1
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Lu HJ, Chuang CY, Chen MK, Su CW, Yang WE, Yeh CM, Lai KM, Tang CH, Lin CW, Yang SF. The impact of ALDH7A1 variants in oral cancer development and prognosis. Aging (Albany NY) 2022; 14:4556-4571. [PMID: 35613852 PMCID: PMC9186774 DOI: 10.18632/aging.204099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/19/2022] [Indexed: 11/25/2022]
Abstract
The gene encoding aldehyde dehydrogenase 7 family member A1 (ALDH7A1) has been associated with the development and prognosis in multiple cancers; however, the role of ALDH7A1 polymorphisms in oral cancer remains unknown. For this purpose, the influences of ALDH7A1 rs13182402 and rs12659017 on oral cancer development and prognosis were analyzed. Our resulted showed that ALDH7A1 rs13182402 genotype had less pathologic nodal metastasis among betel quid chewer. ALDH7A1 rs13182402 also corresponded to higher expressions in upper aerodigestive mucosa, whole blood, the musculoskeletal system and oral cancer tissues than did the ALDH7A1 wild type. Furthermore, ALDH7A1 overexpression in oral cancer cells increased in vitro migration, whereas its silencing reduced cell migration. Conversely, ALDH7A1 expression in tumor tissues and in patients with advanced disease was lower than that in normal tissues and in patients with early-stage disease. When the patients were classified into ALDH7A1-high and -low-expression groups, the high-ALDH7A1 group had superior outcomes in progression-free survival than the low-ALDH7A1 group (5-year survival of 58.7% vs. 48.0%, P = 0.048) did. In conclusion, patients with high ALDH7A1 expression might, however, have more favorable prognoses than those with low ALDH7A1 expression have.
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Affiliation(s)
- Hsueh-Ju Lu
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chun-Yi Chuang
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Mu-Kuan Chen
- Department of Otorhinolaryngology-Head and Neck Surgery, Changhua Christian Hospital, Changhua, Taiwan.,Oral Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan.,Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chun-Wen Su
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Wei-En Yang
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chia-Ming Yeh
- Oral Cancer Research Center, Changhua Christian Hospital, Changhua, Taiwan.,Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Kuan-Ming Lai
- Division of Hematology and Oncology, Department of Medicine, Changhua Christian Hospital, Changhua, Taiwan
| | - Chih-Hsin Tang
- School of Medicine, China Medical University, Taichung, Taiwan.,Chinese Medicine Research Center, China Medical University, Taichung, Taiwan.,Department of Medical Laboratory Science and Biotechnology, College of Medical and Health Science, Asia University, Taichung, Taiwan
| | - Chiao-Wen Lin
- Institute of Oral Sciences, Chung Shan Medical University, Taichung, Taiwan.,Department of Dentistry, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Shun-Fa Yang
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan
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2
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Shen Y, Zhou G, Liang C, Tian Z. Omics-based interdisciplinarity is accelerating plant breeding. CURRENT OPINION IN PLANT BIOLOGY 2022; 66:102167. [PMID: 35016139 DOI: 10.1016/j.pbi.2021.102167] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/08/2021] [Accepted: 12/07/2021] [Indexed: 05/09/2023]
Abstract
Plant breeding is one of the oldest and most important activities accompanying human civilization. During the past thousand years, plant breeding has achieved three significant innovations, each of which derives from introgression of new theories or technologies. These innovations have significantly increased the food supply and allowed for population development. However, with population increases and resource shortages, the world is continuously facing the challenge of food security, which calls for next innovation in plant breeding. Recent technological advances in multiple disciplines have boosted the development of omics, which is accelerating plant breeding. Here, we review the recent advances in omics and discuss our understanding of how interdisciplinary researches will prompt new innovations in plant breeding.
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Affiliation(s)
- Yanting Shen
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China; Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China
| | - Guoan Zhou
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Chengzhi Liang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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3
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Gualtieri CT. Genomic Variation, Evolvability, and the Paradox of Mental Illness. Front Psychiatry 2021; 11:593233. [PMID: 33551865 PMCID: PMC7859268 DOI: 10.3389/fpsyt.2020.593233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/27/2020] [Indexed: 12/30/2022] Open
Abstract
Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.
