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Assessing fitness and virulence of pathogenic organisms is a fundamental aspect of microbiology research. It enables comparisons to be made between strains or between mutated organisms, which allows researchers to determine the importance of certain genes under specific conditions. Traditionally, virulence assessment utilizes an animal model of infection using different bacterial strains and observing the outcome of the infected animal (e.g. Infectious Dose50, Lethal Dose50, time to death, symptom severity, lack of symptoms, etc.). This procedure provides valuable descriptions of virulence, but it requires strains to cause considerable differences in outcomes in order to detect variations from wild-type. Furthermore, results are semi-quantitative because while disease progression and symptom severity can be subjectively quantified over time, interpretation of virulence compared to wild-type is more qualitative (i.e. more, less, or equally virulent). A common alternative to performing animal infectivity assays is to generate competitive indices (CIs), values that directly compare fitness or virulence of a strain to a wild-type counterpart in a mixed infection1. This technique has numerous advantages over a traditional animal model of infection by standardizing virulence to a wild-type strain and determining a quantifiable value to reflect the degree of attenuation. This technique can also be adapted to analyze gene interactions in bacteria by determining a canceled out competitive index (COI)2. Calculating a COI for a group of mutated organisms allows researchers to determine whether two genes independently contribute to pathogenesis or if they are involved in the same virulence pathway and dependent on each other. Additionally, calculating a CI requires enumeration of bacteria which can provide valuable insights into the pathogenesis of organisms. CIs and COIs also allow researchers to asses avirulent strains that do not cause clinical disease but still have differences in fitness. This technique is limited by the use of traditional antibiotic resistance markers to identify strains, thereby limiting the number of input strains to only one or two at a time. Because of this limitation, large numbers of experimental groups and replicates are required, which in addition to adding to labor and material costs, also increases opportunities for variability in experimental conditions and inaccurate results. (For a thorough review of the benefits and applications of using mixed infections to study virulence, fitness, and gene interactions, see C.R. Beuzón and D.W. Holden 1)
Attempts have been made to overcome this limitation, such as the use of fluorescently-labeled cells quantified via flow cytometry3,4,5. This technique quantifies cells using either 1) labeled antibodies to phenotypic markers or 2) endogenously produced fluorescent proteins. The use of labeled antibodies has a limit of detection of 1,000 cells/mL, and therefore requires a high number of cells to analyze3. Cells expressing fluorescent proteins have an altered physiology and are susceptible to fitness changes resulting from high protein expression6. Both methods are limited by the number of fluorescent markers detectable using flow cytometry. An advancement in molecular quantification was achieved through the development of a microarray technique that detected attenuation in 120 strains from an initial mixed infection of over 1,000 strains in a murine model7. This technique utilized a microarray analysis of RNA from mutated strains, which lead to considerable variability in the outcome. Nevertheless, it established that large pools of mixed infections can be a useful tool and that by utilizing sensitive detection techniques, differences in bacterial virulence can be identified. With the development of the next generation sequencing, Tn-seq expanded the utility of transposon mutations, enabling a powerful method for quantifying bacteria that were randomly mutated8,9,10,11. An alternative protocol was recently developed that eliminates the need for transposons and instead uses DNA barcodes to more easily identify and track genomic changes and their impact on fitness12. This technology is a major advancement, but the insertion of the genomic barcodes is still a random process. To overcome the randomness of previous experiments, Yoon et al. developed a method to calculate the CIs of Salmonella strains using unique DNA barcodes inserted at precise locations on the chromosomes of bacteria13. Unique barcoded strains were detected using a qPCR-based method with SYBR green and primers specific to each unique barcode. The technique was limited by constraints imposed by qPCR, including differences in primer efficiencies and low sensitivity, evidenced by the need for nested-PCR prior to qPCR. Nevertheless, this approach demonstrated that targeted genomic modifications could be exploited for detecting and potentially quantifying pools of multiple bacterial strains.
In the following protocol, we describe a novel methodology to perform bacterial competition experiments with large pools of mixed inocula followed by accurate quantification using a highly sensitive digital PCR technique. The protocol involves genetically-labeling bacterial strains with a unique DNA barcode inserted on an innocuous region of the chromosome. This modification allows strains to be quickly and accurately quantified using modern molecular technology instead of traditional serial dilutions, replica plating, and counting colony forming units that rely on phenotypic markers (i.e. antibiotic resistance). The modifications allow for simultaneous assessment of many strains in a single pooled inoculum, substantially reducing the possibility of experimental variability because all strains are exposed to the exact same conditions. Furthermore, while this technique was developed in Salmonella enterica serovar Typhimurium, it is highly adaptable to any genetically malleable organism and nearly any experimental design where accurate bacterial counts are required, providing a new tool to increase accuracy and throughput in microbiology laboratories without the constraints imposed by previous methods.