Evive Biotech (China)

Extracellular vesicles (EVs) are promising drug-delivery vehicles owing to their biocompatibility and low immunogenicity. Genetic engineering of a membrane-bound EV-sorting scaffold protein empowers EVs by installing targeting moieties on the surface and enriching therapeutic cargo in the lumen. However, the choice of scaffold proteins with simple structures and short sequences is limited. Here, we conduct mass spectrometry-based proteomic studies and identify ENPP1 as a superior scaffold protein. Furthermore, we show that a truncated 144-amino acid variant, EN144, efficiently loads diverse therapeutic cargoes and outperforms conventional scaffolds. By fusing EN144 to the IL-6 decoy receptor gp130, we create engineered decoy EVs that potently inhibit inflammatory IL-6 trans-signaling. In mouse models, these EVs reduce inflammation, improve survival in sepsis, and, when targeted to cartilage, alleviate tissue damage in osteoarthritis. Our work establishes EN144 as a minimal, high-performance scaffold for EV engineering and demonstrates its broad therapeutic potential for inflammatory diseases.

Abstract Analyzing metabolites using mass spectrometry can offer valuable insight into an individual’s health or disease status. However, various sources of experimental variation can affect the data, making robust quality control essential. In this context, we introduce mzQuality, a user-friendly software tool designed to evaluate and correct technical variations in mass spectrometry-based metabolomics data. MzQuality offers key quality control features, such as batch correction, outlier identification, and analysis of signal-to-noise ratios. It supports any peak-integrated processed data independent of vendor software and does not require the user to have any programming skills. We demonstrate the functionality of mzQuality with a data set of 419 samples measured across six batches, in which mzQuality effectively minimized experimental variation, ensuring the data’s readiness for statistical analysis and biological interpretation. With customizable settings, mzQuality can be seamlessly integrated into research workflows to produce more accurate and reproducible metabolomics data.

A Pantoea ananatis strain, named LCFJ-001 (GDMCC: 1.6101), was isolated for the first time from bacterial wilt-diseased roots of mulberry (Morus atropurpurea) in the western part of the Guangxi Zhuang Autonomous Region, China. Moreover, through Koch's postulates, it was proven that LCFJ-001 can cause mulberry wilt, which is one of the pathogens of mulberry bacterial wilt. Here, we report a complete, annotated genome sequence of P. ananatis LCFJ-001. The entire genome sequence of P. ananatis strain LCFJ-001 was a 4,499,350 bp circular chromosome with 53.50% GC content. In total, 3521 genes were annotated, of which 3418 were assigned protein-coding genes. In addition, 22 ribosomal RNAs and 81 transfer RNAs were identified. The presented resource will help explore the pathogenetic mechanisms of mulberry wilt disease caused by the genus Pantoea.

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

Abstract This abstract was not presented at the conference. Citation Format: Glaspy J, Tang T, Rutty D, Yan X, Bondarenko I, Burdaeva O. Not presented [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-13-03.