Most currently used antibiotics originate from Streptomycetes and phosphate limitation is an important trigger of their biosynthesis. Understanding the molecular processes underpinning such regulation is of crucial importance to exploit the great metabolic diversity of these bacteria and get a better understanding of the role of these molecules in the physiology of the producing bacteria. To contribute to this field, a comparative proteomic analysis of two closely related model strains,Streptomyces lividansandStreptomyces coelicolorwas carried out. These strains possess identical biosynthetic pathways directing the synthesis of three well-characterized antibiotics (CDA, RED and ACT) but onlyS. coelicolorexpresses them at a high level. Previous studies established that the antibiotic producer,S. coelicolor, is characterized by an oxidative metabolism and a reduced triacylglycerol content compared to the none producer,S. lividans, characterized by a glycolytic metabolism. Our proteomic data support these findings and reveal that these drastically different metabolic features could, at least in part, due to the weaker abundance of proteins of the two component system PhoR/PhoP inS. coelicolorcompared toS. lividans. In condition of phosphate limitation, PhoR/PhoP is known to control positively and negatively, respectively, phosphate and nitrogen assimilation and our study revealed that it might also control the expression of some genes of central carbon metabolism. The tuning down of the regulatory role of PhoR/PhoP inS. coelicoloris thus expected to be correlated with low and high phosphate and nitrogen availability, respectively and with changes in central carbon metabolic features. These changes are likely to be responsible for the observed differences betweenS. coelicolorandS. lividansconcerning energetic metabolism, triacylglycerol biosynthesis and antibiotic production. Furthermore, a novel view of the contribution of the bio-active molecules produced in this context, to the regulation of the energetic metabolism of the producing bacteria, is proposed and discussed.

The Saccharomycotina subphylum (budding yeasts) spans 400 million years of evolution and includes species that thrive in diverse environments. To study niche-adaptation, we identify changes in gene expression in three divergent yeasts grown in the presence of various stressors. Duplicated and non-conserved genes are significantly more likely to respond to stress than genes that are conserved as single-copy orthologs. Next, we develop a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis reveals that genes that emerged in recent evolutionary time are enriched amongst stress-responsive genes for each species. This gene expression pattern suggests that budding yeasts share a stress adaptation mechanism, whereby selective pressure leads to functionalization of young genes to improve growth in adverse conditions. Further characterization of young genes from species that thrive in harsh environments can inform the design of more robust strains for biotechnology.

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.