PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website ( https://thecellvision.org/pifia/ ), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

Expanding TheCellVision.org: A Central Repository for Visualizing and Mining High-Content Cell Imaging Projects

We previously constructed TheCellVision.org, a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.org housed two high-content screening (HCS) projects providing genome-scale protein abundance and localization information for the budding yeast Saccharomyces cerevisiae, as well as a comprehensive analysis of the morphology of its endocytic compartments upon systematic genetic perturbation of each yeast gene. Here, we report on the expansion of TheCellVision.org by the addition of two new HCS projects and the incorporation of new global functionalities. Specifically, TheCellVision.org now hosts images from the Cell Cycle Omics project, which describes genome-scale cell cycle-resolved dynamics in protein localization, protein concentration, gene expression, and translational efficiency in budding yeast. Moreover, it hosts PIFiA, a computational tool for image-based predictions of protein functional annotations. Across all its projects, TheCellVision.org now houses > 800,000 microscopy images along with computational tools for exploring both the images and their associated datasets. Together with the newly added global functionalities, which include the ability to query genes in any of the hosted projects using either yeast or human gene names, TheCellVision.org provides an expanding resource for single-cell eukaryotic biology.

The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2

Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenomics approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued growth inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in promoting G1 progression.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds

Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screening platform by generating a DNA damage response (DDR)-focused custom sgRNA library targeting 1011 genes with 3033 sgRNAs. We performed five proof-of-principle compound screens and found that the compounds' known modes-of-action (MoA) were enriched among the compounds' CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale with 20-fold fewer resources than commonly used genome-wide libraries and produce biologically informative CGI profiles.

Direct structural analysis of a single acyl carrier protein domain in fatty acid synthase from the fungus Saccharomyces cerevisiae

Acyl carrier protein (ACP) is the work horse of polyketide (PKS) and fatty acid synthases (FAS) and acts as a substrate shuttling domain in these mega enzymes. In fungi, FAS forms a 2.6 MDa symmetric assembly with six identical copies of FAS1 and FAS2 polypeptides. However, ACP spatial distribution is not restricted by symmetry owing to the long and flexible loops that tether the shuttling domain to its corresponding FAS2 polypeptide. This symmetry breaking has hampered experimental investigation of substrate shuttling route in fungal FAS. Here, we develop a protein engineering and expression method to isolate asymmetric fungal FAS proteins containing odd numbers of ACP domains. Electron cryomicroscopy (cryoEM) observation of the engineered complex reveals a non-uniform distribution of the substrate shuttling domain relative to its corresponding FAS2 polypeptide at 2.9 Å resolution. This work lays the methodological foundation for experimental study of ACP shuttling route in fungi.

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity

Most eukaryotic proteins are N-terminally acetylated, but the functional impact on a global scale has remained obscure. Using genome-wide CRISPR knockout screens in human cells, we reveal a strong genetic dependency between a major N-terminal acetyltransferase and specific ubiquitin ligases. Biochemical analyses uncover that both the ubiquitin ligase complex UBR4-KCMF1 and the acetyltransferase NatC recognize proteins bearing an unacetylated N-terminal methionine followed by a hydrophobic residue. NatC KO-induced protein degradation and phenotypes are reversed by UBR knockdown, demonstrating the central cellular role of this interplay. We reveal that loss of Drosophila NatC is associated with male sterility, reduced longevity, and age-dependent loss of motility due to developmental muscle defects. Remarkably, muscle-specific overexpression of UbcE2M, one of the proteins targeted for NatC KO-mediated degradation, suppresses defects of NatC deletion. In conclusion, NatC-mediated N-terminal acetylation acts as a protective mechanism against protein degradation, which is relevant for increased longevity and motility.

