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MAP  >  Soil Science and Environment
2023 Volume 2
Article Contents
Abstract
Introduction
Materials and methods
Plant materials and low temperature treatments
Ion leakage rate determination
Analysis of cold-induced transcriptomic changes in a. thaliana and c. equisetifolia
Go enrichment analysis of cold-induced genes
Cis-regulatory binding motifs analysis of cbfs
Qpcr validation of cold-related genes
Transgenic plant generation
Results
Differences in cold resistance of a. thaliana and c. equisetifolia
Differential expression patterns induced by chilling stress
Cold-induced genes go enrichment assay
Comparison of c. equisetifolia and a. thaliana go enriched cold-response entry genes
Confirmation of the expression profiles of selected cold response genes by qpcr in c. equisetifolia
Functional characterization of cbfs in c. equisetifolia
Cbf binding motif analysis in promoter region of cold-induced genes in c. equisetifolia
The expression analysis of cbf upstream genes
Analysis on the cold intolerant mechanism in tropical and subtropical plants
Discussion
Acknowledgments
Conflict of interest
Supplementary information
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References
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Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land

  • 1.

    College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi, China

  • 2.

    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi, China

  • 3.

    Center for Environmental Nanoscience and Risk, Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA

  • 4.

    College of life Sciences, Shanxi Agricultural University, Taigu 030801, Shanxi, China

More Information
  • Carbon-fixing microbes can potentially improve soil fertility. However, the potential and function of carbon-fixing microbes remains largely uninvestigated in reclaimed soil of coal-mining subsidence areas. In this study, treatments included UL (uncultivated land), CK (maize cultivation without fertilization), NPK (maize cultivation with chemical fertilizer), M (maize cultivation with manure), MNPK (maize cultivation with manure and chemical fertilizer) after 1-year reclamation in a typical coal mining subsidence area. Quantitative PCR, enzyme-linked immunosorbent assay (ELISA) and high-throughput sequencing were employed to investigate the topsoil carbon-fixing microbial biomass, RubisCO activity and community composition. The results showed that the dominant taxa (i.e., Proteobacteria, Cyanobacteria, Devosia and Marichromatium) were significantly changed after reclamation (P < 0.05). Carbon-fixing microbial community structure in fertilization treatments (NPK, M and MNPK) obviously differed from non-fertilizer treatments (UL and CK). Soil organic carbon and microbial biomass carbon were significantly higher in fertilization treatments than non-fertilizer treatments (P < 0.05). M significantly increased RubisCO activity and cbbL gene abundance (P < 0.05), MNPK significantly increased carbon-fixing microbial richness (P < 0.05). Carbon-fixing microbial community structure was strongly influenced by soil moisture, catalase, total phosphorus and dissolved organic carbon. Some environmental factors indirectly influenced SOC by affecting carbon-fixing microbial biomass, diversity and community structure. Our study implies that even short-term (1-year) reclamation and fertilization could significantly influence carbon-fixing microbial community structure and promote soil carbon accumulation, and the fertilization treatments with manure (M and MNPK) were more conducive, which indicated that carbon-fixing microbes were greatly conducive to improve soil fertility in reclaimed mining areas and achieve carbon neutrality.

  • Introduction

    Low temperature is one of the main factors restricting plant growth, development and geographic distribution. Cold stress is categorized as chilling (0−15 °C), generally suffered by tropical and subtropical plants, and freezing (< 0 °C), experienced by temperate plants[1]. The adaptability of plants to chilling and freezing is termed as chilling resistance and freezing resistance, respectively.

    To adapt to cold climates, plants have developed a set of complex mechanisms[2]. Lots of temperate plants increase their freezing tolerance through cold acclimation (CA). The molecular mechanism of cold acclimation in Arabidopsis has been well studied, and a relatively clear cold regulatory network has been established. The CBF/DREB1 (C-REPEAT BINDING FACTOR/DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN 1)-dependent transcriptional modulation pathway takes indispensable parts in plant response to both chilling and freezing[3]. In the CBF-dependent pathway, the expression of CBF genes are rapidly and significantly up-regulated by low temperature, and the corresponding CBF proteins activate the cold-regulated (COR) genes' expression through specific binding to the C-repeat/DRE cis-elements (G/ACCGAC) motif contained in the promoters of COR genes, resulting in the improvement of cold acclimation and freezing tolerance[4−6]. Moreover, the deletion of the C-repeat/DRE regulatory motif of AtCBF2 decreases the cold tolerance of plants[7].

    CBFs function as a molecular switch under low temperature stress and is highly conserved in different higher plants. The transgenic plants with the overexpressing of endogenous CBFs represent distinctly improved tolerance to freezing[8−12]. In Arabidopsis, CBF1 (DREB1B), CBF2 (DREB1C), and CBF3 (DREB1A) are highly similar in sequence and redundant in function. The triple mutants of CBF1 CBF2 CBF3 (cbf1 cbf2 cbf3) fail to respond to chilling stress and are hypersensitive to freezing stress after cold acclimation[13]. Previous research reported that the expression of CBFs is directly modulated by some other transcription factors, such as Inducer of CBF expression 1 (ICE1)[14−16], Calmodulin-binding Transcription Activators (CAMTAs)[17,18], MYB15[19], Ethylene-Insensitive 3 (EIN3), Brassinazole-Resistant 1/BRI1-EMS-SUPPRESSOR1 (BZR1/BES1)[20−22], Phytochrome-Interacting Factors 3/4/7 (PIF3/4/7)[23,24], and Circadian Clock-Associated 1/Late Hypocotyl (CCA1/LHY)[25,26]. In addition, some genes independent CBF pathway have also been identified to be involved in cold response, such as HY5 (ELONGATED HYPOCOTYL 5)[27] and HSFA1[28].

    Plants from temperate-zones exhibit a strong resistence to low temperature conditions, whereas plants originating from the tropics and subtropics are generally sensitive to the chilling temperatures, and undergo irreversible damage[29−31]. Many studies have investigated the physiological and biochemical response of tropical and subtropical plants under cold stress, but, besides identifying some cold responsive genes, limited systematic research has been conducted to understand the reasons for the tropical and subtropical plants sensitivity to cold conditions. For example, it has been shown that the CBF-dependent pathway could perform its functions in rubber tree (Hevea brasiliensis)[32] and some genes involved in cold adaptability, such as HbICE1[33], HbCOR47[34] and HbEBP1[35] have been characterized. Compared to Cavendish (a cultivar of banana), the stonger cold resistence of another specie Dajiao could be due to the quick activation and induction of ICE1 and MYBS3[36]. In Moso bamboo, PeDREB1 is highly up-regulated by cold and PheMYB4-1 also mediated the response of bamboo to cold[37,38]. Further transcriptome analysis under different cold conditions in Moso bamboo revealed that most of the genes involved in cold response are late-responsive ones[39]. So, it is very important to improve the cold resistence of tropical and subtropical economic tree species through elucidating the underlying basis of intolerance to cold stress at the molecular level.

    Casuarina equisetifolia (C. equisetifolia) is a drought-, salinity-, and barren-tolerant tree species, and native to southeastern Australia[40]. It is a typical tropical plant with poor cold tolerance with 22.1−26.9 °C being the suitable temperature for its growth[41]. In this study, C. equisetifolia was used as a typical example of tropical and subtropical tree. We compared transcriptome of C. equisetifolia to that of A. thaliana, in which a clear view of the cold signal network has been investigated, under cold stress for different treatment times to identify the factors causing the cold intolerance in C. equisetifolia.

    Materials and methods

    Plant materials and low temperature treatments

    The seeds of C. equisetifolia were sown in the pot and grown in greenhouses for two months and then the chilling treatment was carried out. A. thaliana seeds of wild-type (Col), mutants and transgenic plants were placed on Murashige and Skoog (MS) medium for 7 d at 25 °C with 16 h/8 h light/dark cycle and then transferred into the greenhouse. After two weeks, the seedlings of A. thaliana were exposed to low temperatures.

    The seedlings of both C. equisetifolia and A. thaliana were firstly treated at 4 °C for 7 d for survival rate analysis and at 4 °C for 4 d for ion leakage experiment in a climate-controlled chamber, respectively, and then transferred to −8 °C for 6 h. Subsequently, the treated seedlings were recovered at 4 °C for 12 h and then moved to greenhouses for 7 d. Finally, the survival and ion leakage rates were calculated.

    Ion leakage rate determination

    Leaves (100−200 mg) were detached from cold-treated and untreated A. thaliana and C. equisetifolia, respectively, and were washed three times using deionized water. The samples were then placed on the clean glass chambers with 10 mL deionized water and were shaken at room temperature for 1 h to mix. The electrical conductivity (C1) of the solution was detected using a conductivity meter (DDS-307W, Gallop). Subsequently, the chambers were boiled in a water bath for 20 min, followed cooling for 1 h. After that, the electrical conductivity (C2) of the solution in each tested chamber was surveyed. The relative electrolyte leakage (REL) was obtained based on the formula REL = C1/C2 × 100%. The experiments were performed three times and the data were represented as the means ± standard deviation (SD) of three independently biological replicates.

    Analysis of cold-induced transcriptomic changes in A. thaliana and C. equisetifolia

    In order to identify the differential transcriptomic changes between A. thaliana and C. equisetifolia under cold stress, 21-day old A. thaliana and two-month old C. equisetifolia seedlings were used for low temperature treatments, which were 4 °C for 0, 2, and 24 h for A. thaliana and 0, 10 min, 2 h, 24 h, and 168 h for C. equisetifolia. Total RNA was isolated from the treated seedlings and evaluated by NanoDrop ND-1000. Poly (A)-RNA was enriched for constructing cDNA library and sequencing by Illumina NovaSeq 6000 (2 × 150 bp paired-end reads). Each time point contained three replicates.

    In order to analyze the RNA-seq dataset, we designed a pipeline. In brief, we aligned the clean reads of the samples to the corresponding genome and reference genes using hisat2-2.1.0[42] with the parameters (--min-intronlen 20 --max-intronlen 5,000 --rna-strandness RF). Gene expression in the normal and after the onset of cold treatment was determined using StringTie v2.0.3[43]. Trimmed Means of M values (TMM) values were used to represent gene expression value. The cold-induced genes were defined with more than two-fold expression under cold stress relative to that under normal conditions.

    GO enrichment analysis of cold-induced genes

    The GO term annotations of A. thaliana genes were directly downloaded from the website The Arabidopsis Information Resource (www.arabidopsis.org) (TAIR10)[44]. As reported in previous research[45,46], the protein coding genes of C. equisetifolia were annotated with the highest similarity to A. thaliana proteins through BLASTP searches (E-value < 1e–5). The GO enrichment analysis of cold-induced genes was performed through the OmicShare tools (www.omicshare.com/tools).

