Recommendations about soil Biological Nitrification Inhibition (BNI) studies
Recommendations about soil Biological Nitrification Inhibition (BNI) studies
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DOI：
10.1007/s00374-022-01645-w
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发表时间：
2022-05
影响因子：
6.5
通讯作者：
P. Nardi;Christoph Müller;G. Pietramellara;G. Subbarao;P. Nannipieri
中科院分区：
文献类型：
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作者：
P. Nardi;Christoph Müller;G. Pietramellara;G. Subbarao;P. Nannipieri
在2019-2021年，《土壤生物学与肥力》发表了14篇以生物硝化抑制为重点的研究文章，并以“生物硝化抑制”特刊的形式增加了一份出版物，其中包括一篇社论、两篇评论、一篇立场/意见书和九篇研究文章（见生物学和土壤肥力58第3期，2022），反映了广泛的研究兴趣，在这个话题。此外，使用术语“BNI”在Scopus上进行的文献检索返回了103篇文献，其中97篇是研究文章，6篇是综述。已发表的BNI研究大致分为两类：1）那些侧重于筛选和2）那些更侧重于测试土壤系统中的BNI，一些研究包括BNI研究的两个方面。筛选研究的主要目标是通过收集能够抑制硝化菌纯培养物中氨氧化的化合物并随后对其进行化学表征来鉴定具有BNI潜力的植物。这些研究的一个毋庸置疑的优点是，它们导致了几种具有BNI潜力的化合物的发现，澄清了释放机制以及它们对硝化菌纯培养物的抑制能力。回到土壤中BNI的测试，这些研究的一个共同点是，使用基于DNA的方法结合潜在硝化活性和/或净硝化速率（即NO3-的生产和消耗之间的平衡）评估具有BNI能力的植物或BNI化合物的影响。在这里，通过定量PCR（qPCR）估计的氨氧化菌（即氨氧化古菌（AOA）和氨氧化细菌（AOB））丰度的减少，沿着土壤NO3-浓度、净硝化速率或潜在硝化活性的降低，通常被解释为BNI的支持或证据（Kaur-Bhambra等人，2022）。然而，基于DNA的方法和净速率测量都有重要的局限性，BNI研究人员在推断因果关系之前需要认识到这一点（Stark和哈特，1997年; Nardi等人，2020年）。这些方法的缺点如下所述。1）基于DNA的方法，即评估amoA基因库，不能区分从活细胞释放的DNA和从死细胞释放的DNA。此外，即使来源于活细胞，基因的检测也不一定意味着其表达。因此，基因丰度的变化仅反映了功能潜力的变化，而不是活性的变化（Nannipieri等人，2020）。如果与基因转录本（即amoA mRNA）的测量相结合，假设转录本的半衰期比基因的半衰期短，则可以部分规避基于DNA的方法的局限性。毫无疑问，BNI研究将受益于基因丰度、转录本和转录本/基因丰度比的同时分析，后者是基因丰度的代表。
In 2019-2021, Biology and Fertility of Soils published 14 research articles focusing on BNI and added an additional publication in the form of a Special Issue on “Biological Nitrification Inhibition”, which included an editorial, two reviews, a position/opinion paper and nine research articles (see Biology and Fertility of Soils 58 issue 3, 2022), reflecting the broad research interest in this topic. Moreover, a literature search on Scopus using the term “BNI” returned 103 documents, 97 of which were research articles and 6 were reviews. Published BNI studies fall broadly into two categories: 1) those that focus on screening and 2) those focusing more on testing BNI in soil systems, with some studies including both aspects of BNI research. The main goal of screening studies is to identify plants with BNI potential by collecting and subsequently chemical characterization of compounds capable of inhibiting ammonia oxidation in pure culture of nitrifiers. An unquestionable merit of these studies is that they have led to the discovery of several compounds with BNI potential, clarification of the release mechanisms as well as their inhibitory capacity of pure culture of nitrifiers. Returning to the testing of BNI in soils, a common aspect shared by these studies is that the effects of either plants with BNI capacity or BNI compounds are assessed using DNA-based approaches coupled with measurements of potential nitrification activity and/or net nitrification rate (ie the balance between production and consumption of NO3-). Here, decrease in abundances of ammonia oxidisers, ie ammonia oxidizing archaea (AOA) and ammonia oxidising bacteria (AOB), estimated by quantitative PCR (qPCR), along with reduced soil NO3-concentrations, net nitrification rates or potential nitrification activity, is generally interpreted as support or evidence for BNI (Kaur-Bhambra et al. 2022). However, both DNA-based approaches and net rate measurements have important limitations that BNI researchers need to recognize before inferring causation (Stark and Hart 1997; Nardi et al. 2020). Drawbacks of these approaches are reported below.1) DNA-based approaches, ie assessment of the pool of amoA genes, do not discriminate between DNA released from living cells and DNA released from dead cells. In addition, even if sourced from living cells, the detection of the gene does not necessarily imply its expression. Therefore, changes in gene abundances only mirror changes in the functional potential and not activity (Nannipieri et al. 2020). Limitations of DNA-based approaches may partially be circumvented if combined with measurements of gene transcripts, ie, amoA mRNA, with the assumption of shorter half-life of transcripts compared to that of genes. Undoubtedly, BNI studies would benefit from the concurrent analysis of gene abundance, transcripts and the transcripts/gene abundance ratio, the latter being a proxy of gene