Bac8c (RIWVIWRR-NH 2 ) is an 8-amino-acid peptide derived from Bac2A (RLARIVVIRVAR-NH 2 ), a C3A/ C11A variant of the naturally occurring bovine peptide, bactenecin (also known as bovine dodecapeptide), the smallest peptide with activity against a range of pathogenic Gram-positive and Gram-negative bacteria, as well as yeast. The effects of Bac8c on Escherichia coli were examined by studying its bacteriostatic and bactericidal properties, demonstrating its effects on proton motive force generation, and visually analyzing (via transmission electron microscopy) its effects on cells at different concentrations, in order to probe the complexities of the mechanism of action of Bac8c. Results were consistent with a two-stage model for the Bac8c mode of action. At sublethal concentrations (3 g/ml), Bac8c addition resulted in transient membrane destabilization and metabolic imbalances, which appeared to be linked to inhibition of respiratory function. Although sublethal concentrations resulted in deleterious downstream events, such as methylglyoxal formation and free radical generation, native E. coli defense systems were sufficient for full recovery within 2 h. In contrast, at the minimal bactericidal concentration (6 g/ml), Bac8c substantially but incompletely depolarized the cytoplasmic membrane within 5 min and disrupted electron transport, which in turn resulted in partial membrane permeabilization and cell death.

Our limited ability to predict genotype–phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype–phenotype relationships at a single‐protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR‐EnAbled Trackable genome Engineering to attempt a parallel and high‐resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli. We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Beyond their traditional role of wrapping DNA, histones display antibacterial activity to Gram-negative and -positive bacteria. To identify bacterial components that allow survival to a histone challenge, we selected resistant bacteria from homologous Escherichia coli libraries that harbor plasmids carrying pieces of the chromosome in different sizes. We identified genes required for exopolysaccharide production and for the synthesis of the polysaccharide domain of the lipopolysaccharide, called O-antigen. Indeed, O-antigen and exopolysaccharide conferred further resistance to histones. Notably, O-antigen also conferred resistance to histones in the pathogens Shigella flexneri and Klebsiella pneumoniae.

A fundamental issue in the design and development of antimicrobials is the lack of understanding of complex modes of action and how this complexity affects potential pathways for resistance evolution. Bac8c (RIWVIWRR-NH2) is an 8 amino acid antimicrobial peptide (AMP) that has been shown to have enhanced activity against a range of pathogenic Gram-positive and Gram-negative bacteria, as well as yeast. We have previously demonstrated that Bac8c appears to interfere with multiple targets, at least in part through the disruption of cytoplasmic membrane related functions, and that resistance to this peptide does not easily develop using standard laboratory methods. Here, we applied a genomics approach, SCalar Analysis of Library Enrichement (SCALEs), to map the effect of gene overexpression onto Bac8c resistance in parallel for all genes and gene combinations (up to ∼ 10 adjacent genes) in the E. coli genome (a total of ∼ 500,000 individual clones were mapped). Our efforts identified an elaborate network of genes for which overexpression leads to low-level resistance to Bac8c (including biofilm formation, multi-drug transporters, etc). This data was analyzed to provide insights into the complex relationships between mechanisms of action and potential routes by which resistance to this synthetic AMP can develop.

Genome engineering methodologies are transforming biological research and discovery. Approaches based on CRISPR technology have been broadly adopted and there is growing interest in the generation of massively parallel edited cell libraries. Comparing the libraries generated by these varying approaches is challenging and researchers lack a common framework for defining and assessing the characteristics of these libraries. Here we describe a framework for evaluating massively parallel libraries of edited genomes based on established methods for sampling complex populations. We define specific attributes and metrics that are informative for describing a complex cell library and provide examples for estimating these values. We also connect this analysis to generic phenotyping approaches, using either pooled (typically via a selection assay) or isolate (often referred to as screening) phenotyping approaches. We approach this from the context of creating massively parallel, precisely edited libraries with one edit per cell, though the approach holds for other types of modifications, including libraries containing multiple edits per cell (combinatorial editing). This framework is a critical component for evaluating and comparing new technologies as well as understanding how a massively parallel edited cell library will perform in a given phenotyping approach.