TL;DR: The synthesis of enzymes in bacteria follows a double genetic control, which appears to operate directly at the level of the synthesis by the gene of a shortlived intermediate, or messenger, which becomes associated with the ribosomes where protein synthesis takes place.
TL;DR: A better understanding of how the Bcl2 family controls caspase activation should result in new, more effective therapeutic approaches in tissue homeostasis and cancer.
Abstract: Tissue homeostasis is regulated by apoptosis, the cell-suicide programme that is executed by proteases called caspases. The Bcl2 family of intracellular proteins is the central regulator of caspase activation, and its opposing factions of anti- and pro-apoptotic members arbitrate the life-or-death decision. Apoptosis is often impaired in cancer and can limit conventional therapy. A better understanding of how the Bcl2 family controls caspase activation should result in new, more effective therapeutic approaches.
TL;DR: Interestingly, the growth-promoting activity of c-Jun is mediated by repression of tumour suppressors, as well as upregulation of positive cell cycle regulators, whereas JunB has the converse effect.
Abstract: The transcription factor AP-1 (activator protein-1) is involved in cellular proliferation, transformation and death. Using mice and cells lacking AP-1 components, the target-genes and molecular mechanisms mediating these processes were recently identified. Interestingly, the growth-promoting activity of c-Jun is mediated by repression of tumour suppressors, as well as upregulation of positive cell cycle regulators. Mostly, c-Jun is a positive regulator of cell proliferation, whereas JunB has the converse effect. The intricate relationships between the different Jun proteins, their activities and the mechanisms that mediate them will be discussed.
TL;DR: Findings reveal that the target of rapamycin TOR controls an unusually abundant and diverse set of readouts all of which are important for cell growth, suggesting that this conserved kinase is such a central regulator.
TL;DR: The procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'.
Abstract: Much of a cell’s activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form ‘regulator X regulates module Y under conditions W’. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.