Glutamine synthetase (GS) activity increased over three-fold in developing primary leaves of Phaseolus vulgaris L. This increase was shown to be the result of differential expression of three members of the GS gene family: gln-alpha and gln-beta, which encode cytosolic GS polypeptides, and gln-delta, which encodes the chloroplast-located GS. The gln-delta gene was the most highly expressed GS gene and was regulated in a complex manner with two different transcripts accumulating differentially during leaf development. This gene was expressed weakly in the dark and was induced strongly by light; this induction was shown not to be an indirect effect of photorespiration. In the long term, gln-delta showed increased expression in photorespiring compared with non-photorespiring leaves. However, in the short term, there was no induction of gln-delta following transfer of plants to photorespiratory conditions. These results suggest that regulation of gln-delta by photorespiration was the result of indirect, long-term effects on cellular metabolism. In general, in all these experiments, analysis of cytosolic versus chloroplastic GS polypeptides and of the GS isoenzyme profiles showed the same pattern of changes in abundance as that observed for the mRNAs suggesting that regulation of GS gene expression occurred primarily at the mRNA level. However, it was noteworthy that the delta isoenzyme remained at a high abundance in older leaves, grown in both light and dark, despite a decrease in abundance of gln-delta mRNA.
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants (CCVs) in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium, and enriched genomic features to determine variants with high posterior probabilities (HPPs) of being causal.Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of potentially causal variants, using gene expression (eQTL), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways, were over-represented among the 178 highest confidence target genes.
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