High Level of Iron Inhibited Maize Straw Decomposition by Suppressing Microbial Communities and Enzyme Activities

Abstract

In order to study the linkages between the crop straw decomposition rate and the change in soil biological properties after the straw returned to the soil with different iron (Fe²⁺) contents, a 180-day incubation experiment was performed to examine the decomposition of maize straw (MS) under three Fe²⁺ levels, i.e., 0, 0.3, and 1 mg g⁻¹. Enzyme activities regarding straw decomposition and microbial communities under 0 and 1 mg g⁻¹ Fe addition were also detected. The results showed that Fe²⁺ addition significantly inhibited MS decomposition. This was evidenced by the higher contents of hemicellulose, cellulose, and lignin in Fe²⁺ treatments on day 180. High-Fe addition (1 mg g⁻¹) decreased the activity of Laccase (Lac) by 71.82% compared with control on day 30. Furthermore, the principal coordinates analysis (PCoA) indicated that high-Fe mainly affected the bacterial community. In particular, it suppressed the relative abundance of Microbacteriaceae in phylum Actinomycota that, in turn, is a potential decomposer of crop straw by secreting lignocellulolytic enzymes. A high level of Fe²⁺ inhibited the decomposition of hemicellulose, cellulose, and lignin in MS by reducing the relative abundance of phylum Actinobacteria in bacteria and suppressing Lac activity. Our findings provide guidance for returning crop straws in soils with high-Fe content.

1. Introduction

Globally, the annual production of crop straw reached approximately 4 billion metric tons [1], and the agricultural sector in China produces about one-third of the global crop straw [2]. Crop straw is an important amendment to improve soil health and quality due to its high enrichment of major nutrients, i.e., phosphorus, nitrogen, and potassium [3,4,5]. However, the complete decomposition of returned crop straw in soil within one crop season is still a major concern for farmers due to the popularity of double- or even triple-cropping systems in China.

Crop straw decomposition is predominantly a soil microbially-mediated process. Soil microorganisms secrete a series of specific hydrolases, lyases, oxidoreductases, and auxiliary enzymes for the decomposition of crop straw [6]. Different agricultural management can result in distinctive soil biological properties, such as microbial community structure and enzymatic activities, influencing soil ecological function and capacity that contribute to crop straw decomposition [7]. It was reported that the decomposition of crop straw could be regulated by soil environmental factors, such as soil temperature, soil moisture, soil pH, O₂, nitrogen, and other nutrients [8]. Chen et al. (2018) showed a lower decomposition rate of wheat straw under anaerobic (0.014 d⁻¹) than under aerobic (0.020 d⁻¹) conditions [4], and this was caused by the low oxygen, inhibiting microbial biomass and activity. Xu et al. (2016) revealed that the litter decomposition was suppressed in nitrogen (N) and/or sulfur (S) treatments due to the decrease in microbial biomass in G⁻ bacteria [9]. Chen et al. (2014) found a better fungal decomposition of maize straw at lower moisture content [10]. Due to their low cost and high benefits to crop growth and microbial metabolism, trace elements, such as manganese (Mn) and zinc (Zn), can also be used to accelerate the decomposition of crop straw [11,12]. Mn has been recognized as an inducer of certain lignin-degrading enzymes (e.g., manganese peroxidase), thus promoting the decomposition of lignin components in wheat straw [13]. van Kuijk et al. (2016) indicated that Mn addition of 150 μg g⁻¹ increased lignin decomposition in wheat straw by 10% [14]. However, Tang et al. (2015) indicated that the decomposition of cellulose was strongly inhibited by mercury ion (Hg⁻), copper ion (Cu²⁺), and lead ion (Pb²⁺) [15]. The capability of trace elements to decompose straw is determined by their role as co-factors in various enzymes involved in microbial decomposition [16]. Zhang et al. (2011) found that the proper concentrations (1.2 mg mL⁻¹) of Zn²⁺ and Cu²⁺ increased the activity of endo-l,4-β-D-glucanase, whereas the addition of aluminum ion (Al³⁺) inhibited the activity of endo-l,4-β-D-glucanase and exo-1,4-β-D-glucanase [11].

