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Seasonal and Spatial Variations in Functional Genes and Microbial Community of Feammox and Its Associated Processes in Urban Green Heart Soil

School of New Energy Materials and Chemistry, Leshan Normal University, Leshan 614000, China
South Sichuan Pollution Control and Resource Recovery Research Center, Leshan Normal University, Leshan 614000, China
Author to whom correspondence should be addressed.
Water 2023, 15(6), 1024;
Submission received: 1 February 2023 / Revised: 5 March 2023 / Accepted: 7 March 2023 / Published: 8 March 2023
(This article belongs to the Section Soil and Water)


Research on Fe3+ reduction coupled to anaerobic ammonium oxidation (Feammox) and its associated processes in the moderately low-dissolved oxygen (DO) urban natural environment is lacking. To clarify seasonal and spatial variations in Feammox, iron-reducing, and anaerobic ammonium oxidation (anammox) in urban green spaces, we examined the physicochemical indices and functional genes acd, acm, Geo, and hszA in topsoils and wetland soils across four seasons. Further, we performed 16S rRNA high-throughput sequencing. The Feammox-related gene acm was detected in all topsoil samples. Season and habitat affected soil physicochemical indices influencing gene distributions. Moisture content (45.3%) and Fe3+ (13.3%) mediated genetic changes. Competition between Feammox and iron-reducing bacteria (IRB) lowered the distributions of acd and acm in summer and increased these in winter. The acd and acm distributions were higher in wetland soil than in forestland soil. The dominant phyla, Nitrospirota, Actinobacteriota, and Desulfobacterota, correlated positively. Network analysis revealed that the relative abundances of acd, Geo, and hszA correlated positively with Flavobacterium and Thermomonas, Subgroup_2, and Candidatus_Solibacter, respectively. Feammox, iron-reducing, and anammox microorganisms correlated positively but competition existed between certain taxa. Candidatus, Sphingomonas, and Geobacter are linked to Feammox, iron reduction, and anammox. Here, we demonstrated the theoretical feasibility of developing Feammox-based nitrogen removal technology under moderately low-DO conditions, providing a reference for elucidating the ecological contribution of Feammox in an urban green heart.

1. Introduction

Feammox is ferric iron reduction coupled to anaerobic ammonium oxidation (Feammox). Ammonia is oxidized, and ferric iron is reduced by microorganisms under anaerobic and autotrophic conditions [1]. Feammox has been studied since 2005 as a new type of biological nitrogen removal (BNR) agent, in addition to denitrification and anammox in natural ecosystems [2,3]. Lixun et al. reported that Feammox bacteria oxidize ammonia in paddy soil to a greater extent than ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) [4]. Feammox-based nitrogen removal technology could sustainably and cost-effectively treat wastewater as it requires no aeration or additional carbon sources while producing no greenhouse gases (GHG).
Recently, researchers successively identified Feammox processes in anoxic natural ecological environments such as tropical soils [5], aqueous sediments [6], agricultural fields [7], rice soils [8], and groundwater [9]. Prior studies demonstrated that Feammox bacteria are anaerobic and prefer low or no dissolved oxygen (DO) conditions [10]. Feammox activity is higher in the subsoil than in the topsoil [8]. Thus, air must be excluded from Feammox-based wastewater treatment processes. However, this constraint is not conducive to practical Feammox applications. Nonetheless, Feammox has been detected in moderately low-DO environments [11], and certain researchers have developed a continuous Feammox-based wastewater treatment system through intermittent aeration [12]. Thus, the Feammox process and microecology in moderately low-DO habitats merit further investigation.
Prior studies have extensively addressed Feammox in natural and certain agricultural soils. In contrast, research on Feammox in urban green spaces is scarce. Urban green space planning and construction are receiving increasing attention as requirements for human settlement as climate change evolves. Several studies have shown that urban green spaces provide recreation [13], promote physical and mental health [14], regulate the local climate, buffer stormwater runoff [15], control air and noise pollution [16], and participate in the biogeochemical cycle [17]. The urban green heart is a relatively large urban green space in the center of a city or urban agglomeration [18]. Although researchers understand its sociological contributions, relatively little is known about its environmental significance.
Feammox microorganisms have similar habitats and nutrient requirements and are usually detected together with iron-reducing bacteria (IRB) and anammox bacteria in natural and wastewater treatment systems [19,20]. The activity and nitrogen removal performance of Feammox bacteria are significantly positively correlated with the abundance of IRB and anammox bacteria [21,22]. Some researchers have suggested that Feammox bacteria are essentially autotrophic IRB [23]. Elucidation of the iron-reducing and anammox processes might help to clarify the Feammox mechanism.
Considering the few insights on Feammox in topsoil and in urban natural environments, as well as the close relationship between Feammox and IRB and anammox, in the present study, we used qPCR and 16S rRNA gene high-throughput sequencing to analyze Feammox bacteria and their closely related IRB and anammox bacteria in three types of surface soil in which the DO condition and redox potential were higher than deep soil, including landscape soil, returned farmland soil, and forestland soil, and as well as a type of sediment soil (wetland soil) in an urban green heart. We also comprehensively analyzed the seasonal and spatial successions of Feammox-related microbial communities. The present study provides a reference for understanding the mechanism of Feammox in the environment and its ecological contributions to an urban green heart.

