A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Eukaryotic cells use autophagy and the ubiquitin-proteasome system (UPS) as their major protein degradation pathways. Whereas the UPS is required for the rapid degradation of proteins when fast adaptation is needed, autophagy pathways selectively remove protein aggregates and damaged or excess organelles. However, little is known about the targets and mechanisms that provide specificity to this process. Here we show that mature ribosomes are rapidly degraded by autophagy upon nutrient starvation in Saccharomyces cerevisiae. Surprisingly, this degradation not only occurs by a non-selective mechanism, but also involves a novel type of selective autophagy, which we term 'ribophagy'. A genetic screen revealed that selective degradation of ribosomes requires catalytic activity of the Ubp3p/Bre5p ubiquitin protease. Although ubp3Delta and bre5Delta cells strongly accumulate 60S ribosomal particles upon starvation, they are proficient in starvation sensing and in general trafficking and autophagy pathways. Moreover, ubiquitination of several ribosomal subunits and/or ribosome-associated proteins was specifically enriched in ubp3Delta cells, suggesting that the regulation of ribophagy by ubiquitination may be direct. Interestingly, ubp3Delta cells are sensitive to rapamycin and nutrient starvation, implying that selective degradation of ribosomes is functionally important in vivo. Taken together, our results suggest a link between ubiquitination and the regulated degradation of mature ribosomes by autophagy.

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Single molecule FISH (smFISH) allows studying transcription and RNA localization by imaging individual mRNAs in single cells. We present smiFISH (single molecule inexpensive FISH), an easy to use and flexible RNA visualization and quantification approach that uses unlabelled primary probes and a fluorescently labelled secondary detector oligonucleotide. The gene-specific probes are unlabelled and can therefore be synthesized at low cost, thus allowing to use more probes per mRNA resulting in a substantial increase in detection efficiency. smiFISH is also flexible since differently labelled secondary detector probes can be used with the same primary probes. We demonstrate that this flexibility allows multicolor labelling without the need to synthesize new probe sets. We further demonstrate that the use of a specific acrydite detector oligonucleotide allows smiFISH to be combined with expansion microscopy, enabling the resolution of transcripts in 3D below the diffraction limit on a standard microscope. Lastly, we provide improved, fully automated software tools from probe-design to quantitative analysis of smFISH images. In short, we provide a complete workflow to obtain automatically counts of individual RNA molecules in single cells.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided for instance by flow cytometry, currently remains limited to very small and simple systems. Here we propose a new method that makes use of low-order moments of the measured distribution and thereby keeps the essential parts of the provided information, while still staying applicable to systems of realistic size. We demonstrate how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population. We demonstrate the method for a simple example of gene expression using synthetic data generated by stochastic simulation. Subsequently, we use time-lapsed flow cytometry data for the osmo-stress induced transcriptional response in budding yeast to calibrate a stochastic model, which is then used as a basis for predictions. Our results show that measurements of the mean and the variance can be enough to determine the model parameters, even if the measured distributions are not well-characterized by low-order moments only-e.g., if they are bimodal.extrinsic variability | high-osmolarity glycerol pathway | moment dynamics | parameter inference | stochastic kinetic models B uilding predictive computational models of intracellular reaction kinetics is still a dauntingly ill-posed task (1), characterized by low-dimensional experimental readouts of the hypothesized high-dimensional process. Single-cell technologies hold promise to partly alleviate this ill-posedness by exploiting the observed variability for the calibration of stochastic kinetic models (2, 3). The same technologies, however, also reveal that isogenic cells in a single population exhibit large cell-to-cell variability (4, 5). The variation can be shown to be a convolution of two sources, namely the intrinsic molecular noise and extrinsic factors that render single cells different even in the absence of molecular noise; in many cases, the latter was reported to dominate the former (4, 5). Extrinsic factors comprise difference in cell size, cell-cycle stage, expression capacity, local growth conditionsto name but a few (6, 7). Thus, although single-cell technology offers a way out of the predicament of ill-posedness, it requires new methods to deal properly with intrinsic and extrinsic variability. The effect of extrinsic variability on the dynamics of stochastic models is studied in refs. 7 and 8, whereas first attempts have been made to address the inverse problem of quantifying the extrinsic (9) and any additional intrinsic (10) components from measurements. Because the latter is based on path sampling, its applicability remains limited to small systems. Naturally, extrinsic variability is bypassed when...

Pichon et al. describe a method to visualize translation of single endogenous mRNPs in live cells and provide evidence for specialized translation factories, as well as measurements of translation elongation rate, ribosome loading, and movements of single polysomes.

Autophagy is an intracellular trafficking pathway sequestering cytoplasm and delivering excess and damaged cargo to the vacuole for degradation. The Atg1/ULK1 kinase is an essential component of the core autophagy machinery possibly activated by binding to Atg13 upon starvation. Indeed, we found that Atg13 directly binds Atg1, and specific Atg13 mutations abolishing this interaction interfere with Atg1 function in vivo. Surprisingly, Atg13 binding to Atg1 is constitutive and not altered by nutrient conditions or treatment with the Target of rapamycin complex 1 (TORC1)‐inhibitor rapamycin. We identify Atg8 as a novel regulator of Atg1/ULK1, which directly binds Atg1/ULK1 in a LC3‐interaction region (LIR)‐dependent manner. Molecular analysis revealed that Atg13 and Atg8 cooperate at different steps to regulate Atg1 function. Atg8 targets Atg1/ULK1 to autophagosomes, where it may promote autophagosome maturation and/or fusion with vacuoles/lysosomes. Moreover, Atg8 binding triggers vacuolar degradation of the Atg1–Atg13 complex in yeast, thereby coupling Atg1 activity to autophagic flux. Together, these findings define a conserved step in autophagy regulation in yeast and mammals and expand the known functions of LIR‐dependent Atg8 targets to include spatial regulation of the Atg1/ULK1 kinase.