Effect of Substrate on Identification of Microbial Communities in Poultry

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Effect of Substrate on Identification of Microbial Communities in Poultry Carcass Composting and Microorganisms Associated with Poultry Carcass Decomposition Jie Wang, Xueqing Du, Yitao Zhang, Ting Li, and Xindi Liao J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02442 • Publication Date (Web): 22 Aug 2016 Downloaded from http://pubs.acs.org on August 27, 2016

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Journal of Agricultural and Food Chemistry

Effect of Substrate on Identification of Microbial Communities in Poultry Carcass Composting and Microorganisms Associated with Poultry Carcass Decomposition Jie Wang‡, Xueqing Du†, Yitao Zhang†, Ting Li†, and Xindi Liao*† ‡ Department of Bioengineering, College of Food Science, South China Agricultural University, Guangzhou, 510642, PR China; † Department of Animal Production, College of Animal Science, South China Agricultural University, Guangzhou, 510642, PR China

*Corresponding Author. Phone: +86 020 85280279. Fax: +86 020 85280740. E-mail: [email protected] (Xindi Liao).

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ABSTRACT

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Three composting systems, which consisted of different ratio of chicken manure,

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sawdust and poultry carcasses, were conducted to investigate the effect of substrate on

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the identification of microbial communities and microorganisms associated with poultry

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carcasses decomposition by characterizing of microbial communities and

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physico-chemical parameters. The physico-chemical and Miseq Illumina sequencing

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results showed the composition of substrate had a significant effect on the identification

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and metabolic capabilities of microbial communities in decomposting process. Poultry

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carcasses might be the potential driver for the identification of bacterial communities in

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poultry carcass composting, while initial C/N ratio may mainly contribute to the

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different diversity of fungal communities and the similar dominant microbial

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communities in treatments. Poultry carcasses and initial C/N ratio could respectively

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affect the composition and abundance of microorganisms associated with the

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decomposition of poultry carcasses. Understanding of the potential composting driver

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could allow development of efficient carcasses degradation system.

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KEYWORDS: Poultry carcass composting; Substrate; Identification; Microbial

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community; Functional microorganisms

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As poultry and livestock production operations around the world have become larger

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and concentrated, the intensive production in one area inevitably results in the

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production of high number of animal carcasses. In China, for example, approximately

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one million tons poultry carcasses are produced per year in recent years1. In case of an

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emergency situation such as disease outbreak or ventilation failure, timely and

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effectively handling carcasses has become a significant issue.

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INTRODUCTION

Composting is recognized as an attractive option for disposal of animal carcasses in

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many regions of the world because it is relatively inexpensive as the supplies and

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equipment for the process are usually available on farm and could provide biological

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conversion of carcasses into usable fertilizer products with pathogen destruction 2-4.

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However, one issue which may curtail the widespread adoption of compost for carcass

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disposal is the low efficiency with regards to the rate of carcass breakdown.

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Consequently, improvements on disposal efficiency of animal carcasses are being

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sought by researches, with several advancements in composting supplies and equipment

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5,6

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. Since composting is a microbial consortia-mediated bioprocessing of once-living

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materials, the degradation efficiency relies greatly on the microbial activity involved

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through the process, in which the complex organic materials undergo decomposition

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jointly by microbial communities 7-9. And it is generally accepted that microorganisms

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tend to act not alone but in association with others and efficient degradation occurs by 4 ACS Paragon Plus Environment

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the highly collaborative action of microorganisms 10,11. Therefore, the characterization

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of microorganisms that relate to animal carcass composting could be the starting point

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for novel biotechnological applications related to the efficient conversion of organic

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materials.

