Functional diversity of microbial communities in two contrasting maize rhizosphere soils
Functional diversity of microbial communities in two contrasting maize rhizosphere soils
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DOI：
10.1016/j.rhisph.2020.100282
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发表时间：
2021-03-01
期刊：
影响因子：
3.7
通讯作者：
Kutu, Funso Raphael
中科院分区：
文献类型：
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作者：
Chukwuneme, Chinenyenwa Fortune;Ayangbenro, Ayansina Segun;Kutu, Funso Raphael
微生物功能多样性的表征提供了在各种农业和生物技术过程中理解和操纵它们的机会。本研究采用鸟枪法宏基因组学方法分析了前草地和集约化耕地根际微生物群落的功能多样性和代谢潜力。我们假设功能多样性将在两个玉米田之间变化，并且每个土壤都具有活跃的代谢特征，将其与其他土壤区分开来。从根际和玉米田的大量土壤样品中提取宏基因组DNA，并使用鸟枪法进行测序。结果表明，原草地根际土壤具有14种优势功能，而集约耕地根际土壤具有12种优势功能。在水平2的子系统中，钾代谢是最丰富的功能类别，在GZ 3中观察到的相对丰度最高，为21.32%。在两个领域之间的α多样性研究中未观察到显著差异，Kruskal-Wallis检验也显示样品多样性水平无显著差异（p = 0.99），而从相似性分析（ANOSIM）中获得的R值和p值分别为0.51和0.01。硝酸盐氮（N-NO3）是最具影响力的理化参数，p值为0.01，贡献率为81.7%。此外，未知功能的高丰度解释了玉米微生物组仍然未被充分探索。鉴定具有这些功能的微生物群落将有助于对它们进行建模，以收获它们对农业生产力提高的独特功能效益。
The characterization of microbial functional diversity proffers the opportunity to understand and manipulate them in various agricultural and biotechnological processes. This study analyzed the functional diversity and metabolic potentials of microbial communities in the rhizosphere of a former grassland and an intensively cultivated land using a shotgun metagenomic approach. We assumed that functional diversity will vary between the two maize fields and that each soil has an active metabolic profile that differentiates it from the other. Metagenomic DNA was extracted from the rhizosphere, and bulk soil samples from maize fields and sequencing was performed using the shotgun method. The results showed that 14 functional categories were dominant in the former grassland rhizosphere, while 12 functions predominated the intensively cultivated land. In the subsystems at level 2, potassium metabolism was the most abundant functional category with the highest relative abundance of 21.32% observed in GZ3. No significant difference was observed in the alpha diversity studies between the two fields, the Kruskal-Wallis test also revealed an insignificant difference (p = 0.99) in the diversity levels of samples, while 0.51 and 0.01 were obtained as the R- and p-values, respectively from the analysis of similarity (ANOSIM) performed. Nitrate nitrogen (N-NO3) was the most influential physicochemical parameter, with a pvalue of 0.01 and a contribution % of 81.7%. Furthermore, the high abundance of unknown functions explains that the maize microbiome is still underexplored. Identifying the microbial communities with these functions will help in modeling them to harvest their unique functional benefits for agricultural productivity enhancement.