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Laboratory models are central to microbiology research, advancing the understanding of bacterial physiology by mimicking natural environments, from soil to the human microbiome. When studying host-bacteria interactions, animal models enable investigators to examine bacterial dynamics associated with a host, and in the case of human infections, animal models are necessary to translate basic research into clinical treatments. Efforts toward improving animal infection models are typically based on reproducing host genotypes/phenotypes and disease manifestations, leaving a gap in how well the physiology of microbes reflects their behavior in a human host. Understanding bacterial physiology is vital because it dictates host response and bacterial interactions with antimicrobials. Thus, our goal was to develop an animal model that accurately recapitulates bacterial physiology in human infection. The system we chose to model was a chronic Pseudomonas aeruginosa respiratory infection in cystic fibrosis (CF). To accomplish this goal, we leveraged a framework that we recently developed to evaluate model accuracy by calculating the percentage of bacterial genes that are expressed similarly in a model to how they are expressed in their infection environment. We combined two complementary models of P. aeruginosa infection--an in vitro synthetic CF sputum model (SCFM2) and a mouse acute pneumonia model. This combined model captured the chronic physiology of P. aeruginosa in CF better than the standard mouse infection model, showing the power of a data-driven approach to refining animal models. In addition, the results of this work challenge the assumption that a chronic infection model requires long-term colonization. [ABSTRACT FROM AUTHOR]

Reproducibility is a fundamental expectation in science and enables investigators to have confidence in their research findings and the ability to compare data from disparate sources, but evaluating reproducibility can be elusive. For example, generating RNA sequencing (RNA-seq) data includes multiple steps where variance can be introduced. Thus, it is unclear if RNA-seq data from different sources can be validly compared. While most studies on RNA-seq reproducibility focus on eukaryotes, we evaluate bias in bacteria using Pseudomonas aeruginosa gene expression data from five laboratory models of cystic fibrosis. We leverage a large data set that includes samples prepared in three different laboratories and paired data sets where the same sample was sequenced using at least two different sequencing pipelines. We report here that expression data are highly reproducible across laboratories. In addition, while samples sequenced with different sequencing pipelines showed significantly more variance in expression profiles than between labs, gene expression was still highly reproducible between sequencing pipelines. Further investigation of expression differences between two sequencing pipelines revealed that library preparation methods were the largest source of error, though analyses to identify the source of this variance were inconclusive. Consistent with the reproducibility of expression between sequencing pipelines, we found that different pipelines detected over 80% of the same differentially expressed genes with large expression differences between conditions. Thus, bacterial RNA-seq data from different sources can be validly compared, facilitating the ability to advance understanding of bacterial behavior and physiology using the wide array of publicly available RNA-seq data sets.IMPORTANCERNA sequencing (RNA-seq) has revolutionized biology, but many steps in RNA-seq workflows can introduce variance, potentially compromising reproducibility. While reproducibility in RNA-seq has been thoroughly investigated in eukaryotes, less is known about pipelines and workflows that introduce variance and biases in bacterial RNA-seq data. By leveraging Pseudomonas aeruginosa transcriptomes in cystic fibrosis models from different laboratories and sequenced with different sequencing pipelines, we directly assess sources of bacterial RNA-seq variance. RNA-seq data were highly reproducible, with the largest variance due to sequencing pipelines, specifically library preparation. Different sequencing pipelines detected overlapping differentially expressed genes, especially those with large expression differences between conditions. This study confirms that different approaches to preparing and sequencing bacterial RNA libraries capture comparable transcriptional profiles, supporting investigators' ability to leverage diverse RNA-seq data sets to advance their science.

Grapes have been cultivated for wine production for millennia. Wine production involves a complex biochemical process where sugars in grape must are converted into alcohol and other compounds by microbial fermentation, primarily by the yeast Saccharomyces cerevisiae . Commercially available S. cerevisiae strains are often used in winemaking, but indigenous (native) strains are gaining attention for their potential to contribute unique flavors. Recent advancements in high-throughput DNA sequencing have revolutionized our understanding of microbial communities during wine fermentation. Indeed, transcriptomic analysis of S. cerevisiae during wine fermentation has revealed a core gene expression program and provided insights into how this yeast adapts to fermentation conditions. Here, we assessed how the age of vines impacts the grape fungal microbiome and used transcriptomics to characterize microbial functions in grape must fermented with commercial and native S. cerevisiae . We discovered that ~130-year-old Zinfandel vines harbor higher fungal loads on their grapes compared to 20-year-old Zinfandel vines, but fungal diversity is similar. Additionally, a comparison of inoculated and uninoculated fermentations showed distinct fungal dynamics, with uninoculated fermentations harboring the yeasts Metschnikowia and Pichia . Transcriptomic analysis revealed significant differences in gene expression between fermentations inoculated and not inoculated with a commercial S. cerevisiae strain. Genes related to metabolism, stress response, and cell adhesion were differentially expressed, indicating varied functionality of S. cerevisiae in these fermentations. These findings provide insights into S. cerevisiae function during fermentation and highlight the potential for indigenous yeast to contribute to wine diversity., Importance: Understanding microbial functions during wine fermentation, particularly the role of Saccharomyces cerevisiae , is crucial for enhancing wine quality. While commercially available S. cerevisiae strains are commonly used, indigenous strains can offer unique flavors, potentially reflecting vineyard terroir. By leveraging high-throughput DNA sequencing and transcriptomic analysis, we explored the impact of vine age on the grape mycobiome and characterized microbial functions during grape fermentation. Our findings revealed that older vines harbor higher fungal loads, but fungal diversity remains similar across vine ages. Additionally, uninoculated fermentations exhibited diverse fungal dynamics, including the beneficial wine yeasts Metschnikowia and Pichia. Transcriptomic analysis uncovered significant differences in S. cerevisiae gene expression between inoculated and uninoculated fermentations, highlighting the potential of indigenous yeast to enhance wine diversity and inform winemaking practices., Competing Interests: The authors declare no conflict of interest.