Effect of nutritional energy regulation on the fate of antibiotic resistance genes during composting of sewage sludge

Bioresource Technology 297 (2020) 122513

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Effect of nutritional energy regulation on the fate of antibiotic resistance genes during composting of sewage sludge Huawei Weia,b, Jiaying Maa,b, Yinglong Sua,b, Bing Xiea,b,

T

⁎

a

Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China b Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Sludge composting C/N ratio Antibiotic resistance genes Microbial community Microbial metabolism

Sludge composting is increasingly adopted due to its end product for application as a soil nourishment amendment. Although the ratio of C/N is significant in the quality and process of composting, little information has been obtained from the effects of nutritional energy (carbon and nitrogen) on the fate of antibiotic resistance genes (ARGs) during sludge composting. Dynamic variations of ARGs, microbial community as well as functional characteristics during composting of sludge were investigated in this study. Three levels of carbon to nitrogen (20:1, 25:1 and 30:1) were developed for the composting of sludge with fermented straw plus a control which was just sewage sludge (C/N = 9.5:1). A novel finding of this work is that the highest initial C/N ratio (30:1) could prolong the thermophilic period, which was helpful to reduce some target ARGs. Some ARGs (sul1, sul2, and aadA1) had negative correlation with multiple metabolic pathways, which were difficult to remove.

1. Introduction Antibiotic resistance as a serious issue in contemporary medicine has become a critical challenge to human health worldwide in recent years. An estimated 90 000 people die annually in the world directly or indirectly from the infectious resistant to antibiotics (Zhumabayeva et al., 2016). Although antibiotic resistance occurs as a natural phenomenon, overuse and abuse of antibiotic drugs in animals or humans can exacerbate the phenomenon and then stimulate the rapid emergence of antibiotic resistant bacteria (ARB) and/or antibiotic resistance genes (ARGs) (Qiao et al., 2018). Therefore, the identification and control of environmental pools of ARB and ARGs have been paid more and more attention recently (Auguet et al., 2017). Wastewater treatment plants (WWTPs) are reckoned to be potential hotspots of ARGs for they spread ARGs from environmental reservoirs to human pathogens (Hultman et al., 2018). Sewage sludge as a coproduct during wastewater treatment process, contains a mass of ARGs (Burch et al., 2016). Sentchilo et al. (2013) revealed that the environmental characteristics with high microbial community diversity and abundance in sludge may facilitate horizontal transfer of ARGs through mobile genetic elements (MGEs). Hence sewage sludge treatment was considered as an important topic to reduce the amount of ARGs effluent from sewage-treatment plants. As one of the most promising

technologies, composting can facilitate the decomposition of organic matters and transformations of heavy metals and create an end product for applying as soil fertilizer or ameliorant to ameliorate soil conditions (Su et al., 2015). However, composting represents a potential avenue for ARB and ARGs to soil, where the shifts of ARGs and their underlying mechanisms have not been fully explored. Previous studies have demonstrated that quantities of several types of ARGs in sewage sludge decreased during digestion (Jang et al., 2018). However, some researchers verified that ARGs were determined with higher abundance and diversity during composting (Su et al., 2015). For composting, carbon and nitrogen compounds are used as nutritional energy sources of the microorganism (Bernal et al., 2009). Therefore, the ratio of C/N is one of the most decisive points in the quality and process, and the range of 20–30:1 is considered to be optimal (Huang et al., 2004; Zhu, 2007). Although some researchers have proven that the quality of compost and the emission of odor in composting process were influenced by C/N ratio and bulking agent (Wu et al., 2017a), little information has been obtained about the effects of nutritional energy on the changes of ARGs during sludge composting. What’s more, microbial diversity and metabolic pathways play an essential role in the catabolic phases during OM decomposition (Metcalf et al., 2016). The previous study revealed that the metabolic pathways involved in Metabolism and Cellular Processes were the key metabolic

⁎ Corresponding author at: Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China. E-mail address: [email protected] (B. Xie).

https://doi.org/10.1016/j.biortech.2019.122513 Received 22 August 2019; Received in revised form 25 November 2019; Accepted 28 November 2019 Available online 30 November 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.

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thoroughly and distributed into two parts: one part was stored in refrigerated conditions (4 ℃), and then analyzed for physicochemical parameters variations; the other was preserved at −80 ℃ for further use. DNA extraction was used a Powersoil® Kit (MoBio Laboratories) based on the manufacturer’s protocol. The yields and quality of the extracted DNA were checked using a NanoDrop spectrophotometer (Merinton, Beijing). Concentration of DNA samples ranged from 10 to 20 ng/μL after dilution, and the A260/A280 ratios varied between 1.8 and 2.0 for all DNA extracts.

