Global biogeography of microbial nitrogen-cycling traits in soil

Global biogeography of microbial nitrogen-cycling traits in soil

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Unformatted text preview: COLLOQUIUM PAPER Global biogeography of microbial nitrogen-cycling traits in soil Michaeline B. Nelsona, Adam C. Martinya,b, and Jennifer B. H. Martinya,1 a Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697; and bDepartment of Earth System Science, University of California, Irvine, CA 92697 Microorganisms drive much of the Earth’s nitrogen (N) cycle, but we still lack a global overview of the abundance and composition of the microorganisms carrying out soil N processes. To address this gap, we characterized the biogeography of microbial N traits, defined as eight N-cycling pathways, using publically available soil metagenomes. The relative frequency of N pathways varied consistently across soils, such that the frequencies of the individual N pathways were positively correlated across the soil samples. Habitat type, soil carbon, and soil N largely explained the total N pathway frequency in a sample. In contrast, we could not identify major drivers of the taxonomic composition of the N functional groups. Further, the dominant genera encoding a pathway were generally similar among habitat types. The soil samples also revealed an unexpectedly high frequency of bacteria carrying the pathways required for dissimilatory nitrate reduction to ammonium, a little-studied N process in soil. Finally, phylogenetic analysis showed that some microbial groups seem to be N-cycling specialists or generalists. For instance, taxa within the Deltaproteobacteria encoded all eight N pathways, whereas those within the Cyanobacteria primarily encoded three pathways. Overall, this traitbased approach provides a baseline for investigating the relationship between microbial diversity and N cycling across global soils. | nitrification nitrogen fixation dissimilatory nitrite reduction | ammonia assimilation | metagenomics | A grand challenge for this century is to predict how environmental change will alter global biogeochemical cycles. The field of biogeography has an important role to play in this effort (1). Environmental change is altering the distribution of biodiversity, which in turn is a key driver of biogeochemical processes (2, 3). Historically, biogeography has viewed biodiversity through a taxonomic lens, primarily resolving species distributions. However, a focus on traits—particularly those involved in ecosystem processes—may offer a clearer link between biodiversity patterns and biogeochemistry (4–6). These ideas are particularly relevant for microorganisms. Microbes catalyze most of the biological transformations of the major elements of life (7), and because of their sheer abundance they account for a large pool of elements in living matter (8). Furthermore, like plants and animals, microbial taxonomic composition varies over space (9, 10), and this variation can influence ecosystem processes (11–14). Thus, a consideration of microbial traits should improve efforts to connect biogeographic patterns and ecosystem processes (15). Here, we provide a first characterization of the global biogeographic patterns of microbial nitrogen (N) cycling traits in soil. Microbially driven transformations regulate biologically available N through exchange with the atmosphere (via N fixation and denitrification) and loss by nitrate leaching. They also influence the forms of N available for plant uptake. At the same time, human activities have altered, and continue to alter, the N cycle by increasing the amount of reactive N in the biosphere (16, 17). At local scales, N addition consistently shifts microbial composition in soils and other ecosystems (18, 19). The distribution of microbial traits might therefore be relevant for understanding current and future N cycling. The taxonomic composition of soil microorganisms is correlated with spatial variation in climate, plant diversity, pH, disturbance, and many other factors (20–23). These biogeographic patterns help to identify factors that select on the entire suite of microbial traits. In this study, we reverse this direction of inquiry. We first characterize the patterns and drivers of just handful of traits associated with N cycling and then ask which taxa comprise these functional groups. To quantify the abundance and composition of N-cycling traits, we analyzed ∼2.4 billion short-read sequences from 365 soil metagenomes sampled from around the globe. From this dataset, we identified sequences that indicate the potential for a microorganism to perform one of eight N pathways that convert inorganic N to other inorganic forms or microbial biomass. We then quantified the frequency and taxonomic association of microorganisms carrying these pathways in each sample. If a gene from a pathway was detected, we assumed the presence of the entire pathway in the organism. To compare the frequencies among the N pathways, we standardized for the number of genes (2–20) in each pathway. Although metagenomic sequences provide a measure of a community’s trait diversity (24), the presence of a trait does not indicate how it is being used in the community. Thus, we cannot determine whether genes in the N pathways are expressed or the rate at which N is being transformed. However, assaying traits based on metagenomic sequences are parallel to other trait metrics used to describe an organism’s functional potential, such as nutrient uptake affinity or temperature optimum for growth. The global N trait dataset allowed us to address four main questions. First, what are the overall frequencies of the different N pathways in soil? We expected the frequencies to vary greatly by pathway. Indeed, the ability to perform nitrification is restricted to few microbial taxa, whereas ammonia assimilation is probably present in almost all taxa. Second, what drives variation in the frequencies of N pathways among soil samples? We hypothesized that N pathway frequencies would vary primarily by habitat type, which reflects major differences in plant communities and therefore N inputs into soils. Third, what are the main taxa encoding each N pathway? Surprisingly little is known about the dominant lineages encoding N-cycling traits across global soils. We therefore expected to find previously unrecognized, prominent players, particularly for the less-studied pathways such as dissimilatory nitrate to ammonium (DNRA). Finally, what underlies compositional variation among soil samples in microorganisms encoding N pathways? We hypothesized that the taxa responsible for each pathway would vary greatly by habitat type, because the habitat would select for specialized taxa. We This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “In the Light of Evolution X: Comparative Phylogeography,” held January 8–9, 2016, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA. The complete program and video recordings of most presentations are available on the NAS website at . Author contributions: M.B.N., A.C.M., and J.B.H.M. designed research; M.B.N. performed research; A.C.M. contributed new reagents/analytic tools; M.B.N. and J.B.H.M. analyzed data; and M.B.N., A.C.M., and J.B.H.M. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1 To whom correspondence should be addressed. Email: [email protected] This article contains supporting information online at . 1073/pnas.1601070113/-/DCSupplemental. PNAS | July 19, 2016 | vol. 113 | no. 29 | 8033–8040 ECOLOGY Edited by Francisco J. Ayala, University of California, Irvine, CA, and approved April 21, 2016 (received for review February 12, 2016) Habitat Agriculture Cold Desert Desert Grassland ammonium pathway was slightly more common that these two pathways, detected on average 9.3 times per million sequences. Across all soil samples, N pathway frequencies were overwhelmingly positively correlated for both the Bacteria and Archaea (Fig. 3 A and B). To examine differences in pathways Lawn Pasture A N2 Tundra 8034 | NH4 + Microbial Biomass (2) ficat Nitri Den B (2) NO2- - (4) NO3Bacteria 10-3 10-5 10-7 10-9 Archaea 10-3 10-5 10-7 10-9 Fungi 10-3 10-5 10-7 Dissimilatory Nitrite to Ammonium Denitrification Dissimilatory Nitrate to Nitrite Nitrification Assimilatory Nitrite to Ammonium Assimilatory Nitrate to Nitrite 10-9 Ammonia Assimilation Frequency of Soil N Pathways. On average, 0.5% of all annotated sequences in a soil sample were associated with one of the eight N pathways (Fig. 2A), or an average of 3.3 and 4.7 N pathways per marker gene for Bacteria and Archaea, respectively. The frequency of the individual pathways varied by several orders of magnitude (one-way ANOVA P < 0.001; F = 74.21, df = 7) (Fig. 2B). Bacteria and Archaea displayed similar trends in their relative frequency of N pathways except for the absence of the dissimilatory nitrite reduction to ammonium pathway in Archaea. Fungal sequences were only associated with assimilatory pathways, including ammonia assimilation, assimilatory nitrate to nitrite, and assimilatory nitrite to ammonium. Across all domains, the most common pathway was ammonia assimilation (Fig. 2B). For instance, among the Bacteria, an average of 280 ammonia assimilation pathways were detected for every million annotated bacterial sequences. In comparison, nitrification and N fixation were the least common pathways and detected only 6.1 and 4.6 times per million sequences, respectively. Notably, the relatively unstudied dissimilatory nitrite reduction to Dissimilatory (9) Nitrate Reduction Frequency Results Metagenomic data from surface soil samples were retrieved from the metagenomics analysis server (MG-RAST) (27). After curating the samples for sequence and metadata quality, the final 365 samples represented 118 unique locations from 10 distinct habitat types covering natural and human-dominated systems (Fig. 1 and Dataset S1). Sequencing depth varied greatly among the samples but was not overtly biased toward any particular