C. elegans protein interaction network analysis probes RNAi validated pro-longevity effect of nhr-6, a human homolog of tumor suppressor Nr4a1
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-202666
- Protein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologsProtein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologs of already reported human tumor suppressor and aging associated genes. We select by this nhr-6, vab-3 and gst-23 as predicted longevity genes for RNAi screen. The RNAi results demonstrated the pro-longevity effect of these genes. Nuclear hormone receptor nhr-6 RNAi inhibition resulted in a C. elegans phenotype of 23.46% lifespan reduction. Moreover, we show that nhr-6 regulates oxidative stress resistance in worms and does not affect the feeding behavior of worms. These findings imply the potential of nhr-6 as a common therapeutic target for aging and cancer ailments, stressing the power of in silico PPI network analysis coupled with RNAi screens to describe gene function.…
Autor(en): | Bashir A. Akhoon, Shishir K. Gupta, Sudeep Tiwari, Laxmi Rathor, Aakanksha Pant, Nivedita Singh, Shailendra K. Gupta, Thomas Dandekar, Rakesh Pandey |
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URN: | urn:nbn:de:bvb:20-opus-202666 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Scientific Reports |
Erscheinungsjahr: | 2019 |
Band / Jahrgang: | 9 |
Seitenangabe: | 15711 |
Originalveröffentlichung / Quelle: | Scientific Reports (2019) 9:15711. https://doi.org/10.1038/s41598-019-51649-0 |
DOI: | https://doi.org/10.1038/s41598-019-51649-0 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Freie Schlagwort(e): | Computer modelling; Embryonic induction; RNAi |
Datum der Freischaltung: | 15.05.2020 |
Sammlungen: | Open-Access-Publikationsfonds / Förderzeitraum 2019 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |