Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Integration of multiple stress signals in plants using synthetic Boolean logic gates

As photosynthetic organisms, plants have a potential role in the sustainable production of high-value products such as medicines, biofuels, and chemical feedstocks. With effective engineering using synthetic biology approaches, plant-based platforms could conceivably be designed to minimize the costs and waste of production for materials that would otherwise be uneconomical. Additionally, modern agricultural crops could be engineered to be more productive, resilient, or restorative in different or rapidly changing environments and climates. Information-processing genetic devices and circuits containing multiple interacting parts that behave predictably must be developed to achieve these complex goals. A genetic Boolean AND logic gate is a device that computes the presence or absence of 2 inputs (signals and stimuli) and produces an output (response) only when both inputs are present. We optimized individual genetic components and used synthetic protein heterodimerizing domains to rationally assemble genetic AND logic gates that integrate 2 hormonal inputs in transgenic Arabidopsis thaliana plants. These AND gates produce an output only in the presence of both abscisic acid and auxin but not when either or neither hormone is present. The AND logic gate can also integrate signals resulting from 2 plant stresses, cold temperature and bacterial infection, to produce a response. The design principles used here are generalizable, and, therefore, multiple orthogonal AND gates could be assembled and rationally layered to process complex genetic information in plants. These layered logic gates may be used in genetic circuits to probe fundamental questions in plant biology, such as hormonal crosstalk, in addition to plant engineering for bioproduction.

Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates “small-world” networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein’s position within a module and to the module’s relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

We discovered a large protein network that regulates fat storage in budding yeast. This network contains 94 proteins, almost all of which bind to other proteins in the network. To understand the functions of large protein collections such as these, it will be necessary to move away from one-by-one analysis of individual proteins and create computational models of entire networks. This will allow classification of networks into categories and permit researchers to identify key network proteins on theoretical grounds. We show here that the fat regulation network fits a Watts-Strogatz small-world model. This model was devised to explain the clustering phenomena often observed in real networks, but has not been previously applied to signaling networks within cells. The short path length and high clustering coefficients characteristic of the Watts-Strogatz topology allow for rapid communication between distant nodes and for division of the network into modules that perform different functions. The fat regulation network has modules, and it is divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We experimentally examined communication between nodes within the network using a combination of genetics and pharmacology, and showed that the communication patterns are consistent with the Watts-Strogatz topology.

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

RESULTS

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

CONCLUSIONS

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Metabolic activation of the HOG MAP kinase pathway by Snf1/AMPK regulates lipid signaling at the Golgi

Phosphatidylinositol-4-phosphate (PI(4)P) is an important regulator of Golgi function. Metabolic regulation of Golgi PI(4)P requires the lipid phosphatase Sac1 that translocates between endoplasmic reticulum (ER) and Golgi membranes. Localization of Sac1 responds to changes in glucose levels, yet the upstream signaling pathways that regulate Sac1 traffic are unknown. Here, we report that mitogen-activated protein kinase (MAPK) Hog1 transmits glucose signals to the Golgi and regulates localization of Sac1. We find that Hog1 is rapidly activated by both glucose starvation and glucose stimulation, which is independent of the well-characterized response to osmotic stress but requires the upstream element Ssk1 and is controlled by Snf1, the yeast homolog of AMP-activated kinase (AMPK). Elimination of either Hog1 or Snf1 slows glucose-induced translocation of Sac1 lipid phosphatase from the Golgi to the ER and thus delays PI(4)P accumulation at the Golgi. We conclude that a novel cross-talk between the HOG pathway and Snf1/AMPK is required for the metabolic control of lipid signaling at the Golgi.

Systems biology: new institute and applications

The Yale Systems Biology Institute (YSBI) sponsored its first symposium at the university's West Campus in October 2010. The symposium served to provide Yale's scientific community with a glimpse into the wide range of research at the forefront of this interdisciplinary field. YSBI was conceived less than a year ago, and the event was the perfect forum for its debut, both at Yale and in the U.S. scientific community. This article includes a brief overview of the different topics presented at the symposium, followed by a discussion of the advantages and challenges of practical application of systems biology.