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Effect of fine coal gasification slag on improvement of bacterial diversity community during the pig manure composting
Bioresource Technology 304 (2020) 123024
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Effect of fine coal gasification slag on improvement of bacterial diversity community during the pig manure composting Tao Liu1, Sanjeev Kumar Awasthi1, Yumin Duan, Zengqiang Zhang, Mukesh Kumar Awasthi
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College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Bacterial abundance Fine coal gasification slag Pig manure Composting
In present study, evaluate the effect of fine coal gasification slag (FCGS) as additive on abundance of bacterial diversity during pig manure composting. The six different dosages of FCGS 0% (T1), 2% (T2), 4% (T3), 6% (T4), 8% (T5) and 10% (T6) (dry weight basis) were mixed with original raw materials for 42 days an aerobic composting. The results indicated that FCGS adopted could affect the succession of bacterial diversity in different ways. Among all treatments, Firmicutes, Proteobacteria, Tenericutes, unidentified_Bacteria, and Actinobacteria were the highest abundance in weighted unifrac distance but Firmicutes; Proteobacteria, Actinobacteria, Bacteroidetes, and Spirochaetes were main bacteria in unweighted unifrac distance. The β-diversity and principal component analysis indicated a significant difference in bacterial diversity in all treatments which T4 obtained difference obviously. Therefore, the results showed that T4 was a potential candidate to enhance significantly abundance of bacterial community in PM compost.
1. Introduction With the improvement of living standards and quality, increased the demand for meat products, caused the livestock industry becomes more prosperous. Meanwhile, a large of livestock manure was generated, which is a major source of environmental pollution (i.e. soil, water and air) in world (Ren et al., 2019). Nevertheless, conventional process
represented the unsuitable disposal which could lead to the environmental risks, it’s also a waste of sources because high nutrients, phosphorus and organic matter (OM) in pig manure (PM) (Wang et al., 2018; Agyarko-Mintah et al., 2017). According to Zhu et al. (2013), approximately 618 billion kg of PM was produced in China annually. How to deal with PM in an environmentally friendly for the Chinese government has turn into a serious problem. Hence, it is important to
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Corresponding author. E-mail address: [email protected] (M.K. Awasthi). 1 Equally contributing authors. https://doi.org/10.1016/j.biortech.2020.123024 Received 26 December 2019; Received in revised form 11 February 2020; Accepted 12 February 2020 Available online 13 February 2020 0960-8524/ © 2020 Elsevier Ltd. All rights reserved.
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Table 1 Characteristics of composting materials used in this study (dry weigh basis). Parameters
Pig manure
Wheat straw
Fine coal gasification slag
Moisture content (%) pH EC (mS/cm) Total organic carbon (%) Total Kjeldahl nitrogen (%) Carbon: nitrogen ratio Specific surface area (m2/g) Total pore volume (cm3/g)
79.62 ± 0.01 8.39 ± 0.09 4.68 ± 0.13 41.08 ± 1.19 2.39 ± 0.08 17.19 ± 0.25 ND ND
5.00 ± 0.12 6.98 ± 0.08 1.17 ± 0.11 59.85 ± 2.39 0.99 ± 0.01 60.45 ± 0.98 ND ND
2.18 ± 0.24 8.80 ± 0.14 0.86 ± 0.02 35.63 ± 1.82 0.14 ± 0.01 254.5 ± 5.63 245.95 ± 17.24 0.20 ± 0.001
EC – Electrical conductivity. Results are the mean of three replicates ± standard deviation. ND – Not detected.
of bacterial community variation during PM composting applied with FCGS. This study was mainly focused on the application of different ratio of FCGS to explore the effect on bacterial community during PM composting. Subsequently, the objective was to identify the relative abundance of bacterial communities in PM compost.
discover an environmentally and economically method to recycle PM efficiently. Composting is considered as an ecofriendly method and process to successful conversion manure into stable humic substance and reduction waste of agriculture resources (Gao et al., 2019; Wu et al., 2017). Through a range of active microbial community execute a series complex biological process which is called composting, microoganisms (Bacteria, Actinomystes and Fungi) and environmental characteristics play a key role in rapid degradation of OM (Awasthi et al., 2018; Wu et al., 2019). Due to the strong adaptability to the unfavorable surrounding condition and metabolic, the bacteria show more proactive (Fu et al., 2015; Chen et al., 2019). To explore the knowledge gap of bacteria, so that completely understand the mechanism of composting progress and make it more efficient, safe and clean. In recently, it is an impressive molecular technology that the Illumina MiSeq high throughput sequencing could reveal the detailed interior bacterial dynamics accurately (Awasthi et al., 2017b; Zhang et al., 2018). However, unimproved composting would bring about the negative effects on environmental problem such as odors and nitrogen loss, which could be due to the single substance condition create an adverse environment for the bacteria survival (Galitskaya et al., 2017; Janczak et al., 2017). In order to promote the progress of composting, facilitated the OM degradation and provide a favorable condition for bacteria, many researches have been determined to study the advanced parts of additives (Zhang et al., 2017;, Wang et al., 2016; Liu et al., 2019). Mixing PM with wheat straw and biochar could increase the number of bacteria by 161% (Li et al., 2016). Li et al. (2019a) reported that the bacterial diversity was greatly higher in pine leaf biochar amendment, and observed that 10% treatment got the highest correlation. The environmental factors of temperature and pH, or the diversity of bacterial community might be correlated to the different dosage of additives cocomposting process (Tkachuk et al., 2014; Chen et al., 2017). Fine coal gasification slag (FCGS) comes from a typical entrained-flow gasification which is an inevitable by-product, the characteristic of FCGS is high carbon content and large amount, being comprised with irregular porous particle (Li et al., 2019b). Meanwhile, the amount of FCGS produced is very large, yet the properties of FCGS could provide the enhancement of cation exchange capacity and decrease the density of composting. There are similar characteristics between FCGS and biochar which possess the high porosity and large specific surface area. (Lu et al., 2018; Xiao et al., 2017). Lu et al. (2018) reported that the effects of coal gasification slag (CGS) on antibiotic resistant genes during swine manure composting. There are none of research with the effect of different ratio FCGS amendment on bacterial abundance diversity and relation of environmental parameters in the thermophilic stage of composting. The impacts of the employing of FCGS during PM composting on bacterial diversity have not been reported. Nevertheless, based on widespread literature read and conclusion which there is limited information the influences of FCGS amendment in PM composting on the bacterial diversity have not been reported adopting 16S rRNA sequencing technology. Therefore, there was limited study available about abundance
2. Materials and method 2.1. Collection and processing of composting raw materials The experiment conducted in the drought shed in the composting areas of Northwest Agriculture and Forestry University. A local livestock farm provided the PM in this study, and the wheat straw (WS) was collected by the neighboring farms (Yangling, Shaanxi, China). FCGS was obtained from Huide Company (Baoji, Shaanxi, China). Selected properties of composting raw materials were reveled in Table 1. 2.2. Composting setup and sample collection The 42-days composting experiment was carried out in 100 L steel rectors and its systematic diagram were illustrated in our previous study (Awasthi et al., 2017a; Chen et al., 2018). To achieve the suitable moisture content (~65%) and C/N ratio (~25), PM and WS were mixed 2:1 ratio (dry weight basis). Meanwhile, 1 kg of plastic spheres was blended with initial raw materials to adjust the initial density to 0.5 kg/ L. The six different ratio of FCGS (0%, 2%, 4%, 6%, 8% and 10% on the dry weight of the mixed material) were employed with the mixture initial feed stocks namely T1, T2, T3, T4, T5 and T6. The compost temperature was monitored twice daily every 12 h in the middle of containers by the using of thermos probes, and the average temperature was also recorded. The composting mass of each treatments were mixed on day 1, 3, 7, 14, 21, 28, 35 and 42, and then collected the samples. Separated the fresh samples into two parts, one part was for the moisture, pH and electrical conductivity (EC) determined; after air dried and smash the samples to tested the total Kjeldahl nitrogen (TKN) and OM (TMECC, 2002), while the other was preserved at −20 °C to analyze the microbial. 2.3. DNA extraction and 16S rRNA amplification The 16sDNA amplification and sequencing evaluated the bacterial abundance communities’ ingredient in the process of fresh PM composting. To extract the DNA, 0.1 g of each sample was employed by a Fast DNA SPIN Kit for Soil (Omega Biotek, Inc.) according to the company’s instructions. The nano-spectrophotometer and 1% (W/V) agarose gels electrophoresis determined the DNA and its concentration. Using the primers 515F (5′-GGACTACHVGGGTWTCTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAA-3′) amplified the 16S V4 region. Adopting Qiagen Gel Extraction Kit and PCR Clean-up process (Qiagen, Germany) refined the PCR products. According to PCR Clean-up process, 2% agarose gel electrophoresis and purified detected the PCR 2
