Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.
The lack of integrated resources depicting the complexity of the innate immune response in cancer represents a bottleneck for high-throughput data interpretation. To address this challenge, we ...perform a systematic manual literature mining of molecular mechanisms governing the innate immune response in cancer and represent it as a signalling network map. The cell-type specific signalling maps of macrophages, dendritic cells, myeloid-derived suppressor cells and natural killers are constructed and integrated into a comprehensive meta map of the innate immune response in cancer. The meta-map contains 1466 chemical species as nodes connected by 1084 biochemical reactions, and it is supported by information from 820 articles. The resource helps to interpret single cell RNA-Seq data from macrophages and natural killer cells in metastatic melanoma that reveal different anti- or pro-tumor sub-populations within each cell type. Here, we report a new open source analytic platform that supports data visualisation and interpretation of tumour microenvironment activity in cancer.
PRL-3 belongs to the PRL phosphatase family. Its physiological role remains unclear, but many studies have identified that PRL-3 is a marker of cancer progression and shown it to be associated with ...metastasis. Evidence implicating PRL-3 in various elements of the metastatic process, such as the cell cycle, survival, angiogenesis, adhesion, cytoskeleton remodeling, EMT, motility and invasion, has been reported. Furthermore, several molecules acting as direct or indirect substrates have been identified. However, this information was obtained in many different studies, and it remains difficult to see the larger picture. We therefore systematically collected the published information together and used it to develop a comprehensive signaling network map. By analyzing this network map, we were able to retrieve the signaling pathways via which PRL-3 governs the key steps of the metastatic process in cancer. In this review, we summarize current knowledge of the role of PRL-3 in cancer and the molecular mechanisms involved. We also provide the web-based open-source PRL-3 signaling network map, for use in further studies.
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•The phosphatase PRL-3 regulates numerous pathways driving tumorigenesis and metastasis.
Epithelial-to-mesenchymal transition-like (EMT-like) is a critical process allowing initiation of metastases during tumour progression. Here, to investigate its role in intestinal cancer, we combine ...computational network-based and experimental approaches to create a mouse model with high metastatic potential. Construction and analysis of this network map depicting molecular mechanisms of EMT regulation based on the literature suggests that Notch activation and p53 deletion have a synergistic effect in activating EMT-like processes. To confirm this prediction, we generate transgenic mice by conditionally activating the Notch1 receptor and deleting p53 in the digestive epithelium (NICD/p53(-/-)). These mice develop metastatic tumours with high penetrance. Using GFP lineage tracing, we identify single malignant cells with mesenchymal features in primary and metastatic tumours in vivo. The development of such a model that recapitulates the cellular features observed in invasive human colorectal tumours is appealing for innovative drug discovery.
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.
We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration ( https://navicell.curie.fr/pages/maps_ReconMap%202.html ). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified.
As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cancer treatments using tumor defects in DNA repair pathways have shown promising results but are restricted to small subpopulations of patients. The most advanced drugs in this field are PARP ...inhibitors (PARPi), which trigger synthetic lethality in tumors with homologous recombination (HR) deficiency. Using AsiDNA, an inhibitor of HR and nonhomologous end joining, together with PARPi should allow bypassing the genetic restriction for PARPi efficacy.
We characterized the DNA repair inhibition activity of PARPi (olaparib) and AsiDNA by monitoring repair foci formation and DNA damage. We analyzed the cell survival to standalone and combined treatments of 21 tumor cells and three nontumor cells. In 12 breast cancer (BC) cell lines, correlation with sensitivity to each drug and transcriptome were statistically analyzed to identify resistance pathways.
Molecular analyses demonstrate that olaparib and AsiDNA respectively prevent recruitment of XRCC1 and RAD51/53BP1 repair enzymes to damage sites. Combination of both drugs increases the accumulation of unrepaired damage resulting in an increase of cell death in all tumor cells. In contrast, nontumor cells do not show an increase of DNA damage nor lethality. Analysis of multilevel omics data from BC cells highlighted different DNA repair and cell-cycle molecular profiles associated with resistance to AsiDNA or olaparib, rationalizing combined treatment. Treatment synergy was also confirmed with six other PARPi in development.
Our results highlight the therapeutic interest of combining AsiDNA and PARPi to recapitulate synthetic lethality in all tumors independently of their HR status.
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Systematic analysis of synthetic lethality (SL) constitutes a critical tool for systems biology to decipher molecular pathways. The most accepted mechanistic explanation of SL is that the two genes ...function in parallel, mutually compensatory pathways, known as between-pathway SL. However, recent genome-wide analyses in yeast identified a significant number of within-pathway negative genetic interactions. The molecular mechanisms leading to within-pathway SL are not fully understood. Here, we propose a novel mechanism leading to within-pathway SL involving two genes functioning in a single non-essential pathway. This type of SL termed within-reversible-pathway SL involves reversible pathway steps, catalyzed by different enzymes in the forward and backward directions, and kinetic trapping of a potentially toxic intermediate. Experimental data with recombinational DNA repair genes validate the concept. Mathematical modeling recapitulates the possibility of kinetic trapping and revealed the potential contributions of synthetic, dosage-lethal interactions in such a genetic system as well as the possibility of within-pathway positive masking interactions. Analysis of yeast gene interaction and pathway data suggests broad applicability of this novel concept. These observations extend the canonical interpretation of synthetic-lethal or synthetic-sick interactions with direct implications to reconstruct molecular pathways and improve therapeutic approaches to diseases such as cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, ...signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.
Carcinoma-associated fibroblasts (CAF) are key players in the tumor microenvironment. Here, we characterize four CAF subsets in breast cancer with distinct properties and levels of activation. Two ...myofibroblastic subsets (CAF-S1, CAF-S4) accumulate differentially in triple-negative breast cancers (TNBC). CAF-S1 fibroblasts promote an immunosuppressive environment through a multi-step mechanism. By secreting CXCL12, CAF-S1 attracts CD4+CD25+ T lymphocytes and retains them by OX40L, PD-L2, and JAM2. Moreover, CAF-S1 increases T lymphocyte survival and promotes their differentiation into CD25HighFOXP3High, through B7H3, CD73, and DPP4. Finally, in contrast to CAF-S4, CAF-S1 enhances the regulatory T cell capacity to inhibit T effector proliferation. These data are consistent with FOXP3+ T lymphocyte accumulation in CAF-S1-enriched TNBC and show how a CAF subset contributes to immunosuppression.
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•Four CAF subsets identified in breast cancer accumulate differently in BC subtypes•CAF-S1 subset is associated with an immunosuppressive microenvironment•CAF-S1 cells attract and retain CD4+CD25+ T cells through OX40L, PD-L2, and JAM2•CAF-S1 cells increase CD25+FOXP3+ T lymphocytes, through B7H3, DPP4, and CD73
Costa et al. identify four subsets of carcinoma-associated fibroblasts (CAF) in breast cancer. CAF-S1 promotes an immunosuppressive microenvironment by recruiting CD4+CD25+ T cells, via secreting CXCL12, and promoting their differentiation to Tregs and survival, via expressing T cell interacting proteins.
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