Compost and Compost Tea Microbiology: The “-Omics” Era

Abstract

Composting is largely driven and mediated by microorganisms interacting with abiotic factors. However, until recently our knowledge of compost microbes has been heavily informed by culture-dependent methods that capture <1% of microorganisms involved in composting. This suggests that challenges related to optimizing the process of composting and the effectiveness of its products may be due to a partial understanding of microbial community structure, diversity, and function. Recent advances in molecular biology, bioinformatics, and sequencing technologies have presented opportunities to gain unprecedented insights into the microbiology of compost and compost tea by using “-omics” approaches. This chapter summarizes research aimed at better understanding the microbiology and effect of compost and compost tea using -omics approaches (genomics, metagenomics, metaproteomics, metaprotegenomics, metatranscriptomics, and metabolomics). Reference to findings from metaprofiling work done using genetic fingerprinting and culture-dependent techniques are made when necessary. To this end, a systematic framework that facilitates data integration and analysis from multi-omics and culture-dependent approaches are recommended to continue improving our knowledge of compost microbes.

Full text

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Chapter 1
Compost and compost tea microbiology: the “-omics” era
Chaney C.G. St. Martin
1
, Judy Rouse-Miller
2
, Gem Thomas Barry
3
, Piterson Vilpigue
4
Abstract
Composting is largely driven and mediated by microorganisms interacting with abiotic factors.
However, until recently our knowledge of compost microbes has been heavily informed by
culture-dependent methods that capture <1% of microorganisms involved in composting. This
suggests that challenges related to optimizing the process of composting and the effectiveness
of its products may be due to a partial understanding of microbial community structure,
diversity and functions. Recent advances in molecular biology, bioinformatics and sequencing
technologies have presented opportunities to gain unprecedented insights into the microbiology
of compost and compost tea by using “-omics” approaches. This chapter summarizes research
aimed at better understanding the microbiology and effect of compost and compost tea using -
omics approaches (genomics, metagenomics, metaproteomics, metaprotegenomics,
metatranscriptomics and metabolomics). Reference to findings from metaprofiling work done
1
C.C.G. St. Martin
Inter-American Institute for Cooperation on Agriculture, Couva, Republic of Trinidad
and Tobago.
e-mail: chaney.stmartin@iica.int
2
J. Rouse-Miller
Department of Life Science, The University of the West Indies, St. Augustine, Republic of
Trinidad and Tobago.
e-mail: judy.rouse-miller@sta.uwi.edu
3
G. Thomas-Barry
Department of Life Science, The University of the West Indies, St. Augustine, Republic of
Trinidad and Tobago
e-mail: gem.thomas@my.uwi.edu
4
P. Vilpigue
Department of Food Production, The University of the West Indies, St. Augustine,
Republic of Trinidad and Tobago
e-mail: pitersonvilpigue@gmail.com

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using genetic fingerprinting and culture-dependent techniques are made when necessary. To
this end, a systematic framework that facilitates data integration and analysis from multi-omics
and culture-dependent approaches is recommended to continue improving our knowledge of
compost microbes.
1.1 Introduction
Microbial detection, identification, characterization and quantification often pose several
challenges during composting or in compost products. These challenges result from difficulty
in extracting DNA and cells from complex compost matrices, which contains humic acids and
other compounds that bind to DNA (Ogram et al. 1987; Pfaller et al. 1994; LaMontagne et al.
2002). Further challenges are posed by diverse microbial communities with complex
interaction among biotic and abiotic factors. Despite these challenges, traditional culture-based
methods have used to study the compost microbiology. Though useful, in soils, < 1% of the
genetic diversity of prokaryotes is captured using culture-based methods, with an unknown
percentage captured in compost products (Torsvik et al. 1990; Hugenholtz 2002). This suggests
that the three fundamental questions related to microbial ecology of composting: (1) what
microbial types and community structures are present during composting and in compost
products? (2) what are the roles and functions these microbial types? and (3) what is the
relationship between the activities of these microbial types and predictable results (disease
suppression and plant growth enhancement)? have been incompletely informed by traditional
culture-based methods. Such information is important since mechanism of action for compost-
based products have been attributed in part or in full to the activities of diverse microbial
communities or specific microbial species.
In this context, microbial studies on composting and compost-based products have
advanced considerably since the seminal International Conference (Innsbruck, Austria in 2000)

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and publication on “Microbiology of Composting” (Insam et al. 2002). Research in this
publication mainly used culture-plate methods alone or complemented with culture-
independent approaches to provide "up-to-date" insight into processes and microorganisms
associated with compost production. These include techniques, which allowed partial
microbial community analysis (metaprofiling) such as, Denaturing gradient gel electrophoresis
(DGGE), Phospholipid analysis (PLFA'S), community level physiological profiles (CLPPs),
terminal restriction fragment length polymorphism (TRFLP), clone libraries, and amplified
ribosomal DNA restriction analysis (ANDRA). Limited work was presented on effects of
compost and compost tea on soil, rhizosphere or phyllosphere microbial communities.
Moreover, studies on the characterization of microbes during the compost tea brewing
processes and the resulting end-products was outside the scope of this publication.
Notwithstanding these scope limits, the use of metaprofiling approaches, which
includes polymerase chain reaction (PCR)-based methods with sequencing and phylogenetic
analysis of 16S or 18S rRNA, have added much to the literature on compost and compost tea
microbiology (Peters et al. 2000; Tiquia et al. 2005; Danon et al. 2008; de Gannes et al. 2013;
Larkin and Tavantzis 2013). This includes a “general” consensus that: (1) aerobic composting
is characterized as a microbially driven, self-heating process, which results in temperatures >
50 °C, with subsequent and sustained temperatures of 60–80 °C, followed by a steady cooling
of the compost heap (Ryckeboer et al. 2003; Kumar 2011). (2) mesophilic and thermophilic
microorganisms with different physiological requirements and tolerance levels, decompose the
organic matter as is consistent with continuous environmental fluxes during composting
(Alfreider et al. 2002; Partanen et al. 2010; Jurado et al. 2014) (3) the level of abundance of
routinely found bacterial phyla such as Actinobacteria, Bacteroidetes, Firmicutes and
Proteobacteria is dependent on the characteristics of the starting materials and the type of
composting procedure used (Ryckeboer et al. 2003; de Gannes et al. 2013).

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(4) fungi generally seem to be most important in the cooling and curing stages of composting
(Neher et al. 2015) since they are not readily or abundantly detected at temperatures > 65 °C
(thermophilic stage) (Langarica-Fuentes et al. 2014a, b). This suggests that relative to bacteria,
their degradative activities are minor during the thermophilic phase (Langarica-Fuentes et al.
2014a, b).
However, as approaches informing the biology of composting, there are three main
limitations of PCR-based analyses of 16S and 18S rRNA amplicon (Zhou et al. 2010): (i)
obtaining information to sequence between primers is limited with the use PCR. As such, the
amount of functional information captured using PCR is limited. (ii) most PCR-based
measurements provide mainly relative abundance information since PCR-based analysis is
only somewhat quantitative; (iii) the probability of entirely missing some lineages due to PCR-
primer mismatches is of concern, particularly with complex environmental samples.
Furthermore, , 16S or 18S rRNA amplicon is a highly conserved molecule, as such, they do
not provide sufficient species and strain level resolution as it targets single or few genes
(Konstantinidis et al. 2006).
Most of these challenges have been addressed with advances in molecular biology,
bioinformatics and sequencing technologies (Handelsman 2005; Tringe et al. 2005). These
advances have allowed deeper study (“-omics”) of biomolecules along the central dogma
framework of molecular biology. These include the study of total: DNA/genome (genomics),
the mRNA/transcripts (transcriptomics), proteins (proteomics) and metabolites
(metabolomics) of an organism. When the total complement of these respective biomolecules
is examined for entire communities of organisms, the prefix “meta” (meaning beyond) is added
to the root word that indicates the type of molecule being studied. For example,
“metaproteomics” studies the entire protein complement of microbial communities from
environmental samples. Collectively, these “-omics” studies have advanced an era in microbial

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ecology, which has allowed unprecedented discovery of new taxa, genes and functions.
Specifically, the combination of DNA(genomic)-, mRNA-, protein- and metabolite-based
(postgenomic) analyses of microbial communities from distinct environments has allowed for
in-depth elucidation of the structure, diversity, functions, and interactions of microbial
communities, which are linked various environmental processes (Simon and Daniel 2011).
This chapter aims is to summarize research findings aimed at better understanding the
microbiology and effect of compost and compost tea using -omics approaches (genomics,
metagenomics, metaproteomics, metaprotegenomics, metatranscriptomics and
metametabolomics). The technical definition applied to metagenomics in this chapter does not
include studies that use PCR to amplify gene cassettes (Holmes et al. 2003) or random PCR
primers to access genes of interest (Eschenfeldt et al. 2001; Brzostowicz et al. 2003), since
these methods provide limited genomic information beyond the amplified genes. It also
excludes the broader definitions, which refer to metagenomics as any type of analysis of DNA
acquired directly from environmental samples (Handelsman et al. 1998). Instead, the definition
is more process-oriented, involving either the direct analysis of total community DNA or vector
cloning before analysis (whole metagenome shotgun sequencing).
Owing to the limited -omics-studies on compost tea and aspects of compost and
composting, findings obtained using metaprofiling approaches are included when relevant.
Details on definitions, standards, uses, disease suppression and challenges with composting,
compost products are not presented in this chapter since these have been extensively reviewed
by Litterick et al. (2004), Scheuerell et al. (2005), St. Martin (2014) and St. Martin and
Ramsubhag (2015). Due to the many variations in composting methods, feedstocks and abiotic
factors, a serious attempt is made not to suggest a “typical” microbiological profile, process or
ecology for compost and compost tea, particularly at a genus or species level. Instead, greater
emphasis is placed on detailing unique findings and highlighting emerging research trends on

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compost microbiology. Some specific limitations of -omics approaches are briefly stated in the
conclusions or at the end of some subsections.
1.2 Genomic Approaches
1.2.1 Genomics
Numerous microorganisms involved in composting have been identified and extracted to
evaluate their potential roles in various agricultural, environmental and industrial applications.
Until recently, most of these microorganisms have been identified using phenotypic
characterization techniques (morphology, biochemical profiles, diagnostic staining and media)
and 16S or 18S rRNA gene sequencing and/or analysis of phospholipid profiles (Insam et al.
2002; Ryckeboer et al. 2003). To date, a major application focus of many studies, has been
single-species microbial isolation from compost or compost tea to increase the understanding
and predictability of plant disease suppression or growth enhancement. To this end, the
suppressiveness of compost-based products have been attributed to bacterial species mainly
from the genera Bacillus, Serratia, Pseudomonas, Stenotrophomonas, Flavobacterium,
Streptomyces and Enterobacter (Kwok et al. 1987; Hoitink 1990; Phae et al. 1990; Inbar et al.
2005; Kerkeni et al. 2007; Ryan et al. 2009; Kouki et al. 2012; Khaldi et al. 2015). Whereas,
fungal species from the genera Trichoderma, Penicillium, Aspergillus and Gliocladium and
Fusarium (non-pathogenic) have been reported as the main taxa related to the disease
suppressive effect of compost and compost tea (Hoitink and Fahy 1986; Kwok et al. 1987;
Malandraki et al. 2008; Daami-remadi et al. 2012).
Though useful, the identification of some of these taxa from compost microbiomes are
represented by draft or incomplete genomes in various gene banks (INSDC 2018). This is
partly due to the previously high cost and processing speed limitations of second-generation
sequencing technologies, which limited more extensive and in-depth examination of microbial