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It was reported that the physicochemical and nutritional properties of the raw material

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being composted are mainly responsible for the identification of the microbial

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communities, which could affect the metabolic capabilities for transforming organic

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substrates and then the subsequent microbial functional succession associated to specific

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processes 12-14. In carcasses composting system, the organic matter is composed of

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animal carcasses and composting feedstock. Accordingly, the microbial communities

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involved in the decomposition of organic matter should be respectively affected by

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animal carcasses and composting feedstock. And the initial composition of animal

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carcasses and feedstock in substrate may exert their influence by altering the microbial

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community assembly and function during composting. So, it is important for

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establishing efficient composting system to character the relationship between the raw

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material being composted and the microbial diversity or succession and the change in

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the compost’s physicochemical properties in composting process. In this regard, little

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information has been reported in the literature regarding the microorganisms or their

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metabolic activity associated with the decomposition of animal carcasses and

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composting feedstock at the same time, especially if we consider the potential driver for

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identification and metabolic capabilities of microbial communities and the functional

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microorganisms contributing to carcass decomposition 4,6,15.

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In the present study, a deeper exploration of poultry carcass composting was conducted by establishing different composting systems, which consisted of different 5 ACS Paragon Plus Environment


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ratio of chicken manure, sawdust and poultry carcasses. The aims of this research were

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focused on: 1) how does the raw material being composted affect the diversity and

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composition of bacterial-fungal consortia in composting system; 2) analyzing the effect

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of substrate types on the metabolic capabilities of microbial communities by

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investigating the physico-chemical properties; 3) identifying the potential functional

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microorganisms associated with poultry carcasses decomposition. Investigation the role

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of substrate and their importance in controlling microbial community assembly,

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biomass turnover and nutrient cycling could allow development of efficient carcasses

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degradation.

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MATERIALS AND METHODS Composting schedule. Composting was conducted in the special composting bins

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with mixing device (Figure 1, 60 cm × 60 cm × 80 cm, patent number:

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ZL201320021724.5) from August to September at the Zengcheng experiment station of

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South China Agricultural University, Guangzhou, China. Three substrate materials, fresh

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poultry manure, poultry carcasses weighing 1-1.5kg (18 week-old laying hen) and

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sawdust were collected from a farm in Zhongshan and a wood processing factory in

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Zengcheng, respectively.

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Three treatments with three replicates were carried out in the composting bins,

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namely group A, group B and group C, each of which has the same volume. Group A

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was a treatment without the addition of poultry carcass, in which a thorough mixture of

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poultry manure and sawdust at a ratio of 5:1 (manure weight: sawdust weight) was

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conducted to achieve the initial C/N of 15. Group B was the treatment that consisted of

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12 poultry carcasses and the same ratio of poultry manure and sawdust as group A.


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Group C was the treatment that contained a thorough mixture of poultry manure and

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sawdust at a ratio of 4:1 (weight) with the initial C/N of 22 and 12 poultry carcasses. In

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group B and group C, the compost constituents from bottom to top layer were

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respectively 25 cm manure-sawdust mixture, six poultry carcasses (every chicken was

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cut into four pieces and uniformly placed ), 10 cm manure-sawdust mixture, another six

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poultry carcasses (the same treatment as mentioned above), and 20 cm manure-sawdust

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mixture.

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Tissues obtained from the same part of chicken carcasses were cut into 50 g pieces

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and sealed in nylon bag (100 µm pore size). Then every two nylon bag were

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respectively suspended adjacent to the carcasses layers in each compost bin of group A

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and group B to test the degradation rate.

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Three nanotechnology microporous trachea used for ventilation were fixed on the

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bottom of each composting bin. Forced aeration was applied to the compost piles, and

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the ventilation rate was 1.1 m3 h-1bin-1. Fresh air was introduced into each bin through

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an air pump, which was connected with a flow meter and nanotechnology microporous

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trachea through tubes (Figure 1). The air supply for each treatment was controlled by a

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timer to operate for 40 min and then turn off for 20 min. The air moved upward through

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the compost pile and was exhausted through a outlet above on one side of each bin. An

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sucking pump inlet was connected with the outlet, and the outlet was connected with a

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shunt device to divide the exhaust air into two parts. One part gas was collected by a

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storage bag volume of three M3, and the other part was released into the outside. Pile

