Environmental and Physiological Factors Affecting High-Throughput Measurements of Bacterial Growth

Abstract

Bacterial growth under nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over that of standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multiwell plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth. IMPORTANCE How starved bacteria adapt and multiply under replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allow for accurate growth rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli. Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

AMiGA: Software for Automated Analysis of Microbial Growth Assays

“…Here, we saw that ribotype 027 has the largest noise term and widest confidence intervals relative to other ribotypes. This may reflect larger phenotypic diversity among ribotype 027 isolates or may suggest a need for generating starting cultures in a manner that reduces variability in initial population size and physiology ( 26 ).…”

AMiGA: software for automated Analysis of Microbial Growth Assays

“…Here, we saw that ribotype 027 has the largest noise term and widest confidence intervals relative to other ribotypes. This may reflect larger phenotypic diversity amongst ribotype 027 isolates or may suggest a need for generating starting cultures in a manner that reduces variability in initial population size and physiology (21).…”

“…Important aspects of analyzing these data sets include screening for desired phenotypes (23), discovering genotype-phenotype relationships (22, 24), investigating crosstalk of environmental pressures and microbial dynamics (25, 26), and predicting microbial fitness under a variety of conditions (27, 28). Yet, we are continuing to learn how growth dynamics are affected by a variety of technical and experimental factors (21). Scientists are also increasingly able to cultivate nontraditional or non-lab adapted strains and manipulate them under a wide variety of treatments.…”

The quorum sensing system NprR-NprRB contributes to spreading and fitness in colony biofilms ofBacillus thuringiensis

“…However, B. thuringiensis can also colonize diverse tissues of plants, being found mainly in the roots where exudates rich in carbohydrates and amino acids are secreted and favor its growth (82). The spreading phenotype may be relevant during colonization of these niches, increasing survival after translocation across biotic or abiotic surfaces, allowing access to nutrients and escape from unfavorable environments (83)(84)(85).…”