Skip to main content

Advertisement

SpringerLink
Log in

Assessment of the Utility of the Oral Fluid and Plasma Proteomes for Hydrocodone Exposure

  • Original Article
  • Published:
Journal of Medical Toxicology Aims and scope Submit manuscript

Abstract

Introduction

Non-medical use and abuse of prescription opioids is a growing problem in both the civilian and military communities, with minimal technologies for detecting hydrocodone use. This study explored the proteomic changes that occur in the oral fluid and blood plasma following controlled hydrocodone administration in 20 subjects.

Methods

The global proteomic profile was determined for samples taken at four time points per subject: pre-exposure and 4, 6, or 168 hours post-exposure. The oral fluid samples analyzed herein provided greater differentiation between baseline and response time points than was observed with blood plasma, at least partially due to significant person-to-person relative variability in the plasma proteome.

Results

A total of 399 proteins were identified from oral fluid samples, and the abundance of 118 of those proteins was determined to be significantly different upon metabolism of hydrocodone (4 and 6 hour time points) as compared to baseline levels in the oral fluid (pre-dose and 168 hours).

Conclusions

We present an assessment of the oral fluid and plasma proteome following hydrocodone administration, which demonstrates the potential of oral fluid as a noninvasive sample that may reveal features of hydrocodone in opioid use, and with additional study, may be useful for other opioids and in settings of misuse.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. JAMA. 2016;315(15):1624–45.

    Article  CAS  Google Scholar 

  2. Yokell MA, Delgado MK, Zaller ND, Wang NE, McGowan SK, Green TC. Presentation of prescription and nonprescription opioid overdoses to US emergency departments. JAMA Intern Med. 2014;174(12):2034–7.

    Article  Google Scholar 

  3. Traynor K. White House expands opioid addiction response: Treat chronic pain like other chronic conditions, pharmacists say. Am J Health Syst Pharm. 2016;73(1):e1–2.

    Article  Google Scholar 

  4. FACT SHEET. President Obama proposes $1.1 billion in new funding to address the prescription opioid abuse and heroin use epidemic [press release]. February. 2016;2:2016.

    Google Scholar 

  5. Varney SM, Bebarta VS, Mannina LM, Ramos RG, Ganem VJ, Carey KR. Emergency medicine providers’ opioid prescribing practices stratified by gender, age, and years in practice. World J Emerg Med. 2016;7(2):106–10.

    Article  Google Scholar 

  6. Agarin T, Trescot AM, Agarin A, Lesanics D, Decastro C. Reducing opioid analgesic deaths in America: what health providers can do. Pain Physician. 2015;18(3):E307–22.

    PubMed  Google Scholar 

  7. del Portal DA, Healy ME, Satz WA, McNamara RM. Impact of an opioid prescribing guideline in the acute care setting. J Emerg Med. 2016;50(1):21–7.

    Article  Google Scholar 

  8. Geyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol. 2017;13(9):942.

    Article  Google Scholar 

  9. Jacobs JM, Adkins JN, Qian WJ, Liu T, Shen Y, Camp DG 2nd, et al. Utilizing human blood plasma for proteomic biomarker discovery. J Proteome Res. 2005;4(4):1073–85.

    Article  CAS  Google Scholar 

  10. Wu C, Duan J, Liu T, Smith RD, Qian WJ. Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids. J Chromatogr B Analyt Technol Biomed Life Sci. 2016;1021:57–68.

    Article  CAS  Google Scholar 

  11. Helmerhorst EJ, Oppenheim FG. Saliva: a dynamic proteome. J Dent Res. 2007;86(8):680–93.

    Article  CAS  Google Scholar 

  12. Dominy SS, Brown JN, Ryder MI, Gritsenko M, Jacobs JM, Smith RD. Proteomic analysis of saliva in HIV-positive heroin addicts reveals proteins correlated with cognition. PLoS One. 2014;9(4):e89366.

    Article  Google Scholar 

  13. Nicolardi S, Bogdanov B, Deelder AM, Palmblad M, van der Burgt YE. Developments in FTICR-MS and its potential for body fluid signatures. Int J Mol Sci. 2015;16(11):27133–44.

    Article  CAS  Google Scholar 

  14. Castagnola M, Scarano E, Passali GC, Messana I, Cabras T, Iavarone F, et al. Salivary biomarkers and proteomics: future diagnostic and clinical utilities. Acta Otorhinolaryngol Ital. 2017;37(2):94–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Flood JG, Khaliq T, Bishop KA, Griggs DA. The new Substance Abuse and Mental Health Services Administration Oral fluid cutoffs for cocaine and heroin-related analytes applied to an addiction medicine setting: important, unanticipated findings with LC-MS/MS. Clin Chem. 2016;62(5):773–80.

    Article  CAS  Google Scholar 

  16. Valtier S, Mueck RL, Bebarta VS. Quantitative method for analysis of hydrocodone, hydromorphone and norhydrocodone in human plasma by liquid chromatography-tandem mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2013;925:40–5.

    Article  CAS  Google Scholar 

  17. Yang F, Shen Y, Camp DG 2nd, Smith RD. High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Rev Proteomics. 2012;9(2):129–34.

    Article  CAS  Google Scholar 

  18. Kelly RT, Page JS, Luo Q, Moore RJ, Orton DJ, Tang K, et al. Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal Chem. 2006;78(22):7796–801.

    Article  CAS  Google Scholar 

  19. Maiolica A, Borsotti D, Rappsilber J. Self-made frits for nanoscale columns in proteomics. Proteomics. 2005;5(15):3847–50.

    Article  CAS  Google Scholar 

  20. Granholm V, Kim S, Navarro JC, Sjolund E, Smith RD, Kall L. Fast and accurate database searches with MS-GF+Percolator. J Proteome Res. 2014;13(2):890–7.

