Biology subjects

Magnusson, G.

Publications and source records attributed to Magnusson, G..

3 recordsLinked to original sources

bioRxiv2026

Unifying the genetic landscape of common and rare diseases via latent neighborhoods

Resolving disease mechanisms and identifying safer drug targets remains challenging due to difficulties in integrating inconsistent sources of genetic evidence. Here, we developed a deep-learning (DL) method for obtaining latent representations of human diseases and other terms by coupling a variational autoencoder (VAE) to network embeddings from graph representation learning on a protein interaction network. We apply this approach to map the landscape of human disease, by systematically integrating evidence for [~]42,000 traits and terms from sources such as clinical reports (ClinVar), genome-wide association studies (GWAS), mouse phenotypes and gene ontology. Similar diseases share their latent neighborhoods with the sources of evidence highlighting different aspects of the same cell biology for its role in disease. By decoding latent neighborhoods, we unify the sources of evidence and integrate across diseases to prioritize genes for common and rare diseases. Through examples for diabetes, hearing loss, and familial long QT syndrome, we illustrate how the methodology can be applied to develop hypotheses for disease mechanisms and to propose experimental models, measurements, targets and potential drugs that may improve diagnosis and treatments.

genetics↗

bioRxiv2025

Systematic structural and functional analysis of metal-binding sites in native proteomes

Metal-binding proteins (MBPs) are widespread and critical for many biological processes. Proteomics-based approaches have identified MBPs in various systems but typically cannot determine the location of metal-binding sites or shed light on the functional roles of metals. Thus, a systems-wide map of metal binding sites and an understanding of the consequences of metal binding for protein function are still lacking. Here, we used limited proteolysis-coupled mass spectrometry (LiP-MS) combined with chelator treatment to globally map metal-protein interactions and binding sites in native protein lysates. We applied the pipeline, named Metal-LiP, to Escherichia coli, identified 981 peptides on 297 proteins that exhibited dose responsiveness to chelator treatment, and quantified the relative metal-binding affinity. These proteins are strongly enriched for known or predicted MBPs, based on our novel compiled ground truth dataset of previously annotated (n=1013) and predicted (n=850) MBPs. Further, chelator-responsive peptides mapped at or near annotated metal-binding sites in 3D protein structures, showing that Metal-LiP can identify metal-binding sites, and provided novel experimental evidence for previously predicted sites. Notably, Metal-LiP identified 225 novel candidate MBPs and we validated five of these novel MBPs (GatZ, AhpC, GrxB, MsrC, TrhO) using inductively coupled plasma-mass spectrometry to detect metal binding in purified proteins. Finally, we used orthogonal approaches to probe the broad functional consequences of metal binding. Using a combination of Metal-LiP, thermal proteome profiling, size-exclusion chromatography coupled to mass spectrometry, and structural analyses, we identified cases where metal binding affects protein stability, modulates the assembly states of protein complexes, or is likely to affect enzymatic activity. We thereby propose functional roles for metal binding in 98 of our Metal-LiP hits and in 225 proteins in total. In summary, we describe an approach to identify bona fide MBPs with binding site-level resolution across the proteome. We have combined it with other methods to identify 424 candidate MBPs in the E coli proteome, to map known and novel candidate metal-binding sites for 297 of these proteins, and to suggest functional roles of metal binding.

systems biology↗

bioRxiv2022

NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows

Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP, which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.

neuroscience↗