.The STON framework then parses the SBGN-ML file and maps it on a graph structure (see Workflow section below). The graph representation, which is processable by Neo4j, is built according to the given SBGN-ML file; the location of the obtained graph has to be specified by the user see Fig. . We ran The representation of biological pathways in graph databases requires translation rules. The SBGN-ML format already describes a graph-like structure: Glyph nodes (representing entities) interact with each other by means of glyph arcs (representing relations). The translation process has been facilitated by the similarity of SBGN-ML and Neo4j. SBGN glyph entities are nodes in Neo4j, and SBGN arcs are relationships. Additional information  is retrieved as properties for Neo4j nodes and relationships.Figure STON provides a mapping of all concepts from SBGN PD and SBGN AF. The list of translation rules and properties are available in the supplementary material . Using the exchange format JSON, one can send Cypher queries to the server’s data endpoint and receive a JSON encoded response . It is When analyzing disease pathways, it is highly important to find disease-associated genes or substructures responsible to understand the organisation of a biological system and the underlying mechanisms . TherefoThe linking of different levels of granularity allows researchers to compare biological networks at different levels of detail. In Systems Biology, it is highly beneficial to have access to computational tools that can return detailed information about processes occurring in complex biological networks by connecting information from multiple layers. By using the Neo4j graph database, we are able to link PD and AF diagrams. The purpose is to compare both levels and to help addressing the difficulties related to incomplete knowledge representation. When studying a complex network, for example, we may first be interested in getting an overview of the influences between the biological entities involved (AF level). At a later time, we may want to study the processes occurring in a network in more detail (PD level). To determine matching parts in the two networks, we use a Cypher query  by identifying and linking identical processes. Figure Many models today are represented using standards, including SBGN, and they are getting increasingly extensive. This situation requires new methods and tools for the management and exploration of models. In this paper, we show how the STON framework supports researchers with a visual, graph-based representation of large biological networks.STON manages heterogeneous data at different levels of biological description by integrating i) various types of biological concepts including metabolites, proteins, complexes, genes, subcellular location, and ii) different types of processes such as metabolic reactions, signalling events and gene regulatory machinery. Once represented in a Neo4j database, the networks can be interrogated for different topics of interest using the Cypher query language. Cypher allows for structure-based queries that cannot be answered efficiently on the SBGN-ML file level, nor using SQL databases. We tested the capabilities of STON using a biological reference map from the KEGG pathway database : the larThe modular implementation of the Neo4j framework allows to further extend STON to support the management of additional data types such as tissue-specific expression levels or drug target information for proteins. Another reason for the easy implementation of these extensions is the adaptability of the Neo4j schema. Similarly, the existing mapping from SBGN-ML into Neo4j can easily be adapted and extended by adding new relationships and node types to the database schema.In the last few years there has been a growing interest in reconstruction of large disease networks. For example, the reconstruction of maps describing Alzheimer’s disease  containThe semantic interpretation of links between identified subnetworks and the judgment how feasible these links are in a biological sense remain two open research questions . STON dSTON is a framework that exploits the Neo4j graph database to store biological pathways. We mapped the SBGN standard (PD and AF levels) onto a Neo4j structure and we showed how STON enables i) the identification of subnetworks of interest, ii) the comparison of different layers of granularity in SBGN languages and iii) the merge of SBGN diagrams. STON provides new opportunities for managing and querying biological networks, as well as advanced manipulation of subnetworks. This will add to the infrastructure of tools for model management and exploration, which is necessary for efficient use of network approaches in Systems Biology and Systems Medicine.Additional file 1Supplementary material. This pdf file contains tables with translation rules of STON and a benchmark table on STON’s performances. (PDF 