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product. Nevertheless, the concentrations of the PCR generates were fluorometrically quantified by the Qubit 2.0 dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) before sequenced on the Miseq 25,000 platform (Illumina, San Diego, CA, USA), at Novogene, Beijing, China (Awasthi et al., 2017b). 2.4. Statistical analysis All the data was conducted in triplicate and the data was performed IBM SPSS-v.21 and Microsoft Excel 2010 at mean ± standard deviation. In order to calculate the results for relation of bacterial communities and environmental properties using the Pearson correlation coefficients. The principal component analysis (PCA) was determined by the R language v3.0 prcomp function. The Dice was conducted to analyze the resemblance matrix, and the Un-weighted Pair-group method with arithmetic means (UPGMA) was carried out fusion strategy for detailing the dendrograms. 3. Results and discussions 3.1. Impact of FCGS on composting of bacterial communities Compared with the other technologies, 16S rRNA is a critical method to provide a more accurate and high efficiency DNA amplification under the circumstance of microorganism community and diversity condition, so that become an excellent channel to obtain the richness information about microorganism diversity (Awasthi et al., 2017b; Zhao et al., 2013). As presented in Fig. 1a, total Tags of 60749, 67837, 67287, 60891, 62033, and 63381 and operational taxonomic units (OTUs) of 687, 774, 667, 846, 774, and 829 as well as Taxon Tags 58540, 66366, 66013, 58866, 60395, and 61510 were attained from T1 to T6 in all treatments, respectively. In experiment, the total Tags, OTUs and Taxon Tags were 63696, 763 and 61948, and T2 got the maximum in Tags abundance and the T1 obtained the minimum in abundant of Tags. While the sequences of Unique Tags were 2209, 1471, 1274, 2025, 1638 and 1871 in T1, T2, T3, T4, T5 and T6, respectively, Unclassified Tags were not detect among all treatments. Meanwhile, the classification of microorganism species adopted OTUs, the abundance diversity of bacterial community were reflected by OTUs (Jiang et al., 2019). The OTUs got the maximum abundant in T4, and the control 4% FCGS added treatments was the minimum abundance. According to the Wang et al. (2017), from the mesophilic phase to cooling phase during the composting, the number of OTUs gradually decreased. The range of OTUs in this study was 667–846 which was higher than 270–572 during co-composting for cow manure with biochar and bacterial consortium (Duan et al., 2019), and also higher than188-304 during the pine leaf biochar amendment PM composting (Li et al., 2019a), and higher than 177–546 during the cow manure with corn cobs composting (Zhang et al., 2016), meanwhile, Partanen et al. (2010) reported the total of 522 OTUs during the organic municipal waste compost with wood chip. However, in this study which OTUs number was less than 1552–2269 during the sewage sludge and biochar co-composting (Awasthi et al., 2017b). The increased additive proportion and classified the phase of each treatment procedure, the abundance of bacterial diversity community structure changed greatly. The bacterial relative abundance (RAs) of the phyla status among all treatments was revealed in Figs. 1b and 2. In all treatments, Firmicutes, Proteobacteria and Tenericutes were showed the most remarkable three phyla (Figs. 1b and 2a). Pankratov et al. (2011) reported that Firmicutes had a richness phylum abundance in various of organic substrates composting, which played a great role in degradation of lignocellulose. Meanwhile, many previous findings confirmed that the Firmicutes still belong the highest abundance bacterial in manure composting (Li et al., 2019a; Duan et al., 2019; Lu et al., 2018) but Li et al. (2019a) and Awasthi et al. (2017b) found that Proteobacteria was relatively greater abundant present in dairy manure and sewage sludge composting. The
Fig. 1. Statistic analysis and phylum relative abundance layout in cow manure compost: (a) Tags and OTUs number statistics of different samples. All OTUs with an average relative read abundance > 1% and total tags number (red) refers to the filtered the splicing sequence number. Taxon Tags (blue) refers to the number of Tags for building OTUs and access to classified information. Unclassified Tags (green tea) for building number of Tags OTUs but without access to classified information; Unique Tags (orange) refers to the frequency is 1, and can’t be clustering to the number of Tags OTUs; All the above corresponding with the vertical axis on the left, meanwhile the right vertical axis is the Number of OTUs (purple) refers to the Number of OTUs finally got. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.); (b) The top ten species in phylum level were selected, and the distribution histogram of relative abundance of species, ‘‘Others” represents a total relative abundance of the rest phylum besides the top 10 phylum. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
study observed the similar result that Firmicutes was the highest abundance in all bacterial, and clearly showed that T3 treatment (4%) got the highest relative abundance, T2 was the lowest abundance. The results indicated that the bacterial community abundance of cellulosedegradation may be effected by the FCGS amendment. In T4, the Proteobacteria showed the higher abundance than other treatments. Fig. 2b showed the T4 got the lowest abundance of Firmicutes but the highest abundance of Proteobacteria and Actinobacteria were also observed. Li et al. (2019a) also observed similar result, where biochar amendment PM composting could decrease the abundance of Firmicutes. The nitrogen cycling related the abundance of Proteobacteria community (Zhong et al., 2018; Dees and Ghiorse, 2001). The loss of nitrogen was due to the ammonification during the thermophilic phase of composting. Only T3 treatment (4%) got the lowest abundance of Proteobacteria in all treatments, which indicated that the other FCGS additional treatments would facilitate the microorganism transformation the organic nitrogen. The abundance of Tenericutes was higher in additive adopting of FCGS, revealed that FCGS could enhance the abundance and promote the cellulose degradation. This result was similar the previous reported by Duan et al. (2019), who added the biochar and bacterial consortium in cow manure composting. 3
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Fig. 2. Basic information of the microbial communities in different treatments: (a) the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA gene sequences. Bacterial phyla level (top 10) and clustering tree based on the Weighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance; (b) the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA gene sequences. Bacterial phyla level (top 10) and clustering tree based on the Unweighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance.
The major prominent of five phyla were Firmicutes, Proteobacteria, Actinobacterial, Bacteroidetes and Spirochaetes that occupied for dominant situation during the composting (Fig. 2b). Ma et al. (2018) and Zhang et al. (2018) observed that the most excellent five phyla were Firmicutes, Proteobacteria, Bacteroidetes, Actinobacterial, and Tenericutes in the PM and WS aerobic composting and the compost of a 90-m3 aerobic solid state fermentor, respectively. Meanwhile, Duan et al. (2019) also found the similar results during the cow manure composting. The Firmicutes, Actinobacterial, Proteobacteria, and Bacteroidetes were the four most prominent phyla (Li et al., 2019a), and the same dominant taxonomic phyla were also observed in other wastes composting (Antunes et al., 2016; Wei et al., 2018). The decomposition of cellulose and lignin also relied on the Actinobacterial (Su et al., 2015). It was a possibility influence on OM degradation that the rich abundance of Bacteroidetes due to the degradation cellulose to short chain fatty acids (Zhong et al., 2018). As shown in Fig. 3a, heat map revealed the relative abundance of the top 35 bacterial genera during composting progress. The highest abundance in T1 was including the Thermobifida, Blautia, unidentifiedLachnospiraceae, Roseburia, Catenibacterium and unidentified_Enterobacteriaceae belong to the Firmicutes, Actinobacteria and Proteobacteria. Thermobacillus, Halocella and unidentified_Clostridiales
belong to Firmicutes and unidentified-Bacteria in T2. Caldicoprobacter, Bacillus, unidentified_Clostridiales, Streptococcus, Haloplasma and Limnochorda belong to Firmicutes and Tenericutes in T3. Caldalkalibacillus, Planifilum, Trichococcus and Gemmobacter belong to Firmicutes and Proteobacteria in T4. Pseudomonas, Symbiobacterium, Hydrogenispora, Defluvitalea, Vulgatibacter and Chelativorans belong to Firmicutes and Proteobacteria in T5. Unidentified_Corynebacteriaceae, Corynebacterium, Ureibacillus, Trueperella, Fastidiosipila, Lactobacillus, Candidatus Bacilloplasma and Lactococcus belong to Actinobacteria, Firmicutes and Tenericutes in T6. Therefore, Thermobacillus, unidentified_Corynebacteriaceae, Bacillus, Caldicoprobacter, Thermobifida, unidentified-Lachnospiraceae, Roseburia and Catenibacterium dominated genes during the PM composting. This study revealed that these genera could better suit for the composting. Storey et al. (2015) reported that the predominant abundance genera Streptomyces and Sphaerobacter in the composting. In the PM composting, Ma et al. (2018) observed the Pusillimonas, Ignatzschineria, Pseudomonas, Moheibacter, Prevotella, Bacilli, Clostridia and Negativicute were the main genera. Due to the fact that the FCGS amendment could improve the condition of composting through its unique characteristic and promote the degradation of OM, the adopted of FCGS treatments has the higher abundance bacterial diversity community.
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Fig. 3. The relative abundance of each class based on 16S rDNA sequence analysis: (a) species abundance clustering heat maps selected the top 35 genera of abundance and cluster from the species and sample levels based on their abundance information in each sample. Different color means the different relative abundance of the genus in the all six treatments (red means great abundance); (b) the relative abundance is expressed in percentage and classification tree of complex samples. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.2. Impact of FCGS on composting of interactive metagenomic visualization in a phylogenetic tree
As shown in Fig. 3b, percentage and classification tree indicated the relative abundance, and the average relative abundance Firmicutes, Proteobacteria, Tenericutes and unidentified_Bacteria accounted for 89.67%, 5.63%, 1.74% and 2.96% of bacteria, respectively, which these identified bacteria played an important role in cycling of nitrogen. Among all treatments, the NO3−-N concentration of similar trends, which almost absent in the initial phase and then increased after end of compost. The NO3−-N was generated by the microbial activity and nitrification. At the end of composting, the NH4+-N content decreased which this results was the ammonification, immobilizattion by nitrogen-fixing microbial and the generation of NO3−-N. In the swine manure and cotton stalks co-composting, Zhang et al. (2018) founded the ammonia nitrogen was influenced by the primary bacterial of Proteobacteria (41.7%), Firmicutes (20.8%) and Actinobacteria (16.7%). The result was similar the previous reported by Awasthi et al. (2017b) and Li et al. (2019a), which Firmicutes accounted the largest ratio 54.41% and 64.33%, respectively. Duan et al. (2019) reported that Bacteroidetes accounted the largest proportion (33.85%). The Bacillus, Ureibacillus, and Thermobacillus belong to Bacilli, while Clostridiales, and unidentified_Closttridiales belong to Clostridia occupied 60.51%, 7.33%, 11.98% and 6.82%, 3.02%, respectively, which is confirmed to Firmicutes were dominantly present in each treatments. The Pseudomonas belong to the Proteobacteria, which hold all percentage. On the other hand, a lot unidentified_Bacteria of Halocella and Thermobacillus were showed in T2. Most abundance of Bacillus and Tepidimicrobium belong to Firmicutes observed in T3. Thermobacillus existed in T4, which the dominate genes was Proteobacteria. Richness of Defluviitalea, Symbiobacterium and unidentified_Clostridia belong to Clostridia were presented in T5, which the major genes were Firmicutes. The most abundance of Tenericutes and Firmicutes of Bacillus occupied in T6. Bacillus and Pseudomonas mainly existed in T1. Therefore, the FCGS additive could promote the increase of bacterial diversity.
The phylogenetic tree was established based on the sequence of the first 100 genera (Fig. 4). The most sequence contained Firmicutes, Proteobacteria, unidentified_Bacteria, Tenericutes and Actinobacteria. Li et al. (2019a) and Yin et al. (2017) reported which the Actinobacteria, Proteobacteria Firmicutes, and Bacteroidetes were the main bacterial community abundance in PM composting. The results are different which could be due to the different additives lead to the gap of composting environment. The bacterial of Bacillus, Thermobacillus and Pseudomonas were higher than other bacteria observed in T1. The abundance of Bacillus was significant greater among the all treatments, but the T3 showed the richest, which governed the bacterial community. Bacillus played a critical role in degradation of OM, and the cellulase and dehydrogenase got the highest activities in the thermophilic phase, which may be due to the Bacillus associated (Blanc et al., 1997). The similar reported by the He et al. (2013), who found the microbial community was degraded by the Bacillus during the thermophilic stage. The abundance of Thermobacillus occupied for the largest ratio to T2 than other treatments but absent in T3. The bacterial of Bacilus, Thermobacillus, Halocella, Tepidimicrobium and Pseudomonas could be clearly found in T4. The bacterial of Pseudomonas could be found in all treatments, and T5 obtained the highest abundance, which the Pseudomonas could promote the degradation of WS lignocellulose (Wang et al., 2013; Ventorino et al., 2015). The abundance of Bacillus, Ureibacillus, Thermobacillus, Candidatus_Bacilloplasma, Tepidimicrobium and Pseudomona dominated in T6, which more bacteria explored than other treatments. In order to reveal the correlate abundance of bacterial diversity community classification which were conducted by a new visualization tool phylogenetic tree. Here we concluded that phylogenetic tree is a new visualization instrument which can directly explore the correlative richness of bacterial diversity community. As we know, a bioinformatics instrument based on numbers of compost samples was utilized by phylogenetic tree, which determines the strong role of emerging 5
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Fig. 4. The phylogenetic tree constructed by the representative sequence of the horizontal species; the color of the branch and the fan shape indicates its corresponding gate, circles from inside to outside stand for system composition tree, genes layout on different classification levels (represent by different color, and the area of sector means respective proportion), the stacked column chart outside the fan ring indicates the abundance distribution information of the genus in different treatments.
Candidatus_Bacilloplasma belong to Mollicutes, which occupied 33%, 7%, 8%, 5% and 5% in T6, respectively. The above results showed that higher percentage of FCGS have significantly increased the abundance of bacterial with the exploration classified bacterial ratio. The krona diagram showed that the abundance of Bacillus (21%–61%) is greatly richer than other bacteria, thus, it’s directly indicated that the number of aerobic bacteria community is greater than other bacteria. Awasthi et al. (2019) also found that the relative abundance of Bacillus (28%) is higher than others. In the phyla of Firmicute, with the dosage of FCGS amendment decreased the proportion of Unclassified bacteria. Therefore, try our best, the firstly tool build entirely on the number of samples is adopted by Krona, and it demonstrates the power of generating web methods to broadly research the correlation abundance and easily accessible visualization tools to identify bacterial community.
biology diversity network technology. It offers conditions to explore bacterial diversity that relatively rich and easily accessible visualization method. 3.3. Impact of FCGS on composting of interactive taxonomy web visualization by krona Among all samples, to identify the taxonomic hierarchy conducted the krona taxonomy visualization (Fig. 5). Krona clearly defined the taxon and species, which boundary from inside to outside ascending for diversity levels, the separate ratios of phyla, classes, orders, genera and species were revealed in the square of the two-dimensional figure (Awasthi et al., 2019). Relative abundance of the bacterial community adopting krona diagram to determine the effects of the biochar added into sewage sludge (Awasthi et al., 2017b). In T1, the Firmicutes dominated that contained Bacillus, Thermobacillus, unclassified, Pseudomonas and Unclassified of Proteobacteria occupied of 46%, 8%, 8%, 4% and 4%, respectively. Most apart of Bacillus, Ureibecillus, Thermobacillus, Unclassified, Unidentified_Clostridia belong to Firmicutes in T2, which hold on 21%, 6%, 12%, 5% and 4%. The Firmicutes of Bacillus (61%) and Proteobacteria of Pseudomonas (4%) governed in T3. The Bacillus, Thermobacillus, Ureibecillus, Unclassified were contained by Firmicutes, which accounted 33%, 9%, 4% and 5% in T4. The high abundance of bacterial was Firmicutes and Proteobacteria which included Bacillus (24%), Thermobacillus (6%), Ureibecillus (4%), Unclassified (7%) and Pseudomonas (6%), and Unclassified (4%) in T5. The Bacillus, Thermobacillus, Ureibecillus, and Unclassified belong to the Firmicutes and
3.4. Statistical analysis of bacteria community The β-diversity, petals diagram and PCA were usually carried out to define the wide tendency of differences and similarities in all treatments though a cluster analysis (Fig. 6). The correlation coefficient was conducted for all treatments compost and confirmed that the FCGS concentration related with the primary parameter of bacterial diversity directly (Fig. 6a). The value between treatments is small which means the gap of bacterial diversity is small, so that measure the coefficient of difference between pairwise samples usually adopted weighted unifrac distance and unweighted unifrac distance, which phylogenetic methods are employed largely in 16sDNA. Based on the quantitative data 6
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Fig. 5. Krona is taxonomy web visualization; circles from inside to outside stand for different classification levels, and the area of sector means respective proportion of different operational taxonomic unit annotation results.
44.53% between the six treatments. Lu et al. (2018) also reported that CGS amendment directly changed the composting progress and the statistically impacted the bacterial abundance diversity. The findings reveled that sequences in T2 and T5, T1, T3 and T6 existed in a restrict cluster, but T4 treatments in a broader boundary. It is carried out that the bacterial abundance of T4 is majorly influenced by the FCGS amendment. As well-known, composting progress controls the abundance of bacteria diversity, which is significantly attributed to some important environmental elements (i.e. pH, moisture), composting raw materials characteristics or nutrients (Insam et al., 2010). Awasthi et al. (2017b) also found that the temperature, pH, EC, total organic matter, C/N ratio and the humic and fulvic acids played a critical role in the maximum ingredient turnover for Proteobacteria, Firmicutes and Chloroflexi. In present study, the explanation of most possibly was FCGS can offer an optimal microenvironment for PM composting. In future work, it is necessary that the FCGS amendment PM composting investigates the bacterial diversity models and their correlation with basic parameters and stages. Generally, based on the obvious different between FCGS employing with the control and FCGS can improve the bacterial structure and enhance the progress of biodegradation of organic matter, especially for the decomposition of lignin and cellulose, because of the regulated important enzyme activity, which needs to be explored for fact effectiveness.
