1
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Liu M, Zhao Z, Wang C, Sang S, Cui Y, Lv C, Yang X, Zhang N, Xiong K, Chen B, Dong Q, Liu K, Gu Y. Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells. Cancer Lett 2024; 594:216991. [PMID: 38797232 DOI: 10.1016/j.canlet.2024.216991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/20/2024] [Accepted: 05/23/2024] [Indexed: 05/29/2024]
Abstract
Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.
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Affiliation(s)
- Mingyue Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhangxiang Zhao
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, China
| | - Chengyu Wang
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Shaocong Sang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanrui Cui
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Lv
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiuqi Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Nan Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Xiong
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qi Dong
- Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, China
| | - Kaidong Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
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2
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Hsiung CCS, Wilson CM, Sambold NA, Dai R, Chen Q, Teyssier N, Misiukiewicz S, Arab A, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations. Nat Biotechnol 2024:10.1038/s41587-024-02224-0. [PMID: 38760567 DOI: 10.1038/s41587-024-02224-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/28/2024] [Indexed: 05/19/2024]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.
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Affiliation(s)
- C C-S Hsiung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - C M Wilson
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Tetrad Graduate Program, University of California, San Francisco, CA, USA
| | | | - R Dai
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Q Chen
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - N Teyssier
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - S Misiukiewicz
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - A Arab
- Arc Institute, Palo Alto, CA, USA
| | - T O'Loughlin
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - J C Cofsky
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - J Shi
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - L A Gilbert
- Department of Urology, University of California, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Arc Institute, Palo Alto, CA, USA.
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3
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Katagiri F. An averaging model for analysis and interpretation of high-order genetic interactions. PLoS One 2024; 19:e0299525. [PMID: 38598526 PMCID: PMC11006166 DOI: 10.1371/journal.pone.0299525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/13/2024] [Indexed: 04/12/2024] Open
Abstract
While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by active and inactive alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. The currently common approaches have three major drawbacks. First, although it is a long tradition in genetics, modeling the effect of an inactive allele (a null mutant allele) contrasted against that of the active allele (the wild-type allele) is not suitable for mechanistic understanding of multi-gene systems. Second, a commonly-used additive model (ANOVA with interaction) mathematically fails in estimation of interactions among more than two genes when the phenotypic response is not linear. Third, interpretation of higher-order interactions defined by an additive model is not intuitive. I derived an averaging model based on algebraic principles to solve all these problems within the framework of a general linear model. In the averaging model: the effect of the active allele is contrasted against the effect of the inactive allele for easier mechanistic interpretations; there is mathematical stability in estimation of higher-order interactions even when the phenotypic response is not linear; and interpretations of higher-order interactions are intuitive and consistent-interactions are defined as the mean effects of the last active genes added to the system. Thus, the key outcomes of this study are development of the averaging model, which is suitable for analysis of multi-gene systems, and a new, intuitive, and mathematically and interpretationally consistent definition of a genetic interaction, which is central to the averaging model.
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Affiliation(s)
- Fumiaki Katagiri
- Department of Plant and Microbial Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN, United States of America
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4
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Hsiung CC, Wilson CM, Sambold NA, Dai R, Chen Q, Misiukiewicz S, Arab A, Teyssier N, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558350. [PMID: 37781594 PMCID: PMC10541102 DOI: 10.1101/2023.09.18.558350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.
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5
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Ryan CJ, Devakumar LPS, Pettitt SJ, Lord CJ. Complex synthetic lethality in cancer. Nat Genet 2023; 55:2039-2048. [PMID: 38036785 DOI: 10.1038/s41588-023-01557-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/04/2023] [Indexed: 12/02/2023]
Abstract
The concept of synthetic lethality has been widely applied to identify therapeutic targets in cancer, with varying degrees of success. The standard approach normally involves identifying genetic interactions between two genes, a driver and a target. In reality, however, most cancer synthetic lethal effects are likely complex and also polygenic, being influenced by the environment in addition to involving contributions from multiple genes. By acknowledging and delineating this complexity, we describe in this article how the success rate in cancer drug discovery and development could be improved.
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Affiliation(s)
- Colm J Ryan
- Conway Institute and School of Computer Science, University College Dublin, Dublin, Ireland.
| | - Lovely Paul Solomon Devakumar
- The CRUK Gene Function Laboratory and Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
| | - Stephen J Pettitt
- The CRUK Gene Function Laboratory and Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
| | - Christopher J Lord
- The CRUK Gene Function Laboratory and Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
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6
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Durand R, Jalbert-Ross J, Fijarczyk A, Dubé AK, Landry CR. Cross-feeding affects the target of resistance evolution to an antifungal drug. PLoS Genet 2023; 19:e1011002. [PMID: 37856537 PMCID: PMC10617708 DOI: 10.1371/journal.pgen.1011002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/31/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.
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Affiliation(s)
- Romain Durand
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Jordan Jalbert-Ross
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
| | - Anna Fijarczyk
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Alexandre K. Dubé
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Christian R. Landry
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
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Hernandez Hernandez D, Ding L, Murao A, Dahlin LR, Li G, Arnolds KL, Amezola M, Klein A, Mitra A, Mecacci S, Linger JG, Guarnieri MT, Suzuki Y. Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs. ACS Synth Biol 2023; 12:2778-2782. [PMID: 37582217 PMCID: PMC10510714 DOI: 10.1021/acssynbio.3c00183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Indexed: 08/17/2023]
Abstract
Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.
