Control of the Nitrogen Isotope Composition of the Fungal Biomass: Evidence of Microbial Nitrogen Use Efficiency

Abstract

Changes in ¹⁵N/¹⁴N in the soil microbial biomass during nitrogen (N) mineralization have been hypothesized to influence ¹⁵N/¹⁴N in soil organic matter among ecosystem sites. However, a direct experimental test of this mechanism has not yet been performed. To evaluate the potential control of microbial N mineralization on the natural N isotope composition, we cultured fungi (Aspergillus oryzae) in five types of media of varying C:N ratios of 5, 10, 30, 50, and 100 for 4 d, and tracked changes in δ¹⁵N in the microbial biomass, NH₄⁺, and dissolved organic N (DON: glycine) over the course of the experiment. High rates of NH₄⁺ excretion from A. oryzae were accompanied by an increase in δ¹⁵N in the microbial biomass in low C:N media (i.e., C/N<30). In contrast, NH₄⁺ was strongly retained in higher C/N treatments with only minor (i.e., <1 ‰) changes being detected in δ¹⁵N in the microbial biomass. Differences in δ¹⁵N in the microbial biomass were attributed to the loss of low-δ¹⁵N NH₄⁺ in low, but not high C/N substrates. We also detected a negative linear correlation between microbial nitrogen use efficiency (NUE) and Δ¹⁵N (δ¹⁵N-biomass–δ¹⁵N-glycine). These results suggest an isotope effect during NH₄⁺ excretion in relatively N-repleted environments in which microbial NUE is low, which may explain the vertical patterns of organic matter δ¹⁵N in soil profiles.

Soil microbial processes strongly regulate the terrestrial nitrogen (N) cycle. The microbial decomposition of soil organic matter (SOM) aids in the mineralization of N in plants, which maintains primary productivity (39, 53) and influences the magnitude of denitrification and nitrification (23, 55). Increasing levels of atmospheric CO₂ and elevated N deposition rates markedly affect the N cycle (19, 30, 51), partly by changing the quantity and quality of SOM (61). Therefore, it is important to clarify the relationships between soil microbial processes and N and carbon (C) availabilities in order to predict changes in terrestrial ecosystem N cycling.

The soil microbial biomass (SMB) accounts for a significant fraction of the soil N pool (64) and contributes to SOM production and consumption (40, 49). For example, ¹³C and ¹⁵N NMR analyses revealed that SMB in soil minerals contributed to SOM pools (21, 33). The fungal biomass is a demonstrated precursor of SOM formation (9, 17, 31), e.g. the dominant pathway (62%) through which C enters the SOM pool, exceeding C input via leaf litter and fine root turnover, with potentially similar patterns for N (22).

The natural abundance of ¹⁵N/¹⁴N (δ¹⁵N; δ¹⁵N is expressed as ¹⁵N/¹⁴N_sample/¹⁵N/¹⁴N_standard–1, where ¹⁵N/¹⁴N_standard is atmospheric N₂ and treated in units mil⁻¹ [‰]) was previously used to examine relationships among SOM and SMB (13). While whole-soil δ¹⁵N patterns are driven by variations in the δ¹⁵N of inputs and the balance among leaching and isotopic fractionating gaseous N loss over time (28, 29, 37, 45), the within soil profile δ¹⁵N is a product of the downward transport of δ¹⁵N, microbial processing, and variations in δ¹⁵N in root and litter inputs. The δ¹⁵N of surface (i.e., ~top 10 cm) SOM markedly varies among ecosystems, from −7.8‰ in acidic tundra (3) to as high as 22.0‰ in South Africa (13); however, the mechanisms responsible for these variations remain unclear (13, 26).

A primary factor contributing to δ¹⁵N-SOM patterns may be the rates of SOM decomposition (11, 35, 38, 56, 59). Kramer et al. (38) reported a strong positive relationship between δ¹⁵N-SOM and an index for SOM decomposition (Aliphatic/O-Alkyl), and Sollins et al. (56) demonstrated that δ¹⁵N-SOM increased with greater densities in the SOM fraction. These findings imply that high δ¹⁵N in SMB-derived compounds is either incorporated into SOM, ¹⁵N-depleted SOM is lost during decomposition, or both.

