Drophiobiolins A and B, Bioactive Ophiobolan Sestertepenoids Produced by Dreschslera gigantea

Two new bioactive ophiobolan sestertepenoids, named drophiobiolins A and B (1 and 2) were isolated from Drechslera gigantea, a fungus proposed as a mycoherbicide for biocontrol of Digitaria sanguinalis. They were isolated together with ophiobolin A, the main metabolite, 6-epi-ophiobolin A, 3-anhydro-6-epi-ophiobolin A, and ophiobolin I. Drophiobolins A and B were characterized by NMR, HRESIMS, and chemical methods as 7-hydroxy-7-(6-hydroxy-6-methylheptan-2-yl)-1,9a-dimethyl-3-oxo-3,3a,6,6a,7,8,9,9a,10,10a-decahydrodicyclopenta [a,d][8]annulene-4-carbaldehyde and 6-(hydroxymethyl)-3',9,10a-trimethyl-5'-(2-methylprop-1-en-1-yl)-3a,4,4',5',10,10a-hexahydro-1H,3'H-spiro[dicyclopenta[a,d] [8]annulene-3,2'-furan]-5,7(2H,9aH)-dione. The relative configuration of drophiobolins A and B, which did not afford crystals suitable for X-ray analysis, was determined by NOESY experiments, while the absolute configuration was assigned by comparison of their experimental and TDDFT calculated electronic circular dichroism (ECD) spectra. The phytotoxic activity of drophiobolins A and B was tested by leaf-puncture assay on cultivated (Lycopersicon esculentum L.), as well as on host (Digitaria sanguinalis L.) and nonhost (Chenopodium album L.) weed plants, compared to that of ophiobolin A. Both of the newly identified ophiobolins showed significant phytotoxicity. Drophiobolins A and B exhibited cytotoxicity against Hela B cells with an IC₅₀ value of 10 μM. However, they had a lesser or no effect against Hacat, H1299, and A431 cells when compared to that of ophiobolin A.

Y box binding protein 1 (YB-1) oncoprotein at the hub of DNA proliferation, damage and cancer progression

The Y Box binding protein 1 (YB-1) belongs to the highly conserved Cold Shock Domain protein family and is a major component of messenger ribonucleoprotein particles (mRNPs) in various organisms and cells. Cold Shock proteins are multifunctional nucleic acids binding proteins involved in a variety of cellular functions. Biological activities of YB-1 range from the regulation of transcription, splicing and translation, to the orchestration of exosomal RNA content. The role of YB-1 in malignant cell transformation and fate transition is the subject of intensive investigation. Besides, emerging evidence indicates that YB-1 participates in several DNA damage repair pathways as a non-canonical DNA repair factor thus pointing out that the protein can allow cancer cells to evade conventional anticancer therapies and avoid cell death. Here, we will attempt to collect and summarize the current knowledge on this subject and provide the basis for further lines of inquiry.

Radicinin, a Fungal Phytotoxin as a Target-Specific Bioherbicide for Invasive Buffelgrass (Cenchrus ciliaris) Control

The fungal pathogens Cochliobolus australiensis and Pyricularia grisea have recently been isolated from diseased leaves of buffelgrass (Cenchrus ciliaris) in its North American range, and their ability to produce phytotoxic metabolites that could potentially be used as natural herbicides against this invasive weed was investigated. Fourteen secondary metabolites obtained from in vitro cultures of these two pathogens were tested by leaf puncture assay on the host plant at different concentrations. Radicinin and (10S, 11S)-epi-pyriculol proved to be the most promising compounds. Thus, their phytotoxic activity was also evaluated on non-host indigenous plants. Radicinin demonstrated high target-specific toxicity on buffelgrass, low toxicity to native plants, and no teratogenic, sub-lethal, or lethal effects on zebrafish (Brachydanio rerio) embryos. It is now under consideration for the development of a target-specific bioherbicide to be used against buffelgrass in natural systems where synthetic herbicides cause excessive damage to native plants.

Modelling the distribution of the vector Aedes aegypti in a central Argentine city

Aedes aegypti (Diptera: Culicidae) is an urban mosquito involved in the transmission of numerous viruses, including dengue, chikungunya and Zika. In Argentina, Ae. aegypti is the main vector of dengue virus and has been involved in several outbreaks in regions ranging from northern to central Argentina since 2009. In order to evaluate areas of potential vector-borne disease transmission in the city of Córdoba, Argentina, the present study aimed to identify the environmental, socioeconomic and demographic factors driving the distribution of Ae. aegypti larvae through spatial analysis in the form of species distribution models (SDMs). These models elucidate relationships between known occurrences of a species and environmental data in order to identify areas with suitable habitats for that species and the consequent risk for disease transmission. The maximum entropy species distribution model was able to fit the training data well, with an average area under the receiver operating characteristic curve (AUC) of > 0.8, and produced models with fair extrapolation capacity (average test AUC: > 0.75). Human population density, distance to vegetation and water channels were the main variables predictive of the vector suitability of an area. The results of this work will be used to target surveillance and prevention measures, as well as in mosquito management.

Evaluation of species distribution model algorithms for fine-scale container-breeding mosquito risk prediction

The present work evaluates the use of species distribution model (SDM) algorithms to classify high densities of small container-breeding Aedes mosquitoes (Diptera: Culicidae) on a fine scale in the Bermuda Islands. Weekly ovitrap data collected by the Department of Health, Bermuda for the years 2006 and 2007 were used for the models. The models evaluated included the algorithms Bioclim, Domain, GARP (genetic algorithm for rule-set prediction), logistic regression and MaxEnt (maximum entropy). Models were evaluated according to performance and robustness. The area under the receiver operating characteristic curve was used to evaluate each model's performance, and robustness was assessed according to the spatial correlation between classification risks for the two datasets. Relative to the other algorithms, logistic regression was the best and MaxEnt the second best model for classifying high-risk areas. We describe the importance of covariables for these two models and discuss the utility of SDMs in vector control efforts and the potential for the development of scripts that automate the task of creating risk assessment maps.