Biology subjects

Greco, B. M.

Publications and source records attributed to Greco, B. M..

3 recordsLinked to original sources

bioRxiv2025

PARPAL: PARalog Protein Redistribution using Abundance and Localization in Yeast Database

Whole-genome duplication (WGD) events are common across various organisms however the retention and evolution of WGD paralogs is not fully understood. Quantitative measure of protein redistribution in response to the deletion of their WGD paralog provides insight into sources of gene retention. Here, we describe PARPAL (PARalog Protein Retribution using Abundance and Localization in Yeast), a web database that houses results of high-content screening and deep learning neural network analysis of the redistribution of 164 proteins reflecting how their subcellular localization and protein abundance change in response to their paralog deletion. We interrogated a total of 82 paralog pairs in two genetic backgrounds for a total of [~]3,500 micrographs of [~]460,000 cells. PARPAL also links to other studies on trigenic interactions, protein-protein interactions and protein abundance. PARPAL is available at https://parpal.c3g-app.sd4h.ca and is a valuable resource for the yeast community interested in understanding the retention and evolution of paralogs and can help researchers to investigate protein dynamics of paralogs in other organisms.

systems biology↗

bioRxiv2023

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

systems biology↗

bioRxiv2022

Rapid, scalable, combinatorial genome engineering by Marker-less Enrichment and Recombination of Genetically Engineered loci (MERGE)

Large-scale genome engineering in yeast is feasible primarily due to prodigious homology-directed DNA repair (HDR), a plethora of genetic tools, and simple conversion between haploid and diploid forms. However, a major challenge to rationally building multi-gene processes in yeast arises due to the combinatorics of combining all of the individual edits into the same strain. Here, we present an approach for scalable, precise, multi-site genome editing that combines all edits into a single strain without the need for selection markers by using CRISPR-Cas9 and gene drives. First, we show that engineered loci become resistant to the corresponding CRISPR reagent, allowing the enrichment of distinct genotypes. Next, we demonstrate a highly efficient gene drive that selectively eliminates specific loci by integrating CRISPR-Cas9 mediated Double-Strand Break (DSB) generation and homology-directed recombination with yeast sexual assortment. The method enables Marker-less Enrichment and Recombination of Genetically Engineered loci (MERGE) in yeast. We show that MERGE converts single heterologous yeast loci to homozygous loci at [~]100% efficiency, independent of chromosomal location. Furthermore, MERGE is equally efficient at converting and combining loci, thus identifying viable intermediate genotypes. Finally, we establish the feasibility of MERGE by engineering a fungal carotenoid biosynthesis pathway and most of the human proteasome core into yeast. MERGE, therefore, lays the foundation for marker-less, highly efficient, and scalable combinatorial genome editing in yeast.

synthetic biology↗