Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient

Wang, Cong; Yu, Qing-Yi; Ji, Niu-Niu; Zheng, Yong; Taylor, John W.; Guo, Liang-Dong; Gao, Cheng

doi:10.1038/s41467-023-43297-w

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Published: 17 November 2023

Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient

Nature Communications volume 14, Article number: 7437 (2023) Cite this article

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Abstract

Bacterial gene repertoires reflect adaptive strategies, contribute to ecosystem functioning and are limited by genome size. However, gene functional diversity does not necessarily correlate with taxonomic diversity because average genome size may vary by community. Here, we analyse gene functional diversity (by shotgun metagenomics) and taxonomic diversity (by 16S rRNA gene amplicon sequencing) to investigate soil bacterial communities along a natural pH gradient in 12 tropical, subtropical, and temperate forests. We find that bacterial average genome size and gene functional diversity decrease, whereas taxonomic diversity increases, as soil pH rises from acid to neutral; as a result, bacterial taxonomic and functional diversity are negatively correlated. The gene repertoire of acid-adapted oligotrophs is enriched in functions of signal transduction, cell motility, secretion system, and degradation of complex compounds, while that of neutral pH-adapted copiotrophs is enriched in functions of energy metabolism and membrane transport. Our results indicate that a mismatch between taxonomic and functional diversity can arise when environmental factors (such as pH) select for adaptive strategies that affect genome size distributions.

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Introduction

For decades, the use of ribosomal RNA (16S rRNA) sequence to identify bacteria, infer their phylogenies, and characterize their communities has revolutionized our understanding of the biodiversity, biogeography, and ecology of microbiomes. One of the most important discoveries made using the 16S rRNA gene as a biomarker has been the observation that bacterial diversity peaks at neutral pH as compared to acid pH¹, and in many subsequent studies it has been proven to be one of the most consistent features of microbial communities (Supplementary Table 1). However, this pH-diversity relationship cannot be directly embedded into classic ecological models such as the biodiversity-ecosystem functioning framework, unless the relationship between taxonomic and functional diversities is clarified.

Functional traits can reflect response and adaptation of microbes to changes in resources and stress². Soil pH has been recognized as the essential driver of microbial structure and function^3,4,5, and changes of soil pH at regional and latitudinal scales are coupled with changes in both resources and stress^6,7. For example, acid soil of natural tropical forests can be a harsh environment with low resource availability, whereas neutral pH soil of natural temperate forests can be a benign environment with high resource availability^8,9. Bacteria adapted to these different soils may share some functional traits, but show variation in other traits, such as genome size, 16S rRNA gene copy number, GC content, and growth rate¹⁰. Variation in genome size can involve gene diversity and functional versatility^11,12,13, because the number of genes of a bacterial genome is strongly positively correlated with genome size^14,15. Previous studies suggest that slow-growing oligotrophs living in resource-scarce soil may carry large genomes^16,17, as do stress-tolerators found in harsh environments when compared to non-tolerators in benign environments^11,13. Slow-growing oligotrophs in resource-poor habitats are often characterized by fewer copies of 16S rRNA genes than fast-growing copiotrophs found in high-carbon, resource-rich habitats¹⁰. GC content also varies with environment, with bacteria having lower GC content being more abundant in the nutrient-deficient surface than in the nutrient-rich interior of the ocean^18,19. Guided by these studies, we hypothesize H₁ that the oligotrophic, stress-tolerators found in acid environments will have larger genomes than the copiotrophic, non-tolerators found in neutral pH environments.

Functional gene diversity of communities does not necessarily reflect taxonomic diversity because community-level, functional gene diversity is influenced by both taxonomic diversity and average genome size. An example where both taxonomic and functional diversities of bacteria are positively correlated is provided by Bahram et al.⁷, who researched soils along an environmental gradient of increasing pH and latitude. Conversely, Kerfahi et al.²⁰ found that the diversity of functional genes was negatively correlated with pH of soils collected along a gradient of increasing altitude. Explaining the negative correlation of functional gene diversity and increasing environmental pH, given the consensus that bacterial taxonomic diversity increases from acid to neutral pH¹ (Supplementary Table 1), may require consideration of genome size. As suggested by our H₁, small genomes are likely to be dominated by indispensable, core genes^21,22, whereas large genomes can harbor the core genes as well as genes of diverse function¹⁰. As a result, a relatively low diversity community of large genome taxa can support a higher diversity of functional genes than a high diversity community of small genome taxa. Therefore, we hypothesize H₂ that along a gradient of increasing pH, there will be a mismatch between increasing bacterial taxonomic diversity and decreasing functional diversity.

The traits responsible for the adaptations that underlie the spectrum of adaptive strategies from oligotrophic communities to copiotrophic communities include growth rate, carbon use efficiency, biomass yield, enzyme production, genome size and 16S rRNA gene copy number^23,24,25,26. Recently, our understanding of bacterial adaptive strategy has been improved by the inclusion of genome information, however, these improvements challenge some assumptions about associations of traits with communities of oligotrophic or copiotrophic bacteria^10,25,27,28. Recognizing that our understanding of the gene repertoire underpinning oligotrophy and copiotrophy is in its infancy, we hypothesize, H₃, that the gene repertoire of oligotrophic and copiotrophic communities along an environmental pH gradient will differ in traits of cell motility, chemotaxis, secretion systems, resource transporters, and defense.

To test the three hypotheses proposed here, we analyzed the diversity of bacterial taxa using 16S rRNA gene amplicons and the diversity of functional genes using shotgun metagenomes sequenced from DNA extracted from 36 plots of 12 forests along a latitudinal gradient (N21.6°–N50.9°) in China, where soil pH ranged from 3.68 to 7.22 (Fig. 1a). Our analysis supported H₁ because genome size decreased as soil pH rose from acid to neutral. Second, H₂ was supported as taxonomic and functional gene diversities were significantly negatively correlated. Finally, H₃ was supported as the gene repertoires of acid-adapted, oligotrophic strategists were enriched in signal transduction, cell motility, secretion system, and degradation of complex compounds, while those of neutral pH-adapted, copiotrophic strategists were enriched in functions of energy metabolism and membrane transport.