    Cis-regulatory binding motifs analysis of CBFs

    To explore the difference of CBF-CORs-mediated cold signaling pathway between A. thaliana and C. equisetifolia, the following pipeline was developed. First, we collected the well-known CBF regulons with cold-elevated expression in A. thaliana from previous report[47]. Next, we identified the orthologous of those CBF regulons in C. equisetifolia using BLASTP program (E-value < 1e–10). Then, we extracted 1- and 2-Kb upstream promoter sequences of the regulons in A. thaliana and C. equisetifolia from the corresponding genomes, respectively. Besides, the published A. thaliana CBFs cis-regulatory motifs (G/ACCGAC) from early research were organized[47,48]. Using the modules as query, those promoters were searched by FIMO[49]. Finally, the CBF binding sites in 1- and 2-Kb upstream promoter sequences of CBF regulons were identified.

    qPCR validation of cold-related genes

    This study utilized quantitative reverse transcription PCR (qPCR) following the methodology described by Cheng et al.[35]. The primers of genes for quantification were designed using Primer 5 software and listed in Supplemental Table S1. The primers were synthesized by Hangzhou Youkang Biotechnology Co., Ltd (Hangzhou, China). qPCR was performed using the ChamQTM SYBR Color qPCR Master on a Bio-Rad Iq5 RT-PCR instrument, following the experimental protocols outlined in the instruction manual. The data was analyzed using 2−ΔΔCᴛ method.

    Transgenic plant generation

    The full-length coding sequences (CDS) of CeqCBF genes (CeqCBF1 and CeqCBF3) were amplified and cloned into the pCAMBIA1300-35S::GFP vector between the Sma I and Xba I sites, termed as 35S::CeqCBF1OE/cbfs and 35S::CeqCBF3OE/cbfs. The recombinant plasmid was transformed into Agrobacterium tumefaciens EHA105. The transgenic lines were generated through Agrobacterium-mediated floral-dipping method and screened by Hygromycin B. The seeds from T3 homozygous transgenic plants were used for phenotype detection under low temperature conditions. The primers used are listed in Supplemental Table S2.

    Results

    Differences in cold resistance of A. thaliana and C. equisetifolia

    The molecular regulation mechanism of A. thaliana in response to cold stress is well documented. To investigate the cold stress response of C. equisetifolia, we compared the differences in cold response between A. thaliana and C. equisetifolia, and determined the impact of cold acclimation on improving cold resistance in these plant species. The morphological performance of both A. thaliana and C. equisetifolia with (4 °C for 7 d and then treated at −8 °C for 6 h) and without (directly exposed to −8 °C) cold acclimation was significantly different. Compared to the cold-acclimated (CA) counterparts, plants (both A. thaliana and C. equisetifolia) non-acclimated (NA) showed typical cold injury symptoms like desiccated foliage and eventually almost all plants died (Fig. 1a, b). Moreover, C. equisetifolia displayed higher cold sensitivity than A. thaliana (Fig. 1b). The survival rate of CA plants (both A. thaliana and C. equisetifolia) was higher than that of NA plants. In detail, the survival rate of A. thaliana achievd 95.8% after cold acclimation for 7 d, whereas the survival rate of C. equisetifolia was about 4.93% (Fig. 1c), indicating that cold acclimation could improve the cold resistence of plants and C. equisetifolia had a weaker cold acclimation capacity than A. thaliana.

    Figure 1.  Analysis of cold resistance of A. thaliana and C. equisetifolia. (a), (b) Freezing phenotypes (c) survival rates, and (d) ion leakage, of three-week-old wild type A. thaliana and two-month-old C. equisetifolia. The seedlings were grown under a 16-h-white light/8-h-dark photoperiod (LD) at 25 °C and either directly exposed to freezing treatment (non-acclimated, NA), or treated at 4 °C for 4 d or 7 d before freezing treatment (cold-acclimated, CA). Representative photographs were taken after a 3-d recovery period, survival rates were calculated, and the ion leakage was measured. Data are presented as the mean ± standard deviation of mean of three biological replicates. Asterisks represent significant differences compared to the non-acclimated plant. ** p < 0.01.
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    The relative electrolyte leakage is a vital indicator of membrane permeability. So, the electrolyte leakage assay was carried out in both plants with and without cold acclimation based on the method reported by Jiang et al.[50]. The data showed that the electrolyte leakage of CA plants was dramatically lower than that of the NA counterparts (Fig. 1d). Moreover, the influence of cold acclimation on electrolyte leakage was more obviously in A. thaliana (dropped from 74.2% to 24.5%) than in C. equisetifolia (dropped from 62.9% to 45.8%) (Fig. 1d).

    Differential expression patterns induced by chilling stress

    To further investigate the transcriptional response of C. equisetifolia under cold stress, C. equisetifolia and A. thaliana wild-type (Col) seedlings were exposed to a cold treatment at 4 °C for different time periods (0, 10 min, 2 h, 24 h, 168 h for C. equisetifolia, and 0, 2 h, 24 h for A. thaliana). Leaves from the treated seedlings were harvested at indicated time points and 24 cDNA libraries (15 for C. equisetifolia and nine for A. thaliana) were prepared which were subjected to paired-end sequencing. According to Pearson Correlation Coefficient (PCC), the sample replicates were clustered closely and independently, illustrating that the results were highly reproducible with good quality (Supplemental Fig. S1).

    To identify the genes up-regulated by cold stress, the expression of a particular gene was considered as cold-induced with log2 (fold-change) ≥ 1 (p value < 0.01) in comparison with control. The genes with read counts < 5 in each sample were removed.

    According to the Venn diagram, a total of 3,085 (1,376 at 2 h, 2,416 at 24 h) cold-induced genes were identified in A. thaliana, whereas a total of 3081 genes were up-regulated by chilling in C. equisetifolia (48 at 10 min, 189 at 2 h, 728 at 24 h, and 2,764 at 168 h) (Fig. 2a). Further, we found that there was a considerable difference in the number of cold-induced genes at each treatment time point between C. equisetifolia and A. thaliana, such as 189 vs 1,376 at 2 h, 728 vs 2,416 at 24 h, while the number of cold-induced genes at 168 h in C. equisetifolia was close to the number at 24 h in A. thaliana (Fig. 2b), displaying an obvious delay in cold response in C. equisetifolia. Moreover, expression profiles and clustering of cold-induced genes showed that numerous genes in A. thaliana was significantly induced by cold at 2 and 24 h after the start of cold treatment, while only fewer genes were up-regulated in C. equisetifolia at these time points, indicating that C. equisetifolia may require a more prolonged cold treatment than A. thaliana for transcriptome response (Fig. 2c).

    Figure 2.  Comparison of the number of cold-induced genes at different time points in A. thaliana and C. equisetifolia. (a) Venn diagram illustrating the cold-induced genes at 10 min, 2, 24 and 168 h (C. equisetifolia) and 2 h, 24 h (A. thaliana) after chilling treatment. (b) The number of cold-induced genes between the different plants and time points. (c) Expression profiles and clustering of 3,081 cold-induced genes in C. equisetifolia and 3,085 cold-induced genes in A. thaliana at different times after cold acclimation.
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    Cold-induced genes GO enrichment assay

    GO enrichment analysis was carried out to classify the putative functions of cold induced genes. The top 15 highly enriched functional groups are shown in Fig. 3. For A. thaliana, 15 functional groups and 11 functional groups separately exhibited significant enrichment within the biological processes category during the 2 h and 24 h of cold stress. These enriched groups included response to abiotic stimulus, cold, temperature stimulus, water deprivation and so on (Fig. 3a).

    Figure 3.  GO enrichment analysis of cold-induced genes at different time points in A. thaliana and C. equisetifolia under 4 °C cold treatment. (a) A. thaliana, (b) C. equisetifolia.
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    For C. equisetifolia, 13 functional groups were significantly enriched at the 10 min time point within the biological processes category, and two functional groups were enriched within the molecular process. For the 2 h time point, enriched biological processes included response to endogenous stimulus, chemical, and cold, etc., while the enriched molecular processes included beta-amylase activity and transcription factor activity. At the 24 h time point, 14 groups were enriched within the biological processes category. And at 168 h after the initiation of chilling, the GO enrichment analysis revealed that 11 functional groups were enriched in the category of biological processes (Fig. 3b). It is worth mentioning that, with the exception of the 10 min time point in C. equisetifolia, biological process 'response to cold' was represented in both C. equisetifolia and A. thaliana at all cold treatment time points, indicating the induction of cold response at the transcriptional level.

    To gain further insights, the differences in GO entries between A. thaliana and C. equisetifolia at 2 h and at 24 h treatment time points were analyzed, respectively. The results showed that GO entries related to stress response, such as hormone, abiotic stimulus, cold, and water deprivation were dramatically enriched in A. thaliana at 2 h, whereas in C. equisetifolia at 24 h (Supplemental Fig. S2a). Furthermore, at each time point, the top 200 enriched GO entries were examined and found that the two species shared 115 GO entries at 2 h of A. thaliana and 24 h of C. equisetifolia, as well as at 24 h of A. thaliana and 168 h of C. equisetifolia (Supplemental Fig. S2b). These results implied that though the cold stress response of C. equisetifolia was comparatively delayed, the overall biological enrichment classes are similar to A. thaliana.

    Comparison of C. equisetifolia and A. thaliana GO enriched cold-response entry genes

    As plant cold stress tolerance is a polygenic controlled trait[51], an array of genes and proteins play vital roles in cold tolerance[52−55]. To analyze the cold response pathway in C. equisetifolia, we extracted cold induced genes from A. thaliana transcriptome and then identified the homologous genes in C. equisetifolia using A. thaliana cold-induced genes as the query. A total of 139 and 143 genes were identified in C. equisetifolia and A. thaliana, respectively, in which 87 were significantly up-regulated by low temperature both in A. thaliana and in C. equisetifolia (Fig. 4a). However, the expression of most of them in C. equisetifolia was dramatically increased at 168 h and obviously later than that in A. thaliana, which were induced at 24 h (Fig. 4a), inferring that their delayed expression may influence the cold tolerance of C. equisetifolia. In addition, 52 genes had no change in transcription level under cold conditions in C. equisetifolia but up-regulated A. thaliana (Fig. 4b). Some of these genes have been reported to play notable roles in response to cold, such as RD29A/B[56], COR413PM1[57,58], KIN1/KIN2 (ABA-inducible protein-coding), SUS1, DAG2, and LOS2[47]. Besides, four genes namely STMP2 (Salt Tolerance-Associated Membrane Protein 2), HTT5 (HEAT-INDUCED TAS1 TARGET 5), COR15A (Cold regulated 15A), and COR15B were absent in the transcriptome of C. equisetifolia (Fig. 4c).

    Figure 4.  Comparison of the 'cold response entries' in A. thaliana and C. equisetifolia. (a) Conserved cold-induced genes in A. thaliana and C. equisetifolia. (b) Unresponsive to cold stress/ weakly cold-induced (FC < 2) genes in C. equisetifolia. (c) Gene absent in C. equisetifolia.
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    Based on the above analysis, we postulated three reasons for relative cold sensitivity of C. equisetifolia. First, compared with A. thaliana, the expression of cold responsive genes was delayed. Second, partial key cold-response genes were not up-regulated under cold stress, and last but not least, several functional cold responsive genes reported in A. thaliana were absent in C. equisetifolia.

