RESUMEN
Resveratrol is a natural product associated with wide-ranging effects in animal and cellular models, including lifespan extension. To identify the genetic target of resveratrol in human cells, we conducted genome-wide CRISPR-Cas9 screens to pinpoint genes that confer sensitivity or resistance to resveratrol. An extensive network of DNA damage response and replicative stress genes exhibited genetic interactions with resveratrol and its analog pterostilbene. These genetic profiles showed similarity to the response to hydroxyurea, an inhibitor of ribonucleotide reductase that causes replicative stress. Resveratrol, pterostilbene, and hydroxyurea caused similar depletion of nucleotide pools, inhibition of replication fork progression, and induction of replicative stress. The ability of resveratrol to inhibit cell proliferation and S phase transit was independent of the histone deacetylase sirtuin 1, which has been implicated in lifespan extension by resveratrol. These results establish that a primary impact of resveratrol on human cell proliferation is the induction of low-level replicative stress.
Asunto(s)
Proliferación Celular/efectos de los fármacos , Replicación del ADN/efectos de los fármacos , Resveratrol/farmacología , Sistemas CRISPR-Cas , Línea Celular , Resistencia a Medicamentos/genética , Humanos , Hidroxiurea/farmacología , Células Jurkat , Nucleótidos/metabolismo , Puntos de Control de la Fase S del Ciclo Celular/efectos de los fármacos , Sirtuina 1/metabolismo , Estilbenos/farmacologíaRESUMEN
This study investigated the organic and inorganic constituents of healthy leaves and Candidatus Liberibacter asiaticus (CLas)-inoculated leaves of citrus plants. The bacteria CLas are one of the causal agents of citrus greening (or Huanglongbing) and its effect on citrus leaves was investigated using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics. The information obtained from the LIBS spectra profiles with chemometrics analysis was promising for the construction of predictive models to identify healthy and infected plants. The major, macro- and microconstituents were relevant for differentiation of the sample conditions. The models were then applied to different inoculation times (from 1 to 8 months). The models were effective in the classification of 82-97% of the diseased samples with a 95% significance level. The novelty of this method was in the fingerprinting of healthy and diseased plants based on their organic and inorganic contents.