Biology subjects

Foguet, C.

Publications and source records attributed to Foguet, C..

5 recordsLinked to original sources

bioRxiv2024

Glutaminase as a metabolic target of choice to counter acquired resistance to Palbociclib by colorectal cancer cells

Several mechanisms of resistance of cancer cells to cyclin-dependent kinase inhibitors (CDKi) have been identified, including the upregulation of metabolic regulators such as glutaminase. However, whether such mechanisms and targets are optimal has not been determined. Here, we have systematically analyzed metabolic reprogramming in colorectal cancer cells exposed to Palbociclib, a CDKi selectively targeting CDK4/6, or Telaglenestat, a selective glutaminase inhibitor. Through multiple approaches, we show that Palbociclib and Telaglenestat elicit complementary metabolic responses and are thus uniquely suited to counter the metabolic reprogramming induced by the reciprocal drug. As such, while Palbociclib induced reduced tumor growth in vivo, and Telaglenestat did not show a significant effect, the drug combination displayed a strong synergistic effect on tumor growth. Likewise, initial responses to Palbociclib were followed by signs of adaptation and resistance, which were prevented by combining Palbociclib with Telaglenestat. In conclusion, combination with Telaglenestat optimally forestalls acquired resistance to Palbociclib in cancer cells.

cancer biology↗

bioRxiv2023

Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10

Antiviral signaling, immune response and cell metabolism in human body are dysregulated by SARS-CoV-2, the causative agent of the COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While all four ORFs caused mitochondrial fragmentation and altered mitochondrial function, only ORF3a and ORF9c induced a marked structural alteration in mitochondrial cristae. ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes. In contrast, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes for proteins with critical mitochondrial functions and morphology. Genome-Scale Metabolic Models predicted common and private metabolic flux reprogramming, notably a depressed amino acid metabolism, and an enhanced metabolism of specific lipids distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention. One-Sentence SummaryMitochondria and metabolic alterations induced by SARS- CoV-2 accessory proteins ORF3a, ORF9b, ORF9c, ORF10 in pulmonary cells unravel new targets of intervention.

immunology↗

bioRxiv2022

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗

bioRxiv2018

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organisms metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent - and sometimes incompatible - analysis methods that are difficult to connect into a useful and complete data analysis solution.\n\nFindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the projects continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.\n\nConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and omics research domains.

bioinformatics↗

bioRxiv2017

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

bioinformatics↗