Iron (Fe) is one of the most essential micronutrients in plants and soils [17]. Fe could generate reactive oxygen species with a high destructive capacity for straw decomposition due to the redox properties [18]. Fe is also a critical cofactor for major lignin-degrading enzymes including lignin peroxidase (Lip) and manganese peroxidase (Mnp) in white-rot fungi, and also plays a key role in Fenton reactions [19]. The latter are essential for brown-rotter fungi to depolymerize cellulose. Multicopper oxidases with ferroxidase activity have been implicated in regulating Fe bioavailability in the hyphal proximity, fine-tuning Fenton chemistry, and balancing lignin vs. cellulose decomposition [19]. Previous works have reported contradictory results in regards to the impact of Fe addition on crop straw decomposition. On one hand, the low content of Fe increases straw decomposition, whereas the high content of Fe reduces straw decomposition due to the inhibition of cellulase activities [20]. On the other hand, different forms of Fe may have distinct roles in straw decomposition. It has been found that Fe³⁺ inhibited the decomposition of lignocellulose more effectively than Fe²⁺ [21]. Soil Fe mostly exists in the form of Fe³⁺, which is strongly attached to the soil particles [17].

Acid red soils have significant Fe enrichment and low levels of nutrients due to the high weathering and leaching [22]. Therefore, straw returning has been widely applied as an effective practice to mitigate the acidification and increase fertility in the red soil region in southern China [15,22]. However, the slow decomposition of crop straw in red soil greatly restricts the effect of straw returning in soil fertility [23]. Studies have revealed that the slow decomposition of crop straw was closely associated with the high concentration of Fe in red soils [22,24]. Nevertheless, the potential mechanisms regarding microbiology and enzymology of the high level of Fe in suppressing straw decomposition are still unclear.

Therefore, this study aimed to: (1) explore the decomposition of MS under high Fe contents; (2) identify the potential decomposition mechanisms of Fe undertaking MS decomposition in terms of soil microbial communities, enzyme activities, and lignocellulose. This study can provide a practical basis for farmers and scholars to overcome the slow decomposition of crop straw in Fe-rich soils.

2. Materials and Methods

2.1. Preparation of Soil Microbial Suspension and Maize Straw

Soil microorganism was introduced to observe the maize straw degradation process. In accordance, topsoil samples (0–20 cm) were collected from a wheat-maize rotation field in the Middle-Lower Yangtze River Area of China (117°5′ E, 31°41′ N) in 2020. Soil pH, organic matter, total N, available (Olsen) P, and exchangeable K were 6.46, 26.20 g kg⁻¹, 0.85 g kg⁻¹, 17 mg kg⁻¹, and 398 mg kg⁻¹, respectively. Visible roots and litter residues were removed from the soil samples and then passed through a 2 mm sieve. The sieved soil was soaked with ultrapure water at a 1:1 ratio for 2 h and stirred with glass rods every 30 min for 5 min. Afterwards, the soil supernatant with soil microbes was collected by centrifugation (centrifuged at 9390 g for 5 min) and stored at 4 °C for further analysis [25].

Maize (Zea mays L.) straw (MS) was derived from the stalks and leaves of maize after the grain harvest in the Middle-Lower Yangtze River Area of China (117°5′ E, 31°41′ N) in 2020. The collected MS was air-dried, pulverized with a high-speed grinder, sieved (2 mm), and then autoclaved at 121 °C for 30 min to sterilize before the incubation. The contents of C (416.7 g kg⁻¹), N (14.3 g kg⁻¹), H (56.8 g kg⁻¹), and C/N (29.15) of MS were determined by an elemental analyzer (Vario EL III, Elementar Corporation).

