Mechanism of action-based classification of antibiotics using high-content bacterial image analysis.

Kelly C Peach,Peter F Linington,Walter M Bray,Dustin Winslow,Roger G Linington

doi:10.1039/c3mb70027e

Kelly C Peach, Peter F Linington + Show 3 more

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https://doi.org/10.1039/c3mb70027e

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Abstract

Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. However, very few examples exist of extensions of this approach to bacterial targets. We now report the first high-throughput, high-content platform for the prediction of antibiotic modes of action using image-based screening. This approach employs a unique feature segmentation and extraction protocol to quantify key size and shape metrics of bacterial cells over a range of compound concentrations, and matches the trajectories of these metrics to those of training set compounds of known molecular target to predict the test compound's mode of action. This approach has been used to successfully predict the modes of action of a panel of known antibiotics, and has been extended to the evaluation of natural products libraries for the de novo prediction of compound function directly from primary screening data.

Highlights

Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology
We hypothesized that the development of image-based profiling technologies for bacterial systems would permit the direct assignment of mechanism of action to antibiotic lead compounds from primary screening data, and would provide a new approach to the discovery of novel lead compounds
Using an epifluorescence image-based screening platform we have shown that the antibiotic mechanism of action of unknown compounds against V. cholerae can be correctly predicted using only whole-cell, concentration-dependent, morphological observations

Summary

Introduction

Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. A significant proportion of the antibiotics to reach the market in recent years have been analogues of existing scaffolds.[5] these compounds remedy the immediate need for antibiotic development by incremental improvements in scope or potency, they inevitably suffer from many of the same underlying resistance mechanisms of their predecessors, and are of only modest value in the wider context of controlling the emergence and spread of drug-resistant pathogens.[1] unproven, the possibility of using high-content screening (HCS) to find compounds with unique mechanisms is an exciting new avenue for antibiotic discovery This type of technology and mechanism-based profiling opens up the potential to approach antibiotic drug discovery from a different perspective. By developing novel image analysis technologies we have created a phenotypic profiling screening platform for the direct annotation of drug function from primary screening data, and applied this methodology to the characterization of a natural products library to examine the validity of this approach for clustering drug leads from natural sources

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