Skip to main content
SpringerLink
Log in

Effects of Different Sample Pulverisation Methods on the Extraction of Metabolites from the Fermented Cottonseed Meal Based on UPLC-Q-TOF-MS

  • Published:
Current Microbiology Aims and scope Submit manuscript

Abstract

The precondition of studying biological sample is to extract sample metabolites by the best pretreatment methods. There is already limited information about pretreatments of fermented feed metabolites. The study compared the extraction effects of different pulverisation methods used in the sample pretreatment process for the extraction of metabolites from cottonseed meal fermented by Lactobacillus acidophilus based on UPLC-Q-TOF-MS. The extraction effects of three pretreatments (non-pulverisation (WF), pulverisation (F), and high-speed homogenisation methods (YJ)) were compared with the numbers of metabolites and the normalised peak areas of the metabolites. The results showed that the number of metabolites extracted with three pulverisation methods were 1745, 1896, 2132 (ESI+ mode) and 1447, 1675, 2073 (ESI− mode), respectively. The number of variable importance plot (VIP) metabolites and the relative peak areas of metabolites showed that the trend was YJ > F > WF. The extraction effect of high-speed homogenisation method was the best way to extract metabolites from the fermented cottonseed meal. This study built a foundation work for the further research of the fermented feed metabolomics.

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. Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R (2015) Metabolomics for laboratory diagnostics. J Pharm Biomed Anal 113:108–120

    Article  CAS  Google Scholar 

  2. Liu X, Locasale JW (2017) Metabolomics: a primer. Trends Biochem Sci 42(4):274. https://doi.org/10.1016/j.tibs.2017.01.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Raterink R-J, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T (2014) Recent developments in sample-pretreatment techniques for mass spectrometry-based metabolomics. Trac-Trend Anal Chem 61:157–167

    Article  CAS  Google Scholar 

  4. Schaub J, Schiesling C, Reuss M (2006) Dauner M (2006) Integrated sampling procedure for metabolome analysis. Biotechnol Progr 22(5):1434–1442

    Article  CAS  Google Scholar 

  5. Maharjan RP, Ferenci T (2003) Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli. Anal Biochem 313(1):145–154

    Article  Google Scholar 

  6. Rabinowitz JD, Kimball E (2007) Acidic acetonitrile for cellular metabolomeextraction from Escherichia coli. Anal Chem 79(16):6167–6173

    Article  CAS  Google Scholar 

  7. Winder CL, Dunn WB, Schule S, Broadhurst D, Jarvis R, Stephens GM, Goodacre R (2008) Global metabolic profiling of escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites. Anal Chem 80(8):2939

    Article  CAS  Google Scholar 

  8. Faijes M, Mars AE, Smid EJ (2007) Comparison of quenching and extraction methodologies for metabolome analysis of lactobacillus plantarum. Microb Cell Fact 6(1):27–27

    Article  Google Scholar 

  9. Chen MM, Li AL, Sun MC, Feng Z, Meng XC, Wang Y (2014) Optimization of the quenching method for metabolomics analysis oflactobacillus bulgaricus. J Zhejiang Univ-SC B 15(4):333–342

    Article  CAS  Google Scholar 

  10. Tokuoka M, Sawamura N, Kobayashi K, Mizuno A (2010) Simple metabolite extraction method for metabolic profiling of the solid-state fermentation of aspergillus oryzae. J Biosci Bioeng 110(6):665–669

    Article  CAS  Google Scholar 

  11. Dettmer K, Nadine N, Kaspar H, Gruber MA, Almstetter MF, Oefner PJ (2011) Metabolite extraction from adherently growing mammalian cells for metabolomics studies: optimization of harvesting and extraction protocols. Anal Bioanal Chem 399(3):1127–1139

    Article  CAS  Google Scholar 

  12. Fraga CG, Clowers BH, Moore RJ, Zink EM (2010) Signature-discovery approach for sample matching of a nerve-agent precursor using liquid chromatography-mass spectrometry, xcms, and chemometrics. Anal Chem 82(10):4165–4173