2.2. Experimental Design

Mesh bags (0.48 μm, 20 cm×15 cm) were used to monitor the decomposition of MS. To do so, 30 g of air-dried MS was placed into a 1 L glass beaker, and the C/N ratio was adjusted to 25 via urea addition (N content of 99%, AR, Shanghai Sinopharm Chemical Reagent Co., Ltd.). To prepare Fe solutions, 0, 118, and 390 mg FeSO₄·7H₂O were dissolved into soil suspension (prepared as mentioned above), and the final volume was adjusted to 100 mL. Then, 55 mL of Fe solution was thoroughly mixed with 30 g MS at Fe final concentrations of 0 (control), 0.3 (Fe-0.3), and 1.0 (Fe-1.0) mg g⁻¹. The final mixtures/treatments were transferred to the nylon mesh bags and stored moist at 25 °C. All treatments were performed in three replicates.

2.3. Decomposition Rate and Main Component Content of Maize Straw

The mass decomposition of MS was calculated by gravimetric analysis. For each treatment, the nylon mesh bags were randomly sampled after 7, 30, and 180 days of incubation. The total weight of incubated MS was immediately recorded at the time of sampling. Then, half of each sample was used for the determination of microbial communities and enzyme activities. Another half was vacuum freezing-dried to constant mass for further chemical analysis. The decomposition rate of MS was calculated as follows:

R = W_t/W × 100%,

(1)

where R is the decomposition rate (%) of MS, W_t is the mass loss (g) of MS at the incubation time t (d), and W is the initial mass of MS (30 g). The mass here refers to the weight of vacuum freezing-dried MS.

The decomposition pattern was described with MS decomposing at two rates [26]:

M = M_f × exp (−k_f × t) + M_s × exp (−k_s × t),

(2)

where M_f and M_s give the fractions of a more readily decomposed and a more slowly decomposed component with k₁ and k₂ as rate constants for the two fractions, respectively.

The main components (hemicellulose, cellulose, and lignin) of MS were determined by the neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) techniques, respectively, according to the method of van Soest et al. (1991) [27].

2.4. Enzyme Activity Assays

Prior to the enzyme activity assays, MS samples (10 mg) were combined with 1 mL extracting agents and centrifuged at 12,000× g for 10 min at 4 °C. Then, the supernatant was filtered and stored at 4 °C for testing. The extracting agents were citric acid-phosphate buffer (pH 5.0), 0.05 mol L⁻¹ PBS (pH 7.0), and succinic acid-NaOH solution (pH 4.5) for β-Glucosidase (β-GC), Laccase (Lac), and Manganese peroxidase (Mnp), respectively.

β-GC activity was determined by measuring the production of reducing sugars [28,29]. One enzyme unit (U) of β-GC was defined as the amount of enzyme required to release 1 μmol of reducing sugar per minute under the assay conditions. Lac activity was determined using 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonate) (ABTS; Sigma-Aldrich, St. Louis, MO, USA) as substrate at 420 nm [30]. Mnp activity was determined using 2,6-dimethoxyphenol (DMP) as the substrate at 465 nm. One unit (U) of Lac and Mnp, was defined as the amount of enzyme that oxidizes 1 nmol of ABTS, DMP, and veratryl alcohol per minute, respectively.

2.5. DNA Extraction and High Throughput Sequencing

Total DNA from 0.5 g WS was extracted using OMEGA Soil DNA Kit (D5625-01) (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. The V3-V4 region of 16S rRNA gene was PCR-amplified to investigate bacterial communities using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [31]. Primer sets ITS1 (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′) targeting the internal transcribed spacer (ITS) region 1 were used to investigate the fungal communities. For bacteria, the PCR conditions consisted of 4 min 95 °C and 25 cycles: 30 s 94 °C, 45 s 55 °C, 30 s 72 °C, with a final elongation at 72 °C for 5 min. For fungi, the PCR conditions was consisted of 5 min 95 °C and 35 cycles: 20 s 94 °C, 20 s 48 °C, 30 s 72 °C, with a final elongation at 72 °C for 1 min.

2.6. Bioinformatic Analysis

Microbiome bioinformatics were performed with QIIME 2 with slight modification according to the official tutorials [32]. Briefly, raw sequence data were demultiplexed using the demux plugin followed by primers cutting with cutadapt plugin. Sequences were then quality filtered, denoised, merged, and chimera removed using the DADA2 plugin [33]. Non-singleton amplicon sequence variants (ASVs) were aligned with mafft [34] and used to construct a phylogeny with fasttree2 [35]. Taxon-dependent analyses in bacteria and fungi were carried out by applying the RDP Classifier against the database of SILVA128 for 16S rRNA gene and UNITE7.0 for ITS using a confidence threshold of 70%., respectively [36].