analysis weighted unifrac distance with the increasing of FCGS additives revealed that the β-diversity among all treatments was differentiate greatly, while the T6 showed the lowest value. In T2 (2%) and T5 (8%), the value of β-diversity was high, which could be attributed to FCGS increased the porosity, and led to a suitable condition for bacteria. The similar results were found by Li et al. (2019a), which proved the fact of biochar mixed with PM co-composting could enhance the porosity and abundance of bacteria, also observed that the T1 (0%) treatment could not effectively facilitate the survival of bacteria. Nevertheless, T6 and T1 were not valid to optimum the survival of aerobic bacteria, but it could help improve the condition for anaerobic bacteria. However, according to the unweighted unifrac distance, the difference of species diversity showed that T4 get the best because of large value, but the T5 showed the β-diversity of bacterial was very low. Awasthi et al. (2017b) reported that the biochar amendment could increase the total bacterial abundance and inhibit the bacterial of nitrogen cycling which could be due to the characteristic of biochar, and among all the composting samples were correlated significantly. The abundance of Firmicute, Proteobacteria, Actinobacteria and Tenericutes were significantly influenced by FCGS and the composting rate which were indicated by the phylum-level bacterial diversity and β-diversity. The petals diagram was able to save all kinds of metagenomic data sets, and among all treatments in a document was analyzed with popular metagenomic (Fig. 6b). Each petals in the petal diagram represents a sample, and different colors indicate different treatments. Among all samples, the similar OTUs number were 334, which is the unique OTUs values were 27, 33, 16, 64, 29 and 26 in T1 to T6, respectively. The results showed that the T3 and T6 were lower than control which could be due to the FCGS ratio, however, T4 obtained a huge number OTUs which means the ratio 6% additive provided an optimum condition for the bacteria. The PCA determined each composting samples adopting the correlative number of sequences of accordingly phylum (Fig. 6c), and each treatment distance represented the similar index. The principal component (PC1) illustrated a large value of total bacterial variances
4. Conclusion The FCGS amendment promisingly increased the abundance of bacterial diversity community during PM composting. The correlation analysis of phylum and genus were significantly difference between control and FCGS adopted treatments. The Firmicute, Proteobacteria, Actinobacteria, Bacteroidetes and Tenericutes were the most abundance phyla in the unweighted unifrac distance condition. The petals diagram and β-diversity also showed that T4 obtained the most OTUs number. Therefore, 6% FCGS amendment could be beneficial to bacteria growth and improve the relative abundance of bacteria during the PM 7
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composting. CRediT authorship contribution statement Tao Liu: Formal analysis. Sanjeev Kumar Awasthi: Formal analysis. Yumin Duan: Formal analysis. Zengqiang Zhang: Supervision, Conceptualization, Funding acquisition. Mukesh Kumar Awasthi: Conceptualization, Supervision, Writing - original draft, Funding acquisition, Project administration. 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 The authors are grateful for the financial support from Research Fund for International Young Scientists from National Natural Science Foundation of China (Grant No. 31750110469), China, Shaanxi Introduced Talent Research Funding (A279021901), China and The Introduction of Talent Research Start-up fund (No. Z101021904), College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China. We are also thankful to all our laboratory colleagues and research staff members for their constructive advice and help. References Agyarko-Mintah, E., Cowie, A., Zwieten, L.V., Singh, B.P., Smillie, R., Harden, S., Fornasier, F., 2017. Biochar lowers ammonia emission and improves nitrogen retention in poultry litter composting. Waste Manage. 61, 129–137. Antunes, L.P., Martins, L.F., Pereira, R.V., Thomas, A.M., Barbosa, D., Lemos, L.N., Silva, G.M.M., Moura, L.M.S., Epamino, G.W.C., Digiampietri, L.A., Lombardi, K.C., Ramos, P.L., Quaggio, R.B., De Oliveira, J.C.F., Pascon, R.C., Da Cruz, J.B., Da Silva, A.M., Setubal, J.C., 2016. Microbial community structure and dynamics in thermophilic composting viewed through metagenomics and metatranscriptomics. Sci. Rep. 6, 38915. Awasthi, M.K., Chen, H., Wang, Q., Liu, T., Awasthi, S.K., Wang, Q., Ren, X., Duan, Y., Zhang, Z., 2019. Respond of clay amendment in chicken manure composts to understand the antibiotic resistant bacterial diversity and its correlation with physicochemical parameters. J. Clean. Prod. 236, 117715. Awasthi, M.K., Chen, H., Wang, Q., Liu, T., Duan, Y., Awasthi, S.K., Ren, X., Tu, Z., Li, J., Zhao, J., Zhang, Z., 2018. Succession of bacteria diversity in the poultry manure composted mixed with clay: studies upon its dynamics and associations with physicochemical and gaseous parameters. Bioresour. Technol. 267, 618–625. Awasthi, M.K., Wang, M., Pandey, A., Chen, H., Awasthi, S.K., Wang, Q., Ren, X., Lahori, A.H., Li, D., Li, R., Zhang, Z., 2017a. Heterogeneity of zeolite combined with biochar properties as a function of sewage sludge composting and production of nutrient-rich compost. Waste Manage. 68, 760–773. Awasthi, M.K., Zhang, Z., Wang, Q., Shen, F., Li, R., Li, D., Ren, X., Wang, M., Chen, H., Zhao, J., 2017b. New insight with the effects of biochar amendment on bacterial diversity as indicators of biomarkers support the thermophilic phase during sewage sludge composting. Bioresour. Technol. 238, 589–601. Blanc, M., Marilley, L., Beffa, T., Aragno, M., 1997. Rapid identification of heterotrophic, thermophilic, spore-forming bacteria isolated from hot composts. Int. J. Syst. Bacteriol. 47, 1246–1248. Chen, H., Awasthi, M.K., Liu, T., Zhao, J., Ren, X., Wang, M., Duan, Y., Awasthi, S.K., Zhang, Z., 2018. Influence of clay as additive on greenhouse gases emission and maturity evaluation during chicken manure composting. Bioresour. Technol. 266, 82–88. Chen, W., Liao, X., Wu, Y., Liang, J.B., Mi, J., Huang, J., Zhang, H., Wu, Y., Qiao, Z., Li, X., Wang, Y., 2017. Effects of different types of biochar on methane and ammonia mitigation during layer manure composting. Waste Manage. 61, 506–515. Chen, X., Liu, R., Hao, J., Li, D., Wei, Z., Teng, R., Sun, B., 2019. Protein and carbohydrate drive microbial responses in diverse ways during different animal manures composting. Bioresour. Technol. 271, 482–486. Dees, P.M., Ghiorse, W.C., 2001. Microbial diversity in hot synthetic compost as revealed by PCR-amplified rRNA sequences from cultivated isolates and extracted DNA. FEMS Microbiol. Ecol. 35, 207–216. Duan, Y., Awasthi, S.K., Liu, T., Verma, S., Wang, Q., Chen, H., Ren, X., Zhang, Z., Awasthi, M.K., 2019. Positive impact of biochar alone and combined with bacterial consortium amendment on improvement of bacterial community during cow manure composting. Bioresour. Technol. 280, 79–87. Fu, Y., Li, X., Zheng, S., Du, J., Liang, A., 2015. Classification and identification of
Fig. 6. Beta-diversity analysis; (a) Relationship between heat-map of β-diversity index for all seven treatments with increasing biochar dosage for sewage sludge composting. β-diversity value is the discrepancy coefficient between the two samples. Located the number of reads belong to the same species in different samples into the same table, the profiling table was generated. Here weighted unifra (upper) and unweighted unifra distance were set as the measure index; (b) Rainfall distribution on 10–12 petals figure – each flower petals the picture represents a sample (group), different colors represent different samples (group), in the middle of the core number represents the number of OTUs on all samples, petals. (c) Principal component analysis: PCA based on the normalized OTUs table, and each point represents a sample, plotted by the second principal component on the Y-axis and the first principal component on the X-axis, which was colored by group.
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McAllister, T.A., 2014. Targeted 16S rRNA high-throughput sequencing to characterize microbial communities during composting of livestock mortalities. J. Appl. Microbiol. 116, 1181–1194. TMECC (Test Methods for the Examination of Composts and Composting), 2002. The US Composting Council. US Government Printing Office. http://tmecc.org/tmecc/index. Ventorino, V., Parillo, R., Testa, A., Viscardi, S., Espresso, F., Pepe, O., 2015. Chestnut green waste composting for sustainable forest management: microbiota dynamics and impact on plant disease control. J. Environ. Manage. 166, 168–177. Wang, Y., Liu, Q., Yan, L., Gao, Y., Wang, Y., Wang, W., 2013. A novel lignin degradation bacterial consortium for efficient pulping. Bioresour. Technol. 139, 113–119. Wang, K., Mao, H., Li, X., 2017. Functional characteristics and influence factors of microbial community in sewage sludge composting with inorganic bulking agent. Bioresour. Technol. 249, 527–535. Wang, Q., Awasthi, M.K., Zhao, J., Ren, X., Wang, M., Li, R., Wang, Z., Zhang, Z., 2018. Utilization of medical stone to improve the composition and quality of dissolved organic matter in composted pig manure. J. Clean. Prod. 197, 472–478. Wang, Q., Wang, Z., Awasthi, M.K., Jiang, Y., Li, R., Ren, X., Zhao, J., Shen, F., Wang, M., Zhang, Z., 2016. Evaluation of medical stone amendment for the reduction of nitrogen loss and bioavailability of heavy metals during pig manure composting. Bioresour. Technol. 220, 297–304. Wei, H., Wang, L., Hassan, M., Xie, B., 2018. Succession of the functional microbial communities and the metabolic functions in maize straw composting process. Bioresour. Technol. 256, 334–341. Wu, D., Wei, Z., Zhao, Y., Mohamed, T.A., Zhu, L., Wu, J., Meng, Q., Yao, C., Zhao, R., 2019. Improved lignocellulose degradation efficiency based on Fenton pretreatment during rice straw composting. Bioresour. Technol. 294, 122132. Wu, J., Zhao, Y., Zhao, W., Yang, T., Zhang, X., Xie, X., Cui, H., Wei, Z., 2017. Effect of precursors combined with bacteria communities on the on the formation of humic substances during different materials composting. Bioresour. Technol. 226, 191–199. Xiao, R., Awasthi, M.K., Li, R., Park, J., Pensky, S.M., Wang, Q., Wang, J.J., Zhang, Z., 2017. Recent developments in biochar utilization as an additive in organic solid waste composting: a review. Bioresour. Technol. 246, 203–213. Yin, Y., Gu, J., Wang, X., Song, W., Zhang, K., Zhang, X., Lu, C., Liu, J., 2017. Effects of chromium (III) on enzyme activities and bacterial communities during swine manure composting. Bioresour. Technol. 243, 693–699. Zhao, H., Li, J., Liu, J., Lü, Y., Wang, X., Cui, Z., 2013. Microbial community dynamics during biogas slurry and cow manure compost. J. Integr. Agric. 12, 1087–1097. Zhang, D., Luo, W., Yuan, J., Li, G., Luo, Y., 2017. Effects of woody peat and superphosphate on compost maturity and gaseous emissions during pig manure composting. Waste Manage. 68, 56–63. Zhang, R., Gu, J., Wang, X., Li, Y., Zhang, K., Yin, Y., Zhang, X., 2018. Contributions of the microbial community and environmental variables to antibiotic resistance genes during co-composting with swine manure and cotton stalks. J. Hazard. Mater. 358, 82–191. Zhang, L., Zhang, H., Wang, Z., Chen, G., Wang, L., 2016. Dynamic changes of the dominant functioning microbial community in the compost of a 90–m3 aerobic solid state fermentor revealed by integrated meta-omics. Bioresour. Technol. 203, 1–10. Zhong, X., Ma, S., Wang, S., Wang, T., Sun, Z., Tang, Y., Deng, Y., Kida, K.J., 2018. A comparative study of composting the solid fraction of dairy manure with or without bulking material: performance and microbial community dynamics. Bioresour. Technol. 247, 443–452. Zhu, Y., Johnson, T.A., Su, J., Qiao, M., Guo, G., Stedtfeld, R.D., Hashsham, S.A., Tiedjec, J.M., 2013. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Porc. Natl. Acad. Sci. U.S.A. 110 (9), 3435–3440.
