The functional proteome landscape of Escherichia coli

André Mateus, Johannes Hevler, Jacob Bobonis, Nils Kurzawa, Malay Shah, Karin Mitosch, Camille V. Goemans, Dominic Helm, Frank Stein, Athanasios Typas () and Mikhail M. Savitski ()
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André Mateus: European Molecular Biology Laboratory (EMBL)
Johannes Hevler: European Molecular Biology Laboratory (EMBL)
Jacob Bobonis: European Molecular Biology Laboratory (EMBL)
Nils Kurzawa: European Molecular Biology Laboratory (EMBL)
Malay Shah: European Molecular Biology Laboratory (EMBL)
Karin Mitosch: European Molecular Biology Laboratory (EMBL)
Camille V. Goemans: European Molecular Biology Laboratory (EMBL)
Dominic Helm: European Molecular Biology Laboratory (EMBL)
Frank Stein: European Molecular Biology Laboratory (EMBL)
Athanasios Typas: European Molecular Biology Laboratory (EMBL)
Mikhail M. Savitski: European Molecular Biology Laboratory (EMBL)

Nature, 2020, vol. 588, issue 7838, 473-478

Abstract: Abstract Recent developments in high-throughput reverse genetics1,2 have revolutionized our ability to map gene function and interactions3–6. The power of these approaches depends on their ability to identify functionally associated genes, which elicit similar phenotypic changes across several perturbations (chemical, environmental or genetic) when knocked out7–9. However, owing to the large number of perturbations, these approaches have been limited to growth or morphological readouts10. Here we use a high-content biochemical readout, thermal proteome profiling11, to measure the proteome-wide protein abundance and thermal stability in response to 121 genetic perturbations in Escherichia coli. We show that thermal stability, and therefore the state and interactions of essential proteins, is commonly modulated, raising the possibility of studying a protein group that is particularly inaccessible to genetics. We find that functionally associated proteins have coordinated changes in abundance and thermal stability across perturbations, owing to their co-regulation and physical interactions (with proteins, metabolites or cofactors). Finally, we provide mechanistic insights into previously determined growth phenotypes12 that go beyond the deleted gene. These data represent a rich resource for inferring protein functions and interactions.

Date: 2020
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DOI: 10.1038/s41586-020-3002-5

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