microbiology and nitrogen mineralization in composted
TRANSCRIPT
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Microbiology and Nitrogen Mineralization in Composted Poultry Litter
Amended with Biodiesel Wash Water
by
Jonathan Robert Gaiero
A Thesis
Presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Master of Science
in
Environmental Biology
Guelph, Ontario, Canada
© Jonathan R. Gaiero, April, 2014
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ABSTRACT
MICROBIOLOGY AND NITROGEN MINERALIZATION OF COMPOSTED
POULTRY LITTER AMENDED WITH BIODIESEL WASH WATER
Jonathan R. Gaiero Advisors:
University of Guelph, 2014 Professors M. B. Habash, R. Nicol
Committee member:
J. T. Trevors
Composting of poultry litter is an important method to increase the retention of nutrients
such as nitrogen (N) prior to its use as an agricultural soil amendment. Biodiesel wash water
(BWW) was used as a treatment during composting and compared to municipal water as a
control. Molecular analyses examined the effects of BWW on the microbial communities (via
denaturing gradient gel electrophoresis) and the abundance of bacteria, fungi, pathogens, and
ureolytic/uricolytic N-cycling microorganisms (via quantitative real-time PCR). The lack of
large community perturbence and variable abundance of N-cycling microbes supports the use of
BWW, but higher levels of Campylobacter in the mature BWW compost will need to be
examined further. Ureolytic screening of bacterial isolates identified the dominant group PLUP
(poultry litter urease producers). Incubation of PL with BWW and PLUP microbial inoculant,
revealed active ureolysis by PLUP, and inhibition of the enzymes involved in N-mineralization
by BWW.
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ACKNOWLEDGEMENTS
Firstly, I would like to greatly thank my co-advisors Dr. Marc Habash and Dr. Rob Nicol, and
committee advisor Dr. Jack Trevors for their continued support during my project. My Master’s
education would not have been the same without the insightful conversations I had with Marc on
many occasions.
Family, friends and fellow office-mates were incredibly helpful and supportive during my
Master’s. My parents (Robert and Dianne Gaiero), brother (Andrew) and sister (Angela) were
very encouraging and I’m glad to make them proud. I had the privilege of meeting many students
during my graduate studies. Their friendship and help during my thesis will not be forgotten.
The staff at the Ridgetown biodiesel facility, and Cold Springs composting farm, were
instrumental in preparing the samples required for this project. Peter Smith was very considerate
and timely in his chemical analyses for which I am grateful. I am also grateful to Dr. Lee, Dr.
Trevors, Dr. Kari Dunfield, and Dr. Susan Glasauer for the use of their labs and equipment.
Finally I would like to thank the Government of Ontario, the University of Guelph and private
donors, and the Natural Sciences and Engineering Research Council (NSERC) of Canada for
their generous funding.
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TABLE OF CONTENTS
ABSTRACT .................................................................................................................................... ii
LIST OF TABLES ......................................................................................................................... ix
LIST OF FIGURES ........................................................................................................................ x
LIST OF ABBREVIATIONS AND ACRONYMS ..................................................................... xii
CHAPTER 1: INTRODUCTION ................................................................................................... 1
1.1 Poultry litter (PL) .................................................................................................................. 1
1.2 Potential economic cost of N loss from PL ........................................................................... 2
1.3 PL Composting ...................................................................................................................... 3
1.4 Minimizing NH3 volatilization from PL ............................................................................... 4
1.5 General microbiology of PL .................................................................................................. 7
1.6 Microbial N dynamics in PL ................................................................................................. 9
1.6.1 Uric acid mineralization to NH3 (Fig 2) ...................................................................... 10
1.6.1.1 Uricase in microorganisms ................................................................................... 12
1.6.1.2 Urease in microorganisms..................................................................................... 12
1.6.2 Diversity of ureolytic and uricolytic microbes in PL ................................................... 14
1.6.3 Ureolytic activity in a compost environment ............................................................... 16
1.6.4 Treatments used to minimize NH3 loss from PL by inhibiting mineralization............ 17
1.7 Molecular microbial analysis .............................................................................................. 18
1.7.1 Molecular versus culture based methods ..................................................................... 18
1.7.2 Extraction of nucleic acids ........................................................................................... 21
1.7.3 PCR inhibition limits effectiveness of molecular techniques ...................................... 22
1.7.4 Sensitivity of PCR-based assays and safety of composts ............................................ 23
1.7.5 Molecular methods used to study compost microbial communities ............................ 24
1.7.5.1 Molecular ecology of N mineralizing microbes ................................................... 29
1.7.5.2 Poultry litter urease producer (PLUP) .................................................................. 30
1.7.5.3 Genomic studies of ureolytic microbes ................................................................. 30
1.8 Biodiesel production and biodiesel wash water (BWW) .................................................... 32
1.8.1 BWW and inhibition of microbial growth ................................................................... 33
1.9 Summary ............................................................................................................................. 34
1.10 Research objectives ........................................................................................................... 34
CHAPTER 2: MATERIALS AND METHODS .......................................................................... 36
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2.1 Pilot project: large scale composting of PL treated with BWW or MW............................. 36
2.1.1 Materials ...................................................................................................................... 36
2.1.2 General method ............................................................................................................ 36
2.1.3 Temperature measurements ......................................................................................... 37
2.1.4 Sampling ...................................................................................................................... 37
2.2 Optimization of genomic DNA extraction from PL ............................................................ 38
2.2.1 Initial comparison of commercial gDNA extraction kits ............................................. 38
2.2.2 Pseudomonas sp. UG14Lr ........................................................................................... 38
2.2.3 UG14Lr growth conditions .......................................................................................... 39
2.2.4 qPCR inhibitors from PL gDNA extracts .................................................................... 39
2.2.5 Extraction efficiency of the FASTDNA kit ................................................................. 40
2.2.6 Sensitivity of gDNA extractions and detection by PCR .............................................. 40
2.3 DNA extraction methods used in this study ........................................................................ 41
2.3.1 Final protocol for genomic DNA extractions from PL ................................................ 41
2.3.2 From pure cultures ....................................................................................................... 42
2.3.3 Isolation of plasmid vectors from transformed E. coli ................................................ 42
2.4 General molecular analyses ................................................................................................. 43
2.4.1 Determining DNA purity by spectrophotometry ......................................................... 43
2.4.2 Agarose gel electrophoresis ......................................................................................... 43
2.4.3 Cloning ......................................................................................................................... 43
2.4.4 Sequencing and phylogenetic analysis......................................................................... 44
2.5 Molecular analysis of PL by PCR, DGGE, and qPCR........................................................ 45
2.5.1 Primers used in this study ............................................................................................ 45
2.5.2 Quantitative real-time PCR (qPCR) of nitrogen cycling microbes and pathogens ..... 47
2.5.2.1 General procedure of qPCR .................................................................................. 47
2.5.2.2 Plasmid standard curves used for quantification of samples ................................ 47
2.5.2.3 Cell abundances and copy number calculations ................................................... 48
2.5.3 General end-point PCR ................................................................................................ 48
2.5.4 End-point PCR for DGGE ........................................................................................... 49
2.5.5 DGGE .......................................................................................................................... 49
2.5.5.1 General procedure ................................................................................................. 49
2.5.5.2 Band identification: extraction, cloning, and sequencing ..................................... 50
2.5.5.3 Analysis of DGGE profiles ................................................................................... 51
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2.6 Isolation of culturable ureolytic microbes from composted PL .......................................... 52
2.6.1 Extracting viable microorganisms from PL ................................................................. 52
2.6.2 Media and culture conditions ....................................................................................... 53
2.6.3 Testing ureolytic capacity of cultured microbial isolates ............................................ 54
2.6.4 Identification of positive ureolytic bacterial isolates ................................................... 54
2.6.5 Storage of cultured microorganisms ............................................................................ 55
2.7 Microcosm incubation experiments .................................................................................... 55
2.7.1 Materials ...................................................................................................................... 55
2.7.2 PLUP cultures used for inoculations ............................................................................ 55
2.7.3 Experimental design..................................................................................................... 56
2.7.4 Sampling from the microcosms ................................................................................... 56
2.7.5 Urea extraction and quantification ............................................................................... 57
2.7.6 Urease activity assay .................................................................................................... 57
2.8 Statistical Analysis .............................................................................................................. 58
CHAPTER 3: RESULTS .............................................................................................................. 59
3.1 Optimization of gDNA extraction and qPCR sensitivity of detection from extracts .......... 59
3.1.1 Testing commercial gDNA extraction kits .................................................................. 59
3.1.2 Improved extraction efficiency from PL using the FASTDNA kit ............................. 60
3.1.3 Sensitivity of DNA extraction and detection by qPCR ............................................... 62
3.2 Pilot project: large scale composting of PL treated with BWW or MW............................. 64
3.2.1 Compost temperature ................................................................................................... 64
3.2.2 Quantification of microbes in pilot scale composting ................................................. 66
3.2.2.1 General and nitrogen cycling microbes ................................................................ 66
3.2.2.2 qPCR – Pathogens................................................................................................. 68
3.2.3 Denaturing gradient gel electrophoresis (DGGE)........................................................ 69
3.2.3.1 DGGE - Fungal 18S rRNA gene (Fig 8) .............................................................. 70
3.2.3.1.1 18S rRNA gene DGGE analysis .................................................................... 70
3.2.3.1.2 Band isolations and sequencing ..................................................................... 72
3.2.3.1.3 18S phylotype richness .................................................................................. 74
3.2.3.2 DGGE - Bacterial 16S rRNA gene (Fig 12) ......................................................... 76
3.2.3.2.1 16S rRNA gene DGGE analysis .................................................................... 76
3.2.3.2.2 Band isolations and sequencing ..................................................................... 78
3.2.3.2.3 16S phylotype richness .................................................................................. 80
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3.2.3.3 DGGE - bacterial partial ureC gene (Fig 10a) ...................................................... 82
3.2.3.3.1 PCR of ureC for DGGE ................................................................................. 82
3.2.3.3.2 Partial gene ureC DGGE analysis .................................................................. 82
3.3 Isolation of culturable ureolytic bacteria from poultry litter ............................................... 83
3.3.1 Media and isolate naming ............................................................................................ 83
3.3.2 General culture characteristics ..................................................................................... 84
3.3.3 Testing isolates for ureolytic activity ........................................................................... 84
3.3.4 ureC partial gene analysis of ureolytic bacterial isolates ............................................. 87
3.3.4.1 Preliminary isolate screening by PCR .................................................................. 87
3.3.4.2 ureC partial gene sequencing and sequence alignment......................................... 87
3.3.4.3 Isolation of novel bacterial isolates containing PLUP ureC sequences ................ 93
3.3.4.4 Translated protein vs. nucleotide ureC sequences ................................................ 94
3.3.4.5 Multiple copies of ureC gene per genome ............................................................ 94
3.3.4.6 ureC phylogenetic analysis ................................................................................... 95
3.3.5 16S rRNA gene analysis of ureolytic bacterial isolates ............................................... 97
3.3.5.1 16S rRNA gene sequencing .................................................................................. 97
3.3.5.2 16S rRNA gene phylogenetic analysis ............................................................... 100
3.3.5.3 Comparing 16S rRNA gene identities to ureC BLASTn and BLASTp ............. 102
3.4 Microcosm incubation experiments .................................................................................. 104
3.4.1 PLUP inoculants ........................................................................................................ 105
3.4.2 Moisture content (MC%) and pH .............................................................................. 106
3.4.3 Urea levels in the microcosm ..................................................................................... 107
3.4.4 Urease activity in the microcosms ............................................................................. 109
CHAPTER 4: DISCUSSION ...................................................................................................... 114
4.1 Large scale composting ..................................................................................................... 114
4.1.1 Differences in compost process between BWW and MW treatments ....................... 114
4.1.2 Conclusions ................................................................................................................ 116
4.2 Molecular analysis of microorganisms during PL composting ......................................... 116
4.2.1 Sampling procedure ................................................................................................... 116
4.2.2 Optimization of DNA extraction and detection by qPCR ......................................... 117
4.2.2.1 Extraction efficiency ........................................................................................... 117
4.2.2.2 Sensitivity of detection by qPCR ........................................................................ 118
4.2.2.3 Conclusions ......................................................................................................... 120
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4.2.3 Determining the abundance of microbial groups by qPCR ....................................... 120
4.2.3.1 Pathogens ............................................................................................................ 121
4.2.3.2 Nitrogen cycling microbes .................................................................................. 123
4.2.4 Community analysis by DGGE ................................................................................. 125
4.2.4.1 Bacteria ............................................................................................................... 126
4.2.4.2 Fungi ................................................................................................................... 128
4.2.4.3 ureC DGGE ......................................................................................................... 129
4.3 The isolation and analysis of ureolytic microorganisms from PL .................................... 130
4.3.1 Culturing of ureolytic microbes ................................................................................. 130
4.3.2 Phylogenetic analysis of isolates ............................................................................... 131
4.3.3 Discovery of novel ureolytic bacteria ........................................................................ 131
4.3.4 Variable ureolytic activity of isolates ........................................................................ 133
4.3.5 Identification of PLUP ureC in several isolates ........................................................ 134
4.3.6 Evidence of horizontal gene transfer (HGT) among PL bacteria .............................. 135
4.3.7 Conclusions ................................................................................................................ 137
4.4 Microcosm incubation experiment comparing BWW and MW treatments for N-
mineralization and microbial ureolytic activity ...................................................................... 137
4.4.1 Moisture content (MC%) and aerobicity of microcosms ........................................... 138
4.4.2 pH and enzymatic activity within microcosms .......................................................... 139
4.4.3 N mineralization to urea............................................................................................. 140
4.4.4 Explanations for the minimal accumulation of urea in BWW treated PL microcosms
............................................................................................................................................. 142
4.4.5 Urease activity ........................................................................................................... 143
4.4.6 Conclusions ................................................................................................................ 145
CHAPTER 5: SUMMARY AND CONCLUSIONS .................................................................. 146
CHAPTER 6: LITERATURE CITED ........................................................................................ 150
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LIST OF TABLES
Table 1: Meta-analysis of poultry litter properties from literature findings. .................................. 2
Table 2: Summary of microbial surveys of poultry litters from diverse regions, by culture-based
(cult) or molecular analyses. ........................................................................................................... 8
Table 3: Several of the more common molecular methodologies and their intended purpose in the
analysis of microbial communities in composts. .......................................................................... 28
Table 4: List of primer sets used for PCR and sequencing, including the sequence of the forward
and reverse primers (listed first and second, respectively), the target and size of the amplicon,
and the primer anneal temperature and final concentration (conc) used. ..................................... 45
Table 5: Absorbance measurements of DNA extracts from a number of commercial kits…. ..... 60
Table 6: Quantification of luxAB copy number from gDNA extractions from 100 mg of PL
inoculated with increasingly diluted Pseudomonas sp. UG14Lr stock culture.. .......................... 63
Table 7: Sensitivity of gDNA extraction protocol and qPCR to detect bacterial cells at
concentrations similar to the pathogen detection limits for E. coli in compost (1 x 103 CFU(g)
-1;
CCME 2005). ................................................................................................................................ 64
Table 8: Quantification of pathogenic groups at three time points (Initial, PT – post-treatment,
and final) from the pilot scale study of BWW and MW amended composted poultry litter. Genus
or species specific primers were used to target a number of different pathogens. ....................... 69
Table 9: Summary of total culturable microbe counts and number of ureolytic and PLUP isolates.
Media types (NA, nutrient agar; Enrich, urea enrichment culture followed by NA with added
urea; PLA, poultry litter extract agar). Bacteria (bac) and fungi are both included. .................... 86
Table 10: Translated ureC sequences for the isolates found in composted PL.. .......................... 90
Table 11: 16S sequences from the bacterial isolates cultured from composted PL, and closest
matches found on Genbank via BLASTn ..................................................................................... 98
Table 12: PLUP cultures used to inoculate the PL for incubation microcosms. ........................ 105
Table 13: Moisture content (MC%) averaged across three replicates for each treatment used in
microcosm incubation experiment. ............................................................................................. 106
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LIST OF FIGURES
Figure 1: Nitrogen flow of microbially-mediated processes relating to the production of
ammoniacal-N species.. ................................................................................................................ 10
Figure 2: Generalized pathway of uric acid mineralization to ammonia ...................................... 11
Figure 3: Flow chart of the steps required for appropriate analysis of microbial communities in
diverse environments .................................................................................................................... 20
Figure 4: Flow diagram of the general processing of biodiesel .................................................... 33
Figure 5: Efficiency of gDNA extraction from 100 mg litter spiked with 8log Pseudomonas sp.
UG14Lr (+ PL) using the FASTDNA kit for soil, compared to UG14Lr pure culture (no PL) of
the same quantity of bacteria. ....................................................................................................... 62
Figure 6: Temperature profiles of poultry litter during composting from the start July 14 2011 to
Sept 20 2011.. ............................................................................................................................... 65
Figure 7: Cell abundances of nitrogen cycling microbes found in composted poultry litter
amended with either BWW or MW.. ............................................................................................ 67
Figure 8: Fungal 18S rRNA gene DGGE ..................................................................................... 71
Figure 9: Fungal 18S rRNA gene DGGE analysis. ...................................................................... 72
Figure 10: Dendrogram of 18S sequences obtained from DGGE and relevant Genbank results
from BLASTn searches................................................................................................................. 74
Figure 11: Phylotype richness (R) from the fungal 18S rRNA gene DGGE profiles. .................. 75
Figure 12: Bacterial 16S rRNA gene DGGE. ............................................................................... 77
Figure 13: Bacterial 16S rRNA gene DGGE analysis. ................................................................. 78
Figure 14: Dendrogram of 16S sequences obtained from DGGE and relevant Genbank results
from BLASTn searches................................................................................................................. 80
Figure 15: Phylotype richness (R) values from the bacterial 16S DGGE profiles. ...................... 81
Figure 16: Bacterial partial ureC DGGE ...................................................................................... 83
Figure 17: Bacterial isolates growing on PLA (A), isolation onto master plates (B), and testing
on Christensen’s urea agar: Proteus vulgaris positive control (C), and bacterial isolates (D). .... 85
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Figure 18: 5’ nucleotide sequence (5’ to 3’) of the partial ureC gene from several bacterial
ureolytic isolates, showing differences at the target region for the forward primer ureC1F
(highlighted). ................................................................................................................................. 88
Figure 19: 3’ nucleotide sequence (5’ to 3’) of the partial ureC gene from several bacterial
ureolytic isolates, showing differences at the target region for the reverse primer ureC2R
(highlighted) .................................................................................................................................. 88
Figure 20: Clustal alignment of the translated bacterial ureC sequences obtained from the
cultured isolates from composted PL ............................................................................................ 89
Figure 21: Dendrogram of translated ureC sequences from the ureolytic culture based study and
from ureC DGGE from the PL pilot scale composting................................................................. 97
Figure 22: Dendrogram of 16S sequences obtained from the ureolytic culture based experiment,
16S DGGE from the PL pilot scale composting, and relevant Genbank results from BLASTn
searches. ...................................................................................................................................... 102
Figure 23: Web diagram of 16S rRNA and partial ureC gene sequence identities for the ureolytic
bacteria cultured from PL. .......................................................................................................... 104
Figure 24: Percent abundance of five common ureC alleles among the 28 ureolytic bacterial
isolates from PL, the ones most likely to have undergone HGT. ............................................... 104
Figure 25: Microcosm pH levels [1:10 w(v)-1
] in dH2O; Tiquia 2005) measured daily for each of
the four treatment types (BWW ± PLUP, and MW ± PLUP) .................................................... 107
Figure 26: Urea levels measured in the incubated PL microcosms. ........................................... 109
Figure 27: Urease activity in incubated PL microcosms ............................................................ 111
Figure 28: Net urease activity in incubated PL microcosms ...................................................... 113
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LIST OF ABBREVIATIONS AND ACRONYMS
ANAMMOX: Anaerobic Ammonium Oxidation
ANOVA: Analysis Of Variance
AOA: Ammonia Oxidizing Archaea
AOB: Ammonia Oxidizing Bacteria
ARDRA: Amplified rDNA Restriction Analysis
ARISA: Automated Ribosomal Intergenic Spacer Analysis
ATP: Adenosine Triphosphate
BLASTn: Basic Local Alignment Search Tool (nucleotide vs. nucleotides)
BLASTp: Basic Local Alignment Search Tool (protein vs. proteins)
bp: Base Pair(s)
BSA: Bovine Serum Albumin
BWW: Biodiesel Wash Water
CFU: Colony Forming Unit
cm: Centimetre
CO2: Carbon Dioxide
COD: Chemical Oxygen Demand
col: Colony/Isolate
d: Day
DGGE: Denaturing Gradient Gel Electrophoresis
DNA: DeoxyriboNucleic Acid
dsDNA: Double Stranded DNA
EDTA: Ethylene Diamine Tetraacetic Acid
g: Gram
gDNA: Genomic DNA
GTA: Gene Transfer Agent
h: Hours
HGT: Horizontal Gene Transfer
H2SO4: Sulfuric Acid
KCl: Potassium Chloride
kg: Kilogram
L: Litre
LB: Lysogeny Broth/Luria-Bertani broth
m: Metre
MC%: Moisture Content Percent
min: Minute
mL: Millilitre
µL: Microlitre
µg: Microgram
mg: Milligram
MOE: Ministry of the Environment (Canada)
MPN: Most Probable Number
MW: Municipal Water (i.e. tap water)
NA: Nutrient Agar
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LIST OF ABBREVIATIONS AND ACRONYMS CONTINUED
NaOH: Sodium Hydroxide
NCBI: National Centre for Biotechnology Information
NH3: Ammonia
NH4+: Ammonium
ng: Nanogram
NO3-: Nitrate
N2O: Nitrous Oxide
NTC: No Template Control
OD: Optical Density
ON: Ontario (province of Canada)
PBS: Phosphate Buffered Saline
PCR: Polymerase Chain Reaction
PDA: Potato Dextrose Agar
PL: Poultry Litter (bedding + excreta from poultry production)
PLA: Poultry Litter Extract Agar
PLUP: Poultry Litter Urease Producer
PMA: Phenylmercuric Acetate
PPS: Protein Precipitation Solution
qPCR: Quantitative Real-Time PCR
R: Richness (phylotype)
rDNA: Ribosomal DeoxyriboNucleic Acid
RFLP: Restriction Fragment Length Polymorphism
RNA: RiboNucleic Acid
rpm: Revolutions Per Minute
RT: Room Temperature
RT-qPCR: Reverse Transcription qPCR
s: Second(s)
SD: Standard Deviation
TDL: Theoretical Detection Limit
TE: Tris EDTA
TGGE: Temperature Gradient Gel Electrophoresis
THAM: Tris, tris(hydroxymethyl)aminomethane
x g: Times Gravity (relative centrifugal force)
UA: Christensen’s Urea Agar
UK: United Kingdom
UPGMA: Unweighted Pair Group Method with Arithmetic Mean
USA: United States of America
UV: Ultra Violet
V: Volt
VBNC: Viable But Non Culturable
X-gal: 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside
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CHAPTER 1: INTRODUCTION
1.1 Poultry litter (PL)
Biowaste recycling from agriculture is an important method used to retain nutrients
which is optimal for environmental and financial prudence. Animal manures are often applied to
agricultural land, to dispose of the manure, improve soil quality and to provide nutrients for
crops which helps to increase yields (Edwards and Daniel 1992; Amanullah et al. 2010). Of the
animal biowastes, poultry manure offers the highest quantity of nitrogen (N) and thus the most
potential for use as an organic amendment. Poultry litter (PL) is the waste mixture (excreta and
bedding materials) resulting from broiler production, and offers several advantages over raw
manure as an agricultural soil amendment, including: improved soil characteristics (e.g., water
holding capacity), enhanced nutrient availability and uptake by plants, and reduced damage to
foliage (Edwards and Daniel 1992; Amanullah et al. 2010). The high N content of the manure is
composed of undigested protein (30%), and uric acid (70%) – which is subsequently converted
to ammonia via microbial mineralization (Nahm 2003). The uric acid component is considered
fast mineralizing, occurring within a few days to several weeks following land applications
(Edwards and Daniel 1992), and plants preferably uptake inorganic forms such as NH4+.
However, the rapid mineralization is a concern for nutrient loss and environmental harm
including: eutrophication in aquatic environments from the oxidation of ammonia (NH3) to
nitrate (NO3-), acidification of rain and soils from NH3 gas, and greenhouse gas emissions of
nitrous oxide (N2O) from denitrification (Edwards and Daniel 1992; Van Horn et al. 1994).
2
1.2 Potential economic cost of N loss from PL
A meta-analysis of research on the properties of PL is summarized below (Table 1;
Edwards and Daniel, 1992). Further calculations are hereby derived from this work to yield
important information regarding the economic cost of N loss in PL. Given that about 70% of the
total organic N is uric acid, and each mole of which can be mineralized to four moles of NH3
(Nahm 2003), this indicates that a total of 14.2 g of NH3 per kg PL can be produced. Not all of
the available uric acid will be mineralized, nor will all of the NH3 be volatilized; however, the
enormous potential loss of nitrogen via volatilization, upwards of 60% of the total N (Kithome et
al. 1999; Rothrock et al. 2008a), can result in significant downstream costs since alternative
sources of N input would be needed. An estimate of $1 per kg of N (Agriculture and Agri-Food
Canada 2013) indicates that per kg of PL there is a potential loss of $0.014, and if worldwide
production of poultry is about 50 billion birds, each producing about 1.5 kg of litter (Perkins et
al. 1964), this indicates a potential nutrient shortfall with a cost of about $1B, which is about
12% of the global revenue for chicken exports (Iacobucci et al. 2006).
Table 1: Meta-analysis of poultry litter properties from literature findings, adapted from Edwards
and Daniel (1992).
Property Value (g per kg litter)
Water 245
Total C 376
Total N 40.8
Average C:N 9.22
Organic N 41.0
NH3-N 2.6
NO3-N 0.2
3
1.3 PL Composting
Composting is a microbiologically mediated thermal decomposition of organic wastes,
which include animal manures, crop litters, and other green biowastes (Maeda et al. 2011; Rawat
and Johri 2013). It has also been used in the bioremediation of harmful environmental pollutants
such as petroleum hydrocarbons (Barker and Bryson 2002; Atagana 2008). Composting is also
identified as a superior method of managing organic wastes compared to other means, by
reducing bulk size for easier transport, removing microbial pathogens and weeds, and managing
odours (de Bertoldi et al. 1983; Bollen et al. 1989; Brinson et al. 1994; Larney et al. 2003;
Amanullah et al. 2010; Rawat and Johri 2013). Benefits are observed following land application,
including a reduced environmental impact associated with nutrient loss, improved nutrient
availability to plants and water holding capacity of the soil.
The composting process is usually performed in windrows, bins (in-vessel), or static
piles, and kept aerobic either by passive airflow via convection or forced air (Edwards and
Daniel 1992). During composting there is a rapid succession within the microbial communities
between stages. There are two general compost stages, the first is a bio-oxidative stage
comprised of a short mesophilic period followed by a rapid transition to a thermophilic
environment and then a cooling stage (Bernal et al. 2009). The second stage is a
maturation/curing phase. The process may last several months, during which the compost is
turned periodically to maintain relatively homogeneous heating. Compost guidelines in Canada
stipulate that heating > 55°C for a minimum of 15 days is required in a windrow system for
inactivation of pathogens, along with culture based tests for Salmonella and E. coli (CCME
2005). A primary limitation to culturing methods is the inability to clearly indicate whether these
4
pathogenic species have been eliminated or suppressed since the cultivation does not account for
the viable but non-culturable (VBNC) state (Zaleski et al. 2005). The VBNC state of some
pathogens can be a contributing factor to pathogenic regrowth within the final compost (Elving
et al. 2010; Asano et al. 2011). As such, molecular-based methods would help alleviate this
problem, but have not yet been developed or widely adopted.
Studies on the nitrogen dynamics during PL and manure composting found that
significant losses of N (up to 60% of the total, or more) occurred during aerobic composting of
PL. These losses were almost entirely accounted for as a result of NH3 production, or conversion
from NH4+, and subsequent volatilization (Mondini et al. 1996; Kithome et al. 1999; Tiquia and
Tam 2000). Composted PL thereby has inherently lower N mineralization (Tyson and Cabrera
1993) and NH3 volatilization (Brinson et al. 1994) compared to fresh PL when applied to soil.
While prior composting is beneficial to agricultural lands and the surrounding environment,
research is needed to understand how to reduce the loss of N during composting.
1.4 Minimizing NH3 volatilization from PL
Reviews by Nahm (2003; 2005) succinctly outline the driving factors leading to NH3
volatilization from poultry wastes. These are: increased NH4+/NH3 concentrations, temperatures,
pH, and aeration, and decreased C:N ratios and moisture. These conditions will also affect the
microbiological activity and thus the mineralization leading to NH3 production. Targeting any or
all of the factors leading to NH3 loss should result in a higher retention of N in the compost.
5
While Elliott and Collins (1982) concluded that moisture content percent (MC%) was the
least important parameter influencing final NH3 formation in poultry manure, compared to pH
and temperature, significant increases in NH3 volatilization from manure composting can be
measured outside the range of 40 to 60% MC% (Nahm 2005). A common target MC% for
composting is between 50 to 60% (Bernal et al. 2009; Amanullah et al. 2010) which corresponds
to the maximal aerobic microbial activity at 60% (Linn and Doran 1984). Lower moisture
content thus reduces microbial activity and microbial N-mineralization; for example, a PL
incubation study found that at a lower MC% (around 35%) may be useful to retain high levels of
N in PL compost (Murakami et al. 2011). High MC% of PL (> 60%) will result in reduced NH3
volatilization due to both the effect of urea and uric acid substrate dilution, and increased acidity
from the production of organic acids from anaerobic conditions (Witter and Kirchmann 1989b;
Partanen et al. 2010) which limits conversion of NH4+ to NH3 and reduces microbial activity
(Nahm 2003). Anaerobic conditions due to the high moisture will result in reduced N loss but
malodours in the process and anaerobic conditions increase the length of time required for
composting; an increase of 25% was observed for poultry manure (Amanullah 2007).
The low carbon to nitrogen (C:N) ratio of about 10 in PL is not ideal for retention of N
(Edwards and Daniel 1992; Brinson et al. 1994; Amanullah et al. 2010). Optimal C:N levels for
composting are between 25-35 including for poultry manure (Nahm 2005; Bernal et al. 2009).
Increasing the carbon component of poultry manure can improve N retention. Although, other
studies of compost have shown that high carbon contents (e.g., C:N of 100) can lead to stronger
odours, reduced composting heating, and potentially reduced N retention as well (Larsen and
McCartney 2000; Liang et al. 2006). Additionally, manures and litters amended with readily
6
assimilable carbon such as glucose reduced NH3 volatilization (Subair 1995). A sucrose
amendment was similarly successful in dairy manure with straw (Witter 1986). The reduced NH3
is at least partially attributable to the repression of uricase expression by the presence of these
carbon sources (Shuler et al. 1979).
Ammonia volatilization has been shown to increase dramatically above pH 7-8 (Reece et
al. 1979; Elliott and Collins 1982). The alkaline pH of PL can be remedied by the addition of
acidic amendments, such as alum (aluminum sulfate), phosphoric acid, or elemental sulfur
(Mahimairaja et al. 1994; Moore et al. 1996; Kithome et al. 1999; DeLaune et al. 2004; Rothrock
et al. 2008b). Incubation studies of PL and poultry manure have shown these treatments reduce
the pH into the 4-5 range and concomitantly reduce NH3 volatilization significantly (50-75%). A
reduced pH acts both on the microbial communities, and chemically on the NH4+/ NH3
equilibrium; a lower pH favours NH4+ since the pKa for dissociation is 9.2 (Shuler et al. 1979).
The risk of using acidic amendments for PL composting is the reduction in microbial activity
during the mesophilic stage which extends the length of the process (Smårs et al. 2002), but
increases the final N content (Xie et al. 2012). The reduced microbial activity would certainly
also affect the microbially-mediated N mineralization due to the low pH and high temperatures
(Shuler et al. 1979; Cook et al. 2008); however, composting temperatures would also decrease.
While fixation of ammonia and ammonium onto organic materials occurs, reducing the
amount that is volatilized and lost from the compost, the NH4+ form is much more amenable for
immobilization in soils and onto organic matter. The immobilization of NH4+/NH3 in soils is
greatest between pH 7 and 10 and under aerobic conditions (Nyborg 1969). This greater amount
of fixation corresponds to the measureable reduction in ammonia volatilization below about pH
7-8 as mentioned earlier, indicating a potentially narrow range for optimal N retention.
7
Alternatively, inorganic amendments such as zeolite or sodium bisulfate have been successfully
used to adsorb NH4+/NH3 from PL (Li et al. 2013) or during PL and poultry manure composting,
reducing NH3 loss by about 50-60% (Witter and Kirchmann 1989a; Witter and Kirchmann
1989b; Mahimairaja et al. 1994; Turan 2009).
1.5 General microbiology of PL
A number of studies have examined the microflora of poultry litters, by culture based
approaches (Schefferle 1965a; Lovett et al. 1971; Martin et al. 1998; Terzich and Pope 2000; Lu
et al. 2003; Fries et al. 2005) and by molecular techniques such as PCR and sequencing or
community fingerprinting by DGGE (Lu et al. 2003; Lovanh et al. 2007; Rothrock et al. 2008b).
The results of these studies are summarized below (Table 2). These studies indicate there is
variability between litters, due to local conditions and as a result of sampling from different
geographical areas. Organic wastes such as PL are microbially rich, with a higher total bacterial
count (up to 11log CFU(g)-1
; Schefferle, 1965; Terzich and Pope, 2000) than found in soils (4-
9log CFU(g)-1
); Whitman et al., 1998; Robe et al., 2003; Delmont et al., 2011). Additionally, the
majority of these isolates are gram-positive, and often represented by the coryneform bacteria or
Staphylococcus spp. Of the fungi, moulds are more commonly found. The dominant genera are
Aspergillus, Fusarium, Penicillium, Mucor, Scopulariopsis, and Onychocola (Lovett et al. 1971;
Rothrock et al. 2008b). Pathogenic isolates, such as E. coli O157:H7 and Salmonella, appear to
be absent or infrequently found by culturing or molecular methods, but testing is still required in
order to meet safety standards. E. coli and Enterococci are both found in relatively high
abundance in litters, and are commonly used as indicator organisms for pathogen testing, such as
after composting.
8
Table 2: Summary of microbial surveys of poultry litters from diverse regions, by culture-based (cult) or molecular analyses. Culture-
based counts are represented per gram or as a percentage of the total. The results from PCR-DGGE represent sequencing of prominent
bands and do not reflect total abundances.
Ref (Schefferle
1965a)
(Lovett et al.
1971)
(Martin et
al. 1998)
(Fries et al.
2005)
(Terzich and
Pope 2000)
(Lu et al.
2003)
(Lovanh et
al. 2007)
(Rothrock et
al. 2008b)
Method Cult Cult Cult Cult Cult Bacterial
clone library
+ cult
DGGE DGGE and
fungal clone
library
Total bacteria 10log-
11log
8log-11log 4log-8log 7-10log 11log 9log ----- -----
Gram positive ----- ----- ----- 87.6% 50% 82% 67% - low
%GCa
33% - high
%GCb
38.5% - low
%GC
38.5% - high
%GC
Gram negative 10% ----- None-7log 12.4% 50% ----- none 23%
Staphylococci ----- ----- ----- 24.6% 9-11log 13% ----- -----
Enterococci 7log-8log ----- ----- ----- ----- 0.1% ----- -----
Coliforms ----- 5log-8log None-2log ----- 6-8log ----- ----- -----
E. coli ----- 5log-8log ----- ----- 5-10log 0.01% ----- -----
Total fungi ----- 2log-7log ----- ----- ----- ----- ----- -----
Moulds 6log ----- None-6log ----- ----- ----- ----- 85% -
Arachnomyces
nodosetosusc
15% -
Microascus
cirrosusd
‘-----‘ denote groups that were not examined in the study; a Bacillales, and Lactobacillales such as the genus Lactobacillus;
b
Actinomycetales such as the genera Arthrobacter and Corynebacterium; c teleomorph of Onychocola canadensis;
d teleomorph of
Scopulariopsis cinerea
9
1.6 Microbial N dynamics in PL
The process of mineralization converts organic nitrogen (e.g., uric acid) into inorganic
forms (e.g., NH3) through a pathway of microbially-mediated enzymatic steps. The focus of this
summary will be on the pathway of uric acid degradation, since the majority of organic-N in PL
is in the form of uric acid and urea (70-80% of the total), and is quickly mineralized and thus the
most important contributor to NH3 production (Edwards and Daniel 1992; Nahm 2003; Nahm
2005).
The production of uric acid is the result of purine catabolism, the breakdown of DNA
bases adenine and guanine (Vogels and van der Drift 1976). Similar to reptiles and humans, but
unlike other mammals, birds primarily excrete uric acid as a waste product. Once the excreta are
released into the surrounding environment, microorganisms capitalize on the influx of N by
converting uric acid to urea and subsequently NH3/NH4+ which they can assimilate for protein
synthesis or oxidize for energy. The generalized flow of N-species is outlined below (Figs 1 and
2). In aerobic conditions, the ammonia immobilized in the organic matter would be oxidized by
microbes to nitrate via nitrification (Kowalchuk et al. 1999; Maeda et al. 2011) then converted to
nitrogen gas via denitrification. Alternatively, ammonia is converted to nitrogen gas via
anaerobic ammonia oxidation (ANAMMOX; Graaf et al. 1995; Kartal et al. 2011). However,
ammonia assimilation by heterotrophic microbes, particularly during the early stages of manure
composting is more significant to N dynamics than nitrification (Sasaki et al. 2005).
10
Figure 1: Nitrogen flow of microbially-mediated processes relating to the production of
ammoniacal-N species. Boxes embolded indicate the areas of focus of this research. The process
of N mineralization by the microbial enzymes uricase and urease converts small organic
compounds uric acid and urea to ammonia (NH3) which can then be either assimilated by
microorganisms for growth, converted to nitrate (nitrification), directly converted to nitrogen gas
via anaerobic ammonia oxidation (ANAMMOX), or indirectly converted to N2 gas via nitrate
(denitrification). Figure modified from Geisseler et al. (2010).
1.6.1 Uric acid mineralization to NH3 (Fig 2)
The initial hydrolysis of uric acid is performed by a coenzyme-independent uricase (urate
oxidase; EC 1.7.3.3) under aerobic conditions in bacteria, archaea, fungi, and plants (Vogels and
van der Drift 1976). Two moles of urea are thus produced via intermediate reactions (Ramazzina
et al. 2006; Michiel et al. 2012), and further hydrolyzed by ATP-independent urease (urea
amidohydrolase; EC 3.5.1.5) to yield NH3 and carbamic acid (Mobley and Hausinger 1989).
Carbamic acid spontaneously decomposes into NH3 and CO2, to yield a total of four moles of
11
NH3 and two moles of CO2 for each mole of uric acid. Research on uricase and urease is by far
the most well-established steps of the pathway, owing to a century of past study. Both reactions
are often studied in terms of human health (Mobley et al. 1995; Ramazzina et al. 2006; Morrow
and Fraser 2013); such as gout (buildup of uric acid), urinary infections, ulcer formation or the
life-threatening meningoencephalitis (bacterial and fungal ureases). It is clear that in
environmental systems involving urea fertilizer and N rich materials such as PL that these
reactions are pivotal in relation to the nitrogen cycle and should not be understated (Bachrach
1957). Given the positions of the enzymes flanking the pathway, both the first and final
committed enzymatic steps leading to NH3 production and thus N-loss via volatilization are
critical. As such, research relating to the methods of reducing NH3 loss from composts and PL
has focused on inhibiting these microbial mediated reactions (Kim and Patterson 2003; Cook et
al. 2008; Rothrock et al. 2008b; Rothrock et al. 2010).
Figure 2: Generalized pathway of uric acid mineralization to ammonia. Uric acid is first
converted by uricase (urate oxidase, EC 1.7.3.3), producing two moles of urea via intermediary
reactions. Urea is then hydrolysed by urease (EC 3.5.1.5), for a total of four moles of ammonia
and two moles of carbon dioxide for each mole of uric acid. Figure modified from Bachrach
(1957) and Vogels and van der Drift (1976).
12
1.6.1.1 Uricase in microorganisms
The uricase enzyme is found in animals, plants, and microorganisms. While most
eukaryotic uricases require copper, the prokaryote enzymes are unique in that they do not utilize
co-factors (e.g., transition metals) for the catalytic reaction, only oxygen and water (Bongaerts et
al. 1978; Koyama et al. 1996; Imhoff et al. 2003). The uricase enzyme is found both
intracellularly and extracellularly (Saeed et al. 2004; Khucharoenphaisan and Sinma 2011;
Anderson and Vijayakumar 2012), and up-regulated by the presence of uric acid, and repressed
by its products and by catabolite repression from readily assimilable carbon sources (Bongaerts
et al. 1978; Shuler et al. 1979). In terms of stability, the uricase enzyme activity is variable
between species, and may either be stable at high temperatures or have a wide optimal pH range
between 6 and 9 (Huang and Wu 2004); uricase from Bacillus sp. TB-90 was reported to be both.
At alkaline pH and even above 6.4, a higher level of uric acid degradation was observed than at
low pH (5.7) in poultry wastes (Shuler et al. 1979). The average pH optimum for most uricases is
hypothesized to be 9 (Vogels and van der Drift 1976).
1.6.1.2 Urease in microorganisms
As with uricase, urease is ubiquitous across the kingdoms of life and is an important
enzyme relating to nitrogen cycling and as a virulence factor relating to human urinary health.
Urease has been well studied and is a model in many regards; Jack bean urease was the first
enzyme crystal structure to be resolved (Sumner 1926), and the first metalloenzyme observed to
contain nickel (Dixon et al. 1975). Urease has thus been used as a model enzyme (Collins and
D’Orazio 1993). Extensive reviews on urease enzyme and genetic regulation are available
(Mobley and Hausinger 1989; Mobley et al. 1995), and included in a recent review on nickel
13
metalloenzymes (Boer et al. 2013). X-ray crystallography of the urease from Klebsiella
aerogenes has helped reveal the general structure of bacterial urease as a trimer of three subunits
(ureA, ureB, ureC)3 with a binuclear active site thus two nickel atoms per enzyme (Jabri et al.
1995). The alpha subunit (ureC), which is the largest subunit, contains the Ni binding residues
and active site (Mobley et al. 1995). Phylogenetic protein sequence analysis has revealed that
these regions are quite conserved and thus have been successfully used as a target for molecular
studies (Hammes et al. 2003b; Koper et al. 2004; Gresham et al. 2007). Eukaryotic ureases differ
from their bacterial counterparts in that the subunits (ureA, ureB, ureC) are fused, and the alpha
subunit is homologous to the C-terminus of the protein (Mobley and Hausinger 1989; Mobley et
al. 1995). Microbial ureases also require accessory proteins (e.g., perhaps nickel acquisition or
other functions; D’Orazio and Collins 1993), of which there are four, commonly termed ureD,
ureE, ureF, ureG. Further characterizations may soon reveal how they contribute to the final
functional urease enzyme (Fong et al. 2013; Zambelli et al. 2013). All known microbial ureases
function intracellularly (Mobley et al. 1995; Hammes et al. 2003b); although, a rare report of a
functional extracellular urease from Arthrobacter creatinolyticus was found in the literature
(Ramesh et al. 2013). Urease regulation is variable between species, being either constitutive, or
driven by the surrounding environment; nitrogen status (presence of ammonia or urea), and pH
(Mobley et al. 1995).
Despite the vast amount of knowledge forthcoming regarding urease, there are many
questions to be answered. Recent work has outlined the potential function of the bacterial ureB
subunit to aid in the assembly and stability of the ureC metallocentre (Carter et al. 2011a); and
further work is certainly necessary to characterize the functions of the other urease subunits and
14
accessory proteins. Additionally, the prevalence of an active urease in Helicobacter mustelae
(found within the stomachs of ferrets) containing an alternate metal cofactor (iron) to nickel is a
fascinating discovery outlining the potential diversity of environment adapted ureases (Stoof et
al. 2008; Carter et al. 2011b; Carter et al. 2012).
1.6.2 Diversity of ureolytic and uricolytic microbes in PL
Most microbes utilize the products of urea hydrolysis directly; as a nitrogen source for
chemolithotrophic growth by ammonia oxidizing bacteria (AOB, such as Nitrosospira spp.) or
more recently Archaea (AOA; Alonso-Sáez et al. 2012; Lu and Jia 2013), or to raise the pH of
their surroundings (Koper et al. 2004) (e.g., H. pylori in the human stomach). Some research has
been completed to characterize urease for alternative purposes, such as a storage protein during
times of limiting nutrients (Zawada and Sutcliffe 1981), or as a source of ATP energy which was
uniquely found in Ureaplasma ureolyticum (Romano et al. 1980; Smith et al. 1993).
PL and urea rich environments are important in disseminating the diversity of ureolytic
microbes (Schefferle 1965b; Rothrock et al. 2008a). Ureolytic microbes have been cultured from
other environments as well, such as various soils (Lloyd and Sheaffe 1973; Hammes et al.
2003b; Al-Thawadi and Cord-Ruwisch 2012; Burbank et al. 2012), and water (Gresham et al.
2007). From the only known study of cultured ureolytic bacteria from PL, there was a
discrepancy between isolates that could break down uric acid to NH3 and those that could either
perform the initial steps (uric acid to urea) or the final decomposition of urea to NH3 (Schefferle
1965b). Many isolates of the genera Micrococcus, Corynebacterium, and Alcaligenes were
observed only as ureolytic; however, an even larger fraction of the Corynebacterium isolates
15
were solely uricolytic. From this research, 17% of the total bacteria were ureolytic, which
corroborated previous work which found that 17-30% were ureolytic (Lloyd and Sheaffe 1973).
Urea enrichment was not reported to affect the number of ureolytic bacteria since non-ureolytic
microbes are able to take advantage of NH3 produced from extracellular ureases.
Certain PLs may contain a dominant population of ureolytic Aspergillus spp. (Cook et al.
2008; Rothrock et al. 2008b); otherwise not much is known of ureolytic fungi in PL. While
urease activity is common among fungi (Navarathna et al. 2010), in urea rich environments
bacterial urease may be more dominant (Barua et al. 2012). In environments other than PL,
common ureolytic fungi include the members of the genera Penicillium, Neurospora, Fusarium,
Ustilago, Rhizopus, Coccidioides, Chaetomium, and certain yeasts (Hasan 2000; Strope et al.
2011).
Uricase is fairly common in microbes, and about 25% of total culturable bacteria from PL
were measurably uricolytic (Schefferle 1965b). Of these isolates, many belonged to the genus
Corynebacterium, and less frequently Nocardia, Streptomyces, Pseudomonas, Alcaligenes, and
Achromobacter. A small percentage of the total isolates (< 0.0002%) were anaerobic and
uricolytic. Other major uricolytic genera reported in PL are Bacillus, Arthrobacter and the
fungus Aspergillus (Ritz et al. 2004; Rothrock et al. 2008b). Other known uricolytic fungi
include members of the genera Aspergillus, Neurospora, Candida, Fusarium, and Penicillium
(Lehejčková et al. 1986; Anderson and Vijayakumar 2012).
16
1.6.3 Ureolytic activity in a compost environment
Studies involving poultry manure and PL measured the changes in urease activity
following applications to soil (Mian and Rodriguez-Kabana 1982; Martens et al. 1992; Tejada et
al. 2006; Tejada et al. 2007; Tejada and Masciandaro 2011), but no studies have looked directly
at PL or PL composting. However, urease enzyme activity, among other degradative enzymes
(e.g., cellulases, β-glucosidases, and phosphatases), have been used in composting studies to
examine the negative correlation of microbial activity and compost maturity (Benitez et al. 1999;
Mondini et al. 2004; Castaldi et al. 2008). Composting trials performed with municipal and plant
biowastes indicated that during the initial several weeks, urease activity increased more than
50% and peaked by week three as the temperature continued to climb. Urease activity declined
after week three, one week after the other enzymes, as the compost matured.
Intracellular ureases can be released into the surrounding area from lysed cells (Paulson
and Kurtz 1969; Mobley and Hausinger 1989). These extracellular ureases are adsorbed to soil
particles and thus persist as they are more resistant to microbial degradation. The impact of
extracellular ureases in the highly degradative compost environment is unknown, but in soil the
activity can be significant and may represent 50% or more of the total soil urease activity
(Paulson and Kurtz 1969; Klose and Tabatabai 1999). Urease activity is positively correlated to
levels of organic matter, among other parameters (Dharmakeerthi and Thenabadu 2013).
The high temperatures that develop during composting will also influence microbial
growth and activity. Uricases and ureases are quite variable in pH and temperature tolerance
depending on the species. These enzymes may either be stable at high temperatures or require a
17
narrow range of pH (Huang and Wu 2004). Certain ureases can be active from 0°C as observed
for Proteus mirabilis (Breitenbach and Hausinger 1988), to 65°C for Brevibacterium (now
Corynebacterium) ammoniagenes (Nakano et al. 1984), and 80°C in Providencia rettgeri
(Magana-Plaza et al. 1971) depending on the pH. Urease activity was thus found to be positively
correlated to moisture and temperature, peaking near field capacity and around 70°C (Lai and
Tabatabai 1992; Agehara and Warncke 2005), which correspond to near optimal conditions in
composts for ureolytic activity.
A search of literature revealed that no studies have examined the fate of uricase activity
during composting. Incubation studies have been used to approximate composting, and analytical
methods have been developed to measure changes in uric acid (Fujiwara and Murakami 2007;
Murakami et al. 2011) or its degradation product allantoin (Bao et al. 2008).
1.6.4 Treatments used to minimize NH3 loss from PL by inhibiting mineralization
Multiple approaches have been taken to reduce NH3 volatilization from PL by attempting
to control the uricolytic and ureolytic microbial activity. These have included enzyme inhibitors,
growth inhibitors, competitive inoculations, and metal adsorbent materials (Bachrach 1957; Kim
and Patterson 2003; Cook et al. 2011). Acidic treatments of PL (e.g., alum) that have been shown
to reduce NH3 volatilization may also be affecting the activities of urease, uricase, or the
intermediate enzymes. Uricase (Vogels and van der Drift 1976) and urease (Magana-Plaza et al.
1971; Mobley and Hausinger 1989) respond similarly to pH and are more active in alkaline
conditions. For example, at acidic pH urease activity is irreversibly lost due to nickel removal
from the enzyme (Schneider and Kaltwasser 1984), underscoring the threshold of pH 5 observed
18
for these enzymes, such as that from Brevibacterium (now Corynebacterium) ammoniagenes
(Nakano et al. 1984). Chitosan is capable of adsorbing metals, and was found to be as successful
as acidic treatments in reducing NH3 loss (Cook et al. 2011). Chitosan was hypothesized to bind
nickel which is required for urease function (Dalton et al. 1985) but also negatively affecting
uricolytic and ureolytic microbial populations by reducing the total available trace minerals by
non-specific adsorption. Heavy metals have also been shown to be deleterious to urease
(Magana-Plaza et al. 1971; Mobley and Hausinger 1989), but are unlikely to be used as PL
treatment options.
1.7 Molecular microbial analysis
1.7.1 Molecular versus culture based methods
Culture-based techniques have been quite useful in the identification and enumeration of
certain microbial species in composts, however these methods suffer as a result of our inability to
culture most (99% or more) microorganisms from the environment (Amann et al. 1995), and
many may be viable-but-non-culturable (VBNC; Oliver 2005). The development of molecular
based methodologies has in many ways superseded the requirement for culturability of these
important microbes, which thus represents an opportunity to more thoroughly analyze the
microbially-mediated environments. However, culture–dependent and culture–independent
studies are complementary. In the analysis of microbial richness in diverse environments, the
resulting overlap in species abundances and identities between several molecular methods may
result in less than 10% similarity, and only 1% when also compared with a culture-based
approach (Kisand and Wikner 2003; Wakase et al. 2008). Advances in molecular techniques
have enabled some very important developments in terms of a deeper understanding of microbial
19
diversity (von Wintzingerode et al. 1997; Hugenholtz et al. 1998). For example, Archaea were
observed to be quite abundant in soil environments, despite their previous moniker as
extremophiles (Bintrim et al. 1997). They are now studied more extensively in compost
processes (Yamamoto et al. 2010; Yamamoto et al. 2011; Zeng et al. 2011b). Novel microbial
phylotypes are being discovered in composts; species that may elude researchers using culture-
based approaches (Blanc et al. 1999; Dees and Ghiorse 2001; Bonito et al. 2010; Yamamoto et
al. 2010). Finally, it is important to concede that phylogeny does not necessarily correlate to
physiology (Zengler 2009), and thus the function of microbial isolates requires testing beyond
the level of DNA or RNA sequences. This lends to the importance of complementing molecular
microbial investigations with culture based approaches and in isolating species of interest in
order to fully explore their functional importance.
In the process of composting, it is important to analyze the abundance of particular
microbial species (e.g., pathogens) and the community diversity which changes as a function of
compost conditions (Larney et al. 2003; Tang et al. 2007; Danon et al. 2008; Adams and Frostick
2009). Culture-independent approaches of analyzing microbial communities have been
developed and utilized to evaluate the changes that occur during the composting process, as well
as the effect of amendments, processes, and quality of the composts (Table 3 section 1.7.5;
Herrmann and Shann 1997; Peters et al. 2000). While current methodologies have seen
appropriate application in many studies, it is crucial that robust extraction, purification, and
analytical protocols are continually optimized for varying compost substrates since universal
methodologies without biases do not currently exist (Figure 3 below). The initial sample design
and extraction protocol including the number and size of extractions will determine the limitation
20
on the subsequent molecular analysis (Figure 3 below). Often for environmental samples,
composite sampling is appropriate due to the large spatial scale and potential variance between
samples (Gilbert and Pulsipher 2005). Additionally, considering many of the extraction
methodologies that use small sample ‘mini-preps’, this too may introduce bias due to inadequate
environmental sampling at a large spatial scale.
Figure 3: Flow chart of the steps required for appropriate analysis of microbial communities in
diverse environments. The decision to perform culture-independent molecular methods requires
adequate understanding of the limitations of DNA/RNA extractions and purifications (influenced
by sample type) as well as sufficient optimizations prior to molecular analysis. Community
21
fingerprinting techniques are ideal for rapid profiling of general community members, whereas
deeper meta-analyses can provide information on the rarer taxa, which are likely missed by
fingerprinting.
1.7.2 Extraction of nucleic acids
There are a variety of methods for extracting nucleic acids from environmental samples.
It is outside the scope of this review to analyze all of the methods available to researchers and
there are many other resources on this topic already available (Hultman et al. 2010; Rajesh et al.
2011). The focus of this section is to outline important factors for optimal implementation of
molecular-based techniques on compost samples.
Composts such as PL will contain a considerable soil component, however certain clear
distinctions can be made from soil samples: (1) there is likely a greater concentration of
microbial biomass, based on an estimation of 104 to 10
9 CFU(g)
-1 in soil (Whitman et al. 1998;
Robe et al. 2003; Delmont et al. 2011) compared to 1010
CFU(g)-1
in composts (Dees and
Ghiorse 2001); (2) the composted material will contain a greater number of ‘humics’ (Howeler et
al. 2003), a by-product of decomposition (humification), which confers certain technical
difficulties to molecular analysis. These technical difficulties include the tendency of humics to
strongly bind to microbial cells and nucleic acids thereby biasing cell lysis and nucleic acid
extraction (Sharma et al. 2007; Levy-Booth et al. 2007; Albers et al. 2013).
There are a number of available methods for extracting metagenomic DNA from
environmental samples (Hultman et al. 2010; Rajesh et al. 2011). Firstly, a method using direct
22
lysis of microbial cells via bead beating is less biased and thus preferred to separation of cells
from sample then lysis (Steffan and Goksøyr 1988; Sharma et al. 2007). Secondly, commercial
kits are often preferred over manual methods based on ease of use and reproducibility.
Major constituents of the compost include humic acids, fulvic acids, and humins, many of
which are chemically ill-defined (Campitelli et al. 2006). Through cation bridging, DNA will
bind to these chemicals and resist degradation (Crecchio and Stotzky 1998). Enzymatic lysis and
proprietary silica-based spin column purification appeared to result in pure uninhibited DNA
from swine manure composted with sawdust (Wu et al. 2009). Other studies have also looked at
optimizing extraction and purification of DNA specifically from composts (Arbeli and Fuentes
2007; Yang et al. 2007), including a number of commercially available kits (Tian et al. 2013b).
Sufficient evidence of a high yield and pure extraction is required for the particular
environmental substrate in question in order to confidently assess the microbial communities,
within the limitations of the sampling protocol.
1.7.3 PCR inhibition limits effectiveness of molecular techniques
The tightly bound humics that co-extract with DNA have been shown to negatively affect
PCR analyses (Tsai and Olson 1992; Kreader 1996). PCR inhibition may not require a
substantial amount of these humic substances, and a relatively high amount can be co-extracted
in soil [1-35 ng(µL)-1
; Sharma et al. 2007], and potentially 10-100 times this amount in composts
(Howeler et al. 2003). A two-fold approach can be applied: initial purification of extracts to
remove humics; and alleviation post-extraction during PCR. A number of purification methods
have been described and with varying success; proprietary and non-proprietary methods,
23
ultrasonic pretreatment, sephadex columns, agarose gel electrophoresis, column chromatography,
polyethylene glycol 8000, polyvinylpolypyrrolidone, and others (Tsai and Olson 1992; Wilson
1997; LaMontagne et al. 2002; Robe et al. 2003; Yang et al. 2007; Zhang et al. 2011). While the
mechanics of PCR inhibition by humics are not well understood, it has been proposed to
manifest in several ways: directly interfering with the Taq polymerase, preventing annealing of
primers or binding of Taq to the target sequence, interaction with DNA, or chelation of
magnesium from the PCR mix (Tsai and Olson 1992; Young et al. 1993; Opel et al. 2009; Baar
et al. 2011). Experiments using additives such as bovine serum albumin (BSA) and T4 gene 32
protein to the PCR mix have been successful in relieving PCR inhibition (Kreader 1996).
Another simple solution has been to dilute the DNA extracts, sometimes up to 1:500 (Rothrock
et al. 2008a; Zeng et al. 2011b); however, this will also decrease the ability to detect low copy
target genes (i.e., decreased sensitivity). Determining and quantifying inhibition has been another
important step in improving the analysis of environmental samples. A generally applicable qPCR
assay has been designed, whereby serially diluted soil DNA extracts are added to a known
standard quantity of recombinant plasmid, and amplified using plasmid specific primers
(Schneider et al. 2009).
1.7.4 Sensitivity of PCR-based assays and safety of composts
Nucleic acid extractions of low efficiency and co-purification of PCR inhibitors will limit
the sensitivity of detection. Despite these issues, PCR-based assays still produce several fold
more sensitive results than a culture-based approach (Artz et al. 2006; Klein et al. 2011), and the
gap is widening further with development of inhibitor-insensitive assays such as digital PCR
(Hoshino and Inagaki 2012). Optimizations and high-throughput developments of these assays
24
will enable them to be more accepted by regulatory agencies. One potential pitfall of DNA-based
molecular detection is the persistence of PCR amplifiable extracellular DNA from lysed cells
(Artz et al. 2006). DNA adsorbs to soil particles and humic substances which protects the
molecules from nuclease degradation (England et al. 1998; Levy-Booth et al. 2007). The
persistence of extracellular DNA in composts has not been examined; however, compared to
studies in soil it can be estimated that there will be a higher rate of degradation due to elevated
temperatures and biological activity.
Inactivation of pathogens during composting is time-, temperature-, and substrate-
dependent (Larney et al. 2003; Asano et al. 2010; McCarthy et al. 2011), and the presence of
non-uniform or insufficient heating within the compost can produce refuges of protection and
thus pathogenic species such as E. coli, Enterococcus spp., Salmonella spp., may be detected
post-composting (Inglis et al. 2010; Elving et al. 2010; Asano et al. 2010), and moreso by
molecular methods should the pathogens enter the VBNC state. E. coli is routinely used as an
indicator organism for the presence of these pathogenic species, and to identify the effectiveness
of the treatment procedure, but Enterococcus has been considered for this role as well (Mohee et
al. 2008; Elving et al. 2010). Compost safety guidelines are based on culturability of these
microbes (CCME 2005), and standard molecular methods have yet to be developed.
1.7.5 Molecular methods used to study compost microbial communities
Analyses of compost microbial communities can be hindered by a lack of reproducibility
as a result of being a highly complex and variable bioprocess (Schloss et al. 2003; Schloss and
Handelsman 2005). To compound these issues, biases exist at each level of processing or
25
analysis: in DNA extraction methodologies, PCR amplification, and analysis (Steffan and
Goksøyr 1988; von Wintzingerode et al. 1997; LaMontagne et al. 2002). While it is known that
two molecular techniques may not produce identical results, perhaps similar ecological
conclusions can still be made (Hackl et al. 2004; Danon et al. 2008; Székely et al. 2009).
