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Background We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug–disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. Objective Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. Methods We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. Results Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. Conclusion Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu. [ABSTRACT FROM AUTHOR]

Live-Attenuated Vaccines (LAVs) stimulate robust mucosal and cellular responses and have the potential to protect against Respiratory Syncytial Virus (RSV) and Human Metapneumovirus (HMPV), the main etiologic agents of viral bronchiolitis and pneumonia in children. We inserted the RSV-F gene into an HMPV-based LAV (Metavac®) we previously validated for the protection of mice against HMPV challenge, and rescued a replicative recombinant virus (Metavac®-RSV), exposing both RSV- and HMPV-F proteins at the virion surface and expressing them in reconstructed human airway epithelium models. When administered to BALB/c mice by the intranasal route, bivalent Metavac®-RSV demonstrated its capacity to replicate with reduced lung inflammatory score and to protect against both RSV and lethal HMPV challenges in vaccinated mice while inducing strong IgG and broad RSV and HMPV neutralizing antibody responses. Altogether, our results showed the versatility of the Metavac® platform and suggested that Metavac®-RSV is a promising mucosal bivalent LAV candidate to prevent pneumovirus-induced diseases. [ABSTRACT FROM AUTHOR]

This correction notice is related to an article published in the journal NPJ Vaccines. The correction states that the authors Guy Boivin, Manuel Rosa-Calatrava, and Julia Dubois should have been denoted as jointly supervising authors. The original article has been corrected accordingly. The authors of the article are listed as Daniela Ogonczyk-Makowska, Pauline Brun, Clémence Vacher, Caroline Chupin, Clément Droillard, Julie Carbonneau, Emilie Laurent, Victoria Dulière, Aurélien Traversier, Olivier Terrier, Thomas Julien, Marie Galloux, Stéphane Paul, Jean-François Eléouët, Julien Fouret, Marie-Eve Hamelin, Andrés Pizzorno, Guy Boivin, Manuel Rosa-Calatrava, and Julia Dubois. [Extracted from the article]

Background: We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches., Objective: Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches., Methods: We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations., Results: Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures., Conclusion: Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu., (© The Author(s) 2024. Published by Oxford University Press.)