High-Resolution Chrono-Transcriptome of Lactococcus lactis Reveals That It Expresses Proteins with Adapted Size and pI upon Acidification and Nutrient Starvation

Whole-genome transcriptional analyses performed on microorganisms are traditionally based on a small number of samples. To map transient expression variations, and thoroughly characterize gene expression throughout the growth curve of the widely used model organism Lactococcus lactis MG1363, gene expression data were collected with unprecedented time resolution. The resulting gene expression patterns were globally analyzed in several different ways to demonstrate the richness of the data and the ease with which novel phenomena can be discovered. When the culture moves from one growth phase to another, gene expression patterns change to such an extent that we suggest that those patterns can be used to unequivocally distinguish growth phases from each other. Also, within the classically defined growth phases, subgrowth phases were distinguishable with a distinct expression signature. Apart from the global expression pattern shifts seen throughout the growth curve, several cases of short-lived transient gene expression patterns were clearly observed. These could help explain the gene expression variations frequently observed in biological replicates. A method was devised to estimate a measure of unnormalized/absolute gene expression levels and used to determine how global transcription patterns are influenced by nutrient starvation or acidification of the medium. Notably, we inferred that L. lactis MG1363 produces proteins with on average lower pIs and lower molecular weights as the medium acidifies and nutrients get scarcer. IMPORTANCE This data set is a rich resource for microbiologists interested in common mechanisms of gene expression, regulation and in particular the physiology of L. lactis. Thus, similar to the common use of genome sequence data by the scientific community, the data set constitutes an extensive data repository for mining and an opportunity for bioinformaticians to develop novel tools for in-depth analysis.