A bacterium meticulously regulates its biological processes to survive changing and/or stressful environments. These processes rely on the coordinated effort of multiple genetic components to produce a biological function. A genome thus has a complex functional arrangement of connections between these components to generate a certain phenotype. Such connections are revealed by studying genetic interactions, which are defined by mutations in two or more genes that, compared to the phenotype of the individual mutations, produce an unexpected phenotype when combined. Interactions can be classified into different categories pointing to different types of relationships, including negative or synthetic lethal interactions, which may be an indication of functionally related genes in parallel pathways, whereas a positive interaction that suppresses an individual mutant phenotype highlights regulatory connections between genes. Despite having large potential, it is mostly not possible to reconstruct comprehensive genetic interaction networks in (pathogenic) bacteria due to technical limitations and a lack of high-throughput tools. For instance, conventional array-based approaches that use ordered single and double gene deletion mutant libraries of bacteria to assess genetic interactions are labor intensive. Tn-Seq (Transposon-insertion sequencing), an approach that combines transposon mutagenesis with massively parallel sequencing, does enable genetic interaction network reconstruction in high-throughput. However, neither array-based approaches nor Tn-Seq can assess the importance of essential genes because essential gene knockouts are inviable and thus cannot be made. Instead, chemical-genetics based approaches have been used in bacteria to profile drug-gene interactions, which can be translated to genetic interactions. However, these approaches are limited by the number of existing antimicrobials that have a (highly) specific essential-gene target. Recently, CRISPRi (clustered regularly interspaced short palindromic repeats interference) a gene knockdown approach was developed that enables sub-lethal targeting of essential genes thereby enabling measurements that can determine the contribution of an essential-gene to a phenotype. Here we develop the ?next-generation? tool CRISPRi-Tn-Seq; a fusion of CRISPRi and Tn-Seq, that can reconstruct a bacterial genetic interaction network, including all essential and nonessential genes, in high-throughput. CRISPRi-Tn-Seq will be implemented in the bacterial pathogen Streptococcus pneumoniae, and will be directly applied to projects in the lab that are focused on identifying strain dependent virulence and the emergence of antimicrobial resistance. Moreover, we develop a novel generalizable tool that is transferable to other bacterial species and, if time permits, we aim to develop the system at least for the gram-negative Acinetobacter baumanii as well.

Public Health Relevance

A genetic interaction map gives a genome-wide view of the relationships between all genes in the genome and can thereby aid in identifying novel gene functions, regulatory networks and drug targets. Although relatively complete networks have been generated for model systems such as yeast, high quality networks are lacking in (pathogenic) bacteria, mostly due to technical limitations. Here we develop CRISPRi-Tn-Seq, combining two state-of-the-art technologies enabling comprehensive genetic interaction network reconstruction, including essential genes, in bacteria.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Small Research Grants (R03)
Project #
1R03AI135737-01A1
Application #
9600189
Study Section
Prokaryotic Cell and Molecular Biology Study Section (PCMB)
Program Officer
Lu, Kristina
Project Start
2018-05-04
Project End
2020-04-30
Budget Start
2018-05-04
Budget End
2019-04-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Boston College
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
045896339
City
Chestnut Hill
State
MA
Country
United States
Zip Code