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Salmonella enterica serovar Typhimurium must adapt to rapid environmental shifts, including those encountered upon entry and during replication to survive within macrophages during pathogenesis. Despite extensive RNA-seq-based investigations, questions remain regarding the range, timing and magnitude of response dynamics. Here we constructed a comprehensive GFP-reporter strain library representing 2,901 computationally identified Salmonella promoter regions to study time-resolved Salmonella transcriptional responses. Promoter activity was measured during in vitro growth and during intracellular infection of RAW 264.7 macrophages. Using bulk measurements and single-cell imaging, we uncovered condition-specific transcriptional regulation and population-level heterogeneity in SPI2-related promoter activity. We also discovered previously unidentified transcriptional activity from 234 promoters. These analyses revealed metabolic shifts including requirements for mntS expression to support manganese homeostasis and expression of Entner–Doudoroff pathway-associated genes to support growth within macrophages. Our library and datasets, made available through the online tool SalComKinetics, provide resources for systems-level interrogation of Salmonella transcriptional dynamics. 

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.