Unnatural tetradeoxy echinocandins produced by gene cluster design and heterologous expression.

The hydroxyl groups in the amino acid residues of echinocandin B were related to the biological activity, the instability, and the drug resistance. The modification of hydroxyl groups was expected to obtain the new lead compounds for next generation of echinocandin drug development. In this work one method for heterologous production of the tetradeoxy echinocandin was achieved. A reconstructed biosynthetic gene cluster for tetradeoxy echinocandins composed of ecdA/I/K and htyE was designed and successfully hetero-expressed in Aspergillus nidulans. The target product of echinocandin E (1) together with one unexpected derivative echinocandin F (2), were isolated from the fermentation culture of engineered strain. Both of compounds were unreported echinocandin derivatives and the structures were identified on the basis of mass and NMR spectral data analysis. Compared with echinocandin B, echinocandin E demonstrated superior stability and comparable antifungal activity.

Recent Advances in the Heterologous Expression of Biosynthetic Gene Clusters for Marine Natural Products.

Marine natural products (MNPs) are an important source of biologically active metabolites, particularly for therapeutic agent development after terrestrial plants and nonmarine microorganisms. Sequencing technologies have revealed that the number of biosynthetic gene clusters (BGCs) in marine microorganisms and the marine environment is much higher than expected. Unfortunately, the majority of them are silent or only weakly expressed under traditional laboratory culture conditions. Furthermore, the large proportion of marine microorganisms are either uncultivable or cannot be genetically manipulated. Efficient heterologous expression systems can activate cryptic BGCs and increase target compound yield, allowing researchers to explore more unknown MNPs. When developing heterologous expression of MNPs, it is critical to consider heterologous host selection as well as genetic manipulations for BGCs. In this review, we summarize current progress on the heterologous expression of MNPs as a reference for future research.

Structure and product relationship analysis of acyl homoserine lactone synthases among Ensifer adhaerens reveals distinct chromosome and plasmid origins.

Despite the conservative DNA sequences among LuxI (Acyl Homoserine Lactones synthase gene) homologs, structure-product relationship of AHL synthase remains to be elucidated. In this study, through degenerate primers and in vitro expression methods, we collected the information of the gene sequences and AHL profiles from nine LuxIs among Ensifer adhaerens strains. The chromosome-encoded LuxI (C-LuxI) distinguished themselves from the plasmid-encoded ones (P-LuxI) not only in the DNA sequences, but also in AHL profiles. The C-LuxIs produced only C14-HSL, while the P-LuxIs produced predominantly C8-HSL and 3-oxo-C8-HSL. Sequence-product relationship analysis updated our recognition of the role of T140 (EsaI) in the 3-oxo-HSL production. Computational calculation based on 3D structures of these LuxIs revealed the linear relationship between the chain length and the affinity of amides to AHL synthase in C-LuxI, which was not found in the P-LuxI. We hereby proposed the linear docking affinity as a criterion for the prediction of long-chain AHL production by an AHL synthase. This study extends our understanding on the structure-product relationship of AHL synthases and revealed the distinct chromosome and plasmid origin of this enzyme among E. adhaerens.

EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.

Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.