The preceding root system drives the composition and function of the rhizosphere microbiome
Genome Biology volume 21, Article number: 89 (2020)
The soil environment is responsible for sustaining most terrestrial plant life, yet we know surprisingly little about the important functions carried out by diverse microbial communities in soil. Soil microbes that inhabit the channels of decaying root systems, the detritusphere, are likely to be essential for plant growth and health, as these channels are the preferred locations of new root growth. Understanding the microbial metagenome of the detritusphere, and how it responds to agricultural management such as crop rotations and soil tillage, is vital for improving global food production.
This study establishes an in-depth soil microbial gene catalogue based on the living-decaying rhizosphere niches in a cropping soil. The detritusphere microbiome regulates the composition and function of the rhizosphere microbiome to a greater extent than plant type: rhizosphere microbiomes of wheat and chickpea were homogenous (65–87% similarity) in the presence of decaying root (DR) systems but were heterogeneous (3–24% similarity) where DR was disrupted by tillage. When the microbiomes of the rhizosphere and the detritusphere interact in the presence of DR, there is significant degradation of plant root exudates by the rhizosphere microbiome, and genes associated with membrane transporters, carbohydrate and amino acid metabolism are enriched.
The study describes the diversity and functional capacity of a high-quality soil microbial metagenome. The results demonstrate the contribution of the detritusphere microbiome in determining the metagenome of developing root systems. Modifications in root microbial function through soil management can ultimately govern plant health, productivity and food security.
The establishment of a gene catalogue aids in the understanding and identification of options for potentially manipulating microbial communities in complex environments. Global gene catalogues of microbiomes have been established from the human gut , from mouse  and pig  gut, from the human skin  and from ocean water . Although efforts have been made to capture a microbial gene catalogue from global topsoils , prairie and cornfield soils  and citrus roots , observations indicate that insufficient coverage and under-sampling can affect the estimation of the enormous functional (gene) potential of soil microbiomes. With such complexity, ultradeep sequencing (e.g. 0.6 to 1 Tb required for complete soil genome coverage ) and bioinformatic assemblages are required to draw a saturated gene catalogue of a defined soil metagenome .
Spatial heterogeneity in soil microbiomes is driven by microsites, the biologically relevant spheres of influence such as rhizosphere, detritusphere and porosphere, each with distinct physico-chemical properties operating at different spatial scales . Microbiomes in the rhizosphere, a zone close (typically < 2 mm) to the plant root, are strongly influenced by plant roots, and the rhizosphere generally has much higher chemical and biological activity than bulk soil, due to exudates from roots [11,12,13,14]. Plants selectively influence the composition and activity of the rhizosphere microbiome through chemical communication and provision of carbon and nutrients. Rhizosphere microbiota play important roles in improving the growth of host plants, through the regulation of plant essential functions including nutrient cycling and uptake, root and shoot growth, disease suppression and induced systemic resistance and abiotic stress tolerance [15,16,17,18]. The composition and function of the rhizosphere microbiome have been evaluated using reconstructed soils in controlled conditions [19,20,21] and in on-farm environments [22, 23]. Crop management practices such as tillage, residue retention and crop rotation are crucial components in the functioning of an agroecosystem, but are often neglected when studying the dynamics of rhizosphere microbiota, in particular during the establishment of rhizosphere microbial communities in early seedling growth phases in no-till or disrupted soil profiles. In view of the significant influence that the detritusphere (soil surrounding the decaying root from the previous crop) can have on soil microbial communities, it is hypothesised that early seedling rhizosphere communities would be influenced by the detritusphere microbiomes.
Microbiomes in agricultural systems are altered by both crop and soil management. Growing annual crops invariably involves some level of soil disturbance from tillage and sowing operations, along with variation in the sequence of crops grown (crop rotations) . The practice of no tillage (NT) is used globally in agriculture and limits the destruction of soil structure and retains much of the prior rotation crop’s root residue and microsite structure (aggregates, pores, detritusphere etc.), compared with historical conventional tillage (CT) practices that disrupt the soil structure [25, 26]. The use of NT has provided the opportunity for the development of more intensive crop rotation practices, especially in soils with structural problems. Importantly, the NT system is characterised by an abundance of historic root channels that contain the residues of antecedent root systems referred to here as the detritusphere. The decaying root material typically contains more active microbes than the bulk soil . In the NT systems, root channels are typically maintained. As a consequence, a large portion of the roots of a newly established crop occupy previously established root channels due in part to lower mechanical resistance [28, 29], commonly categorised as part of a “sense-by-growth” mechanism . Therefore, in an NT-intensive cropping system, the formation of the rhizosphere microbiome associated with the new crop’s active roots may be in part determined by the microorganisms existing in the detritusphere, but to date, little is known about the detailed microbiome structure associated with these microsites.
The early seedling phase is crucial for plants in establishing the rhizosphere and endosphere microbiomes, as it is the start of the plant root selecting specific members of the soil microbiome . Microbiome composition of seedlings has been shown to influence the early growth of wheat seedlings under no-till cropping systems . In legumes, specific root exudates signal the bacterial population to encourage successful symbiotic events with rhizobia . These examples demonstrate the presence of a dynamic interaction between soil habitat conditions, plant root and soil microbiome with potential consequences to plant growth, health and overall crop performance.
Here, our study uses the seedlings of wheat (Triticum aestivum L.) and chickpea (Cicer arietinum L.), representing cereal and legume crops, respectively, to evaluate the interaction of plants with differential root exudation and cropping management (+ and − decaying root) on shaping the rhizosphere microbiome composition. Chickpea and wheat have contrasting root exudation, as chickpea is characterised by a great quantity of organic and amino acid root exudates compared with wheat . In the present study, we compare the function and taxonomic structure of the rhizosphere microbiome between these different crops in the presence (+) and absence (−) of decaying root. In particular, we highlight the role of the detritusphere and its decaying root in influencing the development of the rhizosphere microbiomes of a cereal and a legume seedling.
The physical, chemical and biological properties of niches
Here, we collected the intact soil cores with decaying root (+DR), and the second sample of repacked soils with removed decaying root (−DR) from the same field. In each of these +DR and −DR treatments, wheat or chickpea plants were grown in a controlled environment (Additional file 1: Figure S1). In addition, unplanted cores with + and −DR were used as the control for detritusphere and bulk soil sampling, respectively (Additional file 1: Table S1). Therefore, most of the soil physical and chemical properties were similar between + and −DR (Additional file 1: Table S2). The existence of the DR and its physiochemical properties was the key factor evaluated for its effect in influencing the rhizosphere microbiome structure and function of the living roots compared with the non-DR control.
Differentiation of root exudate composition (based on the 26 detected root exudates) between the studied niches had a significant three-way interaction of plant type, decaying root (+ and −) and sterilisation (+ and −), based on P < 0.01 on permutational multivariate ANOVA (PERMANOVA) test using Bray-Curtis distance. Under sterilised conditions, the separation was reliant on plant type whereas under non-sterilised conditions, the plant type effect was stronger for −DR but not for +DR (Fig. 1a). This suggests that the existence of decaying root masked the plant type effect on root exudate metabolism.
Rhizosphere root exudate degradation was calculated by a reduction in root exudate concentrations from sterilised to non-sterilised conditions (Fig. 1b). The degradation of root exudates by the rhizosphere microbiome in +DR was greater than for −DR (P < 0.01). The chickpea rhizosphere metabolised more root exudates than wheat under both + and −DR. Additionally, in the living soil, chickpea without DR formed an acid rhizosphere compared with bulk soil and wheat rhizosphere (P < 0.05, Table 1), but in the presence of DR, there were no significant differences in rhizosphere pH between chickpea and other rhizo-detritusphere niches.
Regarding the influence of rhizosphere from the detritusphere, there was a close contact between the fresh root growth and residue root in the +DR treatment. For both wheat and chickpea, approximately 60–80% of the new roots followed the channels of the previous root residue (Fig. 1c). In +DR pots where fresh roots closely interacted with residue root channels, there was a greater root distortion rate compared to that in the −DR pots (P < 0.01, Additional file 1: Figure S2, visualised in Figure S1).
The bacteria number and activity (cumulative CO2–C evolved) were higher (double; P < 0.01) in the rhizosphere under +DR condition than in −DR (Table 1 and Fig. 1d). Under +DR, only wheat rhizosphere and the detritusphere treatments harboured more fungi than −DR, while compared with bacteria, fungi was a minor proportion of the microbial community, contributing only 1/100–1/200 the copy number of bacteria (Table 1).