**Fig. 1: Bacterial community structure along a pH gradient. a Distribution of 12 forests.**

Results

As a prelude to hypothesis testing, we explored the role of soil pH in shaping the compositions of bacterial communities and functional genes. First, principal coordinate (Pco) analysis showed that both bacterial taxonomic and functional gene compositions were divergent between forests with acid pH soils and forests with neutral pH soils, and envfit analysis showed that soil pH correlated strongly with variation in the composition of bacterial communities (R² = 0.747, P < 0.001) and functional genes (R² = 0.868, P < 0.001) (Fig. 1b, c, Supplementary Fig. 1, and Supplementary Data 1). Furthermore, a loss of Acidobacteria abundance as soil pH increased from acidic to neutral was seen from sequence of both 16S rRNA gene amplicons (R = −0.674, P = 3.925e-06) and metagenomes (R = −0.777, P = 1.52e-08) (Fig. 1d, e).

Testing H₁: bacterial genome size will decrease from acid to neutral pH

To test our H₁, we estimated, from metagenomic data, community-level average genome size using the MicrobeCensus pipeline²⁹ and estimated average 16S rRNA gene copy number using the method of Pereira-Flores et al.¹⁵. These estimates showed that bacterial average genome size was strongly negatively correlated with soil pH (R² = 0.423, P < 0.001; Fig. 2a), and that bacterial average 16S rRNA gene copy number was not significantly correlated with soil pH (R² = 0.063, P = 0.076; Fig. 2b). We then estimated bacterial GC content (GC%) using Quast software³⁰ and found that it increased with soil pH (R² = 0.146, P = 0.012; Fig. 2c); while bacterial growth rate as estimated by gRodon²⁷ was unrelated to soil pH (R² = 0.014, P = 0.489; Fig. 2d). Besides, both bacterial average genome size and GC% are correlated with several biotic and abiotic variables leading by available Ca (Supplementary Fig. 8).

**Fig. 2: Metagenomic traits along a pH gradient.**

The detected changes in bacterial genome size with soil pH may be attributed to shifts in stress and resource, i.e., from an acidic, resource-poor, harsh environment to a neutral, resource-rich benign environment. The larger genome size seen in resource-poor, acidic environments has been proposed to accommodate a diversity of genes needed to cope with abiotic stress and resource scarcity and diversity^11,13,16,17. Small genome size and a high 16S rRNA gene copy number can be linked to fast growth and high resource turnover in resource-rich, benign environments^10,12. The increase of GC% with increasing pH is consistent with a copiotrophic lifestyle in neutral soils, possibly because GC base pairs require more nitrogen than AT base pairs^18,19. In summary, our analysis supports H₁ because the bacterial genome size decreases as the environment becomes less acidic.

In order to challenge our finding that bacterial genome size, calculated from the MicrobeCensus pipeline²⁹, is larger in acidic than in neutral pH environments (Fig. 2a), we additionally calculated the community-weighted genome size by referencing the 16S rRNA gene amplicon dataset against the Genome Taxonomy Database (GTDB)³¹. The results again showed that the average genome size of the bacterial community was significantly, negatively correlated with soil pH (Supplementary Fig. 2). The decrease of bacterial genome size was largely caused by changes with increasing soil pH of the most abundant bacterial taxa (Supplementary Fig. 3), including the gain of a genus with a small genome, DA101 (2.80 Mb), and the loss of large-genome taxa of Ca. Solibacter (5.52 Mb), Ca. Koribacter (5.65 Mb), Burkholderia (8.57 Mb), and Salinispora (5.56 Mb)³¹ (Supplementary Fig. 3). One of the declining taxa, Xiphinematobacter, harbors a very small genome (0.91 Mb, likely related to its parasitism of nematodes³²), but was far less abundant than DA101 and too rare to affect the trend (Supplementary Fig. 3).

The generality of our finding that bacterial genome size decreased from acid to neutral pH in China was tested by re-analyzing the previously published global dataset of Bahram et al.⁷. Using both the MicrobeCensus annotation of metagenomes and the matching of 16S rRNA amplicons with GTDB, our reanalysis of Bahram et al.⁷ again found that bacterial average genome size was negatively correlated with soil pH (Supplementary Fig. 4).

Interestingly, for the datasets of both this study and that of Bahram et al.⁷, average genome size significantly, negatively correlated with GC% based on analysis of the metagenome (Supplementary Figs. 5 and 6); however, the average genome size positively correlated with GC% when based on the matching of 16S rRNA community with GTDB (Supplementary Figs. 5 and 6). This obvious, methodological bias should stimulate research on the causes and consequences of the different results in microbial traits detected from the genomic-based and metagenomic-based methods.

Testing H₂: increasing bacterial taxonomic diversity correlates negatively with decreasing functional diversity along a pH gradient

We tested H₂ by analyzing our 16S rRNA gene amplicon data using the USEARCH pipeline³³ and annotating our metagenome data with the KO database³⁴. We then calculated richness and the Shannon diversity index for bacterial taxonomic and functional diversities in each sample and plotted bacterial taxonomic and KO diversity against soil pH. The results showed that bacterial taxonomic diversity increased from acid to neutral pH (Fig. 3a and Supplementary Figs. 7–10), as demonstrated frequently in previous studies (Supplementary Table 1). However, we found that bacterial KO diversity decreased from acid to neutral pH (Fig. 3b). The analysis of the results, showing that taxonomic diversity increased as functional diversity decreased with increasing soil pH, supported a significantly negative correlation between the two forms of diversities (Fig. 3e). Therefore, H₂ is supported because a positive association between taxonomic and functional diversities is not detected along the pH gradient.