functions of the microbial community in maize straw composting process (Wei et al., 2018). Yet the biochemical metabolic pathways linked to ARGs in sewage sludge composting are not clearly understood. To the authors’ knowledge, there is little experimental evidence or quantitative data that has correlated the metabolic pathways of bacteria and elevated ARGs in sludge composting. Additionally, addition of different types of materials in sludge composting influenced the abundance of ARGs such as natural zeolite or nitrification inhibitor (Zhang et al., 2016). Application of fermented substrate as inoculum is an approved technique to modify the fermentation process (Neumann and Scherer, 2011). However, different nutritional energy adjusted by straw fermented substrate whether or not to affect the abundance of ARGs was still unknown. Therefore, it is needed to understand the operational parameters for reducing ARGs accumulation in sludge compost. This first-attempt study tended to comprehensively unravel the dynamic changes in microbial community, metabolic function as well as ARGs via regulation of carbon and nitrogen. The purposes of the study were to: (1) identify the effect of nutritional energy (C/N ratio) on reducing ARGs; (2) investigate correlation between ARGs and environmental variables; (3) reveal variation characteristics of microbial community and the metabolic function; (4) understand the relationships among ARGs, potential host bacteria for ARGs, and microbial metabolism. The novelty of this work will provide the new understanding for the fate of ARGs during the sludge composting process, which may promote the development of applicable technologies for reducing ARGs through composting and reduce the environmental risk linked to the sludge compost product.

2.3. Physicochemical properties Each sample with deionized water (1:10 w/v) was shaken manually to determine the electrical conductivity (EC) and pH using an inductivity measurer (CD 400, Shanghai) and the pH meter (PHS-3C, Shanghai), respectively. Total organic carbon (TOC) was detected using an automatic elemental analyzer (Elementar Americas, Hanau). Organic matter (OM) content was determined by dry mass loss on combustion at 550 ℃ for 6 h (Zhang et al., 2017). Total phosphorus (TP) and total nitrogen (TN) contents were characterized by an automated chemical analyzer system (WESTCO). The available phosphorus (AP) was measured according to the method of sodium bicarbonate extraction Mo-Sb anti-spectrophotometry. The bio-availability of heavy metals in terms of As, Cd, Cu and Ni in the composting samples were assessed by first extracting the samples using diethylenetriaminepentaacetic acid (DTPA) (Lindsay and Norvell, 1978). The extractant contains DTPA (0.01 mol/L), triethanolamine (0.1 mol/L), and CaCl2 (0.01 mol/L), with a pH of 7.3. During the extracting procedure, 5 g of air-dry composting and 25 mL of extractant were filled into a centrifuge tube (50 mL), and then the centrifuge tube was tumbled in an oscillator (ZD-85A, Shanghai) at 25 ℃ for 2 h. Subsequently, the supernatant in centrifuge tube was filtrated through 0.45 μm filter after being centrifuged at 5000 rpm for 15 min to collect the leachate. The concentration of As, Cd, Cu and Ni in the leachate was measured by ICP-OES (Agilent 710/715, Santa Clara).

2. Materials and methods 2.1. Materials and experimental setup The lab-scale sludge compost was derived from the dewatered sludge sampled from a Sewage Treatment Plant (Minhang district, Shanghai, China). Based on the advantage of application of fermented substrate in the fermentation process (Neumann and Scherer, 2011), fermented straw was selected as the bulking material in this study. Therefore, the compost was prepared by mixing the fermented straw sieved with 2 mm mesh (Table 1) with sewage sludge by manual operation to give the ratio of C/N of 20:1, 25:1, and 30:1, respectively. The composting experiments were in triplicate and conducted for 32 days with the treatments of (i) sewage sludge (C/N = 9.5:1), as control check (CK) and (ii) the ratio of C/N 20:1 (A20), 25:1 (A25), and 30:1 (A30), respectively (Supplementary Data). The composting system was set up in a lab-scale reactor (50 L) with a few drainage holes in the bottom (5-mm-diameter) and covered with plastic mulch to reduce water loss and prevent worm escape. Moisture was kept at 55–65% based on the previous research (Su et al., 2015). The compost was turned on day 5, 10, 15, 30 by manual operation to regulate oxygen content and to obtain very uniform temperature and moisture distributions. In addition, the center temperature of the composting piles was measured using digital thermometers.

2.4. qPCR of ARGs and MGEs qPCR was used to assess thirteen frequently detected and widely existed genes (Wu et al., 2017b; Wu et al., 2018), including eleven subtypes of ARGs (sul1, sul2, tetM, tetQ, blaOXA, blaTEM, ermB, mefA, addA1, strB and mexF) and two marker genes of MGEs (intI1 and intI2) performing with BioRad CFX96 Touch (BioRad, California). The PCR reaction mixture (final volume 10 μL) contained SYBR Green SuperMix (5 μL) (Takara, Japan), each primer (10 μM, 0.5 μL), nuclease free ddH2O (3 μL), and template DNA (1 μL). PCR amplification cycle consisted of an initial denaturation at 95 ℃ for 30 s, followed by 40 cycles of 94 ℃ for 30 s and 72 ℃ for 30 s. The standard curve was performed based on the Ct values of a dilution series of a reference DNA sample. R2 for all target genes was more than 0.99 and the PCR efficiency ranged between 0.91 and 1.10. PCR protocols were listed (Supplementary Data). The relative copy number of target ARGs were normalized according to the formula of 2−(Ct(ARGs)-Ct (16S rRNA)), where Ct (ARG) and Ct (16S rRNA) denote the threshold cycle of each ARG and the 16S rRNA, respectively (Karkman et al., 2016).