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turning was started from the third day and was conducted before every sampling by

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turning in two directions for three minutes. A schematic representation of the

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experimental setup is shown in Figure 1. 7 ACS Paragon Plus Environment


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Compost sample collection and analysis. Samples from day 0, day 1, day 3, day 6,

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day 9, day 12, day 16, day 20, day 24, day 28, and day 32 of compost were collected

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and analyzed. One kilogram of samples collected at different depths and directions were

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mixed. The samples were divided into two sections and stored at -20 °C and -80 °C for

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further analysis, which were respectively used for chemical parameters analysis and

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Illumina sequencing.

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Physical and chemical characterization consisted of analyses for temperature, pH,

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moisture content, total nitrogen (TN), organic matter, C:N ratio of non purgeable

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organic C and total dissolved N, ammonium nitrogen (NH4+-N) and nitrate nitrogen

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(NO3--N). Ambient temperature and pile core temperature were measured real-time by a

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PT100 temperature sensor (Platinum Resistance Thermometers, Shanghai Feilong,

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China) during the composting. Moisture content was measured by drying in an oven at

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105 °C until no change in dry weight was observed. The pH value was determined on a

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1:10 compost: water (w/v) suspension using a Five pH meter (Mettler Toledo,

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Switzerland) after 1 h equilibrium with shaking. The TN, C:N ratio, organic matter,

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ammonium nitrogen and nitrate nitrogen were respectively determined according to the

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methods of Chowdhury et al. 16.

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Gas sample collection and analysis. Gas generated from each composting bin was

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collected with storage bag for gap sampling. Gap was sampled from day 0, day 1, day 3,

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day 6, day 9, day 12, day 16, day 20, day 24, day 28, and day 32 after mixing in storage

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bag and fitted with 1L foil bag. Gap samples were used to determinate the concentration

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of CO2, CH4 and NH3.

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The concentration of CO2 and CH4 were analyzed by gas-chromatography with a 8 ACS Paragon Plus Environment

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7890 A GC-System (Agilent, Santa Clara, CA, USA) connected to an automatic

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headspace-sample injection syste (Dani HSS 86.50 Dani, Cologno Monzese, Italy).

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Helium served as the carrier gas. Concentration of CO2 was determined by HP-PLOT/Q

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column (19095P-Q04, 30 m×0.53 mm×40 µm). Temperatures of the injector oven,

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column oven and detector were respectively 200, 30 and 200 °C, and the flow rate for

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nitrogen, H2 and air were 35, 40 and 400 ml/min, respectively. Peaks were identified by

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comparison with a know concentration of pure CO2. Concentration of CH4 was

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determined by HP-PLOT/Q column (19091P-Q04, 30 m×0.32 mm×20 µm).

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Temperatures of the injector oven, column oven and detector were 200, 50 and 250 °C,

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respectively, and the flow rates for nitrogen, H2 and air were 40, 40 and 400 ml/min,

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respectively. Peaks were identified by comparison with a know concentration of pure

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CH4.

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The concentration of NH3 were measured by PN2000 ammonia gas detector (PNLE

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Technology Co., Ltd., China). The following formula was used for the calculation of

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gas emissions:

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F = (C1-C2) V Where, F = Gas emissions (mg), C1 = the concentration of gas in the outlet port (mg /

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m3), C2 = the concentration of gas in the inlet (mg / m3), V = the total flow of outlet

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(m³).

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Microbial community sampling and DNA extraction. The Miseq Illumina

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sequencing technology was used to explore the microbial ecosystems of three

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treatments. Microbial communities were respectively sampled from the compost at the 0,

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1st, 3rd, 6th, 9th, 16th, 24th and 32th day based on the observation of biochemical 9 ACS Paragon Plus Environment


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changes in composing system. Samples were collected at different depths and directions

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and mixed. The total microbial DNA of triplicate compost samples were extracted by

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EZNATM Soil DNA extraction kit produced by OMEGA company. DNA products of

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the three replicate samples were mixed in equal concentrations and aliquoted for

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analysis by MiSeq high-throughput sequencing.