Hence, the existence of decaying root changed the rhizosphere metabolism, root growth and rhizobiome activities of the living roots, and played a significant role in shaping the plant rhizosphere microbiomes.
Microbial taxonomic and functional composition between niches
Based on the amplicon sequencing of 16S rRNA genes and metagenome sequencing data, differential abundance analysis was conducted using unplanted bulk soil without decaying root material as the control. We compared the differences in the abundance of individual assembled operational taxonomic units (OTUs), non-redundant genes and KOs between rhizosphere niches (including + and −DR) and bulk soil. The enriched and depleted OTUs, non-redundant genes and KOs for each rhizosphere of different crop types under + and −DR were identified based on log2-fold-change > 1 and the FDR adjusted P value < 0.01 using 3 replicates (Fig. 2).
The taxonomy and functional gene composition of the rhizosphere microbiome between wheat and chickpea were compared under + and −DR (Fig. 3). The results demonstrate a high degree of taxonomic and functional similarity of rhizosphere microbiota between these two crops when grown under +DR, while alternatively under –DR, significant variation between wheat and chickpea for both the rhizosphere microbiome composition and function was observed. This conclusion is supported by the results from three different types of statistical analysis (Fig. 3). Firstly, when decaying root existed, wheat- and chickpea-enriched OTUs and genes shared a large proportion of overlap, which represented around 48–77% and 65–87% of the wheat and chickpea individually enriched, respectively (Fig. 3a). In the absence of decaying root, the number of overlapped OTUs and genes enriched by both crops became fewer, with about 5–14% and 3–24% for wheat and chickpea, respectively (Fig. 3a). The results of depleted OTUs and genes were similarly influenced by plant type and decaying root interactions (Additional file 1: Figure S3a).
Under the +DR condition, the contribution to the abundance of each KO and OTU was almost equal between wheat and chickpea, as indicated by the distribution of most of the KOs (Fig. 3b) and OTUs (Additional file 1: Figure S3b) which were distant from both wheat and chickpea corners in the ternary plot. Given that it was difficult to present the large number of genes clearly in the ternary plot, genes were merged into KOs based on their functional similarity in the KEGG database, and KOs were considered as the basic unit to generate the functional ternary plot. In +DR, there were only 9 and 16 out of 5289 KOs, with over 60% counts contributed by the wheat or chickpea rhizospheres, respectively (Fig. 3b). In contrast, in the −DR system, plant type showed a considerable influence on the abundance of KOs: the numbers of KOs with over 60% counts contributed by plant rhizosphere were 621 and 256 out of 5289 for wheat and chickpea rhizospheres, respectively (Fig. 3b).
Principal coordinate analyses (PCoA) on OTUs and genes showed that plant treatments under +DR were closely grouped, but were clearly separated under −DR, especially for gene composition (Fig. 3c). When decaying root and plant type were constrained as two factors, PERMANOVA based on Bray-Curtis distance revealed that decaying root accounted for the majority of variation (about 41% for OTUs and 48% for genes composition, P < 0.01, permutations = 999, Additional file 1: Table S3). The effects of individual factors and their interaction were significant (P < 0.01) on gene composition, while decaying root × plant type interaction did not show a significant effect on the microbial taxonomic structure. Bray-Curtis distance between wheat and chickpea rhizobiome was shorter under +DR than −DR (P < 0.01, Table 1).
Aside from comparing the rhizosphere microbiome between two crops under + or −DR, we studied the enriched and depleted microbial genes (relative to bulk soil) in the detritusphere (soil surrounding the decaying root with no live crops planted). The rhizospheres of wheat and chickpea growing in the presence of decaying roots shared a large proportion of differential genes (both enriched and depleted) with those observed in the detritusphere sample. Of the enriched and depleted genes in the detritusphere sample, 44–53% were observed in wheat or chickpea rhizospheres in +DR, indicating a significant overlap (Additional file 1: Figure S4). Furthermore, under –DR, the wheat rhizosphere microbiome shared a larger number of enriched genes and OTUs with detritusphere than in the chickpea rhizosphere vs detritusphere (Additional file 1: Figure S4).
In addition to beta-diversity variation between sampled niches, the alpha-diversity of microbiomes such as the Shannon index of OTUs and genes was higher in the rhizosphere of wheat and chickpea under +DR than in the −DR treatments (P < 0.01, Table 1).
Therefore, the presence of decaying roots with their physical, chemical and biological properties appeared to promote more similar rhizosphere microbiomes in the living roots of wheat and chickpea.
The relative abundance of sequenced reads grouped into microbial functional and taxonomic families was compared between different rhizospheres and bulk soil (Fig. 4). Statistical differences among niches were identified based on adjusted P < 0.05 by Benjamini-Hochberg FDR method in ANOVA with 3 replicates. Functional and taxonomic groups significantly responding to different niches and having the highest abundance are presented in Fig. 4.
Niches under +DR had a similar abundance of gene functions with a higher proportion of sequences belonging to ‘carbohydrate metabolism’, ‘xenobiotics biodegradation’ and ‘membrane transport’ than the plant rhizosphere niches under −DR (adjusted P < 0.05, Fig. 4a). Genes assigned to genetic information processing such as ‘replication and repair’ and ‘nucleotide metabolism’ in the rhizosphere microbiome were upregulated by −DR rhizobiome than by +DR (adjusted P < 0.05).
Under −DR, there were a similar amount of genes for ‘carbohydrate metabolism’ between the chickpea and the wheat rhizosphere microbiome, while the chickpea rhizosphere was more abundant in functions associated with carbohydrate degradation (glycoside hydrolases (GHs)) and had less abundance related to carbohydrate synthesis (glycosyl transferases (GTs)) compared to −DR wheat (adjusted P < 0.05). In addition, −DR chickpea rhizosphere had the lowest proportion of functions related to ‘membrane transport’ and ‘energy metabolism’, which was associated with its lowest abundance of ‘primary active transporters’.
The number of enriched or depleted genes (relative to bulk soil) was also annotated into different functional and taxonomic groups, and the enrichment of the number of functional genes among niches matched the results from the relative abundance analysis (Additional file 1: Fig. S5a-c). There were more enriched genes related to the functions of ‘membrane transport’ and ‘carbohydrate degradation’, and bacteria order Rhizobiales in plant rhizospheres under +DR than −DR.
Taxonomic profile of niches
OTU analysis based on amplicon sequencing of 16S rRNA genes showed that the two most abundant bacteria genera Kaistobacter and Rubrobacter were more enriched without DR than with DR (adjusted P < 0.05, Fig. 4b). Chickpea rhizosphere without DR harboured a greater abundance of Bacillus than other rhizosphere niches under −DR or +DR. By contrast, the abundance of Nocardioides, Cellulomonas, Skermanella, Methylobacterium, Modestobacter and Aeromicrobium was higher with DR (0.76%) than without DR (0.34%, adjusted P < 0.05).
When the enriched genes of rhizosphere niches were annotated into taxonomic groups compared with bulk soil (Additional file 1: Figure S5d), the proportion of rhizosphere-enriched genes belonging to Rhizobiales (the most abundant order) was higher with +DR, compared with −DR, while the order Rhizobiales did not include the most abundant genera such as Kaistobacter, Rubrobacter or Bacillus.
In addition to analysing the taxonomic composition of the entire gene catalogue outlined above, the taxonomic composition of genes involved in specific functional groups was also evaluated. Non-redundant genes within each of the 8 functional groups were used for taxonomic annotation. Compared with the whole gene catalogue, Rhizobiales contributed more to the functions associated with ‘membrane transport’ (Additional file 1: Figure S6a), and Sphingomonadales contributed more to ‘transcription’ and ‘energy metabolism’. The group ‘xenobiotic biodegradation and metabolism’ involved a higher proportion of unclassified Proteobacteria. Also, the influence of experimental treatments such as decaying root and plant types on microorganism composition at order level was consistent across the tested functional groups and the whole gene catalogue, based on the Mantel analysis (Additional file 1: Figure S6b).
Two approaches to analyse microbiome taxonomic composition, amplicon sequencing on 16S rRNA genes and metagenomics sequencing, were compared. At the genus level, sequencing 16S rRNA genes lead to more sequences mapped to the identified genera than sequencing the metagenome, 39% vs 7.5% (Additional file 1: Figure S7a). There was a greater diversity of identified genera by metagenomics approach (Additional file 1: Fig. S7b). For the highly abundant bacteria genera Kaistobacter and Rubrobacter, the relative abundance was significantly correlated between the two methods (n = 18, Additional file 1: Fig. S7c-d), while for the genera Bacillus and Geodermatophilus which had an extremely low relative abundance (1.6 × 10−4–5.9 × 10−6) in metagenomics sequencing, the correlations between two approaches were not significant (Additional file 1: Fig. S7e-f).
