Next Article in Journal
Examining Relations Between Public Participation and Public Expenditure: Opinions from English and French Users on Environmental Issues in the English Channel
Previous Article in Journal
Northern Warning Lights: Ambiguities of Environmental Security in Finland and Sweden
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 11
Issue 8
10.3390/su11082229
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Physico-Chemical Parameters on Actinomycetes Communities during Composting of Agricultural Waste

by
Yuanping Li
1,†,
Yanrong Chen
2,3,†,
Yaoning Chen
2,3,*,
Yanxin Wu
2,3,
Chun Zhang
1,
Zhen Peng
2,3,
Yihuan Liu
2,3,
Sha Wang
2,3,
Ran Xu
2,3 and
Ziping Zeng
2,3
1
College of Municipal and Mapping Engineering, Hunan City University, Yiyang 413000, China
2
College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
3
Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this article.
Sustainability 2019, 11(8), 2229; https://doi.org/10.3390/su11082229
Submission received: 21 February 2019 / Revised: 13 March 2019 / Accepted: 9 April 2019 / Published: 13 April 2019
Browse Figures
Versions Notes

Abstract

:
The objective of this study was to investigate the influence of physico-chemical parameters on Actinomycetes communities and to prioritize those parameters that contributed to Actinomycetes community composition during the composting of agricultural waste. Denaturing gradient gel electrophoresis of polymerase chain reaction (PCR-DGGE) and redundancy analysis (RDA) were used to determine the relationships between those parameters and Actinomycetes community composition. Quantitative PCR (qPCR) and regression analysis were used to monitor the 16S rDNA copy numbers of Actinomycetes and to analyse the correlations between physico-chemical parameters and Actinomyces 16S rDNA gene abundance, respectively. The RDA results showed that moisture content, water soluble carbon (WSC) and pH (p < 0.05) made the main contributions to the temporal variations of Actinomycetes community composition. The output of the regression analysis indicated that moisture content (R2 = 0.407, p < 0.01) showed a negative linear relationship with the Actinomyces 16S rDNA gene abundance.

1. Introduction

The biodegradation of organic waste is one of the important steps of the carbon cycle in nature. As one of the biodegradation technologies, composting is often used to transform agricultural organic waste into stable usable resources under controlled conditions. It is an aerobic and spontaneous heating process of recycling organic waste which is controlled by microbial communities. Composting has the advantages of being low cost, environmentally friendly and it produces no further pollution [1,2]. As an important basis of agriculture waste, lignocellulose is one of the important sources of renewable carbon on Earth [3]. It is also known as a persistent organic compound, because of its complex cross-linked structure and stable composition which forms a matrix accompanying cellulose. The presence of the matrix strongly impedes the microbial depolymerization of cellulose and hemicellulose, so the degradation of lignin has become a difficult process when lignocellulose degrades in agricultural waste composting [4,5]. It is known that the decomposition of organic substrates in composting is mainly carried out by microorganisms. However, Actinomycetes, considered a specific group of Gram-positive bacteria that can form branching hyphae, make a great contribution to the decomposition of complex organic compounds like lignin [5] and other organic matters [6] in composting. In addition, Actinomycetes also plays a significant role in the bio-decomposition of agricultural waste during composting [7].
Some studies have explored the interaction between changes in environmental factors and microbial communities during composting [8,9,10,11,12]. Sundberg [11] reported that different pH settings had a significantly different influence on microbial communities during the composting process. Liang [9] drew the conclusion that the effect of pile temperature on the metabolism of the microbe was less than that of moisture content. A study by Tang [12] showed that water soluble carbon (WSC) and pile temperature were dominant factors compared with other parameters. These results are conflicting and only a few physico-chemical parameters were measured separately. Therefore, these reports cannot provide enough comprehensive and systematic understanding of the composting of agricultural waste. Zhang [2] separated out and prioritized the relative influence of several environmental factors on the community change of bacterial and fungi by evaluating them simultaneously. However, the influences of physico-chemical parameters on Actinomycetes communities during agricultural waste composting are still not clear.
Denaturing gradient gel electrophoresis of polymerase chain reaction (PCR-DGGE), as a widely applied molecular biological technology, has been used in composting studies. This technology was frequently used to generate a fingerprinting pattern to reveal genetic diversity and the abundance of microbial communities [13,14,15,16,17,18,19,20]. Quantitative PCR (qPCR) is a technology used to monitor the number of copies of a targeted DNA molecule [21]. Redundancy analysis (RDA) is a multivariate regression analysis used to explain the variation in a set of response variables by another set of explanatory variables. The method has been widely applied to extract and summarize the relationships between microbial community composition and environmental factors [22]. Regression analysis is a statistical method which is utilized for studying the dependence relationships between dependent variables and independent variables [23].
Few studies have aimed to explore the influences of physico-chemical parameters on Actinomycetes community composition changes during composting. Therefore, we measured eight physico-chemical parameters, investigated the Actinomycetes community composition by denaturing gradient gel electrophoresis (DGGE), analysed the relationships between the parameters and the Actinomycetes communities by RDA, determined the quantity of Actinomycetes 16S rDNA gene by qPCR and studied the correlations between these parameters and Actinomycetes 16S rDNA gene abundance with regression analysis. It was hypothesized that quantifying and prioritizing the relationships between physico-chemical parameters and Actinomycetes community composition would provide a better understanding of agricultural waste composting for the scientific community and provide some useful references for relevant research and practice.

