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Human calcium-sensing receptor (CaSR) is a G-protein-coupled receptor (GPCR) that maintains extracellular Ca2+ homeostasis through the regulation of parathyroid hormone secretion. It functions as a disulfide-tethered homodimer composed of three main domains, the Venus Flytrap module, cysteine-rich domain, and seven-helix transmembrane region. Here, we present the crystal structures of the entire extracellular domain of CaSR in the resting and active conformations. We provide direct evidence that L-amino acids are agonists of the receptor. In the active structure, L-Trp occupies the orthosteric agonist-binding site at the interdomain cleft and is primarily responsible for inducing extracellular domain closure to initiate receptor activation. Our structures reveal multiple binding sites for Ca2+ and PO43- ions. Both ions are crucial for structural integrity of the receptor. While Ca2+ ions stabilize the active state, PO43- ions reinforce the inactive conformation. The activation mechanism of CaSR involves the formation of a novel dimer interface between subunits.DOI: http://dx.doi.org/10.7554/eLife.13662.001

Lipid composition determines the physical properties of biological membranes and can vary substantially between and within organisms. We describe a specific role for the viscosity of energy-transducing membranes in cellular respiration. Engineering of fatty acid biosynthesis in Escherichia coli allowed us to titrate inner membrane viscosity across a 10-fold range by controlling the abundance of unsaturated or branched lipids. These fluidizing lipids tightly controlled respiratory metabolism, an effect that can be explained with a quantitative model of the electron transport chain (ETC) that features diffusion-coupled reactions between enzymes and electron carriers (quinones). Lipid unsaturation also modulated mitochondrial respiration in engineered budding yeast strains. Thus, diffusion in the ETC may serve as an evolutionary constraint for lipid composition in respiratory membranes.

The Design–Build–Test–Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain development than historical efforts in biofuels and biobased products. Here, we report on implementation of two DBTL cycles to optimize 1-dodecanol production from glucose using 60 engineered Escherichia coli MG1655 strains. The first DBTL cycle employed a simple strategy to learn efficiently from a relatively small number of strains (36), wherein only the choice of ribosome-binding sites and an acyl-ACP/acyl-CoA reductase were modulated in a single pathway operon including genes encoding a thioesterase (UcFatB1), an acyl-ACP/acyl-CoA reductase (Maqu_2507, Maqu_2220, or Acr1), and an acyl-CoA synthetase (FadD). Measured variables included concentrations of dodecanol and all proteins in the engineered pathway. We used the data produced in the first DBTL cycle to train several machine-learning algorithms and to suggest protein profiles for the second DBTL cycle that would increase production. These strategies resulted in a 21% increase in dodecanol titer in Cycle 2 (up to 0.83 g/L, which is more than 6-fold greater than previously reported batch values for minimal medium). Beyond specific lessons learned about optimizing dodecanol titer in E. coli, this study had findings of broader relevance across synthetic biology applications, such as the importance of sequencing checks on plasmids in production strains as well as in cloning strains, and the critical need for more accurate protein expression predictive tools.