Dataset Information

Identification of the bacteriophage nucleus protein interaction network.

ABSTRACT: In the arms race between bacteria and bacteriophages (phages), some large-genome jumbo phages have evolved a protein shell that encloses their replicating genome to protect it against DNA-targeting immune factors. By segregating the genome from the host cytoplasm, however, the "phage nucleus" introduces the need to specifically transport mRNA and proteins through the nuclear shell, and to dock capsids on the shell for genome packaging. Here, we use proximity labeling and localization mapping to systematically identify proteins associated with the major nuclear shell protein chimallin (ChmA) and other distinctive structures assembled by these phages. We identify six uncharacterized nuclear shell-associated proteins, one of which directly interacts with self-assembled ChmA. The structure and protein-protein interaction network of this protein, which we term ChmB, suggests that it forms pores in the ChmA lattice that serve as docking sites for capsid genome packaging, and may also participate in mRNA and/or protein transport.

SUBMITTER: Enustun E

PROVIDER: S-EPMC10245766 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of the bacteriophage nucleus protein interaction network.

Enustun Eray E Deep Amar A Gu Yajie Y Nguyen Katrina T KT Chaikeeratisak Vorrapon V Armbruster Emily E Ghassemian Majid M Villa Elizabeth E Pogliano Joe J Corbett Kevin D KD

bioRxiv : the preprint server for biology 20230518

In the arms race between bacteria and bacteriophages (phages), some large-genome jumbo phages have evolved a protein shell that encloses their replicating genome to protect it against DNA-targeting immune factors. By segregating the genome from the host cytoplasm, however, the "phage nucleus" introduces the need to specifically transport mRNA and proteins through the nuclear shell, and to dock capsids on the shell for genome packaging. Here, we use proximity labeling and localization mapping to ...[more]

PMID: 37292858

Similar Datasets

Project description:BACKGROUND: Finding reliable gene markers for accurate disease classification is very challenging due to a number of reasons, including the small sample size of typical clinical data, high noise in gene expression measurements, and the heterogeneity across patients. In fact, gene markers identified in independent studies often do not coincide with each other, suggesting that many of the predicted markers may have no biological significance and may be simply artifacts of the analyzed dataset. To find more reliable and reproducible diagnostic markers, several studies proposed to analyze the gene expression data at the level of groups of functionally related genes, such as pathways. Studies have shown that pathway markers tend to be more robust and yield more accurate classification results. One practical problem of the pathway-based approach is the limited coverage of genes by currently known pathways. As a result, potentially important genes that play critical roles in cancer development may be excluded. To overcome this problem, we propose a novel method for identifying reliable subnetwork markers in a human protein-protein interaction (PPI) network. RESULTS: In this method, we overlay the gene expression data with the PPI network and look for the most discriminative linear paths that consist of discriminative genes that are highly correlated to each other. The overlapping linear paths are then optimally combined into subnetworks that can potentially serve as effective diagnostic markers. We tested our method on two independent large-scale breast cancer datasets and compared the effectiveness and reproducibility of the identified subnetwork markers with gene-based and pathway-based markers. We also compared the proposed method with an existing subnetwork-based method. CONCLUSIONS: The proposed method can efficiently find reliable subnetwork markers that outperform the gene-based and pathway-based markers in terms of discriminative power, reproducibility and classification performance. Subnetwork markers found by our method are highly enriched in common GO terms, and they can more accurately classify breast cancer metastasis compared to markers found by a previous method.

Dataset Information

Identification of the bacteriophage nucleus protein interaction network.

Publications

Identification of the bacteriophage nucleus protein interaction network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets