Insights into bacterial diversity in compost: Core microbiome and prevalence of potential pathogenic bacteria

Journal Pre-proof Insights into bacterial diversity in compost: Core microbiome and prevalence of potential pathogenic bacteria

Yue Wang, Jingyang Gong, Jiaxin Li, Yuanyuan Xin, Ziyi Hao, Chen Chen, Huixiu Li, Bo Wang, Min Ding, Wanwan Li, Zeyu Zhang, Pengxiang Xu, Ting Xu, Guo-Chun Ding, Ji Li PII:

S0048-9697(20)30814-7

DOI:

https://doi.org/10.1016/j.scitotenv.2020.137304

Reference:

STOTEN 137304

To appear in:

Science of the Total Environment

Received date:

23 October 2019

Revised date:

12 February 2020

Accepted date:

12 February 2020

Please cite this article as: Y. Wang, J. Gong, J. Li, et al., Insights into bacterial diversity in compost: Core microbiome and prevalence of potential pathogenic bacteria, Science of the Total Environment (2020), https://doi.org/10.1016/j.scitotenv.2020.137304

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© 2020 Published by Elsevier.

Journal Pre-proof Insights into bacterial diversity in compost: core microbiome and prevalence of potential pathogenic bacteria

Yue Wang1, Jingyang Gong1, Jiaxin Li1, Yuanyuan Xin1, Ziyi Hao1, Chen Chen1, Huixiu Li1, Bo Wang1, Min Ding1, Wanwan Li1, Zeyu Zhang1, Pengxiang Xu1, Ting Xu1,2, Guo-

College of Resources and Environmental Sciences, Beijing Key Laboratory of

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Chun Ding1,2*, Ji Li 1,2*

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Biodiversity and Organic Farming, China Agricultural University, Yuanmingyuan West

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Road No.2, Haidian District, 100193, Beijing, China;

Organic Recycling Institute (Suzhou) of China Agricultural University and Suzhou

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ViHong Biotechnology, Wuzhong District, 215128, Jiangsu Province, China.

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Corresponding authors E-mail address: [email protected] (Guo-chun Ding) * Corresponding authors E-mail address: [email protected] (Ji Li)

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Journal Pre-proof Abstract Fertilizer-replacement programs by the ministry of agriculture and rural affairs are extraordinary actions for environment protection and sustainable agriculture in China. A national-level survey was performed to acquire consensuses of bio-physiochemical properties for composts. A total of 116 compost samples collected from 16 provinces in China were analyzed by high throughput sequencing of bacterial 16S rRNA gene amplicons.

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The germination index and bacterial alpha-diversity were lower in composts from poultry

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manure than others. This large-scale survey revealed that bacterial communities were

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distinct among different composts and slightly explained by pH, moisture and total nitrogen,

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but not by raw material or composting process. Nevertheless, 26 OTUs affiliated with Firmicutes (Cerasibacillus, Atopostipes and Bacillus) and Actinobacteria (Thermobifida,

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Actinomadura and Nocardiopsis) were present in most (> 90%) composts and majority of

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these bacterial species were possibly associated with the biodegradation of organic materials. Surprisingly, 629 potential human or animal bacterial pathogens accounting an

with

Helicobacter,

Staphylococcus,

Acinotobacter,

Streptococcus,

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affiliated

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average of 1.21% of total 16S rRNA gene were detected and these bacteria were mainly

Mycobacterium and Enterococcus. In summary, this study provides baseline data for the diversity and abundance of core microbiome and potential pathogens in composts. Key words: national-level survey, 16S rRNA, microbial diversity, composting process, pathogens.

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Journal Pre-proof 1. Introduction In China, huge amounts of organic wastes, e.g. ca 3.8 billion tons of animal manure, 2 billion tons of straws were produced annually (Niu and Ju, 2017). Manure alone contains as much as 78 million tons of nitrogen, phosphorus and potassium for recycling, which otherwise could cause severe environmental problems, such as pollutions on surface or ground water, air and emission of greenhouse gases (Bai et al., 2016; Hu et al., 2017; Shen

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et al., 2018). To promote the sustainability of agriculture, re-cycling of nutrients in organic

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wastes and mitigating agricultural non-point source pollution, the ministry of agriculture

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and rural affairs released a serial of fertilizer-replacement programs since 2015. More than

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4,000 organic fertilizer factories were established in China and these factories were varied at several aspects, e.g. annual treatment capacities (2,000 to 100,000 tons), raw organic

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wastes (poultry manure, straws, biogas residue, sewage sludge, soybean meal, etc.),

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technologies (pile, in-vessel and windrow composting or anaerobic digestions), amendments (humic acids, plant ash, bioagents). Application of organic fertilizers such as

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compost were known for several beneficial effects, for instance, suppression of plant

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disease (Hadar and Papadopoulou, 2012; Liu et al., 2018; Mierzwa-Hersztek et al., 2018; Li et al., 2019), increase of soil organic matter (Gattinger et al., 2012; Hartmann et al., 2015; Shen et al., 2018; Ding et al., 2019), or mitigation of greenhouse gases emission (Han et al., 2019; Skinner et al., 2019; Yang et al., 2019). However, large variability of these beneficial effects was often detected (Bernal et al., 2009), especially under filed conditions (Chen et al., 2016; Mie et al., 2017; Zhao et al., 2016). In addition to several context-dependent factors, for example, local climates, plants, soil physicochemical properties, biological and physicochemical properties of organic fertilizers may also contribute to the grand effects in

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Journal Pre-proof field (Chen et al., 2018; Diacono and Montemurro, 2011). While the physicochemical properties of organic fertilizers at large scale are largely unknown. The application of organic fertilizers not only introduced nutrients, organic materials but also spread beneficial or pathogenic microorganisms into agroecosystems (Avery et al., 2012; Zhu et al., 2013; Chen and Jiang, 2014). Thus, it might be important to characterize the microbiome or potential pathogens in organic fertilizers. Previously, the fates of pathogenic bacteria and

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fungi, such as Salmonella, Clostridium, Campylobacter, Listeria monocytogenes and

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Escherichia coli, have been explored during composting or anaerobic digestion (Ferens and

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Hovde, 2011; Manyi-Loh et al., 2016). But the abundance and diversity of several other

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potential pathogens in organic fertilizers were rarely explored. Here, we launched a national-level survey to acquire a consensus of microbial and

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physicochemical properties, heavy metals and antibiotics contamination of organic

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fertilizers in the market of China. Recently, heavy metal concentrations have been reported (Zhang et al., 2018). In the present study, we analyzed the microbiomes and

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physicochemical properties of these composts with specific aims 1) to acquire a consensus

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of taxonomic composition and core-microbiome; 2) to study the influences of composting process, raw materials on the assemblage of microbiome; 3) to evaluate the abundance and diversity of potential pathogens. 2. Materials and methods 2.1. Sampling A national-level survey was performed from June to September in 2017 and a total of 116 compost samples were collected from 16 provinces in China (Fig. 1). All these factories were certificated to produce organic fertilizers. Only those factories with

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Journal Pre-proof composting facilities and in the processes of composting were selected. Composting was performed according to the China national standard (NY 525-2012) and the China industry standard (NY/T 3442-2019), in which the thermophilic stage (> 55 ℃) lasted for at least five days. Composts were produced by pile, windrow or in-vessel. Pile composting is a traditional composting method without mechanical turning. Windrow composting is a convenient way to make compost by piling organic wastes in long rows (windrows) and

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these windrows were turned regularly to improve porosity and oxygen concentration. In-

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vessel composting is a method to produce compost by confine organic wastes in vessel with

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forced aeration at the bottom and regular turning to maintain an optimum aerobic

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fermentation condition. For each factory, ca 2.5 kg of compost was collected with five points from the freshly produced batch with a storage time less than one week.

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Approximately 0.5 kg of sample was transported to the lab in a cool box. One part was kept

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at -20 ℃ prior to the DNA extraction and the other part was stored at 4 ℃ for the measurements of physicochemical properties.

