Fischerella major NIES-592 protein interaction network data
Ontology highlight
ABSTRACT:
ORGANISM(S): Fischerella Major Nies-592
SUBMITTER: 
Sheng Yao
PROVIDER: PXD033465 | iProX | Wed Apr 27 00:00:00 BST 2022
REPOSITORIES: iProX
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Project description:The majority of proteins are organized in protein complexes. Protein complexes represent an important cellular organizational layer, which regulate and catalyze most of the cellular processes. Within the field of proteomics several techniques such as Affinity Purification (AP) and co-fractionation (co-Frac) coupled to mass spectrometry (MS) were developed in order to study protein complexes. Our approach, deep interactome profiling coupled to MS (DIP-MS), is a combination of the benefits of these approaches by combining the selectivity of AP-MS together with a blue native-page size-based fractionation, in order to deconvolute the co-purified complexes, in which the bait is a constitutive subunit. To ensure high-quality quantitation along the fractionation gradient, and to keep data acquisition time limited, we developed a high-throughput (HT) sample preparation method and high-throughput data independent acquisition (DIA) method, enabling us to acquire almost one co-Frac gradient per day. Additionally, a tailored machine learning approach was devised – protein-protein interaction prophet (PPIprophet) which enabled the scoring of the co-elution proteins to retrieve PPIs respectively protein complexes. The method was employed to depict the complex modularity within the prefoldin and prefoldin-like protein complexes, allowing us to I) describe protein complex isoforms, II) derive stoichiometries and abundance distributions of co-purified complexes and III) identify reported and new client proteins and client complexes of the PAQosome, a multi-subunit co-chaperone complex, necessary for the stabilization and formation of multiple complexes such as the RNA polymerases (RNA Pol I, RNA Pol II, RNA Pol III). This study thereby demonstrates the applicability of our method and shows it strength and sensitivity by depicting the prefoldin complexes within only a single experiment.
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Project description:Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
| S-EPMC10148184 | biostudies-literature