Dynamic Ochratoxin A Production by Strains of Aspergillus niger Intended Used in Food Industry of China

Biogeography of Black Mold Aspergillus niger: Global Situation and Future Perspective under Several Climate Change Scenarios Using MaxEnt Modeling

Climate change impacts represent one of the most important ecological and medical issues during this century. Several fungal species will change their distribution through space and time as a response to climate changes. This will rearrange many fungal diseases throughout the world. One of the most important and very common fungi is the black mold Aspergillus niger. The COVID-19 pandemic reforms the way in which mycologists think about this fungus as an emerging healthy issue. Through this work, about one thousand records of Aspergillus niger were used to model its current and future global distribution using 19 bioclimatic variables under several climate change scenarios. Maximum entropy implemented in Maxent was chosen as the modeling tool, especially with its accuracy and reliability over the other modeling techniques. The annual mean temperature (bio 1) forms the most contributed climatological parameter to black mold distribution. The produced current distribution model came compatible with the real distribution of the species with a cosmopolitan range. The rise of temperature due to global warming will form a limitation to Aspergillus niger through several parts of its range. The generated maps of the future status of this fungus under two different RCPs for 2050 and 2070, indicate several parts that become free from black mold due to temperature limitations. The present results need more intensive future evaluation using data science and GIS, especially on a local scale including more ecological parameters other than climatological data.

Mycotoxins occurrence in milk and cereals in North African countries - a review

North African countries; Algeria, Egypt, Libya, Morocco and Tunisia suffer from mycotoxin contamination. Various studies have indicated the presence of mycotoxins in raw milk and cereals (i.e. wheat, barley, maize and cereal-based products). Aflatoxins (AFs), Aflatoxin M1 (AFM1), Ochratoxin A (OTA), Fumonisin (FB1) and Zearalenone (ZEN)-mycotoxin are the most detected due to climatic change in the region. In this review, we will present the kind of foods and feeds cereals and milk based products contaminated and the level of their contaminated mycotoxin. On the other hand, researchers try to find biologic methods to remove/mitigate mycotoxins in food and feed using bio-products. But the research works concerning legislations and mycotoxin risk assessment still rare. Therefore, it appears necessary to make review on the current status of mycotoxins in North African countries in order to explore data related to contamination of basic food in this region and to highlight the problem to the policy-makers to establish a serious legislation on this matter. On the other hand, to give more information to the worldwide readers about the impact of climate change on the food and feed pollution on mycotoxins in the Mediterranean Sea region.

Predictive Quantitative Structure-Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds

Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R² values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.