2.7. Statistical Analysis

Duncan’s multiple range test was performed to determine the significant differences among the treatments using the Statistical Package for Social Science (SPSS v.19.0, IBM Corp., Armonk, NY, USA). The significant level of differences was set at p < 0.05. For β diversity analysis, the Weighted UniFrac based on phylogenetic distances was used to assess the similarities between a pair of samples. Principal coordinate analysis (PCoA) and random forest (SF) were performed by multivariate analyses to compare the differences between the microbial community distances of the samples. The heat map shows the abundance distribution of these species in each group. From the top to the bottom, the importance of species to the model decreases in order; these species with the highest importance can be considered as marker species of differences between groups. The PCoA, SF, and redundancy analysis (RDA) were performed by the genes cloud tools, a free online platform for data analysis. In the result of RDA, the arrow length represents the strength of the correlation between the environmental variables and the microbes. The longer the arrow length, the stronger the correlation. The perpendicular distance between microbes and environmental variable axes in the plot reflects their correlations. The smaller the distance, the stronger the correlation.

3. Results

3.1. Impact of Fe Addition on Maize Straw Decomposition

Figure 1 shows the variations in the decomposition rate of MS. A rapid MS decomposition rate of about 20% and 28% occurred in control after 7 and 30 days of incubation, respectively (Figure 1), and the remaining 18% was decomposed between days 30 to 180. No significant differences appeared between the MS decomposition rate of Fe treatments (Fe-0.3, Fe-1.0) and control on days 7 and 30. The MS decomposition rates of control, Fe-0.3, and Fe-1.0 were 46.19%, 41.75%, and 36.16%, respectively, after incubation for 180 days, indicating significant lower MS decomposition in Fe treatments than in control (p < 0.05) (Figure 1).

Table 1. Regression models of the maize straw mass and incubation time.

Treatments	M = Mf × exp (−kf × t) + Ms × exp (−ks × t)
	Mf	kf	Ms	ks	R2
control	23.79	0.2170	76.21	0.0019	0.9968
Fe-0.3	25.14	0.1582	74.86	0.0014	0.9963
Fe-1.0	26.27	0.1281	73.73	0.0008	0.9805

used the double exponential regression equation to simulate the decomposition rate of MS. Accordingly, the coefficient of regression (R²) of each treatment was greater than 0.95, indicating that the results of this regression model are rather identical to the actual decomposition of MS (Table 1). The readily and the slowly decomposed components of MS accounted for about 25% and 75% in all treatments, respectively. The decomposition rate constants (k_f and k_s) in Fe-0.3 (0.1582 d⁻¹ and 0.1281 d⁻¹) and Fe-1.0 (0.1281 d⁻¹ and 0.0008 d⁻¹) treatments were both less than that in control (0.2170 d⁻¹ and 0.0019 d⁻¹).

3.2. Impact of Fe Addition on Lignocellulose Components in Maize Straw

Figure 2 shows the changes in the contents of hemicellulose, cellulose, and lignin components within 180 days of incubation. At the beginning of incubation, the contents of hemicellulose, cellulose, and lignin were 8.54, 9.00, and 2.60 g, respectively, accounting for 28.47%, 30%, and 8.65% of MS, respectively.

The contents of hemicellulose in control, Fe-0.3, and Fe-1.0 were 5.64, 5.65, and 5.87 g, respectively, and the decomposed parts accounted for 31.34–33.94% of the original hemicellulose within the first 30 days of incubation (Figure 2A). Nevertheless, no significant differences were found between the Fe treatments (Fe-0.3, Fe-1.0) and control in terms of hemicellulose decomposition (Figure 2A). On day 180, hemicellulose contents in control, Fe-0.3, and Fe-1.0 were 3.28, 3.89, and 4.45 g, respectively, and the decomposed parts accounted for 61.59%, 54.44%, and 47.89% of the original hemicellulose, respectively (Figure 2A). Herein, Fe-0.3 and Fe-1.0 had significantly higher contents of hemicellulose than in control, by 18.60 and 35.67%, respectively (p < 0.05) (Figure 2A).