Misregulation of cell cycle-dependent methylation of budding yeast CENP-A contributes to chromosomal instability

Centromere (CEN) identity is specified epigenetically by specialized nucleosomes containing evolutionarily conserved CEN-specific histone H3 variant CENP-A (Cse4 in Saccharomyces cerevisiae, CENP-A in humans), which is essential for faithful chromosome segregation. However, the epigenetic mechanisms that regulate Cse4 function have not been fully defined. In this study, we show that cell cycle-dependent methylation of Cse4-R37 regulates kinetochore function and high-fidelity chromosome segregation. We generated a custom antibody that specifically recognizes methylated Cse4-R37 and showed that methylation of Cse4 is cell cycle regulated with maximum levels of methylated Cse4-R37 and its enrichment at the CEN chromatin occur in the mitotic cells. Methyl-mimic cse4-R37F mutant exhibits synthetic lethality with kinetochore mutants, reduced levels of CEN-associated kinetochore proteins and chromosome instability (CIN), suggesting that mimicking the methylation of Cse4-R37 throughout the cell cycle is detrimental to faithful chromosome segregation. Our results showed that SPOUT methyltransferase Upa1 contributes to methylation of Cse4-R37 and overexpression of UPA1 leads to CIN phenotype. In summary, our studies have defined a role for cell cycle-regulated methylation of Cse4 in high-fidelity chromosome segregation and highlight an important role of epigenetic modifications such as methylation of kinetochore proteins in preventing CIN, an important hallmark of human cancers.

Identification of triazenyl indoles as inhibitors of fungal fatty acid biosynthesis with broad-spectrum activity

Rising drug resistance among pathogenic fungi, paired with a limited antifungal arsenal, poses an increasing threat to human health. To identify antifungal compounds, we screened the RIKEN natural product depository against representative isolates of four major human fungal pathogens. This screen identified NPD6433, a triazenyl indole with broad-spectrum activity against all screening strains, as well as the filamentous mold Aspergillus fumigatus. Mechanistic studies indicated that NPD6433 targets the enoyl reductase domain of fatty acid synthase 1 (Fas1), covalently inhibiting its flavin mononucleotide-dependent NADPH-oxidation activity and arresting essential fatty acid biosynthesis. Robust Fas1 inhibition kills Candida albicans, while sublethal inhibition impairs diverse virulence traits. At well-tolerated exposures, NPD6433 extended the lifespan of nematodes infected with azole-resistant C. albicans. Overall, identification of NPD6433 provides a tool with which to explore lipid homeostasis as a therapeutic target in pathogenic fungi and reveals a mechanism by which Fas1 function can be inhibited.

Reproducibility metrics for context-specific CRISPR screens

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background, or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. A record of this paper's transparent peer review process is included in the supplemental information.

K29-linked unanchored polyubiquitin chains disrupt ribosome biogenesis and direct ribosomal proteins to the Intranuclear Quality control compartment (INQ)

Ribosome assembly requires precise coordination between the production and assembly of ribosomal components. Mutations in ribosomal proteins that inhibit the assembly process or ribosome function are often associated with Ribosomopathies, some of which are linked to defects in proteostasis. In this study, we examine the interplay between several yeast proteostasis enzymes, including deubiquitylases (DUBs), Ubp2 and Ubp14, and E3 ligases, Ufd4 and Hul5, and we explore their roles in the regulation of the cellular levels of K29-linked unanchored polyubiquitin (polyUb) chains. Accumulating K29-linked unanchored polyUb chains associate with maturing ribosomes to disrupt their assembly, activate the Ribosome assembly stress response (RASTR), and lead to the sequestration of ribosomal proteins at the Intranuclear Quality control compartment (INQ). These findings reveal the physiological relevance of INQ and provide insights into mechanisms of cellular toxicity associated with Ribosomopathies.

Overexpression profiling reveals cellular requirements in the context of genetic backgrounds and environments

Overexpression can help life adapt to stressful environments, making an examination of overexpressed genes valuable for understanding stress tolerance mechanisms. However, a systematic study of genes whose overexpression is functionally adaptive (GOFAs) under stress has yet to be conducted. We developed a new overexpression profiling method and systematically identified GOFAs in Saccharomyces cerevisiae under stress (heat, salt, and oxidative). Our results show that adaptive overexpression compensates for deficiencies and increases fitness under stress, like calcium under salt stress. We also investigated the impact of different genetic backgrounds on GOFAs, which varied among three S. cerevisiae strains reflecting differing calcium and potassium requirements for salt stress tolerance. Our study of a knockout collection also suggested that calcium prevents mitochondrial outbursts under salt stress. Mitochondria-enhancing GOFAs were only adaptive when adequate calcium was available and non-adaptive when calcium was deficient, supporting this idea. Our findings indicate that adaptive overexpression meets the cell's needs for maximizing the organism's adaptive capacity in the given environment and genetic context.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

Context-dependent regulation of ferroptosis sensitivity

Ferroptosis is an important mediator of pathophysiological cell death and an emerging target for cancer therapy. Whether ferroptosis sensitivity is governed by a single regulatory mechanism is unclear. Here, based on the integration of 24 published chemical genetic screens combined with targeted follow-up experimentation, we find that the genetic regulation of ferroptosis sensitivity is highly variable and context-dependent. For example, the lipid metabolic gene acyl-coenzyme A (CoA) synthetase long chain family member 4 (ACSL4) appears far more essential for ferroptosis triggered by direct inhibition of the lipid hydroperoxidase glutathione peroxidase 4 (GPX4) than by cystine deprivation. Despite this, distinct pro-ferroptotic stimuli converge upon a common lethal effector mechanism: accumulation of lipid peroxides at the plasma membrane. These results indicate that distinct genetic mechanisms regulate ferroptosis sensitivity, with implications for the initiation and analysis of this process in vivo.

Phenylboronic Ester-Activated Aryl Iodide-Selective Buchwald-Hartwig-Type Amination toward Bioactivity Assay

In this study, a phenylboronic ester-activated aryl iodide-selective Buchwald-Hartwig-type amination was developed. When the reaction of aryl iodides and aryl/aliphatic amines using Ni(acac)₂ is carried out in the presence of phenylboronic ester, the Buchwald-Hartwig-type amination proceeds smoothly to afford the corresponding amines in high yields. This reaction does not proceed in the absence of phenylboronic ester. A wide variety of aryl iodides can be applied in the presence of aryl chlorides and bromides, which remain intact during the reaction. The mechanistic studies of this reaction suggest that the phenylboronic ester acts as an activator for the amines to form the ″ate complex″. Chemical kinetics studies show that the reaction of aryl iodides, base, and Ni(acac)₂ follows first-order kinetics, while that of amines and phenylboronic ester follows zero-order kinetics. The bioactivity screening of the corresponding products showed that some amination products exhibit antifungal activity.

Genetic background and mistranslation frequency determine the impact of mistranslating tRNASerUGG

Transfer RNA variants increase the frequency of mistranslation, the misincorporation of an amino acid not specified by the “standard” genetic code, to frequencies approaching 10% in yeast and bacteria. Cells cope with these variants by having multiple copies of each tRNA isodecoder and through pathways that deal with proteotoxic stress. In this study, we define the genetic interactions of the gene encoding tRNA^Ser_UGG,G26A, which mistranslates serine at proline codons. Using a collection of yeast temperature-sensitive alleles, we identify negative synthetic genetic interactions between the mistranslating tRNA and 109 alleles representing 91 genes, with nearly half of the genes having roles in RNA processing or protein folding and turnover. By regulating tRNA expression, we then compare the strength of the negative genetic interaction for a subset of identified alleles under differing amounts of mistranslation. The frequency of mistranslation correlated with the impact on cell growth for all strains analyzed; however, there were notable differences in the extent of the synthetic interaction at different frequencies of mistranslation depending on the genetic background. For many of the strains, the extent of the negative interaction with tRNA^Ser_UGG,G26A was proportional to the frequency of mistranslation or only observed at intermediate or high frequencies. For others, the synthetic interaction was approximately equivalent at all frequencies of mistranslation. As humans contain similar mistranslating tRNAs, these results are important when analyzing the impact of tRNA variants on disease, where both the individual’s genetic background and the expression of the mistranslating tRNA variant need to be considered.

