Here, we propose an approach to identify active metabolic pathways by integrating gene essentiality analysis and protein abundance. We use two bacterial species (Mycoplasma pneumoniae and Mycoplasma agalactiae) that share a high gene content similarity yet show significant metabolic differences. First, we build detailed metabolic maps of their carbon metabolism, the most striking difference being the absence of two key enzymes for glucose metabolism in M. agalactiae. We then determine carbon sources that allow growth in M. agalactiae, and we introduce glucose-dependent growth to show the functionality of its remaining glycolytic enzymes. By analyzing gene essentiality and performing quantitative proteomics, we can predict the active metabolic pathways connected to carbon metabolism and show significant differences in use and direction of key pathways despite sharing the large majority of genes. Gene essentiality combined with quantitative proteomics and metabolic maps can be used to determine activity and directionality of metabolic pathways.

The understanding of interaction dynamics in signaling pathways can shed light on pathway architecture and provide insights into targets for intervention. Here, we explored the relevance of kinetic rate constants of a key upstream osmosensor in the yeast high-osmolarity glycerol-mitogen-activated protein kinase (HOG-MAPK) pathway to signaling output responses. We created mutant pairs of the Sln1-Ypd1 complex interface that caused major compensating changes in the association (kon) and dissociation (koff) rate constants (kinetic perturbations) but only moderate changes in the overall complex affinity (Kd). Yeast cells carrying a Sln1-Ypd1 mutant pair with moderate increases in kon and koff displayed a lower threshold of HOG pathway activation than wild-type cells. Mutants with higher kon and koff rates gave rise to higher basal signaling and gene expression but impaired osmoadaptation. Thus, the kon and koff rates of the components in the Sln1 osmosensor determine proper signaling dynamics and osmoadaptation.

Viruses need to hijack the translational machinery of the host cell for a productive infection to happen. However, given the dynamic landscape of tRNA pools among tissues, it is unclear whether different viruses infecting different tissues have adapted their codon usage toward their tropism. Here, we collect the coding sequences of 502 human-infecting viruses and determine that tropism explains changes in codon usage. Using the tRNA abundances across 23 human tissues from The Cancer Genome Atlas (TCGA), we build an in silico model of translational efficiency that validates the correspondence of the viral codon usage with the translational machinery of their tropism. For instance, we detect that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is specifically adapted to the upper respiratory tract and alveoli. Furthermore, this correspondence is specifically defined in early viral proteins. The observed tissue-specific translational efficiency could be useful for the development of antiviral therapies and vaccines.