2. Material and Methods

2.1. Composting and Sampling

Soil collected from the surface 10–15 cm of soil on Yuelu Mountain (Changsha, Hunan, China) was sieved through a 40-mesh screen to remove coarse plant debris and was added to a composting system to increase microbial species and provide nutrients for microorganisms. The vegetables used in this study included Chinese cabbage, lettuce and celery, which were chopped into 1–2 cm pieces and used as easily degradable organic material for microorganism. Rice straw, as the typical agricultural waste, was gathered from the suburb of Changsha, Hunan, China, which was used as a difficult-degradable organic material, after air-drying it was cut into 1–2 cm lengths. As a low total carbon/total nitrogen (C/N) raw material, bran was used to adjust the initial C/N ratio of composting which was the optimal C/N ratio for the microbial activity [22]. The characteristics of the above materials are shown in Table 1.
The pilot-scale composting pile was placed in a special experimental container (50 L) with good heat preservation. All materials for composting weighed about 10 kg (dry weight) in total. Materials were mixed adequately at a calculated ratio and packed loosely according to a method reported by Zeng [24]. Rice straw, vegetables, bran and soil were mixed at a ratio of 11:3:2:8 (dry weight) to obtain a mixture with a C/N ratio of about 30:1 and the moisture content was adjusted to about 60%, which was considered to be the optimal range for microorganism activity [2]. The experimental period was 45 days. About 200 g composting samples were collected on days 1, 2, 3, 4, 5, 6, 9, 12, 15, 18, 21, 27, 33, 39 and 45, respectively. In order to minimize sampling errors, three subsamples were collected from different sections of the pile (the top, middle and bottom), respectively. The three subsamples were mixed well and then divided into two parts: one for physico-chemical parameter analysis and the remaining were stored immediately at −20 °C for total DNA extraction. After sampling, the moisture content of the composting pile was adjusted using the method described by Zhang [25]. In order to supply enough oxygen, the materials were turned adequately every day during the first 12 days and once a week after that until the end.

2.2. Physico-Chemical Parameters Analyses

To ensure the accuracy of the results, the composting pile was performed in triplicate and the results were presented as the mean of these data. The C/N ratio, ambient temperature, moisture content, pH, WSC and pile temperature were measured according to our previous study [22]. Besides that, the ammonium and nitrate of materials were extracted with a 2M KCl solution (materials:solution, 1:5) and its concentrations were determined using the Continuous Flow Analyzer (A33, SEAL Analytical, Norderstedt, Germany). For all measurements, three parallels were tested.

2.3. DNA Extraction and PCR-DGGE

The protease K and cetyltrimethyl ammonium bromide (CTAB) method was used to extract the total DNA of all samples [26]. The crude DNA was purified with a TIAN quick Midi Purification Kit (TianGen, Beijing, China) following the manufacturer’s recommendations. A fragment length of about 433 bp of Actinomycetes was amplified by Nested PCR. In the first step, Actinomycetes-specific primer F243 and R1378 were used to amplify Actinomycetes 16S rRNA genes from positions 226 to 1401. Then 2 µL amplification products in the first step were used as the DNA template. Primer F984GC and R1378 were used to amplify the fragment from positions 968 to 1401 with a length of about 433 bp that is appropriate for DGGE. The reaction conditions of the two amplification steps were followed according to the protocol described by Das [27]. The details of the primers used in Nested PCR are shown in Table 2.
The 40 µL amplification products from the second PCR process of each sample were used for DGGE analysis. The analysis was performed with a DCodeTM Universal Detection System apparatus. The amplification products were loaded into 8% (w/v) polyacrylamide gels in 1 × TAE with a denaturing range from 30 to 80%. The gel electrophorese was run at 60 °C and 80 V for 12 h, then the gel was stained for 30 min by using SYBR Green I nucleic acid gel stain (Molecular Probes, Carlsbad, CA, USA) for 30 min, photographed by a UV Transilluminator and analysed using Quantity One software (version 4.5, Bio-Rad, Hercules, CA, USA).