2. Materials and Methods

2.1. Study Area and Soil Sampling

Jiazhou Lvxin Park (, accessed on 31 January 2023) is located in Leshan City, Sichuan Province, China. It has a total area of 9.8 km2, and its forest coverage is >70%. It was founded in 1987 and was China’s first urban green heart planning exercise. A total of 64 soil samples were collected from the park each season between winter 2019 and autumn 2020. Detailed seasonal and habitat information of the soil samples is shown in Table S1. Each seasonal batch sampling was conducted within 1 d, and there had to be at least four consecutive rain-free days before sample collection. Landscape-returned farmland, forestland, and wetland soil were collected each season, and four samples per soil type were taken in each sampling batch. The landscape soil was used to cultivate landscape and ornamental plants and was regularly managed by gardeners. The returned farmland soil was previously used in crop cultivation but was retired under government regulation as of 2018. The forestland soil was covered with trees and was never cultivated. The wetland soil occurred in both the natural and constructed wetlands in the park. The longitude, latitude, and altitude of the sampling sites were in the ranges of 29°34′5″ N–29°35′51″ N, 103°42′42″ E–103°45′11″ E, and 360–410 m, respectively. Soil samples were collected in accordance with the agricultural industry standard Soil Testing, Part 1: Soil Sampling, Processing, and Reposition (NY/T 1121.1-2006) of the government of the People’s Republic of China. Each soil sample was divided into two equal parts. The first part was used to detect various physicochemical indices immediately after the samples were returned to the laboratory. The second part was frozen and maintained in a sealed plastic bag at −20 °C until the subsequent total DNA extraction.

2.2. Physicochemical Analyses

The pH, moisture content, organic matter (OM) content, Fe2+, Fe3+, ammonia nitrogen (NH4+-N), nitrite nitrogen (NO2-N), and nitrate nitrogen (NO3-N) were measured for each soil sample. Each soil sample was mixed with distilled water at a 1:2.5 dry soil weight: water ratio and the pH of the resultant extraction solution was measured with a pH meter (Inesa, Shanghai, China). The moisture content was determined by weighing each sample, drying it in an oven at 105 °C for 8 h, and reweighing it. The OM content was determined according to the Method for the Determination of Soil Organic Matter (GB 9834-1988).
Fe2+ and Fe3+ were detected by phenanthroline spectrophotometry. NH4+-N, NO2-N, and NO3-N were measured by Nessler’ s reagent spectrophotometry (HJ 535-2009), N-(1-naphthalene)-diaminoethane spectrophotometry (HJ/T 346-2007), and ultraviolet spectrophotometry (GB 7493-87). Each 1.00-g soil sample was placed in a 50-mL centrifuge tube containing 10 mL of extraction solution. For Fe2+ and Fe3+, the extraction solution was 1 mM HCl. For NH4+-N, NO2-N, and NO3-N, the extraction solution was 1 M KCl. The centrifuge tube was shaken at 160 rpm and 20 °C for 1 h; then, the suspension was transferred to a clean 10-mL centrifuge tube. The latter was centrifuged at 3000 rpm for 10 min, and the supernatant was taken. Three soil sample extracts were prepared per sample.

2.3. DNA Extraction

Each 0.50-g soil sample was prewashed thrice with phosphate-buffered saline (PBS; pH 8.0) and 1.7% (w/v) polyvinylpyrrolidone K30 to remove impurities such as humic acids before extraction. Total genomic DNA was extracted from the cleaned samples with a FastDNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’ s instructions. The concentration and quality of the extracted DNA were determined with a NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The DNA was successfully extracted when the OD260/OD280 was in the range of 1.8–2.0. All 64 extracted DNA samples were used for qPCR detection. Four soil DNA samples of equal volume, collected in the same season and from the same habitat, were mixed and used in 16S rRNA gene high-throughput sequencing. The extracted DNA was stored at −20 °C until later use.