A significant amount of microbial community analysis in composts has focused on
fingerprinting techniques (Table 3). These methods have been developed as a snapshot overview
of diverse environments, capturing the most abundant organisms down to some of the relatively
less common species, which enables an evaluation of species diversity at a particular point in
time, and the ability to compare samples by a uniform metric. These techniques are often based
on PCR amplification of conserved regions of ribosomal genes (e.g., 16S for bacteria), or
specific gene targets. The PCR amplicons can then be separated by sequence to produce a unique
banding pattern via techniques such as denaturing gradient gel electrophoresis (DGGE; Muyzer
et al. 1993) among others (Table 3). Researchers have previously used DGGE to examine
microbial nitrogen cycling, targeting genes relating to ammonia oxidation (amoA; Yamamoto et
al. 2010; Zeng et al. 2011a), denitrification (nirS, nirK, nosZ; Maeda et al. 2010), and urease
(ureC; Hammes et al. 2003b). DGGE has become the default method of choice for microbial
ecology research (Ercolini 2004). Techniques based on the similarly amplified 16S rDNA gene
sequence are: clone libraries, microarrays, and the increasingly more available high-throughput
metagenomic sequencing.
Fingerprinting methods preclude rare taxa due to insufficient detection limits and
inherent biases in analysis. Only species that represent 1% or more of the community may be
identified by DGGE (Muyzer et al. 1993). While this skews accurate measurements of microbial
26
diversity, it still allows for informative relative comparisons between samples. Additionally, one
can see that a fundamental difference exists between fingerprinting and sequencing
methodologies. Fingerprinting is essentially screening for diversity, where low frequency PCR
amplicons (i.e., rare species) blend into the background noise. Metagenomic analysis, including
clone libraries and sequencing, represents sampling from this pool, which is a more accurate
measure of diversity (Bent and Forney 2008). Often in environmental research, the high spatial
scale of study does not permit a high resolution of the methodology due to constraints of
resources. There is inevitably a trade-off between costs and power of resolution of the data.
For known culturable microorganisms or identified phylotypes, researchers are able to
specifically quantify genomic DNA targets via quantitative real-time PCR (qPCR) or their gene
transcript expression via reverse transcription qPCR (RT-qPCR). Many of these techniques have
been described in detail elsewhere (Hultman et al. 2010; Stefanis et al. 2013). Without a priori
information on species-specific gene targets, qPCR techniques are limited to the general
detection and quantification of microbial groups (e.g., 16S rRNA gene for bacteria). These
assays are powerful, sensitive, and capable of detecting less abundant taxa with appropriate
primer sets. In composts, qPCR has been used to track microbial species throughout the
bioprocess (Yamamoto et al. 2010; Xiao et al. 2011a). A very large number of 16S primer sets
are available, for both end-point PCR and qPCR, and each may represent slightly different
estimates of bacterial diversity or abundances (Baker 2003; Huws et al. 2007). Additionally, 16S
rRNA operons can be present at a frequency of up to 15 copies per bacterial genome (Acinas et
al. 2004), but average frequency indicates a copy number of four per genome should be used
when describing total bacterial abundances in the environment (Klappenbach et al. 2001).
Similarly, fungal 18S rRNA copy numbers are variable (between 50 and 200 copies or more per
27
genome; Ganley and Kobayashi 2007; Haugland et al. 1999). In this sense, species, group, or
genus, specific primers may be more adept at defining abundance in diverse microbial
communities, but this also limits the scalability of the qPCR assays due to the greater time and
resources required.
28
Table 3: Several of the more common molecular methodologies and their intended purpose in the analysis of microbial communities
in composts. Techniques such as denaturing gradient gel electrophoresis (DGGE), a variant using temperature (TGGE), restriction
fragment length polymorphism (RFLP), amplified rDNA restriction analysis (ARDRA), and automated ribosomal intergenic spacer
analysis (ARISA).
Methodology Analyses of microbial communities
ARISA, DGGE Effectiveness of soil amendments and molecular techniques (Cherif et al. 2008)
DGGE Effect of compost storage (Klammer et al. 2005), compost temperatures (Tang et al.
2007), diversity of bacterial and archael ammonia oxidizers (Zeng et al. 2011b),
diversity of ammonia assimilators (Sasaki et al. 2005), diversity of denitrifying bacteria
(Maeda et al. 2010), effect of PCP contaminated soil amendment (Zeng et al. 2011a),
effect on PCP degradation following inoculation of Phanerochaete chrysosporium (Yu
et al. 2011)
DGGE, qPCR Continuous thermophilic composting and Actinomycete diversity (Xiao et al. 2011a),
diversity of archaeal ammonia oxidizers (Yamamoto et al. 2010)
DGGE, clone library Microbial inoculum additives (Wakase et al. 2008)
COMPOCHIP microarray Development of microarray for composts (Franke-Whittle et al. 2005)
COMPOCHIP microarray, DGGE, cloning Microbial diversity during curing (Danon et al. 2008)
Clone libraries Microbial succession (Tian et al. 2013a), effect of scale of composting (Partanen et al.
2010)
DGGE, clone libraries Archaeal ammonia oxidizers and methanogens (Yamamoto et al. 2010; Yamamoto et
al. 2011), identification of fungi in municipal waste compost (Bonito et al. 2010)
ARDRA, clone libraries Microbial diversity during thermophilic composting (Dees and Ghiorse 2001)
T-RFLP Correlation of physical and chemical properties to microbial diversity (Tiquia 2005)
T-RFLP, clone libraries Hyper-thermophilic pretreatment for effective composting (Yamada et al. 2008), spatial
heterogeneity of microbes (Guo et al. 2012), diversity of thermophilic bacteria in hot
composts (Blanc et al. 1999)
454-titanium pyrosequencing Identification of glycoside hydrolases relevant to the production of cellulosic biofuels
(Allgaier et al. 2010)
29
1.7.5.1 Molecular ecology of N mineralizing microbes
Environmental urease producers have been studied in aquatic and terrestrial environments
for basic nitrogen cycling research and even applied engineering and bioremediation. DNA-
based measures of quantifying or characterizing the ureolytic microbes are possible (Fujita et al.
2010), but hindered by the lack of suitable PCR primers capable of general urease amplification,
which is an ongoing issue (Reed 2001; Koper et al. 2004; Gresham et al. 2007). Few studies have
examined the diversity of ureases, but those that have been completed using clone libraries or
RFLP, have indicated an intriguing number of novel urease sequences that were previously
unidentified (Rothrock et al. 2008a; Singh et al. 2009; Collier et al. 2009). Previous work on
calcium carbonate precipitation by ureolytic microbes led researchers to culture a number of
bacterial isolates (Hammes et al. 2003b). These isolates were subjected to ureC PCR-DGGE
which led to the important discovery of several urease isoforms. This was later confirmed by
research on the ammonia oxidizing bacteria Nitrosospira sp. strain NpAV and Nitrosococcus
oceani (Koper et al. 2004). The extent of the DNA based work examining N-mineralizing
microbes in PL focused on changes in abundance of specific ureolytic and uricolytic groups as
proxies for the total populations following various NH3 reducing treatments such as alum to
acidify the litter (Cook et al. 2008; Rothrock et al. 2008a; Rothrock et al. 2008b; Rothrock et al.
2010). Ureolytic microbes were probed via PCR amplification of the poultry litter urease
producers (PLUP) group (representing bacteria) or Aspergillus spp. (representing fungi), and
similarly for uricolytic microbes (Bacillus and Arthrobacter for bacteria) and (Aspergillus for
fungi). Urease producers were often found to represent only 1% of total, a significantly lower
amount than found by culturing from soil (Lloyd and Sheaffe 1973). General fungal urease or
uricase primers were not found in the literature, likely due to difficulties associated with finding
30
conserved regions for primer design. Perhaps future genome sequencing projects will help reveal
gene targets for these studies and concurrently for evolutionary analysis of urease and uricases.
1.7.5.2 Poultry litter urease producer (PLUP)
In PL sampled from farms in Kentucky, Mississippi and Oklahoma, a single dominant
bacterial ureC isoform was found in a clone library sequencing project, and called poultry litter
urease producer (PLUP; Rothrock et al. 2008a). This group was uniquely found in the PL and not
in the surrounding areas sampled around the broiler houses. They represented between 0.1-3.1%
of the total bacterial population, but 90% of the ureolytic population. It was clear from sequence
comparisons that the PLUP group is itself composed of slightly different isoforms with around
95% nucleotide sequence similarities. Since this discovery, the species identity of the PLUP
containing isolates remains unknown, and this information would certainly be valuable for
ureolytic research in PL and perhaps more broadly in other environments as well.
1.7.5.3 Genomic studies of ureolytic microbes
Ureolytic activity can be variable and dependent on the local environment of the
microbial cell (Collins and Falkow 1988; Mobley et al. 1995; Heyndrickx 2004). For example,
the urease genes were not found within a genome survey of Bacillus licheniformis (Rey and
Ramaiya 2004), but the isolate was later identified as ureolytic by another research group
(Gresham et al. 2007). Variable urease activity has also been attributed to chromosomal
arrangement (Collins and Falkow 1988) of the urease operon, or the presence of active plasmid-
borne ureases (Mobley et al. 1995). Evidence of plasmid-borne ureases was found in a number
bacteria, including both gram positive and negative species, such as: Clostridium perfringens
31
(Dupuy et al. 1997); E. coli (Wachsmuth et al. 1979; Collins and Falkow 1990); Providencia
stuartii (Mobley et al. 1985); and members of the Enterobacteriaceae family (D’Orazio and
Collins 1993). These plasmids are often large in size (about 100 kbp) and include genes related
to human pathogenesis or antibiotic resistance. Since the last discovery in 1997, no further
reports in the literature were found regarding plasmid-borne ureases.
The discovery of ureases on mobile genetic elements supported the conclusions from
genetic sequencing that the urease isoforms often differ in phylogeny from each other (i.e., non-
congruent) and with other conserved genes such as 16S rRNA (Koper et al. 2004; Gresham et al.
2007). These mobile elements contribute to horizontal gene transfer (HGT) which is an
important phenomenon associated with prokaryote evolution (Ochman et al. 2000; Koonin et al.
2001). A selective advantage would certainly exist for bacteria that can incorporate functional
genes such as urease that increase their nitrogen acquisition; however, additional copies of urease
may not increase the inherent ureolytic activity of the wild-type species (Mobley et al. 1995).
Alternatively, urease genes may be located within the bacterial genome but transferred via gene
transfer agents (GTA) which are remnants of virus particles that encapsulate fragments of DNA
and enable their transfer between bacterial cells (McDaniel et al. 2010; Lang et al. 2012).
Finally, microbial cells scavenging for nutrients may also uptake and incorporate extracellular
DNA into their genome via homologous recombination, a potentially beneficial circumstance
which may even help to prevent Muller’s ratchet (Takeuchi et al. 2013).
32
1.8 Biodiesel production and biodiesel wash water (BWW)
This section will not serve as an extensive review of biodiesel production (see reviews by
Gerpen 2005; Vasudevan and Briggs 2008), but to identify an area of waste production that may
prove valuable for nitrogen retention in PL composting.
Biodiesel is diesel fuel derived from renewable biological sources, a significant
improvement for reducing CO2 emissions and elevating national energy security. Commonly, the
process involves the transesterification of triglycerides usually from vegetable oils with an
alcohol such as methanol using an alkali catalyst such as sodium hydroxide to produce fatty acid
methyl esters (i.e., biodiesel) (Fig 4). During biodiesel purification, a large quantity of biodiesel
wash water (BWW) is produced in order to clean the biodiesel of remaining catalyst, soaps, salts,
alcohol, or free glycerol, and as a waste represents between 10 and 15% of the total production
volume (Srirangsan et al. 2009; Lamers 2010). The BWW is characterized by high carbon [COD
of 150k-750k mg(L)-1
] and is often very acidic (pH of 1), thus requiring an added cost for
disposal since it is unsuitable for disposal in municipal systems. The use of technologies
(Srirangsan et al. 2009) or re-purposing to remove this waste product is of great importance to
the end-goal of sustainable and efficient biodiesel production.
33
Figure 4: Flow diagram of the general processing of biodiesel. The alcohol, oil and catalyst are
reacted to form biodiesel which is subsequently purified. Much of the methanol and glycerol are
recovered by purification. The neutralization of the catalyst and washing of biodiesel produces a
large quantity of biodiesel wash water (BWW) which requires disposal.
1.8.1 BWW and inhibition of microbial growth
Biological treatment of biodiesel wash water (BWW) is a potential method for
remediating the water prior to disposal, but studies have shown a significant inhibition of
microorganisms tested (Suehara et al. 2005; Lamers 2010). BWW is decidedly not nutrient rich,
with typical ratio C:N:P of 2,430:0.7:1, which may not be sufficient for many microbes.
Additionally, these studies indicate that it is likely the methanol, soaps [8k-800k mg(L)-1
], and
acidic pH which will have the largest effect on the microbial growth and activity. Compared to
bacteria, fungi are more tolerant to low pH and thus less dilution was necessary in order for fungi
to grow with BWW (Lamers 2010). The high abundance of solids in BWW [about 300k mg(L)-1
]
and low growth of microorganisms may be correlated. A BWW solids content of about 2.1k
mg(L)-1
was completely inhibitory to an oil degrading yeast Rhodotorula mucilaginosa (Suehara
et al. 2005). The high soap content of the BWW was suspected as the inhibitory substance, but
unconfirmed by the authors. Determination of these inhibitory substances may prove useful for
downstream applications that may require the control of microbial activity, or inclusively with
discarding of BWW. The use of BWW in these respects may be useful during PL composting for
reducing N-mineralization and control of ureolytic and uricolytic microbes.
34
1.9 Summary
Poultry manure and litters (PL which is manure and bedding material) are byproducts
from the production of poultry and can be significant sources of nitrogen when applied as
organic soil amendments (Edwards and Daniel 1992; Nahm 2003; Amanullah et al. 2010).
However, the majority of nitrogen can be lost in the form of ammonia (NH3) due to
microbiological mineralization of urea and uric acid (Kithome et al. 1999; Nahm 2003; Nahm
2005). Composting, and with acidic amendments to minimize nitrogen loss by reducing the
microbial mineralization (Shuler et al. 1979; Cook et al. 2008), may prove invaluable to
enhancing nutrient retention, and improving soil conditions for optimal plant growth (de Bertoldi
et al. 1983). Biodiesel wash water (BWW) is proposed as an amendment during PL composting
due to its high carbon content, low pH, and inhibition of microbial growth. The large quantities
of BWW produced (10-15% of production; Lamers 2010) are not suitable for municipal disposal
and thus as a PL amendment represent a secondary goal of water conservation. Currently there is
a knowledge gap of the microbial communities present in PL and PL composting, and
particularly those microorganisms cycling nitrogen in this environment. By exploring these
communities via molecular techniques, a vanguard assessment of the important microbes can be
made leading to future applied work, and not withstanding an assessment of the utility and safety
of a BWW amendment used in PL composting.
1.10 Research objectives
The purpose of this project was to explore aspects of the microbial communities within
PL; prevalence, identities, and how they are affected by the addition of BWW. While the
microbiology of composting has been well studied, community differences that exist within PL,
35
and especially following the additional of BWW, are to be evaluated. Prior to the use of BWW-
treated PL compost as an organic soil amendment, effective pathogen safety also needs to be
assessed. BWW or compost related factors affecting the microbial community will also influence
the prevalence and activity of ureolytic and uricolytic microbes, which are abundant in the
environment, but less well studied in PL. Nitrogen mineralization as a result of the uricase (urate
oxidase) and urease (urea amidohydrolase) enzymatic processes is well-established in the
literature and most suitable for characterizing the effects of BWW for this work. Urease activity,
as the committed and rate-limiting step to NH3 generation in PL, will also be a point of focus for
the current study.
Hypotheses:
1. The total microbial community structure will differ between BWW treated PL compost as
compared to the municipal water (MW) control; in that the BWW treated PL will become
more fungal dominant during composting due to acidification by BWW.
2. The BWW treated PL compost will be equally effective in controlling microbial
pathogens as compared to the MW control.
3. Following application of BWW to the PL compost, there will be a reduction in the
abundance of microbial groups mediating N mineralization and concomitantly a
reduction in total N mineralization, as compared to the MW control.
36
CHAPTER 2: MATERIALS AND METHODS
2.1 Pilot project: large scale composting of PL treated with BWW or MW
2.1.1 Materials
The BWW was collected from the biodiesel facility at U of Guelph Ridgetown campus
(Ridgetown, Ontario, Canada). BWW chemical characteristics were previously analyzed (U of
Guelph Laboratory Services, http://www.guelphlabservices.com). PL was collected from Cold
Springs Composting Facility (Putnam, Ontario, Canada).
2.1.2 General method
PL composting was performed by U of Guelph Ridgetown campus (Ridgetown, ON)
compost facility staff. The PL was composted over the course of eight weeks in triplicate
concrete channels. Each channel is 50’ (15.24 m) long 7’ (2.13 m) wide and 6’ (1.829 m) deep,
and held 12920 kg, 12760 kg, and 13810 kg, respectively. A forced aeration system pumped air
through grates underneath the compost for three min per hour. Increased airflow up to three min
per 10 min was needed if the compost temperature exceeded 60°C. The channels were divided in
two sections, one for each treatment, with a 10’ (3.05 m) buffer in the middle which was not
sampled. Application of glycerol (about 5% v/w L:kg) and either municipal water (MW;
treatment 2) or BWW (treatment 1) at about 50% v/w L:kg, followed by thorough mechanical
mixing (cleaned between treatments). Compost was turned several times throughout the eight
week process to ensure homogenous heating throughout, but still maintaining > 55°C
temperatures for several days. Additional municipal water was added to all composts to maintain
37
optimal moisture content (MC%) for composting (50-60%). Once the compost had returned to
ambient temperatures, the mature composted PL was left outside in a pile for several months.
2.1.3 Temperature measurements
Temperatures were recorded by Ridgetown staff, measured at the centre of the compost
pile using a 122 cm compost thermometer.
2.1.4 Sampling
About 2 kg samples were collected weekly from the compost in triplicate. Three samples
were taken from up to 30 cm deep, mixed in a clean pail, and placed in a Ziplock bag, and stored
at 4°C. Two 10 g sub-samples from each time point and treatment replicate were stored at -80°C
for molecular analyses, and the remaining PL compost stored at 4°C. The three main sampling
time points used for subsequent molecular work included the initial uncomposted PL, two days
post treatment, and the final mature compost (68 days). The remaining litter was utilized for
optimization of the genomic DNA extraction, procedure and isolation of poultry litter urease
producing bacteria.
From the PL compost stored at -80°C, three 100 mg samples (wet weight) from each of
the treatment replicates (36 samples), and one from each of the initial/control litters (six samples)
were used for gDNA extractions (total of 42 samples). A composite sample was then made for
the three extracts from each treatment replicate.
38
2.2 Optimization of genomic DNA extraction from PL
2.2.1 Initial comparison of commercial gDNA extraction kits
Changes were made to the protocols outlined by the manufacturer or from published
research to improve gDNA extraction to adequately compare the kits. Parameters initially tested
included: sample extraction size (50 - 250 mg depending on the kit), protocol variations, and
elution volumes (eluted in a single step or two; 100-200 µL total).
For the Powersoil (MoBio, Carlsbad, CA, USA) and Powerlyzer (MoBio) kits,
modifications to protocol: 10 min incubation at 70°C following C1 buffer, followed by 10 min
vortex, 3-5 min centrifuge, brief vortex, and brief centrifuge again. Final elution from spin filter
was done in two sequential 50 µL elutions with water into the same collection tube.
For the FASTDNA Spin Kit for Soil (MP Bio), modification to protocol: a 15 minute
centrifuge was used to pellet the debris after lysis; at step 6 the tubes were inverted not shaken;
elution of DNA from binding matrix used 150 µL sterile nuclease free water.
For SoilMaster (Epicentre Biotech, Madison, WI, USA), modifications to the protocol:
500 µL of soil DNA buffer instead of 250 µL; 300 µL added to spin column instead of 150 µL;
elution was in 100 µL water.
2.2.2 Pseudomonas sp. UG14Lr
The FASTDNA kit for soil was chosen for the current study, and further modifications
were made to the protocol to improve extraction efficiency by using inoculations of
Pseudomonas sp. UG14Lr in the litter followed by extraction.
39
2.2.3 UG14Lr growth conditions
Pseudomonas sp. UG14Lr is an environmental isolate obtained from soil and resistant to
rifampicin (Providenti et al. 1995). The strain was originally modified to include the luxAB
operon for detection (Weir et al. 1995). This strain was further modified via antibiotic resistance
selection on nalidixic acid amended media (van Frankenhuyzen 2010). Thus, the strain used in
this work possesses both rifampicin and nalidixic acid resistance. UG14Lr cultures were initiated
from frozen stocks onto tryptic soy agar (TSA) amended with rifampicin [100 µg(mL)-1
] and
nalidixic acid [100 µg(mL)-1
] and grown at 28°C. For further culture preparation, UG14Lr was
grown at 28°C in a minimal salts media (UG14 media; Providenti et al. 1995). For this study,
UG14 media was amended with nalidixic acid [100 µg(mL)-1
] and inoculated with a single
colony of UG14Lr grown on TSA. The culture was grown to an ODA600 between 0.3 and 0.8, and
triplicate direct cell counts were made using a haemocytometer. These cultures were utilized to
determine gDNA extraction efficacy and sensitivity of qPCR, as described below.
