Biology subjects

Bugno, M.

Publications and source records attributed to Bugno, M..

3 recordsLinked to original sources

bioRxiv2023

mRNA Vaccines Encoding Influenza Virus Hemagglutinin (HA) Elicits Immunity in Mice from Influenza A Virus Challenge

Influenza viruses cause epidemics and can cause pandemics with substantial morbidity with some mortality every year. Seasonal influenza vaccines have incomplete effectiveness and elicit a narrow antibody response that often does not protect against mutations occurring in influenza viruses. Thus, various vaccine approaches have been investigated to improve safety and efficacy. Here, we evaluate an mRNA influenza vaccine encoding hemagglutinin (HA) proteins in a BALB/c mouse model. The results show that mRNA vaccination elicits neutralizing and serum antibodies to each influenza virus strain contained in the current quadrivalent vaccine that is designed to protect against four different influenza viruses including two influenza A viruses (IAV) and two influenza B (IBV), as well as several antigenically distinct influenza virus strains in both hemagglutination inhibition assay (HAI) and virus neutralization assays. The quadrivalent mRNA vaccines had antibody titers comparable to the antibodies elicited by the monovalent vaccines to each tested virus regardless of dosage following an mRNA booster vaccine. Mice vaccinated with mRNA encoding an H1 HA had decreased weight loss and decreased lung viral titers compared to mice not vaccinated with an mRNA encoding an H1 HA. Overall, this study demonstrates the efficacy of mRNA-based seasonal influenza vaccines are their potential to replace both the currently available split-inactivated, and live-attenuated seasonal influenza vaccines.

immunology↗

bioRxiv2023

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, "BigRNA", which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.

bioinformatics↗

bioRxiv2020

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to mRNA and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization-driven, while differential expression events are more common and driven by other mechanisms. We further evaluated the performance of in silico screens for off-target events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR. Despite these large improvements in performance, the approach does not detect the majority of events at a false discovery rate below 99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs.

molecular biology↗