habitat type (Fig. S1). To standardize for sequencing depth, we report the abundance of each N pathway as its frequency in a sample. The trends observed were similar whether pathway frequency was normalized as the number detected per annotated sequence or per marker gene (based on 30 conserved, singlecopy genes) (Fig. S1). Bacteria dominated the metagenomic libraries, comprising 95% of all sequences, followed by 3% for Fungi and only 2% for Archaea. The fraction of fungal sequences in metagenomic libraries is known to be lower than their contribution to soil microbial biomass (10). We therefore concentrate our analyses on Bacteria and Archaea and report only general trends for Fungi. For instance, the proportion of total sequences of Bacteria, Archaea, and Fungi varied across habitat type (G-test of independence; P << 0.001) (Fig. S2). Archaea ranged from 0.9 to 11% of all sequences by habitat, with the highest percentage detected in deserts. The ratio of fungal to bacterial sequences was particularly high in temperate forest soil, as previously observed (28). (20) Assimilatory (2) Nitrate Reduction further predicted that soil pH—previously identified as an important driver of soil composition (25, 26)—would also influence compositional variation within microorganisms encoding N-cycling traits. (10) Nitrogen Fixation itrifi Fig. 1. The locations (n = 118) sampled to create the soil metagenomic libraries (n = 365) used in this analysis. The samples represent 10 distinct habitats including agriculture (n = 19), cold desert (n = 6), desert (n = 15), grassland (n = 14), lawn (n = 4), pasture (n = 2), temperate forest (n = 12), tropical forest (n = 34), tundra (n = 7), and wetland (n = 5). Ammonia Assimilation A R ssim D Re issim edu ila c t du il ct ato tion ory ion r y Ni tri Ni te tri te catio n Wetland (20) ion Tropical Forest Nitrogen Fixation Temperate Forest Pathway Fig. 2. N pathways and their frequencies. (A) N pathways considered in this study. The numbers in parentheses are the number of genes targeted for each pathway. Assimilatory pathways are in orange and dissimilatory pathways in blue. (B) Box plot of the frequency of each N pathway in a metagenomic library for Bacteria, Archaea, and Fungi. To compare across domains, frequencies are calculated as per annotated sequence in each domain. The upper and lower bounds of boxes correspond to the 25th and 75th percentiles, with a median line shown. Whiskers represent 1.5*IQR (interquartile range). Dots represent outliers. Nelson et al. C Total COLLOQUIUM PAPER A Residual Denitrification Dissimilatory Nitrate to Nitrite Bacteria Nitrification Nitrogen Fixation Spearman Correlation Assimilatory Nitrite to Ammonium 1.0 Assimilatory Nitrate to Nitrite 0. 5 Ammonia Assimilation 0.0 All Nitrogen Cycle Archaea Dissimilatory Nitrate to Nitrite B −0.5 D −1.0 Nitrification p <0.01 p <0.001 p <0.0001 Nitrogen Fixation Assimilatory Nitrite to Ammonium Assimilatory Nitrate to Nitrite Ammonia Assimilation Denitrification Dissimilatory Nitrite to Ammonium Nitrification Dissimilatory Nitrate to Nitrite Nitrogen Fixation Assimilatory Nitrate to Nitrite Assimilatory Nitrite to Ammonium Denitrification Dissimilatory Nitrite to Ammonium Nitrification Dissimilatory Nitrate to Nitrite Nitrogen Fixation Assimilatory Nitrite to Ammonium Ammonia Assimilation Assimilatory Nitrate to Nitrite All Nitrogen Cycle beyond the trends shared by all, we calculated the residuals of the frequency of each pathway regressed against the frequency of all N pathways in a sample. This residual variation was also significantly correlated among many of the N pathways (Fig. 3 C and D). For instance, denitrification was highly positively correlated with dissimilatory nitrate reduction to nitrite within both Bacteria and Archaea (R2 = 0.86 and 0.97, respectively, P ≤ 0.001). This relationship is expected, because dissimilatory nitrate reduction to nitrite is the first step of the complete denitrification process; however, we separated the two steps here, because nitrate reduction to nitrate is also the first step in DNRA (29). Similarly, we separated DNRA into its two pathways: dissimilatory nitrate reduction to nitrite and dissimilatory nitrite reduction to ammonium (Fig. 2A). Among Bacteria, the assimilatory nitrite to ammonium pathway residual was negatively correlated with all other pathways. Likewise, the residual frequency of the ammonia assimilation pathway was negatively correlated with all other N pathways in both Bacteria and Archaea. N fixation generally showed weak or no correlation with other pathways. Drivers of N Pathway Frequencies. The frequency of all N-cycling traits (summing across all pathways) varied greatly among soil samples, and initial analyses revealed broad biogeographic patterns. On average, the highest frequencies of total N pathways were detected in tropical forest and human-dominated (pasture, lawn, and agriculture) soils, whereas the lowest frequency was observed in cold deserts (Fig. S3). Total N pathway