CHESS: The future direct geometry spectrometer at the second target station

CHESS, chopper spectrometer examining small samples, is a planned direct geometry neutron chopper spectrometer designed to detect and analyze weak signals intrinsic to small cross sections (e.g., small mass, small magnetic moments, or neutron absorbing materials) in powders, liquids, and crystals. CHESS is optimized to enable transformative investigations of quantum materials, spin liquids, thermoelectrics, battery materials, and liquids. The broad dynamic range of the instrument is also well suited to study relaxation processes and excitations in soft and biological matter. The 15 Hz repetition rate of the Second Target Station at the Spallation Neutron Source enables the use of multiple incident energies within a single source pulse, greatly expanding the information gained in a single measurement. Furthermore, the high flux grants an enhanced capability for polarization analysis. This enables the separation of nuclear from magnetic scattering or coherent from incoherent scattering in hydrogenous materials over a large range of energy and momentum transfer. This paper presents optimizations and technical solutions to address the key requirements envisioned in the science case and the anticipated uses of this instrument.

A concept of a broadband inverted geometry spectrometer for the Second Target Station at the Spallation Neutron Source

BWAVES is an acronym for Broadband Wide-Angle VElocity Selector spectrometer, indicating that a novel WAVES (Wide-Angle VElocity Selector) device will be used to select the velocity/wavelength of the detected neutrons after they are scattered by the sample. We describe a conceptual design of BWAVES, a time-of-flight broadband inverted-geometry neutron spectrometer for the Second Target Station at the Spallation Neutron Source operated by Oak Ridge National Laboratory. Being the first inverted geometry spectrometer where the energy of the detected neutrons can be chosen by a WAVES device mechanically, irrespective of the limitations imposed by the crystal analyzers or filters, BWAVES will feature a uniquely broad, continuous dynamic range of measurable energy transfers, spanning 4.5 decades. This will enable measurements of both vibrational and relaxational excitations within the same, continuous scattering spectra. Novel approaches that are necessary for the implementation of a WAVES device at the BWAVES spectrometer will result in a spectrometer with the design and characteristics much different from those displayed by the neutron spectrometers in existence today.

Retention of duplicated genes in evolution

Gene duplication is a prevalent phenomenon across the tree of life. The processes that lead to the retention of duplicated genes are not well understood. Functional genomics approaches in model organisms, such as yeast, provide useful tools to test the mechanisms underlying retention with functional redundancy and divergence of duplicated genes, including fates associated with neofunctionalization, subfunctionalization, back-up compensation, and dosage amplification. Duplicated genes may also be retained as a consequence of structural and functional entanglement. Advances in human gene editing have enabled the interrogation of duplicated genes in the human genome, providing new tools to evaluate the relative contributions of each of these factors to duplicate gene retention and the evolution of genome structure.

Tubulin isotypes optimize distinct spindle positioning mechanisms during yeast mitosis

Microtubules are dynamic cytoskeleton filaments that are essential for a wide range of cellular processes. They are polymerized from tubulin, a heterodimer of α- and β-subunits. Most eukaryotic organisms express multiple isotypes of α- and β-tubulin, yet their functional relevance in any organism remains largely obscure. The two α-tubulin isotypes in budding yeast, Tub1 and Tub3, are proposed to be functionally interchangeable, yet their individual functions have not been rigorously interrogated. Here, we develop otherwise isogenic yeast strains expressing single tubulin isotypes at levels comparable to total tubulin in WT cells. Using genome-wide screening, we uncover unique interactions between the isotypes and the two major mitotic spindle positioning mechanisms. We further exploit these cells to demonstrate that Tub1 and Tub3 optimize spindle positioning by differentially recruiting key components of the Dyn1- and Kar9-dependent mechanisms, respectively. Our results provide novel mechanistic insights into how tubulin isotypes allow highly conserved microtubules to function in diverse cellular processes.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

The amino acid substitution affects cellular response to mistranslation

Mistranslation, the misincorporation of an amino acid not specified by the "standard" genetic code, occurs in all organisms. tRNA variants that increase mistranslation arise spontaneously and engineered tRNAs can achieve mistranslation frequencies approaching 10% in yeast and bacteria. Interestingly, human genomes contain tRNA variants with the potential to mistranslate. Cells cope with increased mistranslation through multiple mechanisms, though high levels cause proteotoxic stress. The goal of this study was to compare the genetic interactions and the impact on transcriptome and cellular growth of two tRNA variants that mistranslate at a similar frequency but create different amino acid substitutions in Saccharomyces cerevisiae. One tRNA variant inserts alanine at proline codons whereas the other inserts serine for arginine. Both tRNAs decreased growth rate, with the effect being greater for arginine to serine than for proline to alanine. The tRNA that substituted serine for arginine resulted in a heat shock response. In contrast, heat shock response was minimal for proline to alanine substitution. Further demonstrating the significance of the amino acid substitution, transcriptome analysis identified unique up- and down-regulated genes in response to each mistranslating tRNA. Number and extent of negative synthetic genetic interactions also differed depending upon type of mistranslation. Based on the unique responses observed for these mistranslating tRNAs, we predict that the potential of mistranslation to exacerbate diseases caused by proteotoxic stress depends on the tRNA variant. Furthermore, based on their unique transcriptomes and genetic interactions, different naturally occurring mistranslating tRNAs have the potential to negatively influence specific diseases.