δ¹⁵N-SMB, which is measured using a chloroform-fumigation-extraction procedure (14, 62), was shown to be higher than δ¹⁵N-SOM in most soils, with the level of ¹⁵N enrichment of SMB (Δ¹⁵N=δ¹⁵N-SMB–δ¹⁵N-SOM) varying widely across soils (Table 1). Furthermore, Δ¹⁵N increases with decreasing soil C/N (16, 41). Dijkstra et al. (16) hypothesized that the ¹⁵N/¹⁴N of SMB is controlled by the excretion of ¹⁵N-depleted ammonia from the microbial biomass. In this case, δ¹⁵N-SMB is expected to increase when soil microbes actively mineralize organic N at low C/N (N sufficient conditions), while δ¹⁵N-SMB shows markedly less enrichment when net N is immobilized (incorporate) at high C/N (N limited conditions).

Table 1 Previous findings on differences between δ¹⁵N-SMB and δ¹⁵N-SOM (Δ¹⁵N).

Δ15N (‰)(=δ15NSMB–δ15NSOM)	n=	Depth (cm)	Period	Location	Information	Reference
+0.1 to +0.6	3 for each treatment	0–5	October, 1996	Kansas, USA	8-year treated soils including no chamber, an ambient CO2 chamber, and an elevated CO2 chamber	Williams et al., 2006 (62)
+3.1±0.2	136	A0, 0–10, 0–15	1997 to 2004	Arizona and Florida, USA	six experiments covering a broad range of soil types, vegetation cover, climates, land-use practices, and analytical procedures	Dijkstra et al., 2006a (14)
+0 to +4	19	0–10	October 2002 September 2003	Hawaii and Arizona, USA	soils at different elevations	Dijkstra et al., 2008 (16)
−1 to +7 (+5.6±0.5)	22	0–10	March, 2003	Arizona, USA	soils in a cattle manure gradient	Dijkstra et al., 2006b (15)
+2 to +10 (+7.2±0.7)	31	0–10	March, 2005	northern Arizona, USA	four study sites with different substrate ages of 930 y, 55,000 y, 750,000 y, and 3,000,000 y	Coyle et al., 2009 (12)
+0.3±0.3 to +2.2±0.3 (from day 0 to day 180)	3	0–30		Varsailles, France	soil incubation for 6 months	Lerch et al., 2011 (41)
−2 to +1	8 for each plot	0–5	April to September, 2007	Kansas, USA	4 plots including fertilized, unfertilized, hayed, and non-hayed	Tiemann and Billings, 2011 (58)
+0 to +8	15	0–10	November, 2014	Danjiangkou Reservoir area, China	soils in woodland plantation, shrubland plantation, cropland, and open areas	Wu et al., 2016 (63)
+17	30	thin humus layer	July, 2011	nothern Norway	soils in tundra sites	Barthelemy et al., 2017 (1)

¹⁵N/¹⁴N enrichment in organisms vs. substrates is common. Animals are commonly enriched in ¹⁵N by ~3–5‰ vs. food sources, with the δ¹⁵N of excreted compounds (NH₄⁺, urea, and urine) being lower than that of the biomass in cows (57), guppies (44), and zooplankton (6). In contrast, less direct evidence is available for microbial ¹⁵N enrichment vs. N sources; however, several observations point to this possibility. For example, the δ¹⁵N of bacteria grown on alanine as a single nitrogen substrate was 3.6‰ higher than that of alanine (42). Yamaguchi et al. (65) cultured bacteria, fungi, and archaea with casamino acids, and observed ¹⁵N enrichment of the biomass in the order of 0.6±0.2, 3.6±0.2, and 5.0±1.0‰, respectively, vs. substrate δ¹⁵N. In a controlled culture experiment, Collins et al. (10) demonstrated an increase in δ¹⁵N in bacterial cells during N mineralization.

Previous studies suggested that N availability and the degree of N limitation are primary factors contributing to the level of microbial ¹⁵N enrichment, which may be expressed through the concept of nitrogen use efficiency (NUE). NUE is defined as the ratio of N incorporation into the microbial biomass to organic N uptake (46) and is expected to decrease when soil microbes actively mineralize organic N at low C/N. In contrast, NUE increases when net N is immobilized at high C/N owing to microbial N limitations. Thus, we hypothesize that NUE controls Δ¹⁵N and, thus, δ¹⁵N-SOM patterns.

We herein investigated the mechanisms underlying the ¹⁵N enrichment of fungi via a controlled culture experiment, and measured δ¹⁵N in the biomass, substrates, and NH₄⁺. Fungi were cultured in media with different C:N ratios to examine the hypothesis that NUE is a primary factor responsible for the level of isotopic expression. In the present study, we provide direct evidence to show that the δ¹⁵N of fungi is influenced by NUE.

Materials and Methods

Experimental design

Aspergillus oryzae (RIB40, NFRI1599), a well-studied, cosmopolitan strain of fungi, was used in the present study. Five milliliters of A. oryzae (ca. 8×10⁷ cells) was inoculated into 500-mL flasks with 250 mL of medium. Cultures were incubated at 27°C with shaking (180 rpm) for 96 h. Porous silicon plugs were used to maintain aerobic conditions. We used glycine and glucose as the sole C and N sources to adjust the C:N ratio of medium. Five types of media with C:N ratios of 5, 10, 30, 50, and 100 (hereafter CN5, CN10, CN30, CN50, and CN100, respectively) contained glycine and D(+)-glucose at the following amounts: 3.26 and 4 g L⁻¹, 1.63 and 5.2 g L⁻¹, 0.5 and 5.6 g L⁻¹, 0.25 and 4.8 g L⁻¹, and 0.2 and 7.9 g L⁻¹, respectively. The actual initial C:N ratios of media were 5.4±0.1, 11.1±0.2, 31.3±0.5, 52.5±2.0, and 103.6±3.8 (mol mol⁻¹; means±S.D.). All media contained 0.5 g L⁻¹ MgSO₄·7H₂O, 0.07 g L⁻¹ CaCl₂·2H₂O, 0.01 g L⁻¹ FeSO₄·7H₂O, 4.65 mg L⁻¹ MnSO₄·5H₂O, 5.0 mg L⁻¹ ZnSO₄·7H₂O, and 1.0 mg L⁻¹ CoCl·6H₂O. pH was adjusted to between 5.7 and 5.9 by adding 0.92 g L⁻¹ K₂HPO₄ and 6.86 g L⁻¹ KH₂PO₄. Media were filtered through pre-sterilized membrane filters (pore size of 0.22 μm; Stericup and Steritop, Millipore, Burlington, MA, USA), and filtrates were poured into pre-combusted (450°C, 4 h) 500-mL flasks. A total of 1.0 mg L⁻¹ FeSO₄·7H₂O was added to medium without sterilization because of its oxidization during autoclaving as well as its precipitation. We had 3 replicates, except for CN 5 which had 4 replicates.

Samples were collected every 24 h for a total of 4 d. Five milliliters of media, including the biomass, was collected using a 5-mL pipette and filtered through pre-combusted (450°C, 4 h) glass fiber filters (GF/F; Whatman, Tokyo, Japan) with a pore size of 0.22 μm (Steriflip Filter Unit, Millipore), while all media in the flasks were filtered with a membrane filter (pore size of 0.22 μm; Stericup and Steritop, Millipore) to completely recover the biomass in the flask at 96 h. The filtrate was frozen for later analyses. The biomass on the filter was washed 4 times with 5 mL (total of 20 mL) of 0.9% (w/v) NaCl solution, separated from the filter, transferred to a microtube, and freeze-dried. The growth of A. oryzae was calculated from the freeze-dried biomass weight.

Data collection and analysis

The concentrations of NH₄⁺ and NO₃⁻ ([NH₄⁺] and [NO₃⁻], respectively) in filtrates were assessed by colorimetric methods using an autoanalyzer (QuAAtro2-HR; BL-TEC, Tokyo, Japan). [NO₃⁻] was not detected in any samples (the detection limit of 0.1 μM for the sum of NO₂⁻ and NO₃⁻). The concentrations of dissolved organic C ([DOC]) and total dissolved N ([TDN]) were measured using a TOC analyzer with a TN unit (TOC-L/TNM-L; Shimadzu, Kyoto, Japan). The concentration of dissolved organic N ([DON]) was calculated as follows: (DON)=(TDN)–(NH₄⁺). The C:N ratio of medium (DOC/TDN) was calculated as DOC/TDN=(DOC)/(TDN).

The recovery % of N during the incubation was calculated as follows: Recovery N (%)=([TDN]+[biomass N])/(TDN_initial)×100, where (TDN_initial) is the concentration of TDN at 0 h and (biomass N) is the concentration of the biomass N. Microbial NUE was calculated as ([DON_consumption]–[NH₄⁺_production])/(DON_consumption), where (DON_consumption) represents the difference between initial DON and DON concentrations over the course of the experiment, and (NH₄⁺_production) is the difference between NH₄⁺ concentrations and vs. initial NH₄⁺ measured.

The concentrations of biomass carbon ([biomass C]) and (biomass N) and the δ¹⁵N values of the biomass (δ¹⁵N-biomass) were measured using an elemental analyzer (EA1112; Thermo Fisher Scientific, Yokohama, Japan) interfaced with an isotope ratio mass spectrometer (Delta-XP; Thermo Fisher Scientific). Calibrated in-house standards, such as DL-alanine (δ¹⁵N=−1.7‰), glycine (δ¹⁵N=10.0‰), and L-histidine (δ¹⁵N=−8.0‰), were used to obtain the calibration curve to correct the measured isotopic values. The average standard deviations for a replicate analysis of individual samples were ±0.4‰ for δ¹⁵N after blank corrections. The biomass C/N (mol mol⁻¹) was calculated as biomass C/N=(biomass C)/(biomass N).

The δ¹⁵N values of NH₄⁺ (δ¹⁵N-NH₄⁺) were measured for samples with CN5 and CN10 only after 48 h with sufficient concentrations of (NH₄⁺) that allowed for the assessment of δ¹⁵N-NH₄⁺. δ¹⁵N-NH₄⁺ was measured using the methods described by Koba et al. (36). In brief, NH₄⁺ was concentrated on the pre-combusted (450°C, 4 h) glass fiber filter (GF/D, diameter of 1 cm, Whatman) using the diffusion method (27), and concentrated NH₄⁺ was then oxidized to NO₃⁻ using persulfate (34). The δ¹⁵N of converted NO₃⁻ from NH₄⁺ was measured using the denitrifier method (4, 54) with an isotope ratio mass spectrometer (IRMS; 20–22: Sercon, Cheshire, UK) coupled with an autosampler (GX-271: GILSON, Middleton, WI, USA) and CryoPrep (Sercon) equipped with a gas chromatograph (GC-4000, GL Sciences, Tokyo, Japan). The denitrifying bacterium, Pseudomonas aureofaciens (ATCC#13985), which lacks nitrous oxide reductase activity, was used to convert NO₃⁻ into N₂O gas before the isotope analysis. The isotopic standards, USGS25 (δ¹⁵N=−30.4‰), USGS26 (δ¹⁵N=53.7‰), and IAEA N2 (δ¹⁵N=20.3‰), were used for data calibration. The average standard deviations from the replicate analysis of individual samples were large (±1.4‰) for δ¹⁵N after blank corrections, and this was attributed to samples with low (NH₄⁺) (CN10 at 48 h). The average standard deviations from the replicate analysis were ±0.1‰ without these low (NH₄⁺) samples after blank corrections.

The δ¹⁵N of glycine at 0 h (δ¹⁵N-glycine) and δ¹⁵N of TDN (δ¹⁵NTDN) were measured via the denitrifier method, in which total N was digested to NO₃⁻ using the persulfate method (36). Calibrated in-house standards of DL-alanine (δ¹⁵N=−1.7‰), glycine (δ¹⁵N=10.0‰), and L-histidine (δ¹⁵N=−8.0‰) were used for the calibration. The average standard deviations for the replicate analysis of individual samples were ±0.2‰ for δ¹⁵N after blank corrections. Δ¹⁵N was calculated as the difference between δ¹⁵N-biomass and δ¹⁵N-glycine (Δ¹⁵N=δ¹⁵N-biomass–δ¹⁵N-glycine).

In some cases, (TDN) was lower than (NH₄⁺) because of the inaccurate assessment of high (NH₄⁺), and we were unable to calculate δ¹⁵N-DON with the concentrations and δ¹⁵N data of TDN and NH₄⁺. On the other hand, the concentration of Gly_used, glycine utilized by A. oryzae, (Gly_used), was calculated as (Gly_used)=(biomass N)+(NH₄⁺). The δ¹⁵N of Gly_used (δ¹⁵N-Gly_used) was then calculated with the concentrations and δ¹⁵N values of biomass N and NH₄⁺ as δ¹⁵N-Gly_used= δ¹⁵N_microbe×f_microbe+δ¹⁵N-NH₄⁺×f_NH4+ and f_microbe+f_NH4+=1, where f_NH4+ is the ratio of the concentration of initial NH₄⁺ to TDN and f_microbe= 1–f_NH4+. δ¹⁵N-Gly_used was not calculated in CN30, CN50, and CN100 because the concentrations of NH₄⁺ were too low to measure δ¹⁵N-NH₄⁺. The standard deviations of δ¹⁵N-Gly_used were evaluated using the Monte Carlo method described by Koba et al. (37).

Statistical analyses were conducted using R software (R version 3.4.2, R core Team [2017]). An α level of 0.05 was considered to indicate significance. The Student’s t-test was used to investigate whether concentrations or δ¹⁵N values were significantly different between (DOC)/(TDN) and biomass C/N on each d in all treatments, between δ¹⁵N-biomass at 96 h and initial δ¹⁵N-glycine (0.1±0.3‰) in all treatments, and between δ¹⁵N-Gly_used in CN5 and CN10 and initial δ¹⁵N-glycine (0.1±0.3‰) on each d. The paired Student’s t-test was used to clarify whether concentrations or δ¹⁵N values were significantly different between δ¹⁵N-biomass at 24 and 96 h in all treatments, and between δ¹⁵N-NH₄⁺ and δ¹⁵N-biomass on each d in CN5 and CN10. Pearson’s correlation test was used to examine the relationship between (DOC)/(TDN).

Results

The microbial biomass and carbon and nitrogen dynamics

As A. oryzae grew (Fig. 1A), the majority of DOC was consumed by 96 h (97.1±0.1, 96.4±0.1, 95.1±0.3, 77.1±2.1, and 41.9±0.6% [mean±SD] in CN5, CN10, CN30, CN50, and CN100, respectively; Fig. 1B), thereby increasing biomass C over the course of the experiment (Fig. 1C; 24.9±1.0, 25.1±0.9, 29.2±1.6, 39.2±2.6, and 46.3±1.6% of consumed DOC were assimilated in CN5, CN10, CN30, CN50, and CN100, respectively). More than 90% of the DON substrate was consumed in CN30, CN50, and CN100, with DON concentrations decreasing to below the limit of detection in CN5 and CN10 at 72 and 96 h (Fig. 1D). Large amounts of DON were incorporated in biomass N by 96 h (Fig. 1E; 21.4±1.2, 41.1±3.1, 87.6±1.6, 85.0±6.2, and 87.2±5.0% for CN5, CN10, CN30, CN50, and CN100, respectively). Consumed DON was mineralized to NH₄⁺ in CN5 and CN10 (92±2 and 57±10% of consumed DON, respectively), while less than 2% of consumed DON was mineralized to NH₄⁺ in other treatments (Fig. 1F). Nitrate concentrations were below the limit of detection in all treatments. In CN5 and CN10, biomass C/N was significantly higher than DOC/TDN (except for 24 and 48 h in CN10; Fig. 2A and B, Table S1; P<0.001). On the other hand, DOC/TDN was significantly higher than biomass C/N in CN30, 50, and 100 (Fig. 2A and B, Table S1; P<0.01). Moreover, biomass C/N in CN50 and CN100 increased to 21.3±2.5 and 26.6±1.2, respectively, values that were significantly higher than the C/N of soil fungi (4–17) when incubated in medium C/N of 3.9 (47). Although high C/N ratios are not common, fungi may respond to a stoichiometric imbalance by increasing the storage of C in cells (e.g. in lipids [47]), thereby increasing C/N. Another possibility is that the microbial biomass incorporated the degraded necromass at low N concentrations, resulting in the high C/N observed in the total biomass.

Fig. 1

Changes in biomass weights (A), concentrations of DOC (B), biomass C (C), DON (D), biomass N (E), and NH₄⁺ (F) at different C:N treatments. Symbols represent mean values and error bars represent SD. Regarding samples without error bars, the SD was smaller than the symbols.

Fig. 2

Changes in DOC/TDN (A) and biomass C/N (B) at different C:N treatments. Symbols represent mean values and error bars represent SD. Regarding samples without error bars, the SD was smaller than the symbols.

The recovery of N was more than 70% in CN5 and CN10, but was lower (40–62%) from 24 to 72 h in CN30, CN50, and CN100 (except at 96 h; Fig. S1). This low recovery of N (less than 100%) was attributed to the underestimation of biomass N (the largest N fraction in this experiment) between 24 and 72 h. We collected the microbial biomass with large membrane filters (see the Methods section) at 96 h, but used a 5-mL pipette with an aperture that was not sufficiently large to collect the same rate of microbial biomass for 250 mL medium until 72 h. Underestimated biomass N (%) may be defined as ([expected biomass N]/[measured biomass N])×100, where (expected biomass N)=(DON consumption)–(NH₄⁺ production). Between 24 and 72 h, underestimated biomass N was between 36 and 60%, while only 10–12% of biomass N was underestimated at 96 h in CN30, CN50, and CN100. We measured biomass N at 96 h at the end of the incubation more accurately than at other time points because we collected all of the microbial biomass left in media using large membrane filters (see the Materials and Methods section), while we collected microbial biomass with a small ratio for 5 mL medium solution using the 5-mL pipette until 72 h. Since DON was nearly consumed by 48 h in CN30 and by 24 h in CN50 and CN100 (Fig. 1D) and less than 2% of consumed DON was mineralized to NH₄⁺, we assumed that the concentrations of biomass N at 48 and 72 h in CN30 and between 24 and 72 h in CN50 and CN100 were similar to that of biomass N at 96 h in each CN treatment. After we recalculated biomass N based on this assumption, the recovery of N in CN30, CN50, and CN100 at 24, 48, and 72 h increased to 74–92%.

