Sampling period and disease severity of bacterial wilt significantly affected the bacterial community structure and functional prediction in the sesame rhizosphere soil

IF 3.4 3区 生物学 Q1 PLANT SCIENCES
Ruiqing Wang, Fengjuan Lv, Rujie Lv, Hongxin Lin, Zhihua Zhang, Lingen Wei
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引用次数: 3

Abstract

The changes in the structure and function of bacterial communities present in the rhizosphere soils are of great significance for disease control in sesame, but it remains unclear to date. The present study explored the effects of five sampling periods (F0, day 0 after the first flowering; F11; F21; F32; F48) and three disease severities (H0, healthy; D5, moderate diseased; D9, serious diseased) on the structure and function using high throughput sequencing. The results indicated that the values of the Obs, Shannon, Chao1, and ACE first increased and then decreased in the D5 and D9 groups as plants matured, while an increasing trend was observed in the H0 group. The relative abundances of Actinobacteria and Proteobacteria demonstrated a trend from lower values to increased values and then again to lower values in the later sampling period. In the D9 group, Ralstonia experienced a significant 2.15-fold increase during the F0 period, while Gemmatimonas and Streptomyces demonstrated significant increases of 0.65 and 0.30 percentage points, respectively, in the D9 group during the F11 and F21 periods compared to H0. The predominant phyla in the H0 group during the F0 and F11 periods were p_Verrucomicrobiota and p_Firmicutes, respectively. In the D5 group during the F11 period, there were notable increases in xenobiotics biodegradation and metabolism (7.72%) and amino acid metabolism (0.80%) compared to H0, while nucleotide metabolism, replication and repair, and translation experienced significant decreases of 1.85%, 2.16%, and 1.64%, respectively. This critical period preceding the final flowering influenced the structure and function of bacterial communities in the sesame rhizosphere soil. The available potassium and boron exhibited a significant negative correlation with the phyla Proteobacteria and Crenarchaeota, while these phyla were significantly positively correlated with the genera Sphingomonas, Ramlibacter, and Gemmatimonas in the rhizosphere soil, and Carbohydrate_Metabolism and Cell_Motivity of the bacterial functions. The findings contribute to the understanding of the temporal dynamics of microbial communities and their functions in sesame rhizosphere soil, and their response mechanisms to bacterial wilt severity.

芝麻根际土壤细菌群落结构和功能预测受青萎病采样期和病害严重程度的显著影响
根际土壤细菌群落结构和功能的变化对芝麻病害的控制具有重要意义,但目前尚不清楚。本研究采用高通量测序技术探讨了五个取样期(F0,第一次开花后第0天;F11;F21;F32;F48)和三种疾病严重程度(H0,健康;D5,中度疾病;D9,严重疾病)对结构和功能的影响。结果表明,随着植物成熟,D5和D9组的Obs、Shannon、Chao1和ACE值先升高后降低,而H0组则呈升高趋势。放线菌属和变形菌属的相对丰度显示出从较低值到较高值的趋势,然后在后期采样期间再次降低值。在D9组中,Ralstonia在F0期间显著增加了2.15倍,而在F11和F21期间,与H0相比,D9组的Gemmatimonas和Streptomyces分别显著增加了0.65和0.30个百分点。在F0和F11时期,H0组的优势门分别是p_Verrucomicrobiota和p_Firmicutes。在F11期间,D5组的外源性生物降解和代谢(7.72%)以及氨基酸代谢(0.80%)与H0相比显著增加,而核苷酸代谢、复制和修复以及翻译分别显著下降1.85%、2.16%和1.64%。最后开花前的这一关键时期影响了芝麻根际土壤细菌群落的结构和功能。有效钾和有效硼与变形菌门和克氏菌门呈显著负相关,而这些门与根际土壤中的鞘氨醇单胞菌属、Ramlibacter属和Gemmatimomonas属以及细菌功能的碳水化合物代谢和细胞运动性呈显著正相关。这些发现有助于了解芝麻根际土壤微生物群落的时间动态及其功能,以及它们对青萎病严重程度的反应机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Rhizosphere
Rhizosphere Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
5.70
自引率
8.10%
发文量
155
审稿时长
29 days
期刊介绍: Rhizosphere aims to advance the frontier of our understanding of plant-soil interactions. Rhizosphere is a multidisciplinary journal that publishes research on the interactions between plant roots, soil organisms, nutrients, and water. Except carbon fixation by photosynthesis, plants obtain all other elements primarily from soil through roots. We are beginning to understand how communications at the rhizosphere, with soil organisms and other plant species, affect root exudates and nutrient uptake. This rapidly evolving subject utilizes molecular biology and genomic tools, food web or community structure manipulations, high performance liquid chromatography, isotopic analysis, diverse spectroscopic analytics, tomography and other microscopy, complex statistical and modeling tools.
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