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Affiliation(s)
- Diana Hernandez Hernandez
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
| | - Lin Ding
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
| | - Ayako Murao
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
| | - Lukas R. Dahlin
- National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Gabriella Li
- National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | | | - Melissa Amezola
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
| | - Amit Klein
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
- Department
of Bioengineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Aishwarya Mitra
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
- Department
of Bioengineering, University of California
San Diego, La Jolla, California 92093, United States
| | - Sonia Mecacci
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
| | - Jeffrey G. Linger
- National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | | | - Yo Suzuki
- Synthetic
Biology and Bioenergy Group, J. Craig Venter
Institute, La Jolla, California 92037, United States
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8
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Phenomics approaches to understand genetic networks and gene function in yeast. Biochem Soc Trans 2022; 50:713-721. [PMID: 35285506 PMCID: PMC9162466 DOI: 10.1042/bst20210285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 01/03/2023]
Abstract
Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.
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9
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Lin Z, Xue H, Malakhov MM, Knutson KA, Pan W. Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies. Hum Mol Genet 2022; 31:2462-2470. [PMID: 35043938 PMCID: PMC9307319 DOI: 10.1093/hmg/ddac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 01/21/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate genome-wide association study (GWAS) data with gene expression (GE) data to identify (putative) causal genes for complex traits. There are two stages in TWAS: in Stage 1, a model is built to impute gene expression from genotypes, and in Stage 2, gene-trait association is tested using imputed gene expression. Despite many successes with TWAS, in the current practice, one only assumes a linear relationship between GE and the trait, which however may not hold, leading to loss of power. In this study, we extend the standard TWAS by considering a quadratic effect of GE, in addition to the usual linear effect. We train imputation models for both linear and quadratic gene expression levels in Stage 1, then include both the imputed linear and quadratic expression levels in Stage 2. We applied both the standard TWAS and our approach first to the ADNI gene expression data and the IGAP Alzheimer's disease GWAS summary data, then to the GTEx (V8) gene expression data and the UK Biobank individual-level GWAS data for lipids, followed by validation with different GWAS data, suitable model checking and more robust TWAS methods. In all these applications, the new TWAS approach was able to identify additional genes associated with Alzheimer's disease, LDL and HDL cholesterol levels, suggesting its likely power gains and thus the need to account for potentially nonlinear effects of gene expression on complex traits.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haoran Xue
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mykhaylo M Malakhov
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Katherine A Knutson
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- To whom correspondence should be addressed at: A460 Mayo Building, 420 Delaware St SE, Minneapolis, MN 55455, USA. Tel: (612)626-2705; Fax: (612)626-0660;
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Halder V, McDonnell B, Uthayakumar D, Usher J, Shapiro RS. Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions. FEMS Microbiol Rev 2021; 45:fuaa055. [PMID: 33145589 DOI: 10.1093/femsre/fuaa055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022] Open
Abstract
Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .
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Affiliation(s)
- Viola Halder
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Brianna McDonnell
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Deeva Uthayakumar
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Jane Usher
- Medical Research Council Centre for Medical Mycology, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter EX4 4QD, UK
| | - Rebecca S Shapiro
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
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11
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Grinberg NF, Wallace C. Multi-tissue transcriptome-wide association studies. Genet Epidemiol 2021; 45:324-337. [PMID: 33369784 PMCID: PMC8048510 DOI: 10.1002/gepi.22374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/04/2020] [Accepted: 11/18/2020] [Indexed: 12/20/2022]
Abstract
A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.
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Affiliation(s)
- Nastasiya F. Grinberg
- Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge Institute of Therapeutic Immunology and Infectious DiseaseUniversity of CambridgeCambridgeUK
| | - Chris Wallace
- Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge Institute of Therapeutic Immunology and Infectious DiseaseUniversity of CambridgeCambridgeUK
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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Kryazhimskiy S. Emergence and propagation of epistasis in metabolic networks. eLife 2021; 10:e60200. [PMID: 33527897 PMCID: PMC7924954 DOI: 10.7554/elife.60200] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.
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Affiliation(s)
- Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San DiegoLa JollaUnited States
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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast. Nat Protoc 2021; 16:1219-1250. [PMID: 33462440 PMCID: PMC9127509 DOI: 10.1038/s41596-020-00456-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023]
Abstract
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.
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An oxindole efflux inhibitor potentiates azoles and impairs virulence in the fungal pathogen Candida auris. Nat Commun 2020; 11:6429. [PMID: 33353950 PMCID: PMC7755909 DOI: 10.1038/s41467-020-20183-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022] Open
Abstract
Candida auris is an emerging fungal pathogen that exhibits resistance to multiple drugs, including the most commonly prescribed antifungal, fluconazole. Here, we use a combinatorial screening approach to identify a bis-benzodioxolylindolinone (azoffluxin) that synergizes with fluconazole against C. auris. Azoffluxin enhances fluconazole activity through the inhibition of efflux pump Cdr1, thus increasing intracellular fluconazole levels. This activity is conserved across most C. auris clades, with the exception of clade III. Azoffluxin also inhibits efflux in highly azole-resistant strains of Candida albicans, another human fungal pathogen, increasing their susceptibility to fluconazole. Furthermore, azoffluxin enhances fluconazole activity in mice infected with C. auris, reducing fungal burden. Our findings suggest that pharmacologically targeting Cdr1 in combination with azoles may be an effective strategy to control infection caused by azole-resistant isolates of C. auris. The fungal pathogen Candida auris is resistant to multiple drugs including the common antifungal fluconazole. Here, Iyer et al. identify a compound that potentiates fluconazole activity against C. auris in vitro and in vivo by inhibiting a major efflux pump and thus increasing intracellular fluconazole accumulation.
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Protonophore FCCP provides fitness advantage to PDR-deficient yeast cells. J Bioenerg Biomembr 2020; 52:383-395. [DOI: 10.1007/s10863-020-09849-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 08/06/2020] [Indexed: 01/02/2023]
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