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Kleinschmidt H, Xu C, Bai L. Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation. Chromosoma 2023; 132:167-189. [PMID: 37184694 PMCID: PMC10542970 DOI: 10.1007/s00412-023-00796-5] [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: 02/05/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/16/2023]
Abstract
Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.
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Affiliation(s)
- Holly Kleinschmidt
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Cheng Xu
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Lu Bai
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA.
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA.
- Department of Physics, The Pennsylvania State University, University Park, PA, 16802, USA.
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2
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Navigating the pitfalls of applying machine learning in genomics. Nat Rev Genet 2022; 23:169-181. [PMID: 34837041 DOI: 10.1038/s41576-021-00434-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2021] [Indexed: 11/08/2022]
Abstract
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential.
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Paz I, Argoetti A, Cohen N, Even N, Mandel-Gutfreund Y. RBPmap: A Tool for Mapping and Predicting the Binding Sites of RNA-Binding Proteins Considering the Motif Environment. Methods Mol Biol 2022; 2404:53-65. [PMID: 34694603 DOI: 10.1007/978-1-0716-1851-6_3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via http://rbpmap.technion.ac.il/ and as a stand-alone version for download. RBPmap was designed for mapping and predicting the binding sites of any RBP within a nucleic acid sequence, given the availability of an experimentally defined binding motif of the RBP. The algorithm searches for a sub-sequence that significantly matches the RBP motif, considering the clustering propensity of other weak matches within the motif environment. Here, we present different applications of RBPmap for discovering the involvement of RBPs and their targets in a variety of cellular processes, in health and disease states. Finally, we demonstrate the performance of RBPmap in predicting the binding targets of RBPs in large-scale RNA-binding data, reinforcing the strength of the tool in distinguishing cognate binding sites from weak motifs.
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Affiliation(s)
- Inbal Paz
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
| | - Amir Argoetti
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
| | - Noa Cohen
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Computer Sciences, Technion-Israel Institute of Technology, Haifa, Israel
| | - Niv Even
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Computer Sciences, Technion-Israel Institute of Technology, Haifa, Israel
| | - Yael Mandel-Gutfreund
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
- Department of Computer Sciences, Technion-Israel Institute of Technology, Haifa, Israel.
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John E, Singh KB, Oliver RP, Tan K. Transcription factor control of virulence in phytopathogenic fungi. MOLECULAR PLANT PATHOLOGY 2021; 22:858-881. [PMID: 33973705 PMCID: PMC8232033 DOI: 10.1111/mpp.13056] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 05/12/2023]
Abstract
Plant-pathogenic fungi are a significant threat to economic and food security worldwide. Novel protection strategies are required and therefore it is critical we understand the mechanisms by which these pathogens cause disease. Virulence factors and pathogenicity genes have been identified, but in many cases their roles remain elusive. It is becoming increasingly clear that gene regulation is vital to enable plant infection and transcription factors play an essential role. Efforts to determine their regulatory functions in plant-pathogenic fungi have expanded since the annotation of fungal genomes revealed the ubiquity of transcription factors from a broad range of families. This review establishes the significance of transcription factors as regulatory elements in plant-pathogenic fungi and provides a systematic overview of those that have been functionally characterized. Detailed analysis is provided on regulators from well-characterized families controlling various aspects of fungal metabolism, development, stress tolerance, and the production of virulence factors such as effectors and secondary metabolites. This covers conserved transcription factors with either specialized or nonspecialized roles, as well as recently identified regulators targeting key virulence pathways. Fundamental knowledge of transcription factor regulation in plant-pathogenic fungi provides avenues to identify novel virulence factors and improve our understanding of the regulatory networks linked to pathogen evolution, while transcription factors can themselves be specifically targeted for disease control. Areas requiring further insight regarding the molecular mechanisms and/or specific classes of transcription factors are identified, and direction for future investigation is presented.
