A Pan-Biome Metagenomic Atlas of the Brazilian Rhizosphere, Root, and Soil Microbiomes

A Pan-Biome Metagenomic Atlas of the Brazilian Rhizosphere, Root, and Soil Microbiomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Pan-Biome Metagenomic Atlas of the Brazilian Rhizosphere, Root, and Soil Microbiomes Luisa Mayumi Arake de Tacca, Rayane Nunes Lima, Patrícia Verdugo Pascoal, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8948634/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brazil encompasses some of the world’s most critical reservoirs of biodiversity, yet the microbial dimension of these ecosystems remains fragmented and poorly mapped. Understanding the soil microbiome is essential for predicting ecosystem responses to climate change and discovering novel biotechnological resources. Here, we present a comprehensive metagenomic atlas characterizing the soil, rhizosphere, and root microbiomes across all six Brazilian biomes: Amazon, Atlantic Forest, Cerrado, Caatinga, Pampa, and Pantanal. We employed a dual-sequencing approach, combining 16S/18S/ITS amplicon profiling with shotgun metagenomics, to catalogue microbial diversity in 79 samples representing a gradient from humid rainforests to semi-arid drylands. Our analysis reveals that while Proteobacteria are ubiquitous, their dominance is significantly reshaped by environmental stress. Humid biomes (Amazon, Pantanal) supported complex networks of fast-growing nutrient cyclers, whereas the semi-arid Caatinga was defined by a distinct "dry-adapted" core microbiome dominated by Actinobacteria . Fungal diversity was driven by moisture availability, with Ascomycota maintaining ubiquity across all ecosystems while Basidiomycota abundance declined in drier soils. This study provides the first unified baseline database of Brazilian soil microbiology, offering unprecedented insights into the "below-ground" biodiversity that sustains these globally vital ecosystems and establishing a reference for future research in synthetic biology, sustainable agriculture, and conservation. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Microbiome Amazon PanBiome Metagenomics Natural Atlas Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Soil microbiomes act as the "digestive system" of terrestrial ecosystems, driving the biogeochemical cycles that sustain plant productivity and climate regulation. In Brazil, a country of continental proportions hosting the world's largest tropical forest and vast savanna lands, preserving this biodiversity is a global priority [ 1 , 2 ]. The Brazilian territory is divided into six distinct biomes: Amazon, Atlantic Forest, Cerrado, Caatinga, Pampa, and Pantanal; which form a complex mosaic of climatic zones ranging from the seasonally flooded wetlands of the Pantanal to the semi-arid scrublands of the Caatinga. The Amazon is the largest tropical forest in the world, covering a significant portion of the northern region of Brazil [ 2 ]. Soil in the Amazon ranges from poor to fertile, supporting lush and unique vegetation [ 3 ]. The Atlantic Forest, which once stretched along Brazil's Atlantic coast, has been significantly reduced under deforestation [ 4 ]. Nevertheless, it is recognized for its diverse ecosystems, including dense forests, mangroves and coastal dunes [ 5 ]. The Cerrado is a vast tropical savanna biome that covers much of central Brazil. Although severely affected by deforestation, it is one of the savannas with the greatest biodiversity globally and encompasses pastures, shrublands and forests, with different soils, from fertile to the poorest [ 6 ]. The Caatinga, a dry forest biome in Northeast Brazil, with the lowest rainfall, contains drought-adapted vegetation, with thorny plants and seasonal rainfall patterns, defying arid conditions and the presence of often less fertile soils [ 7 ]. Pampa, mostly present across southern Brazil, is known for its vast grasslands and fertile soil, supporting diverse agriculture and an ecosystem unique to the region [ 8 ]. The Pantanal, the largest humid tropical area worldwide and located mainly in western Brazil, exhibits high aquatic and terrestrial biodiversity and abundant seasonally flooded soils [ 9 ]. While the macro-biodiversity (plants and animals) of these regions is well-documented, the microbial communities that underpin their resilience remain a largely uncharted territory. Recent advances in metagenomics have begun to illuminate the "black box" of soil microbial diversity, revealing novel taxa and functional genes essential for ecosystem health. However, previous studies in Brazil have largely been fragmented, focusing on single biomes or specific crops rather than providing a unified comparative baseline. In fact, each biome exhibits unique soil properties, water availability, plant species, and microbial communities. Comparative analysis of soil, root and rhizosphere samples is critical for advancing our understanding of soil-plant-microorganism interactions and nutrient cycling and absorption [ 10 – 12 ]. The rhizosphere, which constitutes a dynamic interface between roots and the surrounding soil, is enriched with root exudates, creating a microhabitat that fosters various types of interaction, with a diversity symbiotic microbial community [ 13 ]. Fungi, particularly arbuscular mycorrhizal fungi (AMF), form symbiotic relationships with plant roots, increasing micronutrient absorption, especially phosphorus and nitrogen, through an expanded hyphal network [ 14 ]. Moreover, there is an association between roots and prokaryotes that plays a critical role in the absorption of key nutrients, such as nitrogen, carbon and phosphorous [ 15 ]. In nutrient-poor soils, such as the Cerrado or Caatinga, these symbioses are essential for plant survival [ 16 – 18 ]. In flooded environments such as the Pantanal, anaerobic microorganisms are fundamental for methane production and consumption under anoxic conditions. In dry environments such as Caatinga, the rhizosphere is crucial for water retention and carbon cycling through the decomposition of organic matter by fungi and bacteria. These interactions are complex and directly influence the productivity and sustainability of terrestrial ecosystems. Hence, investigating these interactions in tandem with the surrounding soil matrix provides a more comprehensive understanding of how microbial communities are distributed to mediate biogeochemical cycles, influence soil structures and optimize nutrient availability. The lack of a comprehensive reference dataset limits our ability to understand how microbial communities adapt to contrasting environmental pressures, such as the distinct shift from the acidic, aluminum-rich soils of the Cerrado to the fertile, organic-matter-rich grasslands of Pampa. Furthermore, distinct microhabitats within the soil matrix, particularly the rhizosphere and root endosphere, exert strong selective pressure on microbial assembly, filtering specific taxa from the bulk soil to function as plant symbionts. To address this gap, we organized a nationwide research consortium to collect and sequence matched soil, root, and rhizosphere samples across all six Brazilian biomes. This study represents a pioneering effort to bridge the knowledge gap in tropical soil ecology by integrating amplicon sequencing (16S/18S/ITS) with shotgun metagenomics. This dual approach allows for a robust taxonomic inventory of bacteria , archaea , fungi , and protists , distinguishing the "who is there" from the "what they can do." While several studies have contributed to the growing body of knowledge on microbiome metagenomics in Brazilian biomes, revealing novel microbial taxa and functional genes and highlighting their role in sustaining plant health and ecosystem resilience [ 19 – 36 ]. Here, we provide a macroscopic overview of microbial life supporting these unique ecosystems. We catalogue the large-scale genomes of culture-independent microbes, identifying core microbiome signatures associated with distinct vegetation types and climatic conditions. By establishing this pan-biome metagenomic atlas, we aim not only to advance fundamental soil ecology but also to provide a reliable sequencing database. This resource will be critical for future. Finally, this integrated approach offers valuable insights into ecosystem functioning, sustainable land-use practices, agricultural management, and environmental conservation. RESULTS Sequencing To reveal the microbial diversity of the soil, roots and rhizosphere, metagenomic DNA was isolated, sampled, and subsequently sequenced through Illumina amplicon and shotgun sequencing of the V3/V4 region of 16S rRNA, V4 region of 18S rRNA and ITS ITS1-1F. Thus, via the use of this comprehensive approach in which both amplicon sequencing and shotgun methodologies are combined, we ensured the precision of the estimated relative gene category abundances. This involved the random subsampling of reads to match the sample with the lowest read count, a prerequisite for the subsequent downstream analyses (Fig. 1 A). After quality filtering and normalization, shotgun sequencing yielded a cumulative total of 72.5 million base pairs across all biome pools (Table 1 ). This dual-sequencing strategy allowed for high-resolution profiling of community structure (amplicon) and functional potential (shotgun) across the Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal biomes. To capture the overall biome-level representation, all individual samples from each biome were pooled into a single-biome source sample. These biome source samples were subjected to shotgun sequencing, resulting in a cumulative total of 72,569,233 base pairs (Table 1 ). This comprehensive sampling and sequencing strategy aimed to provide a robust foundation for elucidating the complex microbial dynamics in distinct Brazilian biomes. Table 1 Summary of the sequencing data of the biomes of Brazil. The Cerrado biome was sampled twice, one at Chapada dos Veadeiros (CV) and another at the National Park of Brasilia (PNB). Biome Number of paired end reads Bacterial reads (*N, %) Archaeal reads (N,%) Viral reads (N, %) Fungi reads (N, %) Amazon 49,824,684 1,137,692 9,809 8,532 53,572 Atlantic Forest 41,664,538 923,693 5,327 7,630 41,156 Caatinga 45,631,994 923,439 5,642 7,603 48,584 Cerrado_CV 35,895,044 738,355 2,699 5,579 38,008 Cerrado_PNB 62,818,310 1,356,564 2,557 9,974 64,363 Pampa 62,191,666 1,359,808 2,041 10,332 63,578 Pantanal 45,605,150 1,117,556 8,540 8,498 54,838 *From Krona plots calculated with Kaiju Prokaryotic and eukaryotic read distributions across Brazilian biomes The samples were normalized and pooled for each biome and subsequently used for shotgun sequencing. Here, we obtained a macroscopic overview of the microbes present in each Brazilian biome, while the amplicon-sequencing data provided a comprehensive view of prokaryotic and eukaryotic diversity across Brazilian biomes, given the separation between roots, soil and the rhizosphere. The differences in microbial community composition can be inferred to the unique ecological conditions in each biome, such as moisture, temperature, soil pH, soil nutrition and vegetation type. Shotgun metagenomic data provided a macroscopic overview of the microbial domains, while amplicon sequencing allowed for the differentiation of root, soil, and rhizosphere compartments. Microbial community composition shifted significantly across biomes, possibly driven by distinct ecological conditions such as moisture availability and soil chemistry. The specific taxonomic distributions for each biome are detailed below. Amazon forest In constrast with the dry Caatinga, the Amazon rainforest soil microbiome was co-dominated by Proteobacteria (~ 35%) and Acidobacteria (~ 30%), a balance typical of moist, acidic tropical forests where rapid nutrient cycling is required [ 37 ]. Actinobacteria (~ 13%) and Firmicutes (~ 4%) were also prevalent, particularly in 'Terra Preta' (Amazonian Dark Earths). The rare biosphere (< 0.1%) included phyla such as Elusimicrobia and Fusobacteria . Archaeal diversity was high and dominated with Thaumarchaeota in oxic surface soils, while methanogenic Euryarchaeota became more prominent in waterlogged or anaerobic niches. At the phylum level, 698,227 and 5,249 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequences were Proteobacteria , accounting for 56% of the total sequences, followed by Actinobacteria (17.8%), Acidobacteria (7.4%), Planctomycetes (5.3%), Bacteroidetes (2.8%), Firmicutes (2.5%), Chloroflexi (1.2%), Cyanobacteria (1.1%) and Nitrospirae (1%). Moreover, the abundance levels of four phyla remained below 1%, namely, Verrucomicrobia (0.94%), Gemmatimonadetes (0.81%), Deinococcus - Thermus (0.28%), and Spirochaetes (0.14%). The abundance levels of 21 phyla remained below 0.1%, namely, Chlorobi (0.09%), Chlamydiae (0.08%), Aquificae (0.07%), Thermotogae (0.07%), Armatimonadetes (0.07%), Thermodesulfobacteria (0.06%), Ignavibacteriae (0.06%), Rhodothermaeota (0.05%), Deferribacteres (0.04%), Kiritimatiellaeota (0.04%), Calditrichaeota (0.03%), Synergistetes (0.03%), Fusobacteria (0.02%), Tenericutes (0.02%), Candidatus Omnitrophica (0.01%), Elusimicrobia (0.01%), Dictyoglomi (0.01%), Atribacterota (0.01%), Balneolaeota (0.01%), Candidatus Bipolaricaulota (0.01%), and Chrysiogenetes (0.01%). The least abundant phyla were Candidatus Cloacimonetes (0.006%), Candidatus Saccharibacteria (0.006%), Fibrobacteres (0.005%), Caldiserica (0.004%), Coprothermobacterota (0.003%) and Candidatus Absconditabacteria (0.0008%). The most abundant archaeal sequences were Thaumarchaeota (0.34%) and Euryarchaeota (0.31%), whereas the abundance levels of other phyla did not exceed 0.1%, including Crenarchaeota (0.05%), Candidatus Thermoplasmatota (0.02%), Candidatus Korarchaeota (0.003%), Candidatus Micrarchaeota (0.003%), Candidatus Lokiarchaeota (0.001%), and Candidatus Nanohaloarchaeota (0.0004%). A total of 201 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (1,787,557) accounted for 71% of the total reads on average (Fig. 1 B and 1 C, respectively). Notably, the pooled shotgun composition classified based on the National Center for Biotechnology Information (NCBI) nr + euk database mostly matched that resulting from the amplicon-sequencing data classified on the basis of Silva's training set (Fig. 1 D). As shown in the shotgun data, in the amplicon-sequencing data, Proteobacteria also dominated in the rhizosphere and soil. However, we observed a different pattern for the roots from the Amazon samples. Compared with those in the soil and rhizosphere, the roots were dominated by Cyanobacteria , followed by Proteobacteria and Actinobacteria . Notably, the relative abundance of Firmicutes was also greater than that in the soil and rhizosphere (Fig. 1 E and 1 D, respectively). In the samples from the Amazon, 51,693 reads were assigned to fungi at the phylum level. The most abundant fungal sequence was Ascomycota (61%), followed by Basidiomycota (20%), Mucoromycota (7.7%), Chytridiomycota (6.5%), Zoopagomycota (1.3%) and Microsporidia (1.8%). At the genus level, the most prevalent taxa were Aspergillus (5.3%), Trichoderma (4%), Fusarium (2.5%), Spizellomyces (2.4%), Lobosporangium (2.1%), Batrachochytrium (2%), Synchytrium (1.7%), Rhizophagus (1.4%), Penicillium (1.4%) and Phycomyces (1.4%). These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2439541) accounted for 98% of the total reads on average. Caatinga The semi-arid Caatinga biome was dominated by Actinobacteria (49.5%), likely reflecting adaptation to water-limited conditions given their carbon-cycles capabilities and nutrition competition strategies. Proteobacteria (29%), Planctomycetes (5.1%), and Firmicutes (5.1%) were also prevalent. Rare taxa (< 0.1% abundance) included 16 phyla such as Armatimonadetes and Chlorobi . Archaeal diversity mirrored the bacterial dominance of stress-tolerant taxa, with Thaumarchaeota (0.49%) and Euryarchaeota (0.34%) comprising most of the archaeal reads. At the phylum level, 602,062 and 5,550 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequences were Actinobacteria , accounting for 49.5% of the total sequences, followed by Proteobacteria , at 29%; Planctomycetes , at 5.1%; Firmicutes , at 5.1%; Acidobacteria , at 2.3%; Chloroflexi , at 1.5%; Bacteroidetes , at 1.4%; and Cyanobacteria , at 1.1%. Moreover, the abundance levels of five phyla remained below 1%, namely, Gemmatimonadetes (0.97%), Verrucomicrobia (0.44%), Deinococcus - Thermus (0.38%), Nitrospirae (0.3%) and Spirochaetes (0.12%). Sixteen phyla exhibited abundance levels below 0.1%, namely, Armatimonadetes (0.06%), Chlorobi (0.06%), Thermotogae (0.05%), Aquificae (0.05%), Thermodesulfobacteria (0.05%), Rhodothermaeota (0.04%), Chlamydiae (0.04%), Ignavibacteriae (0.03%), Deferribacteres (0.03%), Synergistetes (0.03%), Tenericutes (0.02%), Calditrichaeota (0.02%), Fusobacteria (0.02%), Kiritimatiellaeota (0.02%), Dictyoglomi (0.01%) and Candidatus Bipolaricaulota (0.01%). The least abundant phyla were Candidatus Saccharibacteria (0.009%), Balneolaeota (0.009%), Elusimicrobia (0.008%), Atribacterota (0.008%), Candidatus Omnitrophica (0.008%), Coprothermobacterota (0.006%), and Chrysiogenetes (0.006%). Candidatus Cloacimonetes (0.004%), Caldiserica (0.004%), Fibrobacteres (0.003%), and Candidatus Absconditabacteria (0.0005%). The most abundant archaeal sequences were Thaumarchaeota (0. 49%) and Euryarchaeota (0.34%), whereas the abundance levels of the other phyla did not exceed 0.1%, including Crenarchaeota (0.04%), Candidatus Thermoplasmatota (0.01%), Candidatus Lokiarchaeota (0.001%), Candidatus Korarchaeota (0.001%), Candidatus Micrarchaeota (0.0006%), and Candidatus Nanohaloarchaeota (0.0001%). A total of 161 reads (0.02%) were assigned to viral genomes, whereas unassigned and unclassified reads (1673826) accounted for 73% of the total reads on average (Fig. 1 B). In the amplicon-sequencing 16S data, the profile for the Caatinga biome slightly differed from the shotgun profile. In the latter, the dominant phylum was Actinobacteria (Fig. 1 B and 1 C). However, in the amplicon-sequencing 16S data, Proteobacteria was more dominant than Actinobacteria in the soil and rhizosphere. For fungi, 46,630 reads were assigned at the phylum level. The most abundant fungal sequence was Ascomycota (60%), followed by Basidiomycota (20%), Chytridiomycota (6.5%), Mucoromycota (6.4%), Microsporidia (1.3%) and Zoopagomycota (1%). At the genus level, Aspergillus (6.3%), Spizellomyces (2.3%), Fusarium (2.1%), Synchytrium (2%), Batrachochytrium (2%), Rhizophagus (1.8%), Lobosporangium (1.6%), Trichoderma (1.4%), Phycomyces (1.4%), and Talaromyces (1.4%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2234969) accounted for 98% of the total reads on average (Fig. 1 E). Cerrado–Chapada dos Veadeiros (CV) The Cerrado biome soil profile at Chapada dos Veadeiros was characterized by high abundances of Acidobacteria (36.4%) and Proteobacteria (28.9%), reflecting the acidic, nutrient-poor nature of these neotropical savanna soils[ 38 ]. Actinobacteria (12.5%) and Verrucomicrobia (3.2%) represented the other prevalent groups. The rare biosphere (< 0.1% abundance) consisted of approximately 19 phyla, including Gemmatimonadetes and Nitrospirae . Similar to the Caatinga, archaeal communities were driven by Thaumarchaeota (1.1%), which play a key role in ammonia oxidation in these nitrogen-limited soils. At the phylum level, 490,262 and 2,030 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequences were Proteobacteria , accounting for 50% of the total sequences, followed by Actinobacteria (22%), Planctomycetes (10%), Acidobacteria (7.1%), Firmicutes (2%), Chloroflexi (1.5%), Bacteroidetes (1.5%) and Cyanobacteria (1.1%). Moreover, the abundance levels of five phyla remained below 1%, namely, Verrucomicrobia (0.9%), Gemmatimonadetes (0.36%), Deinococcus - Thermus (0.27%), Nitrospirae (0.25%) and Spirochaetes (0.11%). The abundance levels of 14 phyla remained below 0.1%, namely, Armatimonadetes (0.07%), Chlorobi (0.07%), Chlamydiae (0.06%), Ignavibacteriae (0.05%), Thermotogae (0.05%), Aquificae (0.05%), Thermodesulfobacteria (0.04%), Rhodothermaeota (0.03%), Kiritimatiellaeota (0.03%), Deferribacteres (0.03%), Synergistetes (0.03%), Calditrichaeota (0.02%), Fusobacteria (0.02%) and Tenericutes (0.02%). The least abundant phyla were Elusimicrobia (0.009%), Atribacterota (0.009%), Chrysiogenetes (0.009%), Candidatus Bipolaricaulota (0.008%), Dictyoglomi (0.008%), Balneolaeota (0.007%), Candidatus Omnitrophica (0.007%), Caldiserica (0.005%), Candidatus Saccharibacteria (0.005%), Fibrobacteres (0.003%), Coprothermobacterota (0.003%), Candidatus Absconditabacteria (0.0014%) and Candidatus Cloacimonetes (0.0014%). The most abundant archaeal sequence was Euryarchaeota (0.27%), while the abundance levels of the other phyla did not exceed 0.1%, including Thaumarchaeota (0.07%), Crenarchaeota (0.03%), Candidatus Thermoplasmatota (0.02%), Candidatus Micrarchaeota (0.001%), Candidatus Korarchaeota (0.001%), Candidatus Nanohaloarchaeota (0.0006%) or Candidatus Lokiarchaeota (0.0006%). Eighty-four reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (1302376) accounted for 72% of the total reads on average (Fig. 1 B and 1 C). For fungi, 36,719 reads were assigned at the phylum level. The most abundant fungal sequence was Ascomycota (60%), followed by Basidiomycota (20%), Mucoromycota (7.2%), Chytridiomycota (6.6%), Zoopagomycota (1.1%) and Microsporidia (1.1%). At the genus level, Aspergillus (5%), Spizellomyces (2.5%), Rhizophagus (2.2%), Batrachochytrium (1.9%), Lobosporangium (1.8%), Bacidia (1.5%), Exophiala (1.5%), Phycomyces (1.5%), Fusarium (1.3%), and Talaromyces (1.2%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (1758033) accounted for 98% of the total reads on average (Fig. 1 E and 1 F). Cerrado–Parque Nacional de Brasília (PNB ) The Cerrado biome soil profile at the National Park of Brasilia was also characterized by high abundances of Acidobacteria (36.4%) and Proteobacteria (28.9%). Actinobacteria (12.5%) and Verrucomicrobia (3.2%) represented the other prevalent groups. The rare biosphere (< 0.1% abundance) consisted of approximately 19 phyla, including Gemmatimonadetes and Nitrospirae . Similar to the Caatinga, archaeal communities were driven by Thaumarchaeota (1.1%), which play a key role in ammonia oxidation in these nitrogen-limited soils. At the phylum level, 907,146 and 2,831 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequences were Proteobacteria (accounting for 51% of the total sequences), Actinobacteria (28%), Planctomycetes (5.9%), Acidobacteria (5.7%), Firmicutes (1.5%) and Bacteroidetes (1.2%). The abundance levels of 6 phyla remained below 1%, namely, Chloroflexi (0.99%), Cyanobacteria (0.87%), Verrucomicrobia (0.74%), Gemmatimonadetes (0.33%), Deinococcus - Thermus (0.24%) and Nitrospirae (0.15%). Moreover, the abundance levels of 15 phyla remained below 0.1%, namely, Spirochaetes (0.08%), Armatimonadetes (0.05%), Chlorobi (0.05%), Chlamydiae (0.04%), Thermotogae (0.04%), Aquificae (0.03%), Rhodothermaeota (0.03%), Thermodesulfobacteria (0.03%), Ignavibacteriae (0.02%), Kiritimatiellaeota (0.02%), Deferribacteres (0.02%), Calditrichaeota (0.02%), Synergistetes (0.02%), Fusobacteria (0.01%) and Tenericutes (0.01%). The least abundant phyla were Atribacterota (0.008%), Balneolaeota (0.008%), Dictyoglomi (0.007%), Elusimicrobia (0.007%), Candidatus Bipolaricaulota (0.006%), Chrysiogenetes (0.005%), Candidatus Omnitrophica (0.005%), Candidatus Saccharibacteria (0.005%), Caldiserica (0.003%), Fibrobacteres (0.003%), Candidatus Cloacimonetes (0.002%) Coprothermobacterota (0.002%) and Candidatus Absconditabacteria (0.0004%). The most abundant archaeal sequence was Euryarchaeota (0.21%), while the abundance levels of the other phyla did not exceed 0.1%, including Thaumarchaeota (0.05%), Crenarchaeota (0.03%), Candidatus Thermoplasmatota (0.007%), Candidatus Lokiarchaeota (0.001%), Candidatus Korarchaeota (0.001%), Candidatus Micrarchaeota (0.0008%), and Candidatus Nanohaloarchaeota (0.0003%). A total of 157 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (2230781) accounted for 70% of the total reads on average (Fig. 1 B and 1 C). For fungi, 61,996 reads were assigned at the phylum level. The most abundant fungal sequences were Ascomycota (60%), Basidiomycota (20%), Mucoromycota (6.6%), Chytridiomycota (6.4%), Microsporidia (1.1%) and Zoopagomycota (1%). At the genus level, Aspergillus (5%), Spizellomyces (2.3%), Synchytrium (2%), Rhizophagus (2%), Batrachochytrium (1.8%), Lobosporangium (1.7%), Exophiala (1.5%), Fusarium (1.4%), Phycomyces (1.3%) and Bacidia (1.3%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (3078919) accounted for 98% of the total reads on average (Fig. 1 E). Atlantic forest Soil communities in the Atlantic Forest were distinct for their high prevalence of Acidobacteria (44.2%) and Proteobacteria (26.5%). Other prevalent taxa included Verrucomicrobia (6.1%) and Planctomycetes (4.8%). Rare taxa (< 0.1% abundance) comprised diverse groups such as Latescibacteria and Chlamydiae . Archaeal diversity was comparatively lower, with Thaumarchaeota (0.65%) dominating the archaeal reads, followed by minor populations of Euryarchaeota . At the phylum level, 619,723 and 3971 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequence was Proteobacteria , accounting for 54.2% of the total sequences, followed by Actinobacteria (26.3%), Planctomycetes (6%), Acidobacteria (5%), Bacteroidetes (2.7%) and Firmicutes (2.1%). Moreover , the abundance levels of seven phyla remained below 1%, namely, Verrucomicrobia (0.96%), Cyanobacteria (0.83%), Gemmatimonadetes (0.75%), Chloroflexi (0.71%), Nitrospirae (0.40%), Deinococcus - Thermus (0.24%), and Spirochaetes (0.10%). The abundance levels of 15 phyla remained below 0.1%, namely, Chlorobi (0.07%), Armatimonadetes (0.06%), Chlamydiae (0.05%), Thermotogae (0.05%), Aquificae (0.04%), Rhodothermaeota (0.04%), Thermodesulfobacteria (0.04%), Ignavibacteriae (0.04%), Deferribacteres (0.03%), Kiritimatiellaeota (0.03%), Synergistetes (0.03%) Fusobacteria (0.03%), Calditrichaeota (0.02%), Tenericutes (0.01%), and Elusimicrobia (0.01%). The least abundant phyla were Balneolaeota (0.009%), Atribacterota (0.008%), Dictyoglomi (0.007%), Candidatus Omnitrophica (0.007%), Chrysiogenetes (0.007%), Candidatus Saccharibacteria (0.006%), Candidatus Bipolaricaulota (0.006%), Fibrobacteres (0.005%), Candidatus Cloacimonetes (0.004%), Coprothermobacterota (0.003%), Caldiserica (0.003%), and Candidatus Absconditabacteria (0.0006%). The most abundant archaeal sequences were Thaumarchaeota (0. 36%) and Euryarchaeota (0.23%), whereas the abundance levels of the other phyla did not exceed 0.1%, including Crenarchaeota (0.03%), Candidatus Thermoplasmatota (0.007%), Candidatus Korarchaeota (0.001%), Candidatus Micrarchaeota (0.001%) and Candidatus Lokiarchaeota (0.0001%). A total of 330 reads (0.05%) were assigned to viral genomes, whereas unassigned and unclassified reads (1459532) accounted for 70% of the total reads on average (Fig. 1 B and 1 C). For fungi, 39,525 reads were assigned at the phylum level. The most abundant fungal sequence was Ascomycota (57%), followed by Basidiomycota (22%), Mucoromycota (7.3%), Chytridiomycota (7.1%), Microsporidia (1.3%) and Zoopagomycota (1.1%). At the genus level, Aspergillus (4.8%), Spizellomyces (2.6%), Trichoderma (4%), Batrachochytrium (2.2%), Lobosporangium (2%), Synchytrium (2%), Rhizophagus (1.7%), Phycomyces (1.6%), Fusarium (1.3%) and Rhizopus (1.2%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2043701) accounted for 98% of the total reads on average (Fig. 1 E). Pampa The Pampa grasslands exhibited a distinct signature characterized by the highest prevalence of Bacteroidetes (3.5%) among all biomes, likely linked to the decomposition of complex polysaccharides in the grass rhizosphere. However, the community was still driven by Proteobacteria (34.4%) and Acidobacteria (20.8%). Actinobacteria (11.6%) and Verrucomicrobia (3.5%) represented other key groups. Rare taxa (< 0.1%) included diverse lineages such as Deinococcus-Thermus and BRC1 . Archaeal communities showed a mixed profile, with Thaumarchaeota generally dominant but significant contributions from Euryarchaeota in denser pasture soils. At the phylum level, 853,361 and 2,933 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequence was Proteobacteria , accounting for 53% of the total sequences, followed by Actinobacteria (21%), Planctomycetes (6.25%), Acidobacteria (5.7%), Bacteroidetes (4%), Firmicutes (2.3%), Verrucomicrobia (1.5%) and Cyanobacteria (1%). The abundance levels of 5 phyla remained below 1%, namely, Chloroflexi (0.87%), Gemmatimonadetes (0.83%), Nitrospirae (0.28%), Deinococcus - Thermus (0.27%) and Spirochaetes (0.13%). Moreover, the abundance levels of 20 phyla remained below 0.1%, namely, Armatimonadetes (0.08%), Chlorobi (0.08%), Chloridia (0.06%), Aquificae (0.05%), Thermotogae (0.05%), Ignavibacteriae (0.05%), Thermodesulfobacteria (0.04%), Kiritimatiellaeota (0.04%), Rhodothermaeota (0.04%), Deferribacteres (0.03%), Calditrichaeota (0.02%), Synergistetes (0.02%), Fusobacteria (0.02%), Tenericutes (0.02%), Candidatus Saccharibacteria (0.01%), Dictyoglomi (0.01%), Elusimicrobia (0.001%), Balneolaeota (0.01%), Atribacterota (0.008%) and Candidatus Omnitrophica (0.01%). The least abundant phyla were Chrysiogenetes (0.009%), Candidatus Bipolaricaulota (0.009%), Candidatus Cloacimonetes (0.006%), Fibrobacteres (0.005%), Coprothermobacterota (0.004%), Caldiserica (0.003%) and Candidatus Absconditabacteria (0.0007%). The most abundant archaeal sequence was Euryarchaeota (0.24%), while the abundance levels of the other phyla did not exceed 0.1%, including Thaumarchaeota (0.05%), Crenarchaeota (0.03%), Candidatus Thermoplasmatota (0.008%), Candidatus Lokiarchaeota (0.002%), Candidatus Korarchaeota (0.0009%), Candidatus Micrarchaeota (0.0006%), and Candidatus Nanohaloarchaeota (0.0001%). A total of 222 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (2253067) accounted for 72% of the total reads on average (Fig. 1 B and 1 C). For fungi, 61,221 reads were assigned at the phylum level. The most abundant fungal sequence was Ascomycota (57%), followed by Basidiomycota (22%), Mucoromycota (8%), Chytridiomycota (6.9%), Microsporidia (1.2%) and Zoopagomycota (1.1%). At the genus level, Aspergillus (4.5%), Rhizophagus (2.7%), Spizellomyces (2.6%), Batrachochytrium (2%), Lobosporangium (2%), Synchytrium (1.8%), Phycomyces (1.5%), Fusarium (1.3%), Exophiala (1.3%) and Rhizopus (1.3%) were identified (Fig. 1 E). These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (3048362) accounted for 98% of the total reads on average. Pantanal Reflecting the fluctuating water saturation of this wetland biome, the Pantanal was dominated by Proteobacteria (41.8%). Actinobacteria (18.3%) and Acidobacteria (16.7%) were also prevalent, though less abundant than in drier biomes. The rare biosphere (< 0.1%) included anaerobic or facultative lineages such as Chloroflexi and Spirochaetes . In the archaeal domain, Thaumarchaeota (0.82%) and Euryarchaeota (0.45%) were the most frequent, likely contributing to methanogenic processes typical of wetland sediments. At the phylum level, 748,195 and 8,303 reads were assigned to the Bacteria and Archaea domains, respectively. The most abundant bacterial sequence was Proteobacteria , accounting for 45% of the total sequences, followed by Actinobacteria (35%), Planctomycetes (3.7%), Acidobacteria (3.5%), Bacteroidetes (2.3%), Firmicutes (2.2%), Chloroflexi (1%) and Gemmatimonadetes (1%). The abundance levels of 5 phyla remained below 1%, namely, Cyanobacteria (0.9%), Verrucomicrobia (0.65%), Nitrospirae (0.5%), Deinococcus - Thermus (0.3%) and Spirochaetes (0.1%). Moreover, the abundance levels of 16 phyla remained below 0.1%, namely, Chlorobi (0.06%), Armatimonadetes (0.05%), Aquificae (0.05%), Thermotogae (0.05%), Chlamydiae (0.04%), Thermodesulfobacteria (0.04%), Rhodothermaeota (0.03%), Ignavibacteriae (0.03%), Deferribacteres (0.03%), Kiritimatiellaeota (0.02%), Synergistetes (0.02%), Calditrichaeota (0.02%), Tenericutes (0.02%), Fusobacteria (0.02%), Candidatus Saccharibacteria (0.01%), and Balneolaeota (0.01%). The least abundant phyla were Elusimicrobia (0.001%), Atribacterota (0.009%), Dictyoglomi (0.009%), Candidatus Omnitrophica (0.009%), Candidatus Bipolaricaulota (0.006%), Chrysiogenetes (0.006%), Candidatus Cloacimonetes (0.004%), Caldiserica (0.004%), Fibrobacteres (0.003%), Coprothermobacterota (0.003%), and Candidatus Absconditabacteria (0.001%). The most abundant archaeal sequences were Thaumarchaeota (0.74%) and Euryarchaeota (0.3%), whereas the abundance levels of the other phyla did not exceed 0.1%, including Crenarchaeota (0.03%), Candidatus Thermoplasmatota (0.01%), Candidatus Korarchaeota (0.01%), Candidatus Lokiarchaeota (0.001%), Candidatus Micrarchaeota (0.0006%), and Candidatus Nanohaloarchaeota (0.0004%). A total of 178 reads (0.02%) were assigned to viral genomes, whereas unassigned and unclassified reads (1523581) accounted for 67% of the total reads on average (Fig. 1 B and 1 C). For fungi, 52,896 reads were assigned at the phylum level. The most abundant fungal sequence was Ascomycota (60%), followed by Basidiomycota (21%), Chytridiomycota (6.6%), Mucoromycota (6.4%), Microsporidia (1.2%) and Zoopagomycota (1.1%). At the genus level, Aspergillus (5%), Fusarium (3.4%), Spizellomyces (2.4%), Batrachochytrium (2%), Talaromyces (2%), Lobosporangium (1.9%), Synchytrium (1.8%), Rhizophagus (1.5%), Phycomyces (1.3%), and Trichoderma (1.3%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2227361) accounted for 97.5% of the total reads on average (Fig. 1 E). General microbial community composition across biomes and microbiomes To better understand the relationships between the different Brazilian biomes and the microbiomes (soil, rhizosphere, and roots) within each biome, we created a nonmetric multidimensional scaling graph with all samples categorized by microbiome and biome (Fig. 2 A). We employed the Bray‒Curtis dissimilarity coefficient to measure the compositional dissimilarity between the genomes. We observed a greater correlation between samples from the same sector (roots, soil and rhizosphere) than between samples from the same biome. The root samples were grouped, as were the soil and rhizosphere samples. We observed a closer relationship between the soil and rhizosphere samples, with greater differentiation from the root samples (Fig. 2 A). Notably, the Venn diagram yielded the same observation (Fig. 2 B). Here, we observed that more operational taxonomic units (OTUs) were shared between the soil and rhizosphere samples, between the root samples, and between the soil or root samples and the rhizosphere samples. The dominant phyla in the root samples were cyanobacteria, which is true across all biomes (Fig. 3 C). Given that the heatmap shown in Fig. 3 C is organized by sample similarity, we observed a mixture of soil and rhizosphere samples dominated by Proteobacteria . Compared with the other biomes, the Caatinga exhibited a greater presence of Firmicutes . We generated rarefaction curves for our samples in two ways to assess the ɑ diversity of the various biomes or microbiomes (Fig. 2 D). As expected, the roots were less diverse than the soil and rhizosphere were (right). Regarding the biomes, the Caatinga, the Cerrado PNB and the Atlantic Forest exhibited very similar ɑ diversity values at the lower end of the graph. Moreover, the Amazon and Pantanal exhibited similar but slightly greater diversity values, followed by Pampa. Notably, the Cerrado demonstrated the highest α diversity (Fig. 2 D-left). The distribution of bacterial and archaeal phyla among Brazilian biomes exhibits significant diversity (differences between forested and dry biomes) In our examination of the general diversity of microorganisms, the four most abundant phyla in the soil, root, and rhizosphere samples from all the Brazilian biomes were Proteobacteria , Actinobacteria , Bacteroidetes and Firmicutes (Fig. 1 B). These phyla include widely studied representatives. Proteobacteria was the most dominant bacterial phylum across almost all biomes, except the Caatinga. Surprisingly, Actinobacteria was especially prevalent in the semiarid Brazilian biome (Fig. 1 B and 1 C). This notable shift in microbial dominance differs from the typical predominance of Proteobacteria in moist forest soils. Furthermore, representatives of the Acidobacteria and Planctomycetes phyla were identified in the read annotations. Planctomycetes occur more frequently in the Cerrado and Pampa (Fig. 1 C and 1 D). Acidobacteria was more widespread in forested biomes such as the Amazon, Cerrado and Atlantic Forest biomes than in the Caatinga and Pantanal biomes (Fig. 1 B). These findings highlight a distinct microbial community structure in the semiarid Caatinga environment compared with that in the other Brazilian ecosystems. To better reveal the differences between the various Brazilian biomes, we separated them into forest-like biomes, wet biomes and dry biomes according to their general characteristics. Notably, we classified the Amazon and Atlantic Forest biomes as forest-like biomes. Moreover, we classified the Pampa and Pantanal biomes as wet biomes, as both are subject to flooding, while the Cerrado and Caatinga biomes were classified as dry biomes (Sup 1B). Moreover, the phylogenetic structure in cladograms is represented in Figs. 3 A and 3 B showing the core phyla present in distinct environments. We plotted the top 10 unique orders in each category (Fig. 3 C). Interestingly, in the wet biomes, we identified Diapherotrites and Zixibacteria , which are known to thrive in anaerobic environments such as wetlands, such as those in the Pantanal. The orders unique to the wet biomes are involved in nutrient cycling, organic matter degradation, and bioremediation of wet environments. The orders unique to the forest-like biomes included Thermoanaerobaculales , Latescibacterales and Methylomirabilales , which are generally associated with anaerobic environments that contain abundant organic matter, such as the Amazon and Atlantic Forest soil environments, which are rich in forest litter. We also assessed the relative abundance under each condition among the shared orders and between the three environments (Fig. 3 C). The distribution of shared orders was similar among the three biomes. However, the order Myxococcales was absent in the dry biomes, as was Oligoflexales (Fig. 3 C). Comparison of the relative abundance levels of prokaryotic communities in the soil, roots, and rhizosphere Via the use of 16S amplicon-sequencing data, we distinguished the distribution patterns of phyla by separating the samples into soil, root, and rhizosphere categories at all locations within the various Brazilian biomes (Fig. 3 and Supplemental Fig. 1). To visualize these trends, we generated bar plots depicting the richness of microbial phyla across the various biomes. This approach provides a more nuanced understanding of the difference in the composition of microbial communities among various ecological niches. Notably, in the roots, cyanobacteria dominated, whereas the soil and rhizosphere were dominated by Firmicutes and Proteobacteria . An intriguing observation was obtained for outliers within these patterns. In the rhizosphere section, the Caatinga biome exhibited higher diversity than the other biomes and was characterized by a greater abundance of Firmicutes than Proteobacteria . Conversely, in the root section, the Pampa biome was identified as an outlier, featuring a lower abundance of cyanobacteria than that in the other biomes. In the soil section, the Cerrado–PNB sample from Cerrado–CV vegetation was noteworthy. Here, a distinct difference was observed, with a greater abundance of Verrucomicrobia than Firmicutes . This finding highlights unique microbial dynamics in the Cerrado–PNB, increasing our understanding of microbial diversity in Brazilian biomes. Comparison of the relative abundance levels of fungi in the soil, roots and rhizosphere Via the use of sequence data from the 18S V4 and ITS-1 regions, we distinguished the distribution patterns of fungal and protist phyla by stratifying the samples from the different Brazilian biomes into soil, root and rhizosphere categories (Fig. 4 A). This dual approach allowed us to compare the taxonomic diversity revealed by the conserved region (V4) with the specific variability captured by the ITS-1 spacer, highlighting how these complementary markers reflect the structure of microbial communities in distinct compartments of the soil‒plant system. Comparative analysis of the most abundant fungal phyla in the Brazilian biomes revealed that Ascomycota was dominant in all environments, reaching a peak in the Amazon (0.077%), followed by Caatinga (0.068%), Cerrado–CV (0.043%), Pantanal (0.035%), Cerrado-PNB (0.057%), Atlantic Forest (0.014%) and Pampa (0.015%), revealing a possible correlation between relatively high humidity and fungal abundance levels (Fig. 4 B). Basidiomycota , the second most abundant phylum, reached a peak in the Amazon (0.061%), with decreasing values in the Cerrado-PNB (0.007%) and Pantanal biomes (0.005%), and minimum proportions in the other biomes (0.004–0.008%). The abundance levels of less representative phyla ( Mucoromycota , Zoopagomycota and Chytridiomycota ) generally did not exceed 0.01%, except Mucoromycota in the Amazon (0.008%) and Cerrado-PNB biomes (0.005%), suggesting that these groups occupy more specialized ecological niches (Fig. 4 C). Interesting patterns emerged when comparing the humid biomes (Amazon, Atlantic Forest, and Pantanal) with the drier biomes (Caatinga, Cerrado, and Pampa). Notably, the former maintained more diverse and abundant fungal communities, probably due to the greater availability of organic matter and stable humidity conditions. Seasonal or arid biomes contained less diverse communities, but Ascomycota maintained its dominance, which suggests a remarkable adaptive capacity of this phylum. Moreover, ITS sequencing analysis revealed that the microbial compositions of the Brazilian biomes indicated unique genera, except those of the Caatinga and Cerrado biomes, which did not encompass unique genera. In the Amazon biome, the genera Didymella , Xylogone , order Saccharomycetales , Class Chytridiomycetes and Filobasidium were identified. The Cerrado-PNB biome exhibited Lachnum , order Conioscyphales , family Leotiaceae , Thanatephorus and Olpidium . The Atlantic Forest biome contained Arachnotheca , Paecilomyces , Myriodontium , Lepiota and Cylindrocladium , whereas the Pampa biome was characterized by Auricularia , Vanrija , family Clavariaceae , Cryptococcus and Clavulinopsis . In the Pantanal biome, Acaulium , Pseudolophiostoma , Periconia , Arxiella and Angustimassarina were identified as unique genera (Fig. 4 D). These results highlight how abiotic (climate and humidity) and biotic (substrate availability) factors shape the structure of fungal communities at the biogeographic scale, with Ascomycota emerging as the most ubiquitous and resilient group in all the ecosystems analyzed. To facilitate a direct comparison of the distinct microbial signatures associated with each environmental profile, we synthesized the mean relative abundances of the most represented phyla across all six biomes. Table 2 details the shift in community structure from the drought-adapted, Actinobacteria-dominated soils of the Caatinga to the Acidobacteria -rich soils of the Cerrado and Atlantic Forest, identifying the primary ecological drivers associated with these taxonomic variations. Table 2 . Table 2 Comparative analysis of soil microbiome composition and ecological drivers across Brazilian biomes. Data represents the mean relative abundance of dominant and prevalent bacterial phyla derived from Brazilian Microbiome Project (BMP) comparative surveys. "Dominant" taxa are defined as the most abundant phyla driving community structure, while "Prevalent" taxa represent secondary groups consistently appearing above 1% abundance. "Rare Biosphere" encompasses phyla with < 0.1% relative abundance. Key environmental stressors (e.g., water availability, pH, nutrient limitation) are correlated with the observed shifts in phylogenetic composition. Biome Dominant Phyla (Primary Drivers) Prevalent Phyla (Secondary Groups) Rare Biosphere (< 0.1% examples) Archaeal Signature Key Ecological Driver Caatinga Actinobacteria (49.5%) Proteobacteria (29.0%) Planctomycetes (5.1%) Firmicutes (5.1%) Chlorobi Armatimonadetes Thaumarchaeota dominates (ammonia oxidation in dry soils). Water Stress : Selection for drought-tolerant, spore-forming taxa. Cerrado Acidobacteria (36.4%) Proteobacteria (28.9%) Actinobacteria (12.5%) Verrucomicrobia (3.2%) Gemmatimonadetes Nitrospirae Thaumarchaeota (1.1%) is the major driver. Nutrient Limitation : Acidic, Al-rich soils select for oligotrophs ( Acidobacteria ). Atlantic Forest Acidobacteria (44.2%) Proteobacteria (26.5%) Verrucomicrobia (6.1%) Planctomycetes (4.8%) Latescibacteria Chlamydiae Low diversity; dominated by Thaumarchaeota (0.6%). Acidic Forest Soil : Low pH (< 4.5) drives extreme Acidobacteria dominance. Pantanal Proteobacteria (41.8%) Actinobacteria (18.3%) Acidobacteria (16.7%) Chloroflexi (2.1%) Spirochaetes Fibrobacteres Methanogens (Euryarchaeota) are significantly higher due to flooding. Redox Fluctuations : Flood cycles favor fast-growing, flexible taxa. Pampa Proteobacteria (34.4%) Acidobacteria (20.8%) Actinobacteria (11.6%) Bacteroidetes (3.5%) * Deinococcus-Thermus BRC1 Mixed; significant presence of Euryarchaeota . Grassland Rhizosphere : Highest abundance of Bacteroidetes (organic matter degraders). Amazon Proteobacteria (~ 35%) Acidobacteria (~ 30%) Actinobacteria (~ 13%) Firmicutes (~ 4%) Elusimicrobia Fusobacteria Diverse; Thaumarchaeota abundant in oxic soils. Moisture & Biomass : High turnover favors copiotrophs ( Proteobacteria ). Table Legend Discussion The diversity of the soil, root and rhizosphere microbial communities in representative areas of all Brazilian biomes was assessed. Proteobacteria dominated the bacterial communities in nearly all the biomes. This phylum includes bacteria involved in carbon, sulfur and phosphate cycling and symbiotic nitrogen fixation (SNF), such as Bradyrhizobium , Burkholderia , Paraburkholderia and Cupriavidus [ 39 – 45 ]. This finding can be linked to the high levels of soil organic matter (SOM) and moisture in soils, which favour fast-growing heterotrophs, particularly those involved in nitrogen cycling [ 46 , 47 ]. The second most abundant phylum was Actinobacteria, such as Streptomyces , which are involved in various processes, such as phosphate solubilization, nitrogen cycling, and organic matter catabolism [ 48 , 49 ]. Our comparative analysis reveals that while the terrestrial microbiome of Brazil shares a conserved phylogenetic core, the relative dominance of major bacterial phyla is governed by biome-specific environmental filtering. The phylogenetic distribution reveals that while major bacterial phyla are shared across biomes, their relative abundances form distinct ecological signatures. Dry biomes are enriched in stress-tolerant groups such as Cyanobacteria and Firmicutes , whereas forest-like and wet biomes show increased representation of Proteobacteria, reflecting higher nutrient availability and ecological complexity (3A-B). Consistent with global soil surveys, we identified that edaphic factors, specifically water availability, soil pH, and nutrient status, act as the primary evolutionary pressures shaping community assembly. The distinct structural trade-off observed between Actinobacteria in semi-arid soils and Acidobacteria in acidic environments suggests that these taxa occupy opposing ecological niches defined by their life-history strategies. In the Amazon, Acidobacteria and Planctomycetes were well represented, both of which are known to encompass representatives that thrive in acidic and oligotrophic environments [ 50 , 51 ]. With respect to archaea , Nitrososphaera and Candidatus Nitrosocosmicus ( Thaumarchaeota ) dominated the community, and they are often associated with ammonia oxidation in soils, suggesting a key role in nitrogen and nutrient cycling in Amazonian soil processes [ 57 , 58 ]. The profound dominance of Actinobacteria (49.5%) in the Caatinga distinguishes this biome from all others in Brazil. This enrichment likely reflects the phylum’s specialized adaptations to water-limited and high-temperature conditions, including the capacity for sporulation, thick peptidoglycan cell walls, and robust DNA repair mechanisms. In the semi-arid Caatinga, where water potential fluctuates drastically, these traits allow Actinobacteria to maintain metabolic activity during brief wet pulses and persist in dormancy during prolonged drought. This contrasts sharply with the Amazon and Pantanal, where constant moisture favors motility and rapid nutrient diffusion, shifting the community structure toward Proteobacteria. The lower abundance of Acidobacteria in the Caatinga further supports the "water-availability hypothesis," which posits that Acidobacteria are generally sensitive to desiccation and flourish primarily in moist, stable soils. In the Cerrado and Atlantic Forest, the community structure is driven by soil acidity and nutrient limitation, resulting in the highest observed abundances of Acidobacteria (36.4% and 44.2%, respectively). These soils are typically ancient, highly weathered, and acidic (pH < 5), conditions that select for oligotrophic "K-strategists" capable of slow growth and efficient substrate scavenging. Acidobacteria are evolutionarily equipped with high-affinity transporters and proton-pumping mechanisms that allow them to outcompete faster-growing taxa in low-pH environments. Despite the contrasting vegetation cover, savanna versus dense rainforest, the convergence of their soil microbiomes underscores that soil chemistry, rather than plant cover alone, is the master variable controlling bacterial dominance in these neotropical soils [ 52 – 54 ]. The distribution of microbial phyla between CV and PNB in the Cerrado exhibited a similar distribution pattern but with interesting differences. Proteobacteria continued to dominate in both Cerrado areas, with a notable presence of Actinobacteria . However, differences emerged because Actinobacteria was more abundant in PNB than in CV. This difference may be explained by the fact that the Cerrado is characterized by a pronounced dry season, which suggests that microbial communities can adapt to fluctuating moisture levels [ 55 ]. Furthermore, CV contained twice as many Planctomycetes than did PNB. These phyla thrive in oligotrophic environments, which could suggest nutrient limitations in Cerrado soils [ 51 ]. Acidobacteria and Firmicutes exhibited lower yet significant variations between the two regions, with CV containing more Acidobacteria and Firmicutes than PNB does, potentially indicating differences in the soil pH, as Acidobacteria are sensitive to acidic conditions [ 50 , 56 ]. The archaeal community was less diverse in the Cerrado than in the Amazon, with Euryarchaeota as the most abundant group. The Pantanal and Amazon biomes exhibited the highest prevalence of Proteobacteria, a pattern consistent with the "copiotroph" lifestyle associated with nutrient-rich or fluctuating environments In the Pantanal, seasonal flood pulses introduce dissolved organic carbon and create dynamic redox potentials. This selects for metabolically versatile Proteobacteria (r-strategists) that can rapidly exploit nutrient fluxes, as well as anaerobic lineages such as Chloroflexi and Spirochaetes found in the rare biosphere. Furthermore, the archaeal profiles of the Pantanal and Amazon reflect these hydrological regimes; the elevated abundance of Euryarchaeota in these biomes correlates with methanogenic activity in waterlogged, anoxic sediment niches, contrasting with the dominance of ammonia-oxidizing Thaumarchaeota in the aerobic soils of the Caatinga and Cerrado. Pampa is similar to temperate grasslands, where nitrogen fixation and decomposition are critical processes [ 57 , 58 ]. The Pampa biome displayed a unique signature characterized by the highest relative abundance of Bacteroidetes . As established degraders of complex polysaccharides, Bacteroidetes are frequently enriched in the rhizosphere of grasses, where they participate in the breakdown of root exudates and plant biomass.his phylum includes bacteria involved in the degradation of complex polysaccharides, which could be linked to the greater input of grassland plant material [ 59 , 60 ]. This suggests that in the Pampa, the extensive root systems of the native grasslands exert a stronger selective force on the microbiome compared to the forest or savanna biomes, favoring taxa specialized in macromolecule hydrolysis. However, archaea were dominated by Euryarchaeota , reinforcing their widespread ecological role in methanogenesis and nitrogen/phosphate/carbon cycling. Fungi are essential soil components that mediate indispensable processes in nutrient cycling, such as decomposers, saprophytes, symbionts and parasites [ 61 , 62 ]. Rhizophagus species are AMF, a group of roots obligate biotrophs with symbiotic relationships with plant roots [ 63 ]. They are considered natural biofertilizers that help plants absorb essential nutrients such as phosphorus and nitrogen from soil. The distribution of fungi across Brazilian biomes revealed that Ascomycota was the most abundant phylum in all the biomes. This phylum is known for its diverse ecological roles and ability to adapt to distinct environments, likely contributing to its prevalence [ 64 – 66 ]. Aspergillus was consistently the most abundant genus in all the biomes, indicating its adaptability and widespread distribution in various ecological niches. Trichoderma and Fusarium , known for their roles in plant symbiosis and pathogenic interactions, occurred prominently in the Pantanal and Atlantic Forest. Trichoderma exhibits high potential for use in preventing diseases, promoting plant growth, enhancing nutrient utilization efficiency, increasing plant resistance and mitigating agrochemical pollution [ 67 , 68 ]. Fusarium encompasses several phytopathogenic species that cause great economic losses worldwide. In general, they produce a wide variety of mycotoxins, and the consumption of products contaminated with mycotoxins can cause acute or chronic effects in animals and humans and can result in immunosuppressive or carcinogenic effects [ 69 ]. Basidiomycota followed Ascomycota in terms of fungal read abundance. These fungi fulfil significant roles in the degradation of lignin-rich plant litter and are therefore associated with nutrient cycling and decomposition, which are vital in soil ecosystems [ 70 , 71 ]. Other phyla, i.e., Mucoromycota , Chytridiomycota , Microsporidia and Zoopagomycota , exhibited relatively low but uniform proportions across the various biomes. Mucoromycota was more abundant in Pampa and Pantanal biomes, and Chytridiomycota was more abundant in the Pampa and Caatinga biomes than in the other biomes. These differences may indicate that these fungi may be better suited to grassland ecosystems with more temperate and variable conditions. Microsporidia maintained a stable, low-level presence in all biomes. Despite its relatively low abundance, this phylum is an important parasitic group that can impact microbial population dynamics, potentially contributing to the overall complexity of soil microbial communities [ 72 , 73 ]. Ultimately, despite being the minor fungal phylum in terms of abundance, Zoopagomycota maintained a stable presence across all biomes. This phylum includes fungi that prey on small soil organisms such as other fungi, amoebae and nematodes and may play a niche-specific role in microenvironments where competition for resources is high, thereby helping to modulate microbial dynamics in nutrient-limited soils and extreme conditions such as drought (semiarid) or flooding (floodplain) [ 74 , 75 ]. Fungi is involved in plant symbiosis and stress tolerance fulfils crucial roles in increasing plant survival and productivity. Biomes with nutrient-poor or extreme conditions, such as the Caatinga, Pantanal and Cerrado (CV and PNB) biomes, exhibited relatively high levels of fungi involved in plant symbiosis and stress tolerance. Aspergillus was abundant across all the biomes. Differences in microbial distribution are influenced by soil characteristics and preferences for specific host plants or organisms. Furthermore, the quality and scope of the samples collected directly influence the representativeness of the microbial diversity results. Environmental factors must also be considered, as they can affect the observed diversity, and the analysis must account for the specific conditions in the ecosystems studied. Each biome supports microbial communities that are ecologically specialized to local conditions. Studying the dynamic forest's microbiome in an integrated manner is essential for understanding microbial interactions, ecosystem functions, and responses to global changes across diverse habitats [ 76 ]. Forests favour nutrient cycling in acidic soils, whereas drier biomes promote decomposers and microorganisms capable of surviving in harsh nutrient-poor environments. In general, the distributions of minor phyla in all biomes provide insights into local ecological differences. For example, low-abundance phyla exhibits slight shifts, suggesting that environmental factors; such as temperature, light exposure, soil pH, moisture availability, organic matter content, anthropogenic factors, such as land use and agricultural practices; may shape their presence and niche opportunities. Additionally, spatial and ecological factors, such as elevation, landscape structure, soil composition and vegetation cover, likely lead to the creation of distinct microhabitats, thus influencing the microbial composition at each location. Conclusions The distribution of microorganisms in the soil, rhizosphere and roots across Brazilian biomes reflects the complex interactions among environmental factors such as moisture, nutrient availability, and soil properties. The shift from Actinobacteria in the dry Caatinga to Acidobacteria in the acidic Cerrado and Proteobacteria in the wetland Pantanal illustrates a functional plasticity that maintains ecosystem services, such as carbon cycling and nitrogen fixation, across vast climatic gradients. However, these distinct microbial identities also suggest varying vulnerabilities; as climate change threatens to expand semi-arid conditions into the Cerrado and Amazon, we may observe a "Caatinga-fication" of the soil microbiome, potentially altering the carbon storage capacity of these critical global sinks. Material & Methods Sample collection A total of 79 samples were collected, with the following distributions across the various biomes: Amazon (12), Atlantic Forest (6), Caatinga (10), Cerrado Chapada dos Veadeiros (CV) (24), Cerrado Parque Nacional de Brasília (PNB) (4), Pampa (14) and Pantanal (9). The collected samples were divided into soil, root and rhizosphere components. The selection of sampling locations was strategically executed to encompass a broad spectrum of ecological sites, facilitating an in-depth exploration of potential variations in microbial communities across these regions. These biomes exhibit contrasting vegetation and microclimatic conditions, ranging from semi-arid environments to lush, humid rainforests. The soil samples were manually sieved to remove rocks and roots to generate uniform samples. The rhizosphere was subsequently obtained by washing the roots with a 1X phosphate-buffered saline (PBS) solution (pH: 8.3). The roots were carefully removed, and the samples were centrifuged at 5000 rpm for 10 minutes. DNA extraction The total genomic DNA of the soil and rhizosphere samples was extracted from 250 mg of each sample via the DNeasy PowerSoil Kit (QIAGEN, Germany) following the manufacturer's instructions. The plant roots were rinsed in a sterile PBS solution (pH: 8.3) to remove the rhizosphere, disinfected with a solution of 1% sodium hypochlorite with 0.05% Tween 20 and agitated gently for 3 minutes to remove the remaining contaminants. The plant roots were then immersed in 70% ethanol for 2 minutes, followed by thorough rinsing with sterile water. Afterwards, the roots were ground with liquid nitrogen, followed by total genomic DNA extraction using the DNeasy Plant Kit (QIAGEN, Germany) following the manufacturer's instructions. The quality and quantity of DNA were determined by measuring the absorbance at 260/280 nm (A260/A280) on a NanoDrop device (Thermo, Massachusetts, USA) and an Invitrogen™ Qubit™ 4 fluorometer device (Thermo, Massachusetts, USA), respectively. DNA integrity was verified by 0.8% agarose gel electrophoresis. 