Biology subjects

San Jose-Eneriz, E.

Publications and source records attributed to San Jose-Eneriz, E..

4 recordsLinked to original sources

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗

bioRxiv2021

A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

systems biology↗

bioRxiv2020

BOSO: a novel feature selection algorithm for linear regression with high-dimensional data

MotivationWith the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. ResultsWe introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior performance in highdimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. AvailabilityA Matlab implementation of BOSO is available as a Supplementary Material. Contactfplanes@tecnun.es Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on the bone marrow (BM) microenvironment, in particular on mesenchymal stromal cells (MSCs). MSCs from MM patients exhibit an abnormal transcriptional profile, suggesting that epigenetic alterations could be governing the tumor-promoting functions of MSCs and their prolonged osteoblast (OB) suppression in MM. In this study, we analyzed the DNA methylome of BM-derived MSCs from patients with monoclonal gammopathy of undetermined significance, smoldering myeloma and symptomatic MM at diagnosis in comparison with their normal counterparts. DNA methylation alterations were found at each of the myeloma stage in association with deregulated expression levels of Homeobox genes involved in osteogenic differentiation. Moreover, these DNA methylation changes were recapitulated in vitro by exposing MSCs from healthy individuals to MM plasma cells. Pharmacological targeting of DNMTs and G9a with the dual inhibitor CM-272, reverted the expression of aberrantly methylated osteogenic regulators and promoted OB differentiation of MSCs from myeloma patients. Most importantly, in a mouse model of bone marrow-disseminated MM, administration of CM-272 prevented tumor-associated bone loss and reduced tumor burden. Our results demonstrated that not only was aberrant DNA methylation a main contributor to bone formation impairment found in MM patients, but also its targeting by CM-272 was able to reverse MM-associated bone loss.\n\nKEY POINTS- Bone marrow-derived mesenchymal stromal cells (MSCs) from monoclonal gammopathy of undetermined significance, smoldering myeloma and myeloma patients exhibit an aberrant DNA methylome compared to their healthy counterparts.\n- These DNA methylation changes are associated with an altered expression of genes of the Homeobox loci that orchestrate osteogenic differentiation of mesenchymal precursors.\n- MM plasma cell-exposed healthy MSCs recapitulate the DNA methylation alterations observed in MSCs isolated from myeloma patients.\n- Dual targeting of DNMTs and the histone methyltransferase G9a with CM-272 not only controls MM tumor burden but also prevents myeloma-associated bone loss.

cancer biology↗