Changes in δ¹⁵N

δ¹⁵N-biomass by 24 h was −0.1±0.5 and −0.2±0.2‰ in CN5 and CN10, respectively; a significant increase in δ¹⁵N-biomass between 3.1±0.3 and 2.9±0.0‰ was apparent by 96 h (Fig. 3A, Table S2, P=0.001 and P<0.001). δ¹⁵N-biomass at 96 h was significantly higher than δ¹⁵N-glycine (0.1±0.3‰) (Fig. 3A, Table S2, P<0.001 and P<0.001), and Δ¹⁵N was 3.1±0.5 and 2.7±0.2‰ in CN5 and CN10, respectively. On the other hand, in CN30 and CN100, δ¹⁵N-biomass was not significantly different between 24 h (−0.2±0.1 and −0.3±0.9‰) and 96 h (0.4±0.2 and 0.5±0.0‰) (Fig. 3A, Table S2, P=0.076 and P=0.279). In CN50, δ¹⁵N-biomass was significantly different between 24 h (−0.1±0.3‰) and 96 h (0.6±0.1‰) (Fig. 3A, Table S2, P=0.037); however, this difference (0.7±0.2‰) was markedly smaller than that observed for CN5 and CN10. In CN30, CN50, and CN100, δ¹⁵N was not significantly different between glycine and biomass N at 96 h (Fig. 3A, Table S2, P=0.358, P=0.162, P=0.227, respectively), and Δ¹⁵N (0.4±0.2, 0.7±0.1, 0.6±0.0‰, respectively) was smaller than those for CN5 and CN10.

Fig. 3

A) Changes in δ¹⁵N-biomass in CN5 and CN10. The red dotted line represents δ¹⁵N-Glycine (0.1±0.3‰). Asterisks (***) represent a significant difference (P<0.001) between δ¹⁵N-Glycine and δ¹⁵N-biomass at 96 h based on the Student’s t-test. B) Changes in δ¹⁵N-biomass and δ¹⁵N-NH₄⁺ in CN5 and CN10. In CN30, CN50, and CN100, the concentration of NH₄⁺ was too low to measure δ¹⁵N-NH₄⁺. Asterisks (**) represent a significant difference (P<0.01) between δ¹⁵N-NH₄⁺ and δ¹⁵N-biomass based on the paired Student’s t-test. C) Changes in δ¹⁵N-Gly_used in CN5 and CN10. The red dotted line represents δ¹⁵N-Glycine. Asterisks represent a significant difference between δ¹⁵N-Gly_used and δ¹⁵N-Glycine based on the Student’s t-test (*, **, *** indicate P<0.05, P<0.01, P<0.001, respectively). Symbols represent mean values and error bars represent SD. Regarding samples without error bars, the SD was smaller than the symbols.

δ¹⁵N-NH₄⁺ was below the limit of quantification in CN30, CN50, and CN100 (Fig. 3B). In CN5 and CN10, δ¹⁵N-NH₄⁺ was significantly lower than δ¹⁵N-biomass (Table S3); the differences between them at 48, 72, and 96 h were 3.9±0.6, 2.7±0.5, and 3.5±0.6‰ in CN5, and 21.4±1.1, 4.6±0.2, and 4.9±0.2‰ in CN10, respectively. Among δ¹⁵N-Gly_used and δ¹⁵N-glycine (0.1±0.3‰), significant differences were observed at 48 and 72 h in CN5 and at 48 h in CN10 (Fig. 3C, Table S4). However, differences between δ¹⁵N-Gly_used and δ¹⁵N-glycine (average of δ¹⁵N-Gly_used–average of δ¹⁵N-glycine) remained small at 48, 72, and 96 h (+0.35, +1.44, and −0.30 in CN5, and −1.11, −0.65, and −0.22 in CN10, respectively).