    Article  CAS  Google Scholar 

  21. Monroe ME, Tolic N, Jaitly N, Shaw JL, Adkins JN, Smith RD. VIPER: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics. 2007;23(15):2021–3.

    Article  CAS  Google Scholar 

  22. Tolmachev AV, Monroe ME, Purvine SO, Moore RJ, Jaitly N, Adkins JN, et al. Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments. Anal Chem. 2008;80(22):8514–25.

    Article  CAS  Google Scholar 

  23. Yang F, Jaitly N, Jayachandran H, Luo Q, Monroe ME, Du X, et al. Applying a targeted label-free approach using LC-MS AMT tags to evaluate changes in protein phosphorylation following phosphatase inhibition. J Proteome Res. 2007;6(11):4489–97.

    Article  CAS  Google Scholar 

  24. Stanley JR, Adkins JN, Slysz GW, Monroe ME, Purvine SO, Karpievitch YV, et al. A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics. Anal Chem. 2011;83(16):6135–40.

    Article  CAS  Google Scholar 

  25. Polpitiya AD, Qian WJ, Jaitly N, Petyuk VA, Adkins JN, Camp DG 2nd, et al. DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics. 2008;24(13):1556–8.

    Article  CAS  Google Scholar 

  26. DeconMSn [Available from: http://omics.pnl.gov/software/DeconMSn.php.

  27. Mayampurath AM, Jaitly N, Purvine SO, Monroe ME, Auberry KJ, Adkins JN, et al. DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra. Bioinformatics. 2008;24(7):1021–3.

    Article  CAS  Google Scholar 

  28. Iavarone F, Melis M, Platania G, Cabras T, Manconi B, Petruzzelli R, et al. Characterization of salivary proteins of schizophrenic and bipolar disorder patients by top-down proteomics. J Proteome. 2014;103:15–22.

    Article  CAS  Google Scholar 

  29. Grassl N, Kulak NA, Pichler G, Geyer PE, Jung J, Schubert S, et al. Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome. Genome Med. 2016;8(1):44.

    Article  Google Scholar 

  30. Sivadasan P, Gupta MK, Sathe GJ, Balakrishnan L, Palit P, Gowda H, et al. Human salivary proteome—a resource of potential biomarkers for oral cancer. J Proteome. 2015;127(Pt A):89–95.

    Article  CAS  Google Scholar 

  31. Castagnola M, Cabras T, Iavarone F, Fanali C, Nemolato S, Peluso G, et al. The human salivary proteome: a critical overview of the results obtained by different proteomic platforms. Expert Rev Proteomics. 2012;9(1):33–46.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Work was performed in the Environmental Molecular Sciences Laboratory, a US Department of Energy Office of Biological and Environmental Research national scientific user facility located at the Pacific Northwest National Laboratory in Richland, Washington. Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy under Contract No. DE-AC05-76RLO 1830. Many thanks to Robert Mueck for sample processing and metabolic profiling analysis. The opinions represented in this manuscript do not necessarily represent the views and opinions of the US Government, Department of Defense, or US Air Force.

Funding

Funding was provided by the DoD (Department of Defense) CDMRP (Congressionally Directed Medical Research Program) W81WXH-11-2-0126.

Author information

Authors and Affiliations

  1. Chemical and Biological Signature Sciences Group, Pacific Northwest National Laboratory, Richland, WA, USA

    Brooke L. Deatherage Kaiser

  2. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Jon M. Jacobs, Athena A. Schepmoes, Bobbie-Jo M. Webb-Robertson & Joshua N. Adkins

  3. Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA

    Heather M. Brewer

  4. Science and Technology, 59th Medical Wing, JBSA-Lackland AFB, San Antonio, TX, USA

    Sandra Valtier

  5. University of Colorado School of Medicine, Aurora, CO, USA

    Vikhyat S. Bebarta

Authors
  1. Brooke L. Deatherage Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jon M. Jacobs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Athena A. Schepmoes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heather M. Brewer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bobbie-Jo M. Webb-Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sandra Valtier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vikhyat S. Bebarta
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joshua N. Adkins
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vikhyat S. Bebarta.

Ethics declarations

The study was approved by the Wilford Hall Medical Center and Pacific Northwest National Laboratory Institutional Review Boards.

Conflict of Interest

None.

Additional information

Supervising Editor: Peter R. Chai, MD, MMS

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Fig. S1.

Principal Component Analysis of the Blood Plasma Proteome Following Hydrocodone Administration. Principal Component Analysis (PCA) was used to visualize the relatedness of protein observations from blood plasma samples. Blood plasma proteome data demonstrated greater similarity by individual than by time post hydrocodone exposure. This is demonstrated by clustering of individuals (represented by varying colors) and not by time point post-drug administration. PC1 (x-axis) is principal component 1 and PC2 (y-axis) is principal component 2; PC1 and PC2 are the two principal components that account for the most variability in the data, with PC1 being the greatest. Percentages shown are the percent of variability that each PC accounts for. (PNG 164 kb)

High resolution image (TIF 221 kb)

Supplementary Table 1

(PDF 38 kb)

Supplementary Table 2

(PDF 343 kb)

Supplementary Table 3

(PDF 1135 kb)

Supplementary Table 4

(PDF 53 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deatherage Kaiser, B.L., Jacobs, J.M., Schepmoes, A.A. et al. Assessment of the Utility of the Oral Fluid and Plasma Proteomes for Hydrocodone Exposure. J. Med. Toxicol. 16, 49–60 (2020). https://doi.org/10.1007/s13181-019-00731-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13181-019-00731-0

Keywords

Search

Navigation