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Hajiaghabozorgi M, Fischbach M, Albrecht M, Wang W, Myers CL. BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS. Nat Protoc 2024:10.1038/s41596-024-00954-8. [PMID: 38514837 DOI: 10.1038/s41596-024-00954-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/22/2023] [Indexed: 03/23/2024]
Abstract
Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.
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Affiliation(s)
- Mehrad Hajiaghabozorgi
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Mathew Fischbach
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
- Graduate Program in Bioinformatics and Computational Biology (BICB), University of Minnesota, Minneapolis, MN, USA
| | - Michael Albrecht
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
- Graduate Program in Bioinformatics and Computational Biology (BICB), University of Minnesota, Minneapolis, MN, USA.
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2
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Longhurst AD, Wang K, Suresh HG, Ketavarapu M, Ward HN, Jones IR, Narayan V, Hundley FV, Hassan AZ, Boone C, Myers CL, Shen Y, Ramani V, Andrews BJ, Toczyski DP. The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2. bioRxiv 2024:2024.03.18.585604. [PMID: 38562687 PMCID: PMC10983909 DOI: 10.1101/2024.03.18.585604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
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.
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Affiliation(s)
- Adam D Longhurst
- University of California, San Francisco, San Francisco, CA 94158, USA
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kyle Wang
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Harsha Garadi Suresh
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Mythili Ketavarapu
- Gladstone Institute for Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities Minneapolis MN USA
| | - Ian R Jones
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California
| | - Vivek Narayan
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Frances V Hundley
- University of California, San Francisco, San Francisco, CA 94158, USA
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA 02115, USA
| | - Arshia Zernab Hassan
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities Minneapolis MN USA
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities Minneapolis MN USA
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA 02115, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Vijay Ramani
- Gladstone Institute for Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Brenda J Andrews
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - David P Toczyski
- University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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Liu X, Igarashi D, Hillmer RA, Stoddard T, Lu Y, Tsuda K, Myers CL, Katagiri F. Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations. Plant Commun 2024:100882. [PMID: 38486453 DOI: 10.1016/j.xplc.2024.100882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/25/2024] [Accepted: 03/13/2024] [Indexed: 05/02/2024]
Abstract
Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens. Although transcriptome analysis is often used to describe overall immune responses, collection of transcriptome data with sufficient resolution in both space and time is challenging. We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2, which induces effector-triggered immunity in Arabidopsis. Double-peak time-course patterns are prevalent among thousands of upregulated genes. We implemented a multi-compartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene. The decomposed peaks reveal an "echoing" pattern: the peak times of the first and second peaks correlate well across most upregulated genes. We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2. Thus, the peak decomposition has extracted spatial information from the time-course data. The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types. A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity. Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types. We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.
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Affiliation(s)
- Xiaotong Liu
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA
| | - Daisuke Igarashi
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Rachel A Hillmer
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - Thomas Stoddard
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - You Lu
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA
| | - Kenichi Tsuda
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; State Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Hubei Key Lab of Plant Pathology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA
| | - Fumiaki Katagiri
- Department of Plant and Microbial Biology, University of Minnesota - Twin Cities, St Paul, MN 55108, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA.
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Oram MK, Baxley RM, Simon EM, Lin K, Chang YC, Wang L, Myers CL, Bielinsky AK. RNF4 prevents genomic instability caused by chronic DNA under-replication. DNA Repair (Amst) 2024; 135:103646. [PMID: 38340377 PMCID: PMC10948022 DOI: 10.1016/j.dnarep.2024.103646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/26/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
Eukaryotic genome stability is maintained by a complex and diverse set of molecular processes. One class of enzymes that promotes proper DNA repair, replication and cell cycle progression comprises small ubiquitin-like modifier (SUMO)-targeted E3 ligases, or STUbLs. Previously, we reported a role for the budding yeast STUbL synthetically lethal with sgs1 (Slx) 5/8 in preventing G2/M-phase arrest in a minichromosome maintenance protein 10 (Mcm10)-deficient model of replication stress. Here, we extend these studies to human cells, examining the requirement for the human STUbL RING finger protein 4 (RNF4) in MCM10 mutant cancer cells. We find that MCM10 and RNF4 independently promote origin firing but regulate DNA synthesis epistatically and, unlike in yeast, the negative genetic interaction between RNF4 and MCM10 causes cells to accumulate in G1-phase. When MCM10 is deficient, RNF4 prevents excessive DNA under-replication at hard-to-replicate regions that results in large DNA copy number alterations and severely reduced viability. Overall, our findings highlight that STUbLs participate in species-specific mechanisms to maintain genome stability, and that human RNF4 is required for origin activation in the presence of chronic replication stress.
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Affiliation(s)
- Marissa K Oram
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ryan M Baxley
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Emily M Simon
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kevin Lin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ya-Chu Chang
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Liangjun Wang
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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5
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Lin K, Chang YC, Billmann M, Ward HN, Le K, Hassan AZ, Bhojoo U, Chan K, Costanzo M, Moffat J, Boone C, Bielinsky AK, Myers CL. A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds. Sci Rep 2024; 14:2508. [PMID: 38291084 PMCID: PMC10828508 DOI: 10.1038/s41598-024-51735-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/09/2024] [Indexed: 02/01/2024] Open
Abstract
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.
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Affiliation(s)
- Kevin Lin
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Ya-Chu Chang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Khoi Le
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Arshia Z Hassan
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Urvi Bhojoo
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Katherine Chan
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jason Moffat
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
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6
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Hassan AZ, Ward HN, Rahman M, Billmann M, Lee Y, Myers CL. Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens. Mol Syst Biol 2023; 19:e11657. [PMID: 37750448 PMCID: PMC10632734 DOI: 10.15252/msb.202311657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 08/28/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods-autoencoders, robust, and classical principal component analyses (PCA)-for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.
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Affiliation(s)
- Arshia Zernab Hassan
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Mahfuzur Rahman
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Maximilian Billmann
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
- Institute of Human GeneticsUniversity of Bonn, School of Medicine and University Hospital BonnBonnGermany
| | - Yoonkyu Lee
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Chad L Myers
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
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7
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Varland S, Silva RD, Kjosås I, Faustino A, Bogaert A, Billmann M, Boukhatmi H, Kellen B, Costanzo M, Drazic A, Osberg C, Chan K, Zhang X, Tong AHY, Andreazza S, Lee JJ, Nedyalkova L, Ušaj M, Whitworth AJ, Andrews BJ, Moffat J, Myers CL, Gevaert K, Boone C, Martinho RG, Arnesen T. N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity. Nat Commun 2023; 14:6774. [PMID: 37891180 PMCID: PMC10611716 DOI: 10.1038/s41467-023-42342-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
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.
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Affiliation(s)
- Sylvia Varland
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway.
- Department of Biological Sciences, University of Bergen, N-5006, Bergen, Norway.
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.
| | - Rui Duarte Silva
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal.
- Faculdade de Medicina e Ciências Biomédicas, Universidade do Algarve, 8005-139, Faro, Portugal.
| | - Ine Kjosås
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Alexandra Faustino
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal
| | - Annelies Bogaert
- VIB-UGent Center for Medical Biotechnology, B-9052, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, B-9052, Ghent, Belgium
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, D-53127, Bonn, Germany
| | - Hadi Boukhatmi
- Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes 1, CNRS, UMR6290, 35065, Rennes, France
| | - Barbara Kellen
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal
| | - Michael Costanzo
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Adrian Drazic
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Camilla Osberg
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway
| | - Katherine Chan
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Xiang Zhang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
| | - Amy Hin Yan Tong
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Simonetta Andreazza
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Juliette J Lee
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Lyudmila Nedyalkova
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Matej Ušaj
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | | | - Brenda J Andrews
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Jason Moffat
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto, ON, M5G 1×8, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, 55455, USA
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, B-9052, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, B-9052, Ghent, Belgium
| | - Charles Boone
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- RIKEN Centre for Sustainable Resource Science, Wako, Saitama, 351-0106, Japan
| | - Rui Gonçalo Martinho
- Algarve Biomedical Center Research Institute, Universidade do Algarve, 8005-139, Faro, Portugal.
- Departmento de Ciências Médicas, Universidade de Aveiro, 3810-193, Aveiro, Portugal.
- iBiMED - Institute of Biomedicine, Universidade de Aveiro, 3810-193, Aveiro, Portugal.
| | - Thomas Arnesen
- Department of Biomedicine, University of Bergen, N-5021, Bergen, Norway.
- Department of Biological Sciences, University of Bergen, N-5006, Bergen, Norway.
- Department of Surgery, Haukeland University Hospital, N-5021, Bergen, Norway.
