Biology subjects

Bosso, M.

Publications and source records attributed to Bosso, M..

3 recordsLinked to original sources

bioRxiv2026

CD81 is an Ebola virus inhibiting factor that is antagonized by GP and VP40

Viruses manipulate the host cell membrane of infected cells for evasion of antiviral immunity, prevention of superinfection and optimization of viral replication and spread. The Ebola virus glycoprotein (EBOV GP) mediates virus entry, but is also known as important factor for subversion of the hosts antiviral immune response. We characterized the dysregulation of cell surface-residing proteins by EBOV GP and found that among several membrane proteins GP interferes with the tetraspanins CD81, CD63 and CD9. This was a conserved function of several filoviral GPs and not observable for viral glycoproteins of other virus families. While CD63 and CD9 were largely dispensable for EBOV replication, CD81 suppressed virus-like particle entry and replication at multiple steps. This phenotype might be explainable by sustained suppression of NF{kappa}B by CD81, that is otherwise activated by VP40 and EBOV trVLP replication. We further demonstrate that not only GP but also VP40 interferes with CD81 functionality and that antibody-mediated clustering of CD81 suppresses EBOV infection. Altogether, the tetraspanin CD81 emerges as druggable NF{kappa}B and EBOV-inhibiting factor, supporting an important role of NF{kappa}B in EBOV replication and potentially virus-induced immunopathogenesis. HighlightsO_LIEBOV GP and VP40 interfere with CD81 cell surface expression C_LIO_LICD81 suppresses NF{kappa}B signaling which is activated by VP40 C_LIO_LICD81 restricts EBOV VLP uptake and replication C_LIO_LITargeting CD81 by a crosslinking antibody inhibits EBOV infection C_LI

microbiology↗

bioRxiv2025

A novel in vitro 3D cancer model based on modular tissue engineering approach

An emerging tool to better recapitulate the complexity of tumor biology in vitro is 3D culture models. Here, we describe a free-floating collagen-based hydrogel system with embedded cancer cells, called microtissues. The microtissues are based on the well-established modular tissue engineering method. They mimic the natural development of the tumor microenvironment, with features such as hypoxia and treatment resistance. To demonstrate the utility of microtissues as a 3D tumor model system, triple negative breast cancer cells were cultured using this method and were shown to maintain cell viability and proliferation with minimal cell death, along with mimicking natural emergence of tumor properties such as, a hypoxic core. Furthermore, by screening the model with commonly used anti-breast cancer chemotherapeutics, we observed drug resistance to concentrations which are largely in accordance with the used doses in the clinics. Therefore, our model offers the opportunity to naturally reproduce fundamental features of a tumor in vitro, leading to emergence of a similar cell reprogramming which is responsible for clinical drug resistance.

cancer biology↗

bioRxiv2023

Deep generative models predict SARS-CoV-2 Spike infectivity and foreshadow neutralizing antibody escape

Recurrent waves of SARS-CoV-2 infection, driven by the periodic emergence of new viral variants, highlight the need for vaccines and therapeutics that remain effective against future strains. Yet, our ability to proactively evaluate such therapeutics is limited to assessing their effectiveness against previous or circulating variants, which may differ significantly in their antibody escape from future viral evolution. To address this challenge, we develop a deep learning method to predict the effect of mutations on fitness and escape from neutralizing antibodies. We use this model to engineer 83 unique SARS-CoV-2 Spike proteins incorporating novel combinations of up to 46 amino acid changes relative to the ancestral B.1 variant. The designed constructs were infectious and evaded neutralization by nine well-characterized panels of human polyclonal anti-SARS-CoV-2 immune sera (from vaccinated, boosted, bivalent boosted, and breakthrough infection individuals). Designed constructs on contemporary SARS-CoV-2 strains displayed similar levels of antibody neutralization escape and similar antigenic profiles as variants seen subsequently (up to 12 months later) during the COVID-19 pandemic despite differences in exact mutations. Our approach provides targeted datasets of antigenically diverse escape variants for an early evaluation of the protective ability of vaccines and therapeutics to inhibit not only currently circulating but also future variants. This approach is generalizable to other viral pathogens.

immunology↗