7
species (Ku and Roukos 2013). This means that incomplete genomes only provide genomic
information on the genes that are amplified. Furthermore, owing to the genome sequencing of
microbial species being severely skewed towards a few phyla that contain model organisms
(Land et al. 2015), many microorganisms present in compost have not been fully sequenced.
This highlights the tremendous scope for genomic information that can significantly impact
our understanding of microbiology of composting and compost products.
Genomics, which refers to study of the complete genetic complement of a species,
rather than the study of only single genes, provides tremendous opportunities for more in-depth
insights into the structural and functional characteristics of microorganisms in composting.
Specifically, structural genomics offers the opportunity for sequencing the complete DNA of
an organism (genome) and determining the complete set and three-dimensional structure of
proteins produced by an organism. Whereas, functional genomics focuses on gene
transcription, translation and protein-protein interactions. More specifically, it involves the
study of mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a
biological sample.
Most of the genomics work on compost have been done using cultivated “bulk” cell
populations of single microbial species, which falls more aptly in the domain of isolation
genomics. Though useful, particularly for comparative genomics, information from such
studies is limited to microbial species that can be cultured. More so, with exception of rare
instances where cells can be accurately synchronized, bulk measurements destroy important
biological information such as cell phenotypes, metabolic states and transition between states
and cellular functions by averaging individual cell signals (Trapnell 2015). In contrast, single-
cell genomics, which refers to the sequencing of a genome of a single cell selected from a
population of mixed cells, makes possible the study of genomes of uncultivated
microorganisms, particularly from complex communities such as compost (Rinke et al. 2013).

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As such, single-cell genomics provides a critical link between isolate genomics and
metagenomics. Such a link is important to gain insights into growing formerly uncultivable
microorganisms and reconstructing genomes of dominant microbial species in environmental
samples.
Progress in this direction is already evident with the advent of culturomics, a high
diverse culture conditions-rapid microbial identification approach, which has resulted in the
first-time cultivation of many bacteria (Lagier et al. 2015). Culturomics also addresses a
limitation of metagenomics methods, which is inability to detect minority microbial
populations (species <105 per gram) such as, Salmonella enterica serovar (pathogen), in
environmental samples (Lagier et al. 2012). Therefore, single-cell genomics complemented
with culturomics, has the potential to transform our understanding of gene regulation during
plant disease development and suppression with compost or compost tea. Also, a combined
approach of stable isotope-labeled substrates and single-cell analyses could provide insights
into the in situ function of uncultivated microbes during composting or soils treated with
compost-based products (Eichorst et al. 2015).
To this end, Matteoli et al. (2018) full genome sequenced a Serratia marcescens strain
from vermicompost and assessed its plant growth-promoting properties. They reported that S.
marcescens solubilized P and Zn, produced indole compounds, colonized hyphae and
countered the growth of phytopathogenic fungi Fusarium oxysporum and F. solani in vitro.
Using a genome-centric analysis of a group of thermophilic and cellulolytic bacteria isolated
from compost, Lemos et al. (2017) discovered four novel genomes. The novel genomes
encoded several glycoside hydrolases and possessed genes related to lignocellulose
breakdown. Likewise, Akita et al. (2017) isolated and full genome sequenced Ureibacillus
thermosphaericus A1, a thermophilic Bacillus from compost. U. thermosphaericus A1
produced several enzymes of industrially importance including catalase, amino acid

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dehydrogenase and esterase. Additionally, U. thermosphaericus A1, which grew at
temperature ranging from 37°C to 55°C , was used as a biocatalyst for degrading
lignocellulosic biomass. Brumm et al. (2016) also full genome sequenced Geobacillus sp.,
which was one of several thermophilic microorganisms isolated from wood compost.
According to the researchers, this genus is known for thriving in varied harsh environments,
which suggests that this species may contain enzymes suitable for industrial applications,
particularly in challenging environments.
Other interesting microbial isolates with the ability to decaffeinate coffee, tea, and
chocolate (Divine 2014), decolorize dyes in effluents (bioremediation) (Abd El-Kader et al.
2019) and degrade plastics (Dang et al. 2018) have been extracted from compost/compost tea
and identified using 16S rDNA sequencing. Limited published work on the extraction of
viruses from compost for beneficial agricultural use have been done. However, as indicated by
the work of Heringa et al. (2010), viruses extracted from sewage effluent may have a potential
role in disease suppression with compost-based products. Heringa et al. (2010) reported that
within 4 hours of applying an effluent extracted mixture of five strains of bacteriophages to
dairy manure-compost, a > 2-log reduction in Salmonella enterica was observed across
moisture levels compared with controls. It is possible that strains of bacteriophages with similar
human and plant pathogen and disease suppressive effects may be in compost tea. Though the
study of single-species isolates is useful, it is often activities and interactions of different types
of microorganisms that have been attributed to the increase efficacy of compost products (St.
Martin and Brathwaite 2012; Cook and Baker 1983). Therefore, the analysis of the microbial
communities of compost and compost tea is equally important.
1.2.2 Metagenomics
In principle, the basis of metagenomics is that the entire genetic complement of microbial
communities from environmental samples could be sequenced and analyzed in a like manner

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as whole genome sequencing a single microbial isolate. As such, metagenomics refers to the
sequence (computational) and function-based (experimental) analysis of the collective
microbial genomes contained in environmental samples. Isolation and lab cultivation of
individual species are not necessary for such analysis and prior knowledge of the microbial
communities is not required (Riesenfeld et al. 2004). A detailed description of the process of
metagenomics is provided by (Sabree et al. 2009) and can be summarized as: extraction of
DNA directly from the microbial community, followed by cloning of DNA into a surrogate
host then analysis of metagenomic DNA (sequence- or function-driven). Conceptually,
sequence-driven analysis identifies the genes and “metabolic pathways” by comparing
metagenomic DNA with genes found in other samples with known functions. Whereas,
functional-driven analysis screens for expression of activities (enzymes or antibiotic
production) of interest conferred by the metagenomic DNA.
Though crucial for relating microbial ecology to efficacy of processes and compost
products, metagenomics studies on compost and compost tea have been limited. Previous
studies have focused on metaprofiling composting phases (Insam et al. 2002; Klammer et al.
2005; Danon et al. 2008) and elucidating the mechanisms of plant disease suppression using
compost-based products (Scheuerell and Mahaffee 2004). Resulting from this research trend is
a preponderance of work on soil-borne pathogens and compost-induced changes in the
rhizosphere/soil (St. Martin 2015) with less work on aerial pathogens and induced changes in
the phyllosphere. Therefore, compared to rhizosphere, our knowledge of the microbiology of
phyllosphere as affected by compost tea or compost is lagging. Furthermore, Vorholt (2012)
noted that for the most part, basic questions related to which microbial types are present in the
phyllosphere and their functions, remain unanswered.
In one of the first reports of metagenomic studies on composting, Martins et al. (2013)
presented findings that contrasted results from previous works done using culture-dependent

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(Golueke et al. 1954) and -independent studies (Peters et al. 2000; Ishii et al. 2000; Alfreider
et al. 2002; Schloss et al. 2003; Partanen et al. 2010). They reported that Lactobacillus genus
(particularly L. brevis) had a clear dominance in the older (thermophilic) compost sample
whereas mesophilic compost sample was dominated by members of Acinetobacter and
Stenotrophomones genera. Traditionally, composting literature has shown that the initial stage
of composting is dominated by mesophilic organic acid-producing bacteria such as
Lactobacillus spp. and Acetobacter spp., which degrade readily degradable compounds (e.g.,
sugars). This results in lower pH levels (Golueke et al. 1954; Yu 2014), growth inhibition of
other microbes (Yu 2014) and high odor emission, particularly when Clostridia is present
(Sundberg et al. 2011, 2013). The authors contended that the dominance of Lactobacillus spp.
in older compost may be due to competitive advantage of the species, achieved partly by the
production of bacteriocins. Peters et al. (2000) noted that Lactobacilli were typically the
dominant microorganisms under oxygen limitation degrading relatively wet plant material or
substrate. Though scarcely reported in compost, studies have identified thermophilic
Lactobacilli in traditional yogurt and cheeses (Randazzo et al. 2002; Azadnia et al. 2011).
These studies may further support the findings of Martins et al. (2013) and highlight the
usefulness of metagenomics in advancing knowledge in compost microbiology.
Martins et al. (2013) further reported that bacterial enzymes, possibly from
Clostridiales and Actinomycetales were fully responsible for degrading recalcitrant
lignocellulose (Allgaier et al. 2010; Bugg et al. 2011). This finding fits well with the current
understanding of the degradation of recalcitrant lignocellulose during composting as reviewed
by Bugg et al. (2011). However, traditionally, in composting literature, the degradation of
recalcitrant lignocellulose have been mainly attributed to fungi (Tuomela et al. 2000; Sánchez
2009). Martins et al. (2013) explained that the relatively frequent anaerobic and thermophilic

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conditions during composting possibly diminishes the degradation role of fungi as it relates to
recalcitrant lignocellulose.
In a more recent metagenomic study, Antunes et al. (2016) explored the microbial
community structure of large-scale thermophilic composting using shotgun DNA and 16S
rRNA gene sequencing techniques. They reported that at the phylum and order level, results of
the shotgun DNA and 16S amplicon analyzes generally agreed with each other. However, at
the genus level, 16S results on microbial composition structure starkly contrasted shotgun
DNA findings. That is, none of the five most abundant OTUs in 16S analysis seemed to
correspond to the species Rhodothermus marinus, Thermobispora bispora, Symbiobacterium
thermophilum, Sphaerobacter thermophilus and Thermobifida fusca classified using MyTaxa
(Chengwei et al. 2014) through shotgun DNA data. The authors attributed this discrepancy to
the unavailability of complete “reference” genomes of microorganisms present during
composting, which precluded identification during the analyses of the shotgun DNA
metagenomics dataset. The unavailability of complete reference genomes poses a serious
bottleneck challenge in metagenomic works on composting and compost products. This
challenge will persist until taxonomic databases are more comprehensively populated with full
genomic entries and more novel classification schemes are developed.
Nonetheless, Antunes et al. (2016) noted that the most abundant orders for shotgun
DNA and 16S amplicon were Clostridiales, Bacillales and Actinomycetales. These orders
along with Enterobacteriales and Thermoanaerobacterales were proposed as the bacterial core
group mainly responsible for degrading lignocellulosic biomass at different stages of
composting. Actinomycetales played a primary role in lignocellulosic degradation throughout
composting; Bacillales at the start and middle, and Clostridiales and Enterobacteriales at the
start and end, respectively. Interestingly, the relatively high abundance of Clostridiales, which
include micro-aerophilic or anaerobic species, in the initial stages of composting suggests

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quasi-static conditions that favored the fluctuations between anaerobic and aerobic micro-
environments (Ryckeboer et al. 2003; Jurado et al. 2014). Hemsworth et al. (2015) reported
that anaerobic microorganisms play an major role in degrading biomass. Moreover, members
of the Clostridiales and Bacillales orders have been reported to possess genes that encode
enzymes, which degrade hemicellulose and cellulose (Kanokratana et al. 2011; Ventorino et
al. 2015).
In contrast to the findings of Antunes et al. (2016), the relative abundance of
Enterobacter spp. are generally described as highest during early composting phases (Chandna
et al. 2013). Nonetheless, Enterobacter spp. have been associated with lower temperatures (<
60 °C) (Gbolagade 2006; Chandna et al. 2013), which may explain their relative abundance
and lignocellulosic degrading activity during the end phase of composting. This was
particularly evident for Klebsiella pneumoniae, the predominant species of the
Enterobacteriales order, known to perform cellulose and hemicellulose degradation in
composting ecosystems (Droffner et al. 1995) and wood termite guts (Doolittle et al. 2008).
Antunes et al. (2016) also reported the almost-complete genome construction of a novel
biodegrading bacterial species (order Bacillales) capable of bioconverting all components in
plant biomass.
Results from such studies provided the foundation or link to emerging trends in
compost metagenomic studies, which focus on: i. compost microbiomes as rich and diverse
sources for discovering biochemical catalysts and pathways for advanced biofuel production
or other industrial and bioremediation (soil, wetlands, and plastic polluted spaces) applications
(bioprospecting) (Dougherty et al. 2012; Yi-fang et al. 2013; Wang et al. 2016) ii. composting
as a strategy to eliminate antibiotic resistance genes (ARG)/resistome and residues from animal
manure (Wang et al. 2017; Chen et al. 2018; Gou et al. 2018) that pose potential global health
risks, particularly for the antibiotics tetracycline, sulfonamide, and fluoroquinolone, commonly

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used by humans and in livestock production. iii. compost as a carrier medium or enhancer for
biocontrol (entomopathogenic nematodes) (Herren et al. 2018) and plant growth promoting
(Rhizobacteria and Mycorrhiza) agents (Yang et al. 2018).
In this context, in one of the first study to use a functional metagenomics approach, Yeh
et al. (2013) identified and cloned a novel endogluconase gene (RS-EG1) from the metagenome
of rice straw compost. The RS-EG1 shared approx. 70% similarity with its closest known
bacterial cellulase from Micromonospora aurantiaca and Thermobispora sp and was stable
over wide ranges of temperature and pH. The authors concluded that the novel endogluconase
was potentially useful in the production of cellulosic biofuel. Allgaier et al. (2010) and
Dougherty et al. (2012) who successfully used targeted metagenomics to identify several active
enzymes with differing hemicellulose degrading activities reported similar results.
As it relates to ARG studies, Wang et al. (2017) integrated metagenomics and time
series metatranscriptomics data to determine if changes in resistome expression were related
to the evolution of active microbiome profiles during composting. They found that the
principal determinant that defined the diverse transcriptional response of the resistome was the
microbial phylogeny during composting. During the mesophilic and thermophilic stages, the
most prevalent phylum that harbored ARG families were Firmicutes, whereas Actinobacteria
and Ascomycota were primary source in the matured compost. Moreover, amending
composting with biochar significantly reduced the aggregated level of ARG expression by an
additional 38% compared non-amended compost treatment. The researchers suggested that the
quicker microbial succession observed in biochar-amended composting, which was mainly due
to enhanced composting kinetics, may account for this difference.
Further to the composting process, Gou et al. (2018) investigated the effect of compost-
treated soils on the temporal succession of ARGs. They found that diversity and abundance of
ARGs in compost-treated soils were significantly lower compared to manure-treated soils.

15
Therefore, concluded that composting was an effective method to limit ARG dispersal, which
was linked with land application of organic wastes. In a related study, Chen et al. (2018) found
that sewage sludge and manure applications to the soil over 10 years increased the incidence
and abundance of ARGs in phyllosphere. Therefore, they concluded that soil may serve as an
antibiotic resistome reservoir for phyllosphere. Furthermore, ARGs profiles were strongly
correlated with bacterial communities, which were dominated by Proteobacteria,
Bacteroidetes, Actinobacteria and Firmicutes.
As it relates to studies on compost as a carrier medium or enhancer for biocontrol, Yang
et al. (2018) used PCR-based method with sequencing of 18S gene to investigate the impact of
compost applications rates (0, 11.25. 22.5 and 45Mg/ha) on the composition and abundance of
arbuscular mycorrhizal fungi (AMF) communities at the seedling, flowering and mature stage
of soybean. They found that moderate and high compost applications rates significantly
increased AMF root colonization and extraradical hyphal density and the abundance of
Paraglomus sp. generally decreased along the compost application gradient, while
Rhizophagus fasciculatum showed an opposite trend.
Using Nematode Indicator of Compost Maturity as proposed by Steel et al. (2018),
Herren et al. (2018) reported that Steinernema feltiae, an entomopathogenic nematode, which
was applied to soil via mature compost, had the highest survival and virulence against Galleria
mellonella compared to when it was applied without compost or via immature compost. They
suggested that higher survival rate of EFN in mature compost was due to reduced predation
pressure (by mites and collembolans) on the EPN, in favor of other nematodes. These other
nematodes generally had higher and more diverse populations in mature compared immature
compost. Nitrate-N concentration was the only characteristic differentiating mature from
immature composts and was significantly higher in mature compost. Pan et al. (2015) and
Griffiths et al. (1992) reported that with the application of nitrate-N nematode diversity and

16
evenness increased and dominance decreased, which may support the predation pressure
inference made by Herren et al. (2018).
In a recent study, Blaya et al. (2016) assessed the microbial consortia of composts made
of different agro-industrial waste and with varying levels of suppressiveness against P.
nicotianae. They found that Ascomycota phylum had a higher relative abundance in
suppressive composts compared to non-suppressive media and was negatively correlated with
Phytophthora root rot incidence. The researchers postulated that the high proportions (67-75%)
vineyard pruning waste promoted higher relative abundance of Ascomycota (particularly of
Sordariales and Hypocreales taxa) and microbial activity, which were essential for controlling
the disease. No unique fungi or bacteria that would be suggestive of suppressive or conducive
compost were identified. However, conducive compost contained relatively higher abundance
of Actinobacteria and Gemmatimonadetes compared to suppressive composts.
To date and there is limited published articles on the metagenome of compost tea and
how it may differ to that of compost. Currently, a research team from University of Alabama,
Huntsville, USA is undertaking a project on the metagenomic analysis of aerated compost tea
(Cseke 2016). To this end, metaprofiling studies have shown that bacterial phyla
Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Verrucomicrobia, Chloroflexi,
Planctomycetes, Acidobacteria dominated non-aerated compost teas (NCTs) made from
different compost sources (vermicompost and agricultural and municipal wastes) (Mengesha
et al. 2017). Ascomycota was the predominant fungal phylum detected in NCTs. However,
results revealed dissimilarities at genera level with Thiobacillus, Malikia, Hydrogenophaga,
Desulfomicrobium and Prolixibacter observed only in NCTs made from agricultural waste.
Whereas, genera Oligosphaera, Paracoccus, Synergistes, and Anoxybacillus were only
observed in NCTs made from solid municipal waste. Although compost source affected
microbial structure of the NCTs, it is uncertain how the compost tea brewing process affected

17
characteristics such as relative abundance of microbial species. This is definitely an area that
requires further work in most compost-compost tea studies.
To this end, based on a high number of DGGE bands, Diánez et al. (2018) suggested
regardless of the incubation conditions (aeration versus not aeration) or compost source (spent
mushroom and grape marc compost, and crop residues vermicompost), all compost teas
contained a high diversity of species,. It is unclear what can be attributed to the high population
of microbial species in all compost tea given that compost source and incubation conditions
were not suggested as significant factors. Nevertheless, they reported that aerated compost teas
(ACT) provided higher bacterial richness, diversity and evenness values compared to NCT.
That is, during incubation the effect of aeration on bacterial richness and diversity was compost
type-specific. Bacteroidetes and Proteobacteria were reported as the main phyla dominating
the ACTs and NCTs in this study. Kim et al. (2015) who found that the dominant bacterial
genera were Bacillus, Ochrobactrum and Spingomonas reported similar results. However, the
density of fungal populations were significantly lower than those of bacteria and decreased
significantly in all compost teas after two days incubation.
As it relates to soil application, the efficacy of ACT in introducing beneficial
microorganisms and its effect on soil microbial and disease dynamics under crop rotation
systems was investigated by Larkin (2008) using various techniques including soil dilution
plating and profiles of fatty acid methyl ester and substrate utilization. It was reported that
ACT successfully delivered microorganisms into the soil, significantly increased soil microbial
population and activity, reduced soil-borne disease and improved yield under the barley/rye
crop rotation, but not under other rotations or plots continuously cultivated with potato. Due to
these results, Larkin (2008) suggested that to be effective, ACT required a minimum level of
support from the soil microbial environment. It was however evident that the “minimum” soil
support level required was mediated by crop rotation, which had a more dominant, positive and

18
distinct effect on soil microbial community characteristics than ACT. Fritz et al. (2012)
reported similar positive response on plant growth, which was associated with vermicompost
teas with the highest microbial population and diversity. They reported that the addition of
different carbon substrates during brewing significantly affected the richness and diversity of
microbial communities. Furthermore, the microbial communities of the solid composts were
distinct from that of the compost teas produced from them. DGGE profiles showed that
compost tea stored beyond 1 week at >=10 °C resulted in significant change in microbial
communities, which was probably related to a loss in quality. The authors postulated that the
difference between the microbial communities of solid composts and that of the corresponding
compost teas was that compost bacteria were not being extracted proportionally into the tea.
Using COMPOCHIP microarray analyses, the authors found that the main
discriminatory microbial species/group across compost types were Acinetobacter, specifically
Acinetobacter lwoffii and Acinetobacter calcoaceticus. Microbial species reported to be
involved in nitrogen cycling (Nitrosovibrio spp. and Nitrosospira spp.) (Kowalchuk et al.
1999; Innerebner et al. 2006; Danon et al. 2008), degradation of biopolymers (Alfreider et al.
2002), plant disease suppression and human pathogens (Xanthomonas spp. and
Stenotrophomonas spp.) (Franke-Whittle et al. 2009) were also detected in compost teas. Of
concern to the authors were the presence of A. calcoaceticus, which has been linked to bovine
spongiform encephalopathy (Cayuela et al. 2008) and the seemingly inconclusive results on
the reproducibility of compost tea. Both concerns have serious implications, which can limit
the marketability of compost teas. In contrast, Ottesen et al. (2009) reported that in the clone
library of compost tea, neither of the bacterial genera most commonly associated with produce-
related illness outbreaks (Salmonella and Escherichia) were observed.
Findings from metagenomic studies on microbiology of composting process were
generally in agreement with cultivable and partial microbial community analysis at the domain

