Publications by authors named "Brenda J Andrews"

89 Publications

A decade of G3: Genes|Genomes|Genetics: a unified home for genetics and genomics research.

Authors:
Brenda J Andrews

G3 (Bethesda) 2021 Sep;11(9)

University of Toronto, Toronto, ON M5S 3E1, Canada.

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http://dx.doi.org/10.1093/g3journal/jkab247DOI Listing
September 2021

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

Cell Syst 2021 Jun;12(6):608-621

The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.cels.2021.05.010DOI Listing
June 2021

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

Mol Syst Biol 2021 06;17(6):e10207

Calico Life Sciences LLC, South San Francisco, CA, USA.

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, Z EB42, that gives lower expression than Z EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.
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http://dx.doi.org/10.15252/msb.202110207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182650PMC
June 2021

High-Throughput Imaging of Arrays of Fluorescently Tagged Yeast Mutant Strains.

Methods Mol Biol 2021 ;2304:221-242

The Donnelly Centre, University of Toronto, Toronto, ON, Canada.

We describe a protocol for live-cell high-throughput (HTP) screening of yeast mutant strains carrying fluorescent protein markers for subcellular compartments of choice using automated confocal microscopy. This procedure, which combines HTP genetics and microscopy, results in the acquisition of thousands of images that can be analyzed in a systematic and quantitative way to identify morphology defects in the tagged subcellular compartments. This HTP protocol is readily adapted for screening any combination of markers and can be expanded to different growth conditions or higher order mutant genetic backgrounds.
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http://dx.doi.org/10.1007/978-1-0716-1402-0_12DOI Listing
August 2021

A method for benchmarking genetic screens reveals a predominant mitochondrial bias.

Mol Syst Biol 2021 05;17(5):e10013

Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.

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.
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http://dx.doi.org/10.15252/msb.202010013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138267PMC
May 2021

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

Mol Cell 2021 06 10;81(11):2460-2476.e11. Epub 2021 May 10.

Institute of Molecular Biology (IMB), Mainz, Germany. Electronic address:

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.
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http://dx.doi.org/10.1016/j.molcel.2021.04.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189435PMC
June 2021

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

Methods Mol Biol 2021 ;2212:377-400

The Donnelly Centre, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.1007/978-1-0716-0947-7_23DOI Listing
April 2021

τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.

Nat Protoc 2021 02 18;16(2):1219-1250. Epub 2021 Jan 18.

The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.

Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.
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http://dx.doi.org/10.1038/s41596-020-00456-3DOI Listing
February 2021

Genetic profiling of protein burden and nuclear export overload.

Elife 2020 11 4;9. Epub 2020 Nov 4.

Research Core for Interdisciplinary Sciences, Okayama University, Okayama, Japan.

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.
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http://dx.doi.org/10.7554/eLife.54080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673788PMC
November 2020

Systematic analysis of bypass suppression of essential genes.

Mol Syst Biol 2020 09;16(9):e9828

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.15252/msb.20209828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507402PMC
September 2020

TheCellVision.org: A Database for Visualizing and Mining High-Content Cell Imaging Projects.

G3 (Bethesda) 2020 11 5;10(11):3969-3976. Epub 2020 Nov 5.

The Donnelly Centre, University of Toronto, Ontario, M5S 3E1, Canada

Advances in genome engineering and high throughput imaging technologies have enabled genome-scale screens of single cells for a variety of phenotypes, including subcellular morphology and protein localization. We constructed TheCellVision.org, a freely available and web-accessible image visualization and data browsing tool that serves as a central repository for fluorescence microscopy images and associated quantitative data produced by high-content screening experiments. Currently, TheCellVision.org hosts ∼575,590 images and associated analysis results from two published high-content screening (HCS) projects focused on the budding yeast TheCellVision.org allows users to access, visualize and explore fluorescence microscopy images, and to search, compare, and extract data related to subcellular compartment morphology, protein abundance, and localization. Each dataset can be queried independently or as part of a search across multiple datasets using the advanced search option. The website also hosts computational tools associated with the available datasets, which can be applied to other projects and cell systems, a feature we demonstrate using published images of mammalian cells. Providing access to HCS data through websites such as TheCelllVision.org enables new discovery and independent re-analyses of imaging data.
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http://dx.doi.org/10.1534/g3.120.401570DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642925PMC
November 2020

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

Nat Metab 2020 06 1;2(6):499-513. Epub 2020 Jun 1.

Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.

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.
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http://dx.doi.org/10.1038/s42255-020-0211-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566881PMC
June 2020

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

Science 2020 06;368(6498)

Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

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.
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http://dx.doi.org/10.1126/science.aaz5667DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539174PMC
June 2020

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

Mol Syst Biol 2020 02;16(2):e9243

The Donnelly Centre, University of Toronto, Toronto, ON, Canada.

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.
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http://dx.doi.org/10.15252/msb.20199243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025093PMC
February 2020

Genetic interaction networks in cancer cells.

Curr Opin Genet Dev 2019 02 8;54:64-72. Epub 2019 Apr 8.

