Caracterización fenotípica y filogenia molecular de hongos extremófilos
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PROVIDER: PRJEB38011 | ENA |
REPOSITORIES: ENA
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2016-03-07 | GSE71638 | GEO
Project description:DNA methylation is a conserved epigenetic gene regulation mechanism. DOMAINS REARRANGED METHYLTRANSFERASE (DRM) is a key de novo methyltransferase in plants, but how DRM acts mechanistically is poorly understood. Here, we report the crystal structure of the methyltransferase domain of tobacco DRM (NtDRM) and reveal a molecular basis for its rearranged structure. NtDRM forms a functional homo-dimer critical for catalytic activity. We also show that Arabidopsis DRM2 exists in complex with the siRNA effector ARGONAUTE4 (AGO4) and preferentially methylates one DNA strand, likely the strand acting as the template for non-coding Pol V RNA transcripts. This strand-biased DNA methylation is also positively correlated with strand-biased siRNA accumulation. These data suggest a model in which DRM2 is guided to target loci by AGO4-siRNA and involves base-pairing of associated siRNAs with nascent RNA transcripts. Whole-genome bisulfite sequencing was done for a wildtype line (ecotype Col) as well as various transgenic lines in a drm2 mutant background (ecotype Col). Each transgenic line expressed a version of the DRM2 protein that was either wildtype or carried induced mutations in order to test the function of various domains in the DRM2 protein. Two sets of whole-genome bisulfite were performed (130615 or 131216) and comparisons were mainly done within sets although comparisons can also be done between sets. The drm2 mutant methylome was also analyzed in this study using a previously published whole-genome bisulfite library (GSE39901).
2014-05-27 | E-GEOD-54944 | biostudies-arrayexpress
Project description:Theoretical models of the chemical origins of life depend on self-replication or autocatalysis, processes that arise from molecular interactions, recruitment, and cooperation. Such models often lack details about the molecules and reactions involved, giving little guidance to those seeking to detect signs of interaction, recruitment, or cooperation in the laboratory. Here, we develop minimal mathematical models of reactions involving specific chemical entities: amino acids and their condensation reactions to form de novo peptides. Reactions between two amino acids form a dipeptide product, which enriches linearly in time; subsequent recruitment of such products to form longer peptides exhibit super-linear growth. Such recruitment can be reciprocated: a peptide contributes to and benefits from the formation of one or more other peptides; in this manner, peptides can cooperate and thereby exhibit autocatalytic or exponential growth. We have started to test these predictions by quantitative analysis of de novo peptide synthesis conducted by wet-dry cycling of a five-amino acid mixture over 21 days. Using high-performance liquid chromatography, we tracked abundance changes for >60 unique peptide species. Some species were highly transient, with the emergence of up to 17 new species and the extinction of nine species between samplings, while other species persisted across many cycles. Of the persisting species, most exhibited super-linear growth, a sign of recruitment anticipated by our models. This work shows how mathematical modeling and quantitative analysis of kinetic data can guide the search for prebiotic chemistries that have the potential to cooperate and replicate.
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2014-03-26 | E-GEOD-38142 | biostudies-arrayexpress
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Project description:This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model. Graphical abstract .
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