A Pichia biosensor for high-throughput analyses of compounds that can influence mosquito behavior

The GEM-GECO Calcium Indicator Is Useable in Ogataea parapolymorpha Yeast, but Aggravates Effects of Increased Cytosolic Calcium Levels

Ca²⁺ is a ubiquitous second messenger, which allows eukaryotic cells to respond to external stimuli. The use of genetically encoded Ca²⁺ indicators allows real-time monitoring of cytosolic Ca²⁺ levels to study such responses. Here we explored the possibility of using the ratiometric Ca²⁺ indicator GEM-GECO for monitoring cytosolic Ca²⁺ concentration ([Ca²⁺]_cyt) in the yeast Ogataea parapolymorpha. High-level production of GEM-GECO led to a severe growth defect in cells lacking the vacuolar Ca²⁺ ATPase Pmc1, which is involved in [Ca²⁺]_cyt control, and prompted a phenotype resembling that of Pmc1 deficiency, in a strain with wild-type PMC1. This was likely due to the presence of the calmodulin domain in GEM-GECO. In contrast to previous studies of genetically-encoded calcium indicators in neuronal cells, our results suggest that physiological effects of GEM-GECO expression in yeast cells are due not to Ca²⁺ depletion, but to excessive Ca²⁺ signaling. Despite these drawbacks, study of fluorescence in individual cells revealed switching of GEM-GECO from the Ca²⁺-free to Ca²⁺-bound state minutes after external addition of CaCl₂. This was followed by gradual return of GEM-GECO to a Ca²⁺-free-state that was impaired in the pmc1-Δ mutant. These results demonstrate GEM-GECO usability for [Ca²⁺]_cyt monitoring in budding yeast.

The Use of Yeast in Biosensing

Yeast has been used as a model for several diseases as it is the simplest unicellular eukaryote, safe and easy to culture and harbors most of the fundamental processes that are present in almost all higher eukaryotes, including humans. From understanding the pathogenesis of disease to drug discovery studies, yeast has served as an important biosensor. It is not only due to the conservation of genetics, amenable modification of its genome and easily accessible analytical methods, but also some characteristic features such as its ability to survive with defective mitochondria, making it a highly flexible microbe for designing whole-cell biosensing systems. The aim of this review is to report on how yeasts have been utilized as biosensors, reporting on responses to various stimuli.

The Larval Density of Mosquitos (Diptera: Culicidae) in Jiaxiang County, Shandong Province, China: Influence of Bacterial Diversity, Richness, and Physicochemical Factors

As Jiaxiang County of Shandong province is an area with complex mosquito vector composition, it is necessary to investigate the relationship between bacterial diversity, physicochemical factors, and larval density. Therefore, the physicochemical properties of 46 breeding sites for six kinds of habitat types (small puddles, small water containers, paddy fields, large water containers, irrigation channels, and drainage ditches) were investigated by a multiparameter analyzer; the water’s bacterial diversity was analyzed by the 16S rRNA full-length sequencing method. Spearman correlation and multiple linear regression were used to analyze the correlation between larval density and variables. The variables analyzed were dissolved oxygen, pH, hardness, turbidity, conductivity, temperature, ammonia nitrogen, water depth, and distance from the nearest house. One-Way ANOVA was used to understand whether there are differences in bacterial diversity in different habitats. Pearson linear correlation model was used to analyze the effects of bacterial diversity and richness on mosquito densities in breeding sites. A total of 3291 larvae were captured, and a total of 6 species of 4 genera were identified. The identified species were Culex pipiens pallens, Aedes albopictus, Anopheles sinensis, Culex tritaeniorhynchus, Culex bitaeniorhynchus, and Mansonia uniformis. The density and species can be jointly affected by physicochemical properties and bacterial diversity, especially Shannon index and distance from the nearest house. In general, the physicochemical parameters and bacterial diversity of different habitats were significantly different. Even for the same habitat type, the physicochemical parameters varied greatly due to different environments.