19
and phyla levels, which showed bacteria was the dominant domain through all phases of
composting with the abundance of the dominant phyla Firmicutes decreasing after
thermophilic, while that of Proteobacteria, Bacteroidetes, Actinobacteria increased. However,
stark differences in findings across studies were more apparent at the genus and species levels
as dictated by contrasting feedstock characteristics and nutrients, oxygen, temperature and to
some extent pH levels. This ability of differing microbial taxon to carry out
processes/composting at similar rate despite differences in microbial community composition
speaks strongly to extent of functional similarity and/or redundancy in compost microbial
consortia. Although much work is still needed to better access and represent compost
metagenome by investigating DNA extraction biases, obtaining representative samples and
providing better tools to study microbial diversity, current metagenomic libraries are proving
to be a great resource to postgenomic or function-related studies such as metatranscriptomics,
metaproteomics and metametabolomics.
1.3 Postgenomic Approaches
1.3.1 Metatranscriptomics
While metagenomic DNA-based analyses can provide information on the functional and
metabolic capacity of microbial communities, it cannot differentiate expressed and non-
expressed genes. Thus, it does not reflect the actual metabolic activity of microbial
communities (Sorek and Cossart 2010). By focusing on genes expressed by the entire microbial
community, metatranscriptomics is used to examine the active functional profile of a microbial
community. With this approach and the use of point or time sampling, transcriptional profiles
associated with specific microbial populations within a community can be produced
(Carvalhais et al. 2012). Such profiles are useful in obtaining more in-depth

20
understanding of the potential activities and regulatory mechanisms of microbial communities
in compost-based products .
In contrast to qPCR and microarrays, respectively, metatranscriptomics does not use
primers or probes. Rather, as with metagenomics, it consists of the random sequencing of
mRNA of the microbial community. As such, according to Moran (2009) and Carvalhais et al.
(2012), the constraints of preselecting which and how many genes should be surveyed in a
study when using qPCR and microarrays, is overcome with the use of metatranscriptomics.
For this reason, employing metatranscriptomics results in sequencing of transcripts from
microbial communities with less bias compared with the use of microarray or qPCR techniques.
Moreover, Moran (2009) reported that using metatranscriptomics, it is possible to distinguish
paralogous sequences that might cross-hybridize on a microarray.
In this light, to obtain overall profile of gene function during composting, Antunes et
al. (2016) used Cluster of Orthologous Groups (COGs) (Galperin et al. 2015) to classify coding
sequences from two composts assembled shotgun reads. They found the most abundant
functions in the composts were related to cell maintenance and proliferation, signal
transduction and defense mechanisms. Hierarchical clustering of the metatranscriptome coding
sequences (CDSs) revealed distinctions among the beginning/turning, middle and end phases
of composting as evident by their corresponding COG category differential abundance levels.
The main functional groupings of metabolism, cellular processes and signaling, and
information storage and processing seemed more equally distributed with higher COGs
abundance levels in the end phase of composting. Whereas, beginning/turning of composting
phase was more skewed to functions related to metabolism (energy production and
transport/coenzyme metabolism). Among the composting phases, the middle phase of
composting had the lowest in the abundance of COGs related to metabolism but had noticeably
high COGs related to replication/recombination/repair activities. The authors inferred that

21
functions related to the microbial metabolism in the beginning phase or after turning compost
were related to the degradation and utilization of easily degradable organic nutrients. Whereas,
the higher expression levels of genes in the replication/recombination/repair category may be
explained by changes in microbial composition between the beginning/turning and middle
groups as was evident in taxonomic analysis.
A clear and solid inference of the functional profiles obtained at the end of composting
was not provided by the authors. However, the general inferences made about results trend in
beginning and middle of composting are consistent with that made for a typical aerobic
composting process (Ryckeboer et al. 2003; Kumar 2011). Antunes et al. (2016) also confirmed
that most coding sequences (CDSs) related to plant biomass degradation belonged to members
of the Bacillales, Clostridiales, Actinomycetales, and Thermoanaerobacterales. Members of
these orders were associated with 5 CAZyme classes: glycoside hydrolases, carbohydrate-
binding modules, glycosyl transferases, carbohydrate esterases and polysaccharide lyases.
Similar results were reported by Wang et al. (2016). They found that the dominant
phylum Actinobacteria in rice straw-adapted (RSA) microbial consortia enriched from
compost, contained about 46.1 % of Carbohydrate-active enzyme (CAZyme) genes, which
harbored an extensive catalog of the cellobiohydrolase, β-glucosidase, acetyl xylan esterase,
arabinofuranosidase, pectin lyase, and ligninase genes. Both studies showed degradation of
hemicellulose, cellulose, pectin, and lignin occurred throughout composting. However,
Antunes et al. (2016) specifically noted that turning compost temporarily slowdown the
degradation process. This “slowdown” was probably linked to a restoring a microbial
population profile similar to what was seen at the beginning of composting. Neither Antunes
et al. (2016) or Wang et al. (2016) found any CDSs that could be annotated as fungal
ligninolytic enzymes during thermophilic composting. Hence, they concluded that the

22
degradation of lignocellulose during thermophilic composting is mainly or even exclusively
the result of bacterial enzymatic activity.
As it relates to ARGs, Wang et al. (2017) reported that during the active phase of
composting, the relative abundance of expressed resistome significantly increased but
decreased during the cooling and maturing phases. This trend was particularly evident for the
3 most prevalent resistance mechanisms (ATP-binding cassette antibiotic efflux pumps,
tetracycline resistance, and vancomycin resistance proteins). The expression of tetracycline
resistance genes (tetM-tetW-tetO-tetS) declined as composting progressed and composting had
no effect on the expression of sulfonamide and fluoroquinolone resistance genes.
Emerging trends and interest in plant nutritional genomics are expected to further fuel
metatranscriptomics works involving compost and compost tea. That is, metatranscriptomics
works focused on linking plant nutritional status under stressed conditions to plant-microbe
interactions as facilitated or affected by compost or compost tea. In this regard, research works
by Carvalhais et al. (2013) have set a strong foundation for this direction. They found that the
nutritional status of maize affected the transcriptome of a beneficial root colonizing bacterium,
Bacillus amyloliquefaciens, due to compositional changes in root exudates. Exudates from
nitrogen-deprived maize triggered a general stress response in B. amyloliquefaciens and down-
regulated genes associated with chemotaxis and motility in the exponential growth phase.
These results have serious implications for beneficial bacteria such as Pseudomonas
Fluorescens (plant disease control) and Azospirillum brasilense (nitrogen-fixing) that depend
on chemotactic motility to colonize roots/rhizosphere. Notwithstanding these results, much is
needed to extend such works from transcriptome to metatranscriptome level for PGPR focusing
on using compost or compost tea to enhance resident rhizosphere microbial populations or as
a carrier for PGPR with diverse modes of action.

23
As noted by Borek et al. (1958) and Schut et al. (1993), mRNA is more sensitive to
environmental fluxes than proteins due in part to its shorter half-life and lower inventory in
cells. As such, the metatranscriptome may be better able to reflect more near-real-time
regulatory reactions of cells to environmental changes compared to the metaproteome (Moran
2009; Moran et al. 2013). A review of the limitations and biases of metatranscriptomics as it
relates to difficult protocols for synthesizing and amplifying cDNA and isolating mRNA is
provided by Carvalhais et al. (2012) and Moran (2009). Moran (2009) noted that most of these
limitations are associated with the instability and impurity of mRNA during isolation or storage
(Redon et al. 2005; Deutscher 2006; Opel et al. 2010) and its relatively low quantum in
microbial communities from environmental samples (He et al. 2010). Another limitation
related to specifically to prokaryotic microorganisms is the lack of 3’-poly-A tails (Moran
2009).
1.3.2 Metaproteomics and Metaproteogenomics
Metatranscriptomics analyses do not give any insight into whether transcripts are translated to
proteins, or if constitutively expressed genes are differentially post-translationally modified.
As such, metatranscriptomics is arguably a less suitable and direct way of profiling microbial
community function compared to metaproteomics, which refers to the characterization of the
protein composition of microbiota from environmental samples
(Maron et al. 2007; Carvalhais et al. 2012). This is so because ultimately, in a cell, specific
functions are carried out by proteins, more specifically, by enzymes (Chistoserdova 2013).
Interestingly, Maier et al. (2009) found that there was a very weak correlation between protein
synthesis and the abundance of transcripts, which mediated the synthesis process. Moreover,
extracting, separating and identifying proteins pose several technical challenges which includes
removing contaminants (organic and inorganic). In light of these challenges, Moran (2009)

24
noted that metaproteomics may be more arduous than metatranscriptomics, particularly when
dealing with environmental samples with high levels of microbial diversity. In such cases, due
to the dilution of each protein in a complex sample, it is likely that only proteins that are most
abundant will be identified (Schneider and Riedel 2010), which often results in specific
microorganisms being under- or over-represented. Furthermore, microbial species which have
never been genome sequenced or in vitro studied may be present in environmental samples.
This means that the protein sequences for such microorganisms, which are required for
identification using mass spectrometry, are not present in public databases. Banfield et al.
(2005) noted that a combined metaproteomics-metagenomic approach, which is termed
metaproteogenomics can be used to address limitations of the two approaches. More
specifically, metaproteogenomics allows for a more systematic way of linking phylogenetic
identities or diversity of microorganisms with their biological functions since total DNA and
proteins are extracted from the same environmental sample (Rastogi and Sani 2011)
In this light, Liu et al. (2015) used compost metaproteomes to evaluate microbial
succession during composting phases and infer the predominant metabolic processes by
bacteria and fungi. Results showed that the diversity of fungi was lower compared to bacteria,
and the abundance of Actinobacteria and Saccharomyces increased significantly with
composting time. Fungi (Fusarium oxysporum, Neurospora crassa and Phanerochaete
chrysosporium) were the main producers of cellulase in earlier phase of composting, bacterial
communities (Bacillus subtilis and Thermobifida fusca) replaced the cellulolytic fungal
communities during active phase and decomposition of cellulose in the curing phase required
the synergy between bacteria and fungi. Interestingly, thermophilic fungi are not active through
the thermophilic phase and Saccharomycetes, Schizosaccharomycetes, Sordariomycetes,
Eurotiomycetes and Basidiomycota were the most abundant fungal classes.

25
In a study, more akin to cold rather than active composting, Schneider et al. (2012) used
metaproteomics to study the influence of environmental factors and nutrients on the structure
and function of the decomposer during beech litter decomposition. In contrast to Liu et al.
(2015), Schneider et al. (2012) found that the primary producers of extracellular hydrolytic
enzymes were fungi, with no bacterial hydrolases detected. This may be due to the lack of
sustained high temperature phase (thermophilic) during litter decomposition, which tends to
preclude fungal microorganisms and activity. Schneider et al. (2012) noted that the
stoichiometry of C:N:P affected the decomposer community structure. Moreover, microbial
activity was stimulated at higher nutrient contents through higher abundance and activity of
extracellular enzymes. In an in vitro metaproteogenomics study, Ros et al. (2018) reported
differences in phylogenetic structure and functional levels between compost suppressive and
not suppressive to Phytophthora nicotianae. The authors concluded that Proteobacteria could
be indicators of P. nicotianae suppression and annotated proteins using COGs, to the
carbohydrate process, cell wall structure and inorganic ion transport and metabolism.
Metaproteogenomic studies focused on characterizing microbiota in compost tea-
treated phyllospheres have been limited. So have compost-tea studies focused on profiling
microbiota in the phyllosphere and rhizosphere in a single study, such as work done by Knief
et al. (2012) to characterize microbiota in rice cultivars. Such research is important to gain a
greater understanding of disease suppression mechanisms since foliar application of compost
tea have been reported result in a phyllosphere-mediated disease suppressive-effect (Weltzien
1991). More specifically, as it relates to the contrasting environments of NCT and ACT,
metaproteogenomic approaches should prove useful in the identification of specific stress-
protein production linkages and new functional genes and metabolic pathway tracking (Maron
et al 2007). All of which speaks to reevaluating microbial ecology concepts from a more
functional perspective or “lens” (Maron et al 2007).

