Resultados de investigación

Sample Processing Protocol Nuclear extracts of mESCs were perepared as previosly described in Kloet et al. 2016. 2mg of lysate was mixed with 6ug of anti-FLAG antibody (Merck. F3165) and 50uL of Protein G mangnetic beads (Dynabeads). The magnetic beads were cleaned three times with 500 µl of 200 mM ammonium bicarbonate and 60 µl of 6M Urea / 200mM ammonium bicarbonate were added. Samples were then reduced with dithiothreitol (30 nmol, 37 ºC, 60 min), alkylated in the dark with iodoacetamide (60 nmol, 25 ºC, 30 min) and diluted to 1M urea with 200 mM ammonium bicarbonate for trypsin digestion (1 µg, 37ºC, 8h, Promega). After digestion, peptide mix was acidified with formic acid and desalted with a MicroSpin C18 column (The Nest Group, Inc) prior to LC-MS/MS analysis. The samples were then analysed using a LTQ-Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) coupled to an EASY-nLC 1200 (Thermo Fisher Scientific (Proxeon, Odense, Denmark). Peptides were loaded directly onto the analytical column and were separated by reversed-phase chromatography using a 50-cm column with an inner diameter of 75 μm, packed with 2 μm C18 particles. Chromatographic gradients started at 95% buffer A and 5% buffer B with a flow rate of 300 nl/min and gradually increased to 25% buffer B and 75% A in 52 min and then to 40% buffer B and 60% A in 8 min. After each analysis, the column was washed for 10 min with 100% buffer B. Buffer A: 0.1% formic acid in water. Buffer B: 0.1% formic acid in 80% acetonitrile. The mass spectrometer was operated in positive ionization mode with nanospray voltage set at 2.4 kV and source temperature at 305°C. The acquisition was performed in data-dependent adquisition (DDA) mode and full MS scans with 1 micro scans at resolution of 120,000 were used over a mass range of m/z 350-1400 with detection in the Orbitrap mass analyzer. Auto gain control (AGC) was set to ‘standard’ and injection time to ‘auto’. In each cycle of data-dependent acquisition analysis, following each survey scan, the most intense ions above a threshold ion count of 10000 were selected for fragmentation. The number of selected precursor ions for fragmentation was determined by the “Top Speed” acquisition algorithm and a dynamic exclusion of 60 seconds. Fragment ion spectra were produced via high-energy collision dissociation (HCD) at normalized collision energy of 28% and they were acquired in the ion trap mass analyzer. AGC and injection time were set to ‘Standard’ and ‘Dynamic’, respectively and isolation window of 1.4 m/z was used. Digested bovine serum albumin (New England Biolabs) was analysed between each sample to avoid sample carryover and to assure stability of the instrument. QCloud has been used to control instrument longitudinal performance during the project. -- Data Processing Protocol Acquired spectra were analysed using the Proteome Discoverer software suite (v1.4, Thermo Fisher Scientific) and the Mascot search engine (v2.6, Matrix Science). The data were searches against a Swiss-Prot mouse database (as in March 2021, 17082 entries) plus a list of common contaminants and all the corresponding decoy entries. For peptide identification a precursor ion mass tolerance of 7 ppm was used for MS1 level, trypsin was chosen as enzyme and up to three missed cleavages were allowed. The fragment ion mass tolerance was set to 0.5 Da for MS2 spectra. Oxidation of methionine and N-terminal protein acetylation were used as variable modifications whereas carbamidomethylation on cysteines was set as a fixed modification. False discovery rate (FDR) in peptide identification was set to a maximum of 5%. SAINT express algorithm was used to score protein-protein interactions and STRING software was used to identify functional clusters among FLAG-BMAL1-interacting proteins. --, Immunoprecipitation with anti-FLAG antibody in nuclear extracts of FLAG-BMAL1 expressing mESCs. LC MS/MS was used to analyze the interactome of BMAL1 in mESCs., Peer reviewed

Refer to individual Series. This SuperSeries is composed of the SubSeries listed below. Figure 1: scRNAseq of a bulk sample 1014 NRAS*. Sample GSM8195555. Figure 2: RNAseq. Samples GSM8195543 to GSM8195554. Figures 4 - 6: scRNAseq of CX3CR1+/CD45+ samples from NRAS* MBM. Samples from GSM8706608 to GSM8706613., Melanoma is one of the solid tumor types with the highest risk of brain metastasis. However, the biology of melanoma brain metastasis and the contribution of the brain immune microenvironment to the responses to therapies remain insufficiently characterized. By using preclinical models and single-cell transcriptomics, we identify microglia, as critical regulators of melanoma biology in the brain. We show that activation of the Rela/NF-kB pathway in microglia promotes melanoma brain metastasis and that targeting this pathway elicits microglia reprogramming towards a pro-inflammatory phenotype that enhances anti-tumor immunity and reduces brain metastatic burden. Additionally, canonical and pro-inflammatory microglial markers in melanoma brain metastasis correlate with better responses to immune checkpoint inhibitors in patients and we show that Rela/NF-kB targeting improves responses to these therapies in the brain. Thus, we propose targeting Rela/NF-kB in activated microglia as a strategy to promote anti-tumor immunity and to improve the response to immune checkpoint inhibitors in melanoma brain metastasis., Peer reviewed