Donnelly Centre, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada. Electronic address:

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.
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http://dx.doi.org/10.1016/j.gde.2019.03.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820710PMC
February 2019

Complex modifier landscape underlying genetic background effects.

Proc Natl Acad Sci U S A 2019 03 25;116(11):5045-5054. Epub 2019 Feb 25.

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada;

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 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 and , 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 / conditional essentiality can be caused by variation in two independent modifiers, and , each with roles associated with cellular cysteine physiology. Interestingly, the 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.
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http://dx.doi.org/10.1073/pnas.1820915116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421401PMC
March 2019

Identifying Type III Secreted Effector Function via a Yeast Genomic Screen.

G3 (Bethesda) 2019 02 7;9(2):535-547. Epub 2019 Feb 7.

Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada

Gram-negative bacterial pathogens inject type III secreted effectors (T3SEs) directly into host cells to promote pathogen fitness by manipulating host cellular processes. Despite their crucial role in promoting virulence, relatively few T3SEs have well-characterized enzymatic activities or host targets. This is in part due to functional redundancy within pathogen T3SE repertoires as well as the promiscuity of individual T3SEs that can have multiple host targets. To overcome these challenges, we generated and characterized a collection of yeast strains stably expressing 75 T3SE constructs from the plant pathogen This collection is devised to facilitate heterologous genetic screens in yeast, a non-host organism, to identify T3SEs that target conserved eukaryotic processes. Among 75 T3SEs tested, we identified 16 that inhibited yeast growth on rich media and eight that inhibited growth on stress-inducing media. We utilized Pathogenic Genetic Array (PGA) screens to identify potential host targets of T3SEs. We focused on the acetyltransferase, HopZ1a, which interacts with plant tubulin and alters microtubule networks. To uncover putative HopZ1a host targets, we identified yeast genes with genetic interaction profiles most similar (, congruent) to the PGA profile of HopZ1a and performed a functional enrichment analysis of these HopZ1a-congruent genes. We compared the congruence analyses above to previously described HopZ physical interaction datasets and identified kinesins as potential HopZ1a targets. Finally, we demonstrated that HopZ1a can target kinesins by acetylating the plant kinesins HINKEL and MKRP1, illustrating the utility of our T3SE-expressing yeast library to characterize T3SE functions.
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http://dx.doi.org/10.1534/g3.118.200877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6385969PMC
February 2019

Mapping a diversity of genetic interactions in yeast.

Curr Opin Syst Biol 2017 Dec 12;6:14-21. Epub 2017 Aug 12.

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto ON, Canada M5S 3E1.

Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.
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http://dx.doi.org/10.1016/j.coisb.2017.08.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269142PMC
December 2017

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell.

Curr Opin Microbiol 2018 10 28;45:170-179. Epub 2018 Jul 28.

The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada; RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.
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http://dx.doi.org/10.1016/j.mib.2018.06.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295359PMC
October 2018

Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression.

Trends Genet 2018 08 11;34(8):578-586. Epub 2018 Jun 11.

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. Electronic address:

The phenotypic consequences of a given mutation can vary across individuals. This so-called background effect is widely observed, from mutant fitness of loss-of-function variants in model organisms to variable disease penetrance and expressivity in humans; however, the underlying genetic basis often remains unclear. Taking insights gained from recent large-scale surveys of genetic interaction and suppression analyses in yeast, we propose that the genetic network context for a given mutation may shape its propensity of exhibiting background-dependent phenotypes. We argue that further efforts in systematically mapping the genetic interaction networks beyond yeast will provide not only key insights into the functional properties of genes, but also a better understanding of the background effects and the (un)predictability of traits in a broader context.
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http://dx.doi.org/10.1016/j.tig.2018.05.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6085889PMC
August 2018

Systematic analysis of complex genetic interactions.

Science 2018 Apr;360(6386)

The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.
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http://dx.doi.org/10.1126/science.aao1729DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215713PMC
April 2018

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

Elife 2018 04 5;7. Epub 2018 Apr 5.

Department of Computer Science, University of Toronto, Toronto, Canada.

The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.
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http://dx.doi.org/10.7554/eLife.31872DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935485PMC
April 2018

Reporter-Based Synthetic Genetic Array Analysis: A Functional Genomics Approach for Investigating Transcript or Protein Abundance Using Fluorescent Proteins in Saccharomyces cerevisiae.

Methods Mol Biol 2018 ;1672:613-629

The Donnelly Centre, University of Toronto, 160 College St., Toronto, ON, M5S 3E1, Canada.

Fluorescent reporter genes have long been used to quantify various cell features such as transcript and protein abundance. Here, we describe a method, reporter synthetic genetic array (R-SGA) analysis, which allows for the simultaneous quantification of any fluorescent protein readout in thousands of yeast strains using an automated pipeline. R-SGA combines a fluorescent reporter system with standard SGA analysis and can be used to examine any array-based strain collection available to the yeast community. This protocol describes the R-SGA methodology for screening different arrays of yeast mutants including the deletion collection, a collection of temperature-sensitive strains for the assessment of essential yeast genes and a collection of inducible overexpression strains. We also present an alternative pipeline for the analysis of R-SGA output strains using flow cytometry of cells in liquid culture. Data normalization for both pipelines is discussed.
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http://dx.doi.org/10.1007/978-1-4939-7306-4_40DOI Listing
June 2018

The Candida albicans transcription factor Cas5 couples stress responses, drug resistance and cell cycle regulation.

Nat Commun 2017 09 11;8(1):499. Epub 2017 Sep 11.

Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, M5G 1M1.

The capacity to coordinate environmental sensing with initiation of cellular responses underpins microbial survival and is crucial for virulence and stress responses in microbial pathogens. Here we define circuitry that enables the fungal pathogen Candida albicans to couple cell cycle dynamics with responses to cell wall stress induced by echinocandins, a front-line class of antifungal drugs. We discover that the C. albicans transcription factor Cas5 is crucial for proper cell cycle dynamics and responses to echinocandins, which inhibit β-1,3-glucan synthesis. Cas5 has distinct transcriptional targets under basal and stress conditions, is activated by the phosphatase Glc7, and can regulate the expression of target genes in concert with the transcriptional regulators Swi4 and Swi6. Thus, we illuminate a mechanism of transcriptional control that couples cell wall integrity with cell cycle regulation, and uncover circuitry governing antifungal drug resistance.Cas5 is a transcriptional regulator of responses to cell wall stress in the fungal pathogen Candida albicans. Here, Xie et al. show that Cas5 also modulates cell cycle dynamics and responses to antifungal drugs.
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http://dx.doi.org/10.1038/s41467-017-00547-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593949PMC
September 2017

Taxonomically Restricted Genes with Essential Functions Frequently Play Roles in Chromosome Segregation in and .

G3 (Bethesda) 2017 10 5;7(10):3337-3347. Epub 2017 Oct 5.

The Donnelly Centre, University of Toronto, Ontario M5S 1A8, Canada

Genes encoding essential components of core cellular processes are typically highly conserved across eukaryotes. However, a small proportion of essential genes are highly taxonomically restricted; there appear to be no similar genes outside the genomes of highly related species. What are the functions of these poorly characterized taxonomically restricted genes (TRGs)? Systematic screens in and previously identified yeast or nematode TRGs that are essential for viability and we find that these genes share many molecular features, despite having no significant sequence similarity. Specifically, we find that those TRGs with essential phenotypes have an expression profile more similar to highly conserved genes, they have more protein-protein interactions and more protein disorder. Surprisingly, many TRGs play central roles in chromosome segregation; a core eukaryotic process. We thus find that genes that appear to be highly evolutionarily restricted do not necessarily play roles in species-specific biological functions but frequently play essential roles in core eukaryotic processes.
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http://dx.doi.org/10.1534/g3.117.300193DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5633384PMC
October 2017

Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens.

G3 (Bethesda) 2017 08 7;7(8):2719-2727. Epub 2017 Aug 7.

Donnelly Centre, University of Toronto, Ontario M5S3E1, Canada.

The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.
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http://dx.doi.org/10.1534/g3.117.041277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555476PMC
August 2017

Mechanisms of suppression: The wiring of genetic resilience.

Bioessays 2017 07 5;39(7). Epub 2017 Jun 5.

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.

Recent analysis of genome sequences has identified individuals that are healthy despite carrying severe disease-associated mutations. A possible explanation is that these individuals carry a second genomic perturbation that can compensate for the detrimental effects of the disease allele, a phenomenon referred to as suppression. In model organisms, suppression interactions are generally divided into two classes: genomic suppressors which are secondary mutations in the genome that bypass a mutant phenotype, and dosage suppression interactions in which overexpression of a suppressor gene rescues a mutant phenotype. Here, we describe the general properties of genomic and dosage suppression, with an emphasis on the budding yeast. We propose that suppression interactions between genetic variants are likely relevant for determining the penetrance of human traits. Consequently, an understanding of suppression mechanisms may guide the discovery of protective variants in healthy individuals that carry disease alleles, which could direct the rational design of new therapeutics.
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http://dx.doi.org/10.1002/bies.201700042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5681848PMC
July 2017

Identifying pathogenicity of human variants via paralog-based yeast complementation.

PLoS Genet 2017 May 25;13(5):e1006779. Epub 2017 May 25.

Donnelly Centre, Toronto, Ontario, Canada.

To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation.
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http://dx.doi.org/10.1371/journal.pgen.1006779DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466341PMC
May 2017

Automated analysis of high-content microscopy data with deep learning.

Mol Syst Biol 2017 04 18;13(4):924. Epub 2017 Apr 18.

Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408780PMC
http://dx.doi.org/10.15252/msb.20177551DOI Listing
April 2017

Machine learning and computer vision approaches for phenotypic profiling.

J Cell Biol 2017 Jan 9;216(1):65-71. Epub 2016 Dec 9.

Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.
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http://dx.doi.org/10.1083/jcb.201610026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223612PMC
January 2017
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