26
Even more limited is research on compost tea that focused on profiling the phyllosphere and
rhizophere in a single study, as was done by Knief et al. (2012) to characterize microbiota in
rice cultivars.
1.3.3 Metametabolomics
Microbial communities release metabolites (naturally occurring-low molecular weight organic
molecules), often as final responses to environmental fluxes, toxic compounds, disease or
genetic alterations into their immediate environment (Khanfir et al. 2009). As such,
metabolomics refers to the study these molecules and their relationship with microbial
communities and the environment (Dunn and Ellis 2005). Metabolomic profiles per se do not
reflect microbial functionality directly or in totality, however they may indicate a level of
dependency between microbiome and environmental factors such as, climatic stresses and
available nutrients. Thus, providing valuable information not just about the characteristics of
the microbiome but about the interactions of the microbial community with the host
environment. In this way, metametabolomics complements the information provided by the
other omics and is considered the most direct indicator of the health of an environment or of
the alterations in homeostasis (Bernini et al. 2009). That is, metametabolomics aims to improve
our understanding of the role of the microbiome in the transformation of nutrients and
pollutants, and other abiotic factors that may affect the homeostasis of the host environment
(Aguiar-Pulido et al. 2016).
In this light, metametabolomics is also regarded as the end-point of the ‘omics’ cascade
(Dettmer and Hammock 2004) since the metabolome is most predictive of phenotype.
Metametabolomics also represents an approach to pathway analysis since variation in the
production of signature metabolites are related to changes in activity of metabolic routes

27
(Manor et al. 2014). In turn, the combination of metabolomic and pathways information can
lead to new hypotheses. Metabolome analysis can also provide information on the signaling
processes which characterize communication between bacteria such as in quorum sensing
Bassler (2002). Such information is important in advancing or improving strategies for disease
control with compost and compost tea. More so, it is critical for optimising application efficacy
of compost and compost tea, particularly in sustainably intensified farming systems.
In light of this, Cronin et al. (1996) and Scheuerell (2002) reported that the disease
suppressive capacity of compost-based products was related to secondary metabolite
production by native microorganisms. However, for the most part, the identification and
purification of such metabolites have not been very successful (Cronin et al. 1996; Sang and
Kim 2011). As such, there is limited information on the metabolic profiles and function of
compost-based products. In one of the first compost-metametabalome disease suppression
studies, Blaya et al. (2016) reported that 54 major metabolites from compost and peat extracts
clustered in a way that was associated with their suppressive ability of Phytophthora root rot
(P. nicotianae). In suppressive compost, most of these metabolites (identified by values, not
name) were found in lower relative abundance. Whereas, several mass compounds
predominantly contributed to the separation of peat from the composts and others to the
separation of suppressive from conductive composts. Blaya et al. (2016), however, concluded
that results obtained were preliminary and there was a need to further investigate the
relationship between metabolites and the capacity of composts to suppress P. nicotianae.
Other studies have focused on the effect of compost on the metabolome of crops or the
effect of specific metabolites from microbial isolates in compost products on the suppression
pathogens. For example, Vinci et al. (2018) reported that a synergic effect on plant growth,
phosphorus uptake and plant metabolite expression was observed with composts and
Trichoderma harzianum (strain OMG-08) inoculation. That is, plant growth and phosphorus

28
uptake were enhanced, and a relationship between the expression of different metabolites and
improved photosynthetic activity was observed. Conversely, plant metabolome revealed
compounds typical of biotic or abiotic stresses when T. harzianum (strain OMG-08) was
applied with inorganic fertilisers. The authors attributed the stress profile to a reduced capacity
of inorganic fertilizers to provide a sufficient phosphorus availability during plant growth.
Vinci et al. (2018) reported similar results with Bacillus amyloliquefaciens- composts
treatments, which resulted in significant increases in glucose, fructose, alanine and GABA
metabolites in maize leaves. De Juras (2008) also reported a 32% increase in flavonoid content
in sambong (Blumea balsamifera) plants treated with compost. Likewise, Neugart et al. (2018)
demonstrated that the solid biological waste composts induced specific changes in the
metabolite profiles. Furthermore, the changes were dependent on the type of the organic
residues and its concentration in soil. Targeted analysis of selected plant metabolites revealed
a 3.2-fold (maxima) increase in the concentrations of carotenoids and 4.7-fold and 1.5-fold
decrease in glucosinolates and phenolic compounds, respectively (Neugart et al. 2018).
Using a genomics-guided discovery process, Yang et al. (2016) isolated three
antibacterial active metabolites (Aurantinins B, C and D) from compost-associated B. subtilis
fmb60. Aurantinins C and D were identified as new antimicrobial compounds and all three
metabolites showed significant bactericidal activity against multidrug-resistant Staphylococcus
aureus and Clostridium sporogenes. Cell membrane disruption was reported as the main
bactericidal activity of the metabolites. On the premise that secondary metabolite production
is dependent on the nutrient state of microorganisms, Li et al. (2004) investigated geosmin
concentration as a possible indicator of compost stability. Results demonstrated that geosmin
correlated with C/N ratio and could be used as an index for the compost stability assessment
across different composting processes with various organic solid wastes.

29
Results from these studies show the potential applications of metametabolomics, which
is not without limitations. One of the major challenges of metametabolomics, is difficulty in
mapping metabolites to individual species of the microbiome. Furthermore, if conclusions are
to be made about which genes, enzymes, or pathways are associated with a specific metabolite,
the results obtained from a metabolomic study must be combined with other omics- data
(Aguiar-Pulido et al. 2016). Moreover, as noted by Miller (2007), databases with sufficient
information for the identification of metabolites is lacking. So is software for automating the
process of identifying and quantifying metabolites. However, Putri et al. (2013) noted that the
increasing use metabolomics techniques to study microbiology and in related fields, have
resulted in the development of more comprehensive public metabolomics databases. This has
concurrently occurred with more advanced statistical and bioinformatic approaches to process
and manage big data.
1.4 Conclusions and Future Work
High‐throughput
‐
omics techniques have revolutionized molecular biology and advanced our
understanding of compost and compost tea microbiology. However, there is still a need to
better understand and relate microbial dynamics to the effectiveness of the composting process
and compost products. Such an understanding is important in optimising production and use
protocols for compost and compost tea without risking human health. More so, it is critical in
reducing the variability in efficacy associated with using compost products. To achieve this
understanding, scientific consensus on a theoretical framework that examines the interactions
between the physical, chemical and biological parameters of compost and abiotic factors such
temperature and moisture is needed. Embedded in this framework, omics techniques can be
used as a “toolbox” for systematic analyses and integration of information on the diversity,
function, and ecology of microorganisms in compost products. Multi-omics approaches are

30
also important in supporting emerging or next frontier trends such as metaphenomics (Nesme
et al. 2018). Metaphenomics will allow for mapping physiological states of microorganisms to
available resources, outputs and ultimately effects of products such as compost and compost
tea. Therefore, it has the potential to provide deeper insights into predictability problematique
of compost products than metagenomics, which analyzes DNA from microbes with vastly
varying physiological states.
In this light, although extremely useful, -omics tools are not without challenges,
particularly the “meta” approaches. The correct annotation of only a small fraction of a very
large number of ecologically important genes presents a cross-cutting challenge for all “meta”
approaches (Moran 2009). More so, the rapid expansion of sequence database is still
representative of the most abundant genes in the environment (Moran 2009), with a relatively
limited contribution from compost-based samples. To further complex this issue, many of these
sequences cannot be confidently assigned to a function because there are no close matching in
public sequence databases (Poretsky et al. 2005). However, with recent surge of compost
studies using -omics approaches, the severity of this challenge is gradually decreasing.
Developing more comprehensive public sequencing databases through integrative
visualization of multiple biological datasets and gene expression, biomarkers and pathway
analysis is now possible through online multi-omics platforms such as PaintOmics
(Hernández-de-Diego et al. 2018). However, there is still a need for contributions to better
reflect a balance of entries from compost rhizosphere and phyllosphere studies, particularly for
fungal species. Moreover, as more metagenomic datasets are generated, it becomes
increasingly important to have standardize procedures and shared data storage and analysis to
ensure that outputs of individual projects can be assessed and compared (Thomas et al. 2012).
This becomes even more critical with the impending use of real-time metagenomic next

31
generation sequencing in studying species diversity during the composting and compost tea
brewing process.
Other technical challenges persist with -omics approaches including DNA, RNA, and
protein extraction from environmental samples like compost, mRNA instability, and low
abundance of certain gene transcripts in total RNA. Biases associated to nucleic isolation and
PCR result in perhaps the greatest challenge, which is the quantitative assessment of microbial
communities. According to researchers, more specific challenges related to metagenomics is
sequence assembly, particularly from complex microbial communities (Pell et al. 2012;
Nagarajan and Pop 2013). Although metagenome-specific assembly algorithms and methods
for “binning” genomes from metagenome data have resulted in successes, this remains one
of the biggest challenges in bioinformatics (Pell et al. 2012; Verberkmoes et al. 2012).
In advancing the knowledge on compost microbiology, it should be noted that culture-
based and culture-independent molecular techniques are neither contradictory nor excluding
and should be considered complementary. Moreover, none of the molecular approaches
provides complete access to the genetic and functional diversity of complex microbial
communities. As such, an -omics approach should be selected based on the biological questions
and objectives of the study. To this end, metaprofiling remains a cost-effective and viable tool
for exploratory analysis of microbial community to inform the direction of subsequent “omics”
studies. This is particularly relevant for large-scale characterization of complex or differing
environmental samples.
References
Abd El-Kader SF, El-Chaghaby GA, Khalafalla GM, et al (2019) A novel microbial consortium
from sheep compost for decolorization and degradation of Congo red ARTICLE INFO.
Glob J Environ Sci Manag 5:61–70. doi: 10.22034/gjesm.2019.01.05

32
Aguiar-Pulido V, Huang W, Suarez-Ulloa V, et al (2016) Metagenomics, metatranscriptomics,
and metabolomics approaches for microbiome analysis. Evol. Bioinforma. 12:5–16
Akita H, Kimura Z-I, Matsushika A (2017) Complete genome sequence of Ureibacillus
thermosphaericus A1, a Thermophilic Bacillus Isolated from Compost. Genome Announc
5:e00910-17. doi: 10.1128/genomeA.00910-17
Alfreider A, Peters S, Tebbe CC, et al (2002) Microbial Community Dynamics During
Composting of Organic Matter as Determined by 16S Ribosomal DNA Analysis.
Compost Sci Util 10:303–312. doi: 10.1080/1065657X.2002.10702094
Allgaier M, Reddy A, Park JI, et al (2010) Targeted discovery of glycoside hydrolases from a
switchgrass-adapted compost community. PLoS One 5:e8812. doi:
10.1371/journal.pone.0008812
Antunes LP, Martins LF, Pereira RV, et al (2016) Microbial community structure and dynamics
in thermophilic composting viewed through metagenomics and metatranscriptomics. Nat
Publ Gr 1–13. doi: 10.1038/srep38915
Azadnia P, Zamam MH, Ghasemi SA, et al (2011) Isolation and identification of thermophilic
lactobacilli from traditional Yoghurts of tribes of Kazerun. J Anim Vet Adv 10:774–776.
doi: 10.3923/javaa.2011.774.776
Banfield JF, Verberkmoes NC, Hettich RL, Thelen MP (2005) Proteogenomic Approaches for
the Molecular Characterization of Natural Microbial Communities. 9:301–333
Bassler BL (2002) Small talk. Cell-to-cell communication in bacteria. Cell 109:421–4
Bernini P, Bertini I, Luchinat C, et al (2009) Individual human phenotypes in metabolic space
and time. J Proteome Res 8:4264–4271. doi: 10.1021/pr900344m
Blaya J, Marhuenda FC, Pascual JA, Ros M (2016) Microbiota characterization of compost
using omics approaches opens new perspectives for phytophthora root rot control. PLoS
One 11:. doi: 10.1371/journal.pone.0158048