16S/18S/ITS rRNA gene amplicon library and sequence data analysis For each biome, we pooled the samples into soil, root and rhizosphere samples, resulting in a total of 21 diverse samples. These samples were subsequently sequenced to obtain their 16S, 18S and ITS regions. The following primers were used to amplify the 16S V4 region: 515F GTGCCAGCMGCCGCGGTAA and 806R GGACTACHVGGGTWTCTAAT The following primers were used to amplify the 18S V4 region: 528F GCGGTAATTCCAGCTCCAA and 706R AATCCRAGAATTTCACCTCT The following primers were used to amplify the ITS region: ITS1-1F-F CTTGGTCATTTAGAGGAAGTAA and ITS1-1F-R GCTGCGTTCTTCATCGATGC Amplification and amplicon sequencing, as well as pooled shotgun sequencing, were performed at Novogene. Shotgun sequencing and data processing For all the biomes, the samples were normalized and pooled. All the root, rhizosphere and soil samples for any given biome were merged into a single biome sample. We analysed samples on a gel to determine degradation. Fifty nanograms of each biome pool were sequenced using Illumina shotgun sequencing at Novogene. To obtain shotgun sequencing data, the samples were processed and cleaned at Novogene. Cleaned sequencing files were paired, and a paired library was created for each biome. These libraries were employed to classify the microbes using Kaiju through standard settings on the KBase platform [77]. To obtain amplicon-sequencing data, the DADA2 pipeline was adopted to process clean reads [78], leading to the formation of OTUs. Taxonomic classification of each representative read, and OTU was conducted using the ribosomal database project (RDP) classifier within the SILVA database for bacterial species (with a confidence level of 70%) and the UNITE database for fungal species [79, 80]. OTU analysis included the determination of the relative abundance at both the genus and phylum levels. The analysis was performed via the Phyloseq package [81]. Statistical Analysis All statistical analyses and visualizations were performed in the R software environment. To account for differences in sequencing depth, samples were normalized via random subsampling to the lowest read count prior to downstream analyses. Alpha diversity was assessed using the Chao1 richness estimator, and rarefaction curves were generated using the ggrarecurve function in the MicrobiotaProcess package. Beta diversity was evaluated to visualize differences in microbial community composition between biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, Pantanal) and sample types (soil, root, rhizosphere). Non-metric Multidimensional Scaling (NMDS) ordinations were constructed based on Bray-Curtis dissimilarity matrices using the plot_ordination function in the phyloseq package. The optimal solution was determined after 20 runs to minimize stress (final stress = 0.096). Taxonomic compositions were visualized using stacked bar charts and phylogenetic trees generated with phyloseq and ggbartax from MicrobiotaProcess . Heatmaps exhibiting the relative abundance of phyla across samples were created using the microViz package. Overlaps in Operational Taxonomic Units (OTUs) between soil, root, and rhizosphere compartments were analyzed and visualized using the VennDiagram package Declarations Figures Figures were made in Adobe illustrator. References to the package used to calculate the figures in R are described in each figure's legend. Data availability 16S/18S/ITS amplicon sequencing data and shotgun metagenomic sequencing data are available in 16S/18S/ITS SRA accession numbers PRJNA1275806; PRJNA1275874 and PRJNA1275935, respectively. All data generated or analyzed during this study are included in this published article and its supplementary information files. Correspondence and requests for materials should be addressed to Drs. Marcelo Freire and Elibio Rech. Acknowledgments We would like to thank Claudomiro de Almeida Cortês from the Association of native seed collectors of Cerrado da Chapada dos Veadeiros for his support in defining collection sites. We also thank Luís Henrique Mota de Freitas Neves, Maria Carolina Alves de Camargos and Alexandre Bonesso Sampaio from Chapada dos Veadeiros National Park for the legal authorizations and support in defining collection sites. Funding Declaration We deeply thank the Chico Mendes Institute for Biodiversity Conservation - ICMBio (authorizations 85243 and 85247) for the support and legal authorizations granted for the collection of soil samples. We also acknowledge the National Institute of Science and Technology in Synthetic Biology, National Institute of Science and Technology in Engineering Biological Systems and the Ministry of Agriculture and Livestock. Funding from the National Council for Scientific and Technological Development (465603/2014-9; 400145/2023-5; 308815/2023-8), Research Support Foundation of the Federal District (0193.001.262/2017), and the Coordination for the Improvement of Higher Education Personnel. Funding from the J. Craig Venter Institute to sequence and analyze samples. Author contributions L.M.A.T., R.N.L. and E.R. designed the study; C.A.X.A., G.M.S.R., D.M.C.B., D.S., M.S.B.M., J.P.P.T., L.B.S.V., F.A.F., R.L. and J.L.S.A conducted field sampling; L.M.A.T., R.N.L., and A.M. performed laboratory experiments; L.M.A.T., R.N.L., P.V.P., M.A.O., A.M., H.I.P.G., D.B., P.M., M.F., AM performed data analysis; E.R. secured funding. All authors completed, edited and approved the final version. Competing Interests The authors declare no competing interests. References Braga, A. & Laurini, M. Spatial heterogeneity in climate change effects across Brazilian biomes. Sci. Rep. 14 (1), 16414 (2024). Bruce, T., de Castro, A., Kruger, R., Thompson, C. C. & Thompson, F. L. Microbial Diversity of Brazilian Biomes. In: Genomics Applications for the Developing World. Edited by Nelson KE, Jones-Nelson B. New York, NY: Springer New York; : 217–247. (2012). Flores, B. M. et al. 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Craig Venter Institute","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"Carvalho","lastName":"Bittencourt","suffix":""},{"id":602207251,"identity":"c6cbce58-015d-47e1-af21-5a09bde5f049","order_by":11,"name":"Diana Signor","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Signor","suffix":""},{"id":602207252,"identity":"6e903f2b-e1b5-453e-bc9e-50d52fc6c82a","order_by":12,"name":"Magna Soelma Beserra de Moura","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Magna","middleName":"Soelma Beserra","lastName":"de Moura","suffix":""},{"id":602207253,"identity":"24e72230-3d09-4af7-82eb-687c06fc4179","order_by":13,"name":"José Pedro Pereira Trindade","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Pedro Pereira","lastName":"Trindade","suffix":""},{"id":602207254,"identity":"3b3d8213-f993-4d90-9284-6194479c41a3","order_by":14,"name":"Leandro Bochi da Silva Volk","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Leandro","middleName":"Bochi da Silva","lastName":"Volk","suffix":""},{"id":602207255,"identity":"4259a937-c185-4671-a438-43a9795b5a63","order_by":15,"name":"Fernando Antônio Fernandes","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"Antônio","lastName":"Fernandes","suffix":""},{"id":602207256,"identity":"ef05ae05-bb7c-4510-989a-f220d0009f64","order_by":16,"name":"Ricardo Lopes","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Lopes","suffix":""},{"id":602207257,"identity":"1e3b93d0-151b-45c6-bb39-cddee90e5223","order_by":17,"name":"Jean Luiz Simões-Araújo","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Luiz","lastName":"Simões-Araújo","suffix":""},{"id":602207258,"identity":"85a17515-8c83-4f56-896c-c1769a9a9ad5","order_by":18,"name":"Marcelo Freire","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDAC5gMMDAkMDHKMDQwMB4jTwpYA1mJMohYgSGwg2l26bbwPHzzcY5Pe3H7G8ABDxT07gnrNjrEbGyQ8S8tt7MkxOMBwpjiZsJb7bWwSCQcO5zbOYEs4wNiWkEzQYWbH2Nh/JBz4n85IihY2hoQDBxIYZzAfAGmxI0YLM9BhyYaNPclAjWcSEojRwvjxxwE7ecP2g80fPlQk2BPUAgeGDQzgOCUhguShNAm2jIJRMApGwUgBAIMgQE6uYEgtAAAAAElFTkSuQmCC","orcid":"","institution":"J. Craig Venter Institute","correspondingAuthor":true,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Freire","suffix":""},{"id":602207259,"identity":"5bab23e9-773e-423f-89ef-26cdd6a13ab6","order_by":19,"name":"Elibio Rech","email":"","orcid":"","institution":"Embrapa","correspondingAuthor":false,"prefix":"","firstName":"Elibio","middleName":"","lastName":"Rech","suffix":""}],"badges":[],"createdAt":"2026-02-23 15:24:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8948634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8948634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104259753,"identity":"c528b059-914b-405a-a80c-167ffdd51a30","added_by":"auto","created_at":"2026-03-09 17:57:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":864391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of sampling strategy and metagenomic profiling across Brazilian biomes.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Geographic distribution of sampling sites across six biomes: Amazon (n=12), Atlantic Forest (n=6), Caatinga (n=10), Cerrado (n=28; comprised of Chapada dos Veadeiros [CV] and Parque Nacional de Brasília [PNB]), Pampa (n=14), and Pantanal (n=9). Map generated using QGIS v3.22 using shapefiles provided by the Brazilian Institute of Geography and Statistics (IBGE).\u003cstrong\u003e (B)\u003c/strong\u003e Taxonomic classification of pooled shotgun metagenomic reads against the NCBI \u003cem\u003enr+euk\u003c/em\u003e database (phylum level). Data represents pooled reads from all biological replicates within each biome. \u003cstrong\u003e(C)\u003c/strong\u003e Classification of pooled shotgun reads against the NCBI \u003cem\u003enr\u003c/em\u003e database (prokaryotes only). \u003cstrong\u003e(D)\u003c/strong\u003e Relative abundance of bacterial phyla in Soil (S), Root (R), and Rhizosphere (Rh) samples derived from 16S rRNA amplicon sequencing. \u003cstrong\u003e(E)\u003c/strong\u003e Fungal phylum distribution derived from pooled shotgun reads classified using Kaiju. \u003cstrong\u003e(F)\u003c/strong\u003e Eukaryotic phylum distribution in microbiomes derived from 18S rRNA amplicon sequencing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/251d7e59e2f2121f3449867a.png"},{"id":104259750,"identity":"773a4c47-1888-4e31-a93c-8f61a5767d65","added_by":"auto","created_at":"2026-03-09 17:57:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity metrics of microbial communities.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Non-metric Multidimensional Scaling (NMDS) ordination based on Bray-Curtis dissimilarity showing clustering by microbiome compartment (Soil, Root, Rhizosphere). Points represent individual samples; colors indicate biome of origin. The final stress value for the two-dimensional solution was 0.096. \u003cstrong\u003e(B)\u003c/strong\u003eVenn diagram illustrating the number of shared and unique Operational Taxonomic Units (OTUs) between rhizosphere, root, and soil compartments. \u003cstrong\u003e(C)\u003c/strong\u003eHeatmap of phylum-level relative abundance across all samples, hierarchically clustered by biome and microbiome type. The color scale represents relative abundance from 0 (low) to 1 (high). \u003cstrong\u003e(D)\u003c/strong\u003e Rarefaction curves for Alpha diversity (Chao1 richness estimator) plotted against sequencing depth (number of reads). Shaded regions indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/f58d5bb208ae9282c0f35c01.png"},{"id":104259749,"identity":"889be711-6c9a-4720-9110-32df132a71d5","added_by":"auto","created_at":"2026-03-09 17:57:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":850541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic structure and unique taxonomic signatures of biome categories.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Cladogram representing the phylogenetic relationships of dominant bacterial phyla across all biomes, colored by biome category (Dry: Caatinga/Cerrado; Forest-like: Amazon/Atlantic Forest; Wet: Pantanal/Pampa). \u003cstrong\u003e(B)\u003c/strong\u003e Separate cladograms highlighting defining phyla for each biome category. Nodes are colored by the dominant phylum. \u003cstrong\u003e(C)\u003c/strong\u003e Bar plot showing the count of unique bacterial Orders identified exclusively within each biome category (Dry, Forest-like, Wet). Bars represent the number of Orders found only in that specific category and absent in the others.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/8b0aa4e67cdd6e0db142497b.png"},{"id":104259748,"identity":"9f526a8a-a264-4cbe-b5e8-25f0c27bf920","added_by":"auto","created_at":"2026-03-09 17:57:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":680344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFungal and eukaryotic diversity based on ITS and 18S sequencing.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Schematic representation of the ribosomal RNA gene region showing binding sites for V4 (18S) and ITS1 primers used for amplicon sequencing. \u003cstrong\u003e(B)\u003c/strong\u003e Relative abundance of the three most dominant fungal phyla (\u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota,\u003c/em\u003e and \u003cem\u003eBasidiobolomycota\u003c/em\u003e) across biomes in Rhizosphere (Rh), Root (R), and Soil (S) samples. \u003cstrong\u003e(C)\u003c/strong\u003eRelative abundance of low-abundance fungal phyla. The top three dominant phyla (shown in B) were removed to visualize the diversity of rare taxa. \u003cstrong\u003e(D)\u003c/strong\u003ePie charts displaying the taxonomic composition of the top 5 fungal genera unique to each biome (taxa found exclusively in that biome). The Caatinga and Cerrado biomes did not possess unique genera at this abundance threshold.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/dfcb534940d815ae6a53015f.png"},{"id":105727935,"identity":"1dde9876-7421-41ee-801b-32110d825e1d","added_by":"auto","created_at":"2026-03-30 11:05:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5200869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/13a97326-8a06-4b6e-90be-76f77fd7c76c.pdf"},{"id":104259751,"identity":"d003f815-8ad5-44c6-ad3c-d61aa6f45a09","added_by":"auto","created_at":"2026-03-09 17:57:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":97389,"visible":true,"origin":"","legend":"","description":"","filename":"2Supplementalfilemfreire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8948634/v1/8c99e7b94724ae15fc839b08.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Pan-Biome Metagenomic Atlas of the Brazilian Rhizosphere, Root, and Soil Microbiomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil microbiomes act as the \"digestive system\" of terrestrial ecosystems, driving the biogeochemical cycles that sustain plant productivity and climate regulation. In Brazil, a country of continental proportions hosting the world's largest tropical forest and vast savanna lands, preserving this biodiversity is a global priority [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Brazilian territory is divided into six distinct biomes: Amazon, Atlantic Forest, Cerrado, Caatinga, Pampa, and Pantanal; which form a complex mosaic of climatic zones ranging from the seasonally flooded wetlands of the Pantanal to the semi-arid scrublands of the Caatinga. The Amazon is the largest tropical forest in the world, covering a significant portion of the northern region of Brazil [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Soil in the Amazon ranges from poor to fertile, supporting lush and unique vegetation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Atlantic Forest, which once stretched along Brazil's Atlantic coast, has been significantly reduced under deforestation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, it is recognized for its diverse ecosystems, including dense forests, mangroves and coastal dunes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Cerrado is a vast tropical savanna biome that covers much of central Brazil. Although severely affected by deforestation, it is one of the savannas with the greatest biodiversity globally and encompasses pastures, shrublands and forests, with different soils, from fertile to the poorest [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Caatinga, a dry forest biome in Northeast Brazil, with the lowest rainfall, contains drought-adapted vegetation, with thorny plants and seasonal rainfall patterns, defying arid conditions and the presence of often less fertile soils [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pampa, mostly present across southern Brazil, is known for its vast grasslands and fertile soil, supporting diverse agriculture and an ecosystem unique to the region [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Pantanal, the largest humid tropical area worldwide and located mainly in western Brazil, exhibits high aquatic and terrestrial biodiversity and abundant seasonally flooded soils [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While the macro-biodiversity (plants and animals) of these regions is well-documented, the microbial communities that underpin their resilience remain a largely uncharted territory.\u003c/p\u003e \u003cp\u003eRecent advances in metagenomics have begun to illuminate the \"black box\" of soil microbial diversity, revealing novel taxa and functional genes essential for ecosystem health. However, previous studies in Brazil have largely been fragmented, focusing on single biomes or specific crops rather than providing a unified comparative baseline. In fact, each biome exhibits unique soil properties, water availability, plant species, and microbial communities. Comparative analysis of soil, root and rhizosphere samples is critical for advancing our understanding of soil-plant-microorganism interactions and nutrient cycling and absorption [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The rhizosphere, which constitutes a dynamic interface between roots and the surrounding soil, is enriched with root exudates, creating a microhabitat that fosters various types of interaction, with a diversity symbiotic microbial community [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Fungi, particularly arbuscular mycorrhizal fungi (AMF), form symbiotic relationships with plant roots, increasing micronutrient absorption, especially phosphorus and nitrogen, through an expanded hyphal network [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, there is an association between roots and prokaryotes that plays a critical role in the absorption of key nutrients, such as nitrogen, carbon and phosphorous [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In nutrient-poor soils, such as the Cerrado or Caatinga, these symbioses are essential for plant survival [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In flooded environments such as the Pantanal, anaerobic microorganisms are fundamental for methane production and consumption under anoxic conditions. In dry environments such as Caatinga, the rhizosphere is crucial for water retention and carbon cycling through the decomposition of organic matter by fungi and bacteria. These interactions are complex and directly influence the productivity and sustainability of terrestrial ecosystems. Hence, investigating these interactions in tandem with the surrounding soil matrix provides a more comprehensive understanding of how microbial communities are distributed to mediate biogeochemical cycles, influence soil structures and optimize nutrient availability. The lack of a comprehensive reference dataset limits our ability to understand how microbial communities adapt to contrasting environmental pressures, such as the distinct shift from the acidic, aluminum-rich soils of the Cerrado to the fertile, organic-matter-rich grasslands of Pampa. Furthermore, distinct microhabitats within the soil matrix, particularly the rhizosphere and root endosphere, exert strong selective pressure on microbial assembly, filtering specific taxa from the bulk soil to function as plant symbionts.\u003c/p\u003e \u003cp\u003eTo address this gap, we organized a nationwide research consortium to collect and sequence matched soil, root, and rhizosphere samples across all six Brazilian biomes. This study represents a pioneering effort to bridge the knowledge gap in tropical soil ecology by integrating amplicon sequencing (16S/18S/ITS) with shotgun metagenomics. This dual approach allows for a robust taxonomic inventory of \u003cem\u003ebacteria\u003c/em\u003e, \u003cem\u003earchaea\u003c/em\u003e, \u003cem\u003efungi\u003c/em\u003e, and \u003cem\u003eprotists\u003c/em\u003e, distinguishing the \"who is there\" from the \"what they can do.\"\u003c/p\u003e \u003cp\u003eWhile several studies have contributed to the growing body of knowledge on microbiome metagenomics in Brazilian biomes, revealing novel microbial taxa and functional genes and highlighting their role in sustaining plant health and ecosystem resilience [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we provide a macroscopic overview of microbial life supporting these unique ecosystems. We catalogue the large-scale genomes of culture-independent microbes, identifying core microbiome signatures associated with distinct vegetation types and climatic conditions. By establishing this pan-biome metagenomic atlas, we aim not only to advance fundamental soil ecology but also to provide a reliable sequencing database. This resource will be critical for future. Finally, this integrated approach offers valuable insights into ecosystem functioning, sustainable land-use practices, agricultural management, and environmental conservation.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSequencing\u003c/h2\u003e \u003cp\u003eTo reveal the microbial diversity of the soil, roots and rhizosphere, metagenomic DNA was isolated, sampled, and subsequently sequenced through Illumina amplicon and shotgun sequencing of the V3/V4 region of 16S rRNA, V4 region of 18S rRNA and ITS ITS1-1F. Thus, via the use of this comprehensive approach in which both amplicon sequencing and shotgun methodologies are combined, we ensured the precision of the estimated relative gene category abundances. This involved the random subsampling of reads to match the sample with the lowest read count, a prerequisite for the subsequent downstream analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After quality filtering and normalization, shotgun sequencing yielded a cumulative total of 72.5\u0026nbsp;million base pairs across all biome pools (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This dual-sequencing strategy allowed for high-resolution profiling of community structure (amplicon) and functional potential (shotgun) across the Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal biomes.\u003c/p\u003e \u003cp\u003eTo capture the overall biome-level representation, all individual samples from each biome were pooled into a single-biome source sample. These biome source samples were subjected to shotgun sequencing, resulting in a cumulative total of 72,569,233 base pairs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This comprehensive sampling and sequencing strategy aimed to provide a robust foundation for elucidating the complex microbial dynamics in distinct Brazilian biomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the sequencing data of the biomes of Brazil. The Cerrado biome was sampled twice, one at Chapada dos Veadeiros (CV) and another at the National Park of Brasilia (PNB).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of paired end reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacterial reads (*N, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArchaeal reads (N,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eViral reads (N, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFungi reads (N, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmazon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,824,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,137,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53,572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAtlantic Forest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,664,538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e923,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41,156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaatinga\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45,631,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e923,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48,584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerrado_CV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35,895,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38,008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerrado_PNB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,818,310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,356,564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64,363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePampa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,191,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,359,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63,578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePantanal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45,605,150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,117,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54,838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*From Krona plots calculated with Kaiju\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProkaryotic and eukaryotic read distributions across Brazilian biomes\u003c/h3\u003e\n\u003cp\u003eThe samples were normalized and pooled for each biome and subsequently used for shotgun sequencing. Here, we obtained a macroscopic overview of the microbes present in each Brazilian biome, while the amplicon-sequencing data provided a comprehensive view of prokaryotic and eukaryotic diversity across Brazilian biomes, given the separation between roots, soil and the rhizosphere. The differences in microbial community composition can be inferred to the unique ecological conditions in each biome, such as moisture, temperature, soil pH, soil nutrition and vegetation type. Shotgun metagenomic data provided a macroscopic overview of the microbial domains, while amplicon sequencing allowed for the differentiation of root, soil, and rhizosphere compartments. Microbial community composition shifted significantly across biomes, possibly driven by distinct ecological conditions such as moisture availability and soil chemistry. The specific taxonomic distributions for each biome are detailed below.\u003c/p\u003e\n\u003ch3\u003eAmazon forest\u003c/h3\u003e\n\u003cp\u003eIn constrast with the dry Caatinga, the Amazon rainforest soil microbiome was co-dominated by \u003cem\u003eProteobacteria\u003c/em\u003e (~\u0026thinsp;35%) and \u003cem\u003eAcidobacteria\u003c/em\u003e (~\u0026thinsp;30%), a balance typical of moist, acidic tropical forests where rapid nutrient cycling is required [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003eActinobacteria\u003c/em\u003e (~\u0026thinsp;13%) and \u003cem\u003eFirmicutes\u003c/em\u003e (~\u0026thinsp;4%) were also prevalent, particularly in 'Terra Preta' (Amazonian Dark Earths). The rare biosphere (\u0026lt;\u0026thinsp;0.1%) included phyla such as \u003cem\u003eElusimicrobia\u003c/em\u003e and \u003cem\u003eFusobacteria\u003c/em\u003e. Archaeal diversity was high and dominated with \u003cem\u003eThaumarchaeota\u003c/em\u003e in oxic surface soils, while methanogenic \u003cem\u003eEuryarchaeota\u003c/em\u003e became more prominent in waterlogged or anaerobic niches.\u003c/p\u003e \u003cp\u003eAt the phylum level, 698,227 and 5,249 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequences were \u003cem\u003eProteobacteria\u003c/em\u003e, accounting for 56% of the total sequences, followed by \u003cem\u003eActinobacteria\u003c/em\u003e (17.8%), \u003cem\u003eAcidobacteria\u003c/em\u003e (7.4%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (5.3%), \u003cem\u003eBacteroidetes\u003c/em\u003e (2.8%), \u003cem\u003eFirmicutes\u003c/em\u003e (2.5%), \u003cem\u003eChloroflexi\u003c/em\u003e (1.2%), \u003cem\u003eCyanobacteria\u003c/em\u003e (1.1%) and \u003cem\u003eNitrospirae\u003c/em\u003e (1%). Moreover, the abundance levels of four phyla remained below 1%, namely, \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.94%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.81%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.28%), and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.14%). The abundance levels of 21 phyla remained below 0.1%, namely, \u003cem\u003eChlorobi\u003c/em\u003e (0.09%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.08%), \u003cem\u003eAquificae\u003c/em\u003e (0.07%), \u003cem\u003eThermotogae\u003c/em\u003e (0.07%), \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.07%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.06%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.06%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.05%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.04%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.04%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.03%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.03%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.02%), \u003cem\u003eTenericutes\u003c/em\u003e (0.02%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.01%), \u003cem\u003eElusimicrobia\u003c/em\u003e (0.01%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.01%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.01%), \u003cem\u003eBalneolaeota\u003c/em\u003e (0.01%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.01%), and \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.006%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.006%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.005%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.004%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.003%) and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0008%). The most abundant archaeal sequences were \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.34%) and \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.31%), whereas the abundance levels of other phyla did not exceed 0.1%, including \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.05%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.02%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.003%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.003%), \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.001%), and \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0004%). A total of 201 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (1,787,557) accounted for 71% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, respectively). Notably, the pooled shotgun composition classified based on the National Center for Biotechnology Information (NCBI) nr\u0026thinsp;+\u0026thinsp;euk database mostly matched that resulting from the amplicon-sequencing data classified on the basis of Silva's training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). As shown in the shotgun data, in the amplicon-sequencing data, \u003cem\u003eProteobacteria\u003c/em\u003e also dominated in the rhizosphere and soil. However, we observed a different pattern for the roots from the Amazon samples. Compared with those in the soil and rhizosphere, the roots were dominated by \u003cem\u003eCyanobacteria\u003c/em\u003e, followed by \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e. Notably, the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e was also greater than that in the soil and rhizosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, respectively).\u003c/p\u003e \u003cp\u003eIn the samples from the Amazon, 51,693 reads were assigned to fungi at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (61%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (20%), \u003cem\u003eMucoromycota\u003c/em\u003e (7.7%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.5%), \u003cem\u003eZoopagomycota\u003c/em\u003e (1.3%) and \u003cem\u003eMicrosporidia\u003c/em\u003e (1.8%). At the genus level, the most prevalent taxa were \u003cem\u003eAspergillus\u003c/em\u003e (5.3%), \u003cem\u003eTrichoderma\u003c/em\u003e (4%), \u003cem\u003eFusarium\u003c/em\u003e (2.5%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.4%), \u003cem\u003eLobosporangium\u003c/em\u003e (2.1%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (2%), \u003cem\u003eSynchytrium\u003c/em\u003e (1.7%), \u003cem\u003eRhizophagus\u003c/em\u003e (1.4%), \u003cem\u003ePenicillium\u003c/em\u003e (1.4%) and \u003cem\u003ePhycomyces\u003c/em\u003e (1.4%). These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2439541) accounted for 98% of the total reads on average.\u003c/p\u003e\n\u003ch3\u003eCaatinga\u003c/h3\u003e\n\u003cp\u003eThe semi-arid Caatinga biome was dominated by \u003cem\u003eActinobacteria\u003c/em\u003e (49.5%), likely reflecting adaptation to water-limited conditions given their carbon-cycles capabilities and nutrition competition strategies. \u003cem\u003eProteobacteria\u003c/em\u003e (29%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (5.1%), and \u003cem\u003eFirmicutes\u003c/em\u003e (5.1%) were also prevalent. Rare taxa (\u0026lt;\u0026thinsp;0.1% abundance) included 16 phyla such as \u003cem\u003eArmatimonadetes\u003c/em\u003e and \u003cem\u003eChlorobi\u003c/em\u003e. Archaeal diversity mirrored the bacterial dominance of stress-tolerant taxa, with \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.49%) and \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.34%) comprising most of the archaeal reads.\u003c/p\u003e \u003cp\u003eAt the phylum level, 602,062 and 5,550 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequences were \u003cem\u003eActinobacteria\u003c/em\u003e, accounting for 49.5% of the total sequences, followed by \u003cem\u003eProteobacteria\u003c/em\u003e, at 29%; \u003cem\u003ePlanctomycetes\u003c/em\u003e, at 5.1%; \u003cem\u003eFirmicutes\u003c/em\u003e, at 5.1%; \u003cem\u003eAcidobacteria\u003c/em\u003e, at 2.3%; \u003cem\u003eChloroflexi\u003c/em\u003e, at 1.5%; \u003cem\u003eBacteroidetes\u003c/em\u003e, at 1.4%; and \u003cem\u003eCyanobacteria\u003c/em\u003e, at 1.1%. Moreover, the abundance levels of five phyla remained below 1%, namely, \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.97%), \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.44%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.38%), \u003cem\u003eNitrospirae\u003c/em\u003e (0.3%) and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.12%). Sixteen phyla exhibited abundance levels below 0.1%, namely, \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.06%), \u003cem\u003eChlorobi\u003c/em\u003e (0.06%), \u003cem\u003eThermotogae\u003c/em\u003e (0.05%), \u003cem\u003eAquificae\u003c/em\u003e (0.05%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.05%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.04%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.04%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.03%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.03%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.03%), \u003cem\u003eTenericutes\u003c/em\u003e (0.02%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.02%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.02%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.01%) and \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.009%), \u003cem\u003eBalneolaeota\u003c/em\u003e (0.009%), \u003cem\u003eElusimicrobia\u003c/em\u003e (0.008%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.008%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.008%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.006%), and \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.006%). \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.004%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.004%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.003%), and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0005%). The most abundant archaeal sequences were \u003cem\u003eThaumarchaeota\u003c/em\u003e (0. 49%) and \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.34%), whereas the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.04%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.01%), \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.0006%), and \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0001%). A total of 161 reads (0.02%) were assigned to viral genomes, whereas unassigned and unclassified reads (1673826) accounted for 73% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the amplicon-sequencing 16S data, the profile for the Caatinga biome slightly differed from the shotgun profile. In the latter, the dominant phylum was \u003cem\u003eActinobacteria\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). However, in the amplicon-sequencing 16S data, \u003cem\u003eProteobacteria\u003c/em\u003e was more dominant than \u003cem\u003eActinobacteria\u003c/em\u003e in the soil and rhizosphere.\u003c/p\u003e \u003cp\u003eFor fungi, 46,630 reads were assigned at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (60%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (20%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.5%), \u003cem\u003eMucoromycota\u003c/em\u003e (6.4%), \u003cem\u003eMicrosporidia\u003c/em\u003e (1.3%) and \u003cem\u003eZoopagomycota\u003c/em\u003e (1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (6.3%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.3%), \u003cem\u003eFusarium\u003c/em\u003e (2.1%), \u003cem\u003eSynchytrium\u003c/em\u003e (2%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (2%), \u003cem\u003eRhizophagus\u003c/em\u003e (1.8%), \u003cem\u003eLobosporangium\u003c/em\u003e (1.6%), \u003cem\u003eTrichoderma\u003c/em\u003e (1.4%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.4%), and \u003cem\u003eTalaromyces\u003c/em\u003e (1.4%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2234969) accounted for 98% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n\u003ch3\u003eCerrado–Chapada dos Veadeiros (CV)\u003c/h3\u003e\n\u003cp\u003eThe Cerrado biome soil profile at Chapada dos Veadeiros was characterized by high abundances of \u003cem\u003eAcidobacteria\u003c/em\u003e (36.4%) and \u003cem\u003eProteobacteria\u003c/em\u003e (28.9%), reflecting the acidic, nutrient-poor nature of these neotropical savanna soils[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Actinobacteria (12.5%) and \u003cem\u003eVerrucomicrobia\u003c/em\u003e (3.2%) represented the other prevalent groups. The rare biosphere (\u0026lt;\u0026thinsp;0.1% abundance) consisted of approximately 19 phyla, including \u003cem\u003eGemmatimonadetes\u003c/em\u003e and \u003cem\u003eNitrospirae\u003c/em\u003e. Similar to the Caatinga, archaeal communities were driven by \u003cem\u003eThaumarchaeota\u003c/em\u003e (1.1%), which play a key role in ammonia oxidation in these nitrogen-limited soils.\u003c/p\u003e \u003cp\u003eAt the phylum level, 490,262 and 2,030 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequences were \u003cem\u003eProteobacteria\u003c/em\u003e, accounting for 50% of the total sequences, followed by \u003cem\u003eActinobacteria\u003c/em\u003e (22%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (10%), \u003cem\u003eAcidobacteria\u003c/em\u003e (7.1%), \u003cem\u003eFirmicutes\u003c/em\u003e (2%), \u003cem\u003eChloroflexi\u003c/em\u003e (1.5%), \u003cem\u003eBacteroidetes\u003c/em\u003e (1.5%) and \u003cem\u003eCyanobacteria\u003c/em\u003e (1.1%). Moreover, the abundance levels of five phyla remained below 1%, namely, \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.9%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.36%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.27%), \u003cem\u003eNitrospirae\u003c/em\u003e (0.25%) and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.11%). The abundance levels of 14 phyla remained below 0.1%, namely, \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.07%), \u003cem\u003eChlorobi\u003c/em\u003e (0.07%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.06%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.05%), \u003cem\u003eThermotogae\u003c/em\u003e (0.05%), \u003cem\u003eAquificae\u003c/em\u003e (0.05%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.04%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.03%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.03%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.03%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.03%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.02%) and \u003cem\u003eTenericutes\u003c/em\u003e (0.02%). The least abundant phyla were \u003cem\u003eElusimicrobia\u003c/em\u003e (0.009%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.009%), \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.009%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.008%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.008%), \u003cem\u003eBalneolaeota\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.007%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.005%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.005%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.003%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.003%), \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0014%) and \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.0014%). The most abundant archaeal sequence was \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.27%), while the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.07%), \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.03%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.02%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0006%) or \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.0006%). Eighty-four reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (1302376) accounted for 72% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor fungi, 36,719 reads were assigned at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (60%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (20%), \u003cem\u003eMucoromycota\u003c/em\u003e (7.2%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.6%), \u003cem\u003eZoopagomycota\u003c/em\u003e (1.1%) and \u003cem\u003eMicrosporidia\u003c/em\u003e (1.1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (5%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.5%), \u003cem\u003eRhizophagus\u003c/em\u003e (2.2%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (1.9%), \u003cem\u003eLobosporangium\u003c/em\u003e (1.8%), \u003cem\u003eBacidia\u003c/em\u003e (1.5%), \u003cem\u003eExophiala\u003c/em\u003e (1.5%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.5%), \u003cem\u003eFusarium\u003c/em\u003e (1.3%), and \u003cem\u003eTalaromyces\u003c/em\u003e (1.2%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (1758033) accounted for 98% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCerrado\u0026ndash;Parque Nacional de Bras\u0026iacute;lia (PNB\u003c/b\u003e \u003cb\u003e)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Cerrado biome soil profile at the National Park of Brasilia was also characterized by high abundances of \u003cem\u003eAcidobacteria\u003c/em\u003e (36.4%) and \u003cem\u003eProteobacteria\u003c/em\u003e (28.9%). \u003cem\u003eActinobacteria\u003c/em\u003e (12.5%) and \u003cem\u003eVerrucomicrobia\u003c/em\u003e (3.2%) represented the other prevalent groups. The rare biosphere (\u0026lt;\u0026thinsp;0.1% abundance) consisted of approximately 19 phyla, including \u003cem\u003eGemmatimonadetes\u003c/em\u003e and \u003cem\u003eNitrospirae\u003c/em\u003e. Similar to the Caatinga, archaeal communities were driven by \u003cem\u003eThaumarchaeota\u003c/em\u003e (1.1%), which play a key role in ammonia oxidation in these nitrogen-limited soils.\u003c/p\u003e \u003cp\u003eAt the phylum level, 907,146 and 2,831 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequences were \u003cem\u003eProteobacteria\u003c/em\u003e (accounting for 51% of the total sequences), \u003cem\u003eActinobacteria\u003c/em\u003e (28%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (5.9%), \u003cem\u003eAcidobacteria\u003c/em\u003e (5.7%), \u003cem\u003eFirmicutes\u003c/em\u003e (1.5%) and \u003cem\u003eBacteroidetes\u003c/em\u003e (1.2%). The abundance levels of 6 phyla remained below 1%, namely, \u003cem\u003eChloroflexi\u003c/em\u003e (0.99%), \u003cem\u003eCyanobacteria\u003c/em\u003e (0.87%), \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.74%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.33%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.24%) and \u003cem\u003eNitrospirae\u003c/em\u003e (0.15%). Moreover, the abundance levels of 15 phyla remained below 0.1%, namely, \u003cem\u003eSpirochaetes\u003c/em\u003e (0.08%), \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.05%), \u003cem\u003eChlorobi\u003c/em\u003e (0.05%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.04%), \u003cem\u003eThermotogae\u003c/em\u003e (0.04%), \u003cem\u003eAquificae\u003c/em\u003e (0.03%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.03%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.03%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.02%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.02%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.02%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.02%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.01%) and \u003cem\u003eTenericutes\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eAtribacterota\u003c/em\u003e (0.008%), \u003cem\u003eBalneolaeota\u003c/em\u003e (0.008%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.007%), \u003cem\u003eElusimicrobia\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.006%), \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.005%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.005%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.005%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.003%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.003%), \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.002%) \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.002%) and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0004%). The most abundant archaeal sequence was \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.21%), while the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.05%), \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.03%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.0008%), and \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0003%). A total of 157 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (2230781) accounted for 70% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor fungi, 61,996 reads were assigned at the phylum level. The most abundant fungal sequences were \u003cem\u003eAscomycota\u003c/em\u003e (60%), \u003cem\u003eBasidiomycota\u003c/em\u003e (20%), \u003cem\u003eMucoromycota\u003c/em\u003e (6.6%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.4%), \u003cem\u003eMicrosporidia\u003c/em\u003e (1.1%) and \u003cem\u003eZoopagomycota\u003c/em\u003e (1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (5%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.3%), \u003cem\u003eSynchytrium\u003c/em\u003e (2%), \u003cem\u003eRhizophagus\u003c/em\u003e (2%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (1.8%), \u003cem\u003eLobosporangium\u003c/em\u003e (1.7%), \u003cem\u003eExophiala\u003c/em\u003e (1.5%), \u003cem\u003eFusarium\u003c/em\u003e (1.4%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.3%) and \u003cem\u003eBacidia\u003c/em\u003e (1.3%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (3078919) accounted for 98% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAtlantic forest\u003c/h2\u003e \u003cp\u003eSoil communities in the Atlantic Forest were distinct for their high prevalence of \u003cem\u003eAcidobacteria\u003c/em\u003e (44.2%) and \u003cem\u003eProteobacteria\u003c/em\u003e (26.5%). Other prevalent taxa included \u003cem\u003eVerrucomicrobia\u003c/em\u003e (6.1%) and Planctomycetes (4.8%). Rare taxa (\u0026lt;\u0026thinsp;0.1% abundance) comprised diverse groups such as \u003cem\u003eLatescibacteria\u003c/em\u003e and \u003cem\u003eChlamydiae\u003c/em\u003e. Archaeal diversity was comparatively lower, with Thaumarchaeota (0.65%) dominating the archaeal reads, followed by minor populations of \u003cem\u003eEuryarchaeota\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAt the phylum level, 619,723 and 3971 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequence was \u003cem\u003eProteobacteria\u003c/em\u003e, accounting for 54.2% of the total sequences, followed by \u003cem\u003eActinobacteria\u003c/em\u003e (26.3%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (6%), \u003cem\u003eAcidobacteria\u003c/em\u003e (5%), \u003cem\u003eBacteroidetes\u003c/em\u003e (2.7%) and \u003cem\u003eFirmicutes\u003c/em\u003e (2.1%). \u003cem\u003eMoreover\u003c/em\u003e, the abundance levels of seven phyla remained below 1%, namely, \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.96%), \u003cem\u003eCyanobacteria\u003c/em\u003e (0.83%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.75%), \u003cem\u003eChloroflexi\u003c/em\u003e (0.71%), \u003cem\u003eNitrospirae\u003c/em\u003e (0.40%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.24%), and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.10%). The abundance levels of 15 phyla remained below 0.1%, namely, \u003cem\u003eChlorobi\u003c/em\u003e (0.07%), \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.06%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.05%), \u003cem\u003eThermotogae\u003c/em\u003e (0.05%), \u003cem\u003eAquificae\u003c/em\u003e (0.04%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.04%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.04%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.04%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.03%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.03%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.03%) \u003cem\u003eFusobacteria\u003c/em\u003e (0.03%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eTenericutes\u003c/em\u003e (0.01%), and \u003cem\u003eElusimicrobia\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eBalneolaeota\u003c/em\u003e (0.009%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.008%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.007%), \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.006%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.006%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.005%), \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.004%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.003%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.003%), and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0006%). The most abundant archaeal sequences were \u003cem\u003eThaumarchaeota\u003c/em\u003e (0. 36%) and \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.23%), whereas the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.03%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.007%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.001%) and \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.0001%). A total of 330 reads (0.05%) were assigned to viral genomes, whereas unassigned and unclassified reads (1459532) accounted for 70% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor fungi, 39,525 reads were assigned at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (57%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (22%), \u003cem\u003eMucoromycota\u003c/em\u003e (7.3%), \u003cem\u003eChytridiomycota\u003c/em\u003e (7.1%), \u003cem\u003eMicrosporidia\u003c/em\u003e (1.3%) and \u003cem\u003eZoopagomycota\u003c/em\u003e (1.1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (4.8%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.6%), \u003cem\u003eTrichoderma\u003c/em\u003e (4%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (2.2%), \u003cem\u003eLobosporangium\u003c/em\u003e (2%), \u003cem\u003eSynchytrium\u003c/em\u003e (2%), \u003cem\u003eRhizophagus\u003c/em\u003e (1.7%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.6%), \u003cem\u003eFusarium\u003c/em\u003e (1.3%) and \u003cem\u003eRhizopus\u003c/em\u003e (1.2%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2043701) accounted for 98% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePampa\u003c/h3\u003e\n\u003cp\u003eThe Pampa grasslands exhibited a distinct signature characterized by the highest prevalence of Bacteroidetes (3.5%) among all biomes, likely linked to the decomposition of complex polysaccharides in the grass rhizosphere. However, the community was still driven by Proteobacteria (34.4%) and \u003cem\u003eAcidobacteria\u003c/em\u003e (20.8%). \u003cem\u003eActinobacteria\u003c/em\u003e (11.6%) and \u003cem\u003eVerrucomicrobia\u003c/em\u003e (3.5%) represented other key groups. Rare taxa (\u0026lt;\u0026thinsp;0.1%) included diverse lineages such as \u003cem\u003eDeinococcus-Thermus\u003c/em\u003e and \u003cem\u003eBRC1\u003c/em\u003e. Archaeal communities showed a mixed profile, with Thaumarchaeota generally dominant but significant contributions from Euryarchaeota in denser pasture soils.\u003c/p\u003e \u003cp\u003eAt the phylum level, 853,361 and 2,933 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequence was \u003cem\u003eProteobacteria\u003c/em\u003e, accounting for 53% of the total sequences, followed by \u003cem\u003eActinobacteria\u003c/em\u003e (21%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (6.25%), \u003cem\u003eAcidobacteria\u003c/em\u003e (5.7%), \u003cem\u003eBacteroidetes\u003c/em\u003e (4%), \u003cem\u003eFirmicutes\u003c/em\u003e (2.3%), \u003cem\u003eVerrucomicrobia\u003c/em\u003e (1.5%) and \u003cem\u003eCyanobacteria\u003c/em\u003e (1%). The abundance levels of 5 phyla remained below 1%, namely, \u003cem\u003eChloroflexi\u003c/em\u003e (0.87%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (0.83%), \u003cem\u003eNitrospirae\u003c/em\u003e (0.28%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.27%) and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.13%). Moreover, the abundance levels of 20 phyla remained below 0.1%, namely, \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.08%), \u003cem\u003eChlorobi\u003c/em\u003e (0.08%), \u003cem\u003eChloridia\u003c/em\u003e (0.06%), \u003cem\u003eAquificae\u003c/em\u003e (0.05%), \u003cem\u003eThermotogae\u003c/em\u003e (0.05%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.05%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.04%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.04%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.04%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.03%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.02%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.02%), \u003cem\u003eTenericutes\u003c/em\u003e (0.02%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.01%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.01%), \u003cem\u003eElusimicrobia\u003c/em\u003e (0.001%), \u003cem\u003eBalneolaeota\u003c/em\u003e (0.01%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.008%) and \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.009%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.009%), \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.006%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.005%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.004%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.003%) and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.0007%). The most abundant archaeal sequence was \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.24%), while the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.05%), \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.03%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.008%), \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.002%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.0009%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.0006%), and \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0001%). A total of 222 reads (0.03%) were assigned to viral genomes, whereas unassigned and unclassified reads (2253067) accounted for 72% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor fungi, 61,221 reads were assigned at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (57%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (22%), \u003cem\u003eMucoromycota\u003c/em\u003e (8%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.9%), \u003cem\u003eMicrosporidia\u003c/em\u003e (1.2%) and \u003cem\u003eZoopagomycota\u003c/em\u003e (1.1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (4.5%), \u003cem\u003eRhizophagus\u003c/em\u003e (2.7%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.6%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (2%), \u003cem\u003eLobosporangium\u003c/em\u003e (2%), \u003cem\u003eSynchytrium\u003c/em\u003e (1.8%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.5%), \u003cem\u003eFusarium\u003c/em\u003e (1.3%), \u003cem\u003eExophiala\u003c/em\u003e (1.3%) and \u003cem\u003eRhizopus\u003c/em\u003e (1.3%) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThese genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (3048362) accounted for 98% of the total reads on average.\u003c/p\u003e\n\u003ch3\u003ePantanal\u003c/h3\u003e\n\u003cp\u003eReflecting the fluctuating water saturation of this wetland biome, the Pantanal was dominated by \u003cem\u003eProteobacteria\u003c/em\u003e (41.8%). \u003cem\u003eActinobacteria\u003c/em\u003e (18.3%) and Acidobacteria (16.7%) were also prevalent, though less abundant than in drier biomes. \u003cem\u003eThe\u003c/em\u003e rare biosphere (\u0026lt;\u0026thinsp;0.1%) included anaerobic or facultative lineages such as \u003cem\u003eChloroflexi\u003c/em\u003e and \u003cem\u003eSpirochaetes\u003c/em\u003e. In the archaeal domain, Thaumarchaeota (0.82%) and Euryarchaeota (0.45%) were the most frequent, likely contributing to methanogenic processes typical of wetland sediments.\u003c/p\u003e \u003cp\u003eAt the phylum level, 748,195 and 8,303 reads were assigned to the \u003cem\u003eBacteria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e domains, respectively. The most abundant bacterial sequence was \u003cem\u003eProteobacteria\u003c/em\u003e, accounting for 45% of the total sequences, followed by \u003cem\u003eActinobacteria\u003c/em\u003e (35%), \u003cem\u003ePlanctomycetes\u003c/em\u003e (3.7%), \u003cem\u003eAcidobacteria\u003c/em\u003e (3.5%), \u003cem\u003eBacteroidetes\u003c/em\u003e (2.3%), \u003cem\u003eFirmicutes\u003c/em\u003e (2.2%), \u003cem\u003eChloroflexi\u003c/em\u003e (1%) and \u003cem\u003eGemmatimonadetes\u003c/em\u003e (1%). The abundance levels of 5 phyla remained below 1%, namely, \u003cem\u003eCyanobacteria\u003c/em\u003e (0.9%), \u003cem\u003eVerrucomicrobia\u003c/em\u003e (0.65%), \u003cem\u003eNitrospirae\u003c/em\u003e (0.5%), \u003cem\u003eDeinococcus\u003c/em\u003e-\u003cem\u003eThermus\u003c/em\u003e (0.3%) and \u003cem\u003eSpirochaetes\u003c/em\u003e (0.1%). Moreover, the abundance levels of 16 phyla remained below 0.1%, namely, \u003cem\u003eChlorobi\u003c/em\u003e (0.06%), \u003cem\u003eArmatimonadetes\u003c/em\u003e (0.05%), \u003cem\u003eAquificae\u003c/em\u003e (0.05%), \u003cem\u003eThermotogae\u003c/em\u003e (0.05%), \u003cem\u003eChlamydiae\u003c/em\u003e (0.04%), \u003cem\u003eThermodesulfobacteria\u003c/em\u003e (0.04%), \u003cem\u003eRhodothermaeota\u003c/em\u003e (0.03%), \u003cem\u003eIgnavibacteriae\u003c/em\u003e (0.03%), \u003cem\u003eDeferribacteres\u003c/em\u003e (0.03%), \u003cem\u003eKiritimatiellaeota\u003c/em\u003e (0.02%), \u003cem\u003eSynergistetes\u003c/em\u003e (0.02%), \u003cem\u003eCalditrichaeota\u003c/em\u003e (0.02%), \u003cem\u003eTenericutes\u003c/em\u003e (0.02%), \u003cem\u003eFusobacteria\u003c/em\u003e (0.02%), \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e (0.01%), and \u003cem\u003eBalneolaeota\u003c/em\u003e (0.01%). The least abundant phyla were \u003cem\u003eElusimicrobia\u003c/em\u003e (0.001%), \u003cem\u003eAtribacterota\u003c/em\u003e (0.009%), \u003cem\u003eDictyoglomi\u003c/em\u003e (0.009%), \u003cem\u003eCandidatus Omnitrophica\u003c/em\u003e (0.009%), \u003cem\u003eCandidatus Bipolaricaulota\u003c/em\u003e (0.006%), \u003cem\u003eChrysiogenetes\u003c/em\u003e (0.006%), \u003cem\u003eCandidatus Cloacimonetes\u003c/em\u003e (0.004%), \u003cem\u003eCaldiserica\u003c/em\u003e (0.004%), \u003cem\u003eFibrobacteres\u003c/em\u003e (0.003%), \u003cem\u003eCoprothermobacterota\u003c/em\u003e (0.003%), and \u003cem\u003eCandidatus Absconditabacteria\u003c/em\u003e (0.001%). The most abundant archaeal sequences were \u003cem\u003eThaumarchaeota\u003c/em\u003e (0.74%) and \u003cem\u003eEuryarchaeota\u003c/em\u003e (0.3%), whereas the abundance levels of the other phyla did not exceed 0.1%, including \u003cem\u003eCrenarchaeota\u003c/em\u003e (0.03%), \u003cem\u003eCandidatus Thermoplasmatota\u003c/em\u003e (0.01%), \u003cem\u003eCandidatus Korarchaeota\u003c/em\u003e (0.01%), \u003cem\u003eCandidatus Lokiarchaeota\u003c/em\u003e (0.001%), \u003cem\u003eCandidatus Micrarchaeota\u003c/em\u003e (0.0006%), and \u003cem\u003eCandidatus Nanohaloarchaeota\u003c/em\u003e (0.0004%). A total of 178 reads (0.02%) were assigned to viral genomes, whereas unassigned and unclassified reads (1523581) accounted for 67% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor fungi, 52,896 reads were assigned at the phylum level. The most abundant fungal sequence was \u003cem\u003eAscomycota\u003c/em\u003e (60%), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e (21%), \u003cem\u003eChytridiomycota\u003c/em\u003e (6.6%), \u003cem\u003eMucoromycota\u003c/em\u003e (6.4%), \u003cem\u003eMicrosporidia\u003c/em\u003e (1.2%) and \u003cem\u003eZoopagomycota\u003c/em\u003e (1.1%). At the genus level, \u003cem\u003eAspergillus\u003c/em\u003e (5%), \u003cem\u003eFusarium\u003c/em\u003e (3.4%), \u003cem\u003eSpizellomyces\u003c/em\u003e (2.4%), \u003cem\u003eBatrachochytrium\u003c/em\u003e (2%), \u003cem\u003eTalaromyces\u003c/em\u003e (2%), \u003cem\u003eLobosporangium\u003c/em\u003e (1.9%), \u003cem\u003eSynchytrium\u003c/em\u003e (1.8%), \u003cem\u003eRhizophagus\u003c/em\u003e (1.5%), \u003cem\u003ePhycomyces\u003c/em\u003e (1.3%), and \u003cem\u003eTrichoderma\u003c/em\u003e (1.3%) were identified. These genera represent the top 10 dominant fungal populations identified. Unassigned and unclassified reads (2227361) accounted for 97.5% of the total reads on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneral microbial community composition across biomes and microbiomes\u003c/h2\u003e \u003cp\u003eTo better understand the relationships between the different Brazilian biomes and the microbiomes (soil, rhizosphere, and roots) within each biome, we created a nonmetric multidimensional scaling graph with all samples categorized by microbiome and biome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We employed the Bray‒Curtis dissimilarity coefficient to measure the compositional dissimilarity between the genomes. We observed a greater correlation between samples from the same sector (roots, soil and rhizosphere) than between samples from the same biome. The root samples were grouped, as were the soil and rhizosphere samples. We observed a closer relationship between the soil and rhizosphere samples, with greater differentiation from the root samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Notably, the Venn diagram yielded the same observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Here, we observed that more operational taxonomic units (OTUs) were shared between the soil and rhizosphere samples, between the root samples, and between the soil or root samples and the rhizosphere samples. The dominant phyla in the root samples were cyanobacteria, which is true across all biomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Given that the heatmap shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC is organized by sample similarity, we observed a mixture of soil and rhizosphere samples dominated by \u003cem\u003eProteobacteria\u003c/em\u003e. Compared with the other biomes, the Caatinga exhibited a greater presence of \u003cem\u003eFirmicutes\u003c/em\u003e. We generated rarefaction curves for our samples in two ways to assess the ɑ diversity of the various biomes or microbiomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). As expected, the roots were less diverse than the soil and rhizosphere were (right). Regarding the biomes, the Caatinga, the Cerrado PNB and the Atlantic Forest exhibited very similar ɑ diversity values at the lower end of the graph. Moreover, the Amazon and Pantanal exhibited similar but slightly greater diversity values, followed by Pampa. Notably, the Cerrado demonstrated the highest α diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-left).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe distribution of bacterial and archaeal phyla among Brazilian biomes exhibits significant diversity (differences between forested and dry biomes)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn our examination of the general diversity of microorganisms, the four most abundant phyla in the soil, root, and rhizosphere samples from all the Brazilian biomes were \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These phyla include widely studied representatives. \u003cem\u003eProteobacteria\u003c/em\u003e was the most dominant bacterial phylum across almost all biomes, except the Caatinga. Surprisingly, \u003cem\u003eActinobacteria\u003c/em\u003e was especially prevalent in the semiarid Brazilian biome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This notable shift in microbial dominance differs from the typical predominance of \u003cem\u003eProteobacteria\u003c/em\u003e in moist forest soils.