1887

Mass Spectrometry as a Tool to Enhance “-omics” Education

    Authors: Michael J. Wolyniak1,*, Nathan S. Reyna2, Ruth Plymale2, Welkin H. Pope3, Daniel E. Westholm4
    VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA 23943; 2: Department of Biology, Ouachita Baptist University, Arkadelphia, AR 71998; 3: Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260; 4: Department of Biology, The College of St. Scholastica, Duluth, MN 55811
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Received 27 August 2017 Accepted 27 November 2017 Published 16 February 2018
    • ©2018 Author(s). Published by the American Society for Microbiology
    • [open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.

    • Supplemental materials available at http://asmscience.org/jmbe
    • *Corresponding author. Mailing address: Department of Biology, Hampden-Sydney College, Box 183, Hampden-Sydney, VA 23943. Phone: 434-223-6175. E-mail: mwolyniak@hsc.edu.
    Source: J. Microbiol. Biol. Educ. February 2018 vol. 19 no. 1 doi:10.1128/jmbe.v19i1.1459
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    Abstract:

    The rise of "-omics" related technologies makes it essential for students to have experience working with large bioinformatics data sets. Although”-omic” datasets are complex and abstract, effective instruction can be improved when students see the direct connections between the data on a computer screen and the results of "wet lab" experimentation. Here we describe the use of protein mass spectrometry as a means for students to gain experience in connecting bioinformatic data with work done at the lab bench. Course-based Research Experiences (CREs) based on these techniques are accessible to institutions of all types as a result of rapidly declining costs for whole genome and proteome analysis. Our implementation is within a CRE based on viral infection of a bacterial host; however, this basic paradigm may be applied to other experimental systems of interest.

References & Citations

1. Jordan TC, Burnett SH, Carson S, Caruso SM, Clase K, DeJong RJ, Dennehy JJ, Denver DR, Dunbar D, Elgin SCR, Findley AM, Gissendanner CR, Golebiewska UP, Guild N, Hartzog GA, Grillo WH, Hollowell GP, Hughes LE, Johnson A, King RA, Lewis LO, Li W, Rosenzweig F, Rubin MR, Saha MS, Sandoz J, Shaffer CD, Taylor B, Temple L, Vazquez E, Ware VC, Barker LP, Bradley KW, Jacobs-Sera D, Pope WH, Russell DA, Cresawn SG, Lopatto D, Bailey CP, Hatfull GF2014A broadly implementable research course in phage discovery and genomics for first-year undergraduate studentsmBio5e010511310.1128/mBio.01051-13244967953950523 http://dx.doi.org/10.1128/mBio.01051-13
2. Hatfull GF2015Innovations in undergraduate science education: going viralJ Virol898111811310.1128/JVI.03003-14260181684524241 http://dx.doi.org/10.1128/JVI.03003-14
3. Elgin SC, Hauser C, Holzen TM, Jones C, Kleinschmit A, Leatherman JThe Genomics Education Partnership2017The GEP crowd-sourcing big data analysis with undergraduatesTrends Genet33818510.1016/j.tig.2016.11.004 http://dx.doi.org/10.1016/j.tig.2016.11.004
4. Davis E, Sloan T, Aurelius K, Barbour A, Bodey E, Clark B, Dennis C, Drown R, Fleming M, Humbert A, Glasgo E, Kerns T, Lingro K, McMillin M, Meyer A, Pope B, Stalevicz A, Steffen B, Steindl A, Williams C, Wimberly C, Zenas R, Butela K, Wildschutte H2017Antibiotic discovery throughout the small world initiative: a molecular strategy to identify biosynthetic gene clusters involved in antagonistic activityMicrobiologyOpen6e0043510.1002/mbo3.4355458470 http://dx.doi.org/10.1002/mbo3.435
5. Zhang G, Annan RS, Carr SA, Neubert TA2010Overview of peptide and protein analysis by mass spectrometryCurr Protoc Protein SciChapter16Unit16.11021
6. Mageeney C, Pope WH, Harrison M, Moran D, Cross T, Jacobs-Sera D, Hendrix RW, Dunbar D, Hatfull GF2012Mycobacteriophage Marvin: a new singleton phage with an unusual genome organizationJ Virol864762477510.1128/JVI.00075-12223572843347389 http://dx.doi.org/10.1128/JVI.00075-12
7. Grose JH, Belnap DM, Jensen JD, Mathis AD, Prince JT, Merrill BD, Burnett SH, Breakwell DP2014The genomes, proteomes, and structures of three novel phages that infect the Bacillus cereus group and carry putative virulence factorsJ Virol88118461186010.1128/JVI.01364-14251008424178739 http://dx.doi.org/10.1128/JVI.01364-14
8. Pope WH, Jacobs-Sera D, Russell DA, Rubin DH, Kajee A, Msibi ZNP, Larsen MH, Jacobs WRJr, Lawrence JG, Hendrix RW, Hatfull GF2014Genomics and proteomics of mycobacteriophage Patience, an accidental tourist in the Mycobacterium neighborhoodmBio5e021451410.1128/mBio.02145-14254674424324244 http://dx.doi.org/10.1128/mBio.02145-14
9. Searle BC2010Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studiesProteomics101265126910.1002/pmic.20090043720077414 http://dx.doi.org/10.1002/pmic.200900437
10. Stock NL, March RE2014Hands-on electrospray ionization-mass spectrometry for upper-level undergraduate and graduate studentsJ Chem Educ911244124710.1021/ed500062w http://dx.doi.org/10.1021/ed500062w
11. Bedard L, Boyd A, Dyer N, Golay Z, Smith-Kinnaman W, Alakhras N, Mosley AL2017Undergraduate student research in quantitative analysis of transcription elongation perturbation networks using mass spectrometryFASEB J311 Suppl752758
12. Kappler U, Rowland SL, Pedwell RK2017A unique large-scale undergraduate research experience in molecular systems biology for non-mathematics majorsBiochem Mol Biol Educ4523524810.1002/bmb.21033 http://dx.doi.org/10.1002/bmb.21033

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2018-02-16
2018-07-20

Abstract:

The rise of "-omics" related technologies makes it essential for students to have experience working with large bioinformatics data sets. Although”-omic” datasets are complex and abstract, effective instruction can be improved when students see the direct connections between the data on a computer screen and the results of "wet lab" experimentation. Here we describe the use of protein mass spectrometry as a means for students to gain experience in connecting bioinformatic data with work done at the lab bench. Course-based Research Experiences (CREs) based on these techniques are accessible to institutions of all types as a result of rapidly declining costs for whole genome and proteome analysis. Our implementation is within a CRE based on viral infection of a bacterial host; however, this basic paradigm may be applied to other experimental systems of interest.

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Figures

Image of FIGURE 1

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FIGURE 1

Mass spectrometry experimental protocol flowchart. OD = optical density; MOI = multiplicity of infection; LC-MS/MS = liquid chromatography-tandem mass spectrometry; ORF = open reading frame.

Source: J. Microbiol. Biol. Educ. February 2018 vol. 19 no. 1 doi:10.1128/jmbe.v19i1.1459
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Image of FIGURE 2

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FIGURE 2

SCAFFOLD Viewer Sample display window. Gene product names beginning with CDS are linked to the mycobacteriophage Brusacoram. All others are of host or other origin.

Source: J. Microbiol. Biol. Educ. February 2018 vol. 19 no. 1 doi:10.1128/jmbe.v19i1.1459
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Image of FIGURE 3

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FIGURE 3

SCAFFOLD Viewer output for a representative bacteriophage infection experiment using the bacteriophage Brusacoram. (A) Representative recovered peptide from the mass spectrometry reading. Yellow highlights indicate that LC-MS/MS detected peptide overlap with the gene product. Green highlights indicate modified amino acids. (B) In this case, a much smaller percentage of the predicted ORF was detected. Here, four peptides were detected that overlap with this ORF. A minimum of two detected peptides are required to confirm protein expression. ORF = open reading frame; LC-MS/MS = liquid chromatography-tandem mass spectrometry.

Source: J. Microbiol. Biol. Educ. February 2018 vol. 19 no. 1 doi:10.1128/jmbe.v19i1.1459
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