Introduction

Soil organic matter (SOC) plays an important role in maintaining soil quality and it stores the majority of terrestrial carbon (C) (Lal 2004). Atmospheric input of C into soils via rhizodeposition is an essential pathway for building SOC (Pausch and Kuzyakov 2017). Plant roots release about 20–30% of total photosynthetic C into the soil (Calvo et al. 2017), which can be subsequently utilized by the rhizosphere microbiome (Sasse et al. 2018). The structural dynamics of the rhizosphere bacterial community that assimilates rhizodeposited C can control (i) the quantity of C incorporated into soil pools in the form of metabolites or necromass, which are subsequently stabilized by minerals and aggregates (Liang et al. 2017; Jeewani et al. 2020; Luo et al. 2021), and (ii) the mineralization of both the rhizodeposits and SOC (Pausch and Kuzyakov 2017). Both stabilization and mineralization via rhizosphere microbiome are the keys to plant-derived C sequestration in soil, which are determined by climate, edaphic properties, and agriculture practices (Cotrufo et al. 2013; Keiluweit et al. 2015).

There has been a surge of interest over the last 15 years in the utilization of biochar for increasing soil C content due to its recalcitrance (Lehmann et al. 2008). While biochar can supply a stable C component to the soil, biochar and non-biochar interactions are essential while determining biochar half-life (Kuzyakov et al. 2009) and the decomposition of plant-derived C and native SOC (Liang et al. 2010). Biochar can either promote stabilization of plant-derived C, i.e., rhizodeposits (Weng et al. 2017), or result in faster rhizodeposit mineralization and larger SOC mineralization (Luo et al. 2017), which are mainly driven by biochar-induced changes to both soil abiotic and biotic properties (Yu et al. 2020).

The physicochemical properties of biochar can influence soil pH, nutrient availability, and C chemical composition (Dai et al. 2014; Blanco-Canqui 2017; Razzaghi et al. 2020), which in turn shape soil microbial communities and their functions (Chen et al. 2021; Wang et al. 2021). For example, biochar reduced the effect of protists on the bacterial communities by changing physicochemical properties (e.g., size of the micro-pores and nutrient content), thus altering the microbially mediated transformation of N in soil (Asiloglu et al. 2021). Zhang et al. (2017) found biochar increased pH and thus shifted AOA/AOB ratio and following N2O potential. Several studies highlight that the response of certain soil microbial communities to biochar is linked to altered soil C dynamics (Luo et al. 2013; Whitman et al. 2016, 2021; Campos et al. 2020). Previous studies reported that biochar amendment can decrease soil microbial activities, thus mitigating C loss and promoting soil C storage and fertility (Duan et al. 2020; Wu et al. 2021).

Quite recently, those biochar studies, including plants, have mainly emphasized either (i) biochar and rhizodeposit interactions and their impacts on soil effluxes and C sequestration (Whitman et al. 2014; Weng et al. 2015, 2017; Pei et al. 2020), but ignoring the role of microbial community underpinning C processes, or (ii) biochar-induced changes on rhizosphere microbiome and consequential effects on plant nutrient uptake and growth (Efthymiou et al. 2018; Fu et al. 2021), but omitting soil C processes. For example, greater microbial diversity and potential metabolism observed in the rhizosphere by biochar amendment enhanced plant performance (Kolton et al. 2017), but the microbial mechanisms (taxonomic guild and active microorganisms) underlying rhizosphere-C dynamics remain elusive (Liao et al. 2019; Joseph et al. 2021).

To predict the ecosystem function of the soil microbiome, it is vital to specifically target active members of the soil microbial community (Couradeau et al. 2019). Stable isotope probing (SIP) is a powerful tool for investigating specific active microorganisms by incorporating isotopically labeled substrates (e.g., 13C or 15 N) in situ, and thus link community and function. By using SIP of phospholipid fatty acids (PLFA-SIP), actinomycetes were shown to utilize rhizodeposits in a biochar-amended soil (Chen et al. 2021). To have detailed resolution, DNA-SIP is used to identify microorganisms that assimilate labeled organic substances and their functions in C cycling (Dumont and Murrell 2005). To target RNA as the biomarker, RNA-SIP has the advantage of greater sensitivity compared to DNA-SIP, and can reflect the active bacteria involved in C cycling (Lu and Conrad 2005). Some studies have applied the RNA-SIP technique to identify the active microorganisms in various ecosystems, including phenanthrene degraders in sandy soils (Schwarz et al. 2018), methylotrophs, and sulfate-reducing bacteria in paddy soils (Lueders et al. 2004; Liu et al. 2018). Combination of CO2 labeling and RNA-SIP can provide an approach to identify the microorganisms that are stimulated by plant rhizodeposits (Drigo et al. 2010; Hernández et al. 2015). This therefore may assist in unraveling the microbial mechanisms whereby biochar amendment controls or alters C dynamics in a plant-soil system.

The aims of this study were (i) to investigate the influence of biochar on active bacteria utilizing plant-derived C in the maize rhizosphere and (ii) to assess roles of active bacteria that may potentially regulate the mineralization of rhizodeposits and SOC. We conducted continuous labeling of maize (13CO2, 99% atom 13C) experiment coupled with RNA-SIP. We designed a two-compartment chamber to separate aboveground plant shoot respired CO2 from belowground root and SOC-derived CO2. CO2 from the belowground pools can be then separated by an isotopic signature using a mixed model (Fig. S1). This technique allowed us to understand changes to the mineralization of both maize rhizodeposits and SOC and active microbial community by rhizodeposits. We hypothesized that (i) biochar amendment will change rhizosphere bacterial communities that assimilate rhizodeposits due to altered soil properties such as pH and C recalcitrance, and (ii) the activated microorganisms, i.e., upregulated genera, will in turn enhance rhizodeposit loss and contribute to subsequent SOC mineralization via co-metabolism.

Materials and methods

Soil and biochar materials

Soil was collected from the 0–20-cm layer by using the five diagonal point sampling method from Wenling (28°170′ N, 121°126′ E), Zhejiang, China. The plant residues and stones were removed by hand. The soil was sieved (< 5 mm) and stored at 4 °C before use. The soil texture was loamy clay. Soil properties were total C (3.08 ± 0.21%), total N (0.32 ± 0.03%), pH of soil–water slurry (1:5, w/v) (4.86 ± 0.06), and δ13C (− 28.42 ± 0.03‰). Biochar was produced using swine manure and straw (manure:straw = 1:3) feedstock with the method described by Luo et al. (2011). In brief, materials were pyrolyzed with a heating rate of 26 °C per min, and the highest treatment temperature of 700 °C. This biochar contained 56.7 ± 0.67 total C (%), 0.67 ± 0.03 total N (%), pH of 9.60 ± 0.05, and the δ13C was − 29.8 ± 0.14 (‰). The biochar was sieved (2 mm) and stored at 25 °C before use.

Experimental setup

The experiment investigated one treatment (with biochar) and the control (non-biochar). Sixteen pots (four replicates each treatment) were set up, including (1) unlabeled plant in soil without biochar addition; (2) 13CO2 labeled plant in soil without biochar addition; (3) unlabeled plant in soil with biochar addition; and (4) 13CO2 labeled plant in soil with biochar addition.

The glasshouse experiment used a total of 16 polyvinyl chloride (PVC) pots (height 10.5 cm, diameter 11.3 cm) each filled with 400 g soil (dry weight basis). Biochar was applied at 5% (w/w all dry weight basis) (Wang et al. 2019) to 8 pots homogeneously. The soil in this study was adjusted to about 50% of water holding capacity (WHC) and pre-incubated in the greenhouse at 20 °C (night time) and 28 °C (day time) temperatures, and 70% relative humidity for 7 days before sowing.

Maize (Zea mays L.) seeds were sterilized with 30% H2O2 for 30 min, washed thoroughly with distilled water, and then sown in agar media. At 5 days after sowing the seeds, 1 maize seedling was selected and transplanted into each experimental pot. The pots were then placed in a climate-controlled greenhouse; 10-h dark (night) and 14-h light (day), at 20 °C night and 28 °C daytime temperatures, and 70% relative humidity (Chen et al. 2016). Four replicates of each treatment had 14 days of continuous labeling starting at day 18 (ripening stage) (Lu and Conrad 2005). Before the labeling, 100 mL of 1 M NaOH solution was put inside the chamber to exclude the 12CO2. For 13C labeling, maize was exposed to a 13CO2 enriched atmosphere at 400 ppm for 8 h by using the following procedure. A closed Perspex chamber (width 0.8 m, height 1.0 m, and length 1.5 m) housing 8 pots was used, and a glass beaker containing 100 mL H2SO4 (3 M) was put inside the chamber. At each labeling event, 25 mL of 13C labeled Na2CO3 (≥ 99% atom 13C, Cambridge Isotope Laboratories Inc, USA) solution (1 M) was then injected into a glass beaker containing H2SO4 solution through a tube to release 13CO2. The opening for injection of the 13C labeled Na2CO3 was sealed using Vaseline glue after each injection. The injection was repeated four times every day (every 1.5 h). An electric fan was placed in the chamber to deliver the homogenous distribution of CO2. The CO2 concentration was monitored by using a portable infrared sensor (PGD3-C-CO2, Shenzhen, China).

