Biological Nitrification Inhibition by Australian Tussock Grass and Its Impact on the Rhizosphere Ammonia-Oxidizing Microbiome

Abstract

Certain plant species have developed the ability to express biological nitrification inhibition (BNI), suppressing the activity of nitrifying microbes and thereby reducing the conversion of ammonium to nitrate. This study assessed the BNI capacity and the rhizosphere ammonia-oxidizing microbiome of two grass species: the endemic Australian Barley Mitchell grass (Astrebla pectinata) and the introduced koronivia grass (Urochloa humidicola), using soils from both agricultural land and native vegetation. In agricultural soil, koronivia grass exhibited significantly higher BNI capacity compared with Barley Mitchell grass. However, in native soil, this trend was reversed, with Barley Mitchell grass demonstrating a significantly greater BNI capacity than koronivia grass (52% vs. 38%). Koronivia grass significantly altered the composition of the ammonia-oxidizing bacteria community in its rhizosphere, leading to a decrease in the Shannon index and bacteria number. Conversely, Barley Mitchell grass reduced the Shannon index (1.2 vs. 1.7) and population size (3.28 × 10⁷ vs. 7.43 × 10⁷ gene copy number g⁻¹ dry soil) of the ammonia-oxidizing archaea community in its rhizosphere to a greater extent. These findings suggest that Australian Barley Mitchell grass may have evolved mechanisms to suppress soil archaeal nitrifiers, thereby enhancing its BNI capacity and adapting to Australia’s nutrient-poor soils.

1. Introduction

Nitrogen (N) is the most vital nutrient for plant growth, and plants primarily take it up in the forms of ammonium (NH₄⁺) and nitrate (NO₃⁻) [1]. Ammonium, which carries a positive charge, readily binds to negatively charged soil particles, while nitrate, which has a negative charge, is more mobile and susceptible to leaching, especially in soils that are rich in negatively charged organic matter [2]. The nitrification activity of soil microbes converts ammonium into nitrate, thereby leading to the accumulation of excess N in the soil, air, and water, which contributes to environmental pollution and climate change [3].

Certain plant species have developed the ability to perform biological nitrification inhibition (BNI), an ecological process in the rhizosphere where root-secreted secondary metabolites suppress the activity of nitrifying microbes, thereby inhibiting nitrification [4]. In 2009, the BNI function triggered by compounds in plant root exudates was first observed in koronivia grass (Urochloa humidicola) [5]. Following this discovery, the inhibitory effects of root exudates on nitrification were also identified in other species within the grass family, including Sorghum bicolor [6], Oryza sativa [7], Zea mays [8], Leymus racemosus [9], bermudagrass (Cynodon dactylon L.), St. Augustine grass (Stenotaphrum secundatum (Walt.) Kuntze), saltwater couch (Sporobolus virginicus), seashore paspalum (Paspalum vaginatum Swartz.), and kikuyu grass (Pennisetum clandestinum) [10].

The evolution of BNI functions in plants may have originated from N-limited ecosystems. For example, in the nutrient-poor savanna soils of West Africa, dominant grass species have developed a strong BNI capacity, reducing the nitrification rate by up to 72 times compared to tree species [11]. Australia is the driest inhabited continent globally, and its soils are generally low in nutrients. Despite these challenges, certain grasses, particularly C4 grasses, have adapted to the nutrient-poor soils and extreme environmental conditions [12]. Australia has the largest proportion of land covered by C4 grasses compared to any other continent [13]. Previous research has demonstrated that the Australian native dicotyledonous species Hibiscus splendens and Solanum echinatum exhibit BNI capacities comparable to that of sorghum [14]. However, there has been no research assessing BNI activity among Australian endemic grass species.

The physical, chemical, and biological properties of soil are crucial to determining a plant’s BNI capacity. The BNI effect originates from specific root exudate compounds produced by certain plant species inhibiting nitrifying microbes. However, root exudation is influenced by various soil conditions, including nutrient deficiencies and metal toxicity [15,16]. Furthermore, soil properties and management significantly affect the diversity and abundance of ammonia-oxidizing microbes. An analysis of 365 soil metagenomes from various global locations revealed that habitat type, soil carbon, and soil nitrogen were key factors explaining the variation in soil microbial N cycling pathways, including nitrification [17]. Another study, which examined 107 sampling sites across a 44 ha farm, found a strong correlation between soil pH and an abundance of ammonia-oxidizing archaea (AOA) [18]. Additionally, cropping practices were shown to significantly increase the relative abundance of ammonia-oxidizing bacteria (AOB) in the soil compared to forest and transitional soils [19]. However, there have been few studies that utilize different soil types to assess the BNI capacity of various plant species.

Barley Mitchell grass (Astrebla pectinata) is a perennial C4 tussock grass species native to Australia, and is primarily found in semi-arid and arid regions that are characterized by low N and phosphorus levels [20]. Barley Mitchell grass is known for its resilience and ability to thrive in drought conditions, as well as its persistence when subjected to grazing by livestock, making it a dominant species in natural tussock grassland pastures [21]. The objectives of this study were to assess the BNI potential of Australian Barley Mitchell grass across contrasting soil types and to explore how it influences the AOB and AOA communities within its rhizosphere. Exotic koronivia grass (also C4) is used as the reference species for BNI comparison.

2. Materials and Methods

Koronivia grass seeds of the “Tully” cultivar were supplied by the Australian Pastures Genebank (https://apg.pir.sa.gov.au/gringlobal, accessed on 1 March 2024), and Mitchell Barley grass seeds were obtained from Austrahort Pty Ltd., Cleveland, QLD, Australia. The germination rates for the used Koronivia grass and Mitchell Barley grass were low, at 64% and 22%, respectively, so seeds were not directly sown in the soils. Surface-sterilized seeds were germinated in Petri dishes containing autoclaved sand until the roots reached a length of 0.5 cm, after which they were transplanted into pots (10 cm in diameter and 20 cm in height, and one plant per pot) filled with two different soil types. One soil sample was collected from a farm in Roseworthy, South Australia (−34.538, 138.691), where cereal crops have long been rotated with legumes. The second soil sample came from native vegetation in the arid zone at Marree, South Australia (−29.642, 138.091). Soil analyses for the two soil types are shown in

Table 1. Chemical and physical properties of the soils used in the present study.

	pH (H2O)	EC (dS/m)	Organic C (%)	Total N (%)	Colwell P (mg/kg)	Clay (%)	Sand (%)
Agricultural soil	7.54	0.081	1.67	0.089	72.2	32.5	49.2
Native soil	8.20	0.180	0.61	0.021	12.4	7.1	89.2

. Unplanted pots were used as controls for bulk soil sampling.

Plants were cultivated in a controlled environment chamber with a 12 h light period (1200 μmol m⁻² s⁻¹) at 30 °C, followed by 12 h of darkness at 15 °C. Watering was carried out every three days and field capacity was maintained by weighing the pots. The experimental setup was a randomized complete block design with four replicates. After eight weeks, soil attached to the roots was carefully brushed into sterilized plastic bags to collect rhizosphere samples. From each pot, 2 g of rhizosphere soil was stored at −80 °C for DNA extraction, while 15 g was used for BNI analysis.

BNI capacity was assessed using shaken soil slurries to determine the potential nitrification rate (PNR), according to the methodology outlined by Zhou et al. [10]. A solution was prepared by adding 15 mL of (NH₄)₂SO₄, 1.5 mL of KH₂PO₄, and 3.5 mL of K₂HPO₄ to 1 L of water, with the pH adjusted to 7.2, then shaken at 26 °C and 100 rpm. Samples from the slurry were collected at intervals of 2, 4, 12, 24, and 48 h. The NO₃⁻ concentration was measured using a Segmented Flow Analyzer (model AA1, SEAL Analytical, Mequon, WI, USA). Linear regression analysis was performed on the relationship between the measured NO₃⁻ concentration and incubation time, with the slope of the regression line representing the PNR. The BNI capacity (%) was calculated with the following formula: (PNR of bulk soil—PNR of rhizosphere soil)/PNR of bulk soil × 100%.

Soil DNA was extracted using the Powersoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA). The bacterial amoA gene was quantified with the primers amoA-1F (5′-GGGGTTTCTACTGGTGGT-3′) and amoA-2R (5′-CCCCTCKGSAAAGCCTTCTTC-3′), following the method of Rotthauwe et al. [22]. The thermal cycling conditions included an initial denaturation at 94 °C for 10 min, followed by 40 cycles of 30 s at 94 °C, 30 s at 53 °C, and 45 s at 72 °C, as described by Hussain et al. [23]. For the amplification of the archaeal amoA gene, the primers Arch-amoAF (5′-STAATGGTCTGGCTTAGACG-3′) and Arch-amoAR (5′-GCGGCCATCCATCTGTATGT-3′) were employed [24] using the same PCR mixture and thermal cycling conditions as for the bacterial amoA gene. The PCR products were sequenced on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). Following sequencing, the reads were examined and optimized with the Trimmomatic software v0.39 [25]. Operational taxonomic units (OTUs) were identified based on 97% sequence similarity using USEARCH v7 [26].

The same primers for the AOB community were utilized in the qPCR. qPCR was carried out using a CFX96 optical real-time detection system (Bio-Rad Laboratories Inc, Hercules, CA, USA) to quantify the gene copy numbers of ammonia oxidizers. Standard curves were prepared with purified plasmids containing each target gene, following the approach described by Tao et al. [27].

OTU counts were normalized using the Hellinger method through the decostand function in the Vegan Package in R [28]. Bray–Curtis distances were computed between samples to assess differences in microbiome composition. A non-metric multidimensional scaling (NMDS) plot based on Bray–Curtis distances was created to visualize microbial communities using the Vegan Package. The effects of grass species were analyzed using a General Linear Model in Minitab (Minitab Inc., State College, PA, USA). For variables following a normal distribution, Tukey’s HSD post hoc pairwise comparisons were conducted.

3. Results

The effectiveness of BNI activity of the two grass species was strongly influenced by the type of soil (Figure 1). In agricultural soil, koronivia grass demonstrated significantly higher BNI capacity compared with Barley Mitchell grass. Conversely, in native soil, the trend was reversed, and Barley Mitchell grass displayed a significantly higher BNI capacity compared with koronivia grass (52% vs. 38%).

The non-metric multidimensional scaling (NMDS) analysis revealed that the composition of both AOB and AOA communities was predominantly influenced by soil type, as evidenced by the distinct separation of samples based on soil type (Figure 2). The AOB community in the rhizosphere of koronivia grass differed from that in bulk soil and the rhizosphere of Barley Mitchell grass (Figure 2a), while Barley Mitchell grass exhibited a distinct rhizosphere AOB community structure compared to both koronivia grass and bulk soil (Figure 2b). Following this, the dissimilarity in the composition of ammonia oxidizers between bulk soil and rhizosphere was calculated for koronivia grass and Barley Mitchell grass. For the AOB community, this dissimilarity was greater in koronivia grass than in Barley Mitchell grass (Figure 3a), whereas the opposite trend was observed for the AOA community (Figure 3b).

Regarding the diversity of AOB community, a similar pattern of Shannon indices was observed for both the agricultural and native soils. Bulk soil displayed the highest Shannon index, greater than that in the koronivia grass rhizosphere soil (p < 0.05), which had the lowest diversity among the three. The Shannon index of the AOB community diversity in Barley Mitchell grass rhizosphere was intermediate (Figure 4a). For the AOA community in both soils, the Shannon index was also the highest in bulk soil, with significantly lower diversity in the Barley Mitchell grass rhizosphere, which in turn had lower diversity than the koronivia grass rhizosphere (Figure 4b).

The abundance of ammonia-oxidizing communities was measured by the copy number of targeted 16S rRNA genes. The AOB community was found to be less abundant in the rhizosphere compared with the bulk soil, with the rhizosphere of koronivia grass showing lower abundance than that of Barley Mitchell grass, independent of soil type (Figure 5a). For the AOA community in agricultural soil, the abundance was also lower in the rhizosphere compared to the bulk soil, with no significant difference between the two grass species (Figure 5b). However, in native soil, Barley Mitchell grass significantly reduced the AOA populations in its rhizosphere compared with both the bulk soil and the rhizosphere of koronivia grass.

4. Discussion

We observed a significant interaction between the BNI capacity of different grass species and soil type. Previous studies on BNI in grasses have typically focused on comparisons between genotypes or species using a single soil type or using in vitro methods [29,30]. However, our findings suggest that soil influences interspecific variation in BNI. This may be because the BNI ability of a plant stems from its unique root exudates that suppress the growth of nitrifying microbes, and the root exudation activity is sensitive to soil micro-environment factors such as nutrient availability, soil water content, and microbial activity [31,32]. For instance, a study on wheat, barley, and ryegrass across diverse soil types showed that denitrification was regulated by root exudates to a greater extent in the soils with lower C content and higher moisture [33]. Another study found that applying a synthesized biological nitrification inhibitor significantly suppressed soil nitrification, and this effect was consistent across different soil types [34]. In our study, we used two soils from different environments—agricultural land and arid native land—with contrasting properties. These differences in soil characteristics may differentially modulate the root exudation of inhibitors among grass species, thereby affecting their BNI capacity.