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4
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Sarkar D, Maranas CD. SNPeffect: identifying functional roles of SNPs using metabolic networks. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:512-531. [PMID: 32167625 PMCID: PMC9328443 DOI: 10.1111/tpj.14746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/20/2020] [Indexed: 05/04/2023]
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
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5
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Kemble H, Eisenhauer C, Couce A, Chapron A, Magnan M, Gautier G, Le Nagard H, Nghe P, Tenaillon O. Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway. SCIENCE ADVANCES 2020; 6:eabb2236. [PMID: 32537514 PMCID: PMC7269641 DOI: 10.1126/sciadv.abb2236] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 03/31/2020] [Indexed: 05/31/2023]
Abstract
Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4000 fitness interactions between expression variants of two metabolic genes, starting from various environmentally modulated expression levels. We detect a remarkable variety of interactions dependent on initial expression levels and demonstrate that they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity, and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.
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Affiliation(s)
- Harry Kemble
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
- Laboratory of Biochemistry (LBC), Chimie Biologie et Innovation, ESPCI Paris, PSL University, CNRS, 75005 Paris, France
| | | | - Alejandro Couce
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
- Department of Life Sciences, Imperial College, London SW7 2AZ, UK
| | - Audrey Chapron
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Mélanie Magnan
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Gregory Gautier
- Centre de Recherche sur l'Inflammation, INSERM, UMRS 1149, 75018 Paris, France
- Laboratoire d’Excellence INFLAMEX, Université de Paris, Sorbonne Paris Cité, 75018 Paris, France
| | - Hervé Le Nagard
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Philippe Nghe
- Laboratory of Biochemistry (LBC), Chimie Biologie et Innovation, ESPCI Paris, PSL University, CNRS, 75005 Paris, France
| | - Olivier Tenaillon
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
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6
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Kemble H, Nghe P, Tenaillon O. Recent insights into the genotype-phenotype relationship from massively parallel genetic assays. Evol Appl 2019; 12:1721-1742. [PMID: 31548853 PMCID: PMC6752143 DOI: 10.1111/eva.12846] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/21/2019] [Accepted: 07/02/2019] [Indexed: 12/20/2022] Open
Abstract
With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.
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Affiliation(s)
- Harry Kemble
- Infection, Antimicrobials, Modelling, Evolution, INSERM, Unité Mixte de Recherche 1137Université Paris Diderot, Université Paris NordParisFrance
- École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), UMR CNRS‐ESPCI CBI 8231PSL Research UniversityParis Cedex 05France
| | - Philippe Nghe
- École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), UMR CNRS‐ESPCI CBI 8231PSL Research UniversityParis Cedex 05France
| | - Olivier Tenaillon
- Infection, Antimicrobials, Modelling, Evolution, INSERM, Unité Mixte de Recherche 1137Université Paris Diderot, Université Paris NordParisFrance
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7
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Alzoubi D, Desouki AA, Lercher MJ. Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast. Sci Rep 2019; 9:11837. [PMID: 31413270 PMCID: PMC6694147 DOI: 10.1038/s41598-019-47935-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 07/08/2019] [Indexed: 12/15/2022] Open
Abstract
Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in vivo epistasis, as FBA predicted only a minority of experimentally observed genetic interactions between non-essential metabolic genes in yeast. Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.
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Affiliation(s)
- Deya Alzoubi
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Abdelmoneim Amer Desouki
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany.
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8
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Jaffe M, Dziulko A, Smith JD, St Onge RP, Levy SF, Sherlock G. Improved discovery of genetic interactions using CRISPRiSeq across multiple environments. Genome Res 2019; 29:668-681. [PMID: 30782640 PMCID: PMC6442382 DOI: 10.1101/gr.246603.118] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 02/13/2019] [Indexed: 01/01/2023]
Abstract
Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient-sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.
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Affiliation(s)
- Mia Jaffe
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Adam Dziulko
- Joint Initiative for Metrology in Biology, Stanford, California 94305, USA.,SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| | - Justin D Smith
- Stanford Genome Technology Center, Stanford University, Palo Alto, California 94305, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Robert P St Onge
- Stanford Genome Technology Center, Stanford University, Palo Alto, California 94305, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Sasha F Levy
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA.,Joint Initiative for Metrology in Biology, Stanford, California 94305, USA.,SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.,National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Gavin Sherlock
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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9
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Alzoubi D, Desouki AA, Lercher MJ. Alleles of a gene differ in pleiotropy, often mediated through currency metabolite production, in E. coli and yeast metabolic simulations. Sci Rep 2018; 8:17252. [PMID: 30467356 PMCID: PMC6250661 DOI: 10.1038/s41598-018-35092-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022] Open
Abstract
A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for Escherichia coli and Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes in these fast-growing microbes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same metabolic enzyme-encoding gene with increasingly reduced enzymatic function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts of metabolic genes will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.