2.4. Quantitative Polymerase Chain Reaction (qPCR)

In the present study, qPCR was used to determine the target genes acd (Acidobacteriaceae), acm (Acidimicrobiaceae bacterium A6), hszA (Anammox bacteria), and Geo (Geobacteraceae IRB). Total bacteria were quantified using the general 16S rRNA gene. Detailed information on the primers used to target these genes is provided in Table S2. The qPCR was conducted in a 7500 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) using the aforementioned primer pairs. The qPCR was performed in a total volume of 20 μL consisting of 10 μL of 2× Power SYBR Green PCR Master Mix Kit (Applied Biosystems), 0.5 μL of each 10-μM primer, 1 μL of 10 mg mL−1 bovine serum albumin (BSA), 3 μL of DNA, and 5 μL of ddH2O. Each measurement was conducted in triplicate. Thermal cycles and fluorescence signal acquisition were performed according to the protocols described in Table S2.

2.5. 16S rRNA Gene High-Throughput Sequencing

The 16S rRNA gene sequencing was conducted on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) at Oebiotech Co. Ltd. (Shanghai, China). The microbial community structure was determined using the primers 343F (5′-TACGGRAGGCAGCAG-3′) and 798R (5′-AGGGTATCTAATCCT-3′) targeting the V3–V4 region of the 16S rRNA gene. Raw paired-end reads were merged with the FLASH software (, accessed on 31 January 2023). The data obtained were then analyzed using the QIIME pipeline ( Representative sequences for each amplicon sequence variant (ASV) were selected by the UCLUST algorithm (, accessed on 31 January 2023). Taxonomic information was annotated with the Ribosomal Database Project (RDP) classifier (, accessed on 31 January 2023). The raw sequencing data were submitted to the NMDC (National Microbiology Data Center;, accessed on 31 January 2023).

2.6. Data Analysis

Data are represented as means ± standard error. The absolute target gene abundance was expressed as copies per gram of dry soil. The unit of relative target gene abundance was the ratio of absolute target gene abundance to 16S rRNA. The final gene data were calculated and reported in log10 format. Pearson’s correlation coefficient (R-value) was used for correlation analyses. Significant changes in the physicochemical factors, functional gene abundance, and microbial indices of the various groups were compared by one-way- or two-way analysis of variance (ANOVA) and corrected by Tukey’s test. Normality and homoscedasticity were evaluated before the ANOVA. All statistical analyses were conducted in OriginPro v.2021 (OriginLab, Northampton, MA, USA). The statistical significance level (p-value) was set to 0.05. Histograms, boxplots, and bar charts were plotted with OriginPro. The calculation of α diversity indexes, including Chao1, Shannon, Simpson, and Good’s coverage, was conducted by Oebiotech Co. Ltd. (Shanghai, China). The principal component analysis (PCA) and the redundancy analysis (RDA) were conducted in CANOCO v. 5 (Microcomputer Power, Ithaca, NY, USA). Heatmaps were plotted with TBtools software (, accessed on 31 January 2023). A network analysis was conducted with Cytoscape v. 3.4.0 (, accessed on 31 January 2023). Only correlations with R > 0.6 and p < 0.05 were displayed in the networks.

3. Results and Discussion

3.1. Physicochemical Properties

The physicochemical properties of soil are shown in Figure S1. The average soil pH was 6.85 ± 0.04, and the average moisture and OM content were 19.98 ± 1.18% and 1.76 ± 0.10%, respectively. Soluble iron and inorganic nitrogen varied widely among soil types. However, NO3-N dominated the latter. Two-way ANOVA showed that soil pH, moisture content, NH4+-N, and NO3-N significantly differed (p < 0.05) among seasons, while soil moisture, OM content, Fe2+, Fe3+, NH4+-N, and NO3-N significantly differed (p < 0.05) among habitats. Season and habitat had a significant interaction effect on soil pH, moisture content, and NH4+-N (p < 0.05).
Fe2+, Fe3+, and NO3-N significantly contributed to sample separation in the PCA (Figure S2). Soil Fe2+ (R = 0.51; p < 0.01), Fe3+ (R = 0.32; p < 0.01), NH4+-N (R = 0.62; p < 0.01), and OM content (R = 0.46; p < 0.01) significantly positively correlated with soil moisture content. Hence, high soil moisture content promotes iron dissolution and NH4+-N production. Fe2+ (140.43 ± 3.71 mg/kg) and Fe3+ (96.73 ± 26.20 mg/kg) were significantly higher in wetland soil than in the other habitats (one-way ANOVA; p < 0.05). Fe2+ significantly positively correlated with Fe3+ (R = 0.40; p < 0.05) and NH4+-N (R = 0.46; p < 0.01). NO3-N (78.07 ± 9.79 mg/kg) was significantly higher in the landscape soils than in other habitats (p < 0.05).

3.2. Absolute and Relative Target Gene Abundance

All target genes were detected in the 64 urban green heart soil samples (Figure 1) and their absolute and relative abundances followed a normal distribution (Kolmogorov–Smirnov test; p < 0.05). The absolute abundances of 16S rRNA, acd, acm, Geo, and hszA were 3.69–7.11 log, 2.62–6.95 log, 4.11–6.04 log, 3.70–7.77 log, and 3.25–6.64 log, respectively. The range of absolute target gene abundances was similar to those reported in previous studies [4,24,25]. It is generally accepted that Feammox bacteria thrive under very low-DO conditions [6,9,10]. Nevertheless, Feammox bacteria were abundant in the landscape, returned farmland, and forest topsoils with higher redox conditions. Bangjing et al. and Lixun et al. demonstrated the presence of Feammox in agricultural topsoils [4,8]. The foregoing discoveries provide a theoretical basis for the development of the Feammox process under moderately anaerobic conditions. Figure S3 shows the variations in relative target gene abundance. The relative abundance of Geo was significantly (p < 0.05) higher than that of acd, acm, and hszA. The relative abundance of acd and acm was closely clustered.
A correlation analysis (Figure 2) revealed significant positive correlations (p < 0.01) between the absolute and relative abundances of acd and those of acm. Thus, acd and the Feammox-related gene acm were reliably detected. The relative abundance of hszA correlated positively with that of acm and Geo (p < 0.05). The relative abundance of Geo correlated positively with that of acm (p < 0.05).

3.3. Effects of Physicochemical Indices on Target Genes

The RDA showed that soil moisture content and Fe3+ contributed to the observed variations in the target genes by 45.3% (p = 0.002) and 13.3% (p = 0.008), respectively (Figure 3A). Thus, certain physicochemical parameters may be responsible for the occurrence of target genes. Bangjing et al. confirmed the importance of soil moisture content and Fe3+ in controlling Feammox bacteria and IRB [26]. Figure 3B shows that soil moisture content significantly positively correlated with the absolute abundance of Geo (R = 0.25; p < 0.05). High soil moisture increased the OM content, facilitated Fe3+ and NH4+-N leaching in the soil, and met the nutrient requirements for IRB [27]. Fe3+ also significantly positively correlated with the absolute abundance of acm (R = 0.30; p < 0.05) and Geo (R = 0.44; p < 0.01) abundance. Therefore, Fe3+ is vital as a substrate for Feammox bacteria and IRB. Yunbin et al. reported that Fe3+ content described 36.00% of the variation in the surface soil Feammox rate and was considered the best predictor of this parameter [28]. Yaojin et al. found that Fe3+ content strongly influences the compositions of the bacteria participating in ammonia oxidation and iron reduction [9].
OM only significantly positively correlated with the absolute abundance of Geo (R = 0.25; p < 0.05) as IRB is heterotrophic while both Feammox and anammox bacteria are autotrophic. Feammox is widely considered an autotrophic process [29]. Nevertheless, OM can promote Feammox through several pathways [30,31]: (1) The activity of bacteria using OM can induce the release of Fe3+ from the mineral matrix; (2) OM may act as an electron shuttle, accelerating Feammox; (3) OM facilitates Feammox by affecting the abundance of IRB as the latter usually positively correlates with Feammox bacteria; (4) OM degradation releases protons and lowers the pH, providing Feammox with a physicochemically suitable environment. In contrast, OM reportedly inhibits Feammox. Tingting et al. used an up-flow anaerobic sludge blanket reactor to initiate Feammox and found that OM in the influent water significantly inhibited nitrogen removal [32]. Excess organic substrate promoted the overproliferation of IRB; this process would consume the ferric iron and ammonia that are required for Feammox. Despite the foregoing contradictions, it is evident that the effects of OM on Feammox are closely associated with the response of IRB to the OM in the system [32,33,34,35].
Figure 3A shows that hszA was more strongly correlated with NH4+-N and NO2-N than the other physicochemical indices as these nitrogen sources are direct substrates of anammox bacteria. However, the correlations were only weakly significant. The soil samples had low NH4+-N and NO2-N content, which might account for the fact that the relative hszA abundance was significantly (p < 0.05) lower than that of Geo. The latter functional IRB gene is induced in the presence of abundant substrates.

