Microbial-induced carbonate precipitation modulates microbial community composition in long-term lead-contaminated soil

doi:10.21203/rs.3.rs-5274994/v1

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Microbial-induced carbonate precipitation modulates microbial community composition in long-term lead-contaminated soil

https://doi.org/10.21203/rs.3.rs-5274994/v1

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Microbial induced carbonate precipitation (MICP) is known as a significant process for remediating heavy metals contaminated environment. At present, we have studied the effects of microbe induced carbonate precipitation on soil physical structure after remediation of lead (Pb) contamination. However, in the process of soil remediation, the addition of exogenous substances and bacterial solution to the structure of soil bacterial community and the relationship between soil fungi and non-biological factors remains unclear. In this study, a new type of urea decomposing bacteria was isolated and identified as Bacillus pasteurii. Its performances for immobilizing Pb in soil were systematically discussed at different treatment conditions. Results showed that effects of different biological treatments on basic physical and chemical properties of soil. Meanwhile, MICP reduced the available content of Pb in soil. By analyzing the diversity and composition of soil microbial communities, it was found that MICP reduced the diversity of soil microbial community and changed the composition of soil bacterial and fungal microbial communities due to the impact of bioremediation process on soil environmental factors. However, the functional composition of soil bacteria and fungi was not affected by MICP remediation, especially the functional composition of bacterial microbial community.

Pb contaminated soil

Biomineralization

Microbial diversity

Functional prediction

Microorganisms play an important role in the exchange of quality and energy between the soil, hydrosphere, and atmosphere, and they also play an important role in ecological restoration, environmental monitoring, and pollution treatment (Singh et al. 2010, Wagg et al. 2014). Microorganisms are ubiquitous in the soil environment, and they are especially important in the restored soil ecosystem (Zheng et al. 2019, Hannula et al. 2019). Soil microorganisms are very important for the decomposition of soil organic matter, the circulation and transformation of soil nutrients, and the promotion of plant growth (Roesch et al. 2007, Douterelo, Goulder and Lillie 2010). The composition and diversity of soil bacterial communities are closely related to the process of soil nutrient conversion (Xiao, Huang and Lu 2015). Fungi are mainly involved in the decomposition and transformation of soil organic matter and mineral components (Hollister et al. 2010). The composition and ecological functions of soil microorganisms are closely related to the soil ecological environment (Strickland and Rousk 2010). Therefore, changes in soil microbial communities may affect the microbial-mediated processes in the soil environment, thereby the stability of the soil ecosystem, and play a vital role in the efficiency of soil ecological restoration. Soil microorganisms usually depend on changes in soil environmental factors, including pH and EC (Electrical conductivity) levels and nutrient availability (Du et al. 2018, Koranda et al. 2013). In different soil environments, the complexity of different environmental factors and the comprehensive effects related to various environmental factors will Pb to significant differences in the structure and biomass of soil microorganisms (Voriskova et al. 2014, Lopez-Mondejar et al. 2015).

During the accumulation process of non-ferrous metal mines, a large amount of heavy metals are directly discharged into the surrounding soil along with tailings and other mineral dust, becoming the main source of environmental pollution in the surrounding soil (Jia, Wang and Chai 2019). Large amounts of heavy metals have caused serious pollution and degradation of the local ecological environment (Wang et al. 2010, Sun et al. 2018). Pb, the second most toxic element, may have long-lasting and irreversible effects on living organisms (Zulfiqar et al., 2019). Pb causes disruption of the body's cardiac, digestive and endocrine functions (Ayub et al., 2024). Therefore, reasonable and effective ecological restoration of the soil around the tailings dam is necessary. The restoration of soil ecological functions is the key to the sustainable development of terrestrial ecosystems (Huang et al. 2019, Jia et al. 2019). At the same time, in the process of soil restoration, attention must be paid to the dynamic characteristics of soil fertility and soil microbial communities. Soil bioremediation has gradually become the mainstream restoration method due to environmental protection and economy (Ekperusi and Aigbodion 2015, Ayangbenro and Babalola 2017). MICP is an important process to reduce the migration of heavy metals through the metabolic activity of carbonate biomineralization microorganisms (Seifan, Samani and Berenjian 2016). Microbial action catalyzes the hydrolysis of urea into carbonate and ammonium salts, and reacts with free heavy metal ions in the soil to promote the combination of calcium and carbonate with heavy metals to form co-precipitation, thereby reducing the mobility of heavy metals (Achal et al. 2012). Although there have been many studies on bioremediation, few people pay attention to the effect of bioremediation on soil microbial community.