    Confirmation of the expression profiles of selected cold response genes by qPCR in C. equisetifolia

    To confirm the expression profiles of cold responsive genes in C. equisetifolia, the transcriptional level of some genes was analyzed through qPCR. We selected two genes with the similar expression pattern to A. thaliana (CIPK7, GOLS3), seven genes with delayed cold-induced expression (DEAR1, BBX29, CBL1, MPK3, RD29A, WRKY33, CBF1), and seven genes with no change in expression (ADH1, DGK2, KIN1, LOS2, COR413-PM1, ELF3, SUS1) in C. equisetifolia under cold stress. The qPCR results revealed that the expression of CIPK7 and GOLS3 was markedly induced under cold conditions at 2 h and 24 h in C. equisetifolia, which aligned with the transcriptome data of A. thaliana (Fig. 5). On the other hand, the expression of DEAR1, BBX29, CBL1, MPK3, RD29A, WRKY33, and CBF1 in C. equisetifolia was prominently up-regulated at 168 h, whereas in A. thaliana, the up-regulation was observed a significant delay at 24 h (Fig. 5). Furthermore, the expression of ADH1, DGK2, KIN1, LOS2, COR413-PM1, ELF3, and SUS1 was not increased by cold stress in both species, and some of these genes were even down-regulated (Fig. 5). In addition, the expression of ADH1, DGK2, KIN1, LOS2, COR413-PM1, ELF3, and SUS1 was not induced by cold stress, instead a few of these were down-regulated (Fig. 5). These findings demonstrated that the qPCR results for the selected genes in C. equisetifolia were consistent with the transcriptome data of both A. thaliana and C. equisetifolia.

    Figure 5.  Expression of cold-response genes in C. equisetifolia, containing two similar expression pattern genes (CIPK7, GOLS3), seven delayed genes (DEAR1, BBX29, CBL, MPK3, RD29A, WRKY33, CBF1), four weakly responsive genes (ADH1, DGK2, KIN1, LOS2) and three non-responsive genes (COR413-PM1, ELF3, SUS1). CIPK7, CBL-interacting protein kinase 7; GOLS3, galactinol synthase 3; DEAR1, cooperatively regulated by ethylene and jasmonate 1; BBX29, B-box type zinc finger family protein; CBL, calcineurin B-like protein 1; MPK3, mitogen-activated protein kinase 3; RD29A, low-temperature-responsive protein 78 (LTI78); WRKY33, WRKY DNA-binding protein 33; ADH1, alcohol dehydrogenase 1; DGK2, diacylglycerol kinase 2; KIN1, stress-responsive protein (KIN1)/stress-induced protein (KIN1); LOS2, involved in light-dependent cold tolerance and encodes an enolase; COR413-PM1, cold regulated 413 plasma membrane1; ELF3, hydroxyproline-rich glycoprotein family protein; SUS1, encodes a protein with sucrose synthase activity. The values are the mean ± standard deviation of three biological replicates. Relative expression in untreated plants (0 h) was set to 1. *p < 0.05, **p < 0.01.
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    Functional characterization of CBFs in C. equisetifolia

    It is well known that the C-repeat binding factor (CBF) plays a predominant role in cold acclimation to gain the maximum cold resistence in plants[5,59]. We identified the Arabidopsis homologous of CBFs in C. equisetifolia and descovered that the transcriptional level of CeqCBF1 was up-regulated by low temperature (Fig. 5). To verify whether cold intolerance of C. equisetifolia was associated with CBFs, we identified CBF family in C. equisetifolia. There were eight CBF homologous proteins in the transcriptome of C. equisetifolia by BLASP, and we constructed the phylogenetic tree to determine the evolutionary relationship of C. equisetifolia CBFs (Fig. 6a). Further, the conserved structure domains of CBFs were aligned. The results showed that signature1 and AP2 domain were highly conserved, but an amino acid R was absent from signature2 in C. equisetifolia and the first amino acid D in signature2 in A. thaliana was changed into E (ESAW-) in C. equisetifolia (Fig. 6a), inferring that it may impact CBFs' function in cold response pathway. The chromosome localization data showed that A. thaliana CBF1, CBF2, and CBF3 are arranged in series on chromosome 4 (Fig. 6b), which was consistent with the previous research[60,61]. The analysis of CeqCBF genes localization on the chromosomes in C. equisetifolia revealed that four out of eight genes were found in tandem arrangements (Fig. 6c).

    Figure 6.  CBF in C. equisetifolia was involved in cold regulation. (a) Alignment of AP2/ERE domain amino acid sequences between A. thaliana and C. equisetifolia. (b), (c) Chromosome positions of CBF genes. (d) Freezing phenotypes, (e) survival rates and (f) the expression of CeqCBF1 in A. thaliana cbf triple mutants and transgenic lines with overexpressing of CeqCBF1. Two-week-old plants grown on MS plates at 22 °C were treated at −6 °C for 1 h after cold acclimation at 4 °C for 3 d. Asterisks indicate significant differences compared to the cbfs mutant plants. **p < 0.01.
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    To study the function of CeqCBFs in response to cold stress, the recombination plasmids were separately constructed with CeqCBF1 (Casgl53S05669) (exhibiting high homology to AtCBF1/AtCBF2/AtCBF3) and CeqCBF3 (Casgl344S25208) under 35S promoter and then transferred into Arabidopsis cbfs triple mutants (cbf1cbf2cbf3) by floral dipping method. The results of phenotype analysis showed that the transgenic lines with CeqCBF1 overexpression (35s::CeqCBF1OE-25/cbfs, 35s::CeqCBF1OE-27/cbfs, 35s::CeqCBF1OE-28/cbfs) and CeqCBF3 overexpression (35s::CeqCBF3OE-20/cbfs, 35s::CeqCBF3OE-27/cbfs, 35s::CeqCBF3OE-40/cbfs) in the cbfs triple mutants background could restore the sensitivity of cbfs triple mutants to cold stress to a certain extent and display the increased cold tolerance, respectively (Fig. 6d, Supplemental Fig. S3a). The survival rate of transgenic lines was obviously higher than that of cbfs triple mutants (Fig. 6e, Supplemental Fig. S3b). These results indicated that CeqCBF1 and CeqCBF3 dramatically enhance the cold tolerance of transgenic plants, though signature2 domain in CeqCBFs was different from that in AtCBFs.

    CBF binding motif analysis in promoter region of cold-induced genes in C. equisetifolia

    CBFs, vital transcription factors in plant cold acclimation, promote the expression of a subset of cold responsive (COR) genes by specific binding to a CRT/DRE (G/ACCGAC) element in their promoters[4,5]. We identified the downstream target genes of CBFs from the transcriptome of both A. thaliana and C. equisetifolia, and subsequently analyzed the binding motif contained in COR genes' promoter region.

    For A. thaliana, we identified 91 downstream target genes of CBFs in transcriptome[47], and most of them were highly induced by cold stress. Taken them as query, the same number homologous genes in C. equisetifolia were also identified. According to the transcriptome data, only half of the homologous genes in C. equisetifolia were up-regulated under low temperature condition, and a majority of the up-regulated genes exhibited a delayed response (Fig. 7). To investigate the CBF‐binding motif, the sequence of promoter located 1,000−2,000 bp upstream of ATG were analyzed by using FIMO[49]. In the 2,000 bp region, all genes contained CRT/DRE elements in A. thaliana, but only 58 of 91 contained CRT/DRE core motifs in C. equisetifolia. Moreover, the number of CRT/DRE binding motifs in the promoter of COR genes in C. equisetifolia was less than that in A. thaliana (Fig. 7), suggesting that the reduced number of CRT/DRE elements in the promoter region of C. equisetifolia may be associated with the delayed induction of COR genes in response to low temperature. Additionally, only half of the homologous genes in C. equisetifolia were up-regulated under cold treatment further supported the potential link between the decrease in CRT/DRE motifs and the reduced number of up-regulated genes in the downstream targets of CBFs in C. equisetifolia.

    Figure 7.  Heatmap and CRT/DRE element analysis of downstream cold response genes directly regulated by CBF in A. thaliana and C. equisetifolia.
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    To prove this hypothesis, the expression of 13 genes without CRT/DRE element were detected in C. equisetifolia under cold stress. Of the genes, only two (GOLS1 and NRT 1.7) genes were significantly increased at the 24 h time point under low temperature treatment, which was similar to that in A. thaliana (Fig. 8). The expression of five genes (TIL, ZIFL1, PGIP1, CSCL9 and MMI9.1) was increased by cold, but obviously delayed in comparison with that in A. thaliana (Fig. 8). Additionally, the transcriptional levels of six genes, including GRF2, RLP33, GORK, Q8W589, SERPIN1, and BGLU10, did not show any obvious change under cold treatment (Fig. 8). These results implied that the absence of CRT/DRE elements resulted in either non-response or postponed response in the expression of cold induced genes, resulting in the weak cold tolerance in C. equisetifolia. We also determined the expression of the CBF downstream genes containing DRE elements in C. equisetifolia, including ERD7, RD26, EXL2, HVA22D, LEA14, and RAV1. These genes exhibited induced but delayed expression under cold stress (Supplemental Fig. S4), indicating that CRT/DRE elements were contributed to their expression mediated by CBF.

    Figure 8.  The expression of downstream genes without DRE elements but directly regulated by CBF in C. equisetifolia, containing two similar expression pattern genes (GOLS1, NRT1.7), five delayed genes (ZIFL1, TIL, PGIP1, CSCL9, MMI9.1) and six weakly responsive and non-responsive genes (GBF2, RLP33,GORK,Q8W589, SERPIN1, BGLU10), GOLS1, galactinol synthase 1; NRT1.7, NITRATE TRANSPORTER 1.7; ZIFL1, ZINC INDUCED FACILITATOR-LIKE 1; TIL, temperature-induced lipocalin; PGIP1, POLYGALACTURONASE INHIBITING PROTEIN 1; GBF2, G-box binding factor 2; RLP33, receptor like protein 33; GORK, GATED OUTWARDLY-RECTIFYING K+ CHANNEL; SERPIN1, Inhibitor of pro-apoptotic proteases, which is involved in the regulation of the programmed cell death induction; BGLU10, BETA GLUCOSIDASE 10. The values are the mean ± standard deviation of three biological replicates. Relative expression in untreated plants (0 h) was set to 1. *p < 0.05, **p < 0.01.
    DownLoad: Full-Size Img PowerPoint

    The expression analysis of CBF upstream genes

    It has been reported that cold stress rapidly up-regulated the transcriptional expression of the CBFs through a complex signal transduction network in A. thaliana, involving transcription factor ICE1, CAMTAs, MYB15, Circadian Clock‐related components CCA1/LHY, and the light signaling components PIF3/4/7[6,62]. Based on transcriptome analysis, we discovered a stark contrast in the expression patterns of CBF genes between A. thaliana and C. equisetifolia under cold treatment. In A. thaliana, the expressions of three CBF genes were significantly upregulated at 2 h after the start of cold treatment. However, in C. equisetifolia, the noticeable upregulation of their homologous genes was delayed and occurred after 24 h of cold treatment (Fig. 9a). Besides that, few upstream genes of CBFs were identified and their expression profiles were compared to that of A. thaliana. These upstream genes could be divided into three types according to their expression pattern: the first group including genes non-induced by cold stress (PIF4, PIF7, CAMTA5, OST1), the second group including delayed induced genes (PUB25, MYB15, PRR5, JAZ1, LUX, EGR2, ELF4, COR28, COR27) while the third group including genes with similar expression patterns to those in A. thaliana (CCA1, TOC1) (Fig. 9a). Further, the transcription levels of these genes and three CeqCBFs were detected under chilling treatment at different time points using qPCR. The results displayed that compared to the expression of genes in A. thaliana, the obvious increase in the expression of CeqCBFs (CeqCBF1, CeqCBF2, CeqCBF3, CeqCBF4, 2 h vs 12 h), LUX (24 h vs 72 h), ELF4 (24 h vs 168 h), MYB15 (2 h vs 72 h), PRR5 (24 h vs 72 h), TOC1 (24 h vs 168 h) was obviously delayed (Figs 5b, 9b). Chilling treatment did not induce the expression of JAZ1, PIF4, PIF7, CCA1, and OST1. These results demonstrated that one of the factors for the cold stress sensitivity of C. equisetifolia could be delayed response or non-response of the upstream regulatory genes.

