典型农田土壤微生物宏基因组及对人为扰动的响应研究

发布时间：2018-02-03 02:53

本文关键词： 宏基因组学 Geo Chip 微生物群落人为扰动土壤功能过程　出处：《清华大学》2016年博士论文　论文类型：学位论文

【摘要】：微生物是生物地球化学循环的主要驱动者,对甲烷、二氧化碳和氧化亚氮等温室气体的排放起调节作用。气候变化影响微生物群落组成,而微生物所产生的温室气体、碳库和氮库的变化反过来调节气候。因此,气候变化与微生物之间形成环形的反馈机制。但是,微生物调节气候变化的反馈机制尚不明确。另外,土壤类型是否影响微生物对种植植物和施肥的响应还不清楚。基于以上问题,本论文研究了黑龙江海伦站的黑土、河南封丘站的潮土和江西鹰潭站的红壤中的微生物群落组成。2005年,三个站点的土壤被相互移位,利用空间代替时间的概念模拟了环境变化。暨2006年起,每年在原位和移位土壤中种植玉米及施氮磷钾肥。2009年,取黑土在原位和移位样点中的裸土和种植玉米土样,利用Geo Chip 3.0、变性梯度凝胶电泳和磷脂脂肪酸研究环境变化和种植玉米对微生物群落组成和功能基因的影响。结果表明,微生物功能基因多样性、微生物生物量、土壤养分含量和氮循环功能过程都随往南移位而增加。然而,土壤移位对微生物功能基因群落组成的影响被种植玉米掩盖。2011年,取三种土壤在原位和移位点中的裸土、种植玉米和施肥的土样,利用Illumina Mi Seq测序及Geo Chip技术,研究细菌、真菌和功能基因群落的组成。得到如下试验结果:一,不同土壤类型的微生物生物量和群落组成明显不同。土壤p H值、总磷、总钾和年降雨量可以用于解释微生物群落组成。种植玉米改变红壤中的细菌组成和黑土中的真菌组成,但是对潮土中的微生物组成影响较小。二,施肥导致土壤酸化,黑土和红壤中的微生物群落组成的改变与土壤p H值显著相关;然而,潮土中微生物组成没有因施肥而改变,与土壤p H值也没有相关性。三,真菌对环境变化的敏感性高于细菌。对2009年和2011年的样品进行相关性分析,发现裸土中的碳循环基因丰度与呼吸通量强烈相关,氮循环基因与硝化潜力显著相关,为理解这两个功能过程提供了机理解释,增强了微生物群落在温室气体排放模型中的应用潜力。
[Abstract]:Microbes are the main drivers of biogeochemical cycle, which regulate the emission of methane, carbon dioxide and nitrous oxide. Climate change affects the composition of microbial communities. Changes in greenhouse gases, carbon and nitrogen pools produced by microorganisms, in turn, regulate the climate. Thus, there is a circular feedback mechanism between climate change and microbes. The feedback mechanism of microbes regulating climate change is not clear. In addition, it is not clear whether soil types affect the response of microorganisms to planting plants and fertilization. In this paper, the composition of microbial communities in the black soil of Helene station in Heilongjiang, the tidal soil in Fengqiu station in Henan Province and the red soil in Yingtan station in Jiangxi Province were studied. In 2005, the soil of the three stations was shifted to each other. The change of environment was simulated by the concept of space instead of time. Since 2006, maize and nitrogen, phosphorus and potassium fertilizer were planted in situ and in shifting soil every year. 2009. Geo Chip 3.0 was used to take the bare soil of black soil in situ and in situ and maize planting soil samples. Denaturing gradient gel electrophoresis and phospholipid fatty acids were used to study the environmental changes and the effects of maize cultivation on microbial community composition and functional genes. The results showed that microbial functional gene diversity and microbial biomass. Soil nutrient content and nitrogen cycling function increased with southward translocation. However, the effect of soil translocation on the composition of microbial functional gene community was masked by maize planting. 2011. The naked soil of three kinds of soils in situ and translocation sites were used to plant maize and fertilized soil samples. Illumina Mi Seq sequencing and Geo Chip technique were used to study bacteria. The composition of fungi and functional gene communities. The following results were obtained: first, the microbial biomass and community composition of different soil types were significantly different. Total potassium and annual rainfall can be used to explain the composition of microbial community. Maize can change the composition of bacteria in red soil and fungi in black soil, but have little effect on the composition of microorganisms in fluvo-soil. Soil acidification was induced by fertilization, and the change of microbial community composition in black soil and red soil was significantly related to soil pH value. However, microbial composition did not change with fertilization, and there was no correlation with soil pH. The sensitivity of fungi to environmental changes was higher than that of bacteria. The correlation analysis between 2009 and 2011 showed that carbon cycle gene abundance in bare soil was strongly correlated with respiratory flux. Nitrogen cycling genes were significantly correlated with nitrification potential, which provided a mechanism for understanding these two functional processes and enhanced the application potential of microbial communities in greenhouse gas emission models.
【学位授予单位】：清华大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S154.3

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