Many studies have shown that the impact of human activities on the environment have led to changes in the structure and diversity of soil microorganisms (Cycon, Mrozik and Piotrowska-Seget 2019). However, few studies have reported the characteristics of microbial community and its driving factors after soil heavy metal pollution using microbial remediation methods. Research on the microbial community after soil remediation can improve our understanding of the ecology of the microbial community in the environment, and help improve and implement soil bioremediation strategies, so as to solve some problems that may be encountered in the practice of soil bioremediation engineering (Hoostal and Bouzat 2016). Therefore, the use of bioremediation on the relationship between soil environmental conditions and changes in microbial communities deserves further study. This research will help clarify the impact of soil bioremediation on the soil environment and the key environmental factors affecting soil microbial communities. In addition, our experiments have studied the phonology of soil bioremediation on the function of soil microbial communities, revealing the impact of soil bioremediation on the entire soil ecological environment.

In this study, we solved the following questions: (1) How will the bacterial and fungal communities change after the use of MICP to remediate soil Pb pollution? (2) What are the special functional bacteria and fungi before and after biological MICP restoration? (3) What are the main environmental factors that affect the structure and diversity of soil microorganisms? The purpose of this research is to provide an ecological basis for soil bioremediation and to provide guidance for the engineering practice of soil bioremediation.

2.1 Bacterial strains and growth conditions

The strain used for reconditioning was Bacillus pasteurii ATCC 11859 (gram-positive) which was isolated from the soil and stored in Shanghai Huzheng Biotechnology Co., LTD. The mineralization culture medium included Casein peptone 15 g/L, Soya peptone 5 g/L, Sodium chloride 5 g/L and Urea 20 g/L (The urea solution were filter-sterilized through 0.22 µm filter), adjust pH = 7.3.

2.2 Soil samples and experimental groups

The soil was selected from around a gold mine tailing pond in Baotou City, Inner Mongolia. Put the soil in a flowerpot with a diameter of 12 cm and a height of 10 cm. Set up three experimental groups, including control group (CK: soil + water), medium group (CM: soil + medium), bacterial agent group (BS: soil + medium + 1 mol/l CaCl₂ + bacterial agents)(Chen et al., 2021), the bacterial solution every five days, and perform the effect analysis after 15 days, and three parallel samples were taken from each group.

2.3 Determination of soil index

After passing through a 200-mesh sieve, the soil was dried at 105℃ for 8 hours to a constant weight. Soil pH was determined at a fresh soil to water ratio of 1:2.5 (w:v) with a pH meter (Leici, China). The total nitrogen in soil was determined by SKD-1000 type automatic azotometer (PEIOU, China). In the determination of total phosphorus in soil, sodium hydroxide melting method was used for treatment and then ultraviolet spectrophotometer determination. Determination of total soil carbon using a total organic carbon analyzer (XPERT-TOC, Netherlands). The pretreated soil was extracted by “ Solid waste-Extraction procedure for leaching toxicity-Horizontal vibration method “ to verify the leaching amount of heavy metals in pure water after MICP remediation (Feng et al. 2005). The Tessier five-step extraction method was used to analyze the changes of soil heavy metal forms (Sherameti and Varma 2010). The content of Pb in the solution produced by the above treatment was determined by AAS.

2.4 Genomic DNA Extraction

Bacillus pasteurii strains were inoculated onto plates on mineralized solid medium and incubated at 30 ℃ overnight. Single colonies were picked with an inoculating needle and inoculated into 150 mL of mineralized medium, and incubated in a shaker at 30 ℃ until logarithmic growth stage at 180 r/min. Then centrifuged at 12000 rpm for 10 min at 4 ℃, and the supernatant was discarded, and the organisms were collected, and the medium components were washed away from the organisms with sterile water at the same time. Genomic DNA was extracted according to the instructions of FastDNA® Spin Kit (MP Biomedicals, USA). The purified genomic DNA was quantified and the high quality DNA was used for library sequencing later.

2.5 PCR Amplification

PCR (ABIGeneAmp®9700) uses TransGenAP221-02: Trans StartFastpfu DNA Polymerase. All samples are carried out in accordance with the formal experimental conditions (Zheng et al. 2018). Each sample has 3 replicates. The PCR products of the same sample are mixed and detected by 2% agarose gel electrophoresis, and recovered by AxyPrepDNA gel The kit (AXYGEN company) cut the gel to recover the PCR product, eluted with Tris_HCl, and detected by 2% agarose electrophoresis. Measure and refer to the preliminary quantitative results of electrophoresis, and use QuantiFluor™-ST blue fluorescence quantitative system (Promega) to detect and quantify the PCR products, and then mix in corresponding proportions according to the sequencing volume requirements of each sample.

2.6 MiSeq Sequencing

One end of the DNA fragment is complementary to the primer base and is attached to the chip. The DNA fragment was used as the template, the fixed base sequence on the chip was used as the primer for PCR synthesis, and the target DNA fragment to be tested was synthesized on the chip. After denatured annealing, the other end of the DNA fragment on the chip is randomly complementary to another nearby primer and is also immobilized to form a bridge (Wang et al. 2020). PCR was amplified to produce clusters of DNA. DNA amplifiers are linearized into single strands. The modified DNA polymerase and dNTP with four fluorescent markers were added to synthesize only one base at a time. The surface of the reaction plate is scanned with a laser to read the nucleotide types that each template sequence aggregates in the first reaction. The fluorescence group and the terminating group were cut chemically to restore the viscosity of the 3’ end and continue to polymerize the second nucleotide. The fluorescence signal results collected in each round were counted to obtain the sequence of the template DNA fragment (Yang et al. 2015). The libraries were built and sequenced using the Illumina Miseq PE300 platform from Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China).

2.7 Biological analysis process

All experimental data were preliminarily arranged by Excel 2019 (Microsoft, Redmond, WA, USA), and then statistical analysis was conducted by SPSS 26.0 (IBM Corp.). Multiple comparisons were performed using one-way anova and Duncan test (p < 0.05 was the significant level). The graph was drawn using Oirgin 2019b (OriginLab, USA).

3.1 MICP changed the basic physical and chemical properties of soil

The soil physical and chemical properties changed significantly after 15 days of treatment with MICP (Table 1). The soil pH value showed a decreasing trend between 7.96 and 7. The conductivity of CK was 0.81mS/cm, and the highest value of soil after MICP restoration was 19.91mS/cm, with significant change (P < 0.01). Compared with the CK, the contents of total carbon (TC), total nitrogen (TN) and total phosphorus (TP) increased significantly after MICP treatment (P < 0.01). Among them, the TC, TN and TP of CM treated soil increased by 0.994%, 0.61% and 19.35mg/kg, respectively, compared with CK. When adding bacterial agents, TC, TN, and TP treated with BS were reduced compared with CM. Compared with CK, bioremediation increased the content of CaCO₃ by 94.64% and 78.43%.

Table 1

Effects of different biological treatments on basic physical and chemical properties of soil
Site	pH	EC(mS/cm)	TC(%)	TN(%)	C/N	TP (mg/Kg)	CaCO₃(%)
CK	7.96 ± 0.07	0.81 ± 0.14	2.38 ± 0.11	0.02 ± 0.00	118.83 ± 5.51	6.16 ± 1.83	12.33 ± 2.52
CM	6.99 ± 0.03	13.43 ± 0.81	2.94 ± 0.10	0.62 ± 0.04	4.79 ± 0.50	25.51 ± 0.66	24.00 ± 2.01
BS	7.00 ± 0.05	16.80 ± 1.27	2.67 ± 0.08	0.47 ± 0.01	5.68 ± 0.19	22.63 ± 0.83	22.00 ± 1.42
Note: CK is the control group, CM is the medium group, BS is the bacterial agent group; EC, electrical conductivity; TC, soil organic matter; TN, total nitrogen; C/N, ratio of C to N; TP, total phosphorus, CaCO₃, content of CaCO₃.

3.2 MICP reduced the available content of Pb in soil

The bioavailability of Pb in soil can be reduced by MICP. Compared with CK, bioremediation reduced the water leaching amount of Pb by 53.66% and 67.44%, respectively. MICP reduced the content of Pb available in soil, while the exchangeable fraction content of Pb decreased, and the carbonate-bound fraction content increased (Fig. 1). In the case of bioremediation, the available Pb in the soil in groups CM and BS decreased significantly by 8.91% and 20.86%, respectively, compared with the CK. In the tested soil, the Pb content in the soil increased mainly in the form of carbonate-bound and Fe-Mn oxide-bound fractions by 5.06% and 7.41% in groups CM and BS respectively.

Figure 1 Stabilization effect of heavy metal Pb. The broken line is the leaching rate of the heavy metal Pb in the soil; the bar graph is the form distribution in the soil, and different colors represent the content of different forms.

3.3 MICP reduces the diversity of soil microbial community

Through the analysis of the α diversity index, it was found that the species richness and diversity of the microbial community in the soil environment have significantly variation. The Venn diagram (Fig. 2) shows that there were 2184 OTUs in bacterial communities and 668 in fungal communities. We compared the estimated abundance of bacterial and fungal communities between soils with (Kumar et al., 2023) different biological treatments (ie Chao1 and abundance-based coverage estimates (ACE)) and diversity indices (ie Shannon and Simpson) (Table 2). The abundance estimate shows that, compared with CK, the abundance of soil bacterial communities after MICP remediation was significantly reduced. The Shannon Index and Simpson Index showed that the fungal community abundance and diversity of the soil after MICP restoration was significantly higher than that of CK, but there was no significant difference in the bacterial community diversity between the soil before and after restoration (Table 2). Statistical T test method was used to detect whether the index value between any two groups is significantly different (Fig. 2). The results showed that the index value difference between any two groups of fungi was significant (P < 0.05), and the bacterial CK also had significant difference compared with CM and BS.

Table 2

Comparison of systematic coverage and diversity estimator of soil microbial communities after MICP treatment.
	Sample	OTU	Coverage	Richness estimator		Diversity index
	Sample	OTU	Coverage	ACE	Chao1	Shannon	Simpson
Bacterial	CK	2077	0.98513	2004.449	1985.745	6.22288	0.00533
	CM	983	0.98945	1235.988	943.7114	3.32424	0.10322
	BS	843	0.99103	1028.603	794.4192	3.01935	0.11079
Fungal	CK	484	0.99976	283.37217	283.91515	4.12577	0.04076
	CM	305	0.99979	178.02274	182.42677	3.02987	0.12595
	BS	279	0.99982	150.1865	153.31944	2.87069	0.16438
Note: The diversity (Alpha diversity) analysis of a single sample reflects the richness and diversity of the microbial community, including a series of statistical analysis indexes to estimate the species abundance and diversity of the environmental community.

3.4 MICP changes the composition of soil bacterial and fungal microbial communities

According to the results of the soil microbial community, it can be seen that the composition of the soil bacteria and fungal microbial community treated by MICP technology had undergone great changes at both the phylum level and the genus level (Fig. 3). At the phylum level, the bacterial communities in the soil treated with CK are mainly Proteobacteria, Bacteroides, Actinomycetes, Acidobacteria and Gemmatimonadetes, while those soil after MICP treatment was mainly Firmicutes, Bacteroides, and Actinomycetes, and Firmicutes have the highest proportion (Fig. 4A). In addition, the number of soil fungi decreased greatly as a whole, in which Proteobacteria decreased significantly after MICP bioremediation, and Acidobacteria even disappeared after biological treatment. In the environment, most Firmicutes microorganisms can produce spores, so they can grow well in extreme environments such as drought. In this study, the strain added in the remediation process is Bacillus pasteurii Firmicutes, and the main components of its culture mediu may also be suitable for the growth of this kind of microorganisms, so the soil after MICP bioremediation is mainly Firmicutes.

Analyzing the results at the genus level, the community composition of soil bacteria and fungi under different treatments changed greatly (Fig. 4B). Bacteria at the genus level, the microorganisms in the soil treated with CK were mainly Lysobacter, Flavisolibacter, Pontibacter, Halomonas, the distribution was relatively uniform and accounted for Not high. The soil microbial community changed significantly after MICP treatment. The soil bacteria treated by CM were mainly Salinimicrobium, Glutamicibacter, Lysobacter and Pyychrobacter, while the soil treated by BS were mainly Salinimicrobium and Virgibacillus, Glutamicibacter mainly bacteria. For fungi, Fusarium, Pseudeurotium, Geminibasidium and Coprinellus were the main soils treated with CK. After MICP restoration, the proportion of Fusarium and Penicillium in the soil fungus community treated with CM and BS increased significantly (P < 0.05), while there were no significant variation in the proportion of other fungi.