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2.2. Physicochemical properties and germination index

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Physicochemical properties of compost such as total nitrogen and total organic matter, total phosphorus, available potassium, pH and moisture content were analyzed according to standard protocols. The pH was measured in water suspension (1:10, w/v) of compost with a pH meter (PHS-3C, China). The contents of total nitrogen and total organic matter were measured according to the Chinese National Standard (NY 525-2012). Seed germination index was measured with fresh water extraction of compost (1:10, w/v) following the method described by appendix of China Industry Standard (NY/T 3442-2019). 2.3. High throughput sequencing of bacterial 16S rRNA gene amplicon

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Journal Pre-proof Total microbial community DNA from compost was extracted using a FastDNA spin Kit for soil (MP, Biomedicals, Santa Ana, Carlsbad, CA, United States). The 16S rRNA gene fragments was amplified with the universal primers 515F (5’GTGCCAGCMGCCGCGGTAA-3’) and 909R (5’-CCCCGYCAATTCMTTTRAGT-3’) fused with a 12 nt unique barcode (Caporaso et al., 2010). Gel-purified PCR products were mixed with equal molar following illumina sequencing using the platform of Hiseq2500.

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All sequences were submitted to the NCBI (PRJNA605604).

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2.4. Bioinformatic analysis

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All sequence reads were assigned to each sample based on primer and barcodes and the technical regions were trimmed for the following analyses. High quality sequences

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(length > 300 bp, without ambiguous base ‘N’, and average base quality score > 30) were

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used for downstream analyses. Generation of the taxonomic OTU was performed as

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previously described (Caporaso et al., 2010; Schloss et al., 2009; Li et al., 2019). Classification of representative OTUs were performed with RDP naïve bayes classifier

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version 16 (Wang et al., 2007). Chao1 and Pielous’s evenness indices were calculated in a

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manner similar to rarefaction analysis to mitigate the biases caused by different sequencing efforts (Ding et al., 2012). Correlation between alpha-diversity indices and physicochemical properties was calculated with the R 3.1.21 software. Relative abundance of the most five dominant phyla were visualized by bar plot and the samples were reordered according to the relative abundance of dominant phyla using UPGMA cluster analysis. Redundancy analysis was performed to study the relationship between compost physicochemical properties and microbial diversity. It was done with R add-on package ‘vegan’ (Oksanen et al., 2018) using a forward selection model which selected for

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Journal Pre-proof parameters contribute significantly (p < 0.05 via 1000 times permutation tests) to community variation. ANOSIM analysis based on Bray-Curtis distance was performed to compare the effects of composting processes on bacterial communities. To identify potential pathogens in the dataset, a standalone BLASTN analysis against the curate PATRIC (www.patricbrc.org) database with a total of 227,568 sequences of 16S rRNA gene of pathogenic bacteria (Wattam et al., 2014). The relative abundance of potential

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pathogenic bacteria in each sample was calculated by dividing the amount of reads which

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were 99% identical to the 16S rRNA gene of pathogenic bacteria with the total reads for

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each sample. One-way ANOVA was used to tested the effects of composting processes or

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raw materials on physicochemical properties. All statistical analyses and plotting were

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instance (www.freebioinfo.org).

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performed with the R 3.1.21 software, and these tools have been implemented into a galaxy

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Journal Pre-proof 3. Results and discussion 3.1. Maturity and physicochemical properties All 116 composts were measured for physicochemical properties and maturity. The germination index is a maturity test based on seed germination using a liquid extract from the compost (Zucconi et al., 1981). In general, variability in physicochemical properties was high, with 5.00 to 9.57 for pH and 5.32% to 61.87% for moisture content, 14.5% to

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153.3% for germination index, 0.42% to 11.07 % for total nitrogen, 8.43% to 86.9% for

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total organic matter, 0.13% to 9.02% for total phosphorus and 0.03% to 5.42% for available

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potassium (Table 1). The total nitrogen was positively correlated with the total organic

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matter (p = 0.005) and similar findings were also detected for soils (Avramidis et al., 2015; He et al., 2015). The total nitrogen was negatively correlated with pH (p = 0.023), in

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agreement with that nitrogen losses during compost were associated with pH (Wang et al.,

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2013). Interestingly, the germination index was negatively correlated with phosphorus (p = 0.033) and available potassium (p = 0.002), but positively with pH (p = 0.029) (Fig. 2a).

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One-way ANOVA analysis revealed that there was no significant difference on

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physicochemical properties of composts among composting processes (Fig. S1). Raw materials exerted significant effects on the physicochemical properties of composts (Fig. 2b). The germination index was significantly lower in compost from chicken or duck manure, or those amended with rice or wheat straw (Fig. 2b). The content of phosphorus was lower in compost from manure of herbivores (cow, sheep, horse or rabbit), in contrast to those amended with soybean meal (Fig. 2b). The pH was lower in compost from distiller’s grains, mushroom substrates spent or sludge of anaerobic digestion, or those amended with humic acids or plant ash (Fig. 2b).

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Journal Pre-proof 3.2. Bacterial composition varied greatly among compost and was not associated with raw material and compost process Compost fertilization could deliver several beneficial effects, such as suppression of plant diseases, improving soil fertility and quality (Hadar and Papadopoulou, 2012; Liu et al., 2018; Li et al., 2019). Beneficial microorganisms in compost might play significant roles in maintaining plant health and productivities (Arif et al., 2018; Yu et al., 2019). All

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composts were subjected to high throughput sequencing of 16S rRNA analysis to acquire a

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consensus of compost microbiome. Altogether 2,127,886 high quality reads were acquired

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and on average 18,344 reads per sample. These sequences were classified into 274,505

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OTU (> 97% identity) and on average 4,630 OTUs per sample were acquired. Good’s coverage analysis revealed that more than 97.4% of genera have been detected. Most

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sequences were affiliated with Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes

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(Fig. 3). These findings are in agreement with other studies (Xu et al., 2017; Awasthi et al., 2014; Zhang et al., 2016). As compared to soils(Qiu et al., 2012; Ding et al., 2019), the

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fractions of Firmicutes and Actinobacteria were often much higher in compost, suggesting

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that compost fertilization may introduce Firmicutes and Actinobacteria into soils. Several members of these two phyla were bioagents or antibiotics producers (Chaurasia et al., 2018; Chowdhury et al., 2015; Rybakova et al., 2016; Shafi et al., 2017; Sivasakthi et al., 2014). The dominance of Firmicutes in compost might also link with their heat tolerance (Yi et al., 2012; Kosowski et al., 2014). However, the relative abundance of dominant phyla varied greatly among different composts with values ranged between 2.13% to 98.27%, 0.65% to 91.18%, 0.38% to 53.70% and 0.17% to 58.70% for Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes respectively. Multiple ANOVA revealed that no clear effects

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Journal Pre-proof of raw materials and composting processes on the relative abundance of dominant phyla were observed (Fig. 3 and Fig. S2), in contrast to local-scale studies under controlled conditions (Varma et al., 2017). 3.3. Alpha- and beta-diversity Previously, several studies have evaluated alpha-diversity during composting (Kitamura et al., 2016; Zainudin et al., 2017), but little is known about the relationship

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between raw materials and compost processes across large geographic scale. Here,

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ANOVA analysis revealed that no clear effects on bacterial alpha-diversity were observed

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for composting processes (Fig. 4a and 4b). The Pielous’ evenness index was lower in

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compost from chicken or duck manure (Fig. 4c) or those amended with humic acids or plant ash (Fig. 4d). Redundancy analysis revealed that beta-diversity of bacterial

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communities was associated with pH, moisture content and total nitrogen (Fig. 4e). Due to

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the large number of samples were included, these physiochemical properties (pH, moisture and total nitrogen) only explained 7.3% of total variation. Similar results that

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environmental parameters only explained a small fraction of variation within microbial

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communities were also observed in other studies, in which many samples were included (Gunnigle et al., 2017). Bray-Curtis distance was chosen to compare community composition and community between different composting processes. ANOSIM analysis further confirmed that bacterial community composition was comparable among different composting processes (p = 0.76). In summary, these results indicated that bacterial diversity in compost may not be predicted by raw materials or compost processes across large scale. 3.4. Core microbiome

