Transcriptional Response of Nitrifying Communities to Wetting of Dry Soil

Sarah A Placella; Mary K Firestone

doi:10.1128/AEM.00404-13

. 2013 May;79(10):3294–3302. doi: 10.1128/AEM.00404-13

Transcriptional Response of Nitrifying Communities to Wetting of Dry Soil

Sarah A Placella ^a,^*,^✉, Mary K Firestone ^a,^b

PMCID: PMC3685241 PMID: 23524666

Abstract

The first rainfall following a severe dry period provides an abrupt water potential change that is both an acute physiological stress and a defined stimulus for the reawakening of soil microbial communities. We followed the responses of indigenous communities of ammonia-oxidizing bacteria, ammonia-oxidizing archaea, and nitrite-oxidizing bacteria to the addition of water to laboratory incubations of soils taken from two California annual grasslands following a typically dry Mediterranean summer. By quantifying transcripts for a subunit of bacterial and archaeal ammonia monooxygenases (amoA) and a bacterial nitrite oxidoreductase (nxrA) in soil from 15 min to 72 h after water addition, we identified transcriptional response patterns for each of these three groups of nitrifiers. An increase in quantity of bacterial amoA transcripts was detectable within 1 h of wet-up and continued until the size of the ammonium pool began to decrease, reflecting a possible role of transcription in upregulation of nitrification after drought-induced stasis. In one soil, the pulse of amoA transcription lasted for less than 24 h, demonstrating the transience of transcriptional pools and the tight coupling of transcription to the local soil environment. Analysis of 16S rRNA using a high-density microarray suggested that nitrite-oxidizing Nitrobacter spp. respond in tandem with ammonia-oxidizing bacteria while nitrite-oxidizing Nitrospina spp. and Nitrospira bacteria may not. Archaeal ammonia oxidizers may respond slightly later than bacterial ammonia oxidizers but may maintain elevated transcription longer. Despite months of desiccation-induced inactivation, we found rapid transcriptional response by all three groups of soil nitrifiers.

INTRODUCTION

By the end of a hot, dry summer in arid and semiarid ecosystems, microbial activity is extremely low (1), as soil water potential can be below −20 MPa (2). The first rainfall following a dry season results in a sudden increase in water potential and can be characterized by rapid mobilization of the soil microbial community and an accompanying pulse of carbon dioxide so large that it is well documented at the landscape scale (3, 4). This carbon dioxide pulse following wetting of dry soil has become known as the Birch effect due to its initial discovery by Birch and Friend (5) and subsequent investigations by Birch (6, 7). More recently, due to the importance of these carbon dioxide pulses to global carbon dioxide dynamics, researchers are again studying the carbon dioxide released from wetting of dry soils (4, 8–11).

A number of groups have investigated changes in soil microorganisms and biogeochemistry upon wet-up of dry soil. Much of this work has focused on mineralization, with rapid, high rates of carbon mineralization (12, 13) and of nitrogen mineralization (7, 14, 15) documented. Additionally, ammonium is rapidly converted to nitrate following water pulses (14–16). Very high stream nitrate concentrations following early rains have been documented, sometimes with a known nitrogen source (17, 18). Although ammonia oxidizers are generally considered to be slow growers (19, 20), the large pulses of nitrate following wet-up suggest that nitrification may be responding relatively rapidly to wet-up events. We now have the tools to directly assess specific functional responses of indigenous soil microorganisms to wet-up events by following over time the production of transcripts (mRNA) for enzyme groups. Here, we use this molecular approach to monitor the response of nitrifiers to simulated rainfall wet-up of dry soil.

Nitrification requires two groups of organisms to convert ammonium to nitrate; ammonia oxidizers convert ammonia to nitrite, and nitrite oxidizers convert nitrite to nitrate. Ammonia-oxidizing soil bacteria have been relatively well studied and belong to the Betaproteobacteria; more recently, archaeal ammonia oxidizers have been discovered and investigated (20, 21). The respective roles in ammonia oxidation for Bacteria and Archaea are still being explored; some studies suggest that Archaea are more dominant in some soils (22, 23), especially under low pH (24), while in other soils, bacterial ammonia oxidizers may control ammonia oxidation (25–27). Ammonium concentration seems to play a role, with high ammonium favoring bacterial ammonia oxidizers (28, 29). We do not know whether one of these groups recovers from inactivity faster or whether the large changes in nitrogen pools during wet-up are due to the activity of one group versus the other.

In this study, we ask how long it takes for inactive soil nitrifiers to resuscitate and respond to a wet-up event. We have previously reported that the timing of microbial resuscitation is a characteristic conserved at broad phylogenetic levels, phylum and subphylum (4). How do nitrifier response patterns fit into these more-broadly based characteristics? Do the patterns of temporal response vary among groups of nitrifiers? Can insights into microbial mechanisms underlying rain-driven activity pulses help us understand ecosystem responses to changing precipitation patterns? To address these questions, we studied indigenous soil microbial communities, inactive due to extended drought. Specifically, we investigated nitrifier transcriptional response to wet-up and the conversion of ammonium to nitrate on a fine time scale in two soils, both from California annual grasslands. Sampling on a logarithmic time scale from 15 min to 72 h after water addition, we were able to delineate rapidly occurring early responses while still following the response over a 3-day period.

We focused on transcript abundance of key genes for each of three groups of nitrifiers: bacterial ammonia oxidizers of the Betaproteobacteria, archaeal ammonia oxidizers, and nitrite oxidizers of Nitrobacter spp. We targeted amoA, which codes for subunit A of ammonia monooxygenase, the enzyme involved in the first step of ammonia oxidation, for both bacterial and archaeal ammonia oxidizers. We quantified nxrA transcripts (encoding nitrite oxidoreductase) from Nitrobacter spp., one of three conserved clades of nitrite-oxidizing bacteria. Increases in transcript abundances over this brief time scale were interpreted as evidence for functional activation. We examined changes in gene copy numbers as an indicator of changing numbers of cells. Changes in relative abundance of 16S rRNA evaluated by a high-density microarray (PhyloChip) hybridized with cDNA were used to assess responses of taxonomic groups of nitrifiers; these data contributed information on responses by many genera of nitrite oxidizers in addition to Nitrobacter as well as comparisons across taxa more generally.

MATERIALS AND METHODS

Sites and collection.

Soils were collected from a northern and a southern California annual grassland; both sites are coastal annual grasslands featuring a Mediterranean climate with hot, dry summers and cool, wet winters (Table 1). Our northern California site (NCA) is Hopland Field Station, a University of California Research and Extension Center 200 km north of San Francisco. Our southern California site (SCA) is Sedgwick Reserve, part of the University of California Natural Reserve System located in Santa Ynez, CA, 475 km south of San Francisco. Soils were collected shortly before the first rainfall of the wet season, September in NCA and October in SCA. At each grassland, we collected soil cores along 5 transects; surface vegetation (which was dead from the summer) was pushed aside, and 10-cm-diameter cores of the top 10 cm of soil were removed every meter for each 8-m-long transect. For each transect, the cores were combined, homogenized in the field, and sealed in mason jars prior to being sent to the laboratory to prevent an increase in water potential due to the higher humidity in Berkeley, CA. A subsample of the homogenized soil from each transect was frozen on dry ice in the field. Jars were kept in a dark room until the experiments were begun, 1 week after collection.

Table 1.

Soil characteristics for Hopland Field Station (NCA) and Sedgwick Reserve (SCA)

Characteristic	Hopland Field Station	Sedgwick Reserve
Location	Northern California	Southern California
Mean annual precipitation (mm)	940^a	380^b
Mean annual temp^c (°C)	15	17
Soil series	Laughlin	Elder loam
Parent material	Sandstone	Alluvial
Sand/silt/clay (%)	60/27/13	55/29/16
Total carbon content (%)	2.1	3.1
C:N ratio	13	12
pH	5.0	5.6
Sampling date (mo/day/yr)	09/18/2008	10/29/2008
Previous rainfall event (mo/day/yr)	04/23/2008	05/23/2008
Gravimetric soil moisture prewet (%)	1.0	4.7
Soil water potential prewet (MPa)	−38	−33
GPS of site	38°59.5784′N, 123°04.0469′W	34°43.1621′N, 120°03.424′W

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Obtained from reference 58.

Obtained from reference 59.

Mean annual temperature from 1970 to 2000 from RAND California using the nearest towns, Ukiah for Hopland, CA, and Lake Cachuma for Santa Ynez, CA.

Laboratory wet-up.

The wet-up experiment was performed in uncapped 236-ml glass jars. For each soil, 5 jars were allocated for destructive sampling at every time point, with one jar per biological replicate (transect). On the first day, wet-up was initiated by adding and mixing with a spatula 10 ml of double-distilled water to 40 g of field-dry soil in each jar, equivalent to a 17-mm rainfall event being distributed through the top 5 cm. Samples taken before water addition, designated “prewet,” were weighed into jars but never received water.

Soils in the jars were destructively sampled at times approximating a logarithmic progression, before and 15 min, 30 min, 1 h, 3 h, 9 h, 24 h, and 72 h after water addition. Between water addition and sampling, jars were kept at room temperature, open to the air, and in the dark and remained moist throughout the experiment. At the time of sampling, soils were divided for analysis as follows: 6 g for soil moisture analysis, 10 g extracted with 25 ml of 2 M KCl for inorganic nitrogen analysis, and 10 g in Whirlpak bags (Nasco, Fort Atkinson, WI), snap-frozen in liquid nitrogen and then stored at −80°C for subsequent molecular analyses.

Rates of carbon dioxide production.

Rates of carbon dioxide production were determined in parallel but identical jars that were not destructively sampled. Jars were sealed, water was added with a syringe through a septum in each lid, soil was mixed by shaking, and then gas-phase samples were taken by syringe starting 1 min after water addition (t₀). Gas samples were taken from the sealed jars at approximately 30 min, 1 h, and 3 h after water addition. In order to avoid oxygen depletion, jars were opened after the first 3-h time point and then sealed and gas-phase samples were taken at 3 h prior to each subsequent sampling point, which were at approximately 9 h, 24 h, and 72 h after water addition. The measured change in concentration of carbon dioxide between time points was divided by the time elapsed to yield rates of carbon dioxide production. Carbon dioxide production by the northern California soil was determined on a Hewlett Packard 6890 (Palo Alto, CA) with a pulsed discharge detector and that of SCA was analyzed on a Shimadzu 14-A (Columbia, MD) with a thermal conductivity detector. Standard curves were run on both machines.

Soil characteristics.

Soil water potential was determined from soil water content values using a water retention curve based on water potential values set by isopiestic equilibration (30). Organic matter content and the carbon-to-nitrogen ratio were determined on a CE Instruments (Wigan, United Kingdom) NC2100 soil analyzer. Soil nitrogen pools were determined colorimetrically on a Zellweger Analytics QuikChem FIA+ Series 8000 following 2 M KCl extraction. Soil particle size analysis was determined by the UC Davis Analytical Laboratory using the hydrometer method.

Nucleic acid extraction.

We followed the large-scale nucleic acid extraction method presented in Placella et al. (4).

Quantitative PCR for gene and transcript abundances.

For gene abundances, DNA was diluted 1:12 for a final mass of approximately 25 ng per 25-μl reaction. For transcript abundances, cDNA was synthesized from the extracted RNA with the Qiagen (Valencia, CA) QuantiTect reverse transcriptase kit with random hexamers, which included a second DNase treatment; 1 μl of the end product was used directly in each 25-μl reaction. Real-time PCR of the nucleic acids was performed in triplicate using Bio-Rad's EvaGreen Supermix (Hercules, CA) on a Bio-Rad iQ5 thermal cycler with a 500 nM final concentration for each primer, using previously designed primers (31–33). Every plate included purified plasmid standards and negative controls, also in triplicate. More information on standards and on thermocycling parameters is provided in the supplemental material.

Relative ribosomal abundance of nitrifying taxa.

Ribosomal abundance was assessed for a range of nitrifying bacteria using a high-density 16S rRNA microarray (Affymetrix, Santa Clara, CA). There are 202 known nitrifying taxa represented on the G2 PhyloChip (34). For G2 PhyloChip analysis, double-stranded cDNA was synthesized from extracted 16S rRNA transcripts according to the method of DeAngelis et al. (35). cDNA (200 ng in all but 3 samples, which had less material) was applied directly to the microarray. PhyloChips were prepared and filtered as described by Brodie et al. (36) with the same modifications used in DeAngelis et al. (35). Hence, the cDNA PhyloChips assessed the ribosomal abundances of members of the microbial community using 16S rRNA. While hybridization biases interfere with a comparison between organisms on the PhyloChip, we can evaluate the relative response of any one operational taxonomic unit (OTU) by comparing across multiple microarrays. To learn more about the nitrifying community, we selected the data from all of the OTUs representing known nitrifiers. Thus, our PhyloChip analysis is a comparison of nitrifier taxon 16S rRNA transcripts relative to the 16S rRNA transcripts of the entire detectable community. Nitrifier identity was determined using G2 PhyloChip information combined with the Greengenes database (34) using both Hugenholtz and NCBI taxonomies; only consensus data are reported.

Data analyses.

The two soils (NCA and SCA) were analyzed separately. We used two different statistical analyses to address two different questions. The first significant increase in transcript abundances over the time series was determined using helmert contrasts on log-transformed data; helmert contrasts compare the second time point to the first, the third to the first and second, the fourth to the first, second, and third, etc., to determine when a time point is significantly different from all earlier time points (37). We used the 15-min values as the first time point for this analysis because bacterial 16S rRNA transcript abundances followed a logarithmic decline (P = 0.02, r² = 0.25) when measured by quantitative PCR at prewet and 1, 5, and 15 min after water addition (data not shown). This decline suggests that the pool of extant transcripts rapidly degraded following wet-up; hence we selected transcripts at 15 min postwet as a baseline from which to monitor response.

Significant differences among time points for soil nitrogen pools, transcripts, and relative activities were analyzed using Tukey's honestly significant difference (HSD) test and analysis of variance (ANOVA) with transect as a random variable (P < 0.05); for soil nitrogen pools and transcripts, data were log transformed for the analyses. Carbon dioxide was analyzed similarly, only using repeated measures. We performed an ordinary least-squares regression of amoA against soil ammonium. For correlations between transcripts, we excluded prewet and field samples from the analysis to better understand the correlations between active transcripts.

Data from the two soils were analyzed for an effect of soil using ANOVA with time as a continuous variable, transect as a random variable, and an interaction between soil and time where significant to a P value of 0.1. Similarly, differences in gene abundances were determined using log-transformed data in an ANOVA with soil and time point as fixed effects, transect as a random effect, and a soil-time point interaction. Student's t test was then used to determine soil-specific significant differences between the two time points. There were two instances of outlier data exclusion, in one case based on Tukey's outlier criterion (38) and in the other based on recorded procedural error coupled with Dixon's Q test (39).

RESULTS

Rates of carbon dioxide production.

Rates of carbon dioxide production increased rapidly with the highest rates around 1 h after water addition (Fig. 1). In both the northern California soil (NCA) and the southern California soil (SCA), the rates of carbon dioxide production gradually declined from 1 h through 72 h, although the rate of carbon dioxide production at 72 h was still significantly higher than that at prewet. While both NCA and SCA had approximately the same rates (and standard errors of the means [SEM]) of carbon dioxide evolution both at prewet (0.4 ± 0.1 and 0.2 ± 0.1 μg CO₂-C g⁻¹ day⁻¹, respectively) and 72 h postwet (33 ± 1 and 37 ± 2 μg CO₂-C g⁻¹ day⁻¹, respectively), NCA was higher for all intermediate time points, resulting in a significant effect of soil on the rate of carbon dioxide production (Table 2).

Table 2.

Probabilities of soil and of soil-time interaction impacts on biogeochemical variables and functional gene transcript abundances following wet-up

Biogeochemical variable or transcript abundance	P value for effect of^a:
Biogeochemical variable or transcript abundance	Soil	Soil-time interaction
Rate of carbon dioxide efflux	0.001	NS
Soil ammonium pool	0.004	0.004
Soil nitrate pool	0.008	0.03
Bacterial amoA transcript abundance	0.0003	0.001
Archaeal amoA transcript abundance	<0.0001	NS
Nitrobacterspecies nxrA transcript abundance	0.002	0.1

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Values reported are P values determined using ANOVA. NS, not significant to a P value of 0.1. In the instances where a soil-time interaction was not significant, the interaction was excluded from the ANOVA determining the probability of a soil effect.

Gene abundances before and after wet-up.

Bacterial amoA and Nitrobacter species nxrA genes were more abundant in NCA than in SCA (Fig. 2). The only significant increase in gene abundance from prewet to 72 h after water addition was in Nitrobacter species nxrA in the NCA soil. Meanwhile, gene abundance of bacterial amoA declined from prewet to 72 h in SCA. No significant change in archaeal amoA gene abundance was detectable in either soil.

Soil nitrogen pools over the course of wet-up.

Soil ammonium and soil nitrate pools both changed rapidly after water addition, but they did not follow the same pattern (Fig. 3; data presented on both log and linear time scales). Soil ammonium accumulation and decline appeared remarkably similar in the two soils; soil ammonium was high at the start, increased rapidly, and declined significantly between 24 and 72 h. However, the significant interaction between soil and time (Table 2) is consistent with a later ammonium maximum in NCA than in SCA. Large amounts of nitrate began to accumulate when the ammonium pools reached a plateau and began to decline.

Transcript abundances.

Although significantly more transcripts for each gene were detected in NCA than in SCA, the response patterns were similar in the two soils (Fig. 3; linearly scaled transcript abundances from 15 min through 9 h are provided in Fig. S1 in the supplemental material). Bacterial amoA increased within 1 h of water addition in both soils (P ≤ 0.01 for NCA; P ≤ 0.1 for SCA). Archaeal amoA responded later (at 9 h in both soils) than either bacterial amoA or nxrA (by helmert contrast analysis of the first increase); however, archaeal amoA transcripts were also more variable than the other transcripts. The time from water addition to a significant increase in amoA transcript abundance was consistent between soils for both bacteria and archaea. The abundances of bacterial and archaeal amoA transcripts were generally similar even though the number of archaeal gene copies was greater (Fig. 2). By helmert contrast, transcripts for nxrA of Nitrobacter spp. increased within 3 h after water addition. The overall patterns for nxrA in both soils were similar to those of bacterial amoA (see also ANOVA analyses presented in Table 3). The best predictor of nxrA was bacterial amoA, explaining 83% of the variability in NCA (P ≤ 0.005). This relationship was not, however, significant in SCA to a P value of ≤0.05. There was no detectable relationship between archaeal amoA and nxrA.

Table 3.

Analysis of nitrifier transcripts by qPCR and 16S rRNA at the taxon level by PhyloChip by ANOVA with Tukey's HSD test^a

Transcript or taxon	OTU	Soil source	Significance results for each time point
Transcript or taxon	OTU	Soil source	P	15 min	30 min	1 h	3 h	9 h	24 h	72 h
Transcripts
Bacterial amoA	NA	NCA	b	b	b	b	a	a	a	a
Bacterial amoA	NA	SCA	AB	B	AB	AB	AB	A	AB	B
Archaeal amoA	NA	NCA	d	cd	d	bcd	bcd	abc	ab	a
Archaeal amoA	NA	SCA	NS
nxrA(Nitrobacterspp.)	NA	NCA	cd	e	cde	bc	bc	ab	a	ab
nxrA(Nitrobacterspp.)	NA	SCA	AB	B	AB	AB	AB	AB	A	AB
Taxa
Nitrosomonasspp.	7976	NCA	b	b	b	b	b	b	a	a
Nitrosomonasspp.	7976	SCA	NS
Nitrosospiraspp.	7931	NCA	c	c	c	c	c	bc	a	ab
	7931	SCA	AB	B	AB	AB	AB	A	AB	A
	7865	NCA	b	b	b	b	b	b	a	a
	7865	SCA	NS
	7858	NCA	bc	bc	bc	c	c	bc	ab	a
	7858	SCA	NS
	7805	NCA	c	c	c	c	c	bc	ab	a
	7805	SCA	NS
	7796	NCA	b	b	b	b	b	b	a	a
	7796	SCA	AB	B	AB	AB	AB	A	AB	AB
	7789	NCA	abc	abc	bc	c	abc	abc	ab	a
	7789	SCA	NS
	7682	NCA	b	b	b	b	b	b	a	a
	7682	SCA	ABC	B	BC	ABC	AB	A	ABC	ABC
Nitrobacterspp.	7438	NCA	NS
	7438	SCA	NS
	6927	NCA	NS
	6927	SCA	AB	AB	B	B	AB	AB	AB	A
Nitrospinaspp.	594	NCA	a	abc	ab	abcd	bcd	cd	d	abcd
Nitrospinaspp.	594	SCA	NS
Nitrospira phylum	833	NCA	a	ab	ab	ab	ab	ab	b	ab
	833	SCA	NS
	984	NCA	NS
	984	SCA	B	AB	B	AB	AB	AB	AB	A
	864	NCA	NS
	864	SCA	NS
	179	NCA	NS
	179	SCA	NS
	240	NCA	a	ab	ab	ab	ab	ab	b	ab
	697	SCA	NS
	542	SCA	NS
	860	SCA	NS

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Values represented by different letters in the same row and the same soil are significantly different (P < 0.05) across time. Letters shown in bold signify a time point that had significantly higher transcript quantity or relative 16S rRNA expression than at least one other time point in the series. The taxa that did not change significantly are represented by “NS” in the box for the first time point. In some cases, a taxon was detected only in one soil, and the data for only that soil are presented. P, prewet. OTU is the G2 PhyloChip identification for 16S rRNA data and therefore is not applicable (NA) for transcript data.

Bacterial amoA transcript abundances and the soil ammonium pool covaried. The variability in bacterial amoA transcripts was best explained by the variability in the soil ammonium pool, accounting for 88% and 82% of the variability in transcripts by time in NCA and SCA, respectively. While we detected more transcripts in NCA, the relationship between bacterial amoA transcript abundance and soil ammonium pools (Fig. 4) was the same in both soils; the regression of bacterial amoA with ammonium shows a slope of 5.6 for both soils. The difference between the soils was in the intercepts, 0.99 and 0.81 in NCA and SCA, respectively, reflecting the difference in the quantity of transcripts detected.

Fig 4 — Correlation of bacterial *amoA* transcripts with soil ammonium concentrations (n = 5). Bars indicate standard errors. Filled circles represent soil from the northern California site, while open circles represent soil from SCA.

Archaeal amoA transcript abundances did not show a significant correlation with ammonium, bacterial amoA transcripts, or nxrA transcripts in SCA. In NCA, however, archaeal amoA transcript abundance correlated with each of these (for soil ammonium, r² = 0.71, P = 0.008; for bacterial amoA, r² = 0.78, P = 0.004; for nxrA, r² = 0.52, P = 0.04).

Analyses of transcript patterns by ANOVA are presented in Table 3 to facilitate the comparison between patterns of functional gene transcripts and 16S rRNA for relevant taxa. The time points at which increases in functional gene transcripts were recognized as significant by ANOVA are similar to those at which functional gene transcripts increased by helmert contrast analysis except slightly later. Increases in relative 16S rRNA by ANOVA were detectable even later. For example, in NCA, bacterial amoA showed an increase in 1 h by helmert contrast analysis and in 3 h by ANOVA analysis, but bacterial ammonia oxidizers did not show an increase in relative 16S rRNA until 24 h. The amount of time bacterial amoA transcripts remained elevated differed between soils. Whereas bacterial amoA transcripts declined significantly from 9 h to 72 h after water addition in SCA, there was no significant decline in bacterial amoA transcripts in NCA. The difference in bacterial amoA transcript response patterns between the two soils was supported by the significant interaction between soil and time point in an ANOVA analysis (Table 2).

Relative ribosomal abundance of nitrifying taxa.

We detected rRNA from 19 known nitrifying taxa with the G2 PhyloChip. The 16S rRNA expression of each detected nitrifier taxon was assessed over time relative to the 16S rRNA expression of the entire detected microbial community. Among ammonia-oxidizing bacteria, we detected OTUs representing one Nitrosomonas spp. and at least two Nitrosospira spp., including Nitrosospira briensis and Nitrosospira multiformis. Among nitrite-oxidizing bacteria, we found many OTUs for species within the phylum Nitrospira; we also detected at least one taxon of Nitrobacter and one taxon of Nitrospina in each soil. Due to the difficulty of distinguishing some taxa at finer resolution than the genus, we aggregated our results to the genus level; the statistics for each identified operational taxonomic unit are provided in Table 3.

Increases in the relative expression of 16S rRNA were generally consistent with trends in transcript abundances. In addition, for the same soil, we did not see different response patterns for taxa within the same genus. All detected NCA ammonia-oxidizing bacteria, including both Nitrosospira and Nitrosomonas spp., had significantly higher relative ribosomal content at 24 and 72 h after water addition. Similarly, in SCA, three bacterial ammonia oxidizer taxa showed significantly higher relative ribosomal content at 9 h after water addition. However, relative ribosomal content of Nitrosospira spp. did not decline significantly from 9 to 24 h as expression of amoA transcripts did. Among nitrite-oxidizing bacteria, we analyzed nxrA transcript abundance data only for members of Nitrobacter. Relative ribosomal content increased significantly in one of two Nitrobacter taxa 72 h after water addition in SCA. Detectable increases in relative ribosomal content were generally delayed in comparison to detectable increases in both bacterial amoA and nxrA transcript abundances. In NCA, we were unable to detect a significant change in Nitrobacter species ribosomal content by PhyloChip. While SCA showed a significant increase in relative ribosomal content for one taxon of Nitrospira at 72 h, the other six detected Nitrospira taxa did not change significantly in SCA. In contrast to SCA and Nitrobacter spp., in NCA, the significant changes in Nitrospira and Nitrospina relative ribosomal content by PhyloChip were declines from prewet to 24 h after water addition.

DISCUSSION

We expected nitrifying bacteria and archaea to be inactive in California annual grassland soils prior to wet-up because water potential values were below the hydration level of nucleic acids (40) and the rates of CO₂ production by the prewet soils were extremely low. Previous measurements of gross nitrification rates in dry California grassland soils suggest that nitrification is negligible under these extremely dry conditions (41) and little nitrate is present in these soils before wet-up (Fig. 3). Thus, it is somewhat surprising to find significant numbers of transcript copies in these two soils prior to wet-up. While quantities of transcripts may simply reflect the metabolic state of cells at their time of desiccation, it is likely that the relationship between transcription and enzymatic mediation of a process is complicated by whole-cell metabolic state and environmental determinants in addition to accepted mechanisms of posttranscriptional regulation. Wei and coworkers (42) have reported high levels of transcripts in Nitrosomonas europaea starved for ammonium and carbonate.

Soil bacterial ammonia oxidizers responded very quickly, with an increase in amoA transcripts within 1 h of the defined stimulus. Favorable conditions for ammonia oxidizers were present as soon as water was added to the soil, creating an abundance of available ammonium and a defined “start” time. Because ammonium was present in soil before wet-up, ammonia oxidizers must have been either inactive due to cellular impairment or unable to access the ammonium due to diffusional limitations characteristic of dry soils (43). Hence, the time required to measure an increase in bacterial amoA transcripts is likely related to the amount of time required for these organisms to recover their functional capacities from a state of inactivity and dry-induced ammonium starvation. While nitrifiers are not known to form spores, we assume that they were in a state of “dormancy” due to the dry summer condition (44). Our finding of increased bacterial amoA transcripts within 1 h is on par with culture-based literature investigating induction of transcripts for ammonia monooxygenase following ammonium starvation (45–47). Indeed, water addition ended ammonium starvation in this study by removing diffusional limitation and spurring high rates of gross nitrogen mineralization (15) (see Fig. S2 in the supplemental material).

Abundance of bacterial amoA transcripts was dynamic, both temporally and spatially. Ammonia monooxygenase subunit A transcripts from ammonia-oxidizing bacteria increased significantly within 1 h of wet-up in both soils. However, in SCA, transcript abundance declined substantially between 9 and 72 h; this illustrates the dynamic nature of transcription and its transient role in functional activation. The fact that this phenomenon had a different temporal pattern in two California annual grassland soils demonstrates a strong coupling of transcription to the local soil environment.

The rapid increases in transcripts appear to represent upregulation and not an increase in cells; bacterial amoA transcript accumulation was linear until ammonium began to decline, and nitrate accumulation was linear after bacterial amoA transcription declined. We did not detect a significant increase in bacterial amoA gene copies from prewet to 72 h in either soil (Fig. 2), and the linear increase in nitrate concentration is also consistent with no significant growth. The lack of detectable growth is not surprising given that terrestrial ammonia-oxidizing isolates require 2 to 6 days to double in the laboratory (19). Increases in transcript quantity represent upregulation of some portion of the ammonia-oxidizing community; this increase in number of transcripts does not appear to directly coincide with the rate of ammonia oxidation. In addition to transcription, other intracellular and extracellular components, such as posttranscriptional and posttranslational regulation and substrate availability, will also impact the actual rate of ammonia oxidation. The rate of accumulation of bacterial amoA transcripts was relatively constant from water addition through 3 h after wet-up (on a linear scale) (see Fig. S1 in the supplemental material) in both soils. In both soils, bacterial amoA transcripts continued to increase until the size of the ammonium pool began to decrease. The maximum rate of accumulation of bacterial amoA transcripts was approximately three times higher in NCA than in SCA; we detected two to three times more bacterial amoA gene copies in NCA than in SCA (Fig. 2), suggesting that the maximum rates of transcription per cell may be similar in the two ammonia oxidizer communities, provided transcript degradation is proportional. Rates of nitrate accumulation did not decline with declines in amoA or nxrA transcription. Nitrate accumulated linearly from 9 through 72 h in both soils even when amoA transcript abundances declined in SCA starting at 9 h.

Archaeal amoA transcript abundances were highly variable; this variability likely obfuscates temporal trends and diminishes our ability to detect changes in transcript quantity. Thus, although the first detectable increase in archaeal amoA was at 9 h after wet-up, 8 h after bacterial amoA transcripts increased significantly, archaeal amoA may have been increasing for some period before the increase was detectable. While ammonia-oxidizing archaea may be slower responders to wet-up than bacterial ammonia oxidizers, they were still able to respond in 9 h to the wet-up event after a long state of inactivity.

Whereas taxa of ammonia-oxidizing bacteria appeared to respond in tandem as one functional group, nitrite-oxidizing taxa from different genera exhibited somewhat different response patterns, suggesting some niche differentiation. Based on 16S rRNA (Table 3), all taxa of ammonia-oxidizing bacteria appeared to respond in tandem. Transcripts from nitrite-oxidizing Nitrobacter spp. appeared to peak at around 24 h after wet-up in both soils, while ammonia-oxidizing archaea appeared to respond last and not decline throughout 72 h. Examination of relative ribosomal abundances, which would not necessarily be expected to respond in tandem with transcripts for functional genes, showed response patterns similar to those of functional gene transcripts; however, significant differences were not detected as rapidly by ribosomal abundances as by functional gene transcripts. At the genus level, increases in relative ribosomal content followed an overall pattern similar to that of increases in functional gene transcripts. The 16S rRNA data also suggest that most ammonia-oxidizing bacteria respond similarly, as do the two detected Nitrobacter taxa. However, the decline in relative ribosomal content of Nitrospina and of some Nitrospira taxa in NCA indicates that other nitrite oxidizers may play a different functional role than Nitrobacter spp. during response to wet-up.

