Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
School of Biological Sciences, Washington State University, Pullman, Washington, USA.
mSystems. 2024 Jun 18;9(6):e0111223. doi: 10.1128/msystems.01112-23. Epub 2024 May 9.
Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate.
尽管土壤宏基因组数据呈爆炸式增长,但我们对土壤微生物的分布和功能模式仍缺乏综合认识。这些模式对于预测土壤微生物组对气候变化的响应以及由此产生的调节土壤温室气体释放的反馈至关重要。为了解决这一差距,我们使用互补的注释数据库、基于读取的分类学和机器学习来检测 1512 个手动 curated 土壤宏基因组,以提取全球土壤微生物组的多维基因组指纹。我们的目标是用高分子分辨率揭示环境因素和生态生物群落中土壤微生物组的新的生物地理模式。我们揭示了微生物获取养分的潜力在 pH 梯度上的变化;(ii)土壤容重变化时基于压力、运输和氧化还原的过程;以及(iii)生物群落中的温室气体排放。我们还使用无监督的方法来揭示一组具有独特基因组特征的土壤,这些特征的特点是土壤有机碳、氮和阳离子交换能力以及土壤容重和粘粒含量的协调变化,这可能最终反映出具有高微生物活性的土壤环境。这些土壤的基因组指纹突出了资源掠夺、植物-微生物相互作用、真菌和异养代谢的重要性。在所有分析中,我们观察到土壤微生物组的系统发育一致性——更相关的微生物往往会一致地响应土壤因素。总的来说,这里揭示的基因组指纹为土壤生物地球化学背后的微生物机制的全球模式提供了基础,并有助于产生可管理的微生物反应网络,纳入土壤碳和养分循环的基于过程的模型。