2.2. Sample preparation and DNA extraction Six composite samples of 1.0 kg were collected on day 1, 3, 7, 14, 19 and 32, by taking twelve sub-samples at different height of each pile to achieve high representativeness. The collected samples were mixed Table 1 Characteristics of the raw materials. Material

Moisture (%)

pH

Total organic carbon (g/kg)

Organic matter (g/kg)

Total nitrogen (g/kg)

C/N ratio

Sewage sludge Fermented straw

73.91 ± 9.93 11.47 ± 0.03

7.19 ± 0.51 /

359.93 ± 2.73 408.31 ± 2.98

620.28 ± 24.70 704.90 ± 15.14

37.81 ± 3.21 9.48 ± 1.83

9.53 ± 0.92 43.04 ± 4.24

2

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2.5. High-throughput sequencing and data analysis Two primers (338F/806R) corresponding to the V3-V4 hyper variable region were used to amplify 16S ribosomal RNA genes (Wei et al., 2018). The samples were sent to Shanghai Personal Biotechnology for high-throughput sequencing by the Illumina Miseq-PE250 platform. Raw sequences were done with QIIME (http://qiime.org). After sequences in each library were binned into operational taxonomic units (OTUs) at ≥97% identity threshold using USEARCH (v5.2.236). Taxonomic identification of OTUs was classified by the assign_taxonomy along with the Naïve Bayesian Classifier tool obtained through the Ribosomal Database Project (RDP) (Wang et al., 2007). 2.6. Prediction of functional profiles PICRUSt (Phylogenetic investigation of communities by reconstruction of unobserved states) was performed to predict microbiome function inferred from the taxonomic data. KEGG (Kyoto Encyclopedia of Genes and Genomes) was utilized to predict metabolic functions after OTU table generation in the PICRUSt. Nearest Sequenced Taxon Index (NSTI) value indicating a correspondence with the reference genome was calculated to characterize the effect (Langille et al., 2013). 2.7. Statistical analysis Data were expressed on a dry weight basis. One-way ANOVA method was carried out to determine the analytical results and p < 0.05 was considered to be statistically significant. Data analyses were completed via SPSS 23 (IBM, USA). Bar charts were generated using Origin 9.3 software (Origin Lab Corporation, USA). Correlation analysis (Pearson’s correlation coefficient) was performed among the environmental variables, MGEs and the relative abundance of ARGs. Redundancy analysis (RDA) was implemented to assess the relationship between bacterial class and ARGs in R3.4.2 with vegan 2.2–0. The abundance of ARGs through different treatments was obtained by the non-metric multidimensional scaling (NMDS) ordination technique with the Bray-Curtis dissimilarity distance in the R3.4.2. Besides, the heatmap visualization of the relative abundance of genera and bacterial function profiles was generated using “ggplot2” graphics package. The interactive relationship among the target genes, microbial taxa and metabolic function were investigated by network analysis using the Cytoscape 3.6.0 if the Pearson coefficient was greater than 0.6. 3. Results and discussion 3.1. Changes in physicochemical properties during composting The pile temperature in A20, A25 and A30 increased more quickly than the CK, getting 61.5 °C on the 5th day, 65.4 °C on the 3rd day, and 65.7 °C on the 3rd day, respectively (Fig. 1A). Clearly, the peak temperature was lowest in A20 and highest in A30 among the three treatments. In addition, there was no significant difference (p > 0.05) between A25 and A30 for the peak temperature on the 3rd day, whereas the high temperature (> 45 °C) lasted for the longest time (10 days) in A30. Temperature can indicate biological activity in composting system, which is an indispensable factor affecting microbial activity and degradation of OM (Onwosi et al., 2017). It suggested that in this study low C/N ratio might hinder the activity of thermophilic microorganisms which play a significant role in the thermophilic phase. At the beginning of composting under the low initial C/N, owing to lack of carbon source, microorganisms could not obtain the favorable condition for their growth (Huang et al., 2004). In the initial stage of the composting the pH value of CK was 7.51 and then maintained approximately 7.2 (Fig. 1B). However, the changes in pH value for other three piles followed the similar trend with

Fig. 1. Temperature (A), pH (B) and electrical conductivity (C) changes in cocomposting of sludge and fermented straw process. The whiskers are the standard deviation of replica experiments.

a decline to pH 7.26, 7.43, and 7.20 on day 3, 7 and 3, respectively. The decrease in pH at the thermophilic phase was attributable to microbial decomposition of organic matter and production of acidic substances during the composting (Onwosi et al., 2017). Then the pH of these treatments increased immediately before stabilizing at 8.25–8.43, due to release of ammonium and volatile ammonia during organic nitrogen mineralization through microbial activities (Peng et al., 2018). Among the three treatment groups, the A20 and A25 treatment piles followed a similar trend in EC (Fig. 1C). The values increased from the beginning of composting showing a steady value of 6.33 ms/cm and 6.12 ms/cm on day 32. EC of A30 increased in the early stage reaching 3

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Fig. 2. Dynamic of the copy number of target ARGs in the four treatments during sludge composting with fermented straw. The absolute copy numbers of target ARGs (A) and the normalized copy number of ARGs (B). * and ** mean significant differences at p < 0.05 and p < 0.01, respectively. The whiskers are the standard deviation.