3.5 Relationship between Microbial Community Structure and Environmental Characteristics

Spearman correlation analysis showed a significant correlation between abiotic factors and bacterial communities (Fig. 5). We screened five abiotic factors related to most bacterial communities, pH, EC, TC, TN and TP. Among them, pH was positively correlated with Flavisolibacter, Pontibacter, Sphingomonas, Limnobacter, Nitrospira, and Adhaeribacter, and negatively correlated with Arthrobacter (P < 0.05); EC was significantly positively correlated with Glutamicibacter, Salinicoccus, Sporosarcina, Planomicrobium, and is significantly negatively correlated with Rubrobacter, RB41, Sphingomonas, Bryobacter, etc.; EC had a significant positive correlation with Virgibacillus, Halomonas, Janibacter, Arthrobacter, etc., and a very significant negative correlation with Flavibacter, Plavibacter, Massilia, Nitrospira, etc (P < 0.01). TC was positively correlated with Salinimicrobium, Lysobacter, Psychrobacter, and Bhargavaea (P < 0.05), and negatively correlated with Adhaeribacter (P < 0.05). There was a significant positive correlation between TN with Salinimicrobium, Lysobacter, and Psychrobacter, and a very significant positive correlation with Bhargavaea, but a significantly negative correlation with Methylobacillus. At the same time, TP shown a significantly positive correlation with Lysobacter, extremely significantly positively correlated with Psychrobacter and Bhargavaea, while negatively correlated with Methylobacillus. C/N had a significant positive correlation with Methylobacillus, and a significant negative correlation with Lysobacter, Psychrobacter, and Bhargavaea.

Spearman correlation analysis showed that there was a significant correlation between abiotic factors and fungal communities (Fig. 6). pH has a positive correlation with Coprinellus, Acaulium, Cephalotrichum, and Naganishia, a significantly positive correlation with Knufia, and a negative correlation with Penicillium and Microascus. EC had a very significant positive correlation with Penicillium, a significant negative correlation with Coprinellus, and a very significant negative correlation with Naganishia. TC was positively correlated with Fusarium and Acaulium, significantly positively correlated with Microascus, negatively correlated with Chaetomium and Mortierella, and significantly negatively correlated with Coprinellus, Spizellomyces, and Knufia. TN was positively correlated with Fusarium, significantly positively correlated with Acaulium and Microascus, and significantly negatively correlated with Coprinellus, Chaetomium, Spizellomyces, and Knufia. TP has a positive correlation with Acaulium, a significant positive correlation with Microascus, a negative correlation with Coprinellus, Mortierella, Arthrographis, Knufia, Gibberella, and a significant negative correlation with Chaetomium and Spizellomyces. C/N was positively correlated with Coprinellus and Chaetomium, and significantly negatively correlated with Acaulium, Microascus, Spizellomyces, and Knufia.

3.6 Functional prediction of soil microbial communities in different biological treatments

Using the Tools of PICRUSt and FUNGuild, we found that bioremediation had no effect on the function of soil bacteria, but had an obvious effect on the function of soil fungi. Bacteria showed similar functional characteristics in different treated soils (Fig. 7A). These functional characteristics mainly include functions related to energy production and conversion processes, amino acid transport and metabolism processes, carbohydrate transport and metabolism processes, transcription processes, and biological genetics and signal transduction processes related to cell wall, membrane and envelope mechanisms (Fig. 7A). However, we found a significant difference in fungal function between the control group and the repaired soil. In the soil of the control group, animal pathogens-fecal saprophytic-endophytic-epiphyte-plant saprophytic-wood saprophytic and fecal saprophytes were significantly higher, and they were significantly reduced after MICP restoration. And animal pathogens-plant pathogens, soil saprophytic bacteria-undefined saprophytic, endophytic saprophytes-soil saprophytes-undefined saprophytes, plant pathogens and plant pathogens-undefined parasites-undefined saprophytic relative abundance in organisms After the repair are reduced. Undefined Saprotroph accounted for the highest proportion of fungal OTU in soil added with inoculants (Fig. 7B)

4.1 MICP changed the basic physical and chemical properties of soil but still a safety method of bioremediation

MICP is widely used in remediation of soil with heavy metals (Chung et al., 2020). As microorganisms metabolize, the soil pH declines from 7.96 to 7.00 with the bioremediation which means MICP technology could alleviate soil alkalinity. Also the soil electrical conductivity increased after MICP remediation, and it may affect the growth of plants, we should strictly control the adding amount (Robles et al., 2018). What’s more, MICP bioremediation could promoted the soil aggregation structure (Chen et al., 2021). The medium added as nutrition could maintain the growth of microorganisms and accelerate mineralization; on the other hand, it was a probable reason for variations in TC and TP (Hashem et al., 2019).