103 kb)Additional file 2www.sbgnbricks.sourceforge.netusing the SBGN-ED tool. The IFNG forms a complex with the interferon gamma receptor. This will activate the phosphorylation of STAT1alpha. After homodimerization, STAT1alpha will bind to the gene IRF1 to activate the transcription of IRF1. This protein regulates the transcription of the iNOS protein, which will links Calmodulin to create a complex that will activate the synthesis of nitric oxide (NO). (PDF 83.4 kb)Visualization of the iNOS pathway in SBGN PD. This pdf visualizes the iNOS pathway that we designed from the Additional file 3The queries. This text file contains the queries for Figs. Additional file 4SBGN files in a COMBINE Archive. This COMBINE Archive contains the five SBGN-ML files used to generate the benchmark table present in the Additional file


In this The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features.Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions.The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways.Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs. Lower grade gliomas (LGGs), those lesions graded II and III, comprise 10-15% of primary brain tumors , 3. TreaThe complementary assessment of gliomas via imaging and molecular pathology approaches, for understanding the response to treatment and poor outcomes, has spurred the investigation of combining imaging data with molecular (genetic/genomic) information. This approach, termed radiogenomics or imaging-genomics, holds potential to improve LGG/GBM outcomes. Radiogenomics allows inference of molecular characteristics of a tumor from image features obtained via human or computational assessment of pre-operative MR  images. To our knowledge, this study is the first to integrate radiological features with proteomic measurements in lower grade gliomas.In LGGs however, the radioproteomics approach to associate proteomics measurements with radiological feature sets is yet to be investigated, although a small study of four patients showed correlations between mass spectra and enhancement in the corresponding tumor region . We hypoMultiple-response regression of the combined imaging and protein expression datasets from 57 patients  were computed for each combination of imaging feature/molecule. Molecules (proteins and phospho-proteins) with associations that were statistically significant after correction for multiple comparisons were retained for further analysis . For exaEach VASARI feature was matched with a unique set of associated pathways and biological functions using Ingenuity Pathway Analysis software Table . The T1/From the IPA calculation of Z-activation scores, correlations could be inferred that indicate up regulation and down regulation of a portion of IPA canonical pathways Figure , diseaseThe integration of diverse biomedical datasets poses a significant analytical and computational challenge. Combining clinical, gene expression, protein, post-translational modification, and imaging datasets requires robust analytical approaches that minimize false- discovery rates and yield useful correlates between model parameters. The development of such techniques would be a major advancement in analytics and make novel diagnostics and prognostics possible . RadioprWe hypothesized that integrating imaging and protein expression data would reveal imaging biomarkers tying radiological features to the proteomics of LGG. We have demonstrated the use of a novel multiple-response regression approach to integrate imaging features and proteomics data while leveraging the correlation structure between the protein expression values. In the situation where the responses are correlated, and there are multiple predictors associated with multiple responses, standard univariate/multivariate analysis will identify associations incorrectly. Thus the correlated response regression approach is more appropriate in this context, having direct biological significance since it uses the correlation structure of the proteins during regression.Multiple-response regression of the combined VASARI feature annotations and RPPA molecular expression data revealed significantly correlated proteins and phospho-proteins indicating upregulated signal transduction pathways. Querying the highly-correlated molecules using Ingenuity Pathway Analysis probed the biological implications of the regression results. Clustering of the pathway analysis highlighted the most important imaging features tied with underlying molecular alterations. By using this analytics pipeline, corresponding biological hypotheses about the underpinnings of these phenotypic characteristics could be within the framework of the IPA knowledge base.Multiple-response regression revealed associations between imaging features and signal transduction, evidenced by significant correlations with levels of phosphorylated signaling molecules such as Src kinase, Yes-associated