2.2.4 qPCR inhibitors from PL gDNA extracts
A similar procedure was used to test qPCR inhibition as performed by other researchers
(Lovanh et al. 2007; Schneider et al. 2009). Several different PL gDNA extracts were
individually tested for their inhibitory effects on qPCR amplification. PCR reaction mixtures
containing 8log copies of the luxAB standard vector were compared with and without addition of
the PL extracts. Extract volumes between 1-9.5 µL were tested, with 2 µL of BSA [10 mg(mL)-
1]. See section 2.5.2 for qPCR reaction conditions.
40
2.2.5 Extraction efficiency of the FASTDNA kit
These extraction efficiency tests were useful in finalizing the gDNA extraction protocol
(see gDNA below, section 2.3.1). Extractions of 8log UG14Lr cells were performed using the
FASTDNA kit, from 100 mg PL spiked with inoculum (+ PL) and pure culture without PL (no
PL) The initial extraction and subsequent washes/lysis steps were quantified separately using the
luxAB qPCR assay (see section 2.5.1 for primers) and summed in silico. To minimize inhibitors
in the early washes, 1:2 dilutions of the extract template were made along with the addition of 2
µL of BSA [10 mg(mL)-1
]. Extraction efficiency of the PL spiked samples (+ PL) were
calculated relative to the summed values of the control (no PL), and averaged from three separate
experiments.
2.2.6 Sensitivity of gDNA extractions and detection by PCR
UG14Lr cells (7.63 ± 0.69 x 103) and 10-fold dilutions down to 1:100000 were spiked
into 100 mg PL to determine the detection limits inherent to the modified FASTDNA extraction
protocol and qPCR assay.
Sensitivity was compared to the theoretical detection limit (TDL) of cells by PCR
(Pontiroli et al. 2011):
Where TV = PCR reaction volume, w = mass of sample, D = dilution factor, E = elution volume
Extraction efficiency for the theoretical limit equation was assumed to be 100%, although this is
not found in practice.
41
To further test the microbial detection limits of the litter, an experiment was performed
using a small inoculum of 6.67 ± 1.72 x 103 UG14Lr cells into 10 g PL to mimic the pathogen
detection limits required in compost for culturable E. coli at 1000 MPN(g)-1
(CCME 2005).
Following inoculation, the PL was mixed to distribute the inoculum, and six 100 mg samples
were collected, extracted and qPCR performed as described above. A template volume of 9.5 µL
was initially used and samples were re-analyzed using 1 µL template following the modification
of the reaction mix based on the inhibition tests (see section 2.2.4 above). The qPCR assay was
thus used to detect luxAB for each of the extractions.
2.3 DNA extraction methods used in this study
2.3.1 Final protocol for genomic DNA extractions from PL
Total genomic DNA was extracted from the PL and compost using the FASTDNA Spin
Kit for Soil (MP Biomedicals, Solon, OH, USA) using the following manufacturer protocol with
the following modifications:
1. A secondary and tertiary bead-beating step was done each with the addition of 900 µL of 0.2
M sodium phosphate buffer and 90 µL of MT buffer (both supplied with kit). Each of these
extracts were purified using separate 15mL tubes for binding matrix step and separate spin
columns.
2. An additional wash step using a ‘Humic Acid Wash Solution’ detailed by MP Biomedicals
was performed twice for each column. The protocol was as follows:
In a 1.5 mL microcentrifuge tube: 978 µL sodium phosphate buffer, 122 µL MT buffer,
and 250 µL PPS were added. The tube was centrifuged for 1 minute at full speed, and the
supernatant was transferred to a 2 mL microcentrifuge tube. An equal volume of a 5.5 M
42
guanidine thiocyanate solution was added and mixed well. 500 µL of this wash solution
was added to the silica in the spin column, centrifuged at 14 000 x g for 1 minute, and
catch tube was emptied. The washing was repeated until the silica returns to original
colour.
3. Each column was washed two additional times (three total) using the SEWS-EtOH wash. For
the poultry samples, the silica only returned to colour following the SEWS-EtOH washes.
4. Each column was eluted separately in 150 µL eluents. A composite sample was made
comprising at least one half of all three of the eluted volumes.
2.3.2 From pure cultures
Protocol as outlined by manufacturer in the Qiagen DNeasy Blood and Tissue Kit
(Qiagen, Venlo, Netherlands), with the additional pre-treatment cell lysis steps for gram-positive
bacteria.
2.3.3 Isolation of plasmid vectors from transformed E. coli
Selected transformed colony was grown overnight at 37°C in 5 mL LB with ampicillin
(100 µg/mL). Isolation of plasmids from E. coli was performed as per manufacturer’s protocol
for the Promega Wizard Plus SV Miniprep (Promega, Madison, WI, USA).
43
2.4 General molecular analyses
2.4.1 Determining DNA purity by spectrophotometry
Purity of DNA samples was measured using a Nanodrop 2000 spectrophotometer
(Thermo Scientific, Wilmington, DE, USA). Evaluation of DNA purity was compared to optimal
ratios of A260/A280 of 1.8, and A260/A230 of 1.5.
2.4.2 Agarose gel electrophoresis
PCR products were run on agarose gels in a BioRad mini-sub cell GT (Bio-Rad
Laboratories, Hercules, CA, USA). Gels were run at 7 V(cm)-1
using a BioRad PowerPac Basic
(Bio-Rad). The percent agarose was determined based on optimal resolution for the amplicons.
Agarose gels were visualized on a UVP transilluminator (UVP, Upland, CA, USA) and digitally
photographed using the Kodak Gel Logic 100 imaging system (Kodak, Rochester, NY, USA).
All purifications of PCR products were made using the Promega Wizard® SV Gel and PCR
Clean-Up System (Promega), eluted in nuclease free water.
2.4.3 Cloning
Purified PCR amplicons were ligated into the pGem-T-Easy vector system (Promega)
using the manufacturer’s protocol. Half of the ligation volume was used in the transformation of
Top10 E. coli competent cells (CaCl2 treated), followed by plating on LB agar containing X-gal
[40 µg(mL)-1
], IPTG (100 µM), and ampicillin [100 µg(mL)-1
], grown overnight at 37°C.
Blue/white colony selection was initially used for visualizing, followed by colony PCR (cPCR)
using the M13F/M13R or gene specific primers (see Table 4 below) for confirming insert.
44
Selected transformed colonies were grown overnight at 37°C in 5 mL LB with ampicillin [100
µg(mL)-1
] for plasmid isolation and for long term storage at -80°C in 25% glycerol.
2.4.4 Sequencing and phylogenetic analysis
Sequencing of PCR products and cloned amplicons was performed at the Genomics
Facility, University of Guelph (Guelph, Ontario, Canada), using either gene specific or vector
specific primers (M13F, M13R, T7, and SP6 primers were provided by Genomics Facility). For
total 16S rRNA gene sequences, forward and reverse sequencing reactions were required based
on its size (1.5 kbp). Between the two sequence runs, there was enough overlap to allow accurate
reading for the entire amplified fragment. Sequences were aligned to partial or complete
sequences from known organisms in the NCBI Genbank database (National Center for
Biotechnology information: http://ncbi.nlm.nih.gov) for identification via BLASTn, or BLASTp
(Basic local alignment search tool, BLAST; Altschul et al. 1990) once the sequences were
translated by an online tool (http://www.bioinformatics.org/sms2/). Nucleotide or protein
sequence alignments were performed using the online tool Clustal Omega (Sievers et al. 2011;
http://www.ebi.ac.uk/Tools/msa/clustalo/). Phylogenetic trees were computed using an online
tool phylogeny.fr (Dereeper et al. 2008; http://www.phylogeny.fr/), and the newick (.nwk) file
was exported for modification to the program TreeGraph 2 (Stöver and Müller 2010;
http://treegraph.bioinfweb.info/).
45
2.5 Molecular analysis of PL by PCR, DGGE, and qPCR
2.5.1 Primers used in this study
Table 4: List of primer sets used for PCR and sequencing, including the sequence of the forward and reverse primers (listed first and
second, respectively), the target and size of the amplicon, and the primer anneal temperature and final concentration (conc) used.
Primer Target Sequence (5’-3’) Size
(bp)
Anneal temp
(°C), conc (nM)
Ref
341F-GCa±
16S bacterial
universal
CCTACGGGAGGCAGCAG 190 55, 500 (Muyzer et al.
1993)c 534R ATTACCGCGGCTGCTGG
NS1-F 18S fungi universal GTAGTCATATGCTTGTCTC 330 55, 500 (White et al.
1990)c
NS1-R-GCb±
ATTCCCCGTTACCCGTTG (May et al. 2001)
Arthro-uric-F Arthrobacter-like
uricase
CGCAGAAGAACACGGTCTTC 96 60, 300 (Rothrock et al.
2010) Arthro-uric-R AAGCTGCTGGTGAAGTGATC
Bac-uric-F Bacillus-like uricase CGGGTGTGCACGCAAGATAT 92 60, 300 (Rothrock et al.
2010) Bac-uric-R ATGATCACCGGCCGTAAGAAA
ureC-QRT-F PLUP ureC TTCACACCTTCCACACCGAA 103 63, 300 (Rothrock et al.
2008a) ureC-QRT-R AACGTCGGGTTGGTCGAG
ALU 139 F Aspergillus-like
urease
ACCAGCCGCCATTGATACCTG 139 56, 300 (Cook et al.
2008) ALU 139 R GGTAGGTGTGGATTGTGCGGTTCT
ureC1-F-GCa¶ urease, ureC subunit AAGMTSCACGAGGACTGGGG 340 52, 500 (Koper et al.
2004)c ureC2R AGRTGGTGGCASACCATSAGCAT
Enterococcus
spp.
23S rRNA AGAAATTCCAAACGAACTTG 86 60, 600 (Haugland et al.
2005)c CAGTGCTCTACCTCCATCATT
Salmonella
spp.
himA CGTGCTCTGGAAAACGGTGAG 122 57, 500 (Chen et al.
2000) CGTGCTGTAATAGGAATATCTTCA
46
E. coli uidA AGCCAAAAGCCAGACAGAGT 140 53, 500 (Sandhya et al.
2008) CATGACGACCAAAGCCAGTA
C. jejuni ccoN TGGTCTAAGTCTTGAAAAAGTGGCA 81 60, 900 (Toplak et al.
2012) ACTCTTATAGCTTTTCAAATGGCATATCC
pUC/M13F
(-20)
Cloning vector GTAAAACGACGGCCAGT 230 45, 500 Promega
pUC/M13R CAGGAAACAGCTATGAC
luxAB-F luxAB AGGTGGTGCTCCTGTTTATGTC 106 62, 300 Unpublished
luxAB-R CTCGTGAGTGTTGATGATCCAG
GCa (Muyzer et al., 1993): 40mer GC clamp, sequence CGCCCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG
GCb (Das et al. 2007): CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCC
c PCR conditions modified
For ureC1F/2R, decreased primer annealing temperature from 55°C to 52°C
For 341F/534R as performed by Xiao et al. (2011)
For NS1-F/NS1-R as performed by Das et al. (2007)
For Enterococcus 23S, primer concentration was increased from 500 nM to 600 nM
± Primers 341F/534R and NS1-F/NS1-R were used without GC clamp for qPCR
¶ ureC1F/2R were modified for use in DGGE by attaching a 40mer GC clamp
47
2.5.2 Quantitative real-time PCR (qPCR) of nitrogen cycling microbes and pathogens
2.5.2.1 General procedure of qPCR
Reactions were run on 96 well plates using an iQ5 iCycler (Bio-Rad). All PCRs were run
in duplicate 20 µL reactions comprising 10 µL of 2x SsoFast EvaGreen Supermix (Bio-Rad), 2
µL of BSA [10 mg(mL)-1
], 0.5 µL of each primer (final concentrations outlined in Table 4), 4 µL
of 1:4 diluted template gDNA, and nuclease free sterile water. Although several of the published
protocols used TaqMan® probes, these qPCR assays were also suitable for dsDNA intercalating
dyes such as EvaGreen (similar to SYBR green). Additionally, protocols were modified to suit
the qPCR protocol as outlined by the SsoFast EvaGreen Supermix (Bio-Rad) for the iCycler. The
general two-step protocol was as follows: 2 min at 98°C, followed by 45 cycles of 5 sec melt at
98°C, and 10 sec anneal/elongation. A meltcurve followed the amplification to ensure specificity
of the reaction, from primer anneal/elongation temperature to 95°C ramping 0.5°C per cycle.
Baseline and threshold fluorescent values were automatically calculated by the BioRad
iQ5 optical software (Bio-Rad). Minor manual corrections of baseline fluorescence and the
threshold value were sometimes required. Ct threshold values were ensured to be above baseline
and within the linear range of the exponential amplification curve. No template controls (NTCs)
were about 4 Ct or greater than sample wells. In addition, the positive result cut-off used was 1
cycle beyond the average Ct for the lowest standard.
2.5.2.2 Plasmid standard curves used for quantification of samples
qPCR standards were constructed for all assays by cloning the PCR amplified gene
fragment from the species or genus of interest, which was confirmed by sequencing. The
48
absolute copy number of plasmids was calculated from the plasmid DNA concentration and the
molecular mass of the plasmid and insert, using the following formula (also available at
http://cels.uri.edu/gsc/resources/cndna.html):
( )
( )
2.5.2.3 Cell abundances and copy number calculations
Cell abundances were calculated as follows:
( ) ( )
( )
Where the gene copies/genome/cell is known or estimated, total copy number was calculated
from the standard curve , template volume is 1 µL (4 µL of a 1:4 diluted sample), elution volume
was 450 µL, and extraction size was 0.1 g (gDNA extraction method from PL). The gene copy
number per genome for most assays was one. However, 16S rDNA was estimated at four
copies/genome (Klappenbach et al. 2001); Enterococcus 23S rDNA was based on 4
copies/genome found in E. faecalis (Marshall et al. 2002); 18S rDNA at 100 copies/genome
(Ganley and Kobayashi 2007); and ureC was averaged at 1.5 copies/genome (Koper et al. 2004).
2.5.3 General end-point PCR
End-point PCRs were run in individual 0.2 mL thermocycling tubes on a BioRad C1000
thermocycler (Bio-Rad). Generally, PCRs were run in 25 µL reactions comprising 12.5µL of 2x
GoTaq Green Mastermix (Promega), 0.5 µL of each primer (final concentrations outlined in
Table 4), template, and nuclease free sterile water. An initial 3 minute denaturation step at 95°C
49
was used to activate the Taq polymerase, and a 10 min final elongation step was run at 72°C.
Annealing temperatures and elongation times were adjusted based on the specific PCR assay (see
Table 4 above).
2.5.4 End-point PCR for DGGE
PCRs for DGGE were 25 µL reactions comprising 12.5 µL of 2x GoTaq Green
MasterMix (Promega), 2 µL BSA [10 mg(mL)-1
], 0.5 µL of each primer, 4 µL of 1:4 diluted
gDNA template, and sterile nuclease free water. The PCR protocols used were as follows: 3 min
at 95°C, followed by 30 (bacterial), 40 (fungal), or 45 (ureC) cycles of 30 sec at 95°C, 52°C
(ureC) or 55°C (16S and 18S) for 30 sec (16S) or 1 min (18S and ureC), 72°C for 30 sec (16S)
or 1 min (18S and ureC), ending with 10 min at 72°C. PCR products were checked on agarose
gels to confirm a single band and no contamination. Four replicate PCR runs were performed per
sample; replicates were pooled and purified using the PCR Clean-Up System (Promega) as per
manufacturer’s instructions. Samples were eluted in a final volume of 40 µL, DNA
concentrations were determined by spectrophotometry, and stored at -20°C until analyzed.
2.5.5 DGGE
2.5.5.1 General procedure
DGGE gradients were optimized for each of the primer sets; the 18S fungal PCR-DGGE
protocol using primers NS1-F/NS1-R-GC was modified from (Das et al. 2007). The 16S
bacterial PCR-DGGE protocol using primers 341-F-GC/534R was modified from (Mahmoudi et
al. 2011). The ureC DGGE was not found in literature and is novel to this study. DGGE was
performed using 8% (w/v) (bacteria, fungi) or 7% (bacterial ureC) acrylamide gels containing a
50
linear chemical gradient ranging from 25-65% (bacteria), 5-40% (fungal), or 30-80% (bacterial
ureC). A 100% denaturant is defined as 7 mol(L)-1
urea and 40% formamide. A 25-well comb
was used, and in each well 1000 ng (18S, ureC) or 1200 ng (16S) of the PCR product was loaded
in the centre-most lanes. Standard lanes were loaded on either side of the samples; 3 µL of a 100
bp ladder (Fermentas-Thermo Scientific) was loaded on the left, and on the right a mixture of
PCR products from three species were used; lab strains of E. coli, Pseudomonas sp. UG14Lr,
and Enterococcus faecalis for the bacterial DGGE, and fungal isolates isolated from PL on
potato dextrose agar; Aspergillus sp., Debaryomyces sp., and Penicillium sp., for fungal DGGE
(no organism standard was used for ureC DGGE). These ladders were initially planned to be
used as external standards to compare across gels, however they were later only used for visual
comparisons and for identification of similar bands. Each DGGE was run in duplicate to ensure
reproducible results. The gels were run at 70V, 60°C for 16h in 0.5x TAE using the Dcode
Universal Mutation Detection System (Bio-Rad). DNA was stained with SYBR Green I (12.5x,
diluted in 25 mL 0.5x TAE), and visualized under UV transillumination using the GeneSnap
program (Syngene, Synoptics Ltd, Cambridge, UK).
2.5.5.2 Band identification: extraction, cloning, and sequencing
Bands that appeared representative of either treatment - those that appeared to increase in
abundance, stayed the same or appeared or disappeared - were excised using a 1000 µL pipette
tip (cut at the tip to make a wider mouth). Bands were placed into sterilized 1.5 mL
microcentrifuge tubes and left overnight at 4°C in 30 µL of sterilized distilled water to passively
diffuse out of the acrylamide. Eluted rDNA product was re-amplified using the same GC clamp
primers and run on a DGGE against an environmental sample to be confident that the proper
51
band was extracted. If the re-amplified band was confirmed as a single band, or with few
additional bands, the PCR product was ligated into a cloning vector, and then selected clones
were used to screen for the appropriate band. For screening clones, the colony PCR (cPCR) was
performed using the GC-clamp primer set, followed by DGGE run with several selected
environmental samples to compare against the clones (Gonzalez et al. 2003). For the 16S DGGE,
over 100 clones were individually screened. Once the clone harbouring the appropriate amplicon
was identified, sequencing and phylogenetic analysis was performed as outlined above in section
2.4.4.
2.5.5.3 Analysis of DGGE profiles
DGGE analysis was performed visually, and by the software program Gel Compar II
(Applied Maths, Sint-Martens-Latem, Belgium). Gel images that were exported from GeneSnap
(Syngene) were imported into the Gel Compar II program and optimized for resolution and
clarity of bands, including background subtraction (removes large background trends) and least
square filtering (Wiener cut off scale; removes very small peaks).
Once the lanes were defined, band searching was performed at a 2% minimum profiling.
Bands were also manually added which were undetected by the software. Additionally, smearing
at the top of the 16S gel resulted in inconsistent band demarcations, thus this area was not
analyzed. Following the band search, a band matching algorithm was used. A 2% tolerance level
was used for the fungal 18S and bacterial ureC gels, whereas a 0.5% tolerance was used for the
bacterial 16S. For all gels, a 0.5% gel optimization value was used (movement allowed for total
gel). Cluster analysis was performed using the DICE similarity coefficient and unweighted pair
52
group method with arithmetic mean (UPGMA). Band matching information was exported and
used to calculate species richness (R) for each lane profile.
2.6 Isolation of culturable ureolytic microbes from composted PL
2.6.1 Extracting viable microorganisms from PL
A sample taken mid-way through composting (stored at 4°C for 6 months) was used to
extract microbes since at that time, ammonia volatilization peaked (but lower than in previous
year’s work, data not shown), presumably as a result of a spike in PLUP growth and potentially
other ureolytic bacteria and fungi.
Extraction was performed similarly to others (Lu et al. 2003; Brooks and Adeli 2009).