Our results demonstrated that the Australian native Astrebla (Mitchell Barley) grass had a stronger BNI capacity than koronivia grass when grown in Australian native soil. The koronivia grass cultivar Tully is typically used as a reference species due to its superior BNI ability, which was higher than that of other koronivia grass genotypes, Urochloa species, and grasses from different genera such as sorghum [29,30]. The Australian inland arid environment is characterized by low soil fertility, and the native species have adapted to these conditions through co-evolution with soil microbes. For example, legume plants like Acacia and actinorhizal plants like Allocasuarina and Casuarina establish symbiotic relationships with rhizobia and Frankia to fix atmospheric N, enabling them to thrive in nitrogen-poor soils [35,36].

To our knowledge, this is the first study to demonstrate that BNI is a novel mechanism used by Australian native grasses to adapt to environments with low soil fertility. In the West African savanna, the soil nitrification rate was found to be 72 times lower under dominant grass species compared with dominant tree species, which allows ecosystem productivity to be maintained even when the soil fertility is low [11]. The four dominant perennial grass species in the West African savanna exhibited strong BNI capabilities; all of these species were C4 tussock grasses, similar to the Australian Mitchell Barley grass which was examined in our research. Tussock grasses played crucial roles in capturing nutrients and water, with their root exudates most concentrated in fertile patches near the plants, enhancing biological activity in the surrounding soil [37]. Previous studies have also shown that soil microbial biomass decreased progressively from the center of tussocks outward [38]. Thus, tussock grasses may effectively concentrate secreted nitrification inhibitors and suppress nitrifiers in the rhizosphere.

The BNI capacity of Australian Mitchell Barley grass was found to modulate the structure and suppress the growth of the ammonia-oxidizing archaea (AOA) community more effectively than the ammonia-oxidizing bacteria (AOB) community. Previous research showed that grasses with stronger BNI capacities, such as bermudagrass, St. Augustine grass, and koronivia grass, inhibited the populations of Nitrospira—a genus of nitrite-oxidizing bacteria—within the rhizosphere, but did not significantly affect AOA [10]. Similarly, another study on Urochloa pastures demonstrated a suppression of soil nitrifier populations within both the AOB and AOA communities [5]. In line with the current study, research on the BNI of tussock perennial grasses in the savanna soils of Western Africa found that the abundances of AOA were 34 times lower under grasses with BNI capacity compared with trees lacking BNI capacity, whereas the abundances of AOB were only 3 times lower [11].

Previous studies have shown that AOA had a faster growth rate than AOB in soils with low N availability or low organic matter [39], whereas AOB were key contributors to nitrification in soils with high N levels [40]. In this study, the Australian native soil, characterized by extremely low N content, exhibited greater diversity and abundance of AOA compared to the agricultural soil, which had higher nitrogen content (Figure 4). This suggests that the nutrient-poor, arid soils of Australia may have fostered an increase in archaeal diversity and abundance. As a result, Mitchell Barley grass might have evolved the mechanism to suppress soil archaeal nitrifiers, gaining BNI capacity to make its N supply more secure, and as a result better withstand harsh environments.

Overall, our study reveals the critical role of soil type in modulating the BNI capacity of different grass species, with Mitchell Barley grass demonstrating a notable ability to adapt to nutrient-poor, arid conditions through its interaction with soil microbial communities. These findings underscore the importance of understanding plant–soil–microbe interactions to harness the potential of BNI in improving soil fertility and reducing N losses in sustainable agricultural systems. By highlighting the influence of soil properties on the efficacy of BNI, our results open new avenues for research into optimizing grass species to enhance N use efficiency.

5. Conclusions

This study revealed that Barley Mitchell grass, an Australian native grass species, had a strong BNI capacity, particularly in native soils, surpassing that of the well-known BNI species, koronivia grass (52% vs. 38%). Barley Mitchell grass more effectively suppressed the AOA community compared to AOB, suggesting that this grass species may have evolved specific mechanisms to inhibit archaeal nitrifiers in response to the nutrient-poor, arid conditions typical of many Australian soils. The strong BNI capacity observed in Barley Mitchell grass may contribute to its adaptation and persistence in the nutrient-deficient and harsh soil environments of Australia, offering potential benefits for sustainable grazing system with low N fertilizer input.