2.4. qPCR Analysis

16S rRNA gene copy numbers of Actinomycetes were measured with real-time qPCR with the primers F984 and R1378 in iCycler IQ5 Thermocycler (Bio-Rad). The qPCR reaction mix contained 0.5 μL of each primer (10 μM), 10 μL of 2 × SYBR real-time PCR pre-mixture (Invitrogen, Carlsbad, CA, USA), 0.5 μL of BSA (10 mg mL−1), 1 μL of 10-fold dilution of DNA extract and was adjusted to 20 μL with sterile deionized water. The amplification was performed by using the following conditions: 94 °C for 3 min, followed by 40 cycles of 20 s at 94 °C, 30 s at 55 °C and 30 s at 72 °C. Data was obtained at 72 °C. A known copy number of plasmid of the Actinomycetes 16S rDNA gene was ten-fold serially diluted and used as a template in qPCR for the standard curve generation. For all the qPCR experiments, duplicate runs were used in all samples.

2.5. Data Analysis

The One-Way Analysis of Variance (ANOVA) method was used to analyse all physico-chemical parameters and to test the significant differences between the data. Data were obtained as average values of the triplicates, the maximum difference between which was 5%.
After subtracting a rolling disk background value for polyacrylamide gels and each lane, a DGGE banding profile for Actinomycetes communities was digitized and calculated the relative contribution of each band to the total band intensity for each lane by the Quantity One software [28]. Multivariate relationships between Actinomycetes community composition and eight physico-chemical parameters were determined by Canoco (version 4.5, Centre for Biometry, Wageningen, The Netherlands).
Different magnitudes of the units existed in all the physico-chemical parameters, so the multivariate analyses could not be carried out directly. SPSS Statistics (version 11.5, SPSS, Chicago, IL, USA) software was used to standardize those parameters [2,29]. A detrended correspondence analysis (DCA) was used first to analyse the data of Actinomycetes communities to decide whether to use either a linear or unimodal response model. The analysis showed that the first DCA ordination axis length was 2.448. Therefore, the redundancy analysis (RDA) method, based on a linear response model, was used to analyse the response of the Actinomycetes community composition to the eight physico-chemical parameters [30].
The RDA was carried out with default settings [2]. Regression analysis was performed to determine the correlations between each of the eight physico-chemical parameters and the quantity of Actinomycetes 16S rDNA gene abundance by SPSS (version 11.5).

3. Results and Discussion

3.1. The Changes of Physico-Chemical Parameters

Figure 1 described the physico-chemical parameters changes over 45 days. As shown in Figure 1a, the C/N ratio decreased gradually throughout the whole process from 31.15 to 16.62 due to the fact that the organic matter was degraded gradually by the microorganism. Values of C/N ratio lower than 20 at day 27 were considered the threshold value for composting maturity [22]. During the first 6 days, the ammonification and mineralization process produced plenty of ammonium, so the pH increased from 6.74 to 8.87. Then the pH decreased slowly to 8.13 until day 21 as a result of the release of H+ derived from the nitrification process and the volatilization of ammoniacal nitrogen. Due to the abundant growth and metabolism of the microorganism, easy-metabolizable materials were degraded quickly and the pile temperature increased rapidly to 56 °C during the first 4 days and was maintained above 50 °C for 12 days, which is long enough to suppress a pathogenic microorganism, then decreased gradually to become stable at a little higher than ambient level (Figure 1b). Moisture content was kept at about 60% during the first fermentation phase and at about 45–50% during the second fermentation phase, which was considered the optimal range for microbial activity [25].
WSC increased from 217.5 mg kg−1 to 245.6 mg kg−1 during the first 3 days and then decreased gradually to 57.6 mg kg−1 at the end (Figure 1c). The ammonium and nitrate concentration showed an almost opposite variation trend. Nitrate concentration increased rapidly to 1166.8 mg kg−1 (dry weight) on the 2nd day and ammonium increased significantly to a maximum of 2122.2 mg kg−1 (dry weight) on the 5th day respectively, then the nitrate decreased quickly to a minimum of 398.34 mg kg−1 and increased gradually to 1353.4 mg kg−1, after the 5th day ammonium decreased gradually to 296.8 mg kg−1 (Figure 1d). The pH, pile temperature and ammonium all increased during the early stage of composting, which was explained by the abundant growth of the microorganisms which need a large amount of nutrients to maintain their metabolism. Therefore, easy-metabolizable materials were degraded and released heat. Meanwhile, after microorganism degradation, organic nitrogen was transformed to ammonium by the ammonification and to nitrate by the mineralization during the early stage of composting.

3.2. DGGE Analysis and Quantitative Analysis

Composting is a process controlled by microorganisms. PCR-DGGE is a powerful technology to monitor the composition changes in a microbial community that is affected by environmental factors [31]. The DGGE gel result of Actinomycetes community composition is shown in Figure 2. The changes of the band intensity at the same horizontal position suggested a relative change of the species abundance in different samples [32]. Each band of the DGGE gel image represents a group of Actinomycetes species. Bands of Actinomycetes 16S rDNA sequences in the same horizontal position mean they have a similar melting characteristic [33]. Figure 2 indicates that the Actinomycetes species were dynamic and the number of Actinomycetes species during the last phase of composting was more than in other phases.
The Actinomycetes 16S rDNA copy numbers measured by qPCR are shown in Figure 3. The numbers increased quickly at the beginning of the composting, decreased rapidly between day 12 and 15 and then increased slowly until the composting process was ended.

3.3. Redundancy Analyses and Regression Analysis

Redundancy analysis was used to analyse and digitalize the extent to which the eight physico-chemical parameters affected the Actinomycetes community’s composition. Therefore, the influences between physico-chemical parameters and the Actinomycetes community’s composition during composting was analysed by the relative band intensities of the community profile data. The results show that these parameters all influence the composition of the Actinomycetes community because the significance of the first axis (p = 0.006) and all canonical axes (p = 0.004) were significant by Monte Carlo permutation tests carried out with default settings. The first two canonical axes explained 66.4% and 7.8% of the variation in Actinomycetes community composition, respectively. All four significant canonical axes and all canonical axes explained 84.2% and 86.6% of the changes in the Actinomycetes community composition, respectively (Table 3).
Regression analysis was used to analyse the relationships between these physico-chemical parameters and Actinomycetes 16S rDNA gene abundance by SPSS (version 11.5). The quantitative curve of Actinomycetes 16S rDNA gene abundance showed a negative linear relationship (R2 = 0.407, p < 0.01) with the moisture content and other parameters had no obvious correlation relationship with the variation tendency of quantitative curve, which indicated that moisture content had a more important influence on Actinomycetes communities than other parameters during composting (Figure 4).
In this study, the eight physico-chemical parameters accounted for 86.6% of the variation in the composition of the Actinomycetes communities by RDA. The variation of about 13.4% that remained unexplained may be related to other physico-chemical parameters, such as the content of humus and the interaction between microorganisms, which were not measured in this study. Only moisture content showed a significant correlation with the Actinomycetes 16S rDNA gene abundance by regression analysis. We suggest that future studies of Actinomycetes should link Actinomycetes function (e.g., enzyme production) with physico-chemical parameters to explore the interaction effect between them.

3.4. Forward Selections and Variation Partitioning

In this study, in order to make clear the influences of physico-chemical parameters on composition changes in the Actinomycetes community, we sorted the significant parameters based on their relative influence by variation partitioning analysis and expounded changes during composting. Forward selection was carried out to screen the significant impact factors (p < 0.05), which had a statistically significant influence on Actinomycetes community composition. The results indicated that the variation in Actinomycetes community composition was influenced significantly by WSC, moisture and pH (p < 0.05). In order to quantify the effects of these three significant factors on the variation of the actinomycetes community composition, variation partitioning analysis was carried out to conduct further analysis [34]. The results are shown in Table 4; 45.4% of the variation (p = 0.018) was explained statistically by the RDA model in all of the Actinomycetes community composition during composting. WSC, moisture content and pH explained 2.8% (p = 0.002), 19.6% (p = 0.002) and 5.3% (p = 0.03) of the variation of Actinomycetes community composition, respectively and the three significant impact factors explained 17.7% of variation in all, without the effect of other parameters. The positions of each sample with the first two environmental axes are shown in Figure 5, providing a more intuitive expression of the composting condition of each sampling time.
Partial RDAs based on the Monte Carlo permutation (n = 499) kept only the significant parameters in the models. For each partial model, the other significant parameters were used as covariates. F and p values were estimated using Monte Carlo permutations. The sum of all Eigen values for both partial RDAs were 1.000.
WSC is the predominant factor influencing bacterial and fungal community composition [2]. Straathof [35] and Maeda [36] drew a common conclusion that dissolved organic carbon concentration is the main factor affecting microbial communities in the composting process. In this research, WSC showed a significant correlation (p = 0.002) with Actinomycetes community composition. Thus, it can be seen that WSC has a great influence on microbial community composition. It was reported that pH was a remarkably influential factor which had different effects on microbial communities under different conditions during the composting process [11]. In this study, the pH (p = 0.03) explained 5.3% of the variation of Actinomycetes community composition. In addition, moisture content induced a significant modification in the Actinomycetes community composition (19.6% of the variation, p = 0.002), which suggested that Actinomycetes communities were more sensitive to moisture content fluctuation than other parameters. Ryckeboer [6] also reported that moisture content was an important factor for Actinomycetes. However, it was reported that WSC and pile temperature were the main factors affecting bacterial and fungal community composition, respectively [2]. That is to say, compared with bacterial and fungal communities, the composition of the Actinomycete community was more sensitive to moisture content.
Ammonium and nitrate have a large influence on bacterial communities [2]. However, no significant relationships with Actinomycetes communities were found in this study. Although several researchers [22,37,38] have reported that pile temperature had a significant impact on microbial activity and biomass during composting, no significant correlation between pile temperature and Actinomycetes communities were found during composting in this study. Maybe the influence of pile temperature on Actinomycetes communities were covered by moisture content because of adding deionized water to compost at regular intervals. Ambient temperature had almost no influence on Actinomycetes communities because of less direct interaction.