Ten grams of poultry litter was suspended in 95 mL of 1x phosphate-buffered saline (PBS) and
then mixed for 5 min in a shake incubator (215 rpm, 28°C). The suspension was transferred to 50
mL sterile conical tubes (Fisher), and the large litter debris was separated from the PBS
microbial supernatant by low-speed centrifugation (50 x g for 15 min at 4°C, Sorvall Legend
RT+, Thermo Scientific). The supernatant was transferred to a new sterile 50 mL conical tube
and centrifuged (3,650 x g for 15 min at 4°C) to pellet the cells. The pellet was resuspended in 1
ml of 1x PBS, transferred to a sterile 1.5 mL Eppendorf and serial dilutions were prepared for
spread plating. Samples of the cellular pellet and of the supernatant were stored in 25% glycerol
at -80°C.
53
2.6.2 Media and culture conditions
Serial dilutions of bacterial cells were grown aerobically and in the dark at 28°C for up to
two weeks on the following media: poultry litter extract agar (PLA, see below), and nutrient agar
(NA). These media were cooled to 55°C after autoclaving and supplemented with cycloheximide
[50 µg(mL)-1
] to prevent fungal growth. Potato dextrose agar (PDA; Fisher) was used to cultivate
fungal isolates. PDA plates were also grown at 28°C. Additionally, PLA was used to grow
bacterial isolates in an anaerobic chamber (Ar with 2.5% H2).
PLA was a novel preparation of a modified soil extract agar (James 1958). In a 1 L media
bottle, PL was mixed with MilliQ water in a 1:1 v/v ratio (500 mL of each). Uncomposted PL
stored at 4°C was used in the media preparation. The mixture was autoclaved for 20 min at
121°C. The extract was left at room temperature (RT) to cool overnight. To pellet debris, the
extract was centrifuged at 3700 x g (Sorvall Legend RT+) for 10 min at RT. 450-500 mL of the
extract supernatant was retrieved, mixed with 15 g of agar, 0.2 g potassium phosphate dibasic,
and 1 g glucose, and made up to 1 L using MilliQ water. PLA was then autoclaved at 121°C for
20 min.
NA was also used to culture bacteria from the litter. In 1 L MilliQ water, 0.5% peptone,
0.3% yeast extract, 0.5% NaCl, and 1.5% agar, pH to 6.8. Autoclaved at 121°C for 20 min.
In addition, NA was made to include urea (8% final w/v) following the enrichment
protocol to enrich soil ureolytic bacteria (Al-Thawadi and Cord-Ruwisch 2012). The enrichment
media consisted of 10 g of yeast extract, 1 M urea, 152 mM ammonium sulphate, and 100 mM
sodium acetate dissolved in 1 L of water. Growth media without urea was autoclaved at 121°C
54
for 20 min. The urea solution was made separately using MilliQ filtered water, filter sterilized,
and added after cooling the agar to 55°C.
Christensen’s urea agar (UA) was used to indicate the presence of ureolytic organisms
via colour change of the pH indicator phenol red from yellow (pH 6.8) to pink (pH 8.2)
associated with the release of ammonia into the media. Urea plates were divided into sections for
each isolate, up to 16 per plate. Proteus vulgaris was used as a positive control, and E. coli as the
negative control. This procedure was repeated at a later date to ensure continued urease activity
of the freezer stocks, and to determine how quickly the colour changed.
2.6.3 Testing ureolytic capacity of cultured microbial isolates
Each day, growth of colonies was observed, and a number of colonies were selected for
sub-culturing on distinct ‘master plates’ of the same media type. Each colony was numbered on
the master plate and streaked on UA to test for ureolytic activity. A similar protocol was used for
culturing bacteria anaerobically, except only PLA was used.
2.6.4 Identification of positive ureolytic bacterial isolates
Genomic DNA was extracted from pure cultures of the bacterial isolates, followed by
PCR, cloning and sequencing of the partial ureC gene. In addition, the near full length 16S
rRNA gene was sequenced as well, using primers 8F and 1541R (see Table 4). Phylogenetic
analysis of the sequences was performed as outlined above in section 2.4.4.
55
2.6.5 Storage of cultured microorganisms
Long term storage of all bacterial isolates was attempted by growing them in nutrient
broth, growth media, or other media types. All positive ureolytic bacteria were cultured
successfully. No further work was performed on the fungal isolates; however, they were
carefully stored for potential future application by placing excised cultures from agar in sterile 2
mL cryogenic vials filled with sterile MilliQ water and stored at 4°C.
2.7 Microcosm incubation experiments
2.7.1 Materials
Materials used for the microcosm experiment were the same as those from the pilot scale
study; however, the PL and BWW were from new batches but were collected from the same
facilities and produced in the same manner. The MW was from the University of Guelph campus
(Guelph, Ontario, Canada).
2.7.2 PLUP cultures used for inoculations
Several of the recently cultured and identified PLUP bacterial isolates were used to
examine their effect on nitrogen dynamics associated with the ureolytic bacteria in PL. Two of
the PLUP isolates, one fast ureolytic (NA M3 col 9) and one slow ureolytic (PL M2 col 5), were
grown in LB broth for three days at 30°C, shaking at 160 rpm. The fast ureolytic NA M3 col 9
also grew faster and thus had a higher number of cells added to the PL. Cultures were
centrifuged to pellet the cells and mixed to form a final pellet that was resuspended in 0.25x PBS
to a final volume of about 16 mL.
56
2.7.3 Experimental design
Mesophilic compost temps were reached using a small incubator, at 37°C for the
experiment duration of four days. The incubation microcosms were placed in a fume hood to
control odours. The experiment was replicated three times.
Four treatments were tested in triplicate (one replicate per week): BWW +/- PLUP, and
MW +/- PLUP. Plastic 1 L containers were filled with PL (about 220 g), MW and BWW added
as done for the pilot scale study (50% v/w L:kg). PLUP was added several mL at a time followed
by mixing to produce a more homogeneous mixture.
The final MC% was aimed to be between 50-60%. After the initial treatments, no
additional applications of water were used to keep the composting litter moist (as done in the
2011 pilot study). Composts were turned daily before sampling using a sterile spatula, to collect
a representative sample and to introduce some oxygen into the microcosms.
2.7.4 Sampling from the microcosms
Four samples from the uncomposted PL were used to indicate the starting conditions
prior to the treatment applications. During the experiment, daily samples were taken: 1 g was
used to measure pH (determined electronically using a 1:10 w/v compost:water mixture) (Tiquia
2005), and 3 g for the MC% (dried at 105°C for 24h) (Tiquia et al. 2002). Litter was also sieved
(< 2mm) to collect 1 g for urea extraction, and 6 g for the urease activity assay (3 g for each of
the incubation and water control flasks). About 10 g was placed in a 50 mL sterile centrifuge
tube and stored at -80°C for future use.
57
2.7.5 Urea extraction and quantification
There were a total of 48 samples, 12 for each treatment type, and four samples from the
initial litter. The protocol for urea extraction was modified from (Rothrock et al. 2010; from
Douglas and Bremner, 1970). Briefly, 1 g of sieved (< 2mm) litter was placed in a 125 mL
Erlenmeyer flask, to which 30 mL of a KCl-PMA solution [2 M KCl + 50 mg(L)-1
]
phenylmercuric acetate) was added. Samples were shaken at 200 rpm for 1 hour, and then
vacuum filtered through Whatman 42 filters (Fisher) into side-arm flasks. The urea extract
volume was measured and transferred to a 50 mL centrifuge tube and centrifuged at 3700 x g
(Sorvall Legend RT+) for 10 min to pellet any fine particles. Subsequently, the extract was
aliquoted into two parts; one part was kept at 4°C for analysis during the experiment and the
other half frozen at -20°C for longer term storage.
Urea was quantified colorimetrically using the QuantiChrom Urea Assay Kit (BioAssay
Systems, Hayfield, CA) according to manufacturer’s specifications with several changes. Assays
were performed in clear, flat-bottomed, 96-well microtiter plates (Fisher Scientific), and 75 μL
from each urea extract was used per well. For each plate, urea standards [0.5, 0.25, 0.1, and 0.01
mg(mL)-1
] and blanks (KCl-PMA) were used. All samples, standards and blanks were run in
duplicate. The plate was incubated at RT for 50 min and then analyzed on a BioRad microplate
reader model 680 (BioRad) at 450 nm.
2.7.6 Urease activity assay
Methodology for determining urease activity was performed as outlined by Tabatabai and
Bremner (1972), modified as in Kandeler and Gerber (1988) and using KCl-PMA to stop the
58
reaction in lieu of acidified KCl, as done by Douglas and Bremner (1970). The scale of the
reaction was decreased from 5 g to 3 g of PL (sieved < 2mm). Litter was placed in a 50 mL
volumetric flask with stopper, to which 0.1 mL of toluene and 6 mL of THAM buffer (0.05 M;
titrated to pH 9 using 0.1 M NaOH and 0.2 M H2SO4) was added, and the flask was swirled for a
few seconds to mix. Then 0.6 mL of urea solution (0.2 M in the THAM buffer) was added to the
flask, and swirled to mix. The flask was stoppered and placed in incubator at 37°C for 2 h. The
stopper was then removed, and 30 mL of KCl-PMA was added and mixed to stop the reaction.
Mixture was set on counter to cool and settle debris, vacuum filtered through Whatman 42 filter
paper; two aliquots for storage were made as done for the urea extracts. As a control, another
flask was set up; however, water was added instead of urea prior to the incubation. The filtrates
for all samples and controls were then analyzed for ammonium to calculate the urease rate of
activity. Ammonium was measured by the indophenol blue method using the Seal Analytical
AA3 (Porvair Sciences, Leatherhead, England). Ammonium sulphate stock [1000 mg(L)-1
] was
diluted to 0 – 10 mg(L)-1
for the standard curve.
2.8 Statistical Analysis
Statistical analyses of extraction efficiency, gene abundance, band richness, and pH, urea,
and urease levels from the microcosm study were completed using Statistical Analysis Software
for Windows version 9.3 (SAS Institute, Cary, NC, USA). Gene abundances, urea, and urease
levels were log10 transformed prior to analysis. Data were examined for normality by graphical
methods such as normal probability plots, and by theoretical measures such as the Shapiro-Wilk
test. Data sets were analysed using a mixed model (Proc MIXED), two-factor ANOVA with
repeated measures. A multiple means comparison was done using a Tukey adjustment under an error
rate of less than 5%.
59
CHAPTER 3: RESULTS
3.1 Optimization of gDNA extraction and qPCR sensitivity of detection from extracts
3.1.1 Testing commercial gDNA extraction kits
Four commercial DNA extraction kits were examined for their efficacy of extraction
from PL. The quantity of DNA and purity were investigated initially by spectrophotometry
(Table 5) and then also by an end-point PCR inhibition test (data not shown). Evaluation of DNA
purity was compared to optimal absorbance ratios of A260/A280 around 1.8 for minimal protein
contamination, and A260/A230 around 1.5 for minimal salts. The first extraction tests followed
manufacturer protocols (see section 2.2.1), and although many of the kits suggest a maximal
extraction of 250 mg, this did not appear appropriate for the PL since it left minimal headspace
which is required for adequate bead beating. This was particularly reflected in the low
A260/A280 absorbance ratio indicative of low DNA purity for most kits. Of those kits tested, the
FASTDNA extraction kit for soil had the purest and highest quantity of DNA recovered.The
FASTDNA protocol was further optimized to maximize the extraction of DNA to improve the
downstream (qPCR) detection sensitivity. Based solely on absorbance data, an extraction of 150
mg and 150 µL appeared the most suitable for future work. However, subsequent findings that a
smaller extraction (100 mg) improved extraction efficiency (data not shown), likely as a result of
reduced inhibitors, indicated that the smaller sample should be used.
60
Table 5: Absorbance measurements of DNA extracts from a number of commercial kits.
Outlined below are sample amounts, elution volumes, spectrophotometric ratios, and DNA
concentrations (conc) calculated from the A260 value. Extracted DNA from the Powersoil and
Powerlyzer kits were eluted twice into the same collection tube.
Sample
(mg)
Elution
(µL)
A260/A280 A260/A230 Conc
[ng(µL)-1
]
Powersoil 250 50 0.57 0.26 24.23
50 0.67 0.26 19.23
Preliminary Powerlyzer 250 50 1.21 0.53 11.43
50 0.82 0.16 0.8651
SoilMaster 100 100 1.24 0.36 2.319
FASTDNA 250 150 1.47 0.09 60.66
Follow up FASTDNA 50 100 1.24 0.07 44.18
100 100 1.45 0.10 71.62
100 150 1.40 0.06 37.23
150 100 1.46 0.12 86.33
150 150 1.58 0.13 78.60
250 100 1.42 0.18 126.5
250 200 1.41 0.08 41.70
3.1.2 Improved extraction efficiency from PL using the FASTDNA kit
The final protocol for gDNA extraction using the FASTDNA kit was developed in
relation to understanding the extraction efficiency of this kit, which is to say the amount of
gDNA that can be detected or quantified based on the total that is in the sample. Optimizing the
genomic DNA extraction from the litter is important to accurately examine the microbial groups
for this study, found in variable concentrations in the litter, and pathogens in particular, which
are often in low abundance. The efficiency of gDNA extraction from 100 mg litter spiked with
8log Pseudomonas sp. UG14Lr (+ PL) using the FASTDNA kit for soil, compared to UG14Lr
pure culture (no PL) of the same quantity of bacteria. From these results, it was observed that the
typical procedure without additional ‘wash’ steps led to an extraction efficiency of about 37%
from the PL UG14Lr spiked sample (+ PL) compared to a nearly 100% extraction efficiency
61
with ‘no PL’ (Figure 5). The use of additional wash/lysis steps increased the efficiency but with
diminishing returns, achieving an almost 2-fold increase following a single additional wash,
almost 1.5-fold from the second additional wash, and thereafter no increases in efficiency (p >
0.05). Therefore it was determined, that by using two additional wash/lysis steps (three total per
sample) per sample, purified and eluted separately, the efficiency increased by almost 3-fold to
100%, the same level seen from the initial extraction of ‘no PL’. Initial PCR screening of the PL
indicated that the luxAB gene was not found, confirming that the detected amplification by PCR
is in fact only from the UG14Lr inocula.
62
Figure 5: Efficiency of gDNA extraction from 100 mg litter spiked with 8log Pseudomonas sp.
UG14Lr (+ PL) using the FASTDNA kit for soil, compared to UG14Lr pure culture (no PL) of
the same quantity of bacteria. Two additional wash/lysis steps were utilized to obtain an
efficiency of 100% from PL. This experiment was repeated three times, with the average
efficiency labelled on the graph and standard errors are shown. The PL samples that were
significantly different from the pure cultures are identified by asterisk (p < 0.0001).
3.1.3 Sensitivity of DNA extraction and detection by qPCR
Pseudomonas sp. UG14Lr cells were again used as a proxy for microbial cells in litter to
determine the lowest concentration of cells(g)-1
that is detectable or quantifiable by qPCR
following the use of the finalized FASTDNA extraction procedure. From preliminary PCR
inhibition tests, BSA [800 ng(µL)-1
] was shown to alleviate inhibition.
The theoretical detection limit, TDL (Pontiroli et al. 2011), for this work was calculated to be:
g
The first experiment identified the difficulty of detecting luxAB gene copies nearing the
TDL. Increasingly dilute UG14Lr cell cultures (7.63 ± 0.68 x 103 cells down to 1:1000000 fold
diluted) were inoculated into PL and extracted for this test (Table 6). The initial inoculation was
63
accurately quantified from the extracts (7.53 ± 3.73 x 103 gene copies) with no loss in copy
number based on the inoculation quantity (p > 0.05), thus supporting the prior conclusion of
100% extraction efficiency. The 1:10 and 1:100 dilutions, at the threshold of the TDL, were also
positively detected; however, the 1:10 dilution (3.48 ± 2.31 x 103 gene copies) and the 1:100
dilution (1.30 ± 0.594 x 104 gene copies) were overestimations and not entirely consistent with
the dilution. At these low copy numbers, the qPCR results are at the limit of detection
capabilities, and more sensitive to inter-well carryover. Below the 1:100 dilution, the number of
positive signals continually decreased and thus were not quantifiable. These results suggest that
the gDNA extraction procedure and qPCR assay to be used in this study should detect a
particular target, for example a particular microorganism, approaching the TDL of this matrix.
Table 6: Quantification of luxAB copy number from gDNA extractions from 100 mg of PL
inoculated with increasingly diluted Pseudomonas sp. UG14Lr stock culture.
Dilution Expected copies (± SD) Total copies (± SD) Positive Signals
NA 7.63 (± 0.68) x 103 7.53 ± 3.73 x 10
3 3/3
1:10 7.63 (± 0.68) x 102 3.48 ± 2.31 x 10
3 3/3
1:100 7.63 (± 0.68) x 101 1.30 ± 0.594 x 10
4 3/3
1:1000 7.63 (± 0.68) x 100 N/A 2/3
1:10000 < 1 N/A 1/3
1:100000 < 1 N/A 0/3
A second experiment examined the detection of UG14Lr cells in 10 g of PL. The
UG14Lr cells were spiked to a final concentration [6.67 ± 1.72 x 102 cells(g)
-1] matching the
standard pathogen limits for E. coli detection [1 x 103 CFU(g)
-1; CCME 2005]. The results of the
six gDNA extractions from the 10 g of PL are shown below (Table 7). Based on the current
methodology, it was difficult to detect the luxAB gene from UG14Lr at this inoculum
concentration. The initial qPCR assay was based on a template volume of 9.5 µL; however,
64
further testing had shown that a template volume of 1 µL was necessary to alleviate PCR
inhibition for accurate quantification. Comparing the two template volumes, the reduced
template volume decreased the ability to detect luxAB in low abundance but increased the
accuracy of quantification (data not shown). Perhaps for presence and absence determination, a
larger template volume might be suitable.
Table 7: Sensitivity of gDNA extraction protocol and qPCR to detect bacterial cells at
concentrations similar to the pathogen detection limits for E. coli in compost [1 x 103 CFU(g)
-1;
CCME 2005]. A sample of 6.67 ± 1.72 x 103 UG14Lr cells were added to 10 g PL and mixed.
Quantification by qPCR was performed twice (template amounts 9.5 µL and 1 µL) with three
replicates for each of the six gDNA extractions.
Template 9.5 µL Template 1 µL
Positive qPCR replicates 15/18a 7/18
b
Positive extractions 4/6 2/6 a three negative wells; two from one sample and one from another
b the positive wells were concentrated in two samples, and one positive well from another sample
3.2 Pilot project: large scale composting of PL treated with BWW or MW
3.2.1 Compost temperature
Temperature measurements were recorded by the Ridgetown compost facility staff and
provided for reference after the end of the composting trial. The temperature profiles of each
treatment differed during composting (Fig 6). Generally, the MW treated litter proceeded more
rapidly to a thermophilic temperature and maintained a higher temperature for the duration of
composting. The BWW treated litter was slow to increase in temperature, with fewer heating and
cooling cycles than MW, and slowly reached an average thermophilic temperature around 50°C
several weeks after MW litter. One of the three BWW replicates did not reach temperatures
65
above 55°C necessary for pathogen and weed control. A difference was also seen during the
mesophilic stage during the first several weeks of composting. The MW treated PL maintained a
higher mesophilic temperature (40-45°C) than BWW (low 30°C’s), before briefly dropping to
30°C for both treatments, prior to the rise in temperature in the thermophilic stage.
Figure 6: Temperature profiles of poultry litter during composting from the start July 14 2011 to
Sept 20 2011. Temperature was measured several times per week using a 122 cm compost
thermometer. Average temperatures were calculated for the three replicates per treatment, and
standard deviations are shown.
Composting time (days)
0 20 40 60 80
Tem
pera
ture
°C
0
10
20
30
40
50
60
70Ambient temperature
CG-BWW
CG-MW
66
3.2.2 Quantification of microbes in pilot scale composting
Quantification of microbial groups, pathogens (e.g., Salmonella, Campylobacter) and
nitrogen cycling microbes (e.g., ureolytic bacteria and fungi) was performed to assess both the
safety of the final BWW treated PL compost as well as the impact on the microbes responsible
for nitrogen volatilization during composting.
3.2.2.1 General and nitrogen cycling microbes
The BWW and MW treatments were compared for their effects on the abundance of
general microbial communities and specific nitrogen cycling microbes, at the three time points
examined (initial, two days post-treatment, and final). Genus and group specific qPCR primers
were used to target important nitrogen mineralizing microbes, and general bacterial and fungal
primers indicate community sizes for comparison. By these measures, differences were observed
between treatment types BWW and MW, and between time points within each treatment (Fig 7).
