METAGENOMICS IN-SIGHT INTO THE MICROBIAL STRUCTURAL DIVERSITY OF ANAEROBIC DIGESTER UTILISING FRUIT WASTE AS SUBSTRATE

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April – May 2020, vol. 9, no. 5
pages: 965-969
Article type: Microbiology of Microbiology
DOI: 10.15414/jmbfs.2020.9.5.965-969
Abstract: Anaerobic digester has emerged as a technology of choice in management of waste and production of biogas. However, the microbial ecology of digesters utilizing various substrates are very poorly understood. The ecology of anaerobic digester utilizing Citrullus lanatus fruit waste was analyzed using metagenomics. Slurry substrate sample was collected from a functional digester aseptically and anaerobically. Metagenomic DNA was extracted using ZYMO DNA extraction Kit (Model D 6001, Zymo Research, USA) following manufacturer’s instruction. Extracted DNA was amplified using the 16S rRNA gene amplicon PCR primers set and sequenced using Illumina MiSeq platform. Taxonomic analysis of the reads was performed using NCBI-BLAST-2.2.24 and CLC bio Genomics workbench v7.5.1. Taxonomic classification of the sequences revealed that bacteria and archae were the top two kingdoms with reads counts of 57,554 and 80, respectively. The top 7 phyla were Unknown, Firmicutes, Proteobacteria, Planctomycetes, Bacteroidetes, Actinobacteria and Euryarchaeota in decreasing order of counts. A total of 30 microbial classes, 43 orders, 79 families and 210 species were further classified. Over half of the 210 species detected were not routinely cultured species in the laboratory, indicating that there is still a relatively wide gap between culturable and non-culturable species in an anaerobic digester. The Acidogens captured in this study were Clostridium, Uncultured rumen bacteria and Bacteroidetes species. Furthermore, we also detected uncultured syntrophic Acetogens such as Syntrophomonas species and the hydrogenotrophic thermophile, Methanothermobacter sp. The Syntrophomonas species is known to breakdown short chain fatty acids, like propionate and butanoate in concert with hydrogenotrophic Methanogens indicating methane generation was via the hydrogenotrophic route. However, the main representative hydrogenotrophic methanogens detected were Methanoculleus bourgensis and Methanoculleus marisnigri, with the former being more abundant. In addition to the aforementioned species, other species captured were largely classified as unknown or uncultured species and they include Uncultured species of Clostridium, Syntrophomonas, Synergistetes, Synergistaceae Anaerobic sp, Ruminococcaceae, Rumen sp, Thermomonas, Thermoanaerobacteriales, Bacterium, Compost, Firmicutes, Bacteroidetes, Chloroflexi, Clostriaceae, Acetobacter. Eubacterium, Alpha and Aacteroides. The results of the study revealed that culture-independent approach is better able to capture the anaerobes including both culturable and unknown that dominate anaerobic digesters and are responsible for the bioconversion of organic waste into biogas.
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METAGENOMICS IN-SIGHT INTO THE MICROBIAL STRUCTURAL DIVERSITY OF ANAEROBIC DIGESTER UTILISING FRUIT WASTE AS SUBSTRATE


AUTHORS

Anika, Ogemdi Chinwendu, Akin-Osanaiye, Bukola Catherine, Edet, Uwem Okon

ABSTRACT

Anaerobic digester has emerged as a technology of choice in management of waste and production of biogas. However, the microbial ecology of digesters utilizing various substrates are very poorly understood. The ecology of anaerobic digester utilizing Citrullus lanatus fruit waste was analyzed using metagenomics. Slurry substrate sample was collected from a functional digester aseptically and anaerobically. Metagenomic DNA was extracted using ZYMO DNA extraction Kit (Model D 6001, Zymo Research, USA) following manufacturer’s instruction. Extracted DNA was amplified using the 16S rRNA gene amplicon PCR primers set and sequenced using Illumina MiSeq platform. Taxonomic analysis of the reads was performed using NCBI-BLAST-2.2.24 and CLC bio Genomics workbench v7.5.1. Taxonomic classification of the sequences revealed that bacteria and archae were the top two kingdoms with reads counts of 57,554 and 80, respectively. The top 7 phyla were Unknown, Firmicutes, Proteobacteria, Planctomycetes, Bacteroidetes, Actinobacteria and Euryarchaeota in decreasing order of counts. A total of 30 microbial classes, 43 orders, 79 families and 210 species were further classified. Over half of the 210 species detected were not routinely cultured species in the laboratory, indicating that there is still a relatively wide gap between culturable and non-culturable species in an anaerobic digester. The Acidogens captured in this study were Clostridium, Uncultured rumen bacteria and Bacteroidetes species. Furthermore, we also detected uncultured syntrophic Acetogens such as Syntrophomonas species and the hydrogenotrophic thermophile, Methanothermobacter sp. The Syntrophomonas species is known to breakdown short chain fatty acids, like propionate and butanoate in concert with hydrogenotrophic Methanogens indicating methane generation was via the hydrogenotrophic route. However, the main representative hydrogenotrophic methanogens detected were Methanoculleus bourgensis and Methanoculleus marisnigri, with the former being more abundant. In addition to the aforementioned species, other species captured were largely classified as unknown or uncultured species and they include Uncultured species of Clostridium, Syntrophomonas, Synergistetes, Synergistaceae Anaerobic sp, Ruminococcaceae, Rumen sp, Thermomonas, Thermoanaerobacteriales, Bacterium, Compost, Firmicutes, Bacteroidetes, Chloroflexi, Clostriaceae, Acetobacter. Eubacterium, Alpha and Aacteroides. The results of the study revealed that culture-independent approach is better able to capture the anaerobes including both culturable and unknown that dominate anaerobic digesters and are responsible for the bioconversion of organic waste into biogas.


KEYWORDS

Metagenome; 16S rRNA; Illumina MiSeq; Syntrophic Acetogens; Hydrogenotrophic Methanogens

INTRODUCTION

With increasing awareness of the health benefits of fruits, there has been an increased cultivation and consumption of fruits around the world (Ijah et al., 2015; Sagar et al., 2018). In most developing countries such as Nigeria, fruit waste disposal is usually not properly handled despite enormous potentials (Oladepo et al., 2015; Sagar et al., 2018). Furthermore, the lack of post-harvest technology or preservation techniques for fruits increases the quantity of wastes generated from fruits in Nigeria. Anaerobic degradation of waste is a well-known method of biological waste treatment with concomitant production of biogas such as methane and carbon dioxide using a complex community of microorganisms (Kleinsteuber, 2018).

The processes of anaerobic biogas generation are divided into four major stages namely: hydrolysis, acidogenesis, acetogenesis and methanogenesis (Sagagi et al., 2009). During hydrolysis, complex organic polymers are broken down into simpler forms (Cirne et al., 2007; Doi, 2008). If the substrate is not readily degradable, this phase may limit the entire process (Parawira, 2005). However, if the substrate is easily digested, then methanogenesis would be the phase that may limit the process in the reactor (Bjornsson, 2001). The process of acidogenesis involves further disintegration of the components into compounds such as organic acids, ketones, alcohols and carbon dioxide (Ding et al., 2008). Meanwhile, the resulting acidogenic products are converted into hydrogen, carbon dioxide and acetic acid during acetogenesis (Schink, 1997). In the methanogenic stage, the acetogenic products are transformed into methane and CO2   which make up the bulk of the biogas liberated from the process (Verma et al., 2007).

Anaerobic digestion (AD) is a very complex biological process. Despite being so widely used around the world, the microbial ecology of this process is very poorly understood (Nelson et al., 2010; Amha et al., 2018). Unfolding and proper understanding of the complex structural diversity is very important in understanding functional relationship between the various metabolic groups of microorganisms (hydrolytic, acidogenic, acetogenic and methanogenic). Understanding this synergy will help improve and optimize the process of AD thereby making it more effective (Manyi-Loh et al., 2013; Amha et al., 2018).

Most of the microorganisms involved in AD are anaerobes and their cultivation in the laboratory is one of the most challenging areas of microbial research (Mori and Kamagata, 2014).  Before the advent of molecular tools such as metagenomics, microbial ecology of various environments including those of the anaerobic world were largely elusive (Edet et al., 2017a; Edet et al., 2017b; Edet et al., 2018a; Edet et al., 2018b; Mori and Kamagata, 2014). Metagenomics, a culture-independent method allows for the direct examination of microbial community structure and function in an ecosystem using various bioinformatics pipelines (Edet et al., 2017a; Manyi-Loh et al., 2013). Application of omics-based studies have revealed a number of things previously unknown to the anaerobic microbial world such as new taxa and their roles in various anaerobic systems (Manyi-Loh et al., 2013; Mori and Kamagata, 2014).

Microbial composition in an anaerobic digester is driven by a number of factors. Prominent amongst the factors is the type of substrate utilized in the AD and free ammonia (Zhang et al., 2014; Li et al., 2015). Other factors include design of the reactor and its operational condition such as hydraulic retention time, organic loading rates pH, temperature and mixing (Lin et al., 2013; Town et al., 2014; Manyi-Loh et al., 2013) and even co-substrate (Sun et al., 2015). In Nigeria, a number of studies abound on the utilization of anaerobic digestion systems in biogas production and waste treatment but none have utilized metagenomics in the evaluation of microbial structure. Thus, the primary aim of this study was characterization of the microbial communities in an anaerobic digester utilizing water melon fruit waste in biogas production process.

MATERIAL AND METHODS

Sampling of slurry

Slurry sample from an operational digester was collected for characterization of the microbial community composition of the digesters after shaking to achieve a uniform mixture. The samples from the digester were collected from the outlet tap using a sterile capped bottle and transported immediately for immediate analysis. The operational conditions of the digester have been described previously (Anika et al., 2019).

Metagenomic DNA extraction

DNA extraction from slurry sample was performed using ZYMO DNA extraction Kit (Model D 6001, Zymo Research, USA) adhering to the manufacturer’s instructions.

DNA amplification and electrophoresis

The universal bacterial 16S rRNA gene amplicon PCR primers set TruSeq tailed 341F TGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTACGGGNGGCWGCAG and TruSeq tailed 785R ACACTCTTTCCCCACACGACGCTCTTCCGATCTGACTACHVGGGTATCTAATCC was used to amplify and produce more copies of target region in the extracted metagenome. The reactants (substrates) for DNA amplification were prepared using  2.5 μL (5 ng/μL) microbial DNA, 5 μL (1 μM) amplicon PCR forward primer, 5 μL (1 μM) amplicon PCR reverse primer, and 2× KAPA HiFi Hot Start Ready Mix 12.5 μL (total 25 μL).  Using the reaction mixture above, the following protocol was used to run a PCR for the slurry extracted DNAs in a thermal cycler (Applied Biosystems9700, USA) for twenty five cycles: The first cycle was allowed to denature for three minutes at 95 ℃, but for the rest cycles that followed, the DNAs were denatured for five minutes at 95 ℃. Following, the temperature of the reaction mixture was brought down to 55 ℃ for three minutes to allow for DNA annealing and later increased to 72 °C for thirty seconds for elongation to take place, then, another five minutes was allowed at same temperature for a final extension. Exactly 1 μL was run on a bioanalyzer DNA 1000 chip to verify the size of the amplified product (approximately 550bp)

Illumina sequencing

In order to assess all the nucleotide sequences present in the DNAs eluted from digesting slurry samples (to reveal all the microbial communities present), DNA sequencing was carried out using Next Generation Sequencing Technique, following library preparation, using automated PCR cycle- Genome Sequencer™ MiSeq (Illumina). Vecton NTI suite 9 (InforMax, Inc.) was used to analyze and align the sequences (Salam et al., 2017; Edet et al., 2017a).

Taxonomic analysis

Following next generation sequencing, quality control and trimming of the sequences was performed as reported previously (Salam et al., 2017; Edet et al., 2017a). Taxonomic analysis of the reads was performed using NCBI-BLAST-2.2.24 and CLC bio Genomics workbench v7.5.1 (Edet et al., 2017a).

RESULTS

The results of the study are presented in the tables below. Table 1 shows the various kingdom in our studied sample. The various kingdoms classified were Bacteria, Archae, Unknown, Fungi, Plantae, Animalia, and Protozoa in decreasing order of abundance. Their read counts were 57554, 80, 23, 7, 4, 3, and 2, respectively. As can be seen from the read counts, bacteria were the most dominant kingdom representing 99.79% and the followed by archae with 0.14%. Unknown kingdom represented 0.04% while fungi, plantae, and animalia represented 0.01% each. The least abundant kingdom was protozoa with a representation that was less than 0.00%.

Phylum classification revealed a total of 18 phyla with various read counts as shown in Table 2. These were Unknown, Firmicutes, Proteobacteria, Planctomycetes, Bacteroidetes, Actinobacteria, Euryarchaeota, Chloroflexi, Lentisphaerae, Ascomycota, Basidiomycetes, Chordata, Tracheophyta, Ciliophora, Cyanobacteria, Deinococcus-Thermus, Bryophyta, and Crenarchaeota with reads of 53739, 2206, 1177, 186, 186, 116,84, 79, 59, 8, 4, 3, 3, 2, 1, 1, 1, 1 and 1, respectively.

Class classification as presented in table 3, revealed a total of 30 classes including the Unknown and Not Assigned categories. The top 15 classes were Unknown, Clostridia, Gammaproteobacteria, Bacilli, Planctomycetacia, Alphaproteobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Betaproteobacteria, Halobacteria, Flavobacteria, Methanomicrobia, Lentisphaerae, and Deltaproteobacteria. Their corresponding read counts were 53740, 1792, 1010, 414, 186, 107, 84, 72, 58, 54, 46, 43, 32, 8 and 6.

A total of 43 orders were captured in our sample. The top 30 orders are as presented in Table 4.  The top 15 orders were Unknown, Clostridiales, Clostridiales, Bacillales, Planctomycetales,

Planctomycetales, Xanthomonadales, Actinomycetales, Bacteroidales, Chloroflexales, Burkholderiales, Halobacteriales, Flavobacteriales, Methanomicrobiales and Rhodospirillales. Their corresponding read counts were 53748, 1792, 908, 401, 186, 401, 186, 85, 79, 72, 58, 54, 46 and 43. Also included in the top 30 orders was an important methanogen order, Methanomicrobiales.

The various families were 79 in total. However, the top 30 orders in our AD sample is presented in table 5. In increasing order of read counts, they were Unknown, Clostridiaceae, Enterobacteriaceae, Listeriaceae, Eubacteriaceae, Planctomycetaceae, Xanthomonadaceae, Prevotellaceae, Halobacteriaceae,  Flavobacteriaceae,  Not assigned, Bacillaceae, Comamonadaceae, Syntrophomonadaceae, Rhodobacteraceae, Dietziaceae, Acetobacteraceae, Rhodospirillaceae, Phyllobacteriaceae, Sphingomonadaceae, Burkholderiaceae, Pseudomonadaceae, Lactobacillaceae, Caulobacteraceae, Alcaligenaceae, Victivallaceae, Pseudonocardiaceae, Bradyrhizobiaceae, Nocardioidaceae and Micrococcaceae. Their respective read counts were 54160, 1060, 908, 365, 349, 186, 85, 69, 46, 43, 35, 30, 30, 36, 23, 21, 16, 15, 13, 12, 12, 10, 10, 10, 9, 8, 7, 7, 7 and 7. The remaining families are as captured in the supplementary material attached. In addition to the aforementioned families, Methanobacteriaceae and Methylobacteriaceae were also detected in our sample.

Genus/species level classification revealed a total of 210 species majority of which were uncultured species. Selected 45 species are as presented in Table 6. These were dominated by strict anaerobes or facultative anaerobes as well as methanogens and methane utilizing species.

Table 1 Kingdom classification

Kingdoms Read counts Percentage (%)
Bacteria 57554 99.79
Archae 80 0.14
Unknown 23 0.04
Fungi 7 0.01
Plantae 4 0.01
Animalia 3 0.01
Protozoa 2 0.00

Table 2 Phyla classification

Phyla Read count %
Unknown 53739 93.18
Firmicutes 2206 3.83
Proteobacteria 1177 2.04
Planctomycetes 186 0.32
Bacteroidetes 116 0.20
Actinobacteria 84 0.15
Euryarchaeota 79 0.14
Chloroflexi 59 0.10
Lentisphaerae 8 0.01
Ascomycota 4 0.01
Basidiomycota 3 0.01
Chordata 3 0.01
Tracheophyta 3 0.01
Ciliophora 2 0.00
Cyanobacteria 1 0.00
Deinococcus-thermus 1 0.00
Bryophyta 1 0.00
Crenarchaeota 1 0.00