Chickpea symbiotic rhizobia
Nodules were not observed in the chickpea root in the short-term soil microbiome experiment (experiment 2), due to the short growing period of 12 days. We conducted another experiment with the same chickpea cultivar in the same soil for 4 weeks, and then sequenced the 16S rRNA genes of the harvested nodule tissue. One OTU, annotated as Mesorhizobium, accounted for over 90% abundance in the nodule sequences was identified as the chickpea symbiotic rhizobial OTU (csrOTU). By blasting all the OTUs from the rhizosphere niches (experiment 2) against the csrOTU sequence, one OTU was confirmed as csrOTU (398/404 matching with 0 gap, Additional file 1: Fig. S8). Analysis of csrOTU in different niches showed that it was over 10 times more abundant in the niches under +DR than in –DR (P < 0.01). Within +DR (Fig. 5), csrOTU was less enriched in the chickpea rhizosphere than in the wheat rhizosphere and detritusphere.
A 2-year field experiment was conducted to evaluate if rhizosphere microbial assemblages in different crop types and decaying roots observed in the pot study influenced crop field performance. In the same soil, wheat and chickpea were planted with pre-cropped wheat root residues retained and removed. The results showed that the increase in grain yield in the transition from −DR to +DR was more significant for wheat than chickpea (P < 0.01, Additional file 1: Table S4).
A soil microbial gene catalogue from the living-decaying rhizosphere niches
By combing all the metagenomics sequences, a non-redundant gene catalogue with 19.8 M genes, 513 bp average length and 10.16 Gbp total length was established (Additional file 1: Table S5) which had an average of 39.6% mapping rate reads (Additional file 1: Table S6). The non-redundant gene catalogue was annotated by aligning them to taxonomic and functional databases (Additional file 1: Table S7) including NCBI microbial NR database (76% genes mapped), KEGG (76% genes mapped), eggNOG (70% genes mapped for NOG+COG), CAZy (10% genes mapped) and TCDB (1.6% genes mapped).
As saturation was reached in rarefaction curves, the sequencing depth and established gene catalogue in the present study were considered adequate to cover the database they interrogated (NCBI Reference Sequence Database, Fig. 6a).
The proportion of genes with unknown functions was about 5% in the KEGG database, but larger in the eggNOG database at about 50% (Fig. 6b, c). The genes related to functions of membrane transport and the metabolisms of carbohydrate, amino acid, DNA and energy accounted for a majority of known functions, ~ 46% in the KEGG database (Fig. 6b) and ~ 32% in the eggNOG database (Fig. 6c).
Taxonomic classification of the gene catalogue showed that around 95% of organisms belonged to the kingdom Bacteria (Fig. 6d). Within bacteria, a large proportion (47%) of genes was detected in more than one phylum (defined as “unidentified” here). The genes belonging to a group of non-bacteria organisms (fungi, archaea and viruses) were quite minor, at less than 0.4%.
We present a soil microbial gene catalogue providing high coverage of the non-redundant genes, based on the niches of bulk soil, rhizosphere (living root) and detritusphere (decaying root) in an agricultural land that is representative of soils from many parts of the world (cambisols in the International World Reference Base for Soil Resources). According to the gene catalogue that we established, there was a significant interaction between crop type and cropping management on the composition and function of the rhizosphere microbiome. The detritusphere, formed by the decaying roots, modified the rhizosphere microbiome of a cereal (wheat) and a legume (chickpea) plant during the early stage of crop growth, probably due to the tight contact between the decaying roots and fine roots, and the rich and active microbiome of decaying root, capable of degrading root exudates from both cereal and legume plants (Fig. 1). A previous study demonstrated that soil microorganisms were able to utilise more carbon sourced from the decaying roots than from root exudates of a living plant, and the microbial use of residue carbon was restricted to 1 mm from the decaying root . Beneficial microbial groups, e.g. Rhizobiales (including N2 fixers) and Sphingomonadales (root disease suppressors; 52, 53), were the the dominant groups that responded to plant type and management, especially under the influence of the detritusphere.
The composition and functional capacity of the rhizosphere microbiome between wheat and chickpea were similar when growing in the presence of decaying roots, but heterogenous when grown in the absence of decaying roots. The possible reason for the influence of the decaying roots was that 60 to 80% of plant roots grew through existing root channels left by the previous crop, as these likely had lower resistance than the surrounding bulk soil (Fig. 1c). Thus, the detritusphere microbiome existing in the root channels of the +DR treatments determined the composition and function of rhizosphere microbiome to a greater extent than plant type did.
Furthermore, the larger number of microbes (Table 1) and their greater activity in the detritusphere (Fig. 1d) most likely degrade root exudates in the rhizosphere (Fig. 1b), and thus dilutes the influence of plant rhizosphere processes, such as root exudation, in shaping the microbiome. Similarly, it has been reported that the detritusphere stimulated enzyme activities more strongly than the rhizosphere did in roots of barley . Another study using 13C signatures showed that carbon incorporation into microbial biomass was stronger from the decaying root than from the living root of plants up to 3 mm from the residues/roots .
When roots grew in the soil without decaying roots, plant root exudates drove the selection of the rhizosphere microbiome. For example, under −DR, the rhizosphere of chickpea showed a more restricted selection of microbiomes relative to wheat, as indicated by fewer microbes; a lower diversity index (Table 1); and fewer enriched/depleted genes in the chickpea rhizosphere (Fig. 2). This was correlated with the greater quantity (Fig. 2) of acidic root exudates (Table 1) detected in the chickpea rhizosphere than those in wheat.
Here, we demonstrate that the existence of decaying roots (with no change in soil chemical properties compared with −DR) can reduce the selective effects of plants on the rhizosphere microbiome structure and function, through the strong influence of the detritusphere. The capacity for young seedlings (as used in our study) to influence their rhizosphere microbiome may be restricted at such an early age when carbon allocation to growth may impede the production of root exudates. As such, our sampling of material at the early seedling stage may place important caveats on any conclusions.
No tillage, with no disturbance to the decaying root of the previous crop, is an important component of conservation agriculture (CA) that is estimated to be applied globally to 125 million hectares and has increased 3-fold during the past decade [26, 36]. Longer-term implementation of no-tillage and stubble retention practices (two components of CA) that maintain decomposing particulate organic materials from above- and below-ground plant residues (i.e. detritusphere) has been shown to affect total soil bacterial and fungal community structure (genomic community structure [17, 37, 38] and active communities (metatranscriptomics ) leading to biological disease suppression against soilborne pathogens . Our findings here suggest that the detritusphere, shaped from the decaying root, is a key niche influencing the rhizosphere microbiome in no-tillage cropping systems. Furthermore, we did not focus on the influence of tillage on soil chemical traits such as organic C and total N, which can be changed by the long-term influence of CA .
The composition and function of the detritusphere microbiome might be determined by the previous crop’s rhizosphere microbiome and the quality of the root residues. Our results showed that the function and structure of the microbial community in the wheat detritusphere (soil surrounding the decaying root under +DR) was quite similar to that of wheat rhizosphere under −DR (Additional file 1: Fig. S3). The importance of dead cell wall residues in harbouring microbiota in soils has been highlighted, which accounted for around 40% of the live root-associated inhabiting bacteria in Arabidopsis . Interestingly, wheat detritusphere microbiomes in our study were similar to wheat rhizosphere microbiomes but differed from chickpea rhizosphere microbiomes in microbial function and composition (Additional file 1: Fig. S3). This observation indicates that the microbial community formed by the rhizosphere of wheat can have a lasting impact, even when root cells become inactive. Furthermore, the high similarity of the microbiome between the wheat rhizosphere and wheat detritusphere possibly led to an improved adaptation of wheat roots to its detritusphere microsite. Our field experiment supported the finding that grain yield of wheat was more responsive to the cropping system (+/− wheat decaying root) than chickpea was (Additional file 1: Table S4).
Taxonomic and functional annotation
The abundance of major microbial taxonomic groups is associated with their roles in agricultural production systems. For example, the present results showed that more chickpea symbiotic rhizobia enriched in rhizosphere and detritusphere under +DR than −DR (Fig. 5). More symbiotic rhizobia inhabiting the detritusphere might have a greater probability to nodulate with the host legume roots, as the legume roots grow through the detritusphere in the +DR treatment. Our findings support the previous result that retaining decaying roots through no-tillage maintained legume rhizobial populations and diversity, compared with the removal of decaying roots .
