Next Article in Journal
Face Verification Based on Deep Learning for Person Tracking in Hazardous Goods Factories
Previous Article in Journal
Comparative Study between Flatness-Based and Field-Oriented Control Methods of a Grid-Connected Wind Energy Conversion System
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Processes
Volume 10
Issue 2
10.3390/pr10020379
processes-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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Metagenomic Analysis of Bacterial Community Structure and Dynamics of a Digestate and a More Stabilized Digestate-Derived Compost from Agricultural Waste

by
Stefania Mirela Mang
*,
Vincenzo Trotta
,
Antonio Scopa
and
Ippolito Camele
*
School of Agricultural, Forestry, Food and Environmental Sciences (SAFE), University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy
*
Authors to whom correspondence should be addressed.
Processes 2022, 10(2), 379; https://doi.org/10.3390/pr10020379
Submission received: 3 December 2021 / Revised: 9 February 2022 / Accepted: 13 February 2022 / Published: 16 February 2022
(This article belongs to the Topic Anaerobic Digestion Processes)
Browse Figures
Versions Notes

Abstract

:
Recycling of different products and waste materials plays a crucial role in circular economy, where the anaerobic digestion (AD) constitutes an important pillar since it reuses nutrients in the form of organic fertilizers. Knowledge about the digestate and compost microbial community structure and its variations over time is important. The aim of the current study was to investigate the microbiome of a slurry cow digestate produced on a farm (ADG) and of a more stabilized digestate-derived compost (DdC) in order to ascertain their potential uses as organic amendments in agriculture. The results from this study, based on a partial fragment of 16S bacterial rRNA NGS sequencing, showed that there is a greater microbial diversity in the DdC originated from agricultural waste compared to the ADG. Overall, the existence of a higher microbial diversity in the DdC was confirmed by an elevated number (1115) of OTUs identified, compared with the ADG (494 OTUs identified). In the DdC, 74 bacterial orders and 125 families were identified, whereas 27 bacterial orders and 54 families were identified in the ADG. Shannon diversity and Chao1 richness indexes were higher in DdC samples compared to ADG ones (Shannon: 3.014 and 1.573, Chao1: 68 and 24.75; p < 0.001 in both cases). A possible association between the microbiome composition at different stages of composting process and the role that these microorganisms may have on the quality of the compost-like substrate and its future uses is also discussed.

1. Introduction

Anaerobic digestion constitutes an adaptable technology able to process urban, industrial, and agricultural residues. Throughout this microbiological process, a complex community of microorganisms acts to decompose organic matter (OM), transforming it into two main final products: digestate and biogas [1,2,3]. The key reactions during the decomposition of OM have been extensively studied [4,5,6,7,8,9]. Briefly, it has been demonstrated that the degradable OM would be utilized as the energy source and few compounds such as CO2, NH3, and moisture were released along with large quantities of heat [4,5,6,7,8,9]. After the anaerobic digestion (AD) of a biodegradable feedstock, the residual material is known as digestate (ADG). The digestate is a secondary waste that has unpleasant features such as being viscous, containing high volatile fatty acids (VFA), and possessing an elevated level of humidity [10]. Due to its richness in nutrients and nitrogen, the ADG can be further processed and used as a fertilizer, while the biogas represents an important source of re-usable energy. The bioprocess of anaerobic digestion of biodegradable waste has widely been recognized by the EU as a recycling method by explicitly including it in Annex II of the Waste Framework Directive under point ‘R3´, in which a list of recovery operations was established [11,12,13]. Many studies have shown that the AD process is affected by several factors and is very sensitive to environmental changes [14,15,16]. In addition, maintaining the stability of the microbial community in AD is of crucial importance as its equilibrium can be easily affected by any variation of the operative parameters.
Agricultural waste recycling is an important key in the circular economy concept worldwide [17,18,19]. In Italy, the regulations on agricultural waste recycling always follow the EU laws and recommendations on this matter. Their implementations are coordinated by the Italian Ministry of the Environment fitting within the European Circular Economy Action Plan [13,20].
In Europe, several countries successfully implemented the agricultural waste recycling processes starting from various substrates like: biogenic waste in Austria [21]; organic waste, by-products, and energy crops in Germany [22,23]; chicken manure, animal slurry, food waste, or other organic materials in the UK [24]. In Italy, the agricultural waste recycling processes start from food waste, organic municipal, wastewater, and livestock waste [19,25] and produce energy or biofertilizers/soil amendments.
Organic material can easily be decomposed by the natural process of composting and then re-used as it is rich in humus soil amendment [26,27,28]. Several methods are employed for performing the composting procedures. For this reason, different types of composting systems exist, such as the conventional windrow, aerated static pile, in-vessel, and the newly developed two-stage composting system [6,29]. It is important to stress that many parameters such as temperature, pH, oxygen, porosity, moisture content, etc. need to be verified during the whole process and constantly optimized for a good composting result [30,31,32]. Due to their ability to improve health, physical and chemical properties of soil, plant growth, and also to suppress pathogens and plant diseases, compost materials have been greatly recommended and widely used in agriculture [19,33,34,35].
Above all, the quality of the fertilizer obtained after the composting greatly depends on the microbial structure of the digestate or compost [36,37,38]. It is known that composting is a very fragile process. The results of this process could be predicted only if any modification that may arise in the microbial communities is taken into account. In addition, knowledge about the microbial community composition during the whole process of composting allows us to make the best choices for compost stabilizers and microbial agents. This also establishes the basis for future screenings of microorganisms with some important and special roles [37,38,39].
Despite all previous efforts made to reveal the compost structure, a full and precise description of the microbial communities present in it is still incomplete. This is mainly due to the technical limitations of the traditional microorganism culture methods, which are not suitable to isolate and characterize such complex microbial groups. As previously stated by Hultman [40], classic methods generally provide a reduction of dozens (at maximum hundreds) of Operational Taxonomic Units (OTUs). When modern techniques were applied, the presence of thousands of species in digestate and compost was revealed [41,42,43]. The very high levels of microbial diversity discovered indicated that the traditional methods, which are dependent by the in vitro culture of the investigated species, did not show sufficient resolution to fully understand the microbial communities from digestate, compost, or soil. This is due to the fact that the majority of the bacterial species found in these matrices are not cultivable in vitro. A useful technique has been provided by modern technology such as the new sequencing tools (e.g., pyrosequencing, Roche, Illumina). These technologies are able to provide, in a very short time frame, a huge amount of information that allows us to better understand and describe the complexity of the microbial communities from various substrates [41,44,45,46,47,48].
Molecular investigations on both digestate and compost microbiomes are generally rather difficult because of the particular nature of these matrices, which contain great quantities of OM (especially humic acids). Those substances are produced by bacteria, fungi, and protozoa in soil or water sediments during the degradation of plants or of other organic substances. Due to their very high molecular weight and their mainly poly-anionic nature, the humic substances easily interfere with the nucleic acid extraction methods. They also act as potent PCR inhibitors and their presence, even in very small quantities, is able to inhibit the DNA polymerase and shift the PCR or sequencing assay results towards a complete lack of success. To overcome all these issues, specific kits and adequate molecular protocols have been developed and successfully employed in many studies [46,47,48,49,50]. They facilitate total DNA extraction from OM rich complex matrices such as digestate and compost.
The aim of this study was to: (i) investigate the microbiome composition of a slurry cow digestate (ADG) produced on farm and taxonomically classify its microorganisms; (ii) examine the microbiome of a more stabilized digestate-derived compost (DdC), obtained after 50 days of a composting process, and determine its taxonomic entities. The role that these microorganisms may have on the quality of the DdC and its use as a final product, more suitable for agricultural purposes, is also discussed.

2. Materials and Methods

2.1. Digestate and Stabilized Digestate-Derived Compost Sources

A zoo-technical farm located in Atella (Basilicata Region, Italy) (40°51′ N; 15°38′ E) that produces biogas derived from an anaerobic digestion of livestock effluent and agricultural waste provided the anaerobic digestate (ADG) utilized in this study. All ADG samples were collected in sterile containers, transported in ice to the laboratories at the University of Basilicata, and maintained in a refrigerator at 4 °C until further use.
In order to obtain the stabilized digestate-derived compost (DdC), wheat straw was also used, in the digesters, in this experiment. Details about its origin, full description, and usage were previously reported by Vitti and co-authors [51]. In this study, the authors investigated the chemical properties and the biologic activity of an ADG and DdC from agro-waste in Southern Italy.
The digestate-derived compost (DdC) was obtained in hand-made thermally isolated semi-batch reactors of 50 L, following an extraction method that had been entirely described by Vitti et al. [51]. Briefly, each reactor had a cylindrical shape, with a 0.05 m3 volume. It was made of plastic material and the control systems for both qualitative and quantitative parameters were present. The experiment had semi-batch sets of 12 continually stirred digesters, which were filled with 90% of ADG and 10% wheat straw. The reactions were allowed to develop for 50 days and at the end of this period the DdC samples were collected. In order to improve homogeneity, aeration, and temperature control, agitating of the composting substrates for 5 min every 3 h was applied. Each reactor was filled up to 35% of the total volume and kept constant throughout the experiment. Finally, the DdC was separated and air-dried for 4 days [51].

2.2. Molecular Analysis of Microbiota from Digestate and Stabilized Digestate-Derived Compost

2.2.1. DNA Extraction

Bacterial community composition (BCC), diversity, and taxonomy of the ADG and DdC samples were investigated using Illumina MiSeq platform and NGS-sequencing technology followed by bioinformatics analyses. From ADG and DdC samples (3 samples each of 0.25 g), the genomic DNA (gDNA) were extracted with three different methods: Qiagen-DNeasy Plant kit Mini, Omega Bio-Tek- E.Z.N.A.® Soil DNA kit and PowerSoil® kit (MO BIO Laboratories Inc., Carlsbad, CA, USA). For our samples, the most efficient method was the PowerSoil® kit, which was used following manufacturer’s instructions. The quantity and quality of the gDNA were evaluated with a NanoDrop ND-1000 spectrophotometer (Thermo Scientific Inc., Wilmington, DE, USA). The extracted gDNA was stored at −20 °C for further analyses.

2.2.2. PCR Amplification, Sequencing, and Bioinformatics Analyses

For metagenomics analysis, the extracted gDNA was amplified and sequenced by the BMR Genomics Company (Padua, Italy) to obtain the DNA sequences of the 16S rRNA bacterial gene. A double step PCR was performed, using primers with tails for the 16S gene region [52] Pro341F:5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNBCASCAG-3′ and Pro 805R:3′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACNVGGGTATCTAATCC-5′ amplifying a partial 16S fragment (428 bp in size). The amplification was performed using a PCR mix containing: Taq Platinum HiFi enzyme 0.2 µL (Thermofisher Scientific Inc., Waltham, MA, USA), MgSO4 (50 mM) 1 µL, Forward and Reverse primers (10 µM) 1 µL each, dNTPs mix (10 mM) 0.5 1 µL, buffer 2.5 µL, genomic DNA (3–10 ng/µL) 5 µL, and double distilled water till 25 µL. PCR cycling program was the following: 94 °C for 1 min (1 cycle), followed by 25 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s, and extension at 68 °C for 45 s, and a final extension at 68 °C for 7 min. Samples were performed in triplicate and a positive control of a previously tested DNA and a negative control with distilled water were always included. The PCR outcomes were verified running 5 µL of the PCR product on agarose gel electrophoresis at 1.5% stained with SYBR™ Safe DNA Gel Stain (Invitrogen, Thermo Scientific Inc., Wilmington, NC, USA) and visualized by an UV transilluminator model Euro Clone Combi Light (Euro Clone S.p.A., Milan, Italy). PCR products were sequenced using the MiSeq technology (Illumina). For that purpose, they were further purified with magnetic beads and the Index Illumina Nextera XT were linked to the universal tails in a second PCR reaction. Subsequently, the samples were normalized and multiplied until 188. The library created was loaded to MiSeq with the 300PE strategy, the quality of the run was controlled and the reads revealed in fastq format. Regarding the bioinformatics analysis, processing the raw reads started with quality check and filtering of low-quality ones (Q < 25). The forward and reverse sequences (R1 and R2 reads) were united with the software FLASHv1.2.11 and filtered for their quality (Q > 30). All reads containing ambiguous bases were removed. The clustering of the Operational Taxonomic Units (OTUs) [cluster of similar (97% identity) sequences, which represent a “bacterial species”] was based on the method pick_closed_reference_otus.py of Qiime 1.9.1 and on the database Greengenes v.13–8. The Quantitative Insights into Microbial Ecology (QIIME) pipeline was applied in order to control the data quality, including length-based filtering and read-quality filtering. The OTUs were filtered at 0.005% of abundance to eliminate the OTUs with less frequency [53,54]. Finally, the relative abundance (RA %) of each sample type (ADG and DdC), at different taxonomic levels, was calculated as the average of the three independent samples. All the outcomes of sequencing and bioinformatics analyses for both substrates were identically processed at the Laboratory of Plant Pathology of the University of Basilicata, School of Agriculture, Forestry and Environmental Sciences (SAFE) using Microsoft Excel Office 2007 advanced functions. The results were further used for the statistical analyses.

2.3. Statistical Analyses

For each microbial group, as the mean abundances were measured at the beginning (ADG) and at the end (DdC) of the same composting process, the differences between treatments were analyzed with a paired sample Student t-test using the R-3.6.2 software [55]. Species diversity (Shannon) and richness (Chao-1) indices were computed and compared using random permutation tests in PAST version 4.0 [56].

3. Results

3.1. Taxonomic Classification of Digestate and Stabilized Digestate-Derived Compost Microbiome

The physico-chemical properties of ADG and DdC have been previously reported by Vitti et al. [51]. According to the Italian Legislative Decree 75/2010 about “Reorganization and Revision of the Discipline on Fertilizers”, the DdC could be considered a mixed composted amendment. Moreover, the ADG could be considered an amendment until its full maturation, such as the obtained DdC [51].
The NGS results showed 124,468 total reads and 55,691 filtered reads in the ADG, whereas, in the DdC the total reads were 51,542 and 11,301 filtered reads. A total number of 494 and 1113 OTUs have been identified in the ADG and in DdC, respectively. Both in the ADG and in the DdC, the bacterial species were the most abundant (80 and 99% of the RA), whereas the remaining species belonged to the Archaea kingdom (20 and 0.01% RA) (Figure 1).
The metagenomics analysis performed on the ADG and DdC samples allowed us to classify more than 90% of the high number of nucleotide sequences at various taxonomic levels. Furthermore, in our study only a lower percentage of sequences (about 1.34% in the case of ADG and around 9% in the case of DdC) remained unclassified (Table 1). In general, a great variation of the microbial community composition and diversity in the ADG and DdC matrices was observed. High-throughput 16S rRNA sequencing allowed the identification of a lower number of taxonomic orders and families inADG (27 and 54, respectively) than in DdC (74 and 125, respectively). Moreover, the ADG and DdC samples analyzed shared 23 orders and 37 families, while the remaining taxa were specific to the ADG (4 and 17) or to the DdC (51 and 88) (Table 1).
At order level, a high number of bacteria in the DdC compared to the ADG were identified. The taxonomic profile of the ADG microbiome identified showed 5 top orders: Bacillales (43.3% RA), Bacteriodales (22.5% RA), Methanomicrobiales (18.7% RA), Clostridiales (6% RA), and Acholeplasmatales (1.7% RA). The other orders were less abundant (<1.6% RA) (Figure 2). In the DdC microbiome, the more abundant identified orders were: Bacteriodales (14.2% RA), Alteromonadales (10.7% RA), Xanthomonadales (9.1% RA), Cytophagales (8% RA), and Rhizobiales (7% RA). The remaining orders were unclassified (Figure 2).
In addition, four orders were only found in the ADG, namely, Sphaerochetales (1.55% RA), Neisseriales (0.01% RA), Dehalococcoidales (0.01% RA), and Fusobacteriales (0.01% RA). However, a very complex microbiome was observed in the DdC where the abundant OTUs (≥5% RA) identified were members of Bacteroidales (14.21% RA), Rhizobiales (8.38% RA), Xanthomonadales (8.89% RA), Alteromonadales (10.38% RA), and Bacillales (5.6% RA) orders, while the other bacterial orders were less represented (0.3–4% RA). A morecomplex microbiome was then observed in the DdC than in the ADG (Figure 3). In DdC, the microbes identified were grouped in 74 taxonomical orders. In the ADG samples, the microorganisms identified were classified in 24 bacterial orders (Figure 3). The most abundant bacterial orders identified in ADG were Bacillales and Bacteroidales (22–44% RA), while in DdC the most abundant orders were Bacteroidales and Alteromonadales (10–14% RA). In the DdC samples, some orders such as Xanthomonadales (9.16% RA), Cytophagales (8.04% RA), Rhizobiales (7.05% RA), and Sphingomonadales (2.53% RA) are specific for this substrate (<10% RA) (Figure 3).
At order level, for all the microbiota found in the ADG and DdC, the mean values (±standard errors) of the relative abundance were calculated and reported in Table 2. In particular, the mean differences were statistically significant for the majority of the orders identified, except for Enterobacteriales, Methylcoccales, Aeromonadales, Fibrobacterales, Lactobacillales, Podosphaerales, and Synergistales (Table 2). Additionally, for Dehalococcoidales, Fusobacteriales, Gaiellales, Gemmatales, Legionellales, Neiseriales, Nitrospirales, and Puniceicoccales orders, it was not possible to perform the Stundent’s t-test since their abundances were extremely low or zero (Table 2).

