The Proteomic Landscape of Genome-Wide Genetic Perturbations

Abstract

SummaryFunctional genomic strategies help to address the genotype phenotype problem by annotating gene function and regulatory networks. Here, we demonstrate that combining functional genomics with proteomics uncovers general principles of protein expression, and provides new avenues to annotate protein function. We recorded precise proteomes for all non-essential gene knock-outs in Saccharomyces cerevisiae. We find that protein abundance is driven by a complex interplay of i) general biological properties, including translation rate, turnover, and copy number variations, and ii) their genetic, metabolic and physical interactions, including membership in protein complexes. We further show that combining genetic perturbation with proteomics provides complementary dimensions of functional annotation: proteomic profiling, reverse proteomic profiling, profile similarity and protein covariation analysis. Thus, our study generates a resource in which nine million protein quantities are linked to 79% of the yeast coding genome, and shows that functional proteomics reveals principles that govern protein expression.Highlights-Nine million protein quantities recorded in ~4,600 non-essential gene deletions in S. cerevisiae reveal principles of how the proteome responds to genetic perturbation-Genome-scale protein expression is determined by both functional relationships between proteins, as well as common biological responses-Broad protein expression profiles in slow-growing strains can be explained by chromosomal aneuploidies-Protein half-life and ribosome occupancy are predictable from protein abundance changes across knock-outs-Functional proteomics annotates missing gene function in four complementary dimensions

Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology

“…Vice versa, many low abundant proteins that are difficult to be detected by proteomics, can be efficiently deleted genetically. Hence, combining functional genomics with proteomics provides complementary information with increased genome-wide coverage [18].…”

“…Thus, using left‐censored imputation strategies (such as zeros, minimum values, and others) can introduce additional batch effects and artifacts. Large‐scale proteomics datasets thus require special imputation strategies, such as mixed imputations that distinguish between batch and non‐batch related missing values based on defined cutoffs [18].…”

“…(C) High‐throughput proteomics allows new dimensions in gene function annotation: (1) Reverse proteome profiling for protein centric analysis: studying the behavior of proteins across many conditions (e.g., mutants, knock‐outs) can provide functional information. (2) Covariation analysis: s functionally related proteins are co‐regulated, the covariation of proteins across many conditions can be used for functional assignment [18].…”

“…This not only hampers a major goal of molecular biology-the explanation of phenotypes from genotypes-but also means that we miss potential targets for drug design and biotechnology. Studies in yeast have shown that combining genome-wide perturbation approaches with transcriptomics, proteomic, or metabolomic technologies constitutes a powerful tool for systematically capturing missing gene function [16][17][18].…”

“…(2) Covariation analysis: s functionally related proteins are co-regulated, the covariation of proteins across many conditions can be used for functional assignment [18]. demands on sample numbers and measurement consistency over large sample sets or over multiple batches.…”