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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

Protein Networks

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted

Ying Sun1, Yan Weng2, Ying Zhang3

  • 1Department of Cadres' Ward, China Meitan General Hospital, Beijing 100028, China.

Microbial Pathogenesis
|March 22, 2017
PubMed
Summary

Researchers identified specific gene modules associated with ribosomes as potential biomarkers for active tuberculosis (TB). These findings could lead to more sensitive and efficient diagnostic tests for TB detection.

Keywords:
Active tuberculosisAttractorsDys-regulated modulesProtein-protein interaction network

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Area of Science:

  • Systems biology
  • Molecular biology
  • Infectious diseases

Background:

  • Tuberculosis (TB) affects millions globally, with latent infections posing a significant risk of progression to active disease.
  • Current diagnostic methods for active TB lack optimal efficiency and sensitivity, necessitating improved approaches.
  • Understanding the molecular mechanisms underlying TB progression is crucial for developing better diagnostics.

Purpose of the Study:

  • To identify potential molecular signatures for active tuberculosis (TB).
  • To enhance the understanding of biological roles of functional modules in active TB.
  • To discover novel biomarkers for improved TB diagnosis.

Main Methods:

  • Constructed targeted protein-protein interaction (PPI) networks for active TB and control groups using Pearson's correlation coefficient (PCC).
  • Identified candidate modules and defined 'attractors' based on Jaccard scores (>0.7).
  • Detected dys-regulated modules using the 'attract' method and performed Gene Ontology (GO) enrichment analyses.

Main Results:

  • Identified 33 candidate modules in controls and 65 in active TB patients.
  • Discovered 13 'attractor' modules, with 4 identified as significantly dys-regulated (Module 1, 2, 3, and 4).
  • GO analysis revealed that genes in Modules 1, 2, and 4 are primarily involved in translation and associated with ribosomes.

Conclusions:

  • Dys-regulated modules, particularly those related to ribosomal function and translation, show promise as potential biomarkers for active TB.
  • These findings may contribute to the development of more efficient and sensitive diagnostic assays for active TB.
  • Further validation of these modules could significantly advance TB diagnostics.