frequency also tended to decrease with increasing latitude (R2 = 0.22, P < 0.05; Fig. S4). To disentangle the drivers behind these patterns, we performed a multivariate regression analysis including habitat type and environmental parameters known to influence microbial abundance and composition (30, 31). Local measurements were not available for most samples; instead, we estimated these variables from secondary sources. For Bacteria, the regression model explained a large and significant proportion of the variability in the frequency of total N pathways (R2 = 0.58, P << 0.001; Table 1). Habitat type Nelson et al. contributed most to this model, both directly (positively related to total N pathways) and through interactions with soil carbon and N. The regression model for Archaea explained less variability in total N pathway frequency than for Bacteria (R2 = 0.43, P < 0.001; Table 1). An interactive effect between carbon and N contributed the most to the model, and habitat was only important through an interactive effect with temperature. We next examined the drivers of individual N pathway frequencies. Due to high covariance between pathways (Fig. 3 A and B), we fitted regression models to the total-frequency-corrected residuals for each pathway. These models varied greatly in their ability to explain this additional variation (Table 1). For example, the models for the N fixation pathway explained 80% and 63% of the variation among samples in Bacteria and Archaea, respectively (P << 0.001). In contrast, the same parameters did not explain any variation in the frequency of the dissimilatory nitrite reduction to ammonium pathway in Bacteria. Among the significant models, habitat type was an important predictor of the individual pathway frequencies (Table 1). Habitat also interacted with other factors including precipitation, temperature, and soil N to influence the frequency of some pathways. For instance, denitrification frequency increased with temperature in deserts but decreased with temperature in tropical forests. Similarly, ammonia assimilation frequency increased with soil N in temperate forests but decreased with soil N in tropical forests. Soil carbon, which seemed to be a primary driver of total N pathway frequency, did not explain differences in the frequency of individual pathways in Bacteria. Including estimates of N deposition in these models only improved the denitrification model (R2 increased from 0.41 to 0.48); denitrification frequency increased with increasing N deposition. The models for individual pathway frequencies in Archaea generally explained less variation than those for Bacteria, perhaps due to the lower number of sequences per sample (Dataset S1). However, for the significant models, the individual N pathways were often best explained by the same parameters as the Bacteria. For instance, habitat type and habitat by temperature were the most PNAS | July 19, 2016 | vol. 113 | no. 29 | 8035 ECOLOGY Fig. 3. The relationships between N pathway frequencies. Correlations between N pathways encoded by Bacteria (A) and Archaea (B) across the samples. (C and D) Correlations between the residuals of each pathway regressed against the total frequency of all N pathways. Table 1. Variation explained by the environmental variables in the regression models of the frequency of all (total) and individual N pathways Individual pathways (residuals) Environmental variables Bacteria Habitat (H) Precipitation (P) Temperature (T) pH Organic carbon (C) Total N H×P H×T H × pH H×C H×N P×T C×N Adjusted R2 Archaea Habitat Precipitation Temperature pH Organic carbon Total N H×P H×T H × pH H×C H×N P×T C×N Adjusted R2 Total 0.14 Ammonia assimilation Assimilatory nitrate to nitrite Assimilatory nitrite to ammonia 0.23 0.07 0.02 <0.01 <0.01 0.29 0.06 Denitrification 0.09 0.02 0.13 0.07 0.09 0.09 0.49 0.23 0.31 0.05 0.51 NS 0.32 0.03 0.31 0.5 0.52 <0.01 0.05 0.36 0.05 0.21 0.18 0.09 0.08 0.21 0.06 0.08 0.34 0.43 0.11 0.02 0.02 0.58 Nitrification Dissimilatory nitrite to ammonia <0.01 0.12 0.05 <0.01 <0.01 0.1 0.17 N fixation Dissimilatory nitrate to nitrite <0.01 0.41 0.8 0.45 0.41 0.09 <0.01 0.03 0.12 0.02 0.03 <0.01 NS 0.04 NS 0.09 0.33 0.12 0.13 0.63 0.22 0.06 NS 0.21 NA The models for the individual pathways are based on the residual frequencies of the pathway after correcting for the Total N pathway frequency (see text). Estimates of the fraction of explained variation are only reported for significant variables (P < 0.05). Samples were only included when all environmental variables could be obtained for that location (n = 99). NA, not assessed; NS, not statistically significant. important predictors of N fixation frequency within both domains. Likewise, habitat, habitat by precipitation, and habitat by temperature contributed to the variation in assimil...
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  • Fall '16
  • Statistics, Denitrification, dissimilatory nitrite reduction, N Pathways

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