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The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources. The IMEx consortium provides one of the largest resources of curated, experimentally verified molecular interaction data. Here, the authors review how IMEx evolved into a fundamental resource for life scientists and describe how IMEx data can support biomedical research.

Presented is an update on the status and current practices of the International Molecular Exchange (IMEx) consortium and on its efforts to create a single nonredundant set of protein interactions curated from the scientific literature. The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website ( http://www.imexconsortium.org/ ). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool’s ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.

Background Metabolic syndrome represents a pancreatic ductal adenocarcinoma (PDAC) risk factor. Metabolic alterations favor PDAC onset, which occurs early upon dysmetabolism. Pancreatic neoplastic lesions evolve within a dense desmoplastic stroma, consisting in abundant extracellular matrix settled by cancer associated fibroblasts (CAFs). Hereby, dysmetabolism and PDAC association was analyzed focusing on CAF functions. Methods PDAC development upon dysmetabolic conditions was investigated in: 1) high fat diet fed wild type immunocompetent syngeneic mice by orthotopic transplantation of pancreatic intraepithelial neoplasia (PanIN) organoids; and 2) primary pancreatic CAFs isolated from chemotherapy naïve PDAC patients with/without an history of metabolic syndrome. Results The dysmetabolic-associated higher PDAC aggressiveness was paralleled by collagen fibril enrichment due to prolyl 4-hydroxylase subunit alpha 1 (P4HA1) increased function. Upon dysmetabolism, P4HA1 boosts collagen proline hydroxylation, intensifies collagen contraction strength, precluding PDAC infiltration. Noteworthy, semaglutide, an incretin agonist, prevents the higher dysmetabolism-dependent PDAC stromal deposition and allows T lymphocyte infiltration, reducing tumor development. Conclusions These results shed light on novel therapeutic options for PDAC patients with metabolic syndrome aimed at PDAC stroma reshape.

The Cyclin-dependent kinase 1, as a serine/threonine protein kinase, is more than a cell cycle regulator as it was originally identified. During the last decade, it has been shown to carry out versatile functions during the last decade. From cell cycle control to gene expression regulation and apoptosis, CDK1 is intimately involved in many cellular events that are vital for cell survival. Here, we provide a comprehensive catalogue of the CDK1 upstream regulators and substrates, describing how this kinase is implicated in the control of key ‘cell cycle-unrelated’ biological processes. Finally, we describe how deregulation of CDK1 expression and activation has been closely associated with cancer progression and drug resistance.

Background About 40% of relapsed or non-responder tumors exhibit therapeutic resistance in the absence of a clear genetic cause, suggesting a pivotal role of intracellular communication. A deeper understanding of signaling pathways rewiring occurring in resistant cells is crucial to propose alternative effective strategies for cancer patients. Methods To achieve this goal, we developed a novel multi-step strategy, which integrates high sensitive mass spectrometry-based phosphoproteomics with network-based analysis. This strategy builds context-specific networks recapitulating the signaling rewiring upon drug treatment in therapy-resistant and sensitive cells. Results We applied this strategy to elucidate the BCR::ABL1-independent mechanisms that drive relapse upon therapy discontinuation in chronic myeloid leukemia (CML) patients. We built a signaling map, detailing - from receptor to key phenotypes - the molecular mechanisms implicated in the control of proliferation, DNA damage response and inflammation of therapy-resistant cells. In-depth analysis of this map uncovered novel therapeutic vulnerabilities. Functional validation in patient-derived leukemic stem cells revealed a crucial role of acquired FLT3-dependency and its underlying molecular mechanism. Conclusions In conclusion, our study presents a novel generally applicable strategy and the reposition of FLT3, one of the most frequently mutated drivers of acute leukemia, as a potential therapeutic target for CML relapsed patients.

FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.

Background The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data. Results The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms. Conclusion The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.