Nervous systems are complex biological networks with largely unknown structural and functional characteristics. Motif analysis is a robust tool that can reveal the unique aspects of connectivity in a complex network. An ideal candidate for motif analysis is the connectome of the nematode Caenorhabditis elegans, which is the first fully reconstructed nervous system. We performed, for the first time, edge polarity-based signed motif analysis on the C. elegans connectome using recent data on the connection signs of this network and a novel structure-preserving randomization method. We identified 56 significantly over- and 1 underrepresented three-node signed motifs and revealed that certain motifs (e.g., positive feedforward, negative feedback, disinhibitory feedback, and incoherent feedforward loops) are overabundant in the C. elegans connectome. We further distinguished nodes by their corresponding neuron modalities (e.g., sensory vs. motor neurons), and found that each significant feedforward and feedback loop has a characteristic neuronal layout. Our findings demonstrate the importance and potential of signed motif analysis in understanding biological networks. The motif enumeration tool and definition system we developed can be used to analyze signed motifs in other complex networks.

Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We therefore designed a hypothesis-generating framework that integrates network motifs and protein disorder to explore their contribution to predictive biomarker discovery. This encouraged us to develop MarkerPredict by using literature evidence-based positive and negative training sets of 880 target-interacting protein pairs total with Random Forest and XGBoost machine learning models on three signalling networks. MarkerPredict classified 3670 target-neighbour pairs with 32 different models achieving a 0.7–0.96 LOOCV accuracy. We defined a Biomarker Probability Score (BPS) as a normalised summative rank of the models. The scores identified 2084 potential predictive biomarkers to targeted cancer therapeutics, 426 was classified as a biomarker by all 4 calculations. We detailed the biomarker potential of LCK and ERK1. This study encourages further validation of the high-ranked predictive biomarkers. The development of the MarkerPredict tool (which is available on GitHub) for predictive biomarker identification may have a significant impact on clinical decision-making in oncology.

Drug resistance is a major cause of tumor mortality. Signaling networks became useful tools for driving pharmacological interventions against cancer drug resistance. Signaling datasets now cover the entire human cell. Recently, network adaptation became understood as a learning process. We review rapidly increasing evidence showing that the development of cancer drug resistance can be described as learning of signaling networks. During drug adaptation, the network forgets drug-affected pathways by desensitization and relearns by strengthening alternative pathways. Thus, resistant cancer cells develop a drug resistance memory. We show that all key players of cellular learning (i.e., IDPs, protein translocation, microRNAs/lncRNAs, scaffolding proteins and epigenetic/chromatin memory) have important roles in the development of cancer drug resistance. Moreover, all of them are central components of the epithelial-mesenchymal transition leading to metastases and resistance. Phenotypic plasticity was recently listed as a hallmark of cancer. We review how network plasticity induces rare, pre-existent drug-resistant cells in the absence of drug treatment. Key network methods assessing the development of drug resistance and network pharmacological interventions against drug resistance are summarized. Finally, we highlight the class of cellular memory drugs affecting cellular learning and forgetting, and we summarize current challenges to prevent or break drug resistance using network models.

Drug resistance is responsible for >90% of cancer related deaths. Cancer drug resistance is a system level network phenomenon covering the entire cell. Small-scale interactomes and signalling network models of drug resistance guide directed drug development. Recently, proteome-wide human interactome and signalling network data have become available, which have been extended by drug-target interactions, drug resistance-inducing mutations, as well as by several cancer and drug resistance-related multi-omics datasets. System level signalling network models have become available examining therapy resistance, performing in silico clinical trials, and conducting large, in silico drug combination screens. Drug resistance network data and models have become interoperable and reliable. These advances paved the road for building proteome-wide drug resistance models. LINKED ARTICLES: This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc.

Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and 'forgetting' of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Ageing occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and ageing may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-κB signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g. helping to overcome drug resistance.

Prostate cancer metastasis is a significant cause of mortality in men. PKD3 facilitates tumor growth and metastasis, however, its regulation is largely unclear. The Hsp90 chaperone stabilizes an array of signaling client proteins, thus is an enabler of the malignant phenotype. Here, using different prostate cancer cell lines, we report that Hsp90 ensures PKD3 conformational stability and function to promote cancer cell migration. We found that pharmacological inhibition of either PKDs or Hsp90 dose-dependently abrogated the migration of DU145 and PC3 metastatic prostate cancer cells. Hsp90 inhibition by ganetespib caused a dose-dependent depletion of PKD2, PKD3, and Akt, which are all involved in metastasis formation. Proximity ligation assay and immunoprecipitation experiments demonstrated a physical interaction between Hsp90 and PKD3. Inhibition of the chaperone–client interaction induced misfolding and proteasomal degradation of PKD3. PKD3 siRNA combined with ganetespib treatment demonstrated a specific involvement of PKD3 in DU145 and PC3 cell migration, which was entirely dependent on Hsp90. Finally, ectopic expression of PKD3 enhanced migration of non-metastatic LNCaP cells in an Hsp90-dependent manner. Altogether, our findings identify PKD3 as an Hsp90 client and uncover a potential mechanism of Hsp90 in prostate cancer metastasis. The molecular interaction revealed here may regulate other biological and pathological functions.

Lysosome-related organelles (LROs) play diverse roles and their dysfunction causes immunodeficiency. However, their primordial functions remain unclear. Here, we report that C. elegans LROs (gut granules) promote organismal defenses against various stresses. We find that toxic benzaldehyde exposure induces LRO autofluorescence, stimulates the expression of LRO-specific genes and enhances LRO transport capacity as well as increases tolerance to benzaldehyde, heat and oxidative stresses, while these responses are impaired in glo-1/Rab32 and pgp-2 ABC transporter LRO biogenesis mutants. Benzaldehyde upregulates glo-1- and pgp-2-dependent expression of heat shock, detoxification and antimicrobial effector genes, which requires daf-16/FOXO and/or pmk-1/p38MAPK. Finally, benzaldehyde preconditioning increases resistance against Pseudomonas aeruginosa PA14 in a glo-1- and pgp-2-dependent manner, and PA14 infection leads to the deposition of fluorescent metabolites in LROs and induction of LRO genes. Our study suggests that LROs may play a role in systemic responses to stresses and in pathogen resistance.

Network topology is a common tool in systems biology. The focus of our work were disordered proteins whose importance in signaling have long been discussed. A good tool for studying these regulatory proteins is to study triangles i.e., three-nodal signaling motifs where each node is related to the other two, which infers a closer connection between the nodes. Our research shows that oncotherapeutical targets and disordered proteins often form three-nodal motifs together. Our aim was to analyse of these motifs for better understanding the complex relationship of these proteins with oncotherapeutical targets. The directed edges of three networks were extracted, and motifs were identified using the FANMOD program. For network analysis, Cytoscape and the Python NetworkX package were used. Then, disordered proteins were identified using the DisProt database, and CIViCmine were used for identifying biomarkers. Then, binary classification models were developed. We have shown that disordered proteins were more frequently in triangles and in regulatory motifs like unbalanced triangles and cycles. They form common motifs with known oncotherapeutical targets, where many disordered proteins are predictive biomarkers for the target (Human Cancer Signaling Network: 23%). Certain topological parameters are associated with predictive properties. Thus, we developed a machine learning model based on network topological data and biological characteristics of 110 target-disordered protein pairs, whose predictive properties couldbe identified from the literature . With this model we are able tocan predict potential predictive biomarker properties for the other identified 694 pairs in our networks. We show based on network topology analysis that intrinsically disordered proteins have great potential as predictive biomarkers. Our results imply that investigation into these motifs may help to identify novel predictive biomarkers, which can be further studied individually and validated experimentally. As predictive biomarkers are vital for therapeutical decision making, developing a tool for predictive biomarker identification may have effect on the daily clinical practice.

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

Background The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein–protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model. Results We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein–protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species. Conclusion We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred .

Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data.

Regulation of translocating proteins is crucial in defining cellular behaviour. Epithelial-mesenchymal transition (EMT) is important in cellular processes, such as cancer progression. Several orchestrators of EMT, such as key transcription factors, are known to translocate. We show that translocating proteins become enriched in EMT-signalling. To simulate the compartment-specific functions of translocating proteins we created a compartmentalized Boolean network model. This model successfully reproduced known biological traits of EMT and as a novel feature it also captured organelle-specific functions of proteins. Our results predicted that glycogen synthase kinase-3 beta (GSK3B) compartment-specifically alters the fate of EMT, amongst others the activation of nuclear GSK3B halts transforming growth factor beta-1 (TGFB) induced EMT. Moreover, our results recapitulated that the nuclear activation of glioma associated oncogene transcription factors (GLI) is needed to achieve a complete EMT. Compartmentalized network models will be useful to uncover novel control mechanisms of biological processes. Our algorithmic procedures can be automatically rerun on the https://translocaboole.linkgroup.hu website, which provides a framework for similar future studies.

SummaryThe Bruton's tyrosine kinase (BTK) inhibitor ibrutinib has revolutionised the therapeutic landscape of chronic lymphocytic leukaemia (CLL). Acquired mutations emerging at position C481 in the BTK tyrosine kinase domain are the predominant genetic alterations associated with secondary ibrutinib resistance. To assess the correlation between disease progression, and the emergence and temporal dynamics of the most common resistance mutation BTKC481S, sensitive (10−4) time‐resolved screening was performed in 83 relapsed/refractory CLL patients during single‐agent ibrutinib treatment. With a median follow‐up time of 40 months, BTKC481S was detected in 48·2% (40/83) of the patients, with 80·0% (32/40) of them showing disease progression during the examined period. In these 32 cases, representing 72·7% (32/44) of all patients experiencing relapse, emergence of the BTKC481S mutation preceded the symptoms of clinical relapse with a median of nine months. Subsequent Bcl‐2 inhibition therapy applied in 28/32 patients harbouring BTKC481S and progressing on ibrutinib conferred clinical and molecular remission across the patients. Our study demonstrates the clinical value of sensitive BTKC481S monitoring with the largest longitudinally analysed real‐world patient cohort reported to date and validates the feasibility of an early prediction of relapse in the majority of ibrutinib‐treated relapsed/refractory CLL patients experiencing disease progression.

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

Network theory as part of complex systems theory has over the past decades developed powerful tools to analyze and make sense of the enormous amounts of molecular interaction data obtained by modern molecular biology methods at the DNA, RNA, and protein levels. These complex network models of intracellular molecular interactions (from here on: cellular networks) can reveal aspects of cellular function and overall molecular interaction behavior that cannot be described by other means. We will give here a brief introduction into the general logic of such network models and how we can gain novel, biologically, and medically relevant insights from applying them to the cancer context. This chapter describes networkbased adaptive mechanisms that bring about the “creativity” of cancer cells to survive and expand in the unpredictable, often hostile, environments of tumor tissues. First, we describe the dominance shift from “businessasusual” processes driven by the core of cellular molecular interaction networks to changes in the network periphery that lead to “creative” shortcuts between distant network regions and thus allow the network to respond to novel challenges. This forms a general adaptation/learning mechanism that characterizes the initial stages of cancer development. Such adaptive changes may change the topology of cellular networks from a rigid to a plastic state. Rigid networks have a dense core, disjunct modules (network groups), prominent hierarchy, low network entropy and socalled sink dominance. Rigid networks have only a few dominant attractors (i.e., stable states to where the network converges). Plastic networks have a fuzzy core, overlapping modules, less hierarchy/more loops, high network entropy, and source dominance. Plastic networks have many attractors, which are often dispersed. Alternating changes of network plasticity and rigidity help to encode novel information into the network structure, thus remodeling the network core and developing novel system attractors. Cancer stem cells are characterized by exceptionally high evolvability involving rapid alternations between plasticity and rigidity. Plastic and rigid networks (characterizing early and latestage tumors, respectively) require conceptually different drug design strategies. Plastic networks (which dissipate stimuli very well) should only be attacked Adaptation of Molecular Interaction Networks in Cancer Cells

Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.

Molecular processes of neuronal learning have been well described. However, learning mechanisms of non-neuronal cells are not yet fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins (IDPs) and prions, signaling cascades, protein translocation, RNAs [miRNA and long noncoding RNA (lncRNA)], and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and we discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods.

Here we present Translocatome, the first dedicated database of human translocating proteins. The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13,066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein-protein interaction database. As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field. The database is available here: http://translocatome.linkgroup.hu

Motivation: Network visualizations of complex biological datasets usually result in 'hairball' images, which do not discriminate network modules. Results: We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major protein complexes in protein-protein interaction and signalling networks. Importantly, the tool gives a quality score of the network visualization by calculating the information loss between the input data and the visual representation showing a 3- to 25-fold improvement over conventional methods. Availability and implementation: The plug-in (running on Windows, Linux, or Mac OS) and its tutorial (both in written and video forms) can be downloaded freely under the terms of the MIT license from: http://apps.cytoscape.org/apps/entoptlayout. Supplementary data are available at Bioinformatics online. Contact: csermely.peter@med.semmelweis-univ.hu