The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission. Funded by and rooted in a number of these European private-public partnership projects, the Data Catalog is built on FAIR-enabling community standards, and its mission is to ensure that datasets are findable and accessible by machines. Here we present its creation, content, value and adoption, as well as the next steps for sustainability within the ELIXIR ecosystem.

Melanoma is an aggressive skin cancer with increasing incidence worldwide. The development of BRAF kinase inhibitors as targeted treatments for patients with BRAF-mutant tumours contributed profoundly to an improved overall survival of patients with metastatic melanoma. Despite these promising results, the emergence of rapid resistance to targeted therapy remains a serious clinical issue.

Upon stimulation with Th1 cytokines or bacterial lipopolysaccharides, resting macrophages shift their phenotype toward a pro-inflammatory state as part of the innate immune response. LPS-activated macrophages undergo profound metabolic changes to adapt to these new physiological requirements. One key step to mediate this metabolic adaptation is the stabilization of HIF1α, which leads to increased glycolysis and lactate release, as well as decreased oxygen consumption. HIF1 abundance can result in the induction of the gene encoding pyruvate dehydrogenase kinase 1 (PDK1), which inhibits pyruvate dehydrogenase (PDH) via phosphorylation. Therefore, it has been speculated that pyruvate oxidation through PDH is decreased in pro-inflammatory macrophages. However, to answer this open question, an in-depth analysis of this metabolic branching point was so far lacking. In this work, we applied stable isotope-assisted metabolomics techniques and demonstrate that pyruvate oxidation is maintained in mature pro-inflammatory macrophages. Glucose-derived pyruvate is oxidized via PDH to generate citrate in the mitochondria. Citrate is used for the synthesis of the antimicrobial metabolite itaconate and for lipogenesis. An increased demand for these metabolites decreases citrate oxidation through the tricarboxylic acid cycle, whereas increased glutamine uptake serves to replenish the TCA cycle. Furthermore, we found that the PDH flux is maintained by unchanged PDK1 abundance, despite the presence of HIF1. By pharmacological intervention, we demonstrate that the PDH flux is an important node for M(LPS) macrophage activation. Therefore, PDH represents a metabolic intervention point that might become a research target for translational medicine to treat chronic inflammatory diseases.

Selecting the most beneficial treatment regimens for colorectal cancer (CRC) patients remains challenging due to a lack of prognostic markers. Members of the Myosin family, proteins recognised to have a major role in trafficking and polarisation of cells, have recently been reported to be closely associated with several types of cancer and might thus serve as potential prognostic markers in the context of CRC.

Cytokines orchestrate responses to pathogens and in inflammatory processes, but they also play an important role in cancer by shaping the expression levels of cytokine response genes. Here, we conducted a large profiling study comparing miRNome and mRNA transcriptome data generated following different cytokine stimulations. Transcriptomic responses to STAT1- (IFNγ, IL-27) and STAT3-activating cytokines (IL6, OSM) were systematically compared in nine cancerous and non-neoplastic cell lines of different tissue origins (skin, liver and colon). The largest variation in our datasets was seen between cell lines of the three different tissues rather than stimuli. Notably, the variability in miRNome datasets was a lot more pronounced than in mRNA data. Our data also revealed that cells of skin, liver and colon tissues respond very differently to cytokines and that the cell signaling networks activated or silenced in response to STAT1- or STAT3-activating cytokines are specific to the tissue and the type of cytokine. However, globally, STAT1-activating cytokines had stronger effects than STAT3-inducing cytokines with most significant responses in liver cells, showing more genes upregulated and with higher fold change. A more detailed analysis of gene regulations upon cytokine stimulation in these cells provided insights into STAT1- versus STAT3-driven processes in hepatocarcinogenesis. Finally, independent component analysis revealed interconnected transcriptional networks distinct between cancer cells and their healthy counterparts.

