p38 MAPK is involved in CB2 receptor-induced apoptosis of human leukaemia cells

AbstractCannabinoids have been shown to inhibit the growth of a broad spectrum of tumour cells. However, the molecular mechanisms involved in that effect have not been completely elucidated. Here, we investigated the possible involvement of mitogen-activated protein kinases (MAPKs) in CB2 receptor-induced apoptosis of human leukaemia cells. Results show that stimulation of the CB2 receptor leads to p38 MAPK activation and that inhibition of this kinase attenuates CB2 receptor-induced caspase activation and apoptosis. These findings support a role for p38 MAPK in CB2 receptor-induced apoptosis of human leukaemia cells

Implication of Ceramide Kinase/C1P in Cancer Development and Progression

Cancer cells rewire their metabolic programs to favor biological processes that promote cell survival, proliferation, and dissemination. Among this relevant reprogramming, sphingolipid metabolism provides metabolites that can favor or oppose these hallmarks of cancer. The sphingolipid ceramide 1-phosphate (C1P) and the enzyme responsible for its biosynthesis, ceramide kinase (CERK), are well established regulators of cell growth and survival in normal, as well as malignant cells through stress-regulated signaling pathways. This metabolite also promotes cell survival, which has been associated with the feedback regulation of other antitumoral sphingolipids or second messengers. C1P also regulates cancer cell invasion and migration of different types of cancer, including lung, breast, pancreas, prostate, or leukemia cells. More recently, CERK and C1P have been implicated in the control of inflammatory responses. The present review provides an updated view on the important role of CERK/C1P in the regulation of cancer cell growth, survival, and disseminationWork in AGM lab is supported by ‘Departamento de Educación, Universidades e Investigación del Gobierno Vasco’, Basque Country, Spain (Grant IT1106-16). The work of A. Carracedo is supported by the Basque Department of Industry, Tourism and Trade (Elkartek), the Department of Education (IKERTALDE IT1106-16) and Health (RIS3), the MICINN (PID2019-108787RB-I00 (FEDER/EU), Severo Ochoa Excellence Accreditation SEV-2016-0644, Excellence Networks RED2018-102769-T), the AECC (GCTRA18006CARR), La Caixa Foundation (ID 100010434), under the agreement LCF/PR/HR17/, and the European Research Council (Consolidator Grant 819242). CIBERONC was co-funded with FEDER funds and funded by ISCIII

Nanocomposite Scaffolds for Monitoring of Drug Diffusion in Three-Dimensional Cell Environments by Surface-Enhanced Raman Spectroscopy

[EN]Monitoring dynamic processes in complex cellular environments requires the integration of uniformly distributed detectors within such three-dimensional (3D) networks, to an extent that the sensor could provide real-time information on nearby perturbations in a non-invasive manner. In this context, the development of 3D-printed structures that can function as both sensors and cell culture platforms emerges as a promising strategy, not only for mimicking a specific cell niche but also toward identifying its characteristic physicochemical conditions, such as concentration gradients. We present herein a 3D cancer model that incorporates a hydrogel-based scaffold containing gold nanorods. In addition to sustaining cell growth, the printed nanocomposite inks display the ability to uncover drug diffusion profiles by surface-enhanced Raman scattering, with high spatiotemporal resolution. We additionally demonstrate that the acquired information could pave the way to designing novel strategies for drug discovery in cancer therapy, through correlation of drug diffusion with cell death.J.P. acknowledges an FPU fellowship from the Spanish Ministry of Science, Innovation and Universities. L.M.L.-M. acknowledges funding from the European Research Council (Grants ERC AdG 787510, 4DbioSERS) and the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (Grant MDM-2017-0720). A.C. was funded by MICINN (Grant PID2019-108787RB-I00 (FEDER/EU)) and the European Research Council (ERC Consolidator Grant 819242)

Machine learning‐assisted high‐throughput SERS classification of cell secretomes

During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.Universidade de Vigo/CISUGAgencia Estatal de Investigación | Ref. PID2019-108787RB-I0

