Metagenomic insights into the structure and function of the soil microbiome in typical ecological regions across China

doi:10.21203/rs.3.rs-3105452/v1

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Metagenomic insights into the structure and function of the soil microbiome in typical ecological regions across China

https://doi.org/10.21203/rs.3.rs-3105452/v1

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The structure and functioning of soil microbiomes in the eco-regions of China remain largely undetermined. Thus, we report on a comprehensive, continental-scale metagenomic study of topsoil samples (0–20 cm depth; n = 41 locations) collected from six eco-regions of China. A total of 54.7 million non-redundant genes were deposited in our metagenomic libraries and could be assigned to 105 taxonomic phyla, consisting of 82 bacterial, 12 archaea, 10 eukaryota phyla, and 1 virus phylum. Of these, the bacteria as represented by Actinobacteria, Proteobacteria and Acidobacteria dominated the metagenomic libraries across all samples. A total of 76 differentially abundant KEGG pathways spanning the six eco-regions could be classified into 23 classes, these chiefly concerning carbohydrate metabolism, amino acid metabolism, xenobiotics biodegradation and metabolism, energy metabolism, metabolism of cofactors and vitamins, replication and repair, and lipid metabolism. According to the KEGG database, we identified six CAZyme-encoding genes (GHs, GTs, PLs, CEs, AAs, and CBMs) involved in the decomposition and biosynthesis of SOC (soil organic carbon); 41 KOs of seven clusters—nitrogen (N) fixation, nitrification, denitrification, DNRA, ANRA, N transport, and organic N metabolism—encoding enzymes critical for N cycling; and 21 KOs of three clusters (ASR, DSR, and thiosulfate oxidation) encoding enzymes crucial to sulfur (S) cycling, from all samples across the six regions. In addition, the functional gene abundance related to the cycling of SOC, N, and S were associated with the NDVI, soil properties, and climatic variables. Among these, SOC (both for SOC and S cycling) and MAP (for N cycling) as being two dominant drivers on the functional gene abundance distribution pattern. Altogether, these findings advance our understanding of the soil microbial functional characteristics of typical eco-regions across China.

eco-regions

metagenomics

functional genes

SOC decomposition and biosynthesis

N cycle

S cycle

The biogeochemical cycling of biogenic elements, such as carbon (C), nitrogen (N), and sulfur (S), affects the dynamic equilibrium and stability of material turnover in the terrestrial biosphere. Soil is the largest organic matter pool on land, storing ~ 1500 Gt C, which is at least as much as the atmosphere (~ 750 Gt C) and vegetation (~ 560 Gt C) combined [1]. Even very tiny changes to the global soil C pool would lead to a significantly altered carbon dioxide (CO₂) concentration in the atmosphere and affect the C-cycle feedback to climate change [2]. The soil C pool is often conceptually divided into two fractions: soil organic carbon (SOC) and soil inorganic carbon (SIC), with SOC generally considered to be far more active than SIC. An essential element for all living organisms, N is required for the biosynthesis of key cellular components, such as proteins and nucleic acids [3, 4]. As another essential element, S is one of the most common volatiles on the Earth and necessary raw material for the synthesis of amino acids (methionine and cysteine) [5, 6]. The roles of S in cellular nutrition show many similarities to those of N. Both must be reduced by living systems to form proteins and other macromolecules; hence either appears among the macroelements needed by all plants and plant-like microorganisms [7]. Accordingly, one of the most fundamental, yet challenging, issues is to describe, trace, and predict the cycling processes of C, N, and S (as well as other elements) across the world’s terrestrial ecosystems.

Microorganisms are ubiquitous in the natural environment, where they play key driving roles in biogeochemical cycles due to their diverse types, wide distribution, and abundant metabolic patterns [8, 9]. For microbial ecologists, however, it remains a formidable task to robustly link the soil microbiome to the decomposition and biosynthesis of SOC as well as both N and S cycles. In the last two decades, investigation of biogeochemical element cycling vis-à-vis microbial activity has mainly relied on the sequencing of functional genes and Geochip technology. But these amplicons and Geochip sequencing techniques depend on primers and probes, respectively, whose inherent defects such as bias, specificity, and low coverage, make it problematic to interpret the dynamics of SOC, N, and S thoroughly. Fortunately, recent advances in metagenomic techniques now provide unprecedented opportunities to study microbial functional composition in depth, by utilizing short-read next-generation sequencing data, and benefiting from the ability to quickly quantify thousands of notably transmissible resistances genes in a single sample [10]. Likewise, it can provide additional information about the presence of soil microbial species, pathogens, and virulence genes, whose data can be readily analyzed should novel genes of interest be identified. Recently, metagenomic techniques were used to reveal the microbial communities involved in the cycling of C (as SOC), N, and S in various ecosystems [9, 11–14]. Admittedly, however, short reads metagenomics may provide limited information about the genes’ host or the genetic environment.

China is one of the countries with the largest coverage of eco-regions, the greatest number of eco-types. Recently, based on an extensive field survey and next-generation sequencing (Illumina MiSeq PE300 Platform) [15], our team described biogeographic patterns of soil (topsoil) microbiomes in eco-regions across China, including their taxonomy characterization and assembly mechanisms. Yet, we still lack an understanding of the variety and prevalence of functional traits associated with soil microbiomes across China’s typical eco-regions. To fill that knowledge gap, here we used the metagenomic approach to (1) clarify the soil microbial genes and traits functionally involved in the cycling of C, N, and S from 41 sampled sites across six eco-regions in China; and (2) explore their divergent and convergent responses to climatic, edaphic, and vegetation factors.

Study area and soil sampling

We studied six typical eco-regions: the karst area of southwestern China (KS), the agro–pastoral ecotone of southwestern China (AS), the Qinghai-Tibet Plateau (QT), the Loess Plateau (LP), the forest–grassland ecotone (FG), and the deserts of Hexi Corridors (HC) (Fig. 1a). This study area has a mean annual temperature (MAT) spanning − 22.9°C to 28.6°C, a mean annual precipitation (MAP) of 44.9–1815 mm, and an elevation that ranges from 27 m to 8305 m a.s.l.

Soil sampling was carried out in June through July 2019 and also in July 2020. By tracking the gradient of MAP, a total of n = 41 locations—6 locations for KS, and likewise 6 for AS, 9 for QT, 5 for LP, 6 for FG, and 9 for HC—were chosen at intervals of ca. 100–110 km along the four transects (Fig. 1a). All samples were collected during the peak of the growing season from minimally disturbed natural soils. At each location, surface litter was removed within a 10-m × 10-m plot and then along its diagonal line five 1-m × 1-m quadrats were established. From each quadrat, three replicate soil samples (0–20 cm depth) were obtained along that diagonal line and homogenized to provide a single composite soil sample per plot. All composite soil samples were individually packed in sterilized polyethylene bags, and taken as quickly as possible to the lab in portable car refrigerators. There, each composite soil sample was split into two subsamples: one that was stored at 4°C until its properties were determined, and another that was stored at − 80°C prior to its DNA extraction. All relevant variables and location information of our study’s soil samples are detailed in Table S1.

Climatic factors and soil physicochemical properties

The MAP (mean annual precipitation), MAT (mean annual temperature), and NDVI (normalized difference vegetation index) were obtained from the Chinese Meteorological Database (http://data.cma.cn/). Soil pH was measured with an E20-FiveEasy pH meter (Mettler Toledo, Giessen, Germany), and EC (electrical conductivity), an indicator of the soluble salt content of soil, was measured with an electric conductometer. Both soil measurements were made using a soil–water suspension (5:1, v/v mixture of deionized water and fresh soil) previously shaken for 30 min. Both the content of SOC (soil organic carbon) and TN (soil total nitrogen) were quantified using a carbon–hydrogen–nitrogen elemental analyzer (2400 II CHN Elemental Analyzer; Perkin Elmer, Boston, MA, USA). Climatic factors and soil properties are summarized in Table S1.

DNA extraction, library construction, and metagenomic sequencing

Total genomic DNA was extracted from each soil sample using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) as per the manufacturer’s instructions. The concentration and purity of extracted DNA was respectively determined using TBS-380 and NanoDrop2000 spectrophotometers. DNA extract quality was checked on 1% agarose gel.

The DNA extracts were fragmented to an average size of about 400 bp, using the Covaris M220 (Gene Company Limited, China). For the paired-end library construction, we used NEXTFLEX Rapid DNA-Seq (Bio Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt-end of each fragment. Next, the paired-end sequencing was carried out on an Illumina HiSeq Xten system (Illumina Inc., San Diego, CA, USA) at the Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), by using HiSeq X Reagent Kits and following the manufacturer’s instructions (www.illumina.com).

Sequence data processing, assembly, and annotation

The ensuing data were analyzed on the free Majorbio Cloud Platform online (www.majorbio.com) [15]. Briefly, the paired-end Illumina reads were trimmed of their adaptors, and any low-quality reads (i.e., having a length < 50 bp, or a quality value < 20, or N bases) were removed by the fastp (v 0.20.0) tool (https://github.com/OpenGene/fastp) [16]. The resulting high-quality read pairs originating from the same sample were then assembled into contigs, by using MEGAHIT (v1.1.2) software (https://github.com/voutcn/megahit) [17] with kmer values ranging from 47 to 97. Contigs having a length ≥ 300 bp were selected for gene prediction and functional annotation.

An open reading frames (ORF) was predicted for each contig by MetaGene (http://metagene.cb.k.u-tokyo.ac.jp/) [18]. Those predicted ORFs with a length ≥ 100 bp were retrieved and translated into amino acid sequences, using the NCBI translation table. Next, a non-redundant gene catalog was constructed using CD-HIT (v4.6.1) (http://www.bioinformatics.org/cd-hit/) [19] based on a minimal 90% sequence identity and 90% coverage. To calculate the abundance of genes at a 95% identity threshold, the obtained high-quality reads were aligned to non-redundant gene catalogs by SOAP aligner (v 2.21) (http://soap.genomics.org.cn/) [20]. For their taxonomic identification, Diamond (v0.8.35) (http://www.diamondsearch.org/index.php) [21] was used with an e-value < 1e^− 5 and the alignments searched against the NCBI microbial NR database. To annotate the genes according to the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (http://www.genome.jp/keeg/), Diamond was also used (with an e-value cutoff = 1e^− 5). For the annotation of carbohydrate-active enzymes, hmmscan (http://hmmer.janelia.org/search/hmmscan) was used to search against the Carbohydrate-Active Enzymes (CAZy) database (http://www.cazy.org/) (e-value cutoff = 1e^− 5).

Statistical analysis

The Wilcoxon rank-sum test was used to compare the α-diversity of taxonomic abundance (species) between any two of the six eco-regions. The α-diversity estimates were calculated using the diversity function of the ‘vegan’ package, in the R computing platform (v 3.2.1) (www.r-project.org). One-way ANOVA followed by Tukey’s HSD (honest significant difference) post-hoc test was implemented using the ‘agricolae’ package in R. To identify taxa and metabolic pathways as biomarkers, we applied the linear discriminant analysis (LDA) effect size (LEfSe) algorithm [22]. Principal co-ordinates analysis (PCoA) of taxonomic traits (species) and functional traits (KOs, KEGG Orthology database), based on their Bray–Curtis (BC) distances, was carried out using the ‘vegan’ package in R. The abundances (i.e., RPKM, reads per kilobase per million mapped reads) of all microbial groups (bacteria, archaea, viruses, and eukaryota) at the phylum level were visualized in a histogram. The relative abundances (RPKM, reads per kilobase per million mapped reads) of SOC decomposition and biosynthesis genes, as well as those of N cycle- and S cycle-related genes were log-transformed and visualized in a heatmap, to reveal the 32 most abundant genes. The gene-based dendrogram was based on values of Pearson’s product moment correlation coefficient (PPMC), while the sample-based dendrogram was based on the above BC dissimilarity matrices not just for the visualized genes, but all genes. Both dendrograms are characterized by complete linkage clustering. The abundance of SOC-, N cycle- and S-cycle-related genes were z-score standardized and visualized in a heatmap, by using the ‘Pheatmap’ package in R. Mantel tests were used to evaluate the relationships of the measured site-level environmental variables to the abundance of genes involved in SOC’s decomposition and biosynthesis, N cycling, and S cycling, this also carried out in R software using the ‘vegan’ package. Furthermore, random forest algorithm was used to assess and rank the environmental factors to the abundance of genes involved in SOC’s decomposition and biosynthesis, N cycling, and S cycling. To identify taxa and metabolic pathways as biomarkers, we applied the linear discriminant analysis (LDA) effect size (LEfSe) algorithm [22].

Cataloguing microbiome genes from the topsoil of China’s eco-regions

Topsoil samples were collected from n = 41 locations, covering six distinctive geographical regions (Fig. 1a, Table S1). Altogether, 579.3 gigabases (Gb) of paired-end sequence data were generated, averaging 93.6 million paired reads per sample. De novo assembly of that sequence data yielded a non-redundant gene catalogue for the 41 locations. The total length of this non-redundant assembly was 24.6 Gb (mean contig N50 length of 543 bp), from which 54.7 million partial genes > 100 bp were predicted. After removing redundancy by clustering the genes with a shared identity > 90% and a coverage > 90% of the shorter gene, a total of 25.4 million non-redundant genes were deposited in our metagenomic libraries. Bacterial genes were clearly dominant, comprising 97.69% of all sequences, followed by 2.22% for archaea, 0.07% for eukaryota, and only 0.02% for viruses (Fig. 1b, Table S2).

The Chao1 index values for taxonomic (species) and functional (KOs, KEGG Orthology) traits for all microbial groups in China’s six eco-regions are shown in Fig. 1c and Fig. 1d, respectively. For taxonomic traits, the Chao1 richness of the entire soil microbiome ranged from 12 383.95 (at AS group) to 13 569.53 (at HC group), with an overall mean (± SD) and median of 12 961.03 ± 551.81 and 12 938.09, respectively (Table S3, S5). For functional traits, whole microbiome’s Chao1 richness based on the KOs ranged from 6540.04 (at LP group) to 7505.37 (at HC group), having an overall mean of 6875.65 ± 476.89 and a median of 6771.20 across the six regions (Table S4, S6). In addition, the ACE and Simpson indices for α-diversity based on species and KOs are summarized in Tables S3 and S4, respectively. Significant differences arose between regions for their soil microbiome’s Chao1 index when based on species or KOs (Table S5, S6).

Principal coordinates analysis (PCoA) revealed significant differences in the β-diversity of taxonomic traits (species) (PERMANOVA, R² = 0.415, p = 0.001; Fig. 1e) as well as functional traits (KOs) (PERMANOVA, R² = 0.432, p = 0.001; Fig. 1f) between different regions. Evidently, whether taxonomic or functional, the microbiome traits of five regions (KS, AS, QT, LP, and FG) were geographically separated along the first principal coordinate from those of HC.

Taxonomic analysis identified a total of 105 phyla of soil microorganisms: 82 for bacteria, 12 for archaea, 10 for eukaryota, plus 1 virus phylum (Fig. 2a, Table S7). Most of the metagenomes were dominated by Actinobacteria (18.01–62.09%) and Proteobacteria (14.66–50.31%), followed by Acidobacteria (0.61–13.10%). In addition, Chloroflexi (1.37–8.01%), Firmicutes (1.19–3.78%), Bacteria_unclassified (0.96–5.42%), Gemmatimonadetes (0.65–5.49%), Cyanobacteria (0.89–21.54%), Planctomycetes (0.72–2.54%), Euryarchaeota (0.19–27.94%), Verrucomicrobia (0.23–3.85%), Bacteroidetes (0.47–6.38%), Thaumarchaeota (0.05–3.90%), Candidatus_Rokubacteria (0.05–3.45%), Candidatus_Tectomicrobia (0.14–2.36%), Nitrospirae (0.15–1.70%), and Deinococcus-Thermus (0.22–1.51%), together accounted for ca. 98% of all metagenomic sequences derived from the topsoil samples (Table S7).

The RPKM abundance data were screened for KEGG pathways significantly enriched in the six eco-regions, using the linear discriminant analysis (LDA) effect size (LEfSe) method (LDA > 2.5, p < 0.05). Collectively, 76 KEGG pathways (18.5% of total) were differentially abundant among the six regions, with 4, 27, 34, 4, 7, and 0 pathways enriched in the LP, KS, HC, FG, AS, and QT, regions respectively (Fig. 2b, Table S8). Further analysis revealed that the 76 KEGG pathways could be classified into 23 classes that mainly dealt with seven biological processes (Fig. 2c, Table S8). (1)Carbohydrate metabolism: e.g., pentose phosphate pathway, amino sugar and nucleotide sugar metabolism, fructose and mannose metabolism, galactose metabolism, starch and sucrose metabolism, and glycolysis/gluconeogenesis. (2)Amino acid metabolism: e.g., valine, leucine and isoleucine biosynthesis; glycine, serine and threonine metabolism; arginine biosynthesis; arginine and proline metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; and lysine biosynthesis. (3)Xenobiotics biodegradation and metabolism: e.g., aminobenzoate degradation, benzoate degradation, caprolactam degradation, chloroalkane and chloroalkene degradation, metabolism of xenobiotics by cytochrome P450, nitrotoluene degradation, polycyclic aromatic hydrocarbon degradation, and styrene degradation. (4)Energy metabolism: e.g., nitrogen metabolism, sulfur metabolism, methane metabolism, carbon fixation pathways in prokaryotes, and oxidative phosphorylation. (5)Metabolism of cofactors and vitamins: e.g., folate biosynthesis, one carbon pool by folate, porphyrin and chlorophyll metabolism, thiamine metabolism, as well as ubiquinone and other terpenoid-quinones’ biosynthesis. (6)Replication and repair: e.g., base excision repair, DNA replication, homologous recombination, mismatch repair, and nucleotide excision repair. (7)Lipid metabolism: e.g., steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, and fatty acid degradation (Fig. 2c, Table S8). Further, the RPKM-normalized read counts differed markedly (more than five-fold) across six regions, being 7.29×10⁶, 6.27×10⁶, 1.22×10⁷, 8.63×10⁶, 6.44×10⁶, and 6.37×10⁶ in LP, KS, HC, FG, AS, and QT, respectively (Table S8).

Profiling of functional genes involved in metabolism essential for the cycling of C, N, and S

To detect the functional genes conferring traits involved in C, N, and S cycling, the obtained soil metagenomic reads were annotated using the databases of CAZy [23] and KEGG [24]

For carbohydrate metabolism, the 41 soil samples contained six critical CAZyme-encoding genes involved in SOC decomposition and biosynthesis (Fig. 3, Table S9). The genes were distributed unequally among glycoside hydrolases (GHs), glycosyl transferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), auxiliary activities (AAs), and carbohydrate-binding modules (CBMs), these generally considered to reflect the microbial potential of decomposition and biosynthesis of SOC [25]. Evidently, the genes encoding the GHs (organic carbon decomposition) and GTs (organic carbon biosynthesis) enzymes were the most abundant in all samples across the six regions (Fig. 3), followed by those coding for CEs and AAs, and least for CBMs and PLs. In addition, there is considerable variation in the total abundances of genes participating in SOC decomposition and biosynthesis across all 41 samples from the different regions, ranging from 61 267.39 to 77 190.28 (Fig. 3, Table S9).

For nitrogen metabolism, biogeochemical N cycling between inventories is often attributed to the six classical N-transformation processes: nitrogen fixation, nitrification, denitrification, nitrate reduction, nitrogen transport/nitrate assimilation, and organic nitrogen metabolism [3, 26] (see the N-cycling model in Fig. 4). Here, 41 KOs encoding key enzymes responsible for N cycling were detected from all samples across the six regions; these genes could be categorized into seven individual gene clusters (Fig. 4, Table S10). (1)Nitrogen fixation: The conversion of N₂ to biologically available ammonia (NH₄⁺) is carried out by the nitrogenase complex [26]. Three key genes (nifD, nifH, nifK) encoding the nitrogenase complex were identified in four regional groups (KS, AS, QT, FG samples), but the nifH and nifK were not identified in either the LP or HC group. The highest and lowest abundance of genes involved in nitrogen fixation were found in the QT group (7.43) and HC group (0.135), respectively, the former nearly 55 times greater than the latter. Overall, the abundance of these three genes was measurable yet consistently low across all soil samples from the 41 sites.

(2) Nitrification

Microbial enzymes (ammonia monooxygenase) catalyze the process whereby ammonia (NH₃) is oxidized to nitrite (NO₂⁻) and subsequently to nitrate (NO₃⁻) [27]. Four key genes (pmoA-amoA, pmoB-amoB, pmoC-amoC, hao) encoding ammonia monooxygenase were identified in all six regions. The genes involved in nitrification were most abundant in the FG group (43.50) and least so in the HC group (15.57), the former 2.79 times higher than the latter. Of them, pmoB-amoB (80.28) and pmoC-amoC (58.67) were two most abundant genes participating in the nitrification process.

(3) Denitrification

The conversion of NO₃⁻ to N₂ proceeds via four intermediate steps (NO₃⁻→NO₂⁻→NO→N₂O→N₂), producing several nitrogenous compounds with notable roles as air polluting gases (N₂O and NO) [28]. Eleven marker genes (nirS, nirK, norB, norC, nosZ, narG-nxrA, narH-nxrB, narI, napA, napB, napC) encoding key enzymes for denitrification were identified in all six regions. These genes attained their highest abundance in the QT group (754.21), being lowest in the HC group (296.37), with the former 2.54 times the latter. The three most abundant genes involved in the denitrification process were narG-nxrA (747.29), nirK (649.48), and norB (631.83).

(4) Nitrate reduction: The reduction of NO₃⁻ to NH₄⁺ ultimately leads to the incorporation of N into microbial biomass [26]. Of these, DNRA (dissimilatory nitrate reduction to ammonia) is an anaerobic process in which NO₃⁻ serves as an electron acceptor to oxidize and release energy from organic carbon. It is mediated by nitrate reductases that form NO₂⁻ and nitrite reductases that convert NO₂⁻ to NH₄⁺ [29]. DNRA is a novel a biological pathway of N-cycling, and the shortest, in terrestrial ecosystems where NO₃⁻ is reduced to NH₄⁺ in soils [30]. Four key genes (nrfA, nrfH, nirB, nirD) encoding key enzymes for DNRA were identified in all six regions. The highest abundance of these genes involved in the DNRA pathway was found in the LP group (656.28) and the lowest in the FG group (329.02), the former being about double the latter. Of these, nirB (1988.84) was the gene present in greatest abundance. Compared with the DNRA pathway, ANRA (assimilatory nitrate reduction) pathway is an energetically costly process that depends on different families of nitrate reductases and nitrite reductases. A total of six marker genes (nasA, nasB, narB, nirA, NR, NIT-6) encoding key enzymes for ANRA pathway were detected across all six regions. The first four of those genes were present in all six regions, with nasA (1607.44) and nirA (1252.56) being the most abundant, where NR and NIT-6 had negligible abundances in three regional groups (LP, FG, and HC) and the FG group, respectively. Further, the highest abundance of genes participating in the ANRA pathway occurred in the HC group (667.09) and the lowest in the FG group (406.41), with the former 1.64 times higher than the latter.

(5) Nitrogen transport/nitrate assimilation: Five marker genes (NRT, nrtA, nrtC, nrtB, nrtD) encoding key enzymes for nitrogen transport/nitrate assimilation were identified in all six regions. These genes were found most and least abundant in the KS (702.81) and HC (358.00) groups, respectively, the former being 1.96 times higher than the latter.

(6) Organic nitrogen metabolism

Eight marker genes (gltB, gltD, gdhA, gdhB, glnA, ureA, ureB, ureC) encoding key enzymes for organic nitrogen metabolism were identified in all six regions. The highest abundance of these genes was found in the LP group (7014.40) and the lowest in the HC group (4937.93); the former was 1.42 times the latter. Of these, glnA (16 187.50) and gltB (10 596.90) were two most abundant genes involved in the organic nitrogen metabolism process.

Overall, then, we uncovered significant differences in the total gene abundances of N metabolism among the different eco-regions, these indicating the soil microbiome of LP harbors the greatest gene abundance for N metabolism (9203.69), followed by that of QT (8976.28), KS (8471.59), and AS (8363.10), with FG (7113.39) and HC (6673.01) having the lowest abundance (Fig. 4, Table S10).

For sulfur metabolism, biogeochemical cycling of S between inventories is often attributed to the three distinct sulfate-transforming processes (Fig. 5): sulfur assimilation, anaerobic sulfate respiration, and sulfide oxidation [31] (see the S-cycling model in Fig. 5). In this study, a total of 21 KOs encoding key enzymes responsible for S cycling were detected from all soil samples across the six regions. These genes could be categorized into three individual gene clusters (Fig. 5, Table S11). (1)Sulfate reduction: The redox reaction in which the divalent anion sulfate (SO₄²⁻) is reduced to sulfide (S²⁻) occurs after the addition of eight electrons. This reaction can be coupled to the oxidation of other chemical species, including a variety of organic compounds (e.g., acetate, pyruvate, methane) or inorganic compounds such as hydrogen (H₂) [32]. Sulfate reduction can happen via assimilatory sulfate reduction (ASR) and dissimilatory sulfate reduction (DSR). Almost all microbes use the ASR pathway to reduce SO₄²⁻ or sulfite (SO₃²⁻) to build biomolecules. Here, 10 marker genes (PAPSS, sat, cysNC, cysN, cysD, cysC, cysH, cysJ, cysI, sir) encoding key enzymes participating in the ASR pathway were identified in all six eco-regions. The highest abundance of these genes was found in the LP group (2845.11) and the lowest in the HC group (2017.40). Of these, cysNC (2530.80), cysD (2425.40), cysI (2168.48), and cysH (2041.78) were four most abundant genes involved in the ASR pathway, followed by sat (1718.80), sir (1429.45), cysC (860.12), cysN (770.90), cysJ (659.60), and PAPSS (9.71). In contrast to the ASR pathway, many microbial phyla in anoxic habitats rely on sulfur (either as [thio-]sulfate, sulfite, or elemental S0) as a terminal electron acceptor, hence the so-called “DSR” [33, 34]. Like the ASR pathway, hydrogen sulfide (H₂S) is also produced in the DSR pathway, but in much larger quantities and as a ‘waste product’ rather than a precursor [35]. Here, five marker genes (sat, aprA, aprB, dsrA, dsrB) encoding key enzymes crucial to the DSR pathway were identified across six regions. Of these, sat (1718.80), aprA (91.21), and aprB (53.69) were three most abundant genes present and evenly distributed among the six regions. In addition, a very low abundance of dsrA (0.34) and dsrB (1.03) was respectively found in two (AS and FG) and three (KS, AS, and QT) regional groups.

(2) Thiosulfate oxidation by the SOX complex

In chemotrophic and phototrophic sulfur oxidizers that do not form sulfur deposits, a periplasmic thiosulfate-oxidizing multienzyme complex (SOX complex) has been described as responsible for sulfate’s formation from thiosulfate [36]. Here, six marker genes (soxA, soxB, soxC, soxX, soxY, soxZ) encoding key enzymes for thiosulfate oxidation were identified in all six regions. The highest and lowest of abundance of these was in the KS (808.33) and HC group (74.73) group, respectively, the former being 10.8 times higher than the latter. Furthermore, soxC (1106.70) was the most abundant gene involved in the thiosulfate oxidation process, followed by soxY (472.73), soxA (379.14), soxB (331.35), soxZ (262.72), and finally soxX (215.41).

Altogether, we detected significant differences in the total abundances of genes related to S metabolism among different regions. Specifically, the KS (3552.47) and LP (3545.35) regional groups have the highest gene abundance for N metabolism, followed by QT (3396.34) and AS (3394.62), with FG (2955.29) and HC (2404.06) having the lowest abundance (Fig. 5, Table S11).

Effects of environmental factors to microbial genes involved in the cycling of C, N, and S

To understand the relationship between the respect abundances of microbial functional genes involved in C, N, and S cycling vis-à-vis environmental factors across the six eco-regions in China, a Mantel analysis using Bray–Curtis’s dissimilarity measures (geographic distance) was firstly conducted. For carbohydrate functional traits, our results showed that gene abundances for GHs, GTs, PLs, CEs, AAs, and CBMs had positive relationships with multiple environmental factors (Fig. 6a, Table S12). Evidently, across all six regions, NDVI was the most critical environmental factor that individually explained the gene abundance involved in carbon metabolism, except with respect to PLs (soil C:N ratio). Soil pH and MAP were also contributing environmental factors driving the geographic distribution of the genes’ abundance. For N-cycling functional traits, the abundances of genes for nitrogen fixation, nitrification, denitrification, DNRA, ANRA, nitrogen transport, and organic N metabolism were correlated with either a single factor or a suite of environmental factors (Fig. 6b, Table S13). Evidently, MAT and MAP were the most critical environmental factors that individually explained the gene abundance of nitrogen fixation (r = 0.253, p = 0.017) as well as nitrification (r = 0.310, p = 0.004) and DNRA (r = 0.264, p = 0.004), respectively. Meanwhile, the gene abundance of denitrification was more positively correlated with MAT (r = 0.328, p = 0.002) than either SOC (r = 0.177, p = 0.007) or MAP (r = 0.160, p = 0.026), while gene abundance of ANRA was modestly positively correlated with the NDVI (r = 0.300, p = 0.001), soil pH (r = 0.236, p = 0.002), and soil EC (r = 0.236, p = 0.055). For nitrogen transport, its gene abundance was strongly positively correlated with MAP (r = 0.551, p = 0.001) and NDVI (r = 0.470, p = 0.001), as what that of organic N metabolism with NDVI (r = 0.480, p = 0.001) and soil C:N ratio (r = 0.480, p = 0.001). For S-cycling functional traits, the abundances of genes for ASR, DSR, and thiosulfate oxidation (by SOX complex) had positive relationships with multiple environmental factors (Fig. 6c, Table S14). Evidently, ASR’s gene abundance was moderately positively correlated with MAP (r = 0.354, P = 0.001), NDVI (r = 0.333, p = 0.001), and soil C:N ratio (r = 0.306, p = 0.001); the DSR’s gene abundance was moderately positively correlated with MAP (r = 0.413, p = 0.001), NDVI (r = 0.385, p = 0.001), and SOC (r = 0.300, p = 0.001); finally, the gene abundance of thiosulfate oxidation was strongly positively correlated with MAP (r = 0.649, p = 0.001), NDVI (r = 0.585, p = 0.001), and to a lesser extent, soil pH (r = 0.400, p = 0.001). Overall, these Mantel tests revealed that the functional gene abundance of microbes involved in C, N, and S cycling across all six eco-regions were each associated with the NDVI, soil properties, and climatic variables.