Moreover, a dominant bacterial genus, Kaistobacter, which is associated with disease suppressiveness  was less abundant in +DR than in −DR, which indicated a higher chance for plants to be infected by pathogen disease under +DR. Kaistobacter is a member of the bacterial community that responds to the availability of easily degradable C compounds, especially plant root exudates [43, 44]. Chickpea rhizosphere under −DR assembled more Kaistobacter than wheat (Fig. 4b), possibly due to its acidic pH and the greater presence of root exudates (Fig. 2 and Table 1).
Another dominant bacterial genus, Rubrobacter, was also more abundant under −DR than +DR (Fig. 4). It has been reported that Rubrobacter was widely distributed in Australian arid soils , and firstly isolated from an Australian acid soil . Rubrobacter was physiologically active in the plant rhizosphere soil with high mineral nutrients, but low organic carbon .
Under the presence of a decaying root, the rhizosphere microbiome was enriched with more genes associated with the metabolism of carbohydrate (especially C degradation) and membrane transporters. This possibly relates to the influence of the detritusphere providing C and N pools and more active microbes to enhance C and N utilisation and uptake through microbial cell transporters, as indicated by the significant degradation of root exudation in the rhizosphere under +DR (Fig. 1b). Additionally, the enriched chickpea symbiotic rhizobia in +DR rhizospheres possibly contribute to the higher abundance of transporter functions in that region, as the rhizobial genome is rich in transporter genes, representing over 10% of the total annotated proteins .
When plants grow in the absence of decaying roots, rhizosphere microbial functions of chickpea were enriched more in carbohydrate degradation metabolism than in wheat. Greater carbohydrate degradation may be associated with stronger exudation activity from the chickpea root and its acidic root exudation compounds (Fig. 1a, b; Table 1 in the present study and ). Under −DR, the microbiome functions of the wheat rhizosphere were characterised by enhanced carbohydrate synthesis and its related energy metabolism compared with chickpea, possibly due to the weaker C exudation of wheat root than chickpea, as measured in the present study.
The functional relatedness between niches was independent of their taxonomic assembly, as the distance among samples in taxonomic composition was consistent across functional groups with homogeneous and heterogeneous niche effects (Additional file 1: Fig. S6b). Our results are similar to previous metagenomic analyses on the niches of the phyllosphere  and land use  and support the neutral theory  that the taxonomic composition of the microbial community is assembled by stochastic processes in the rhizo-detritusphere microsite environment.
A soil microbial gene catalogue from the living-decaying rhizosphere niches
The microbial gene catalogue presented here represents multiple samples collected in an agricultural field that is a typical cropping system in a Mediterranean semi-arid climate, representative of environments that are important regions for global crop production and crop diversity hotspots [52, 53].
Our soil metagenome has higher assembly quality with greater coverage compared with other assembled soil metagenomes. For example, when compared with the standard soil metagenome of Iowa corn and native prairie soils in the USA , which also applied de Bruijn graph-based approach for sequence assembly, our soil metagenome has more assembled contigs (12.9 million vs 1.9 million and 3.1 million with a minimum length of 300 bp), a longer total assembly length (11 Gbp vs 0.912 Gbp and 1.5 Gbp) and a higher assembly rate (50% vs 20%) with sequenced clean data of 205 Gbp vs 140 Gbp and 252 Gbp.
To assess the sequence diversity present in a soil sample, the estimation of the coverage provided by the specific metagenome dataset and the extent of the real diversity of the community in an environment were assessed . A previous estimation of the soil metagenome was 1012 genes per gram of soil for bacteria, based either on the number of bacteria and effective genome size [54, 55] or 50 Tbp sequence required to cover the whole metagenome of 1 g of soil [7, 56]. Our assembled gene catalogue has less genes than the previous estimations but with improved assembly quality. The possible reasons include, firstly, that our soil samples are from a cropping agroecosystem with low diversity of above-ground vegetation, with mostly 1–2 crop species present in the field each year, especially over the last 50 years. Additionally, soil organic carbon in our study is low (1.4%) compared with more diverse systems such as prairie grasslands and forest ecosystems with higher soil organic C concentrations. Consequently, agricultural land may have reduced richness of functional genes in the soil microbiome [7, 57]. Secondly, our sampling site was from a semi-arid Mediterranean climate with extremely dry and hot summers for > 4 months per year. Soils from global drylands have demonstrated that diversity and abundance of microorganisms are reduced with increasing aridity . Finally, microbial genomes might share the same, or similar genes, when adapted to the same environment. Horizontal gene transfer can enrich the proportion of shared genes , and the percentage of shared genes rises with the inclusion of more genomes . In more disturbed systems, e.g. in our cropping soil with low organic matter and microbial number and diversity, the annual disturbance would potentially facilitate the increased interactions between microsites.
Bacteria were the dominant microbial taxa in the gene catalogue and in soil samples, demonstrated by their dominance in gene number (~ 95%) and higher copy number. While the fungal communities were quite minor (gene number < 0.4% of the gene catalogue, and copy number only 1/100–1/200 of the bacterial community), this is possibly due to the environment where our soils were collected. Previous studies on the global fungal diversity showed that annual rainfall was the best predictor of fungal richness with a positive effect, and South Australian soil had a low level of fungal diversity .
In this study, we established a microbial gene catalogue for a long-term agricultural soil from a Mediterranean-type climate. We demonstrated the important role that the detritusphere (formed by decaying root) plays in determining the rhizosphere microbiome structure and potential function. The existence of the decaying root homogenised the rhizosphere microbiome of different crops, through close root contact of the decaying root with fine living root, and the development of an active microbiome that degrades plant root exudates. These results demonstrate that under conservation agriculture systems, microsites such as detritusphere and rhizosphere are in constant interaction, providing microbial hotspots moderated by the crop type and physico-chemical properties of the system.
The foundation of crop selection is based on bio-environment (soil type, aspect and climate), market opportunities, crop history (rotation) and management (tillage and nutrition). Underpinning these holistic factors is the emerging awareness that the soil can also be considered in a more reductionist way, particularly pertaining to the functioning of the soil microbiome. Our study examined the basis of a soil microbiome in typical cropping soils including functional and taxonomic diversity, to understand the opportunities that exist for managing these microbiomes. This greater understanding of the soil microbiome can lead to better management of the soil resource and sustainable crop production.
Our findings illustrate the role that the microbiome plays at the functional level. Indeed, it may now be possible to manage and stabilise within any preferred crop rotation functions such as carbon decomposition, nutrient cycling and disease suppression through the selection of crops and management practices. However, the results also show that the microbiome is extremely dynamic. In particular, it will be valuable to compare the microbiome composition of other soils from comparable and contrasting dryland environments, to identify common edaphic and environmental drivers and to establish any underlying common compositional structures.
General experimental design
The intact soil cores with decaying root (+DR) and the soils for repacked columns representing the removal of decaying roots through tillage (−DR) were collected from the same field. Wheat (Triticum aestivum cv. Justica CL Plus), chickpea (Cicer arietinum cv. Hattrick) and unplanted controls were grown under +DR and −DR conditions in a controlled environment with three replicates. Using the same plant-soil design and growing system, 4 individual microcosm experiments were conducted to evaluate the plant root development, rhizosphere microbiome assemblages, rhizosphere metabolism and soil microbial respiration.
Field site and soil collection
Soil was collected from a farming system at the Roseworthy Campus (34° 53′ S, 138° 724′ E) of The University of Adelaide, Australia. Roseworthy is a typical dryland cropping area with a Mediterranean-type climate: 463 mm annual rainfall (334 mm in the April to October growing season), mean maximum temperature of 22.5 °C and mean minimum temperature of 10.0 °C over the last 50 years . The soil at the sample area is a brown Earth soil (hypercalcic, red, chromosol ). Soils with wheat stubble residues were sampled in January 2015 with less than 10 mm rainfall in the previous 40 days. The cropping history of the sampled site was in accordance with the local farmers’ current practice: one season legume crop followed by 3–4 cereal crops. The detailed rotation was lentil (Lens culinaris) in 2012 and wheat (Triticum aestivum cv. Justica CL Plus) in 2013 and 2014. The sample site was previously managed under no-tillage and stubble retention practices for more than 15 years. Other management practices such as fertilisation, herbicide/fungicide application and row spacing at seeding followed local farmer practices.
