The Response of the Estrogen-Related Receptor to 20-Hydroxyecdysone in Bombyx mori: Insight Into the Function of Estrogen-Related Receptor in Insect 20-Hydroxyecdysone Signaling Pathway.

Estrogen-related receptor (ERR) is an orphan nuclear receptor that was first discovered in animals, and play an important role in metabolism, development, and reproduction. Despite extensive research on the function of ERR, its transcriptional regulation mechanism remains unclear. In this study, we obtained the upstream region of Bombyx mori ERR (BmERR) and confirmed the promoter activity of this region. Interestingly, we found that 10 and 50 nM 20-hydroxyecdysone (20E) up-regulated the transcriptional activity of BmERR promoter. In addition, eight putative ecdysone response elements (EcREs) were predicted in the upstream sequence of BmERR. Based on their positions, the upstream sequence of BmERR was truncated into different fragments. Finally, an EcRE-like sequence (5'-AGTGCAGTAAACTGT-3') was identified. Electrophoretic mobility shift assay (EMSA) and cell transfection experiments confirmed that this motif specifically binds to the complex formed between ecdysone receptor (BmEcR) and the ultraspiracle (BmUSP), a key complex in the 20E signaling pathway. Interference of BmERR or BmEcR mRNA in the embryonic cells of Bombyx mori significantly affected the expression of BmEcR and BmUSP. Overall, these results suggested that an EcRE element was identified from BmERR, and this will help understanding the detailed regulatory mechanism of ERR in insects.

Identification and Characterization of Aldehyde Oxidase 5 in the Pheromone Gland of the Silkworm (Lepidoptera: Bombycidae).

Aldehyde oxidases (AOXs) are a subfamily of cytosolic molybdo-flavoenzymes that play critical roles in the detoxification and degradation of chemicals. Active AOXs, such as AOX1 and AOX2, have been identified and functionally analyzed in insect antennae but are rarely reported in other tissues. This is the first study to isolate and characterize the cDNA that encodes aldehyde oxidase 5 (BmAOX5) in the pheromone gland (PG) of the silkworm, Bombyx mori. The size of BmAOX5 cDNA is 3,741 nucleotides and includes an open reading frame, which encodes a protein of 1,246 amino acid residues. The theoretical molecular weight and isoelectric point of BmAOX5 are approximately 138 kDa and 5.58, respectively. BmAOX5 shares a similar primary structure with BmAOX1 and BmAOX2, containing two [2Fe-2S] redox centers, a FAD-binding domain, and a molybdenum cofactor (MoCo)-binding domain. RT-PCR revealed BmAOX5 to be particularly highly expressed in the PG (including ovipositor) of the female silkworm moth, and the expression was further confirmed by in situ hybridization, AOX activity staining, and anti-BmAOX5 western blotting. Further, BmAOX5 was shown to metabolize aromatic aldehydes, such as benzaldehyde, salicylaldehyde, and vanillic aldehyde, and fatty aldehydes, such as heptaldehyde and propionaldehyde. The maximum reaction rate (Vmax) of benzaldehyde as substrate was 21 mU and Km was 1.745 mmol/liter. These results suggested that BmAOX5 in the PG could metabolize aldehydes in the cytoplasm for detoxification or participate in the degradation of aldehyde pheromone substances and odorant compounds to identify mating partners and locate suitable spawning sites.

Robust MST-Based Clustering Algorithm.

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.