We designed a MERSCOPE panel targeting 265 genes using the Vizgen Gene Panel Design Portal. Formalin-fixed paraffin-embedded (FFPE) samples from three distinct MMTV-PyMT tumors were processed according to Vizgen MERSCOPE protocols. The resulting data were utilized to analyze transcriptional heterogeneity at the spatial level within the tumors., Disseminated cancer cells rarely succeed in forming macrometastases, yet they continue to be the most life-threatening cause of patient mortality. The determinants driving metastatic competence, however, are still poorly understood. Using data from human breast cancer patients and genetic mouse models, we uncover a non-linear relationship between the expression of the EMT transcription factor Prrx1 in the primary tumor and metastatic outcome. Multi-omics analyses in mouse tumors reveal that Prrx1 functions as a dual regulator, promoting invasive behavior while simultaneously inducing dormancy. Cells with intermediate Prrx1 levels exhibit the highest metastatic fitness, whereas extreme levels (negative or too high) lead to less metastasis, typical of a hormetic behavior, and respectively due to insufficient invasiveness or to dormancy. These findings provide mechanistic insight into the regulation of dormancy and its link to metastatic potential. Additionally, we propose that combined signatures of invasion and proliferation allow robust prognosis prediction in breast cancer patients. Our study further highlights Prrx1-driven heterogeneity, established in the primary tumor, as a key determinant of metastatic competence., Peer reviewed

Here, we use single cell sequencing to delineate the heterogeneity of tumours from the genetically engineered MMTV-PyMT mouse model of breast cancer (MMTV-PyMT::K14Cre::Rosa-tdTomato mice) upon Prrx1 knock out (both heterozygous and homozygous), Disseminated cancer cells rarely succeed in forming macrometastases, yet they continue to be the most life-threatening cause of patient mortality. The determinants driving metastatic competence, however, are still poorly understood. Using data from human breast cancer patients and genetic mouse models, we uncover a non-linear relationship between the expression of the EMT transcription factor Prrx1 in the primary tumor and metastatic outcome. Multi-omics analyses in mouse tumors reveal that Prrx1 functions as a dual regulator, promoting invasive behavior while simultaneously inducing dormancy. Cells with intermediate Prrx1 levels exhibit the highest metastatic fitness, whereas extreme levels (negative or too high) lead to less metastasis, typical of a hormetic behavior, and respectively due to insufficient invasiveness or to dormancy. These findings provide mechanistic insight into the regulation of dormancy and its link to metastatic potential. Additionally, we propose that combined signatures of invasion and proliferation allow robust prognosis prediction in breast cancer patients. Our study further highlights Prrx1-driven heterogeneity, established in the primary tumor, as a key determinant of metastatic competence., Peer reviewed

Experiment type: Genome binding/occupancy profiling by high throughput sequencing. -- Here, we use single cell sequencing to delineate the chromatin heterogeneity of tumours from the genetically engineered MMTV-PyMT mouse model of breast cancer (MMTV-PyMT::K14Cre::Rosa-tdTomato mice), Disseminated cancer cells rarely succeed in forming macrometastases, yet they continue to be the most life-threatening cause of patient mortality. The determinants driving metastatic competence, however, are still poorly understood. Using data from human breast cancer patients and genetic mouse models, we uncover a non-linear relationship between the expression of the EMT transcription factor Prrx1 in the primary tumor and metastatic outcome. Multi-omics analyses in mouse tumors reveal that Prrx1 functions as a dual regulator, promoting invasive behavior while simultaneously inducing dormancy. Cells with intermediate Prrx1 levels exhibit the highest metastatic fitness, whereas extreme levels (negative or too high) lead to less metastasis, typical of a hormetic behavior, and respectively due to insufficient invasiveness or to dormancy. These findings provide mechanistic insight into the regulation of dormancy and its link to metastatic potential. Additionally, we propose that combined signatures of invasion and proliferation allow robust prognosis prediction in breast cancer patients. Our study further highlights Prrx1-driven heterogeneity, established in the primary tumor, as a key determinant of metastatic competence., Peer reviewed

