Dataset Information

On-chip AP-MS: Interaction proteomics on a microfluidics platform

ABSTRACT: Essentially all cellular processes are orchestrated by protein-protein interactions (PPIs). In recent years, affinity purification coupled to mass spectrometry (AP-MS) has been the preferred method to identify cellular PPIs. Here we present a microfluidics-based (AP-MS) workflow to identify PPIs using minute amounts of input material. The use of this automated platform allowed us to identify the human cohesin, CCC and Mediator complex from as little as 4 micrograms of input lysate, representing a 50─100-fold downscaling compared to regular microcentrifuge tube-based protocols. We show that our platform can be used to affinity purify tagged baits as well as native cellular proteins and their interaction partners. As such, our method holds great promise for future biological and clinical AP-MS applications in which sample amounts are limited.

INSTRUMENT(S): Orbitrap Fusion ETD

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Cristina Furlan

LAB HEAD: Michiel Vermeulen

PROVIDER: PXD012800 | Pride | 2019-04-02

REPOSITORIES: Pride

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Publications

Miniaturised interaction proteomics on a microfluidic platform with ultra-low input requirements.

Furlan Cristina C Dirks René A M RAM Thomas Peter C PC Jones Robert C RC Wang Jing J Lynch Mark M Marks Hendrik H Vermeulen Michiel M

Nature communications 20190404 1

Essentially all cellular processes are orchestrated by protein-protein interactions (PPIs). In recent years, affinity purification coupled to mass spectrometry (AP-MS) has been the preferred method to identify cellular PPIs. Here we present a microfluidic-based AP-MS workflow, called on-chip AP-MS, to identify PPIs using minute amounts of input material. By using this automated platform we purify the human Cohesin, CCC and Mediator complexes from as little as 4 micrograms of input lysate, repres ...[more]

PMID: 30948724

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Project description:Intracellular bacterial pathogens inject a cocktail of effector proteins into host cells to hijack diverse cellular processes and promote their survival and proliferation. Salmonella enterica serovar Typhimurium (STm) uses two Type 3 secretion system (T3SS) to deliver a suite of effector proteins into host cells, however, knowledge of their host-targets remains sparse. To systematically map STm effector-host protein-protein interactions (PPIs) during active infection, we generated a library of 32 STm strains expressing affinity-tagged effector proteins from their endogenous chromosomal loci. Tagged-effector strains were then used to infect macrophages or epithelial cells, and PPIs were detected by Affinity-Purification Quantitative Mass-Spectrometry (AP-QMS) under both native and cross-linking conditions. We recovered 25 previously described effector-host PPIs, along with 421 novel interactions across the two cell lines tested, as well as several effector-effector PPIs. While effectors converged on specific host cellular processes, which varied across cell types, most had multiple host targets. Using reciprocal co-immunoprecipitations, we could validated 13 out of 22 new PPIs, demonstrating an accuracy of at least 59% for the AP-QMS approach. To illustrate the utility of this resource for discovering novel infection biology, we first showed that SseJ and SseL cooperate to control cholesterol transport at the Salmonella Containing Vacuole (SCV) via the Niemann-Pick C1 protein (NPC1). Second, we uncovered that PipB directly binds and recruits the organelle contact site protein PDZD8 to the SCV. Third, we elucidated a novel mechanism whereby the effector kinase SteC promotes host actin bundling, by directly interacting and phosphorylating formin-like proteins. Overall, we provide a systems-wide host-bacterial pathogen physical interactome resource, to our knowledge the first in an infection context, with broad implications for effector function and cooperation.

Dataset Information

On-chip AP-MS: Interaction proteomics on a microfluidics platform

Publications

Miniaturised interaction proteomics on a microfluidic platform with ultra-low input requirements.

Similar Datasets

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