Imidazolium ionic liquids (IILs) underpin promising technologies that generate fermentable sugars from lignocellulose for future biorefineries. However, residual IILs are toxic to fermentative microbes such as Saccharomyces cerevisiae, making IIL-tolerance a key property for strain engineering. To enable rational engineering, we used chemical genomic profiling to understand the effects of IILs on S. cerevisiae.

Microbial conversion of lignocellulosic feedstocks into biofuels remains an attractive means to produce sustainable energy. It is essential to produce lignocellulosic hydrolysates in a consistent manner in order to study microbial performance in different feedstock hydrolysates. Because of the potential to introduce microbial contamination from the untreated biomass or at various points during the process, it can be difficult to control sterility during hydrolysate production. In this study, we compared hydrolysates produced from AFEX-pretreated corn stover and switchgrass using two different methods to control contamination: either by autoclaving the pretreated feedstocks prior to enzymatic hydrolysis, or by introducing antibiotics during the hydrolysis of non-autoclaved feedstocks. We then performed extensive chemical analysis, chemical genomics, and comparative fermentations to evaluate any differences between these two different methods used for producing corn stover and switchgrass hydrolysates.

We applied a yeast-two-hybrid (Y2H) analysis to screen for ubiquitin variant (UbV) inhibitors of a human deubiquitinase (DUB), ubiquitin-specific protease 2 (USP2). The Y2H screen used USP2 as the bait and a prey library consisting of UbVs randomized at four specific positions, which were known to interact with USP2 from phage display analysis. The screen yielded numerous UbVs that bound to USP2 both as a Y2H interaction in vivo and as purified proteins in vitro. The Y2H-derived UbVs inhibited the catalytic activity of USP2 in vitro with nanomolar-range potencies, and they bound and inhibited USP2 in human cells. Mutational and structural analysis showed that potent and selective inhibition could be achieved by just two substitutions in a UbV, which exhibited improved hydrophobic and hydrophilic contacts compared to the wild-type ubiquitin interaction with USP2. Our results establish Y2H as an effective platform for the development of UbV inhibitors of DUBs in vivo, providing an alternative strategy for the analysis of DUBs that are recalcitrant to phage display and other in vitro methods.

The phenotypic consequence of a given mutation can be influenced by the genetic background. For example, conditional gene essentiality occurs when the loss of function of a gene causes lethality in one genetic background but not another. Between two individual Saccharomyces cerevisiae strains, S288c and Σ1278b, ∼1% of yeast genes were previously identified as "conditional essential." Here, in addition to confirming that some conditional essential genes are modified by a nonchromosomal element, we show that most cases involve a complex set of genomic modifiers. From tetrad analysis of S288C/Σ1278b hybrid strains and whole-genome sequencing of viable hybrid spore progeny, we identified complex sets of multiple genomic regions underlying conditional essentiality. For a smaller subset of genes, including CYS3 and CYS4, each of which encodes components of the cysteine biosynthesis pathway, we observed a segregation pattern consistent with a single modifier associated with conditional essentiality. In natural yeast isolates, we found that the CYS3/CYS4 conditional essentiality can be caused by variation in two independent modifiers, MET1 and OPT1, each with roles associated with cellular cysteine physiology. Interestingly, the OPT1 allelic variation appears to have arisen independently from separate lineages, with rare allele frequencies below 0.5%. Thus, while conditional gene essentiality is usually driven by genetic interactions associated with complex modifier architectures, our analysis also highlights the role of functionally related, genetically independent, and rare variants.

Unlike most checkpoint proteins, Mec1, an ATM/ATR kinase, is essential. We utilized mec1-4, a missense allele (E2130K) that confers diminished kinase activity, to interrogate the question. Unbiased screen for genetic interactors of mec1-4 identified numerous genes involved in proteostasis. mec1-4 confers sensitivity to heat, an amino acid analog, and Htt103Q, an aggregation-prone model peptide of Huntingtin. Oppositely, mec1-4 confers resistance to cycloheximide, a translation inhibitor. In response to heat, mec1-4 leads to widespread protein aggregation and cell death. Key components of the Mec1 signaling network, Rad53CHK1, Dun1, and Sml1, also impact survival in response to proteotoxic stress. Activation of autophagy or sml1Δ promotes aggregate resolution and rescues mec1-4 lethality. These findings show that proteostasis is a fundamental function of Mec1 and that Mec1 is likely to utilize its checkpoint response network to mediate resistance to proteotoxic stress, a role that may be conserved from yeast to mammalian cells.

