A metabolism-centric proteomic map on the genomic scale: enabling functional annotation of the unknown genome
Lead Research Organisation:
University of Cambridge
Department Name: Biochemistry
Abstract
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Technical Summary
We will create a genome-spanning functional proteomic map of the model eukaryote Saccharomyces cerevisiae, and use it to dissect the compendium of metabolism-regulating genes, and to annotate yet unknown genes on the basis of their proteomic footprint. This map will be created by systematically recording SWATH-MS based proteome profiles from 4800 yeast single gene knock-out strains, representing the fraction of gene deletions viable in the absence of rich nutrient supplementation, chosen to enable growth that is informative about biosynthetic metabolism. This unique large-scale project will be facilitated by an academia/industry partnership to establish a high-throughput variant of SWATH-MS technology invented by the collaborating laboratory of Ruedi Aebersold, and be capable to process thousands of samples in a proteomic experiment by combining microflow-chromatography, advanced ionisation techniques, and data independent acquisition.
Planned Impact
This is an academia-industry collaboration with direct impact. The biotechnological industry will obtain the information about which gene has to be manipulated in the genome to affect 80% of enzymes and can use this information to develop new production strains, save money by improving existing production cycles for instance to reduce greenhouse gas emissions. The mass spectrometry manufacturing industry will get access to a workflow that allows implementing proteomics technology in industrial scale projects, including diagnostics and environmental analytics, and improve access to a new market for proteomics. Enabling proteomics for the large scale will directly benefit society, that depends on diagnostics in medicine and environmental analytics for food production and marketing. Finally, the results clearly impact basic science, as we will substantially improve the annotation of ~2000 budding yeast genes that are functionally yet uncharacterized.
People |
ORCID iD |
Kathryn Lilley (Principal Investigator) |
Publications
Messner CB
(2023)
The proteomic landscape of genome-wide genetic perturbations.
in Cell
Messner CB
(2020)
Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.
in Cell systems
Demichev V
(2021)
A time-resolved proteomic and prognostic map of COVID-19.
in Cell systems
Messner CB
(2021)
Ultra-fast proteomics with Scanning SWATH.
in Nature biotechnology
Kustatscher G
(2022)
An open invitation to the Understudied Proteins Initiative.
in Nature biotechnology
Demichev V
(2022)
dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.
in Nature communications
Demichev V
(2020)
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.
in Nature methods
Kustatscher G
(2022)
Understudied proteins: opportunities and challenges for functional proteomics
in Nature Methods
Demichev V
(2022)
A proteomic survival predictor for COVID-19 patients in intensive care.
in PLOS digital health
Description | We have developed a piece of open source software, DIA-NN which will allow mass spectrometrists using Sciex mass spectrometers to more accurately quantify their data |
Exploitation Route | The software we have developed will be used by many researchers world wide |
Sectors | Agriculture Food and Drink Education Healthcare Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
Description | A piece of software - DIA-NN has been published in Nature Methods in January 2020. Bruker, an mass spectrometer instrument manufacturer have been interested in incorporating the software into their analysis pipelines |
First Year Of Impact | 2019 |
Sector | Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Title | DIA-NN |
Description | a computation approach to interrogate data independent acquisition mass spectrometry data |
Type Of Material | Technology assay or reagent |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | The tool is described in a manuscript that is currently under review in Nature Methods |
Description | Collabaration with Sciex |
Organisation | AB SCIEX |
Country | United States |
Sector | Private |
PI Contribution | This project is funded by a BBSRC LINK grant where Sciex is the industrial partner. MY group and that of Markus Ralser, provide experimental data and it interpretation |
Collaborator Contribution | Sciex assist with setting up of the mass spectrometers, one of which was donated by them to this project. |
Impact | A tool, DIA-NN has been developed by the Lilley/Ralser labs to enable robust quantitation of data produced from the Sciex 6600 mass spectrometers |
Start Year | 2017 |
Title | DIA-NN |
Description | DIA-NN is a tool that enables robust quantitation of SWATH mass spectrometry data generated by Sciex mass spectrometers with SWATh capabilities |
Type Of Technology | Software |
Year Produced | 2018 |
Impact | The tool has just been developed hence its impact is too early to measure |