这是用户在 2024-6-26 10:22 为 https://app.immersivetranslate.com/pdf-pro/36549c74-831e-4da0-8332-e3c1ec6ebda3 保存的双语快照页面,由 沉浸式翻译 提供双语支持。了解如何保存?
2024_06_26_39360094b552ddfc8d60g

In-depth characterization of denitrifier communities across different soil ecosystems in the tundra
在苔原不同土壤生态系统中对反硝化细菌群落进行深入表征

Igor S. Pessi , Sirja Viitamäki , Anna-Maria Virkkala , Eeva Eronen-Rasimus , Tom O. Delmont ,
伊戈尔·S·佩西 ,西尔雅·维塔马基 ,安娜-玛丽亚·维尔卡拉 ,埃娃·埃罗宁-拉西穆斯 ,汤姆·O·德尔蒙特
Maija E. Marushchak , Miska Luoto and Jenni Hultman
Maija E. Marushchak ,Miska Luoto 和 Jenni Hultman

Abstract 摘要

Background: In contrast to earlier assumptions, there is now mounting evidence for the role of tundra soils as important sources of the greenhouse gas nitrous oxide ( . However, the microorganisms involved in the cycling of in this system remain largely uncharacterized. Since tundra soils are variable sources and sinks of , we aimed at investigating differences in community structure across different soil ecosystems in the tundra.
背景:与早期的假设相反,现在有越来越多的证据表明苔原土壤作为温室气体一氧化二氮的重要来源。然而,在这个系统中参与 循环的微生物仍然大多没有被表征。由于苔原土壤是 的可变来源和汇,我们的目标是调查苔原不同土壤生态系统之间的群落结构差异。

Results: We analysed 1.4 Tb of metagenomic data from soils in northern Finland covering a range of ecosystems from dry upland soils to water-logged fens and obtained 796 manually binned and curated metagenome-assembled genomes (MAGs). We then searched for MAGs harbouring genes involved in denitrification, an important process driving emissions. Communities of potential denitrifiers were dominated by microorganisms with truncated denitrification pathways (i.e., lacking one or more denitrification genes) and differed across soil ecosystems. Upland soils showed a strong sink potential and were dominated by members of the Alphaproteobacteria such as Bradyrhizobium and Reyranella. Fens, which had in general net-zero fluxes, had a high abundance of poorly characterized taxa affiliated with the Chloroflexota lineage Ellin6529 and the Acidobacteriota subdivision Gp23.
结果:我们分析了来自芬兰北部土壤的 1.4 Tb 的宏基因组数据,涵盖了从干旱高地土壤到水浸泽地的一系列生态系统,并获得了 796 个手动分选和筛选的宏基因组组装基因组(MAGs)。然后,我们搜索了携带参与反硝化作用的基因的 MAGs,这是驱动 排放的重要过程。潜在反硝化细菌群落以缺少一个或多个反硝化基因的微生物为主导,并在土壤生态系统之间存在差异。高地土壤显示出强烈的 汇潜力,并以 Alphaproteobacteria 的成员(如 Bradyrhizobium 和 Reyranella)为主导。总体上净零 通量的泥炭地具有与 Chloroflexota 谱系 Ellin6529 和 Acidobacteriota 亚门 Gp23 相关的未充分表征的类群丰度较高。

Conclusions: By coupling an in-depth characterization of microbial communities with in situ measurements of fluxes, our results suggest that the observed spatial patterns of fluxes in the tundra are related to differences in the composition of denitrifier communities.
结论:通过将微生物群落的深入表征与原位 通量测量相结合,我们的研究结果表明,苔原地区 通量的观察到的空间模式与反硝化菌群落组成的差异有关。

Keywords: Arctic, Denitrification, Genome-resolved metagenomics, Nitrous oxide
关键词:北极,脱氮,基因组解析的宏基因组学,一氧化二氮

