Transcriptomes of soybean roots and nodules inoculated with Sinorhizobium fredii with NopP and NopI variants.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Kejing Fan, Zhixia Xiao, Liping Wang, Wai-Lun Cheung, Fuk-Ling Wong, Feng Zhang, Man-Wah Li, Hon-Ming Lam
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Abstract

The major crop, soybean, forms root nodules with symbiotic rhizobia, providing energy and carbon to the bacteria in exchange for bioavailable nitrogen. The relationship is host-specific and highly host-regulated to maximize energy efficiency. Symbiotic nitrogen fixation (SNF) is greener than synthetic fertilizer for replenishing soil fertility, contributing to yield increase. Nodulation Outer Protein P (NopP) and NopI of the type 3 secretion system (T3SS) of the rhizobium determine host specificity. Sinorhizobium fredii CCBAU25509 (R2) and CCBAU45436 (R4) have different NopP and NopI variants, affecting their respective symbiotic compatibilities with the cultivated soybean C08 and the wild soybean W05. Swapping the NopP variants between R2 and R4 has been shown to switch their compatibility with C08 with the rj2/Rfg1 genotype. To understand the effects of Nops on host compatibility, analyses on the transcriptomic data of W05 roots and nodules inoculated with S. fredii strains containing Nop variants uncovered many differentially expressed genes related to nodulation and nodule functions, providing important information on the effects of Nops on hosts and nodules.

用带有 NopP 和 NopI 变体的 fredii 中果镰刀菌接种大豆根部和结节的转录组。
主要作物大豆与共生根瘤菌形成根瘤,为细菌提供能量和碳,以换取生物可用氮。这种关系具有宿主特异性,并受到宿主的高度调控,以最大限度地提高能量效率。在补充土壤肥力方面,共生固氮(SNF)比合成肥料更环保,有助于提高产量。根瘤菌 3 型分泌系统(T3SS)的结节外蛋白 P(NopP)和 NopI 决定了宿主的特异性。Fredii 中根瘤杆菌 CCBAU25509(R2)和 CCBAU45436(R4)具有不同的 NopP 和 NopI 变体,这影响了它们与栽培大豆 C08 和野生大豆 W05 的共生相容性。事实证明,交换 R2 和 R4 的 NopP 变体,就能改变它们与 C08 的 rj2/Rfg1 基因型的相容性。为了了解 Nops 对宿主兼容性的影响,对接种了含有 Nop 变体的 S. fredii 菌株的 W05 根和结节的转录组数据进行了分析,发现了许多与结瘤和结节功能有关的差异表达基因,为了解 Nops 对宿主和结瘤的影响提供了重要信息。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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