Lysosomes are organelles central to degradation and recycling processes in animal cells. Whether lysosomal activity is coordinated to respond to cellular needs remains unclear. We found that most lysosomal genes exhibit coordinated transcriptional behavior and are regulated by the transcription factor TFEB. Under aberrant lysosomal storage conditions, TFEB translocated from the cytoplasm to the nucleus, resulting in the activation of its target genes. TFEB overexpression in cultured cells induced lysosomal biogenesis and increased the degradation of complex molecules, such as glycosaminoglycans (GAGs) and the pathogenic protein causing Huntington disease. Thus, a genetic program controls lysosomal biogenesis and function, providing a potential therapeutic target to enhance cellular clearing in lysosomal storage disorders and neurodegenerative diseases.

Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.

Summary: The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and possibly high-dimensional setting. Its originality lies in the fact that it searches for a latent modular structure to drive the inference procedure through adaptive penalization of the concentration matrix. Availability: Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/