4. Conclusions

The pile entered the maturity stage at 27 days. These eight physico-chemical parameters statistically explained 86.6% variation of the Actinomycetes community composition during agriculture composting. Moisture content, pH and WSC had the predominant effect, statistically explaining 19.6%, 5.3% and 2.8% variation, respectively and 45.4% variation in all of the Actinomycetes community composition during composting. Only moisture content showed a negative linear relationship with the quantity of Actinomycetes 16S rDNA gene abundances. Actinomycetes species were dynamic and the number of Actinomycetes species during the last phase of composting was more than other phases. In addition, compared with bacterial and fungal communities, the composition of the Actinomycete community was more sensitive to moisture content.

Author Contributions

Y.L. (Yuanping Li), Y.C. (Yanrong Chen) and Y.C. (Yaoning Chen) conceived and designed the experiments; Y.L. (Yuanping Li) and Y.C. (Yanrong Chen) performed the experiments; Y.L. (Yihuan Liu), Z.P., Z.Z., R.X. and S.W. analyzed the date; Y.C. (Yaoning Chen), Y.W. and C.Z. contributed materials and analysis tools; Y.L. (Yuanping Li) and Y.C. (Yanrong Chen) wrote the paper; Y.W. and C.Z. provided comments to the paper.

Funding

This study was financially supported by the Research Foundation of Education Department of Hunan Province (17C0305, 17K019), Natural Science Foundation of Hunan Province, China (2017JJ2020), Outstanding Youth Project of Hunan Province, China (16B049) and the Natural Science Foundation of Hunan Province (13JJB002).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Xiao, Y.; Zeng, G.M.; Yang, Z.H.; Ma, Y.H.; Huang, C.; Xu, Z.Y.; Huang, J.; Fan, C.Z. Changes in the actinomycetal communities during continuous thermophilic composting as revealed by denaturing gradient gel electrophoresis and quantitative PCR. Bioresour. Technol. 2011, 102, 1383–1388. [Google Scholar] [CrossRef]
  2. Zhang, J.; Zeng, G.; Chen, Y.; Yu, M.; Yu, Z.; Li, H.; Yu, Y.; Huang, H. Effects of physico-chemical parameters on the bacterial and fungal communities during agricultural waste composting. Bioresour. Technol. 2011, 102, 2950–2956. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, Y.; Chen, Y.; Li, Y.; Wu, Y.; Zhu, F.; Zeng, G.; Zhang, J.; Li, H. Application of Fenton pretreatment on the degradation of rice straw by mixed culture of, Phanerochaete chrysosporium, and, Aspergillus niger. Ind. Crops Prod. 2018, 112, 290–295. [Google Scholar] [CrossRef]
  4. Chen, Y.; Huang, J.; Li, Y.; Zeng, G.; Zhang, J.; Huang, A.; Zhang, J.; Ma, S.; Tan, X.; Xu, W.; et al. Study of the rice straw biodegradation in mixed culture of Trichoderma viride and Aspergillus niger by GC-MS and FTIR. Environ. Sci. Pollut. Res. Int. 2015, 22, 9807–9815. [Google Scholar] [CrossRef]
  5. Tuomela, M.; Vikman, M.; Hatakka, A.; Itävaara, M. Biodegradation of lignin in a compost environment: A review. Bioresour. Technol. 2000, 72, 169–183. [Google Scholar] [CrossRef]
  6. Ryckeboer, J.; Mergaert, J.; Vaes, K.; Klammer, S.; De Clercq, D.; Coosemans, J.; Insam, H.; Swings, J. A survey of bacteria and fungi occurring during composting and self-heating processes. Ann. Microbiol. 2010, 53, 349–410. [Google Scholar]
  7. Godden, B.; Ball, A.S.; Helvenstein, P.; Mccarthy, A.J.; Penninckx, M.J. Towards elucidation of the lignin degradation pathway in actinomycetes. Microbiology 1992, 138, 2441–2448. [Google Scholar] [CrossRef] [Green Version]
  8. Chen, Y.; Zhou, W.; Li, Y.; Zhang, J.; Zeng, G.; Huang, A.; Huang, J. Nitrite reductase genes as functional markers to investigate diversity of denitrifying bacteria during agricultural waste composting. Appl. Microbiol. Biotechnol. 2014, 98, 4233–4243. [Google Scholar] [CrossRef] [PubMed]
  9. Liang, C.; Das, K.; McClendon, R. The influence of temperature and moisture contents regimes on the aerobic microbial activity of a biosolids composting blend. Bioresour. Technol. 2003, 86, 131–137. [Google Scholar] [CrossRef]
  10. Paetsch, L.; Mueller, C.W.; Rumpel, C.; Houot, S.; Kögel-Knabner, I. Urban waste composts enhance OC and N stocks after long-term amendment but do not alter organic matter composition. Agric. Ecosyst. Environ. 2016, 223, 211–222. [Google Scholar] [CrossRef]
  11. Sundberg, C.; Yu, D.; Franke-Whittle, I.; Kauppi, S.; Smars, S.; Insam, H.; Romantschuk, M.; Jonsson, H. Effects of pH and microbial composition on odour in food waste composting. Waste Manag. 2013, 33, 204–211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Tang, J.C.; Shibata, A.; Zhou, Q.; Katayama, A. Effect of temperature on reaction rate and microbial community in composting of cattle manure with rice straw. J. Biosci. Bioeng. 2007, 104, 321–328. [Google Scholar] [CrossRef] [PubMed]
  13. Aydin, S.; Shahi, A.; Ozbayram, E.G.; Ince, B.; Ince, O. Use of PCR-DGGE based molecular methods to assessment of microbial diversity during anaerobic treatment of antibiotic combinations. Bioresour. Technol. 2015, 192, 735–740. [Google Scholar] [CrossRef] [PubMed]
  14. Camara, B.; De los Rios, A.; Urizal, M.; de Buergo, M.A.; Varas, M.J.; Fort, R.; Ascaso, C. Characterizing the microbial colonization of a dolostone quarry: Implications for stone biodeterioration and response to biocide treatments. Microb. Ecol. 2011, 62, 299–313. [Google Scholar] [CrossRef]
  15. Chen, Y.; Liu, Y.; Li, Y.; Wu, Y.; Chen, Y.; Zeng, G.; Zhang, J.; Li, H. Influence of biochar on heavy metals and microbial community during composting of river sediment with agricultural wastes. Bioresour. Technol. 2017, 243, 347–355. [Google Scholar] [CrossRef] [PubMed]
  16. He, F.; Huang, D.; Liu, L.; Shu, X.; Yin, H.; Li, X. A novel PCR-DGGE-based method for identifying plankton 16S rDNA for the diagnosis of drowning. Forensic Sci. Int. 2008, 176, 152–156. [Google Scholar] [CrossRef] [PubMed]
  17. Matussek, A.; Stark, L.; Dienus, O.; Aronsson, J.; Mernelius, S.; Lofgren, S.; Lindgren, P.E. Analyzing multiclonality of Staphylococcus aureus in clinical diagnostics using spa-based denaturing gradient gel electrophoresis. J. Clin. Microbiol. 2011, 49, 3647–3648. [Google Scholar] [CrossRef] [PubMed]
  18. Pandey, J.; Chauhan, A.; Jain, R.K. Integrative approaches for assessing the ecological sustainability of in situ bioremediation. FEMS Microbiol. Rev. 2009, 33, 324–375. [Google Scholar] [CrossRef]
  19. Ruan, A.D.; Liu, C.X. Analysis of effect of nicotine on microbial community structure in sediment usinpog PCR-DGGE fingerprinting. Water Sci. Eng. 2015, 8, 309–314. [Google Scholar] [CrossRef]
  20. Tatsadjieu, N.L.; Maïworé, J.; Hadjia, M.B.; Loiseau, G.; Montet, D.; Mbofung, C.M.F. Study of the microbial diversity of Oreochromis niloticus of three lakes of Cameroon by PCR-DGGE: Application to the determination of the geographical origin. Food Control 2010, 21, 673–678. [Google Scholar] [CrossRef]
  21. Corbisier, P.; Pinheiro, L.; Mazoua, S.; Kortekaas, A.M.; Chung, P.Y.; Gerganova, T.; Roebben, G.; Emons, H.; Emslie, K. DNA copy number concentration measured by digital and droplet digital quantitative PCR using certified reference materials. Anal. Bioanal. Chem. 2015, 407, 1831–1840. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Chen, Y.; Ma, S.; Li, Y.; Yan, M.; Zeng, G.; Zhang, J.; Zhang, J.; Tan, X. Microbiological study on bioremediation of 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) contaminated soil by agricultural waste composting. Appl. Microbiol. Biotechnol. 2016, 100, 9709–9718. [Google Scholar] [CrossRef] [PubMed]
  23. D’Urso, P.; Santoro, A. Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable. Comput. Stat. Date Anal. 2006, 51, 287–313. [Google Scholar] [CrossRef]
  24. Zeng, G.; Yu, M.; Chen, Y.; Huang, D.; Zhang, J.; Huang, H.; Jiang, R.; Yu, Z. Effects of inoculation with Phanerochaete chrysosporium at various time points on enzyme activities during agricultural waste composting. Bioresour. Technol. 2010, 101, 222–227. [Google Scholar] [CrossRef] [PubMed]
