Search

Abstract Background Anthraquinone-fused enediynes (AFEs) are excellent payloads for antibody-drug conjugates (ADCs). The yields of AFEs in the original bacterial hosts are extremely low. Multiple traditional methods had been adopted to enhance the production of the AFEs. Despite these efforts, the production titers of these compounds are still low, presenting a practical challenge for their development. Tiancimycins (TNMs) are a class of AFEs produced by Streptomyces sp. CB03234. One of their salient features is that they exhibit rapid and complete cell killing ability against various cancer cell lines. Results In this study, a combinatorial metabolic engineering strategy guided by the CB03234-S genome and transcriptome was employed to improve the titers of TNMs. First, re-sequencing of CB03234-S (Ribosome engineered mutant strains) genome revealed the deletion of a 583-kb DNA fragment, accounting for about 7.5% of its genome. Second, by individual or combined inactivation of seven potential precursor competitive biosynthetic gene clusters (BGCs) in CB03234-S, a double-BGC inactivation mutant, S1009, was identified with an improved TNMs titer of 28.2 ± 0.8 mg/L. Third, overexpression of five essential biosynthetic genes, including two post-modification genes, and three self-resistance auxiliary genes, was also conducted, through which we discovered that mutants carrying the core genes, tnmE or tnmE10, exhibited enhanced TNMs production. The average TNMs yield reached 43.5 ± 2.4 mg/L in a 30-L fermenter, representing an approximately 360% increase over CB03234-S and the highest titer among all AFEs to date. Moreover, the resulting mutant produced TNM-W, a unique TNM derivative with a double bond instead of a common ethylene oxide moiety. Preliminary studies suggested that TNM-W was probably converted from TNM-A by both TnmE and TnmE10. Conclusions Based on the genome and transcriptome analyses, we adopted a combined metabolic engineering strategy for precursor enrichment and biosynthetic pathway reorganization to construct a high-yield strain of TNMs based on CB03234-S. Our study establishes a solid basis for the clinical development of AFE-based ADCs.

Lactobacillus is a recognized probiotic and has been widely used in food and medicine. As a new type of low-temperature resistant Lactobacillus, the fermentation products of Dellaglioa algida have multiple physiological activities. This study focuses on the exopolysaccharide (EPS) produced by Dellaglioa algida. The composition of the EPS is analyzed by FTIR, UV, GPC, HPLC, NMR, and SEM, and its antioxidant and immune activities are explored. The experimental results show that the EPS is a polymer composed of nine monosaccharides such as rhamnose, glucose, and mannose, connected by α- and β-glycosidic bonds, with an average molecular weight of 2.163×104 Da. When the EPS concentration reaches 100 μg/mL, the scavenging activities of DPPH and ABTS+ are 60.0% and 51.2%, respectively. The EPS promotes the secretion of NO by regulating the iNOS/NO pathway, reduces oxidative damage, and reduces the secretion of inflammatory factors such as IL-6, IL-1β, and TNF-α, and downregulates the mRNA expression of inflammatory factors, thereby alleviating the cell inflammation stimulated by the cold-resistant bacteria Pseudomonas fluorescens and Pseudomonas fragi. By virtue of these properties, the EPS produced by Dellaglioa algida fermentation has the potential to act as an antioxidant and immunomodulator.

The aim of this study was to examine the components of the cell-free supernatant (CFS) derived from a novel strain of psychrophilic Lactobacillus, Dellaglioa algida, and to further elucidate the impact of this CFS on various cellular processes. Specifically, we sought to understand its effects on the cell membrane, protein and DNA release, protease activity, and metabolites of Pseudomonas fluorescens and Pseudomonas fragi, thereby clarifying the antibacterial mechanism involved. The CFS components were analyzed using Gas Chromatography–Mass Spectrometry (GC-MS), the Coomassie Brilliant Blue method, and the phenol–sulfuric acid method. The inhibitory effect of the CFS on Pseudomonas fluorescens and Pseudomonas fragi was assessed using the ethidium bromide (EB) assay, Oxford cup assay, and ultramicroassay. Additionally, we analyzed the metabolites produced by Pseudomonas fluorescens and Pseudomonas fragi when treated with the CFS. The findings reveal that the CFS of Dellaglioa algida contains 94 volatile components, with protein and sugar concentrations of 32.857 ± 0.9705 mg/mL and 98.250 ± 4.210 mg/L, respectively. The CFS induces varying degrees of damage to the cell membranes of both Pseudomonas fluorescens and Pseudomonas fragi, leading to the release of intracellular proteins and DNA. Furthermore, the CFS reduced the protease activity and metabolic capacity of Pseudomonas fluorescens and Pseudomonas fragi. These results enhance our understanding of the mechanism by which psychrophilic Dellaglioa algida inhibits Pseudomonas fluorescens and Pseudomonas fragi, confirming that its inhibitory effect predominantly occurs through damage to the biological cell membranes of Pseudomonas. Dellaglioa algida is a newly identified cold-adapted inhibitor of Pseudomonas, indicating that its CFS is an effective microbial inhibitor in cold environments. This discovery suggests potential applications in inhibiting the growth and reproduction of Pseudomonas fluorescens and Pseudomonas fragi in food, pharmaceuticals, perfumes, and other chemicals, providing a valuable new reference for industrial preservation.

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems. [ABSTRACT FROM AUTHOR]