scholarly journals Insight Into Unprecedented Diversity of Cyanopeptides in Eutrophic Ponds Using a MS/MS Networking Approach

2020 ◽  
Author(s):  
Andreja Kust ◽  
Klára Řeháková ◽  
Jaroslav Vrba ◽  
Vincent Maicher ◽  
Jan Mareš ◽  
...  
Keyword(s):  
Social Networking ◽  
Natural Product ◽  
Harmful Algal Bloom ◽  
Simultaneous Detection ◽  
Species Abundance ◽  
Chemical Properties ◽  
Binary Logistic Regression ◽  
Molecular Networking ◽  
Liquid Chromatography Tandem Mass ◽  
Web Platform

Man-made shallow fishponds in the Czech Republic have been facing a high eutrophication since 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physico-chemical properties of water and its aquatic community composition leading to harmful algal bloom formation. In our current study, we have characterised the phytoplankton community across three hypertrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprised of non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography tandem mass spectrometry (LC–MS/MS) data coupled with dereplication strategy. This MS networking approach coupled with dereplication on online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin and epidolastatin12. We have applied the binary logistic regression to estimate the CNPs producer by correlating the GNPS data with the species abundance. Usage of The combination of molecular networking and dereplication on online global natural product social networking (GNPS) web platform has proved as a valuable approach for rapid and simultaneous detection of high number of peptides, and rapidly assessing the risk for harmful bloom.

scholarly journals Insight into Unprecedented Diversity of Cyanopeptides in Eutrophic Ponds Using an MS/MS Networking Approach

Toxins ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 561 ◽  
Author(s):  
Andreja Kust ◽  
Klára Řeháková ◽  
Jaroslav Vrba ◽  
Vincent Maicher ◽  
Jan Mareš ◽  
...  
Keyword(s):  
Harmful Algal Bloom ◽  
Simultaneous Detection ◽  
Species Abundance ◽  
Binary Logistic Regression ◽  
The Czech Republic ◽  
Bloom Formation ◽  
Liquid Chromatography Tandem Mass ◽  
Web Platform ◽  
Insight Into ◽  
Eutrophic Ponds

Man-made shallow fishponds in the Czech Republic have been facing high eutrophication since the 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physicochemical properties of water and its aquatic community composition, leading to harmful algal bloom formation. In our current study, we characterized the phytoplankton community across three eutrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprising non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data coupled with a dereplication strategy. This MS networking approach, coupled with dereplication, on the online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin, and epidolastatin 12. We applied the binary logistic regression to estimate the CNPs producers by correlating the GNPS data with the species abundance. The usage of the GNPS web platform proved a valuable approach for the rapid and simultaneous detection of a large number of peptides and rapid risk assessments for harmful blooms.

scholarly journals Shifts in soil and plant functional diversity along an altitudinal gradient in the French Alps

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Alexia Stokes ◽  
Guillermo Angeles ◽  
Fabien Anthelme ◽  
Eduardo Aranda-Delgado ◽  
Isabelle Barois ◽  
...  
Keyword(s):  
Plant Community ◽  
Functional Diversity ◽  
Plant Communities ◽  
Species Abundance ◽  
Chemical Properties ◽  
Water Infiltration ◽  
Temperate Climate ◽  
Biophysical Properties ◽  
Tropical Zone ◽  
Soil Microbial

Abstract Objectives Altitude integrates changes in environmental conditions that determine shifts in vegetation, including temperature, precipitation, solar radiation and edaphogenetic processes. In turn, vegetation alters soil biophysical properties through litter input, root growth, microbial and macrofaunal interactions. The belowground traits of plant communities modify soil processes in different ways, but it is not known how root traits influence soil biota at the community level. We collected data to investigate how elevation affects belowground community traits and soil microbial and faunal communities. This dataset comprises data from a temperate climate in France and a twin study was performed in a tropical zone in Mexico. Data description The paper describes soil physical and chemical properties, climatic variables, plant community composition and species abundance, plant community traits, soil microbial functional diversity and macrofaunal abundance and diversity. Data are provided for six elevations (1400–2400 m) ranging from montane forest to alpine prairie. We focused on soil biophysical properties beneath three dominant plant species that structure local vegetation. These data are useful for understanding how shifts in vegetation communities affect belowground processes, such as water infiltration, soil aggregation and carbon storage. Data will also help researchers understand how plant communities adjust to a changing climate/environment.

scholarly journals The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking

mSystems ◽  
2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Daniela B. B. Trivella ◽  
Rafael de Felicio
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Chemical Compounds ◽  
Gene Clusters ◽  
Tandem Mass ◽  
Molecular Networking ◽  
Natural Product Discovery

ABSTRACT Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches—genome mining, silent pathway induction, and MS-based molecular networking—compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

scholarly journals Application of a Liquid Chromatography Tandem Mass Spectrometry Method for the Simultaneous Detection of Seven Allergenic Foods in Flour and Bread and Comparison of the Method with Commercially Available ELISA Test Kits

2011 ◽  
Vol 94 (4) ◽  
pp. 1060-1068 ◽  
Author(s):  
Julia Heick ◽  
Markus Fischer ◽  
Sandra Kerbach ◽  
Ulrike Tamm ◽  
Bert Popping
Keyword(s):  
Simultaneous Detection ◽  
Elisa Test ◽  
Spectrometry Method ◽  
Chromatography Tandem Mass Spectrometry ◽  
Model Matrix ◽  
Before And After ◽  
Liquid Chromatography Tandem Mass ◽  
Novel Method ◽  
Detection Capabilities ◽  
Baked Products

Abstract To protect the allergic consumer, analytical methods need to be capable of detecting allergens in fnished products that typically contain multiple allergens. An LC/MS/MS method for simultaneous detection of seven allergens was developed and compared with commercially available ELISA kits. The detection capabilities of this novel method were demonstrated by analyzing incurred material containing milk, egg, soy, peanut, hazelnut, walnut, and almond. Bread was chosen as a model matrix. To assess the influence of baking on the method’s performance, analysis was done before and after baking. The same samples were analyzed with ELISA test kits from ELISA Systems, Morinaga, Neogen, and r-Biopharm. Peanut, hazelnut, walnut, and almond could be detected with both ELISA and LC/MS/MS regardless of whether the product was baked or not. LC/MS/MS clearly showed superior detection of milk in processed matrixes compared to ELISA, which exhibited signifcantly lower sensitivities when analyzing the baked products. Similar results were obtained when analyzing egg; however, one kit was capable of detecting egg in the processed samples as well.

scholarly journals Molecular Networking: A Useful Tool for the Identification of New Psychoactive Substances in Seizures by LC–HRMS

2020 ◽  
Vol 8 ◽  
Author(s):  
Flaminia Vincenti ◽  
Camilla Montesano ◽  
Francesca Di Ottavio ◽  
Adolfo Gregori ◽  
Dario Compagnone ◽  
...  
Keyword(s):  
Natural Product ◽  
Illicit Drugs ◽  
Synthetic Cannabinoids ◽  
New Psychoactive Substances ◽  
Nmr Analysis ◽  
Psychoactive Substances ◽  
Molecular Networking ◽  
Fentanyl Analogs ◽  
Global Concern ◽  
Product Search

New Psychoactive Substances (NPS) are a global concern since they are spreading at an unprecedented rate. Despite their commerce still being limited compared to traditional illicit drugs, the identification of NPS in seizures may represent a challenge because of the variety of possible structures. In this study we report the successful application of molecular networking (MN) to identify unexpected fentanyl analogs in two seizures. The samples were extracted with 1 mL of methanol and analyzed with an untargeted data-dependent acquisition approach by LC–HRMS. The obtained data were examined using the MN workflow within the Global Natural Product Search (GNPS). A job was submitted to GNPS by including both seizures and standard mixtures containing synthetic cannabinoids and fentanyls raw files; spectra obtained from standards were used to establish representative networks for both molecular classes. All synthetic cannabinoids in the mixture were linked together resulting in a molecular network despite their different fragmentation spectra. Looking at fentanyls, all the molecules with the typical 188.143 and 105.070 fragments were combined in a representative network. By exploiting the standard networks two unexpected fentanyls were found in the analyzed seizures and were putatively annotated as para-fluorofuranylfentanyl and (iso)butyrylfentanyl. The identity of these two fentanyl analogs was confirmed by NMR analysis. Other m/z ratios in the seizures were compatible with fentanyl derivatives; however, they appeared to be minor constituents, probably impurities or synthetic byproducts. The latter might be of interest for investigations of common fingerprints among different seizures.

scholarly journals Dhb Microcystins Discovered in USA Using an Online Concentration LC–MS/MS Platform

Toxins ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 653 ◽  
Author(s):  
Johnna A. Birbeck ◽  
Nicholas J. Peraino ◽  
Grace M. O’Neill ◽  
Julia Coady ◽  
Judy A. Westrick
Keyword(s):  
United States ◽  
Mass Spectrometry ◽  
United States Of America ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
The United States ◽  
Tandem Mass ◽  
Chemical Derivatization ◽  
Chromatography Tandem Mass Spectrometry ◽  
Liquid Chromatography Tandem Mass

Based on current structural and statistical calculations, thousands of microcystins (MCs) can exist; yet, to date, only 246 MCs were identified and only 12 commercial MC standards are available. Standard mass spectrometry workflows for known and unknown MCs need to be developed and validated for basic and applied harmful algal bloom research to advance. Our investigation focuses on samples taken in the spring of 2018 from an impoundment fed by Oser and Bischoff Reservoirs, Indiana, United States of America (USA). The dominant cyanobacterium found during sampling was Planktothrix agardhii. The goal of our study was to identify and quantify the MCs in the impoundment sample using chemical derivatization and mass spectrometry. Modifying these techniques to use online concentration liquid chromatography tandem mass spectrometry (LC–MS/MS), two untargeted MCs have been identified, [d-Asp3, Dhb7]-MC-LR and [Dhb7]-MC-YR. [Dhb7]-MC-YR is not yet reported in the literature to date, and this was the first reported incidence of Dhb MCs in the United States. Furthermore, it was discovered that the commercially available [d-Asp3]-MC-RR standard was [d-Asp3, Dhb7]-MC-RR. This study highlights a workflow utilizing online concentration LC–MS/MS, high-resolution MS (HRMS), and chemical derivatization to identify isobaric MCs.

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