4.2 Soil Microbial Community Composition

Microorganisms are decomposers of soil organic matter, but also a participant in the soil food chain (Morrison et al. 2017, Philippot et al. 2013, Romaniuk et al. 2011). In this study, the relationship between soil bacterial and fungal communities and the changes of soil environmental factors after heavy metal remediation with MICP was analyzed, and changes in bacterial fungal abundance and diversity. The abundance of bacteria and fungi were significantly reduced after biomineralization and restoration. This may be because the added influencing substances and inoculants changed the soil environment (Jangid et al. 2008, Death and Barquin 2012). In other words, the addition of specific nutrients will promote the growth of microorganisms that make better use of these nutrients and inhibit the growth of other microorganisms (Luan et al. 2020, Jiao et al. 2016). Another possible explanation is that the added various exogenous substances change the physical and chemical properties of the soil, and changes in the physical and chemical properties of the soil will also affect the growth of microorganisms (Lammel et al. 2018). The MICP technology used in this study remediation of heavy metal pollution in soil. The bacterial agent added during the repair process is Bacillus pasteurii, and the nutrients contain urea and other nutrients, which are more suitable for the growth of urease-producing bacterial agents. Therefore, these foreign aid substances inhibit and promote the growth of urease-producing bacteria, while inhibiting the growth of other microorganisms in the soil, reducing the diversity of soil microbial communities.

4.3 Soil Microbial community composition and Function

Compared with other treatments, soil treated without MICP had the highest microbial abundance and indicated the most bacterial and fungal species. At the phylum level, the bacterial communities in the soil that have not been treated with MICP technology are mainly Proteobacteria, Bacteroidetes, Actinobacteria and Acidobacteria. Proteobacteria is the dominant bacteria in many saline soils in China (Yang et al. 2016), and Bacteroides has a certain ability to resist environmental stress and can produce spores that can resist dehydration and extreme environment (Fazi et al. 2005). After the MICP treatment, Firmicutes, Bacteroidetes and Actinobacteria were the main bacterial categories in the soil. The results of this study might be due to the addition of large amounts of calcium chloride and various nutrients in the soil remediation process. At the genus level, the proportions of Salinimicrobium, Virgibacillus, Glutamicibacter, and Lysobacter increased, and the changes in Salinimicrobium were the most significant. Studies have shown that Salinimicrobium was distributed in saline soil and could tolerate 1%-3% NaCl stress (Chen et al. 2008). Therefore, the addition of NaCl and other substances in the process of MICP restoration increased the proportion of Salinimicrobium.

Fusarium, Pseudeurotium and Penicillium are the dominant phylum in MICP treated soils (Fig. 4B). They were the main decomposers in soil, while also playing a vital role in nutrient cycling. The proportion of Fusarium and Penicillium increased significantly with the addition of many nutrients, because Fusarium and Penicillium belong to the dominant flora in soil environment (Klein, Katan and Gamliel 2011, Amezcua-Allieri et al. 2003).We study the genetic function of soil fungal communities that are not as sensitive to changes in environmental factors as bacterial community structures. This may be because bacteria are more likely than fungi to degrade the different nutrients added during the repair process, enabling them to extract resources from many related available substances in the first place (Chapin, Matson and Vitousek 2011). In the three biological treatments, the fungal communities showed significant differences in functional characteristics, but no significant differences in bacterial functional groups were observed in this study. The different soil treatment responses between the community functions of soil bacteria and soil fungi may be due to the lower sensitivity of fungi to all environmental changes. This is because fungi are generally produced more slowly than bacteria, and thus more slowly respond to soil disturbances (Fiorentino et al. 2018).