3.2. Species Diversity and Richness

Shannon diversity and Chao-1 richness indices for all microbiota found in digestate and compost were reported in Table 3. In particular, the Shannon diversity index was statistically higher in the DdC than in the ADG samples (3.014 and 1.573 respectively, p < 0.001) and Chao-1 richness index followed the same pattern, with registering higher values in DdC than in ADG (68 and 24.75, respectively, p < 0.001). The Chao-1 and the Shannon indexes increased with time since the compost showed higher values than the digestate, suggesting that the quantity and the composition of the microorganisms had increased following the composting process.

3.3. Shared Microbioma Composition

Twenty-three orders (including those belonging to Archaea) constitute the core microbiota shared by the ADG and the DdC. The large majority of these OTUs (≥80% RA) were included in Bacteriodales (22.47% RA in the ADG and 14.21% RA in the DdC) and Bacilalles (43.27% RA in the ADG and 5.27% RA in the DdC) (Figure 4). Archaea were found at 20% RA in the ADG, whereas they were less abundant in the DdC (Figure 4).
The mean relative abundances of the shared orders in the ADG and in the DdC has been reported in Table 4. Specifically, the mean differences were statistically significant for the majority of the orders identified, except for four namely, Fibrobacterales, Lactobacillales, Podosphaerales, and Synergistales (Table 4).
In the ADG, 54 bacterial families of microorganisms were identified. Furthermore, the identified taxa grouped in five prevalent families, namely, Planococcaceae (42.86% RA), Porphyromonadaceae (5.65% RA), Bacteroidaceae (5.56% RA), Tissierellaceae (1.94% RA), and one Archaea family, Methanocorpusculaceae (18.62% RA). In the DdC samples, the five most abundant families were: Marinilabiaceae (11.49% RA), Chromatiaaceae (9.42% RA), Xanthomonadaceae (8.03% RA), Cytophagaceae (7.56% RA), and Hyphomicrobiaceae (3.56% RA), while the others were less abundant (<2.5% RA). Even at the family level, the taxonomic profiles of the ADG and the DdC samples were very different. In the DdC, a strong change in the abundance of some of the core families along with the presence of many new ones has been detected. For example, the Planococcaceae, which were abundant at the beginning of the transformation process (42.86% RA in the ADG) dropped in the final stages (2.22% RA in the DdC). The same trend was observed for the Bacteroidaceae (5.56% RA in the ADG and 0.03% RA in the DdC) and for some Archaea (18.62% RA in the ADG but not identified in the DdC). An opposite trend was observed for the members of the Marinilabiaceace family: they were less abundant at the first stage of the agricultural waste transformation process (0.17% RA in the ADG) compared to the final stages (11.49% RA in the DdC).

4. Discussion

4.1. Microbiota Diversity and Characterization of the Bacterial Community

An earlier study by Thummes et al. [57] on thermophilic methanogenic Archaea in compost material showed that cattle manure used for soil fertilization is known to contain anaerobic methanogens. The compost material acts as an effective carrier for the distribution of thermophilic methanogens by fertilization and wind [57]. Moreover, in the process of composting, Archaea ammonia-oxidizing (AOA) coexists with ammonia-oxidizing bacteria as shown by the Oshi et al. study. [58]. The authors demonstrated that the predominant AOA in cattle manure compost can grow and can probably oxidize ammonia under moderately thermophilic conditions [58]. In the present study, the microbiome of a slurry cow digestate produced on the farm (ADG) and of a more stabilized digestate-derived compost (DdC) were investigated. Our results on the microbial composition showed that the predominant species belonged to the bacteria Kingdom, whereas Archaea were less abundant, in agreement with Antunes et al. [59]. These authors, using metagenomic and metatranscriptomic approaches, investigated the microbial community structure and dynamics in thermophilic composting and showed that the Bacteria were always dominant compared to Archaea, regardless of the composting age [59].
In our study, in all the ADG samples the top five Phylum identified were Firmicutes (50.4% RA), Bacteroidetes (23.3% RA), Tenericutes (2% RA), Proteobacteria (2.3% RA), and Euryarchaeota (19.8% RA), while in all the DdC samples the most abundant phyla identified were Bacteroidetes (30.74% RA), Firmicutes (5.89% RA), Tenericutes (5.89% RA), Proteobacteria (50.02% RA), and Actinobacteria (2.78% RA). It was also observed that the Euryarchaeota were very less abundant (0.09% RA) in the DdC compared to the ADG. Our results on the microbial composition observed in the ADG is in agreement with what has been previously reported by Liu et al. [41]. These authors showed that, within an anaerobic digester, Bacteroidetes and Firmicutes increased in their relative abundance. In addition, the same authors reported that Proteobacteria was the most dominant phylum in feedstock samples. This shift in the composition of the microbial community could be explained as an outcomeof their adaptation to the digested environment [41].

4.2. Environmental Factors Influencing Micobioma, the ADG, and the DdC

In our study, Firmicutes were found to be more dominant (50.4% RA) than Proteobacteria (2.3% RA). Similar results were found by Xia et al. [60] in thermophilic digesters. It is well known that community composition in digesters is mostly influenced by operational temperatures [41,61,62]. Overall, Firmicutes and Proteobacteria diversity is greatly influenced, among other factors, by temperature variation during the composting process [63].
Zhang et al. [64] pointed out that in digesters operating at lower temperatures, Proteobacteria were more dominant while in thermophilic digesters and Firmicutes were the most abundant. Regarding the roles played by these organisms, several studies showed that Proteobacteria contains members thatare able to convert glucose, butyrate, propionate, and acetate [61,65,66,67]. The Firmicutes, which dominated the chicken manure compost microbiota, are considered to be degraders with strong amino acid metabolism, secreting a diverse array of proteases. It has been suggested that they mainly participate in the composting process [68]. Our results seem to confirm this statement since Firmicutes were more abundant in the ADG compared to the DdC.
Overall, the results of this study are in agreement with other studies showing that the most abundant phyla and orders (those with an elevated number of reads) throughout the composting process are: Firmicutes, Proteobacteria, Bacteroidales, and Actinobacteria [59,63,68,69,70,71].
We also obtained different taxonomic profiles where some orders were dominant over the others. These differences have been influenced by the composting period (initial stage in the ADG; a more mature stage in the DdC). This finding is in agreement with what has been previously reported by Li et al. [38]. These authors stated that the abundance of some microbial species increased with the compost fermentation. At the beginning of the process, the phyla of Firmicutes and Actinomycetes were dominant while at the maturity stage, Proteobacteria, Bacteroidetes, Firmicutes, and Chloroflexi but not Actinomycetes were identified. The same authors also showed that some bacteria involved in lignocellulose degradation such as Streptomyces, Rhodococcus, and Mycobacterium were found to be more abundant in the maturity stages of composting [38]. The Firmicutes and Proteobacteria were present when the temperature was high, generally at the early stage of composting, whereas Bacteroidetes increased their abundance during the cooling period.
Some core orders identified in our study were present at different times during the composting process. In particular, at the beginning of the transformation process we found that Bacteroidales, Bacillales, Clostridiales, and Acholepasmatales registered higher relative abundance values in digestate than in compost. Other orders such as Flavobacteriales, Campylobacterales, Pseudomonadales, and Actinomycetales were found to be more abundant at the end of the process, thus being more present in the DdC than in the ADG samples.
The relatively high abundance of Bacillales in the ADG and, to some extent, in the DdC is in accordance with what has been reported by Franke-Whittle et al. [72] and by Ryckeboer et al. [73]. This order was the most abundant group of bacteria in composting during the thermophilic phase and throughout the entire composting process. The Bacillales presence observed at the beginning of the transformation process, in our study, is also similar with what has been reported by Varma et al. [74]. The authors noted their abundance in the initial stages of the composting and showed that these organisms are involved in the degradation of cellulose and lignocellulose residues.
In our research, the variation in the orders of the Actinomycetales and Pseudomonadales (more abundant at the end of the transformation stages) showed that the degradation of cellulose and lignocellulose residues occurs at the end of the process. The greater abundance of Actinomycetales and Pseudomonadales and their involvement in the degradation of the material was also reported by Antunes et al. [59]. We also showed the presence of Bacteroidaceae only at the first stage of the transformation of the agricultural waste residues in compost. These results confirm that, in thermophilic composting, the microbial communities structure and dynamics change over time.

