How genes and gene pathways interact is crucial for a complete understanding of a cell. However, most genes in any given organism are not essential, indicating that many cellular functions have genetic redundancy. For instance, three quarters of the genes encoded by the human pathogen Streptococcus pneumoniae are non-essential. A powerful approach to unravel genetic redundancy is by identifying gene-gene interactions. To uncover genetic interactions in S. pneumoniae on a genome-wide scale, we developed a generally applicable dual CRISPRi-Seq method and associated analysis pipeline. By constructing a library of 869 dual sgRNAs targeting high-confidence operons and more than 70% of all genetic elements in the pneumococcal genome, we generated 378,015 unique sgRNA combinations and assessed their genetic interactions. We identified 4026 unique genetic interactions (1935 negative and 2091 positive interactions), involving both non-essential and essential genes. Besides known genetic interactions, we found and confirmed several genetic interactions involving genes responsible for key cellular processes such as cell division, cell shape, and chromosome segregation that were previously unknown. The interactions discovered in this study are available for exploration via the Pneumococcal Genetic Interaction Network (PneumoGIN) at https://veeninglab.shinyapps.io/PneumoGIN. This work demonstrates the scalability of dual CRISPRi-Seq, enabling the investigation of essential genes and providing insights into gene-drug interactions. The here-described methods and bioinformatic approaches can serve as a roadmap for genome-wide gene interaction studies in other organisms and the generated pneumococcal gene interaction network can serve as a starting point for new biological discovery and translational research.
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