Sekhon, P. | Search bioRxiv

bioRxiv2026

Telmisartan alters the transcriptomes of cancer associated fibroblasts and fibrocytes in a solid tumour model

Cancer associated fibroblasts (CAFs) are the main contributors to fibrosis in solid tumours. Fibrosis can lead to increased tumoural stiffness, immune cell exclusion, and poor response to cancer therapies. However, therapeutically depleting CAFs has been shown to increase tumour progression and metastasis in some pre-clinical tumour models. Targeted approaches to inhibit CAF activity and decrease fibrosis without abrogating CAFs altogether may improve the effect of existing cancer therapies. We have previously shown that the blood pressure medication telmisartan decreases tumoural collagen, resulting in improved vascular perfusion, decreased hypoxia, and improved radiation treatment efficacy. To understand how telmisartan influences CAF populations in solid tumours, we conducted single cell RNA sequencing analyses of host cells isolated from tumours in mice treated with telmisartan. Telmisartan modified the transcriptomes of CAF populations, promoting the expression of chemokine signaling while decreasing the expression of extracellular matrix components in myCAFs. Telmisartan decreased expression of collagen transcripts in myCAFs and fibrocytes, indicating that telmisartan does not remove myCAFs but changes the myCAF transcriptome to become less fibrillar. Our work provides further support for using telmisartan to manipulate CAF populations in the solid tumour microenvironment to decrease collagen deposition and render tumours more responsive to current cancer therapies.

cancer biology↗

bioRxiv2021

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

bioinformatics↗