Background 背景

Nitrous oxide is a greenhouse gas (GHG) that has approximately 300 times the global warming potential of carbon dioxide on a 100-year scale [1]. Atmospheric concentrations have increased by nearly since preindustrial times, with soils-both natural and anthropogenic-accounting for up to of the global emissions
一氧化二氮 是一种温室气体(GHG),在 100 年的时间尺度上,其全球变暖潜力约为二氧化碳的 300 倍[1]。大气 浓度自工业化前几乎增加了 ,其中包括天然和人为的土壤占全球排放量的高达
[2]. Despite being nitrogen limited and enduring low temperatures throughout most of the year, tundra soils are increasingly recognized as important sources of [3-7]. The relative contribution of tundra soils to global GHG emissions is predicted to increase in the future , 9], as the warming rate at high latitude environments is more than twice as high than in other regions [10].
尽管苔原土壤受氮素限制且大部分时间内温度较低,但越来越多地认识到苔原土壤是重要的温室气体排放源[3-7]。预测未来苔原土壤对全球温室气体排放的相对贡献将增加[9],因为高纬度环境的升温速度是其他地区的两倍以上[10]。
Microbial denitrification is an important source of [11]. Denitrification is a series of enzymatic steps in which nitrate is sequentially reduced to nitrite , nitric oxide , and dinitrogen via the activity of the Nar, Nir, Nor, and Nos enzymes, respectively. The denitrification trait is common across a wide range of archaea, bacteria, and some fungi, most of which are facultative anaerobes that switch to oxides as electron acceptor when oxygen becomes limiting [12]. Denitrification is a modular community process performed in synergy by different microbial taxa that execute only a subset of the complete denitrification pathway . With the growing number of microbial genomes sequenced in recent years, it has become evident that only a fraction of the microorganisms involved in the denitrification pathway encode the enzymatic machinery needed for complete denitrification .
微生物反硝化是一种重要的 来源[11]。反硝化是一系列酶催化的步骤,其中硝酸盐 依次还原为亚硝酸盐 、一氧化氮 和二氮 ,通过 Nar、Nir、Nor 和 Nos 酶的活性分别完成。反硝化特征在广泛的古菌、细菌和一些真菌中普遍存在,其中大多数是需氧性厌氧菌,在氧气限制时切换为 氧化物作为电子受体[12]。反硝化是一个模块化的群落过程,由不同微生物类群协同执行,只执行完整反硝化途径 的子集。随着近年来微生物基因组测序数量的增加,已经明显地看到,参与反硝化途径的微生物只有一小部分编码了完成反硝化所需的酶机制
Compared to high -emitting systems such as agricultural and tropical soils, our knowledge of denitrifier communities in tundra soils is limited. As denitrification leads to the loss of to the atmosphere, it enhances the -limited status of tundra systems thus impacting both microbial and plant communities [16, 17]. Investigations of denitrifier diversity in the tundra have been largely limited to gene-centric surveys using microarrays, amplicon sequencing, qPCR, and read-based metagenomics, which provide limited information on the taxonomic identity and genomic composition of community members. These studies have shown that denitrifier communities in the tundra are dominated by members of the phyla Proteobacteria, Actinobacteria, and Bacteroidetes, and that the potential for complete denitrification is usually present at the community level [18-22]. However, it is not known whether the complete denitrification potential occurs within discrete microbial populations or is widespread throughout populations of truncated denitrifiers lacking one or more denitrification genes. In addition, tundra soils encompass many different ecosystems, some of which are notorious sources (e.g. bare peat surfaces [3]). consumption is usually favoured in wetlands, where low availability due to anoxia promotes the reduction of to [23]. In upland soils, fluxes vary in both time and space. Strong sinks have been observed specially in sparsely vegetated upland soils [7], but the microbial processes underlying consumption in these systems are largely unknown [24]. Altogether, these large differences in fluxes across tundra ecosystems indicate differences in the structure of microbial communities, but a comprehensive understanding of the microorganisms driving fluxes in tundra soils is lacking.
与高排放系统(如农业和热带土壤)相比,我们对苔原土壤中反硝化细菌群落的了解有限。由于反硝化导致 流失到大气中,它增强了苔原系统的 限制状态,从而影响了微生物和植物群落[16, 17]。对苔原中反硝化细菌多样性的研究主要限于基因为中心的调查,包括微阵列、引物测序、qPCR 和基于读取的宏基因组学,这些方法提供了有限的关于群落成员的分类身份和基因组组成的信息。这些研究表明,苔原中的反硝化细菌群落主要由变形菌门、放线菌门和拟杆菌门的成员主导,而完全反硝化的潜力通常存在于群落水平[18-22]。然而,目前尚不清楚完全反硝化潜力是在离散微生物种群内发生,还是在缺少一个或多个反硝化基因的截短反硝化细菌种群中广泛存在。 此外,苔原土壤包括许多不同的生态系统,其中一些是臭名昭著的来源(例如裸露的泥炭表面)。在湿地中通常偏好消耗,因为缺氧导致低氧化还原电位促进氧化还原反应。在高地土壤中,气体通量在时间和空间上都有所变化。尤其在稀疏植被的高地土壤中观察到强烈的甲烷汇,但这些系统中甲烷消耗的微生物过程大部分是未知的。总的来说,苔原生态系统中甲烷通量的巨大差异表明微生物群落结构存在差异,但对驱动苔原土壤甲烷通量的微生物的全面理解仍然缺乏。
Modelling emissions based on microbial community structure is challenging. fluxes are characterized by a high temporal and spatial heterogeneity driven by several environmental constraints related to soil , , moisture, and oxygen content [11]. In addition, our knowledge of the regulation of the denitrification process is largely based on the activity of model organisms such as the complete denitrifier Paracoccus denitrificans [25].
基于微生物群落结构建模 排放是具有挑战性的。 通量的特点是高时空异质性,受到与土壤 、湿度和氧含量相关的几个环境约束的驱动[11]。此外,我们对反硝化过程调控的认识主要基于模式生物的活性,如完全反硝化菌 Paracoccus denitrificans[25]。

It has been suggested that incomplete denitrifiers that contain Nir and Nor but lack Nos contribute substantially to soil emissions [26], while non-denitrifying reducers, i.e., microorganisms that contain Nos but lack Nir, can represent an important sink [27-29]. Furthermore, the partitioning of metabolic pathways across different populations with truncated pathwaysalso known as metabolic handoffs [30]-has been linked to higher efficiencies in substrate consumption compared to complete pathways . However, it remains largely unclear how populations of truncated denitrifiers with different sets of denitrification genes interact with each other and the environment impacting in situ emissions.
已有研究表明,含有 Nir 和 Nor 但缺乏 Nos 的不完全反硝化细菌在土壤 排放中起着重要作用[26],而非反硝化 还原细菌,即含有 Nos 但缺乏 Nir 的微生物,可能代表一个重要的 汇[27-29]。此外,代谢途径在不同群体之间的分配,即被称为代谢交接[30],已被证明与废物利用效率比完整途径 更高有关。然而,截断反硝化细菌群体如何相互作用以及如何影响环境从而影响原位 排放仍然不太清楚。
The paucity of in-depth knowledge on denitrifying communities in the tundra impairs our ability to model current and future fluxes from this biome. A better understanding of the ecological, metabolic, and functional traits of denitrifiers is thus critical for improving current models and mitigating emissions [32]. This invariably relies on the characterization of the so-called uncultured majority, i.e., microorganisms that have not been cultured to date but which comprise a high proportion of the microbial diversity in complex ecosystems [33, 34]. Genome-resolved metagenomics is a powerful tool to access the genomes of uncultured microorganisms and has provided important insights into carbon cycling processes in tundra soils [35-37]. However, this approach has not yet been applied to investigate the mechanisms driving fluxes in the tundra. Here, we used genomeresolved metagenomics to investigate the diversity and metabolic capabilities of denitrifiers across different tundra soil ecosystems characterised by a high variability in net fluxes in an area of mountain tundra in Kilpisjärvi, northern Finland.
缺乏对苔原反硝化群落的深入了解,影响了我们模拟当前和未来从这一生物群落中排放的氮气通量的能力。因此,更好地理解反硝化细菌的生态、代谢和功能特征对于改进当前模型和减少氮气排放至关重要。这不可避免地依赖于所谓的未培养多数的表征,即迄今尚未培养的微生物,但它们在复杂生态系统中占据着很高的微生物多样性比例。基因组解析宏基因组学是一种强大的工具,可用于获取未培养微生物的基因组,并已为苔原土壤中的碳循环过程提供了重要见解。然而,这种方法尚未应用于研究驱动苔原氮气通量的机制。在这里,我们使用基因组解析宏基因组学研究了不同苔原土壤生态系统中反硝化细菌的多样性和代谢能力,这些生态系统的净氮气通量在芬兰北部 Kilpisjärvi 的山地苔原地区具有很高的变异性。

Methods 方法

Study area and sampling
研究区域和取样

The Saana Nature Reserve ( is located in Kilpisjärvi, northern Finland (Fig. 1a). The area is part of the mountain tundra biome and is characterized by a mean annual temperature of and annual precipitation of [38]. Sampling was performed across 43 sites during the peak of the growing season in the northern hemisphere. Our study sites are distributed across Mount Saana and Mount Korkea-Jehkas and the valley in between (Fig. 1b), and include barren soils ), heathlands (dominated by evergreen and deciduous shrubs) ), meadows (dominated by graminoids and forbs) , and fens (Fig. 1c). Elevation across the sampling sites varies from 586.6 to 904.5 m.a.s.l. (Additional file 1: Table S1). Fen sites were sampled in July 2018 and all other sites in July 2017. Samples were obtained
萨阿纳自然保护区( 位于芬兰北部基尔皮斯亚尔维(图 1a)。该地区属于山地苔原生物群落,其特点是平均年温度为 ,年降水量为 [38]。在北半球生长季节的高峰期间,我们在 43 个地点进行了采样。我们的研究地点分布在萨阿纳山和科尔凯亚-耶赫卡斯山之间的山谷(图 1b),包括贫瘠土壤 ),荒地(以常绿和落叶灌木为主) ),草地(以禾本科植物和草本植物为主) ,以及沼泽地 (图 1c)。采样地点的海拔从 586.6 米至 904.5 米不等(附加文件 1:表 S1)。沼泽地点于 2018 年 7 月进行采样,其他所有地点于 2017 年 7 月进行采样。样本已获得。

with a soil corer sterilized with ethanol and, when possible, cores were split into organic and mineral samples using a sterilized spatula. In total, 69 samples ( 41 organic and 28 mineral) were obtained from the 43 sites (Fig. 1c, Additional file 1: Table S1). Samples were transferred to a whirl-pack bag and immediately frozen in dry ice. Samples were transported frozen to the laboratory at the University of Helsinki and kept at until analyses.
使用用 乙醇消毒的土壤取样器,尽可能地,使用消毒过的铲子将样品分为有机和矿物样品。总共从 43 个站点(图 1c,附加文件 1:表 S1)获得了 69 个样品(41 个有机样品和 28 个矿物样品)。样品被转移到旋转包装袋中,并立即在干冰中冷冻。样品被冷冻运送到赫尔辛基大学实验室,并保存在 进行分析。

Soil physicochemical characterization and in situ measurement of GHG fluxes
土壤理化特性表征和温室气体通量原位测量

