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Anthropogenic warming is caused mainly by emissions of greenhouse gases (GHGs), such as carbon dioxide, methane, and nitrous oxide, with agriculture as a main contributor for the latter 2 gases. Other parts of the food system contribute carbon dioxide emissions that emanate from the use of fossil fuels in transportation, processing, retailing, storage, and preparation. Food items differ substantially when GHG emissions are calculated from farm to table. A recent study of approximately 20 items sold in Sweden showed a span of 0.4 to 30 kg CO(2) equivalents/kg edible product. For protein-rich food, such as legumes, meat, fish, cheese, and eggs, the difference is a factor of 30 with the lowest emissions per kilogram for legumes, poultry, and eggs and the highest for beef, cheese, and pork. Large emissions for ruminants are explained mainly by methane emissions from enteric fermentation. For vegetables and fruits, emissions usually are

The backbone conformation of DNA plays an important role in the indirect readout mechanisms for protein-DNA recognition events. Thus, investigating the backbone dynamics of each step in DNA binding sequences provides useful information necessary for the characterization of these interactions. Here we use 31 P Dynamic NMR to characterize the backbone conformation and dynamics in the Dickerson Dodecamer, a sequence containing the EcoRI binding site, and confirm solid-state 2 H-NMR results showing that the C3pG4 and C9pG10 steps experience unique dynamics and that these dynamics are quenched upon cytosine methylation. In addition, we show that cytosine methylation affects the conformation and dynamics of neighboring nucleotide steps but this effect is localized to only near neighbors and base pairing partners. Lastly, we have been able to characterize the %BII in each backbone step and illustrate that the C3pG4 and C9pG10 favor the non-canonical BII conformation, even at low temperatures. Our results demonstrate that 31 P Dynamic NMR provides a robust and efficient method for characterizing the backbone dynamics in DNA. This allows simple, rapid determination of sequence-dependent dynamical information, providing a useful method for studying trends in protein-DNA recognition events.