Systematic analysis of bypass suppression of essential genes

Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole-genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane-associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line-specific essentiality in the Cancer Dependency Map (DepMap) project.

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical–genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical–genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Timer-based proteomic profiling of the ubiquitin-proteasome system reveals a substrate receptor of the GID ubiquitin ligase

Selective protein degradation by the ubiquitin-proteasome system (UPS) is involved in all cellular processes. However, the substrates and specificity of most UPS components are not well understood. Here we systematically characterized the UPS in Saccharomyces cerevisiae. Using fluorescent timers, we determined how loss of individual UPS components affects yeast proteome turnover, detecting phenotypes for 76% of E2, E3, and deubiquitinating enzymes. We exploit this dataset to gain insights into N-degron pathways, which target proteins carrying N-terminal degradation signals. We implicate Ubr1, an E3 of the Arg/N-degron pathway, in targeting mitochondrial proteins processed by the mitochondrial inner membrane protease. Moreover, we identify Ylr149c/Gid11 as a substrate receptor of the glucose-induced degradation-deficient (GID) complex, an E3 of the Pro/N-degron pathway. Our results suggest that Gid11 recognizes proteins with N-terminal threonines, expanding the specificity of the GID complex. This resource of potential substrates and relationships between UPS components enables exploring functions of selective protein degradation.

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

Environmental robustness of the global yeast genetic interaction network

Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.

Genome-wide Screening of Genes Associated with Momilactone B Sensitivity in the Fission Yeast

Momilactone B is a natural product with dual biological activities, including antimicrobial and allelopathic properties, and plays a major role in plant chemical defense against competitive plants and pathogens. The pharmacological effects of momilactone B on mammalian cells have also been reported. However, little is known about the molecular and cellular mechanisms underlying its broad bioactivity. In this study, the genetic determinants of momilactone B sensitivity in yeast were explored to gain insight into its mode of action. We screened fission yeast mutants resistant to momilactone B from a pooled culture containing genome-wide gene-overexpressing strains in a drug-hypersensitive genetic background. Overexpression of pmd1, bfr1, pap1, arp9, or SPAC9E9.06c conferred resistance to momilactone B. In addition, a drug-hypersensitive, barcoded deletion library was newly constructed and the genes that imparted altered sensitivity to momilactone B upon deletion were identified. Gene Ontology and fission yeast phenotype ontology enrichment analyses predicted the biological pathways related to the mode of action of momilactone B. The validation of predictions revealed that momilactone B induced abnormal phenotypes such as multiseptated cells and disrupted organization of the microtubule structure. This is the first investigation of the mechanism underlying the antifungal activity of momilactone B against yeast. The results and datasets obtained in this study narrow the possible targets of momilactone B and facilitate further studies regarding its mode of action.

A method for benchmarking genetic screens reveals a predominant mitochondrial bias

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.