By the final compost, total bacteria increased in abundance in MW by 110% from the initial
compost (p < 0.05), and is significantly higher than the total bacteria found from BWW
treatment (p < 0.05). The fungal count increased 1000% in BWW (p > 0.05) and decreased 75%
in MW (p > 0.05) by the final compost sampling, but the difference was not significant between
treatments in the final compost (p > 0.05). In terms of uricolytic microbes, Bacillus spp.
decreased in abundance for both treatments, however in MW it was significantly lower (81%
decrease, p < 0.05). Similarly, Arthrobacter spp. abundance was significantly different between
treatments by the final compost sample. This was due to a significant decrease from BWW (61%
decrease, p < 0.05). The ureolytic microbes, Aspergillus-like fungal urease and PLUP bacteria,
responded similarly. There were no differences between the treatments BWW or MW (p > 0.05),
67
however both groups significantly decreased (98% and 79%, respectively) in abundance in the
final MW treated compost compared to the initial litter (p < 0.05). The Aspergillus counts
differed significantly (p < 0.05) between treatments at the initial time point, which was unique to
all other assays, and unexpected since the PL was considered very similar at this point.
Figure 7: Cell abundances of nitrogen cycling microbes found in composted poultry litter
amended with either BWW or MW. Three sampling time points are represented: initial PL (I),
post-treatment (PT), and final compost (F). Values from the three treatment replicates were
averaged and positive standard deviations are shown (error bars on graph). Significant
differences (p < 0.05) between treatments (*), and between initial and final time points (**) are
indicated.
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3.2.2.2 qPCR – Pathogens
The pathogenic groups were generally much lower in abundance, if detected at all, than
the nitrogen cycling microbes (Table 8). The Enterococcus group was found at the highest
concentration, about 9log cells per gram litter. The abundance of this group decreases
significantly by the final compost sample for both treatments (p < 0.05). This occurred more so
for the MW treatment, with a 95.0% decrease in cell counts compared to 63.5% in BWW (p <
0.05). Escherichia coli was also quantifiable in the litter for both treatments, and found to be
about 5log cells(g)-1
. Despite the 86.9% decrease in cell count by the final compost for BWW,
this was not significant due to a large standard deviation in the results from the uncomposted
litter. However, the cell counts from the MW final compost sample were not significantly
different from BWW (p > 0.05), but significantly lower than from the initial litter with a 93.5%
decrease (p < 0.05). Statistical analysis could not be completed for the Salmonella and C. jejuni
results since not all treatment replicates showed positive qPCR results. In general there was a
lack of Salmonella detection in the compost, based on the limit of detection of about 103 cells(g)
-
1. Only 1 of 3 treatment replicates were shown to have a positive detectable result for each of the
treatments (Initial for BWW, and PT for MW). For the samples that were positive, the results
indicated the presence of these cells about 5log per gram. No Salmonella were detected in the
final compost samples for either treatment [< 103 cells(g)
-1]. Campylobacter jejuni was similarly
found on the order of about 5log cells(g)-1
, and were detected more often than Salmonella. No C.
jejuni targets were detected in the final compost of MW, and although the cells were detected in
BWW [7.67 ± 7.48 x 105 cells(g)
-1] there was high variability of abundances between replicates.
69
Table 8: Quantification of pathogenic groups at three time points (Initial, PT – 2 days post-
treatment, and F - final) from the pilot scale study of BWW and MW amended composted
poultry litter. Genus or species specific primers were used to target a number of different
pathogens.
Treatment [cells(g)-1
± SD]
Time Target [cells(g)-1
] BWW MW
Initial Enterococcus spp. (x 109) 1.74 ± 0.910 2.61 ± 1.44
PT 2.05 ± 1.03 1.33 ± 0.730
Final 0.636 ± 0.288*≠
0.131 ± 0.0576*≠
Initial E. coli (x 105) 31.5 ± 33.5 10.8 ± 5.74
PT 5.98 ± 3.22 9.16 ± 4.97
Final 4.12 ± 1.63 0.705 ± 0.551≠
Initial Salmonella spp. (x 105) 2.53 ± 0.232
a ND
¶
PT ND 0.792 ± 0.223a
Final ND ND
Initial C. jejuni (x 105) 47.7 ± 21.0
b 15.4 ± 11.1
PT 3.83 ± 1.29 9.27 ± 5.61
Final 7.67 ± 7.48 ND a only compost replicate 2 and 3 were positive for BWW and MW, respectively
b for Initial time point, only BWW replicates 1 and 2 were positive
* significant difference found between treatments (p < 0.05)
≠ significant difference found between initial and final sample (p < 0.05)
¶ ND, not detected. Below limit of detection of around 10
3 cells(g)
-1
3.2.3 Denaturing gradient gel electrophoresis (DGGE)
Microbial community changes in response to BWW and MW PL treatments were
analyzed via DGGE: bacterial (16S rRNA), fungal (18S rRNA), and bacterial ureolytic (ureC)
genes. The same genomic DNA extracts were used for PCR-DGGE as done for qPCR, as well as
the PCR primers for bacterial 16S and fungal 18S but with the added GC clamp. The number of
unique phylotypes (richness) was assessed, as well as sequence information of the prominent
bands observed.
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3.2.3.1 DGGE - Fungal 18S rRNA gene (Fig 8)
3.2.3.1.1 18S rRNA gene DGGE analysis
Pure fungal isolations from poultry litter samples were used as standards for the fungal
DGGE experiments and to improve the identification procedure for DGGE bands. These isolates
are represented by DGGE bands X, Y, and Z (Figs 8, 10; Debaryomyces or Candida sp.,
Aspergillus niger, and Penicillium spp., respectively). Sequencing revealed their identities (Fig
10). The fungal isolate known as band Y was of particular interest since it is the same fungus that
grows in blooms in PL, even at cool temperatures (4°C). This band is not dominant in the later
fungal communities, but is present in the early time points. Fungal isolates represented by bands
X and Z are more ubiquitous and remain mostly unchanged throughout composting and between
treatments.
The fungal DGGE profiles (Figs 8, 9) indicate that the compost process (i.e., sampling
times) had a larger impact on the fungal communities than the treatment type. The treatment
replicates did not cluster distinctly, such as replicate 1 from BWW final compost (BWW-1-F)
compared to replicates 2 and 3, but the clustering of sampling time points irrespective of
treatment type was clearly distinguishable. While the final (F) compost samples clustered
distinctly, the initial (I) and post-treatment (PT) samples clustered together. The fungal
communities were perhaps resistant to the changes associated with the addition of BWW
compared to MW.
71
Figure 8: Fungal 18S rRNA gene DGGE. Composite PCR products were used to represent each
of the three replicates for both treatments, MW and BWW. Three sampling time points are
represented chronologically: initial PL (I), post-treatment (PT), and final compost (F). On either
side of the sample profiles are the visual ladders used; 100 bp molecular weight ladder (M1), and
the fungal strains (M2; bands X, Y, Z, Debaryomyces or Candida sp., Aspergillus niger, and
Penicillium sp., respectively). Extracted bands of interest, D and A, are also shown.
72
Figure 9: Fungal 18S rRNA gene DGGE. Composite PCR products were used to represent each
of the three replicates for treatments BWW and MW. Three sampling time points are
represented: initial PL (I), post-treatment (PT), and final compost (F). Bands of interest, D and
A, are identified and correspond to Figs 8 and 10. Profile clustering was performed using
UPGMA and the DICE similarity coefficient. Bootstrap values (valued out of 100) are reported
on the nodes as a measure of the cophenetic correlation.
3.2.3.1.2 Band isolations and sequencing
For bands not represented by the cultured isolates described in the previous section, band
isolation from the DGGE gels was performed. Bands excised and purified from a gel were re-
amplified by PCR and run again on another gel to ensure single bands were observed. However,
multiple bands were often detected. After several attempts and with the addition of a cloning
procedure, two isolated bands were sequenced (Fig 10; bands A and D). Following this work it
73
was concluded that cloning and screening the clones was the best way to isolate single sequences
from the DGGE.
The two bands that were successfully isolated are noted as dominant community
members due to their high band intensities (Figs 8, 9; bands A and D). However, the conditions
present in the final composted PL treated with MW adversely affected the species represented by
band D (Fig 10; potentially Scopulariopsis brevicaulis), and more so than in BWW, and the band
intensity is diminished compared to earlier time points. Band A (Fig 10; Candida sp.) is also
unique in that it was not found ubiquitously across time in the composts, nor within treatment
replicates. Interestingly, it was only found in replicate three of the final compost samples for
both treatments.
74
Figure 10: Dendrogram of 18S sequences obtained from DGGE and relevant Genbank results
from BLASTn searches. Species names were given when known, and the Genbank accession
numbers were placed to the right of the names. Branches were collapsed with less than 50%
support (unlabeled branches).
3.2.3.1.3 18S phylotype richness
There was a declining trend in fungal phylotype richness (Fig 11) as the litter proceeds
through composting, from an average of 31 bands down in the initial PL to 22 bands in the final
compost (a 30% decrease); however, no significant differences were found at any time point or
between treatments (p > 0.05). Several lanes had inconclusive numbers of bands as a result of
smearing, particularly in replicate 2 (Figs 8, 9; BWW-2-F, MW-2-F, MW-2-I, MW-2-PT).
75
Figure 11: Phylotype richness (R) from the fungal 18S rRNA gene DGGE profiles. Three
sampling time points are represented: initial PL (I), post-treatment (PT), and final compost (F). R
values across the three replicates were averaged, and standard deviations are shown. No
significant differences were observed (p > 0.05).
76
3.2.3.2 DGGE - Bacterial 16S rRNA gene (Fig 12)
3.2.3.2.1 16S rRNA gene DGGE analysis
Similar to the fungal DGGE results, hierarchical cluster analyses of the bacterial profiles
indicate that the compost process had a larger impact on the community than the treatment type
(Fig 13). The cluster analysis for the 16S DGGE shows that the final (F), the initial (I), and post-
treatment (PT) compost samples are distinct from one another, and both treatment replicates
clustered together by these time point. Thus, it appears as though the bacteria communities are
more sensitive to local environmental changes associated with the BWW treatment as compared
to the fungal communities, which showed a similar structure at initial and post-treatment time
points.
77
Figure 12: Bacterial 16S rRNA gene DGGE. Composite PCR products were used to represent
each of the three replicates for both treatments, MW and BWW. Three sampling time points are
represented chronologically: initial PL (I), post-treatment (PT), and final compost (F). On either
side of the sample profiles are the visual ladders used; 100 bp molecular weight ladder (M1), and
the laboratory bacterial strains (M2; bands 1, 2, 3, Pseudomonas sp. UG14Lr, Enterococcus
faecalis, and E. coli, respectively). Extracted bands of interest, numbered 4-11, are also shown.
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Figure 13: Bacterial 16S rRNA gene DGGE. Composite PCR products were used to represent
each of the three replicates for treatments BWW and MW. Three sampling time points are
represented: initial PL (I), post-treatment (PT), and final compost (F). Bands of interest are
identified (numbers 4-11) and correspond to Figs 12 and 14. Profile clustering was performed
using UPGMA and the DICE similarity coefficient. Bootstrap values (valued out of 100) are
reported on the nodes as a measure of the cophenetic correlation.
3.2.3.2.2 Band isolations and sequencing
The standard microorganisms used in DGGE (Fig 12; bands 1-3) did not all correspond to
compost bacterial bands. However, band 3 representing E. coli, was ubiquitous throughout the
composting and appears to spike in abundance during the post-treatment of MW but not BWW,
and then decreased in band intensity except for a single replicate of the MW treated final
compost (Fig 12).
79
Certain sequenced DGGE bands corresponded closer to changes in compost process than
treatment type, e.g., bands 9-11 (Figs 12, 13) identified as uncultured bacterium, Clostridium
spp., and Corynebacterium spp., respectively (Fig 14). Other bands corresponded to and were
more characteristic of the treatment type, such as the unidentified bands found above band 4 in
MW (Fig 12). The bands may be related to Staphylococcus spp., the identity of band 4 (Fig 14)
since they are located nearby but this is not certain. Additionally, certain species became more
dominant in the MW treated final compost, as bands 5, 7, and 8 are much brighter compared to
the BWW compost (Figs 12, 13). Sequencing revealed prominent bands from the Bacillaceae
family; an uncultured bacterium related to Virgibacillus, an uncultured bacterium related to
Lentibacillus, and Lentibacillus salinarum, respectively (Fig 14).
80
Figure 14: Dendrogram of 16S sequences obtained from DGGE and relevant Genbank results
from BLASTn searches. Species names were given when known, and the Genbank accession
numbers were placed to the right of the names. Branches were collapsed with less than 50%
support (unlabeled branches).
3.2.3.2.3 16S phylotype richness
The bacterial community present in PL is diverse; more so than the fungal community,
with a phylotype richness in the range of 34 to 43 bands (Fig 15). Similar to the fungi, there is a
81
declining trend in bacterial richness. A 22% decline was observed in the average richness of the
final compost samples for MW treated PL compost (Initial and PT compared to final, p > 0.05),
but was not different from the final BWW treated compost.
Figure 15: Phylotype richness (R) values from the bacterial 16S DGGE profiles. Three sampling
time points are represented: initial PL (I), post-treatment (PT), and final compost (F). R values
across the three replicates were averaged, and standard deviations are shown. No significant
differences were observed (p > 0.05).
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3.2.3.3 DGGE - bacterial partial ureC gene (Fig 10a)
3.2.3.3.1 PCR of ureC for DGGE
The published protocol for PCR using the ureC1F-GC/2R primers (Koper et al. 2004)
showed difficulty at amplifying total bacterial ureC sequences by PCR, which was evident by the
low band intensities observed on agarose gels. To improve the abundance of targets amplified by
these primers, a reduced annealing temperature (from 55°C down to 52°C) was used, and the
number of cycles increased to 45 (see section 2.5.4). The reason for the low amplification
efficiency was due to a lack of nucleotide sequence conservation, as outlined later (see Figs 18,
19, section 3.3.4.2)
3.2.3.3.2 Partial gene ureC DGGE analysis
The gene specific ureC subunit DGGE indicated a much lower phylotype richness, with
only a few bands of interest per profile, and no distinctive changes between sampling time points
or treatments (Fig 16). The final compost sample from the MW treatment had a reduced band
signal, despite indication from visualization on an agarose gel that the end-point PCR had a
similar amount of amplification based on band brightness. Across the profiles there was a single
prominent and ubiquitous band, which was sequenced and revealed to be the PLUP phylotype.
Similar observations from clone libraries have been previously described (Rothrock et al. 2008a).
83
Figure 16: Bacterial partial ureC DGGE. Composite PCR products were used to represent each
of the three replicates for treatments 1 and 2, BWW and MW, respectively. Three sampling time
points are represented: initial PL (I), post-treatment (PT), and final compost (F). Profile
clustering was performed using UPGMA and the DICE similarity coefficient. Bootstrap values
(valued out of 100) are reported on the nodes as a measure of the cophenetic correlation.
3.3 Isolation of culturable ureolytic bacteria from poultry litter
3.3.1 Media and isolate naming
Using traditional microbiological techniques, many culturable microorganisms were
isolated from a litter sample from MW treated compost about mid-point during the composting,
at a point of elevated ammonia loss (20 ppm), therefore suspected to contain a greater number of
N-mineralizing (ureolytic) microbes. The sampling time corresponded to compost three weeks
(Aug 8 2011, day 25) from initiation. Higher ammonia levels were also measured in preliminary
research in 2010 (not shown), at thermophilic temperature ranges (50-60°C).
Standard nutrient agar (NA), a novel poultry litter extract agar (PLA), and potato
dextrose agar (PDA) plates were successful in culturing a diverse set of microorganisms. In total,
314 bacterial, and 26 fungal isolates were cultured.
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3.3.2 General culture characteristics
Morphological diversity was highest on PLA, including colony shapes, sizes, and
pigmentation. The NA plates also had several different morphologies visible, whereas the
enrichment process resulted in colonies with identical circular, cream coloured morphology. As a
result, ureolytic bacteria might have remained uncultured in the litter without the use of this
unique PLA media. Finally, although anaerobic isolates were recovered, none were observed to
have ureolytic activity via Christensen’s urea agar. Although ureolytic activity is known to occur
both aerobically and anaerobically (Lloyd and Sheaffe 1973), and confirmed by the facultatively
anaerobic Proteus vulgaris control, the lack of anaerobic ureolytic bacteria indicates that the
degradation of urea and production of ammonia is performed by aerobically growing microbes in
the PL during composting.
There was an interesting diversity among the fungi, with a common isolate morphology
as a round colony, glossy and white in colour. One of the colonies demonstrated antifungal
activity with a zone of clearing around it, an important adaptation for thriving in the high
microbial density of the PL environment.
3.3.3 Testing isolates for ureolytic activity
The protocol used in this study for testing the ureolytic capacity of the fungal and
bacterial isolates is shown visually below (Fig 17). Colonies from the general culture plate (Fig
17a) were picked and grown on a separate master plate (Fig 17b), from which the isolates were
then plated on urea agar (Fig 17d). Urea plates of isolates were compared to positive (Proteus
vulgaris, Fig 17c) or negative (E. coli; not shown) controls.
85
Figure 17: Bacterial isolates growing on PLA (A), isolation onto master plates (B), and testing
on Christensen’s urea agar: Proteus vulgaris positive control (C), and bacterial isolates (D).
A total of 28 ureolytic bacteria were cultured from the three media types (Table 9). While
the greatest number of bacterial isolates were cultured on the PLA plates, this media yielded the
smallest number (4%) of ureolytic bacteria compared to NA and enrichment media (Table 9).
Many of the isolates that are the same species were found from the same media, which indicates
86
that the type of culture media will impact the type of ureolytic isolate that is cultured. The PLUP
ureolytic group was mostly found on PLA (2/3 of the total PLUP isolates). No PLUP isolates
were found on the enrichment media, which might be due to the probability of selection since
fewer isolates were selected from the enrichment media, and one PLUP isolate was found on
NA. NA was the most effective at cultivating general ureolytic isolates at 26% of the total
bacteria tested.
Table 9: Summary of total culturable microbe counts and number of ureolytic and PLUP isolates.
Media types (NA, nutrient agar; Enrich, urea enrichment culture followed by NA with added
urea; PLA, poultry litter extract agar). Bacteria (bac) and fungi are both included.
Media Total isolates Ureolytic (%)
NA (bac) 53 14 (26.4)
Enrich (bac) 32 5 (15.6)
PLA (bac) 229 9 (3.9)
PDA (fungi) 26 3 (11.5)
Of the 26 fungal isolates, three were shown to be ureolytic (12%). Fungal sequencing was
not completed, since the focus was on bacterial urease producers. However, the isolates were
stored and this work can be continued.
The majority of the bacterial isolates that were stored in the freezer for several weeks and
then re-plated tested positive again on urease plates. Three isolates, despite repeated attempts,
were unable to produce a pink colour. These isolates (NA M2 col 1, PL M5 col 6, and PL M5 col
8) are therefore concluded to be variable in urease activity.
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3.3.4 ureC partial gene analysis of ureolytic bacterial isolates
3.3.4.1 Preliminary isolate screening by PCR
Initially targeting isolates using PCR with PLUP specific and ureC general primers was
used to screen for isolates that should be sequenced, however this proved difficult. The ureC1F-
GC/2R primers were again showing difficulty in amplifying bacterial ureC sequences during
PCR as found for the ureC PCR-DGGE (Fig 16, section 3.2.3.3.2). The reasons were revealed
following sequencing (Fig 18, 19 below)
The PLUP primers are more specific, and many isolates were thought to be PLUP based
on a positive result. However, ureC partial gene sequencing later revealed that most of these
were in fact false positives due to non-specific amplification of that ureC region. Sequencing of
ureC was then used to identify the remaining isolates since it was considered a more robust
method to properly characterize an isolate as PLUP.
3.3.4.2 ureC partial gene sequencing and sequence alignment
The nucleotide sequences of the isolates differ at the region of the forward ureC primer
(ureC1F; Fig 18), and even at the protein level shows a lack of conservation (Fig 20). The region
of the reverse primer was more conserved between isolates at the nucleotide and protein levels
(Fig 19 and 20, respectively). Sequence inconsistencies were noted at the 5’ end of several ureC
protein sequences when compared to heavily conserved residues and were removed as PCR
artifacts. The protein sequence between the primer sites was more conserved and often showed
identical or strongly similar properties (Fig 20).