**Fig. 3: Contrasting distribution patterns for diversities of bacterial taxonomy and functional genes along a pH gradient.**

The mismatch between taxonomic and functional diversities may be attributed to the changes in genome size along the pH gradient. Finding that genome size decreased from acid to neutral pH, coupled with previous findings that suggest a positive correlation between bacterial genome size and gene number³⁵, leads to the expectation of more genes in communities in acid soil compared to those in neutral pH soil. Given that all taxa should share the core of indispensable genes^21,22, a small community of large genome taxa could harbor more genes than a large community of small genome taxa. Indeed, our results showed that bacterial average genome size was positively correlated with KO richness (Fig. 3f), but negatively correlated with bacterial taxonomic diversity (Supplementary Fig. 11).

Our result, including all types of genes, is in line with a recent report by Kerfahi et al.²⁰, who found that microbial functional gene diversity was negatively correlated with soil pH along a gradient of increasing altitude. Our result is also in line with a previous study focusing on a specific gene group, antibiotic resistance (AR) genes. With AR genes, Delgado-Baquerizo et al.³⁶ found a negative correlation between soil pH and AR gene diversity for 1012 soil samples collected globally. To compare our approach to these results, we annotated AR genes from our metagenome data using the Resfams database³⁷, and used these data to show that soil pH was significantly negatively correlated with diversity of AR genes (Fig. 3c and Supplementary Fig. 7). We then extended our analyses of specific gene groups to genes encoding carbohydrate-active enzymes (CAZy genes)³⁸, again found that soil pH significantly negatively correlated with the diversity of CAZy genes (Fig. 3d and Supplementary Fig. 7). Moreover, diversities of both AR and CAZy genes were also positively correlated with average genome size (Fig. 3g, h and Supplementary Fig. 7).

Testing H₃: gene repertoire reflects functional trait differences between oligotrophic bacteria adapted to acid pH environments and copiotrophic bacteria adapted to neutral pH environments

To investigate the distribution of bacterial specific functional traits across the gradient from acidic to neutral pH, we used the co-occurrence network to investigate relationships among KOs. Firstly, we annotated our metagenome data using the KO database to find 11,065 KOs (Supplementary Fig. 12). We then filtered for KOs that occurred in >18 of all 36 samples, yielding 7717 KOs. Co-occurrence among annotated KOs was detected using pairwise Spearman’s correlations. These correlations were filtered by Spearman’s rho >0.6 and FDR P < 0.05 and used to construct a co-occurrence network composed of 7481 vertices and 1,359,406 edges (Fig. 4a). The most startling features of the co-occurrence network are two, large modules: module 1 (M1) containing 2777 vertices, and module 2 (M2) containing 4309 vertices (Fig. 4a). Having detected these two modules, we then investigated the relationships between soil pH and KOs in them, finding that soil pH negatively correlated with the larger module (M2, 4309 vertices, conditional R² = 0.953, F_1,150814 = 1825, P < 0.001) and positively correlated with the smaller module (M1, 2777 vertices, conditional R² = 0.930, F_1,97194 = 8169, P < 0.001) (Fig. 4b, c).

**Fig. 4: Functional enrichment and depletion along the pH gradient.**

We then searched within the modules for enriched functions, finding that the acid module (M2) was enriched many key functions: bacterial secretion system, cell motility, xenobiotics biodegradation and metabolism, signal transduction (two component system), metabolism of terpenoids and polyketides, glycan biosynthesis and metabolism, porphyrin metabolism, synthesis of siderophore, and synthesis of lipopolysaccharide (Fig. 4d, e). Correlating specific genes annotated to these functions with environmental variables showed, as expected, that a remarkably large number of genes were negatively correlated with soil pH (Supplementary Figs. 13–15). These genes are involved with essential functions such as motility, bacterial secretion system, and xenobiotics biodegradation and metabolism, a finding largely in line with that of Ramoneda et al.³ (Supplementary Table 2 and Supplementary Data 2). These results are also consistent with the recent finding that extracellular enzyme investment in substrate acquisition is higher in acid than in neutral soils³⁹.

Turning to the neutral model (M1), we found enriched functions of energy metabolism, membrane transport, citrate cycle, glyoxylate and dicarboxylate metabolism, and metabolism of amino acids (Fig. 4d, e). Analysis of specific genes for these functions again revealed, as expected, a prevalence of positive correlations with soil pH (Figs. 4 and 5 and Supplementary Figs. 16–18). Our detection of positive associations of pH with essential functions such as carbon metabolism and membrane transport is, again, largely consistent with the findings of Ramoneda et al.³ and Malik et al.²⁴ (Supplementary Table 2 and Supplementary Data 2). These results are also in line with the recent findings of higher microbial turnover rate and carbon use efficiency in neutral soil^24,26,39.

**Fig. 5: Genes involving biogeochemical cycle associated with environmental variables.**

The detection of differentially enriched functions in the acid (M2) and neutral (M1) modules enables us to speculate about the adaptive strategy of microbiomes along pH gradients. Our speculations follow a perspective by Malik et al.²⁵, who suggested that microbes in less stressful, resource-abundant, neutral pH environments would be characterized by functions of carbon metabolism, while those in resource-limited (e.g., acid) environments would have more microbial transporters. As predicted by these authors, we found that microbes adapted to neutral pH environments were enriched for energy generation. In neutral pH environments, we also found enrichments for functions related to fast growth, such as resource importation and energy metabolism, indicative of copiotrophic communities. Several previous studies based on rRNA gene copy number estimation also supported a copiotrophic strategy for microbiomes in resource-abundant environments^40,41. However, not all of our findings are in agreement with previous reports, for example, Malik et al.²⁵ suggested that there would be more microbial transporters in resource-limited environments, whereas we found more transporters in the resource-abundant environment.

Turning to module 2, our results indicate that microbes adapted to more stressful, resource-limited, acid pH, environments are oligotrophic strategists, based on the enrichments of functions related to resource scavenging and stress tolerance, such as, signal transduction, cell motility, secretion system, and degradation of complex compounds. Two of these traits, motility and degradation of complex substrates, were suggested for microbes in resource-limited environments by Malik et al.²⁵, as well as in the publication on trait dimensions of Westoby et al.¹⁰, which highlighted the importance of signal transduction in functional versatility. Considering the functions enriched at acid pH, signal transduction is essential to perceive the gradients of resources and stress in the environment. Secretion enables microbes to produce and excrete extracellular enzymes that support degradation of complex compounds in resource-limited environment. Lastly, motility may benefit microbes in terms of resource acquisition and stress defense/avoidance^42,43,44. To summarize, our H₃ is supported because pH-driven copiotroph- and oligotroph-strategists differ in gene repertoires involving energy metabolism, membrane transport, chemotaxis, motility, secretion system, signal transduction, and defense (Fig. 4).

Discussion

Our study shows that decoupling between taxonomic and functional diversity can happen when environmental factors (such as pH) select for life history strategies that influence genome size distributions. The detected changes to genome size at the community-level derive from taxonomic changes along the pH gradient, i.e., a gain of small genome taxa and a loss of large genome taxa from acid to neutral pH. This taxonomic change showed responses to specific functional adaptations along a pH gradient, where bacterial taxa in acid pH soils are enriched in functions of signal transduction, cell motility, secretion system, and degradation of complex compounds, but bacterial taxa in neutral pH soils are enriched in functions of energy metabolism and membrane transport.

Our results challenge the longstanding paradigm that bacterial diversity peaks at neutral pH¹ by going beyond taxonomic diversity to also consider bacterial functional gene diversity which is closely linked to genome size. Finding that taxonomic and functional diversities exhibited contrasting patterns along the pH gradient, our result raises serious questions about the relative contribution of these different types of bacterial diversity to ecosystem function. We found that variation in genome size influenced the relationship between taxonomic and functional diversity in systems where resource availability was poor, as in acid pH soil, or rich, as in neutral pH soil. However, it is known that resource availability can be low in neutral pH soil and high in acid pH soil⁶, showing that studies combining taxonomic diversity with functional diversity should be extended to additional ecosystems and additional microbial communities. Thus, our understanding of the structure and function of soil microbiomes would be advanced by a thorough investigation of the functional traits, diversity and adaptive strategy of microbiomes that represent the four quadrants defined by the axes of resource and stress (Fig. 6). A group of microbes in need of such studies is fungi. It is reported that the genome size of fungi also varies by ecosystem⁴⁵, but fungal gene numbers may be less predictable due to the existence of non-coding regions.

**Fig. 6: Conceptual model on the adaptive strategy of soil microbiome along a latitudinal pH gradient across 12 forests.**

Expanding the characterization of microbial communities by adding functional gene diversity to taxonomic diversity opens the possibility of using genome size to study the ecological processes of community assembly and ecological networking⁴⁶. Microbial community biodiversity occupies a central role in ecology, as the driver of ecosystem function and stability, and as the force behind emergent properties of microbial communities^47,48. Given the central role of microbial communities in ecosystems, we hope that our research will stimulate additional research and debates over the characterization of microbial communities and the integration of taxonomic and functional diversity, which can deepen our understanding of processes of community assembly and ecosystem function.

Methods

Sites and soil sampling

This study sampled soils from 12 forest sites covering temperate, subtropical, and tropical climatic zones (Fig. 1a). Soil samples in the forest of Genhe (GH) were collected in long-term observation plots. In the other 11 forests, the Chinese Forest Biodiversity Monitoring Network (CForBio) had established a permanent plot (15–25 ha) consisting of hundreds of 20 m × 20 m quadrats (375–625 quadrats)⁴⁹. Three quadrats in each site were randomly selected for soil sampling in this study. In each quadrat, ten soil cores (10 cm in depth, 5 cm in diameter) were evenly collected and mixed to make one composite sample, resulting in a total of 36 soil samples (3 quadrats × 12 forests). The soil samples were immediately sealed in plastic bags and transported to laboratory on ice. After removing stones and plant debris, fresh soil samples were sieved to 2 mm mesh size. Then, each sample was divided into two subsamples. One subsample was stored at −80 °C until DNA extraction, and another subsample was air-dried and used to measure soil abiotic properties.

Soil properties and climatic factors

Soil pH was measured in a 1:2.5 soil/water suspension. Soil total carbon (TC) and total nitrogen (TN) were measured with an Elementar Vario EL III (Elementar Analysensysteme GmbH, Germany), and soil total phosphorus (TP) was measured by an Inductively Coupled Plasma-Atomic Emission Spectrometer (iCAP 6300, Thermo Jarrell Ash Co.). Soil available calcium, magnesium, iron and potassium were extracted by Mehlich-III solution and measured with Atomic Emission-Inductively Coupled Plasma (ICP-AES, Avio 500, PerkinElmer)⁵⁰. Latitude, longitude, soil bulk density, soil available cations and plant data (abundance, richness and basal area) of the study sites were provided by plot founders and the CForBio organization. Latitude and longitude were recorded by GPS device. Soil bulk density was calculated as the dry weight of soil divided by its volume. Plant community data were surveyed by manual. The mean annual temperature (MAT) and mean annual precipitation (MAP) were obtained from the WorldClim database (www.worldclim.org) with a resolution of 2.5 min⁵¹.

DNA extraction and sequencing

The soil DNA of each sample was extracted by using the PowerSoil DNA isolation Kit (MoBio, Carlsbad, CA, USA). The concentration and purity of each DNA sample was determined by a Qubit 2.0 Flurometer (Life Technologies, CA, USA) and a NanoDrop2000 (Thermo Fisher Scientific, Waltham, MA, USA), respectively. The quality of DNA extracts was checked by electrophoresis in 1% agarose gels. Shotgun metagenomic sequencing of each DNA sample was sequenced on an Illumina HiSeq 2500 instrument (San Diego, CA, USA) with the PE125 run mode at Novogene, Inc. Beijing, China. Before sequencing, the DNA was fragmented into small components, and paired-end libraries were constructed using NEXTflexTM Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt end of DNA fragments. The raw sequence data underwent the following quality control process: paired reads were discarded if either read contained adapter contamination, or if more than 10% of bases were uncertain in either read, or if the proportion of low quality (Phred quality <5) bases was over 50% in either read. After data quality control, we had 509.64 GB data of clean sequence for the 36 samples with average of 14 GB per sample.

From the same DNA, the bacterial 16S rRNA gene was amplified with the primer pair, 515 F/806 R⁵², in the following PCR amplification solution: 2.5 μl 10 × buffer, 1.5 mM MgSO₄, 200 μM of each dNTP, 0.75 μM of each primer, 0.5 U KOD-plus-Neo Polymerase (Toyobo Co., Ltd., Osaka, Japan), and ~10 ng template DNA. A 12-base pair barcode, which is unique for each sample, was on the forward primer 515 F. The thermal cycling consisted of an initial denaturation at 95 °C for 3 min, followed by 30 cycles of denaturation at 95 °C for 50 s, annealing at 56 °C for 1 min, and extension at 68 °C for 1 min, followed by a final extension at 68 °C for 10 min. The PCR products were purified using a gel purification kit (Axygen), and 50 ng of DNA from each sample was pooled and adjusted to 10 ng μl⁻¹. After adding sequencing adapter to the PCR products using an Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina) following the manufacturer’s instructions, the library was then sequenced on an Illumina MiSeq PE250 instrument (San Diego, CA, USA) at Chengdu Institute of Biology, Chinese Academy of Sciences.

Metabarcoding analyses

Sequences of 16S rRNA gene amplicon were aligned to each sample according to the unique barcode at the 5’-end of the forward primer 515 F. Overall sequencing quality was evaluated using FastQC v0.11.5⁵³. Forward and reverse reads were merged using the fastq_mergepairs command in USEARCH v8.0³³. Primers were removed using cutadapt v1.9.1⁵⁴. Quality control was carried out using the fastq_filter command (-fastq_maxee 1.0-fastq_minlen 200) in USEARCH³³. Chimeras were detected and removed in USEARCH with -uchime_ref command³³. High-quality non-chimeric sequences were subjected to de-replication and de-singleton, and then clustered into operational taxonomic units (OTUs) at a 97% sequence similarity level using the cluster_otus command in USEARCH³³. OTUs were identified by a BLAST search of the most abundant sequence representing that OTU against the Greengenes database for bacteria⁵⁵.

Metagenomic analyses

To analyze microbial genomic traits based on metagenomes, the clean sequences returned from the sequencing facility were assembled into contigs for each sample using MEGAHIT v1.2.9 with --k-list 21, 29, 39, 59, 79, 99, 119, 141⁵⁶. To focus on bacteria¹³, we detected and removed potential eukaryotic contigs using EukRep v0.6.7⁵⁷, and detected and removed potential viral contigs using VIBRANT v1.2.1⁵⁸ and BBMap v39.01 (https://github.com/BioInfoTools/BBMap/). Average genome size of each sample was evaluated by using MicrobeCensus pipeline²⁹. In brief, the pipeline evaluates genome size based on sequence coverage of 30 core single-copy genes which were found to universally present in bacteria and archaea. These essential genes can be sequenced at a higher rate, i.e., higher coverage, when average genome size of community is smaller, as these genes should make up a higher fraction in a small genome size. Overall, higher sequence coverage of these 30 genes indicates smaller average genome size. Average 16S rRNA gene copy number was evaluated by following method from ref. ¹⁵. In brief, average 16S rRNA gene copy number was estimated by dividing coverage of 16S rRNA gene by number of genomes in a metagenome. GC% was calculated by using Quast v5.2.0³⁰. Minimal doubling time was estimated by gRodon v2.3.0²⁷ which analyzed codon usage bias and calculated minimal doubling time based on the tight relationship between codon usage bias and bacterial maximum growth rate⁵⁹. GC% was calculated based on all assembled contigs, while average genome size, average 16S rRNA gene copy number and minimal doubling time were calculated based on contigs >500 bp as length could affect the accuracy of estimation methods.

We also calculated bacterial average genome size, protein counts and GC% by relating annotated 16S rRNA gene sequence to the Genome Taxonomy Database (GTDB)³¹. Metadata file and FASTA file of 16S rRNA gene sequences of bacterial representative genomes were download from GTDB (https://gtdb.ecogenomic.org/downloads). The FASTA file was made as a reference database by using makeblastdb v2.13.0⁶⁰. Then, we annotated our 16S rRNA gene amplicon against the reference database by using BLAST v2.13.0⁶⁰ to get genome accession ID. Data of genome size, protein counts and GC% were acquired by matching accession ID to the metadata file. Bacterial average genome size, protein counts and CG% of each sample were calculated with weighing reads counts.

To analyze specific genes, all assembled contigs were combined. Then, the redundans pipeline (https://github.com/lpryszcz/redundans) was used to detect and selectively remove redundant contigs with the settings --overlap 80 and identity 0.90⁶¹. Prodigal v2.6.3 was used to predict protein-coding genes⁶², resulting in 20,817,601 genes. These gene sequences were clustered by using CD-HIT v4.8.1 with sequence identity threshold 0.95 and alignment coverage 0.9⁶³, resulting in a non-redundant gene catalog with 20,038,815 genes. Salmon v1.6.0 was used to estimate gene abundance in each sample by mapping raw reads to the non-redundant gene catalog, considering reads counts and gene length⁶⁴. The non-redundant gene catalog was annotated by using EggNOG-mapper tool through against eggNOG database (http://eggnog5.embl.de)⁶⁵ that integrated databases of Clusters of Orthologous Groups of proteins (COGs)⁶⁶, Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO)³⁴ and the carbohydrate-active enzymes (CAZy)⁶⁷. In addition, antibiotic resistance (AR) genes were annotated against Resfams database³⁷ by using DIAMOND tool (https://github.com/bbuchfink/diamond)⁶⁸. Besides functional annotations, taxonomic classification based on sequences of metagenome was done by Kaiju (https://kaiju.binf.ku.dk/), which is a program for fast taxonomic classification based on sequence comparison to a reference database of microbial proteins⁶⁹.

