RESUMEN

Didemnins are a class of cyclic depsipeptides derived from sea tunicates that exhibit potent anticancer, antiviral, and immunosuppressive properties. Although certain Tistrella species can produce didemnins, their complete biosynthetic potential remains largely unexplored. In this study, we utilize feature-based molecular networking to analyze the metabolomics of Tistrella mobilis and Tistrella bauzanensis, focusing on the production of didemnin natural products. In addition to didemnin B, we identify nordidemnin B and [hysp2]didemnin B, as well as several minor didemnin analogs. Heterologous expression of the didemnin biosynthetic gene cluster in a Streptomyces host results in the production of only didemnin B and nordidemnin B in limited quantities. Isotope-labeling studies reveal that the substrate promiscuity of the adenylation domains during biosynthesis leads to the accumulation of nordidemnin B and [hysp2]didemnin B. Additionally, precursor-directed biosynthesis is applied to generate eight novel didemnin derivatives by supplementing the culture with structurally related amino acids. Furthermore, we increased the titers of nordidemnin B and [hysp2]didemnin B by supplementing the fermentation medium with l-valine and l-isoleucine, respectively. Finally, both compounds undergo side-chain oxidation to enhance their biological activity, with their anticancer properties found to be as potent as plitidepsin.

RESUMEN

Ammonia nitrogen (AN) pollution frequently occurs in urban rivers with the continuous acceleration of industrialization. Monitoring AN pollution levels and tracing its complex sources often require large-scale testing, which are time-consuming and costly. Due to the lack of reliable data samples, there were few studies investigating the feasibility of water quality prediction of AN concentration with a high fluctuation and non-stationary change through data-driven models. In this study, four deep-learning models based on neural network algorithms including artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were employed to predict AN concentration through some easily monitored indicators such as pH, dissolved oxygen, and conductivity, in a real AN-polluted river. The results showed that the GRU model achieved optimal prediction performance with a mean absolute error (MAE) of 0.349 and coefficient of determination (R2) of 0.792. Furthermore, it was found that data preprocessing by the VMD technique improved the prediction accuracy of the GRU model, resulting in an R2 value of 0.822. The prediction model effectively detected and warned against abnormal AN pollution (> 2 mg/L), with a Recall rate of 93.6% and Precision rate of 72.4%. This data-driven method enables reliable monitoring of AN concentration with high-frequency fluctuations and has potential applications for urban river pollution management.

RESUMEN

The assembly line biosynthesis of the powerful anticancer-antiviral didemnin cyclic peptides is proposed to follow a prodrug release mechanism in Tristella bacteria. This strategy commences with the formation of N-terminal prodrug scaffolds and culminates in their cleavage during the cellular export of the mature products. In this study, a comprehensive exploration of the genetic and biochemical aspects of the enzymes responsible for both the assembly and cleavage of the acylated peptide prodrug scaffolds is provided. This process involves the assembly of N-acyl-polyglutamine moieties orchestrated by the nonribosomal peptide synthetase DidA and the cleavage of these components at the post-assembly stage by DidK, a transmembrane CAAX hydrolase homolog. The findings not only shed light on the complex prodrug mechanism that underlies the synthesis and secretion of didemnin compounds but also offer novel insights into the expanded role of CAAX hydrolases in microbes. Furthermore, this knowledge can be leveraged for the strategic design of genome mining approaches aimed at discovering new bioactive natural products that employ similar prodrug biochemical strategies.

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RESUMEN

A novel Gram-stain-negative, aerobic, rod-shaped strain designated PG04(T) was isolated from the rhizosphere of watermelon plants cultivated in Beijing, China. A polyphasic taxonomic study was performed on the new isolate. On the basis of 16S rRNA gene sequence similarity studies, isolate PG04(T) belonged clearly to the genus Hansschlegelia and was most closely related to Hansschlegelia zhihuaiae (97.3 % similarity to the type strain). The predominant respiratory quinone was ubiquinone 10 (Q-10) and the polar lipid profile was composed of the major lipids diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and phosphatidylcholine. The major fatty acids were C18 : 1ω7c (41.3 %), C19 : 0 cyclo ω8c (30.6 %) and C16 : 0 (19.1 %). The G+C content of the DNA was about 64.4 mol%. DNA-DNA hybridization experiments showed 34.4 % relatedness between strain PG04(T) and H. zhihuaiae DSM 18984(T). The results of physiological and biochemical tests and differences in fatty acid profiles allowed clear phenotypic differentiation of strain PG04(T) from the most closely related species in the genus, H. zhihuaiae. Strain PG04(T) therefore represents a novel species within the genus Hansschlegelia, for which the name Hansschlegelia beijingensis sp. nov. is proposed, with the type strain PG04(T) ( = DSM 25481(T) = ACCC 05759(T)).

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