Table 3 Class classification

Class Read Count %
Unknown 53740 93.18
Clostridia 1792 3.11
Gammaproteobacteria 1010 1.75
Bacilli 414 0.72
Planctomycetacia 186 0.32
Alphaproteobacteria 107 0.19
Actinobacteria 84 0.15
Bacteroidetes 72 0.12
Chloroflexi 58 0.10
Betaproteobacteria 54 0.09
Halobacteria 46 0.08
Flavobacteria 43 0.07
Methanomicrobia 32 0.06
Lentisphaerae 8 0.01
Deltaproteobacteria 6 0.01
Mammalia 3 0.01
Agaricomycetes 2 0.00
Saccharomycetes 2 0.00
Leotiomycetes 2 0.00
Heterotrichea 2 0.00
Bryopsida 1 0.00
Sphingobacteria 1 0.00
Lycopodiopsida 1 0.00
Anaerolineae 1 0.00
Methanobacteria 1 0.00
Not assigned 1 0.00
Thermoprotei 1 0.00
Polypodiopsida 1 0.00
Deinococci 1 0.00
Cycadopsida 1 0.00

Table 4 Top 30 orders

Order Read Count %
Unknown 53748 93.19
Clostridiales 1792 3.11
Enterobacteriales 908 1.57
Bacillales 401 0.70
Planctomycetales 186 0.32
Xanthomonadales 85 0.15
Actinomycetales 79 0.14
Bacteroidales 72 0.12
Chloroflexales 58 0.10
Burkholderiales 54 0.09
Halobacteriales 46 0.08
Flavobacteriales 43 0.07
Methanomicrobiales 32 0.06
Rhodospirillales 31 0.05
Rhizobiales 28 0.05
Rhodobacterales 23 0.04
Lactobacillales 13 0.02
Sphingomonadales 12 0.02
Caulobacterales 10 0.02
Pseudomonadales 10 0.02
Victivallales  8 0.01
Myxococcales  4 0.01
Alteromonadales  4 0.01
Primates  3 0.01
Thelebolales  2 0.00
Agaricales 2 0.00
Saccharomycetales 2 0.00
Heterotrichida 2 0.00
Chromatiales 1 0.00

Table 5 Top 30 families

Family Read Count %
Unknown 54160 93.91
Clostridiaceae 1060 1.84
Enterobacteriaceae 908 1.57
Listeriaceae 365 0.63
Eubacteriaceae 349 0.61
Planctomycetaceae 186 0.32
Xanthomonadaceae 85 0.15
Prevotellaceae 69 0.12
Halobacteriaceae 46 0.08
Flavobacteriaceae 43 0.07
Not assigned 35 0.06
Bacillaceae 30 0.05
Comamonadaceae 30 0.05
Syntrophomonadaceae 26 0.05
Rhodobacteraceae 23 0.04
Dietziaceae 21 0.04
Acetobacteraceae 16 0.03
Rhodospirillaceae 15 0.03
Phyllobacteriaceae 13 0.02
Sphingomonadaceae 12 0.02
Burkholderiaceae 12 0.02
Pseudomonadaceae 10 0.02
Lactobacillaceae 10 0.02
Caulobacteraceae 10 0.02
Alcaligenaceae 9 0.02
Victivallaceae 8 0.01
Pseudonocardiaceae 7 0.01
Bradyrhizobiaceae 7 0.01
Nocardioidaceae 7 0.01
Micrococcaceae 7 0.01

Table 6 Selected genus/species

Top BLAST hits Read counts Percentage (%)
Uncultured bacterium 49712 83.52
Bacterium enrichment 2543 4.32
Uncultured compost 955 1.62
Uncultured clostridium 947 1.61
Enterobacter cloacae 388 0.66
Uncultured synergistetes 355 0.60
Uncultured firmicutes 355 0.60
Iron-reducing bacterium 83 0.14
Uncultured Bacteroidetes 83 0.14
Clostridium species 59 0.10
Uncultured chloroflexi 58 0.10
Listeria innocua 51 0.09
Uncultured clostriaceae 51 0.09
Uncultured rumen sp 27 0.05
Bacillus sp. 27 0.05
Uncultured syntrophomonas 24 0.04
Uncultured anaerobic sp 20 0.03
Clostridium pasterianum 20 0.03
Uncultured eubacterium 15 0.03
Uncultured ruminococcaceae 12 0.02
Uncultured ruminococcaceae 12 0.02
Lactobacillus fermentum 10 0.02
Uncultured acetobacter 5 0.01
Paracoccus denitrificans 5 0.01
Uncultured thermomonas 5 0.01
Enterobacter sp. 3 0.01
Uncultured thermoanaerobacteriales 3 0.01
Uncultured alpha 3 0.01
Methanoculleus marisnigri 3 0.01
Anaeromyxobacter dehalogenans 3 0.01
Maricaulis maris 3 0.01
Methanoculleus bourgensis 2 0.00
Uncultured bacteroides 2 0.00
Propionibacterium acnes 2 0.00
Uncultured synergistaceae 1 0.00
Ethylobacteriumpopuli 1 0.00
Ruminococcaceae bacterium 1 0.00
Marinobacter hydrocarbonlasticus 1 0.00
Bifidobacterium bifidum 1 0.00
Methanothermobacter sp. 1 0.00
Clostridiaceae bacterium 1 0.00
Leuconostoc pseudomesenteroides 1 0.00
Clostridium autoethanogenum 1 0.00
Methylobacterium platani 1 0.00
Methylobacterium nodulans 1 0.00

DISCUSSION

Citrullus lanatus waste is usually composed of the entire spoiled fruits or the seeds and the edible pulp. Studies indicate that the seeds, whole fruits and rind contains mineral, proximate nutrients such as proteins, fat, moisture, fiber and higher amounts of carbohydrate (Fila et al., 2013; Tabiri et al., 2016; Olayinka and Etejere, 2018). These nutrients provide an enabling environment for the growth of microorganisms. This has made fruit waste an attractive substrate for anaerobic digestion (AD). In anaerobic digestion, microorganisms function synergistically to achieve high biogas output (Asikong et al., 2014). The microbial diversity in a biogas digester is enormous and rarely captured with cultural techniques.

In this study, the structural composition of anaerobic digester utilizing water melon (Citrullus lanatus) fruit waste was evaluated. The study revealed the dominant phyla to include: unknown (53739 read counts), Firmicutes (2206), Proteobacteria (1177 read counts), Planctomycetes (186 read counts), Bacteroidetes (116 read counts), Actinobacteria (84 read counts), Euryarchaeota (79 read counts) and Chloroflexi (59 read counts). The results of microbial composition of the anaerobic digester studied indicate that the digesting slurry of Citrullus lanatus waste had unique taxonomic groups and overlapping taxa with other substrate types described previously. The kingdoms taxa identified were predominantly bacteria followed by archae. This confirms the fact that bacteria and archae are the main players in anaerobic digesters.

Li et al (2015) detected firmicutes, Bacteroidetes, Proteobacteria, Chloroflexi, Synergistetes, Verrumicrobia, Spirochaetes, Actinobacteria, Tenericutes, Acidobacteria and Planctomycetes as their unique phyla using different animal manures as substrates. Compared to our finding, Tenericutes, Verrumicrobia, Spirochaetes and Acidobacteria were not found. Studies have shown that Tenericutes and Spirochaetes are obligate intracellular parasites (Ludwig et al., 2010; Gupta et al., 2013) while Verrumicrobia and Acidobacteria are common to soil (Navarrete et al., 2015; Kielak, et al., 2016). Furthermore, Deinococcus-thermus and Cyanobacteria amongst others were unique to our sample even though they have been implicated in earlier studies (Vincent et al, 2018; Detman et al., 2018).

Using metagenomics, a number of taxa have been identified in AD utilizing various operating conditions. These include archae such as Methanomicrobia, Methanobacteria, Methanosarcinaceae, Methanobacteriacea, Methanomicrobiales, and Methanosarcina. Dominant bacterial taxa include Clostridia, Bacilli, Bacteroidetes, Chloroflexi, Proteobacteria, Actinobacteria, Synergistetes and Thermotoga (Kleinsteuber, 2018). These taxa and archae were also detected in our study.

It was observed in this study that Firmicutes, Proteobacteria, Actinobacteria Bacteriodetes and Chloroflexi in descending order of abundance were the top five phyla present in our studied anaerobic digester. This agrees with the work of Kirkegaard et al (2017) who also reported these phyla. Among the phyla, Firmicutes was the most dominant after the Unknown phylum. The class Clostridia was the predominant of the bacterial classes in our digester. This class have been reported by Kirkegaard et al (2017) and Jha et al. (2012) to be active bio-degraders of a wide assortment of organic polymers.