    Figure 9.  Comparison of upstream regulatory genes for CBF in A. thaliana and C. equisetifolia. (a) Heatmap of CBF upstream regulatory genes in A. thaliana and C. equisetifolia. (b) The expression of CBF upstream gene in C. equisetifolia. CBF, C-repeat/DRE binding factor; LUX, Homeodomain-like superfamily protein; ELF4, early flowering-like protein; MYB15, myb domain protein 15; PRR5, two-component response regulator-like protein; TOC1, CCT motif -containing response regulator protein; JAZ1, jasmonate-zim-domain protein 1; PIF4/7, phytochrome-interacting factor4/7; CCA1, circadian clock associated 1; OST1, Protein kinase superfamily protein. Relative expression in untreated plants (0 h) was set to 1. *p < 0.05, **p < 0.01.
    DownLoad: Full-Size Img PowerPoint

    Analysis on the cold intolerant mechanism in tropical and subtropical plants

    C. equisetifolia is one of the typical tropical and subtropical plants, exhibiting sensitivity to cold stress, which is also the property of most of the tropical and subtropical plants. According to the results of our analysis, the absence of CBFs binding motif located in the promoters of their downstream genes maybe one of the main reasons for C. equisetifolia' cold intolerance (Fig. 7). To investigate whether the loss of CRT/DRE elements is common to tropical and subtropical plants and how it affects the cold response, the homologous proteins of CBFs in different tropical and subtropical plants, including Theobroma cacao, Citrus sinensis, Citrus clementina, and Musa nana Lour, Citrus clementina, Citrus sinensis, Populus trichocarpa, and Hevea brasiliensis, were identified by MEGA11 and phylogenetic tree was constructed. The result of CBF conserved domains analysis showed that typical domains of CBFs in these homologous proteins were highly conserved (Supplemental Fig. S5), indicating that CBFs may play similar roles in response to cold stress in different tropical and subtropical plants. Further, CRT/DRE motif was analyzed in CBF's downstream target genes by using FIMO[49]. The number of CRT/DRE motif was significantly reduced in the detected promoter range of downstream genes in different species (Fig. 10), demonstrating that absence of CRT/DRE may be a common feature for tropical and subtropical plants.

    Figure 10.  CRT/DRE element analysis of downstream cold response genes directly regulated by CBF in other tropical and subtropical plants.
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    Discussion

    Cold acclimation is a vital strategy for improving plant cold resistance. Many tropical/subtropical plants, such as Litchi chinensis[63], Ananas comosus[64], Citrus[65], Musa spp[66], and C. equisetifolia[67], can gradually distribute and survive in wider geographic and temperature ranges through cold acclimation. In this study, whether acclimated or not, C. equisetifolia maintained a low relative ion leakage rate when exposed to −2 °C, −4 °C, and −6 °C for 2 h, respectively (Supplemental Table S3), demonstrating that C. equisetifolia had a certain degree of tolerance to freezing stress for short treatment time. However, with longer exposure to low temperature, the ion leakage rate dropped to 17.16% after treatment at −6 °C for 6 h, compared to 53.89% of non-acclimated C. equisetifolia seedlings. On the contrary, it was prominent difference in the survival rate between the NA plants (21.15%) and CA plants (59.02%) (Supplemental Table S3). Further, when treating plants with the following condition (at 4 °C for 4 d and then −8 °C for 6 h), the rate of survival and the ion leakage of C. equisetifolia seedlings was separately 5.22% and 45.84% (Supplemental Table S3, Fig. 1c, d). In contrast, under the same treatment condition, the survival rate of A. thaliana increased from 0 to 100% after cold acclimation (Fig. 1c). This proposed that cold acclimation can improve the cold resistance of C. equisetifolia to some extent although this improvement was significantly lower than that of A. thaliana. It is speculated that C. equisetifolia may lack certain low temperature response or regulatory factors that are present in A. thaliana.

    Comparing the low temperature transcriptomes of C. equisetifolia and A. thaliana showed that many low temperature responsive genes in C. equisetifolia exhibited no expression or weak or delayed response under low temperature stress, particularly in the CBF regulation pathway (Figs 4, 7 & 9). Further analysis revealed that numerous downstream target genes of CBF lacked the DRE binding element (Figs 7, 10), leading to influence the binding of CBFs to the downstream genes and then effect the expression of downstream genes, therefore decrease the cold adaptability of tropical and subtropical plants.

    In A. thaliana, cold acclimation triggers altering the expression of a large number of cold regulated genes[62]. According to whether directly regulated by CBF or not, these COR genes are divided into CBF-dependent pathway and CBF-independent pathway. Many studies have reported that different transcription factors and proteins are involved in CBF-dependent signal pathways[6]. It has been shown that plenty of COR genes, including KIN1/2, COR15A/B, LTI78[68,69], play major roles in regulating plant' cold response. Overexpressing of COR15A, encoding a chloroplast-targeted polypeptide in A. thaliana, results in a marked increase in the survival rate of separated protoplast frozen at −4.5 °C to −7 °C[70,71]. By comparing the homologous COR genes in C. equisetifolia and A. thaliana, we found that C. equisetifolia lacked homologous genes of COR15A, COR15B, HTT5, and STMP2 (Fig. 4), and the similar case was also discovered in other tropical/subtropical plants, such as Theobroma cacao, Musa nana Lour, Citrus clementina, Citrus sinensis, and Hevea brasiliensis (Fig. 11), speculating that the loss of key COR genes may be a crucial factor for their low temperature sensitivity and this may happen during the evolution process of tropical/subtropical plants in high temperature environments.

    Figure 11.  Homologous cold response genes in A. thaliana and other tropical/subtropical plants.
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    Cold-induced transcription factors CBF/DREB1 from AP2/ERF gene family respond to low temperature condition by directly modulating the expression of COR genes[47,59]. In Arabidopsis, the expression of CBFs shows rapid and early response to cold acclimation, and reaches the highest transcription level at 4 °C within 2 h[61,72]. Some studies have displayed that the expression of CBFs often exhibits a delayed response in Hevea brasiliensis, which is one of the typical tropical trees and sensitive to low temperature[32,34,73]. C. equisetifolia contained five CBF homologous genes, and cold acclimation highly induced the expression of CeqCBFs, which was accordance with the expression pattern of CBFs in A. thaliana, except that the peak expression of CeqCBFs occurred at 12 h after the start of cold acclimation (Fig. 5, Fig. 9b). In addition, CBF in C. equisetifolia, as well as in other tropical/subtropical plants, shared high conservation with Arabidopsis CBF in conserved structure domains, despite of certain differences in individual amino acid sites (Fig. 6a, Supplemental Fig. S5), suggesting that CBF maintained the conservation during the evolutionary process in plants at different latitudes. Nevertheless, our experiments had revealed that C. equisetifolia CBF could effectively restore the sensitive phenotype of Arabidopsis cbf1/2/3 triple mutants to low temperature conditions (Fig. 6d, Supplemental Fig. S3a), indicating that CBFs were functionally conserved in C. equisetifolia. Considering that cold acclimation may take a relatively long time, the postpone response of CeqCBFs may not be the primary factor causing its sensitivity to low temperatures. Therefore, the main reason for the cold intolerance of C. equisetifolia could be the absence of the CBF binding element (DRE) of the COR genes in C. equisetifolia and multiple tropical/subtropical plants (Fig. 7, Fig. 11). We speculated that long-term exposure to high-temperature environments may result in the loss of these elements during the evolutionary process of tropical/subtropical plants, thereby, leading to the sensitivity to low temperatures. It is worth noting that, both in C. equisetifolia and A. thaliana, COR genes-mediated by the low temperature at different time points exhibited a typical time-dependent cascade amplification. In detail, most early cold responsive genes quickly returned to the expression levels before induction, while other cold responsive genes started to respond (Fig. 2c). In such a situation, the delayed expression of CBF may affect this cascade regulation mode, and may also be one of the reasons for C. equisetifolia's cold intolerance.

    Previous studies focused on the certification of low temperature responsive genes in order to understand the molecular regulation mechanisms and to enhance plant cold resistance through overexpression these genes. By comparing and analyzing differences in low-temperature response between the model plant A. thaliana and C. equisetifolia, we hypothesized that the loss of DRE elements in multiple COR genes in C. equisetifolia was associated with its response to low temperatures, and the absence of key COR genes was also a significant factor contributing to C. equisetifolia's and other tropical/subtropical plants' sensitivity to low temperatures (Fig. 12). Therefore, introducing DRE elements into the related genes or expressing the absent genes in tropical/subtropical plants using modern biotechnological tools could be a new and important research ideas for improving the tropical/subtropical plants' cold resistance.

    Figure 12.  Comparative pattern plot of cold-response regulatory pathway in A. thaliana and C. equisetifolia.
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    Acknowledgments

    This work was supported by the National Key R&D Program for Young Scientists (grant number: 2021YFD2200900), the State Key Laboratory of Subtropical Silviculture (grant number: SKLSS-KF2022-08).

  • The authors declare that they have no conflict of interest.

  • Supplemental Fig. S1 Schematic diagram of the geographic location of the study site.
    Supplemental Fig. S2 16S rRNA abundance (a) and cbbL/16S rRNA gene ratio (b) in different treatments. Values are means (n=3), and error bars represent standard deviation. Different lowercase letters above columns indicate significant differences (one-way ANOVA, P<0.05) among treatments.
    Supplemental Fig. S3 Rarefaction curves of operational taxonomic unit (OTU) numbers.
    Supplemental Fig. S4 Sobs Richness Index (a), Shannon-Weaver Diversity Index (b), Pielou Evenness Index (c) charts of the carbon-fixing microbial community. The error bars represent standard deviation. Different lowercase letters above columns indicate difference (one-way ANOVA, P<0.05) among treatments.
    Supplemental Table S1 The relative abundance of dominant phylum in different treatments.
    Supplemental Table S2 The relative abundance of dominant classes in different treatments.

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  • Cite this article

    Shang Y, Wu M, Zhang J, Meng H, Hong J, et al. 2023. Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land. Soil Science and Environment 2:2 doi: 10.48130/SSE-2023-0002
    Shang Y, Wu M, Zhang J, Meng H, Hong J, et al. 2023. Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land. Soil Science and Environment 2:2 doi: 10.48130/SSE-2023-0002
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Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land

  • 1.