Multiplex SERS Detection of Metabolic Alterations in Tumor Extracellular Media

The composition and intercellular interactions of tumor cells in the tissues dictate the biochemical and metabolic properties of the tumor microenvironment. The metabolic rewiring has a profound impact on the properties of the microenvironment, to an extent that monitoring such perturbations could harbor diagnostic and therapeutic relevance. A growing interest in these phenomena has inspired the development of novel technologies with sufficient sensitivity and resolution to monitor metabolic alterations in the tumor microenvironment. In this context, surface-enhanced Raman scattering (SERS) can be used for the label-free detection and imaging of diverse molecules of interest among extracellular components. Herein, the application of nanostructured plasmonic substrates comprising Au nanoparticles, self-assembled as ordered superlattices, to the precise SERS detection of selected tumor metabolites, is presented. The potential of this technology is first demonstrated through the analysis of kynurenine, a secreted immunomodulatory derivative of the tumor metabolism and the related molecules tryptophan and purine derivatives. SERS facilitates the unambiguous identification of trace metabolites and allows the multiplex detection of their characteristic fingerprints under different conditions. Finally, the effective plasmonic SERS substrate is combined with a hydrogel-based three-dimensional cancer model, which recreates the tumor microenvironment, for the real-time imaging of metabolite alterations and cytotoxic effects on tumor cells.J.P. acknowledges an FPU fellowship from the Spanish Ministry of Science, Innovation and Universities. L.M.L.-M. acknowledges funding from the European Research Council (ERC AdG 787510, 4DbioSERS) and the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (Grant No. MDM-2017-0720). C.G.-A. acknowledges a Juan de la Cierva Fellowship from the Spanish Ministry of Science, Innovation and Universities (FJCI-2016-28887). The authors thank Dr. J. Calvo and Dr. D. Otaegui at CIC biomaGUNE for support with LC/ESI-HRMS measurements. The work of A.C. was supported by the Basque Department of Industry, Tourism and Trade (Elkartek), and the department of education (IKERTALDE IT1106-16, also participated by A. Gomez-Munoz), the BBVA foundation, the MINECO (SAF2016-79381-R (FEDER/EU); Severo Ochoa Excellence Program SEV-2016-0644-18-1; Excellence Networks SAF2016-81975-REDT), European Training Networks Project (H2020-MSCA-ITN-308 2016 721532), the AECC (IDEAS175CARR, GCTRA18006CARR), La Caixa Foundation (HR17-00094), and the European Research Council (starting Grant 336343, PoC 754627). CIBERONC was co-funded with FEDER funds and funded by ISCIII. A.M. acknowledges funding from the European Research Council (Consolidator Grant 819242) and the Spanish Ministry of Science, Innovation and Universities for the excellence program SEV-2015-049

LUZP1 Controls Cell Division, Migration and Invasion Through Regulation of the Actin Cytoskeleton

LUZP1 is a centrosomal and actin cytoskeleton-localizing protein that regulates both ciliogenesis and actin filament bundling. As the cytoskeleton and cilia are implicated in metastasis and tumor suppression, we examined roles for LUZP1 in the context of cancer. Here we show that LUZP1 exhibits frequent genomic aberrations in cancer, with a predominance of gene deletions. Furthermore, we demonstrate that CRISPR/Cas9-mediated loss of Luzp1 in mouse fibroblasts promotes cell migration and invasion features, reduces cell viability, and increases cell apoptosis, centriole numbers, and nuclear size while altering the actin cytoskeleton. Loss of Luzp1 also induced changes to ACTR3 (Actin Related Protein 3, also known as ARP3) and phospho-cofilin ratios, suggesting regulatory roles in actin polymerization, beyond its role in filament bundling. Our results point to an unprecedented role for LUZP1 in the regulation of cancer features through the control of actin cytoskeleton.We are grateful to the Fundacion Inocente, Inocente for their support. We also acknowledge funding by the grants BFU2017-84653-P (MINECO/FEDER, EU), SEV-2016-0644 (Severo Ochoa Excellence Program), 765445-EU (UbiCODE Program), SAF2017-90900-REDT (UBIRed Program), and IT1165-19 (Basque Country Government). Additional support was provided by the Department of Industry, Tourism, and Trade of the Basque Country Government (Elkartek Research Programs) and by the Innovation Technology Department of the Bizkaia County. LB-B acknowledges POSTD19048BOZA (Fundacion Cientifica AECC). VM acknowledges PRE2018086230 (MINECO/FEDER, EU). AC acknowledges the Basque Department of education (IKERTALDE IT1106-16), the MCIU [PID2019-108787RB-I00 (FEDER/EU)], the AECC (IDEAS175CARR; GCTRA18006CARR), La Caixa Foundation (ID 100010434), under the agreement LCF/PR/HR17/and the European Research Council (Starting Grant 336343, PoC 754627, Consolidator grant 819242). CIBERONC was co-funded with FEDER funds. We are also grateful to Maria Vivanco's lab for providing reagents

In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on diferent leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identifcation in cance