In an earlier study, unique operational taxonomic units (OTU) were observed for different substrates even though some phyla did overlap (Li et al., 2015).  In swine abundant manure sludge sample, Syntrophomonadaceae, Lachnospiraceae, Clostridiales, Clostridium XI, and Bacteria were unique OTU. In cattle abundant sample, Sporobacter, Corynebacterineae, Bacteria, Planococcaceae, Firmicutes, Clostridium sensustricto, Bacteria, Anaerovorax and Ruminococcaceae were unique. These taxonomic groups were also detected in our sample especially the Firmicutes.

In a study utilizing cow dung and straw as substrates, some of the reported dominant bacterial classes were Bacilli, Clostridia, Alphaproteobacteria and Actinobacteria (Sun et al., 2015). Compared to our study, all these bacterial classes were also reported but Clostridia had the highest reads followed by Gammaproteobacteria, Bacilli, Planctomycetes, Alphaproteobacteria, Actinobacteria and Bacteroidetes. Liu et al (2018) reported Syntrophomonas, Clostridium, Lactobacillus, and Pseudomonas as relative abundant in AD and these also detected in our samples.

Microbial community within a digester have been grouped into acidogens, syntropic acetogens and methanogens (Manyi-Loh et al., 2013). The acidogens are well known for hydrolysis ability and are often referred to as fermentative bacteria. Reported acidogens in AD include members of the family Enterobacteriaceae which in our study was the third most abundant family. Clostridium, Bacteroides, Succinivibrio, Prevotella and Ruminococcus have also been described as an important genera in the hydrolysis extensively in ruminants and biogas digesters. In our study, the abundance of these genuses Clostridium, Uncultured rumen bacteria, Bacteroidetes, and Prevotella confirms their hydrolytic roles in our cellulose rich substrate (Dowd et al., 2008; Callaway et al., 2010).

The syntrophic acetogens are usually responsible for the syntrophic metabolisms of C3 to C6 short chain fatty acids, alcohols, and amino acids (McInerney et al., 2008).  This ability is almost limited to Syntrophobacter and Syntrophomonas species. In our sample, uncultured Syntrophomonas was detected. Others groups capable of this metabolism include the thermophilic bacteria (Manyi-Loh et al., 2013). These were found amongst our detected genera including the Methanothermobacter sp.

Among the archae, the genus Methanoculleusis predominant in anaerobic reactors previously studied digesters (Krober et al., 2009; Feng et al., 2010; Jaenicke et al., 2011; Jha et al., 2012). This genus was also detected in our study and the two representatives were Methanoculleus bourgensis and Methanoculleus marisnigri, with the former being more predominant member. This may be because M. bourgensis have been reported by Weiss et al (2009) to easily adjust to unfavorable conditions that typically inhibit the success of an anaerobic digestion operation. The important archaeal community responsible for methane gas production, at the class level were dominated by Methanomicrobia and Methanobacteria. In addition to Methanoculleus, Methanothermobacter was also detected in our sample.

Using cultural techniques to characterize microbial community in an anaerobic digester failed to reveal important member of significant phyla Firmicutes such as Clostridium species and other species that are largely unculturable due to their anaerobic requirement (Ofoefule et al., 2010; Asikong et al., 2016).

CONCLUSION

The result indicates that bacteria and archae dominated the microbial kingdoms in our anaerobic digester utilizing Citrullus lanatus fruit waste. Dominant phyla were Unknown, Firmicutes, Proteobacteria, Planctomycetes, Bacteroidetes, Actinobacteria and Euryarchaeota in decreasing order of counts. As expected, classes, orders, families belonging to these top phyla dominated the class, order and family classifications. A total of 210 phylotypes were obtained out of which half of these were not routinely culturable isolates. These findings confirm the fact that AD systems harbor complex microbial communities that are unculturable using routine media. Interestingly, phylotypes involved in key processes of AD such as hydrolysis, acidogenesis, acetogenesis, acetogenesis and methanogenesis were obtained indicating a self-sustaining and synergistic AD.

Conflict of interest: The authors hereby declare that no conflict of interest exist.

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