scholarly journals Top-Down Proteomics of Medicinal Cannabis

Proteomes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 33 ◽  
Author(s):  
Vincent ◽  
Binos ◽  
Rochfort ◽  
Spangenberg
Keyword(s):  
Electron Transfer Dissociation ◽  
Protein Extraction ◽  
Sequence Coverage ◽  
Top Down ◽  
Post Translational Modifications ◽  
Atp Production ◽  
Denatured Protein ◽  
Apical Buds ◽  
Medicinal Cannabis

The revised legislation on medicinal cannabis has triggered a surge of research studies in this space. Yet, cannabis proteomics is lagging. In a previous study, we optimised the protein extraction of mature buds for bottom-up proteomics. In this follow-up study, we developed a top-down mass spectrometry (MS) proteomics strategy to identify intact denatured protein from cannabis apical buds. After testing different source-induced dissociation (SID), collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), and electron transfer dissociation (ETD) parameters on infused known protein standards, we devised three LC-MS/MS methods for top-down sequencing of cannabis proteins. Different MS/MS modes produced distinct spectra, albeit greatly overlapping between SID, CID, and HCD. The number of fragments increased with the energy applied; however, this did not necessarily translate into greater sequence coverage. Some precursors were more amenable to fragmentation than others. Sequence coverage decreased as the mass of the protein increased. Combining all MS/MS data maximised amino acid (AA) sequence coverage, achieving 73% for myoglobin. In this experiment, most cannabis proteins were smaller than 30 kD. A total of 46 cannabis proteins were identified with 136 proteoforms bearing different post-translational modifications (PTMs), including the excision of N-terminal M, the N-terminal acetylation, methylation, and acetylation of K resides, and phosphorylation. Most identified proteins are involved in photosynthesis, translation, and ATP production. Only one protein belongs to the phytocannabinoid biosynthesis, olivetolic acid cyclase.

scholarly journals A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds

2019 ◽  
Vol 20 (22) ◽  
pp. 5630 ◽  
Author(s):  
Delphine Vincent ◽  
Vilnis Ezernieks ◽  
Simone Rochfort ◽  
German Spangenberg
Keyword(s):  
Cannabis Sativa ◽  
Protein Extraction ◽  
Shotgun Proteomics ◽  
Reproductive Organs ◽  
Sequence Coverage ◽  
Post Translational Modifications ◽  
Apical Buds ◽  
Medicinal Cannabis ◽  
Close Relatives

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules 2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes 2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules 2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

scholarly journals Optimisation of Protein Extraction from Medicinal Cannabis Mature Buds for Bottom-Up Proteomics

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 659 ◽  
Author(s):  
Delphine Vincent ◽  
Simone Rochfort ◽  
German Spangenberg
Keyword(s):  
Mass Spectrometry ◽  
Cannabis Sativa ◽  
Protein Extraction ◽  
Guanidine Hydrochloride ◽  
Shotgun Proteomics ◽  
Extraction Methods ◽  
Reproductive Organs ◽  
Apical Buds ◽  
Liquid Chromatography Tandem Mass ◽  
Medicinal Cannabis

Medicinal cannabis is used to relieve the symptoms of certain medical conditions, such as epilepsy. Cannabis is a controlled substance and until recently was illegal in many jurisdictions. Consequently, the study of this plant has been restricted. Proteomics studies on Cannabis sativa reported so far have been primarily based on plant organs and tissues other than buds, such as roots, hypocotyl, leaves, hempseeds and flour. As far as we know, no optimisation of protein extraction from cannabis reproductive tissues has been attempted. Therefore, we set out to assess different protein extraction methods followed by mass spectrometry-based proteomics to recover, separate and identify the proteins of the reproductive organs of medicinal cannabis, apical buds and isolated trichomes. Database search following shotgun proteomics was limited to protein sequences from C. sativa and closely related species available from UniprotKB. Our results demonstrate that a buffer containing the chaotrope reagent guanidine hydrochloride recovers many more proteins than a urea-based buffer. In combination with a precipitation with trichloroacetic acid, such buffer proved optimum to identify proteins using a trypsin digestion followed by nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) analyses. This is validated by focusing on enzymes involved in the phytocannabinoid pathway.

The Features of a Standard INSET

2017 ◽  
pp. 1-14
Author(s):  
Selma Deneme ◽  
Handan Çelik
Keyword(s):  
Teacher Training ◽  
Needs Assessment ◽  
Training Program ◽  
Top Down ◽  
General Picture ◽  
Active Involvement ◽  
Hands On ◽  
Qualitative Descriptive ◽  
Shed Light

Adopting a qualitative descriptive methodology, the current study aims to explore whats and hows of planning, delivery, and follow-up in in-service teacher training. While doing this, together with presenting the general picture of in-service teacher trainings in Turkey, the study also makes use of a delivered in-service teacher training program so as to find how issues regarding planning, delivery, and follow-up were dealt with. The data collected through semi-structured written interview and supported with informal dialogues and telephone conversations revealed what was done and how was done for the three components. However, similar to many other trainings, the findings showed that lack of needs assessment, hands-on practice, and follow-up unfortunately makes the training to be restricted to what is known as traditional and top-down. For this reason, the findings shed light on the reality to consider teachers' needs, their active involvement, and on-going practice for effective in-service teacher trainings.

scholarly journals Increased Single-Spectrum Top-Down Protein Sequence Coverage in Trapping Mass Spectrometers with Chimeric Ion Loading

2020 ◽  
Vol 92 (18) ◽  
pp. 12193-12200
Author(s):  
Chad R. Weisbrod ◽  
Lissa C. Anderson ◽  
Joseph B. Greer ◽  
Caroline J. DeHart ◽  
Christopher L. Hendrickson
Keyword(s):  
Protein Sequence ◽  
Sequence Coverage ◽  
Top Down ◽  
Single Spectrum ◽  
Mass Spectrometers ◽  
Protein Sequence Coverage

scholarly journals 2018 YPIC Challenge: A case study in characterizing an unknown protein sample

2019 ◽  
Author(s):  
Lindsay Pino ◽  
Andy Lin ◽  
Wout Bittremieux
Keyword(s):  
Protein Sequence ◽  
De Novo ◽  
Signal To Noise Ratio ◽  
3D Structure ◽  
Sequence Information ◽  
Sequence Coverage ◽  
Post Translational Modifications ◽  
E Coli ◽  
Unknown Protein ◽  
Synthetic Protein

For the 2018 YPIC Challenge contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the 3D structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.

scholarly journals TDFragMapper: a visualization tool for evaluating experimental parameters in top-down proteomics

2021 ◽  
Author(s):  
Jonathan Steven Dhenin ◽  
Diogo Borges Lima ◽  
Mathieu Dupre ◽  
Julia Chamot-Rooke
Keyword(s):  
Software Tool ◽  
Rapid Evaluation ◽  
Visualization Tool ◽  
Sequence Coverage ◽  
Top Down ◽  
Intact Proteins ◽  
Fragment Ions ◽  
Experimental Parameters ◽  
Demonstration Video

We present a new software-tool allowing an easy visualization of fragment ions and thus a rapid evaluation of key experimental parameters on the sequence coverage obtained for the MS/MS analysis of intact proteins. Our tool can deal with multiple fragmentation methods. We demonstrate that TDFragMapper can rapidly highlight the experimental fragmentation parameters that are critical to the characterization of intact proteins of various size using top-down proteomics. TDFragMapper, a demonstration video and user tutorial are freely available at https://msbio.pasteur.fr/tdfragmapper, for academic use; all data are thus available from the ProteomeXchange consorti-um (identifier PXD024643).

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