4.3. Microbial Community Changes over Time

Warma et al. [74], while examining the bacterial community structure during in-vessel composting of agricultural waste, demonstrated that a similar microbial composition was found in cow manure compost. An earlier study by Chandna et al. [75] on the biochemical analysis of the bacterial diversity during composting of agricultural by-products reported different taxonomic profiles based on the composting stage along with a higher diversity of species found in the final compost. They described the presence of microorganisms like Firmicutes and Proteobacteria at earlier stages of the composting during the mesophilic phase, while the presence of Firmicutes and Actinobacteria was reported in the thermophilic phase, thus confirming our results.
Meng et al. [76], while exploring the microbial community succession during cow manure and corn straw composting using the high throughput sequencing technology, showed that the major phyla identified were Proteobacteria, Bacteriodetes, Firmicutes, and Actinobacteria. These phyla were also found with similar higher percentages of OTUs in our study. During the composting process, their evolution over time was strictly linked to temperature variation. Initially, Bacteriodetes and Proteobacteria were the dominant phyla, followed by Firmicutes. In the latest stage, Firmicutes decreased. In addition, an earlier study of Ishii and Takki [77] on the microbial communities during four different composting processes, showed that the main factor affecting the microbial communities in the composting process is the concentration of dissolved organic materials. They detected Gram negative bacteria, Actinobacteria and various Bacillus species in four different composting processes. Similarly, Fracchia et al. [78], while looking at the bacterial diversity in finished compost and vermicompost by cultivation, described the abundant presence of Gram positive organisms belonging to Firmicutes and Actinobacteria, confirming the results of the present study. Basically, many studies reported that microorganism variation found in different composting systems depends on the initial materials composted and on the composting process used [58,75,76,77].

4.4. Role of the Microbial Composition in Controlling Phytopathogens

The higher abundance of microbial genera/families from the DdC samples compared to the ADG ones revealed that the present study could be used as an effective biological control strategy of soil-borne phytopathogens. This statement is supported by a recent study by De Corato [47], which reported a high diversity of bacteria in compost with positive effects on soil-borne plant pathogens.
Literature data [51,79,80,81,82] demonstrates thatDdC plays a role in the suppression of important phytopathogens such as Rhizoctonia solani, Pythium aphanidermatum, Fusarium solani, and Verticillium dahliae. DdC also increases the growth and development of tomato, beans, or cabbage plants [47,51,79,80,81,82]. The maturation process of waste materials is then able to provide a compost that is useful for agricultural purposes. In small farms using self-built semi-batch reactors, this compost could be considered as an ideal alternative to chemical fertilizers and pesticides.
Environmental sustainability is a fundamental concept for circular economy and is very important in the evaluation of the reuse of wastes responsible for environmental contamination by xenobiotic compounds, microorganisms, and all the waste produced by agro-industrial processes, as well as household activities. Therefore, applying better methodologies is necessary to satisfy the energy needs of the agro-industrial productive areas through the exploitation of renewable sources available on site, and their transformation into organic matter, such as digestate and DdC, to be used in agricultural systems. Starting with cow slurry digestate (ADG) produced on-farm and/or a stabilized digestate-derived compost (DdC), with an anaerobic composting process in self-built semi-batch reactors, it is then possible to obtain an organic amendment useful for agricultural purposes. However, a short anaerobic process determines that the digestate cannot be considered as a good amendment, but it requires a further maturation process.

5. Conclusions

The NGS sequencing of 16S rRNA gene uncovered significant data about the relative abundances of different microorganisms at various taxonomic levels. Highly relevant changes were observed in the relative abundance of the microbiome components in ADG and in DdC, showing a different dynamic over time. Overall, the elevate dabundance of microbial biomass found in the DdC, demonstrates the presence of a rich and complex microbial community, which could also be a good indicator of a superior quality, a valuable feature, which can influence its further uses.
However, further investigation on all bacteria identified in this study but also on the archaeal community seem necessary to provide additional information for a better understanding and characterization of the biological processes involved in anaerobic digesters. Our results provided new data, which can be used for the benefit of the local economy, creating additional value for waste. Furthermore, the complete and updated knowledge about digestate and compost microbiome from agricultural waste is useful for its future manipulation since the application of compost and compost like-derivatives can help in case of soil-borne plant disease suppression in various cropping systems [79,80,81,82,83,84,85,86]. This knowledge is also useful to enhance soil chemical and biological quality [87,88], which can be a successful part of the circular economy.

Author Contributions

Conceptualization, S.M.M. and I.C.; Methodology, S.M.M.; Software, V.T. and S.M.M.; Validation, S.M.M. and I.C.; Formal analysis, S.M.M. and V.T.; Investigation, S.M.M. Resources: I.C. and A.S.; Data curation, S.M.M. and V.T.; Writing—original draft preparation, S.M.M.; Writing—review and editing, S.M.M., V.T., I.C. and A.S.; Visualization, S.M.M.; Supervision, I.C. and S.M.M.; Project administration, I.C. and A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was carried out in the framework of the project ‘Smart Basilicata’ (Contract n. 6386-3, 20 July 2016). Smart Basilicata was approved by the Italian Ministry of Education, University and Research (Notice MIUR n.84/Ric 2012, PON 2007–2013 of 2 March 2012) and was funded with the Cohesion Fund 2007–2013 of the Basilicata Regional Authority.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors wish to thank “Fattorie Donna Giulia”, Atella (PZ), Italy (40°51′ N; 15°38′ E) for providing digestate samples and Di Renzo G.C.—operative unit SAFE-UNIBAS for providing the digestate-derived compost.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Salminen, E.; Rintala, J. Anaerobic digestion of organics solid poultry slaughterhouse waste—A review. Bioresour. Technol. 2002, 83, 13–26. [Google Scholar] [CrossRef]
  2. Zhang, C.; Su, H.; Baeyens, J.; Tan, T. Reviewing the anaerobic digestion of food waste for biogas production. Renew. Sustain. Energy Rev. 2014, 38, 383–392. [Google Scholar] [CrossRef]
  3. Obileke, K.; Nuwokolo, N.; Makaka, G.; Mukumba, P.; Onyeaka, H. Anaerobic digestion: Technology for biogas production as a source of renewable energy—A review. Energy Environ. 2021, 32, 191–225. [Google Scholar] [CrossRef]
  4. Pavlostathis, S.G.; Giraldo-Gomez, E. Kinetics of anaerobic treatment: A critical review. Crit. Rev. Environ. Control 1991, 21, 411–490. [Google Scholar] [CrossRef]
  5. Maeda, K.; Hanajima, D.; Toyoda, S.; Yoshida, N.; Morioka, R.; Osada, T. Microbiology of nitrogen cycle in animal compost. Microb. Biotechnol. 2011, 4, 700–709. [Google Scholar] [CrossRef]
  6. Bernal, M.; Alburquerque, J.; Moral, R. Composting of animal manures and chemical criteria for compost maturity assessment. A review. Bioresour. Technol. 2009, 100, 5444–5545. [Google Scholar] [CrossRef]
  7. Rudolf, M.; Kroneck, P. The nitrogen cycle: Its biology. Met. Ions. Biol. Syst. 2005, 43, 75–104. [Google Scholar]
  8. Batstone, D.J.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S.V.; Pavlostathis, S.G.; Rozzi, A.; Sanders, W.T.M.; Siegrist, H.; Vavilin, V.A. Anaerobic Digestion Model No. 1. IWA Task Group on Mathematical Modelling of Anaerobic Digestion Processes; IWA Publishing: London, UK, 2002; 80p. [Google Scholar]
  9. Gilbert, J.; Jürgensen-Ricci, M.; Ramola, A. Benefits of Compost and Anaerobic Digestate When Applied to Soil; The International Solid Waste Association (ISWA): Rotterdam, The Netherlands, 2020. [Google Scholar]
  10. Ezemagu, I.G.; Ejimofor, M.I.; Menkiti, M.C. Biofertilizer production via composting of digestate obtained from anaerobic digestion of postbiocoagulation sludge blended with saw dust: Physiochemical characterization and kinetic study. Environ. Chall. 2021, 5, 100288. [Google Scholar] [CrossRef]
  11. European Biogas Association (EBA). Contribution of Anaerobic Digestion to Circular Economy. Available online: www.european.biogas.eu (accessed on 14 January 2021).
  12. Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on Waste and Repealing Certain Directives (Text with EEA Relevance). An Official Website of the European Union. 2008. Available online: https://data.europa.eu/eli/dir/2008/98/oj (accessed on 10 June 2020).
  13. European Environment Agency. Bio-Waste in Europe—Turning Challenges into Opportunities; EEA Report, No. 4; Publications Office of the European Union: Luxembourg, 2020; Available online: https://www.eea.europa.eu/publications/bio-waste-in-europe/ (accessed on 1 September 2021).
  14. Cioabla, A.E.; Ionel, I.; Dumitrel, G.-A.; Popescu, F. Comparative study on factors affecting anaerobic digestion of agricultural vegetal residues. Biotechnol. Biofuels 2012, 5, 39–48. [Google Scholar] [CrossRef] [Green Version]
  15. Gholamreza, M.; Samaneh, G.; Anoshiravan, M.B. The biodegradation and COD removal of 2-chlorophenolina granular anoxic baffled reactor. J. Biotechnol. 2014, 184, 111–117. [Google Scholar] [CrossRef]
  16. Ward, A.J.; Hobbs, P.J.; Holliman, P.J.; Jones, D.L. Optimization of the anaerobic digestion of agricultural resources. Bioresour. Technol. 2008, 99, 7928–7940. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, B.; Dong, F.; Chen, M.; Zhu, J.; Tan, J.; Fu, X.; Wang, Y.; Chen, S. Advances in recycling and utilization of agricultural wastes in China: Based on environmental risk, crucial pathways, influencing factors, policy mechanism. Procedia Environ. Sci. 2016, 31, 12–17. [Google Scholar] [CrossRef] [Green Version]
  18. Maina, S.; Kachrimanidou, V.; Koutinas, A. A roadmap towards a circular and sustainable bioeconomy through waste valorization. Curr. Oppin. Green Sustain. Chem. 2017, 8, 18–23. [Google Scholar] [CrossRef]
  19. De Corato, U. Agricultural waste recycling in horticultural intensive farming systems by on-farm composing and compost-based tea application improve soil quality and plant health: A review under the perspective of a circular economy. Sci. Total Environ. 2020, 738, 139840. [Google Scholar] [CrossRef] [PubMed]
  20. Zarbá, C.; Chinnici, G.; La Via, G.; Bracco, S.; Pecorino, B.; D’Amico, M. Regulatory Elements on the Circular Economy: Driving into the Agri-Food System. Sustainability 2021, 13, 8350. [Google Scholar] [CrossRef]
  21. Smidt, E.; Tintner, J.; Böhm, K.; Binner, E. Transforming of Biogenic Waste Materials through Anaerobic Digestion and Subsequent Composting of the Residues—A Case Study. Dyn. Soil Dyn. Plant 2011, 5, 63–69. [Google Scholar]
  22. Weiland, P. Biomass Digestion in Agriculture: A Successful Pathway for the Energy Production and Waste Treatment in Germany. Review. Eng. Life Sci. 2003, 6, 302–309. [Google Scholar] [CrossRef]
  23. Sedlmeier, R.; Rombach, M.; Bitsch, V. Making Food Rescue Your Business: Cases Studies in Germany. Sustainability 2019, 11, 5101. [Google Scholar] [CrossRef] [Green Version]
  24. Bywater, A. A Review of Anaerobic Digestion Plants on UK Farms. Barriers, Benefits and Case Studies; Royal Agricultural Society of England: Stoneleigh Park, UK, 2011; 104p. [Google Scholar]
  25. Demichelis, F.; Piovano, F.; Fiore, S. Biowaste management in Italy: Challenges and Perspectives. Sustainability 2019, 11, 4213. [Google Scholar] [CrossRef] [Green Version]
  26. Diacono, M.; Montemurro, F. Long-term effects of organic amendments on soil fertility. A review. Agron. Sustain. Dev. 2010, 30, 401–422. [Google Scholar] [CrossRef] [Green Version]
  27. Larney, F.J.; Angers, D.A. The role of organic amendments in soil reclamation: A review. Can. J. Soil Sci. 2012, 92, 19–38. [Google Scholar] [CrossRef]
  28. Ramos, T.M.; Jay-Rusell, M.T.; Milner, P.D.; Shade, J.; Misiewicz, T.; Sorge, U.S.; Hutchinson, M.; Lilley, J.; Pires, A.F.A. Assessment of Biological Soil Amendments of Animal Origin use, Research Needs, and Extension Opportunities in Organic Production. Front. Sustain. Food Syst. 2019, 3, 73. [Google Scholar] [CrossRef] [Green Version]
  29. Lim, Y.L.; Bong, C.P.C.; Lee, C.T.; Kleměs, J.J.; Sarmidi, M.J.; Lim, J.S. Review on the Current Composting Practices and the Potential of Improvement Using Two-Stage Composting. Chem. Eng. Trans. 2017, 61, 1051–1056. [Google Scholar] [CrossRef]
  30. Bian, B.; Hu, X.; Zhang, S.; Lv, C.; Yang, Z.; Yang, W.; Zang, L. Pilot-scale composting of typical multiple agricultural wastes: Parameter optimization and mechanisms. Bioresour. Technol. 2019, 287, 121482. [Google Scholar] [CrossRef]
  31. Sayara, T.; Salimia-Basheer, R.; Hawamnde, F.; Sánchez, A. Recycling of Organic Wastes through Composting: Process Performance and Compost Application in Agriculture. Agronomy 2020, 10, 1838. [Google Scholar] [CrossRef]
  32. Palese, A.M.; Persiani, A.; D’Adamo, C.; Pergola, M.; Pastore, V.; Sileo, R.; Ippolito, G.; Lombardi, M.A.; Celano, G. Composting as Manure Disposal Strategy in Small/Medium-Size Livestock Farms: Some Demonstrations with Operative Indications. Sustainability 2020, 12, 3315. [Google Scholar] [CrossRef] [Green Version]
  33. Ahmad, R.; Jilani, G.; Arshad, M.; Zahir, Z.A.; Khalid, Z. Bio-conversion of organic wastes for their recycling in agriculture: An overview of perspectives and prospects. Ann. Microbiol. 2007, 57, 471–479. [Google Scholar] [CrossRef]
  34. Martinéz-Balnco, J.; Lazcano, C.; Christensen, T.H.; Muňoz, P.; Rieradevall, J.; Møller, J.; Antón, A.; Boldrin, A. Compost benefits for agriculture evaluated by life cycle assessment. A review. Agron. Sustain. Dev. 2013, 33, 721–732. [Google Scholar] [CrossRef] [Green Version]
  35. Tully, K.L.; McAskill, C. Promoting soil health in organically managed systems: A review. Org. Agric. 2020, 10, 339–358. [Google Scholar] [CrossRef]
  36. Cozzolino, V.; Di Meo, V.; Monda, H.; Spaccini, R.; Piccolo, A. The molecular characteristics of compost affect plant growth, arbuscular mycorrhizal fungi, and soil microbial composition. Biol. Fertil. Soils 2016, 52, 15–29. [Google Scholar] [CrossRef]
  37. Sánchez, Ó.J.; Ospina, D.A.; Montoya, S. Compost supplementation with nutrients and microorganisms in composting process. Waste Manag. 2017, 69, 156–163. [Google Scholar] [CrossRef] [PubMed]
  38. Li, Z.; Yang, Y.; Xia, Y.; Wu, T.; Zhu, J.; Wang, Z.; Yang, J. The succession pattern of bacterial diversity in compost using pig manure mixed with wood chips analyzed by 16S rRNA gene analysis. bioRxiv 2019, 674069. [Google Scholar] [CrossRef]
  39. 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. 2003, 53, 349–410. [Google Scholar]
  40. Hultman, J. Microbial Diversity in the Municipal Composting Process and Development of Detection Methods. Ph.D. Thesis, Department of Ecological and Environmental Sciences, Faculty of Biosciences, Institute of Biotechnology, Viikki Graduate School in Biosciences, University of Helsinki, Helsinki, Finland, 2009; 47p. [Google Scholar]
  41. Liu, A.-C.; Chou, C.-Y.; Chen, L.-L.; Kuo, C.-H. Bacterial community dynamics in a swine wastewater anaerobic reactor revealed by 16S rDNA sequence analysis. J. Biotechnol. 2015, 194, 124–131. [Google Scholar] [CrossRef] [Green Version]
  42. Bhatia, A.; Rajpal, A.; Madan, S.; Kazmi, A.A. Techniques to analyze microbial diversity during composting—A mini review. Indian J. Biotechnol. 2015, 14, 19–25. [Google Scholar]
  43. Palaniveloo, K.; Amran, M.A.; Norhashim, N.A.; Mohamad-Fauzi, N.; Peng-Hui, F.; Hui-Wen, L.; Kai-Lin, Y.; Jiale, L.; Chian-Yee, M.G.; Jing-Yi, L.; et al. Food Waste Composting and Microbial Community Profiling. Review. Processes 2020, 8, 723. [Google Scholar] [CrossRef]
  44. Martins, L.F.; Antunes, L.P.; Pascon, R.C.; de Oliveira, J.C.F.; Digiampietri, L.A.; Barbosa, D.; Peixoto, B.M.; Vallim, M.A.; Viana-Niero, C.; Ostroski, E.H. Metagenomic analysis of a tropical composting operation at the São Paulo Zoo Park reveals diversity of biomass degradation functions and organisms. PLoS ONE 2013, 8, e61928. [Google Scholar] [CrossRef]
  45. Mahir, B.; Çağrı, A.; Orhan, I.; Sevcan, A.; Bahar, I. Application of next-generation sequencing methods for microbial monitoring of anaerobic digestion of lignocellulosic biomass. Appl. Microbiol. Biotechnol. 2017, 101, 6849–6864. [Google Scholar] [CrossRef]
  46. Meneghine, A.K.; Nielsen, S.; Varani, A.M.; Thomas, T.; Alves, L.M.C. Metagenomic analysis of soil and freshwater from zoo agricultural area with organic fertilization. PLoS ONE 2017, 12, e0190178. [Google Scholar] [CrossRef] [Green Version]
  47. De Corato, U. Use of omic approaches for characterizing microbiota from suppressive compost to control soil-borne plant pathogens. Arch. Phytopathol. Plant Prot. 2019, 52, 757–775. [Google Scholar] [CrossRef]
  48. Enebe, M.C.; Babalola, O.O. Effects of inorganic and organic treatments on the microbial community of maize rhizosphere by a shotgun metagenomics approach. Ann. Microbiol. 2020, 70, 49. [Google Scholar] [CrossRef]
  49. Young, J.P.W.; Downer, H.L.; Eardly, B.D. Phylogeny of the phototropic Rhizobium strain BTAi1by polymerase chain reaction-based sequencing of a 16S rRNA gene segment. J. Bacteriol. 1991, 173, 2271–2277. [Google Scholar] [CrossRef] [Green Version]
  50. Dong, B.; Yi, J.; Dai, L.; Dai, X. Evaluation of Several DNA Extraction Methods for Obtaining Total Community DNA from Anaerobic Digestion Sludge. Procedia Environ. Sci. 2013, 18, 856–863. [Google Scholar] [CrossRef] [Green Version]
  51. Vitti, A.; Elshafie, H.S.; Logozzo, G.; Marzario, S.; Scopa, A.; Camele, I.; Nuzzaci, M. Physico-Chemical Characterization and Biological Activities of a Digesate and a more Stabilized Digestate-Derived Compost from Agro-Waste. Plants 2021, 10, 386. [Google Scholar] [CrossRef] [PubMed]
  52. Takahashi, S.; Tomita, J.; Nishioka, K.; Hisada, T.; Nishijima, M. Development of a Prokaryotic Universal Primer for Simultaneous Analysis of Bacteria and Archaea Using Next-Generation Sequencing. PLoS ONE 2014, 9, e105592. [Google Scholar] [CrossRef] [Green Version]
  53. Bokulich, N.A.; Subramanian, S.; Faith, J.J.; Gevers, D.; Gordon, J.I.; Knight, R.; Mills, D.A.; Caporaso, J.G. Quality-filtering vastly improves diversity estimates Illumina amplicon sequencing. Nat. Methods 2013, 10, 57–59. [Google Scholar] [CrossRef]
  54. Caporaso, G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Pena, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  55. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019; Available online: https://www.R-project.org/ (accessed on 5 May 2019).
  56. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological Statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 9. [Google Scholar]
  57. Thummes, K.; Schäfer, J.; Kämpfer, P.; Jäckel, U. Thermophilic methanogenic Archaea in compost material: Occurrence, persistence and possible mechanisms for their distribution to other environments. Syst. Appl. Microbiol. 2007, 30, 634–643. [Google Scholar] [CrossRef] [PubMed]
  58. Oshi, R.; Tada, C.; Asano, R.; Yamamoto, N.; Suyama, Y.; Nakai, Y. Growth of ammonia-oxidizing archaea and bacteria in cattle manure compost under various temperatures and ammonia concentrations. Microb. Ecol. 2012, 63, 787–793. [Google Scholar] [CrossRef]
  59. Antunes, L.P.; Martins, L.F.; Pereira, L.V.; Thomas, A.M.; Barbosa, D.; Lemos, L.N.; Machado Silva, G.M.; Moura, L.M.S.; Epamino Condomiti, G.W.; Digiampietri, L.A.; et al. Microbial community structure and dymamics in thermophilic composting viewed through metagenomics and metatranscriptomics. Sci. Rep. 2016, 6, 38915. [Google Scholar] [CrossRef] [PubMed]
  60. Xia, Y.; Massé, D.; McAllister, T.A.; Kong, Y.; Seviour, R.; Beaulieu, C. Identity and diversity of archaeal communities during anaerobis co-digestion of chicken feathers and other animal wastes. Bioresour. Technol. 2012, 110, 111–119. [Google Scholar] [CrossRef] [PubMed]
  61. Kundu, K.; Bergmann, I.; Kloche, M.; Sharma, S.; Sreekrishnan, T.R. Impact of abrupt temperature increase on the performance of an anaerobic hybrid bioreactor and its intrinsic microbial community. Bioresour. Technol. 2014, 168, 72–79. [Google Scholar] [CrossRef] [PubMed]
  62. De Vrieze, J.; Saunders, A.M.; He, Y.; Fang, J.; Nielsen, P.H.; Vestraete, W.; Boon, N. Ammonia and temperature determine potential clustering in the anaerobic digestion microbiome. Water Res. 2015, 75, 312–323. [Google Scholar] [CrossRef]
  63. Chang, R.; Li, Y.; Li, N.; Wu, X.; Chen, Q. Effect of microbial transformation induced by metallic compound additives and temperature variations during composting on suppression of soil-borne pathogens. J. Environ. Manag. 2021, 279, 111816. [Google Scholar] [CrossRef]
  64. Zhang, L.; Zhang, H.; Wang, Z.; Chen, G.; Wang, L. 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. 2016, 203, 1–10. [Google Scholar] [CrossRef]
  65. Cardinali-Rezende, J.; Colturato, L.; Colturato, T.D.B.; Chartone-Souza, E.; Nascimento, A.M.A.; Sanz, J.L. Prokaryotic divesity and dynamics in a full-scale municipal solid waste anaerobic reactor from start-up to steady-state conditions. Bioresour. Technol. 2012, 119, 373–383. [Google Scholar] [CrossRef] [Green Version]
  66. Werner, J.J.; Knights, D.; Garcia, M.L.; Scalfone, N.B.; Smith, S.; Yarasheski, K.; Cummings, T.A.; Beers, A.R.; Knight, R.; Angenent, L.T. Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proc. Natl. Acad. Sci. USA 2011, 108, 4158–4163. [Google Scholar] [CrossRef] [Green Version]
  67. Nelson, M.C.; Morrison, M.; Yu, Z.T. A meta-analysis of the microbial diversity observed in anaerobic digesters. Bioresour. Technol. 2011, 102, 3730–3739. [Google Scholar] [CrossRef]
  68. Zhang, L.; Li, L.; Pan, X.; Shi, Z.; Feng, X.; Gong, B.; Li, J.; Wang, L. Enhanced Growth and Activities of the Dominant Functional Microbiota of Chicken Manure Composts in the Presence of Maize Straw. Front. Microbiol. 2018, 9, 1131. [Google Scholar] [CrossRef] [Green Version]
  69. Jurado, M.; Lopéz, M.; Suárez-Estrella, F.; Vargas-Garcia, M.C.; López-Gonzáles, J.A.; Moreno, J. Exploiting compost biodiversity: Study of the persistent biotechnologically relevant microorganisms from lignocellulose-based composting. Bioresour. Technol. 2014, 162, 283–293. [Google Scholar] [CrossRef]
  70. Partanen, P.; Hultman, J.; Paulin, L.; Auvinen, P.; Romantschuk, M. Bacterial diversity at different stages of composting process. BMC Microbiol. 2010, 10, 94. [Google Scholar] [CrossRef] [Green Version]
  71. Holman, D.B.; Hao, X.; Topp, E.; Yang, H.E.; Alexander, T.W. Effect of Co-Composting Cattle Manure with Construction and Demolition Waste on the Archaeal, Bacterial, and Fungal Microbiota, and on Antimicrobial Resistance Determinants. PLoS ONE 2016, 11, e0157539. [Google Scholar] [CrossRef] [Green Version]
  72. Franke-Whittle, I.H.; Confaloneri, A.; Insam, H.; Schlegelmilch, M.; Körner, I. Changes in the microbial communities during co-composting of digestates. Waste Manag. 2014, 34, 632–641. [Google Scholar] [CrossRef] [Green Version]
  73. Ryckeboer, J.; Mergaert, J.; Coosemans, J.; Deprins, K.; Swings, J. Microbiological aspects of biowaste during composting in a monitored compost bin. J. Appl. Microbiol. 2003, 94, 127–137. [Google Scholar] [CrossRef] [Green Version]
  74. Varma, V.S.; Dhamodharan, K.; Kalamdhad, A.S. Characterization of bacterial community structure during in-vessel composting of agricultural waste by 16S rRNA sequencing. Biotech 2018, 8, 301. [Google Scholar] [CrossRef]
  75. Chandna, P.; Nain, L.; Singh, S.; Kuhad, R.C. Assessment of Bacterial Diversity during Composting of Agricultural Byproducts. BMC Microbiol. 2013, 13, 99. Available online: https//www.bmcmicrobiol.biomedcentral.com/1471.2180/13/99 (accessed on 1 September 2021). [CrossRef] [Green Version]
  76. Meng, Q.; Wang, W.; Men, M.; Bello, A.; Xu, X.; Xu, B.; Deng, L.; Jiang, X.; Sheng, S.; Wu, X.; et al. Microbial Community Succession and Response to Environmental Variables during Cow Manure and Corn Straw Composting. Front. Microbiol. 2019, 10, 529. [Google Scholar] [CrossRef] [Green Version]
  77. Ishii, K.; Takii, S. Comparison of microbial communities in four different composting processes as evaluated by denaturing gradient gel electrophoresis analysis. J. Appl. Micobiol. 2003, 95, 109–119. [Google Scholar] [CrossRef]
  78. Fracchia, L.; Dohrmann, A.B.; Martinotti, M.G.; Tebbe, C.C. Bacterial diversity in a finished compost and vermicompost: Differences revealed by cultivation independent analyses of PCR-amplified 16S rRNA genes. Appl. Microbiol. Biotechnol. 2006, 71, 942–952. [Google Scholar] [CrossRef]
  79. Hadar, Y.; Mandelbaum, R. Suppressive compost for biocontrol of soil-borne plant pathogens. Phytoparasitica 1992, 20, S113–S116. [Google Scholar] [CrossRef]
  80. Tubeileh, A.M.; Stephenson, G.T. Soil amendment by composted plant wastes reduces the Verticillium dahliae abundance and changes soil chemical properties in a bell pepper cropping system. Curr. Plant Biol. 2020, 22, 100148. [Google Scholar] [CrossRef]
  81. Rady, M.M.; Semida, W.M.; Hemida, K.A.; Abdelhamid, M.T. The effect of compost on growth and yield of Phaseolus vulgaris plants grown under saline soil. Int. J. Recycl. Org. Waste Agric. 2016, 5, 311–321. [Google Scholar] [CrossRef] [Green Version]
  82. Ibukunoluwa Moyin-Jesu, E. Use of different organic fertilizers on soil fertility improvement, growth and head yield parameters of cabbage (Brassica oleraceae L.). Int. J. Recycl. Org. Waste Agric. 2015, 4, 291–298. [Google Scholar] [CrossRef] [Green Version]
  83. Postma, J.; Montanari, M.; van den Boorget, P.H.J.F. Microbial enrichment to enhance the disease suppressive activity of compost. Eur. J. Soil Biol. 2003, 39, 157–163. [Google Scholar] [CrossRef]
  84. Zaccardelli, M.; de Nicola, F.; Villecco, D.; Scotti, R. The development and suppressive activity of soil microbial communities under compost amendment. J. Soil Sci. Plant Nutr. 2013, 13, 740–742. [Google Scholar] [CrossRef] [Green Version]
  85. Bellini, A.; Ferrocino, I.; Cucu, M.A.; Pugliese, M.; Garibaldi, A.; Gullino, M.L. A Compost Treatment Acts as a Suppressive Agent in Phytophthora capsici—Cucurbita pepo Pathosystem by Modifying the Rhizosphere Microbiota. Front. Plant Sci. 2020, 11, 885. [Google Scholar] [CrossRef]
  86. Lutz, S.; Thuerig, B.; Oberhaensli, T.; Mayerhofer, J.; Fuchs, J.G.; Widmer, F.; Freimoser, F.M.; Ahrens, C.H. Harnessing the Microbiomes of Suppressive Composts for Plant Protection: From Metagenomes to Beneficial Microorganisms and Reliable Diagnostics. Front. Microbiol. 2020, 11, 1810. [Google Scholar] [CrossRef]
  87. Piciarello, E.; Pucci, L.; Carotenuto, M.; Libralato, G.; Lofrano, G.; Baldantoni, D. Compost and Sewage Sludge for the Improvement of Soil Chemical and Biological Quality of Mediterranean Agroecosystems. Sustainability 2021, 13, 26. [Google Scholar] [CrossRef]
  88. Baldantoni, D.; Saviello, G.; Alfani, A. Nutrients and non-essential elements in edible crops following long-term mineral and compost fertilization of a Mediterranean agricultural soil. Environ. Sci. Pollut. Res. 2018, 26, 35353–35364. [Google Scholar] [CrossRef]
Processes 10 00379 g001 550
Figure 1. Relative abundance (RA%) of Bacteria and Archaea in digestate (ADG) and compost (DdC). The taxonomic assignments were obtained from 16S rRNA gene sequencing analysis.
Figure 1. Relative abundance (RA%) of Bacteria and Archaea in digestate (ADG) and compost (DdC). The taxonomic assignments were obtained from 16S rRNA gene sequencing analysis.
Processes 10 00379 g001
Processes 10 00379 g002 550
Figure 2. Top 5 bacterial orders identified in digestate (ADG) and a more stabilized digestate-derived compost (DdC) microbiome. The taxonomic profile was identified using sequencing of the amplicons of 16S rRNA gene region. Unassigned 16S sequences were excluded.
Figure 2. Top 5 bacterial orders identified in digestate (ADG) and a more stabilized digestate-derived compost (DdC) microbiome. The taxonomic profile was identified using sequencing of the amplicons of 16S rRNA gene region. Unassigned 16S sequences were excluded.
Processes 10 00379 g002
Processes 10 00379 g003 550