33
Borek E, Ponticorvo L, Rittenberg D (1958) Protein Turnover in Micro-organisms. Proc Natl
Acad Sci U S A 44:369–374
Brumm PJ, Land ML, Mead DA (2016) Complete genome sequences of Geobacillus sp.
WCH70, a thermophilic strain isolated from wood compost. Stand Genomic Sci 11:33.
doi: 10.1186/s40793-016-0153-y
Brzostowicz PC, Walters DM, Thomas SM, et al (2003) mRNA differential display in a
microbial enrichment culture: Simultaneous identification of three cyclohexanone
monooxygenases from three species. Appl Environ Microbiol 69:334–342. doi:
10.1128/AEM.69.1.334-342.2003
Bugg TD, Ahmad M, Hardiman EM, Singh R (2011) The emerging role for bacteria in lignin
degradation and bio-product formation. Curr Opin Biotechnol 22:394–400. doi:
10.1016/J.COPBIO.2010.10.009
Carvalhais LC, Dennis PG, Fan B, et al (2013) Linking Plant Nutritional Status to Plant-
Microbe Interactions. PLoS One 8:e68555. doi: 10.1371/journal.pone.0068555
Carvalhais LC, Dennis PG, Tyson GW, Schenk PM (2012) Application of metatranscriptomics
to soil environments. J. Microbiol. Methods 91:246–251
Cayuela ML, Millner PD, Meyer SLF, Roig a (2008) Potential of olive mill waste and compost
as biobased pesticides against weeds, fungi, and nematodes. Sci Total Environ 399:11–8.
doi: 10.1016/j.scitotenv.2008.03.031
Chandna P, Nain L, Singh S, Kuhad RC (2013) Assessment of bacterial diversity during
composting of agricultural byproducts. BMC Microbiol 13:. doi: 10.1186/1471-2180-13-
99
Chen QL, An XL, Zheng BX, et al (2018) Long-term organic fertilization increased antibiotic
resistome in phyllosphere of maize. Sci Total Environ 645:1230–1237. doi:
10.1016/j.scitotenv.2018.07.260

34
Chengwei L, Rodriguez-R LM, Konstantinidis KT (2014) MyTaxa: An advanced taxonomic
classifier for genomic and metagenomic sequences. Nucleic Acids Res 42:e73. doi:
10.1093/nar/gku169
Chistoserdova L (2013) Biotechnology and Genetic Engineering Reviews Functional
Metagenomics : Recent Advances and Future Challenges. 37–41. doi: 10.5661/bger-26-
335
Cook RJ, Baker KF (1983) The Nature and Practice of Biological Control of Plant Pathogens.
American Phytopathological Society, St. Paul, MN
Cronin MJ, Yohalem DS, Harris RF, Andrews JH (1996) Putative mechanism and dynamics
of inhibition of the apple scab pathogen Venturia inaequalis by compost extracts. Soil Biol
Biochem 28:1241–1249. doi: 10.1016/0038-0717(96)00131-9
Cseke L (2016) Metagenomic Analysis of Aerated Compost Tea. In: Metagenomic Anal.
Aerated Compost Tea. https://www.researchgate.net/project/Metagenomic-Analysis-of-
Aerated-Compost-Tea. Accessed 14 Nov 2018
Daami-remadi M, Dkhili I, Jabnoun-Khiareddine H, El Mahjoub M (2012) Biological Control
of Potato Leak with Antagonistic Fungi Isolated from Compost Teas and Solarized and
Non-Solarized Soils. Pest Technol 6:32–40
Dang TCH, Nguyen DT, Thai H, et al (2018) Plastic degradation by thermophilic Bacillus sp.
BCBT21 isolated from composting agricultural residual in Vietnam. Adv Nat Sci Nanosci
Nanotechnol 9:015014. doi: 10.1088/2043-6254/aaabaf
Danon M, Franke-whittle IH, Insam H, et al (2008) Molecular analysis of bacterial community
succession during prolonged compost curing. doi: 10.1111/j.1574-6941.2008.00506.x
de Gannes V, Eudoxie G, Hickey WJ (2013) Prokaryotic successions and diversity in composts
as revealed by 454-pyrosequencing. Bioresour Technol 133:573–580. doi:
10.1016/j.biortech.2013.01.138

35
De Juras RJ (2008) Growth of and secondary metabolite production in sambong (Blumea
balsamifera) as influenced by compost and nitrogen. University of the Philippines, Los
Baños
Dettmer K, Hammock BD (2004) Metabolomics--a new exciting field within the “omics”
sciences. Environ Health Perspect 112:A396-7. doi: 10.1007/s11060-008-9572-y
Deutscher MP (2006) Degradation of RNA in bacteria: Comparison of mRNA and stable RNA.
Nucleic Acids Res 34:659–666. doi: 10.1093/nar/gkj472
Diánez F, Marín F, Santos M, et al (2018) Genetic Analysis and In Vitro Enzymatic
Determination of Bacterial Community in Compost Teas from Different Sources Genetic
Analysis and In Vitro Enzymatic Determination of Bacterial Community in Compost Teas
from Different Sources. Compost Sci Util 1–15. doi: 10.1080/1065657X.2018.1496045
Divine R (2014) Isolation of bacteria from compost for potential use in biodecaffeination.
Cornell University
Doolittle M, Raina A, Lax A, Boopathy R (2008) Presence of nitrogen fixing Klebsiella
pneumoniae in the gut of the Formosan subterranean termite (Coptotermes formosanus).
Bioresour Technol 99:3297–3300. doi: 10.1016/j.biortech.2007.07.013
Dougherty MJ, Patrik D, Hazen TC, et al (2012) Glycoside Hydrolases from a targeted
Compost Metagenome , activity-screening and functional characterization. BMC
Biotechnol 12:1. doi: 10.1186/1472-6750-12-38
Droffner ML, Brinton WF, Evans E (1995) Evidence for the prominence of well characterized
mesophilic bacteria in thermophilic (50-70°C) composting environments. Biomass and
Bioenergy 8:191–195. doi: 10.1016/0961-9534(95)00002-O
Dunn WB, Ellis DI (2005) Metabolomics: Current analytical platforms and methodologies.
TrAC Trends Anal Chem 24:285–294. doi: 10.1016/j.trac.2004.11.021
Eichorst SA, Strasser F, Woyke T, et al (2015) Advancements in the application of NanoSIMS

36
and Raman microspectroscopy to investigate the activity of microbial cells in soils. FEMS
Microbiol Ecol 91:fiv106. doi: 10.1093/femsec/fiv106
Eschenfeldt W, Stols L, Rosenbaum H, et al (2001) DNA from Uncultured Organisms as a
Source of 2,5-Diketo-d-Gluconic Acid Reductases. Appl Environ Microbiol 67:4206–
4214. doi: 10.1128/AEM.67.9.4206-4214.2001
Franke-Whittle IH, Knapp B a, Fuchs J, et al (2009) Application of COMPOCHIP microarray
to investigate the bacterial communities of different composts. Microb Ecol 57:510–21.
doi: 10.1007/s00248-008-9435-2
Fritz JI, Franke-Whittle IH, Haindl S, et al (2012) Microbiological community analysis of
vermicompost tea and its influence on the growth of vegetables and cereals. Can J
Microbiol 58:836–847. doi: 10.1139/w2012-061
Galperin MY, Makarova KS, Wolf YI, Koonin E V. (2015) Expanded Microbial genome
coverage and improved protein family annotation in the COG database. Nucleic Acids
Res 43:D261–D269. doi: 10.1093/nar/gku1223
Gbolagade JS (2006) Bacteria associated with compost used for cultivation of Nigerian edible
mushrooms Pleurotus tuber-regium (Fr.) Singer, and Lentinus squarrosulus (Berk.).
African J Biotechnol 5:338–342. doi: 10.5897/AJB05.408
Golueke CG, Card BJ, McGauhey PH (1954) A critical evaluation of inoculums in composting.
Appl Microbiol 2:45–53
Gou M, Hu H, Zhang Y, JT Wang (2018) Aerobic composting reduces antibiotic resistance
genes in cattle manure and the resistome dissemination in agricultural soils. Sci Total
Environ 612:1300–1310
Griffiths BS, Welschen R, van Arendonk JJCM, Lambers H (1992) The effect of nitrate-
nitrogen supply on bacteria and bacterial-feeding fauna in the rhizosphere of different
grass species. Oecologia 91:253–259. doi: 10.1007/BF00317793

37
Handelsman J (2005) Metagenomics: Application of Genomics to Uncultured
Microorganisms. Microbiol Mol Biol Rev 69:195–195. doi:
10.1128/MMBR.69.1.195.2005
Handelsman J, Rondon MR, Brady SF, et al (1998) Molecular biological access to the
chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol
5:R245–R249. doi: 10.1016/S1074-5521(98)90108-9
He S, Wurtzel O, Singh K, et al (2010) Validation of two ribosomal RNA removal methods for
microbial metatranscriptomics. Nat Methods 7:807–812. doi: 10.1038/nmeth.1507
Hemsworth GR, Johnston EM, Davies GJ, Walton PH (2015) Lytic Polysaccharide
Monooxygenases in Biomass Conversion. Trends Biotechnol 33:747–761. doi:
10.1016/j.tibtech.2015.09.006
Heringa SD, Kim JK, Jiang X, et al (2010) Use of a mixture of bacteriophages for biological
control of salmonella enterica strains in compose. Appl Environ Microbiol 76:5327–5332.
doi: 10.1128/AEM.00075-10
Hernández-de-Diego R, Tarazona S, Martínez-Mira C, et al (2018) PaintOmics 3: a web
resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids
Res 46:W503–W509. doi: 10.1093/nar/gky466
Herren GL, Binnemans I, Joos L, et al (2018) Compost as a carrier medium for
entomopathogenic nematodes – The influence of compost maturity on their virulence and
survival. Biol Control 125:29–38. doi: 10.1016/j.biocontrol.2018.06.007
Hoitink HAJ, Fahy P (1986) Basis for the control of soilborne plant pathogens with composts.
93–114
Hoitink HAJ (1990) Production of disease suppressive compost and container media, and
microorganism culture for use therein. U.S. Patent 4,900,348
Holmes AJ, Gillings MR, Nield BS, et al (2003) The gene cassette metagenome is a basic

