1
|
Rozentsvet O, Bogdanova E, Nesterov V, Bakunov A, Milekhin A, Rubtsov S, Rozentsvet V. Phenotyping of Potato Plants Using Morphological and Physiological Tools. PLANTS (BASEL, SWITZERLAND) 2024; 13:647. [PMID: 38475492 DOI: 10.3390/plants13050647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024]
Abstract
Potato (Solanum tuberosum L.) is one of the main non-grain agricultural crops and one of the main sources of food for humanity. Currently, growing potatoes requires new approaches and methods for cultivation and breeding. Phenotyping is one of the important tools for assessing the characteristics of a potato variety. In this work, 29 potato varieties of different ripeness groups were studied. Linear leaf dimensions, leaf mass area, number of stems, number of tubers per plant, average tuber weight, signs of virus infection, dry weight, pigment content, and number of stomata per unit leaf area were used as phenotyping tools. The strongest positive relationship was found between yield and bush area in the stage of full shoots (R = 0.77, p = 0.001), linear dimensions of a complex leaf (R = 0.44, p = 0.002; R = 0.40, p = 0.003), number of stems (R = 0.36, p = 0.05), and resistance to viruses X (R = 0.42, p = 0.03) and S (R = 0.43, p = 0.02). An inverse relationship was found between growth dynamics and yield (R = -0.29, p = 0.05). Thus, the use of morphological and physiological phenotyping tools in the field is informative for predicting key agricultural characteristics such as yield and/or stress resistance.
Collapse
Affiliation(s)
- Olga Rozentsvet
- Samara Federal Research Scientific Center RAS, Institute of Ecology of the Volga Basin RAS, 10, Komzina, Togliatti 445003, Russia
| | - Elena Bogdanova
- Samara Federal Research Scientific Center RAS, Institute of Ecology of the Volga Basin RAS, 10, Komzina, Togliatti 445003, Russia
| | - Viktor Nesterov
- Samara Federal Research Scientific Center RAS, Institute of Ecology of the Volga Basin RAS, 10, Komzina, Togliatti 445003, Russia
| | - Alexey Bakunov
- Samara Federal Research Scientific Center RAS, Samara Scientific Research Agriculture Institute Named after N.M. Tulaykov, Bezenchuk 446254, Russia
| | - Alexey Milekhin
- Samara Federal Research Scientific Center RAS, Samara Scientific Research Agriculture Institute Named after N.M. Tulaykov, Bezenchuk 446254, Russia
| | - Sergei Rubtsov
- Samara Federal Research Scientific Center RAS, Samara Scientific Research Agriculture Institute Named after N.M. Tulaykov, Bezenchuk 446254, Russia
| | - Victor Rozentsvet
- Samara Federal Research Scientific Center RAS, Institute of Ecology of the Volga Basin RAS, 10, Komzina, Togliatti 445003, Russia
| |
Collapse
|
2
|
Brainard SH, Sanders DM, Bruna T, Shu S, Dawson JC. The first two chromosome-scale genome assemblies of American hazelnut enable comparative genomic analysis of the genus Corylus. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:472-483. [PMID: 37870930 PMCID: PMC10826982 DOI: 10.1111/pbi.14199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/11/2023] [Accepted: 09/29/2023] [Indexed: 10/25/2023]
Abstract
The native, perennial shrub American hazelnut (Corylus americana) is cultivated in the Midwestern United States for its significant ecological benefits, as well as its high-value nut crop. Implementation of modern breeding methods and quantitative genetic analyses of C. americana requires high-quality reference genomes, a resource that is currently lacking. We therefore developed the first chromosome-scale assemblies for this species using the accessions 'Rush' and 'Winkler'. Genomes were assembled using HiFi PacBio reads and Arima Hi-C data, and Oxford Nanopore reads and a high-density genetic map were used to perform error correction. N50 scores are 31.9 Mb and 35.3 Mb, with 90.2% and 97.1% of the total genome assembled into the 11 pseudomolecules, for 'Rush' and 'Winkler', respectively. Gene prediction was performed using custom RNAseq libraries and protein homology data. 'Rush' has a BUSCO score of 99.0 for its assembly and 99.0 for its annotation, while 'Winkler' had corresponding scores of 96.9 and 96.5, indicating high-quality assemblies. These two independent assemblies enable unbiased assessment of structural variation within C. americana, as well as patterns of syntenic relationships across the Corylus genus. Furthermore, we identified high-density SNP marker sets from genotyping-by-sequencing data using 1343 C. americana, C. avellana and C. americana × C. avellana hybrids, in order to assess population structure in natural and breeding populations. Finally, the transcriptomes of these assemblies, as well as several other recently published Corylus genomes, were utilized to perform phylogenetic analysis of sporophytic self-incompatibility (SSI) in hazelnut, providing evidence of unique molecular pathways governing self-incompatibility in Corylus.
Collapse
Affiliation(s)
- Scott H. Brainard
- Department of Plant and Agroecosystem SciencesUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Dean M. Sanders
- University of Wisconsin Biotechnology CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Tomas Bruna
- U.S. Department of Energy Joint Genome InstituteLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Shengqiang Shu
- U.S. Department of Energy Joint Genome InstituteLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Julie C. Dawson
- Department of Plant and Agroecosystem SciencesUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| |
Collapse
|
3
|
Chan YO, Biová J, Mahmood A, Dietz N, Bilyeu K, Škrabišová M, Joshi T. Genomic Variations Explorer (GenVarX): a toolset for annotating promoter and CNV regions using genotypic and phenotypic differences. Front Genet 2023; 14:1251382. [PMID: 37928239 PMCID: PMC10623549 DOI: 10.3389/fgene.2023.1251382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 09/27/2023] [Indexed: 11/07/2023] Open
Abstract
The rapid growth of sequencing technology and its increasing popularity in biology-related research over the years has made whole genome re-sequencing (WGRS) data become widely available. A large amount of WGRS data can unlock the knowledge gap between genomics and phenomics through gaining an understanding of the genomic variations that can lead to phenotype changes. These genomic variations are usually comprised of allele and structural changes in DNA, and these changes can affect the regulatory mechanisms causing changes in gene expression and altering the phenotypes of organisms. In this research work, we created the GenVarX toolset, that is backed by transcription factor binding sequence data in promoter regions, the copy number variations data, SNPs and Indels data, and phenotypes data which can potentially provide insights about phenotypic differences and solve compelling questions in plant research. Analytics-wise, we have developed strategies to better utilize the WGRS data and mine the data using efficient data processing scripts, libraries, tools, and frameworks to create the interactive and visualization-enhanced GenVarX toolset that encompasses both promoter regions and copy number variation analysis components. The main capabilities of the GenVarX toolset are to provide easy-to-use interfaces for users to perform queries, visualize data, and interact with the data. Based on different input windows on the user interface, users can provide inputs corresponding to each field and submit the information as a query. The data returned on the results page is usually displayed in a tabular fashion. In addition, interactive figures are also included in the toolset to facilitate the visualization of statistical results or tool outputs. Currently, the GenVarX toolset supports soybean, rice, and Arabidopsis. The researchers can access the soybean GenVarX toolset from SoyKB via https://soykb.org/SoybeanGenVarX/, rice GenVarX toolset, and Arabidopsis GenVarX toolset from KBCommons web portal with links https://kbcommons.org/system/tools/GenVarX/Osativa and https://kbcommons.org/system/tools/GenVarX/Athaliana, respectively.
