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利用 Populus deltoides 'Danhong'×Populus simonii 'Tongliao1' 杂交种进行与干旱相关性状的 QTL 作图。

QTL mapping of drought-related traits in the hybrids of Populus deltoides 'Danhong'×Populus simonii 'Tongliao1'.

机构信息

State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.

Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China.

出版信息

BMC Plant Biol. 2022 May 11;22(1):238. doi: 10.1186/s12870-022-03613-w.

DOI:10.1186/s12870-022-03613-w
PMID:35545765
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9092850/
Abstract

BACKGROUND

Poplar trees provide a large amount of wood material, but many parts of the world are arid or semi-arid areas because of insufficient annual precipitation, which seriously affects the growth of poplar trees. Populus simonii 'Tongliao1' shows strong tolerance to stress environments, and Populus deltoides 'Danhong' shows a stronger growth rate in a suitable environment. To identify drought tolerance-related QTLs and genes, an F population derived from the cross between the 'Danhong' and 'Tongliao 1' Populus was assessed under drought stress.

RESULTS

We measured drought-related traits such as the relative height growth, relative diameter growth, leaf senescence number, specific leaf area, and leaf relative water content in the population under control and drought environments. The results showed that drought stress reduced the plant height relative growth, ground diameter relative growth, specific leaf area and leaf relative water content and increased the number of leaf drops. A total of 208 QTLs were identified by QTL mapping analysis, and they consisted of 92, 63 and 53 QTLs under control, drought stress treatment and drought index conditions, respectively. A molecular identification marker for drought tolerance, np2841, which was associated with a QTL (qDLRWC-LG10-1) for relative leaf water content, was initially developed. We mined 187 candidate genes for QTL regions of five traits under a drought environment. The reference genome annotation for Populus trichocarpa and a homologous gene analysis of Arabidopsis thaliana identified two candidate genes, Potri.003G171300 and Potri.012G123900, with significant functions in response to drought stress. We identified five key regulatory genes (Potri.006G273500, Potri.007G111500, Potri.007G111600, Potri.007G111700, and Potri.007G111800) related to drought tolerance through the poplar coexpression network.

CONCLUSION

In this study, our results indicate that the QTLs can effectively enhance the drought tolerance of poplar. It is a step closer towards unravelling the genetic basis of poplar drought tolerance-related traits, and to providing validated candidate genes and molecular markers for future genetic improvement.

摘要

背景

杨树提供了大量的木材,但由于年降水量不足,世界上许多地区都是干旱或半干旱地区,这严重影响了杨树的生长。‘通辽 1 号’银中杨对胁迫环境具有较强的耐受性,‘丹红’垂柳在适宜的环境中具有更强的生长速度。为了鉴定抗旱相关的 QTLs 和基因,我们在控制和干旱环境下评估了来自‘丹红’垂柳和‘通辽 1 号’银中杨杂交的 F 群体。

结果

我们测量了群体在对照和干旱环境下与干旱相关的性状,如相对生长高度、相对地径生长、叶片衰老数、比叶面积和叶片相对含水量。结果表明,干旱胁迫降低了植物的相对生长高度、地径相对生长、比叶面积和叶片相对含水量,增加了叶片脱落数。通过 QTL 作图分析共鉴定出 208 个 QTL,分别为 92、63 和 53 个在对照、干旱胁迫处理和干旱指数条件下的 QTL。初步开发了一个与叶片相对含水量 QTL(qDLRWC-LG10-1)相关的抗旱分子鉴定标记 np2841。我们在干旱环境下对五个性状的 QTL 区域进行了 187 个候选基因的挖掘。对杨树参考基因组注释和拟南芥同源基因分析鉴定了两个候选基因,Potri.003G171300 和 Potri.012G123900,它们在响应干旱胁迫方面具有重要功能。我们通过杨树共表达网络鉴定了五个与抗旱性相关的关键调控基因(Potri.006G273500、Potri.007G111500、Potri.007G111600、Potri.007G111700 和 Potri.007G111800)。

结论

本研究结果表明,QTL 可以有效提高杨树的抗旱性。这是揭示杨树抗旱相关性状遗传基础的重要一步,并为未来的遗传改良提供了有效的候选基因和分子标记。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/ef47e36a9be6/12870_2022_3613_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/338f309575d2/12870_2022_3613_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/072bcd63dc92/12870_2022_3613_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/a3c9dd11d1a6/12870_2022_3613_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/6a7da2242f36/12870_2022_3613_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/950aa4f451a7/12870_2022_3613_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/ef47e36a9be6/12870_2022_3613_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/338f309575d2/12870_2022_3613_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/072bcd63dc92/12870_2022_3613_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/a3c9dd11d1a6/12870_2022_3613_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/6a7da2242f36/12870_2022_3613_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/950aa4f451a7/12870_2022_3613_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94a/9092850/ef47e36a9be6/12870_2022_3613_Fig6_HTML.jpg

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