3.4. Seasonal and Spatial Variations in the Target Genes

Two-way ANOVA was used to compare the effects of season and habitat on the target genes (Table 1). Season significantly affected the absolute abundance of all five target genes (p < 0.05), whereas habitat only significantly affected the absolute abundance of acd and Geo (p < 0.05). Season and habitat had a significant interactive effect on the absolute abundance of 16S rRNA, acm, and hszA (p < 0.05). The foregoing results suggest that the seasonal and spatial distributions of Feammox bacteria and their related microorganisms are heterogeneous in urban green heart soils. Wu et al. described the heterogeneity, which is the inherent law of nature and the essence of the biodiversity of an ecosystem [36]. A review of nitrogen removal in the hypercurrent zone and riparian zone revealed that precipitation, fire, and snowmelt caused the increase in nitrogen removal, which led to changes in water, temperature, nutrition, and other environmental factors, indicating the contribution of environmental factors to the heterogeneity of nitrogen-removing organisms [37].
Seasonal changes affect nitrogen-removing microorganisms by altering soil moisture, temperature, OM, and other factors. Bangjing et al. found that the Feammox rates in the soils of the Taihu Lake basin were higher in summer than in the other seasons [7]. Shan et al. reported that the abundances of nitrifying and denitrifying bacteria in urban river sediments affected by agricultural activity were higher, and the microorganisms were more active in summer than in winter [38]. Here, the absolute abundances of acd and the Feammox-related gene acm were significantly (p < 0.05) lower in summer and autumn than they were in winter and spring (Figure 4A). Conversely, the absolute abundance of Geo was significantly (p < 0.05) higher in summer and autumn than it was in spring and autumn. Soil moisture content, Fe3+, and Fe2+ significantly affected Geo, whereas Fe3+ significantly influenced acm (Figure 3B). Nevertheless, soil moisture content made the largest contribution to the observed variations in target genes (Figure 3A). On the other hand, two-way ANOVA revealed that the season significantly affected the soil moisture content but not Fe3+. Feammox and IRB have the same electron acceptor and nitrogen source requirements. Hence, they compete in their complex linkages. The relative abundance of Geo significantly positively correlated with that of acm (Figure 2). However, the increase in soil moisture content in summer made IRB the dominant functional iron-reducing bacterium.
Habitat significantly impacted the absolute abundance of acd and Geo (Table 1). Figure 4B shows that the absolute abundances of acd and Geo in the wetlands of aquatic ecosystems were significantly higher (p < 0.05) than those in other habitats of terrestrial ecosystems. Wetland soils had significantly higher (p < 0.05) moisture content, OM content, Fe2+, Fe3+, and NH4+-N than other habitats (Figure S1). Krichels et al. found that the depressional soils in uplands had higher iron-reducing potential than other environments as the former were susceptible to flooding [39]. Wendy et al. reported the oxidation of NH4+-N to NO2-N under iron-reducing conditions in wetland soils but not in terrestrial ecosystems [40]. Thus, Feammox occurs relatively more frequently in aquatic ecosystems. This phenomenon is consistent with the results of the present study.
Figure 4B shows that the absolute abundance of acd was significantly lower in forestland soil than in other habitats (p < 0.05). Forestland soil has significantly lower Fe3+ and NH4+-N (p < 0.05) than other soil types (Figure S1); therefore, it is not conducive to Feammox growth. Several studies have demonstrated that bare soil has lower Feammox potential than vegetated soil [41,42,43]. In the present study, the forestland was covered by trees, but most of its understory soil was bare.
Overall, the distribution of Feammox bacteria was influenced by season, habitat, the presence of other microorganisms, and especially IRB in the same system. Therefore, it is necessary to elucidate the relationships between Feammox bacteria and other microorganisms in a certain system.

