Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time

A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products

LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

In order to accelerate the isolation and characterization of structurally new or novel secondary metabolites, it is crucial to develop efficient strategies that prioritize samples with greatest promise early in the workflow so that resources can be utilized in a more efficient and cost-effective manner. We have developed a metrics-based prioritization approach using exact LC-HRMS, which uses data for 24 618 marine natural products held in the PharmaSea database. Each sample was evaluated and allocated a metric score by a software algorithm based on the ratio of new masses over the total (sample novelty), ratio of known masses over the total (chemical novelty), number of peaks above a defined peak area threshold (sample complexity), and peak area (sample diversity). Samples were then ranked and prioritized based on these metric scores. To validate the approach, eight marine sponges and six tunicate samples collected from the Fiji Islands were analyzed, metric scores calculated, and samples targeted for isolation and characterization of new compounds. Structures of new compounds were elucidated by spectroscopic techniques, including 1D and 2D NMR, MS, and MS/MS. Structures were confirmed by computer-assisted structure elucidation methods (CASE) using the ACD/Structure Elucidator Suite

In vitro reconstruction of human junctional and sulcular epithelium

Background: The aim of this study was to develop and characterize standardized in vitro three-dimensional organotypic models of human junctional epithelium (JE) and sulcular epithelium (SE). Methods Organotypic models were constructed by growing human normal gingival keratinocytes on top of collagen matrices populated with gingival fibroblasts (GF) or periodontal ligament fibroblasts (PLF). Tissues obtained were harvested at different time points and assessed for epithelial morphology, proliferation (Ki67), expression of JE-specific markers (ODAM and FDC-SP), cytokeratins (CK), transglutaminase, filaggrin, and basement membrane proteins (collagen IV and laminin1). Results: The epithelial component in 3- and 5-day organotypics showed limited differentiation and expressed Ki-67, ODAM, FDC-SP, CK 8, 13, 16, 19, and transglutaminase in a similar fashion to control JE samples. PLF supported better than GF expression of CK19 and suprabasal proliferation, although statistically significant only at day 5. Basement membrane proteins started to be deposited only from day 5. The rate of proliferating cells as well as the percentage of CK19-expressing cells decreased significantly in 7- and 9-day cultures. Day 7 organotypics presented higher number of epithelial cell layers, proliferating cells in suprabasal layers, and CK expression pattern similar to SE. Conclusion: Both time in culture and fibroblast type had impact on epithelial phenotype. Five-day cultures with PLF are suggested as JE models, 7-day cultures with PLF or GF as SE models, while 9-day cultures with GF as gingival epithelium (GE) models. Such standard, reproducible models represent useful tools to study periodontal bacteria–host interactions in vitro