Valdeolivas Urbelz, A. | Search bioRxiv

bioRxiv2025

Spatial Transcriptomics and Single-Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

The liver is a primary target for recombinant adeno-associated viral (rAAV) vectors, yet the influence of serotype, sex, and liver zonation on transduction and transcriptomic changes remain incompletely understood. This proof-of-concept study employs spatial transcriptomics alongside single-nucleus RNA sequencing to map the spatial distribution and impacts of rAAV2- and rAAV9-CMV-EGFP vectors in male and female mouse livers. Spatial transcriptomics provided precise transgene mapping and highlighted that rAAV vectors deregulate hepatocellular lipid metabolism, the circadian clock, and the immune/stress response with sex specific differences. Lipid metabolism genes (Elovl3, Chka, Irs2, Ppard), were deregulated independent of zonation, serotype, and sex, while Srebf1, Tlcd4, Cpt2, and Acot1 exhibited sex-specific patterns. Circadian clock modulators (Dbp, Tef, Arntl, Nfil3, Nr1d1/Nr1d2) were altered independent of zonation. The study found sex-specific downregulation of immune and stress-response genes and pathways, including Gadd45g and hypoxia pathways. TGF-{beta} and EGFR pathways were upregulated sex-independently. Spatial transcriptomics further enabled examination of transgene and rAAV entry factor co-expression, identifying known and novel factors like Rpsa, Dpp4, Sdc1, and solute carrier proteins highlighting its role in supporting targeted screening. Our findings demonstrate spatial transcriptomics as a powerful tool in gene therapy research and reveal novel rAAV vector effects on liver biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/643011v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@af87b9org.highwire.dtl.DTLVardef@1f79165org.highwire.dtl.DTLVardef@6aa2ccorg.highwire.dtl.DTLVardef@e6274c_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity - and not inverse similarity - between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect. O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/459786v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@7cd823org.highwire.dtl.DTLVardef@51f699org.highwire.dtl.DTLVardef@114c555org.highwire.dtl.DTLVardef@a774ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