The contents of cellulose in control, Fe-0.3, and Fe-1.0 were 6.31, 6.35, and 6.15 g, respectively, and the decomposed parts accounted for 29.44–31.67% of the original cellulose within the first 30 days of incubation, although no significant difference occurred among the Fe treatments (Fe-0.3, Fe-1.0) and control (Figure 2B). On day 180, the cellulose contents in control, Fe-0.3, and Fe-1.0 were 3.75, 4.38, and 4.43 g, respectively, and the decomposed parts accounted for 50.84–58.38% of the original cellulose (Figure 2B). Fe-0.3 and Fe-1.0 had significantly higher contents of cellulose than control, by 16.80 and 18.13%, respectively (Figure 2B).

The lignin contents in Fe treatments (Fe-0.3, Fe-1.0) were reduced by 18.22–23.35% after 30 days of incubation, indicating no significant differences compared with control (Figure 2C). On day 180, the lignin contents of MS in control, Fe-0.3, and Fe-1.0 were 1.78, 1.87, and 1.93 g, respectively, and the decomposed parts accounted for 25.74–31.28% of original lignin in MS (Figure 2C). Finally, the lignin contents of Fe-0.3 and Fe-1.0 treatments were higher than that in control by 5.05% and 8.43%, respectively (Figure 2C). Herein, there was no significant change in lignin content between Fe-0.3 and control, although Fe-1.0 and control had a significant difference (p < 0.05) (Figure 2C).

The above results indicated that at the early stage (day 30) of MS decomposition, the addition of exogenous Fe had no significant effects on hemicellulose, cellulose, and lignin components. However, significant differences appeared between Fe treatments and control at the later stage (day 180) of MS decomposition (p < 0.05), and the inhibitory effect of Fe on MS decomposition was enhanced with increasing its concentration.

3.3. Microbial Community Composition

Figure 3 shows the variation of bacterial and fungal communities in different treatments by PCoA analysis. Results showed that β-diversity of the bacterial community in high-Fe treatment (Fe-1.0) was significantly different from that in control (p < 0.05), while it was almost identical between control and low-Fe treatment (Fe-0.3) (Figure 3A). There was no significant difference in β-diversity of fungus community among Fe treatments (Fe-0.3 and Fe-1.0) and control (p < 0.05) (Figure 3B). This suggests that Fe addition had a greater impact on the bacterial community than that on the fungal community during MS decomposition.

Bacterial community structure and composition were investigated at phylum and genus classification levels, as shown in Figure 4. The results of Figure 4A shows that during the whole 180 days incubation time, the bacterial community was dominated by phylum Proteobacteria (57.84–68.93%), followed by Actinobacteria (18.16–28.50%), Bacteroides (10.31–27.99%), Firmicutes (1.44–6.03%), Patescibacteria (0.10–4.79%), Gemmatimonadetes (0.35–3.43%), Chloroflexi (0.01–1.44%), Verrucomicrobia (0.26–0.62%), Planctomycetes (0.00–0.67%), Cyanobacteria (0.00–0.21%) and Others (0.15–0.47%) (Figure 4A). On day 30, the relative abundances of phyla Proteobacteria, Bacteroides, and Firmicutes were 19%, 45.62%, and 318%, respectively, higher in Fe-1.0 than that in control, whereas that of phylum Actinobacteria was 70.06% lower in Fe-1.0 than in control. On day 180, the relative abundances of phyla Proteobacteria, Bacteroides, and Firmicutes were 14.39%, 12.37%, and 4.91%, respectively, higher in Fe-1.0 than in control, while that of Actinobacteria in Fe-1.0 was 25.31% lower than in control (Figure 4A).

The results of random forest diagram analysis further displayed that 18 ASVs of the top 20 bacterial units in Fe-1.0 were lower than in control, among which 14 ASVs belong to the phylum Actinobacteria, mainly including Microbacteriaceae (ASV_1311, ASV_264, ASV_1795, ASV_730, ASV_750, ASV_683, ASV_410), Citrococcus (ASV_1093), Bacillus (ASV_23), Brevibacterium (ASV_461), Cellulomonas (ASV_1103, ASV_339, ASV_770), and Micrococcus (ASV_2352); 2 ASVs belong to Brevundomonas (ASV_375, ASV_275) in the phylum Proteobacteria; 2 ASVs belong to Bacillus in the phylum Firmicutes (ASV_269), and LD29 (ASV_959) in the phylum Verrucobacterium, respectively. Simultaneously, Devosia (ASV_89) and Phenylbacterium (ASV_630) in the phylum Proteus in Fe-1.0 were higher than in control (Figure 4B).

3.4. Enzyme Activities

Figure 5 shows the variation of β-GC, Lac, and Mnp activities during the WS decomposition. On day 30 of incubation, β-GC activities of 113.29 and 111.20 U g⁻¹ in Fe-1.0 and control treatments, respectively, showed no significant difference (p > 0.05) (Figure 5A). While significant reductions of 71.82% and 17.18% appeared in the activities of Lac and Mnp, respectively, in Fe-1.0 compared with control (p < 0.05) (Figure 5B,C).

On day 180, compared with control, the activities of β-GC and Lac showed a slight increase in Fe-1.0, while that of Mnp was decreased by 12.37%, indicating no significant difference between Fe-1.0 and control (p > 0.05) (Figure 5).

The above results revealed that high-Fe addition increased β-GC activities, whereas it inhibited Lac and Mnp activities during the WS decomposition. However, the activities of β-GC, Lac, and Mnp had no significant differences between Fe-1.0 and control, except for the Lac activities during the first 30 days of incubation.

3.5. RDA Analysis of Correlation between Bacteria and Enzyme Activities

The relationships between bacterial community composition and enzyme activities are revealed by redundancy analysis (RDA) in Figure 6. RDA results showed that the first and the second axes explained 30.08% and 10.8% of the variation between the bacterial community composition and enzyme activities, respectively. The bacterial community composition had a significant correlation with the enzyme activities (p = 0.024). Compared with β-GC (R² = 0.712, p = 0.003) and Mnp (R² = 0.664, p = 0.003), Lac had a higher correlation with bacterial community (R² = 0.786, p = 0.002).

The bacterial communities in high-Fe treatment (Fe-1.0) are mainly distributed in the first and the second quadrants, whereas it is mainly distributed in the second and the third quadrants in control, revealing that the addition of exogenous Fe significantly influenced the bacterial community distribution. Moreover, both phylum Actinobacteria and Lac activity were distributed in the third quadrants, indicating that they had a positive correlation (Figure 6).

4. Discussion

The 180-day incubation experiments indicated that Fe addition (Fe-0.3 and Fe-1.0) significantly decreased MS decomposition compared with control. Fe is labeled as an essential element for the regular growth and maintenance of most living organisms but in high concentrations, it causes a number of adverse effects, including toxicity to lignocellulolytic microorganisms and enzymes [37]. Consistent with our results, Cai et al. (2017) also indicated that the high-Fe addition (1000 mg L⁻¹) inhibits the decomposition of rice straw [38]. High-Fe treatment (Fe-1.0) mainly inhibited the decomposition of hemicellulose and cellulose in MS, where the hemicellulose and cellulose contents were ultimately 35.67% and 18.13% higher than control, while lignin was only 8% higher than control. Similarly, Wang et al. (2013) also point out that Fe was shown to be inhibitory in the cellulose hydrolysis when the concentration of Fe increased from 0 to 10 mmol [20]. The inhibitory effects of high Fe are caused by disruption of enzyme structure and function via replacement of original metals in enzyme prosthetic groups by Fe, or through binding of functional groups with protein molecules [39]. In addition, excessive addition of Fe may disrupt the structure of extracellular polymeric substances of microbe, which play a vital role in protecting the microbe against possible inhibition by metals [40].