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8
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Chang YC, Lin K, Baxley RM, Durrett W, Wang L, Stojkova O, Billmann M, Ward H, Myers CL, Bielinsky AK. RNF4 and USP7 cooperate in ubiquitin-regulated steps of DNA replication. Open Biol 2023; 13:230068. [PMID: 37607592 PMCID: PMC10444366 DOI: 10.1098/rsob.230068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/27/2023] [Indexed: 08/24/2023] Open
Abstract
DNA replication requires precise regulation achieved through post-translational modifications, including ubiquitination and SUMOylation. These modifications are linked by the SUMO-targeted E3 ubiquitin ligases (STUbLs). Ring finger protein 4 (RNF4), one of only two mammalian STUbLs, participates in double-strand break repair and resolving DNA-protein cross-links. However, its role in DNA replication has been poorly understood. Using CRISPR/Cas9 genetic screens, we discovered an unexpected dependency of RNF4 mutants on ubiquitin specific peptidase 7 (USP7) for survival in TP53-null retinal pigment epithelial cells. TP53-/-/RNF4-/-/USP7-/- triple knockout (TKO) cells displayed defects in DNA replication that cause genomic instability. These defects were exacerbated by the proteasome inhibitor bortezomib, which limited the nuclear ubiquitin pool. A shortage of free ubiquitin suppressed the ataxia telangiectasia and Rad3-related (ATR)-mediated checkpoint response, leading to increased cell death. In conclusion, RNF4 and USP7 work cooperatively to sustain a functional level of nuclear ubiquitin to maintain the integrity of the genome.
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Affiliation(s)
- Ya-Chu Chang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kevin Lin
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ryan M. Baxley
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wesley Durrett
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Liangjun Wang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Olivera Stojkova
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Henry Ward
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
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9
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Antony ML, Chang D, Noble-Orcutt KE, Kay A, Jensen JL, Mohei H, Myers CL, Sachs K, Sachs Z. CD69 marks a subpopulation of acute myeloid leukemia with enhanced colony forming capacity and a unique signaling activation state. Leuk Lymphoma 2023; 64:1262-1274. [PMID: 37161853 DOI: 10.1080/10428194.2023.2207698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/10/2023] [Accepted: 04/09/2023] [Indexed: 05/11/2023]
Abstract
In acute myeloid leukemia (AML), leukemia stem cells (LSCs) have self-renewal potential and are responsible for relapse. We previously showed that, in Mll-AF9/NRASG12V murine AML, CD69 expression marks an LSC-enriched subpopulation with enhanced in vivo self-renewal capacity. Here, we used CyTOF to define activated signaling pathways in LSC subpopulations in Mll-AF9/NRASG12V AML. Furthermore, we compared the signaling activation states of CD69High and CD36High subsets of primary human AML. The human CD69High subset expresses low levels of Ki67 and high levels of NFκB and pMAPKAPKII. Additionally, the human CD69High AML subset also has enhanced colony-forming capacity. We applied Bayesian network modeling to compare the global signaling network within the human AML subsets. We find that distinct signaling states, distinguished by NFκB and pMAPKAPKII levels, correlate with divergent functional subsets, defined by CD69 and CD36 expression, in human AML. Targeting NFκB with proteasome inhibition diminished colony formation.
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Affiliation(s)
- Marie Lue Antony
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Daniel Chang
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Klara E Noble-Orcutt
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Anna Kay
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jeffrey L Jensen
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hesham Mohei
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Karen Sachs
- Next Generation Analytics, Palo Alto, CA, USA
| | - Zohar Sachs
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
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10
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Billmann M, Ward HN, Aregger M, Costanzo M, Andrews BJ, Boone C, Moffat J, Myers CL. Reproducibility metrics for context-specific CRISPR screens. Cell Syst 2023; 14:418-422.e2. [PMID: 37201508 DOI: 10.1016/j.cels.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 08/17/2022] [Accepted: 04/07/2023] [Indexed: 05/20/2023]
Abstract
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.
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Affiliation(s)
- Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn 53127, Germany.
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
| | - Michael Aregger
- National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA; Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A8, Canada; Program in Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Research and Learning Centre, 686 Bay Street, Toronto, ON M5G0A4, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA.
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11
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Rogers CB, Kram RE, Lin K, Myers CL, Sobeck A, Hendrickson EA, Bielinsky AK. Fanconi anemia-associated chromosomal radial formation is dependent on POLθ-mediated alternative end joining. Cell Rep 2023; 42:112428. [PMID: 37086407 DOI: 10.1016/j.celrep.2023.112428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/25/2023] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Activation of the Fanconi anemia (FA) pathway after treatment with mitomycin C (MMC) is essential for preventing chromosome translocations termed "radials." When replication forks stall at MMC-induced interstrand crosslinks (ICLs), the FA pathway is activated to orchestrate ICL unhooking and repair of the DNA break intermediates. However, in FA-deficient cells, how ICL-associated breaks are resolved in a manner that leads to radials is unclear. Here, we demonstrate that MMC-induced radials are dependent on DNA polymerase theta (POLθ)-mediated alternative end joining (A-EJ). Specifically, we show that radials observed in FANCD2-/- cells are dependent on POLθ and DNA ligase III and occur independently of classical non-homologous end joining. Furthermore, treatment of FANCD2-/- cells with POLθ inhibitors abolishes radials and leads to the accumulation of breaks co-localizing with common fragile sites. Uniformly, these observations implicate A-EJ in radial formation and provide mechanistic insights into the treatment of FA pathway-deficient cancers with POLθ inhibitors.
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Affiliation(s)
- Colette B Rogers
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Rachel E Kram
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kevin Lin
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alexandra Sobeck
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Eric A Hendrickson
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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12
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Nanda S, Jacques MA, Wang W, Myers CL, Yilmaz LS, Walhout AJ. Systems-level transcriptional regulation of Caenorhabditis elegans metabolism. Mol Syst Biol 2023; 19:e11443. [PMID: 36942755 PMCID: PMC10167481 DOI: 10.15252/msb.202211443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
Metabolism is controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated subpathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where subpathways exhibit strong coexpression. Finally, we develop "WormClust," a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.
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Affiliation(s)
- Shivani Nanda
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Marc-Antoine Jacques
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - L Safak Yilmaz
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Albertha Jm Walhout
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
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13
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Zernab Hassan A, Ward HN, Rahman M, Billmann M, Lee Y, Myers CL. Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens. bioRxiv 2023:2023.02.22.529573. [PMID: 36993440 PMCID: PMC10054965 DOI: 10.1101/2023.02.22.529573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods - autoencoders, robust, and classical principal component analyses (PCA) - for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.
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Affiliation(s)
- Arshia Zernab Hassan
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
| | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Yoonkyu Lee
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA
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14
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Diers BW, Specht JE, Graef GL, Song Q, Rainey KM, Ramasubramanian V, Liu X, Myers CL, Stupar RM, An YQC, Beavis WD. Genetic architecture of protein and oil content in soybean seed and meal. Plant Genome 2023; 16:e20308. [PMID: 36744727 DOI: 10.1002/tpg2.20308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/09/2023] [Indexed: 05/10/2023]
Abstract
Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.
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Affiliation(s)
- Brian W Diers
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, USA
| | - George L Graef
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | | | | | - Xiaotong Liu
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA
| | - Robert M Stupar
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA
| | - Yong-Qiang Charles An
- USDA-ARS Plant Genetic Research Unit at Donald Danforth Plant Science Center, St. Louis, MO, USA
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15
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Magtanong L, Mueller GD, Williams KJ, Billmann M, Chan K, Armenta DA, Pope LE, Moffat J, Boone C, Myers CL, Olzmann JA, Bensinger SJ, Dixon SJ. Context-dependent regulation of ferroptosis sensitivity. Cell Chem Biol 2022; 29:1409-1418.e6. [PMID: 35809566 PMCID: PMC9481678 DOI: 10.1016/j.chembiol.2022.06.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 04/11/2022] [Accepted: 06/15/2022] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Leslie Magtanong
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Grace D Mueller
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Kevin J Williams
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; UCLA Lipidomics Laboratory, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Katherine Chan
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - David A Armenta
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Lauren E Pope
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Jason Moffat
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA; Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - James A Olzmann
- Departments of Molecular and Cell Biology and Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Steven J Bensinger
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; UCLA Lipidomics Laboratory, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Scott J Dixon
- Department of Biology, Stanford University, Stanford, CA 94305, USA.