Furthermore, random forest model was used to estimate the importance of environmental factors and presented their importance value on the abundances of microbial functional genes involved in C, N, and S cycling (Fig. 7). Our random forest model indicates that the individual functional traits within carbohydrate metabolism were regulated by different predictors. Among them, SOC and Latitude (p < 0.05) were the two most important predictors in regulating entire carbohydrate functional traits (Fig. 7a); MAP (p < 0.01) was the most important predictor in regulating N-cycling functional traits, followed by C: N ratio, SOC, and Latitude (Fig. 7b); SOC, MAP, and Latitude (p < 0.01) were the three most important predictors in regulating S-cycling functional traits, followed by TN, Longitude, and C:N ratio (p < 0.05) (Fig. 7c).

A vast number of diverse microorganisms have thus far eluded cultivation and remain accessible to microbiologists only via cultivation-independent molecular approaches [37]. The metagenomic approach ‘breaks the logjam’ of traditional culture-dependent approaches and enables the comprehensive and unbiased access to extensive information on microbial diversity characteristics and functional traits on Earth. Crucially, it also enables microbial ecologists to analyze various interrelations between microorganisms and their local environment. Here, our comprehensive metagenomic investigation preliminarily revealed regionally distinct microbial taxonomic traits and their associated functional gene traits of six typical eco-regions across China. We obtained a total of 54.7 million non-redundant genes from all soil samples, and of which 97.69% belonged to bacteria and the rest to archaea/eukaryota/viruses (Fig. 1b, Table S2). The abundance of non-redundant genes was significantly different among the six regions. Our results show that genes were at their lowest abundance in the deserts of the Hexi Corridor (region HC) (1 632 676) vis-à-vis the five other eco-regions (KS, AS, QT, LP, FG). This stark disparity could be largely explained by the harsh external conditions of desert ecosystems, which are characterized by extreme drought conditions and poor soil nutrient status [38, 39]. Interestingly, although soil microbial abundance and diversity in desert ecosystems are generally considerably lower than in less stressful environments, the Chao1 richness of taxonomic traits (species) in the HC region exceeded that in other five regions (KS, AS, QT, LP, FG), and likewise when comparing their soil microbiomes’ functional traits (KOs) (Fig. 1c, d). Additionally, the community structure and functional composition of the soil microbiome in the HC region differed significantly from the other five eco-regions (Fig. 1e, f). Similarly, these pronounced differences mostly attributable to the unique desert habitat [39, 40].

Our results demonstrate the occurrence of highly abundant bacterial taxa (at the phylum level) and less-abundant taxa of archaea/eukaryota/viruses (at the phylum level) in the topsoil of China’s eco-regions (Fig. 2a). Assigning all non-redundant genes into 105 phyla (82 bacterial, 12 archaea, 10 eukaryota, and 1 virus) showed them dominated by Actinobacteria, Proteobacteria, and Acidobacteria (Fig. 2a, Table S7). Overall, these latter bacterial phyla exhibit no site-specificity across China, and are likewise ubiquitous in terrestrial ecosystems on a global scale [41].

The number of enriched KEGG pathways differed starkly among the LP (4), KS (27), HC (34), FG (4), AS (7), and QT (0) regions (Fig. 2b, Table S8). Intriguingly, it was the desert (HC) and karst (KS) ecosystems which harbored the most abundant KEGG pathways, far more than the other regions, whereas curiously QT lacked a distinctive KEGG pathway entirely (Fig. 2b, Table S8). This phenomenon could be explained, in part, by the functional redundancy of soil microorganisms [42]. Compared with LP, FG, AS, and QT, the HC region is an oligotrophic environment characterized by extreme drought, strong ultraviolet radiation, sparse vegetation, and poor soil nutrient status [38]. Meanwhile, the KS region is also a typical oligotrophic environment due to many bare rocks on the surface, a thin or almost absent soil surface layer, creating difficult conditions for plant growth. Moreover, KS is mainly located in temperate tropical regions where high temperatures accelerate soil nutrient loss and the humus organic fertility is insufficient [43]. Such a harsh environment encourages microbes in the HC or KS regions to evolve richer and more diverse metabolic pathways to survive in soil habitats there. Additionally, our results reveal that different eco-regions may form different functional metabolic niches. The heatmap analysis reveals that the three KS, AS, and QT regions in southern China are clustered together; likewise, the HC, LP, and FG in northern China are clustered together. Interestingly, along the latitudinal gradient, functional gene abundances are much greater in the three eco-regions (i.e., HC, LP, and FG) of northern than southern China (i.e., KS, AS, and QT) (Fig. 2c, Table S8). Along the longitudinal gradient, the functional gene abundances in the three southern regions KS, AS, and QT are extremely similar, while the for the three northern regions FG, LP, and HC they tended to increasing going from east to west. The differential KEGG pathway patterns found between different eco-regions are most likely attributable to habitat heterogeneity, which is driven from climatic factors along the MAP and MAT geographical gradients.