Reduced gene dosage of histone H4 prevents CENP-A mislocalization and chromosomal instability in Saccharomyces cerevisiae

Mislocalization of the centromeric histone H3 variant (Cse4 in budding yeast, CID in flies, CENP-A in humans) to noncentromeric regions contributes to chromosomal instability (CIN) in yeast, fly, and human cells. Overexpression and mislocalization of CENP-A have been observed in cancers, however, the mechanisms that facilitate the mislocalization of overexpressed CENP-A have not been fully explored. Defects in proteolysis of overexpressed Cse4 (GALCSE4) lead to its mislocalization and synthetic dosage lethality (SDL) in mutants for E3 ubiquitin ligases (Psh1, Slx5, SCFMet30, and SCFCdc4), Doa1, Hir2, and Cdc7. In contrast, defects in sumoylation of overexpressed cse4K215/216/A/R prevent its mislocalization and do not cause SDL in a psh1Δ strain. Here, we used a genome-wide screen to identify factors that facilitate the mislocalization of overexpressed Cse4 by characterizing suppressors of the psh1Δ GALCSE4 SDL. Deletions of histone H4 alleles (HHF1 or HHF2), which were among the most prominent suppressors, also suppress slx5Δ, cdc4-1, doa1Δ, hir2Δ, and cdc7-4 GALCSE4 SDL. Reduced dosage of H4 leads to defects in sumoylation and reduced mislocalization of overexpressed Cse4, which contributes to suppression of CIN when Cse4 is overexpressed. We determined that the hhf1-20, cse4-102, and cse4-111 mutants, which are defective in the Cse4-H4 interaction, also exhibit reduced sumoylation of Cse4 and do not display psh1Δ GALCSE4 SDL. In summary, we have identified genes that contribute to the mislocalization of overexpressed Cse4 and defined a role for the gene dosage of H4 in facilitating Cse4 sumoylation and mislocalization to noncentromeric regions, leading to CIN when Cse4 is overexpressed.

Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis

Complex genetic interactions occur when mutant alleles of multiple genes combine to elicit an unexpected phenotype, which could not be predicted given the expectation based on the combination of phenotypes associated with individual mutant alleles. Trigenic Synthetic Genetic Array (τ-SGA) methodology was developed for the systematic analysis of complex interactions involving combinations of three gene perturbations. With a series of replica pinning steps of the τ-SGA procedure, haploid triple mutants are constructed through automated mating and meiotic recombination. For example, a double-mutant query strain carrying two mutant alleles of interest, such as a deletion allele of a nonessential gene and a conditional temperature-sensitive allele of an essential gene, is crossed to an input array of yeast mutants, such as the diagnostic array set of ~1200 mutants, to generate an output array of triple mutants. The colony-size measurements of the resulting triple mutants are used to estimate cellular fitness and quantify trigenic interactions by incorporating corresponding single- and double-mutant fitness estimates. Trigenic interaction networks can be further analyzed for functional modules using various clustering and enrichment analysis tools. Complex genetic interactions are rich in functional information and provide insight into the genotype-to-phenotype relationship, genome size, and speciation.

Genetic profiling of protein burden and nuclear export overload

Overproduction (op) of proteins triggers cellular defects. One of the consequences of overproduction is the protein burden/cost, which is produced by an overloading of the protein synthesis process. However, the physiology of cells under a protein burden is not well characterized. We performed genetic profiling of protein burden by systematic analysis of genetic interactions between GFP-op, surveying both deletion and temperature-sensitive mutants in budding yeast. We also performed genetic profiling in cells with overproduction of triple-GFP (tGFP), and the nuclear export signal-containing tGFP (NES-tGFP). The mutants specifically interacted with GFP-op were suggestive of unexpected connections between actin-related processes like polarization and the protein burden, which was supported by morphological analysis. The tGFP-op interactions suggested that this protein probe overloads the proteasome, whereas those that interacted with NES-tGFP involved genes encoding components of the nuclear export process, providing a resource for further analysis of the protein burden and nuclear export overload.

Exploring whole-genome duplicate gene retention with complex genetic interaction analysis

Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.

Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism

The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase (FASN), whose product catalyses the formation of long-chain fatty acids. FASN-mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2 and ACACA. Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.