38
resource for bacterial genome evolution. Environ Microbiol 5:383–394. doi:
10.1046/j.1462-2920.2003.00429.x
Hugenholtz P (2002) Exploring prokaryotic diversity in the genomic era. Genome Biol 3:1–8.
doi: 10.1186/gb-2002-3-2-reviews0003
Inbar E, Green SJ, Hadar Y, Minz D (2005) Competing factors of compost concentration and
proximity to root affect the distribution of streptomycetes. Microb Ecol 50:73–81. doi:
10.1007/s00248-004-0111-x
Innerebner G, Knapp B, Vasara T, et al (2006) Traceability of ammonia-oxidizing bacteria in
compost-treated soils. Soil Biol Biochem 38:1092–1100. doi:
10.1016/j.soilbio.2005.09.008
Insam H, Riddech N, Klammer S (2002) Microbiology of composting. Springer Berlin
Heidelberg
INSDC (2018) No Title. In: Int. Nucleotide Seq. Database Collab. http://www.insdc.org/about.
Accessed 14 Nov 2018
Ishii K, Fukui M, Takii S (2000) Microbial succession during a composting process as
evaluated by denaturing gradient gel electrophoresis analysis. J Appl Microbiol 89:768–
777. doi: 10.1046/j.1365-2672.2000.01177.x
Jurado M, López MJ, Suárez-Estrella F, et al (2014) Exploiting composting biodiversity: Study
of the persistent and biotechnologically relevant microorganisms from lignocellulose-
based composting. Bioresour Technol 162:283–293. doi: 10.1016/j.biortech.2014.03.145
Kanokratana P, Uengwetwanit T, Rattanachomsri U, et al (2011) Insights into the Phylogeny
and Metabolic Potential of a Primary Tropical Peat Swamp Forest Microbial Community
by Metagenomic Analysis. Microb Ecol 61:518–528
Kerkeni A, Daami-Remadi M, Tarchoun N, Khedher M Ben (2007) In vitro assessment of the
antifungal activity of several compost extracts obtained from composted animal manure

39
mixtures. Int J Agric Res 2:786–794. doi: 10.3923/ijar.2007.786.794
Khaldi R El, Daami-remadi M, Hamada W, et al (2015) Plant Pathology & Microbiology The
Potential of Serratia marcescens : An Indigenous Strain Isolated from Date Palm Compost
as Biocontrol Agent of Rhizoctonia solani on Potato. doi: 10.4172/2157-7471.S3-006
Khanfir R, Jenana B, Haouala R, et al (2009) COMPOSTS , COMPOST EXTRACTS AND
BACTERIAL SUPPRESSIVE ACTION ON PYTHIUM APHANIDERMATUM IN
TOMATO. 41:315–327
Kim MJ, Shim CK, Kim YK, et al (2015) Effect of aerated compost tea on the growth
promotion of lettuce, soybean, and sweet corn in organic cultivation. Plant Pathol J
31:259–268. doi: 10.5423/PPJ.OA.02.2015.0024
Klammer S, Mondini C, Insam H (2005) Microbial community fingerprints of composts stored
under different conditions. 55:299–305
Knief C, Delmotte N, Chaffron S, et al (2012) Metaproteogenomic analysis of microbial
communities in the phyllosphere and rhizosphere of rice. ISME J 6:1378–1390. doi:
10.1038/ismej.2011.192
Konstantinidis KT, Ramette A, Tiedje JM (2006) The bacterial species definition in the
genomic era. In: Philosophical Transactions of the Royal Society B: Biological Sciences.
pp 1929–1940
Kouki S, Saidi N, Rajeb A Ben, et al (2012) Control of Fusarium Wilt of Tomato Caused by
Fusarium oxysporum F . Sp . Radicis-Lycopersici Using Mixture of Vegetable and
Posidonia oceanica Compost. 2012:. doi: 10.1155/2012/239639
Kowalchuk GA, Naoumenko ZS, Derikx PJL, et al (1999) Molecular analysis of ammonia-
oxidizing bacteria of the subdivision of the class Proteobacteria in compost and composted
materials. Appl Environ Microbiol 65:396–403
Ku C, Roukos DH (2013) From next-generation sequencing to nanopore sequencing

40
technology : paving the way to personalized genomic medicine. 1–6
Kumar S (2011) Composting of municipal solid waste. Crit Rev Biotechnol 31:112–136. doi:
10.3109/07388551.2010.492207
Kwok O, Fahy P, Hoitink H, Kuter G (1987) Interactions Between Bacteria and Trichoderma
hamatum in Suppression of Rhizoctonia Damping-off in Bark Compost Media.
Phytopathology 77:1206. doi: 10.1094/Phyto-77-1206
Lagier J, Armougom F, Million M, et al (2012) Microbial culturomics: Paradigm shift in the
human gut microbiome study. Clin Microbiol Infect 18:1185–1193. doi: 10.1111/1469-
0691.12023
Lagier J, Hugon P, Khelaifia S, et al (2015) The Rebirth of Culture in Microbiology through
the Example of Culturomics To Study Human Gut Microbiota. Clin Microbiol Rev
28:237–264. doi: 10.1128/CMR.00014-14
LaMontagne MG, Michel FC, Holden P a, Reddy C a (2002) Evaluation of extraction and
purification methods for obtaining PCR-amplifiable DNA from compost for microbial
community analysis. J Microbiol Methods 49:255–64
Land M, Hauser L, Jun S-R, et al (2015) Insights from 20 years of bacterial genome
sequencing. Funct Integr Genomics 15:141–161. doi: 10.1007/s10142-015-0433-4
Langarica-Fuentes A, Handley PS, Houlden A, et al (2014a) An investigation of the
biodiversity of thermophilic and thermotolerant fungal species in composts using culture-
based and molecular techniques. Fungal Ecol 11:132–144. doi:
10.1016/j.funeco.2014.05.007
Langarica-Fuentes A, Zafar U, Heyworth A, et al (2014b) Fungal succession in an in-vessel
composting system characterized using 454 pyrosequencing. FEMS Microbiol Ecol
88:296–308. doi: 10.1111/1574-6941.12293
Larkin RP (2008) Relative effects of biological amendments and crop rotations on soil

41
microbial communities and soilborne diseases of potato. Soil Biol Biochem 40:1341–
1351. doi: 10.1016/j.soilbio.2007.03.005
Larkin RP, Tavantzis S (2013) Use of Biocontrol Organisms and Compost Amendments for
Improved Control of Soilborne Diseases and Increased Potato Production. 261–270. doi:
10.1007/s12230-013-9301-8
Lemos LN, Pereira R V., Quaggio RB, et al (2017) Genome-centric analysis of a thermophilic
and cellulolytic bacterial consortium derived from composting. Front Microbiol 8:1–16.
doi: 10.3389/fmicb.2017.00644
Li HF, Imai T, Ukita M, et al (2004) Compost stability assessment using a secondary
metabolite: geosmin. Environ Technol 25:1305–12. doi: 10.1080/09593332508618374
Litterick a. M, Harrier L, Wallace P, et al (2004) The Role of Uncomposted Materials,
Composts, Manures, and Compost Extracts in Reducing Pest and Disease Incidence and
Severity in Sustainable Temperate Agricultural and Horticultural Crop Production—A
Review. CRC Crit Rev Plant Sci 23:453–479. doi: 10.1080/07352680490886815
Liu D, Li M, Xi B, et al (2015) Metaproteomics reveals major microbial players and their
biodegradation functions in a large-scale aerobic composting plant. Microb Biotechnol
8:950–960. doi: 10.1111/1751-7915.12290
Maier T, Güell M, Serrano L (2009) Correlation of mRNA and protein in complex biological
samples. FEBS Lett 583:3966–3973. doi: 10.1016/j.febslet.2009.10.036
Malandraki I, Tjamos SE, Pantelides IS, Paplomatas EJ (2008) Thermal inactivation of
compost suppressiveness implicates possible biological factors in disease management.
44:180–187. doi: 10.1016/j.biocontrol.2007.10.006
Manor O, Levy R, Borenstein E (2014) Mapping the inner workings of the microbiome:
Genomic- and metagenomic-based study of metabolism and metabolic interactions in the
human microbiome. Cell Metab 20:742–752. doi: 10.1016/j.cmet.2014.07.021

42
Maron P-A, Ranjard L, Mougel C, Lemanceau P (2007) Metaproteomics: A New Approach
for Studying Functional Microbial Ecology. Microb Ecol 53:486–493. doi:
10.1007/s00248-006-9196-8
Martins LF, Antunes LP, Pascon RC, et al (2013) Metagenomic Analysis of a Tropical
Composting Operation at the São Paulo Zoo Park Reveals Diversity of Biomass
Degradation Functions and Organisms. PLoS One 8:e61928. doi:
10.1371/journal.pone.0061928
Matteoli FP, Passarelli-Araujo H, Reis RJA, et al (2018) Genome sequencing and assessment
of plant growth-promoting properties of a Serratia marcescens strain isolated from
vermicompost. BMC Genomics 19:750. doi: 10.1186/s12864-018-5130-y
Mengesha WK, Powell SM, Evans KJ, Barry KM (2017) Diverse microbial communities in
non-aerated compost teas suppress bacterial wilt. World J Microbiol Biotechnol 33:1–14.
doi: 10.1007/s11274-017-2212-y
Miller MG (2007) Environmental Metabolomics: A SWOT Analysis (Strengths, Weaknesses,
Opportunities, and Threats). J Proteome Res 6:540–545. doi: 10.1021/pr060623x
Moran MA (2009) Metatranscriptomics: Eavesdropping on Complex Microbial Communities.
Microbe Mag 4:329–335. doi: 10.1128/microbe.4.329.1
Moran MA, Satinsky B, Gifford SM, et al (2013) Sizing up metatranscriptomics. ISME J.
7:237–243
Nagarajan N, Pop M- (2013) Sequence assembly demystified. Nat. Rev. Genet. 7:157–167
Neher DA, Weicht TR, Dunseith P (2015) Compost for Management of Weed Seeds, Pathogen,
and Early Blight on Brassicas in Organic Farmer Fields. Agroecol Sustain Food Syst
39:3–18. doi: 10.1080/21683565.2014.884516
Nesme J, Bailey M, Wagner M (2018) The soil microbiome—from metagenomics to
metaphenomics. Curr Opin Microbiol 43:162–168.