  25. Zhang, L.; Zeng, G.; Zhang, J.; Chen, Y.; Yu, M.; Lu, L.; Li, H.; Zhu, Y.; Yuan, Y.; Huang, A. Response of denitrifying genes coding for nitrite (nirK or nirS) and nitrous oxide (nosZ) reductases to different physico-chemical parameters during agricultural waste composting. Appl. Microbiol. Biotechnol. 2015, 99, 4059–4070. [Google Scholar] [CrossRef] [PubMed]
  26. Yang, Z.H.; Xiao, Y.; Zeng, G.M.; Xu, Z.Y.; Liu, Y. Comparison of methods for total community DNA extraction and purification from compost. Appl. Microbiol. Biotechnol. 2007, 74, 918–925. [Google Scholar] [CrossRef] [PubMed]
  27. Das, M.; Royer, T.V.; Leff, L.G. Diversity of fungi, bacteria and actinomycetes on leaves decomposing in a stream. Appl. Environ. Microbiol. 2007, 73, 756–767. [Google Scholar] [CrossRef]
  28. Roesti, D.; Gaur, R.; Johri, B.; Imfeld, G.; Sharma, S.; Kawaljeet, K.; Aragno, M. Plant growth stage, fertiliser management and bio-inoculation of arbuscular mycorrhizal fungi and plant growth promoting rhizobacteria affect the rhizobacterial community structure in rain-fed wheat fields. Soil Biol. Biochem. 2006, 38, 1111–1120. [Google Scholar] [CrossRef] [Green Version]
  29. Gilbride, K.A.; Frigon, D.; Cesnik, A.; Gawat, J.; Fulthorpe, R.R. Effect of chemical and physical parameters on a pulp mill biotreatment bacterial community. Water Res. 2006, 40, 775–787. [Google Scholar] [CrossRef]
  30. Lepš, J.; Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO; Cambridge University Press: Cambridge, UK, 2003. [Google Scholar]
  31. Novo, A.; André, S.; Viana, P.; Nunes, O.C.; Manaia, C.M. Antibiotic resistance, antimicrobial residues and bacterial community composition in urban wastewater. Water Res. 2013, 47, 1875–1887. [Google Scholar] [CrossRef]
  32. Liu, H.; Yang, C.; Tian, Y.; Lin, G.; Zheng, T. Using population dynamics analysis by DGGE to design the bacterial consortium isolated from mangrove sediments for biodegradation of PAHs. Int. Biodeterior. Biodegrad. 2011, 65, 269–275. [Google Scholar] [CrossRef]
  33. Ovreås, L.; Forney, L.; Daae, F.L.; Torsvik, V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl. Environ. Microbiol. 1997, 63, 3367–3373. [Google Scholar] [CrossRef] [PubMed]
  34. Borcard, D.; Legendre, P.; Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 1992, 73, 1045–1055. [Google Scholar] [CrossRef]
  35. Straathof, A.L.; Chincarini, R.; Comans, R.N.J.; Hoffland, E. Dynamics of soil dissolved organic carbon pools reveal both hydrophobic and hydrophilic compounds sustain microbial respiration. Soil Biol. Biochem. 2014, 79, 109–116. [Google Scholar] [CrossRef]
  36. Maeda, K.; Morioka, R.; Osada, T. Effect of covering composting piles with mature compost on ammonia emission and microbial community structure of composting process. J. Environ. Qual. 2009, 38, 598–606. [Google Scholar] [CrossRef]
  37. Wei, L.; Shutao, W.; Jin, Z.; Tong, X. Biochar influences the microbial community structure during tomato stalk composting with chicken manure. Bioresour. Technol. 2014, 154, 148–154. [Google Scholar] [CrossRef] [PubMed]
  38. Xiao, Y.; Zeng, G.; Yang, Z.; Shi, W.; Huang, C.; Fan, C.; Xu, Z. Continuous thermophilic composting (CTC) for rapid biodegradation and maturation of organic municipal solid waste. Bioresour. Technol. 2009, 100, 4807–4813. [Google Scholar] [CrossRef]
Sustainability 11 02229 g001a 550Sustainability 11 02229 g001b 550
Figure 1. Changes of physico-chemical parameters during composting process: (a) pH and C/N ratio; (b) ambient and pile temperature; (c) WSC and moisture content; (d) ammonium and nitrate.
Figure 1. Changes of physico-chemical parameters during composting process: (a) pH and C/N ratio; (b) ambient and pile temperature; (c) WSC and moisture content; (d) ammonium and nitrate.
Sustainability 11 02229 g001aSustainability 11 02229 g001b
Sustainability 11 02229 g002 550
Figure 2. DGGE profiles of amplified Actinomyces 16S rDNA fragments from the compost samples. The numbers refer to the sampling days.
Figure 2. DGGE profiles of amplified Actinomyces 16S rDNA fragments from the compost samples. The numbers refer to the sampling days.