A30 might be more advantageous to plants, compared to A20 and A25.

a peak value of 6.32 ms/cm on day 19, followed by 5.55 ms/cm at the end. The increment of EC at the initial stage was because of the production of phosphates and ammonium ions by the degradation of OM (Huang et al., 2004; Onwosi et al., 2017). Subsequently, ammonia volatilization and mineral salt precipitation occurred at the later phase of the composting that appeared to be the main reasons for the decline in EC (Onwosi et al., 2017; Wong et al., 1995). On day 32, EC of A30 was significantly lower than that of A20 and A25 (p < 0.05), respectively. EC value is a key indicator of salinity during the sludge composting with fermented straw, suggesting the possible toxic effect on growth of plants when applied to soil. Thus, the lowest EC value (5.5 ms/cm) in

3.2. Changes in the abundance of ARGs The total gene copy number of the target ARGs was 7.65 × 1012 copies/g dry basis (Fig. 2). The eleven subtypes of genes were detected throughout the processes. Compared with the initial day of three treatments (A20, A25 and A30), the total gene copy number of the target ARGs during the thermophilic (days 3–7) was reduced by 1.94 times in A20 (p < 0.05), 2.06 times in A25 (p < 0.05) and 3.61 times in A30 (p < 0.05). Meanwhile, the total gene copy number of target 4

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Fig. 3. Correlation matrices (Pearson’s correlation) of relative abundance of ARGs, environmental variables and MGEs across all treatments.

respectively. The NMDS ordination revealed that (i) the different compost samples were clearly separated from CK, and tightly clustered together; (ii) no significant differences were observed in the three treatments (A20, A25 and A30) with different ratio of C/N (Supplementary Data). However, the relative abundances of ARGs were tending to increase after the thermophilic phase (Fig. 2B). It is probably because many other factors (i.e. antibiotics, physicochemical properties, concentration of metal) as the selective pressure can influence the development and transmission of ARGs (Zhang et al., 2018). The concentration and bioavailability of heavy metals may impose a strong selection pressure for a long time (Hu et al., 2017). Other than bioavailability of heavy metal pressure, some nutrients (such as TOC, TN, TP) were linked to the total ARGs in the eco-agricultural systems, implying that co-attenuation of nutrients with ARGs occurred (Zhang et al., 2018). Thus, the bioavailability of heavy metals and nutrients were considered to make enormous contributions to the shift in ARGs based on different ratio of C/N in this study and the associations among them were discussed in the next section.

ARGs in CK increased by 1.79 times on day 7 (p < 0.01). Additionally, pile temperatures in three treatments were higher than in CK (p < 0.05) (Fig. 1). Temperature is of significant importance to destruct and inactive the ARGs (Diehl and LaPara, 2010; Liao et al., 2018). No statistically significant differences (p > 0.05) were found among the three treatments (C/N = 20, 25 and 30) on day 7 (Fig. 2). For CK, the total gene copy number of target ARGs was higher than day 1 after 32 days with 8.57 × 1012 copies/g (p < 0.05), whereas the total gene copy number of target ARGs in the A20, A25 and A30 treatments decreased by 15.6%, 13.9% and 21.2% compared with day 32 in CK (p < 0.05), respectively (Fig. 2A). The total gene copy numbers of target ARGs were suffered some degree of reduction in three treatment groups after day 32, but this phenomenon did not happen in CK. However, for each treatment, the total gene copy number of target ARGs increased after thermophilic stage (day 3–7). The reason might be that the number of sulfanilamide resistance genes (sul1 and sul2) was not effectively reduced in the composting process (Fig. 2A). More concretely, the copy number of sul1 increased after day 32 in three treatment groups from 3.22 to 4.52 × 1012 copies/g to 5.05–5.93 × 1012 copies/g (Fig. 2A). It had been reported that sul1 gene was difficult to remove by composting (Peng et al., 2018) and the gene was high-temperature tolerant which might use of thermophilic bacteria for its hosts (Liao et al., 2018). For another type of sulfonamide resistance genes, sul2 gene was increased in A20 and A25 treatments, whereas the copy number in A30 treatment declined slightly after day 32. In this study, the initial ratio of C/N as 30:1 of composting could accelerate the decline of sul2 from 1.29 × 1012 copies/g to 6.29 × 1011 copies/g (Fig. 2A). Similarly, aadA1 gene encoding resistance to aminoglycosides was not significantly decreased in three treatment groups (p > 0.05), which suggested that the ratio of C/N did not contribute to the decrease in aadA1 abundance. However, the copy number of tetracycline resistance genes (tetM and tetQ), beta-lactamase resistance genes (blaTEM and blaOXA), macrolide resistance genes (ermB and mefA) and gene encoding for the multidrug efflux pump could be reduced by 91.7%-98.0%, 68.5%-90.2%, 23.9%-99.3% and 94.4%-97.5%, respectively after the composting process (Fig. 2A). Therefore, some complementary approaches are necessary to reduce these obstinate ARGs during the composting, such as sul1, sul2 and aadA1. The relative abundance of the total ARGs detected declined on day 3 and the relative abundance between CK and other treatments (A20, A25 and A30) had statistical significance (p < 0.05, p < 0.01 and p < 0.01, respectively) (Fig. 2B). This result was consistent with previous findings that temperature is a critical variable in reducing ARGs, which probably controls the inactivation of pathogenic microorganism to block horizontal genes transfer to other organisms (Diehl and LaPara, 2010). Overall, the relative abundances of ARGs in the three groups were lower than that in CK after composting (p < 0.01),

3.3. Associations among environmental variables, MGEs and ARGs The C/N ratio decreased with composting (Supplementary Data), which showed significantly negative association with sul2 and tetQ (p < 0.01), respectively (Fig. 3 and Supplementary Data). The C/N ratio is commonly affected by differences of the OM and the characteristics of composting material (Awasthi et al., 2014). Raut et al. (2008) pointed out that higher C/N ratios in compost would cause ammonia volatilization while the lower value would release lots of soluble basic salt. Indeed, except for aadA1, ermB and mexF genes, the relative abundances of other ARGs were negatively related to C/N ratio (p > 0.05). The relative abundance of target ARGs in A30 group, namely the C/N ratio of 30:1, exhibited the best removal efficiency by attenuating most of ARGs detected (Table 2). This finding is novel, and it is firstly reported herein. Since the temperature is the important environmental factor for destroying pathogens (Zhang et al., 2016), the C/N ratio of 30:1 prolonged the thermophilic period and then reduced a large number of pathogens which carried on ARGs in this study. On the other hand, pH and EC were correlated with the target ARGs. Among the 11 subtypes of ARGs, 63.6% and 72.7% had significant correlations with pH and EC (p < 0.05), respectively (Fig. 3 and Supplementary Data). The ARGs that were negatively correlated with pH mainly belonged to tetracycline resistance genes and macrolide resistance genes, where they accounted for 57.1% of the total detected number of target ARGs with significant correlations. However, sul1 and aadA1 genes were positively related to pH. The pH has clearly been shown to exert a strong selective pressure on sludge microorganisms (Rousk et al., 2010) thus affecting the ARG abundance. Moreover, the 5

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Table 2 The average removal rate of the relative abundance of ARGs in different treatments after composting (on day 32). Treatment

tetM

tetQ

ermB

mefA

blaOXA

blaTEM

mexF

aadA1

strB

sul1

sul2

CK (C/N = 9.5:1) A20 (C/N = 20:1) A25 (C/N = 25:1) A30 (C/N = 30:1)

−357.58% 88.65% 92.24% 94.69%

−9.17% 97.28% 97.67% 95.78%

−76.17% 99.06% 98.58% 96.87%

−204.96% −4.37% 82.09% 76.69%

68.84% 68.08% 96.27% 96.20%

56.73% 56.84% 74.66% 90.24%

−115.26% 92.32% 94.94% 96.66%

−98.34% −62.30% −106.34% 17.46%

29.60% 30.64% 46.77% 65.89%

−30.43% −114.88% −217.72% −65.04%

−471.28% −140.74% −348.61% −19.78%

Fig. 4. Mean relative abundance of bacterial class (A) and genus (B) during the composting. Classes are displayed the 20 most abundant bacterial classes. Genera are displayed the 30 most abundant genera.

6

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in other treatment groups and followed by Actinobacteria (6.69%62.60%). The microbial communities in the three treatments (A20, A25 and A30) changed rapidly. Under the temperature, the different conditions of nutrients (TN, TP and AP) and C/N ratio over the composting period (Supplementary Data), the changes of microbial communities among three treatments followed the similar trend. However, at the same stage, the abundance of the dominant bacterial populations changed among the different treatments. For instance, the abundance of Bacilli was enhanced by 2.01–2.05 times, 3.47–3.96 times and 0.88–4.18 times in A20, A25 and A30, respectively during the thermophilic phase (day 3–7). In addition, the population of Gammaproteobacteria declined in A20 and A25, while Alphaproteobacteria and Betaproteobacteria shrank in A20, A25 and A30, respectively at days 3–7. Compared with three composting treatments, Gammaproteobacteria including the drug-resistant bacteria that pose the greater threat to human health (Willyard, 2017), was more abundant in the CK samples (15.82%-37.52%). The class Actinobacteria was detected at an abundance of 3.85%-11.15% in CK during the composting process, which was lowest comparing with other three treatments (6.69%-52.60%). Actinobacteria is one of the key microorganisms, which is actively in charge of rapid decomposition of composting mass (Awasthi et al., 2017). From these results, it seemed like that the co-composting of sewage sludge with fermented straw enriched Actinobacteria, which accelerated OM degradation (Supplementary Data). The relationships between the nine most abundant classes and ARGs and MGEs were explored (Supplementary Data). The intI2 gene was in positive correlation with Bacilli in three treatments (A20, A25 and A30) during the thermophilic phase, where the bacterial communities were dominated by Bacilli (Fig. 4A). Moreover, the abundance of intI2 of days 3–7 was highest with 1.77 × 1011 copies/g −2.85 × 1012 copies/g (Supplementary Data), indicating that the increase in intI2 gene abundance might be partly due to the proliferation of class Bacilli. Actinobacteria, Alphaproteobacteria, Betaproteobacteria and Sphingobacteriia were associated positively with the sul1, blaOXA, blaTEM and intI1 genes, respectively in the four treatment groups (CK, A20, A25 and A30). As one of the more important predicted source bacteria of ARGs, Actinobacteria often show multi-resistance and self-resistance (Gao et al., 2018). One explanation for the persistence of sul1 and intI1 was that the elevated abundance of Actinobacteria during the maturation phase (Fig. 4A). Although the abundances of Alphaproteobacteria, Betaproteobacteria and Sphingobacteriia were slightly decreased after composting (Fig. 4A), they did not help to reduce the quantity of sul1 and intI1. Furthermore, sul2 was positively correlated with some classes including Gammaproteobacteria, Clostridia, Flavobacteriia and Deltaproteobacteria during composting process (Supplementary Data), demonstrating that it was not only pathogens but also environmental bacteria possibly carry ARGs (Su et al., 2014). To conclude, shifts in bacterial communities and nutritional energy were the key factors affecting the transmission of ARGs. The 30 most abundant genera in compost samples are presented in Fig. 4B. Compared with three treatments (A20, A25 and A30), genera comprising Arenimonas (average 2.13%), Thiopseudomonas (average 6.84%), Pseudomonas (average 1.88%), Acinetobacter (average 2.16%), Thauera (average 1.62%), and Comamonas (average 3.33%) were significantly more abundant in the CK (p < 0.05); while the populations in other three treatments changed significantly, where the similar trends had been obtained under the different C/N ratios. The genera Georgenia, Pusillimonas, Sinibacillus, Saccharomonospora, Streptomyces, Glycomyces, Thermobifida, Oceanobacillus, Actinomadura and Bacillus were reckoned as the dominated genera evolving in composting progress, which could be detected through the compost process of each treatment. Therefore, the microbial community composition was not significantly affected by the different initial C/N ratios in this present study. Specifically, the abundance of Actinomadura in A20, A25 and A30 increased in composting process and reached a peak on day 32 with values of 29.81%, 26.20% and 11.77%, respectively. The