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Affiliation(s)
- Evan John
- Centre for Crop and Disease ManagementCurtin UniversityBentleyWestern AustraliaAustralia
- School of Molecular and Life SciencesCurtin UniversityBentleyWestern AustraliaAustralia
| | - Karam B. Singh
- Agriculture and FoodCommonwealth Scientific and Industrial Research OrganisationFloreatWestern AustraliaAustralia
| | - Richard P. Oliver
- School of Molecular and Life SciencesCurtin UniversityBentleyWestern AustraliaAustralia
| | - Kar‐Chun Tan
- Centre for Crop and Disease ManagementCurtin UniversityBentleyWestern AustraliaAustralia
- School of Molecular and Life SciencesCurtin UniversityBentleyWestern AustraliaAustralia
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Dantas Machado AC, Cooper BH, Lei X, Di Felice R, Chen L, Rohs R. Landscape of DNA binding signatures of myocyte enhancer factor-2B reveals a unique interplay of base and shape readout. Nucleic Acids Res 2020; 48:8529-8544. [PMID: 32738045 PMCID: PMC7470950 DOI: 10.1093/nar/gkaa642] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 07/16/2020] [Accepted: 07/22/2020] [Indexed: 01/08/2023] Open
Abstract
Myocyte enhancer factor-2B (MEF2B) has the unique capability of binding to its DNA target sites with a degenerate motif, while still functioning as a gene-specific transcriptional regulator. Identifying its DNA targets is crucial given regulatory roles exerted by members of the MEF2 family and MEF2B's involvement in B-cell lymphoma. Analyzing structural data and SELEX-seq experimental results, we deduced the DNA sequence and shape determinants of MEF2B target sites on a high-throughput basis in vitro for wild-type and mutant proteins. Quantitative modeling of MEF2B binding affinities and computational simulations exposed the DNA readout mechanisms of MEF2B. The resulting binding signature of MEF2B revealed distinct intricacies of DNA recognition compared to other transcription factors. MEF2B uses base readout at its half-sites combined with shape readout at the center of its degenerate motif, where A-tract polarity dictates nuances of binding. The predominant role of shape readout at the center of the core motif, with most contacts formed in the minor groove, differs from previously observed protein-DNA readout modes. MEF2B, therefore, represents a unique protein for studies of the role of DNA shape in achieving binding specificity. MEF2B-DNA recognition mechanisms are likely representative for other members of the MEF2 family.
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Affiliation(s)
- Ana Carolina Dantas Machado
- Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Brendon H Cooper
- Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Xiao Lei
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Rosa Di Felice
- Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Department of Physics & Astronomy, University of Southern California, Los Angeles, CA 90089, USA
| | - Lin Chen
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Remo Rohs
- Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Department of Physics & Astronomy, University of Southern California, Los Angeles, CA 90089, USA
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
- Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
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Ambrosini G, Vorontsov I, Penzar D, Groux R, Fornes O, Nikolaeva DD, Ballester B, Grau J, Grosse I, Makeev V, Kulakovskiy I, Bucher P. Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study. Genome Biol 2020; 21:114. [PMID: 32393327 PMCID: PMC7212583 DOI: 10.1186/s13059-020-01996-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 03/11/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets. RESULTS Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity. CONCLUSIONS In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.
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Affiliation(s)
- Giovanna Ambrosini
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Ilya Vorontsov
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, Moscow, Russia, 119991
- Institute of Protein Research, Russian Academy of Sciences, Institutskaya 4, Pushchino, Russia, 142290
| | - Dmitry Penzar
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, Moscow, Russia, 119991
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye gory 1-73, Moscow, Russia, 119234
- Moscow Institute of Physics and Technology (State University), Institutskiy per. 9, Dolgoprudny, Russia, 141700
| | - Romain Groux
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC V5Z 4H4, Canada
| | - Daria D Nikolaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye gory 1-73, Moscow, Russia, 119234
| | | | - Jan Grau
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Ivo Grosse
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Vsevolod Makeev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, Moscow, Russia, 119991
- Moscow Institute of Physics and Technology (State University), Institutskiy per. 9, Dolgoprudny, Russia, 141700
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, Moscow, Russia, 119991
| | - Ivan Kulakovskiy
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, Moscow, Russia, 119991
- Institute of Protein Research, Russian Academy of Sciences, Institutskaya 4, Pushchino, Russia, 142290
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, Moscow, Russia, 119991
| | - Philipp Bucher
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), CH-1015, Lausanne, Switzerland.
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7
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Lin W, Li Y, Lu Q, Lu H, Li J. Combined Analysis of the Metabolome and Transcriptome Identified Candidate Genes Involved in Phenolic Acid Biosynthesis in the Leaves of Cyclocarya paliurus. Int J Mol Sci 2020; 21:ijms21041337. [PMID: 32079236 PMCID: PMC7073005 DOI: 10.3390/ijms21041337] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 12/12/2022] Open
Abstract
To assess changes of metabolite content and regulation mechanism of the phenolic acid biosynthesis pathway at different developmental stages of leaves, this study performed a combined metabolome and transcriptome analysis of Cyclocarya paliurus leaves at different developmental stages. Metabolite and transcript profiling were conducted by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. Transcriptome identification showed that 58 genes were involved in the biosynthesis of phenolic acid. Among them, 10 differentially expressed genes were detected between every two developmental stages. Identification and quantification of metabolites indicated that 14 metabolites were located in the phenolic acid biosynthetic pathway. Among them, eight differentially accumulated metabolites were detected between every two developmental stages. Association analysis between metabolome and transcriptome showed that six differentially expressed structural genes were significantly positively correlated with metabolite accumulation and showed similar expression trends. A total of 128 transcription factors were identified that may be involved in the regulation of phenolic acid biosynthesis; these include 12 MYBs and 10 basic helix–loop–helix (bHLH) transcription factors. A regulatory network of the phenolic acid biosynthesis was established to visualize differentially expressed candidate genes that are involved in the accumulation of metabolites with significant differences. The results of this study contribute to the further understanding of phenolic acid biosynthesis during the development of leaves of C. paliurus.
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Affiliation(s)
- Weida Lin
- College of Life Science, Zhejiang Sci-Tech University, Hangzhou 310018, China; (W.L.); (H.L.)
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou 318000, China; (Y.L.); (Q.L.)
| | - Yueling Li
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou 318000, China; (Y.L.); (Q.L.)
| | - Qiuwei Lu
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou 318000, China; (Y.L.); (Q.L.)
| | - Hongfei Lu
- College of Life Science, Zhejiang Sci-Tech University, Hangzhou 310018, China; (W.L.); (H.L.)
| | - Junmin Li
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou 318000, China; (Y.L.); (Q.L.)
- Correspondence:
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Wetzel JL, Singh M. Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination. Nucleic Acids Res 2020; 48:e9. [PMID: 31777934 PMCID: PMC7028011 DOI: 10.1093/nar/gkz1087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/03/2019] [Accepted: 11/01/2019] [Indexed: 01/31/2023] Open
Abstract
We are now in an era where protein–DNA interactions have been experimentally assayed for thousands of DNA-binding proteins. In order to infer DNA-binding specificities from these data, numerous sophisticated computational methods have been developed. These approaches typically infer DNA-binding specificities by considering interactions for each protein independently, ignoring related and potentially valuable interaction information across other proteins that bind DNA via the same structural domain. Here we introduce a framework for inferring DNA-binding specificities by considering protein–DNA interactions for entire groups of structurally similar proteins simultaneously. We devise both constrained optimization and label propagation algorithms for this task, each balancing observations at the individual protein level against dataset-wide consistency of interaction preferences. We test our approaches on two large, independent Cys2His2 zinc finger protein–DNA interaction datasets. We demonstrate that jointly inferring specificities within each dataset individually dramatically improves accuracy, leading to increased agreement both between these two datasets and with a fixed external standard. Overall, our results suggest that sharing protein–DNA interaction information across structurally similar proteins is a powerful means to enable accurate inference of DNA-binding specificities.
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Affiliation(s)
- Joshua L Wetzel
- The Lewis-Sigler Institute for Integrative Genomics, Princeton, NJ 08544, USA.,Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- The Lewis-Sigler Institute for Integrative Genomics, Princeton, NJ 08544, USA.,Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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Jayaraman P, Yeoh JW, Zhang J, Poh CL. Programming the Dynamic Control of Bacterial Gene Expression with a Chimeric Ligand- and Light-Based Promoter System. ACS Synth Biol 2018; 7:2627-2639. [PMID: 30359530 DOI: 10.1021/acssynbio.8b00280] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
To program cells in a dynamic manner, synthetic biologists require precise control over the threshold levels and timing of gene expression. However, in practice, modulating gene expression is widely carried out using prototypical ligand-inducible promoters, which have limited tunability and spatiotemporal resolution. Here, we built two dual-input hybrid promoters, each retaining the function of the ligand-inducible promoter while being enhanced with a blue-light-switchable tuning knob. Using the new promoters, we show that both ligand and light inputs can be synchronously modulated to achieve desired amplitude or independently regulated to generate desired frequency at a specific amplitude. We exploit the versatile programmability and orthogonality of the two promoters to build the first reprogrammable logic gene circuit capable of reconfiguring into logic OR and N-IMPLY logic on the fly in both space and time without the need to modify the circuit. Overall, we demonstrate concentration- and time-based combinatorial regulation in live bacterial cells with potential applications in biotechnology and synthetic biology.