Sustainability 11 02229 g002
Sustainability 11 02229 g003 550
Figure 3. Actinomyces 16 S rDNA gene abundance in different sampling time during agricultural waste composting.
Figure 3. Actinomyces 16 S rDNA gene abundance in different sampling time during agricultural waste composting.
Sustainability 11 02229 g003
Sustainability 11 02229 g004 550
Figure 4. Negative linear relationship between Actinomyces 16S rDNA abundance and moisture content.
Figure 4. Negative linear relationship between Actinomyces 16S rDNA abundance and moisture content.
Sustainability 11 02229 g004
Sustainability 11 02229 g005 550
Figure 5. DGGE band data redundancy analysis for Actinomyces species. Solid lines with filled arrows and grey dotted lines with filled arrows represent significant composting parameters and supplementary parameters, respectively. Samples are represented by open circles and sample numbers refer to the sampling days.
Figure 5. DGGE band data redundancy analysis for Actinomyces species. Solid lines with filled arrows and grey dotted lines with filled arrows represent significant composting parameters and supplementary parameters, respectively. Samples are represented by open circles and sample numbers refer to the sampling days.
Sustainability 11 02229 g005
Table
Table 1. Basic characteristics of the composting raw materials.
Table 1. Basic characteristics of the composting raw materials.
MaterialsMoisture Content %Organic Materials %C%N%C/N
Rice straw0.11730.73800.42800.008848.7600
Bran0.14060.81740.47410.041211.5000
Vegetables0.79060.16760.09720.005019.5500
soil0.26130.09500.05510.002422.6700
All the above data are dry sample data.
Table
Table 2. Details of the primers.
Table 2. Details of the primers.
PrimerPrimer Sequence
F2435′-GGA TGA GCC CGC GGC CTA-3′
R13785′-CGG TGT GTA CAA GGC CCG GGA ACG-3′
F984GC5′-GC-AAC GCG AAG AAC CTT AC-3′
Table
Table 3. Redundancy analysis results of Actinomyces DGGE profile.
Table 3. Redundancy analysis results of Actinomyces DGGE profile.
AxisEigen ValueSpecies-Environment CorrelationCumulative % Variation of SpeciesCumulative % Variation of Species-EnvironmentSum of All Canonical Eigen Values
Axis 10.6640.98666.476.70.866
Axis 20.0780.8874.285.7
Axis 30.0720.91281.494
Axis 40.0280.88384.297.3
Monte Carlo significance tests: sum of all Eigen values, 1.000; significance of first canonical axis, F-ratio = 11.869, p = 0.006; significance of all canonical axes, F-ratio = 4.841, p = 0.004.
Table
Table 4. The influences of the significant parameters on Actinomyces community composition were tested by Eigen values, F values and p values which were obtained from the partial RDA.
Table 4. The influences of the significant parameters on Actinomyces community composition were tested by Eigen values, F values and p values which were obtained from the partial RDA.
Parameters Included in the ModelEigen Value% Variation Explains SolelyF Valuep Value
moisture0.19619.67.790.002
pH0.0535.32.710.03
WSC0.0282.86.580.002
All the above together0.45445.43.0470.018

Share and Cite

MDPI and ACS Style

Li, Y.; Chen, Y.; Chen, Y.; Wu, Y.; Zhang, C.; Peng, Z.; Liu, Y.; Wang, S.; Xu, R.; Zeng, Z. Effects of Physico-Chemical Parameters on Actinomycetes Communities during Composting of Agricultural Waste. Sustainability 2019, 11, 2229. https://doi.org/10.3390/su11082229

AMA Style

Li Y, Chen Y, Chen Y, Wu Y, Zhang C, Peng Z, Liu Y, Wang S, Xu R, Zeng Z. Effects of Physico-Chemical Parameters on Actinomycetes Communities during Composting of Agricultural Waste. Sustainability. 2019; 11(8):2229. https://doi.org/10.3390/su11082229

Chicago/Turabian Style

Li, Yuanping, Yanrong Chen, Yaoning Chen, Yanxin Wu, Chun Zhang, Zhen Peng, Yihuan Liu, Sha Wang, Ran Xu, and Ziping Zeng. 2019. "Effects of Physico-Chemical Parameters on Actinomycetes Communities during Composting of Agricultural Waste" Sustainability 11, no. 8: 2229. https://doi.org/10.3390/su11082229

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop