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From coarse to fine: The absolute Escherichia coli proteome under diverse growth conditions

ABSTRACT: Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in E. coli for >2000 proteins over >60 growth conditions, including nutrient limitations, non-metabolic stresses and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of larg e-scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Escherichia Coli

SUBMITTER: Christina Ludwig

LAB HEAD: Chrisitina Ludwig

PROVIDER: PXD014948 | Pride | 2021-03-11

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	Ecoli_DDA_search_results.zip	Other
	Ecoli_PQP_library_formats.zip	Other
	Ecoli_SWATH_quant_results.zip	Other
	chludwig_K150203_001_IDA_ecoli_1243_1.wiff	Wiff
	chludwig_K150203_001_IDA_ecoli_1243_1.wiff.scan	Wiff

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Project description:TARGET (Tumour characterisation to Guide Experimental Targeted therapy) seeks to optimise the pathway for molecular characterisation of all patients entering early phase cancer trials to inform clinical decision-making on optimal therapy. Performance status (PS) was developed to assess a patient’s ability to perform a variety of activities to further inform clinical decision making on optimal therapy including appropriate inclusion in clinical trials. However, the quality of present algorithms to assess performance status are limited and based on a semi-quantitative clinician assessment. In particular, a common eligibility criterion for entry into early phase cancer clinical trials is a life expectancy of >3 or 6 months. Here we investigate proteomics as a means of more objective and quantitative assessment of both performance status and thus likely prognosis prior to clinical trial enrolment. Improved patient stratification at enrolment would reduce the number of patients unnecessarily exposed to potentially toxic drugs and decrease withdrawal from trials. Plasma samples from patients enrolled into the TARGET trial were analysed using the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used on a discovery cohort of 73 patients to identify proteins that could differentiate patients into either a good or poor prognosis. A three-protein panel showed a stronger prediction of overall survival (p = 0.000086) compared to PS (p = 0.001). This panel was then tested against a validation cohort of 77 patients. The panel was determined as being a significant (p = 0.000451) predictor of overall survival whereas PS was not significant. The panel had a receiver operating characteristic curve area under the curve (AUC) of 0.73 in the validation set; the AUC of PS was 0.54. This signature can now be developed further in larger patient populations to assess its utility in a clinical setting.

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From coarse to fine: The absolute Escherichia coli proteome under diverse growth conditions

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