Adam R Martin, Guangrui Li, Boya Cui, Rachel O Mariani, Kale Vicario, Kimberley A Cathline, Allison Findlay, Gavin Robertson
{"title":"量化葡萄水分流失点的高通量方法。","authors":"Adam R Martin, Guangrui Li, Boya Cui, Rachel O Mariani, Kale Vicario, Kimberley A Cathline, Allison Findlay, Gavin Robertson","doi":"10.1186/s13007-024-01304-1","DOIUrl":null,"url":null,"abstract":"<p><p>Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point (π<sub>tlp</sub>) is gaining attention as an indicator of drought tolerance in plants, though estimating π<sub>tlp</sub> often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing π<sub>tlp</sub> and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π<sub>tlp</sub>, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π<sub>tlp</sub> across and within 12 clones of grape vine (Vitis vinifera subsp. vinifera) and one wild relative (Vitis riparia), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4-5% of the variation in π<sub>tlp</sub> while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π<sub>tlp</sub> variation in grape vine. Also at the leaf level, π<sub>tlp</sub> measured using a dewpoint hygrometer approximated π<sub>tlp</sub> values (r<sup>2</sup> = 0.254) and conserved π<sub>tlp</sub> rankings from P-V curves (Spearman's ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t-test p = 0.01), average difference in π<sub>tlp</sub> for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of π<sub>tlp</sub> measured by the two methods were also statistically correlated (r<sup>2</sup> = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π<sub>tlp</sub> rankings across clones (Spearman's ρ = 0.692). The dewpoint hygrometer (taking ∼ 10-15 min on average per measurement) captures fine-scale intraspecific variation in π<sub>tlp</sub>, with results that approximate those from P-V curves (taking 2-3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π<sub>tlp</sub>, and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"20 1","pages":"180"},"PeriodicalIF":4.7000,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587569/pdf/","citationCount":"0","resultStr":"{\"title\":\"A high-throughput approach for quantifying turgor loss point in grapevine.\",\"authors\":\"Adam R Martin, Guangrui Li, Boya Cui, Rachel O Mariani, Kale Vicario, Kimberley A Cathline, Allison Findlay, Gavin Robertson\",\"doi\":\"10.1186/s13007-024-01304-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point (π<sub>tlp</sub>) is gaining attention as an indicator of drought tolerance in plants, though estimating π<sub>tlp</sub> often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing π<sub>tlp</sub> and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating π<sub>tlp</sub>, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in π<sub>tlp</sub> across and within 12 clones of grape vine (Vitis vinifera subsp. vinifera) and one wild relative (Vitis riparia), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4-5% of the variation in π<sub>tlp</sub> while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific π<sub>tlp</sub> variation in grape vine. Also at the leaf level, π<sub>tlp</sub> measured using a dewpoint hygrometer approximated π<sub>tlp</sub> values (r<sup>2</sup> = 0.254) and conserved π<sub>tlp</sub> rankings from P-V curves (Spearman's ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t-test p = 0.01), average difference in π<sub>tlp</sub> for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of π<sub>tlp</sub> measured by the two methods were also statistically correlated (r<sup>2</sup> = 0.304), did not deviate statistically from a 1:1 relationship, and conserved π<sub>tlp</sub> rankings across clones (Spearman's ρ = 0.692). The dewpoint hygrometer (taking ∼ 10-15 min on average per measurement) captures fine-scale intraspecific variation in π<sub>tlp</sub>, with results that approximate those from P-V curves (taking 2-3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in π<sub>tlp</sub>, and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.</p>\",\"PeriodicalId\":20100,\"journal\":{\"name\":\"Plant Methods\",\"volume\":\"20 1\",\"pages\":\"180\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11587569/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plant Methods\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13007-024-01304-1\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Methods","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13007-024-01304-1","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
A high-throughput approach for quantifying turgor loss point in grapevine.
Quantifying drought tolerance in crops is critical for agriculture management under environmental change, and drought response traits in grape vine have long been the focus of viticultural research. Turgor loss point (πtlp) is gaining attention as an indicator of drought tolerance in plants, though estimating πtlp often requires the construction and analysis of pressure-volume (P-V) curves which are very time consuming. While P-V curves remain a valuable tool for assessing πtlp and related traits, there is considerable interest in developing high-throughput methods for rapidly estimating πtlp, especially in the context of crop screening. We tested the ability of a dewpoint hygrometer to quantify variation in πtlp across and within 12 clones of grape vine (Vitis vinifera subsp. vinifera) and one wild relative (Vitis riparia), and compared these results to those derived from P-V curves. At the leaf-level, methodology explained only 4-5% of the variation in πtlp while clone/species identity accounted for 39% of the variation, indicating that both methods are sensitive to detecting intraspecific πtlp variation in grape vine. Also at the leaf level, πtlp measured using a dewpoint hygrometer approximated πtlp values (r2 = 0.254) and conserved πtlp rankings from P-V curves (Spearman's ρ = 0.459). While the leaf-level datasets differed statistically from one another (paired t-test p = 0.01), average difference in πtlp for a given pair of leaves was small (0.1 ± 0.2 MPa (s.d.)). At the species/clone level, estimates of πtlp measured by the two methods were also statistically correlated (r2 = 0.304), did not deviate statistically from a 1:1 relationship, and conserved πtlp rankings across clones (Spearman's ρ = 0.692). The dewpoint hygrometer (taking ∼ 10-15 min on average per measurement) captures fine-scale intraspecific variation in πtlp, with results that approximate those from P-V curves (taking 2-3 h on average per measurement). The dewpoint hygrometer represents a viable method for rapidly estimating intraspecific variation in πtlp, and potentially greatly increasing replication when estimating this drought tolerance trait in grape vine and other crops.
期刊介绍:
Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences.
There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics.
Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.