Synthesis and insecticidal activity of N-(5-phenylpyrazin-2-yl)-benzamide derivatives: Elucidation of mode of action on chitin biosynthesis through symptomology and genetic studies.

Among the six-membered heterocycles, the pyrazine ring is poorly explored in crop protection and does not feature in any product listed in the current IRAC MoA classification. In an effort to identify new leads for internal research, we synthesized a series of N-(5-phenylpyrazin-2-yl)-benzamide derivatives and evaluated them for their insecticidal activity. N-(5-phenylpyrazin-2-yl)-benzamide derivatives 3 were prepared using an automated two-step synthesis protocol. These compounds were tested for their initial biological activity against a wide range of sucking and chewing insect pests and found to be active against lepidopterans only. More detailed experiments, including symptomology studies on the diamondback moth, Plutella xylostella (L.) and the Egyptian cotton leafworm, Spodoptera littoralis (Boisduval) showed that analog 3q causes severe abnormalities in the lepidopteran cuticle leading to larval mortality. Compound 3q shows strong potency against both P. xylostella and S. littoralis, whereas analog 3i shows better potency against S. littoralis causing also impaired cuticular structure and death of the larvae. Additionally, P. xylostella genetic studies showed that compound 3q resistance is linked to Chitin Synthase 1. Our studies show that N-(5-phenylpyrazin-2-yl)-benzamide derivatives 3, and in particular analogs 3i and 3q, act as insect growth modulator insecticides. Conformational similarities with lufenuron are discussed.

Cytosolic fumarase acts as a metabolic fail-safe for both high and low temperature acclimation of Arabidopsis thaliana.

Plants acclimate their photosynthetic capacity (Pmax) in response to changing environmental conditions. In Arabidopsis thaliana, photosynthetic acclimation to cold requires the accumulation of the organic acid fumarate, catalysed by a cytosolically localized fumarase, FUM2. However, the role of this accumulation is currently unknown. Here, we use an integrated experimental and modelling approach to examine the role of FUM2 and fumarate across the physiological temperature range. We have studied three genotypes: Col-0; a fum2 mutant in a Col-0 background; and C24, an accession with reduced FUM2 expression. While low temperature causes an increase in Pmax in the Col-0 plants, this parameter decreases following exposure of plants to 30 °C for 7 d. Plants in which fumarate accumulation is partially (C24) or completely (fum2) abolished show a reduced acclimation of Pmax across the physiological temperature range (i.e. Pmax changes less in response to changing temperature). To understand the role of fumarate accumulation, we have adapted a reliability engineering technique, Failure Mode and Effect Analysis (FMEA), to formalize a rigorous approach for ranking metabolites according to the potential risk that they pose to the metabolic system. FMEA identifies fumarate as a low-risk metabolite, while its precursor, malate, is shown to be high risk and liable to cause system instability. We propose that the role of FUM2 is to provide a fail-safe in order to control malate concentration, maintaining system stability in a changing environment. We suggest that FMEA is a technique that is not only useful in understanding plant metabolism but can also be used to study reliability in other systems and synthetic pathways.

COVID-19 vaccination strategies depend on the underlying network of social interactions.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.