    College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi, China

  • 2.

    National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Taigu 030801, Shanxi, China

  • 3.

    Center for Environmental Nanoscience and Risk, Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA

  • 4.

    College of life Sciences, Shanxi Agricultural University, Taigu 030801, Shanxi, China

  • Received: 02 December 2022
  • Accepted: 03 March 2023
  • Published online: 03 April 2023
Soil Science and Environment  2,  Article number: 2  (2023)  |  Cite this article

Abstract: Carbon-fixing microbes can potentially improve soil fertility. However, the potential and function of carbon-fixing microbes remains largely uninvestigated in reclaimed soil of coal-mining subsidence areas. In this study, treatments included UL (uncultivated land), CK (maize cultivation without fertilization), NPK (maize cultivation with chemical fertilizer), M (maize cultivation with manure), MNPK (maize cultivation with manure and chemical fertilizer) after 1-year reclamation in a typical coal mining subsidence area. Quantitative PCR, enzyme-linked immunosorbent assay (ELISA) and high-throughput sequencing were employed to investigate the topsoil carbon-fixing microbial biomass, RubisCO activity and community composition. The results showed that the dominant taxa (i.e., Proteobacteria, Cyanobacteria, Devosia and Marichromatium) were significantly changed after reclamation (P < 0.05). Carbon-fixing microbial community structure in fertilization treatments (NPK, M and MNPK) obviously differed from non-fertilizer treatments (UL and CK). Soil organic carbon and microbial biomass carbon were significantly higher in fertilization treatments than non-fertilizer treatments (P < 0.05). M significantly increased RubisCO activity and cbbL gene abundance (P < 0.05), MNPK significantly increased carbon-fixing microbial richness (P < 0.05). Carbon-fixing microbial community structure was strongly influenced by soil moisture, catalase, total phosphorus and dissolved organic carbon. Some environmental factors indirectly influenced SOC by affecting carbon-fixing microbial biomass, diversity and community structure. Our study implies that even short-term (1-year) reclamation and fertilization could significantly influence carbon-fixing microbial community structure and promote soil carbon accumulation, and the fertilization treatments with manure (M and MNPK) were more conducive, which indicated that carbon-fixing microbes were greatly conducive to improve soil fertility in reclaimed mining areas and achieve carbon neutrality.

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    INTRODUCTION

    • Coal is one of the most crucial energy sources worldwide. Chinese coal production increased from 3.41 billion tons in 2016 to 4.13 billion tons in 2021 (National Bureau of Statistics, 2022). In China, about 95% of the coal is from underground mining (Wang et al., 2020). This activity can lead to a decline in soil fertility, arable land reduction and environmental pollution in mining areas. Therefore, it is urgent to conduct ecological reconstruction of mining areas. Reclamation is an efficient solution to reconstruct the ecological environment in coal-mining subsidence area, and soil fertility quality restoration is critical to land reclamation. Soil organic carbon (SOC) is an important soil fertility quality indicator. Higher SOC usually indicates better soil quality (Bandyopadhyay & Maiti, 2019). Microbes in soil can assimilate CO2 and convert it into SOC (Antonelli et al., 2018). Carbon-fixing microbes play a crucial role in increasing SOC in barren soils where plant growth is limited (Su et al., 2013). Most of the carbon-fixing microbes are autotrophic. At present, six carbon-fixing pathways of autotrophic microbes have been elucidated (Fuchs, 2011), among which Calvin Benson-Bassham (CBB) cycle is the dominant pathway (Yu King Hing et al., 2019). Ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) is the key enzyme to control Calvin cycle rate, and has four (I, II, III, IV) forms (Tabita, 2004). Form I RubisCO was dominant in different soil environments and encoded by cbbL gene. The cbbL gene was highly conserved and widely present in the environment (Kusian & Bowien, 1997). Therefore, this gene is an effective biomarker to investigate autotrophic carbon-fixing microbial community in mining reclaimed soil.

      Many studies carried out in coal-mining areas have investigated the effects of reclamation on the diversity and activity of bacteria, archaea and fungi (Hou et al., 2018; Li et al., 2018; Wang et al., 2020). However, few of them have focused on carbon-fixing microbes in coal-mining areas. Notably, Čížková et al. (2018) found that the carbon fixation potential and microbial biomass in reclaimed soil were significantly higher than in unreclaimed lignite mining soil. Moreover, reclamation time of coal-mining areas significantly affect the abundance of carbon-fixing Acidobacteria, Bacteroidetes, Cyanobacteria, Firmicutes and Proteobacteria, and these phyla positively correlated with SOC (Ma et al., 2022). In addition to SOC, other environmental factors, such as pH and total nitrogen (Liu et al., 2022), can significantly affect community structure of carbon-fixing microbes. Moreover, fertilization treatments also significantly altered cbbL-carrying bacterial community composition and diversity (Liu et al., 2022). However, the carbon-fixing microbial biomass, diversity and activity under different reclamation and fertilization treatments in reclaiming soil of coal-mining subsidence areas still remain unclear.

      In this study, real-time quantitative PCR, ELISA and high-throughput sequencing were used to measure the effects on carbon-fixing microbial community under different reclamation and fertilization treatments were measured in an underground coal-mining subsidence area of Shanxi Province, northern China. We aim to: (1) determine the effects of reclamation and fertilization on the soil carbon-fixing microbial biomass, RubisCO activity and community structure in coal-mining subsidence areas, (2) reveal the main soil biophysicochemical factors that influenced soil carbon-fixing microbial community. We hypothesize that carbon-fixing microbial community composition will markedly change after reclamation and fertilization due to altered soil properties, and the fertilization treatments with manure is more conducive to improving the soil carbon-fixing potential in a coal-mining subsidence area. We hope our study would provide implications for the effective soil fertility improvement from the perspective of carbon-fixing microorganisms in coal-mining area, which may also be conducive to achievement of carbon neutrality in agricultural ecosystems.

    MATERIALS AND METHODS

      Experiment design and soil sampling

    • The experiment was conducted in a reclamation field in a coal-mining subsidence area of Shanxi Yuci Guanyao Yong'an Coal Industry Co., Ltd. (37°50′19.97″ N, 112°48′21.58″ E) (Supplemental Fig. S1), Shanxi Province, northern China. This region has a temperate continental monsoon climate, with mean annual precipitation of 462 mm, 175 frost-free days and 9−10 °C mean annual temperature. A large area of this land subsided due to underground coal mining. To reuse the subsided land, after the discontinuation of gangue discharge in 2019, the gangue landfill area was covered with 1 m-thick soil and mechanically leveled. The covering soil is raw and classified as calcareous cinnamon soil (Calciustepts). Its physicochemical properties were as follows: soil organic matter (SOM) 3.20 g·kg−1, total nitrogen (TN) 0.21 g·kg−1, available phosphorus (AP) 1.48 mg·kg−1, available potassium (AK) 79.00 mg·kg−1, pH 8.34.

      After the above engineering reclamation, the land was further biologically reclaimed since 2020 and five treatments were performed, including UL (uncultivated land), CK (maize cultivation without fertilization), NPK (maize cultivation with chemical fertilizer), M (maize cultivation with manure), MNPK (maize cultivation with co-fertilization of manure and chemical fertilizer). The manure was chicken manure compost containing 27.8% organic matter, 1.68% nitrogen, 1.54% P2O5 and 0.82% K2O. The chemical fertilizer contained urea (N, 46%), calcium superphosphate (P2O5, 12%) and potassium sulfate (K2O, 60%). The fertilizing quantity of each treatment is listed in Supplemental Table S1. Each treatment had three replicates, and each replicate plot was 10 m × 5 m = 50 m2 (n = 15). Maize (Zea mays Linn.) was sown in late April with a planting density of 60,000 ha−1 and harvested in late September in each plot.

      A total of 15 topsoil samples (0−20 cm, five treatments × three replicates) were collected using the five-point mixed sampling method in each plot at maize harvest in September, 2020 (1-year reclamation). The samples were sieved through a 2 mm mesh sieve after removal of plant residues and detritus. Each sample was then subdivided and respectively stored at 4 °C (for enzyme analysis), −80 °C (for microbial molecular biological analysis), or room temperature (for soil chemical analyses).

      Analyses of soil properties

    • All the chemical properties were measured using routine methods (Tedesco et al., 1995). Soil moisture (SM) was determined by oven drying at 105 °C until a constant weight. pH was measured using a 1:2.5 (w/v) soil-water slurry. TN and alkaline nitrogen (AN) were determined using semimicro-Kjeldahl method and alkali N-proliferation method, respectively. Total phosphorus (TP) was measured via the alkali fusion-Mo-Sb anti-spectrophotometric method. AP was extracted using sodium bicarbonate and measured via the colorimetric method. Total potassium (TK) and AK were respectively extracted using sodium hydroxide and ammonium acetate, and measured by flame photometry. SOC was determined using potassium dichromate volumetric method. Soil microbial biomass carbon (MBC) was determined using the chloroform-fumigation extraction method by Vance et al. (1987). Soil dissolved organic carbon (DOC) was extracted following the procedure by Zhu et al. (2015). Soil particulate organic carbon (POC) concentration was measured according to Cambardella & Elliott (1992). Soil easily oxidizable organic carbon (EOC) was measured using the potassium permanganate oxidation method (Blair et al., 1995).

      Soil enzymatic activity analyses

    • The activity of soil catalase (CAT) was determined from titration of KMnO4 consumption (Lin, 2010). RubisCO enzyme activity of the soil samples was measured by immunoassay (ELISA) kit of RubisCO enzyme (Sangon Biotech, China) according to the manufacturer’s instructions.

      DNA extraction, qPCR and sequencing of microbial cbbL gene

    • DNA of each sample was extracted from 0.25 g −80 °C stored soil using PowerSoil DNA Isolation Kit (QIAGEN, Germany). Then 1% agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) were used to evaluate the quality and quantity of extracted DNA.

      The abundance of carbon-fixing microbes and bacteria were quantified using absolute quantitative PCR (qPCR) with the cbbL gene primer (K2F: 5′-ACCAYCAAGCCSAAGCTSGG-3′, V2R: 5′-GCCTTCSAGCTTGCCSACCRC-3′) and bacterial 16S rRNA gene primer (338F: 5′-CCTACGGGAGGCAGCAG-3′, 518R: 5′-ATTACCGCGGCTGCTGG-3′), respectively (Rasche et al., 2011; Tolli & King, 2005). Consequently, the cbbL/16S rRNA gene ratio was calculated to reflect the proportion of carbon-fixing bacteria in soil bacterial community. The qPCR mixture (final volume, 20 μL) included 10 μL of 2× SYBR® Premix (Biomed, China), 0.8 μL of each primer, 1.4 μL of DNA template and 7 μL of ddH2O. The thermocycling conditions were: initial denaturation at 95 °C (2 min in cbbL, 10 min in 16S rRNA), followed by 40 cycles of denaturation at 95 °C (15 s in cbbL, 30 s in 16S rRNA) and annealing at 60 °C for 1 min. Then the melting curve was used to verify the amplification specificity. The standard curves for both genes were constructed using tenfold dilution series (ranging from 102 to 108) of the recombinant plasmids containing target gene fragments from the soil. The qPCR efficiency of the cbbL and 16S rRNA gene were 102% (R2 = 0.999) and 98% (R2 = 0.998).