protein YAP, ribosomal protein S6, and MAPK, consistent with the established role of these molecules in tumor cell proliferation, migration and invasion. Kinase activity of over-expressed Src drives tumor cell proliferation . YAP is In conclusion, a multiple response regression framework with correlated errors was used to assess the relationship between image-derived radiologist annotations (features) and protein measurements in TCGA LGG cases. Pathway analysis highlighted the functional effects of proteomic alterations in LGGs on MR imaging features, revealing a unique pattern of protein expression and post-translational modifications associated with each VASARI imaging feature. The downstream effects of these gene expression changes were consistent with the current understanding of tumor biology. One such example is that a physiological event, such as hemorrhage, could cause massive cell death, inducing stress response signaling in affected cells. This was reflected in the results of the pathway analysis, which associated the LGG specific protein expression correlated with hemorrhage with top canonical pathways, including MAP kinase signaling induced by ultraviolet damage. Regression and pathway analysis implicated IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways, mechanisms that exert control over the cell cycle, growth, and proliferation and are known to be significantly altered in gliomas . These pMRI data for 57 patients were obtained from The Cancer Imaging Archive (TCIA), and protein  molecular data were obtained from The Cancer Genome Atlas (TCGA) data portal. These patients correspond to the TCGA/TCIA subset on whom imaging data along with matched proteomics (RPPA) information is jointly available. Clinical data were obtained from cBioPortal . InstituAdjudicated reader (radiologist) scores of the pre- operative MR images using an updated version of the standardized Visually Accessible Rembrandt Images (VASARI) feature-set were obtained from TCGA Glioma Phenotype Research Group . The TCGTo estimate the relationship between the LGG- VASARI imaging features and RPPA-derived protein levels, we used a multiple response regression model developed by Bhadra and Mallick . The modVariable selection is a ubiquitous problem in modern genomic data analysis. We provide a brief review below to explain the novelty of our approach more clearly. Further details are in the First we threshold the posterior probabilities of the covariates (imaging features) by controlling FDR at 0.25, yielding a sparse set of imaging predictors.To determine which responses (proteomic measurements) this sparse set of covariates (imaging features) were associated with, we did another level of FDR control. We performed t-tests, of “no association” null hypothesis vs. ‘non-zero association’ alternative hypothesis, between a given imaging feature and a given protein's expression. We then declared those associations significant which had the lowest 10% of the p-values resulting from the above t-tests. All these associations are significant at the adjusted p-value of 0.05. A similar approach has previously been used successfully to analyze eQTL data  and gliohttp://www.qiagen.com/ingenuity). IPA Core Analyses were run on each list of mapped identifiers for each VASARI feature. In the IPA software, p-values were computed by applying the Fisher's exact test based on the number of biological functions, pathways, or molecules in the annotation as defined by the molecules in the selected Reference set, the number of molecules in the Reference set known to be associated with that function, the number of functions, pathways, and molecules in the Reference set, and the number of molecules in the Reference set [Proteins from the RPPA dataset that were found to be strongly-associated with each VASARI feature were queried using the Ingenuity Pathway Analysis software package (IPA™ QIAGEN, Redwood City, CA, ence set .To interpret VASARI associations with pathways, diseases and functions, agglomerative unsupervised hierarchical clustering was performed on the p-values associated with each VASARI feature using the “stats” package in R. Distance matrix calculations were performed using the Euclidean method, and within-cluster variance was minimized using Ward's method .