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Affiliation(s)
- Deya Alzoubi
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Abdelmoneim Amer Desouki
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Universitätsstraße 1, Düsseldorf, D-40221, Germany.
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10
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Awdeh A, Phenix H, Karn M, Perkins TJ. Dynamics in Epistasis Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:878-891. [PMID: 28092574 DOI: 10.1109/tcbb.2017.2653110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations. Here, we explore the possibility of dynamic epistasis analysis, in which, in addition to performing genetic perturbations of a pathway, we drive the pathway by a time-varying upstream signal. We explore the theoretical power of dynamical epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We find that even relatively simple input dynamics greatly increases the power of epistasis analysis to discriminate alternative network structures. Further, we explore the question of experiment design, and show that a subset of short time-varying signals, which we call dynamic primitives, allow maximum discriminative power with a reduced number of experiments.
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11
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Sánchez BJ, Nielsen J. Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr Biol (Camb) 2016; 7:846-58. [PMID: 26079294 DOI: 10.1039/c5ib00083a] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.
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Affiliation(s)
- Benjamín J Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden.
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12
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Jagdishchandra Joshi C, Prasad A. Epistatic interactions among metabolic genes depend upon environmental conditions. MOLECULAR BIOSYSTEMS 2015; 10:2578-89. [PMID: 25018101 DOI: 10.1039/c4mb00181h] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
When the effect of the state of one gene is dependent on the state of another gene in more than an additive or a neutral way, the phenomenon is termed epistasis. In particular, positive epistasis signifies that the impact of the double deletion is less severe than the neutral combination, while negative epistasis signifies that the double deletion is more severe. Epistatic interactions between genes affect the fitness landscape of an organism in its environment and are believed to be important for the evolution of sex and the evolution of recombination. Here we use large-scale computational metabolic models of microorganisms to study epistasis computationally using Flux Balance Analysis (FBA). We study what the effects of the environment are on epistatic interactions between metabolic genes in three different microorganisms: the model bacterium E. coli, the cyanobacteria Synechocystis PCC6803 and the model green algae, C. reinhardtii. Prior studies have shown that under standard laboratory conditions epistatic interactions between metabolic genes are dominated by positive epistasis. We show here that epistatic interactions depend strongly upon environmental conditions, i.e. the source of carbon, the carbon/oxygen ratio, and for photosynthetic organisms, the intensity of light. By a comparative analysis of flux distributions under different conditions, we show that whether epistatic interactions are positive or negative depends upon the topology of the carbon flow between the reactions affected by the pair of genes being considered. Thus complex metabolic networks can show epistasis even without explicit interactions between genes, and the direction and the scale of epistasis are dependent on network flows. Our results suggest that the path of evolutionary adaptation in fluctuating environments is likely to be very history dependent because of the strong effect of the environment on epistasis.
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13
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Barker B, Xu L, Gu Z. Dynamic epistasis under varying environmental perturbations. PLoS One 2015; 10:e0114911. [PMID: 25625594 PMCID: PMC4308068 DOI: 10.1371/journal.pone.0114911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Accepted: 11/15/2014] [Indexed: 01/17/2023] Open
Abstract
Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes. We explored this issue by applying flux balance analysis to simulate epistatic landscapes under various environmental perturbations. Specifically, we looked at gene-gene epistatic interactions, where the mutations were assumed to occur in different genes. We predicted that epistasis tends to become more positive from glucose-abundant to nutrient-limiting conditions, indicating that selection might be less effective in removing deleterious mutations in the latter. We also observed a stable core of epistatic interactions in all tested conditions, as well as many epistatic interactions unique to each condition. Interestingly, genes in the stable epistatic interaction network are directly linked to most other genes whereas genes with condition-specific epistasis form a scale-free network. Furthermore, genes with stable epistasis tend to have similar evolutionary rates, whereas this co-evolving relationship does not hold for genes with condition-specific epistasis. Our findings provide a novel genome-wide picture about epistatic dynamics under environmental perturbations.
