Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool.
Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool.
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DOI：
10.1128/mbio.00592-13
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发表时间：
2013-09-17
期刊：
影响因子：
6.4
通讯作者：
Cole JR
中科院分区：
文献类型：
--
作者：
Wang Q;Quensen JF 3rd;Fish JA;Lee TK;Sun Y;Tiedje JM;Cole JR
生物固氮是土壤可持续肥力的重要组成部分，也是氮素循环的关键组成部分。我们以固氮酶还原酶(NifH)基因为靶点，使用有针对性的元基因组学来研究具有固氮能力的陆地细菌群落。我们从来自阿拉斯加、夏威夷、犹他州和佛罗里达州的4个国家生态观测网络(NEON)站点的222个土壤样本中获得了110万个nifH454扩增序列。为了准确地检测和纠正因Indel测序错误引起的移码，我们开发了一个移码校正和最近邻分类工具FrameBot，并与另外两种快速移码工具进行了精度比较。我们发现，一般而言，只要与查询具有80%或更高同源性的参考蛋白质序列可用，FrameBot就更准确，就像4个霓虹灯站点的几乎所有nifH读取一样。12.7%的读数出现了移码现象。与变形杆菌门相关的nifH序列最丰富，其次是阿拉斯加和犹他州的蓝藻。NifH序列与REDS相似的优势属包括固氮螺菌、慢生根瘤菌和根瘤菌，后两者在这些地点没有明显的植物寄主。令人惊讶的是，80%的序列与已知的nifH基因序列具有95%以上的氨基酸同源性。这些样本按地点分组，并与土壤环境因素相关，特别是排水、光照强度、年平均温度和年平均降水量。FrameBot在三个生态功能基因上测试成功，但应该适用于任何基因。使用rRNA定向测序对微生物群落进行高通量系统发育分析现在很常见；然而，这种数据往往不能对最重要的生态过程中所涉及的基因的存在或多样性进行推断。为了研究这些过程的基因库，评估直接负责生态功能的基因(生态功能基因)更为直接。然而，分析这些基因涉及的技术挑战超出了rRNA的范围。特别值得一提的是，移码错误会导致下游蛋白质翻译混乱。我们在这里描述的FrameBot工具既可以纠正查询读取中的移码错误，又可以在一组参考序列中确定它们最匹配的蛋白质序列。我们用定义的群落的序列验证了这个新工具，并展示了该工具在从具有良好特征和主要陆地生态系统类型的土壤中测序的nifH基因片段上的实用性。
Biological nitrogen fixation is an important component of sustainable soil fertility and a key component of the nitrogen cycle. We used targeted metagenomics to study the nitrogen fixation-capable terrestrial bacterial community by targeting the gene for nitrogenase reductase (nifH). We obtained 1.1 million nifH 454 amplicon sequences from 222 soil samples collected from 4 National Ecological Observatory Network (NEON) sites in Alaska, Hawaii, Utah, and Florida. To accurately detect and correct frameshifts caused by indel sequencing errors, we developed FrameBot, a tool for frameshift correction and nearest-neighbor classification, and compared its accuracy to that of two other rapid frameshift correction tools. We found FrameBot was, in general, more accurate as long as a reference protein sequence with 80% or greater identity to a query was available, as was the case for virtually all nifH reads for the 4 NEON sites. Frameshifts were present in 12.7% of the reads. Those nifH sequences related to the Proteobacteria phylum were most abundant, followed by those for Cyanobacteria in the Alaska and Utah sites. Predominant genera with nifH sequences similar to reads included Azospirillum, Bradyrhizobium, and Rhizobium, the latter two without obvious plant hosts at the sites. Surprisingly, 80% of the sequences had greater than 95% amino acid identity to known nifH gene sequences. These samples were grouped by site and correlated with soil environmental factors, especially drainage, light intensity, mean annual temperature, and mean annual precipitation. FrameBot was tested successfully on three ecofunctional genes but should be applicable to any. High-throughput phylogenetic analysis of microbial communities using rRNA-targeted sequencing is now commonplace; however, such data often allow little inference with respect to either the presence or the diversity of genes involved in most important ecological processes. To study the gene pool for these processes, it is more straightforward to assess the genes directly responsible for the ecological function (ecofunctional genes). However, analyzing these genes involves technical challenges beyond those seen for rRNA. In particular, frameshift errors cause garbled downstream protein translations. Our FrameBot tool described here both corrects frameshift errors in query reads and determines their closest matching protein sequences in a set of reference sequences. We validated this new tool with sequences from defined communities and demonstrated the tool’s utility on nifH gene fragments sequenced from soils in well-characterized and major terrestrial ecosystem types.