Acute myeloid leukemia (AML) is a disease with poor outcome but patients harbouring certain chromosomal rearrangements or complex karyotypes have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor risk AML we profiled 55 poor risk AML patients using a multiomics approach that included transcriptomics (n=39), proteomics (n=55), phosphoproteomics (n=55) and an ex vivo drug sensitivity screening (482 compounds tested in at least 30 patients). We identified a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and IMPDH relative to other cases. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to IMPDH inhibition. In summary, our multilayer molecular profiling of poor risk AML matched to the response to hundreds of compounds identified a phosphoproteomics signature that define two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the development of specific therapies for KMT2A subgroups characterised by the MLLGA phosphoproteomics signature identified in this study., El dataset se puede consultar y descargar en el siguiente enlace: https://dx.doi.org/10.6019/PXD032980, The raw mass spectrometry proteomics, phosphoproteomics, mRNA sequencing, DNA sequencing data and associated clinical data generated during this study are available for non-commercial research purposes only, through an Attribution CC-BY-NC-ND license (Anyone can share this material, provided it remains unaltered in any way, this is not done for commercial purposes, and the original authors are credited and cited). Access to additional clinical data and omics data can granted upon review of a project proposal that will be evaluated by a data access committee and entering into an appropriate data access agreement subject to any applicable ethical approvals. The raw mass spectrometry phosphoproteomics data generated during this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (https://www.ebi.ac.uk/pride/), Peer reviewed

Acute myeloid leukemia (AML) is a disease with poor outcome but patients harbouring certain chromosomal rearrangements or complex karyotypes have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor risk AML we profiled 55 poor risk AML patients using a multiomics approach that included transcriptomics (n=39), proteomics (n=55), phosphoproteomics (n=55) and an ex vivo drug sensitivity screening (482 compounds tested in at least 30 patients). We identified a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and IMPDH relative to other cases. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to IMPDH inhibition. In summary, our multilayer molecular profiling of poor risk AML matched to the response to hundreds of compounds identified a phosphoproteomics signature that define two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the development of specific therapies for KMT2A subgroups characterised by the MLLGA phosphoproteomics signature identified in this study., The raw mass spectrometry proteomics, phosphoproteomics, mRNA sequencing, DNA sequencing data and associated clinical data generated during this study are available for non-commercial research purposes only, through an Attribution CC-BY-NC-ND license (Anyone can share this material, provided it remains unaltered in any way, this is not done for commercial purposes, and the original authors are credited and cited). Access to additional clinical data and omics data can granted upon review of a project proposal that will be evaluated by a data access committee and entering into an appropriate data access agreement subject to any applicable ethical approvals. The raw mass spectrometry phosphoproteomics data generated during this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (https://www.ebi.ac.uk/pride/), El dataset se puede consultar y descargar en el siguiente enlace: https://www.ebi.ac.uk/pride/archive/projects/PXD038980, Peer reviewed

El texto completo del artículo en el que se han utilizado estos datos se ha publicado en la revista Economic History Review 67.3 (2014): 607-626 y está disponbile en http://dx.doi.org/10.1111/1468-0289.12047, The dataset that supports the findings of this study are archived in the Universidad Autónoma de Madrid data repository e‐cienciaDatos in https://doi.org/10.21950/5SCIIO, Datos de investigación generados por José Ignacio Andrés Ucendo (Universidad del País Vasco) y Ramón Lanza García (Universidad Autónoma de Madrid) y utilizados en la elaboración del artículo "Prices and real wages in seventeeth-century in Madrid" que se ha publicado en la revista Economic Historic Review 67.3 (2014): 607-626, Research data compiled by José Ignacio Ucendo (Universidad del País Vasco) and Ramón Lanza García (Universidad Autónoma de Madrid) and used in the article "Prices and real wages in seventeeth-century in Madrid". This article is published by the Economic History Review 67.3 (2014): 607-626, Funding for the research was provided by the Spanish Economy Ministry through the projects ‘Ciudades, gentes e intercambios en la Monarquía Hispánica: política económica, cambio institucional y desarrollo de los mercados en la Edad Moderna’ (HAR2012-39034-C03-02) and ‘Consumo, niveles de vida y desigualdad en España, 1400–1870’ (HAR 2012-35965)

Peer Reviewed

Peer reviewed, Fecha 2014-09-02.--Fujitsu ScanSnap iX500.--Unidad técnica IHMC.--Archivo depósito IHMC.--Solo se permite el uso con fines educativos y de investigación