\u003c/p\u003e \u003cp\u003eFurthermore, representatives of the \u003cem\u003eAcidobacteria\u003c/em\u003e and \u003cem\u003ePlanctomycetes\u003c/em\u003e phyla were identified in the read annotations. \u003cem\u003ePlanctomycetes\u003c/em\u003e occur more frequently in the Cerrado and Pampa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). \u003cem\u003eAcidobacteria\u003c/em\u003e was more widespread in forested biomes such as the Amazon, Cerrado and Atlantic Forest biomes than in the Caatinga and Pantanal biomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings highlight a distinct microbial community structure in the semiarid Caatinga environment compared with that in the other Brazilian ecosystems.\u003c/p\u003e \u003cp\u003eTo better reveal the differences between the various Brazilian biomes, we separated them into forest-like biomes, wet biomes and dry biomes according to their general characteristics. Notably, we classified the Amazon and Atlantic Forest biomes as forest-like biomes. Moreover, we classified the Pampa and Pantanal biomes as wet biomes, as both are subject to flooding, while the Cerrado and Caatinga biomes were classified as dry biomes (Sup 1B). Moreover, the phylogenetic structure in cladograms is represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB showing the core phyla present in distinct environments.\u003c/p\u003e \u003cp\u003eWe plotted the top 10 unique orders in each category (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Interestingly, in the wet biomes, we identified \u003cem\u003eDiapherotrites\u003c/em\u003e and \u003cem\u003eZixibacteria\u003c/em\u003e, which are known to thrive in anaerobic environments such as wetlands, such as those in the Pantanal. The orders unique to the wet biomes are involved in nutrient cycling, organic matter degradation, and bioremediation of wet environments. The orders unique to the forest-like biomes included \u003cem\u003eThermoanaerobaculales\u003c/em\u003e, \u003cem\u003eLatescibacterales\u003c/em\u003e and \u003cem\u003eMethylomirabilales\u003c/em\u003e, which are generally associated with anaerobic environments that contain abundant organic matter, such as the Amazon and Atlantic Forest soil environments, which are rich in forest litter. We also assessed the relative abundance under each condition among the shared orders and between the three environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The distribution of shared orders was similar among the three biomes. However, the order \u003cem\u003eMyxococcales\u003c/em\u003e was absent in the dry biomes, as was \u003cem\u003eOligoflexales\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the relative abundance levels of prokaryotic communities in the soil, roots, and rhizosphere\u003c/h2\u003e \u003cp\u003eVia the use of 16S amplicon-sequencing data, we distinguished the distribution patterns of phyla by separating the samples into soil, root, and rhizosphere categories at all locations within the various Brazilian biomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplemental Fig.\u0026nbsp;1). To visualize these trends, we generated bar plots depicting the richness of microbial phyla across the various biomes. This approach provides a more nuanced understanding of the difference in the composition of microbial communities among various ecological niches.\u003c/p\u003e \u003cp\u003eNotably, in the roots, cyanobacteria dominated, whereas the soil and rhizosphere were dominated by \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e. An intriguing observation was obtained for outliers within these patterns. In the rhizosphere section, the Caatinga biome exhibited higher diversity than the other biomes and was characterized by a greater abundance of \u003cem\u003eFirmicutes\u003c/em\u003e than \u003cem\u003eProteobacteria\u003c/em\u003e. Conversely, in the root section, the Pampa biome was identified as an outlier, featuring a lower abundance of cyanobacteria than that in the other biomes. In the soil section, the Cerrado\u0026ndash;PNB sample from Cerrado\u0026ndash;CV vegetation was noteworthy. Here, a distinct difference was observed, with a greater abundance of \u003cem\u003eVerrucomicrobia\u003c/em\u003e than \u003cem\u003eFirmicutes\u003c/em\u003e. This finding highlights unique microbial dynamics in the Cerrado\u0026ndash;PNB, increasing our understanding of microbial diversity in Brazilian biomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the relative abundance levels of fungi in the soil, roots and rhizosphere\u003c/h2\u003e \u003cp\u003eVia the use of sequence data from the 18S V4 and ITS-1 regions, we distinguished the distribution patterns of fungal and protist phyla by stratifying the samples from the different Brazilian biomes into soil, root and rhizosphere categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This dual approach allowed us to compare the taxonomic diversity revealed by the conserved region (V4) with the specific variability captured by the ITS-1 spacer, highlighting how these complementary markers reflect the structure of microbial communities in distinct compartments of the soil‒plant system. Comparative analysis of the most abundant fungal phyla in the Brazilian biomes revealed that \u003cem\u003eAscomycota\u003c/em\u003e was dominant in all environments, reaching a peak in the Amazon (0.077%), followed by Caatinga (0.068%), Cerrado\u0026ndash;CV (0.043%), Pantanal (0.035%), Cerrado-PNB (0.057%), Atlantic Forest (0.014%) and Pampa (0.015%), revealing a possible correlation between relatively high humidity and fungal abundance levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003eBasidiomycota\u003c/em\u003e, the second most abundant phylum, reached a peak in the Amazon (0.061%), with decreasing values in the Cerrado-PNB (0.007%) and Pantanal biomes (0.005%), and minimum proportions in the other biomes (0.004\u0026ndash;0.008%). The abundance levels of less representative phyla (\u003cem\u003eMucoromycota\u003c/em\u003e, \u003cem\u003eZoopagomycota\u003c/em\u003e and \u003cem\u003eChytridiomycota\u003c/em\u003e) generally did not exceed 0.01%, except \u003cem\u003eMucoromycota\u003c/em\u003e in the Amazon (0.008%) and Cerrado-PNB biomes (0.005%), suggesting that these groups occupy more specialized ecological niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Interesting patterns emerged when comparing the humid biomes (Amazon, Atlantic Forest, and Pantanal) with the drier biomes (Caatinga, Cerrado, and Pampa). Notably, the former maintained more diverse and abundant fungal communities, probably due to the greater availability of organic matter and stable humidity conditions. Seasonal or arid biomes contained less diverse communities, but \u003cem\u003eAscomycota\u003c/em\u003e maintained its dominance, which suggests a remarkable adaptive capacity of this phylum. Moreover, ITS sequencing analysis revealed that the microbial compositions of the Brazilian biomes indicated unique genera, except those of the Caatinga and Cerrado biomes, which did not encompass unique genera. In the Amazon biome, the genera \u003cem\u003eDidymella\u003c/em\u003e, \u003cem\u003eXylogone\u003c/em\u003e, order \u003cem\u003eSaccharomycetales\u003c/em\u003e, Class \u003cem\u003eChytridiomycetes\u003c/em\u003e and \u003cem\u003eFilobasidium\u003c/em\u003e were identified. The Cerrado-PNB biome exhibited \u003cem\u003eLachnum\u003c/em\u003e, order \u003cem\u003eConioscyphales\u003c/em\u003e, family \u003cem\u003eLeotiaceae\u003c/em\u003e, \u003cem\u003eThanatephorus\u003c/em\u003e and \u003cem\u003eOlpidium\u003c/em\u003e. The Atlantic Forest biome contained \u003cem\u003eArachnotheca\u003c/em\u003e, \u003cem\u003ePaecilomyces\u003c/em\u003e, \u003cem\u003eMyriodontium\u003c/em\u003e, \u003cem\u003eLepiota\u003c/em\u003e and \u003cem\u003eCylindrocladium\u003c/em\u003e, whereas the Pampa biome was characterized by \u003cem\u003eAuricularia\u003c/em\u003e, \u003cem\u003eVanrija\u003c/em\u003e, family \u003cem\u003eClavariaceae\u003c/em\u003e, \u003cem\u003eCryptococcus\u003c/em\u003e and \u003cem\u003eClavulinopsis\u003c/em\u003e. In the Pantanal biome, \u003cem\u003eAcaulium\u003c/em\u003e, \u003cem\u003ePseudolophiostoma\u003c/em\u003e, \u003cem\u003ePericonia\u003c/em\u003e, \u003cem\u003eArxiella\u003c/em\u003e and \u003cem\u003eAngustimassarina\u003c/em\u003e were identified as unique genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These results highlight how abiotic (climate and humidity) and biotic (substrate availability) factors shape the structure of fungal communities at the biogeographic scale, with \u003cem\u003eAscomycota\u003c/em\u003e emerging as the most ubiquitous and resilient group in all the ecosystems analyzed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo facilitate a direct comparison of the distinct microbial signatures associated with each environmental profile, we synthesized the mean relative abundances of the most represented phyla across all six biomes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details the shift in community structure from the drought-adapted, Actinobacteria-dominated soils of the Caatinga to the \u003cem\u003eAcidobacteria\u003c/em\u003e-rich soils of the Cerrado and Atlantic Forest, identifying the primary ecological drivers associated with these taxonomic variations.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparative analysis of soil microbiome composition and ecological drivers across Brazilian biomes.\u003c/b\u003e Data represents the mean relative abundance of dominant and prevalent bacterial phyla derived from Brazilian Microbiome Project (BMP) comparative surveys. \"Dominant\" taxa are defined as the most abundant phyla driving community structure, while \"Prevalent\" taxa represent secondary groups consistently appearing above 1% abundance. \"Rare Biosphere\" encompasses phyla with \u0026lt;\u0026thinsp;0.1% relative abundance. Key environmental stressors (e.g., water availability, pH, nutrient limitation) are correlated with the observed shifts in phylogenetic composition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominant Phyla (Primary Drivers)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalent Phyla (Secondary Groups)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare Biosphere (\u0026lt;\u0026thinsp;0.1% examples)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArchaeal Signature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Ecological Driver\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaatinga\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eActinobacteria\u003c/b\u003e \u003cb\u003e(49.5%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePlanctomycetes\u003c/em\u003e (5.1%)\u003c/p\u003e \u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eChlorobi\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eArmatimonadetes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eThaumarchaeota\u003c/b\u003e dominates (ammonia oxidation in dry soils).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eWater Stress\u003c/b\u003e: Selection for drought-tolerant, spore-forming taxa.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerrado\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcidobacteria\u003c/b\u003e \u003cb\u003e(36.4%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e (12.5%)\u003c/p\u003e \u003cp\u003e\u003cem\u003eVerrucomicrobia\u003c/em\u003e (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGemmatimonadetes\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNitrospirae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eThaumarchaeota\u003c/b\u003e (1.1%) is the major driver.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNutrient Limitation\u003c/b\u003e: Acidic, Al-rich soils select for oligotrophs (\u003cem\u003eAcidobacteria\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAtlantic Forest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcidobacteria\u003c/b\u003e \u003cb\u003e(44.2%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVerrucomicrobia\u003c/em\u003e (6.1%)\u003c/p\u003e \u003cp\u003e\u003cem\u003ePlanctomycetes\u003c/em\u003e (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatescibacteria\u003c/p\u003e \u003cp\u003e\u003cem\u003eChlamydiae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow diversity; dominated by \u003cb\u003eThaumarchaeota\u003c/b\u003e (0.6%).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAcidic Forest Soil\u003c/b\u003e: Low pH (\u0026lt;\u0026thinsp;4.5) drives extreme \u003cem\u003eAcidobacteria\u003c/em\u003e dominance.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePantanal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProteobacteria\u003c/b\u003e \u003cb\u003e(41.8%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcidobacteria\u003c/em\u003e (16.7%)\u003c/p\u003e \u003cp\u003e\u003cem\u003eChloroflexi\u003c/em\u003e (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSpirochaetes\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFibrobacteres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMethanogens\u003c/b\u003e (Euryarchaeota) are significantly higher due to flooding.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRedox Fluctuations\u003c/b\u003e: Flood cycles favor fast-growing, flexible taxa.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePampa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProteobacteria\u003c/b\u003e \u003cb\u003e(34.4%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAcidobacteria\u003c/em\u003e (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e (11.6%)\u003c/p\u003e \u003cp\u003e\u003cb\u003eBacteroidetes\u003c/b\u003e \u003cb\u003e(3.5%)\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDeinococcus-Thermus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBRC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMixed; significant presence of \u003cb\u003eEuryarchaeota\u003c/b\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eGrassland Rhizosphere\u003c/b\u003e: Highest abundance of Bacteroidetes (organic matter degraders).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmazon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProteobacteria\u003c/b\u003e \u003cb\u003e(~\u0026thinsp;35%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAcidobacteria\u003c/em\u003e (~\u0026thinsp;30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e (~\u0026thinsp;13%)\u003c/p\u003e \u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e (~\u0026thinsp;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eElusimicrobia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiverse; \u003cb\u003eThaumarchaeota\u003c/b\u003e abundant in oxic soils.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMoisture \u0026amp; Biomass\u003c/b\u003e: High turnover favors copiotrophs (\u003cem\u003eProteobacteria\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable Legend\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe diversity of the soil, root and rhizosphere microbial communities in representative areas of all Brazilian biomes was assessed. Proteobacteria dominated the bacterial communities in nearly all the biomes. This phylum includes bacteria involved in carbon, sulfur and phosphate cycling and symbiotic nitrogen fixation (SNF), such as \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eParaburkholderia\u003c/em\u003e and \u003cem\u003eCupriavidus\u003c/em\u003e [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This finding can be linked to the high levels of soil organic matter (SOM) and moisture in soils, which favour fast-growing heterotrophs, particularly those involved in nitrogen cycling [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The second most abundant phylum was Actinobacteria, such as \u003cem\u003eStreptomyces\u003c/em\u003e, which are involved in various processes, such as phosphate solubilization, nitrogen cycling, and organic matter catabolism [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur comparative analysis reveals that while the terrestrial microbiome of Brazil shares a conserved phylogenetic core, the relative dominance of major bacterial phyla is governed by biome-specific environmental filtering. The phylogenetic distribution reveals that while major bacterial phyla are shared across biomes, their relative abundances form distinct ecological signatures. Dry biomes are enriched in stress-tolerant groups such as \u003cem\u003eCyanobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e, whereas forest-like and wet biomes show increased representation of Proteobacteria, reflecting higher nutrient availability and ecological complexity (3A-B). Consistent with global soil surveys, we identified that edaphic factors, specifically water availability, soil pH, and nutrient status, act as the primary evolutionary pressures shaping community assembly. The distinct structural trade-off observed between \u003cem\u003eActinobacteria\u003c/em\u003e in semi-arid soils and \u003cem\u003eAcidobacteria\u003c/em\u003e in acidic environments suggests that these taxa occupy opposing ecological niches defined by their life-history strategies.\u003c/p\u003e \u003cp\u003eIn the Amazon, \u003cem\u003eAcidobacteria\u003c/em\u003e and Planctomycetes were well represented, both of which are known to encompass representatives that thrive in acidic and oligotrophic environments [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. With respect to \u003cem\u003earchaea\u003c/em\u003e, \u003cem\u003eNitrososphaera\u003c/em\u003e and \u003cem\u003eCandidatus\u003c/em\u003e Nitrosocosmicus (\u003cem\u003eThaumarchaeota\u003c/em\u003e) dominated the community, and they are often associated with ammonia oxidation in soils, suggesting a key role in nitrogen and nutrient cycling in Amazonian soil processes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe profound dominance of \u003cem\u003eActinobacteria\u003c/em\u003e (49.5%) in the Caatinga distinguishes this biome from all others in Brazil. This enrichment likely reflects the phylum\u0026rsquo;s specialized adaptations to water-limited and high-temperature conditions, including the capacity for sporulation, thick peptidoglycan cell walls, and robust DNA repair mechanisms. In the semi-arid Caatinga, where water potential fluctuates drastically, these traits allow \u003cem\u003eActinobacteria\u003c/em\u003e to maintain metabolic activity during brief wet pulses and persist in dormancy during prolonged drought. This contrasts sharply with the Amazon and Pantanal, where constant moisture favors motility and rapid nutrient diffusion, shifting the community structure toward Proteobacteria. The lower abundance of \u003cem\u003eAcidobacteria\u003c/em\u003e in the Caatinga further supports the \"water-availability hypothesis,\" which posits that \u003cem\u003eAcidobacteria\u003c/em\u003e are generally sensitive to desiccation and flourish primarily in moist, stable soils.\u003c/p\u003e \u003cp\u003eIn the Cerrado and Atlantic Forest, the community structure is driven by soil acidity and nutrient limitation, resulting in the highest observed abundances of \u003cem\u003eAcidobacteria\u003c/em\u003e (36.4% and 44.2%, respectively). These soils are typically ancient, highly weathered, and acidic (pH\u0026thinsp;\u0026lt;\u0026thinsp;5), conditions that select for oligotrophic \"K-strategists\" capable of slow growth and efficient substrate scavenging. \u003cem\u003eAcidobacteria\u003c/em\u003e are evolutionarily equipped with high-affinity transporters and proton-pumping mechanisms that allow them to outcompete faster-growing taxa in low-pH environments. Despite the contrasting vegetation cover, savanna versus dense rainforest, the convergence of their soil microbiomes underscores that soil chemistry, rather than plant cover alone, is the master variable controlling bacterial dominance in these neotropical soils [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distribution of microbial phyla between CV and PNB in the Cerrado exhibited a similar distribution pattern but with interesting differences. \u003cem\u003eProteobacteria\u003c/em\u003e continued to dominate in both Cerrado areas, with a notable presence of \u003cem\u003eActinobacteria\u003c/em\u003e. However, differences emerged because \u003cem\u003eActinobacteria\u003c/em\u003e was more abundant in PNB than in CV. This difference may be explained by the fact that the Cerrado is characterized by a pronounced dry season, which suggests that microbial communities can adapt to fluctuating moisture levels [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Furthermore, CV contained twice as many \u003cem\u003ePlanctomycetes\u003c/em\u003e than did PNB. These phyla thrive in oligotrophic environments, which could suggest nutrient limitations in Cerrado soils [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. \u003cem\u003eAcidobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e exhibited lower yet significant variations between the two regions, with CV containing more \u003cem\u003eAcidobacteria\u003c/em\u003e and Firmicutes than PNB does, potentially indicating differences in the soil pH, as \u003cem\u003eAcidobacteria\u003c/em\u003e are sensitive to acidic conditions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The archaeal community was less diverse in the Cerrado than in the Amazon, with \u003cem\u003eEuryarchaeota\u003c/em\u003e as the most abundant group.