Search bioRxiv⌕ Search

Biology subjects

Vaccaro, L.

Publications and source records attributed to Vaccaro, L..

2 recordsLinked to original sources

Identification of druggable host dependency factors shared by multiple SARS-CoV-2 variants of concern

The high mutation rate of SARS-CoV-2 leads to emergence of several variants, some of which are resistant to vaccines and drugs targeting viral elements. Targeting host dependency factors - cell proteins required for viral replication - would help avoid resistance. However, whether different SARS-CoV-2 variants induce conserved cell responses and exploit the same core host factors is still unclear. We compared three variants of concern and observed that the host transcriptional response was conserved, differing only in kinetics and magnitude. By CRISPR screening we identified the host genes required for infection by each variant: most of the identified genes were shared by multiple variants, both in lung and colon cells. We validated our hits with small molecules and repurposed FDA-approved drugs. All drugs were highly effective against all tested variants, including delta and omicron, new variants that emerged during the study. Mechanistically, we identified ROS production as a pivotal step in early virus propagation. Antioxidant drugs, such as N-acetyl cysteine (NAC), were effective against all variants both in human lung cells, and in a humanised mouse model. Our study supports the use of available antioxidant drugs, such as NAC, as a general and effective anti-COVID-19 approach.

microbiology↗

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

bioinformatics↗