Martin Kuiper

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

Psoriasis is a dermatologic disease that affects 2% of the world population. Psoriasis is characterized by chronic inflammation and aberrant behavior of keratinocytes, which display increased levels of proliferation, and decreased differentiation and apoptosis. Stimulation of keratinocytes by psoriatic cytokines leads to the increased production of immunostimulatory ligands that further attract immune cells and amplify inflammatory responses. Psoriasis can have severe, moderate, or mild outcomes and while these severity levels demand custom medical treatment schemes, assigning an effective treatment to patients with moderate or severe disease is a demanding task. The varied responses of patients to treatments highlight a large disease complexity, demanding that new ways to analyze and integrate patients’ molecular profiles are developed to design patient-specific therapies. We have used gene expression values from psoriasis biopsies to separate patients into two clusters, each with distinct expression profiles, but nevertheless not correlating with any of the available clinical data, such as disease severity. When using these gene expression levels in logical model simulations these data became highly descriptive of patient-specific phenotype characteristics. Starting from a psoriatic keratinocyte model that we published recently, we added additional pathways highlighted by a differential gene expression analysis between the subgroups. This included components from the Interleukin-1 family, IFN-alpha/beta and IL-6 signaling pathways. Model personalization was performed by using patient gene expression levels in model configurations, exploiting the PROFILE pipeline. Personalized simulations revealed that the two patient clusters represent more innate immunity-driven, highly inflammatory phenotypes and adaptive immunity-driven, chronic phenotypes, respectively. The model was also able to finely capture differences between responses in patients with a known disease severity. A treatment response analysis among the patient cohort predicted differential responses to the inhibition of psoriatic stimuli, with IL-17, TNFα and PGE2 inhibition reducing proliferation and inflammatory phenotypes. Alternative treatment with PGE2 or TNFα inhibition instead of IL-17 was suggested for patients with high NF-κB activity and prosurvival factors, such as CREB1. With this project, we aim to highlight the value of combining omics data with logical modeling for the detection of ‘emergent’ phenotypes and for gaining disease knowledge on the individual patient level.

Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies.

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

A hallmark of the development of solid and hematological malignancies is the dysregulation of apoptosis, which leads to an imbalance between cell proliferation, cell survival and death. Halogenated boroxine [K-2(B3O3F4OH)] (HB) is a derivative of cyclic anhydride of boronic acid, with reproducible anti-tumor and anti-proliferative effects in different cell models. Notably, these changes are observed to be more profound in tumor cells than in normal cells. Here, we investigated the underlying mechanisms through an extensive evaluation of (a) deregulated target genes and (b) their interactions and links with main apoptotic pathway genes upon treatment with an optimized concentration of HB. To provide deeper insights into the mechanism of action of HB, we performed identification, visualization, and pathway association of differentially expressed genes (DEGs) involved in regulation of apoptosis among tumor and non-tumor cells upon HB treatment. We report that HB at a concentration of 0.2 mg.mL(-1) drives tumor cells to apoptosis, whereas non-tumor cells are not affected. Comparison of DEG profiles, gene interactions and pathway associations suggests that the HB effect and tumor-'selectivity' can be explained by Bax/Bak-independent mitochondrial depolarization by ROS generation and TRAIL-like activation, followed by permanent inhibition of NF kappa B signaling pathway specifically in tumor cells.