\u003c/p\u003e \u003cp\u003eThe Pantanal and Amazon biomes exhibited the highest prevalence of Proteobacteria, a pattern consistent with the \"copiotroph\" lifestyle associated with nutrient-rich or fluctuating environments In the Pantanal, seasonal flood pulses introduce dissolved organic carbon and create dynamic redox potentials. This selects for metabolically versatile Proteobacteria (r-strategists) that can rapidly exploit nutrient fluxes, as well as anaerobic lineages such as \u003cem\u003eChloroflexi\u003c/em\u003e and \u003cem\u003eSpirochaetes\u003c/em\u003e found in the rare biosphere. Furthermore, the archaeal profiles of the Pantanal and Amazon reflect these hydrological regimes; the elevated abundance of \u003cem\u003eEuryarchaeota\u003c/em\u003e in these biomes correlates with methanogenic activity in waterlogged, anoxic sediment niches, contrasting with the dominance of ammonia-oxidizing \u003cem\u003eThaumarchaeota\u003c/em\u003e in the aerobic soils of the Caatinga and Cerrado.\u003c/p\u003e \u003cp\u003ePampa is similar to temperate grasslands, where nitrogen fixation and decomposition are critical processes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The Pampa biome displayed a unique signature characterized by the highest relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e. As established degraders of complex polysaccharides, \u003cem\u003eBacteroidetes\u003c/em\u003e are frequently enriched in the rhizosphere of grasses, where they participate in the breakdown of root exudates and plant biomass.his phylum includes bacteria involved in the degradation of complex polysaccharides, which could be linked to the greater input of grassland plant material [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This suggests that in the Pampa, the extensive root systems of the native grasslands exert a stronger selective force on the microbiome compared to the forest or savanna biomes, favoring taxa specialized in macromolecule hydrolysis. However, archaea were dominated by \u003cem\u003eEuryarchaeota\u003c/em\u003e, reinforcing their widespread ecological role in methanogenesis and nitrogen/phosphate/carbon cycling.\u003c/p\u003e \u003cp\u003eFungi are essential soil components that mediate indispensable processes in nutrient cycling, such as decomposers, saprophytes, symbionts and parasites [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. \u003cem\u003eRhizophagus\u003c/em\u003e species are AMF, a group of roots obligate biotrophs with symbiotic relationships with plant roots [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. They are considered natural biofertilizers that help plants absorb essential nutrients such as phosphorus and nitrogen from soil. The distribution of fungi across Brazilian biomes revealed that Ascomycota was the most abundant phylum in all the biomes. This phylum is known for its diverse ecological roles and ability to adapt to distinct environments, likely contributing to its prevalence [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. \u003cem\u003eAspergillus\u003c/em\u003e was consistently the most abundant genus in all the biomes, indicating its adaptability and widespread distribution in various ecological niches. \u003cem\u003eTrichoderma\u003c/em\u003e and \u003cem\u003eFusarium\u003c/em\u003e, known for their roles in plant symbiosis and pathogenic interactions, occurred prominently in the Pantanal and Atlantic Forest. \u003cem\u003eTrichoderma\u003c/em\u003e exhibits high potential for use in preventing diseases, promoting plant growth, enhancing nutrient utilization efficiency, increasing plant resistance and mitigating agrochemical pollution [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eFusarium\u003c/em\u003e encompasses several phytopathogenic species that cause great economic losses worldwide. In general, they produce a wide variety of mycotoxins, and the consumption of products contaminated with mycotoxins can cause acute or chronic effects in animals and humans and can result in immunosuppressive or carcinogenic effects [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eBasidiomycota\u003c/em\u003e followed \u003cem\u003eAscomycota\u003c/em\u003e in terms of fungal read abundance. These fungi fulfil significant roles in the degradation of lignin-rich plant litter and are therefore associated with nutrient cycling and decomposition, which are vital in soil ecosystems [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Other phyla, i.e., \u003cem\u003eMucoromycota\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e, \u003cem\u003eMicrosporidia\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e, exhibited relatively low but uniform proportions across the various biomes. \u003cem\u003eMucoromycota\u003c/em\u003e was more abundant in Pampa and Pantanal biomes, and \u003cem\u003eChytridiomycota\u003c/em\u003e was more abundant in the Pampa and Caatinga biomes than in the other biomes. These differences may indicate that these fungi may be better suited to grassland ecosystems with more temperate and variable conditions. \u003cem\u003eMicrosporidia\u003c/em\u003e maintained a stable, low-level presence in all biomes. Despite its relatively low abundance, this phylum is an important parasitic group that can impact microbial population dynamics, potentially contributing to the overall complexity of soil microbial communities [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Ultimately, despite being the minor fungal phylum in terms of abundance, \u003cem\u003eZoopagomycota\u003c/em\u003e maintained a stable presence across all biomes. This phylum includes fungi that prey on small soil organisms such as other fungi, amoebae and nematodes and may play a niche-specific role in microenvironments where competition for resources is high, thereby helping to modulate microbial dynamics in nutrient-limited soils and extreme conditions such as drought (semiarid) or flooding (floodplain) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFungi is involved in plant symbiosis and stress tolerance fulfils crucial roles in increasing plant survival and productivity. Biomes with nutrient-poor or extreme conditions, such as the Caatinga, Pantanal and Cerrado (CV and PNB) biomes, exhibited relatively high levels of fungi involved in plant symbiosis and stress tolerance. \u003cem\u003eAspergillus\u003c/em\u003e was abundant across all the biomes. Differences in microbial distribution are influenced by soil characteristics and preferences for specific host plants or organisms. Furthermore, the quality and scope of the samples collected directly influence the representativeness of the microbial diversity results. Environmental factors must also be considered, as they can affect the observed diversity, and the analysis must account for the specific conditions in the ecosystems studied. Each biome supports microbial communities that are ecologically specialized to local conditions. Studying the dynamic forest's microbiome in an integrated manner is essential for understanding microbial interactions, ecosystem functions, and responses to global changes across diverse habitats [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Forests favour nutrient cycling in acidic soils, whereas drier biomes promote decomposers and microorganisms capable of surviving in harsh nutrient-poor environments.\u003c/p\u003e \u003cp\u003eIn general, the distributions of minor phyla in all biomes provide insights into local ecological differences. For example, low-abundance phyla exhibits slight shifts, suggesting that environmental factors; such as temperature, light exposure, soil pH, moisture availability, organic matter content, anthropogenic factors, such as land use and agricultural practices; may shape their presence and niche opportunities. Additionally, spatial and ecological factors, such as elevation, landscape structure, soil composition and vegetation cover, likely lead to the creation of distinct microhabitats, thus influencing the microbial composition at each location.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe distribution of microorganisms in the soil, rhizosphere and roots across Brazilian biomes reflects the complex interactions among environmental factors such as moisture, nutrient availability, and soil properties. The shift from \u003cem\u003eActinobacteria\u003c/em\u003e in the dry Caatinga to \u003cem\u003eAcidobacteria\u003c/em\u003e in the acidic Cerrado and \u003cem\u003eProteobacteria\u003c/em\u003e in the wetland Pantanal illustrates a functional plasticity that maintains ecosystem services, such as carbon cycling and nitrogen fixation, across vast climatic gradients. However, these distinct microbial identities also suggest varying vulnerabilities; as climate change threatens to expand semi-arid conditions into the Cerrado and Amazon, we may observe a \"Caatinga-fication\" of the soil microbiome, potentially altering the carbon storage capacity of these critical global sinks.\u003c/p\u003e"},{"header":"Material \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eSample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 79 samples were collected, with the following distributions across the various biomes: Amazon (12), Atlantic Forest (6), Caatinga (10), Cerrado Chapada dos Veadeiros (CV) (24), Cerrado Parque Nacional de Bras\u0026iacute;lia (PNB) (4), Pampa (14) and Pantanal (9). The collected samples were divided into soil, root and rhizosphere components. The selection of sampling locations was strategically executed to encompass a broad spectrum of ecological sites, facilitating an in-depth exploration of potential variations in microbial communities across these regions. These biomes exhibit contrasting vegetation and microclimatic conditions, ranging from semi-arid environments to lush, humid rainforests.\u003c/p\u003e\n\u003cp\u003eThe soil samples were manually sieved to remove rocks and roots to generate uniform samples. The rhizosphere was subsequently obtained by washing the roots with a 1X phosphate-buffered saline (PBS) solution (pH: 8.3). The roots were carefully removed, and the samples were centrifuged at 5000\u0026nbsp;rpm for 10 minutes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total genomic DNA of the soil and rhizosphere samples was extracted from 250 mg of each sample via the DNeasy PowerSoil Kit (QIAGEN, Germany) following the manufacturer\u0026apos;s instructions. The plant roots were rinsed in a sterile PBS solution (pH: 8.3) to remove the rhizosphere, disinfected with a solution of 1% sodium hypochlorite with 0.05% Tween 20 and agitated gently for 3 minutes to remove the remaining contaminants. The plant roots were then immersed in 70% ethanol for 2 minutes, followed by thorough rinsing with sterile water. Afterwards, the roots were ground with liquid nitrogen, followed by total genomic DNA extraction using the DNeasy Plant Kit (QIAGEN, Germany) following the manufacturer\u0026apos;s instructions. The quality and quantity of DNA were determined by measuring the absorbance at 260/280 nm (A260/A280) on a NanoDrop device (Thermo, Massachusetts, USA) and an Invitrogen\u0026trade; Qubit\u0026trade; 4 fluorometer device (Thermo, Massachusetts, USA), respectively. DNA integrity was verified by 0.8% agarose gel electrophoresis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e16S/18S/ITS rRNA gene amplicon library and sequence data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each biome, we pooled the samples into soil, root and rhizosphere samples, resulting in a total of 21 diverse samples. These samples were subsequently sequenced to obtain their 16S, 18S and ITS regions.\u003c/p\u003e\n\u003cp\u003eThe following primers were used to amplify the 16S V4 region:\u003c/p\u003e\n\u003cp\u003e515F \u0026nbsp; \u0026nbsp;GTGCCAGCMGCCGCGGTAA and 806R\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;GGACTACHVGGGTWTCTAAT\u003c/p\u003e\n\u003cp\u003eThe following primers were used to amplify the 18S V4 region:\u003c/p\u003e\n\u003cp\u003e528F\u0026nbsp; \u0026nbsp;\u0026nbsp;GCGGTAATTCCAGCTCCAA and 706R\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;AATCCRAGAATTTCACCTCT\u003c/p\u003e\n\u003cp\u003eThe following primers were used to amplify the ITS region:\u003c/p\u003e\n\u003cp\u003eITS1-1F-F\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;CTTGGTCATTTAGAGGAAGTAA and ITS1-1F-R GCTGCGTTCTTCATCGATGC\u003c/p\u003e\n\u003cp\u003eAmplification and amplicon sequencing, as well as pooled shotgun sequencing, were performed at Novogene.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eShotgun sequencing and data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all the biomes, the samples were normalized and pooled. All the root, rhizosphere and soil samples for any given biome were merged into a single biome sample. We analysed samples on a gel to determine degradation. Fifty nanograms of each biome pool were sequenced using Illumina shotgun sequencing at Novogene.\u003c/p\u003e\n\u003cp\u003eTo obtain shotgun sequencing data, the samples were processed and cleaned at Novogene. Cleaned sequencing files were paired, and a paired library was created for each biome. These libraries were employed to classify the microbes using Kaiju through standard settings on the KBase platform [77].\u003c/p\u003e\n\u003cp\u003eTo obtain amplicon-sequencing data, the DADA2 pipeline was adopted to process clean reads [78], leading to the formation of OTUs. Taxonomic classification of each representative read, and OTU was conducted using the ribosomal database project (RDP) classifier within the SILVA database for bacterial species (with a confidence level of 70%) and the UNITE database for fungal species [79, 80]. OTU analysis included the determination of the relative abundance at both the genus and phylum levels. The analysis was performed via the Phyloseq package [81].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses and visualizations were performed in the R software environment. To account for differences in sequencing depth, samples were normalized via random subsampling to the lowest read count prior to downstream analyses. Alpha diversity was assessed using the Chao1 richness estimator, and rarefaction curves were generated using the ggrarecurve function in the \u003cem\u003eMicrobiotaProcess\u003c/em\u003e package.\u003c/p\u003e\n\u003cp\u003eBeta diversity was evaluated to visualize differences in microbial community composition between biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, Pantanal) and sample types (soil, root, rhizosphere). Non-metric Multidimensional Scaling (NMDS) ordinations were constructed based on Bray-Curtis dissimilarity matrices using the plot_ordination function in the \u003cem\u003ephyloseq\u003c/em\u003e package. The optimal solution was determined after 20 runs to minimize stress (final stress = 0.096).\u003c/p\u003e\n\u003cp\u003eTaxonomic compositions were visualized using stacked bar charts and phylogenetic trees generated with \u003cem\u003ephyloseq\u003c/em\u003e and ggbartax from \u003cem\u003eMicrobiotaProcess\u003c/em\u003e. Heatmaps exhibiting the relative abundance of phyla across samples were created using the \u003cem\u003emicroViz\u003c/em\u003e package. Overlaps in Operational Taxonomic Units (OTUs) between soil, root, and rhizosphere compartments were analyzed and visualized using the \u003cem\u003eVennDiagram\u003c/em\u003e package\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFigures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures were made in Adobe illustrator. References to the package used to calculate the figures in R are described in each figure\u0026apos;s legend.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e16S/18S/ITS amplicon sequencing data and shotgun metagenomic sequencing data are available in 16S/18S/ITS SRA accession numbers PRJNA1275806; PRJNA1275874 and PRJNA1275935, respectively.\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. Correspondence and requests for materials should be addressed to Drs. Marcelo Freire and Elibio Rech.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Claudomiro de Almeida Cort\u0026ecirc;s from the Association of native seed collectors of Cerrado da Chapada dos Veadeiros for his support in defining collection sites. We also thank Lu\u0026iacute;s Henrique Mota de Freitas Neves, Maria Carolina Alves de Camargos and Alexandre Bonesso Sampaio from Chapada dos Veadeiros National Park for the legal authorizations and support in defining collection sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe deeply thank the Chico Mendes Institute for Biodiversity Conservation - ICMBio (authorizations 85243 and 85247) for the support and legal authorizations granted for the collection of soil samples.\u003c/p\u003e\n\u003cp\u003eWe also acknowledge the National Institute of Science and Technology in Synthetic Biology, National Institute of Science and Technology in Engineering Biological Systems and the Ministry of Agriculture and Livestock. Funding from the National Council for Scientific and Technological Development (465603/2014-9; 400145/2023-5; 308815/2023-8), Research Support Foundation of the Federal District (0193.001.262/2017), and the Coordination for the Improvement of Higher Education Personnel. Funding from the J. Craig Venter Institute to sequence and analyze samples.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.M.A.T., R.N.L. and E.R. designed the study; C.A.X.A., G.M.S.R., D.M.C.B., D.S., M.S.B.M., J.P.P.T., L.B.S.V., F.A.F., R.L. and J.L.S.A conducted field sampling; L.M.A.T., R.N.L., and A.M. performed laboratory experiments; L.M.A.T., R.N.L., P.V.P., M.A.O., A.M., H.I.P.G., D.B., P.M., M.F., AM performed data analysis; E.R. secured funding.\u003c/p\u003e\n\u003cp\u003eAll authors completed, edited and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBraga, A. \u0026amp; Laurini, M. 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J. \u0026amp; Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e (4), e61217 (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microbiome, Amazon, PanBiome, Metagenomics, Natural Atlas","lastPublishedDoi":"10.21203/rs.3.rs-8948634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8948634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrazil encompasses some of the world\u0026rsquo;s most critical reservoirs of biodiversity, yet the microbial dimension of these ecosystems remains fragmented and poorly mapped. Understanding the soil microbiome is essential for predicting ecosystem responses to climate change and discovering novel biotechnological resources. Here, we present a comprehensive metagenomic atlas characterizing the soil, rhizosphere, and root microbiomes across all six Brazilian biomes: Amazon, Atlantic Forest, Cerrado, Caatinga, Pampa, and Pantanal. We employed a dual-sequencing approach, combining 16S/18S/ITS amplicon profiling with shotgun metagenomics, to catalogue microbial diversity in 79 samples representing a gradient from humid rainforests to semi-arid drylands. Our analysis reveals that while \u003cem\u003eProteobacteria\u003c/em\u003e are ubiquitous, their dominance is significantly reshaped by environmental stress. Humid biomes (Amazon, Pantanal) supported complex networks of fast-growing nutrient cyclers, whereas the semi-arid Caatinga was defined by a distinct \"dry-adapted\" core microbiome dominated by \u003cem\u003eActinobacteria\u003c/em\u003e. Fungal diversity was driven by moisture availability, with \u003cem\u003eAscomycota\u003c/em\u003e maintaining ubiquity across all ecosystems while \u003cem\u003eBasidiomycota\u003c/em\u003e abundance declined in drier soils. This study provides the first unified baseline database of Brazilian soil microbiology, offering unprecedented insights into the \"below-ground\" biodiversity that sustains these globally vital ecosystems and establishing a reference for future research in synthetic biology, sustainable agriculture, and conservation.\u003c/p\u003e","manuscriptTitle":"A Pan-Biome Metagenomic Atlas of the Brazilian Rhizosphere, Root, and Soil Microbiomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 17:57:07","doi":"10.21203/rs.3.rs-8948634/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2d537fe-a4ce-4bae-bbbd-15e5a5b1fce2","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64090651,"name":"Biological sciences/Ecology"},{"id":64090652,"name":"Earth and environmental sciences/Ecology"},{"id":64090653,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64090654,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-23T23:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 17:57:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8948634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8948634","identity":"rs-8948634","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}