Drosophila Genetics: The Power of Genetic Mosaic Approaches

Improvement of cellulase and xylanase production in Penicillium oxalicum under solid-state fermentation by flippase recombination enzyme/ recognition target-mediated genetic engineering of transcription repressors

Penicillium oxalicum has received increasing attention as a potential cellulase-producer. In this study, a copper-controlled flippase recombination enzyme/recognition target (FLP/FRT)-mediated recombination system was constructed in P. oxalicum, to overcome limited availability of antibiotic resistance markers. Using this system, two crucial transcription repressor genes atf1 and cxrC for the production of cellulase and xylanase under solid-state fermentation (SSF) were simultaneously deleted, thereby leading to 2.4- to 29.1-fold higher cellulase and 78.9% to 130.8% higher xylanase production than the parental strain under SSF, respectively. Glucose and xylose released from hydrolysis of pretreated sugarcane bagasse achieved 10.6%-13.5% improvement by using the crude enzymes from the engineered strain Δatf1ΔcxrC::flp under SSF in comparison with that of the parental strain. Consequently, these results provide a feasible strategy for improved cellulase and xylanase production by filamentous fungi.

Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists.