Analytical workflow integrating LC-HRMS untargeted analysis and CyanoMetDB for fast and extensive detection of cyanobacterial metabolites
Start Date
24-5-2022 2:45 PM
End Date
24-5-2022 3:00 PM
Abstract
Cyanobacteria produce a large number of secondary metabolites including cyanotoxins and a variety of bioactive peptides with great structural diversity. Identification of these metabolites (cyanometabolites) is a great analytical challenge due to the extremely limited availability of analytical standards and lack of a well-documented fragmentation mass spectra database. In the present study, an analytical workflow was developed for the detection of the cyanometabolites in bloom samples from Greek lakes. Samples were extracted [1,2] and analyzed by LC-HRMS (Orbitrap Fusion Lumos Tribrid MS) in data depended acquisition (DDA) mode. Fragmentation spectra of compounds were obtained with collision-induced dissociation (CID) and higher-energy C-trap dissociation (HCD) modes. Acquired data were processed with Compound Discoverer software in combination to the recently published CyanoMetDB mass list [3] and other related tools for the annotation and structural elucidation of cyanometabolites. Verification of proposed structures was performed based on in silico fragmentation and fragment ion search (FISh) scoring. Application of the workflow revealed the presence of numerous congeners belonging to the cyanotoxins class microcystins and to the understudied cyanopeptides classes of cyanopeptolines, microginins, aeruginosins, anabaenopeptins and aeruginosamides. Furthermore, new congeners were annotated clearly demonstrating the suitability of the approach for the characterization of cyanobacterial chemodiversity.
[1] C. Christophoridis, S.-K. Zervou, K. Manolidi, M. Katsiapi, M. Moustaka-Gouni, T. Kaloudis, T. M. Triantis and A. Hiskia, Scientific Reports 8 (2018) 17877.
[2] S.-K. Zervou, K. Moschandreou, A. Paraskevopoulou, C. Christophoridis, E. Grigoriadou, T. Kaloudis, T. M. Triantis, V. Tsiaoussi and A. Hiskia, Toxins 13 (2021) 394.
[3] M. R. Jones, E. Pinto, M. A. Torres, F. Dörr, H. Mazur-Marzec, K. Szubert, L. Tartaglione, C. Dell’Aversano, C. O. Miles, D. G. Beach, P. McCarron, K. Sivonen, D. P. Fewer, J. Jokela and E. M.-L. Janssen, Water Research 196 (2021) 117017.
Analytical workflow integrating LC-HRMS untargeted analysis and CyanoMetDB for fast and extensive detection of cyanobacterial metabolites
Cyanobacteria produce a large number of secondary metabolites including cyanotoxins and a variety of bioactive peptides with great structural diversity. Identification of these metabolites (cyanometabolites) is a great analytical challenge due to the extremely limited availability of analytical standards and lack of a well-documented fragmentation mass spectra database. In the present study, an analytical workflow was developed for the detection of the cyanometabolites in bloom samples from Greek lakes. Samples were extracted [1,2] and analyzed by LC-HRMS (Orbitrap Fusion Lumos Tribrid MS) in data depended acquisition (DDA) mode. Fragmentation spectra of compounds were obtained with collision-induced dissociation (CID) and higher-energy C-trap dissociation (HCD) modes. Acquired data were processed with Compound Discoverer software in combination to the recently published CyanoMetDB mass list [3] and other related tools for the annotation and structural elucidation of cyanometabolites. Verification of proposed structures was performed based on in silico fragmentation and fragment ion search (FISh) scoring. Application of the workflow revealed the presence of numerous congeners belonging to the cyanotoxins class microcystins and to the understudied cyanopeptides classes of cyanopeptolines, microginins, aeruginosins, anabaenopeptins and aeruginosamides. Furthermore, new congeners were annotated clearly demonstrating the suitability of the approach for the characterization of cyanobacterial chemodiversity.
[1] C. Christophoridis, S.-K. Zervou, K. Manolidi, M. Katsiapi, M. Moustaka-Gouni, T. Kaloudis, T. M. Triantis and A. Hiskia, Scientific Reports 8 (2018) 17877.
[2] S.-K. Zervou, K. Moschandreou, A. Paraskevopoulou, C. Christophoridis, E. Grigoriadou, T. Kaloudis, T. M. Triantis, V. Tsiaoussi and A. Hiskia, Toxins 13 (2021) 394.
[3] M. R. Jones, E. Pinto, M. A. Torres, F. Dörr, H. Mazur-Marzec, K. Szubert, L. Tartaglione, C. Dell’Aversano, C. O. Miles, D. G. Beach, P. McCarron, K. Sivonen, D. P. Fewer, J. Jokela and E. M.-L. Janssen, Water Research 196 (2021) 117017.