      Adequate amount of −80 °C stored soil samples were delivered to Shanghai Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China) for sequencing of microbial cbbL gene with the primer set K2F/V2R on an Illumina MiSeq PE300 platform. The raw reads are available in SRA (Sequence Read Archive) database of NCBI with accession number PRJNA852533.

      Data processing and statistical analyses

    • The raw cbbL gene sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0 and merged based on overlaps by FLASH version 1.2.7. Sequencing reads were assigned to each sample according to the unique individual barcodes. For further improvement of sequencing data quality, the original sequences were controlled and filtered by QIIME (Quantitative Insights into Microbial Ecology) software package. UPARSE standard pipeline (v7.0.1090, http://drive5.com/uparse/) was utilized to cluster high-quality sequences into operational taxonomic unit (OTU) with a 97% similarity, and chimera was identified and removed (Edgar, 2013). The representative sequences of each OTU were compared with related sequences retrieved from NCBI (National Center for Biotechnology Information) database to assign a taxonomic classification using BLAST (Huang et al., 2021a).

      Alpha diversity analysis, including Sobs index (S), Shannon-Wiener diversity (H) (Wei et al., 2011), and Pielou index (J) (Yuan et al., 2016), were calculated using Microsoft Excel 2019 software. Sobs index was the observed OTU number (Qin et al., 2019). Linear discriminant analysis (LDA) effect size (LEfSe, http://huttenhower.sph.harvard.edu/galaxy) (Segata et al., 2011) was performed to screen enriched bacterial taxa in soils under different treatments.

      Principal component analysis (PCA) and biclustering heatmap analysis were used to identify the differences of the carbon-fixing microbial community among different treatments using ade4 package and ComplexHeatmap package of R (v.4.0.3), respectively.

      The Vegan package (v2.5.2) in the R (v4.0.3) was used to conduct calculation of Variance inflation factors (VIF), Redundancy analysis (RDA) between the carbon-fixing microbial community structure and environmental variables, and variance partitioning analysis (VPA). VIF were used as the criterion for distinguish collinearity among explanatory variables, environmental variables with VIF > 5 were eliminated before RDA and VPA (Zhang et al., 2022). According to the RDA results, structural equation modeling (SEM) was constructed using IBM SPSS AMOS 24.0. Based on the influence and relationship among known factors, the model was fitted with the maximum likelihood estimation method. The fitness of the model was evaluated via low χ2/df (χ2/df < 3, the closer χ2/ df is to 1, the better is the model fit, P > 0.05), high goodness-of-fit index (GFI > 0.89), low root mean square error of approximation (RMSEA < 0.01, if RMSEA = 0, it means complete fitness of the model) and low akaike information criterion (AIC) (Shipley, 2000).

      Microsoft Excel 2016 software was utilized for initial data analysis, SPSS 26.0 software was utilized for one-way ANOVA and multiple comparisons (Duncan post hoc test, P < 0.05).

    RESULTS

      Soil properties under different treatments

    • Soil properties among different treatments are listed in Table 1. For basic physicochemical and enzymatic properties, compared with UL, TN was significantly increased in NPK, M and MNPK (P < 0.05), AP was significantly increased in NPK and MNPK (P < 0.05), TK was significantly decreased in the other four treatments (P < 0.05). The highest pH was in UL. Compared with CK, NPK, M and MNPK increased contents of TN, AP, AK and CAT, but decreased contents of SM, AN and TP. Compared with CK, TN and CAT were significantly higher in M and MNPK (P < 0.05).

      Table 1.  Soil biophysicochemical properties in different treatments

      IndexTreatments
      ULCKNPKMMNPK
      SM (g·kg−1)0.17 ± 0.02ab0.19 ± 0.02a0.15 ± 0.01b0.18 ± 0.01ab0.15 ± 0.01b
      pH8.27 ± 0.09a7.86 ± 0.49a7.85 ± 0.42a8.15 ± 0.06a8.14 ± 0.13a
      TN (g·kg−1)0.27 ± 0.04c0.32 ± 0.06bc0.40 ± 0.07b0.56 ± 0.01a0.59 ± 0.06a
      AN (mg·kg−1)304.05 ± 0.00ab344.59 ± 11.50a302.85 ± 30.84ab315.97 ± 145.67ab174.08 ± 99.02b
      TP (g·kg−1)0.38 ± 0.00a0.69 ± 0.46a0.39 ± 0.20a0.35 ± 0.28a0.39 ± 0.20a
      AP (mg·kg−1)5.09 ± 0.00b10.93 ± 0.26ab17.68 ± 5.80a12.14 ± 0.80ab18.65 ± 7.16a
      TK (g·kg−1)5.61 ± 0.00a3.72 ± 0.74b2.88 ± 1.40b3.20 ± 0.03b4.01 ± 0.70b
      AK (mg·kg−1)80.06 ± 0.00a74.71 ± 8.34a77.39 ± 8.34a86.73 ± 10.08a86.74 ± 15.17a
      CAT (ml·g−1)2.15 ± 0.06a1.52 ± 0.22b1.65 ± 0.03b2.22 ± 0.05a1.97 ± 0.26a
      SOC (g·kg−1)1.74 ± 0.00c1.55 ± 0.08c2.07 ± 0.17b3.35 ± 0.24a3.12 ± 0.04a
      MBC (mg·kg−1)9.85 ± 0.45d17.06 ± 1.50c19.72 ± 0.58b37.78 ± 2.29a37.45 ± 0.82a
      DOC (g·kg−1)0.09 ± 0.01a0.09 ± 0.02a0.08 ± 0.01a0.08 ± 0.01a0.08 ± 0.01a
      POC (g·kg−1)0.24 ± 0.23a0.28 ± 0.15a0.52 ± 0.12a0.60 ± 0.18a0.71 ± 0.57a
      EOC (g·kg−1)1.58 ± 0.22a1.48 ± 0.58a1.34 ± 0.35a1.93 ± 0.46a1.65 ± 0.69a
      Values indicate mean ± standard deviations (n = 3). Different letters in each row represent a significant difference among treatments (one-way ANOVA, P < 0.05).

      For soil carbon relevant properties, NPK, M and MNPK increased POC, and significantly increased SOC, MBC compared with UL and CK (P < 0.05). MBC was significantly higher in four other treatments than UL (P < 0.05).

      RubisCO activity and cbbL gene abundance in different treatments

    • Soil RubisCO activity are depicted in Fig.1a and significant differences were observed among treatments. Compared with other treatments, M treatment significantly increased RubisCO activity (P < 0.05). RubisCO activity was the lowest in NPK and had no significant difference among UL, CK and MNPK (P > 0.05).

      Figure 1. 

      (a) Soil RubisCO activity and (b) cbbL gene abundances in different treatments. Values are means (n = 3), and error bars represent standard deviation. Different lowercase letters above columns indicate significant differences (one-way ANOVA, P < 0.05) among treatments.

      Soil cbbL gene abundance was depicted in Fig. 1b and was significantly the highest in M (2.07 × 107 copies/g dry soil, P < 0.05), which was consistent with RubisCO activity. Moreover, the ratio of cbbL/16S rRNA in M was also significantly the highest (3.37%) (Supplemental Fig. S2). The lowest cbbL gene abundance was in CK (7.92 × 105 copies/g dry soil). Compared with CK, cbbL gene abundance was increased 4.18, 25.18 and 8.74 times in NPK, M and MNPK, respectively. But there was no statistical difference among UL, CK, NPK and MNPK treatments.

      Alpha diversity and composition of carbon-fixing microbial community in different treatments

    • A total of 6,140,983 raw sequences were obtained. Each sample contained 34931 high quality sequences after quality filtering and subsampling (normalizing the sequence number according to the minimum sample). All the sequences were further classified into 17 phyla, 43 classes, 84 orders, 160 families, 361 genera and 3719 OTUs. The rarefaction curve (Supplemental Fig. S3) showed that the current sampling depth included most carbon-fixing microbial taxa in the samples and was sufficient for further analyses.

      Based on sequencing data, the α diversity indices (Sobs Richness, Shannon-Weaver Diversity, Pielou Evenness indices) of the carbon-fixing microbes in different treatments are depicted in Supplemental Fig. S4. The highest values of Sobs, Shannon-Weaver and Pielou indices were in MNPK, and Sobs index was significantly higher than the other four treatments (P < 0.05). The Shannon-Weaver and Pielou indices were higher in NPK, M and MNPK than CK. They suggested that fertilization increased soil microbial diversity and evenness, especially in the MNPK, and MNPK also significantly improved soil microbial richness (P < 0.05).

      Furthermore, carbon-fixing microbial community composition at phylum level and class level are shown in Fig. 2a & b, respectively. The dominant phyla were Proteobacteria (73.26%−86.23%), Cyanobacteria (5.58%−17.69%) and Actinobacteria (2.16%−5.40%), comprising 91.15%−96.00% of the sequences. The dominant classes were Alphaproteobacteria (43.07%−70.18%), Betaproteobacteria (11.68%−21.60%), Gammaproteobacteria (1.77%−8.55%), Cyanophyceae (4.81%−15.14%), Cyanobacteria (0.64%−1.96%) and Actinobacteria (1.93%−5.04%), comprising 90.36%−95.78% of the sequences. It is worth noting that carbon-fixing fungi and archaea were also detected, which were not reflected in the figure due to relatively low abundance. In addition, the top 10 dominant genera were Bradyrhizobium, Rhodopseudomonas, Noviherbaspirillum, Cyanobium, Variovorax, Devosia, Marichromatium, Mesorhizobium, Nitrobacter and Thermoleptolyngbya, comprising 66.84%−88.26% of the sequences.

      Figure 2. 

      Taxonomic composition of carbon-fixing microbial communities in soils at (a) phylum level and (b) class level.

      Supplemental Table S1 showed that the relative abundances of the phyla Proteobacteria, Cyanobacteria and Actinobacteria were significantly different among treatments. Compared with UL, the other four treatments increased Proteobacteria, while significantly decreased Cyanobacteria (P < 0.05). NPK, M and MNPK increased Cyanobacteria and Actinobacteria, but decreased Proteobacteria compared with CK.

      Supplemental Table S2 showed that the relative abundances of the classes Alphaproteobacteria, Gammaproteobacteria, Cyanophyceae, Cyanobacteria and Actinobacteria were significantly different among treatments. Compared with UL, the other four treatments increased Alphaproteobacteria, but significantly decreased Gammaproteobacteria, Cyanophyceae and Cyanobacteria (P < 0.05). NPK, M and MNPK increased Cyanobacteria and Actinobacteria, but decreased Alphaproteobacteria compared with CK.

