Analysis of Gene Function Using DNA Microarrays

Integrated TORC1 and PKA signaling control the temporal activation of glucose-induced gene expression in yeast

The growth rate of a yeast cell is controlled by the target of rapamycin kinase complex I (TORC1) and cAMP-dependent protein kinase (PKA) pathways. To determine how TORC1 and PKA cooperate to regulate cell growth, we performed temporal analysis of gene expression in yeast switched from a non-fermentable substrate, to glucose, in the presence and absence of TORC1 and PKA inhibitors. Quantitative analysis of these data reveals that PKA drives the expression of key cell growth genes during transitions into, and out of, the rapid growth state in glucose, while TORC1 is important for the steady-state expression of the same genes. This circuit design may enable yeast to set an exact growth rate based on the abundance of internal metabolites such as amino acids, via TORC1, but also adapt rapidly to changes in external nutrients, such as glucose, via PKA.

Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces cerevisiae

The Target of Rapamycin kinase Complex I (TORC1) is a master regulator of cell growth and metabolism in eukaryotes. Studies in yeast and human cells have shown that nitrogen/amino acid starvation signals act through Npr2/Npr3 and the small GTPases Gtr1/Gtr2 (Rags in humans) to inhibit TORC1. However, it is unclear how other stress and starvation stimuli inhibit TORC1, and/or act in parallel with the TORC1 pathway, to control cell growth. To help answer these questions, we developed a novel automated pipeline and used it to measure the expression of a TORC1-dependent ribosome biogenesis gene (NSR1) during osmotic stress in 4700 Saccharomyces cerevisiae strains from the yeast knock-out collection. This led to the identification of 440 strains with significant and reproducible defects in NSR1 repression. The cell growth control and stress response proteins deleted in these strains form a highly connected network, including 56 proteins involved in vesicle trafficking and vacuolar function; 53 proteins that act downstream of TORC1 according to a rapamycin assay—including components of the HDAC Rpd3L, Elongator, and the INO80, CAF-1 and SWI/SNF chromatin remodeling complexes; over 100 proteins involved in signaling and metabolism; and 17 proteins that directly interact with TORC1. These data provide an important resource for labs studying cell growth control and stress signaling, and demonstrate the utility of our new, and easily adaptable, method for mapping gene regulatory networks.

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry-based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set-wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery.

State transitions in the TORC1 signaling pathway and information processing in Saccharomyces cerevisiae

TOR kinase complex I (TORC1) is a key regulator of cell growth and metabolism in all eukaryotes. Previous studies in yeast have shown that three GTPases-Gtr1, Gtr2, and Rho1-bind to TORC1 in nitrogen and amino acid starvation conditions to block phosphorylation of the S6 kinase Sch9 and activate protein phosphatase 2A (PP2A). This leads to downregulation of 450 Sch9-dependent protein and ribosome synthesis genes and upregulation of 100 PP2A-dependent nitrogen assimilation and amino acid synthesis genes. Here, using bandshift assays and microarray measurements, we show that the TORC1 pathway also populates three other stress/starvation states. First, in glucose starvation conditions, the AMP-activated protein kinase (AMPK/Snf1) and at least one other factor push the TORC1 pathway into an off state, in which Sch9-branch signaling and PP2A-branch signaling are both inhibited. Remarkably, the TORC1 pathway remains in the glucose starvation (PP2A inhibited) state even when cells are simultaneously starved for nitrogen and glucose. Second, in osmotic stress, the MAPK Hog1/p38 drives the TORC1 pathway into a different state, in which Sch9 signaling and PP2A-branch signaling are inhibited, but PP2A-branch signaling can still be activated by nitrogen starvation. Third, in oxidative stress and heat stress, TORC1-Sch9 signaling is blocked while weak PP2A-branch signaling occurs. Together, our data show that the TORC1 pathway acts as an information-processing hub, activating different genes in different conditions to ensure that available energy is allocated to drive growth, amino acid synthesis, or a stress response, depending on the needs of the cell.

Inositol pyrophosphates regulate cell growth and the environmental stress response by activating the HDAC Rpd3L

Cells respond to stress and starvation by adjusting their growth rate and enacting stress defense programs. In eukaryotes this involves inactivation of TORC1, which in turn triggers downregulation of ribosome and protein synthesis genes and upregulation of stress response genes. Here we report that the highly conserved inositol pyrophosphate (PP-IP) second messengers (including 1-PP-IP5, 5-PP-IP4, and 5-PP-IP5) are also critical regulators of cell growth and the general stress response, acting in parallel with the TORC1 pathway to control the activity of the class I histone deacetylase Rpd3L. In fact, yeast cells that cannot synthesize any of the PP-IPs mount little to no transcriptional response to osmotic, heat, or oxidative stress. Furthermore, PP-IP-dependent regulation of Rpd3L occurs independently of the role individual PP-IPs (such as 5-PP-IP5) play in activating specialized stress/starvation response pathways. Thus, the PP-IP second messengers simultaneously activate and tune the global response to stress and starvation signals.

Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities

Muscle volume measurements are essential for an array of diseases ranging from peripheral arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In the clinical setting, muscle volume is not routinely measured due to the lack of standardized ways for its repeatable quantification. In this paper, we present magnetic resonance muscle quantification (MRMQ), a method for the automatic quantification of thigh muscle volume in magnetic resonance imaging (MRI) scans. MRMQ integrates a thigh segmentation and nonuniform image gradient correction step, followed by feature extraction and classification. The classification step leverages prior probabilities, introducing prior knowledge to a maximum a posteriori classifier. MRMQ was validated on 344 slices taken from 60 MRI scans. Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively.