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Identifying Autophagy-Associated Proteins and Chemicals with a Random Walk-Based Method within Heterogeneous Interaction Network.
Huang, FeiMing; Guo, Wei; Chen, Lei; Feng, KaiYan; Huang, Tao; Cai, Yu-Dong.
Afiliación
  • Huang F; School of Life Sciences, Shanghai University, 200444 Shanghai, China.
  • Guo W; Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), 200030 Shanghai, China.
  • Chen L; College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China.
  • Feng K; Department of Computer Science, Guangdong AIB Polytechnic College, 510507 Guangzhou, Guangdong, China.
  • Huang T; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 200031 Shanghai, China.
  • Cai YD; CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 200031 Shanghai, China.
Front Biosci (Landmark Ed) ; 29(1): 21, 2024 01 17.
Article en En | MEDLINE | ID: mdl-38287832
ABSTRACT

BACKGROUND:

Autophagy is instrumental in various health conditions, including cancer, aging, and infections. Therefore, examining proteins and compounds associated with autophagy is paramount to understanding cellular biology and the origins of diseases, paving the way for potential therapeutic and disease prediction strategies. However, the complexity of autophagy, its intersection with other cellular pathways, and the challenges in monitoring autophagic activity make the experimental identification of these elements arduous.

METHODS:

In this study, autophagy-related proteins and chemicals were catalogued on the basis of Human Autophagy-dedicated Database. These entities were mapped to their respective PubChem identifications (IDs) for chemicals and Ensembl IDs for proteins, yielding 563 chemicals and 779 proteins. A network comprising protein-protein, protein-chemical, and chemical-chemical interactions was probed employing the Random-Walk-with-Restart algorithm using the aforementioned proteins and chemicals as seed nodes to unearth additional autophagy-associated proteins and chemicals. Screening tests were performed to exclude proteins and chemicals with minimal autophagy associations.

RESULTS:

A total of 88 inferred proteins and 50 inferred chemicals of high autophagy relevance were identified. Certain entities, such as the chemical prostaglandin E2 (PGE2), which is recognized for modulating cell death-induced inflammatory responses during pathogen invasion, and the protein G Protein Subunit Alpha I1 (GNAI1), implicated in ether lipid metabolism influencing a range of cellular processes including autophagy, were associated with autophagy.

CONCLUSIONS:

The discovery of novel autophagy-associated proteins and chemicals is of vital importance because it enhances the understanding of autophagy, provides potential therapeutic targets, and fosters the development of innovative therapeutic strategies and interventions.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteínas / Neoplasias Tipo de estudio: Clinical_trials / Risk_factors_studies Límite: Humans Idioma: En Revista: Front Biosci (Landmark Ed) Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteínas / Neoplasias Tipo de estudio: Clinical_trials / Risk_factors_studies Límite: Humans Idioma: En Revista: Front Biosci (Landmark Ed) Año: 2024 Tipo del documento: Article País de afiliación: China