NUE and Δ¹⁵N

Microbial NUE changed according to the nutrient imbalance (Fig. S2). NUE was examined in CN5 and CN10 only because of the low recovery of biomass N in CN30, CN50, and CN100 (Table S1). A negative linear correlation was observed between DOC/TDN and Δ¹⁵N (Fig. 4A) and NUE and Δ¹⁵N (Fig. 4B). Linear regression lines for CN5 (Δ¹⁵N=−3.0×NUE+3.0 [R²=0.89, P<0.001]) and CN10 (Δ¹⁵N=−4.2×NUE+3.8 [R²=0.86, P<0.001]; Fig. 4B) had similar slopes, but slightly different intercepts (P=0.06 for slopes and P<0.0001 for intercepts by an analysis of co-variance; Fig. 3B); however, the difference in intercepts was smaller than the error for isotope measurements (±0.6‰). The isotopic fractionation of NH₃ volatilization was negligible because the pH of all media were lower than 7 through the experiment. The data obtained are shown in Table S5.

Fig. 4

A) Relationship between DOC/TDN and Δ¹⁵N in CN5 and CN10. The black line represents the linear regression line of two treatments (Δ¹⁵N=−0.4*DOC/TDN+2.4, R²=0.86, P<0.00). B) Relationship between NUE and Δ¹⁵N in CN5 and CN10. The black line represents the linear regression line of two treatments (Δ¹⁵N=−3.3*NUE+3.1, R²=0.76, P<0.001). The yellow and green lines represent the linear regression lines of CN5 and CN10 (Δ¹⁵N=−3.0*NUE+3.0, R²=0.89, P<0.001 and Δ¹⁵N=−4.2*NUE+3.8, R²=0.86, P<0.001).

Discussion

Mechanism of ¹⁵N enrichment

We tested hypotheses that the δ¹⁵N of extracted substrates explains the patterns of microbial biomass enrichment, with implications for the pattern and regulation of surface SOM δ¹⁵N across ecosystems. We predicted that the δ¹⁵N of NH₄⁺ may be depleted more at higher levels of N available to microbes (i.e., low C/N), with N functioning as more of an excess nutrient and, thus, being more rapidly mineralized from organic substrates. The present results confirmed these hypotheses; the mineralization (excretion of NH₄⁺) of DON by A. oryzae in CN5 and CN10 (Fig. 1D and F) was accompanied by increases in δ¹⁵N-biomass over time (Fig. 3A), revealing that N supplies from DON exceeded N demands at low C/N levels. Moreover, A. oryzae retained DON in CN30, CN50, and CN100 without any detectable changes in δ¹⁵N-biomass, thereby demonstrating the importance of N availability in assessing the loss of low-δ¹⁵N NH₄⁺ from microbes. According to the mass balance, any elevation in δ¹⁵N in the microbial biomass vs. substrates must be balanced by a loss of low δ¹⁵N-NH₄⁺. δ¹⁵N-NH₄⁺ was significantly lower than δ¹⁵N-biomass in CN5 and CN10 (Table S3), with prominent isotopic excursions that varied between 2.0 and 22.7‰ (Fig. 3B). This level of isotopic depletion is similar to observations for cows (7.3‰; 57), fish (23.3–31.8‰; 44), and plankton (3‰; 6).

Mechanistically, our results revealed that the loss of ¹⁵N-dpeleted NH₄⁺ was the principal determinant of fungal biomass δ¹⁵N, which was very important when N was high (i.e., low C/N substrate ratios) (Fig. 1F, 3A, and B). A small isotope effect was observed during DON assimilation by A. oryzae, with only minor Δ¹⁵N values observed for C/N ratios in excess of 30 (Fig. 3A, Table S2). In contrast, Δ¹⁵N increased over time in CN5 and CN10 (Fig. 3A, Table S2), with little evidence for changes in δ¹⁵N-Gly_used (Fig. 3C, Table S4). These results revealed negligible isotope fractionation during DON uptake by fungi, as reported previously for mycorrhiza (18). The negative correlation between DOC/TDN and Δ¹⁵N in CN5 and CN10 (R=−0.88, P<0.001, Fig. 4A), which is similar to the negative correlation between the soil C:N ratio and soil microbial Δ¹⁵N (16, 41), supports the hypothesis that the mineralization of low δ¹⁵N-NH₄⁺ is the primary driver of microbial δ¹⁵N (16).