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16
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Hallacli E, Kayatekin C, Nazeen S, Wang XH, Sheinkopf Z, Sathyakumar S, Sarkar S, Jiang X, Dong X, Di Maio R, Wang W, Keeney MT, Felsky D, Sandoe J, Vahdatshoar A, Udeshi ND, Mani DR, Carr SA, Lindquist S, De Jager PL, Bartel DP, Myers CL, Greenamyre JT, Feany MB, Sunyaev SR, Chung CY, Khurana V. The Parkinson's disease protein alpha-synuclein is a modulator of processing bodies and mRNA stability. Cell 2022; 185:2035-2056.e33. [PMID: 35688132 DOI: 10.1016/j.cell.2022.05.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 04/05/2022] [Accepted: 05/06/2022] [Indexed: 12/13/2022]
Abstract
Alpha-synuclein (αS) is a conformationally plastic protein that reversibly binds to cellular membranes. It aggregates and is genetically linked to Parkinson's disease (PD). Here, we show that αS directly modulates processing bodies (P-bodies), membraneless organelles that function in mRNA turnover and storage. The N terminus of αS, but not other synucleins, dictates mutually exclusive binding either to cellular membranes or to P-bodies in the cytosol. αS associates with multiple decapping proteins in close proximity on the Edc4 scaffold. As αS pathologically accumulates, aberrant interaction with Edc4 occurs at the expense of physiologic decapping-module interactions. mRNA decay kinetics within PD-relevant pathways are correspondingly disrupted in PD patient neurons and brain. Genetic modulation of P-body components alters αS toxicity, and human genetic analysis lends support to the disease-relevance of these interactions. Beyond revealing an unexpected aspect of αS function and pathology, our data highlight the versatility of conformationally plastic proteins with high intrinsic disorder.
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Affiliation(s)
- Erinc Hallacli
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Can Kayatekin
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Sumaiya Nazeen
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115
| | - Xiou H Wang
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Zoe Sheinkopf
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Shubhangi Sathyakumar
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Souvarish Sarkar
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Xin Jiang
- Yumanity Therapeutics, Boston, MA 02135, USA
| | - Xianjun Dong
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Genomics and Bioinformatics Hub, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Roberto Di Maio
- Pittsburgh Institute for Neurodegenerative Diseases and Department of Neurology, Pittsburgh, PA 15213, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matthew T Keeney
- Pittsburgh Institute for Neurodegenerative Diseases and Department of Neurology, Pittsburgh, PA 15213, USA
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics and Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada
| | - Jackson Sandoe
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Aazam Vahdatshoar
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | | | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Susan Lindquist
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Cambridge, MA 02142, USA; Department of Biology, MIT, Cambridge, MA 02139, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - David P Bartel
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Cambridge, MA 02142, USA; Department of Biology, MIT, Cambridge, MA 02139, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - J Timothy Greenamyre
- Pittsburgh Institute for Neurodegenerative Diseases and Department of Neurology, Pittsburgh, PA 15213, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115
| | | | - Vikram Khurana
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA.
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17
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Nelson AB, Chow LS, Stagg DB, Gillingham JR, Evans MD, Pan M, Hughey CC, Myers CL, Han X, Crawford PA, Puchalska P. Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners. JCI Insight 2022; 7:158037. [PMID: 35192550 PMCID: PMC9057596 DOI: 10.1172/jci.insight.158037] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/18/2022] [Indexed: 11/23/2022] Open
Abstract
Background Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO2max) and longer-term outcomes, including the development of diabetes and its complications. Methods Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO2max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome. Results NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO2max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO2max. Conclusion These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO2max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance. Trial registration ClinicalTrials.gov, NCT02150889. Funding NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.
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Affiliation(s)
- Alisa B Nelson
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - David B Stagg
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Jacob R Gillingham
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, United States of America
| | - Meixia Pan
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Curtis C Hughey
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, United States of America
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Peter A Crawford
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Patrycja Puchalska
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
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18
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Coyote-Maestas W, Nedrud D, Suma A, He Y, Matreyek KA, Fowler DM, Carnevale V, Myers CL, Schmidt D. Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling. Nat Commun 2021; 12:7114. [PMID: 34880224 PMCID: PMC8654947 DOI: 10.1038/s41467-021-27342-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 11/16/2021] [Indexed: 11/10/2022] Open
Abstract
Protein domains are the basic units of protein structure and function. Comparative analysis of genomes and proteomes showed that domain recombination is a main driver of multidomain protein functional diversification and some of the constraining genomic mechanisms are known. Much less is known about biophysical mechanisms that determine whether protein domains can be combined into viable protein folds. Here, we use massively parallel insertional mutagenesis to determine compatibility of over 300,000 domain recombination variants of the Inward Rectifier K+ channel Kir2.1 with channel surface expression. Our data suggest that genomic and biophysical mechanisms acted in concert to favor gain of large, structured domain at protein termini during ion channel evolution. We use machine learning to build a quantitative biophysical model of domain compatibility in Kir2.1 that allows us to derive rudimentary rules for designing domain insertion variants that fold and traffic to the cell surface. Positional Kir2.1 responses to motif insertion clusters into distinct groups that correspond to contiguous structural regions of the channel with distinct biophysical properties tuned towards providing either folding stability or gating transitions. This suggests that insertional profiling is a high-throughput method to annotate function of ion channel structural regions.
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Affiliation(s)
- Willow Coyote-Maestas
- grid.17635.360000000419368657Department of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, MN 55455 USA
| | - David Nedrud
- grid.17635.360000000419368657Department of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, MN 55455 USA
| | - Antonio Suma
- grid.264727.20000 0001 2248 3398Department of Chemistry, Temple University, Philadelphia, PA 19122 USA
| | - Yungui He
- grid.17635.360000000419368657Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN 55455 USA
| | - Kenneth A. Matreyek
- grid.67105.350000 0001 2164 3847Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA
| | - Douglas M. Fowler
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA 98115 USA ,grid.34477.330000000122986657Department of Bioengineering, University of Washington, Seattle, WA 98115 USA
| | - Vincenzo Carnevale
- grid.264727.20000 0001 2248 3398Department of Chemistry, Temple University, Philadelphia, PA 19122 USA
| | - Chad L. Myers
- grid.17635.360000000419368657Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Daniel Schmidt
- Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, MN, 55455, USA.
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19
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Fu C, Zhang X, Veri AO, Iyer KR, Lash E, Xue A, Yan H, Revie NM, Wong C, Lin ZY, Polvi EJ, Liston SD, VanderSluis B, Hou J, Yashiroda Y, Gingras AC, Boone C, O’Meara TR, O’Meara MJ, Noble S, Robbins N, Myers CL, Cowen LE. Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets. Nat Commun 2021; 12:6497. [PMID: 34764269 PMCID: PMC8586148 DOI: 10.1038/s41467-021-26850-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/22/2021] [Indexed: 02/08/2023] Open
Abstract
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.
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Affiliation(s)
- Ci Fu
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Xiang Zhang
- grid.17635.360000000419368657Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Amanda O. Veri
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Kali R. Iyer
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Emma Lash
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Alice Xue
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Huijuan Yan
- grid.266102.10000 0001 2297 6811Department of Microbiology and Immunology, UCSF School of Medicine, San Francisco, CA 94143 USA
| | - Nicole M. Revie
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Cassandra Wong
- grid.250674.20000 0004 0626 6184Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada
| | - Zhen-Yuan Lin
- grid.250674.20000 0004 0626 6184Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada
| | - Elizabeth J. Polvi
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Sean D. Liston
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Benjamin VanderSluis
- grid.17635.360000000419368657Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Jing Hou
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada ,grid.17063.330000 0001 2157 2938Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Yoko Yashiroda
- grid.509461.fRIKEN Center for Sustainable Resource Science, Wako, Saitama 351-0198 Japan
| | - Anne-Claude Gingras
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada ,grid.250674.20000 0004 0626 6184Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada
| | - Charles Boone
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada ,grid.17063.330000 0001 2157 2938Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1 Canada ,grid.509461.fRIKEN Center for Sustainable Resource Science, Wako, Saitama 351-0198 Japan
| | - Teresa R. O’Meara
- grid.214458.e0000000086837370Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Matthew J. O’Meara
- grid.214458.e0000000086837370Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Suzanne Noble
- grid.266102.10000 0001 2297 6811Department of Microbiology and Immunology, UCSF School of Medicine, San Francisco, CA 94143 USA
| | - Nicole Robbins
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
| | - Chad L. Myers
- grid.17635.360000000419368657Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Leah E. Cowen
- grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1M1 Canada
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20
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Ward HN, Aregger M, Gonatopoulos-Pournatzis T, Billmann M, Ohsumi TK, Brown KR, Blencowe BJ, Moffat J, Myers CL. Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline. Nat Protoc 2021; 16:4766-4798. [PMID: 34508259 PMCID: PMC9084619 DOI: 10.1038/s41596-021-00596-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 06/03/2021] [Indexed: 02/08/2023]
Abstract
The continued improvement of combinatorial CRISPR screening platforms necessitates the development of new computational pipelines for scoring combinatorial screening data. Unlike for single-guide RNA (sgRNA) pooled screening platforms, combinatorial scoring for multiplexed systems is confounded by guide design parameters such as the number of gRNAs per construct, the position of gRNAs along constructs, and additional features that may impact gRNA expression, processing or capture. In this protocol we describe Orthrus, an R package for processing, scoring and analyzing combinatorial CRISPR screening data that addresses these challenges. This protocol walks through the application of Orthrus to previously published combinatorial screening data from the CHyMErA experimental system, a platform we recently developed that pairs Cas9 with Cas12a gRNAs and enables programmed targeting of multiple genomic sites. We demonstrate Orthrus' features for screen quality assessment and two distinct scoring modes for dual guide RNAs (dgRNAs) that target the same gene twice or dgRNAs that target two different genes. Running Orthrus requires basic R programming experience, ~5-10 min of computational time and 15-60 min total.