sul1 and sul2 genes had significant correlations with OM (p < 0.05), suggesting the changes of sulfanilamide resistance genes were linked to decomposing OM during composting. This is because OM impacts on the bacterial populations that can carry the genes (Ren et al., 2018). Meanwhile, these sulfanilamide resistance genes were negatively correlated with the temperature. Therefore, changes of temperature are the key factors affecting the decrease of sul genes. In addition, the sul2 and tetQ had also been found to be positively correlated with AP, TP and TN (p < 0.05), and the tetM and blaOXA genes had positive relation with TN (p < 0.01), suggesting that co-attenuation of these ARGs with nutrients occurred. Base on statistical correlation, these findings suggested that the C/N ratio, pH, EC, TN, TP and AP might act as drivers and played an indispensable role in controlling the proliferation of ARGs during the compost process. The concentration of bio-Ni appeared strongly positive relation to the abundances of tetM, tetQ, mefA and sul2 genes (p < 0.05); while bio-Cd was positively correlated with the blaOXA and strB (p < 0.05). The results indicated that some ARGs were affected significantly by bioavailability of heavy metals during composting, which is similar to those obtained in chicken manure composting (Li et al., 2017). Additionally, there were significantly positive correlation between bio-Cu and intI2 gene. The bio-Ni concentrations in A20, A25 and A30 decreased by 10.12%, 27.93% and 64.09% on day 32 (p < 0.05), respectively, compared with their initial contents on day 1 (Supplementary Data). The similar phenomenon was seen in the bio-Cd and bio-Cu, respectively. The treatments containing high C/N ratio at initial stage had significantly lower concentrations (p < 0.05) of bioNi, bio-Cd and bio-Cu, respectively compared with CK after composting. Since heavy metals do not undergo microbial degradation, they tend to provide the persistent pressure on antibiotic resistance (Zhu et al., 2013). Hence it was possible that a higher C/N ratio in the composting inhibited the mobility of heavy metals, thereby decreasing the co-selection of ARGs. The class I integron gene (intI1) is important to gene transfer among bacteria, which has been considered to be an indicator of horizontal gene transfer potential (Ma et al., 2011). The previous study confirmed that intI1 carries several antibiotic resistance cassettes (Mendes et al., 2007). In the present study, the Pearson’s correlation coefficients indicated that only sul1 and aadA1 genes were observed to a significant positive correlation with intI1 in the composting (p < 0.05) (Supplementary Data), suggesting that mobile genetic element played roles in the transmission of sul1 and aadA1. However, significant correlation was not obtained between intI2 and target ARGs. Surprisingly, there was not a significant relationship between the C/N ratio and intI genes (intI1 and intI2). It suggested that adjustment of C/N ratio could not directly affect the change of intI genes in this study. Notably, intI1 went up together with ARGs during the composting after thermophilic phase and increased by approximately 1 log unit in the maturation phase (Supplementary Data), indicating that reduction of intI1 might be very vital in preventing the transmission of ARGs horizontal gene transfer during the composting, which was validated in a previous study (Liao et al., 2018). 3.4. Evolution of microbial community structure The high quality sequences (1323793) were obtained from clone libraries after trimming and quality filtering. The sequences were clustered into 161,486 OTUs at 97% sequence similarity. The rarefaction curves indicated a sufficient sequencing depth (31450), and the number of OTUs was nearly saturated (Supplementary Data). Bacilli, Actinobacteria, Gammaproteobacteria, Clostridia, Alphaproteobacteria and Betaproteobacteria were the six most dominant bacterial classes, which accounted for over 60% in each sample (Fig. 4A). Gammaproteobacteria was the most abundant class in CK and the abundance increased from 16.21% to 37.52% on day 32, followed by Clostridia (8.37%-12.39%). On the contrary, Bacilli (11.72%-60.58%) was the most abundant class 7

