Biology subjects

Synnergren, J.

Publications and source records attributed to Synnergren, J..

4 recordsLinked to original sources

bioRxiv2025

Leveraging Neural Network Models for Drug Repurposing: A case study on Cardiac Hypertrophy

BackgroundDrug repurposing has emerged as an attractive strategy in contemporary pharmaceutical research, presenting an opportunity to expedite drug discovery, minimize developmental costs, and mitigate risks associated with developing new pharmaceuticals. In this study, we investigated a novel approach based on deep learning of human transcriptomic mechanisms for systematic identification of additional therapeutic potential in preexisting drugs. MethodWe trained a composite feedforward neural network model using gene expression data sourced from the ARCHS4 compilation of the GEO, encompassing extensive human datasets. Subsequently, disease-associated gene expression data were generated from our stem cell-derived in vitro model of cardiac hypertrophy induced by Endothelin-1 stimulation. These data were employed to identify latent variables associated with genes showing differential expression due to Endothelin-1 stimulation. By examining the differential expression profiles within the models latent space, we successfully correlated the disease signal with known drug targets found in pharmaceutical compounds cataloged in DrugBank. ResultsThe model accurately encoded additional disease-related genes beyond the curated gene set, demonstrating its ability to generalize disease associations. Leveraging the model, we identified potential drug candidates, such as lapatinib and amiodarone showing promise in mitigating proBNP concentration associated with cardiac hypertrophy. ConclusionThis study demonstrates the power of deep learning of human transcriptomic mechanisms in swiftly identifying new therapeutic potentials for existing drugs, highlighting the pivotal role of artificial intelligence technologies in accelerating drug development for other complex medical conditions. HighlightsO_LIAn interpretable neural network model for identification of candidate drugs for drug repurposing C_LIO_LIEncoding expression data from our cardiac hypertrophy model highlights important disease mechanisms C_LIO_LILapatinib and amiodarone experimentally validated as candidate drugs for cardiac hypertrophy therapies C_LI

bioinformatics↗

bioRxiv2025

Targeted delivery of mRNA to the heart via extracellular vesicles or lipid nanoparticles

Targeted mRNA transport plays a crucial role in enhancing the therapeutic efficacy of the molecule, reducing its side effects, and minimizing off-target effects. Systemic administration of mRNA through lipid nanoparticles (LNPs) or extracellular vesicles (EVs) predominantly results in mRNA accumulation in the liver. We hypothesized that cardiac-specific EVs could more effectively target the transport of mRNA to the heart, in comparison to non-cardiac-specific EVs or LNPs. In mice, after intravenous administration, EVs from cardiac progenitor cells (CPC-EVs) were the most efficient to transport the modified mRNA, encoding vascular endothelial growth factor A (VEGF-A), to mouse heart, with minimal liver accumulation compared to non-cardiac-specific EVs or LNPs. Additionally, intracardiac injections of CPC-EVs not only demonstrate that they are the most adapted vehicle for interacting with heart tissue, delivering the mRNA to cells, and inducing maximal VEGF-A protein production, but RNA-seq analyses also revealed their minimal impact on overall gene expression, compared to LNPs or non-cardiac-specific EVs. Furthermore, immunofluorescence staining of CD31 and -SMA, markers of microvascular density, showed increased vessel density in mouse aortic rings following the delivery of VEGF-A mRNA via CPC-EVs. These findings suggest that CPC-EVs are superior in mRNA targeting to heart, communication with cardiac cells, and causing minimal transcriptomic changes during VEGF-A mRNA delivery. Therefore, CPC-EVs could be promising vectors for heart-targeted mRNA delivery, potentially reducing liver accumulation.

molecular biology↗

bioRxiv2023

Representational Learning from Healthy Multi-Tissue Human RNA-seq Data such that Latent Space Arithmetics Extracts Disease Modules

1Developing computational analyses of transcriptomic data has dramatically improved our understanding of complex multifactorial diseases. However, such approaches are limited to small sample sets of disease-affected material, thus being sensitive to statistical biases and noise. Here, we ask if a variational autoencoder (VAE) trained on large groups of healthy, human RNA-seq data of multiple tissues can capture the fundamental healthy gene regulation system such that the learned representation generalizes to account for unseen disease changes. To this end, we trained a multi-scale representation to encode cellular processes ranging from cell types to genegene interactions. Importantly, we found that the learned healthy representations could predict unseen gene expression changes from 25 independent disease datasets. We extracted and decoded disease-specific signals from the VAE latent space to dissect this finding. Interestingly, the gene modules corresponding to this signal contained more disease-specific genes than the respective differential expression analysis in 20 of 25 cases. Finally, we matched genes related to the disease signals to known drug targets. We could extract sets of known and potential pharmaceutical candidates from this analysis and demonstrate the utility in three use cases. In summary, our study showcases how data-driven representation learning using a VAE as a foundational model allows an arithmetic deconstruction of the latent space such that biological insights enable the dissection of disease mechanisms and drug targets. Our model is available at https://github.com/ddeweerd/VAE_Transcriptomics/.

systems biology↗

bioRxiv2023

Artificial intelligence supports automated characterization of differentiated human pluripotent stem cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

cell biology↗