Mechanism and Kinetics of Copper Complexes Binding to the Influenza A M2 S31N and S31N/G34E Channels

Copper(II) is known to bind in the influenza virus His37 cluster in the homotetrameric M2 proton channel and block the proton current needed for uncoating. Copper complexes based on iminodiacetate also block the M2 proton channel and show reduced cytotoxicity and zebrafish-embryo toxicity. In voltage-clamp oocyte studies using the ubiquitous amantadine-insensitive M2 S31N variant, the current block showed fast and slow phases, in contrast to the single phase found for amantadine block of wild-type M2. Here, we evaluate the mechanism of block by copper adamantyl iminodiacitate and copper cyclooctyl iminodiacitate complexes and address whether the complexes can coordinate with one or more of the His37 imidazoles. The current traces were fitted to parametrized master equations. The energetics of binding and the rate constants suggest that the first step is copper complex binding within the channel, and the slow step in the current block is the formation of a Cu-histidine coordination complex. Solution-phase isothermal titration calorimetry and density functional theory (DFT) calculations indicate that imidazole binds to the copper complexes. Structural optimization using DFT reveals that the complexes fit inside the channel and project the Cu(II) toward the His37 cluster, allowing one imidazole to form a coordination complex with Cu(II). Electrophysiology and DFT studies also show that the complexes block the G34E amantadine-resistant mutant despite some crowding in the binding site by the glutamates.     

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.