Collapse
Affiliation(s)
- Yen On Chan
- MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States
| | - Jana Biová
- Department of Biochemistry, Faculty of Science, Palacky University in Olomouc, Olomouc, Czechia
| | - Anser Mahmood
- Division of Plant Science and Technology, University of Missouri-Columbia, Columbia, MO, United States
| | - Nicholas Dietz
- Division of Plant Science and Technology, University of Missouri-Columbia, Columbia, MO, United States
| | - Kristin Bilyeu
- Division of Plant Science and Technology, University of Missouri-Columbia, Columbia, MO, United States
- Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Columbia, MO, United States
| | - Mária Škrabišová
- Department of Biochemistry, Faculty of Science, Palacky University in Olomouc, Olomouc, Czechia
| | - Trupti Joshi
- MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States
- Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, United States
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, United States
- Department of Biomedical Informatics, Biostatistics and Medical Epidemiology, University of Missouri-Columbia, Columbia, MO, United States
| |
Collapse
|
4
|
Gogolev YV, Ahmar S, Akpinar BA, Budak H, Kiryushkin AS, Gorshkov VY, Hensel G, Demchenko KN, Kovalchuk I, Mora-Poblete F, Muslu T, Tsers ID, Yadav NS, Korzun V. OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security. PLANTS (BASEL, SWITZERLAND) 2021; 10:1423. [PMID: 34371624 PMCID: PMC8309286 DOI: 10.3390/plants10071423] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 12/22/2022]
Abstract
The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.
Collapse
Affiliation(s)
- Yuri V. Gogolev
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | | | - Hikmet Budak
- Montana BioAg Inc., Missoula, MT 59802, USA; (B.A.A.); (H.B.)
| | - Alexey S. Kiryushkin
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Vladimir Y. Gorshkov
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Goetz Hensel
- Centre for Plant Genome Engineering, Institute of Plant Biochemistry, Heinrich-Heine-University, 40225 Dusseldorf, Germany;
- Centre of the Region Haná for Biotechnological and Agricultural Research, Czech Advanced Technology and Research Institute, Palacký University Olomouc, 78371 Olomouc, Czech Republic
| | - Kirill N. Demchenko
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | - Tugdem Muslu
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey;
| | - Ivan D. Tsers
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Viktor Korzun
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37555 Einbeck, Germany
| |
Collapse
|
5
|
Calderan-Rodrigues MJ, de Barros Dantas LL, Cheavegatti Gianotto A, Caldana C. Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential. FRONTIERS IN PLANT SCIENCE 2021; 12:637166. [PMID: 33679852 PMCID: PMC7935522 DOI: 10.3389/fpls.2021.637166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/27/2021] [Indexed: 05/21/2023]
Abstract
Sugarcane (Saccharum spp.), a C4 grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products (e.g., polymers and succinate), and bioelectricity, earning the title of the world's leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how "omics" advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.
Collapse
Affiliation(s)
| | | | | | - Camila Caldana
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- *Correspondence: Camila Caldana,
| |
Collapse
|
6
|
Plant Proteomics and Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1346:51-66. [DOI: 10.1007/978-3-030-80352-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
7
|
Shen T, Zhang C, Liu F, Wang W, Lu Y, Chen R, He Y. High-Throughput Screening of Free Proline Content in Rice Leaf under Cadmium Stress Using Hyperspectral Imaging with Chemometrics. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3229. [PMID: 32517150 PMCID: PMC7308835 DOI: 10.3390/s20113229] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 11/17/2022]
Abstract
Tracking of free proline (FP)-an indicative substance of heavy metal stress in rice leaf-is conducive to improve plant phenotype detection, which has important guiding significance for precise management of rice production. Hyperspectral imaging was used for high-throughput screening FP in rice leaves under cadmium (Cd) stress with five concentrations and four periods. The average spectral of rice leaves were used to show differences in optical properties. Partial least squares (PLS), least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models based on full spectra and effective wavelengths were established to detect FP content. Genetic algorithm (GA), competitive adaptive weighted sampling (CARS) and PLS weighting regression coefficient (Bw) were compared to screen the most effective wavelengths. Distribution map of the FP content in rice leaves were obtained to display the changes in the FP of leaves visually. The results illustrated that spectral differences increased with Cd stress time and FP content increased with Cd stress concentration. The best result for FP detection is the ELM model based on 27 wavelengths selected by CARS and Rp is 0.9426. Undoubtedly, hyperspectral imaging combined with chemometrics was a rapid, cost effective and non-destructive technique to excavate changes of FP in rice leaves under Cd stress.
Collapse
Affiliation(s)
- Tingting Shen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- Huanan Industrial Technology Research Institute of Zhejiang University, Guangzhou 510700, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
| | - Yi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (T.S.); (C.Z.); (W.W.); (Y.L.); (R.C.); (Y.H.)
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| |
Collapse
|
8
|
Pathan M, Patel N, Yagnik H, Shah M. Artificial cognition for applications in smart agriculture: A comprehensive review. ARTIFICIAL INTELLIGENCE IN AGRICULTURE 2020; 4:81-95. [PMID: 0 DOI: 10.1016/j.aiia.2020.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
|
9
|
Née G, Tilak P, Finkemeier I. A Versatile Workflow for the Identification of Protein-Protein Interactions Using GFP-Trap Beads and Mass Spectrometry-Based Label-Free Quantification. Methods Mol Biol 2020; 2139:257-271. [PMID: 32462592 DOI: 10.1007/978-1-0716-0528-8_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Protein functions often rely on protein-protein interactions. Hence, knowledge about the protein interaction network is essential for an understanding of protein functions and plant physiology. A major challenge of the postgenomic era is the mapping of protein-protein interaction networks. This chapter describes a mass spectrometry-based label-free quantification approach to identify in vivo protein interaction networks. The procedure starts with the extraction of intact protein complexes from transgenic plants expressing the protein of interest fused to a GFP-Tag (bait-GFP), as well as plants expressing a free GFP as background control. Enrichment of the GFP-tagged protein together with its interaction partners, as well as the free GFP, is performed by immunoaffinity purification. The pull-down quality can be evaluated by simple gel-based techniques. In parallel, the captured proteins are trypsin-digested and relatively quantified by label-free mass spectrometry-based quantification. The relative quantification approach largely relies on the normalization of protein abundances of background-binding proteins, which occur in both bait-GFP and free GFP pull-downs. Therefore, relative quantification of the protein pull-down is superior over methods that solely rely on protein identifications and removal of often copurified high-abundance proteins from the bait-GFP pull-downs, which might remove real interaction partners. A further strength of this method is that it can be applied to any soluble GFP-tagged protein.
Collapse
Affiliation(s)
- Guillaume Née
- Plant Physiology, Institute of Plant Biology and Biotechnology, University of Münster, Münster, Germany
| | - Priyadarshini Tilak
- Plant Physiology, Institute of Plant Biology and Biotechnology, University of Münster, Münster, Germany
| | - Iris Finkemeier
- Plant Physiology, Institute of Plant Biology and Biotechnology, University of Münster, Münster, Germany.
| |
Collapse
|
10
|
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2019; 10:714. [PMID: 31214228 PMCID: PMC6557228 DOI: 10.3389/fpls.2019.00714] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/14/2019] [Indexed: 05/19/2023]
Abstract
Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
Collapse
|
11
|
|
12
|
Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. Review: New sensors and data-driven approaches-A path to next generation phenomics. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:2-10. [PMID: 31003608 PMCID: PMC6483971 DOI: 10.1016/j.plantsci.2019.01.011] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/15/2018] [Accepted: 01/09/2019] [Indexed: 05/19/2023]
Abstract
At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.
Collapse
Affiliation(s)
- Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark; Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czech Republic
| | | | - Antoine Fournier
- Arvalis, Institut du végétal, 45, voie Romaine 41240 Beauce la Romaine, France
| | - Kioumars Ghamkhar
- Forage Science, Grasslands Research Centre, AgResearch, Tennent Drive, Fitzherbert, Palmerston North 4410, New Zealand
| | - José Jiménez-Berni
- Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas (CSIC) Avenida Menéndez Pidal, Campus Alameda del Obispo, 14004 Córdoba, Spain
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, México C.P. 56237, Mexico
| | - Eric S Ober
- National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge, CB3 0LE, UK.
| |
Collapse
|
13
|
Leonelli S. What distinguishes data from models? EUROPEAN JOURNAL FOR PHILOSOPHY OF SCIENCE 2019; 9:22. [PMID: 30873249 PMCID: PMC6383958 DOI: 10.1007/s13194-018-0246-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 12/21/2018] [Indexed: 05/23/2023]
Abstract
I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes' characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes' attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.
Collapse
Affiliation(s)
- Sabina Leonelli
- Exeter Centre for the Study of the Life Sciences (Egenis) & Department of Sociology, Philosophy and Anthropology, University of Exeter, Byrne House, St Germans Road, Exeter, EX4 4PJ UK
| |
Collapse
|
14
|
Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KFX, Lange M, Scholz U, Usadel B. Computational aspects underlying genome to phenome analysis in plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:182-198. [PMID: 30500991 PMCID: PMC6849790 DOI: 10.1111/tpj.14179] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/06/2018] [Accepted: 11/16/2018] [Indexed: 05/18/2023]
Abstract
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.
Collapse
Affiliation(s)
- Anthony M. Bolger
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Hendrik Poorter
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
- Department of Biological SciencesMacquarie UniversityNorth RydeNSW2109Australia
| | - Kathryn Dumschott
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Marie E. Bolger
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Sonia Osorio
- Department of Molecular Biology and BiochemistryInstituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”Universidad de Málaga‐Consejo Superior de Investigaciones CientíficasCampus de Teatinos29071MálagaSpain
| | - Heidrun Gundlach
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Klaus F. X. Mayer
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Björn Usadel
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
| |
Collapse
|
15
|
Bai Y, Kissoudis C, Yan Z, Visser RGF, van der Linden G. Plant behaviour under combined stress: tomato responses to combined salinity and pathogen stress. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2018; 93:781-793. [PMID: 29237240 DOI: 10.1111/tpj.13800] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Accepted: 12/07/2017] [Indexed: 05/21/2023]
Abstract
Crop plants are subjected to a variety of stresses during their lifecycle, including abiotic stress factors such as salinity and biotic stress factors such as pathogens. Plants have developed a multitude of defense and adaptation responses to these stress factors. In the field, different stress factors mostly occur concurrently resulting in a new state of stress, the combined stress. There is evidence that plant resistance to pathogens can be attenuated or enhanced by abiotic stress factors. With stress tolerance research being mostly focused on plant responses to individual stresses, the understanding of a plant's ability to adapt to combined stresses is limited. In the last few years, we studied powdery mildew resistance under salt stress conditions in the model crop plant tomato with the aim to understand the requirements to achieve plant resilience to a wider array of combined abiotic and biotic stress combinations. We uncovered specific responses of tomato plants to combined salinity-pathogen stress, which varied with salinity intensity and plant resistance genes. Moreover, hormones, with their complex regulation and cross-talk, were shown to play a key role in the adaptation of tomato plants to the combined stress. In this review, we attempt to understand the complexity of plant responses to abiotic and biotic stress combinations, with a focus on tomato responses (genetic control and cross-talk of signaling pathways) to combined salinity and pathogen stresses. Further, we provide recommendations on how to design novel strategies for breeding crops with a sustained performance under diverse environmental conditions.
Collapse
Affiliation(s)
- Yuling Bai
- Plant Breeding, Wageningen University & Research, P.O. Box 386, Wageningen, 6700AJ, The Netherlands
| | - Christos Kissoudis
- Plant Breeding, Wageningen University & Research, P.O. Box 386, Wageningen, 6700AJ, The Netherlands
| | - Zhe Yan
- Plant Breeding, Wageningen University & Research, P.O. Box 386, Wageningen, 6700AJ, The Netherlands
| | - Richard G F Visser
- Plant Breeding, Wageningen University & Research, P.O. Box 386, Wageningen, 6700AJ, The Netherlands
| | - Gerard van der Linden
- Plant Breeding, Wageningen University & Research, P.O. Box 386, Wageningen, 6700AJ, The Netherlands
| |
Collapse
|