Statistical analyses

To check if soil pH drives bacterial taxonomic and function distribution along the pH gradient across 12 forests, principal coordinate (Pco) analysis in the “stats” package was used to visualize bacterial taxonomic and functional composition, and envfit analysis in the “vegan” package was used to analyzed the relationship between bacterial taxonomic and functional composition and plant, soil, climate and geography variables⁷⁰. The linear regression model in the “stats” package was used to test if the relative abundance of Acidobacteria decreased with increasing soil pH.

To analyze the distribution pattern of bacterial genome traits across the pH gradient, the linear regression model in the “stats” package was used to analyze the relationships between soil pH and average genome size, average 16S rRNA gene copy number, GC%, and minimal doubling time. Relationships between these genome traits and plant, soil, climate and geographic variables were tested by Spearman’s correlation analysis, using corr.test function in the “psych” package⁷¹.

To check previous findings that bacterial taxonomic diversity of 16S rRNA gene amplicon increases from acid to neutral pH (Supplementary Table 1), we used our 16S rRNA gene amplicon dataset to calculate richness (S.16S) and the Shannon diversity index (H’.16 S) using the diversity function in the “vegan” package⁷⁰. These parameters were subsequently regressed against soil pH. To explore if bacterial functional diversity decreases from acid to neutral pH, for KEGG-annotated metagenomic results we calculated the richness (S.KO) and Shannon diversity index (H’.KO), which were subsequently regressed against soil pH. Similarly, regression was carried out to explore the relationship between soil pH and the richness and Shannon diversity of specific functions of carbohydrate-active enzymes (annotated by CAZy databse) and antibiotic resistance genes (annotated by Resfams database). Relationships between bacterial taxonomic diversity and functional diversity were analyzed by a linear regression model in the “stats” package. Furthermore, to test if functional diversity positively correlated with average genome size, regression was carried out between functional diversity indices and average genome size.

To explore the distribution of abundance of functional genes along the pH gradient, we employed co-occurrence network analysis to demonstrate the associations among all KEGG Orthology terms (KOs). First, only KOs that occurred in >18 of all 36 samples were kept for network analysis, yielding 7717 KOs. Then, co-occurrence among annotated KOs was detected using pairwise Spearman’s correlations in the “psych” package⁷¹; the correlations were used to construct co-occurrence by the “igraph” package⁷² if its Spearman’s rho >0.6 and FDR P < 0.05. The co-occurrence network was composed of 7481 vertices (KOs) and 1,359,406 edges, and clustered into two big modules: module 1 (M1) containing 2,777 vertices and module 2 (M2) containing 4309 vertices. Regression analysis in the “nlme” package⁷³ was used to test the relationship between soil pH and vertices in the two biggest modules, M1 and M2.

To explore functional difference of KOs between these two modules, from these KOs we reconstructed KEGG pathway maps using KEGG Mapper (https://www.kegg.jp/kegg/mapper/). Then, KEGG pathways with significantly different KO counts between M1 and M2 (FDR < 0.05 and |logFC | >= 0.5) were explored using differential analysis implemented in the “edgeR” package⁷⁴.

In addition, Mantel analysis in the “linkET” package⁷⁵ was used to analyse the relationships between genome traits, diversity indices and taxonomic and functional compositions. Spearman’s correlation analysis in the “psych” package⁷¹ was further used to test the relationship between relative abundance of functional genes and plant, soil, climate and geographic variables. Threshold indicator taxa analysis (TITAN) was used to explore pH preference of KO, using the “TITAN2” package⁷⁶. Data were log- or sqrt-transformed to meet homogeneity of variance when necessary. All statistical analyses were conducted on R statistical programming language version 4.2.1 if else stated⁷⁷.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All metabarcoding and metagenomics sequences in this study have been deposited in the National Center for Biotechnology Information database (PRJNA986291). The WorldClim database is available through https://www.worldclim.org. Source data are provided with this paper.

Code availability

The source code implementing the analyses in this manuscript is available on Github (https://github.com/FunCongWang/CForBio.metagenome) or Zenodo⁷⁸ (https://zenodo.org/records/10017052).