The release of cytokines by neutrophils constitutes an essential process in the development of inflammation by recruiting and activating additional cells. Neutrophils are also able to secrete a complex of S100A8 and S100A9 proteins (S100A8/A9), which can amplify the general inflammatory state of the host and is involved in the pathogenesis of several chronic inflammatory diseases, such as rheumatoid arthritis (RA). S100A8/A9 have received renewed attention due to their susceptibility to several function-altering post-translational modifications. In that context, it has been recently demonstrated that only the phosphorylated form of S100A8/A9 (S100A8/A9-P) is able to induce the secretion of several cytokines in neutrophils. Here, we investigate the mechanism by which this post-translational modification of S100A8/A9 can regulate the extracellular activity of the protein complex and its impact on the inflammatory functions of neutrophils. We found that S100A8/A9-P are present in large amounts in the synovial fluids from RA patients, highlighting the importance of this form of S100A8/A9 complex in the inflammation process. Using miRNA-sequencing on S100A8/A9-P-stimulated differentiated HL-60 cells, we identified a dysregulation of miR-146a-5p and miR-155-5p expression through TRL4 signaling pathways. Our data reveal that overexpression of these miRNAs in neutrophil-like cells reduces S100A8/A9-P-mediated secretion of pro-inflammatory cytokines.

Interleukin-6 (IL-6)-type cytokines share the common receptor glycoprotein 130 (gp130), which activates a signaling cascade involving Janus kinases (JAKs) and signal transducer and activator of transcription (STAT) transcription factors. IL-6 and/or its signaling pathway is often deregulated in diseases, such as chronic liver diseases and cancer. Thus, the identification of compounds inhibiting this pathway is of interest for future targeted therapies. We established novel cellular screening systems based on a STAT-responsive reporter gene (Cypridina luciferase). Of a library containing 538 microRNA (miRNA) mimics, several miRNAs affected hyper-IL-6-induced luciferase activities. When focusing on candidate miRNAs specifically targeting 3' UTRs of signaling molecules of this pathway, we identified, e.g., miR-3677-5p as a novel miRNA affecting protein expression of both STAT3 and JAK1, whereas miR-16-1-3p, miR-4473, and miR-520f-3p reduced gp130 surface expression. Interestingly, combination treatment with 2 or 3 miRNAs targeting gp130 or different signaling molecules of the pathway did not increase the inhibitory effects on phospho-STAT3 levels and STAT3 target gene expression compared to treatment with single mimics. Taken together, we identified a set of miRNAs of potential therapeutic value for cancer and inflammatory diseases, which directly target the expression of molecules within the IL-6-signaling pathway and can dampen inflammatory signal transduction.

The balance between stem cell maintenance and differentiation has been proposed to depend on antagonizing ubiquitination and deubiquitination reactions of key stem cell transcription factors (SCTFs) mediated by pairs of E3 ubiquitin ligases and deubiquitinating enzymes. Accordingly, increased ubiquitination results in proteasomal degradation of the SCTF, thereby inducing cellular differentiation, whereas increased deubiquitination stabilizes the SCTF, leading to maintenance of the stem cell fate. In neural stem cells, one of the key SCTFs is c-Myc. Previously, it has been shown that c-Myc is ubiquitinated by the E3 ligase TRIM32, thereby targeting c-Myc for proteasomal degradation and inducing neuronal differentiation. Accordingly, TRIM32 becomes upregulated during adult neurogenesis. This upregulation is accompanied by subcellular translocation of TRIM32 from the cytoplasm of neuroblasts to the nucleus of neurons. However, we observed that a subpopulation of proliferative type C cells already contains nuclear TRIM32. As these cells do not undergo neuronal differentiation, despite containing TRIM32 in the nucleus, where it can ubiquitinate c-Myc, we hypothesize that antagonizing factors, specifically deubiquitinating enzymes, are present in these particular cells. Here we show that TRIM32 associates with the deubiquitination enzyme USP7, which previously has been implicated in neural stem cell maintenance. USP7 and TRIM32 were found to exhibit a dynamic and partially overlapping expression pattern during neuronal differentiation both in vitro and in vivo. Most importantly, we are able to demonstrate that USP7 deubiquitinates and thereby stabilizes c-Myc and that this function is required to maintain neural stem cell fate. Accordingly, we propose the balanced ubiquitination and deubiquitination of c-Myc by TRIM32 and USP7 as a novel mechanism for stem cell fate determination.

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cell line (ST2) toward adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages and identifed the shared TFs involved in both differentiation processes. To take an alternative approach to prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. The combination of the two approaches identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic cell type-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and their overexpression can inhibit the lineage commitment of the multipotent bone marrow-derived ST2 cells.

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.