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Journal Pre-proof Compost application to agricultural field can fight against plant pathogens (Antoniou et al., 2017), possibly due to the microbial consortia in compost. Those species commonly in compost may contribute our understanding on compost microbiology. Here, a total of 26 OTUs (> 97% identity) were present in more 90% of compost samples and there OTUs were regarded as core microbiome following the criteria by Henderson et al (2015). In other studies, OTUs appeared at 50% or 75% sample were regarded as core microbiome

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(Ainsworth et al., 2015). Relative abundance of these OTUs in compost ranged between

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0.42% to 48.09% with an average relative abundance of 13.91% (sd = 12.45%).These

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OTUs were affiliated with 18 genera belonging to Firmicutes (Cerasibacillus, Atopostipes,

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Bacillus, Lysinibacillus, Ureibacillus and Staphylococcus) and Actinobacteria (Thermobifida, Actinomadura, Nocardiopsis, Aciditerrimonas, Georgenia,

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Corynebacterium, Glycomyces, Jiangella, Yaniella, Mycobacterium, Micropolyspora,

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Saccharomonospora). Based on their relative abundance across different composts, these OTUs mainly grouped into two clusters (Fig. 5). The first cluster contains OTUs share high

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similarity (> 97%) with Georgenia ruanii, Allobacillus halotolerans, Corynebacterium

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xerosis, Atopostipes suicloacalis, Yaniella fodinae, Nocardiopsis alkaliphila, Bacillus aidingensis, Micropolyspora internatus, B. badius, B. thermoamylovorans, Staphylococcus fleurettii, Cerasibacillus quisquiliarum and B. thermocloacae (Fig. 5). OTUs of the second cluster were similar with B. odysseyi, B. infernus, Actinomadura sputi, Mycobacterium thermoresistibile, Thermobifida alba, T. cellulosilytica, Aciditerrimonas ferrireducens, Jiangella muralis and Glycomyces arizonensis (Fig. 5). Majority of these bacterial species were possibly involved in the biodegradation of organic materials during composting (Sarwade et al., 2014; Hu et al., 2010; Gamerith et al., 2017), but so far as we know these

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Journal Pre-proof species were not reported to be involved in plant disease suppression. Previously, some studies have demonstrated that soil microbial community shifted greatly after compost application over time and restored homoeostasis later, suggesting that majority of compost bacteria may not thrive after being introduced into soils (Antoniou et al., 2017; Ye et al., 2016). 3.5. Potential bacterial pathogens

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Manure from intensive animal farming contains several pathogens which may pollute

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soil, air and water bodies (Sahlström et al., 2004; Leifert et al., 2008). It was assumed that

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pathogens were possibly killed during the thermophilic stages (Fröschle et al., 2015) but

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little is known on the diversity and abundance of pathogens in compost. Here we performed a standalone BLASTN analysis against the curated databases containing human and animal

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pathogens. A total of 629 potential bacterial pathogens were detected and they affiliated

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with 48 genera. Majority of them were affiliated with Helicobacter, Staphylococcus, Acinotobacter, Streptococcus, Mycobacterium and Enterococcus (Fig. 6a). Among them,

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several potential pathogens affiliated with Mycobacterium, Staphylococcus and

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Streptococcus were reported to as opportunistic pathogens and these genera were commonly detected in several other environments, such as air, reclaimed water, soil (Jjemba et al., 2010; Primm et al., 2004) and rhizosphere (Berg et al., 2005). On average, these potential pathogens accounted for 1.21% (sd = 3.64%) of total 16S rRNA gene. However, high fractions up to 33.7% of potential pathogens were also detected (Fig. 6a). Surprisingly, the fractions of pathogens in compost made by the in-vessel method were higher (p = 0.03) than windrows (Fig. 6b). But no significant effects of raw materials or physicochemical properties on fractions of potential pathogens were detected (Fig. 6a). It is

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Journal Pre-proof also worth to note that further studies are still needed to validate the pathogenicity of bacterial pathogens in compost, as the resolution of 16S rRNA gene is still limited (Partanen et al., 2010). Nevertheless, this study still provides a baseline the abundance and diversity of potential pathogens in compost. 4. Conclusions Bacterial community in compost might be formed to a large extend opportunistically

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and these results indicated that bacterial diversity in compost may not be predicted by raw

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materials or compost processes across large scale. The core microbiome with bacterial

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species affiliated with Firmicutes and Actinobacteria were possibly associated with the

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biodegradation of organic materials, and diverse potential pathogens were prevalent in compost.

