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Start Over Topic gene silencing Publication Year Range Last 50 years
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1. Detection of a G-Quadruplex as a Regulatory Element in Thymidylate synthase for Gene Silencing Using Polypurine Reverse Hoogsteen Hairpins.

Author

Aubets E, J Félix A, Garavís M, Reyes L, Aviñó A, Eritja R, Ciudad CJ, and Noé V

Subjects
Base Sequence, Cell Survival, DNA genetics, HeLa Cells, Humans, RNA, Messenger genetics, Regulatory Sequences, Nucleic Acid, G-Quadruplexes, Gene Silencing, Gene Targeting, Thymidylate Synthase genetics
Abstract

Thymidylate synthase (TYMS) enzyme is an anti-cancer target given its role in DNA biosynthesis. TYMS inhibitors (e.g., 5-Fluorouracil) can lead to drug resistance through an autoregulatory mechanism of TYMS that causes its overexpression. Since G-quadruplexes (G4) can modulate gene expression, we searched for putative G4 forming sequences (G4FS) in the TYMS gene that could be targeted using polypurine reverse Hoogsteen hairpins (PPRH). G4 structures in the TYMS gene were detected using the quadruplex forming G-rich sequences mapper and confirmed through spectroscopic approaches such as circular dichroism and NMR using synthetic oligonucleotides. Interactions between G4FS and TYMS protein or G4FS and a PPRH targeting this sequence (HpTYMS-G4-T) were studied by EMSA and thioflavin T staining. We identified a G4FS in the 5'UTR of the TYMS gene in both DNA and RNA capable of interacting with TYMS protein. The PPRH binds to its corresponding target dsDNA, promoting G4 formation. In cancer cells, HpTYMG-G4-T decreased TYMS mRNA and protein levels, leading to cell death, and showed a synergic effect when combined with 5-fluorouracil. These results reveal the presence of a G4 motif in the TYMS gene, probably involved in the autoregulation of TYMS expression, and the therapeutic potential of a PPRH targeted to the G4FS.

Published
2020
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2. A computational framework for gene regulatory network inference that combines multiple methods and datasets

Author

Philipp Antczak, Anna Stincone, Sarah Durant, Francesco Falciani, Andreas Bikfalvi, Rita Gupta, Roy Bicknell, School of Biosciences, University of Birmingham [Birmingham], Institute of Biomedical Research, Mecanismes Moleculaires de l'Angiogenese, Université Sciences et Technologies - Bordeaux 1-Institut National de la Santé et de la Recherche Médicale (INSERM), The work described in this paper was funded by the CRUK grant C8504/A9488 and partially funded by the BBSRC grant BBC5151041. AS is a recipient of a Darwin Trust PhD fellowship and PA is a recipient of a BBSRC PhD studentship., BMC, Ed., and Université Sciences et Technologies - Bordeaux 1 (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
MESH: Hydrogen-Ion Concentration, Time Factors, Gene regulatory network, Inference, Multiple methods, computer.software_genre, 0302 clinical medicine, Structural Biology, Gene regulatory network inference, Neoplasms, MESH: Gene Silencing, MESH: Neoplasms, Gene Regulatory Networks, MESH: Stress, Physiological, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], MESH: Gene Regulatory Networks, Genetics, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Escherichia coli, Applied Mathematics, Systems Biology, Methodology Article, Hydrogen-Ion Concentration, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Modeling and Simulation, MESH: Systems Biology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Reverse engineering, MESH: Cell Line, Tumor, Systems biology, Biology, Machine learning, Models, Biological, 03 medical and health sciences, Stress, Physiological, Modelling and Simulation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Cell Line, Tumor, Escherichia coli, Humans, Gene Silencing, Molecular Biology, 030304 developmental biology, MESH: Humans, business.industry, MESH: Time Factors, Ode, MESH: Models, Biological, lcsh:Biology (General), Time course, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery
Abstract

Background Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. Results This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies. Conclusions The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.

Published
2011