Biology subjects

Levander, F.

Publications and source records attributed to Levander, F..

4 recordsLinked to original sources

bioRxiv2026

MTDH-SND1 disruption sensitizes ovarian cancer to ferroptosis and PARP inhibition

BRCA-deficient high-grade serous ovarian cancer is characterized by profound genomic instability and elevated replication-associated DNA damage, rendering these tumors initially sensitive to platinum-based chemotherapy and PARP inhibition. However, despite this vulnerability, most patients ultimately develop resistance, underscoring the need for therapeutic strategies that extend beyond DNA repair-targeted mechanisms. Here, we introduce the MTDH-SND1 complex as a complementary therapeutic target that may expose additional stress vulnerabilities in ovarian cancer cells. We show that pharmacological disruption of the MTDH-SND1 interaction using C26-A6 increases susceptibility to ferroptosis-associated stress, an iron-dependent form of regulated cell death and that BRCA-deficient models are particularly more sensitive to this perturbation. Notably, when combined with PARP inhibition, MTDH-SND1 disruption is associated with increased MHC class I expression in tumor cells, suggesting enhanced tumor visibility to the immune system. Together, these findings support a combination strategy that couples DNA repair disruption with metabolic and immunogenic remodeling in BRCA-deficient ovarian cancer.

cancer biology↗

bioRxiv2023

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

Proteomics research encompasses a wide array of experimental designs, resulting in diverse datasets varying in structure and properties. This diversity has led to a considerable variety of software solutions for data analysis, each of them using multiple tools with different algorithms for operations like peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. Computational workflows combine these algorithms to facilitate end-to-end analysis, spanning from raw data to detecting differentially regulated proteins. We introduce WOMBAT-P, a versatile platform designed for the automatic benchmarking and comparison of bottom-up label-free proteomics workflows. By standardizing software parameterization and workflow outputs, WOMBAT-P empowers an objective comparison of four commonly utilized data analysis workflows. Furthermore, WOMBAT-P streamlines the processing of public data based on the provided metadata, with an optional specification of 30 parameters. Wombat-P can use Sample and Data Relationship Format for Proteomics (SDRF-Proteomics) as the file input to simply process annotated local or ProteomeXchange deposited datasets. This feature offers a shortcut for data analysis and facilitates comparisons among diverse outputs. Through an examination of experimental ground truth data and a realistic biological dataset, we unveil significant disparities and a low overlap between identified and quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of four workflows (on the same dataset) using a wide range of benchmarking metrics but also provides insights into the capabilities of different software solutions. These metrics support researchers in selecting the most suitable workflow for their specific dataset. The modular architecture of WOMBAT-P promotes extensibility and customization, making it an ideal platform for testing newly developed software tools within a realistic data analysis context.

bioinformatics↗

bioRxiv2020

OmicLoupe: Facilitating biological discovery by interactive exploration of multiple omic datasets and statistical comparisons

Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. The OmicLoupe software was developed to facilitate such exploration and provides more than 15 interactive cross-dataset visualizations for omic data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three diverse studies, including an analysis of SARS-CoV-2 infection across omic layers, based on previously published proteomics and transcriptomics studies. OmicLoupe is available at quantitativeproteomics.org/omicloupe

bioinformatics↗

bioRxiv2018

e-workflow for recording of glycomic mass spectrometric data in compliance with reporting guidelines

Glycomics targets released glycans from proteins, lipids and proteoglycans. High throughput glycomics is based on mass spectrometry (MS) that increasingly depends on exchange of data with databases and the use of software. This requires an agreed format for accurately recording of experiments, developing consistent storage modules and granting public access to glycomic MS data. The introduction of the MIRAGE (Mimimum Requirement for A Glycomics Experiment) reporting standards for glycomics was the first step towards automating glycomic data recording. This report describes a glycomic e-infrastructure utilizing a well established glycomics recording format (GlycoWorkbench), and a dedicated web tool for submitting MIRAGE-compatible MS information into a public experimental repository, UniCarb-DR. The submission of data to UniCarb-DR should be a part of the submission process for publications with glycomics MSn that conform to the MIRAGE guidelines. The structure of this pipeline allows submission of most MS workflows used in glycomics.

bioinformatics↗