43
Neugart S, Wiesner-Reinhold M, Frede K, et al (2018) Effect of Solid Biological Waste
Compost on the Metabolite Profile of Brassica rapa ssp. chinensis. Front Plant Sci 9:305.
doi: 10.3389/fpls.2018.00305
Ogram A, Sayler GS, Barkay T (1987) The extraction and purification of microbial DNA from
sediments. J Microbiol Methods 7:57–66. doi: 10.1016/0167-7012(87)90025-X
Opel KL, Chung D, McCord BR (2010) A study of PCR inhibition mechanisms using real time
PCR. In: Journal of Forensic Sciences. pp 25–33
Ottesen AR, White JR, Skaltsas DN, et al (2009) Impact of Organic and Conventional
Management on the Phyllosphere Microbial Ecology of an Apple Crop. 72:2321–2325
Pan K, Gong P, Wang J, et al (2015) Applications of nitrate and ammonium fertilizers alter
soil nematode food webs in a continuous cucumber cropping system in Southwestern
Sichuan, China. Eurasian J Soil Sci 4:287. doi: 10.18393/ejss.2015.4.287-300
Partanen P, Hultman J, Paulin L, et al (2010) Bacterial diversity at different stages of the
composting process. BMC Microbiol 10:. doi: 10.1186/1471-2180-10-94
Pell J, Hintze A, Canino-Koning R, et al (2012) Scaling metagenome sequence assembly with
probabilistic de Bruijn graphs. Proc Natl Acad Sci 109:13272–13277. doi:
10.1073/pnas.1121464109
Peters S, Koschinsky S, Schwieger F, Tebbe CC (2000) Succession of microbial communities
during hot composting as detected by PCR-single-strand-conformation polymorphism-
based genetic profiles of small-subunit rRNA genes. Appl Environ Microbiol 66:930–6
Pfaller SL, Vesper SJ, Moreno H (1994) The use of PCR to detect a pathogen in compost.
Compost Sci Util 2:48–54. doi: 10.1080/1065657X.1994.10771139
Phae CGUN, Shoda M, Kubota AYDH (1990) Suppressive Effect of Bacillus subtil & and It ’
s Products on Phytopathogenic Microorganisms. 69:1–7
Poretsky RS, Bano N, Buchan A, et al (2005) Analysis of microbial gene transcripts in

44
environmental samples. Appl Environ Microbiol 71:4121–4126. doi:
10.1128/AEM.71.7.4121-4126.2005
Putri SP, Nakayama Y, Matsuda F, et al (2013) Current metabolomics: Practical applications.
J Biosci Bioeng 115:579–589. doi: 10.1016/j.jbiosc.2012.12.007
Randazzo CL, Torriani S, Akkermans ADL, et al (2002) Diversity, dynamics, and activity of
bacterial communities during production of an artisanal Sicilian cheese as evaluated by
16S rRNA analysis. Appl Environ Microbiol 68:1882–92
Rastogi G, Sani RK (2011) Molecular Techniques to Assess Microbial Community Structure,
Function, and Dynamics in the Environment. In: Microbes and Microbial Technology.
Springer New York, New York, NY, pp 29–57
Redon E, Loubière P, Cocaign-Bousquet M (2005) Role of mRNA stability during genome-
wide adaptation of Lactococcus lactis to carbon starvation. J Biol Chem 280:36380–
36385. doi: 10.1074/jbc.M506006200
Riesenfeld CS, Schloss PD, Handelsman J (2004) Metagenomics: genomic analysis of
microbial communities. Annu Rev Genet 38:525–52. doi:
10.1146/annurev.genet.38.072902.091216
Rinke C, Schwientek P, Sczyrba A, et al (2013) Insights into the phylogeny and coding
potential of microbial dark matter. Nature 499:431–437. doi: 10.1038/nature12352
Ros M, Blaya J, Baldrian P, et al (2018) In vitro elucidation of suppression effects of composts
to soil-borne pathogen Phytophthora nicotianae on pepper plants using 16S amplicon
sequencing and. Renew Agric Food Syst 1–9. doi: 10.1017/S1742170518000467
Ryan RP, Monchy S, Cardinale M, et al (2009) The versatility and adaptation of bacteria from
the genus Stenotrophomonas. Nat Rev Microbiol 7:514–525. doi: 10.1038/nrmicro2163
Ryckeboer J, Mergaert J, Vaes K, Klammer S (2003) A survey of bacteria and fungi occurring
during composting and self-heating processes. 53:349–410

45
Sabree Z, Rondon M, Handelsman J (2009) Metagenomics. In: Schaechter M (ed)
Encylcopedia of Microbiology, 1st edn. Elsevier, Amsterdam, pp 622–632
Sánchez C (2009) Lignocellulosic residues: Biodegradation and bioconversion by fungi.
Biotechnol Adv 27:185–194. doi: 10.1016/J.BIOTECHADV.2008.11.001
Sang MK, Kim KD (2011) Biocontrol activity and primed systemic resistance by compost
water extracts against anthracnoses of pepper and cucumber. Phytopathology 101:732–
40. doi: 10.1094/PHYTO-10-10-0287
Scheuerell SJ (2002) Compost Teas and Compost Amended Container Media for Plant Disease
Control. Oregon State University
Scheuerell SJ, Mahaffee WF (2004) Compost Tea as a Container Medium Drench for
Suppressing Seedling Damping-Off Caused by Pythium ultimum. Phytopathology
94:1156–63. doi: 10.1094/PHYTO.2004.94.11.1156
Scheuerell SJ, Sullivan DM, Mahaffee WF (2005) Suppression of Seedling Damping-Off
Caused by Pythium ultimum, P. irregulare, and Rhizoctonia solani in Container Media
Amended with a Diverse Range of Pacific Northwest Compost Sources. Phytopathology
95:306–15. doi: 10.1094/PHYTO-95-0306
Schloss PD, Hay AG, Wilson DB, Walker LP (2003) Tracking temporal changes of bacterial
community fingerprints during the initial stages of composting. FEMS Microbiol Ecol
46:1–9. doi: 10.1016/S0168-6496(03)00153-3
Schneider T, Keiblinger KM, Schmid E, et al (2012) Who is who in litter decomposition
Metaproteomics reveals major microbial players and their biogeochemical functions.
ISME J 6:1749–1762. doi: 10.1038/ismej.2012.11
Schneider T, Riedel K (2010) Environmental proteomics : Analysis of structure and function
of microbial communities. 785–798. doi: 10.1002/pmic.200900450
Schut F, De Vries EJ, Gottschal JC, et al (1993) Isolation of typical marine bacteria by dilution

46
culture: Growth, maintenance, and characteristics of isolates under laboratory conditions.
Appl Environ Microbiol 59:2150–2160. doi: 10.1007/s000120300002
Simon C, Daniel R (2011) Metagenomic analyses: past and future trends. Appl Environ
Microbiol 77:1153–61. doi: 10.1128/AEM.02345-10
Sorek R, Cossart P (2010) Prokaryotic transcriptomics: A new view on regulation, physiology
and pathogenicity. Nat Rev Genet 11:9–16. doi: 10.1038/nrg2695
St. Martin CCG (2015) Enhancing Soil Suppressiveness Using Compost and Compost Tea. In:
Meghvansi MK, Varma A (eds) Organic Amendments and Soil Suppressiveness in Plant
Disease Management, 46th edn. Springer, London, pp 25–49
St. Martin CCG (2014) Potential of compost tea for suppressing plant diseases. CAB Rev
Perspect Agric Vet Sci Nutr Nat Resour 9:1–38. doi: 10.1079/PAVSNNR20149032
St. Martin CCG, Brathwaite RAI (2012) Compost and compost tea: Principles and prospects
as substrates and soil-borne disease management strategies in soil-less vegetable
production. Biol Agric Hortic 28:1–33. doi: 10.1080/01448765.2012.671516
St. Martin CCG, Ramsubhag A (2015) Potential of Compost for Suppressing Plant Diseases.
In: Sangeetha Ganesan, Kurucheve Vadivel JJ (ed) Sustainable Crop Disease
Management Using Natural Products. CABI, pp 345–388
Steel H, Moens T, Vandecasteele B, Hendrickx F, De Neve S, Neher DA, Bert W (2018)
Factors influencing the nematode community during composting and nema- tode based
criteria for compost maturity. Ecol Ind 85:409–421
Sundberg C, Franke-Whittle IH, Kauppi S, et al (2011) Characterisation of source-separated
household waste intended for composting. Bioresour Technol 102:2859–2867. doi:
10.1016/j.biortech.2010.10.075
Sundberg C, Yu D, Franke-Whittle I, et al (2013) Effects of pH and microbial composition on
odour in food waste composting. Waste Manag 33:204–211. doi:

47
10.1016/j.wasman.2012.09.017
Thomas T, Gilbert J, Meyer F (2012) Metagenomics - a guide from sampling to data analysis.
Microb Inform Exp 2:3. doi: 10.1186/2042-5783-2-3
Tiquia SM, Ichida JM, Keener HM, et al (2005) Bacterial community profiles on feathers
during composting as determined by terminal restriction fragment length polymorphism
analysis of 16S rDNA genes. Appl Microbiol Biotechnol 67:412–9. doi: 10.1007/s00253-
004-1788-y
Torsvik V, Goksøyr J, Daae FL (1990) High diversity in DNA of soil bacteria. Appl Environ
Microbiol 56:782–7
Trapnell C (2015) Defining cell types and states with single-cell genomics. Genome Res.
25:1491–1498
Tringe SG, von Mering C, Kobayashi A, et al (2005) Comparative metagenomics of microbial
communities. Science 308:554–7. doi: 10.1126/science.1107851
Tuomela M, Vikman M, Hatakka A, It M (2000) Biodegradation of lignin in a compost
environment : a review. 72:169–183
Ventorino V, Aliberti A, Faraco V, et al (2015) Exploring the microbiota dynamics related to
vegetable biomasses degradation and study of lignocellulose-degrading bacteria for
industrial biotechnological application. Sci Rep 5:8161. doi: 10.1038/srep08161
Verberkmoes NC, Wilkins MJ, Hettich RL, Lipton MS (2012) Fermentation, Hydrogen, and
Sulfur Metabolism in Multiple Uncultivated Bacterial Phyla. Science (80- ) 337:1661–
1665
Vinci G, Cozzolino V, Mazzei P, et al (2018) An alternative to mineral phosphorus fertilizers:
The combined effects of Trichoderma harzianum and compost on Zea mays, as revealed
by 1H NMR and GC-MS metabolomics. PLoS One 13:e0209664. doi:
10.1371/journal.pone.0209664

48
Vorholt JA (2012) Microbial life in the phyllosphere. Nat Publ Gr 10:828–840. doi:
10.1038/nrmicro2910
Wang C, Dong D, Strong PJ, et al (2017) Microbial phylogeny determines transcriptional
response of resistome to dynamic composting processes. Microbiome 5:103. doi:
10.1186/s40168-017-0324-0
Wang C, Dong D, Wang H, et al (2016) Metagenomic analysis of microbial consortia enriched
from compost: New insights into the role of Actinobacteria in lignocellulose
decomposition. Biotechnol Biofuels 9:22. doi: 10.1186/s13068-016-0440-2
Weltzien HC (1991) Biocontrol of Foliar Fungal Diseases with Compost Extracts. In: Andrews
J, Hirano S (eds). Springer, New York, NY, pp 430–450
Yang J, Zhu X, Cao M, et al (2016) Genomics-Inspired Discovery of Three Antibacterial
Active Metabolites, Aurantinins B, C, and D from Compost-Associated Bacillus subtilis
fmb60. J Agric Food Chem 64:8811–8820. doi: 10.1021/acs.jafc.6b04455
Yang W, Gu S, Xin Y, et al (2018) Compost Addition Enhanced Hyphal Growth and
Sporulation of Arbuscular Mycorrhizal Fungi without Affecting Their Community
Composition in the Soil. Front Microbiol 9:. doi: 10.3389/fmicb.2018.00169
Yeh Y, Chang S, Kuo H, et al (2013) A metagenomic approach for the identification and
cloning of an endoglucanase from rice straw compost. Gene 519:360–366
Yi-fang Y, Chia-yu CS, Hsion-wen K, et al (2013) A metagenomic approach for the identi fi
cation and cloning of an endoglucanase from rice straw compost. Gene 519:360–366. doi:
10.1016/j.gene.2012.07.076
Yu D (2014) Microbial Community Profiling of Biodegradable Municipal Solid Waste
Treatments – Aerobic Composting and Anaerobic Digestion. University of Helsinki,
Finland
Zhou J, Deng Y, He Z, et al (2010) Applying GeoChip Analysis to Disparate Microbial

49
Communities. mICROBE 5:60–65. doi: 10.1128/microbe.5.60.1