1 research outputs found
LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems
The value of synthetic microbial communities in biotechnology
is
gaining traction due to their ability to undertake more complex metabolic
tasks than monocultures. However, a thorough understanding of strain
interactions, productivity, and stability is often required to optimize
growth and scale up cultivation. Quantitative proteomics can provide
valuable insights into how microbial strains adapt to changing conditions
in biomanufacturing. However, current workflows and methodologies
are not suitable for simple artificial coculture systems where strain
ratios are dynamic. Here, we established a workflow for coculture
proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus
elongatus. Factors affecting the quantitative accuracy
of coculture proteomics were investigated, including peptide physicochemical
characteristics such as molecular weight, isoelectric point, hydrophobicity,
and dynamic range as well as factors relating to protein identification
such as varying proteome size and shared peptides between species.
Different quantification methods based on spectral counts and intensity
were evaluated at the protein and cell level. We propose a new normalization
method, named “LFQRatio”, to reflect the relative contributions
of two distinct cell types emerging from cell ratio changes during
cocultivation. LFQRatio can be applied to real coculture proteomics
experiments, providing accurate insights into quantitative proteome
changes in each strain