3.5. Microbial Richness and Diversity

A total of 15,091 ASVs were obtained from 16 samples (NMDC Succession Nos.: NMDC40025931–NMDC40025946) by 16S rRNA high-throughput sequencing with a 100% similarity clustering threshold. All samples shared core ASVs from nine genera under two phyla (Figure S4), Proteobacteria (Ralstonia and Sphingomonas) and Bacteroidota (Alloprevotella, Rikenellaceae_RC9_gut_group, Burkholderia-Caballeronia, and four Muribaculaceae subgenera). Sphingomonas is a proven IRB [19], Ralstonia has broad-spectrum heavy metal resistance [44], and Burkholderia-Caballeronia can degrade OM [45].
Good’s coverage of all samples exceeded >99% (Table 2). Accordingly, two-way ANOVA (Table S3) showed that season and habitat significantly affected Chao1 (p < 0.05), with a significant interaction effect (p < 0.05). Chao1 and ASV were the highest in autumn soils and lowest in winter soils (p < 0.05). They were also the highest in landscape soils and lowest in forest soils (p < 0.05).

3.6. Microbial Community Structure and Succession

The phylum-level microbial community composition was stable and consistent in urban green heart soil. The dominant taxa included Proteobacteria, Bacteroidetes, Actinobacteria, Acidobacteriota, Gemmatimonadota, Myxococcota, Desulfobacterota, and Nitrospirota, accounting for 85.73–97.90% of all sequences (Figure 5A). High abundances of these microorganisms were detected and reported in previous soil studies [46,47,48]. In an autotrophic BNR system designed to treat low-C/N wastewater, Proteobacteria and Acidobacteriota were highly abundant and played important roles in the Feammox system [49]. Acidobacteriaceae and the Feammox bacteria Acidimicrobiaceae sp. A6 examined here belong to phylum Actinobacteria, while Geobacteraceae IRB belong to phylum Desulfobacterota. Actinobacteria and Desulfobacterota predominate in iron-rich soils, wastewater treatments, and composting systems [50,51,52,53]. Two-way ANOVA showed that the foregoing dominant bacterial taxa significantly differed across soil seasons and habitats (p < 0.05). Nitrospirota is the dominant nitrite-oxidizing bacterium (NOB) in BNR [54]. A correlation analysis of the dominant phylum-level bacteria revealed significant correlations among Nitrospirota and Actinobacteriota (R = −0.52; p < 0.05) and Desulfobacterota (R = 0.86; p < 0.01). Thus, there is a strong link between nitrogen and iron metabolism in the soil.
The top 30 genera did not show regular clustering with season or habitat. Figure 5B shows that the dominant genera were Sphingomonas (4.84 ± 0.38%), Arenimonas (3.36 ± 1.18%), Rhizobacter (2.58 ± 0.94%), and MND1 (2.54 ± 0.17%). Sphingomonas is the core genus common to all samples and is a known IRB, Arenimonas is apparently a denitrifier, and MND1 (family Nitrosomonadaceae) is a known AOB [55]. Ellin6067 (Nitrosomonas family) and Nitrospira (family Nitrospiraceae) are known NOB. Hence, iron-reducing and BNR microorganisms are essential in urban green heart soils.
Microorganisms previously identified that have the ability of Feammox or found to be directly involved in the Feammox process were also detected in this study, including the genera Ignavibacterium, Geobacter, Anaeromyxobacter, Dechloromonas, Bradyrhizobium, and Rhodobacter [9,56,57], and the families Acidimicrobiaceae, Bacillaceae, and Clostridiaceae [58,59]. Geobacter, Anaeromyxobacter, and Dechloromonas are also IRB [60,61], while Ignavibacterium has been identified as a an anammox bacterium [56]. Thus, there are close associations between Feammox, IRB, and anammox. Other IRB were detected, including Thiobacillus, Bacillus, Crenothrix, Acinetobacter, Thermomonas, and Clostridium [20,60,61,62]. We also detected the genus SM1A02, which might contribute to the anammox process [63].