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Affiliation(s)
- Brandon Barker
- Center for Advanced Computing, Cornell University, Ithaca, New York, United States of America
| | - Lin Xu
- Division of Hematology/Oncology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Zhenglong Gu
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America
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14
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Wei WH, Guo Y, Kindt ASD, Merriman TR, Semple CA, Wang K, Haley CS. Abundant local interactions in the 4p16.1 region suggest functional mechanisms underlying SLC2A9 associations with human serum uric acid. Hum Mol Genet 2014; 23:5061-8. [PMID: 24821702 PMCID: PMC4159153 DOI: 10.1093/hmg/ddu227] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Human serum uric acid concentration (SUA) is a complex trait. A recent meta-analysis of multiple genome-wide association studies (GWAS) identified 28 loci associated with SUA jointly explaining only 7.7% of the SUA variance, with 3.4% explained by two major loci (SLC2A9 and ABCG2). Here we examined whether gene-gene interactions had any roles in regulating SUA using two large GWAS cohorts included in the meta-analysis [the Atherosclerosis Risk in Communities study cohort (ARIC) and the Framingham Heart Study cohort (FHS)]. We found abundant genome-wide significant local interactions in ARIC in the 4p16.1 region located mostly in an intergenic area near SLC2A9 that were not driven by linkage disequilibrium and were replicated in FHS. Taking the forward selection approach, we constructed a model of five SNPs with marginal effects and three epistatic SNP pairs in ARIC-three marginal SNPs were located within SLC2A9 and the remaining SNPs were all located in the nearby intergenic area. The full model explained 1.5% more SUA variance than that explained by the lead SNP alone, but only 0.3% was contributed by the marginal and epistatic effects of the SNPs in the intergenic area. Functional analysis revealed strong evidence that the epistatically interacting SNPs in the intergenic area were unusually enriched at enhancers active in ENCODE hepatic (HepG2, P = 4.7E-05) and precursor red blood (K562, P = 5.0E-06) cells, putatively regulating transcription of WDR1 and SLC2A9. These results suggest that exploring epistatic interactions is valuable in uncovering the complex functional mechanisms underlying the 4p16.1 region.
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Affiliation(s)
- Wen-Hua Wei
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK, Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK,
| | - Yunfei Guo
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, USA
| | - Alida S D Kindt
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK
| | - Tony R Merriman
- Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand
| | - Colin A Semple
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK
| | - Kai Wang
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, USA
| | - Chris S Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK
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15
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Grennan KS, Chen C, Gershon ES, Liu C. Molecular network analysis enhances understanding of the biology of mental disorders. Bioessays 2014; 36:606-16. [PMID: 24733456 DOI: 10.1002/bies.201300147] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.
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Affiliation(s)
- Kay S Grennan
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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16
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Shestov AA, Barker B, Gu Z, Locasale JW. Computational approaches for understanding energy metabolism. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2013; 5:733-50. [PMID: 23897661 PMCID: PMC3906216 DOI: 10.1002/wsbm.1238] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism). Here, we discuss several approaches used to quantitatively model metabolic pathways relating to energy metabolism and discuss their formalisms, successes, and limitations.
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Affiliation(s)
| | - Brandon Barker
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Zhenglong Gu
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Jason W Locasale
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
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17
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Velenich A, Gore J. The strength of genetic interactions scales weakly with mutational effects. Genome Biol 2013; 14:R76. [PMID: 23889884 PMCID: PMC4053755 DOI: 10.1186/gb-2013-14-7-r76] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 07/26/2013] [Indexed: 11/18/2022] Open
Abstract
Background Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study, we analyze a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae. Results We discuss a geometric definition of epistasis that reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilized this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis, and discuss the consequences for evolutionary dynamics. Conclusions Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques, it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.
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18
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Heavner BD, Smallbone K, Barker B, Mendes P, Walker LP. Yeast 5 - an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC SYSTEMS BIOLOGY 2012; 6:55. [PMID: 22663945 PMCID: PMC3413506 DOI: 10.1186/1752-0509-6-55] [Citation(s) in RCA: 100] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Accepted: 06/04/2012] [Indexed: 11/18/2022]
Abstract
Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Conclusions Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.
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Affiliation(s)
- Benjamin D Heavner
- Department of Biological & Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
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