Threats Posed by the Fungal Kingdom to Humans, Wildlife, and Agriculture

The fungal kingdom includes at least 6 million eukaryotic species and is remarkable with respect to its profound impact on global health, biodiversity, ecology, agriculture, manufacturing, and biomedical research. Approximately 625 fungal species have been reported to infect vertebrates, 200 of which can be human associated, either as commensals and members of our microbiome or as pathogens that cause infectious diseases. These organisms pose a growing threat to human health with the global increase in the incidence of invasive fungal infections, prevalence of fungal allergy, and the evolution of fungal pathogens resistant to some or all current classes of antifungals. More broadly, there has been an unprecedented and worldwide emergence of fungal pathogens affecting animal and plant biodiversity. Approximately 8,000 species of fungi and Oomycetes are associated with plant disease. Indeed, across agriculture, such fungal diseases of plants include new devastating epidemics of trees and jeopardize food security worldwide by causing epidemics in staple and commodity crops that feed billions. Further, ingestion of mycotoxins contributes to ill health and causes cancer. Coordinated international research efforts, enhanced technology translation, and greater policy outreach by scientists are needed to more fully understand the biology and drivers that underlie the emergence of fungal diseases and to mitigate against their impacts. Here, we focus on poignant examples of emerging fungal threats in each of three areas: human health, wildlife biodiversity, and food security.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Skp, Cullin, F-box (SCF)-Met30 and SCF-Cdc4-Mediated Proteolysis of CENP-A Prevents Mislocalization of CENP-A for Chromosomal Stability in Budding Yeast

Restricting the localization of the histone H3 variant CENP-A (Cse4 in yeast, CID in flies) to centromeres is essential for faithful chromosome segregation. Mislocalization of CENP-A leads to chromosomal instability (CIN) in yeast, fly and human cells. Overexpression and mislocalization of CENP-A has been observed in many cancers and this correlates with increased invasiveness and poor prognosis. Yet genes that regulate CENP-A levels and localization under physiological conditions have not been defined. In this study we used a genome-wide genetic screen to identify essential genes required for Cse4 homeostasis to prevent its mislocalization for chromosomal stability. We show that two Skp, Cullin, F-box (SCF) ubiquitin ligases with the evolutionarily conserved F-box proteins Met30 and Cdc4 interact and cooperatively regulate proteolysis of endogenous Cse4 and prevent its mislocalization for faithful chromosome segregation under physiological conditions. The interaction of Met30 with Cdc4 is independent of the D domain, which is essential for their homodimerization and ubiquitination of other substrates. The requirement for both Cdc4 and Met30 for ubiquitination is specifc for Cse4; and a common substrate for Cdc4 and Met30 has not previously been described. Met30 is necessary for the interaction between Cdc4 and Cse4, and defects in this interaction lead to stabilization and mislocalization of Cse4, which in turn contributes to CIN. We provide the first direct link between Cse4 mislocalization to defects in kinetochore structure and show that SCF-mediated proteolysis of Cse4 is a major mechanism that prevents stable maintenance of Cse4 at non-centromeric regions, thus ensuring faithful chromosome segregation. In summary, we have identified essential pathways that regulate cellular levels of endogenous Cse4 and shown that proteolysis of Cse4 by SCF-Met30/Cdc4 prevents mislocalization and CIN in unperturbed cells.

Genetic material on each chromosome must be faithfully transmitted to the daughter cell during cell division and chromosomal instability (CIN) results in aneuploidy, a hallmark of cancers. The kinetochore (centromeric DNA and associated proteins) regulates faithful chromosome segregation. Restricting the localization of CENP-A (Cse4 in yeast) to kinetochores is essential for chromosomal stability. Mislocalization of CENP-A contributes to CIN in yeast, fly and human cells and is observed in cancers where it correlates with increased invasiveness and poor prognosis. Hence, identification of pathways that regulate CENP-A levels will help us understand the correlation between CENP-A mislocalization and aneuploidy in cancers. We used a genetic screen to identify essential genes for Cse4 homeostasis and identified a major ubiquitin-dependent pathway where both nuclear F-box proteins, Met30 and Cdc4 of the SCF complex, cooperatively regulate proteolysis of Cse4 to prevent its mislocalization and CIN under physiological conditions. Our studies define a role for SCF-mediated proteolysis of Cse4 as a critical mechanism to ensure faithful chromosome segregation. These studies are significant because mutations in human homologs of Met30 (β-TrCP) and Cdc4 (Fbxw7) have been implicated in cancers, and future studies will determine if SCF-mediated proteolysis of CENP-A prevents its mislocalization for chromosomal stability in human cells.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability

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.

Integrating yeast chemical genomics and mammalian cell pathway analysis

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Genetic interaction networks in cancer cells

The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.