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Illumina sequencing and quality filtering. PCR primers were designed to amplify a

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459-bp region across the V3-V4 region of the 16S rRNA gene and a 200-600 bp

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spanning ITS1 region of the 18S rRNA gene, respectively. The 16S rRNA gene was

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amplified with primers 341F (GTACTCCTACGGGAGGCAGCA) and R806

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(GTGGACTACHVGGGTWTCTAAT), as previously described 17. 18S primers were

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designed as F (CTTGGTCATTTAGAGGAAGTAA) and R

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(R:TGCGTTCTTCATCGATGC) based on Courtin et al.18and Ihrmark et al.19.

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The amplification conditions were as follows: 94 °C for 5 min, 30 cycles of PCR

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(94 °C for 30 s, 56 °C for 30 s, and 72 °C for 30s ), and a final elongation step of 72 °C

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for 10 min. The samples were kept in 4 °C. Libraries were constructed and quantitated

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using a KAPA Hyper Prep Kit (Illumina). Equimolar amounts of each of the 96 libraries

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were pooled and submitted for sequencing on a MiSeq Sequencer using a 250-bp

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paired-end protocol at the Honor tech company of Beijing.

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The sample containing indexed ampllicons was loaded onto the Miseq reagent, and

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then onto the instrument along with the flow cell. Automated cluster generation and

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paried-end sequencing with dual index reads was performed. Total run time for this

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2×300 bp run was 40 hours. The primary date analysis were performed directly on

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Illumina MiSeq platform. Removing low quality data reads according to the Q20, 90% 10 ACS Paragon Plus Environment

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of the filter standard. The quality score of most reads were above 20. After splicing, 16S

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rRNA sequences less than 200 bp and 18S rRNA sequences less than 100 bp in length

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were removed using mothur software, where the control condition is minlength = 100,

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maxhomop=10 and maxambig=0. The date were used for further information analysis.

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16S rRNA and 18S rRNA gene Sequence Analysis. The 16S rRNA dataset were

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processed and analyzed using QIIME software (v1.8.0). After optimizing, sequences

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with ≥ 97% similarities were selected and grouped into the Operational Taxonomic

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Units (OTU) for species classification. Alpha diversity was estimated on the abundance

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information of OTUs in each sample by calculating the Shannon index, Chao1 index,

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Phylogenetic diversity and observed number of species.

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The mothur software (v1.8.0) was used to process the 18S rRNA dataset obtained

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from Illumine sequencing. The optimized sequences were grouped into OTUs based on

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a 97 % sequence similarity. Community diversity indices, including Shannon, Chao1,

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ace, Coverage and Simpson, were calculated to estimate the diversity within each

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treatment.

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Beta diversity patterns were analyzed to compare the community diversity among

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treatments by performing UniFrac-based Principal Coordinates Analysis (PCoA), and

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the heatmaps were drawn by R (v3.1.1) software. Taxonomic classifications at the

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phylum and genus or species level of each OTU were done using RDP classifier and

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BLAST algorithms against the Greengenes (16S rRNA), UNITE and GenBank (ITS1)

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databases. The possible functional profiles of microbial consortia in three treatments

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were speculated by analyzing the OTU overlap among samples from Venn diagrams

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drawn by R (v3.1.1) software. 11 ACS Paragon Plus Environment


Statistical analysis. All the physicochemical properties of samples were carried out

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in triplicates, and the results were expressed as mean values and standard errors. A

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one-way ANOVA analysis was used to examine the effects of substrate on

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physico-chemical parameters and microbial communities. Significant differences

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between the treatments were compared by Tukey test at p ˂ 0.05 using the general

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linear model procedures of the SPSS v. 18.0 programs.