4.4 Soil Microbial Community and Soil Factor Relationships

There is a significant correlation between soil environmental factors and soil microbial community diversity and abundance (Zhang et al. 2019). It has been proved that the pH and TC of the soil have a significant impact on the structure of the microbial community, and people can affect the characteristics of the soil during the MICP bioremediation process (Yao et al. 2017). In this study, Spearman analysis showed that the available TC and TN content significantly affected the bacterial community structure. The content of organic matter in soil is the main factor for the change of soil bacterial community. This indicates that the bacterial community has adaptive changes to the type and content of soil nutrients, which leads to the change of soil bacterial community. Studies have shown that the relative abundance of soil microorganisms is significantly positively correlated with soil nutrients (Hu et al. 2002). In this study, due to the addition of specific nutrients, the microbial utilization of specific nutrients was different, which, to a certain extent, resulted in the decrease of soil microbial abundance. Soil pH and EC affect the biological activity of soil microorganisms by affecting the physical and chemical properties of soil and the composition of soil substrate (Wu et al. 2017, Zhang et al. 2020). In our studies, Virgibacillus and Arthrobacter were found to be positively correlated with EC. It is well known that Virgibacillus is a moderately halophilic bacterium (Chen et al. 2009), due to the increased abundance of inorganic salts added during MICP bioremediation.

Furthermore, our study found that MICP repair also alters the structure of the fungal microbial community. Among them, pH, TN, TP and C/N are all important factors affecting the change of soil fungal community. Coprinellus, Spicellomyces and Phialemoniopsis were negatively correlated with soil TC and TN. Phialemoniopsis is a common soil fungus. Studies have shown that these strains can significantly reduce the number of potato nematode populations in the field (Atkins et al. 2003). Soil fungi have a strong ability to decompose unstable organic carbon, which depends on various enzymes that can decompose intractable organic matter (Jia et al. 2020). We further found that in all treated soils, Basidiomycetes and Ascomycetes are the main fungal phyla. These two fungal phyla have key genes that encode cellulolytic enzymes and promote the carbon conversion process (Hannula et al. 2012, Bastida et al. 2013). Basidiomycetes mainly rely on soil organic matter as their main carbon source, and members of this phylum participate in the process of soil carbon conversion (Jiang et al. 2006).

MICP forms a stable co-precipitation of Pb compounds in the soil, which reduces the effective content and mobility of Pb in the soil. At the same time, the response of MICP remediation to soil heavy metal pollution to soil microbial community was studied through partial 16S/18S rRNA gene analysis. MiSeq results showed that bioremediation changed the structure of soil microbial community and reduced the diversity and relative abundance of bacterial community. The biological stimulation process and the addition of bactericidal agent had different effects on soil microorganisms, but the effect was relatively small in CM. In addition, considering the microbial community structure and environmental factors, the microbial community changes are mainly attributed to the addition of exogenous substances, soil EC, TN and TP. Our findings are an important step in the impact of soil bioremediation on soil microbial communities. It provides important guidance for bioremediation of heavy metals in soil.

Acknowledgements

This work was financially supported by National key research and development program (SQ2024YFE0101168), Natural Science Foundation of Inner Mongolia (2021MS05033)，Kundulun Science and Technology Program (JB2021007) , Fundamental Research Funds for Inner Mongolia University of Science & Technology (2023XKJX015), National Science Foundation of China (52264013), The basic research funds for universities directly under the Inner Mongolia Autonomous Region (2023CXPT004)

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Microbial-induced carbonate precipitation modulates microbial community composition in long-term lead-contaminated soil

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Bacterial strains and growth conditions

2.2 Soil samples and experimental groups

2.3 Determination of soil index

2.4 Genomic DNA Extraction

2.5 PCR Amplification

2.6 MiSeq Sequencing

2.7 Biological analysis process

3 Results

3.1 MICP changed the basic physical and chemical properties of soil

3.2 MICP reduced the available content of Pb in soil

3.3 MICP reduces the diversity of soil microbial community

3.4 MICP changes the composition of soil bacterial and fungal microbial communities

3.5 Relationship between Microbial Community Structure and Environmental Characteristics

3.6 Functional prediction of soil microbial communities in different biological treatments

4 Discussion

4.2 Soil Microbial Community Composition

4.3 Soil Microbial community composition and Function

4.4 Soil Microbial Community and Soil Factor Relationships

5 Conclusions

Declarations

References

Additional Declarations

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