Diffusion-based models (48) predict that nitrite oxidizers require more time to respond during increases in nitrification than do ammonia oxidizers because nitrite must first accumulate and diffuse to nitrite oxidizers. In our study, the transcript abundance patterns of nitrite-oxidizing bacteria were similar to those of ammonia-oxidizing bacteria but lagged behind by a few hours in NCA. Wetting of dry Israeli soils resulted in an approximately 50-hour lag between ammonia oxidation and nitrite oxidation based on soil nitrite concentrations (49). Physiological differences, as well as diffusional control, may impact response differences between ammonia-oxidizing bacteria and nitrite-oxidizing bacteria; laboratory culture studies show that a strain of Nitrosomonas europaea recovers faster from starvation than one of Nitrobacter winogradskyi (50). While our laboratory-based wet-ups of complex soil communities may be more relevant to field soil responses than are pure culture studies, the homogenization of soil and incubation in the laboratory do potentially alter carbon availability and reduce macroaggregate structure.

The length of nitrifier inactivity has been suggested to correlate with the length of time needed for recovery (50). We expect that these soil microorganisms had been inactive for several months, as the previous rainfall was 5 months prior to this wet-up experiment (Table 1). In comparison to culture studies, a 9-h recovery following such a prolonged state of inactivity is very rapid (50). Soil microorganisms indigenous to Mediterranean climates may, however, be adapted to annual drought. Indeed, microorganisms from semiarid California soils have been shown to withstand large water potential increases far better than isolates from less-dynamic environments (51). Thus, the rapid response of soil nitrifiers may reflect a soil microbial community that is adapted to respond to wet-up of dry soil (4).

Despite commonly being considered slow growers, all of the groups of nitrifiers in this study responded within hours to wet-up, demonstrating their abilities to respond rapidly to favorable environmental conditions. While a strong positive relationship between the number of rRNA operons per genome and the speed of response has been previously demonstrated (52), each of the groups of nitrifiers in this study is known to contain only one 16S rRNA copy per genome (53–57). Different groups of nitrifiers, functionally and phylogenetically, displayed different response patterns following wet-up; however, at the genus level, we did not detect any differences in response characteristics. Nitrifiers were likely not the fastest organisms to respond to the water addition; the rates of carbon dioxide production during wet-up indicate that some heterotrophs respond more quickly than any of the nitrifier groups, and analysis of 16S rRNA indicates that a number of groups of soil bacteria respond more rapidly than the nitrifiers reported here (4). Even with fast transcriptional response, we saw little indication of nitrifier growth. Together, the data from this laboratory study suggest that the water-pulse-driven characteristic of these semiarid soils may select for fast-responding nitrifiers as opposed to fast-growing nitrifiers. The large amount of available nitrogen, coupled with prompt nitrifier response, may explain rapid increases in stream nitrate when rain pulses follow drought; this nitrogen export may be important when considering ecosystem function and changing precipitation patterns.

Supplementary Material

Supplemental material

supp_79_10_3294__index.html^{(881B, html)}

ACKNOWLEDGMENTS

We thank Eoin Brodie, Donald Herman, Dara Goodheart, Steve Blazewicz, Catherine Osborne, Kallista Bley, Maude David, and Erin Nuccio for technical assistance. Yvette Piceno and Gary Andersen generously allowed use of their quantitative PCR (qPCR) facilities, while Whendee Silver, Marissa Lafler, and Rebecca Ryals provided access to a GC and C:N analyzer. Joshua Schimel, James Prosser, Margaret Torn, David Ackerly, and Dennis Baldocchi provided financial, technical, and moral support for the research. We especially thank three reviewers for valuable comments on the manuscript.

Part of this work was completed at Lawrence Berkeley National Laboratory under contract no. DE-AC02-05CH11231 with funding from the U.S. DOE Program for Ecosystem Research and the Terrestrial Ecosystem Sciences program. Support was also received from the Berkeley Atmospheric Sciences Center. Support to S.A.P. came from a Department of Energy Global Change Education Program Graduate Research Environmental Fellowship and a James P. Bennett Fellowship from the Department of Environmental Science, Policy and Management.

We declare no conflict of interest.

Footnotes

Published ahead of print 22 March 2013

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.00404-13.

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Supplementary Materials

Supplemental material

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[B1] 1.Chou WW, Silver WL, Jackson RD, Thompson AW, Allen-Diaz B. 2008. The sensitivity of annual grassland carbon cycling to the quantity and timing of rainfall. Glob. Change Biol. 14:1382–1394 [Google Scholar]

[B2] 2.Kieft TL, Soroker E, Firestone MK. 1987. Microbial biomass response to a rapid increase in water potential when dry soil is wetted. Soil Biol. Biochem. 19:119–126 [Google Scholar]

[B3] 3.Xu LK, Baldocchi DD. 2004. Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California. Agric. For. Meteorol. 123:79–96 [Google Scholar]

[B4] 4.Placella SA, Brodie EL, Firestone MK. 2012. Rainfall-induced carbon dioxide pulses result from sequential resuscitation of phylogenetically clustered microbial groups. Proc. Natl. Acad. Sci. U. S. A. 109:10931–10936 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B5] 5.Birch HF, Friend MT. 1956. Humus decomposition in East African soils. Nature 178:2 [Google Scholar]

[B6] 6.Birch HF. 1958. The effect of soil drying on humus decomposition and nitrogen availability. Plant Soil 10:23 [Google Scholar]

[B7] 7.Birch HF. 1964. Mineralization of plant nitrogen following alternate wet and dry conditions. Plant Soil 20:7 [Google Scholar]

[B8] 8.Harper CW, Blair JM, Fay PA, Knapp AK, Carlisle JD. 2005. Increased rainfall variability and reduced rainfall amount decreases soil CO₂ flux in a grassland ecosystem. Glob. Change Biol. 11:13 [Google Scholar]

[B9] 9.Austin AT, Yahdjian L, Stark JM, Belnap J, Porporato A, Norton U, Ravetta DA, Schaeffer SM. 2004. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141:221–235 [DOI] [PubMed] [Google Scholar]

[B10] 10.Davidson EA, Janssens IA, Luo YQ. 2006. On the variability of respiration in terrestrial ecosystems: moving beyond Q. Glob. Change Biol. 12:154–164 [Google Scholar]

[B11] 11.Huxman TE, Snyder KA, Tissue D, Leffler AJ, Ogle K, Pockman WT, Sandquist DR, Potts DL, Schwinning S. 2004. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems. Oecologia 141:254–268 [DOI] [PubMed] [Google Scholar]

[B12] 12.Fierer N, Schimel JP. 2002. Effects of drying-rewetting frequency on soil carbon and nitrogen transformations. Soil Biol. Biochem. 34:777–787 [Google Scholar]

[B13] 13.Miller AE, Schimel JP, Meixner T, Sickman JO, Melack JM. 2005. Episodic rewetting enhances carbon and nitrogen release from chaparral soils. Soil Biol. Biochem. 37:2195–2204 [Google Scholar]

[B14] 14.Hungate BA, Lund CP, Pearson HL, Chapin FS. 1997. Elevated CO₂ and nutrient addition alter soil N cycling and N trace gas fluxes with early season wet-up in a California annual grassland. Biogeochemistry 37:89–109 [Google Scholar]

[B15] 15.Saetre P, Stark JM. 2005. Microbial dynamics and carbon and nitrogen cycling following re-wetting of soils beneath two arid plant species. Oecologia 142:247–260 [DOI] [PubMed] [Google Scholar]

[B16] 16.Herman DJ, Halverson LJ, Firestone MK. 2003. Nitrogen dynamics in an annual grassland: oak canopy, climate, and microbial population effects. Ecol. Appl. 13:593–604 [Google Scholar]

[B17] 17.Baron JS, Schmidt TM, Hartman MD. 2009. Climate-induced changes in high elevation stream nitrate dynamics. Glob. Change Biol. 15:1777–1789 [Google Scholar]

[B18] 18.Holloway JM, Dahlgren RA, Hansen B, Casey WH. 1998. Contribution of bedrock nitrogen to high nitrate concentrations in stream water. Nature 395:785–788 [Google Scholar]

[B19] 19.Jiang QQ, Bakken LR. 1999. Comparison of Nitrosospira strains isolated from terrestrial environments. FEMS Microbiol. Ecol. 30:171–186 [DOI] [PubMed] [Google Scholar]

[B20] 20.Konneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. 2005. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437:543–546 [DOI] [PubMed] [Google Scholar]

[B21] 21.Leininger S, Urich T, Schloter M, Schwark L, Qi J, Nicol GW, Prosser JI, Schuster SC, Schleper C. 2006. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442:806–809 [DOI] [PubMed] [Google Scholar]

[B22] 22.Offre P, Prosser JI, Nicol GW. 2009. Growth of ammonia-oxidizing archaea in soil microcosms is inhibited by acetylene. FEMS Microbiol. Ecol. 70:99–108 [DOI] [PubMed] [Google Scholar]

[B23] 23.Prosser JI, Nicol GW. 2008. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ. Microbiol. 10:2931–2941 [DOI] [PubMed] [Google Scholar]

[B24] 24.Nicol GW, Leininger S, Schleper C, Prosser JI. 2008. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ. Microbiol. 10:2966–2978 [DOI] [PubMed] [Google Scholar]

[B25] 25.Di HJ, Cameron KC, Shen JP, Winefield CS, O'Callaghan M, Bowatte S, He JZ. 2009. Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nat. Geosci. 2:621–624 [Google Scholar]

[B26] 26.Jia ZJ, Conrad R. 2009. Bacteria rather than Archaea dominate microbial ammonia oxidation in an agricultural soil. Environ. Microbiol. 11:1658–1671 [DOI] [PubMed] [Google Scholar]

[B27] 27.Petersen DG, Blazewicz SJ, Firestone M, Herman DJ, Turetsky M, Waldrop M. 2012. Abundance of microbial genes associated with nitrogen cycling as indices of biogeochemical process rates across a vegetation gradient in Alaska. Environ. Microbiol. 14:993–1008 [DOI] [PubMed] [Google Scholar]

[B28] 28.Verhamme DT, Prosser JI, Nicol GW. 2011. Ammonia concentration determines differential growth of ammonia-oxidising archaea and bacteria in soil microcosms. ISME J. 5:1067–1071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B29] 29.Taylor AE, Zeglin LH, Wanzek TA, Myrold DD, Bottomley PJ. 2012. Dynamics of ammonia-oxidizing archaea and bacteria populations and contributions to soil nitrification potentials. ISME J. 6:2024–2032 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B30] 30.Winston PW, Bates DH. 1960. Saturated solutions for the control of humidity in biological-research. Ecology 41:232–237 [Google Scholar]

[B31] 31.Francis CA, Roberts KJ, Beman JM, Santoro AE, Oakley BB. 2005. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc. Natl. Acad. Sci. U. S. A. 102:14683–14688 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] 32.Poly F, Wertz S, Brothier E, Degrange V. 2008. First exploration of Nitrobacter diversity in soils by a PCR cloning-sequencing approach targeting functional gene nxrA. FEMS Microbiol. Ecol. 63:132–140 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Transcriptional Response of Nitrifying Communities to Wetting of Dry Soil

Sarah A Placella

Mary K Firestone

Abstract

INTRODUCTION