Chemical analyses

Soil pH was measured in suspension (1:5, soil: Millipore water) by using an ISFET electrode. TC content and TN content (air-dried, milled < 200 µm) were determined by using the PerkinElmer EA2400 (Shelton, CT, USA). The stable isotope was determined using an Elementar vario MICRO cube elemental analyzer coupled to a GV Isoprime 100 isotope ratio mass spectrometer (IRMS; GV Instruments, UK).

Chemical fractionations of SOC were measured according to Rovira and Vallejo (2002). Briefly, 500 mg of ground soil sample was hydrolyzed with 20 mL 2.5 mol L−1 H2SO4 for 30 min at the temperature of 105 °C. Then, the solution was centrifuged at 2795 × g min−1 for 10 min, and the supernatant hydrolysate was transferred to a 50-mL tube. The carbon content of the hydrolysate in the 50-mL tube was measured by using a Multi 3100 N/C TOC analyzer. This carbon content was considered labile organic C (LOC). The remaining soil residues were hydrolyzed and shacked overnight with 2 mL 13 mol L−1 H2SO4 at 25 °C, and then the H2SO4 concentration was diluted to 1 mol L−1 by adding deionized water. The diluted hydrolysate was kept at a temperature of 105 °C for 3 h, and centrifuged for 10 min. The C content of this supernatant hydrolysate was regarded as intermediate organic C (IOC). Finally, the C content of remaining soil residues was measured by using a Vario EL III Elemental Analyzer, which is treated as recalcitrant organic C (ROC).

Soil respiration and 13C-CO2

Total soil respiration was measured using a CO2 trap consisting of 10 mL 1.5 M NaOH solution. The amount of CO2 trapped was determined using a TIM840 auto titrator (Radiometer Analytical, Villeurbanne Cedex, France) with standard HCl (0.0501 M L−1). To determine the δ13C (‰) of the trapped CO2, 5 mL aliquots of each sample were added to 10 mL 1 M BaCl2 in a centrifuge tube. The precipitated BaCO3 was carefully rinsed 3 times with distilled water and dried overnight at 60 ℃ in the centrifuge tube. Finally, 1 mg BaCO3 was accurately weighed into tin caps and the δ13C was analyzed using an elemental analyzer-coupled-isotope ratio mass spectrometer (EA-IRMS) (Sercon Ltd, Crewe, UK). SOC-derived CO2 was separated from root-derived CO2 in planted treatments according to Lu et al. (2019):

$${C}_{SOC}={C}_{total}*\left({\delta }^{13}{C}_{Root}-{\delta }^{13}{C}_{Total}\right)-\left({\delta }^{13}{C}_{Root}-{\delta }^{13}{C}_{Soil}\right)$$
(1)
$${C}_{Root}={C}_{total}-{C}_{Soil}$$
(2)

where, CSoil, CRoot, and CTotal are the CO2-C derived from soil, CO2-C derived from roots, and total CO2-C derived from belowground in planted treatments, respectively. δ13CSoil, δ13CRoot, and δ13CTotal are defined as the δ13C values of CSoil, CRoot, and CTotal in planted treatments. The mean δ13C value of soil respiration in unplanted soil was used as δ13CSoil.

RNA extraction and isolation of 13C-labeled RNA

Soil RNA extraction of rhizosphere soil of each sample was completed using the RNeasy Power-Soil Total RNA kit (Qiagen) (Ding et al. 2015). The extracted RNA was added with DNase I (Ambion) and purified by using the RNA Clean and Concentrator kit (ZymoResearch). The integrity of the purified RNA was measured by Bio-Rad Experion™ and RNA HighSens Chips (Bio-Rad). Density gradient centrifugation was carried out to separate the 13C-labeled RNA from total RNA (Dumont et al. 2011; Li et al. 2019). Briefly, approximately 500 ng of extracted RNA was mixed with cesium trifluoroacetate (CsTFA) gradients to achieve an original buoyant density (1.790 g mL−1). The above mixtures were centrifuged at 130 000 g for 65 h at 20 °C. RNA fractionation was performed by displacing the gradient medium with sterile water from the top of the ultracentrifuge tube using a NE-1000 single syringe pump (New Era Pump Systems Inc. Farmingdale, NY, USA), with the controlled flow rate of 0.34 mL min−1. A total of 14 RNA gradient fractions with equal volumes of about 340 mL were generated, and the refractive index of these fractions was measured using an AR200 digital hand-held refractometer (Reichert, Inc., Buffalo, NY, USA). Each RNA fraction was converted to the complementary DNA (cDNA) according to the protocol provided by PrimeScript™ II 1st Strand cDNA Synthesis Kit (Takara, Dalian, China). The copy numbers of bacterial 16S rRNA in each cDNA fraction were determined on an iCycleriQ 5 thermocycler (Roche Diagnostics, Meylan, France) using primer pairs 515F (5′-GTGYCAGCMGCCGCGGTAA) and 907R (5′-CCGYCAATTYMTTTRAGTTT) (Biddle et al. 2008). Each reaction was performed in a 20-μL volume containing 10 μL SYBR Premix Ex Taq (TaKaRa Biotechnology, Otsu, Shiga, Japan), 0.4 μM of each primer, and 1 μL cDNA template (1–10 ng). The thermocycling conditions were denaturation at 95 °C for 30 s, then 40 cycles of denaturation at 94 °C for 5 s, annealing at 58 °C for 15 s, and extension at 72 °C for 10 s. The bacterial 16S rRNA copy numbers of each cDNA fraction are shown in Fig. S3.

16S rRNA Illumina sequencing

cDNA from the “heavy” gradient fractions of each sample was subjected to 16S rRNA amplicon sequencing. Briefly, 16S rRNA fragments were amplified with barcoded and indexed universal prokaryotic V4-V5 primers 515f/907r, and the products were pooled and sequenced on the Illumina Miseq platform (Illumina, San Diego, USA), run by Majorbio, Inc. (Shanghai, China). PCR amplicons pooled from the triplicate reactions were purified using a QIAquick PCR purification kit (Qiagen, Shenzhen, China) and quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The amplicons from all samples were combined in equal mass. According to the Illumina MiSeq reagent kit preparation guide (Illumina, San Diego, CA, USA), the purified mixture was diluted and denatured to obtain the 8 pmol l−1 amplicon library and mixed with an equal volume of 8 pmol l−1 PhiX (Illumina). Finally, 600 μL of the mixture amplicons was loaded with read-1 and read-2, and the index sequencing primers and paired-end sequencing (each 250 bp) were completed on the Illumina MiSeq platform.

Statistical analysis

The quality control and OTU assignment from Quantitative Insights Into Microb Ecol (QIIME) might be outdated, but still reliable and valid, while some studies found that different bioinformatics pipelines (for example, QIIME vs QIIME2) compared in the microbiome were capable of discriminating samples by treatment, leading to similar biological conclusions (Allal et al. 2017; Moossavi et al. 2020). In our study, QIIME 1.9.0-dev pipeline was used to process the gene sequencing data (Caporaso et al. 2010a, b). In brief, reads which were less than length 200 bp and ambiguous bases were discarded. The sequences were then binned into operational taxonomic units (OTUs) based on 97% similarity by using UCLUST (Edgar 2010). Then chimeric sequences were identified and removed by using UCHIME (Edgar et al. 2011). The most highly connected sequence (i.e., the sequence with the highest similarity to all other sequences in the cluster) was chosen to represent each OTU. The representative OTU sequences were aligned by the PyNAST tool (Caporaso et al. 2010a, b). Taxonomy was assigned to bacterial phylotypes against the SILVA database (https://www.arb-silva.de/). Volcano plots were generated using two-sided t-tests to follow genera changes in response to the biochar amendment. Principal coordinate analysis (PCoA) was conducted using the “capscale” function. Shannon diversity was calculated using the “diversity” function from the Vegan package (Dixon 2003). Partial Mantel tests were also conducted to explore the effects of soil properties on the active bacterial community composition in R (vegan package). Phylogenetic trees were displayed using the “plot_tree” function from the PhyloSeq package (McMurdie and Holmes 2013). A heatmap was drawn by using the function “heatmap.2” in the R package “gplots.” All analyses were performed by using SPSS19.0 (SPSS Inc. Chicago, IL, USA).

Results

Soil physicochemical properties and CO2 efflux

At the completion of the experiment (37 days after sowing), we found that biochar amendment significantly increased soil pH soil–water slurry (1:5, w/v) from 4.53 to 6.17, ROC from 10.69 to 25.77 g·kg−1, and TOC from 14.32 to 29.94 mg·kg−1; in contrast, other properties (e.g., DOC, DON, LOC, IOC, and root biomass) had no significant differences between the soils with or without biochar amendment (Table S1).

Biochar amendment significantly decreased the incorporation of 13C into the soil (by 58.50%), compared to the non-amended treatment (Fig. 1a) (p < 0.05). However, there were no statistical differences in the root/rhizodeposit-derived CO2 efflux or SOC-derived CO2 efflux (Fig. 1c).

Fig. 1
figure 1

13C content remaining in the loamy clay soil (a), rhizodeposit-derived CO2 (b), and SOC-derived CO2 (c). Error bars represent standard errors for four replicates

Full size image

Biochar amendment shifts the rhizodeposit-utilizing bacterial community

The diversity indices (richness and Shannon) of bacteria that utilize 13C-rhizodeposits remained unchanged between biochar-amended soil and the control (Fig. 2a). We observed a wide range of bacteria actively utilizing 13C-rhizodeposits (Fig. 2b). Biochar amendment had effect on the active bacterial communities utilizing rhizodeposits (p < 0.05) (Table S3). Actinobacteria abundance was enhanced by biochar (p < 0.05) (Fig. S4b). Proteobacteria dominated the microbial communities that utilize maize rhizodeposits with 39.64% of the communities accounted for in biochar-amended soils and 46.51% in the control (Fig. 2b; Fig. S4a). Firmicutes, Acidobacteria, Planctomycetes, and Bacteroidetes also participated in rhizodeposit utilization, but with a lesser abundance.

Fig. 2
figure 2

The diversity (a), composition (b), and top 30 genera (c) of bacterial communities utilizing rhizodeposits in the biochar-amended and non-amended in the loamy clay soils

Full size image

The most common genera utilizing 13C-rhizodeposits were the genus affiliated to Burkholderiaceae, Massilia, and Bacillus (Fig. 2c). Biochar amendment resulted in the upregulation of 45 genera that utilized 13C-rhizodeposits compared to the control (Fig. 3a). The genera with the greatest alteration (log2 FC ≥ 1, p < 0.05) were classified into Actinobacteria and Proteobacteria. Among these upregulated genera, members of Actinobacteria had high relative abundance in the biochar-amended soil, and included genera affiliated to Micrococcaceae, Oryzihumus, Nocardioides, and Methylophilus (Fig. 3b).

Fig. 3
figure 3

The volcano plot represents the filtering threshold for the t-test for differential expression analysis of genera utilizing rhizodeposits between non-amended treatment and biochar-amended treatment (a). The relative abundance of upregulated genera by biochar (b) and the Pearson correlation between these genera and environmental factors (c) and SOC-derived CO2 (d)

Full size image

Correlation between soil properties, the bacterial community, and SOC mineralization

Bacterial communities in soil with biochar amendment were distinguished from the non-amended soil (Table S2). The largest source of variation (PCoA1) explained 61.96% of the variation in the soil’s active bacterial community, and the Mantel test showed a positive correlation between soil pH (r = 0.89, p < 0.05), TOC (r = 0.54, p < 0.05), ROC (r = 0.58, p < 0.05), and the active bacterial communities (Fig. S5a). Most of the upregulated genera were strongly and positively correlated with soil pH, TOC, and ROC (Fig. 3c).

In addition, we found that most upregulated genera showed a positive correlation with the mineralization of SOC, indicating the potential role of these genera in SOC mineralization (Fig. 3d).

Discussion

Changes in the rhizosphere bacterial community following biochar amendment

We investigated the bacterial community changes in biochar-amended soil using RNA-SIP, which revealed the microorganisms that were activated by rhizodeposits (Fig. 2). Although numerous studies have investigated the changes in the relative abundance of individual phylum using high-throughput sequencing in biochar-treated soils, the generalizations and consistent conclusions are far from being reached as the responses are a result of a wide range of factors, including the alteration of soil properties following biochar amendment, application rates, experimental conditions, and study duration (Lehmann et al. 2011; Luo et al. 2013; Whitman et al. 2016, 2021; Blanco-Canqui 2017; Campos et al. 2020). Considering (i) the large variation of biochar properties and (ii) overwhelmed effect by pre-biochar soil properties over biochar itself, the responders to biochar addition at either the phylum level or the genus level are not consistent across studies (Woolet and Whitman 2020). However, most studies found a positive response of the phylum Actinobacteria to biochar addition (Dai et al. 2017; Yu et al. 2018). Here, we found phylum Actinobacteria were the dominant taxa that utilized maize rhizodeposits in biochar-amended soil (Fig. 2; Fig. S4b). Previous studies that used PLFA found that biochar promotes the abundance of Actinobacteria relative to fungi and other bacteria (Chen et al. 2021), while sequencing analysis also revealed an increase in the abundance of phylum Actinobacteria in biochar-treated soils (Khodadad et al. 2011).

By adopting RNA-SIP and differential expression analysis, this study provided a much higher resolution to reveal the detailed taxonomic information of responders (genera utilizing rhizodeposits) to biochar (Fig. 3a). However, the 14-day continuous labeling and RNA-SIP have the potential to show cross-feeding of label from primary consumers to other soil microbes, resulting in the overestimation of label incorporation in RNA. Therefore, we discuss the more salient results below and compare the results with the previous literatures. As shown by the upregulated genera in biochar-amended soil, Actinobacteria (e.g., genus affiliated to family Micrococcaceae, Nocardioides, Janibacter, Corynebacteriaceae, Terrabacter) and Alphaproteobacteria (e.g., genus Caulobacter, Qipengyuania) were the main active microorganisms that utilized the rhizodeposits (Fig. 3a). The most heavily labeled genus was affiliated with Micrococcaceae, which was reported to be distributed in the rhizosphere of biochar-amended soil (Kolton et al. 2017). These two phyla (i.e., Actinobacteria and Proteobacteria) have also been previously identified in the post-fire soils (Cobo-Díaz et al. 2015; Mikita-Barbato et al. 2015). The changes in soil physical structure and increased abundance of recalcitrant components in pyrolyzed organic matter controlled the bacterial community assemblage of post-fire soils (Luo et al. 2016). Previous studies have widely reported changes in soil physicochemical properties (e.g., labile C and mineral nutrients, soil pH) following biochar amendment that influenced the growth, activity, distribution, and composition of the microbial community (Lehmann et al. 2011; Dai et al. 2021). By correlation analysis, the shift in the microbiome can be attributed to the difference in microbial adaptation ability to biochar itself (e.g., pore size and surface area) and changes in soil properties such as soil pH and chemical structure, e.g., aromatic components (Fig. 3c).

Drivers that shape the rhizosphere bacterial community

Biochar can have a vast range of properties that can alter a range of chemical and physical conditions (e.g., soil bulk density, porosity, pH, C and N availability) in soil (Bolan et al. 2021). These changed physicochemical properties may influence a range of biological interactions. We provide data on changes to available N and C substrates, as well as providing physical niches for colonization (Fig. S2; Table S1). The physical structure (e.g., surface area and porosity) of biochar was reported to be a determinant in shaping soil microbial community (Jaafar et al. 2015). Actinobacteria are particularly adapted to biochar structures, as they have a mycelium-like network. A previous study provides direct evidence that biochar was surrounded by a hyphal network of Actinobacteria (Luo et al. 2013), which are further identified (using PLFA-SIP) as the main microorganism in biochar-amended soil (Luo et al. 2018b).

The biochar-mediated increase of pH in soil from 4.53 to 6.17 (Table S1) may improve the viability of Actinobacteria and enhance their ability to compete with fungi. The optimal pH for Actinobacteria growth has been shown to range between 6.0 and 9.0 (Gohain et al. 2020), and increased pH enhances the bacteria to fungi ratio (Bååth and Anderson 2003). We found a significant positive correlation between upregulated genera of Actinobacteria and soil pH (Fig. 3c). For instance, new species of Nocardia pseudosphaeroides (phylum Actinobacteria) from highly saline and alkaline habitats account for more than 30% of the total number of published new species of Nocardia (He et al. 2007; Ding et al. 2010; Tian et al. 2013). This makes the natural environs a hotspot for the discovery of Nocardia pseudonose. As a response to increasing pH, we found a concomitant decrease in Acidobacteria which is consistent with other studies (Jenkins et al. 2017). Acidobacteria are usually acidophilic (Mao et al. 2012) and therefore less likely to compete in the more neutral soil pH environment following biochar addition. Biochar was found to adjust the competition and interactions between microbial groups via shifting soil pH (Luo et al. 2018a; Chen et al. 2019).

The upregulated genera following biochar amendment might be due to their affinity towards recalcitrant organic C (e.g., aromatic components) (Fabbri et al. 2010; Kolton et al. 2017). We found that the significantly upregulated genera show a positive correlation with ROC (Fig. 3c). Biochar favored the growth of some oligotrophs (e.g., genera belonging to Actinobacteria and Alphaproteobacteria) in the rhizosphere (Fig. 2). The genera affiliated with Micrococcaceae have been previously identified as fire-responders (Woolet and Whitman 2020; You et al. 2021). The post-fire soil contained increased quantities of phenolic compounds, which shaped the oligotrophs (Ling et al. 2021). These upregulated members of Actinobacteria (e.g., genus affiliated to family Micrococcaceae, Nocardioides, Janibacter, Corynebacteriaceae, Terrabacter) and Alphaproteobacteria (e.g., Caulobacter, Qipengyuania) were known to be oligotrophs (Mallory et al. 1977; Entcheva-Dimitrov and Spormann 2004; Khessairi et al. 2014; Huang and Shen 2016; Gao et al. 2019), which are better adapted to conditions where C and nutrient resources are limited, with a role therefore in the degradation of recalcitrant compounds. Biochar is considered to increase C and nutrients due to a small fraction of easily mineralizable C and nutrients. However, the labile C component of biochar is likely to be small when compared to inputs of rhizodeposits (Weng et al. 2020). Instead, the availability of DOC and nutrients might be lowered due to the absorption of these resources onto biochar (Bolan et al. 2021). Li et al. (2019) suggested that recalcitrant components are the main driver of the microbial community in biochar-amended soil, which was consistent with our study (Table S1, Fig. S5, and Fig. 3c). Taken together, a possible explanation for the significantly upregulated genera in the biochar-amended soil could be the adaption of these oligotrophs to changes in soil structure, pH, and particularly the affinity to aromatic components, which possibly further degrade more recalcitrant components from SOC.

Potential SOC decomposers in the biochar-plant-soil system

Short-term biochar-induced increase of SOC mineralization was both abiotically and biotically mediated. Physicochemical properties such as pH have some direct effects on SOC mineralization. For example, Wei et al. (2021) observed that a colloidal mobility was depressed in acidic environments, where SOM was stabilized by Al3+ in the absence of Ca2+. However, the transitory and large increase in SOC mineralization caused by biochar is mainly attributed to microorganisms activated by abiotic properties in our study. The enhanced taxa activated by rhizodeposits might utilize other C sources, e.g., SOC. Indeed, we found larger short-term SOC mineralization in biochar-amended than non-amended soil (Fig. 1c) which may be caused by the upregulation of some genera (Fig. 3a). Most of these dominant upregulated genera are correlated with SOC mineralization (Fig. 3d), including genera affiliated to Micrococcaceae and other oligotrophs such as the genera Oryzihumus, Caulobacter, Qipengyuania, and Dongia (Fig. 3b, d). Most of these genera that correlated with SOC decomposition belonged to the phylum Actinobacteria and Proteobacteria, which have been found in soil after wildfire (Woolet and Whitman 2020). Importantly, the abundance of Actinobacteria was associated with the degradation of SOC mostly from recalcitrant compounds (Blagodatskaya and Kuzyakov 2008). Positive interactions between dominant oligotrophs, e.g., Actinobacteria, and recalcitrant components, such as phenol-like substances, have been shown (Ling et al. 2021). The increased abundance of these upregulated genera might be due to their affinity and ability to degrade the aromatic components brought by biochar to soils, which in turn cause decomposition of SOC via the production of extracellular enzymes (Goodfellow and Williams 1983; Bao et al. 2021).