3.7. Correlations between Target Genes and Microbial Community Structure

The correlations between the target genes and the microbial community structure were investigated by network analysis (Figure 6). Previously reported microorganisms with potential Feammox, iron-reducing, and anammox capacity, as well as the top 30 genera (Figure 5B), were included in the network. Fifty microbial taxa were analyzed (Table S4).
Figure 6 shows that the relative acd abundance in Cluster 1 significantly positively correlated with Flavobacterium (R = 0.606; p < 0.05) and Thermomonas (R = 0.828; p < 0.01). Flavobacterium was the dominant microbial genus (1.47 ± 0.33%) in urban green heart soil, and Thermomonas was implicated in iron redox reactions [62]. In Cluster 2, no microorganism positively correlated with acm. However, the relative acm abundance positively correlated with the relative acd abundance (R = 0.710; p < 0.01; Figure 2). Acidimicrobiaceae bacterium A6 may be influenced by other IRB, such as Thermomonas, Geobacter, Anaeromyxobacter, and Dechloromonas through the Acidobacteriaceae.
Cluster 3 shows that hszA significantly positively correlated with Candidatus_Solibacter (relative abundance = 0.56 ± 0.09%; R = 0.902; p < 0.01; Figure 6). Several studies have reported that Candidatus_Bracadia, Candidatus_Kuenenia, and Candidatus_Jettenia are common major anammox bacterial genera [22,63,64]. Hu et al. studied the combined reaction between Feammox and anammox and found that Candidatus was the dominant bacterial phylum and Candidatus Magasanikbacteria substantially contributed to the Feammox process [65]. Hence, the relative acm abundance significantly correlated with hszA, and the latter significantly positively correlated with Geo (Figure 2), which is closely related to Candidatus_Solibacter through Subgroup_2 (1.01 ± 0.24%; Figure 6). These findings indicate that Candidatus is a key bacterial phylum linking Feammox, iron-reducing, and anammox bacteria.
Sphingomonas was the core genus in all samples (Figure S4). It had the highest relative abundance (4.84 ± 0.38%) of all genera detected (Figure 5B) and linked the three main clusters. Geobacter is a putative Feammox and IRB; it occupied an important position in Cluster 1 and significantly positively correlated with 12 other microorganisms. The coefficients of correlation between Crenothrix (a known IRB), Dechloromonas (a known Feammox, IRB, and denitrifier), OLB12, and Methylocystis were all >0.9 (Table S4). OLB12 was identified as the dominant genus in wastewater treatments [66], and Methylocystis is a known methanotroph [67]. However, neither of these was previously associated with Feammox or iron reduction. Thus, OLB12 and Methylocystis deserve further investigation in Feammox studies. Overall, it can be concluded that Feammox, iron reducers, and anammox in urban green heart soils have close, complex relationships, albeit not necessarily synergistic (Figure 6), and some microbes may be competing with each other (Figure 4).

4. Conclusions

This study makes a novel contribution to the literature by characterizing the Feammox reaction in urban green heart soil. Our results revealed that Feammox bacteria are ubiquitous in topsoil and disclosed their complex associations and related microorganisms. Season and habitat induced changes in physicochemical indexes, which further caused changes in functional genes, including acd, acm, Geo, and hszA. Moisture content and Fe3+ contributed 45.3% and 13.3% to the changes in these target genes, respectively. Furthermore, seasonal and habitat changes caused changes in microbial community structure, with the highest biodiversity in autumn landscape soil and the lowest in winter, in forestland. Network analysis showed that microorganisms Candidatus and the genera of Sphingomonas and Geobacter have important positions in linking the three processes of Feammox, iron reduction, and anammox in urban green heart soil.
However, this work also had a few limitations. (1) The selection of the soil physicochemical indices evaluated was not comprehensive, and other metrics should be considered going forward. Future research should investigate the roles of oxidation-reduction potential (ORP) and substances that function as electron shuttles in the soil and potentially influence Feammox and related microorganisms. (2) Only the top 30 microorganisms were used in the network analysis. Thus, other potentially important Feammox, iron-reducing, and anammox bacteria may have been omitted from the analysis. Future research work shall enrich the Feammox microbial consortium in urban greenheart soils under moderately low-DO conditions, establishing and regulating Feammox-based BNR processes. In conclusion, the information provided in this study could be applied toward sustainable, cost-effective maintenance and BNR in city green spaces.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1: Box charts of soil pH (A), moisture content, OM content (B), Fe2+, Fe3+ (C), and NH4+-N, NO2-N, and NO3-N (D) of all 64 samples.; Figure S2: Principal component analysis (PCA) of soil physicochemical factors. Figure S3: Heatmap of relative target gene abundance in soil samples. Figure S4: Flower plot of soil ASV and nine core ASVs. Table S1: Seasonal and habitat information of soil samples. Table S2: Primers and PCR conditions. Table S3: Effects of season and habitat on indices of microbial richness and biodiversity according to two-way ANOVA. Table S4: Coefficients of correlation among target genes, top 30 bacterial genera, and other putative Feammox, iron-reducing, and anammox bacteria. References [68,69,70,71] are cited in the Supplementary Materials file.