Intact soil cores were collected from a 50 m × 200 m area in the middle of the 20-ha field, with an even distribution between sample sites. Sterilised PVC tubes (50 mm diameter × 100 mm long) were pushed gently into the soil with a hydraulic probe, ensuring that there were 4 stems of wheat stubble residues from the previous crop in the centre of each tube. The aboveground stubble was cut off after sampling. For the −DR treatment, simulating conventional tillage effects, the intact soil cores were firstly cracked to remove the root residues by sieving (5 mm) and then mixed and repacked into PVC tubes with the same bulk density (1.48 g cm−3). The other soil cores were kept intact as the +DR treatment. Soil analysis for physio-chemical properties tested both + and −DR using the protocol from Rayment and Lyons .
PVC tubes were transferred to a controlled environment growth chamber (12 h light, 800 μmol m−2 s−1 at 20 °C and 12 h darkness at 10 °C) within 2 h of collection. In these experiments, wheat (cultivar Justica CL Plus) and chickpea (Cicer arietinum cv. Hattrick) represented cereal and legume crops, respectively, with contrasting root exudation profiles . Surface-sterilised seeds were germinated in petri dishes first and then planted into the soils at 1 cm depth after the first root had emerged to 5 mm. Unplanted tubes of + and −DR were used as controls. All tubes were weighed every day and irrigated by autoclaved water as necessary to keep the soil moisture content at 80% of field capacity.
Controlled environment experiments
Experiment 1: Root development
The experiment was arranged as a completely randomised design with 3 factors (decaying root: + and −; plant type: wheat, chickpea and unplanted; and harvest timing) and 4 replications. Replicated tubes of each treatment were harvested at 3, 6, 8, 10 and 12 days after sowing, to evaluate the root development relative to the stubble residue roots. The experimental duration was short (12 days) because (1) the active roots of young seedlings more accurately reflect the genetic variation in root exudation, and its relationship with the establishment of rhizosphere microbiome, and (2) early harvest can reduce the impacts such as uptake of immobile nutrients and root tissue decay, which may affect the rhizosphere microbiota formation between different crop species.
After cutting off the plant shoot, the tube was soaked in water at 4 °C for 24 h to disperse most of the root-attached soil. Subsequently, the roots were washed free of soil using a high-pressure shower (Additional file 1: Fig. S1). The stubble residue roots and plant fresh roots were separated to analyse for root length, using the WinRHIZO system (Regent Instruments, Inc., Quebec City, QC, Canada) with a scanner (Epson Expression 10000XL, Epson Inc., CA, USA). Root length density was calculated based on the total root length divided by the tube volume. Using the same scanned image, root distortion, which reflects changed root direction from the original trajectory due to physical impediment (Additional file 1: Fig. S1), was estimated using the method from Zhou et al. .
At 12 DAS, when the root length density of sown wheat seedlings was similar to that of the stubble residue root (Additional file 1: Fig. S9), the sampling for root channel occupation was initiated. Root channel occupation is defined here as the proportion of the number of newly grown roots inside the antecedent stubble residue root channel. This was evaluated using a modified method from White and Kirkegaard . Firstly, the intact soil core under +DR treatments was transversely cut into 3 sections at 4, 6 and 8 cm depth of the profile. On the horizontal surface of each section, the total number of fresh roots and the number of fresh roots occupying the residue root channel were recorded under a stereo microscope. Roots on the tube wall were not considered in calculations.
Experiment 2: Rhizosphere microbiome
Using the same growing condition to experiment 1, experiment 2 was arranged as a completely randomised design with 2 factors (decaying root: + and −; plant type: wheat, chickpea and unplanted) and 3 replications. Rhizosphere soils were sampled at 12 DAS, following a protocol modified from that of Bulgarelli et al. . Firstly, after discarding the roots on the tube wall and bottom, plants were shaken by hand to further remove bulk soil. The roots, which had ~ 1 mm soil attached, were transferred into 50-mL Falcon tube with 15-mL sterile PBS buffer. The Falcon tube was located on an orbital shaker for 20 min at 180 rpm. Roots were removed using sterilised tweezers, and the soil suspension was passed through a 0.5-mm sieve to remove any root residue. Finally, the resulting soil suspension was centrifuged at 2000 rpm for 20 min. After removing the supernatant, the pellet was defined as rhizosphere soil. The bulk soil in the unplanted −DR and soil surrounding the decaying root in unplanted +DR (detritusphere) were collected using the same procedure as for the controls. The total DNA for the 18 samples (Additional file 1: Table S1) were extracted using a PowerSoil DNA isolation kit (Mo Bio, Carlsbad, CA, USA) based on the manufacturer’s instructions.
Experiment 3: Root exudation
A similar tube experiment was conducted to measure the root exudates from the plant rhizosphere. The changes of root exudate abundance between sterilised and non-sterilised condition were determined to indicate the exudate degradation by rhizosphere microbial activity. Experiment 3 was arranged as a completely randomised design with 3 factors (decaying root: + and −; plant type: wheat, chickpea and unplanted; and sterilisation: + and −) and 5 replications. PVC tubes with intact and repacked soils were sterilised by 25k Gray gamma radiation, and another group of tubes was the non-sterilised control. Wheat, chickpea and unplanted controls were established in both sterilised and non-sterilised tubes under the same environment and management as before. A soil-based approach was applied to sample root exudate . The attached soil on live or decayed root sections was washed into 50-mL Falcon tube with 10-mL deionised water and then microwaved by a 700-W microwave oven for 10 s to prevent metabolic activities. Samples were washed on an orbital shaker for 30 min at 180 rpm. Roots were discarded. Soil suspensions were transferred through a filter paper (Whatman no. 42). The collected soil was dried at 105 °C to measure the dry weight. The filtered solution was transferred through a syringe filter (0.1 μm) and then stored in − 80 °C freezer for further analysis. Carbohydrates and amino acid compounds were analysed by Centre for Carbon, Water and Food in The University of Sydney, following the method in . The measured compounds were averaged with the amount of dry soil and calibrated by the bulk soil from unplanted −DR and unplanted +DR to measure root exudates. Meanwhile, the same rhizosphere soils collected from both sterilised and non-sterilised conditions were analysed for pH.
Experiment 4: Soil microbial respiration
Five replicates of unplanted tubes with and without DR were used to study the soil respiration. All tubes were sealed at the bottom to keep the moisture content at 80% field capacity and then transferred to 1 L glass jars with sealed lids. Soil respiration was determined by measuring the CO2 concentration in the headspace of the jars using an infrared analyser (Model 1450, Servomex Group, Crowborough, UK ). After each measurement, jars were vented using a fan to refresh the headspace. The amount of CO2 accumulated during this interval was used to indicate soil respiration. Linear regression based on the injection of known amounts of CO2 into empty jars of the same size was used to calibrate the relationship between CO2 concentration and detector reading.
Chickpea symbiotic rhizobia identification
To identify the 16S rRNA gene sequence of chickpea symbiotic rhizobia and analyse its abundance in different niches, the same chickpea cultivar was grown in 3 pots for 4 weeks using the soil with decaying residues removed as per experiments 1 to 4. Three nodules of each pot were harvested, and the attached soil was removed as described for root cleaning in experiment 2. The DNA of cleaned nodules were extracted, amplified for 16S rRNA genes and sequenced using the same methods as experiment 2.
To compare grain yield production of wheat and chickpea under different cropping management, a 2-year field experiment on the same agricultural land as soil collected was performed with two types of decaying root management: DR removed by tillage and DR retained with no tillage, two crops (wheat and chickpea), and 9 replicates of a paired samples in a t test design. The detailed management of the field trial is reported in Kitonyo et al. . In each year, plants were sown into the field with pre-cropped wheat residue. Grain yield was harvested at maturity.
Metagenome sequencing and bioinformatics
The bioinformatic analysis of the DNA sequence in our study was modified from the guideline of Quince et al. . The summarised flowchart is presented in Additional file 1: Fig. S10. Library construction, DNA sequencing and quality control (QC) of the sequence were performed by BGI (Shenzhen, China) based on the HiSeq 2000 platform according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). The sequencing was conducted under 100-bp pair-end runs. The sequenced reads were removed if they met any one of the following criteria: (1) with ≥ 20% low-quality bases, (2) with adapter contamination (15 bases overlapped by reads and adapter with maximal 3 base mismatches allowed), (3) with N base (also removing paired reads) and (4) with low complexity (reads with 10 consecutive reads of the same base).
All clean reads of the 18 samples were pooled together for metagenomic de novo assembly rather than the assembly of each sample independently following the method from . This was done (1) since all of our samples are soils from different niches from the same farm, which may have high similarity among microbial communities; (2) to establish a soil microbial gene catalogue covering all the niches of one particular farm including bulk soil, rhizosphere and detritusphere; and (3) to provide more information through pooling samples . Single-sample assembly of 6 samples was also performed, but the assembly rate (percentage of reads able to map the assembled contigs) was much lower than the mix-sample assembly (9–25% vs 50.7%).
MEGAHIT  based on a succinct de Bruijn graph  was used to assemble all the clean reads with the “meta-large” parameters set up in the software. To evaluate the quality of metagenomic assembly, we calculated the total number, total length, N50 and average length of the assembled contigs. Also, all the clean reads were mapped to the assembled contigs to generate the assembly rate for each sample using SoapAligner software with 90% identity .
Using the assembled contigs with a length of > 300 bp, gene prediction was carried out on the MetaGeneMark software  with default parameters. Also, the number, total length, average length and GC content of predicted genes were calculated.
Using the CD-HIT software with 95% sequence identity , the redundant predicted metagenomic genes were removed to generate the non-redundant gene catalogue. To generate the gene abundance, the clean reads for each sample were aligned to the non-redundant genes using SoapAligner software  with 90% identity. The relative abundance of genes in each sample was adjusted by gene length, as reads have a higher chance to hit the longer genes .
The non-redundant protein sequences were aligned against NCBI microbial NR database (Bacteria, Fungi, Archaea and Virus) and four functional databases including KEGG , eggNOG , CAZy  and TCDB  using BLAST software with an e value ≤ 1e−5. The taxonomic annotation for each gene was carried out using the lowest common ancestor (LCA) method  applied by MEGAN . Blast coverage ratio (BCR, the percentage of aligned length between query and reference) was selected with a cut-off of ≥ 40%. Based on the lowest blast e value, the functional information of the best aligned hit was selected as the metagenomic gene functional annotation . The functional gene abundance profile was generated using non-redundant gene catalogue and abundance.
In the functional database, orthologous genes were classified into one catalogue and named as a KEGG orthologous group (KO).
16S rRNA gene sequencing and bioinformatics
Using the same extracted DNA, the V3–V4 regions of 16S rRNA genes were amplified by the forward primer 341F (CCTAYGGGRBGCASCAG) and reverse primer 806R (GGACTACNNGGGTATCTAAT). PCR products were sequenced on an Illumina MiSeq platform with 300-bp paired-end reads. The flowchart for bioinformatics analysis of the 16S rRNA gene sequence is shown in Additional file 1: Fig. S10. Raw reads were processed for QC using the method as described above for metagenome sequencing. The clean reads were passed to the open reference operational taxonomic unit (OTU) picking and assignment pipeline as described by Bissett et al. . The codes and detailed method can be found from the link http://www.bioplatforms.com/wp-content/uploads/OTU_pipelines.pdf. Simply, sequences with ≥ 97% similarity were clustered as one OTU using UPARSE . To obtain OTU abundance, clean reads were mapped back to OTUs with a minimum identity of 97% by USEARCH (version 8.0.1623) . OTUs were annotated for taxonomies using the Greengenes database (vs 13.8)  and Wang classifier  in MOTHUR  at 60% sequence similarity cut-off.
The same bioinformatics was applied to the 16S rRNA gene sequence of the chickpea nodule tissue. One OTU with over 90% abundance in the nodule was identified as the chickpea symbiotic rhizobial OTU (csrOTU). All the OTUs assembled from the rhizosphere and bulk soil were blasted against the csrOTU using ViroBLAST . The abundance of one matched OTU was used to indicate the proportion of chickpea symbiotic rhizobia at different niches.
The quantity of microbial DNA in the samples was measured by RT-qPCR using the Femto™ Bacterial and Fungal DNA Quantification Kit (Zymo Research, USA) with primers targeting the 16S rRNA gene and internal transcribed spacer (ITS) gene. Three technical replicates per sample were analysed. According to the protocol from the kit manual, the concentration of bacteria and fungi in the samples was converted into gene copy number per unit dry soil calibrated with the standards of Escherichia coli strain JM109 and Saccharomyces cerevisiae strain TMY18.
Non-redundant gene was firstly used as the basic unit for our statistical analysis. Differentially abundant genes were identified using a generalised linear model (GLM) from edgeR [88, 89]. Firstly, low abundant genes were filtered out based on the criteria of at least 3 samples with over 5 reads. A trimmed mean of M values (TMM) method  was applied to normalise the library size across all the samples. The fitness of the negative binomial model was tested by estimating the biological coefficient of variation before the analysis was carried out. Differential abundance for each gene was determined by the GLM likelihood ratio test with the estimated dispersions using the Cox-Reid profile-adjusted likelihood method. Rather than test for genes that have log-fold-changes different from 0 compared with the control, we used glmTreat to test whether the log2-fold-change is greater than 1 (whether the treatment is statistically twice more than the control). The bulk soil treatment was considered as the control to compare with each of the other treatments for detecting differentially abundant (enriched/depleted) gene with the FDR adjusted P value < 0.01.
The identified differentially abundant genes were assigned into different databases including the NCBI microbial NR database, KEGG, CAZy and TCDB to illustrate the proportional distribution profile of gene number. Similar differential abundance analysis using KOs and OTUs as the basic unit was also performed.
Besides differential abundance analysis, the relative abundance of sequenced reads grouped into microbial functional and taxonomic families was compared between different rhizospheres and bulk soil (Additional file 1: Table S1) using the Statistical Analysis of Metagenomic Profiles (STAMP) package  and Benjamini-Hochberg FDR method to correct for the P value.
Multivariate analysis was conducted for non-redundant genes, OTUs and root exudate compounds. Unconstrained principal coordinate analyses (PCoA) based on Bray-Curtis distance were calculated by the pcoa() function from the R Package Vegan . Permutational MANOVA with a maximum of 999 permutations using the adonis() function was conducted to test the effect of experimental factors on β-diversity. Alpha-diversity indices for each sample were estimated also using R Package Vegan based on rarefied OTUs and genes table.
Eight major functional groups in KEGG pathways were analysed for the function-taxonomy relationship. All non-redundant genes within each of the 8 functional groups were aligned for taxonomic annotation, and then the taxonomic data were analysed as described above.
Individual-based rarefaction analysis was conducted for non-redundant gene catalogues using the R package rareNMtests. All the sequences were re-sampled by randomisation to calculate the richness of non-redundant genes in the subsample.
One-way ANOVA was conducted to test the effect of experimental factors on soil physiochemical properties, root morphological traits, soil respiration, shoot biomass ratio, root exudate compound concentrations and alpha-diversity indices using GenStat vs15 (VSN International Ltd., Hertfordshire, UK).
Availability of data and materials
Sequenced metagenomic clean data during the current study were submitted to the European Nucleotide Archive (http://www.ebi.ac.uk/ena/) with the study accession number PRJEB30524 . The amplicon sequences on 16S rRNA genes were uploaded to Sequence Read Archive of The National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/sra) with BioProject ID: PRJNA609629 .
The original data and source code for data analysis are available in the GitHub repository licenced under GNU GPLv3 https://github.com/DentonLab/decaying-root-microbiome  and deposited in Zenodo with DOI https://doi.org/10.5281/zenodo.3692465 .
Qin JJ, Li RQ, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.
Xiao L, Feng Q, Liang SS, Sonne SB, Xia ZK, Qiu XM, Li XP, Long H, Zhang JF, Zhang DY, et al. A catalog of the mouse gut metagenome. Nat Biotechnol. 2015;33:1103–8.
Xiao L, Estelle J, Kiilerich P, Ramayo-Caldas Y, Xia ZK, Feng Q, Liang SS, Pedersen AO, Kjeldsen NJ, Liu C, et al. A reference gene catalogue of the pig gut microbiome. Nat Microbiol. 2016;1:16161.
Oh J, Byrd AL, Deming C, Conlan S, Kong HH, Segre JA, Progra NCS. Biogeography and individuality shape function in the human skin metagenome. Nature. 2014;514:59–64.
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359-1–1261359-9.
Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, Bengtsson-Palme J, Anslan S, Coelho LP, Harend H, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.
Howe AC, Jansson JK, Malfatti SA, Tringe SG, Tiedje JM, Brown CT. Tackling soil diversity with the assembly of large, complex metagenomes. Proc Natl Acad Sci. 2014;111:4904–9.