Mitochondrial dysfunction is linked to pathogenesis of Parkinson's disease (PD). However, individual mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria interaction networks (MINs). Here we show that MINs formed nonclassical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients; however, altered network topological patterns were observed in PD patients. These patterns were highly correlated with PD clinical scores and a machine-learning approach based on the MIN features alone accurately distinguished between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also displayed specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reversed the changes in MINs of mDANs. Our organelle-interaction network analysis opens another critical dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS2 database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

Cancer stem cells, also known as tumor-initiating cells (TICs), are a population of aggressive and self-renewing cells that are responsible for the initiation and progression of many cancers, including colorectal carcinoma. Intratumoral hypoxia, i.e. reduced oxygen supply following uncontrolled proliferation of cancer cells, is thought to support TIC activity by inducing specific hypoxia-responsive mechanisms that are not yet entirely understood. Using previously established and fully characterized patient-derived TIC cultures, we could observe increased sphere and colony formation under hypoxic conditions. Mechanistically, microRNA (miRNA)-profiling experiments allowed us to identify miR-215 as one of the main hypoxia-induced miRNAs in primary colon TICs. Through stable overexpression of miR-215, followed by a set of functional in vitro and in vivo investigations, miR-215 was pinpointed as a negative feedback regulator, working against the TIC-promoting effects of hypoxia. Furthermore, we could single out LGR5, a bona fide marker of non-neoplastic intestinal stem cells, as a downstream target of hypoxia/miR-215 signaling. The strong tumor- and TIC-suppressor potential of miR-215 and the regulatory role of the hypoxia/miR-215/LGR5 axis may thus represent interesting points of attack for the development of innovative anti-CSC therapy approaches.

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.

Treatment options for patients with NRAS-mutant melanoma are limited and lack an efficient targeted drug combination that significantly increases overall and progression-free survival. In addition, targeted therapy success is hampered by the inevitable emergence of drug resistance. A thorough understanding of the molecular processes driving cancer cells' escape mechanisms is crucial to tailor more efficient follow-up therapies. We performed single-cell RNA sequencing of NRAS-mutant melanoma treated with MEK1/2 plus CDK4/6 inhibitors to decipher transcriptional transitions during the development of drug resistance. Cell lines resuming full proliferation (FACs [fast-adapting cells]) and cells that became senescent (SACs [slow-adapting cells]) over prolonged treatment were identified. The early drug response was characterized by transitional states involving increased ion signaling, driven by upregulation of the ATP-gated ion channel P2RX7. P2RX7 activation was associated with improved therapy responses and, in combination with targeted drugs, could contribute to the delayed onset of acquired resistance in NRAS-mutant melanoma.

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method-FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable electric field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2 s,  ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications.

MicroRNAs are key post-transcriptional gene regulators often displaying aberrant expression patterns in cancer. As microRNAs are promising disease-associated biomarkers and modulators of responsiveness to anti-cancer therapies, a solid understanding of their targetome is crucial. Despite enormous research efforts, the success rates of available tools to reliably predict microRNAs (miRNA)-target interactions remains limited. To investigate the disease-associated miRNA targetome, we have applied modified cross-linking ligation and sequencing of hybrids (qCLASH) to BRAF-mutant melanoma cells. The resulting RNA-RNA hybrid molecules provide a comprehensive and unbiased snapshot of direct miRNA-target interactions. The regulatory effects on selected miRNA target genes in predicted vs. non-predicted binding regions was validated by miRNA mimic experiments. Most miRNA-target interactions deviate from the central dogma of miRNA targeting up to 60% interactions occur via non-canonical seed pairing with a strong contribution of the 3' miRNA sequence, and over 50% display a clear bias towards the coding sequence of mRNAs. miRNAs targeting the coding sequence can directly reduce gene expression (miR-34a/CD68), while the majority of non-canonical miRNA interactions appear to have roles beyond target gene suppression (miR-100/AXL). Additionally, non-mRNA targets of miRNAs (lncRNAs) whose interactions mainly occur via non-canonical binding were identified in melanoma. This first application of CLASH sequencing to cancer cells identified over 8 K distinct miRNA-target interactions in melanoma cells. Our data highlight the importance non-canonical interactions, revealing further layers of complexity of post-transcriptional gene regulation in melanoma, thus expanding the pool of miRNA-target interactions, which have so far been omitted in the cancer field.

The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease.