Ilyonectria root rot of ginseng can greatly decrease crop yields often by 20–30% at time of harvest. We previously determined that the Ilyonectria siderophore N,N′,N″-triacetylfusarinine C (TAFC) is critical for the ginseng pathogens' virulence. TAFC enables the fungus to acquire iron from its environment, and once inside the cell, iron is typically released via enzymatic hydrolysis of TAFC's three ester moieties. This iron acquisition system has been extensively studied in Aspergillus fumigatus. Based on this system, we hypothesized that AfEstB, a well-characterized TAFC esterase produced by A. fumigatus, could be used for siderophore-targeted biocontrol of Ilyonectria. Application of purified recombinant AfEstB to ginseng roots inoculated with virulent I. mors-panacis demonstrated a significant protective effect, which disappeared when AfEstB was no longer applied. These data support the targeting of Ilyonectria iron acquisition mechanisms though enzymatic degradation of TAFC as an effective means of controlling Ilyonectria root rot of ginseng.
Sample preparation is key for optimal detection and visualization of analytes in Matrix-assisted Laser Desorption/Ionization (MALDI) Imaging Mass Spectrometry (IMS) experiments. Determining the appropriate protocol to follow throughout the sample preparation process can be difficult as each step must be optimized to comply with the unique characteristics of the analytes of interest. This process involves not only finding a compatible matrix that can desorb and ionize the molecules of interest efficiently, but also selecting the appropriate matrix deposition technique. For example, a wet matrix deposition technique, which entails dissolving a matrix in solvent, is superior for desorption of most proteins and peptides, whereas dry matrix deposition techniques are particularly effective for ionization of lipids. Sublimation has been reported as a highly efficient method of dry matrix deposition for the detection of lipids in tissue by MALDI IMS due to the homogeneity of matrix crystal deposition and minimal analyte delocalization as compared to many wet deposition methods 1,2. Broadly, it involves placing a sample and powdered matrix in a vacuum-sealed chamber with the samples pressed against a cold surface. The apparatus is then lowered into a heated bath (sand or oil), resulting in sublimation of the powdered matrix onto the cooled tissue sample surface. Here we describe a sublimation protocol using 1,5-diaminonaphthalene (DAN) matrix for the detection and visualization of gangliosides in the rat brain using MALDI IMS.
Pyrolysis converts biomass such as agricultural and forestry waste into bio-oil, preserving some chemicals while creating other, new ones. Nicotine, a chemical present in tobacco leaves and a known pesticide, was found to remain intact during pyrolysis. As expected, insecticidal properties were observed for tobacco bio-oil. Pesticide characteristics of tobacco bio-oil have been observed on the Colorado potato beetle (CPB), a pest currently resistant to all major insecticides, as well as a few bacteria and fungi that do not currently respond well to chemical treatment. Unexpectedly, nicotine-free fractions of the bio-oil were also found to be highly lethal to the beetles and successful at inhibiting the growth of select microorganisms. Through GC-MS, it was found that the active, nicotine-free fractions were rich in phenolics, chemicals likely created from lignin during pyrolysis. While bio-oils in general are known to contain phenolic chemicals, such as cresols, to our best knowledge, quantitative analysis has not been performed to determine if these chemicals are solely responsible for the observed pesticide activities. Based on GC-MS results, ten of the most abundant chemicals, eight of which were phenolic chemicals, were identified and examined through bio-assays. A mixture of these chemicals at the concentration levels found in the bio-oil did not account for the bio-oil activity towards the microorganisms. Tobacco bio-oil may have potential as a pesticide, however, further analyses using liquid chromatography is necessary to identify the remaining active chemicals.
Many diverse species of fungi naturally occur as endophytes in plants. The majority of these fungi produce secondary metabolites of diverse structures and biological activities. Culture extracts from 288 fungi isolated from surface-sterilized blueberries, cranberries, raspberries, and grapes were analyzed by LC-HRMS/MS. Global Natural Products Social (GNPS) Molecular Networking modeling was used to investigate the secondary metabolites in the extracts. This technique increased the speed and simplicity of dereplicating the extracts, targeting new compounds that are structurally related. In total, 60 known compounds were dereplicated from this collection and seven new compounds were identified. These previously unknown compounds are targets for purification, characterization, and bioactivity testing in future studies. The fungal endophytes characterized in this study are potential candidates for providing bio-protection to the host plant with a reduced reliance on chemical pesticides.
Surfactants such as didodecyldimethyl ammonium bromide (DDAB) and 1,2-dilauroyl-sn-phosphatidylcholine (DLPC) form bilayers at the walls of bare silica capillaries. Once formed, these bilayers are stable in the absence of surfactant in the buffer. DDAB provides a cationic bilayer coating which yields a strong reversed EOF and is effective for separation of cationic proteins. DLPC provides a zwitterionic bilayer coating which is effective for both cationic and anionic proteins. The electroosmotic flow (EOF) is strongly suppressed in DLPC-coated capillaries, thus low mobility proteins are slow to elute, and so the coating is favored for separation of high mobility proteins.
Rationale Microbial natural products are often biosynthesized as classes of structurally related compounds that have similar tandem mass spectrometry (MS/MS) fragmentation patterns. Mining MS/MS datasets for precursor ions that share diagnostic or common features enables entire chemical classes to be identified, including novel derivatives that have previously been unreported. Analytical data analysis tools that can facilitate a class‐targeted approach to rapidly dereplicate known compounds and identify structural variants within complex matrices would be useful for the discovery of new natural products. Methods A diagnostic fragmentation filtering (DFF) module was developed for MZmine to enable the efficient screening of MS/MS datasets for class‐specific product ions(s) and/or neutral loss(es). This approach was applied to series of the structurally related chaetoglobosin and cytochalasin classes of compounds. These were identified from the culture filtrates of three fungal genera: Chaetomium globosum , a putative new species of Penicillium (called here P . cf. discolor : closely related to P. discolor ), and Xylaria sp. Extracts were subjected to LC/MS/MS analysis under positive electrospray ionization and operating in a data‐dependent acquisition mode, performed using a Thermo Q‐Exactive mass spectrometer. All MS/MS datasets were processed using the DFF module and screened for diagnostic product ions at m / z 130.0648 and 185.0704 for chaetoglobosins, and m / z 120.0808 and 146.0598 for cytochalasins. Results Extracts of C. globosum and P . cf. discolor strains revealed different mixtures of chaetoglobosins, whereas the Xylaria sp. produced only cytochalasins; none of the strains studied produced both classes of compounds. The dominant chaetoglobosins produced by both C. globosum and P . cf. discolor were chaetoglobosins A, C, and F. Tetrahydrochaetoglobosin A was identified from P . cf. discolor extracts and is reported here for the first time as a natural product. The major cytochalasins produced by the Xylaria sp. were cytochalasin D and epoxy cytochalasin D. A larger unknown “cytochalasin‐like” molecule with the molecular formula C 38 H 47 NO 10 was detected from Xylaria sp. culture filtrate extracts and is a current target for isolation and structural characterization. Conclusions DFF is an effective LC/MS data analysis approach for rapidly identifying entire classes of compounds from complex mixtures. DFF has proved useful in the identification of new natural products and allowing for their partial characterization without the need for isolation.
