Physio-molecular responses of tomato cultivars to biotic stress: Exploring the interplay between Alternaria alternata OP881811 infection and plant defence mechanisms

交替链格孢 栽培 生物 园艺 植物抗病性 植物 链格孢 叶斑病 植物对草食的防御 生物化学 基因
作者
Ibrahim A. Adss,Asma A. Al-Huqail,Faheema Khan,Sawsan S. EL-Shamy,Ghoname Amer,E. S. E. Hafez,Omar M. Ibrahim,Sherien E. Sobhy,Ahmed A. Saleh
出处
期刊:Plant Physiology and Biochemistry [Elsevier BV]
卷期号:207: 108421-108421 被引量:3
标识
DOI:10.1016/j.plaphy.2024.108421
摘要

Plant fungal diseases impose a formidable challenge for global agricultural productivity, a meticulous examination of host-pathogen interactions. In this intricate study, an exhaustive investigation was conducted on infected tomatoes obtained from Egyptian fields, leading to the precise molecular identification of the fungal isolate as Alternaria alternata (OP881811), and the isolate showed high identity with Chinese isolates (ON973896 and ON790502). Subsequently, fourteen diverse tomato cultivars; Cv Ferment, Cv 103, Cv Damber, Cv 186, Cv 4094, Cv Angham, Cv N 17, Cv Gesma, Cv 010, Cv branch, cv 2020, Cv 023, Cv Gana and Cv 380 were meticulously assessed to discern their susceptibility levels upon inoculation with Alternaria alternata. Thorough scrutiny of disease symptom manifestation and the extent of tomato leaf damage ensued, enabling a comprehensive evaluation of cultivar responses. Results unveiled a spectrum of plant susceptibility, with three cultivars exhibiting heightened vulnerability (Cv Ferment, Cv 103 and Cv Damber), five cultivars displaying moderate susceptibility (Cv 186, Cv 4094, Cv Angham, Cv N 17 and Cv Gesma), and six cultivars demonstrating remarkable resilience to the pathogen (Cv 010, Cv branch, cv, 2020; Cv 023, Cv Gana and Cv 380). In order to gain a thorough understanding of the underlying physiological patterns indicative of plant resistance against A. alternata, an in-depth exploration of polyphenols, flavonoids, and antioxidant enzymes ensued. These key indicators were closely examined, offering valuable insights into the interplay between plant physiology and pathogen response. Robust correlations emerged, with higher contents of these compounds correlating with heightened susceptibility, while lower levels were indicative of enhanced plant tolerance. In tandem with the physiological assessment, a thorough investigation of four pivotal defensive genes (PR5, PPO, PR3, and POX) was undertaken, employing cutting-edge Real-Time PCR technology. Gene expression profiles displayed intriguing variations across the evaluated tomato cultivars, ultimately facilitating the classification of cultivars into distinct groups based on their levels of resistance, moderate susceptibility, or heightened sensitivity. By unravelling the intricate dynamics of plant susceptibility, physiological responses, and patterns of gene expression, this comprehensive study paves the way for targeted strategies to combat plant fungal diseases. The findings contribute valuable insights into host-pathogen interactions and empower agricultural stakeholders with the knowledge required to fortify crop resilience and safeguard global food security.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
user_huang发布了新的文献求助10
1秒前
1秒前
王楠雪发布了新的文献求助10
1秒前
Bao蕊发布了新的文献求助10
1秒前
2秒前
在水一方应助靓丽的若云采纳,获得10
4秒前
6秒前
7秒前
鑫儿完成签到,获得积分20
8秒前
molihuakai应助justgold采纳,获得10
8秒前
10秒前
10秒前
秋秋完成签到 ,获得积分10
11秒前
shary发布了新的文献求助10
12秒前
小二郎应助内向的振家采纳,获得10
14秒前
14秒前
15秒前
大梅子清清淡淡完成签到,获得积分10
16秒前
17秒前
leecarp发布了新的文献求助10
18秒前
pancake发布了新的文献求助20
19秒前
江xy发布了新的文献求助10
20秒前
whl发布了新的文献求助10
21秒前
李健应助风中的赛凤采纳,获得10
22秒前
huhdcid完成签到,获得积分10
22秒前
25秒前
25秒前
打你完成签到,获得积分10
26秒前
李爱国应助成功的院士采纳,获得10
26秒前
27秒前
RR发布了新的文献求助10
27秒前
jasonwu2024完成签到,获得积分10
28秒前
汉堡包应助111采纳,获得10
30秒前
Ava应助齐小安采纳,获得10
30秒前
腼腆的雪珊完成签到,获得积分10
31秒前
pancake发布了新的文献求助20
31秒前
刘佳辉完成签到,获得积分20
32秒前
32秒前
32秒前
32秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583552
求助须知:如何正确求助?哪些是违规求助? 9162285
关于积分的说明 19606612
捐赠科研通 7165597
什么是DOI,文献DOI怎么找? 3266296
关于科研通互助平台的介绍 2431182
邀请新用户注册赠送积分活动 2257764