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
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    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
    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 GF 2014 A broadly implementable research course in phage discovery and genomics for first-year undergraduate students mBio 5 e01051 13 10.1128/mBio.01051-13 24496795 3950523 http://dx.doi.org/10.1128/mBio.01051-13
2. Hatfull GF 2015 Innovations in undergraduate science education: going viral J Virol 89 8111 8113 10.1128/JVI.03003-14 26018168 4524241 http://dx.doi.org/10.1128/JVI.03003-14
3. Elgin SC, Hauser C, Holzen TM, Jones C, Kleinschmit A, Leatherman J The Genomics Education Partnership 2017 The GEP crowd-sourcing big data analysis with undergraduates Trends Genet 33 81 85 10.1016/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 H 2017 Antibiotic discovery throughout the small world initiative: a molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity MicrobiologyOpen 6 e00435 10.1002/mbo3.435 5458470 http://dx.doi.org/10.1002/mbo3.435
5. Zhang G, Annan RS, Carr SA, Neubert TA 2010 Overview of peptide and protein analysis by mass spectrometry Curr Protoc Protein Sci Chapter16 Unit16.1 10 21
6. Mageeney C, Pope WH, Harrison M, Moran D, Cross T, Jacobs-Sera D, Hendrix RW, Dunbar D, Hatfull GF 2012 Mycobacteriophage Marvin: a new singleton phage with an unusual genome organization J Virol 86 4762 4775 10.1128/JVI.00075-12 22357284 3347389 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 DP 2014 The genomes, proteomes, and structures of three novel phages that infect the Bacillus cereus group and carry putative virulence factors J Virol 88 11846 11860 10.1128/JVI.01364-14 25100842 4178739 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 WR Jr, Lawrence JG, Hendrix RW, Hatfull GF 2014 Genomics and proteomics of mycobacteriophage Patience, an accidental tourist in the Mycobacterium neighborhood mBio 5 e02145 14 10.1128/mBio.02145-14 25467442 4324244 http://dx.doi.org/10.1128/mBio.02145-14
9. Searle BC 2010 Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies Proteomics 10 1265 1269 10.1002/pmic.200900437 20077414 http://dx.doi.org/10.1002/pmic.200900437
10. Stock NL, March RE 2014 Hands-on electrospray ionization-mass spectrometry for upper-level undergraduate and graduate students J Chem Educ 91 1244 1247 10.1021/ed500062w http://dx.doi.org/10.1021/ed500062w
11. Bedard L, Boyd A, Dyer N, Golay Z, Smith-Kinnaman W, Alakhras N, Mosley AL 2017 Undergraduate student research in quantitative analysis of transcription elongation perturbation networks using mass spectrometry FASEB J 31 1 Suppl 752 758
12. Kappler U, Rowland SL, Pedwell RK 2017 A unique large-scale undergraduate research experience in molecular systems biology for non-mathematics majors Biochem Mol Biol Educ 45 235 248 10.1002/bmb.21033 http://dx.doi.org/10.1002/bmb.21033

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2018-02-16
2019-01-23

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
Download as Powerpoint

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