Soil , moisture, and soil organic matter (SOM) content were measured from the 69 samples according to Finnish (SFS) and international (ISO) standards (SFS 300, ISO 10390, and SFS 3008). Carbon (C) and N content were measured using a Vario Micro Cube machine (Elementar, Langenselbold, Germany). In situ ecosystem-level and methane fluxes were measured from the 43 sites using a static, non-steady state, non-flow-through system composed of a darkened acrylic chamber diameter, height) [4,39]. Measurements were conducted between 2nd July and 2nd August 2018, between 10 am and . Simultaneous measurement of GHG fluxes and sampling for metagenomic sequencing was not possible due to limited resources and logistic constraints. At each site, five gas samples were taken during a 50 -min chamber closure and transferred to evacuated Exetainer vials (Labco, Lampeter, UK). Gas samples were analysed using an Agilent 7890B gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with an autosampler (Gilson, Middleton, WI, USA) and a flame ionization detector for and an electron capture detector for . Gas concentrations were calculated from the gas chromatograph peak areas based on standard curves with a concentration of and a concentration of .
土壤 ,湿度和土壤有机质(SOM)含量根据芬兰(SFS)和国际(ISO)标准(SFS 300,ISO 10390 和 SFS 3008)从 69 个样品中测量。碳(C)和氮含量使用 Vario Micro Cube 机器(Elementar,Langenselbold,德国)测量。在 43 个站点使用静态、非稳态、非流通系统(由一个暗色丙烯酸室组成,直径 ,高 )测量原位生态系统水平 和甲烷 通量[4,39]。测量时间为 2018 年 7 月 2 日至 8 月 2 日,时间为上午 10 点至 。由于资源有限和后勤限制,无法同时测量温室气体通量并进行元基因组测序取样。在每个站点,通过 50 分钟的室内关闭取五个 气体样品,并转移到抽空的 Exetainer 瓶(Labco,Lampeter,英国)。气体样品使用装有自动进样器(Gilson,Middleton,WI,美国)和用于 的火焰离子化检测器以及用于 的电子俘获检测器的 Agilent 7890B 气相色谱仪(Agilent Technologies,Santa Clara,CA,美国)进行分析。 气体浓度是根据气相色谱峰面积和标准曲线计算得出的,其中 浓度为 浓度为
Differences in physicochemical composition and rates of GHG fluxes across soil ecosystems and depths were assessed using one-way analysis of variance (ANOVA) followed by Tukey's HSD test with the and TukeyHSD functions in R v3.6.3 [40]. The relationship between soil ecosystem, depth, and physicochemical properties was also verified using a multivariate approach consisting of principal component analysis (PCA) and permutational ANOVA (PERMANOVA) with the package vegan v2.5-6 in v3.6.3 (functions and adonis, respectively) [40, 41]. C, N, and C:N ratio were not included in the multivariate dataset due to a high amount of missing data, and moisture and SOM were log-transformed prior to analysis. Due to the limited number of samples from barren sites, these were not included in the ANOVA and PERMANOVA procedures.
通过使用一元方差分析(ANOVA)评估了土壤生态系统和深度之间的物理化学组成差异和温室气体通量速率,随后使用 R v3.6.3 中的 和 TukeyHSD 函数进行图基的 HSD 检验[40]。还使用了多元方法验证了土壤生态系统、深度和物理化学性质之间的关系,包括主成分分析(PCA)和包含 vegan v2.5-6 的 permutational ANOVA(PERMANOVA)[40, 41]。由于缺失数据量较大,C、N 和 C:N 比率未包含在多元数据集中,而湿度和 SOM 在分析之前进行了对数转换。由于贫瘠地点样本数量有限,因此未将其纳入 ANOVA 和 PERMANOVA 程序中。

Metagenome sequencing and processing of raw data
宏基因组测序和原始数据处理

Total DNA and RNA were co-extracted as previously described [42]. Briefly, extraction was performed on of soil using a hexadecyltrimethyl ammonium bromide (CTAB), phenol-chloroform, and bead-beating protocol. DNA was purified using the AllPrep DNA Mini Kit (QIAGEN, Hilden, Germany) and quantified using the Qubit dsDNA BR Assay Kit (ThermoFisher Scientific, Waltham, MA, USA). Library preparation for Illumina metagenome sequencing was performed using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). Metagenomes were obtained for the 69 samples across two paired-end NextSeq (132-170 bp) and one NovaSeq ( runs. Two samples were additionally sequenced with Nanopore MinION. For this, libraries were prepared using the SQK-LSK109 Ligation Sequencing Kit with the long fragment buffer (Oxford Nanopore Technologies, Oxford, UK) and the NEBNext Companion Module for Oxford Nanopore Technologies Ligation Sequencing Kit (New England Biolabs). Each sample was sequenced for on one R9.4 flow cell.
总 DNA 和 RNA 如前所述一起提取[42]。简而言之,使用十六烷基三甲基溴化铵(CTAB)、苯酚-氯仿和珠破碎方案在 土壤上进行提取。DNA 使用 AllPrep DNA Mini Kit(QIAGEN,德国希尔登)纯化,并使用 Qubit dsDNA BR Assay Kit(ThermoFisher Scientific,美国马萨诸塞州沃尔瑟姆)进行定量。 Illumina 宏基因组测序的文库制备使用 Nextera XT DNA 文库制备套件(Illumina,美国加利福尼亚州圣地亚哥)。69 个样本的宏基因组分别通过两个配对末端 NextSeq(132-170 bp)和一个 NovaSeq( 运行获得。另外两个样本还使用 Nanopore MinION 进行测序。为此,使用 SQK-LSK109 连接测序套件与长片段缓冲液(Oxford Nanopore Technologies,英国牛津)和 NEBNext Companion Module for Oxford Nanopore Technologies 连接测序套件(New England Biolabs)制备文库。每个样本在一个 R9.4 流式细胞上测序
We obtained more than 9 billion Illumina (1.4 Tb) and 7 million Nanopore ( ) reads from the 69 soil metagenomes (mean: , minimum: , maximum: ) (Additional file 1: Table S1). The quality of the raw Illumina data was verified with fastQC v0.11.9 [43] and multiQC v1.8 [44]. Cutadapt v1.16 [45] was then used to trim sequencing adapters and low-quality base calls and to filter out short reads ( ). Nanopore data were basecalled with GPU guppy v4.0.11 using the high-accuracy model and applying a minimum quality score of 7. The quality of the basecalled Nanopore data was assessed with pycoQC v2.5.0.21 [46] and adapters were trimmed with Porechop v0.2.4 [47].
我们从 69 个土壤宏基因组中获得了超过 90 亿个 Illumina(1.4 Tb)和 700 万个 Nanopore( )reads(平均值: ,最小值: ,最大值: )(附加文件 1:表 S1)。原始 Illumina 数据的质量经过了 fastQC v0.11.9 [43]和 multiQC v1.8 [44]的验证。然后使用 Cutadapt v1.16 [45]修剪测序适配器和低质量碱基呼叫 ,并过滤掉短 reads( )。Nanopore 数据使用 GPU guppy v4.0.11 进行碱基呼叫,使用高准确度模型,并应用最小质量分数为 7。通过 pycoQC v2.5.0.21 [46]评估了碱基呼叫的 Nanopore 数据质量,并使用 Porechop v0.2.4 [47]修剪了适配器。
c
Fig. 1 (See legend on previous page.)
图 1(请参见前一页的图例。)

Taxonomic profiling 分类概况

Taxonomic profiles of the microbial communities were obtained using a read-based approach, i.e., based on unassembled Illumina data. Due to differences in sequencing depth across the samples, the dataset was resampled to reads per sample with seqtk v1.3 [48]. Reads matching the SSU rRNA gene were identified with METAXA v2.2 [49] and classified against the SILVA database release 138.1 [50] in mothur v1.44.3 [51] using the Wang's Naïve Bayesian Classifier [52] and a confidence cut-off. Differences in community structure across soil ecosystems and depths were assessed using non-metric multidimensional scaling (NMDS) and PERMANOVA with the package vegan v2.5-6 in v3.6.3 (functions metaMDS and adonis, respectively) [40, 41]. The relationship between community structure, soil physicochemical properties, and elevation was also assessed using PERMANOVA and distance-based redundancy analysis (db-RDA) with forward selection with the package vegan v2.5-6 in v3.6.3 (functions adonis and capscale/ordistep, respectively) [40, 41]. The physicochemical dataset included only , moisture, and SOM due to a high amount of missing data for the other variables, and barren sites were not included in the PERMANOVA procedure due to the limited number of samples. Moisture and SOM were log-transformed prior to analysis. Relationships between the abundance of individual genera and flux rates were assessed using linear regression in v3.6.3 [40].
使用基于读取的方法获得了微生物群落的分类学概况,即基于未组装的 Illumina 数据。由于样本间测序深度的差异,数据集使用 seqtk v1.3 [48]重新采样至每个样本 个读取。使用 METAXA v2.2 [49]识别与 SSU rRNA 基因匹配的读取,并在 mothur v1.44.3 [51]中使用 Wang 的朴素贝叶斯分类器 [52]和 置信度截断值对其进行分类,参考 SILVA 数据库发布版本 138.1 [50]。使用 vegan v2.5-6 软件包在 v3.6.3 中(分别使用 metaMDS 和 adonis 函数)[40, 41]进行非度量多维尺度分析(NMDS)和 PERMANOVA 评估土壤生态系统和深度间的群落结构差异。还使用 vegan v2.5-6 软件包在 v3.6.3 中(分别使用 adonis 和 capscale/ordistep 函数)[40, 41]进行 PERMANOVA 和基于距离的冗余分析(db-RDA)以及前向选择评估群落结构、土壤理化性质和海拔之间的关系。 物理化学数据集仅包括 、湿度和 SOM,因为其他变量的数据缺失较多,贫瘠地点未包括在 PERMANOVA 程序中,因为样本数量有限。在分析之前,湿度和 SOM 进行了对数转换。使用线性回归在 v3.6.3 [40]中评估个体属的丰度与 通量率之间的关系。

Metagenome assembling and binning
宏基因组组装和分箱

Metagenome assembling of the Illumina data was performed as two co-assemblies. One co-assembly comprised the upland soils (barren, heathland, and meadow; and the other the fen samples . For each co-assembly, reads from the respective samples were pooled and assembled with MEGAHIT v1.1.1.2 [53]. Assembling of the Nanopore data was done for each sample individually with metaFlye v2.7.1 [54], and contigs were corrected based on Illumina data from the respective sample with bowtie v2.3.5 [55], SAMtools v1.9 [56], and pilon v1.23 [57]. Quality assessment of the (co-) assemblies was obtained with metaQUAST v5.0.2 [58].
Illumina 数据的宏基因组组装分为两个共同组装。一个共同组装包括高地土壤(贫瘠、荒地和草地; ,另一个是沼泽样本 。对于每个共同组装,来自相应样本的 reads 被汇集并使用 MEGAHIT v1.1.1.2 [53]进行组装。Nanopore 数据的组装是针对每个样本单独进行的,使用 metaFlye v2.7.1 [54],并根据相应样本的 Illumina 数据使用 bowtie v2.3.5 [55]、SAMtools v1.9 [56]和 pilon v1.23 [57]进行校正 contigs。使用 metaQUAST v5.0.2 [58]对(共同)组装的质量进行评估。
Binning of metagenome-assembled genomes (MAGs) was done separately for each Illumina and Nanopore (co-)assembly with anvi'o v6.2 [59] after discarding contigs shorter than . The two Illumina co-assemblies and the two individual Nanopore assemblies yielded more than 4 million contigs longer than , with a total assembly size of . Gene calls were predicted with prodigal v2.6.3 [60]. Single-copy genes were identified with HMMER v.3.2.1 [61] and classified with DIAMOND v0.9.14 [62] against the Genome Taxonomy
对于每个 Illumina 和 Nanopore(共同)组装,使用 anvi'o v6.2 [59] 分别对宏基因组组装的基因组(MAGs)进行分箱,丢弃长度小于 的 contigs。两个 Illumina 共同组装和两个单独的 Nanopore 组装产生了超过 4 百万个长度大于 的 contigs,总组装大小为 。基因调用使用 prodigal v2.6.3 [60] 进行预测。单拷贝基因使用 HMMER v.3.2.1 [61] 进行识别,并使用 DIAMOND v0.9.14 [62] 对 Genome Taxonomy 进行分类。

Database (GTDB) release 04-RS89 [63, 64]. Illumina reads were mapped to the contigs with bowtie v2.3.5 [55] and SAM files were sorted and indexed using SAMtools v1.9 [56]. The co-assemblies covered a significant fraction of the original metagenomic data, with an average read recruitment rate of across samples (minimum: , maximum: ). Due to their large sizes, Illumina co-assemblies were split into 100 smaller clusters based on differential coverage and tetranucleotide frequency with CONCOCT v1.0.0 [65]. Contigs were then manually sorted into bins based on the same composition and coverage metrics using the anvi-interactive interface in anvi'o v6.2 [59]. Nanopore contigs were binned directly without pre-clustering. Bins that were complete according to the presence of single-copy genes were further refined using the anvi-refine interface in anvi'o v6.2 [59]. In addition to taxonomic signal (based on singlecopy genes classified against GTDB), either differential coverage or tetranucleotide frequency was used to identify and remove outlying contigs. The former was used for bins with a large variation in contig coverage across samples, and the latter for those with marked differences in GC content across contigs. Medium- and high-quality bins ( complete and redundant according to the MIMAG standard [66]) were renamed as MAGs and kept for downstream analyses.
数据库(GTDB)发布 04-RS89 [63, 64]。 Illumina 读取与 bowtie v2.3.5 [55]映射到 contigs,并使用 SAMtools v1.9 [56]对 SAM 文件进行排序和索引。共同组装覆盖了原始宏基因组数据的显著部分,平均读取招募率为 (最小值: ,最大值: )。由于其较大的大小,Illumina 共同组装根据差异覆盖和四核苷酸频率被分成 100 个较小的簇,使用 CONCOCT v1.0.0 [65]。然后,根据相同的组成和覆盖度指标,使用 anvi'o v6.2 [59]中的 anvi-interactive 界面手动将 contigs 分类到不同的垃圾箱中。 Nanopore contigs 直接进行分类,无需预先聚类。根据单拷贝基因的存在来进一步优化完整的垃圾箱,使用 anvi'o v6.2 [59]中的 anvi-refine 界面。除了基于单拷贝基因对 GTDB 进行分类的分类信号外,还使用差异覆盖或四核苷酸频率来识别和删除异常的 contigs。 前者用于在样本之间具有较大的 contig 覆盖变化的垃圾箱,后者用于在 contig 之间具有明显 GC 含量差异的垃圾箱。根据 MIMAG 标准[66],中等和高质量的垃圾箱( 完整和 冗余)被重新命名为 MAGs,并保留用于下游分析。

Gene-centric analyses 基因中心分析

Functional profiles of the microbial communities were obtained using a gene-centric approach based on assembled data. For each (co-)assembly, gene calls were translated to amino acid sequences and searched against the KOfam hidden Markov model (HMM) database with KofamScan v1.3.0 [67]. Only matches with scores above the pre-computed family-specific thresholds were kept. Genes putatively identified as denitrification genes (nirK, nirS, norB, and nosZ) were submitted to further analyses to identify false positives consisting of distant homologues that are not involved in denitrification. Amino acid sequences were aligned with MAFFT v7.429 [68] and alignments were visualized with Unipro UGENE v38.1 [69]. Sequences were then inspected for the presence of conserved residues at positions associated with the binding of co-factors and active sites: nirK, Cu-binding and active sites [70]; nirS, c-heme and -heme binding sites [71]; norB, binding of the catalytic centres cyt , , and [72]; nos : binding of the and centres [72]. Sequences which did not contain the correct amino acid at these positions were removed. Finally, resulting amino acid sequences were aligned with MAFFT v7.429 [68] along with reference sequences from the genome of cultured denitrifiers [14] and a maximum-likelihood tree was computed with FastTree v2.1.11 [73] using
微生物群落的功能概况是通过基于组装数据的基因中心方法获得的。对于每个(共同)组装,基因调用被翻译成氨基酸序列,并与 KOfam 隐藏马尔可夫模型(HMM)数据库进行搜索,使用 KofamScan v1.3.0 [67]。只保留得分高于预先计算的特定家族阈值的匹配项。被推断为反硝化基因(nirK、nirS、norB 和 nosZ)的基因被提交进行进一步分析,以识别不参与反硝化的远缘同源基因。氨基酸序列与 MAFFT v7.429 [68]进行对齐,并使用 Unipro UGENE v38.1 [69]可视化对齐。然后检查序列中与辅因子结合和活性位点相关位置上的保守残基的存在:nirK,Cu 结合和活性位点[70];nirS,c-血红素和 -血红素结合位点[71];norB,催化中心细胞 的结合[72];nos 中心的结合[72]。不包含这些位置上正确氨基酸的序列将被移除。 最终,得到的氨基酸序列与 MAFFT v7.429 [68]中的参考序列以及培养的反硝化细菌基因组中的参考序列进行了比对,并使用 FastTree v2.1 计算了最大似然树。11 [73] 使用