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RESULTS AND DISCUSSION Mass loss. Mass loss represented the degradation degree of material composted to

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some extent 20. As observed in Table 1, the highest loss rates of mass was found for

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group C, followed by group B and group A. Poultry carcasses in group B and group C

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started to degrade rapidly within the first 10 days, and no whole carcasses were evident

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after 20 days. At the end of composting, only some solid material was present in the

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form of bones, and the loss rates of poultry carcasses in these two treatments were about

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98%. The chicken degradation rates of group B and group C were lower than the loss

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rates of poultry carcasses, which might be ascribed to the inadequate contact of chicken

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inside the nylon bags with substrate. Degradation rates of chicken in group C were

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higher than those of group B, especially after 16 days of composting (Figure 2A). These

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results indicated that the C:N ratio may exerted more influence on substrate

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decomposition, but poultry carcasses may promote the decomposition of substrate,

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although no significant difference was observed between treatments (p > 0.05).

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Temperature, pH and moisture content. Temperature course in three treatments fit

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the typical evolution of composting processes operated with turning treatments (Figure

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2B). The thermophilic phase (> 55 °C) started within 12 h and lasted for about 16 days 12 ACS Paragon Plus Environment

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in all treatments. The peak temperature of group B and group C was obtained at 10 days,

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but group A was on the 7 th day. In the first 16 days of composting, average temperature

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of group A was significant lower (p < 0.05) than those of group B and group C. The

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subsequent cooling stage of group B registered values slightly higher than group A and

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group C.

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A increasing tendency in pH curves was observed in three treatments except for the

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slight decreasing during the 3th to 6th days in group B and group C (Figure 2C). During

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6 to 16 days, pH in group A was significantly higher (p < 0.05) than the other two

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treatments. Thereafter, no significant difference (p > 0.05) was observed among three

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treatments. Moisture content in three treatments followed similar decreasing profile

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(Figure 2D), with differences observed in decreasing amplitude on trail day, where

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group A exhibited significantly lower (p < 0.05) moisture contents than group B and C.

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Rapid decomposition of organic matter could generate more heat, and high water

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retention of substrate also could increase the thermophilic temperature of composting 12.

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Accordingly, we observed that samples from group B and C had higher degradation

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rates, temperature profile and moisture content. The results indicated that initial

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composition of raw material, and especially the combined addition of carcasses, resulted

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in the differences on the factors of the surroundings.

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Organic matter and C:N ratio. It was found in other composting systems that rapid

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biodegradation of organic matter could result in the disappearance of organic carbon 21.

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In the present study, group C had the highest initial organic matter content. During 6 to

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20 days, the highest organic matter content was found for group B, followed by group C

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and group A, respectively (Figure 2E). The highest initial value and a decreasing

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tendency was observed on C:N ratio of group C (Figure 2F). The C:N ratio of group A 13 ACS Paragon Plus Environment


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and group B presented a slightly increasing and then significantly decreasing tendency.

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At the last stage, group B had the lowest C:N ratio, while group A had the highest C:N

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ratio. In addition, the standard errors on day 3 and day 6 were higher in Figure 2E and

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Figure 2F, which might result from the partially nonuniform mixture of poultry manure

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and sawdust.

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TN content, ammonium nitrogen and nitrate nitrogen content. The change of TN

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content was shown in Figure 3A, where a decreasing tendency was observed on group A,

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which differed from the increasing first and then decreasing profile of group B and

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group C. According to Moorhead and Sinsabaugh 22, nitrogen seems to accelerate

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decomposition due to a higher microbial nutrient demand. The initial TN content in

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group C was lowest, but in later composting stages, group C exhibited the highest

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degradation rates and TN content.

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Previous findings showed that high NH4 + concentrations generally promote N losses

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via volatilization 8,23. Ammonium nitrogen (NH4+-N) content in three treatments all

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increased significantly during composting (Figure 3B). Accordingly, group A had the

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highest NH4+-N content and the lowest TN content. Three treatments had similar initial

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value and increasing profile of nitrate nitrogen (NO3--N) (Figure 3C). The highest and

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lowest NO3--N contents were observed in group A and group B, respectively. Nutrients

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in compost system could affect the conversion of ammonia to nitrate and the retain of N

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in compost 24. Compared to group A, treatments with addition of carcasses had lower

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NH4 + and NO3 -N concentrations by the end of composting, indicating a much more

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dynamic N transformation during composting.


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Gaseous emissions. A rapid increase of initial daily NH3 emission volume was

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registered in three treatments on the second day (Figure 3D). Subsequently, the daily

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NH3 emission volume decreased in all treatments. The highest total NH3 emission

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volume was observed in group A, while the lowest was in group C. The most important

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factors that influence the emission of NH3 during composting are pH value and C:N

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ratio 25, which supported by our results, where the highest total NH3 emission volume

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was accompanied by high pH value and low C:N ratio in group A.

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The CO2 emissions of three treatments had similar change profile and fluctuated over

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time during composting (Figure 3E). Group C exhibited highest daily and total CO2

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emission, followed by group B and group A until the last stage. The change of CO2

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emission was found to be consistent with the changes of C:N ratio, which ranked in

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order of group C, group B and group A.

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The daily CH4 emission significantly decreased (P < 0.05) on the first day, and then

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slight increased during day 6 to day 12, followed by a slowly decreasing trend (Figure

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3F). Production of CH4 occurs through the degradation of lipids, carbohydrates and

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proteins in anaerobic conditions and reduces in aerobic condition 26. We speculated that

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central region of compost group was under anaerobic conditions due to the lack of

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oxygen at the beginning, and thus resulted in the highest CH4 production. With the

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supply of fresh air, CH4 production significant decreased. In addition, addition of

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carcasses in group B and group C resulted in larger gap among substrate material, which

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might be responsible for the less CH4 production.

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Analysis of the community diversities of three microbial consortia. After sequence

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quality filtering and removal of failed samples with low numbers of sequences,



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the Miseq Illumina sequence dataset included 24 samples and 642242 16S rRNA

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sequences that generated 60212 OTUs based on a 97 % sequence similarity . The 18S

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rRNA dataset included 23 samples and 2574729 18S rRNA sequences that generated

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9884 OTUs.

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Alpha diversity. Alpha diversity analysis showed that both richness and diversity

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estimates of the 16S rRNA and 18S rRNA sequences were variable across sample days.

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16S rRNA diversity indices showed that richness and diversity of samples from group B

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and group C exhibited the similar change pattern, where two increasing peaks were

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observed during the sampling days 1- 6 and days 16-24 (Table S1), which was different

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from the steady increasing trend of group A. With respect to fungal community,

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diversity indices of samples from group B presented similar change profile with those of

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group A, but different from those of group C (Table S2).

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Beta diversity. The abundance-unweighted UniFrac analysis of bacterial and fungal

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communities (Figure 4A, C) revealed the substrate-specific clusters, and the separation

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of samples at all sample sites in three treatments was significant (P < 0.05), reflecting

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the effect of substrate composition on the identification of microbial communities.

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However, the abundance-weighted analysis (Figure 4B, D) results showed that

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microbial communities clusters formed in a substrate-independent pattern, where three

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treatments presented similar bacterial and fungal diversity at most sample sites. We

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speculated that the similar microbial diversity may be ascribed to the decomposition of

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poultry manure and sawdust commonly contained in three treatments. In addition, it was

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found that weighted UniFrac distances of bacterial communities between samples from

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group A and group B during 0-16 days were lower than between samples from group A

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and group C or from group B and group C. While the shorter distances of fungal 16 ACS Paragon Plus Environment

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communities were observed between samples from group B and group C when

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compared with the samples from group A and group B or from group A and group C.

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In conjunction with the analysis results of alpha diversity, we speculated that the

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presence of carcasses might be the potential contributing factor to the different change

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patterns of bacterial diversity between group A and the other two groups, and the initial

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C/N ratio might contribute to the diversity difference of fungal community among

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treatments, supporting previous reports that raw material being composting could affect

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the richness and diversity of microbial community 27,28.

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Analysis of community structure and composition of three dominant microbial

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consortia. Three treatments displayed the involvement of similar phyla of bacteria and

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fungi, but differed in relative abundance at all sampled days (Figure 5A, C) . For

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bacterial community, the Firmicutes, members of which were considered to play an

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important role in degrading organic materials in composting process, were dominant in

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these treatments. Proteobacteria was the next most abundant phylum, followed by

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Actinobacteria and Bacteroidetes. The similar structure phylum has been observed in

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many bacterial diversity surveys of composting, including carcasses decomposition 7,15.

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For fugal community, Ascomycota phyla were predominant during 0-16 days, thereafter

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the relative abundance decreased sharply. At the last stage of 24 to 32 days, the relative

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abundance of Basidiomycota and Glomeromycota significantly increased in samples

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from group A and group B. The fungal community structure was different from the

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dominant fungal community observed in a mouse model system 29.

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A closer look of bacterial community at genus level (Figure 5B) revealed that beginning at the initial day (0), three treatments were populated by several genera,



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including Corynebacterium, Virgibacillus, Bacteroides and Gallicola. Corynebacterium

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and Gallicola were significantly more abundant in group A and group B than in group C,

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whereas Virgibacillus and Bacteroides were significantly more abundant in group C

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than in the other two group. According to previous reports, some species from

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Corynebacterium genus are generally recognized as compost inhabitants associated with

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the transformation of nitrogen transformation in composting 30. Virgibacillus members

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were found to be more abundant in cellulose decomposing 31. These data corroborated

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the evidences analyzed above of chemical properties, where group A and B had higher

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initial TN content and exhibited more efficient N transformation, while group C

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contained more cellulase because of the high content of sawdust. During 6-16 days,

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similar communities of microbes dominated within each treatments, but the relative

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abundance and change pattern were different. Among these dominant genera, members

360

of Corynebacterium and Lactobacillus could break down lipids and complex

361

carbohydrates associated with animal carcass tissue 32,33. Caldicoprobacter is generally

362

recognized as bacterial community associated with keratin decomposition of carcasses

363

34

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in group A and highest in group B. Group C contained higher abundances of

365

Tepidimicrobium and Lactobacillus. In addition, it was found that group C and group B

366

had the same dominant taxa and similar change profiles of Caldicoprobacter and

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Lactobacillus, which may contribute to decomposition of keratin and lipids, suggesting

368

that this same functional community may generalize across composting systems with

369

the induction of poultry carcasses. After 16 days composting, the prominent genera

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sharply decreased in three treatments.

371

. The abundances of Corynebacterium and Caldicoprobacter were significantly lowest

As shown in Figure 5D, two key stages were observed before and after the sampling 18 ACS Paragon Plus Environment

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day 16 on change profile of fungal community at specie level. During 0-16 days, fungal

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community at all sampled sites were dominated by Aspergillus, Trichocomaceae and

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Penicillium at specie level, with other minor fungal populations of Ophiostoma and

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Lasiodiplodia in three treatments, of which species of Aspergillus and Penicillium were

376

thought to play a major role in the decomposition of cellulose and lipids, respecitvely

377

35-37

378

community at all sites became dominated by a nematode, O. tipulae, in the family

379

Rhabditidae and fungal community composition did not change significantly over time

380

in mouse model system 29. After 16 days composting, Several genera, such as Gigaspora,

381

Pleurotus and Cladosporium, differently dominated in samples. Pleurotus and

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Cladosporium are known to be common members of the fresh poultry manure or

383

sawdust 38. Carcasses decomposition might be responsible for the shifts because of the

384

breakdown of tissue, which release an ammonia-rich, high nutrient fluid that alters both

385

the pH and nutrient content of the substrate 39. Accordingly the predictable spike in pH

386

(Figure 1B) and total nitrogen and the rapid degradation rates of chicken were observed

387

on the samples collected at the 16 days from group B and group C. In conjunction with

388

the analysis results of bacterial community, we speculated that the initial C/N ratio

389

might the potential contributor for the identification of dominant microbial

390

communities.

391

. These data differed from the findings of Metcalf et al., who found that the fungal

Predicted (potential) functional profiles of microbial consortia. The Venn

392

diagrams represented the shared bacterial and fungal OTUs between treatments as well

393

as the unique OTUs in each treatment (Figure 6). The OTUs shared by three treatments

394

were observed throughout composting. The common in three treatments was the

395

comprising of poultry manure and sawdust in substrate, indicating that the shared 19 ACS Paragon Plus Environment


396

microbial communities might be the feedstock-specific microbiota. Most of the OTUs

397

unique to group A and B and the OTUs unique to group A and C sharply decreased after

398

6 days composting. Group A contained the same ratio of poultry manure and sawdust as

399

group B, while group B had the same amounts of poultry carcasses with group C.

400

Results illustrated that these shared microbiota may originally present in substrate, and

401

decreased because of the poor adaption to severe environment. The OTUs unique to

402

each treatment were found to be generally abundant in the first six days or during the

403

last composting stage. According to previous reports, some microorganism are

404

generally considered to play roles on the adaptation to the composting conditions 9.

405

Thus, we speculated that microbial consortia unique to each treatment might consist of

406

microbiota originally presenting in the substrate or coming from the surroundings and

407

those adapting to the unique composting condition in each treatment changed with

408

composting.

409

Group B and C shared 1158 bacterial OTUs and 109 fungal OTUs, most of which

410

exhibited higher relative abundance during 3-24 days, which was in accordance with the

411

stage of poultry carcasses decomposition. And the dominant microbiota of these shared

412

OTUs belonged to carcasses-specific microbial phyla, indicating that the microbial

413

communities may contribute to the decomposition of poultry carcasses. In addition, the

414

relative abundances of these shared OTUs were different between group B and group C.

415

These results suggested that poultry carcasses and initial C/N ratio could respectively

416

affect the composition and abundance of microorganisms associated with the

417

decomposition of poultry carcasses.

418

In conclusion, this is the first report on microorganisms and their metabolic activity

419

associated with the decomposition of animal carcasses and composting feedstock at the 20 ACS Paragon Plus Environment

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same time. It is important for establishment of efficient composting system to character

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the relationship between the raw material being composted and the microbial

422

community and metabolic activity in composting process. Further investigation is still

423

necessary to determine the optimum growth conditions of the functional microbiota

424

associated with poultry carcass decomposition.

425

426

Funding

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This work was funded by the earmarked fund for Modern Agro-industry Technology

428

Research System, China (Grant Nos. CARS-41); and the National Special Fund for

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Agro-scientific Research in the Public Interest, China (Grant Nos. 201303091).

430

Notes

431

The authors declare no competing financial interest.

432

Supporting Information Available

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Analysis of microbial alpha-diversity indices was found in Supporting Information. This

434

material is available free of charge via the Internet at http://pubs.acs.org.

435

436

(1) National Bureau of Statistics of the People’s Republic of China. Agriculture,

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forestry, animal husbandry and fishery statistics. In China Rural Statistical Yearbook,

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China Statistics Press: Beijing. China, 2015; 174.

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(2) Akdeniz, N.; Koziel, J.A.; Ahn, H.K.; Glanville, T.D.; Crawford, B.P.; Raman, D.R.

440

Air sampling and analysis method for volatile organic compounds (VOCs) related to

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Figure captions

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Figure 1. Schematic representation of the experimental setup (M=poultry manure,

566

S=sawdust)

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Figure 2. Chicken degradation rate, temperature, pH value, moisture, organic matter

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and C/N ratio at all sampled sites in three treatments (A, B, C, D, E and F). Different

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lowercase letters indicate significant difference between days in each treatment (p

Effect of Substrate on Identification of Microbial Communities in Poultry

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