    Article  CAS  Google Scholar 

  13. Wu N, Clausen AM (2007) Fundamental and practical aspects of ultrahigh pressure liquid chromatography for fast separations. J Sep Sci 30(8):1167–1182

    Article  CAS  Google Scholar 

  14. Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung KH, Ronald PC (2010) Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogenxanthomonas oryzaepv.oryzae. Metabolomics 6(3):451–465

    Article  CAS  Google Scholar 

  15. Badran M, Morsy R, Soliman H, Elnimr T (2015) Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis. J Trace Elem Med Bio 33:114–119

    Article  Google Scholar 

  16. Zhao YY, Cheng XL, Cui JH, Yan XR, Wei F, Bai X, Lin RC (2012) Effect of ergosta-4,6,8(14),22-tetraen-3-one (ergone) on adenine-induced chronic renal failure rat: a serum metabonomic study based on ultra performance liquid chromatography/high-sensitivity mass spectrometry coupled with masslynx i-fit algorithm. Clin Chim Acta 413(19–20):1438–1445

    Article  CAS  Google Scholar 

  17. Zhu ZJ, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, Siuzdak G (2013) Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the metlin database. Nat Protoc 8(3):451–460

    Article  CAS  Google Scholar 

  18. Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, Koellensperger G, Huan T, Uritboonthai W, Aisporna AE, Wolan DW, Spilker ME, Benton HP, Siuzdak G (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90(5):3156–3164

    Article  CAS  Google Scholar 

  19. Vorkas PA, Isaac G, Anwar MA, Davies AH, Want EJ, Nicholson JK, Holmes E (2015) Untargeted uplc-ms profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease. Anal Chem 87(8):4184–4193

    Article  CAS  Google Scholar 

  20. Li Y, Peng Y, Wang M, Zhou G, Zhang Y, Li X (2016) Rapid screening and identification of the differences between metabolites of Cistanche deserticola and C. tubulosa water extract in rats by UPLC-Q-TOF-MS combined pattern recognition analysis. J Pharm Biomed 131:364–372

    Article  CAS  Google Scholar 

  21. Want EJ (2018) LC-MS untargeted analysis method. Mol Biol 1738:99

    CAS  Google Scholar 

  22. Raterink RJ, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T (2014) Recent developments in sample-pretreatment techniques for mass spectrometry-based metabolomics. TrAC Trends Anal Chem 61:157–167

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was financially supported by the Science and Technology Research Project of Henan province (No.192102110071), the Program for Innovative Research Team (No.20IRTSTHN025), and the Key Scientific Research Projects of Institutions of Higher Learning in Henan province (No.19B230006). We would like to thank Suzhou BioNovoGene (https://www.bionovogene.com) for technical supports in the comprehensive analysis of the MS/MS data.

Author information

Authors and Affiliations

  1. College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang, 453003, People’s Republic of China

    Yongqiang Wang, Hongbing Xie, Dongyang Liu, Yimin Wang, Changzhong Liu & Jinqing Jiang

  2. College of Animal Science and Technology, Shihezi University, Shihezi, 832000, People’s Republic of China

    Yongqiang Wang & Wenjv Zhang

  3. Department of Pathology, Faculty of Veterinary and Animal Science, PMAS-Arid Agriculture University, Rawalpindi, Pakistan

    Muhammad Akram Khan

Authors
  1. Yongqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongbing Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongyang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yimin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Changzhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Akram Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinqing Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wenjv Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jinqing Jiang or Wenjv Zhang.

Ethics declarations

Conflict of interest

The authors indicate no potential conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Xie, H., Liu, D. et al. Effects of Different Sample Pulverisation Methods on the Extraction of Metabolites from the Fermented Cottonseed Meal Based on UPLC-Q-TOF-MS. Curr Microbiol 77, 2751–2757 (2020). https://doi.org/10.1007/s00284-020-02057-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00284-020-02057-5

Search

Navigation