Figure 3. Microbioma composition (at order level) of the digestate (ADG) and the digestate-derived compost (DdC). The taxonomic profile is based on the sequencing of the 16S rRNA gene region. Unassigned 16S sequences were excluded.
Figure 3. Microbioma composition (at order level) of the digestate (ADG) and the digestate-derived compost (DdC). The taxonomic profile is based on the sequencing of the 16S rRNA gene region. Unassigned 16S sequences were excluded.
Processes 10 00379 g003
Processes 10 00379 g004 550
Figure 4. Shared microbiome composition (at order level) of the digestate (ADG) and the digestate-derived compost (DdC). The taxonomic profile is based on the sequencing of the 16S rRNA gene region. Unassigned 16S sequences were excluded.
Figure 4. Shared microbiome composition (at order level) of the digestate (ADG) and the digestate-derived compost (DdC). The taxonomic profile is based on the sequencing of the 16S rRNA gene region. Unassigned 16S sequences were excluded.
Processes 10 00379 g004
Table
Table 1. Overview of metagenomic results based on 16S rRNA gene amplicons high-throughput sequencing of digestate and stabilized digestate-derived compost samples from agro-waste.
Table 1. Overview of metagenomic results based on 16S rRNA gene amplicons high-throughput sequencing of digestate and stabilized digestate-derived compost samples from agro-waste.
Total No./Taxonomic ClassificationDigestate
(ADG)		Compost
(DdC)
Sequences obtained55,61611,229
Unclassified sequences (%)1.349.09
Classified sequences * (%)98.6690.91
Orders identified2774
Orders included (shared by the two types of matrices and including Archaea) 2323
Orders excluded451
Families identified54125
Families included (shared by the two types of matrices)3737
Families excluded1788
* Classification was counted when it was obtained at least at the order level in allthree samples.
Table
Table 2. Relative abundance (mean ± standard errors) of all microbiota at orders level in digestate and digestate derived-compost and p values of Student’s t-test.
Table 2. Relative abundance (mean ± standard errors) of all microbiota at orders level in digestate and digestate derived-compost and p values of Student’s t-test.
OrderDigestate (ADG)Compost (DdC)p (t)
Acholeplasmatales1.7 ± 0.020.02 ± 0.00588.27 × 10−5
Acidimicrobiales0 ± 02.4 ± 0.11550.0023
Actinomycetales0.08 ± 0.00581.25 ± 0.012.43 × 10−5
Aeromonadales0.02 ± 0.00580.01 ± 00.225
Alteromonadales0 ± 010.66 ± 0.03461.06 × 10−5
Anaerolineales0 ± 00.03 ± 0.00580.035
Bacillales43.27 ± 0.05195.27 ± 0.02305.778 × 10−5
Bacteroidales22.47 ± 0.479814.21 ± 0.0600.004
Bedellovibrionales0 ± 00.08 ± 0.00580.0041
Burkholderiales1.11 ± 0.00580.82 ± 0.00580.00013
Caldilineales0 ± 00.13 ± 0.00580.0019
Campylobacterales0.6 ± 0.02880.23 ± 0.010.00315
Caulobacterales0 ± 00.5 ± 0.01520.0009
Chromatiales0 ± 00.32 ± 0.00580.00032
Chrysiogenales0 ± 00.03 ± 0.00580.035
Chthoniobacterales0 ± 00.7 ± 0.00580.0067
Clostridiales5.72 ± 0.07232.13 ± 0.01150.00028
Cytophagales0 ± 08.04 ± 0.01152.06 × 10−6
Deferribacterales0.01± 00 ± 00.0078
Dehalococcoidales0.01 ± 00 ± 0NA *
Desulfobacterales0 ± 00.05 ± 0.00580.013
Desulfovibrionales0.11 ± 0.00580.34 ± 0.00580.00063
Enterobacteriales0 ± 00.03 ± 0.010.09
Erysipelotrichales1.25 ± 0.01730.03 ± 0.00588.96 × 10−5
Fibrobacterales0.04 ± 0.00580.03 ± 0.010.22
Flavobacteriales0.8 ± 0.02084.32 ± 0.011.08 × 10−5
Fusobacteriales0.01 ± 00 ± 0NA *
Gaiellales0 ± 00.01 ± 0NA *
Gammatimonadales0 ± 00.04 ± 0.00580.02
Gemmatales0 ± 00.01 ± 0NA *
Ignavibacteriales0 ± 00.15 ± 0.00580.0015
Kiloniellales0 ± 00.33 ± 0.00580.00148
Lactobacillales0.08 ± 0.00580.07 ± 0.00580.225
Legionellales0 ± 00.02 ± 0NA *
Methylcoccales0 ± 00.02 ± 0.00580.074
Methylophilales0 ± 00.51 ± 0.010.00038
Myxococcales0 ± 00.44 ± 0.020.0021
Natranaerobiales0 ± 00.04 ± 0.00580.02
Neiseriales0.010NA *
Nitrosomonadales0 ± 00.41 ± 0.01520.0014
Nitrospirales0 ± 00.01 ± 0NA *
Oceanospirillales0 ± 03.64 ± 0.02304.03 × 10−5
Optitutales0 ± 00.24 ± 0.00580.00058
Phycisphaerales0 ± 00.12 ± 0.00580.0023
Pirelullales0 ± 00.37 ± 0.00580.00024
Planctomycetales0 ± 00.08 ± 0.00580.0052
Podosphaerales0.02 ± 0.010.02 ± 01
Pseudomonadales0.5 ± 0.00582.13 ± 0.011.25 × 10−5
Puniceicoccales0 ± 00.01 ± 0NA *
Rhizobiales0 ± 07.05 ± 0.00586.71 × 10−7
Rhodobacterales0 ± 02.05 ± 0.012.38 × 10−5
Rhodocylales0 ± 00.95 ± 0.01530.000258
Rhodospirillales0 ± 01.88 ± 0.02510.000179
Rhodothermales0 ± 01.04 ± 0.00583.08 × 10−5
Rickettsiales0 ± 00.22 ± 0.00580.000688
Saprospirales0 ± 01.64 ± 0.10010.0037
Solibacterales0 ± 00.09 ± 0.00580.0041
Solirubrobacterales0 ± 00.07 ± 0.010.019
Spaherochaetales1.55 ± 0.00580 ± 01.39 × 10−5
Sphingobacteriales0.02 ± 0.00581.47 ± 0.01150.00011
Sphingomonadales0 ± 02.53 ± 0.04040.00025
Spirobacillales0 ± 00.04 ± 0.00580.02
Spirochetales0.22 ± 0.00581.64 ± 0.00581.97 × 10−6
Synergistales0.09 ± 0.00580.09 ± 0.011
Syntrophobacterales0 ± 00.11 ± 0.00580.0027
Thiotrichales0 ± 00.24 ± 0.01150.0023
Turicibacterales0.06 ± 0.00580.15 ± 0.01530.012
Verrucomicrobiales0 ± 00.27 ± 0.010.00137
Xanthomonadales0 ± 09.16 ± 0.00583.97 × 10−7
Others Archaea19 ± 0.03050.08 ± 0.00581.77 × 10−5
* Note: NA = not applicable.
Table
Table 3. Average values of the Alpha diversity indices at order level in digestate and digestate derived-compost.
Table 3. Average values of the Alpha diversity indices at order level in digestate and digestate derived-compost.
Diversity IndexADG DdC
Shannon1.5733.014
Chao-124.7568
Table
Table 4. Relative abundance of the shared orders in digestate and digestate derived-compost and results of the statistical Student’s t-test.
Table 4. Relative abundance of the shared orders in digestate and digestate derived-compost and results of the statistical Student’s t-test.
OrderDigestate (ADG)Compost (DdC)p (t)
Acholeplasmatales1.7 ± 0.05770.22 ± 0.00580.0013
Actinomycetales0.08 ± 0.00581.25 ± 0.00588.094 × 10−8
Bacillales43.27 ± 0.58325.27 ± 0.01150.00024
Bacteroidales22.47 ± 0.479814.21 ± 0.06820.0041
Burkholderiales1.11 ± 0.00580.82 ± 0.00580.00119
Campylobacterales0.6 ± 0.05770.23 ± 0.00580.0214
Clostridiales5.72 ± 0.03212.33 ± 0.00580.00012
Desulfovibrionales0.11 ± 0.00580.34 ± 0.00580.00188
Erysipelotrichales1.25 ± 0.00580.03 ± 0.00586.72 × 10−5
Fibrobacterales0.04 ± 0.00580.03 ± 0.00580.225
Flavobacteriales0.8 ± 0.02084.32 ± 0.011.07 × 10−5
Lactobacillales0.08 ± 0.00580.07 ± 0.00580.422
Podosphaerales0.02 ± 0.00580.02 ± 01
Pseudomonadales0.5 ± 0.05772.13 ± 0.00580.00139
Sphingobacteriales0.02 ± 0.00581.47 ± 0.01150.00011
Spirochetales0.22 ± 0.00581.64 ± 0.00581.04 × 10−6
Synergistales0.09 ± 0.0050.09 ± 01
Turicibacterales0.06 ± 0.00580.15 ± 0.00580.004
Others Archaea19 ± 0.05770.07 ± 0.00580.00092
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mang, S.M.; Trotta, V.; Scopa, A.; Camele, I. Metagenomic Analysis of Bacterial Community Structure and Dynamics of a Digestate and a More Stabilized Digestate-Derived Compost from Agricultural Waste. Processes 2022, 10, 379. https://doi.org/10.3390/pr10020379

AMA Style

Mang SM, Trotta V, Scopa A, Camele I. Metagenomic Analysis of Bacterial Community Structure and Dynamics of a Digestate and a More Stabilized Digestate-Derived Compost from Agricultural Waste. Processes. 2022; 10(2):379. https://doi.org/10.3390/pr10020379

Chicago/Turabian Style

Mang, Stefania Mirela, Vincenzo Trotta, Antonio Scopa, and Ippolito Camele. 2022. "Metagenomic Analysis of Bacterial Community Structure and Dynamics of a Digestate and a More Stabilized Digestate-Derived Compost from Agricultural Waste" Processes 10, no. 2: 379. https://doi.org/10.3390/pr10020379

APA Style

Mang, S. M., Trotta, V., Scopa, A., & Camele, I. (2022). Metagenomic Analysis of Bacterial Community Structure and Dynamics of a Digestate and a More Stabilized Digestate-Derived Compost from Agricultural Waste. Processes, 10(2), 379. https://doi.org/10.3390/pr10020379

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