      LefSe analysis was applied to ascertain the biomarkers, which were significantly enriched carbon-fixing microbial taxa in certain treatments (Fig. 3a). There were 17 biomarkers with LDA score > 3.5, 16 of which belonged to Proteobacteria phylum. Therefore, reclamation mainly influenced the assembly of proteobacteria, which was consistent with the results of carbon-fixing microbial community composition (Fig. 2a). Compared with UL, class Alphaproteobacteria was significantly enriched in four other treatments; family Bradyrhizobiaceae was significantly enriched in NPK and MNPK; family Comamonadaceae was significantly enriched in M and MNPK; family Phyllobacteriaceae, genus Mesorhizobium and genus Nitrobacter were significantly enriched in MNPK; family Devosiaceae and genus Devosia were significantly enriched in CK. Compared with four other treatments, five biomarkers were significantly enriched in UL, namely class Gammaproteobacteria, order Chromatiales, family Chromatiaceae and genus Marichromatium, i.e. these taxa were significantly decreased in all bioreclamation treatments (CK, NPK, M and MNPK).

      Figure 3. 

      Linear discriminant analysis (LDA) effect size analysis determined biomarkers (a) between UL and other treatments (CK, NPK, M and MNPK) and (b) between CK and fertilization treatments (NPK, M and MNPK). The LDA score indicates the effect size and ranking of each differentially abundant taxon (P < 0.05, LDA score > 3.5, a) (P < 0.05, LDA score > 3.0, b). The ordinate is the taxon with significant difference among groups, and the abscissa is a bar chart to visually show the LDA log score of each taxon. Blue, red, gray and orange bars represent the biomarkers in CK, NPK, M and MNPK having significantly greater abundances than in UL, respectively (a). Green bars represent the biomarkers in UL having significantly greater abundances than in all the other four treatments (a). Gray orange and red bars represent the biomarkers in M MNPK and NPK having significantly greater abundances than in CK, respectively (b). Blue bars represent the biomarkers in CK having significantly greater abundances than in all the other three treatments (b).

      There were 17 biomarkers with LDA score > 3.0 (Fig. 3b), 10, five and two of them respectively belonged to Proteobacteria, Actinobacteria and Cyanobacteria phylum. Therefore, fertilization mainly influenced the assembly of these three phyla, which was consistent with the results of taxa composition mentioned in Fig. 2a. Compared with CK, genus Nitrobacter was significantly enriched in NPK M and MNPK; family Phyllobacteriaceae and genus Mesorhizobium were significantly enriched in M and MNPK; class Actinobacteria was significantly enriched in NPK and MNPK; Phylum Cyanobacteria, class Cyanophyceae, family Thiobacillaceae, genus Sulfuritortus, family Thermomonosporaceae and genus Thermomonospora were significantly enriched in NPK. Compared with NPK, M and MNPK, family Devosiaceae and genus Devosia were significantly enriched in CK, which means these taxa were significantly decreased in all fertilization treatments (NPK, M and MNPK).

      Carbon-fixing microbial community structure in different treatments

    • Afterwards, PCA and biclustering heatmap analysis were applied to explore β-diversity of carbon-fixing microbes. PCA biplot showed that 35.93% of total variance in carbon-fixing microbial community structure was explained by the first two axes (Fig. 4). It also showed that five treatments were clearly differentiated into three clusters, UL and CK as cluster 1, M and MNPK as cluster 2, and NPK as cluster 3 along the PC2 axis, which indicated that the carbon-fixing microbial community structure among the three clusters was different. The score in PC2 axis significantly differed among each cluster (P < 0.05).

      Figure 4. 

      Principal component analysis (PCA) of the carbon-fixing microbial community in different treatments.

      Biclustering heatmap were also applied to explore the community structure of carbon-fixing microbes under different treatments (Fig. 5). Vertical clustering of heatmap showed that five treatments were classified into two clusters: M and MNPK were clustered together, while UL, CK and NPK formed a second cluster, and the second cluster was divided into two subclusters, UL and CK as a subcluster, NPK as another subcluster, which was consistent with PCA. Horizontal clustering of heatmap showed that the carbon-fixing microbial community structure differed among treatments. Compared with UL and CK, Actinomadura was increased in NPK, M and MNPK. Compared with the other three treatments Variovorax, Hydrogenophaga, Novosphingobium and Polumorphum harbored a higher relative abundance in M and MNPK. Pseudonocardia relative abundance was higher in NPK and MNPK than in three other treatments. Compared with four other treatments, Mesorhizobium, Nitrobacter, Cupriavidus, Rhodoferax, Rhodoblastus, Mycobacterium, Sphingomonas and Sinorhizobium were more abundant in MNPK. The relative abundance of Noviherbaspirillum, Marichromatium and Thermoleptolyngbya were higher in UL than in four other treatments.

      Figure 5. 

      Biclustering heatmap of the carbon-fixing microbial distributions of the top 30 abundant genera is present in different treatments. The color intensity of the color lumps represents the abundance of the carbon-fixing microbial genera in different treatments, with red representing higher abundance and blue representing lower abundance.

      Correlations between the carbon-fixing microbial communities and soil properties

    • RDA were utilized to investigate the correlation of soil properties with soil carbon-fixing microbial community structure. Soil properties with VIF > 5 were removed before RDA to avoid the effect of collinearity. Then, eight variables, including CAT, EOC, AP, DOC, AK, SM, TP and AN were screened out for RDA.

      The RDA (Fig. 6a) showed that the first two axes explained 41.93% and 11.45% of the total variance in soil carbon-fixing microbial community, respectively. The score in RDA2 axis significantly differed among treatments (P < 0.05). M and MNPK were clustered together and obviously separated from UL, CK and NPK, which was consistent with PCA and biclustering heatmap results. The soil properties with the highest explanatory proportion of soil carbon-fixing microbial community structure were SM (r = 0.6288, P = 0.001), CAT (r = 0.5508, P = 0.006), TP (r = 3972, P = 0.048), DOC (r = 0.3875, P = 0.041).

      Figure 6. 

      (a) Redundancy analysis (RDA) linking carbon-fixing microbial communities with environmental variables in different treatments. Arrows represent the correlation between the soil properties and carbon-fixing microbial communities. Variables that are angled at more than 90° of each other have the least correlation. The length of the arrow represents the correlation. Variables that have arrows extended in opposite directions correlate negatively to each other. (b) Diagrams explaining variance partitioning (VPA) show the relative contribution of ecological drivers with VIF < 5 to soil carbon-fixing microbial community structure. The abiotic variables include EOC, AP, DOC, AK, SM, TP and AN; the biotic variables include CAT. The numbers are the percentages of the total variables explained by the factors.

      To further unveil the relationship between carbon-fixing microbial community and environmental variables, the explanatory variables included in RDA were divided into two groups (abiotic variables and biotic variables) for VPA. The VPA (Fig. 6b) showed that abiotic variables were the more important factors affecting carbon-fixing microbial community assembly. The abiotic variables and biotic variables explained 22.38% and 0.15% of the variation in the carbon-fixing microbial community, respectively. Moreover, 64.98% of variation remained unexplained.

      Based on RDA and PCA, the dominant soil properties (CAT, DOC, TP and EOC) and fertilization treatments with different manure application doses that affected the structure of carbon-fixing microbial community, were further included in SEM analysis. SEM was applied to reveal the causal relationship among soil properties, treatments (manure application dose), RubisCO activity, community composition (loading score on the first PCA axis) (Li et al., 2015), cbbL gene abundance, α diversity (Shannon-Weaver diversity index) (Zhang et al., 2021) and soil carbon accumulation (Fig. 7). It also used to further explain and quantify the contribution of key factors influencing both carbon-fixing microbial community and soil carbon fixation.

      Figure 7. 

      Structural equation model (SEM) shows the causal influences of treatments, DOC, EOC, CAT, TP, RubisCO activity, α diversity of cbbL, community composition (carbon-fixing microbial community), cbbL gene abundance, SOC and MBC. Positive and negative effects are respectively showed in red and green, and significant and non-significant effects are showed with solid and dashed arrow lines, respectively. The standardized coefficients are marked above each path (only marked significant effect paths) and indicate the expected impact of a unit standard-deviation change at one node on units of standard-deviation change in connected nodes. R2 values represent the proportion of the variance explained for each endogenous variable.

      This model is completely consistent with our causal hypothesis (χ2 = 13.060, df = 23, P = 0.951, GFI = 0.892, RMSEA = 0.000, AIC = 99.060) and it could explain 99%, 96%, 94%, 57% and 43% variance of the SOC, MBC, community composition, RubisCO activity and α diversity of cbbL, respectively. SOC was directly affected by treatments, MBC, TP, CAT, RubisCO activity, α diversity of cbbL and community composition and indirectly affected by DOC (standardized indirect effects = −0.15) and EOC (standardized indirect effects = 0.06). MBC was directly affected by treatments, CAT, DOC, RubisCO activity, community composition and cbbL gene abundance. Community composition was directly affected by treatments, EOC, DOC and α diversity of cbbL.

    DISCUSSION

      Effects of reclamation on carbon-fixing microbial communities

    • This study was defined to assess the restoration and responses of carbon-fixing microbial communities in coal-mining areas after different treatments. Among the different treatments, the comparison between UL and CK reveals the variations of carbon-fixing microbial communities before and after reclamation without fertilization. This study revealed significant increase of MBC after reclamation. This could be attributed to the increase of exogenous carbon and other nutrients needed for microbial growth, which promotes the proliferation of microbes, and leads to the increase of MBC after reclamation. Moreover, microbial residues, crop litter, roots and exudates were decomposed and transformed into humus, which promoted soil aggregates formation and changed soil structure, increased microbial proliferation and MBC.

      Additionally, Proteobacteria, Cyanobacteria and Actinobacteria were dominant phyla of carbon-fixing microbial communities under different treatments, which suggests that these were the major contributors to soil microbial carbon fixation, which was similar to the results of Badger & Bek (2008). LefSe analysis (Fig. 3) further suggested the importance of Proteobacteria and Cyanobacteria phyla in the soil reclamation process, Cao et al. (2020) and Ma et al. (2022) have shown similar results in their studies on Chinese coal-mining soils. In addition, compared to unreclaimed soil (UL), our study revealed that reclamation (CK) significantly enriched Proteobacteria and Devosia (P < 0.05), while significantly reduced Cyanobacteria and Marichromatium (P < 0.05). This may be because reclamation enriched soil nutrient such as TN and AP (Table 1), and Proteobacteria is classified as copiotrophs, which prefer nutrient-rich conditions (Lienhard et al., 2013). Devosia, a potential plant-associated nitrogen-fixing bacterium, enriched within the root endosphere (Sun et al., 2021), thereby there may be a link between the enrichment of Devosia and the increase of TN and AN with the growth of plants after reclamation. Cyanobacteria evolved and thrived in low-nutrient systems and oligotrophic systems, respectively (Reinl et al., 2021), the significant increase of other microbes reduced the proportion of Cyanobacteria after reclamation. Marichromatium, a photosynthetic bacterium, could utilize variety of carbon, nitrogen and sulfur sources (Parag et al., 2013), thereby Marichromatium has higher relative abundance in uncultivated land because of its adaptability to barren environments. Moreover, Zhang et al. (2017) reported that organic carbon source type would also affect Marichromatium abundance, so we speculated that reclamation may also alter the organic carbon composition, which provides a new direction for further research.

