Identification of Soybean Genes Whose Expression is Affected by the Ensifer fredii HH103 Effector Protein NopP

Abstract

In some legume–rhizobium symbioses, host specificity is influenced by rhizobial nodulation outer proteins (Nops). However, the genes encoding host proteins that interact with Nops remain unknown. We generated an Ensifer fredii HH103 NopP mutant (HH103ΩNopP), and analyzed the nodule number (NN) and nodule dry weight (NDW) of 10 soybean germplasms inoculated with the wild-type E. fredii HH103 or the mutant strain. An analysis of recombinant inbred lines (RILs) revealed the quantitative trait loci (QTLs) associated with NopP interactions. A soybean genomic region containing two overlapping QTLs was analyzed in greater detail. A transcriptome analysis and qRT-PCR assay were used to identify candidate genes encoding proteins that interact with NopP. In some germplasms, NopP positively and negatively affected the NN and NDW, while NopP had different effects on NN and NDW in other germplasms. The QTL region in chromosome 12 was further analyzed. The expression patterns of candidate genes Glyma.12g031200 and Glyma.12g073000 were determined by qRT-PCR, and were confirmed to be influenced by NopP.

Mapping quantitative trait loci related to nodule number in soybean (Glycine max (L.) Merr.) in response to the Sinorhizobium (Ensifer) fredii HH103 NopT type III effector

“…The QTL and candidate genes underlying the symbiosis-related traits were identified in recombinant inbred lines (RILs) based on a linkage map with SNP markers. The responses of QTL to NopL, NopP, and NopD have been studied (Wang et al 2018;Zhang et al 2018;Wang et al 2020). Although previous research has identified several QTL associated with symbiosis-related traits in soybean, more work is needed to identify the genes involved in QTL responses to different T3Es.…”

QTL Mapping and Data Mining to Identify Genes Associated With the Sinorhizobium fredii HH103 T3SS Effector NopD in Soybean

“…To detect the QTL underlying nodule-related traits, a composite interval mapping method was used with WinQTL Cartographer (Wang et al, 2018;Zhang et al, 2018). The detailed parameters followed the published method (Zhang et al, 2018).…”

“…The differences in phenotypic values were used to determine the location of conditional QTL. At the same time, the phenotype differences in RILs inoculated with HH103 TtsI or the parental strains were used to determine the conditional QTL's location (Wang et al, 2018). The detailed method is as follows.…”

“…Our laboratory completed the construction of a high-density genetic map of the RIL population in the early stage (Qi et al, 2014). So the soybean genes in the major QTL intervals could be identified by combining the high-density genetic map information with two important database tools, and corresponding gene annotations were performed (Wang et al, 2018). Candidate genes involved in the plant immunity and signal conduction were selected from the annotation data, which were used for subsequent verification.…”

“…In recent studies in soybean, PP2C-related gene and RPK were detected by QTL mapping and were shown to interact with S. fredii HH103 NopL to regulate the infection of soybean root cells by rhizobia (Zhang et al, 2018). Via QTL mapping, S. fredii HH103 NopP was found to induce the expression of TLP and MAPK3 during rhizobium infection (Wang et al, 2018). The identification and study of T3Es and their interacting genes could enhance the understanding of the signal communication between host plants and rhizobia during the establishment of symbiosis.…”