Microbial communities are generally considered to affect soil C stabilization in two ways: (1) they incorporate external C into their cellular biomass production, which may subsequently be stabilized by mineral associations, and (2) they supply enzymes that catalyze the decomposition of plant-derived and native soil C (Kögel-Knabner 2002; Malik et al. 2020). Different microbial groups with metabolic strategies likely influence these two pathways of C substrates. In our study, the co-metabolism stimulated in the biochar-amended soil caused both increased efflux of CO2 originating from rhizodeposits (Fig. 1b) and native SOC (Fig. 1c). This indicates these upregulated microbial genera mainly invested in enzyme production to increase the decomposition of C substrates, thus causing greater CO2 emissions and lower 13C incorporation in soil.

Specifically, the genus Nocardioides enriched in biochar-amended soil was identified as a putative degrader of recalcitrant components, including triazine herbicides (Topp et al. 2000) and crude oil (Schippers et al. 2005). Nocardiopsis can secrete cellulolytic enzymes to degrade carboxymethyl and microcrystalline cellulose (Saratale and Oh 2011). A variety of enzymes that can degrade starch, protein, cellulose, and xylan have been isolated from Nocardia pseudobacteria, showing their potential in the mineralization of organic matter in the soil. These extracellular enzymes including endoglucanases, exoglucanases, xylanase, and glucoamylase are highly involved in metabolisms of alkanes and phenanthrene, the benzene ring within straw and polycyclic aromatic hydrocarbon, and thus possibly drive decomposition of recalcitrant components of SOC via co-metabolism. It was reported that activated microorganisms following the addition of organic substances can accelerate SOC mineralization via co-metabolism (Fontaine et al. 2003). For instance, Johnson-Rollings et al. (2014) detected increases in the abundance of Nocardiopsis and associated chitinase of glycoside hydrolase family 18 (GH18) from soil extracts, and confirmed this enzyme is responsible for the degradation of chitin in the soil. Additionally, members of Micrococcaceae, being well known for high cellulolytic activity, are the primary decomposer of organic material (España et al. 2011). Other genera within the phylum Actinobacteria, such as Oryzihumus, were the dominant microorganism in the biochar-amended soil, but little information exists about their ecological role in SOC decomposition (Wang et al. 2020). The α-Proteobacteria may also produce SOC-degrading enzymes, enabling their growth by utilizing nutrients from the soil. For instance, the genus Caulobacter (Alphaproteobacteria), considered as facultative oligotrophs, is presumed to be responsible for considerable mineralization of organic matter through the production of cellulases, including endoglucanases and β-glucosidases (Song et al. 2013).

Taken together, amendment of swine manure biochar in soil stimulated the growth of oligotrophs (e.g., Actinobacteria and Alphaproteobacteria) by increasing pH (Fig. 3c), which accelerated SOC mineralization and offset C sequestration potential by biochar incorporation. To our knowledge, this is the first experimental evidence from the use of RNA-SIP to confirm the role of Actinobacteria in SOC mineralization in a biochar-plant-soil system. Here, we highlight the significance of oligotrophs, particularly Actinobacteria, in the biochar-amended soil and their potential role in SOC mineralization. This study demonstrates the value of RNA-SIP in providing targeted insights for coupling metabolic activity (i.e., decomposition of rhizodeposits) to phylogeny. However, to further investigate the physiological characteristic of active microorganisms, a combination of RNA-SIP with metatranscriptomic sequencing should be provided to get information on the metabolic potential produced by active taxa.

Conclusion

Combination of continuous 13CO2 labelling and RNA-SIP was used to detect the core rhizosphere microorganisms involved in soil C processes following swine manure biochar amendment to maize-planted soil. Here, we showed that (i) rhizosphere bacterial communities were dominated by Actinobacteria and Alphaproteobacteria in the biochar-amended soil, most likely due to the removal of soil acidity and the increased content of recalcitrant organic C and (ii) majority of these upregulated genera (e.g., genus affiliated to Micrococcaceae, Oryzihumus, Caulobacter) by biochar were found to be positively correlated with SOC-derived CO2, indicating the significance of these genera in C mineralization in biochar-amended soil. This study highlights the core rhizosphere-associated bacterial communities that function in SOC mineralization in the biochar-amended soil–plant system. Considering the short period in this study, further research should focus on the long-term effects of biochar on the temporal dynamics of the microbial communities and the expression of C-related genes during plant growth.