Quick microbial molecular phenotyping by differential shotgun proteomics

Abstract

Summary Differential shotgun proteomics identifies proteins that discriminate between sets of samples based on differences in abundance. This methodology can be easily applied to study (i) specific microorganisms subjected to a variety of growth or stress conditions or (ii) different microorganisms sampled in the same condition. In microbiology, this comparison is particularly successful because differing microorganism phenotypes are explained by clearly altered abundances of key protein players. The extensive description and quantification of proteins from any given microorganism can be routinely obtained for several conditions within a few days by tandem mass spectrometry. Such protein‐centred microbial molecular phenotyping is rich in information. However, well‐designed experimental strategies, carefully parameterized analytical pipelines, and sound statistical approaches must be applied if the shotgun proteomic data are to be correctly interpreted. This minireview describes these key items for a quick molecular phenotyping based on label‐free quantification shotgun proteomics.

Shotgun proteomics analysis of SARS-CoV-2-infected cells and how it can optimize whole viral particle antigen production for vaccines

“…Principal component analysis was performed as previously described [20]. Co-expression cluster analysis was performed using the Bioconductor R package coseq v1.5.2 [21].…”

Shotgun proteomics of SARS-CoV-2 infected cells and its application to the optimisation of whole viral particle antigen production for vaccines

“…Principal component analysis was done as previously described [18]. Co-expression cluster analysis was obtained using the Bioconductor R package coseq v1.5.2 [19].…”

Dual transcriptomics and proteomics analyses of the early stage of interaction between Caballeronia mineralivoransPML1(12) and mineral

“…To obtain a global view of our data set, multivariate analyses [i.e., partial least squares regression (PLS)] were performed as recommended (Gouveia et al ., 2020) because it can readily handle multiple dependent categorical variables and is compatible with noisy data sets. To allow a good and interpretable visualization of the genes associated to each treatment, variable selection was performed using a sparse partial least squares approach (sPLS).…”