References

Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA. 103, 626–631 (2006).
Article ADS CAS PubMed PubMed Central Google Scholar
Gao, C. & Guo, L. Progress on microbial species diversity, community assembly and functional traits. Biodivers. Sci. 30, 22429 (2022).
Ramoneda, J. et al. Building a genome-based understanding of bacterial pH preferences. Sci. Adv. 9, eadf8998 (2023).
Article CAS PubMed PubMed Central Google Scholar
Luan, L. et al. Integrating pH into the metabolic theory of ecology to predict bacterial diversity in soil. Proc. Natl. Acad. Sci. USA. 120, e2207832120 (2023).
Article CAS PubMed PubMed Central Google Scholar
Piton, G. et al. Life history strategies of soil bacterial communities across global terrestrial biomes. Nat. Microbiol. https://doi.org/10.1038/s41564-023-01465-0 (2023).
Oliverio, A. M. et al. The role of phosphorus limitation in shaping soil bacterial communities and their metabolic capabilities. mBio. 11, e01718–e01720 (2020).
Article CAS PubMed PubMed Central Google Scholar
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
Article ADS CAS PubMed Google Scholar
Vitousek, P. M. & Sanford, R. L. Nutrient cycling in moist tropical forest. Ann. Rev. Ecol. Syst. 17, 137–167 (1986).
Article Google Scholar
Jordan, C. F. Nutrient Cycling in Tropical Forest Ecosystems (John Wiley and Sons, 1985).
Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).
Article PubMed Google Scholar
Bentkowski, P., Van Oosterhout, C. & Mock, T. A model of genome size evolution for prokaryotes in stable and fluctuating environments. Genome Biol. Evol. 7, 2344–2351 (2015).
Article CAS PubMed PubMed Central Google Scholar
Moran, N. A. Microbial minimalism: genome reduction in bacterial pathogens. Cell 108, 583–586 (2002).
Article CAS PubMed Google Scholar
Ngugi, D. K. et al. Abiotic selection of microbial genome size in the global ocean. Nat. Commun. 14, 1384 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Kuo, C. H., Moran, N. A. & Ochman, H. The consequences of genetic drift for bacterial genome complexity. Genome Res. 19, 1450–1454 (2009).
Article CAS PubMed PubMed Central Google Scholar
Pereira-Flores, E., Glockner, F. O. & Fernandez-Guerra, A. Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data. BMC Bioinforma. 20, 453 (2019).
Article Google Scholar
Konstantinidis, K. T. & Tiedje, J. M. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc. Natl. Acad. Sci. USA. 101, 3160–3165 (2004).
Article ADS CAS PubMed PubMed Central Google Scholar
Rodriguez-Gijon, A. et al. A genomic perspective across earth’s microbiomes reveals that genome size in archaea and bacteria is linked to ecosystem type and trophic strategy. Front. Microbiol. 12, 761869 (2021).
Article PubMed Google Scholar
Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).
Article CAS PubMed Google Scholar
Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl. Acad. Sci. USA. 110, 11463–11468 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Kerfahi, D. et al. Elevation trend in bacterial functional gene diversity decouples from taxonomic diversity. Catena 199, https://doi.org/10.1016/j.catena.2020.105099 (2021).
Yus, E. et al. Impact of genome reduction on bacterial metabolism and its regulation. Science 326, 1263–1268 (2009).
Article ADS CAS PubMed Google Scholar
Moran, N. A. & Bennett, G. M. The tiniest tiny genomes. Annu. Rev. Microbiol. 68, 195–215 (2014).
Article CAS PubMed Google Scholar
Treseder, K. K. Ecological strategies of microbes: thinking outside the triangle. J. Ecol. 00, 1–12 (2023).
Google Scholar
Malik, A. A. et al. Land use driven change in soil pH affects microbial carbon cycling processes. Nat. Commun. 9, 3591 (2018).
Article ADS PubMed PubMed Central Google Scholar
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J 14, 1–9 (2020).
Article CAS PubMed Google Scholar
Jones, D. L., Cooledge, E. C., Hoyle, F. C., Griffiths, R. I. & Murphy, D. V. pH and exchangeable aluminum are major regulators of microbial energy flow and carbon use efficiency in soil microbial communities. Soil Biol. Biochem. 138, https://doi.org/10.1016/j.soilbio.2019.107584 (2019).
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl. Acad. Sci. USA. 118, e2016810118 (2021).
Article CAS PubMed PubMed Central Google Scholar
Chen, Y., Neilson, J. W., Kushwaha, P., Maier, R. M. & Barberan, A. Life-history strategies of soil microbial communities in an arid ecosystem. ISME J 15, 649–657 (2021).
Article CAS PubMed Google Scholar
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).
Article PubMed PubMed Central Google Scholar
Mikheenko, A., Prjibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).
Article CAS PubMed PubMed Central Google Scholar
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).
Article CAS PubMed Google Scholar
Vandekerckhove, T. T., Willems, A., Gillis, M. & Coomans, A. Occurrence of novel verrucomicrobial species, endosymbiotic and associated with parthenogenesis in Xiphinema americanum-group species (Nematoda, Longidoridae). Int. J. Syst. Evol. Microbiol. 50, 2197–2205 (2000).
Article PubMed Google Scholar
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Article CAS PubMed Google Scholar
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
Article CAS PubMed Google Scholar
Podar, M. et al. A genomic analysis of the archaeal system Ignicoccus hospitalis-Nanoarchaeum equitans. Genome Biol. 9, R158 (2008).
Article PubMed PubMed Central Google Scholar
Delgado-Baquerizo, M. et al. The global distribution and environmental drivers of the soil antibiotic resistome. Microbiome 10, 219 (2022).
Article PubMed PubMed Central Google Scholar
Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J 9, 207–216 (2015).
Article CAS PubMed Google Scholar
Drula, E. et al. The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Res. 50, D571–D577 (2022).
Article CAS PubMed Google Scholar
Malik, A. A., Puissant, J., Goodall, T., Allison, S. D. & Griffiths, R. I. Soil microbial communities with greater investment in resource acquisition have lower growth yield. Soil Biol. Biochem. 132, 36–39 (2019).
Article CAS Google Scholar
Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328–1333 (2000).
Article ADS CAS PubMed PubMed Central Google Scholar
Roller, B. R., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).
Article CAS PubMed PubMed Central Google Scholar
Wadhwa, N. & Berg, H. C. Bacterial motility: machinery and mechanisms. Nat. Rev. Microbiol. 20, 161–173 (2022).
Article CAS PubMed Google Scholar
Flemming, H. C. et al. The biofilm matrix: multitasking in a shared space. Nat. Rev. Microbiol. 21, 70–86 (2022).
Article PubMed Google Scholar
Schwartzman, J. A. et al. Bacterial growth in multicellular aggregates leads to the emergence of complex life cycles. Curr. Biol. 32, 3059–3069 e3057 (2022).
Article CAS PubMed PubMed Central Google Scholar
Zhang, H. Y., Bissett, A., Aguilar-Trigueros, C. A., Liu, H. W. & Powell, J. R. Fungal genome size and composition reflect ecological strategies along soil fertility gradients. Ecol. Lett. 26, 1108–1118 (2023).
Article PubMed Google Scholar
Dai, T. et al. Nutrient supply controls the linkage between species abundance and ecological interactions in marine bacterial communities. Nat. Commun. 13, 175 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Guerra, C. A. et al. Global hotspots for soil nature conservation. Nature 610, 693–698 (2022).
Article ADS CAS PubMed Google Scholar
Hall, E. K. et al. Understanding how microbiomes influence the systems they inhabit. Nat. Microbiol. 3, 977–982 (2018).
Article CAS PubMed Google Scholar
Feng, G. et al. CForBio: a network monitoring Chinese forest biodiversity. Science Bulletin 61, 1163–1170 (2016).
Article ADS Google Scholar
Zhang, L., Mi, X., Shao, H. & Ma, K. Strong plant-soil associations in a heterogeneous subtropical broad-leaved forest. Plant Soil 347, 211–220 (2011).
Article CAS Google Scholar
Ji, N.-N. et al. Late Quaternary climate change explains soil fungal community composition rather than fungal richness in forest ecosystems. Ecol. Evol. 9, 6678–6692 (2019).
Article PubMed PubMed Central Google Scholar
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA. 108, 4516–4522 (2011).
Article ADS CAS PubMed Google Scholar
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Article Google Scholar
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
Article ADS CAS PubMed PubMed Central Google Scholar
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Article CAS PubMed Google Scholar
West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).
Article CAS PubMed PubMed Central Google Scholar
Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).
Article CAS PubMed PubMed Central Google Scholar
Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (Meta) genomics. PLoS Genet. 6, e1000808 (2010).
Article PubMed PubMed Central Google Scholar
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma. 10, 421 (2009).
Article Google Scholar
Pryszcz, L. P. & Gabaldon, T. Redundans: an assembly pipeline for highly heterozygous genomes. Nucleic Acids Res. 44, e113 (2016).
Article PubMed PubMed Central Google Scholar
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).
Article Google Scholar
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Article CAS PubMed Google Scholar
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Article CAS PubMed PubMed Central Google Scholar
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
Article CAS PubMed Google Scholar
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinforma. 4, 41 (2003).
Article Google Scholar
Levasseur, A., Drula, E., Lombard, V., Coutinho, P. M. & Henrissat, B. Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes. Biotechnol. Biofuels 6, 41 (2013).
Article CAS PubMed PubMed Central Google Scholar
Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).
Article CAS PubMed PubMed Central Google Scholar
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).
Revelle, W. psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 2.2.9. (2022).
Csárdi, G. & Nepusz, T. The igraph software package for complex network research, InterJournal, Complex Systems 1695. https://igraph.org (2006).
Pinheiro, J., Bates, D. & RCoreTeam. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-160, https://CRAN.R-project.org/package=nlme (2022).
Chen, Y., Lun, A. T. L. & Smyth, G. K. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438–1438 (2016).
PubMed PubMed Central Google Scholar
Huang, H. linkET: everything is linkable. R package version 0.0.7.1. (2021).
Baker, M., King, R. & Kahle, D. TITAN2: Threshold Indicator Taxa Analysis. R package version 2.4.2, https://CRAN.R-project.org/package=TITAN2 (2023).
RCoreTeam. R.: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).
Wang, C. et al. Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient. Zenodo. https://doi.org/10.5281/zenodo.10017052 (2023).