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Fig. 5. The variation of predicted metabolic pathway during sludge composting analyzed by PICRUSt (metabolism). KEGG pathway gene copy numbers obtained at the “categorize by function” command (level 2) was normalized to reflect the levels observed on days 1, 3, 7, 14, 32 samples across all four treatments (CK (C/ N = 9.5:1), A20 (C/N = 20:1), A25 (C/N = 25:1) and A30 (C/N = 30:1)).

3.5. Functional potential bacterial community composition

population of Sinibacillus increased on day 3 and then declined until on day 32. However, the microbial abundance changed among the three treatments at the same stage. For example, the abundance of Glycomyces was enriched to 2.35%, 2.02% and 6.64% in A20, A25 and A30, respectively, during the maturation phase (day 32), which were not obtained in CK. The abundance of genera Bacillus was highest in A30 (31.21%) and lowest in A20 (19.11%) at the thermophilic stage (day 3). The results implied that the dominant microbial populations had barely changed due to the C/N ratio (20–30:1) at the same stage but the abundance was varied according to composting treatment.

In view of the metabolic function of bacterial communities dominating nutrients transformation and organic matter stabilization in sewage sludge composting process (Wang et al., 2018a), it is essential to exploit metabolic characteristics of bacterial communities. The majority of the metabolic functions of bacterial communities in the four treatments involved in metabolism (49.4–51.02%), genetic information processing (15.88–16.6%), environmental information processing (14.26–14.89%), cellular processes (3.18%-4.07%), human diseases (0.90–0.97%), organismal systems (0.81–0.87%) and unclassified (13.25–13.90%), respectively (Supplementary Data). The mean relative abundances of predicted protein sequences belonging to carbohydrate 8

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hosted in the microbes and transferred among them. The numbers of these three genera were greater in CK (Thauera: 5.45–0.13%, Christensenellaceae_R-7 group: 2.58–0.92%, and Acidovorax: 0.69%-0.03%) (Fig. 4B); while they were reduced to less than 0.01%, respectively by composting with fermented straw in the three treatments. Simultaneously, the relative abundance of blaOXA gene decreased in the three treatments (Fig. 2B), indicating that reduction of the genera of Thauera, Christensenellaceae_R-7 group and Acidovorax hindered transmission of blaOXA gene. Furthermore, strB gene was negatively related to Georgenia, suggesting increment of the population of Georgenia after composting in three treatments (Fig. 5B) inhibiting transmission of strB gene. The second cluster was involved in sul1, sul2, aadA1, mexF and intI1, which were related to bacterial function metabolism or bacteria. The negative correlation of intI1 with Sinibacilluss and Thermobacillus was found. However, mexF gene was positively correlated with Candidatus_Microthrix and Phodovulun, respectively. Microorganism are the carriers of ARGs, so controlling the proliferation of potential host bacteria may produce variations in ARGs (Udikovic-Kolic et al., 2014). Although some bacteria probably carried and disseminated ARGs, the relative abundances of genera positively related with ARGs were reduced after composting, such as genera Candidatus_Microthrix and Phodovulun. Therefore, the decrement in the population of some special bacteria which were positively related to ARGs might be the explanation for the removal of some types of ARGs during the sludge composting. Because the host bacteria of ARGs showed the same tendency when the abundance of ARGs decreased (Zhang et al., 2016). Despite intI1 gene is considered to be very vital in gene transfer among bacteria, only Thermobacillus and Sinibacillus were negatively related to it in the network. On the other hand, the pathways of metabolism and genetic information processing and intI1 appeared in the negative correlations. The results indicated that when bacteria carrying intI1 are seen at increased abundance, bacteria carrying a lot of genes involved in the indicated pathways are seen at decreased abundance. In addition, sul1 gene was significantly related to the pathways of genetic information processing, human diseases, metabolism, environmental information processing, cellular processes and unclassified. It indicated that when bacteria carrying the sul1 gene increase in abundance, bacteria that carry many genes associated with the specified KEGG pathways decrease in abundance and vice versa. The similar results were reported by Zeng et al., (2019), who found that genes associated with some metabolic pathways, such as cellular processes and genetic information processing dropped while the abundance of sul1 and sul2 genes increased in intestinal microbiota. Interestingly, the target ARGs with higher copy number such as sul1, sul2, and aadA1 (Fig. 2A) had negative correlation with metabolic pathways. In the present study, the relative abundance of Actinomadura was increased on day 32 (Fig. 4B), which had been also found in swine manure composting (Wang et al., 2018b). The appearance of Actinomadura in straw and sludge co-composting implied that this genus was also very vital in the degradation of cellulose-rich material. Furthermore, Actinomadura had a negative correlation with the metabolic pathways of human diseases, cellular processes and unclassified. It has been ascertained that Actinomadura could produce nucleoside antibiotics (Gao et al., 2017). These suggested that the population of Actinomadura likely affected the metabolic pathways of human diseases and cellular processes. Besides, it was noticed that there was negative correlation between intI1 gene and unclassified pathway, suggesting that when bacteria carrying the intI1 gene increase in abundance, bacteria that carry many genes associated with the unclassified KEGG pathways decrease in abundance and vice versa.