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Acknowledgments

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This work was supported by the National key research and development program (2016YFD0800601, 2016YFD0800602 and 2016YFD0501404); the National Nature

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Science Foundation of China (Grant no: 31570441 and 31400095).

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Appendix A. Supplementary data E-supplementary data of this work can be found in online version of the paper.

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Journal Pre-proof Figure captions Fig.1. Locations of compost factories in this study. Fig.2. Relationship between different physicochemical properties (a), and their association with raw materials (b). Note: TN: total nitrogen, TOM: total organic matter, GI: germination index. Fig.3. Relative abundance of dominant phyla of compost made by different processes or different raw materials.

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Fig.4. Association of bacterial alpha- and beta-diversity with composting processes, raw materials or physicochemical properties.

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Fig.5. Heatmap analysis of core microbiome in compost by different processes or from different

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raw materials. Relative abundance (in permile) of different OTUs was indicated by the color code

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grades from black (not detected) to red (high abundance).

Fig.6. Relative abundance of potential human or animal pathogens in composts in compost by

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different processes or from different raw materials (a) and boxplot analysis of relative abundance of

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potential pathogens by different composting processes (b). Fig.S1. Relationship between physicochemical properties and composting processes. Note: TN:

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total nitrogen, TOM: total organic matter, GI: germination index.

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Fig.S2. Boxplot of relative abundance for Firmicutes (a), Actinobacteria (b), Proteobacteria (c), Bacteroidetes (d), and Chloroflexi (e) under different composting processes. Note: the p value of the one-way ANOVA which was used to test the effects of composting process on relative abundance was given at right top of each box plot. The composting processes had no effects on the relative abundance of dominant phyla of compost.

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Journal Pre-proof Table 1 Range of physical and fertility parameters for composts of national surveys (n=116) pH

Moisture Content/ %

GI/ %

Total Nitrogen/%

Minimum

5.00

5.32

14.46

0.42

8.43

0.13

0.03

Median1

7.88

27.37

75.19

2.05

35.20

1.52

1.28

Mean2

7.80

27.90

70.99

2.64

38.14

2.20

1.56

90th percentile3

9.10

46.50

100.22

6.12

61.00

4.72

3.37

Maximum

9.57

61.87

153.31

11.07

86.91

9.02

5.42

Phosphorus/ %

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Total organic matter/ %

1

The Median is the value that half of tested samples were greater than and half were less than. The Mean is the arithmetic average of all samples. 3 The 90th percentile is the value that 90% of samples were less than. 2

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Available potassium/ %

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Yue Wang, Jingyang Gong, Chen Chen and Huixiu Li: Methodology, Validation, Formal analysis, Writing - Original Draft; Jiaxin Li, Yuanyuan Xin, Ziyi Hao, Bo Wang, Min Ding, Wanwan Li, Zeyu Zhang and Pengxiang Xu: Methodology, Investigation; Guo-Chun Ding, Ji Li and Ting Xu: Conceptualization, Resources, Software, Supervision, Funding acquisition. All authors: Writing - Review & Editing.

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Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Journal Pre-proof Highlights: 1. Microbiome was analyzed for 116 compost from 16 provinces in China. 2. Core microbiome consisting of 26 OTUs affiliated with Firmicutes and Actinobacteria were identified. 3. A total of 629 potential human or animal bacterial pathogens accounting an average of 1.21% of

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total 16S rRNA gene were detected.

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4. Bacterial communities were slightly explained by pH, moisture and total nitrogen, but not by raw

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material or composting process at large scale.

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Figure 1

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Figure 4

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