Author Contributions

Conceptualization, M.C. and T.J.; methodology, X.M. and X.A.; software, S.W.; investigation, M.C., Y.L. and L.L.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.


This research was funded by the Key R & D Projects of the Sichuan Provincial Department of Science and Technology in 2022 (No. 2022YFS0457), the Research and Cultivation Project of Leshan Normal University in 2023 (No. KYPY2023-0002), and the Innovation and Entrepreneurship Training Program for College Students (No. S202210649112).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Histograms (AE) and box chart (F) of absolute target gene abundance in soil samples.
Figure 1. Histograms (AE) and box chart (F) of absolute target gene abundance in soil samples.
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Figure 2. Heatmap of correlation coefficients of target gene abundances. Note: gene name suffixes “ab” and “re” indicate absolute and relative abundance, respectively; bold black circles indicate significant correlation coefficients (p < 0.05).
Figure 2. Heatmap of correlation coefficients of target gene abundances. Note: gene name suffixes “ab” and “re” indicate absolute and relative abundance, respectively; bold black circles indicate significant correlation coefficients (p < 0.05).
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Figure 3. RDA of physicochemical indices (hollow red arrows) and target genes (solid blue arrows; (A)). Coefficients of correlation (p < 0.05) between physicochemical indices and target genes. Note: absolute abundance was used for target gene data. “MC” in (B) refers to “moisture content”. Error bars indicate statistically significant differences; * denotes p < 0.01.
Figure 3. RDA of physicochemical indices (hollow red arrows) and target genes (solid blue arrows; (A)). Coefficients of correlation (p < 0.05) between physicochemical indices and target genes. Note: absolute abundance was used for target gene data. “MC” in (B) refers to “moisture content”. Error bars indicate statistically significant differences; * denotes p < 0.01.
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Figure 4. Absolute abundance of genes in soil samples under different seasonal (A) and habitat (B) conditions.
Figure 4. Absolute abundance of genes in soil samples under different seasonal (A) and habitat (B) conditions.
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Figure 5. Phylum-level microbial community structure (A) and heatmap of top 30 genera (B). Note: in the heatmap, relative abundance data are normalized by column.
Figure 5. Phylum-level microbial community structure (A) and heatmap of top 30 genera (B). Note: in the heatmap, relative abundance data are normalized by column.
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Figure 6. Network analysis based on correlation coefficients among relative target gene abundance, top 30 genera, and other potential Feammox, iron-reducing, and anammox bacteria. Note: connecting lines represent significant correlations (p < 0.05) according to Spearman’s rank correlation analysis.
Figure 6. Network analysis based on correlation coefficients among relative target gene abundance, top 30 genera, and other potential Feammox, iron-reducing, and anammox bacteria. Note: connecting lines represent significant correlations (p < 0.05) according to Spearman’s rank correlation analysis.
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Table 1. Effects of season and habitat on target genes according to two-way ANOVA. Note: “+” indicates the significant influence of each factor on genes or clear interaction between season and habitat; “−” indicates the non-significant influence of each factor on genes or unclear interaction between season and habitat.
Table 1. Effects of season and habitat on target genes according to two-way ANOVA. Note: “+” indicates the significant influence of each factor on genes or clear interaction between season and habitat; “−” indicates the non-significant influence of each factor on genes or unclear interaction between season and habitat.
FactorAbsolute AbundanceRelative Abundance
16S rRNAacdacmGeohszAacdacmGeohszA
Season–habitat interaction+++++
Table 2. Microbial richness and diversity indices of soil samples.
Table 2. Microbial richness and diversity indices of soil samples.
SampleChao1ShannonSimpsonGood’s Coverage (%)
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Chen, M.; Ma, X.; Wei, S.; An, X.; Li, Y.; Liang, L.; Jiang, T. Seasonal and Spatial Variations in Functional Genes and Microbial Community of Feammox and Its Associated Processes in Urban Green Heart Soil. Water 2023, 15, 1024.

AMA Style

Chen M, Ma X, Wei S, An X, Li Y, Liang L, Jiang T. Seasonal and Spatial Variations in Functional Genes and Microbial Community of Feammox and Its Associated Processes in Urban Green Heart Soil. Water. 2023; 15(6):1024.

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Chen, Maoxia, Xuguang Ma, Shiqian Wei, Xin An, Yanjun Li, Liye Liang, and Tao Jiang. 2023. "Seasonal and Spatial Variations in Functional Genes and Microbial Community of Feammox and Its Associated Processes in Urban Green Heart Soil" Water 15, no. 6: 1024.

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