Fast phage detection and quantification: An optical density-based approach

Abstract

Since 1959 with the proposal of Double Agar Layer (DAL) method for phage detection and quantification, many sophisticated methods have emerged meanwhile. However, many of them are either too complex/expensive or insensitive to replace routine utilization of DAL method in clinical, environmental and industrial environments. For that purpose, we have explored an alternative method for the detection and quantification of bacteriophages that fulfills the criteria of being rapid, simple and inexpensive. In this paper we have developed a method based on the analysis of optical density kinetics in bacterial cultures exposed to phage-containing samples. Although the decrease in optical density caused by cell lysis was one of the first observable consequences of the effect of viral infection in bacterial cultures, the potential of the method for the assessment of phage abundance has never been fully exploited. In this work we carry out a detailed study of optical density kinetics in phage-infected bacterial cultures, as a function of both, phage abundance and initial concentration of the host organisms. In total, 90 different combinations of bacteria/phage concentrations have been used. The data obtained provide valuable information about sensitivity ranges, duration of the assay, percentages of inhibition and type of lysing behavior for each phage concentration. The method described can detect, as few as 10 phage particles per assay volume after a phage incubation period of 3.5h. The duration of the assay can be shortened to 45min at the expense of losing sensitivity and increasing the limit of detection to 10 8 pfu/ml. Despite using non-sophisticated technology, the method described has shown sensitivity and response time comparable to other high-end methods. The simplicity of the technology and of the analytical steps involved, make the system susceptible of miniaturization and automation for high-throughput applications which can be implemented in routine analysis in many environments.

Host-Dependent Differences in Replication Strategy of the Sulfolobus Spindle-Shaped Virus Strain SSV9 (a.k.a., SSVK1): Infection Profiles in Hosts of the Family Sulfolobaceae

“… Area under the curve values for virally challenged hosts were then standardized to the AUC value for the averaged uninfected host control growth curve in order to calculate the percent inhibition (PI) for the individual growth curves (see Tables S1 – S3 ). The equation used to calculate the percent inhibition of growth is shown in Equation (6) below, from Rajnovic et al ( 2019 ) which is reformatted in Equation (7). For a full discussion of this method (see Ceballos and Stacy, under review).…”

Quantifying relative virulence: When μmaxfails and AUC alone just isn’t enough

“…Given limitations of μ max in determining V R in non-lytic viral infections, an alternative approach is to determine a percent inhibition (PI) based on area-under-the-curve (AUC) [13–16]. Specifically, determining AUC for infected host growth (AUC infected ) and uninfected control (AUC CTL ) provides a calculated PI AUC on non-transformed data such that: …”

Quantifying relative virulence: when μ max fails and AUC alone just is not enough

“…In comparing host growth using AUC as a metric for relative virulence, two sets of limits were also used. Given the demonstrated inadequacy of µ max in determining V R in non-lytic viral infections, an alternative approach is to calculate a percent inhibition (PI AUC ) of host growth [15][16][17][18] based on AUC for infected (AUC infected ) and uninfected controls (AUC CTL ).…”