The lignocellulosic enzyme can catalyze the decomposition of lignocellulose [41,42], which is the main component of crop straw [43]. Therefore, the lignocellulosic enzyme is recognized as the rate-limiting step in straw decomposition [7]. The temporal changes in three common enzyme activities (β-GC, Lac, and Mnp) of MS decomposition were different with high Fe treatment. Compared with control, the Lac activity with high-Fe addition (Fe-1.0) showed a significant decrease on day 30 (p < 0.05), while β-GC and Mnp activities showed no significant changes on days 30 and 180 (p > 0.05). In nature, biomass-degrading microbial enzymes (e.g., hemicellulases and cellulases) are likely to come into contact with variety of metal ions (including redox-active ones) [21]. Tejirian et al. (2010) indicated that the concentrations of Fe were correlated with the activity of ligninolytic enzyme, and Fe (10 mmol) significantly inhibited cellulose activity [21]. Similar to our result, the maximum loss in Lac activity was also observed for the Fe metal ions at 10 mmol concentration (96% inhibition) by Navada et al. (2021) [44]. AS one of the best-known biocatalysts, Lac could degrade a variety of phenolic and non-phenolic compounds such as lignin, thereby contributing to the decomposition of organic matter [45]. Similarly, the inhibitory effect of high Fe (Fe-1.0) addition on straw degradation may be related to the decrease in Lac activity.

Straw decomposition is predominantly a microbially-mediated process and different microbial communities are responsible for specific functions. It was reported that bacteria prefer to decompose labile compounds while fungi can decompose more recalcitrant material [7]. The PCoA results reflected that the bacterial community was more sensitive than the fungal community with high Fe (Fe-1.0) addition. Coincident with our result, Rajapaksha et al. (2004) also found that fungi were less sensitive to heavy metals (Cu and Zn) than bacteria [46]. Through further analysis of the bacterial community, we found that the high Fe (Fe-1.0) addition mainly decreased the relative abundance of phylum Actinobacteria, especially the Microbacteriaceae in Actinobacteria. Similarly, Zhang et al. (2021) found that the relative abundance of phylum Actinobacteria decreased from 4% to 0.2% when Fe concentration increased from 0 to 40 mg·l⁻¹ [47]. It was also shown that over 97% of Streptococcus mutans cells were killed at a concentration of 1 mmol Fe under anaerobic conditions [48]. This might attribute to the production of free radicals due to the Fe reduction, damaging macromolecules in the cytoplasm and leading to cell death [49]. Furthermore, free intracellular Fe might also cause cellular damage by displacing different divalent metals in metalloproteins. Actinobacteria are the potential members of the straw-degrading community in agricultural soils [50], and as the main community of the signature difference community between Fe-1.0 and control treatments, Actinobacteria may lead to the lower decomposition rate of MS occurred in Fe-1.0 treatment.

Furthermore, the RDA results revealed the suppression of phylum Actinobacteria and Lac activity in Fe-1.0, while the positive correlation between the relative abundance of phylum Actinobacteria and Lac activity in the degradation MS. Consistent with our result, Feng et al. (2015) also found that Lac activity mainly had a bacterial origin [30]. It is believed that Actinomycetes, which belong to the phylum Actinobacteria, are potent producers of Lac in nature [51]. Therefore, the suppression of high-Fe (Fe-1.0) addition in MS decomposition may be accounted for reducing the relative abundance of phylum Actinobacteria, in turn decreasing Lac activities and thus reducing the decomposition of lignocellulose components in MS.

5. Conclusions

The addition of 1.0 mg g⁻¹ Fe²⁺ considerably reduced the decomposition rate of MS. This was accomplished by reducing not only the degradation of cellulose and hemicellulose but also that of lignin. Simultaneously, the bacterial community had a higher sensitivity than the fungal community to the high-Fe addition, decreasing the relative abundance of Microbacteriaceae. The significant correlation between Lac activity and the relative abundance of bacteria (p = 0.02), especially phylum Actinobacteria, suggested that their reduction accounted for the suppression of high-Fe addition on MS decomposition.