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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

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Published on: October 20, 2020

Quantitative proteogenomics of human pathogens using DIA-MS.

Lars Malmström¹, Anahita Bakochi², Gabriel Svensson²

¹S3IT, University of Zurich, Zurich, Switzerland.

Journal of Proteomics

|September 19, 2015

Summary

This summary is machine-generated.

Integrating bacterial genome and proteome data is challenging. We developed a new strategy using a composite genome and data-independent analysis mass spectrometry (DIA-MS) to link genetic variation to bacterial virulence.

Keywords:

DIA Data integration Proteogenomics Quantitative mass spectrometry Streptococcus pyogenes

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Published on: October 30, 2016

Area of Science:

Microbiology
Genomics
Proteomics

Background:

Bacterial genome and proteome data integration is crucial for understanding virulence.
Existing methods face challenges in combining diverse genomic and proteomic datasets.

Purpose of the Study:

To develop a strategy for integrating genome and quantitative proteome data from genetically similar bacterial strains.
To enable rapid interrogation of combined datasets for insights into bacterial virulence.

Main Methods:

Construction of a composite genome integrating all genetic data.
Application of data-independent analysis mass spectrometry (DIA-MS) for quantitative proteome measurements.
Fusion of genomic data with quantitative peptide and protein information.

Main Results:

Demonstrated method with 34 clinical isolates of Streptococcus pyogenes using whole genome sequencing, shotgun MS, and DIA-MS.
Developed a data structure for fast exploration, revealing undetected proteins are more amenable to amino acid substitution.
Identified significantly differentially expressed proteins between invasive and non-invasive strains.

Conclusions:

The integrated approach advances the interpretation of genome-proteome-virulence relationships.
Accurate quantification of proteomes alongside whole genome sequencing enhances understanding of bacterial pathogens.