the LG+GAMMA model. Annotation of denitrification genes was also performed for previously published genomes retrieved from GenBank. These included a set of 1529 MAGs obtained from soils in Stordalen Mire, northern Sweden [37], and all genomes of Acidobacteriota strains and candidate taxa (accessed on 9 October 2020).
LG+GAMMA 模型。还对从 GenBank 检索的先前发表的基因组进行了反硝化基因的注释。这些基因组包括从瑞典北部 Stordalen Mire 土壤中获得的 1529 个 MAGs 集合[37],以及所有酸杆菌门菌株和候选分类群的 基因组(于 2020 年 10 月 9 日访问)。
The abundance of functional genes was computed based on read coverage with CoverM v0.6.1 [74]. For this, Illumina reads were mapped to the contigs with minimap v2.17 [75] and coverage was normalized to reads per kilobase million (RPKM). Differences in functional community structure were assessed using NMDS, PERMANOVA, and db-RDA as described above for the taxonomic profiles. Differences in the abundance of individual genes across soil ecosystems were assessed using ANOVA followed by Tukey's HSD test with the and TukeyHSD functions in R v3.6.3 [40]. Due to the limited number of samples from barren sites, these were not included in the ANOVA and PERMANOVA procedures. Relationships between the abundance of denitrification genes and flux rates were assessed using linear regression in R v3.6.3 [40].
基于 CoverM v0.6.1 [74]的读取覆盖率计算了功能基因的丰度。为此,使用 minimap v2.17 [75]将 Illumina 读取映射到 contigs,并将覆盖率标准化为每千碱基百万读取(RPKM)。使用 NMDS、PERMANOVA 和 db-RDA 评估了功能群落结构的差异,方法与对分类学文件进行描述时相同。使用 ANOVA 后跟 R v3.6.3 [40]中的 Tukey's HSD 测试和 TukeyHSD 函数评估了土壤生态系统中个体基因丰度的差异。由于贫瘠地点样本数量有限,因此未包括在 ANOVA 和 PERMANOVA 程序中。使用 R v3.6.3 [40]中的线性回归评估了反硝化基因丰度与 通量率之间的关系。

Phylogenomic analyses of MAGs and metabolic reconstruction
MAGs 和代谢重建的系统发育基因组学分析

Phylogenetic placement of MAGs was done based on 122 archaeal and 120 bacterial single-copy genes with GTDB-Tk v1.3.0 [76] and the GTDB release 05-RS95 [63, 64]. Acidobacteriota MAGs containing denitrification genes were submitted to further phylogenomic analyses alongside all genomes of Acidobacteriota strains and candidate taxa available on GenBank ( ; accessed on 9 October 2020). For this, the amino acid sequence of 23 ribosomal proteins was retrieved for each genome with anvi'o v6.2 [59] and aligned with MUSCLE v3.8.1551 [77]. A maximum likelihood tree was then computed based on the concatenated alignments with FastTree v2.1.11 using the LG+GAMMA model [73]. Escherichia coli ATCC 11,775 was used to root the tree.
MAGs 的系统发育定位是基于 122 个古菌和 120 个细菌的单拷贝基因,使用 GTDB-Tk v1.3.0 [76] 和 GTDB release 05-RS95 [63, 64]。含有反硝化基因的酸杆菌 MAGs 被提交进行进一步的系统发育分析,与 GenBank 上所有酸杆菌菌株和候选分类单元的基因组一起进行分析( ;于 2020 年 10 月 9 日访问)。为此,使用 anvi'o v6.2 [59] 检索了每个基因组的 23 个核糖体蛋白质的氨基酸序列,并使用 MUSCLE v3.8.1551 [77] 进行了比对。然后,基于连接的比对使用 FastTree v2.1.11 和 LG+GAMMA 模型计算了最大似然树[73]。大肠杆菌 ATCC 11,775 用于根据树。
For metabolic reconstruction, MAGs were annotated against the KOfam HMM database [67] with HMMER v.3.2.1 [61] using the pre-computed score thresholds of each HMM profile. The anvi-estimate-metabolism program in anvi'o v6.2 [59] was then used to predict the metabolic capabilities of the MAGs. A metabolic pathway was considered present in MAGs containing at least of the genes involved in the pathway. Carbohydrateactive enzymes (CAZymes) were annotated with dbCAN v.2.0 based on the dbCAN v7 HMM database [78]. Only hits with an e-value and coverage were considered.
对于代谢重建,MAGs 使用 HMMER v.3.2.1 [61] 对 KOfam HMM 数据库 [67] 进行了注释,使用每个 HMM 概要文件的预先计算的得分阈值。然后使用 anvi'o v6.2 [59] 中的 anvi-estimate-metabolism 程序来预测 MAGs 的代谢能力。包含至少 条参与通路的基因的 MAGs 被认为存在代谢通路。碳水化合物活性酶(CAZymes)根据 dbCAN v7 HMM 数据库 [78] 使用 dbCAN v.2.0 进行注释。仅考虑具有 e-值 和覆盖率 的匹配。