While the absorption of isotopically depleted δ¹⁵N-NH₄⁺ by A. oryzae may decrease, δ¹⁵N-biomass may, in principle, decrease δ¹⁵N in the microbial biomass (−14.1±0.8‰ [60]); however, this was not the case in the present study. Although DON and NH₄⁺ absorption both occur in ecosystems (53), A. oryzae did not appear to use excreted NH₄⁺ due to C limitations at low C/N ratios; the low concentrations of NH₄⁺ in CN30, CN50, and CN100 may have strongly limited the importance of this N source in the present study (Fig. 1F, 2A, and B).

We used glycine as the sole N source because it accounts for a large fraction of amino acids in soil (32), making it a meaningful indicator of amino acid utilization in trees and microbes (5). The glycine cleavage system, one of the main degradation pathways of glycine, has been hypothesized to favor ¹⁵N and cause the ¹⁵N enrichment of NH₄⁺ products (20). Thus, δ¹⁵N-NH₄⁺ may be higher than δ¹⁵N-biomass when the glycine cleavage system is rate-limiting and NH₄⁺ is excreted from the microbial biomass. Since δ¹⁵N-NH₄⁺ was significantly lower than δ¹⁵N-biomass in the present study (Table S3), we obtained little or no evidence for glycine cleavage.

Collins et al. (10) incubated Escherichia coli with glycine as the sole N source. The findings obtained demonstrated that δ¹⁵N-biomass was high in the early stage of the experiments (before 50 h), while δ¹⁵N-biomass and δ¹⁵N-NH₄⁺ approached the δ¹⁵N of substrates in the later stages (after 50 h) in CN5 media. These findings are in contrast to the present results showing that δ¹⁵N-biomass increased over time (Fig. 3A), with δ¹⁵N-NH₄⁺ being significantly lower than δ¹⁵N-biomass (Table S3) in CN5 media. These differences were attributed to the presence of NH₄⁺ re-assimilation or differences in glycine metabolic pathways. In the study by Collins et al. (10), NH₄⁺ re-assimilation, with large isotope fractionation (25), occurred in the later stages (the stationary phase of E. coli), such that δ¹⁵N-biomass decreased and δ¹⁵N-NH₄⁺ approached the δ¹⁵N of substrates. On the other hand, in our experiment, NH₄⁺ re-assimilation did not occur in the later stages when A. oryzae actively mineralized organic N. Furthermore, the glycine cleavage system may have caused the enrichment of δ¹⁵N-NH₄⁺ and the depletion of δ¹⁵N-biomass in the study by Collins et al. (10), but not the present study.

NUE controls ¹⁵N enrichment

The present results on A. oryzae revealed a negative linear correlation between NUE and Δ¹⁵N (Fig. 4B), indicating the control of N limitations on δ¹⁵N in the microbial biomass and mineralized NH₄⁺ under laboratory conditions. Soil microbes may widely change their NUE, while NUE by animals remains low (less than 0.5 [7, 8]), with a constant and large difference (3–4.8‰) between the δ¹⁵N of animals and substrates (6, 44, 52, 57). Therefore, the present results imply that NUE influences ¹⁵N enrichment; relatively constant ¹⁵N enrichment and NUE for animals and variable ¹⁵N enrichment and NUE for microbes.

These results imply similar control over δ¹⁵N-SOM patterns in the field; however, further research is needed to test this hypothesis under natural ecosystem settings. δ¹⁵N-SOM has been shown to markedly change with soil depth, with more substantial ¹⁵N enrichment being observed from shallow to deep soil layers (2, 24, 43, 48, 50). Since microbially-derived organic N may contribute to SOM (40, 49), the δ¹⁵N of SOM is also affected by δ¹⁵N in the microbial biomass, which is controlled by microbial NUE. Further research is warranted to understand the control of soil microbial stoichiometry on δ¹⁵N-SOM, and the present results suggest a hypothesis that has the potential to link microbial N demands to δ¹⁵N, thereby offering a non-intrusive and integrative tool through which to understand nutrient limitations in soil decomposer communities.

Acknowledgements

We thank Ryo Kobayashi, Shotoku Kotajima, Ayami Koba, Eriko Murata, Motoki Murata, Kazuaki Sumi, Shota Takino, and Bin Hiratsuka at TUAT for their fruitful discussions on our research. We also thank Yuka Kojima and Kiwamu Umezawa at Yoshida laboratory in TUAT for helping with A. oryzae treatments. This study was supported by Grants-in-Aid for Scientific Research (KAKENHI Grants 26252020, 16H02524, 17H06297, and 18H04138) from the Japan Society for the Promotion of Science.

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