PtdIns(3)P binding to Phafin2 occurs with high affinity, triggering minor conformational changes in the protein. Taken together, these studies represent a platform for establishing the structural basis of Phafin2 molecular interactions and the role of the two potentially redundant PtdIns(3)P‐binding domains of the protein in endomembrane compartments.Phafin2 is a phosphatidylinositol 3‐phosphate (PtdIns(3)P) binding protein involved in the regulation of endosomal cargo trafficking and lysosomal induction of autophagy. Binding of Phafin2 to PtdIns(3)P is mediated by both its PH and FYVE domains. However, there are no studies on the structural basis, conformational stability, and lipid interactions of Phafin2 to better understand how this protein participates in signaling at the surface of endomembrane compartments. Here, we show that human Phafin2 is a moderately elongated monomer of ∼28 kDa with an intensity‐average hydrodynamic diameter of ∼7 nm. Circular dichroism (CD) analysis indicates that Phafin2 exhibits an α/β structure and predicts ∼40% random coil content in the protein. Heteronuclear NMR data indicates that a unique conformation of Phafin2 is present in solution and dispersion of resonances suggests that the protein exhibits random coiled regions, in agreement with the CD data. Phafin2 is stable, displaying a melting temperature of 48.4°C. The folding‐unfolding curves, obtained using urea‐ and guanidine hydrochloride‐mediated denaturation, indicate that Phafin2 undergoes a two‐state native‐to‐denatured transition. Analysis of these transitions shows that the free energy change for urea‐ and guanidine hydrochloride‐induced Phafin2 denaturation in water is ∼4 kcal mol AUCanalytical ultracentrifugationCDcircular dichroismDLSdynamic light scattering0ƒ/ƒfrictional ratioGuHClguanidine hydrochloridePtdIns(3)Pphosphatidylinositol 3‐phosphatessedimentation coefficientS20,wsedimentation coefficient under standard conditionsSPRsurface plasmon resonancePleckstrin homology) and C‐terminal FYVE  domains ‐transverse relaxation optimized spectroscopy (TROSY)‐heteronuclear single quantum coherence (HSQC) was performed for the protein sample. Data were processed and analyzed using Topspin 3.2 and NMRpipe.NMR experiments of F339 to F356, CD spectral changes of Phafin2 at 222 nm) against denaturant concentration were analyzed to calculate the Gibbs free energy of unfolding (ΔGH2O0) in the absence of denaturant. To calculate ΔGH2O0 for each spectral analysis, a model in which monomeric native Phafin2 (fN) was converted to a denatured (fD) form without the presence of highly populated intermediates was first considered. This state, known as the equilibrium two‐state is represented as:The transition curves acquired after representing each spectral property  using the relation:R is the universal gas constant  and T is the temperature on the Kelvin scale. The ΔG value varies linearly with denaturant concentration following the equation:x] is the molar concentration of the denaturant and m is the slope of the plot. Thus, linear plots of ΔG versus denaturant concentration were obtained. Data was fitted to the nonlinear least‐squares method using Microsoft Excel  for the calculation of the ΔGH2O0 and m for each experimental condition.Cm, the denaturant concentration at the midpoint of the unfolding transition when ΔG is 0, was estimated. Similarly, the TM of Phafin2 was calculated by following the denaturation of the protein by its CD ellipticity at 222 nm and by determining the temperature at which ΔG is 0 using Eq. The estimation of sn‐glycero‐3‐phosphatidylcholine , or DOPC and dipalmitoyl PtdIns(3)P (1.5%) were dissolved in chloroform/methanol/water (65:35:0.8). The lipid mixture was first dried under N2 and further under vacuum to remove residual chloroform. Phospholipids were resuspended in 20 mM HEPES (pH 7.0) and 100 mM NaCl to a final concentration of 4 mM, sonicated, and extruded for 100‐nm liposome size at 65°C. SPR analysis was performed on a BIAcore X100 instrument with a liposome‐coated L1 sensor chip at room temperature. Typical liposome loading was ∼5,000 RU/channel. Kinetic SPR measurements were performed with the flow rate set at 30 μL/min. Apparent KD values were estimated using the BIAevaluation software, version 2.0 . Experiments determining KD values for PtdIns(3)P were carried out by collecting four independent experiments.Liposomes were prepared as previously described.Supporting InformationClick here for additional data file.


Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes  that are ubiquitous in biological systems.We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis . Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses.The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.The online version of this article (doi:10.1186/s12920-017-0263-4) contains supplementary material, which is available to authorized users. Technologies, such as RNA-Seq, provide precise, timely, and cost-effective quantification of whole genome expression . Howeverpathways, which is a novel framework for single-subject transcriptome analysis based on a pair of samples  from the same individual [pathways relies on three principles: (1) the sole unit of observation is a single patient (case and control); (2) gene-level information are aggregated into gene sets (pathways); and (3) pathway results are summarized into personal biological profiling for clinical interpretation. Two methods under N-of-1-pathways framework were developed, N-of-1-pathways Wilcoxon (Wilcoxon) [pathways Mahalanobis distance (MD) [pathways Wilcoxon and MD analyze the dynamic change of mRNA expression and uncover dysregulated pathways (gene sets) from single-subject paired samples. The use of gene sets derived from gene ontology [Recognizing the limitations of conventional methods, we recently designed and validated in different disease contexts the N-of-1-dividual –10. N-ofilcoxon) –8 using ilcoxon)  and the nce (MD)  using a ontology  providesontology , 15. Whiontology  analyzinontology –19.pathways framework: N-of-1-pathways MixEnrich (MixEnrich) using a mixture model  followed by a competitive-based [pathways MixEnrich in comparison to the only other single-subject paired-sample gene set tests (Wilcoxon and MD), we performed a simulation study and validation case study. We show that MixEnrich outperforms Wilcoxon and MD under various scenarios of simulated dysregulated pathways. This synthetic result was validated in a case study using head and neck squamous cell carcinomas (HNSCCs) RNA-Seq dataset, where MixEnrich uncovered biological relevant dysregulated pathways.To address the shortcomings of the current single-subject transcriptome analysis methods, we developed a novel approach within the N-of-1-ve-based  enrichmeve-based . MixEnrive-based . Then pave-based . Notablyve-based . To testpathways MixEnrich, we used another RNA-Seq dataset derived from paired samples of head and neck squamous cell carcinomas (HNSCCs) patients [An RNA-Seq dataset of 55 normal lung tissue samples from The Cancer Genome Atlas (TCGA)  was usedpatients .In the HNSCCs case study, gene sets were defined using Gene Ontology Biological Process, GO-BP , 25. The-pathways. MixEnrich identifies dysregulated pathways by: (1) clustering mRNAs as unaltered and dysregulated mRNAs and (2) detecting gene sets enriched with dysregulated genes. We named this two-stage procedure MixEnrich for Mixture model clustering followed by an Enrichment analysis. As illustrated in Fig. We propose a novel method, MixEnrich, under the framework of N-of-12 transformed fold change, |log2FC|, as |log2(E2/E1)|, where E1 is the expression level of this mRNA in condition 1  and E2 is the expression level in condition 2 . Under the mixture model, each mRNA is assumed to belong to a cluster k  with a prior probability πk. The cluster membership of each mRNA is a Bernoulli trial (Eq. 1).For each mRNA, we calculated its absolute value of logZi is a latent variable and G is the total number of mRNAs in the transcriptome. An mRNA for a gene index i is a member of cluster k when Zi is equal to k. We use xi to represent |log2FCi|, and in cluster k, the absolute value of log fold change, xi follows a certain distribution whose parameters need to be estimated. For simplicity, we assumed that the distribution of xi in each cluster followed a normal distribution, whose probability density function is denoted as φ (Eq. 2).where μk andσk are the mean and standard deviation of the normal distribution for the cluster k. The marginal distribution of X can be obtained by the sum of two weighted normal distributions, hence providing the (discrete) mixture model (Eq. 3).Here The estimation of the parameters of the mixture model is implemented by maximum likelihood using an Expectation-Maximization (EM) algorithm . The likWe defined an mRNA as dysregulated when its posterior probability of belonging to the dysregulated cluster is above 0.5, where the dysregulated cluster is defined as the cluster with the larger mean.M mRNAs among which d mRNAs are dysregulated; while the entire genome consists of N mRNAs among which D mRNAs are dysregulated  was applied to detect the gene sets (pathways) enriched with dysregulated mRNAs . Assume ekutieli  that acc-pathways methods: MixEnrich, Wilcoxon, and MD. It has been shown, for biological replicates collected from different subjects, that a negative binomial distribution [pathways framework since it analyzes the paired samples from the same tissue of the same individual [Single-subject paired RNA-Seq data were simulated to evaluate the performance of three N-of-1ribution , 30 moderibution , 32, as dividual . Therefog, from 55 RNA-Seq normal lung samples downloaded from TCGA  using Poisson distribution, Poisson (λg).Generate a pair of expression values, Ybg.dPct) of mRNAs, in the second transcriptome (tumor), without replacement, and then replace their values by their corresponding values in the first transcriptome  multiplied by a fold change (bg.FC).Randomly sample a proportion (p.S) from the transcriptome without replacement.Designate the target pathway by randomly sampling mRNAs (the number of sampled mRNAs = p.S × p.dPct) from the target pathway without replacement, and designate the sampled mRNAs as dysregulated.Randomly sample mRNAs (the number of sampled mRNAs = p.upPct) of these mRNAs as up-regulated. The rest of the designated dysregulated mRNAs are