In this study, a total of six CAZyme-encoding genes, 41 KOs of seven clusters encoding key enzymes in N cycling, and 21 KOs of three clusters encoding key enzymes related to S cycling were identified from all samples across the six regions (Fig. 3). Although these functional genes are ubiquitously retrieved from a variety of habitats and drive a variety of biogeochemical processes [12, 26, 37, 44–47], the abundances of N- and S-cycling-related differed considerably between different eco-regions, whereas CAZyme-encoding genes’ abundances differed little geographically. Specifically, for N and S cycling in both HC and FG regions, their gene abundances were far lower than in the other four regions (Table S10, S11). One explanation for that discrepancy is the inherent limitation of DNA-based metagenomic technique, in that it can only detect the presence/absence of functional genes and not their expression level characteristics.

Our results show the abundance of genes relevant to SOC decomposition and biosynthesis, N metabolism, and S metabolism were strongly associated with individual or multiple ecological drivers (Fig. 6 and Fig. 7). Generally, ecological factors are crucial drivers of the distribution of soil microorganisms and entail (i) climatic factors, such as MAT and MAP; (ii) edaphic factors, such as soil pH; and (iii) vegetation productivity and diversity. Globally though, soil pH is firmly recognized as a fundamental driver of the distribution of bacterial diversity and community composition across contrasting biomes [48, 49]. Our results suggest the SOC was the most critical ecological factor individually responsible for the geographic distribution in the abundances of genes involved in SOC decomposition and biosynthesis (Fig. 7a), this is mainly due to plant root activity in the rhizosphere zone, which may promote microbial C decomposition potential by providing a dependable energy source for soil microorganisms [11, 50]. Meanwhile, soil pH has been highlighted in previous studies as being dominant driver on soil microbial N cycling genes [51]. However, our study was not consistent with the above conclusion, and MAP was the most essential driver on the soil microbial N cycling genes across eco-regions in China (Fig. 7b). Although metagenomics is a viable and powerful tool for assessing and conveying the potential contribution of microbial functional traits to C, N, and S cycling in soil, complementary meta-transcriptome and metaproteome analyses are needed to fully verify microbial activity and functioning in future studies.

In this study, metagenomics and statistical tools were used in tandem to explore the functional characteristics of topsoil microbiota in the eco-regions of China. We retrieved abundant non-redundant functional genes, annotating them as CAZy encoding genes (SOC decomposition and biosynthesis), 41 KOs encoding genes for N cycling, and 21 KOs encoding genes for S cycling. Further, we linked the abundance of functional genes related to the C, N, and S cycles to multiple ecological drivers (NDVI, soil pH, SOC, MAT, and MAP). Overall, our data and findings provide a reliable base of evidence that accurately describes and characterizes the functioning of soil microbiomes in typical eco-regions across China.

MAP

Mean annual precipitation

MAT

Mean annual temperature

NDVI

Normalized difference vegetation index

SOC

Soil organic carbon

Electrical conductivity

Total nitrogen

RPKM

Reads per kilobase per million mapped reads

KEGG

Kyoto Encyclopedia of Genes and Genomes

CAZy

Carbohydrate-Active Enzymes

LDA

Linear discriminant analysis

PCoA

Principal co-ordinates analysis

PPMC

Pearson’s product moment correlation coefficient

GHs

glycoside hydrolases

GTs

glycosyltransferases

PLs

polysaccharide lyases

CEs

carbohydrate esterases

AAs

auxiliary activities

CBMs

carbohydrate–binding modules

DNRA

Dissimilatory nitrate reduction to ammonia

ANRA

Assimilatory nitrate reduction

karst area of southwest China. AS:agro–pastoral ecotone of southwest China. QT:Qinghai-Tibet Plateau. LP:Loess Plateau. FG:forest–grassland ecotone. HC:deserts of the Hexi Corridor.

Author’s contributions

Yuqiang Li, Yulong Duan, Lilong Wang, Yun Chen, and Xuyang Wang collected the soil samples in the field. Yulong Duan, Yuqiang Li, Jianhua Zhao, Chun Luo, and Junbiao Zhang curated the data. Yuqiang Li, Yulong Duan, Jianhua Zhao, Chun Luo, and Junbiao Zhang, Rongliang Jia, and Yulin Li carried out the methodologies. Yuqiang Li and Yulong Duan wrote the manuscript. All authors reviewed the manuscript.

Funding

This research was supported by the National Key R&D Program of China (grant no. 2017YFA0604803); the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (grant no. 2019QZKK0305); and the National Natural Science Foundation of China (grant no. 31971466).

Ethics approval and consent to participate

No ethics approvals or consents were required.

Competing interests

No competing interests are declared.

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No competing interests reported.

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Metagenomic insights into the structure and function of the soil microbiome in typical ecological regions across China

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Abstract

Figures

Introduction

Materials and methods

Study area and soil sampling

Climatic factors and soil physicochemical properties

DNA extraction, library construction, and metagenomic sequencing

Sequence data processing, assembly, and annotation

Statistical analysis

Results

Cataloguing microbiome genes from the topsoil of China’s eco-regions

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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