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LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems.

Mengxun Shi1, Caroline A Evans1, Josie L McQuillan1

  • 1Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K.

Journal of Proteome Research
|February 14, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new proteomics workflow for analyzing synthetic microbial communities. This method accurately quantifies protein changes in cocultures, even with dynamic strain ratios, using the novel LFQRatio normalization technique.

Keywords:
AzotobacterSynechococcuslabel-free quantificationmicrobial coculturequantitative proteomics

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Area of Science:

  • Microbiology
  • Biotechnology
  • Proteomics

Background:

  • Synthetic microbial communities offer enhanced metabolic capabilities over monocultures for biotechnology applications.
  • Optimizing coculture productivity and stability requires understanding strain interactions, which is challenging with current proteomics methods.
  • Quantitative proteomics is crucial for studying microbial adaptation in biomanufacturing but lacks suitable workflows for dynamic coculture systems.

Purpose of the Study:

  • To establish a robust proteomics workflow for analyzing artificial cocultures with dynamic strain ratios.
  • To investigate factors influencing quantitative accuracy in coculture proteomics, including peptide properties and proteome characteristics.
  • To develop and validate a novel normalization method for accurate strain-specific quantification in cocultures.

Main Methods:

  • Established a proteomics workflow using a model coculture of *Azotobacter vinelandii* and *Synechococcus elongatus*.
  • Investigated the impact of peptide physicochemical properties (molecular weight, isoelectric point, hydrophobicity, dynamic range) on quantitative accuracy.
  • Evaluated different quantification strategies based on spectral counts and intensity at both protein and cell levels.
  • Developed and proposed a new normalization method, LFQRatio, for dynamic coculture systems.

Main Results:

  • Identified key factors affecting quantitative proteomics accuracy in cocultures, including proteome size and shared peptides.
  • Demonstrated that LFQRatio effectively normalizes quantitative proteome data to reflect dynamic cell ratio changes.
  • Validated the workflow and LFQRatio method using the exemplar coculture system, showing accurate strain-specific insights.

Conclusions:

  • The developed proteomics workflow and LFQRatio normalization method enable accurate quantitative analysis of dynamic cocultures.
  • This approach provides valuable insights into strain-specific proteome changes, crucial for optimizing synthetic microbial communities in biomanufacturing.
  • The methodology is applicable to various coculture systems, advancing the field of microbial community analysis.