assigned as down-regulated.Among the designated dysregulated mRNAs in the target pathway, randomly assign a proportion (p.FC); for the down-regulated mRNAs in the target pathway, replace their values in the second sample (tumor) by their corresponding values in the first sample  divided by a fold change (p.FC);For the up-regulated mRNAs in the target pathway, replace their values in the second sample (tumor) by their corresponding values in the first sample  multiplied by a fold change (bg.dPct) of mRNAs without replacement, and then assign these mRNAs to the non-dysregulated pathway.Generate an unaltered pathway: randomly sample a proportion , those identified as dysregulated are true positives (TP) and those identified as unaltered are false negatives (FN). Similarly, among the 100 unaltered pathways (p.dPct = 0), those identified as unaltered are true negatives (TN) and those identified as dysregulated are false positives (FP). We calculated the true positive rate  and false positive rate  by equation 5 and equation 6:For each scenario of pathway dysregulation, we calculated an Area Under the receiver operating characteristic Curve (AUC) value as follows: Each scenario corresponds to 100 ‘virtual patients’, and each ‘virtual patient’ possesses one dysregulated pathway and one unaltered pathway. At a given p-value thresholds. Areas under the ROC curves (AUCs) were computed approximately using Riemann sum in R.Receive Operating Characteristic (ROC) curves were generated by plotting FPR against TPR at various p.S and p.dPct, on method performance. A contour plot was used to present the joint impact on AUCs induced by the two parameters, p.S and p.dPct, while fixing the other four simulation parameters listed in Table 95%) was calculated as an overall measure of method accuracy when the two simulation parameters vary simultaneously. Specifically, using color-coded values, we plotted AUCs corresponding to any combination of the two parameters p.S and p.dPct while fixing the four other parameters, p.Fc, p.upPct, bf.FC, and bg.dPct. The horizontal and vertical axes in the contour plot represent the values of p.S and p.dPct, respectively. AUC values on the contour plot are indicated by color gradient. All points with an AUC value of 95% on the contour plot were connected to construct the 95% curve, demarcating the ACC95% boundary.We investigated the interaction effect of two simulation parameters, pathways MixEnrich, in the context of head and neck squamous cell carcinomas (HNSCCs) . Every pathway was then tested for enrichment by a Fisher’s Exact Test [p-values are dependent, the multiplicity adjustment developed by Benjamini and Yekutieli was used to calculate FDRBY [BY < 0.05.We further evaluated the performance of N-of-1-(HNSCCs) , using pact Test  to deterte FDRBY  for adjupathways methods, MixEnrich, MD, and Wilcoxon, are single-subject methods and were conducted on every single patient of the 15 testing patients. 15 area under the ROC curves (AUCs) were calculated for each N-of-1-pathways methods. Since GSEA and DESeq + Enrichment can only perform on a group of patients, they were evaluated on 50 distinct subsets, which contain 3, 6, or 12 patients, of the 15 testing patients. Taking the subset of 3 patients as an example, 15 testing patients can yield 455 distinct combinations of three patients. To mitigate computational burden, we randomly chose 50 distinct combinations from the 455 combinations as a test set. GSEA and DESeq + Enrichment were conducted on every combination of the 50 distinct patient combinations, which yielded 50 AUCs for each method when compared to the quasi-gold standard. The AUCs resulted from each N-of-1-pathways methods and the AUCs resulted from cohort-based methods performed on 3, 6, or 12 patients were plotted by boxplots. With the same strategy, we also evaluated MixEnrich in the context of breast invasive carcinoma (BRCA)   using papathways MixEnrich, we produced synthetic datasets corresponding to 107,640 scenarios of pathway dysregulation by varying six simulation parameters  the overall performance across all types of dysregulated pathways (p.S) and percentage of the dysregulated mRNAs in the target pathway (p.dPct), vary simultaneously  chapathways , and (3)aneously .pathways MixEnrich with MD and Wilcoxon for their overall performance across all types of pathway dysregulation by combing all 107,640 AUCs  and MD . This result is further supported by the boxplots . On the other hand, the performance of Wilcoxon and MD decreased dramatically as the value of p.upPct increased. Unlike MixEnrich, both N-of-1-pathways Wilcoxon and MD were designed to identify the pathways only with concordant dysregulation, i.e., dysregulated mRNAs within a pathway are either exclusively up-regulated (p.upPct = 1) or exclusively down-regulated (p.upPct = 0). Wilcoxon and MD aim to identify the central tendency shift of pathway