Systematic analysis of gene overexpression phenotypes provides an insight into gene function, enzyme targets, and biological pathways. Here, we describe a novel functional genomics platform that enables a highly parallel and systematic assessment of overexpression phenotypes in pooled cultures. First, we constructed a genome-level collection of ~5100 yeast barcoder strains, each of which carries a unique barcode, enabling pooled fitness assays with a barcode microarray or sequencing readout. Second, we constructed a yeast open reading frame (ORF) galactose-induced overexpression array by generating a genome-wide set of yeast transformants, each of which carries an individual plasmid-born and sequence-verified ORF derived from the Saccharomyces cerevisiae full-length EXpression-ready (FLEX) collection. We combined these collections genetically using synthetic genetic array methodology, generating ~5100 strains, each of which is barcoded and overexpresses a specific ORF, a set we termed "barFLEX." Additional synthetic genetic array allows the barFLEX collection to be moved into different genetic backgrounds. As a proof-of-principle, we describe the properties of the barFLEX overexpression collection and its application in synthetic dosage lethality studies under different environmental conditions.

Because protonation affects the properties of almost all molecules in cells, cytosolic pH (pH(c)) is usually assumed to be constant. In the model organism yeast, however, pH(c) changes in response to the presence of nutrients and varies during growth. Since small changes in pH(c) can lead to major changes in metabolism, signal transduction, and phenotype, we decided to analyze pH(c) control.

It is generally held that random-coil polypeptide chains undergo a barrier-less continuous collapse when the solvent conditions are changed to favor the fully folded native conformation. We test this hypothesis by probing intramolecular distance distributions during folding in one of the paradigms of folding reactions, that of cytochrome c. The Trp59-to-heme distance was probed by time-resolved Förster resonance energy transfer in the microsecond time range of refolding. Contrary to expectation, a state with a Trp59-heme distance close to that of the guanidinium hydrochloride (GdnHCl) denatured state is present after ~27 μs of folding. A concomitant decrease in the population of this state and an increase in the population of a compact high-FRET (Förster resonance energy transfer) state (efficiency>90%) show that the collapse is barrier limited. Small-angle X-ray scattering (SAXS) measurements over a similar time range show that the radius of gyration under native favoring conditions is comparable to that of the GdnHCl denatured unfolded state. An independent comprehensive global thermodynamic analysis reveals that marginally stable partially folded structures are also present in the nominally unfolded GdnHCl denatured state. These observations suggest that specifically collapsed intermediate structures with low stability in rapid equilibrium with the unfolded state may contribute to the apparent chain contraction observed in previous fluorescence studies using steady-state detection. In the absence of significant dynamic averaging of marginally stable partially folded states and with the use of probes sensitive to distance distributions, barrier-limited chain contraction is observed upon transfer of the GdnHCl denatured state ensemble to native-like conditions.

Transcriptome studies have revealed that many eukaryotic genomes are pervasively transcribed producing numerous long non-coding RNAs (lncRNAs). However, only a few lncRNAs have been ascribed a cellular role thus far, with most regulating the expression of adjacent genes. Even less lncRNAs have been annotated as essential hence implying that the majority may be functionally redundant. Therefore, the function of lncRNAs could be illuminated through systematic analysis of their synthetic genetic interactions (GIs).

Establishment of cell polarity--or symmetry breaking--relies on local accumulation of polarity regulators. Although simple positive feedback is sufficient to drive symmetry breaking, it is highly sensitive to stochastic fluctuations typical for living cells. Here, by integrating mathematical modelling with quantitative experimental validations, we show that in the yeast Saccharomyces cerevisiae a combination of actin- and guanine nucleotide dissociation inhibitor-dependent recycling of the central polarity regulator Cdc42 is needed to establish robust cell polarity at a single site during yeast budding. The guanine nucleotide dissociation inhibitor pathway consistently generates a single-polarization site, but requires Cdc42 to cycle rapidly between its active and inactive form, and is therefore sensitive to perturbations of the GTPase cycle. Conversely, actin-mediated recycling of Cdc42 induces robust symmetry breaking but cannot restrict polarization to a single site. Our results demonstrate how cells optimize symmetry breaking through coupling between multiple feedback loops.

Farnesyltransferase inhibitors (FTIs) are anticancer agents with a spectrum of activity in Ras-dependent and independent tumor cellular and xenograph models. How inhibition of protein farnesylation by FTIs results in reduced cancer cell proliferation is poorly understood due to the multiplicity of potential FTase targets. The low toxicity and oral availability of FTIs led to their introduction into clinical trials for the treatment of breast cancer, hematopoietic malignancy, advanced solid tumor and pancreatic cancer treatment, and Hutchinson-Gilford Progeria Syndrome. Although their efficacy in combinatorial therapies with conventional anticancer treatment for myeloid malignancy and solid tumors is promising, the overall results of clinical tests are far below expectations. Further exploitation of FTIs in the clinic will strongly rely on understanding how these drugs affect global cellular activity.

