Penicillium raistrickii ATCC 10490 is used for the commercial preparation of 15α-13-methy-estr-4-ene-3,17-dione, a key intermediate in the synthesis of gestodene, which is a major component of third-generation contraceptive pills. Although it was previously shown that a cytochrome P450 enzyme in P. raistrickii is involved in steroid 15α-hydroxylation, the gene encoding the steroid 15α-hydroxylase remained unknown. In this study, we report the cloning and characterization of the 15α-hydroxylase gene from P. raistrickii ATCC 10490 by combining transcriptomic profiling with functional heterologous expression in Saccharomyces cerevisiae. The full-length open reading frame (ORF) of the 15α-hydroxylase gene P450pra is 1563 bp and predicted to encode a cytochrome P450 protein of 520 amino acids. Targeted gene deletion revealed that P450pra is solely responsible for 15α-hydroxylation activity on 13-methy-estr-4-ene-3,17-dione in P. raistrickii ATCC 10490. The identification of the 15α-hydroxylase gene from P. raistrickii should help elucidate the molecular basis of regio- and stereo-specificity of steroid 15α-hydroxylation and aid in the engineering of more efficient industrial strains for useful steroid 15α-hydroxylation reactions.
Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%.
Cryptococcus neoformans is a major cause of fungal meningitis in individuals with impaired immunity. Our previous studies have shown that the VPS41 gene plays a critical role in the survival of Cryptococcus neoformans under nitrogen starvation; however, the molecular mechanisms underlying VPS41-mediated starvation response remain to be elucidated. In the present study, we show that, under nitrogen starvation, VPS41 strongly enhanced ICL1 expression in C. neoformans and that overexpression of ICL1 in the vps41 mutant dramatically suppressed its defects in starvation response due to the loss of VPS41 function. Moreover, targeted deletion of ICL1 resulted in a dramatic decline in viability of C. neoformans cells under nitrogen deprivation. Taken together, our data suggest a model in which VPS41 up-regulates ICL1 expression, directly or indirectly, to promote survival of C. neoformans under nitrogen starvation.
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