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Precision phenotyping of pines for resistance against Fusarium circinatum

Dissertation (MSc (Genetics))--University of Pretoria, 2023.

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Other Authors: Naidoo, Sanushka
Format: Thesis
Language:English
Published: University of Pretoria 2024
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author2 Naidoo, Sanushka
author_browse Naidoo, Sanushka
author_facet Naidoo, Sanushka
collection Thesis
dc_rights_str_mv © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc (Genetics))--University of Pretoria, 2023.
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:40:33.839Z
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provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
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publishDateRange 2024
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spelling oai:repository.up.ac.za:2263/94266 Precision phenotyping of pines for resistance against Fusarium circinatum Naidoo, Sanushka ruby.ebbeling@up.ac.za Visser, Erik Ebbeling, Ruby May UCTD Fusarium circinatum Pinus patula Pinus tecunumanii Precision phenotyping Quantitative real-time PCR Dissertation (MSc (Genetics))--University of Pretoria, 2023. Pine trees are globally important agricultural and ecological species, with Pinus patula being a key species in South Africa’s economically valuable forestry industry. Natural and commercial pine forests are significantly threatened by the pitch canker fungus, Fusarium circinatum. In the nursery, F. circinatum infection manifests as a wilt, thereby hindering pine production. Since Pinus spp. exhibit a wide range of inter- and intra-specific variation in resistance, insights into the pine resistance phenotype and the pine-F. circinatum interaction may aid in producing resistant planting stock and mitigating F. circinatum damage. Currently, this pine variation is quantified by visually measuring lesion lengths, which is predictive of the pine response phenotype. Visual estimates, however, are limited by subjectivity, inaccuracy, and low reproducibility. This highlights the requirement for a more reliable, precise disease phenotyping method. Quantifying fungal load in infected plants holds promise for greater sensitivity, specificity, timeliness, and high-throughput. To the best of our knowledge, this phenotyping method has not been explored for quantifying F. circinatum resistance in pines. Therefore, our study aimed to develop and optimise a quantitative real-time PCR (qPCR) precision phenotyping tool to quantify the fungal load of F. circinatum in different Pinus patula (susceptible) and Pinus tecunumanii high (intermediately resistant) and low elevation (resistant) families. We also assessed the application of this tool on F1 hybrid pine genotypes of unknown resistance. As an initial approach to estimate resistance, lesion lengths of plants artificially inoculated with F. circinatum were measured weekly for 8 weeks to calculate percentage live stem. The visible lesion length measurements showed the expected differences in resistance for the pine species as well as variation within and between each pine species and family. Next, the fungal load from artificially inoculated pine samples was quantified at 1, 2, 3, 7, 14, 21, and 28 days post-inoculation (dpi) to calculate percentage fungal load, which was compared to percentage live stem. To assess the efficacy of our precision phenotyping tool, we identified correlations between the median percentage live stem and median percentage fungal load. Strong correlations were observed between the median percentage fungal load and median percentage live stem at 3 and 49 dpi, respectively, (0.66, p<0.01) as well as at 28 and 21 dpi, respectively (−0.76, p<0.01). We also observed variation between the F1 genotypes, which allowed them to be classified into three phenotypic groups: susceptible, intermediately resistant, and resistant. We concluded that this phenotyping tool has the potential for phenotypic classes to be developed on a continuum from highly susceptible to highly resistant, to timely and accurately class different pine individuals. This study represents the first step in developing an optimal qPCR precision phenotyping tool to capture the subtle variation in pine responses to F. circinatum. Future studies would involve optimising this tool further and using it to help characterise the genetic architecture of the pine defence response and identify quantitative trait loci governing resistance against F. circinatum. Forestry Sector Innovation Fund The University of Pretoria The Technology Innovation Agency The National Research Foundation The Department of Science and Innovation Forestry South Africa Mondi Sappi York timbers Genetics MSc (Genetics) Unrestricted Faculty of Natural and Agricultural Sciences SDG-09: Industry, innovation and infrastructure SDG-15: Life on land 2024-02-05T07:31:56Z 2024-02-05T07:31:56Z 2024-04-15 2023 Dissertation * A2024 http://hdl.handle.net/2263/94266 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
Fusarium circinatum
Pinus patula
Pinus tecunumanii
Precision phenotyping
Quantitative real-time PCR
Precision phenotyping of pines for resistance against Fusarium circinatum
title Precision phenotyping of pines for resistance against Fusarium circinatum
title_full Precision phenotyping of pines for resistance against Fusarium circinatum
title_fullStr Precision phenotyping of pines for resistance against Fusarium circinatum
title_full_unstemmed Precision phenotyping of pines for resistance against Fusarium circinatum
title_short Precision phenotyping of pines for resistance against Fusarium circinatum
title_sort precision phenotyping of pines for resistance against fusarium circinatum
topic UCTD
Fusarium circinatum
Pinus patula
Pinus tecunumanii
Precision phenotyping
Quantitative real-time PCR
url http://hdl.handle.net/2263/94266