      The cbbL gene abundance, RubisCO activity and α diversity did not significantly differ before (UL) and after reclamation (CK). Moreover, PCA and biclustering heatmap showed that carbon-fixing microbial community structure was similar in CK and UL, which was inconsistent with other studies (Li et al., 2014). Cao et al. (2020) reported that soil microbial diversity and construction were strongly influenced by reclamation time. Soil improvement effect was positively correlated with soil reclamation time. Therefore, the above results of this study maybe because reclamation time in our study is too short to show obvious improvement effect of reclamation. Above all, reclamation even without fertilization in the initial stage could contribute to significant shifts in dominant taxa and MBC, and a trend of ecological recovery.

      Effects of fertilization on carbon-fixing microbial communities

    • Previous studies have shown that one-year of reclamation with fertilization does improve soil fertility (Cheng, 2022; Gao et al., 2021). Different fertilization treatments greatly influence various fractions of carbon as well as carbon fixation (Wang et al., 2020). In our study, the comparison of M, NPK and MNPK with CK could reveal the effect of fertilization on carbon-fixing microbial communities. Our study showed that the reclamation with addition of fertilizers (NPK, M and MNPK) increased POC, SOC and MBC (P < 0.05) (Table 1), which was consistent with Anandakumar’s study (Anandakumar et al., 2022) in semi-arid areas of India. Carbon-fixing microbial community composition was significantly affected by fertilization (Figs 5 & 6). This may be because fertilization increased the nutrient elements required by carbon-fixing microbes, and thereby altered carbon-fixing microbial community structure. Moreover, Devosia was significantly decreased, while Nitrobacter was significantly enriched after fertilization (P < 0.05) (Fig. 3b). Xu et al. (2017) reported that Devosia was strongly positively correlated with AN. Therefore, it might because AN was the highest in CK (Table 1), Devosia was more suitable for growth and reproduction under CK treatment. Nitrobacter fixed carbon through Calvin cycle and may play a crucial role in coupling soil carbon (C) and nitrogen (N) cycles (Wang et al., 2019). These bacterial taxa are related to soil N cycle, which imply that influence of N cycle cannot be neglected in the study of soil C cycle, and it is essential to further simultaneously investigate microbes involved in soil C and N cycle.

      In addition, it should be noted that there were significant separations of the carbon-fixing microbial community structure between the chemical fertilizer treatment (NPK) and manure and manure with chemical fertilizer treatments (M and MNPK) based on Figs 5 & 6. Moreover, MBC, SOC and TN were significantly higher in manure treatments (M and MNPK), compared with chemical fertilizer, which was consistent with Huang et al. (2021b). The application of manure (M) significantly increased carbon-fixing microbial biomass, the cbbL/16S rRNA ratio and RubisCO diversity. It may be because that mining area soil is relatively barren, and manure can increase organic matter more directly and effectively (Dennis et al., 2010), higher organic carbon content promoted facultative autotrophic bacterial growth and resulted in a high carbon-fixing microbial biomass (Yuan et al., 2012). Compared with CK, the application of manure with chemical fertilizer (MNPK) significantly increased richness of carbon-fixing microbial community, which was similar with Ding et al. (2016). This is because the combination of manure and chemical fertilizer not only supplemented the input of organic carbon, improved the availability of nutrients and water retention capacity, but also improved soil physical properties, which greatly stimulated carbon-fixing microbial community and activity (Guo et al., 2010), increased carbon-fixing microbial richness. However, RubisCO activity was significantly reduced by chemical fertilizer (NPK) compared with non-fertilized treatments (CK), but the carbon-fixing microbial biomass and α diversity was not significantly affected, which was consistent with the report by Jing et al. in China (Jing et al., 2021). This may be due to the application of chemical fertilizer leads to the great increase in soil phosphorus (P) (Table 1), which limit or co-limit the growth of soil autotrophic microorganisms (Yuan et al., 2015). In summary, compared with CK, the application of manure (M) improves the carbon-fixing microbial activity and abundance significantly, thereby promotes soil carbon fixation and ecological restoration in the mining area, while the application of manure with chemical fertilizer (MNPK) is more conducive to the improvement of carbon-fixing microbial diversity.

      Effects of soil properties and manure application dose on carbon-fixing microbial communities

    • Environmental factors affecting soil carbon storage remain largely unknown, particularly in mining reclamation ecosystems, where biophysicochemical properties were key factors indirectly affecting variation in soil carbon storage by affecting the biomass, diversity and community structure of carbon-fixing microbes (Li et al., 2018). This study indicated that SM CAT, TP and DOC were the key factors significantly influencing soil carbon-fixing microbial community structure based on RDA. The effect of SM and CAT on carbon-fixing microbial community structure were significant and greater than that of TP and DOC.

      This study showed that even the differences of DOC and EOC did not reach statistically significant levels after reclamation and fertilization, they still exert marked effect on the variations of soil carbon-fixing microbial community. Carbon-fixing microbes are sensitive to the DOC and EOC content (Li et al., 2020), thereby DOC and EOC directly influence microbial composition. Our study revealed that the changes in DOC and EOC caused by reclamation and fertilization can affect carbon-fixing potential through affecting carbon-fixing microbial community composition. Relative to carbon-fixing microbial abundance and diversity, DOC and EOC made a greater contribution to the alteration of microbial carbon-fixing potential, which was consistent with previous research in Chinese Loess Plateau (Xiao et al., 2018).

      RubisCO activity was positively correlated with CAT, which was because CAT accelerates the decomposition of H2O2 and other harmful substances, thereby promoting the conversion of substances and energy in the soil and providing favorable environment for the survival of carbon-fixing microbes (Ma et al., 2022). A great deal of research revealed that SOC was positively correlated with RubisCO activity (Tang et al., 2015; Techtmann et al., 2012; Yuan et al., 2011), which was opposite with our study result. This may be because at the beginning of the reclamation, new equilibrium relationship between SOC fixation and mineralization has not been established, and SOC has not reached the stage of gradual accumulation.

      SEM results indicated that in addition to soil properties, fertilization treatments with different manure application doses also affected carbon-fixing microbial community composition. This may due to application of manure could loosen soil, improve soil aeration, enhance carbon-fixing microbial activity, and change carbon-fixing microbial community composition (Shao et al., 2019). SOC and MBC were positively correlated with fertilization treatments with different manure application doses, which was because manure was rich in organic matter, SOC increased with the increase of manure application dose. Moreover, the application of manure promoted the decomposition of original SOC by priming effect, increased the carbon and other nutrients needed for microbial growth, thereby promotes microbial proliferation and increases MBC (Martín-Lammerding et al., 2015). Our study showed that microbial evolution trends in the complex environment of mining areas was not completely consistent with other studies. This is because there are other factors not included that altered carbon-fixing microbial community structure (Fig. 7), and soil types and soil characteristics varied in different regions, it is difficult to obtain an entirely consistent effect pattern of soil biophysicochemical properties on carbon-fixing microbial communities in different regions.

      Overall, the results confirmed the hypothesis that carbon-fixing microbes play a crucial part in soil carbon sequestration in barren coal mining areas even after only a short-term reclamation and fertilization. The results also highlighted reclamation and fertilization significantly altered carbon-fixing microbial diversity and community, and improved potential ecosystem function. Our future research will further explore the isolation of carbon-fixing microbial strain and its application as microbial fertilizer in reclaimed soil of coal-mining subsidence areas, which would further facilitate soil fertility improvement in coal mining subsidence area.

    CONCLUSIONS

    • Our study revealed that reclamation even without fertilization in the initial stage could bring significant shifts in MBC and dominant taxa (i.e., Proteobacteria, Cyanobacteria, Devosia and Marichromatium) and showed a trend of ecological recovery. Moreover, reclamation with fertilization significantly increased SOC and MBC (P < 0.05) and significantly altered carbon-fixing microbial community composition. Among these fertilization treatments, the application of manure (M) is more conducive to increasing carbon-fixing microbial abundance, the cbbL/16S rRNA ratio and RubisCO activity in the current short-term reclamation. SM, CAT, TP and DOC were the key factors significant influencing soil carbon-fixing microbial community structure, which provided a theoretical basis for improving the carbon-fixing potential. Reclamation and fertilization could significantly influence carbon-fixing microbial community structure (P < 0.05) and increase soil carbon storage due to altered soil properties and manure application dose. The fertilization treatments with manure were more conducive to improving the soil carbon-fixing potential. These findings contributed to improving soil fertility and accelerating ecological restoration and reconstruction in the mining area.

      Acknowledgments

      • This work was financially supported by the National Natural Science Foundation of China (41907215), the Incentive Research Foundation of Shanxi Province for Recruited Doctoral Talents (SXYBKY2018009), the Science and Technology Innovation Foundation of Shanxi Agricultural University (2018YJ24) and the Natural Science Research Project of Shanxi Province (202103021224171).

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Supplementary information
      • Supplemental Fig. S1 Schematic diagram of the geographic location of the study site.
      • Supplemental Fig. S2 16S rRNA abundance (a) and cbbL/16S rRNA gene ratio (b) in different treatments. Values are means (n=3), and error bars represent standard deviation. Different lowercase letters above columns indicate significant differences (one-way ANOVA, P<0.05) among treatments.
      • Supplemental Fig. S3 Rarefaction curves of operational taxonomic unit (OTU) numbers.
      • Supplemental Fig. S4 Sobs Richness Index (a), Shannon-Weaver Diversity Index (b), Pielou Evenness Index (c) charts of the carbon-fixing microbial community. The error bars represent standard deviation. Different lowercase letters above columns indicate difference (one-way ANOVA, P<0.05) among treatments.
      • Supplemental Table S1 The relative abundance of dominant phylum in different treatments.
      • Supplemental Table S2 The relative abundance of dominant classes in different treatments.
      Rights and permissions
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (7)  Table (1) References (59)
  • About this article
    Cite this article
    Shang Y, Wu M, Zhang J, Meng H, Hong J, et al. 2023. Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land. Soil Science and Environment 2:2 doi: 10.48130/SSE-2023-0002
    Shang Y, Wu M, Zhang J, Meng H, Hong J, et al. 2023. Nutrient enhanced reclamation promoted growth, diversity and activities of carbon fixing microbes in a coal-mining subsistence land. Soil Science and Environment 2:2 doi: 10.48130/SSE-2023-0002
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Catalog

  • Html

  • Introduction
  • Materials and methods

  • Experiment design and soil sampling

  • Analyses of soil properties

  • Soil enzymatic activity analyses

  • Dna extraction, qpcr and sequencing of microbial cbbl gene

  • Data processing and statistical analyses

  • Results

  • Soil properties under different treatments

  • Rubisco activity and cbbl gene abundance in different treatments

  • Alpha diversity and composition of carbon-fixing microbial community in different treatments

  • Carbon-fixing microbial community structure in different treatments

  • Correlations between the carbon-fixing microbial communities and soil properties

  • Discussion

  • Effects of reclamation on carbon-fixing microbial communities

  • Effects of fertilization on carbon-fixing microbial communities

  • Effects of soil properties and manure application dose on carbon-fixing microbial communities

  • Conclusions
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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