expression; mRNAs dysregulated in opposing directions counterbalance each other. In contrast, MixEnrich can identify complex bidirectional dysregulation of a pathway since mRNAs dysregulated in both directions contribute additively to the over-representation of a pathway in dysregulated mRNAs.We further explored the relative effect of each of the six simulation parameters Table  on the pp.dPct), pathway size (p.S), or fold change of the dysregulated mRNAs in a pathway (p.FC) increase  does not affect the performance of MixEnrich  and the proportion of these mRNAs that are dysregulated (p.dPct) are two factors most relevant to biology. A comparison . MixEnrich obtained an AAC95% > 0.8 for 228 of the 234 tested scenarios while N-of-1-pathways MD and N-of-1-pathways Wilcoxon yielded AAC95% > 0.8 for 15 and 22 of scenarios, respectively. In the scenarios in which MixEnrich yielded AAC95% < 0.8, the fold change of dysregulated mRNAs was small (1.3 FC) in both the target pathway (p.FC) and the background noise (bg.FC).The number of mRNAs in the pathway  derived from head and neck squamous cell carcinoma patients, HNSCCs [pathways MD and Wilcoxon as well as conventional cohort-based methods GSEA and DESeq + Enrichment in uncovering dysregulated GO-BP terms for HNSCCs. Since it is not feasible to biologically test each pathway to determine the truly dysregulated pathways and unaltered pathways, we conducted DESeq + Enrichment on the 30 patients of HNSCCs cohort to produce a quasi-gold standard  presents in the MixEnrich results, which carries a lower AUC of 0.707. We investigated the dysregulated pathways identified by MixEnrich from patient A6H7 but are not present in the quasi-gold standard  algorithm  for estiImportantly, the simulation study results highlight that MixEnrich detects pathways more dysregulated than the background. This is not addressed by the self-contained methods of MD and Wilcoxon. The fact that self-contained tests do not perform well according to the criteria imposed in the simulation does not invalidate the use of these approaches. Further, self-contained tests are useful to test a small panel of genes such as obtained by real-time PCR.pathways analytic tools can provide more statistical power as more comprehensive N-of-1 experimental studies and assays may be conceived. For example, future studies may include (i) multiple biological and technical replicates of both tumor and control samples from a single subject or (ii) multiple omics measurements beyond the transcriptome . Future improvements will need to address N-of-1 studies designed with time-series datasets using multi-gene measurement and genomic information based on data derived from normal, treated, and withdrawn treatment samples from a single patient. As single-cell transcriptome datasets from a single patient are increasingly being studied [pathways framework can be applied and further improved as demonstrated by our recent work in profiling circulating tumor cells using N-of-1-pathways MD [The current study is based on two paired samples from a single subject. Further improvements and new features of the N-of-1- studied , N-of-1-hways MD . As we spathways methods, Wilcoxon and MD, N-of-1-pathways MixEnrich is more effective in detecting non-concordant pathway dysregulation, better reflecting what one would expect in biological pathways. Moreover, this novel two-stage competitive gene set testing strategy provides more resistance to background noise, which is ubiquitous in biological systems. Results based on the head and neck squamous cell carcinomas study demonstrate that the dysregulated pathways discovered using MixEnrich overlap highly with the quasi-gold standard compared to the two single-subject methods (Wilcoxon and MD). In addition, we have shown the robust performance of N-of-1-pathways MixEnrich operating on single subjects in identifying dysregulated pathways when compared to small-sample, cohort-based methods (DESeq + Enrichment and GSEA).Compared to our previously developed N-of-1-pathways MixEnrich is an innovative framework that bridges this gap by analyzing paired samples, one patient at a time, and is ostensibly extensible to other quantitative ‘omics measurements . MixEnrich is a valuable tool for studying rare and orphan diseases for which sample sizes remain small whereas cohort-based methods are underpowered in that setting. Lastly, the mRNA- and pathway-level analysis performed patient-by-patient by N-of-1-pathways MixEnrich offers more interpretable results for biologists and physicians such as dysregulated mRNAs of interest that can be potentially validated and identified as biomarker candidates for diagnosis.In this era of precision medicine, it becomes crucial to develop unbiased and personalized transcriptome analytics for single-subject diagnosis and prognosis, rather than using methods that aggregate signals across heterogeneous patients. N-of-1-