bacteria in the soil treated by AcMNPV using high-throughput sequencing technique. Biotechnol. Bioprocess. Eng. 20, 931–936. Galitskaya, P., Biktasheva, L., Saveliev, A., Grigoryeva, T., Boulygina, E., Selivanovskaya, S., 2017. Fungal and bacterial successions in the process of co-composting of organic wastes as revealed by 454 pyrosequencing. PLoS One 12, 0186051. Gao, X., Tan, W., Zhao, Y., Wu, J., Sun, Q., Qi, H., Xie, X., Wei, Z., 2019. Diversity in the mechanisms of humin formation during composting with different materials. Environ. Sci. Technol. 53, 3653–3662. He, Y., Xie, K., Xu, P., Huang, X., Gu, W., Zhang, F., Tang, S., 2013. Evolution of microbial community diversity and enzymatic activity during composting. Res. Microbiol. 164, 189–198. Insam, H., Franke-Whittle, I., Goberna, M., 2010. Microbes in aerobic and anaerobic waste treatments. In: Insam, H. (Ed.), Microbes at Work. Springer-Verlag, Berlin Heidelberg, pp. 1–34. Janczak, D., Malińska, K., Czekała, W., Cáceres, R., Lewicki, A., Dach, J., 2017. Biochar to reduce ammonia emissions in gaseous and liquid phase during composting of poultry manure with wheat straw. Waste Manage. 66, 36–45. Jiang, Z., Lu, Y., Li, M., Shan, G., Li, Q., 2019. Exploring the characteristics of dissolved organic matter and succession of bacterial community during composting. Bioresour. Technol. 292, 121942. Li, J., Xing, W., Bao, H., Wang, J., Tong, X., Zhang, H., Luo, W., Wu, F., 2019a. Impact of pine leaf biochar amendment on bacterial dynamics and correlation of environmental factors during pig manure composting. Bioresour. Technol. 293, 122031. Li, Z., Zhang, Y., Zhao, H., Chen, H., He, R., 2019b. Structure characteristics and composition of hydration products of coal gasification slag mixed cement and lime. Constr. Build. Mater. 213, 265–274. Li, M., Liu, M., Li, Z., Jiang, C., Wu, M., 2016. Soil N transformation and microbial community structure as affected by adding biochar to a paddy soil of subtropical China. J. Integr. Agric. 15, 209–219. Liu, T., Awasthi, M.K., Awasthi, S.K., Duan, Y., Chen, H., 2019. Effects of clay on nitrogen cycle related functional genes abundance during chicken manure composting. Bioresour. Technol. 291, 121886. Lu, C., Gu, J., Wang, X., Liu, J., Zhang, K., Zhang, X., Zhang, R., 2018. Effects of coal gasification slag on antibiotic resistance genes and the bacterial community during swine manure composting. Bioresour. Technol. 268, 20–27. Ma, S., Fang, C., Sun, X., Han, L., He, X., Huang, G., 2018. Bacterial community succession during pig manure and wheat straw aerobic composting covered with a semipermeable membrane under slight positive pressure. Bioresour. Technol. 259, 221–227. Pankratov, T.A., Ivanova, A.O., Dedysh, S.N., Liesack, W., 2011. Bacterial populations and environmental factors controlling cellulose degradation in an acidic Sphagnum peat. Environ. Microbiol. 13, 1800–1814. Partanen, P., Hultman, J., Paulin, L., Auvinen, P., Romantschuk, M., 2010. Bacterial diversity at different stages of the composting process. BMC Microbiol. 10, 94. Ren, X., Wang, Q., Awasthi, M.K., Zhao, J., Wang, J., Liu, T., Li, R., Zhang, Z., 2019. Improvement of cleaner composting production by adding Diatomite: from the nitrogen conservation and greenhouse gas emission. Bioresour. Technol. 286, 121377. Storey, S., Chualain, D.N., Doyle, O., Clipson, N., Doyle, E.M., 2015. Comparison of bacterial succession in green waste composts amended with inorganic fertiliser and wastewater treatment plant sludge. Bioresour. Technol. 179, 71–77. Su, J., Wei, B., Yang, O., Huang, F., Zhao, Y., Xu, H., Zhu, Y., 2015. Antibiotic resistome and its association with bacterial communities during sewage sludge composting. Environ. Sci. Technol. 49, 7356–7363. Tkachuk, V.L., Krause, D.O., Knox, N.C., Hamm, A.C., Zvomuya, F., Ominski, K.H.,
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Effect of fine coal gasification slag on improvement of bacterial diversity community during the pig manure composting Tao Liu1, Sanjeev Kumar Awasthi1, Yumin Duan, Zengqiang Zhang, Mukesh Kumar Awasthi
T
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College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: Bacterial abundance Fine coal gasification slag Pig manure Composting
In present study, evaluate the effect of fine coal gasification slag (FCGS) as additive on abundance of bacterial diversity during pig manure composting. The six different dosages of FCGS 0% (T1), 2% (T2), 4% (T3), 6% (T4), 8% (T5) and 10% (T6) (dry weight basis) were mixed with original raw materials for 42 days an aerobic composting. The results indicated that FCGS adopted could affect the succession of bacterial diversity in different ways. Among all treatments, Firmicutes, Proteobacteria, Tenericutes, unidentified_Bacteria, and Actinobacteria were the highest abundance in weighted unifrac distance but Firmicutes; Proteobacteria, Actinobacteria, Bacteroidetes, and Spirochaetes were main bacteria in unweighted unifrac distance. The β-diversity and principal component analysis indicated a significant difference in bacterial diversity in all treatments which T4 obtained difference obviously. Therefore, the results showed that T4 was a potential candidate to enhance significantly abundance of bacterial community in PM compost.
1. Introduction With the improvement of living standards and quality, increased the demand for meat products, caused the livestock industry becomes more prosperous. Meanwhile, a large of livestock manure was generated, which is a major source of environmental pollution (i.e. soil, water and air) in world (Ren et al., 2019). Nevertheless, conventional process
represented the unsuitable disposal which could lead to the environmental risks, it’s also a waste of sources because high nutrients, phosphorus and organic matter (OM) in pig manure (PM) (Wang et al., 2018; Agyarko-Mintah et al., 2017). According to Zhu et al. (2013), approximately 618 billion kg of PM was produced in China annually. How to deal with PM in an environmentally friendly for the Chinese government has turn into a serious problem. Hence, it is important to
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Corresponding author. E-mail address: [email protected] (M.K. Awasthi). 1 Equally contributing authors. https://doi.org/10.1016/j.biortech.2020.123024 Received 26 December 2019; Received in revised form 11 February 2020; Accepted 12 February 2020 Available online 13 February 2020 0960-8524/ © 2020 Elsevier Ltd. All rights reserved.
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Table 1 Characteristics of composting materials used in this study (dry weigh basis). Parameters
Pig manure
Wheat straw
Fine coal gasification slag
Moisture content (%) pH EC (mS/cm) Total organic carbon (%) Total Kjeldahl nitrogen (%) Carbon: nitrogen ratio Specific surface area (m2/g) Total pore volume (cm3/g)
79.62 ± 0.01 8.39 ± 0.09 4.68 ± 0.13 41.08 ± 1.19 2.39 ± 0.08 17.19 ± 0.25 ND ND
5.00 ± 0.12 6.98 ± 0.08 1.17 ± 0.11 59.85 ± 2.39 0.99 ± 0.01 60.45 ± 0.98 ND ND
2.18 ± 0.24 8.80 ± 0.14 0.86 ± 0.02 35.63 ± 1.82 0.14 ± 0.01 254.5 ± 5.63 245.95 ± 17.24 0.20 ± 0.001
EC – Electrical conductivity. Results are the mean of three replicates ± standard deviation. ND – Not detected.
of bacterial community variation during PM composting applied with FCGS. This study was mainly focused on the application of different ratio of FCGS to explore the effect on bacterial community during PM composting. Subsequently, the objective was to identify the relative abundance of bacterial communities in PM compost.
discover an environmentally and economically method to recycle PM efficiently. Composting is considered as an ecofriendly method and process to successful conversion manure into stable humic substance and reduction waste of agriculture resources (Gao et al., 2019; Wu et al., 2017). Through a range of active microbial community execute a series complex biological process which is called composting, microoganisms (Bacteria, Actinomystes and Fungi) and environmental characteristics play a key role in rapid degradation of OM (Awasthi et al., 2018; Wu et al., 2019). Due to the strong adaptability to the unfavorable surrounding condition and metabolic, the bacteria show more proactive (Fu et al., 2015; Chen et al., 2019). To explore the knowledge gap of bacteria, so that completely understand the mechanism of composting progress and make it more efficient, safe and clean. In recently, it is an impressive molecular technology that the Illumina MiSeq high throughput sequencing could reveal the detailed interior bacterial dynamics accurately (Awasthi et al., 2017b; Zhang et al., 2018). However, unimproved composting would bring about the negative effects on environmental problem such as odors and nitrogen loss, which could be due to the single substance condition create an adverse environment for the bacteria survival (Galitskaya et al., 2017; Janczak et al., 2017). In order to promote the progress of composting, facilitated the OM degradation and provide a favorable condition for bacteria, many researches have been determined to study the advanced parts of additives (Zhang et al., 2017;, Wang et al., 2016; Liu et al., 2019). Mixing PM with wheat straw and biochar could increase the number of bacteria by 161% (Li et al., 2016). Li et al. (2019a) reported that the bacterial diversity was greatly higher in pine leaf biochar amendment, and observed that 10% treatment got the highest correlation. The environmental factors of temperature and pH, or the diversity of bacterial community might be correlated to the different dosage of additives cocomposting process (Tkachuk et al., 2014; Chen et al., 2017). Fine coal gasification slag (FCGS) comes from a typical entrained-flow gasification which is an inevitable by-product, the characteristic of FCGS is high carbon content and large amount, being comprised with irregular porous particle (Li et al., 2019b). Meanwhile, the amount of FCGS produced is very large, yet the properties of FCGS could provide the enhancement of cation exchange capacity and decrease the density of composting. There are similar characteristics between FCGS and biochar which possess the high porosity and large specific surface area. (Lu et al., 2018; Xiao et al., 2017). Lu et al. (2018) reported that the effects of coal gasification slag (CGS) on antibiotic resistant genes during swine manure composting. There are none of research with the effect of different ratio FCGS amendment on bacterial abundance diversity and relation of environmental parameters in the thermophilic stage of composting. The impacts of the employing of FCGS during PM composting on bacterial diversity have not been reported. Nevertheless, based on widespread literature read and conclusion which there is limited information the influences of FCGS amendment in PM composting on the bacterial diversity have not been reported adopting 16S rRNA sequencing technology. Therefore, there was limited study available about abundance
2. Materials and method 2.1. Collection and processing of composting raw materials The experiment conducted in the drought shed in the composting areas of Northwest Agriculture and Forestry University. A local livestock farm provided the PM in this study, and the wheat straw (WS) was collected by the neighboring farms (Yangling, Shaanxi, China). FCGS was obtained from Huide Company (Baoji, Shaanxi, China). Selected properties of composting raw materials were reveled in Table 1. 2.2. Composting setup and sample collection The 42-days composting experiment was carried out in 100 L steel rectors and its systematic diagram were illustrated in our previous study (Awasthi et al., 2017a; Chen et al., 2018). To achieve the suitable moisture content (~65%) and C/N ratio (~25), PM and WS were mixed 2:1 ratio (dry weight basis). Meanwhile, 1 kg of plastic spheres was blended with initial raw materials to adjust the initial density to 0.5 kg/ L. The six different ratio of FCGS (0%, 2%, 4%, 6%, 8% and 10% on the dry weight of the mixed material) were employed with the mixture initial feed stocks namely T1, T2, T3, T4, T5 and T6. The compost temperature was monitored twice daily every 12 h in the middle of containers by the using of thermos probes, and the average temperature was also recorded. The composting mass of each treatments were mixed on day 1, 3, 7, 14, 21, 28, 35 and 42, and then collected the samples. Separated the fresh samples into two parts, one part was for the moisture, pH and electrical conductivity (EC) determined; after air dried and smash the samples to tested the total Kjeldahl nitrogen (TKN) and OM (TMECC, 2002), while the other was preserved at −20 °C to analyze the microbial. 2.3. DNA extraction and 16S rRNA amplification The 16sDNA amplification and sequencing evaluated the bacterial abundance communities’ ingredient in the process of fresh PM composting. To extract the DNA, 0.1 g of each sample was employed by a Fast DNA SPIN Kit for Soil (Omega Biotek, Inc.) according to the company’s instructions. The nano-spectrophotometer and 1% (W/V) agarose gels electrophoresis determined the DNA and its concentration. Using the primers 515F (5′-GGACTACHVGGGTWTCTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAA-3′) amplified the 16S V4 region. Adopting Qiagen Gel Extraction Kit and PCR Clean-up process (Qiagen, Germany) refined the PCR products. According to PCR Clean-up process, 2% agarose gel electrophoresis and purified detected the PCR 2
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product. Nevertheless, the concentrations of the PCR generates were fluorometrically quantified by the Qubit 2.0 dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) before sequenced on the Miseq 25,000 platform (Illumina, San Diego, CA, USA), at Novogene, Beijing, China (Awasthi et al., 2017b). 2.4. Statistical analysis All the data was conducted in triplicate and the data was performed IBM SPSS-v.21 and Microsoft Excel 2010 at mean ± standard deviation. In order to calculate the results for relation of bacterial communities and environmental properties using the Pearson correlation coefficients. The principal component analysis (PCA) was determined by the R language v3.0 prcomp function. The Dice was conducted to analyze the resemblance matrix, and the Un-weighted Pair-group method with arithmetic means (UPGMA) was carried out fusion strategy for detailing the dendrograms. 3. Results and discussions 3.1. Impact of FCGS on composting of bacterial communities Compared with the other technologies, 16S rRNA is a critical method to provide a more accurate and high efficiency DNA amplification under the circumstance of microorganism community and diversity condition, so that become an excellent channel to obtain the richness information about microorganism diversity (Awasthi et al., 2017b; Zhao et al., 2013). As presented in Fig. 1a, total Tags of 60749, 67837, 67287, 60891, 62033, and 63381 and operational taxonomic units (OTUs) of 687, 774, 667, 846, 774, and 829 as well as Taxon Tags 58540, 66366, 66013, 58866, 60395, and 61510 were attained from T1 to T6 in all treatments, respectively. In experiment, the total Tags, OTUs and Taxon Tags were 63696, 763 and 61948, and T2 got the maximum in Tags abundance and the T1 obtained the minimum in abundant of Tags. While the sequences of Unique Tags were 2209, 1471, 1274, 2025, 1638 and 1871 in T1, T2, T3, T4, T5 and T6, respectively, Unclassified Tags were not detect among all treatments. Meanwhile, the classification of microorganism species adopted OTUs, the abundance diversity of bacterial community were reflected by OTUs (Jiang et al., 2019). The OTUs got the maximum abundant in T4, and the control 4% FCGS added treatments was the minimum abundance. According to the Wang et al. (2017), from the mesophilic phase to cooling phase during the composting, the number of OTUs gradually decreased. The range of OTUs in this study was 667–846 which was higher than 270–572 during co-composting for cow manure with biochar and bacterial consortium (Duan et al., 2019), and also higher than188-304 during the pine leaf biochar amendment PM composting (Li et al., 2019a), and higher than 177–546 during the cow manure with corn cobs composting (Zhang et al., 2016), meanwhile, Partanen et al. (2010) reported the total of 522 OTUs during the organic municipal waste compost with wood chip. However, in this study which OTUs number was less than 1552–2269 during the sewage sludge and biochar co-composting (Awasthi et al., 2017b). The increased additive proportion and classified the phase of each treatment procedure, the abundance of bacterial diversity community structure changed greatly. The bacterial relative abundance (RAs) of the phyla status among all treatments was revealed in Figs. 1b and 2. In all treatments, Firmicutes, Proteobacteria and Tenericutes were showed the most remarkable three phyla (Figs. 1b and 2a). Pankratov et al. (2011) reported that Firmicutes had a richness phylum abundance in various of organic substrates composting, which played a great role in degradation of lignocellulose. Meanwhile, many previous findings confirmed that the Firmicutes still belong the highest abundance bacterial in manure composting (Li et al., 2019a; Duan et al., 2019; Lu et al., 2018) but Li et al. (2019a) and Awasthi et al. (2017b) found that Proteobacteria was relatively greater abundant present in dairy manure and sewage sludge composting. The
Fig. 1. Statistic analysis and phylum relative abundance layout in cow manure compost: (a) Tags and OTUs number statistics of different samples. All OTUs with an average relative read abundance > 1% and total tags number (red) refers to the filtered the splicing sequence number. Taxon Tags (blue) refers to the number of Tags for building OTUs and access to classified information. Unclassified Tags (green tea) for building number of Tags OTUs but without access to classified information; Unique Tags (orange) refers to the frequency is 1, and can’t be clustering to the number of Tags OTUs; All the above corresponding with the vertical axis on the left, meanwhile the right vertical axis is the Number of OTUs (purple) refers to the Number of OTUs finally got. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.); (b) The top ten species in phylum level were selected, and the distribution histogram of relative abundance of species, ‘‘Others” represents a total relative abundance of the rest phylum besides the top 10 phylum. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
study observed the similar result that Firmicutes was the highest abundance in all bacterial, and clearly showed that T3 treatment (4%) got the highest relative abundance, T2 was the lowest abundance. The results indicated that the bacterial community abundance of cellulosedegradation may be effected by the FCGS amendment. In T4, the Proteobacteria showed the higher abundance than other treatments. Fig. 2b showed the T4 got the lowest abundance of Firmicutes but the highest abundance of Proteobacteria and Actinobacteria were also observed. Li et al. (2019a) also observed similar result, where biochar amendment PM composting could decrease the abundance of Firmicutes. The nitrogen cycling related the abundance of Proteobacteria community (Zhong et al., 2018; Dees and Ghiorse, 2001). The loss of nitrogen was due to the ammonification during the thermophilic phase of composting. Only T3 treatment (4%) got the lowest abundance of Proteobacteria in all treatments, which indicated that the other FCGS additional treatments would facilitate the microorganism transformation the organic nitrogen. The abundance of Tenericutes was higher in additive adopting of FCGS, revealed that FCGS could enhance the abundance and promote the cellulose degradation. This result was similar the previous reported by Duan et al. (2019), who added the biochar and bacterial consortium in cow manure composting. 3
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Fig. 2. Basic information of the microbial communities in different treatments: (a) the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA gene sequences. Bacterial phyla level (top 10) and clustering tree based on the Weighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance; (b) the relative abundance of the dominant bacterial taxonomic groups separated using total 16S rDNA gene sequences. Bacterial phyla level (top 10) and clustering tree based on the Unweighted Unifrac distance assessed based on their relative abundance across all samples and dominant groups were chosen for each having grater relative abundance.
The major prominent of five phyla were Firmicutes, Proteobacteria, Actinobacterial, Bacteroidetes and Spirochaetes that occupied for dominant situation during the composting (Fig. 2b). Ma et al. (2018) and Zhang et al. (2018) observed that the most excellent five phyla were Firmicutes, Proteobacteria, Bacteroidetes, Actinobacterial, and Tenericutes in the PM and WS aerobic composting and the compost of a 90-m3 aerobic solid state fermentor, respectively. Meanwhile, Duan et al. (2019) also found the similar results during the cow manure composting. The Firmicutes, Actinobacterial, Proteobacteria, and Bacteroidetes were the four most prominent phyla (Li et al., 2019a), and the same dominant taxonomic phyla were also observed in other wastes composting (Antunes et al., 2016; Wei et al., 2018). The decomposition of cellulose and lignin also relied on the Actinobacterial (Su et al., 2015). It was a possibility influence on OM degradation that the rich abundance of Bacteroidetes due to the degradation cellulose to short chain fatty acids (Zhong et al., 2018). As shown in Fig. 3a, heat map revealed the relative abundance of the top 35 bacterial genera during composting progress. The highest abundance in T1 was including the Thermobifida, Blautia, unidentifiedLachnospiraceae, Roseburia, Catenibacterium and unidentified_Enterobacteriaceae belong to the Firmicutes, Actinobacteria and Proteobacteria. Thermobacillus, Halocella and unidentified_Clostridiales
belong to Firmicutes and unidentified-Bacteria in T2. Caldicoprobacter, Bacillus, unidentified_Clostridiales, Streptococcus, Haloplasma and Limnochorda belong to Firmicutes and Tenericutes in T3. Caldalkalibacillus, Planifilum, Trichococcus and Gemmobacter belong to Firmicutes and Proteobacteria in T4. Pseudomonas, Symbiobacterium, Hydrogenispora, Defluvitalea, Vulgatibacter and Chelativorans belong to Firmicutes and Proteobacteria in T5. Unidentified_Corynebacteriaceae, Corynebacterium, Ureibacillus, Trueperella, Fastidiosipila, Lactobacillus, Candidatus Bacilloplasma and Lactococcus belong to Actinobacteria, Firmicutes and Tenericutes in T6. Therefore, Thermobacillus, unidentified_Corynebacteriaceae, Bacillus, Caldicoprobacter, Thermobifida, unidentified-Lachnospiraceae, Roseburia and Catenibacterium dominated genes during the PM composting. This study revealed that these genera could better suit for the composting. Storey et al. (2015) reported that the predominant abundance genera Streptomyces and Sphaerobacter in the composting. In the PM composting, Ma et al. (2018) observed the Pusillimonas, Ignatzschineria, Pseudomonas, Moheibacter, Prevotella, Bacilli, Clostridia and Negativicute were the main genera. Due to the fact that the FCGS amendment could improve the condition of composting through its unique characteristic and promote the degradation of OM, the adopted of FCGS treatments has the higher abundance bacterial diversity community.
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Fig. 3. The relative abundance of each class based on 16S rDNA sequence analysis: (a) species abundance clustering heat maps selected the top 35 genera of abundance and cluster from the species and sample levels based on their abundance information in each sample. Different color means the different relative abundance of the genus in the all six treatments (red means great abundance); (b) the relative abundance is expressed in percentage and classification tree of complex samples. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.2. Impact of FCGS on composting of interactive metagenomic visualization in a phylogenetic tree
As shown in Fig. 3b, percentage and classification tree indicated the relative abundance, and the average relative abundance Firmicutes, Proteobacteria, Tenericutes and unidentified_Bacteria accounted for 89.67%, 5.63%, 1.74% and 2.96% of bacteria, respectively, which these identified bacteria played an important role in cycling of nitrogen. Among all treatments, the NO3−-N concentration of similar trends, which almost absent in the initial phase and then increased after end of compost. The NO3−-N was generated by the microbial activity and nitrification. At the end of composting, the NH4+-N content decreased which this results was the ammonification, immobilizattion by nitrogen-fixing microbial and the generation of NO3−-N. In the swine manure and cotton stalks co-composting, Zhang et al. (2018) founded the ammonia nitrogen was influenced by the primary bacterial of Proteobacteria (41.7%), Firmicutes (20.8%) and Actinobacteria (16.7%). The result was similar the previous reported by Awasthi et al. (2017b) and Li et al. (2019a), which Firmicutes accounted the largest ratio 54.41% and 64.33%, respectively. Duan et al. (2019) reported that Bacteroidetes accounted the largest proportion (33.85%). The Bacillus, Ureibacillus, and Thermobacillus belong to Bacilli, while Clostridiales, and unidentified_Closttridiales belong to Clostridia occupied 60.51%, 7.33%, 11.98% and 6.82%, 3.02%, respectively, which is confirmed to Firmicutes were dominantly present in each treatments. The Pseudomonas belong to the Proteobacteria, which hold all percentage. On the other hand, a lot unidentified_Bacteria of Halocella and Thermobacillus were showed in T2. Most abundance of Bacillus and Tepidimicrobium belong to Firmicutes observed in T3. Thermobacillus existed in T4, which the dominate genes was Proteobacteria. Richness of Defluviitalea, Symbiobacterium and unidentified_Clostridia belong to Clostridia were presented in T5, which the major genes were Firmicutes. The most abundance of Tenericutes and Firmicutes of Bacillus occupied in T6. Bacillus and Pseudomonas mainly existed in T1. Therefore, the FCGS additive could promote the increase of bacterial diversity.
The phylogenetic tree was established based on the sequence of the first 100 genera (Fig. 4). The most sequence contained Firmicutes, Proteobacteria, unidentified_Bacteria, Tenericutes and Actinobacteria. Li et al. (2019a) and Yin et al. (2017) reported which the Actinobacteria, Proteobacteria Firmicutes, and Bacteroidetes were the main bacterial community abundance in PM composting. The results are different which could be due to the different additives lead to the gap of composting environment. The bacterial of Bacillus, Thermobacillus and Pseudomonas were higher than other bacteria observed in T1. The abundance of Bacillus was significant greater among the all treatments, but the T3 showed the richest, which governed the bacterial community. Bacillus played a critical role in degradation of OM, and the cellulase and dehydrogenase got the highest activities in the thermophilic phase, which may be due to the Bacillus associated (Blanc et al., 1997). The similar reported by the He et al. (2013), who found the microbial community was degraded by the Bacillus during the thermophilic stage. The abundance of Thermobacillus occupied for the largest ratio to T2 than other treatments but absent in T3. The bacterial of Bacilus, Thermobacillus, Halocella, Tepidimicrobium and Pseudomonas could be clearly found in T4. The bacterial of Pseudomonas could be found in all treatments, and T5 obtained the highest abundance, which the Pseudomonas could promote the degradation of WS lignocellulose (Wang et al., 2013; Ventorino et al., 2015). The abundance of Bacillus, Ureibacillus, Thermobacillus, Candidatus_Bacilloplasma, Tepidimicrobium and Pseudomona dominated in T6, which more bacteria explored than other treatments. In order to reveal the correlate abundance of bacterial diversity community classification which were conducted by a new visualization tool phylogenetic tree. Here we concluded that phylogenetic tree is a new visualization instrument which can directly explore the correlative richness of bacterial diversity community. As we know, a bioinformatics instrument based on numbers of compost samples was utilized by phylogenetic tree, which determines the strong role of emerging 5
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Fig. 4. The phylogenetic tree constructed by the representative sequence of the horizontal species; the color of the branch and the fan shape indicates its corresponding gate, circles from inside to outside stand for system composition tree, genes layout on different classification levels (represent by different color, and the area of sector means respective proportion), the stacked column chart outside the fan ring indicates the abundance distribution information of the genus in different treatments.
Candidatus_Bacilloplasma belong to Mollicutes, which occupied 33%, 7%, 8%, 5% and 5% in T6, respectively. The above results showed that higher percentage of FCGS have significantly increased the abundance of bacterial with the exploration classified bacterial ratio. The krona diagram showed that the abundance of Bacillus (21%–61%) is greatly richer than other bacteria, thus, it’s directly indicated that the number of aerobic bacteria community is greater than other bacteria. Awasthi et al. (2019) also found that the relative abundance of Bacillus (28%) is higher than others. In the phyla of Firmicute, with the dosage of FCGS amendment decreased the proportion of Unclassified bacteria. Therefore, try our best, the firstly tool build entirely on the number of samples is adopted by Krona, and it demonstrates the power of generating web methods to broadly research the correlation abundance and easily accessible visualization tools to identify bacterial community.
biology diversity network technology. It offers conditions to explore bacterial diversity that relatively rich and easily accessible visualization method. 3.3. Impact of FCGS on composting of interactive taxonomy web visualization by krona Among all samples, to identify the taxonomic hierarchy conducted the krona taxonomy visualization (Fig. 5). Krona clearly defined the taxon and species, which boundary from inside to outside ascending for diversity levels, the separate ratios of phyla, classes, orders, genera and species were revealed in the square of the two-dimensional figure (Awasthi et al., 2019). Relative abundance of the bacterial community adopting krona diagram to determine the effects of the biochar added into sewage sludge (Awasthi et al., 2017b). In T1, the Firmicutes dominated that contained Bacillus, Thermobacillus, unclassified, Pseudomonas and Unclassified of Proteobacteria occupied of 46%, 8%, 8%, 4% and 4%, respectively. Most apart of Bacillus, Ureibecillus, Thermobacillus, Unclassified, Unidentified_Clostridia belong to Firmicutes in T2, which hold on 21%, 6%, 12%, 5% and 4%. The Firmicutes of Bacillus (61%) and Proteobacteria of Pseudomonas (4%) governed in T3. The Bacillus, Thermobacillus, Ureibecillus, Unclassified were contained by Firmicutes, which accounted 33%, 9%, 4% and 5% in T4. The high abundance of bacterial was Firmicutes and Proteobacteria which included Bacillus (24%), Thermobacillus (6%), Ureibecillus (4%), Unclassified (7%) and Pseudomonas (6%), and Unclassified (4%) in T5. The Bacillus, Thermobacillus, Ureibecillus, and Unclassified belong to the Firmicutes and
3.4. Statistical analysis of bacteria community The β-diversity, petals diagram and PCA were usually carried out to define the wide tendency of differences and similarities in all treatments though a cluster analysis (Fig. 6). The correlation coefficient was conducted for all treatments compost and confirmed that the FCGS concentration related with the primary parameter of bacterial diversity directly (Fig. 6a). The value between treatments is small which means the gap of bacterial diversity is small, so that measure the coefficient of difference between pairwise samples usually adopted weighted unifrac distance and unweighted unifrac distance, which phylogenetic methods are employed largely in 16sDNA. Based on the quantitative data 6
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Fig. 5. Krona is taxonomy web visualization; circles from inside to outside stand for different classification levels, and the area of sector means respective proportion of different operational taxonomic unit annotation results.
44.53% between the six treatments. Lu et al. (2018) also reported that CGS amendment directly changed the composting progress and the statistically impacted the bacterial abundance diversity. The findings reveled that sequences in T2 and T5, T1, T3 and T6 existed in a restrict cluster, but T4 treatments in a broader boundary. It is carried out that the bacterial abundance of T4 is majorly influenced by the FCGS amendment. As well-known, composting progress controls the abundance of bacteria diversity, which is significantly attributed to some important environmental elements (i.e. pH, moisture), composting raw materials characteristics or nutrients (Insam et al., 2010). Awasthi et al. (2017b) also found that the temperature, pH, EC, total organic matter, C/N ratio and the humic and fulvic acids played a critical role in the maximum ingredient turnover for Proteobacteria, Firmicutes and Chloroflexi. In present study, the explanation of most possibly was FCGS can offer an optimal microenvironment for PM composting. In future work, it is necessary that the FCGS amendment PM composting investigates the bacterial diversity models and their correlation with basic parameters and stages. Generally, based on the obvious different between FCGS employing with the control and FCGS can improve the bacterial structure and enhance the progress of biodegradation of organic matter, especially for the decomposition of lignin and cellulose, because of the regulated important enzyme activity, which needs to be explored for fact effectiveness.
analysis weighted unifrac distance with the increasing of FCGS additives revealed that the β-diversity among all treatments was differentiate greatly, while the T6 showed the lowest value. In T2 (2%) and T5 (8%), the value of β-diversity was high, which could be attributed to FCGS increased the porosity, and led to a suitable condition for bacteria. The similar results were found by Li et al. (2019a), which proved the fact of biochar mixed with PM co-composting could enhance the porosity and abundance of bacteria, also observed that the T1 (0%) treatment could not effectively facilitate the survival of bacteria. Nevertheless, T6 and T1 were not valid to optimum the survival of aerobic bacteria, but it could help improve the condition for anaerobic bacteria. However, according to the unweighted unifrac distance, the difference of species diversity showed that T4 get the best because of large value, but the T5 showed the β-diversity of bacterial was very low. Awasthi et al. (2017b) reported that the biochar amendment could increase the total bacterial abundance and inhibit the bacterial of nitrogen cycling which could be due to the characteristic of biochar, and among all the composting samples were correlated significantly. The abundance of Firmicute, Proteobacteria, Actinobacteria and Tenericutes were significantly influenced by FCGS and the composting rate which were indicated by the phylum-level bacterial diversity and β-diversity. The petals diagram was able to save all kinds of metagenomic data sets, and among all treatments in a document was analyzed with popular metagenomic (Fig. 6b). Each petals in the petal diagram represents a sample, and different colors indicate different treatments. Among all samples, the similar OTUs number were 334, which is the unique OTUs values were 27, 33, 16, 64, 29 and 26 in T1 to T6, respectively. The results showed that the T3 and T6 were lower than control which could be due to the FCGS ratio, however, T4 obtained a huge number OTUs which means the ratio 6% additive provided an optimum condition for the bacteria. The PCA determined each composting samples adopting the correlative number of sequences of accordingly phylum (Fig. 6c), and each treatment distance represented the similar index. The principal component (PC1) illustrated a large value of total bacterial variances
4. Conclusion The FCGS amendment promisingly increased the abundance of bacterial diversity community during PM composting. The correlation analysis of phylum and genus were significantly difference between control and FCGS adopted treatments. The Firmicute, Proteobacteria, Actinobacteria, Bacteroidetes and Tenericutes were the most abundance phyla in the unweighted unifrac distance condition. The petals diagram and β-diversity also showed that T4 obtained the most OTUs number. Therefore, 6% FCGS amendment could be beneficial to bacteria growth and improve the relative abundance of bacteria during the PM 7
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composting. CRediT authorship contribution statement Tao Liu: Formal analysis. Sanjeev Kumar Awasthi: Formal analysis. Yumin Duan: Formal analysis. Zengqiang Zhang: Supervision, Conceptualization, Funding acquisition. Mukesh Kumar Awasthi: Conceptualization, Supervision, Writing - original draft, Funding acquisition, Project administration. 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 The authors are grateful for the financial support from Research Fund for International Young Scientists from National Natural Science Foundation of China (Grant No. 31750110469), China, Shaanxi Introduced Talent Research Funding (A279021901), China and The Introduction of Talent Research Start-up fund (No. Z101021904), College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China. We are also thankful to all our laboratory colleagues and research staff members for their constructive advice and help. References Agyarko-Mintah, E., Cowie, A., Zwieten, L.V., Singh, B.P., Smillie, R., Harden, S., Fornasier, F., 2017. Biochar lowers ammonia emission and improves nitrogen retention in poultry litter composting. Waste Manage. 61, 129–137. Antunes, L.P., Martins, L.F., Pereira, R.V., Thomas, A.M., Barbosa, D., Lemos, L.N., Silva, G.M.M., Moura, L.M.S., Epamino, G.W.C., Digiampietri, L.A., Lombardi, K.C., Ramos, P.L., Quaggio, R.B., De Oliveira, J.C.F., Pascon, R.C., Da Cruz, J.B., Da Silva, A.M., Setubal, J.C., 2016. Microbial community structure and dynamics in thermophilic composting viewed through metagenomics and metatranscriptomics. Sci. Rep. 6, 38915. Awasthi, M.K., Chen, H., Wang, Q., Liu, T., Awasthi, S.K., Wang, Q., Ren, X., Duan, Y., Zhang, Z., 2019. Respond of clay amendment in chicken manure composts to understand the antibiotic resistant bacterial diversity and its correlation with physicochemical parameters. J. Clean. Prod. 236, 117715. Awasthi, M.K., Chen, H., Wang, Q., Liu, T., Duan, Y., Awasthi, S.K., Ren, X., Tu, Z., Li, J., Zhao, J., Zhang, Z., 2018. Succession of bacteria diversity in the poultry manure composted mixed with clay: studies upon its dynamics and associations with physicochemical and gaseous parameters. Bioresour. Technol. 267, 618–625. Awasthi, M.K., Wang, M., Pandey, A., Chen, H., Awasthi, S.K., Wang, Q., Ren, X., Lahori, A.H., Li, D., Li, R., Zhang, Z., 2017a. Heterogeneity of zeolite combined with biochar properties as a function of sewage sludge composting and production of nutrient-rich compost. Waste Manage. 68, 760–773. Awasthi, M.K., Zhang, Z., Wang, Q., Shen, F., Li, R., Li, D., Ren, X., Wang, M., Chen, H., Zhao, J., 2017b. New insight with the effects of biochar amendment on bacterial diversity as indicators of biomarkers support the thermophilic phase during sewage sludge composting. Bioresour. Technol. 238, 589–601. Blanc, M., Marilley, L., Beffa, T., Aragno, M., 1997. Rapid identification of heterotrophic, thermophilic, spore-forming bacteria isolated from hot composts. Int. J. Syst. Bacteriol. 47, 1246–1248. Chen, H., Awasthi, M.K., Liu, T., Zhao, J., Ren, X., Wang, M., Duan, Y., Awasthi, S.K., Zhang, Z., 2018. Influence of clay as additive on greenhouse gases emission and maturity evaluation during chicken manure composting. Bioresour. Technol. 266, 82–88. Chen, W., Liao, X., Wu, Y., Liang, J.B., Mi, J., Huang, J., Zhang, H., Wu, Y., Qiao, Z., Li, X., Wang, Y., 2017. Effects of different types of biochar on methane and ammonia mitigation during layer manure composting. Waste Manage. 61, 506–515. Chen, X., Liu, R., Hao, J., Li, D., Wei, Z., Teng, R., Sun, B., 2019. Protein and carbohydrate drive microbial responses in diverse ways during different animal manures composting. Bioresour. Technol. 271, 482–486. Dees, P.M., Ghiorse, W.C., 2001. Microbial diversity in hot synthetic compost as revealed by PCR-amplified rRNA sequences from cultivated isolates and extracted DNA. FEMS Microbiol. Ecol. 35, 207–216. Duan, Y., Awasthi, S.K., Liu, T., Verma, S., Wang, Q., Chen, H., Ren, X., Zhang, Z., Awasthi, M.K., 2019. Positive impact of biochar alone and combined with bacterial consortium amendment on improvement of bacterial community during cow manure composting. Bioresour. Technol. 280, 79–87. Fu, Y., Li, X., Zheng, S., Du, J., Liang, A., 2015. Classification and identification of
Fig. 6. Beta-diversity analysis; (a) Relationship between heat-map of β-diversity index for all seven treatments with increasing biochar dosage for sewage sludge composting. β-diversity value is the discrepancy coefficient between the two samples. Located the number of reads belong to the same species in different samples into the same table, the profiling table was generated. Here weighted unifra (upper) and unweighted unifra distance were set as the measure index; (b) Rainfall distribution on 10–12 petals figure – each flower petals the picture represents a sample (group), different colors represent different samples (group), in the middle of the core number represents the number of OTUs on all samples, petals. (c) Principal component analysis: PCA based on the normalized OTUs table, and each point represents a sample, plotted by the second principal component on the Y-axis and the first principal component on the X-axis, which was colored by group.
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