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Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences [XDA28030401 (C.G.), XDB31030000 (L.G.)], National Natural Science Foundation of China [No. 32322053 (C.G.), 32101286 (C.W.), 32170129 (C.G.)]. We thank support from the Chinese Forest Biodiversity Monitoring Network and Chinese Soil Microbial Observation Network. We also thank support from the Inner Mongolia Daxing’anling Forest Ecosystem Research Station, Liangshui Experimental Forest Farm, the Research Station of Changbai Mountain Forest Ecosystem, the Beijing Forest Experimental Station, the Baotianman National Nature Reserve, the East China Normal University Tiantong National Forest Ecosystem Observation and Research Station, the Badagong Mountain National Nature Reserve, the Zhejiang Qianjiangyuan Forest Biodiversity National Observation and Research Station, the Heishiding Nature Reserve, the Dinghu Mountain National Nature Reserve, the Nonggang National Nature Reserve, and the National Forest Ecosystem Research Station at Xishuangbanna. We thank Mohammad Bahram for sharing some data of Bahram et al.⁷. We are grateful to Min Cao, Liang Chen, Xiao-Yong Chen, Xiao-Bao Deng, Xiao-Jun Du, Zhan-Qing Hao, Ming-Xi Jiang, Guang-Ze Jin, Bu-Hang Li, Xian-Kun Li, Xing-Chun Li, Ke-Ping Ma, Wei-Guo Sang, Xiang Sun, Yong-Long Wang, Wan-Hui Ye, Ming-Jian Yu, and Xiao-Liang Zhang for help in field sampling. We thank Kabir G. Peay and Noah Fierer for feedback on the manuscript, and Qi Wu for help in bioinformatics. We thank all members of Gao Lab for their discussion and communications.

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State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, 100101, Beijing, China
Cong Wang, Qing-Yi Yu, Niu-Niu Ji, Yong Zheng, Liang-Dong Guo & Cheng Gao
College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
Cong Wang, Qing-Yi Yu, Liang-Dong Guo & Cheng Gao
Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
Niu-Niu Ji
School of Geographical Sciences, Fujian Normal University, 350007, Fuzhou, China
Yong Zheng
Department of Plant and Microbial Biology, University of California, Berkeley, CA, 94720, USA
John W. Taylor

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C.W., L.G., J.T., and C.G. conceived of and wrote the manuscript. C.G., N.J., Y.Z., and L.G. coordinated the field sampling and molecular analysis. C.W., Q.Y., and C.G. performed the bioinformatics and statistical analyses.

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Wang, C., Yu, QY., Ji, NN. et al. Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient. Nat Commun 14, 7437 (2023). https://doi.org/10.1038/s41467-023-43297-w

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Received: 06 June 2023
Accepted: 06 November 2023
Published: 17 November 2023
DOI: https://doi.org/10.1038/s41467-023-43297-w

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