metabolism and amino acid metabolism were over 9.0%, which were significantly lowest in CK (p < 0.05) (Fig. 5). The cause might be that the composting of sludge mixed with fermented straw was more prone to release bio-heat at thermophilic period, where carbohydrate and amino acids decreased, which was coincided with the previous study (Wang et al., 2018b). Metabolism of carbohydrates during composting can produce different compounds due to degradation of cellulose and hemicellulose (Toledo et al., 2018). In this study, the treatment of A30 with the higher C/N ratio might decompose the more content of carbohydrates since the relative abundance of genes involved in carbohydrate metabolism were higher than other treatments after the composting (p < 0.05) (Fig. 5). It can be inferred that during cocomposting process of sludge and fermented straw carbohydrate metabolism was essential to degrade hemicellulose and cellulose. As composting proceeded, the increment of the sequences associated with amino acid metabolism were observed during the maize straw composting (Wei et al., 2018), which are also the nutritional carbon and energy source for microorganism growth and metabolism (Liu et al., 2016). Thus, the higher abundance of genes involved in amino acid metabolism in three treatments (A20, A25 and A30) (Fig. 5) might promote effect on amino acid production compared with CK. The bacterial function profiles involved in genetic information processing, environmental information processing, cellular processes, human diseases, organismal systems and unclassified were presented (Supplementary Data). With initial C/N ratio increasing, the relative abundances of these bacterial function profiles during the composting showed irregular changes. By comparing the sequences related to bacterial function among the four treatments, most of the sequences were shared by different samples (Supplementary Data). The Venn diagram depicted 5832 of common sequences between the composting samples. Interestingly, 61 sequences were unique in CK while the number was reduced during composting. This might suggest the sewage sludge composting with fermented straw can generate a homogeneous function profile. The genes associated with nitrogen and sulfur metabolism, methane, oxidative phosphorylation and carbon fixation pathways in prokaryotes were identified (Supplementary Data). The abundance of encoded genes of the oxidative phosphorylation pathway in A30 increased after the thermophilic stage (from 1.36% to 1.49%), which was similar to sewage sludge composting with pumice (Wang et al., 2018a) whereas the genes did not increase in other treatments. For all treatments, the genes belonged to methane metabolism pathways declined from 1.02% to 1.12% (day 1) to 0.96–1.01% (day 32), because supplementary oxygen during composting gradually strengthened the aerobic metabolism (Wang et al., 2018a), which limited the methane metabolism pathways. Moreover, nitrogen metabolism gradually decreased over the composting time, which meant that nitrogen transformation mainly happened in the warming and thermophilic period. The sequences coincided with lipid metabolism were marginally higher in the maturation stage, such as biosynthesis of unsaturated fatty acids, fatty acid biosynthesis and fatty acid metabolism (Supplementary Data). Similar processes were generally found to occur in swine manure composting, where biosynthesis of unsaturated fatty acids and fatty acid biosynthesis increased after composting (Wang et al., 2018b). It suggested that maturation stage appeared not to be centered on the residual easily degradable substrates but more complex compounds (Khatua et al., 2018). 3.6. Relationships among ARGs, potential host bacteria for ARGs, and microbial metabolism

4. Conclusions

The relationships among ARGs, potential host bacteria for ARGs and microbial metabolism were divided into two clusters (Fig. 6). The first cluster of ARGs involved in blaOXA gene which was related to several bacteria rather than microbial metabolism, such as Thauera, Christensenellaceae_R-7 group, Acidovorax, suggesting blaOXA gene was likely

This work hints that the production of compost from sludge mixed with fermented straw can to some extent reduce the risk of ARGs transmission according to nutritional energy (C/N ratio) regulation. 9

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Fig. 6. The relationships among target ARGs, MGEs, the microbial metabolism and their bacterial. The significant positive correlation (p < 0.05) based on Pearson correlation coefficient (r > 0.6) was showed in the connection. The red edge represents a negative correlation while green edge represents a positive correlation. The size of each node means the abundance of bacteria, ARGs, MGEs and microbial metabolism in all samples (days 1, 3, 7, 14 and 32). The blue, turquoise blue and red bars in the box represent the number of sample, degree and abundance, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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CRediT authorship contribution statement Huawei Wei: Conceptualization, Methodology, Data curation, Formal analysis, Visualization, Investigation, Writing - original draft, Writing - review & editing. Jiaying Ma: Formal analysis, Investigation, Visualization, Writing - review & editing. Yinglong Su: Resources, Visualization, Investigation, Writing - review & editing. Bing Xie: Supervision, Writing - review & editing, Project administration, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the National Key Research and Development Program of China (2018YFC1901000), Natural Science Foundation of China (21577038), Key Project of Shanghai Science and Technology Commission (18391902600), Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration (SHUES2018B08) and the Fundamental Research Funds for the Central Universities. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.122513. 10

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