Fatty acid synthetase reversibly sequesters into storage granules upon nutrient limitation

Fatty acid synthetase reversibly sequesters into storage granules upon nutrient limitation. A detailed analysis of nature and physiological significance of starvation-induced protein sequestrations in S.cerevisiae

Profiling Ssb-Nascent Chain Interactions Reveals Principles of Hsp70-Assisted Folding

The yeast Hsp70 chaperone Ssb interacts with ribosomes and nascent polypeptides to assist protein folding. To reveal its working principle, we determined the nascent chain-binding pattern of Ssb at near-residue resolution by in vivo selective ribosome profiling. Ssb associates broadly with cytosolic, nuclear, and hitherto unknown substrate classes of mitochondrial and endoplasmic reticulum (ER) nascent proteins, supporting its general chaperone function. Ssb engages most substrates by multiple binding-release cycles to a degenerate sequence enriched in positively charged and aromatic amino acids. Timely association with this motif upon emergence at the ribosomal tunnel exit requires ribosome-associated complex (RAC) but not nascent polypeptide-associated complex (NAC). Ribosome footprint densities along orfs reveal faster translation at times of Ssb binding, mainly imposed by biases in mRNA secondary structure, codon usage, and Ssb action. Ssb thus employs substrate-tailored dynamic nascent chain associations to coordinate co-translational protein folding, facilitate accelerated translation, and support membrane targeting of organellar proteins

Prolonged starvation drives reversible sequestration of lipid biosynthetic enzymes and organelle reorganization in Saccharomyces cerevisiae

Cells adapt to changing nutrient availability by modulating a variety of processes, including the spatial sequestration of enzymes, the physiological significance of which remains controversial. These enzyme deposits are claimed to represent aggregates of misfolded proteins, protein storage, or complexes with superior enzymatic activity. We monitored spatial distribution of lipid biosynthetic enzymes upon glucose depletion in Saccharomyces cerevisiae. Several different cytosolic-, endoplasmic reticulum–, and mitochondria-localized lipid biosynthetic enzymes sequester into distinct foci. Using the key enzyme fatty acid synthetase (FAS) as a model, we show that FAS foci represent active enzyme assemblies. Upon starvation, phospholipid synthesis remains active, although with some alterations, implying that other foci-forming lipid biosynthetic enzymes might retain activity as well. Thus sequestration may restrict enzymes' access to one another and their substrates, modulating metabolic flux. Enzyme sequestrations coincide with reversible drastic mitochondrial reorganization and concomitant loss of endoplasmic reticulum–mitochondria encounter structures and vacuole and mitochondria patch organelle contact sites that are reflected in qualitative and quantitative changes in phospholipid profiles. This highlights a novel mechanism that regulates lipid homeostasis without profoundly affecting the activity status of involved enzymes such that, upon entry into favorable growth conditions, cells can quickly alter lipid flux by relocalizing their enzymes

Data File S3. Genetic interaction profile similarity matrices

Matrix files containing genetic interaction profile similarity values (as measured by Pearson correlation) for every pair of mutant strains in the dataset. Similarity values were computed for essential (ExE), non-essential (NxN) and the global similarity network derived from a combined set of all genetic interactions (ExE, NxN, ExN) as described above (see "Constructing genetic interaction profile similarity networks"). Each matrix contains 2 sets of row and column headers, providing a unique allele name for every mutant strain (row & column header #1) as well as a systematic ORF name (row & column header #2)

Data File S4. GO bioprocess functions predicted by the nonessential and essential similarity networks using a K-nearest neighbor approach

This file reports the performance of gene function prediction for non-essential or essential genes based on genetic interaction profiles. For both classes of genes (either nonessential or essential), the performance of a KNN classifier is reported as the Precision at 25% Recall based on interactions derived from TS queries (PR_TSQ) or nonessential deletion queries (PR25_SN). Although analyses were performed using complete genetic interaction profiles (e.g. negative and positive genetic interactions), similar prediction performance was obtained using genetic interaction profiles based on negative interactions alone

Data File S7. Pleiotropic gene analysis

This file lists nonessential and essential query genes associated with high confidence pleiotropy scores based on their genetic interactions derived from the TSA (Essential derived pleiotropy) and DMA (Nonessential derived pleiotropy). The file also contains a second list of nonessential and essential query genes that participate in many genetic interactions but exhibited low pleiotropy scores indicating that these genes are more functionally specific

Data from: A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION: Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE: Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS: We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION: A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships

Data File S6. Genetic profile similarity-based hierarchy analysis

The first tab (“Gene to hierarchy cluster mapping”) lists the clusters identified at each level of the genetic interaction-based hierarchy and the deletion and TS allele array mutants assigned to each cluster. Examples of clusters described in the main text are highlighted. The subsequent 9 tabs indicate enrichment of clusters resolved at the specified profile similarity range for specific cell compartments (Cyclops_enrich), biological processes (GO BP_enrich), protein complexes (complex_enrich) and KEGG pathways (KEGG_enrich). The final tab in the file indicates the clusters used to map the functional distribution of negative and positive interactions shown in Fig. 5D

Data File S16. Genetic suppression analysis

This file includes raw data from spot dilution growth assays to identify positive interactions that can be classified as genetic suppression. The suppression score is based on visual assessment of double mutant strain growth relative to a wild type and single mutant control strains. The score reflects strength of suppression with a score of 4 indicative of a strong suppression interaction where double mutant growth exceeded growth of the sickest single mutant and a score of 0 indicates failure to confirm a suppression interaction