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Affiliation(s)
- Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Thomas Gonatopoulos-Pournatzis
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- RNA Biology Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | | | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin J Blencowe
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA.
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21
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Van Dyke K, Lutz S, Mekonnen G, Myers CL, Albert FW. Trans-acting genetic variation affects the expression of adjacent genes. Genetics 2021; 217:6126816. [PMID: 33789351 DOI: 10.1093/genetics/iyaa051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/16/2020] [Indexed: 11/13/2022] Open
Abstract
Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.
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Affiliation(s)
- Krisna Van Dyke
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sheila Lutz
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gemechu Mekonnen
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
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22
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van Leeuwen J, Pons C, Tan G, Wang JZ, Hou J, Weile J, Gebbia M, Liang W, Shuteriqi E, Li Z, Lopes M, Ušaj M, Dos Santos Lopes A, van Lieshout N, Myers CL, Roth FP, Aloy P, Andrews BJ, Boone C. Systematic analysis of bypass suppression of essential genes. Mol Syst Biol 2021; 16:e9828. [PMID: 32939983 PMCID: PMC7507402 DOI: 10.15252/msb.20209828] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/15/2022] Open
Abstract
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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Affiliation(s)
- Jolanda van Leeuwen
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Guihong Tan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Jason Zi Wang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jing Hou
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Jochen Weile
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Marinella Gebbia
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Wendy Liang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Ermira Shuteriqi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Zhijian Li
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Maykel Lopes
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland
| | - Matej Ušaj
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Andreia Dos Santos Lopes
- Center for Integrative Genomics, Bâtiment Génopode, University of Lausanne, Lausanne, Switzerland
| | - Natascha van Lieshout
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Frederick P Roth
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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23
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Safizadeh H, Simpkins SW, Nelson J, Li SC, Piotrowski JS, Yoshimura M, Yashiroda Y, Hirano H, Osada H, Yoshida M, Boone C, Myers CL. Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions. J Chem Inf Model 2021; 61:4156-4172. [PMID: 34318674 PMCID: PMC8479812 DOI: 10.1021/acs.jcim.0c00993] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
![]()
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.
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Affiliation(s)
- Hamid Safizadeh
- Department of Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States.,Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Scott W Simpkins
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Justin Nelson
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Sheena C Li
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Jeff S Piotrowski
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Mami Yoshimura
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Hiroyuki Hirano
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Hiroyuki Osada
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan.,Department of Biotechnology and Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo City, Tokyo 113-8654, Japan
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States.,Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
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24
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Nudelman I, Kudrin D, Nudelman G, Deshpande R, Hartmann BM, Kleinstein SH, Myers CL, Sealfon SC, Zaslavsky E. Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses. Front Immunol 2021; 12:691758. [PMID: 34335598 PMCID: PMC8317020 DOI: 10.3389/fimmu.2021.691758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.
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Affiliation(s)
- Irina Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Division of General Internal Medicine, New York University Langone Medical Centre, New York, NY, United States
| | - Daniil Kudrin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Boris M Hartmann
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States.,Program in Biomedical Informatics and Computational Biology, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
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25
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Costanzo M, Hou J, Messier V, Nelson J, Rahman M, VanderSluis B, Wang W, Pons C, Ross C, Ušaj M, San Luis BJ, Shuteriqi E, Koch EN, Aloy P, Myers CL, Boone C, Andrews B. Environmental robustness of the global yeast genetic interaction network. Science 2021; 372:372/6542/eabf8424. [PMID: 33958448 DOI: 10.1126/science.abf8424] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/30/2021] [Indexed: 12/18/2022]
Abstract
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.
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Affiliation(s)
- Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Jing Hou
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Vincent Messier
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Justin Nelson
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Catherine Ross
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Matej Ušaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Emira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca I Estudis Avaçats (ICREA), Barcelona, Spain
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. .,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.,RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Brenda Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
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26
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Rahman M, Billmann M, Costanzo M, Aregger M, Tong AHY, Chan K, Ward HN, Brown KR, Andrews BJ, Boone C, Moffat J, Myers CL. A method for benchmarking genetic screens reveals a predominant mitochondrial bias. Mol Syst Biol 2021; 17:e10013. [PMID: 34018332 PMCID: PMC8138267 DOI: 10.15252/msb.202010013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 12/29/2022] Open
Abstract
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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Affiliation(s)
- Mahfuzur Rahman
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | - Maximilian Billmann
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | | | | | - Amy H Y Tong
- Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | - Henry N Ward
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
| | | | - Brenda J Andrews
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Charles Boone
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Jason Moffat
- Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Chad L Myers
- Department of Computer Science and EngineeringUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
- Bioinformatics and Computational Biology Graduate ProgramUniversity of Minnesota – Twin CitiesMinneapolisMNUSA
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27
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Henningsen EC, Omidvar V, Della Coletta R, Michno JM, Gilbert E, Li F, Miller ME, Myers CL, Gordon SP, Vogel JP, Steffenson BJ, Kianian SF, Hirsch CD, Figueroa M. Identification of Candidate Susceptibility Genes to Puccinia graminis f. sp. tritici in Wheat. Front Plant Sci 2021; 12:657796. [PMID: 33968112 PMCID: PMC8097158 DOI: 10.3389/fpls.2021.657796] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/22/2021] [Indexed: 05/30/2023]
Abstract
Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.
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Affiliation(s)
- Eva C. Henningsen
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Vahid Omidvar
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - Jean-Michel Michno
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, United States
| | - Erin Gilbert
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Feng Li
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Marisa E. Miller
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Chad L. Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, United States
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - John P. Vogel
- Joint Genome Institute, Walnut Creek, CA, United States
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Brian J. Steffenson
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Shahryar F. Kianian
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
- USDA-ARS Cereal Disease Laboratory, St. Paul, MN, United States
| | - Cory D. Hirsch
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, Canberra, ACT, Australia
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28
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Chen D, Xu W, Wang Y, Ye Y, Wang Y, Yu M, Gao J, Wei J, Dong Y, Zhang H, Fu X, Ma K, Wang H, Yang Z, Zhou J, Cheng W, Wang S, Chen J, Grant BD, Myers CL, Shi A, Xia T. Revealing Functional Crosstalk between Distinct Bioprocesses through Reciprocal Functional Tests of Genetically Interacting Genes. Cell Rep 2020; 29:2646-2658.e5. [PMID: 31775035 DOI: 10.1016/j.celrep.2019.10.076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/08/2019] [Accepted: 10/17/2019] [Indexed: 12/30/2022] Open
Abstract
To systematically explore the genes mediating functional crosstalk between metazoan biological processes, we apply comparative genetic interaction (GI) mapping in Saccharomyces cerevisiae and Caenorhabditis elegans to generate an inter-bioprocess network consisting of 178 C. elegans GIs. The GI network spans six annotated biological processes including aging, intracellular transport, microtubule-based processes, cytokinesis, lipid metabolic processes, and anatomical structure development. By proposing a strategy called "reciprocal functional test" for interacting gene pairs, we discover a group of genes that mediate crosstalk between distinct biological processes. In particular, we identify the ribosomal S6 Kinase/RSKS-1, previously characterized as an mTOR (mechanistic target of rapamycin) effector, as a regulator of DAF-2 endosomal recycling transport, which traces a functional correlation between endocytic recycling and aging processes. Together, our results provide an alternative and effective strategy for identifying genes and pathways that mediate crosstalk between bioprocesses with little prior knowledge.
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Affiliation(s)
- Dan Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei Xu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yu Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yongshen Ye
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yue Wang
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Miao Yu
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jinghu Gao
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jielin Wei
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yiming Dong
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Honghua Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xin Fu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ke Ma
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhenrong Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jie Zhou
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenqing Cheng
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shu Wang
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Juan Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Barth D Grant
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08854, USA
| | - Chad L Myers
- Department of Computer Science & Engineering, University of Minnesota-Twin Cities, 200 Union St., Minneapolis MN 55455, USA
| | - Anbing Shi
- Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Institute for Brain Research, Huazhong University of Science and Technology, Wuhan 430030, China; Key Laboratory of Neurological Disease of National Education Ministry, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Tian Xia
- Department of Informatics Engineering, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
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29
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Stroik S, Kurtz K, Lin K, Karachenets S, Myers CL, Bielinsky AK, Hendrickson EA. EXO1 resection at G-quadruplex structures facilitates resolution and replication. Nucleic Acids Res 2020; 48:4960-4975. [PMID: 32232411 PMCID: PMC7229832 DOI: 10.1093/nar/gkaa199] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 02/08/2020] [Accepted: 03/16/2020] [Indexed: 01/25/2023] Open
Abstract
G-quadruplexes represent unique roadblocks to DNA replication, which tends to stall at these secondary structures. Although G-quadruplexes can be found throughout the genome, telomeres, due to their G-richness, are particularly predisposed to forming these structures and thus represent difficult-to-replicate regions. Here, we demonstrate that exonuclease 1 (EXO1) plays a key role in the resolution of, and replication through, telomeric G-quadruplexes. When replication forks encounter G-quadruplexes, EXO1 resects the nascent DNA proximal to these structures to facilitate fork progression and faithful replication. In the absence of EXO1, forks accumulate at stabilized G-quadruplexes and ultimately collapse. These collapsed forks are preferentially repaired via error-prone end joining as depletion of EXO1 diverts repair away from error-free homology-dependent repair. Such aberrant repair leads to increased genomic instability, which is exacerbated at chromosome termini in the form of dysfunction and telomere loss.
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Affiliation(s)
- Susanna Stroik
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel, Hill, NC 27514, USA
| | - Kevin Kurtz
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Kevin Lin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Sergey Karachenets
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anja-Katrin Bielinsky
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Eric A Hendrickson
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
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30
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Kuzmin E, VanderSluis B, Nguyen Ba AN, Wang W, Koch EN, Usaj M, Khmelinskii A, Usaj MM, van Leeuwen J, Kraus O, Tresenrider A, Pryszlak M, Hu MC, Varriano B, Costanzo M, Knop M, Moses A, Myers CL, Andrews BJ, Boone C. Exploring whole-genome duplicate gene retention with complex genetic interaction analysis. Science 2020; 368:eaaz5667. [PMID: 32586993 PMCID: PMC7539174 DOI: 10.1126/science.aaz5667] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 05/06/2020] [Indexed: 12/25/2022]
Abstract
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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Affiliation(s)
- Elena Kuzmin
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Anton Khmelinskii
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
| | | | | | - Oren Kraus
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Amy Tresenrider
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Michael Pryszlak
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ming-Che Hu
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda Varriano
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Knop
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
- Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alan Moses
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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31
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Aregger M, Lawson KA, Billmann M, Costanzo M, Tong AHY, Chan K, Rahman M, Brown KR, Ross C, Usaj M, Nedyalkova L, Sizova O, Habsid A, Pawling J, Lin ZY, Abdouni H, Wong CJ, Weiss A, Mero P, Dennis JW, Gingras AC, Myers CL, Andrews BJ, Boone C, Moffat J. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat Metab 2020; 2:499-513. [PMID: 32694731 PMCID: PMC7566881 DOI: 10.1038/s42255-020-0211-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
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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Affiliation(s)
- Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Urology, Department of Surgery, University of Toronto
- Corresponding authors: , , ,
| | - Maximillian Billmann
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Amy H. Y. Tong
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Katherine Chan
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
| | - Kevin R. Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Catherine Ross
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lucy Nedyalkova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Olga Sizova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Habsid
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Judy Pawling
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Zhen-Yuan Lin
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Hala Abdouni
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Cassandra J. Wong
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Patricia Mero
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - James W. Dennis
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Brenda J. Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
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32
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Michno J, Liu J, Jeffers JR, Stupar RM, Myers CL. Identification of nodulation-related genes in Medicago truncatula using genome-wide association studies and co-expression networks. Plant Direct 2020; 4:e00220. [PMID: 32426691 PMCID: PMC7229696 DOI: 10.1002/pld3.220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 02/04/2020] [Accepted: 02/24/2020] [Indexed: 05/17/2023]
Abstract
Genome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in M. truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.
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Affiliation(s)
- Jean‐Michel Michno
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Junqi Liu
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Joseph R. Jeffers
- Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisMinnesota
| | - Robert M. Stupar
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Chad L. Myers
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisMinnesota
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Gonatopoulos-Pournatzis T, Aregger M, Brown KR, Farhangmehr S, Braunschweig U, Ward HN, Ha KCH, Weiss A, Billmann M, Durbic T, Myers CL, Blencowe BJ, Moffat J. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9-Cas12a platform. Nat Biotechnol 2020; 38:638-648. [PMID: 32249828 DOI: 10.1038/s41587-020-0437-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Accepted: 01/27/2020] [Indexed: 12/11/2022]
Abstract
Systematic mapping of genetic interactions (GIs) and interrogation of the functions of sizable genomic segments in mammalian cells represent important goals of biomedical research. To advance these goals, we present a CRISPR (clustered regularly interspaced short palindromic repeats)-based screening system for combinatorial genetic manipulation that employs coexpression of CRISPR-associated nucleases 9 and 12a (Cas9 and Cas12a) and machine-learning-optimized libraries of hybrid Cas9-Cas12a guide RNAs. This system, named Cas Hybrid for Multiplexed Editing and screening Applications (CHyMErA), outperforms genetic screens using Cas9 or Cas12a editing alone. Application of CHyMErA to the ablation of mammalian paralog gene pairs reveals extensive GIs and uncovers phenotypes normally masked by functional redundancy. Application of CHyMErA in a chemogenetic interaction screen identifies genes that impact cell growth in response to mTOR pathway inhibition. Moreover, by systematically targeting thousands of alternative splicing events, CHyMErA identifies exons underlying human cell line fitness. CHyMErA thus represents an effective screening approach for GI mapping and the functional analysis of sizable genomic regions, such as alternative exons.
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Affiliation(s)
| | - Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Shaghayegh Farhangmehr
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | | | - Henry N Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, USA
| | - Kevin C H Ha
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Tanja Durbic
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, MN, USA.,Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin J Blencowe
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. .,Institute for Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
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Sachs K, Sarver AL, Noble-Orcutt KE, LaRue RS, Antony ML, Chang D, Lee Y, Navis CM, Hillesheim AL, Nykaza IR, Ha NA, Hansen CJ, Karadag FK, Bergerson RJ, Verneris MR, Meredith MM, Schomaker ML, Linden MA, Myers CL, Largaespada DA, Sachs Z. Single-Cell Gene Expression Analyses Reveal Distinct Self-Renewing and Proliferating Subsets in the Leukemia Stem Cell Compartment in Acute Myeloid Leukemia. Cancer Res 2019; 80:458-470. [PMID: 31784425 DOI: 10.1158/0008-5472.can-18-2932] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 05/30/2019] [Accepted: 11/25/2019] [Indexed: 12/12/2022]
Abstract
Standard chemotherapy for acute myeloid leukemia (AML) targets proliferative cells and efficiently induces complete remission; however, many patients relapse and die of their disease. Relapse is caused by leukemia stem cells (LSC), the cells with self-renewal capacity. Self-renewal and proliferation are separate functions in normal hematopoietic stem cells (HSC) in steady-state conditions. If these functions are also separate functions in LSCs, then antiproliferative therapies may fail to target self-renewal, allowing for relapse. We investigated whether proliferation and self-renewal are separate functions in LSCs as they often are in HSCs. Distinct transcriptional profiles within LSCs of Mll-AF9/NRASG12V murine AML were identified using single-cell RNA sequencing. Single-cell qPCR revealed that these genes were also differentially expressed in primary human LSCs and normal human HSPCs. A smaller subset of these genes was upregulated in LSCs relative to HSPCs; this subset of genes constitutes "LSC-specific" genes in human AML. To assess the differences between these profiles, we identified cell surface markers, CD69 and CD36, whose genes were differentially expressed between these profiles. In vivo mouse reconstitution assays resealed that only CD69High LSCs were capable of self-renewal and were poorly proliferative. In contrast, CD36High LSCs were unable to transplant leukemia but were highly proliferative. These data demonstrate that the transcriptional foundations of self-renewal and proliferation are distinct in LSCs as they often are in normal stem cells and suggest that therapeutic strategies that target self-renewal, in addition to proliferation, are critical to prevent relapse and improve survival in AML. SIGNIFICANCE: These findings define and functionally validate a self-renewal gene profile of leukemia stem cells at the single-cell level and demonstrate that self-renewal and proliferation are distinct in AML. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/3/458/F1.large.jpg.
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Affiliation(s)
- Karen Sachs
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Next Generation Analytics, Palo Alto, California
| | - Aaron L Sarver
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Klara E Noble-Orcutt
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Rebecca S LaRue
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Marie Lue Antony
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Daniel Chang
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Yoonkyu Lee
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Connor M Navis
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Alexandria L Hillesheim
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Ian R Nykaza
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Ngoc A Ha
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Conner J Hansen
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Fatma K Karadag
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Rachel J Bergerson
- Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | - Michael R Verneris
- Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | - Matthew M Meredith
- Molecular Lab, Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota
| | - Matthew L Schomaker
- Molecular Lab, Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota
| | - Michael A Linden
- Division of Hematopathology, Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota
| | - David A Largaespada
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota.,Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
| | - Zohar Sachs
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota. .,Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
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35
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Zhou FL, Li SC, Zhu Y, Guo WJ, Shao LJ, Nelson J, Simpkins S, Yang DH, Liu Q, Yashiroda Y, Xu JB, Fan YY, Yue JM, Yoshida M, Xia T, Myers CL, Boone C, Wang MW. Integrating yeast chemical genomics and mammalian cell pathway analysis. Acta Pharmacol Sin 2019; 40:1245-1255. [PMID: 31138898 PMCID: PMC6786357 DOI: 10.1038/s41401-019-0231-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/14/2019] [Indexed: 12/27/2022] Open
Abstract
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.
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Affiliation(s)
- Fu-Lai Zhou
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Sheena C Li
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, 3510198, Japan
| | - Yue Zhu
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wan-Jing Guo
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li-Jun Shao
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Justin Nelson
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota, 55455, USA
| | - Scott Simpkins
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota, 55455, USA
| | - De-Hua Yang
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
| | - Qing Liu
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, 3510198, Japan
| | - Jin-Biao Xu
- The State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yao-Yue Fan
- The State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jian-Min Yue
- The State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, 3510198, Japan
- Department of Biology, The University of Tokyo, Bunkyo-ku, Tokyo, 1138657, Japan
- Collaborative Research for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo, 1138657, Japan
| | - Tian Xia
- Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chad L Myers
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota, 55455, USA.
| | - Charles Boone
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, 3510198, Japan.
- Donnelly Centre and Department of Molecular Genetics, University of Toronto, Ontario, M5S 3E1, Canada.
| | - Ming-Wei Wang
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai, 201203, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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36
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Simpkins SW, Deshpande R, Nelson J, Li SC, Piotrowski JS, Ward HN, Yashiroda Y, Osada H, Yoshida M, Boone C, Myers CL. Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments. Nat Protoc 2019; 14:415-440. [PMID: 30635653 DOI: 10.1038/s41596-018-0099-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
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.
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Affiliation(s)
- Scott W Simpkins
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Justin Nelson
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Sheena C Li
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Jeff S Piotrowski
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.,Yumanity Therapeutics, Cambridge, MA, USA
| | - Henry Neil Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Hiroyuki Osada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Charles Boone
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.,Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA. .,Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
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37
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Coyote-Maestas W, He Y, Myers CL, Schmidt D. Domain insertion permissibility-guided engineering of allostery in ion channels. Nat Commun 2019; 10:290. [PMID: 30655517 PMCID: PMC6336875 DOI: 10.1038/s41467-018-08171-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 12/17/2018] [Indexed: 01/01/2023] Open
Abstract
Allostery is a fundamental principle of protein regulation that remains hard to engineer, particularly in membrane proteins such as ion channels. Here we use human Inward Rectifier K+ Channel Kir2.1 to map site-specific permissibility to the insertion of domains with different biophysical properties. We find that permissibility is best explained by dynamic protein properties, such as conformational flexibility. Several regions in Kir2.1 that are equivalent to those regulated in homologs, such as G-protein-gated inward rectifier K+ channels (GIRK), have differential permissibility; that is, for these sites permissibility depends on the structural properties of the inserted domain. Our data and the well-established link between protein dynamics and allostery led us to propose that differential permissibility is a metric of latent allosteric capacity in Kir2.1. In support of this notion, inserting light-switchable domains into sites with predicted latent allosteric capacity renders Kir2.1 activity sensitive to light. Allostery is a fundamental principle of protein regulation that remains challenging to engineer. Here authors screen human Inward Rectifier K + Channel Kir2.1 for permissibility to domain insertions and propose that differential permissibility is a metric of latent allosteric capacity in Kir2.1.
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Affiliation(s)
- Willow Coyote-Maestas
- Department of Biochemistry, Molecular Biology & Biophysics, University of Minnesota, Minneapolis, 55455, MN, USA
| | - Yungui He
- Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, 55455, MN, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55455, MN, USA
| | - Daniel Schmidt
- Department of Genetics, Cell Biology & Development, University of Minnesota, Minneapolis, 55455, MN, USA.
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38
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Nelson J, Simpkins SW, Safizadeh H, Li SC, Piotrowski JS, Hirano H, Yashiroda Y, Osada H, Yoshida M, Boone C, Myers CL. MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action. Bioinformatics 2018; 34:1251-1252. [PMID: 29206899 DOI: 10.1093/bioinformatics/btx732] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 11/29/2017] [Indexed: 11/13/2022] Open
Abstract
Summary Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation MOSAIC is available at http://mosaic.cs.umn.edu. Contact hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justin Nelson
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Scott W Simpkins
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Hamid Safizadeh
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.,Department of Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Sheena C Li
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan
| | | | - Hiroyuki Hirano
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan
| | - Hiroyuki Osada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan
| | - Charles Boone
- RIKEN Center for Sustainable Resource Science, Wako, Saitama 3510198, Japan.,University of Toronto, Donnelly Centre, Toronto, Ontario, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA.,Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
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39
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Schaefer RJ, Michno JM, Jeffers J, Hoekenga O, Dilkes B, Baxter I, Myers CL. Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize. Plant Cell 2018; 30:2922-2942. [PMID: 30413654 PMCID: PMC6354270 DOI: 10.1105/tpc.18.00299] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 10/08/2018] [Accepted: 10/31/2018] [Indexed: 05/02/2023]
Abstract
Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.
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Affiliation(s)
- Robert J Schaefer
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jean-Michel Michno
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Joseph Jeffers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Owen Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, New York 14850
| | - Brian Dilkes
- Department of Biochemistry, Purdue University, West Lafayette, Indiana 47907
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- U.S. Department of Agriculture-Agricultural Research Service Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
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40
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Simpkins SW, Nelson J, Deshpande R, Li SC, Piotrowski JS, Wilson EH, Gebre AA, Safizadeh H, Okamoto R, Yoshimura M, Costanzo M, Yashiroda Y, Ohya Y, Osada H, Yoshida M, Boone C, Myers CL. Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. PLoS Comput Biol 2018; 14:e1006532. [PMID: 30376562 PMCID: PMC6226211 DOI: 10.1371/journal.pcbi.1006532] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 11/09/2018] [Accepted: 09/26/2018] [Indexed: 02/01/2023] Open
Abstract
Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes. Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.
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Affiliation(s)
- Scott W. Simpkins
- University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, United States of America
| | - Justin Nelson
- University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, United States of America
| | - Raamesh Deshpande
- University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, United States of America
| | - Sheena C. Li
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | | | - Erin H. Wilson
- University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, United States of America
| | - Abraham A. Gebre
- University of Tokyo, Department of Integrated Biosciences, Graduate School of Frontier Sciences, Kashiwa, Chiba, Japan
| | - Hamid Safizadeh
- University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, United States of America
- University of Minnesota, Department of Electrical and Computer Engineering, Minneapolis, Minnesota, United States of America
| | - Reika Okamoto
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Mami Yoshimura
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Michael Costanzo
- University of Toronto, Donnelly Centre, Toronto, Ontario, Canada
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Yoshikazu Ohya
- University of Tokyo, Department of Integrated Biosciences, Graduate School of Frontier Sciences, Kashiwa, Chiba, Japan
| | - Hiroyuki Osada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Charles Boone
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
- University of Toronto, Donnelly Centre, Toronto, Ontario, Canada
- * E-mail: (CB); (CLM)
| | - Chad L. Myers
- University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, United States of America
- University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, United States of America
- * E-mail: (CB); (CLM)
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VanderSluis B, Costanzo M, Billmann M, Ward HN, Myers CL, Andrews BJ, Boone C. Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell. Curr Opin Microbiol 2018; 45:170-179. [PMID: 30059827 DOI: 10.1016/j.mib.2018.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 06/26/2018] [Accepted: 06/27/2018] [Indexed: 01/01/2023]
Abstract
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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Affiliation(s)
- Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Henry N Ward
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
| | - Brenda J Andrews
- 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.
| | - Charles Boone
- 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
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42
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Raju R, Chau D, Notelaers T, Myers CL, Verfaillie CM, Hu WS. In Vitro Pluripotent Stem Cell Differentiation to Hepatocyte Ceases Further Maturation at an Equivalent Stage of E15 in Mouse Embryonic Liver Development. Stem Cells Dev 2018; 27:910-921. [PMID: 29851366 DOI: 10.1089/scd.2017.0270] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Hepatocyte-like cells (HLCs) can be derived from pluripotent stem cells (PSCs) by sequential treatment of chemical cues to mimic the microenvironment of embryonic liver development. However, these HLCs do not reach the full maturity level of primary hepatocytes. In this study, we carried out a meta-analysis of cross-species transcriptome data of in vitro differentiation of human PSCs to HLCs and in vivo mouse embryonic liver development to identify the developmental stage at which HLC maturation was blocked at. Systematic variations were found associated with the data source and removed by batch correction. Using principal component analysis, HLCs from different stages of differentiation were aligned with mouse embryonic liver development chronologically. A "unified developmental time" (DT) scale was developed after aligning in vitro HLC differentiation and in vivo embryonic liver development. HLCs were found to cease further maturation at an equivalent stage of mouse embryonic day (E)13-15. Genes with discordant time dynamics were identified by aligning in vivo and in vitro data set onto a common DT scale. These genes may be targets of genetic intervention for enhancing the maturity of PSC-derived HLCs.
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Affiliation(s)
- Ravali Raju
- 1 Department of Chemical Engineering and Materials Science, University of Minnesota , Minneapolis, Minnesota.,2 Stem Cell Institute, University of Minnesota , Minneapolis, Minnesota
| | - David Chau
- 1 Department of Chemical Engineering and Materials Science, University of Minnesota , Minneapolis, Minnesota.,2 Stem Cell Institute, University of Minnesota , Minneapolis, Minnesota.,3 Department of Biomedical Engineering, University of Minnesota , Minneapolis, Minnesota
| | - Tineke Notelaers
- 4 Department of Development and Regeneration, KU Leuven , Leuven, Belgium .,5 Stem Cell Institute Leuven , KU Leuven, Leuven, Belgium
| | - Chad L Myers
- 6 Department of Computer Science and Engineering, University of Minnesota , Minneapolis, Minnesota
| | - Catherine M Verfaillie
- 4 Department of Development and Regeneration, KU Leuven , Leuven, Belgium .,5 Stem Cell Institute Leuven , KU Leuven, Leuven, Belgium
| | - Wei-Shou Hu
- 1 Department of Chemical Engineering and Materials Science, University of Minnesota , Minneapolis, Minnesota.,2 Stem Cell Institute, University of Minnesota , Minneapolis, Minnesota
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43
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Rizzolo K, Huen J, Kumar A, Phanse S, Vlasblom J, Kakihara Y, Zeineddine HA, Minic Z, Snider J, Wang W, Pons C, Seraphim TV, Boczek EE, Alberti S, Costanzo M, Myers CL, Stagljar I, Boone C, Babu M, Houry WA. Features of the Chaperone Cellular Network Revealed through Systematic Interaction Mapping. Cell Rep 2018; 20:2735-2748. [PMID: 28903051 DOI: 10.1016/j.celrep.2017.08.074] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 07/21/2017] [Accepted: 08/23/2017] [Indexed: 10/18/2022] Open
Abstract
A comprehensive view of molecular chaperone function in the cell was obtained through a systematic global integrative network approach based on physical (protein-protein) and genetic (gene-gene or epistatic) interaction mapping. This allowed us to decipher interactions involving all core chaperones (67) and cochaperones (15) of Saccharomyces cerevisiae. Our analysis revealed the presence of a large chaperone functional supercomplex, which we named the naturally joined (NAJ) chaperone complex, encompassing Hsp40, Hsp70, Hsp90, AAA+, CCT, and small Hsps. We further found that many chaperones interact with proteins that form foci or condensates under stress conditions. Using an in vitro reconstitution approach, we demonstrate condensate formation for the highly conserved AAA+ ATPases Rvb1 and Rvb2, which are part of the R2TP complex that interacts with Hsp90. This expanded view of the chaperone network in the cell clearly demonstrates the distinction between chaperones having broad versus narrow substrate specificities in protein homeostasis.
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Affiliation(s)
- Kamran Rizzolo
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada
| | - Jennifer Huen
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada
| | - Ashwani Kumar
- Department of Computer Science, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sadhna Phanse
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - James Vlasblom
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Yoshito Kakihara
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada
| | | | - Zoran Minic
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Jamie Snider
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Wen Wang
- Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA; Program in Bioinformatics and Computational Biology, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Thiago V Seraphim
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada; Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Edgar Erik Boczek
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Simon Alberti
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Chad L Myers
- Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA; Program in Bioinformatics and Computational Biology, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Igor Stagljar
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada; The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada.
| | - Walid A Houry
- Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada; Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada.
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Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R, Chen Y, Usaj M, Balint A, Mattiazzi Usaj M, van Leeuwen J, Koch EN, Pons C, Dagilis AJ, Pryszlak M, Wang JZY, Hanchard J, Riggi M, Xu K, Heydari H, San Luis BJ, Shuteriqi E, Zhu H, Van Dyk N, Sharifpoor S, Costanzo M, Loewith R, Caudy A, Bolnick D, Brown GW, Andrews BJ, Boone C, Myers CL. Systematic analysis of complex genetic interactions. Science 2018; 360:eaao1729. [PMID: 29674565 PMCID: PMC6215713 DOI: 10.1126/science.aao1729] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 02/23/2018] [Indexed: 12/11/2022]
Abstract
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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Affiliation(s)
- Elena Kuzmin
- 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
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Yiqun Chen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Attila Balint
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Mojca Mattiazzi Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jolanda van Leeuwen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Andrius J Dagilis
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Michael Pryszlak
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jason Zi Yang Wang
- 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
| | - Julia Hanchard
- 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
| | - Margot Riggi
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- Department of Biochemistry, University of Geneva, 1211 Geneva, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Kaicong Xu
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Hamed Heydari
- 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
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Ermira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Hongwei Zhu
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Nydia Van Dyk
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Sara Sharifpoor
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Robbie Loewith
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Amy Caudy
- 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
| | - Daniel Bolnick
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Grant W Brown
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- 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
| | - Charles Boone
- 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
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
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Lu Y, Truman W, Liu X, Bethke G, Zhou M, Myers CL, Katagiri F, Glazebrook J. Different Modes of Negative Regulation of Plant Immunity by Calmodulin-Related Genes. Plant Physiol 2018; 176:3046-3061. [PMID: 29449432 PMCID: PMC5884591 DOI: 10.1104/pp.17.01209] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 02/06/2018] [Indexed: 05/23/2023]
Abstract
Plant immune responses activated through the perception of microbe-associated molecular patterns, leading to pattern-triggered immunity, are tightly regulated. This results in low immune responses in the absence of pathogens and a rapid return to the resting state following an activation event. Here, we show that two CALMODULIN-LIKE genes, CML46 and CML47, negatively regulate salicylic acid accumulation and immunity in Arabidopsis (Arabidopsis thaliana). The double mutant cml46 cml47 is highly resistant to the pathogen Pseudomonas syringae pv maculicola (Pma). The effects of cml46 cml47 on Pma growth are genetically additive to that of cbp60a, a known negative regulator in the CALMODULIN-BINDING PROTEIN60 (CBP60) family. Transcriptome profiling revealed the effects of cbp60a and cml46 cml47 on both common and separate sets of genes, with the majorities of these differentially expressed genes being Pma responsive. CBP60g, a positive regulator of immunity in the CBP60 family, was found to be transcriptionally regulated by CBP60a, CML46, and CML47 Analysis of the flg22-induced mRNA levels of CBP60g in cbp60a and cml46 cml47 revealed that cml46 cml47 plants have higher induced expression while cbp60a plants retain elevated levels longer than wild-type plants. Assays for the effect of flg22 treatment on Pma growth showed that the effect is stronger in cml46 cml47 plants and lasts longer in cbp60a plants. Thus, the expression pattern of CBP60g is reflected in flg22-induced resistance to Pma.
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Affiliation(s)
- You Lu
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
| | - William Truman
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
| | - Xiaotong Liu
- Department of Computer Science and Engineering and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455
- Graduate Program in Bioinformatics and Computational Biology, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455
| | - Gerit Bethke
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
| | - Man Zhou
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
| | - Chad L Myers
- Department of Computer Science and Engineering and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455
| | - Fumiaki Katagiri
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
| | - Jane Glazebrook
- Department of Plant and Microbial Biology and Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108
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Kayatekin C, Amasino A, Gaglia G, Flannick J, Bonner JM, Fanning S, Narayan P, Barrasa MI, Pincus D, Landgraf D, Nelson J, Hesse WR, Costanzo M, Myers CL, Boone C, Florez JC, Lindquist S. Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity. Cell 2018. [PMID: 29526462 DOI: 10.1016/j.cell.2018.02.026] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Aggregates of human islet amyloid polypeptide (IAPP) in the pancreas of patients with type 2 diabetes (T2D) are thought to contribute to β cell dysfunction and death. To understand how IAPP harms cells and how this might be overcome, we created a yeast model of IAPP toxicity. Ste24, an evolutionarily conserved protease that was recently reported to degrade peptides stuck within the translocon between the cytoplasm and the endoplasmic reticulum, was the strongest suppressor of IAPP toxicity. By testing variants of the human homolog, ZMPSTE24, with varying activity levels, the rescue of IAPP toxicity proved to be directly proportional to the declogging efficiency. Clinically relevant ZMPSTE24 variants identified in the largest database of exomes sequences derived from T2D patients were characterized using the yeast model, revealing 14 partial loss-of-function variants, which were enriched among diabetes patients over 2-fold. Thus, clogging of the translocon by IAPP oligomers may contribute to β cell failure.
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Affiliation(s)
- Can Kayatekin
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.
| | - Audra Amasino
- Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Giorgio Gaglia
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Jason Flannick
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Julia M Bonner
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Saranna Fanning
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Priyanka Narayan
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | | | - David Pincus
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Dirk Landgraf
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Justin Nelson
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - William R Hesse
- Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | | | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Diabetes Research Center, Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Susan Lindquist
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Cambridge, MA 02139, USA
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47
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Bottoms S, Dickinson Q, McGee M, Hinchman L, Higbee A, Hebert A, Serate J, Xie D, Zhang Y, Coon JJ, Myers CL, Landick R, Piotrowski JS. Chemical genomic guided engineering of gamma-valerolactone tolerant yeast. Microb Cell Fact 2018; 17:5. [PMID: 29329531 PMCID: PMC5767017 DOI: 10.1186/s12934-017-0848-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 12/14/2017] [Indexed: 11/13/2022] Open
Abstract
Background Gamma valerolactone (GVL) treatment of lignocellulosic bomass is a promising technology for degradation of biomass for biofuel production; however, GVL is toxic to fermentative microbes. Using a combination of chemical genomics with the yeast (Saccharomyces cerevisiae) deletion collection to identify sensitive and resistant mutants, and chemical proteomics to monitor protein abundance in the presence of GVL, we sought to understand the mechanism toxicity and resistance to GVL with the goal of engineering a GVL-tolerant, xylose-fermenting yeast. Results Chemical genomic profiling of GVL predicted that this chemical affects membranes and membrane-bound processes. We show that GVL causes rapid, dose-dependent cell permeability, and is synergistic with ethanol. Chemical genomic profiling of GVL revealed that deletion of the functionally related enzymes Pad1p and Fdc1p, which act together to decarboxylate cinnamic acid and its derivatives to vinyl forms, increases yeast tolerance to GVL. Further, overexpression of Pad1p sensitizes cells to GVL toxicity. To improve GVL tolerance, we deleted PAD1 and FDC1 in a xylose-fermenting yeast strain. The modified strain exhibited increased anaerobic growth, sugar utilization, and ethanol production in synthetic hydrolysate with 1.5% GVL, and under other conditions. Chemical proteomic profiling of the engineered strain revealed that enzymes involved in ergosterol biosynthesis were more abundant in the presence of GVL compared to the background strain. The engineered GVL strain contained greater amounts of ergosterol than the background strain. Conclusions We found that GVL exerts toxicity to yeast by compromising cellular membranes, and that this toxicity is synergistic with ethanol. Deletion of PAD1 and FDC1 conferred GVL resistance to a xylose-fermenting yeast strain by increasing ergosterol accumulation in aerobically grown cells. The GVL-tolerant strain fermented sugars in the presence of GVL levels that were inhibitory to the unmodified strain. This strain represents a xylose fermenting yeast specifically tailored to GVL produced hydrolysates. Electronic supplementary material The online version of this article (10.1186/s12934-017-0848-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Scott Bottoms
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA.,Lehrstuhl für Chemie Biogener Rohstoffe, Technische Universität München, Schulgasse 16, 94315, Straubing, Germany
| | - Quinn Dickinson
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA.,School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Mick McGee
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Li Hinchman
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Alan Higbee
- University of Wisconsin Biotechnology Center, Madison, WI, USA
| | - Alex Hebert
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jose Serate
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Dan Xie
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Yaoping Zhang
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Joshua J Coon
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA.,Morgridge Institute for Research, Madison, WI, USA.,Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.,Genome Center of Wisconsin, Madison, WI, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Robert Landick
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA
| | - Jeff S Piotrowski
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1552 University Ave, Madison, WI, USA. .,Yumanity Therapeutics, 790 Memorial Drive, Suite 2C, Cambridge, MA, 02139, USA.
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48
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Adnani N, Chevrette MG, Adibhatla SN, Zhang F, Yu Q, Braun DR, Nelson J, Simpkins SW, McDonald BR, Myers CL, Piotrowski JS, Thompson CJ, Currie CR, Li L, Rajski SR, Bugni TS. Coculture of Marine Invertebrate-Associated Bacteria and Interdisciplinary Technologies Enable Biosynthesis and Discovery of a New Antibiotic, Keyicin. ACS Chem Biol 2017; 12:3093-3102. [PMID: 29121465 DOI: 10.1021/acschembio.7b00688] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Advances in genomics and metabolomics have made clear in recent years that microbial biosynthetic capacities on Earth far exceed previous expectations. This is attributable, in part, to the realization that most microbial natural product (NP) producers harbor biosynthetic machineries not readily amenable to classical laboratory fermentation conditions. Such "cryptic" or dormant biosynthetic gene clusters (BGCs) encode for a vast assortment of potentially new antibiotics and, as such, have become extremely attractive targets for activation under controlled laboratory conditions. We report here that coculturing of a Rhodococcus sp. and a Micromonospora sp. affords keyicin, a new and otherwise unattainable bis-nitroglycosylated anthracycline whose mechanism of action (MOA) appears to deviate from those of other anthracyclines. The structure of keyicin was elucidated using high resolution MS and NMR technologies, as well as detailed molecular modeling studies. Sequencing of the keyicin BGC (within the Micromonospora genome) enabled both structural and genomic comparisons to other anthracycline-producing systems informing efforts to characterize keyicin. The new NP was found to be selectively active against Gram-positive bacteria including both Rhodococcus sp. and Mycobacterium sp. E. coli-based chemical genomics studies revealed that keyicin's MOA, in contrast to many other anthracyclines, does not invoke nucleic acid damage.
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Affiliation(s)
- Navid Adnani
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Marc G. Chevrette
- Department
of Bacteriology, University of Wisconsin, Madison, Wisconsin 53705, United States
- Department
of Genetics, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Srikar N. Adibhatla
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Fan Zhang
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Qing Yu
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Doug R. Braun
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Justin Nelson
- Bioinformatics
and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Scott W. Simpkins
- Bioinformatics
and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Bradon R. McDonald
- Department
of Bacteriology, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Chad L. Myers
- Bioinformatics
and Computational Biology Program, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
- Department
of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | | | | | - Cameron R. Currie
- Department
of Bacteriology, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Scott R. Rajski
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Tim S. Bugni
- Pharmaceutical
Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
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49
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Piotrowski JS, Li SC, Deshpande R, Simpkins SW, Nelson J, Yashiroda Y, Barber JM, Safizadeh H, Wilson E, Okada H, Gebre AA, Kubo K, Torres NP, LeBlanc MA, Andrusiak K, Okamoto R, Yoshimura M, DeRango-Adem E, van Leeuwen J, Shirahige K, Baryshnikova A, Brown GW, Hirano H, Costanzo M, Andrews B, Ohya Y, Osada H, Yoshida M, Myers CL, Boone C. Errata: Functional annotation of chemical libraries across diverse biological processes. Nat Chem Biol 2017; 13:1286. [PMID: 29161244 DOI: 10.1038/nchembio1217-1286b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This corrects the article DOI: 10.1038/nchembio.2436.
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50
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Mechanic LE, Lindström S, Daily KM, Sieberts SK, Amos CI, Chen HS, Cox NJ, Dathe M, Feuer EJ, Guertin MJ, Hoffman J, Liu Y, Moore JH, Myers CL, Ritchie MD, Schildkraut J, Schumacher F, Witte JS, Wang W, Williams SM, Gillanders EM. Up For A Challenge (U4C): Stimulating innovation in breast cancer genetic epidemiology. PLoS Genet 2017; 13:e1006945. [PMID: 28957327 PMCID: PMC5619686 DOI: 10.1371/journal.pgen.1006945] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Leah E. Mechanic
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - Sara Lindström
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, United States of America
| | | | | | - Christopher I. Amos
- Department of Biomedical Data Sciences, Dartmouth College, Lebanon, New Hampshire, United States of America
| | - Huann-Sheng Chen
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Nancy J. Cox
- Division of Genetic Medicine, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Marina Dathe
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Eric J. Feuer
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Michael J. Guertin
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Joshua Hoffman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
| | - Yunxian Liu
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason H. Moore
- Division of Informatics, Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota, United States of America
| | - Marylyn D. Ritchie
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, Pennsylvania, United States of America
- Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Joellen Schildkraut
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Fredrick Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota, United States of America
| | - Scott M. Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | | | | | - Elizabeth M. Gillanders
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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