Short-term effects of i.v. injected murine 99MTc-F(ab')2 fragments of an anti-melanoma antibody (HMW-MAA 225.28 S) on haemato-immunological parameters in patients with melanoma

To evaluate alterations induced by injected murine radiolabelled F(ab')2 fragments of the anti HMW-MAA MoAb 225.28S on the principal haemato-immunological parameters, 32 patients with advanced malignant melanoma were studied. No statistically significant change was found after MoAb administration, but monocytes (3 h after injection) and granular eosinophils (24 h after) were reduced and circulating immune complexes increased (3 h after). No toxic effect or adverse reaction was observed. Therefore, the controlled administration of purified MoAb fragments for diagnostic purposes seems to involve only a very low risk of immediate adverse reactions.     

The effect of experimental warming and precipitation change on proteolytic enzyme activity: positive feedbacks to nitrogen availability are not universal

Nitrogen regulates the Earth's climate system by constraining the terrestrial sink for atmospheric CO₂. Proteolytic enzymes are a principal driver of the within‐system cycle of soil nitrogen, yet there is little to no understanding of their response to climate change. Here, we use a single methodology to investigate potential proteolytic enzyme activity in soils from 16 global change experiments. We show that regardless of geographical location or experimental manipulation (i.e., temperature, precipitation, or both), all sites plotted along a single line relating the response ratio of potential proteolytic activity to soil moisture deficit, the difference between precipitation and evapotranspiration. In particular, warming and reductions in precipitation stimulated potential proteolytic activity in mesic sites – temperate and boreal forests, arctic tundra – whereas these manipulations suppressed potential activity in dry grasslands. This study provides a foundation for a simple representation of the impacts of climate change on a central component of the nitrogen cycle.     

Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia.