Biology subjects

Ayuda-Duran, P.

Publications and source records attributed to Ayuda-Duran, P..

4 recordsLinked to original sources

bioRxiv2024

Genome-wide profiling of the hierarchical control of autophagy dynamics using deep learning

Recycling of cellular components through autophagy maintains homeostasis in dynamic nutrient environments, and its dysregulation is linked to several human disorders. Although extensive research has characterized the core mechanisms of autophagy, limited insight into its systems-wide dynamic control has hampered predictive modeling and effective in vivo manipulation. In this study, we mapped the genetic network that controls both the dynamic activation and inactivation of autophagy during nitrogen changes, using a combination of time-resolved high-content imaging, deep learning, and latent feature analysis. This approach generated a comprehensive genome-wide profiling repository, termed AutoDRY, categorizing 5919 mutants based on their nutrient response kinetics and differential contributions to autophagosome formation and clearance. Integrating these profiles with functional and genetic network data unveiled a hierarchical and multi-layered control of autophagy, identifying new regulatory aspects of the core machinery and established nutrient-sensing pathways. By leveraging multi-omics resources and explainable machine learning to predict genetic perturbation effects and infer new regulatory mechanisms, we identified the retrograde pathway as a pivotal, time-varying autophagy modulator through transcriptional tuning of core genes. By charting the systems-wide dynamical control of autophagy, we have laid the groundwork for connecting the complexity of genome-wide influences with specific core mechanisms. This represents a significant advancement in studying complex genetic phenotypes, guides functional genomics of dynamic cellular processes in any organism, and provides a powerful starting point for hypothesis-based research on autophagy.

systems biology↗

bioRxiv2022

Risk Stratification of Acute Myeloid Leukemia Using Ex Vivo Drug Sensitivity Profiling

Acute Myeloid Leukemia (AML) is a heterogeneous malignancy involving the clonal expansion of myeloid stem and progenitor cells in the bone marrow and peripheral blood. Most AML patients eligible for potentially curative treatment receive intensive chemotherapy. Risk stratification is used to optimize treatment intensity and transplant strategy, and is mainly based on cytogenetic screening for structural chromosomal alterations and targeted sequencing of a selection of common mutations. However, the forecasting accuracy of treatment response remains modest. Recently, ex vivo drug screening has gained traction for its potential in personalized treatment selection, as well as a tool for identifying and mapping patient groups based on relevant cancer dependencies. We systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can accurately assess risk of individual AML patients and may guide clinical decision-making.

bioinformatics↗

bioRxiv2022

metascreen: A modular tool for the design and analysis of drug combination screens

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. metascreen is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. metascreen is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. metascreen is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens. Availability and implementationThe R-package and technical documentation is available at https://github.com/Enserink-lab; the R source code is publicly available at https://github.com/Enserink-lab/metascreen under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy/ Selected modules will be available through Galaxy, an open-source platform for FAIR data analysis, Norway: https://usegalaxy.no

bioinformatics↗

bioRxiv2020

A cell competition-based drug screen identifies a novel compound that induces dual c-Myc degradation and p53 activation

BCR-Abl is a driver oncogene that causes chronic myeloid leukemia and a subset of acute lymphoid leukemias. Although tyrosine kinase inhibitors provide an effective treatment for these diseases, they generally do not kill leukemic stem cells. Leukemic stem cells are cancer-initiating cells that compete with normal hematopoietic stem cells for the bone marrow niche. Using BCR-Abl as a model oncogene, we performed a drug screen based on competition between isogenic untransformed cells and BCR-Abl-transformed cells, and identified several compounds that selectively target BCR-Abl-transformed cells. Systems-level analysis of one of these novel compounds, DJ34, revealed that it induced depletion of c-Myc and activation of p53. c-Myc depletion occurred in a wide range of tumor types, including leukemia, lymphoma, lung, glioblastoma and breast cancer. Further analyses revealed that DJ34 interferes with c-Myc synthesis at the level of transcription, and we provide data showing that DJ34 is a DNA intercalator and topoisomerase II inhibitor. Physiologically, DJ34 induced apoptosis, cell cycle arrest and cell differentiation, and primary leukemic stem cells were particularly sensitive to DJ34. Taken together, we have identified a novel compound that dually targets c-Myc and p53 in a wide variety of cancers, and with particularly strong activity against leukemic stem cells.

cancer biology↗