Recipient 2023 : Carla Bautista
Navigating the genomic landscape of hybridization
Interspecific hybrids display plastic genomic architecture resulting from the interaction of two genomic backgrounds. This increase in genome plasticity generates phenotypic diversity available for natural selection, which could ultimately facilitate adaptation. However, there are likely limits to the adaptive potential of hybrids caused by their enhanced genomic instability. Genomic instability includes a higher rate of DNA damage, gene and chromosome copy number alteration, and chromosomal rearrangements. We challenged hybrids to evolve in a condition expected to increase genomic instability by increasing damage using exposure to a UV mimetic molecule. We exposed 180 populations of hybrids between species which diverge by about 15% at the nucleotide level (Saccharomyces cerevisiae and Saccharomyces paradoxus) and their parental strains to UV mimetic and control conditions for 100 generations. Although we found that adaptation occurs in both hybrids and parents, hybrids achieved a lower rate of adaptation. We suggested that the lower adaptive potential of hybrids in this condition may result from the interaction between DNA damage and the inherent genetic instability of hybrids. Accordingly, we hypothesize that such a slower adaptive response is caused by the accumulation of a larger number of genomic changes. Therefore, we sequenced 300 genomes of parental species and hybrids and compared the number of genomic changes by analyzing their ancestral state and after 100 generations of evolution in UV mimetic. Overall, our results will inform to what degree the inherent genetic instability of hybrids plays an important role in their slower extent of adaptation to conditions of genetic damage.
Recipient 2022: Natalia Teruel Fagundes Borges
Computational modeling of conformational state dynamics and viral fitness prediction
Several viral glycoproteins go through conformational changes, fundamental to infection processes. The SARS-CoV-2 Spike protein is of particular importance during the current pandemic. This protein interacts with the human acetylcholinesterase 2 (ACE2) receptor as part of the viral entry mechanism. To do so, the receptor-binding domain (RBD) of Spike needs to be in an open state conformation. Here we utilize coarse-grained Normal Mode Analyses to model the dynamics of SARS-CoV-2 Spike protein variants as well as the transition probabilities between open and closed conformations. We performed 17081 possible in silico single mutations of Spike to determine positions and mutations that may affect the occupancy of the conformational states. Based on that, we successfully predicted some of the main mutations that constitute Alpha, Beta and Gamma variants, offering an explanation for their epidemiological success. Given the shift in evolutionary pressure towards promoting immune escape, we built a simplified model for binding evaluation. It was validated with experimental data from deep mutation scanning of the binding between RBD mutants and ACE2, and is now being applied to the evaluation of interfaces between conformational ensembles of Spike, ACE2 and antibody structures. With this new tool that accounts for structure flexibility we were able to propose a consensus among the various experimental interfaces determined for Omicron structures. In summary, we present a method to evaluate mutants that integrates dynamics, binding, and immune escape and that can be used to screen a large number of mutations to predict future highly transmissible variants and possibly guide public health decisions.
Recipient 2021: Moïra Dion
A pipeline to improve phage host predictions using CRISPR spacers
Moïra B. Dion1, E. Zufferey1, P.-L. Plante2, S.A. Shah3, L. Deng4, J.L.C. Mejia4, M.-A. Petit5, D.S. Nielsen4, H. Bisgaard3, J. Corbeil2, S. Moineau1*,
1Département de biochimie, de microbiologie et de bio-informatique, Université Laval; 2Big Data Research Center, Université Laval, Québec, Canada; 3Copenhagen Prospective Studies on Asthma in Childhood, University of Copenhagen; 4Department of Food Science, University of Copenhagen, Denmark; 5INRA, Jouy-en-Josas, France
Phages are viruses that infect bacteria. Most of the newly discovered phages from viral metagenomics have no match in reference databases: this is the viral dark matter obstacle. To begin unraveling the impact of phages on their ecosystem, it is essential to improve phage-host predictions, even with unknown phages, using bioinformatics approaches. CRISPR spacers in the bacterial chromosome are useful for host predictions because they are molecular archives of past phage infections. Indeed, many bacteria use their CRISPR spacers, together with Cas proteins, to recognize invading phages and block subsequent infections. Since spacer sequences are identical to a short target sequence found in the phage genome, they can be used to link a phage to its host. The performance of CRISPR spacers-based host predictions largely depend on the extensiveness of the database against which phages are compared. We implemented a phage host prediction pipeline by building a database of more than 11 million CRISPR spacers. Using phages infecting known bacterial hosts, we identified biologically meaningful criteria that optimize the recall and precision of the predictions, reaching more than 80% accuracy at the bacterial family level. We built a web platform to explore the CRISPR spacers database and developed a command line tool to perform host predictions. This tool allowed us to uncover phages that infect bacteria with previously no known phages in the human gut virome.
Recipient 2020: Dr Ximena Zottig
Self-assembled peptide nanorod vaccine confers protection against influenza A virus
Ximena Zottig a,b,c,d, Soultan Al-Halifa a,b,c,d, Mélanie Côté-Cyr a,b,c,d, Cynthia Calzas e, Ronan Le Goffic e, Christophe Chevalier e, DenisArchambault c,d and SteveBourgault a,b,d
aChemistry Department, Université du Québec à Montréal, Montreal, Canada, bQuebec Network for Research on Protein Function, Engineering and Applications (PROTEO), Quebec, Canada, cDepartment of Biological Sciences, Université du Québec à Montréal, Montreal, Canada, dThe Swine and Poultry Infectious Diseases Research Centre (CRIPA), Sainte-Hyacinthe, Canada, eUR892 VIM, Equipe Virus Influenza, Université Paris-Saclay, INRAE, Jouy-en-Josas, France
Proteinaceous nanostructures have emerged as a promising strategy to develop safe and efficient subunit vaccines. The ability of synthetic β-sheet self-assembling peptides to stabilize antigenic determinants and to potentiate the epitope-specific immune responses have highlighted their potential as an immunostimulating platform for antigen delivery. Nonetheless, the intrinsic polymorphism of the resulting cross-β fibrils, their length in the microscale and their close structural similarity with pathological amyloids could limit their usage in vaccinology. In this study, we harnessed electrostatic capping motifs to control the self-assembly of a chimeric peptide comprising a 10-mer β-sheet sequence and a highly conserved epitope derived from the influenza A virus (M2e). Self-assembly led to the formation of 100–200 nm long uniform nanorods (NRs) displaying the M2e epitope on their surface. These cross-β assemblies differed from prototypical amyloid fibrils owing to low polydispersity, short length, non-binding to thioflavin T and Congo Red dyes, and incapacity to seed homologous amyloid assembly. M2e-NRs were efficiently uptaken by antigen presenting cells and the cross-β quaternary architecture activated the Toll-like receptor 2 and stimulated dendritic cells. Mice subcutaneous immunization revealed a robust M2e-specific IgG response, which was dependent on self-assembly into NRs. Upon intranasal immunization in combination with the polymeric adjuvant montanide gel, M2e-NRs conferred complete protection with absence of clinical signs against a lethal experimental infection with the H1N1 influenza A virus. These findings indicate that by acting as an immunostimulator and delivery system, synthetic peptide-based NRs constitute a versatile self-adjuvanted nanoplatform for the delivery of subunit vaccines.