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Affiliation(s)
- Premkumar Jayaraman
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 117456
| | - Jing Wui Yeoh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 117456
| | - Jingyun Zhang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 117456
| | - Chueh Loo Poh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 117456
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Li Y, Fang J, Qi X, Lin M, Zhong Y, Sun L, Cui W. Combined Analysis of the Fruit Metabolome and Transcriptome Reveals Candidate Genes Involved in Flavonoid Biosynthesis in Actinidia arguta. Int J Mol Sci 2018; 19:ijms19051471. [PMID: 29762529 PMCID: PMC5983832 DOI: 10.3390/ijms19051471] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 05/09/2018] [Accepted: 05/09/2018] [Indexed: 12/12/2022] Open
Abstract
To assess the interrelation between the change of metabolites and the change of fruit color, we performed a combined metabolome and transcriptome analysis of the flesh in two different Actinidia arguta cultivars: "HB" ("Hongbaoshixing") and "YF" ("Yongfengyihao") at two different fruit developmental stages: 70d (days after full bloom) and 100d (days after full bloom). Metabolite and transcript profiling was obtained by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. The identification and quantification results of metabolites showed that a total of 28,837 metabolites had been obtained, of which 13,715 were annotated. In comparison of HB100 vs. HB70, 41 metabolites were identified as being flavonoids, 7 of which, with significant difference, were identified as bracteatin, luteolin, dihydromyricetin, cyanidin, pelargonidin, delphinidin and (-)-epigallocatechin. Association analysis between metabolome and transcriptome revealed that there were two metabolic pathways presenting significant differences during fruit development, one of which was flavonoid biosynthesis, in which 14 structural genes were selected to conduct expression analysis, as well as 5 transcription factor genes obtained by transcriptome analysis. RT-qPCR results and cluster analysis revealed that AaF3H, AaLDOX, AaUFGT, AaMYB, AabHLH, and AaHB2 showed the best possibility of being candidate genes. A regulatory network of flavonoid biosynthesis was established to illustrate differentially expressed candidate genes involved in accumulation of metabolites with significant differences, inducing red coloring during fruit development. Such a regulatory network linking genes and flavonoids revealed a system involved in the pigmentation of all-red-fleshed and all-green-fleshed A. arguta, suggesting this conjunct analysis approach is not only useful in understanding the relationship between genotype and phenotype, but is also a powerful tool for providing more valuable information for breeding.
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Affiliation(s)
- Yukuo Li
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Jinbao Fang
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Xiujuan Qi
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Miaomiao Lin
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Yunpeng Zhong
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Leiming Sun
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
| | - Wen Cui
- Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009, China.
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Samad AFA, Sajad M, Nazaruddin N, Fauzi IA, Murad AMA, Zainal Z, Ismail I. MicroRNA and Transcription Factor: Key Players in Plant Regulatory Network. FRONTIERS IN PLANT SCIENCE 2017; 8:565. [PMID: 28446918 PMCID: PMC5388764 DOI: 10.3389/fpls.2017.00565] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 03/29/2017] [Indexed: 05/14/2023]
Abstract
Recent achievements in plant microRNA (miRNA), a large class of small and non-coding RNAs, are very exciting. A wide array of techniques involving forward genetic, molecular cloning, bioinformatic analysis, and the latest technology, deep sequencing have greatly advanced miRNA discovery. A tiny miRNA sequence has the ability to target single/multiple mRNA targets. Most of the miRNA targets are transcription factors (TFs) which have paramount importance in regulating the plant growth and development. Various families of TFs, which have regulated a range of regulatory networks, may assist plants to grow under normal and stress environmental conditions. This present review focuses on the regulatory relationships between miRNAs and different families of TFs like; NF-Y, MYB, AP2, TCP, WRKY, NAC, GRF, and SPL. For instance NF-Y play important role during drought tolerance and flower development, MYB are involved in signal transduction and biosynthesis of secondary metabolites, AP2 regulate the floral development and nodule formation, TCP direct leaf development and growth hormones signaling. WRKY have known roles in multiple stress tolerances, NAC regulate lateral root formation, GRF are involved in root growth, flower, and seed development, and SPL regulate plant transition from juvenile to adult. We also studied the relation between miRNAs and TFs by consolidating the research findings from different plant species which will help plant scientists in understanding the mechanism of action and interaction between these regulators in the plant growth and development under normal and stress environmental conditions.
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Affiliation(s)
- Abdul F. A. Samad
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
| | - Muhammad Sajad
- Department of Plant Breeding and Genetics, University College of Agriculture and Environmental Sciences, The Islamia University of Bahawalpur, PunjabPakistan
- Centre of Plant Biotechnology, Institute of Systems Biology, National University of Malaysia, SelangorMalaysia
| | - Nazaruddin Nazaruddin
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Syiah Kuala University, Darussalam, Banda AcehIndonesia
| | - Izzat A. Fauzi
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
| | - Abdul M. A. Murad
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
| | - Zamri Zainal
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
- Centre of Plant Biotechnology, Institute of Systems Biology, National University of Malaysia, SelangorMalaysia
| | - Ismanizan Ismail
- School of Biosciences and Biotechnology, Faculty of Science and Technology, National University of Malaysia, SelangorMalaysia
- Centre of Plant Biotechnology, Institute of Systems Biology, National University of Malaysia, SelangorMalaysia
- *Correspondence: Ismanizan Ismail,
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