Xu J, Zhang Y, Zhang P, Trivedi P, Riera N, Wang Y, Liu X, Fan G, Tang J, Coletta-Filho HD, et al. The structure and function of the global citrus rhizosphere microbiome. Nat Commun. 2018;9:4894.
Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems. 2018;3:e00039–18.
Beare MH, Coleman DC, Crossley DA, Hendrix PF, Odum EP. A hierarchical approach to evaluating the significance of soil biodiversity to biogeochemical cycling. Plant Soil. 1995;170:5–22.
Rovira AD. Plant root exudates. Bot Rev. 1969;35:35–57.
Bowen GD, Rovira AD. The rhizosphere and its management to improve plant growth. Adv Agron. 1999;66:1–102.
Sasse J, Martinoia E, Northen T. Feed your friends: do plant exudates shape the root microbiome? Trends Plant Sci. 2017;23:25–41.
Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, Cho H, Karaoz U, Loqué D, Bowen BP. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470.
Hacquard S, Garrido-Oter R, Gonzalez A, Spaepen S, Ackermann G, Lebeis S, McHardy AC, Dangl JL, Knight R, Ley R, Schulze-Lefert P. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe. 2015;17:603–16.
Mendes R, Kruijt M, de Bruijn I, Dekkers E, van der Voort M, Schneider JHM, Piceno YM, DeSantis TZ, Andersen GL, Bakker PAHM, Raaijmakers JM. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332:1097–100.
Penton CR, Gupta V, Tiedje JM, Neate SM, Ophel-Keller K, Gillings M, Harvey P, Pham A, Roget DK. Fungal community structure in disease suppressive soils assessed by 28S LSU gene sequencing. PLoS One. 2014;9:e93893.
van der Heijden MGA, Schlaeppi K. Root surface as a frontier for plant microbiome research. Proc Natl Acad Sci. 2015;112:2299–300.
Bulgarelli D, Rott M, Schlaeppi K, van Themaat EVL, Ahmadinejad N, Assenza F, Rauf P, Huettel B, Reinhardt R, Schmelzer E, et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature. 2012;488:91–5.
Bulgarelli D, Garrido-Oter R, Muench PC, Weiman A, Droege J, Pan Y, McHardy AC, Schulze-Lefert P. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe. 2015;17:392–403.
Yan Y, Kuramae EE, de Hollander M, Klinkhamer PG, van Veen JA. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 2016;11:56–66.
Mendes LW, Kuramae EE, Navarrete AA, van Veen JA, Tsai SM. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 2014;8:1577–87.
Peiffer JA, Spor A, Koren O, Jin Z, Tringe SG, Dangl JL, Buckler ES, Ley RE. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc Natl Acad Sci. 2013;110:6548–53.
Pratley JE. Principles of field crop production. South Melbourne: Oxford University Press; 2003.
Roper MM, Gupta V. Management practices and soil biota. Aust J Soil Res. 1995;33:321–39.
Pittelkow CM, Liang X, Linquist BA, Van Groenigen KJ, Lee J, Lundy ME, van Gestel N, Six J, Venterea RT, van Kessel C. Productivity limits and potentials of the principles of conservation agriculture. Nature. 2015;517:365–8.
Kramer S, Dibbern D, Moll J, Huenninghaus M, Koller R, Krueger D, Marhan S, Urich T, Wubet T, Bonkowski M. Resource partitioning between bacteria, fungi, and protists in the detritusphere of an agricultural soil. Front Microbiol. 2016;7:1524.
White RG, Kirkegaard JA. The distribution and abundance of wheat roots in a dense, structured subsoil - implications for water uptake. Plant Cell Environ. 2010;33:133–48.
Zhou Y, Coventry DR, Denton MD. A quantitative analysis of root distortion from contrasting wheat cropping systems. Plant Soil. 2016;404:173–92.
Robbins NE, Dinneny JR. Growth is required for perception of water availability to pattern root branches in plants. Proc Natl Acad Sci. 2018;115:E822–31.
Simpfendorfer S, Kirkegaard JA, Heenan DP, Wong PTW. Reduced early growth of direct drilled wheat in southern New South Wales - role of root inhibitory pseudomonads. Aust J Agric Res. 2002;53:323–31.
Canarini A, Kaiser C, Merchant A, Richter A, Wanek W. Root exudation of primary metabolites: mechanisms and their roles in plant responses to environmental stimuli. Front Plant Sci. 2019;10:157.
Wang X, Tang C, Severi J, Butterly CR, Baldock JA. Rhizosphere priming effect on soil organic carbon decomposition under plant species differing in soil acidification and root exudation. New Phytol. 2016;211:864–73.
Marschner P, Marhan S, Kandeler E. Microscale distribution and function of soil microorganisms in the interface between rhizosphere and detritusphere. Soil Biol Biochem. 2012;49:174–83.
Liu S, Razavi BS, Su X, Maharjan M, Zarebanadkouki M, Blagodatskaya E, Kuzyakov Y. Spatio-temporal patterns of enzyme activities after manure application reflect mechanisms of niche differentiation between plants and microorganisms. Soil Biol Biochem. 2017;112:100–9.
Kassam A, Friedrich T, Derpsch R, Kienzle J. Overview of the worldwide spread of conservation agriculture. Field Actions Sci Rep. 2015;8:3966.
Donn S, Almario J, Mullerc D, Moenne-Loccoz Y, Gupta V, Kirkegaard JA, Richardson AE. Rhizosphere microbial communities associated with Rhizoctonia damage at the field and disease patch scale. Appl Soil Ecol. 2014;78:37–47.
Yin CT, Hulbert SH, Schroeder KL, Mavrodi O, Mavrodi D, Dhingra A, Schillinger WF, Paulitz TC. Role of bacterial communities in the natural suppression of Rhizoctonia solani bare patch disease of wheat (Triticum aestivum L.). Appl Environ Microbiol. 2013;79:7428–38.
Hayden HL, Savin K, Wadeson J, Gupta V, Mele PM. Comparative metatranscriptomics of wheat rhizosphere microbiomes in disease suppressive and non-suppressive soils for Rhizoctonia solani AG8. Front Microbiol. 2018;9:859.
Gupta VV, Rovira AD, Roget DK. Principles and management of soil biological factors for sustainable rainfed farming systems. In: Rainfed farming systems. Dordrecht: Springer; 2011. p. 149–84.
Laudicina VA, Novara A, Barbera V, Egli M, Badalucco L. Long-term tillage and cropping system effects on chemical and biochemical characteristics of soil organic matter in a Mediterranean semiarid environment. Land Degrad Dev. 2015;26:45–53.
Ferreira MC, Andrade DD, Chueire LMD, Takemura SM, Hungria M. Tillage method and crop rotation effects on the population sizes and diversity of Bradyrhizobia nodulating soybean. Soil Biol Biochem. 2000;32:627–37.
Liu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, Ding W. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016;6:36773.
Haichar FEZ, Marol C, Berge O, Rangel-Castro JI, Prosser JI, Balesdent J, Heulin T, Achouak W. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2008;2:1221–30.
Holmes AJ, Bowyer J, Holley MP, O’Donoghue M, Montgomery M, Gillings MR. Diverse, yet-to-be-cultured members of the Rubrobacter subdivision of the Actinobacteria are widespread in Australian arid soils. FEMS Microbiol Ecol. 2000;33:111–20.
Liesack W, Stackebrandt E. Occurrence of novel groups of the domain Bacteria as revealed by analysis of genetic material isolated from an Australian terrestrial environment. J Bacteriol. 1992;174:5072–8.
McCaig AE, Glover LA, Prosser JI. Molecular analysis of bacterial community structure and diversity in unimproved and improved upland grass pastures. Appl Environ Microbiol. 1999;65:1721–30.
Young JPW, Crossman LC, Johnston AW, Thomson NR, Ghazoui ZF, Hull KH, Wexler M, Curson AR, Todd JD, Poole PS. The genome of Rhizobium leguminosarum has recognizable core and accessory components. Genome Biol. 2006;7:R34.
Finkel OM, Delmont TO, Post AF, Belkin S. Metagenomic signatures of bacterial adaptation to life in the phyllosphere of a salt-secreting desert tree. Appl Environ Microbiol. 2016;82:2854–61.
Castañeda LE, Barbosa O. Metagenomic analysis exploring taxonomic and functional diversity of soil microbial communities in Chilean vineyards and surrounding native forests. PeerJ. 2017;5:e3098.
Ofiţeru ID, Lunn M, Curtis TP, Wells GF, Criddle CS, Francis CA, Sloan WT. Combined niche and neutral effects in a microbial wastewater treatment community. Proc Natl Acad Sci. 2010;107:15345–50.
Mauser W, Klepper G, Zabel F, Delzeit R, Hank T, Putzenlechner B, Calzadilla A. Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nat Commun. 2015;6:11.
von Wettberg EJ, Chang PL, Başdemir F, Carrasquila-Garcia N, Korbu LB, Moenga SM, Bedada G, Greenlon A, Moriuchi KS, Singh V. Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation. Nat Commun. 2018;9:649.
Raes J, Korbel JO, Lercher MJ, Von Mering C, Bork P. Prediction of effective genome size in metagenomic samples. Genome Biol. 2007;8:R10.
Trevors J. One gram of soil: a microbial biochemical gene library. Antonie Van Leeuwenhoek. 2010;97:99–106.
Gans J, Wolinsky M, Dunbar J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science. 2005;309:1387–90.
Paula FS, Rodrigues JLM, Zhou J, Wu L, Mueller RC, Mirza BS, Bohannan BJM, Nuesslein K, Deng Y, Tiedje JM, Pellizari VH. Land use change alters functional gene diversity, composition and abundance in Amazon forest soil microbial communities. Mol Ecol. 2014;23:2988–99.
Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, Quero JL, García-Gómez M, Gallardo A, Ulrich W. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci. 2015;112:15684–9.
Zaneveld JR, Lozupone C, Gordon JI, Knight R. Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives. Nucleic Acids Res. 2010;38:3869–79.
Konstantinidis KT, Tiedje JM. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol. 2007;10:504–9.
Tedersoo L, Bahram M, Polme S, Koljalg U, Yorou NS, Wijesundera R, Villarreal Ruiz L, Vasco-Palacios AM, Pham Quang T, Suija A, et al. Global diversity and geography of soil fungi. Science. 2014;346:1078.
Bureau of Meteorology. Australian climate variability & change Canberra. Canberra: Bureau of Meteorology; 2017.
Isbell R. Australian soil classification. Melbourne: CSIRO Publishing; 1996.
Rayment GE, Lyons DJ. Soil chemical methods: Australasia. Victoria: CSIRO publishing; 2011.
Wouterlood M, Cawthray GR, Scanlon TT, Lambers H, Veneklaas EJ. Carboxylate concentrations in the rhizosphere of lateral roots of chickpea (Cicer arietinum) increase during plant development, but are not correlated with phosphorus status of soil or plants. New Phytol. 2004;162:745–53.
Conselvan GB, Fuentes D, Merchant A, Peggion C, Francioso O, Carletti P. Effects of humic substances and indole-3-acetic acid on Arabidopsis sugar and amino acid metabolic profile. Plant Soil. 2018;429:1–16.
Setia R, Marschner P, Baldock J, Chittleborough D, Smith P, Smith J. Salinity effects on carbon mineralization in soils of varying texture. Soil Biol Biochem. 2011;43:1908–16.
Kitonyo OM, Zhou Y, Coventry DR, Denton MD. Canopy development and grain yield of dryland wheat is modified by strategic nitrogen supply and stubble management. Eur J Agron. 2018;99:195–205.
Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833.
Magasin JD, Gerloff DL. Pooled assembly of marine metagenomic datasets: enriching annotation through chimerism. Bioinformatics. 2015;31:311–7.
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
Bowe A, Onodera T, Sadakane K, Shibuya T. Succinct de Bruijn graphs. In: International workshop on algorithms in bioinformatics. Berlin: Springer; 2012. p. 225–235.
Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008;24:713–4.
Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;38:e132.
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.
Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34:D354–7.
Powell S, Forslund K, Szklarczyk D, Trachana K, Roth A, Huerta-Cepas J, Gabaldon T, Rattei T, Creevey C, Kuhn M, et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 2014;42:D231–9.
Lombard V, Ramulu HG, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–5.
Saier MH, Reddy VS, Tamang DG, Vastermark A. The Transporter Classification Database. Nucleic Acids Res. 2014;42:D251–8.
Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17:377–86.
Bissett A, Fitzgerald A, Meintjes T, Mele PM, Reith F, Dennis PG, Breed MF, Brown B, Brown MV, Brugger J, et al. Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database. Gigascience. 2016;5:21.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996.
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
Deng W, Nickle DC, Learn GH, Maust B, Mullins JI. ViroBLAST: a stand-alone BLAST web server for flexible queries of multiple databases and user’s datasets. Bioinformatics. 2007;23:2334–6.
Edwards J, Johnson C, Santos-Medellin C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci. 2015;112:E911–20.
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.
Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, Suggests M. The vegan package. Community Ecol Package. 2007;10:631–7.
Zhou Y, Coventry DR, Gupta VVSR, Fuentes D, Merchant A, Kaiser BN, Li J, Wei Y, Liu H, Wang Y, et al: A metagenomic gene catalogue of soil microbiota from different niches. European Nucleotide Archive. Study PRJEB30524. https://www.ebi.ac.uk/ena/data/view/PRJEB30524. Accessed Mar 2020.
Zhou Y, Coventry DR, Gupta VVSR, Fuentes D, Merchant A, Kaiser BN, Li J, Wei Y, Liu H, Wang Y, et al: The preceding roots driving rhizosphere microbiome. NCBI BioProject PRJNA609629. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA609629. Accessed Mar 2020.
Zhou Y, Coventry DR, Gupta VVSR, Fuentes D, Merchant A, Kaiser BN, Li J, Wei Y, Liu H, Wang Y, et al. DentonLab/decaying-root-microbiome. Github. 2020; https://github.com/DentonLab/decaying-root-microbiome.
Zhou Y, Coventry DR, Gupta VVSR, Fuentes D, Merchant A, Kaiser BN, Li J, Wei Y, Liu H, Wang Y, et al. DentonLab/decaying-root-microbiome. Zenodo. 2020; https://doi.org/10.5281/zenodo.3692465.
We acknowledge Judith Rathjen, Onesmus Kitonyo and Hugh Cameron for their assistance with the field and growth chamber operations.
Peer review information
Andrew Cosgrove and Kevin Pang were the primary handling editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
The review history is available as Additional file 3.
Financial support of this work was provided by the Australian Centre for International Agricultural Research (project ID: CIM/2008/027); the Australian Research Council (project ID: IH140100013); the Grains Research and Development Corporation; the International Technology Cooperation Project from Shandong Academy of Sciences (project ID: 2019GHZD11); the Department of Trade, Tourism and Investment of the South Australian Government; Waite Research Institute; and The University of Adelaide. The participation of Vadakattu VSR Gupta in the preparation of this manuscript is funded by the CSIRO Agriculture and Food.
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The experimental design. Table S2. Physical and chemical properties of soils. Table S3. MANOVA table. Table S4. The grain yield production influenced by decaying roots of pre-crop in the field experiment. Table S5. Statistical results of the assembled metagenome. Table S6. Statistical summary of the metagenomics sequencing and assembly. Table S7. The number of genes and reads aligned to the non-redundant gene catalogue and the annotated databases. Figure S1. Pictures of fresh root-residue root contact and root distortion of wheat and chickpea. Figure S2. Root distortion rate of wheat and chickpea influenced by decaying root. Figure S3. Comparing the functional and taxonomic structure of rhizosphere microbiome between wheat and chickpea growing under + and – decaying root. Figure S4. Comparison of differentially abundant genes between rhizosphere and detritusphere microbiome under + and – decaying root. Figure S5. Functional and taxonomic annotation of the differentially abundant genes in the rhizosphere of wheat and chickpea under + and – decaying root. Figure S6. Taxonomic structure of rhizosphere microbiome at different metabolic pathways. Figure S7. Comparison of two approaches, amplicon sequencing on 16S rRNA genes and metagenomics sequencing, to analyse microbiome taxonomic composition at genus level. Figure S8. Identification of chickpea symbiotic rhizobia. Figure S9. Root length density of wheat and chickpea under + and – decaying root (DR) changed with days after planting. Figure S10. Flowchart of bioinformatics analysis for metagenomics sequencing and 16S rRNA gene sequencing.
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Zhou, Y., Coventry, D.R., Gupta, V.V.S.R. et al. The preceding root system drives the composition and function of the rhizosphere microbiome. Genome Biol 21, 89 (2020). https://doi.org/10.1186/s13059-020-01999-0
- Soil microbiome
- Agricultural system