MAG dereplication and read recruitment analysis
MAG 去重和读取招聘分析

Prior to read recruitment analyses, Illumina and Nanopore MAGs were dereplicated based on a average nucleotide identity (ANI) threshold with fastANI v1.3 [79] to remove redundancy (i.e., MAGs that were recovered multiple times across the different assemblies). Read recruitment analyses were then performed with CoverM v0.6.1 [74]. For this, Illumina reads were mapped to the set of non-redundant MAGs with minimap v2.17 [75] and relative abundances were calculated as a proportion of the reads mapping to each MAG.
在进行招聘分析之前,使用 fastANI v1.3 [79]根据 平均核酸同源性(ANI)阈值对 Illumina 和 Nanopore MAGs 进行去重,以消除冗余(即,在不同组装中多次恢复的 MAGs)。然后使用 CoverM v0.6.1 [74]进行读取招聘分析。为此,使用 minimap v2.17 [75]将 Illumina 读取映射到非冗余 MAG 组,并计算相对丰度,作为映射到每个 MAG 的读取的比例。

Results 结果

Environmental characterization and in situ GHG fluxes
环境特征和原位温室气体排放

Our sampling design in Kilpisjärvi included two soil depths across four ecosystems that are characteristic of the tundra biome (barren soils, heathlands, meadows, and fens) (Fig. 1a-c). In previous studies, we have established in the area a systematic fine-scale sampling of microclimate, soil conditions, and vegetation in topographically distinct environments [42, 80, 81]. Local variation in topography and soil properties creates a mosaic of habitats characterized by contrasting ecological conditions. This makes the study setting ideal to investigate species-environment relationships and ecosystem functioning in the tundra [42, 82, 83].
我们在基尔皮斯亚尔维的采样设计包括了四种典型的苔原生态系统中的两种土壤深度(贫瘠土壤、荒地、草地和沼泽)(图 1a-c)。在先前的研究中,我们在该地区建立了一个系统性的微气候、土壤条件和植被的精细尺度采样,覆盖了地形明显不同的环境[42, 80, 81]。地形和土壤性质的局部变化形成了一系列栖息地,其生态条件迥异。这使得研究环境成为研究苔原中物种与环境关系和生态系统功能的理想场所[42, 82, 83]。
Physicochemical composition varied across samples (Additional file 1: Table S1). Soil ecosystem and depth explained a significant fraction of the variation in , gravimetric soil moisture, and SOM across samples (PERMANOVA, ) (Additional file 2: Fig. S1). Samples from the organic layer had higher moisture and SOM than samples from the mineral layer (one-way ANOVA, ), while did not vary significantly between soil layers (oneway ANOVA, ). Soil physicochemical properties did not differ across soil ecosystems in the mineral layer (one-way ANOVA, ). In the organic layer, however, fens were characterized by higher , moisture, and content (one-way ANOVA, , and, together with the meadows, lower C:N ratio (one-way ANOVA, ) (Fig. 1d).
不同样本之间的物理化学组成有所变化(附加文件 1:表 S1)。土壤生态系统和深度解释了样本之间 、重量土壤湿度和 SOM 变化的显著部分(PERMANOVA, )(附加文件 2:图 S1)。有机层样本的湿度和 SOM 高于矿质层样本(单因素方差分析, ),而 在土壤层间没有显著变化(单因素方差分析, )。矿质层土壤物理化学性质在土壤生态系统间没有差异(单因素方差分析, )。然而,在有机层,沼泽地的 、湿度和 含量较高(单因素方差分析, ,以及与草地一起,较低的 C:N 比(单因素方差分析, )(图 1d)。
In situ measurements showed a high sink-source variability in net fluxes across the ecosystems (Fig. 1e). Although the average flux across all sites was small (net consumption of day ), high emission at rates of up to day was observed at the meadow sites. Likewise, strong consumption (up to day ) was observed particularly at the heathland and meadow sites. Net emissions were observed exclusively at the fen sites.
现场测量显示生态系统中净 通量存在很高的汇-源变异性(图 1e)。尽管所有站点的平均 通量较小( 天净消耗),但在草地站点观察到高达 的高 排放。同样,在荒地和草地站点尤其观察到强烈的 消耗(高达 )。净 排放仅在沼泽站点观察到。

Differences in microbial community structure across soils ecosystems
土壤生态系统中微生物群落结构的差异

Read-based analyses of unassembled SSU rRNA gene sequences showed that microbial community composition differed across the ecosystems, with fen soils harbouring contrasting microbial communities compared to the other ecosystems (PERMANOVA, , ) (Additional file 2: Fig. S2a). No differences in community structure were observed between soil depths or the interaction between soil ecosystem and depth (PERMANOVA, ). A significant relationship was observed between community structure and soil physicochemical properties , gravimetric soil moisture, and SOM; PERMANOVA, ), but not elevation (PERMANOVA, ). Due to the significant overlap between soil ecosystem and physicochemical composition (Additional file 2: Fig. S1), we used db-RDA with forward selection to investigate in more detail the links between community structure and the environment. The best model explaining community structure comprised soil ecosystem and , . Addition of elevation did not improve the model (db-RDA, ).
基于未组装的 SSU rRNA 基因序列的阅读分析显示,微生物群落组成在生态系统之间存在差异,与其他生态系统相比,沼泽土壤中存在截然不同的微生物群落(PERMANOVA, )(附加文件 2:图 S2a)。在土壤深度或土壤生态系统与深度之间的相互作用方面没有观察到群落结构的差异(PERMANOVA, )。观察到群落结构与土壤理化性质