We present a strategy for generating and analyzing comprehensive genetic-interaction maps, termed E-MAPs (epistatic miniarray profiles), comprising quantitative measures of aggravating or alleviating interactions between gene pairs. Crucial to the interpretation of E-MAPs is their high-density nature made possible by focusing on logically connected gene subsets and including essential genes. Described here is the analysis of an E-MAP of genes acting in the yeast early secretory pathway. Hierarchical clustering, together with novel analytical strategies and experimental verification, revealed or clarified the role of many proteins involved in extensively studied processes such as sphingolipid metabolism and retention of HDEL proteins. At a broader level, analysis of the E-MAP delineated pathway organization and components of physical complexes and illustrated the interconnection between the various secretory processes. Extension of this strategy to other logically connected gene subsets in yeast and higher eukaryotes should provide critical insights into the functional/organizational principles of biological systems.

Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To "humanize" this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

Genetic, epidemiological and experimental evidence implicate lysosomal dysfunction in Parkinson's disease (PD) and related synucleinopathies. Investigate several mouse models of lysosomal storage diseases (LSDs) and evaluate pathologies reminiscent of synucleinopathies. We obtained brain tissue from symptomatic mouse models of Gaucher, Fabry, Sandhoff, Niemann-Pick A (NPA), Hurler, Pompe and Niemann-Pick C (NPC) diseases and assessed for the presence of Lewy body-like pathology (proteinase K-resistant α-synuclein and tau aggregates) and neuroinflammation (microglial Iba1 and astrocytic GFAP) by immunofluorescence. All seven LSD models exhibited evidence of proteinopathy and/or inflammation in the central nervous system (CNS). However, these phenotypes were divergent. Gaucher and Fabry mouse models displayed proteinase K-resistant α-synuclein and tau aggregates but no neuroinflammation; whereas Sandhoff, NPA and NPC showed marked neuroinflammation and no overt proteinopathy. Pompe disease animals uniquely displayed widespread distribution of tau aggregates accompanied by moderate microglial activation. Hurler mice also demonstrated proteinopathy and microglial activation. The present study demonstrated additional links between LSDs and pathogenic phenotypes that are hallmarks of synucleinopathies. The data suggest that lysosomal dysregulation can contribute to brain region-specific protein aggregation and induce widespread neuroinflammation in the brain. However, only a few LSD models examined exhibited phenotypes consistent with synucleinopathies. While no model can recapitulate the complexity of PD, they can enable the study of specific pathways and mechanisms contributing to disease pathophysiology. The present study provides evidence that there are existing, previously unutilized mouse models that can be employed to study pathogenic mechanisms and gain insights into potential PD subtypes, helping to determine if they are amenable to pathway-specific therapeutic interventions.

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene-pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria-associated signal within co-essentiality networks derived from these data and explore the basis of this signal. Our analysis and time-resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.

Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we exploited the yeast GFP-fusion collection in a pipeline combining automated genetics, high-throughput microscopy, and computational feature analysis. We developed an ensemble of binary classifiers to generate localization data from single-cell measurements and constructed maps of ∼3,000 proteins connected to 16 localization classes. To survey proteome dynamics in response to different chemical and genetic stimuli, we measure proteome-wide abundance and localization and identified changes over time. We analyzed >20 million cells to identify dynamic proteins that redistribute among multiple localizations in hydroxyurea, rapamycin, and in an rpd3Δ background. Because our localization and abundance data are quantitative, they provide the opportunity for many types of comparative studies, single cell analyses, modeling, and prediction. VIDEO ABSTRACT.

Bacterial AvrE-family Type-III effector proteins (T3Es) contribute significantly to the virulence of plant-pathogenic species of Pseudomonas, Pantoea, Ralstonia, Erwinia, Dickeya and Pectobacterium, with hosts ranging from monocots to dicots. However, the mode of action of AvrE-family T3Es remains enigmatic, due in large part to their toxicity when expressed in plant or yeast cells. To search for targets of WtsE, an AvrE-family T3E from the maize pathogen Pantoea stewartii subsp. stewartii, we employed a yeast-two-hybrid screen with non-lethal fragments of WtsE and a synthetic genetic array with full-length WtsE. Together these screens indicate that WtsE targets maize protein phosphatase 2A (PP2A) heterotrimeric enzyme complexes via direct interaction with B' regulatory subunits. AvrE1, another AvrE-family T3E from Pseudomonas syringae pv. tomato strain DC3000 (Pto DC3000), associates with specific PP2A B' subunit proteins from its susceptible host Arabidopsis that are homologous to the maize B' subunits shown to interact with WtsE. Additionally, AvrE1 was observed to associate with the WtsE-interacting maize proteins, indicating that PP2A B' subunits are likely conserved targets of AvrE-family T3Es. Notably, the ability of AvrE1 to promote bacterial growth and/or suppress callose deposition was compromised in Arabidopsis plants with mutations of PP2A genes. Also, chemical inhibition of PP2A activity blocked the virulence activity of both WtsE and AvrE1 in planta. The function of HopM1, a Pto DC3000 T3E that is functionally redundant to AvrE1, was also impaired in specific PP2A mutant lines, although no direct interaction with B' subunits was observed. These results indicate that sub-component specific PP2A complexes are targeted by bacterial T3Es, including direct targeting by members of the widely conserved AvrE-family.

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.