88
PLM1col33 --------------AAGCTGCACGAGGACTGGGGCGCAACGTATCAC
PLM1col42 -------AATTCGATTAGATGACGAGGACTGGGGCACCACGCCGGCT
NAM1col12 --------AATTCGATTCTGCACGAGGACTGGGGCACCACGCCGGCG
NAM2col16a -------------AATTCGATTCGAGGACTGGGGCACCACGCCATCG
PLM2col5b --------------AAGATGCACGAGGACTGGGGCACCACGCCATCG
PLM5col6 --------------AAGCTGCACGAGGACTGGGGCACCACGCCATCG
PLM5col27a AATTCACTAGTGATTAGCTGCACGAGGACTGGGGCACCACGCCATCG
NAM3col4 --------AATTCGATTATCCACGAGGACTGGGGCACCACGCCATCG
Enrichcol8 ------AATTCGATTAGATCCACGAGGACTGGGGCACCACGCCATCG
Figure 18: 5’ nucleotide sequence (5’ to 3’) of the partial ureC gene from several bacterial
ureolytic isolates, showing differences at the target region for the forward primer ureC1F
(highlighted). The ureC1F forward primer has a sequence of AAGMTSCACGAGGACTGGGG
(5’ to 3’).
PLM1col42 ACCTCGACATGCTCATGGTGTGCCACCACCT
NAM1col12 ACCTCGACATGCTGATGGTCTGCCACCACCT
NAM2col16a ATCTGGACATGCTCATGGTGTGCCACCATCT
PLM5col6 ATCTGGACATGCTCATGGTCTGCCACCACCT
PLM5col27a ATCTGGACATGCTCATGGTCTGCCACCACCT
NAM3col4 ATCTGGACATGCTCATGGTCTGCCACCATCT
Enrichcol8 ATCTGGACATGCTCATGGTCTGCCACCACCT
Figure 19: 3’ nucleotide sequence (5’ to 3’) of the partial ureC gene from several bacterial
ureolytic isolates, showing differences at the target region for the reverse primer ureC2R
(highlighted). The ureC2R reverse primer has a sequence of
AGRTGGTGGCASACCATSAGCAT (5’ to 3’).
PLM1col33 --KLHEDWGATYHAIDVALAVAEEMDIQVAIHSDTLNEGGFADNTIAAFKDRVIHTFHTE
PLM2col5a --KLHEDWGATYHAIDVALAVAEEMDIQVAIHSDTLNEGGFADNTIAAFKDRVIHTFHTE
NAM3col9 --KLHEDWGATYHAIDVALAVAEEMDIQVAIHSDTLNEGGFADNTIAAFKDRVIHTFHTE
NAM2col2 --KMHEDWGATASVIDHALSVADKYDVQVALRADTLNEGGFMENTMAAIKNRVIHMYHTE
NAM3col12 --KMHEDWGATPSVLDHALSVADEYDVQIALHADTLNEAGFFEDTMRAIKDRVIHMYHTE
Enrichcol48 --KIHEDWGATPSVLDHALSVADEYDVQIALHADTLNEAGFFEDTMRAIKDRVIHMYHTE
Enrichcol49 --KLHEDWGATPSVLDHALSVADEYDVQIALHADTLNEAGFFEDTMRAIKDRVIHMYHTE
PLM7col8 --KLHEDWGATPSALDQSLKIADEYDIQIALHSDTLNEAGFVEDTINAIDGRVIHVFHTE
NAM3col3 --KMHEDWGATPSALDQSLKIADEYDIQIALHSDTLNEAGFVEDTINAIDGRVIHVFHTE
NAM1col6 --KMHEDWGATPAALDQSLSVADEYDIQVALHSDTLNEAGFVEDTINAIDGRVIHIFHTE
NAM3col14 NSILHEDWGATPAALDQSLSVADEYDIQVALHSDTLNEAGFVEDTINAIDGRVIHIFHTE
NAM1col12 NSILHEDWGTTPAAIDCCLSVADDHDIQVMIHTDTLNESGFVEETIKAFKGRTIHTLHTE
PLM2col5b --KMHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
NAM2col16a --IRFEDWGTTPSAIDTCLSVADDYDV*VMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
NAM3col4 NSIIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
NAM2col1 NSILHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
Enrichcol7 NSIIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
Enrichcol8 NSIMHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
89
Enrichcol22 --KIHEDWGTTPSAIDTRLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
NAM1col16b -NSIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
NAM2col5b -NSIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM5col27a SD*LHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM3col15 --KIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM5col8 --KIHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM5col6 --KLHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM7col5 --KMHEDWGTTPSAIDTCLSVADDYDVQVMLHSDTLNESGFVEDTVKAFKGRTIHAFHTE
PLM1col42 IRLDDEDWGTTPASIDTCLSVAEKYDVQIAIHTDTLNESGFVEDTLAAFKERGIHTYHTE
NAM1col2 --KLHEDWGTTASAIDTSLQVADEYDVQIAIHTDTLNEGGFVEDTIAAIGDRVIHTYHTE
NAM1col7 --KMHEDWGTTASAIDTSLQVADEYDVQIAIHTDTLNEGGFVEDTIAAIGDRVIHTYHTE
NAM2col5a --FDYEDWGSTPAAIDQCLSVADEYDVQVAIHTDTLNEAGFVEDTIAAIKDRVIHTYHTE
NAM2col16b NSIMHEDWGSTPAAIDQCLSVADEYDVQVAIHTDTLNEAGFVEDTIAAIKDRVIHTYHTE
PLM5col27b ---IHEDWGSTPAAIDQCLSVADEYDVQVAIHTDTLNEAGFVEDTIAAIKDRVIHTYHTE
NAM1col16a -NSIHEDWGSTPAAIDQCLSVADEYDVQVAIHTDTLNEAGFVEDTIAAIKDRVIHTYHTE
****:* :* * :*:. *: : :::*****.** ::*: *: * ** ***
PLM1col33 GAGGGHAPDILKVAGLNNVLPASTNPTLTYTDNTIDEHLDMLMVCHH
PLM2col5a GAGGGHAPDILKVAGLNNVLPASTNPTLTYTDNTIDEHLDMLMVCHH
NAM3col9 GAGGGHAPDILKVAGLNNVLPASTNPTLTYTDNTIDEHLDMLMVCHH
NAM2col2 GAGGGHAPDLIKSASFNNVLPSSTNPTLPYTVNTVDEHLDMLMVCHH
NAM3col12 GAGGGHAPDLIKSAGYMNVLPASTNPTLPYTVNTIDEHLDMLMVCHH
Enrichcol48 GAGGGHAPDLIKSAGYMNVLPASTNPTLPYTVNTIDEHLDMLMVCHH
Enrichcol49 GAGGGHAPDLIKSAGYMNVLPASTNPTLPYTVNTIDEHLDMLMVCHH
PLM7col8 GAGGGHAPDQLKMASLPNVLPASTNPTKPFTTNTIDEHMDMLMVCHH
NAM3col3 GAGGGHAPDQLKMASLPNVLPASTNPTKPFTTNTIDEHMDMLMVCHH
NAM1col6 GAGGGHAPDQLVMASLPNILPASTNPTKPFTTNTIDEHLDMLMVCHH
NAM3col14 GAGGGHAPDQLVMASLPNILPASTNPTKPFTTNTIDEHLDMLMVCHH
NAM1col12 GAGGGHAPDIITIAGLDNVLPSSTNPTRPFTTNTLDEHLDMLMVCHH
PLM2col5b GAGGGHAPDIIKIAGLKNVLPSSTNPTRP------------------
NAM2col16a GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
NAM3col4 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFIRNTIDEHLDMLMVCHH
NAM2col1 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
Enrichcol7 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
Enrichcol8 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
Enrichcol22 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
NAM1col16b GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
NAM2col5b GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM5col27a GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM3col15 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM5col8 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM5col6 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM7col5 GAGGGHAPDIIKIAGLKNVLPSSTNPTRPFTRNTIDEHLDMLMVCHH
PLM1col42 GAGGGHAPDILIACSKPYVLPSSTNPTRPYTVNTIDEHLDMLMVCHH
NAM1col2 GAGGGHAPDIMEMASFPNVLPSSTNPTRPYTVNTLEEHLDMLMVCHH
NAM1col7 GAGGGHAPDIMEMASFPNVLPSSTNPTRPYTVNTLEEHLDMLMVCHP
NAM2col5a GAGGGHAPDIMKIASLPNVLPSSTNPTRPFTVNTLEEHLDMLMVCHH
NAM2col16b GAGGGHAPDIMKIASLPNVLPSSTNPTRPFTVNTLEEHLDMLMVCHH
PLM5col27b GAGGGHAPDIMKIASLPNVLPSSTNPTRPFTVNTLEEHLDMLMVCHH
NAM1col16a GAGGGHAPDIMKIASLPNVLPSSTNPTRPFTVNTLEEHLDMLMVCHH
********* : .. :**:***** .
Figure 20: Clustal alignment of the translated bacterial ureC sequences obtained from the
cultured isolates from composted PL. The grey shading outlines the sequence corresponding to
the ureC1F-GC/2R primers. ‘*’ indicates a fully conserved residue, ‘:’ indicates conservation
between groups of strongly similar properties, and ‘.’ indicates conservation between groups of
weakly similar properties.
90
When compared to Genbank, BLASTn and BLASTp sometimes had different results. As
a result, BLASTp was used for phylogenetic comparison since the conservation of ureC is at the
protein sequence level (Fig 20 above). The list of ureC sequences obtained during this study are
shown below (Table 10). It is evident that multiple isolates may be the same, since ureC
sequences garnered the same results. The Genbank hits are sometimes inconclusive as to the
identities and so multiple results are shown. Sequence differences noted between the cultured
litter isolates and the sequences from Genbank are often accounted for by a higher positive
count, meaning that the substituted amino acid residue likely retains the same function as the
residue being compared against.
Table 10: Translated ureC sequences for the isolates found in composted PL. The isolates labeled
as ‘b’ were results from a secondary ureC sequencing that took place. Isolated colonies names
are prefixed by the media they were cultured on (PL, poultry litter extract agar; NA, nutrient
agar; Enrich, urea enrichment culture followed by NA with added urea) and the master plate
number (M). Identities are based on Genbank BLASTp results, with only the highest ranking
results shown. ‘Positive’ values account for conserved substitutions. All E values were 9e-53
or
lower.
Isolate Genbank matches Accession # Identity (%) Positive (%)
PL M1 col 33 Corynebacterium
ammoniagenes
WP_003847881.1 101/105
(96%)
104/105
(99%)
PL M1 col 42 Halomonas sp. KM-1 WP_010628277 99/102
(97%)
101/102
(99%)
PL M2 col 5a Corynebacterium
ammoniagenes
WP_003847881.1 101/105
(96%)
104/105
(99%)
PL M2 col 5b Bradyrhizobium sp.
DFCI-1
WP_021076830.1 86/87 (99%) 87/87
(100%)
PL M3 col 15 Bradyrhizobium sp.
DFCI-1
WP_021076830.1 104/105
(99%)
105/105
(100%)
PL M5 col 6 Bradyrhizobium sp. WP_021076830.1 105/105 105/105
91
DFCI-1 (100%) (100%)
PL M5 col 8 Bradyrhizobium sp.
DFCI-1
WP_021076830.1 104/105
(99%)
105/105
(100%)
PL M5 col 27a Bradyrhizobium sp.
DFCI-1
WP_021076830.1 104/104
(100%)
104/104
(100%)
PL M5 col 27b Trichodesmium
erythraeum IMS101
YP_720654.1 94/104
(90%)
100/104
(96%)
Bacillus sp. L1 WP_017729055.1 94/104
(90%)
100/104
(96%)
Desulfosporosinus sp.
OT
WP_009621836.1 95/104
(91%)
99/104
(95%)
PL M7 col 5 Bradyrhizobium sp.
DFCI-1
WP_021076830.1 104/105
(99%)
105/105
(100%)
PL M7 col 8 Bacillus sp. B14905 WP_008174846.1 97/105
(92%)
103/105
(98%)
Lysinibacillus
boronitolerans
WP_016992587.1 96/105
(91%)
103/105
(98%)
Lysinibacillus
sphaericus C3-41
YP_001698371.1 96/105
(91%)
102/105
(97%)
NA M1 col 2 Bacillus megaterium
WSH-002
YP_005494413.1 105/105
(100%)
105/105
(100%)
NA M1 col 6 Lysinibacillus
boronitolerans
WP_016992587.1 104/105
(99%)
105/105
(100%)
NA M1 col 7 Bacillus megaterium
WSH-002
YP_005494413.1 103/104
(99%)
104/104
(100%)
NA M1 col 12 Beijerinckia indica
subsp. indica ATCC
9039
YP_001832173.1 95/104
(91%)
99/104
(95%)
Bradyrhizobium
japonicum USDA 6
YP_005613220.1 94/104
(90%)
98/104
(94%)
Chelatococcus sp. GW1 WP_019404066.1 94/104
(90%)
98/104
(94%)
NA M1 col 16a Trichodesmium
erythraeum IMS101
YP_720654.1 94/104
(90%)
100/104
(96%)
Bacillus sp. L1 WP_017729055.1 94/105
(90%)
100/104
(96%)
Desulfosporosinus sp.
OT
WP_009621836.1 95/104
(91%)
99/104
(95%)
NA M1 col 16b Bradyrhizobium sp.
DFCI-1
WP_021076830 103/104
(99%)
104/104
(100%)
NA M2 col 1 Bradyrhizobium sp.
DFCI-1
WP_021076830 104/104
(100%)
104/104
(100%)
NA M2 col 2 Bacillus licheniformis
9945A
YP_008078139.1 102/105
(97%)
103/105
(98%)
NA M2 col 5a Trichodesmium
erythraeum IMS101
YP_720654.1 93/102
(91%)
98/102
(96%)
92
Bacillus sp. L1 WP_017729055.1 93/102
(91%)
98/102
(96%)
Desulfosporosinus sp.
OT
WP_009621836.1 94/102
(92%)
97/102
(95%)
NA M2 col 5b Bradyrhizobium sp.
DFCI-1
WP_021076830.1 103/104
(99%)
104/104
(100%)
NA M2 col 16a Bradyrhizobium sp.
DFCI-1
WP_021076830.1 101/102
(99%)
101/102
(99%)
NA M2 col 16b Trichodesmium
erythraeum IMS101
YP_720654.1 94/104
(90%)
100/104
(96%)
Bacillus sp. L1 WP_017729055.1 94/105
(90%)
100/104
(96%)
Desulfosporosinus sp.
OT
WP_009621836.1 95/104
(91%)
99/104
(95%)
NA M3 col 3 Bacillus sp. B14905 WP_008174846.1 97/105
(92%)
103/105
(98%)
Lysinibacillus
boronitolerans
WP_016992587.1 96/105
(91%)
103/105
(98%)
Lysinibacillus
sphaericus C3-41
YP_001698371.1 96/105
(91%)
102/105
(97%)
NA M3 col 4 Bradyrhizobium sp.
DFCI-1
WP_021076830 102/104
(98%)
103/104
(99%)
NA M3 col 9 Corynebacterium
ammoniagenes
WP_003847881.1 101/105
(96%)
104/105
(99%)
NA M3 col 12 Batrachochytrium
dendrobatidis JAM81
(hypothetical protein)
EGF75835.1 91/105
(87%)
100/105
(95%)
Sporosarcina
newyorkensis
WP_009498986.1 93/105
(89%)
100/105
(95%)
Salinicoccus carnicancri WP_007083931.1 93/105
(89%)
99/105
(94%)
NA M3 col 14 Lysinibacillus
boronitolerans
WP_016992587.1 103/104
(99%)
104/104
(100%)
Enrich col 7 Bradyrhizobium sp.
DFCI-1
WP_021076830 103/104
(99%)
104/104
(100%)
Enrich col 8 Bradyrhizobium sp.
DFCI-1
WP_021076830 103/104
(99%)
104/104
(100%)
Enrich col 22 Bradyrhizobium sp.
DFCI-1
WP_021076830 103/105
(98%)
104/105
(99%)
Enrich col 48 Batrachochytrium
dendrobatidis JAM81
(hypothetical)
EGF75835.1 91/105
(87%)
100/105
(95%)
Salinicoccus carnicancri WP_017549533.1 93/105
(89%)
99/105
(94%)
Sporosarcina
newyorkensis
WP_009498986.1 93/105
(89%)
100/105
(95%)
93
Enrich col 49 Batrachochytrium
dendrobatidis JAM81
(hypothetical)
EGF75835.1 91/105
(87%)
100/105
(95%)
Sporosarcina
newyorkensis
WP_009498986.1 93/105
(89%)
100/105
(95%)
Salinicoccus carnicancri WP_017549533.1 93/105
(89%)
99/105
(94%)
ureC DGGE 1 Corynebacterium
ammoniagenes
WP_003847881.1 101/105
(96%)
104/105
(99%)
ureC DGGE 2 Corynebacterium
ammoniagenes
WP_003847881.1 102/105
(97%)
105/105
(100%)
3.3.4.3 Isolation of novel bacterial isolates containing PLUP ureC sequences
The isolation of PLUP isolates was a novel observation for this study, despite the
ongoing use of the PLUP sequence information for molecular work in the current and previous
studies. The PL compost sample selected based on elevated ammonia levels was therefore
suitable for the isolation of abundant ureolytic bacteria, and perhaps necessary for the PLUP
discovery.
A total of three PLUP ureC sequences were found, in PL M1 col 33, PL M2 col 5 and
NA M3 col 9. All media types except for the enrichment media were able to culture these types
of ureolytic bacteria. The three PLUP sequences are identical in protein sequence – identified as
Corynebacterium ammoniagenes (Table 10 above). The nucleotide sequences match 99% to each
other, and 95-96% to the known PLUP results from Genbank. Of the two PLUP sequences from
the ureC DGGE, one matched closely to the three isolates, and the other closely matched a
Genbank result at 99% nucleotide similarity, and 100% protein identity to C. ammoniagenes
(allowing conserved substitutions). PLUP sequences differed by one amino acid residue for
DGGE band 1, and two for band 2, compared to the three bacteria isolates (not shown). Thus,
several variations of the PLUP ureC sequence exist.
94
3.3.4.4 Translated protein vs. nucleotide ureC sequences
The ureC nucleotide sequences were less well characterized relative to the protein
sequences, given that the protein BLAST results are often different than the nucleotide BLASTs
and result in greater sequence identity.
The isolates determined to have the unknown ureC allele colloquially labeled as PLUP,
which corresponds to a known protein sequence identified as Corynebacterium ammoniagenes
(Table 10 above; PL M1 col 33, PL M2 col 5, NA M3 col 9). Additionally, the inconclusive
single result from Genbank BLASTn ureC known as Bacillus coagulans (PL M5 col 27b, NA
M1 col 16a, NA M2 col 5a, NA M2 col 16b; identities of about 75%) were identified as protein
sequences from Trichodesmium erythraeum, Bacillus sp., or Desulfosporosinus sp. (about 90%
identity).
3.3.4.5 Multiple copies of ureC gene per genome
Performed at different times, ureC genes were re-cloned and sequenced for a number of
isolates. All were recognized as having a total of two ureC gene copies per genome (PL M2 col
5, PL M5 col 27, NA M1 col 16, NA M2 col 5, and NA M2 col 16; Table 11 below), and for all
five isolates one of the alleles was identified by BLASTp as Bradyrhizobium sp. More of the
isolated bacteria may have contained a second ureC gene copy, potentially PLUP, which should
be considered.
95
3.3.4.6 ureC phylogenetic analysis
Certain aspects of the identities, similarities and relationships are best gleaned via
dendrogram of the translated partial ureC gene sequences, seen below (Fig 21). This region of
the ureC sequence is conserved between kingdoms as the plant Jack Bean (Canavalia
ensiformis) is more closely related to certain bacterial species such as Mycobacterium smegmatis
than other bacteria are to each other. Additionally, identities can be made from high ranking
Genbank results; however, certain sequences appear to cluster more closely which likely reveals
a stronger identity. For instance, Batrachochytrium was the highest ranking result for NA M3 col
12, and Enrich col 48 and col 49; however, Sporosarcina newyorkensis clusters the closest to
these isolates. Given that the Batrachochytrium gene is only a hypothetical ureC sequence, this is
perhaps to be expected (Table 10 above). In a second instance, Desulfosporosinus does not
cluster as closely as Trichodesmium to isolates that were matched in Genbank as both (Table 10
above).
The isolates with PLUP ureC sequence clustered closest with C. ammoniagenes, the
result from BLASTp. While C. urealyticum was found on an adjacent sub-branch as the PLUP
group, C. halotolerans is found on a separate main branch entirely, along with Proteus mirabilis.
The ureC divergence within this genus potentially indicates a strong selection pressure on ureC,
resulting in closer similarities across genera than within the genus as ureC gene variants are
transferred.
96
97
Figure 21: Dendrogram of translated ureC sequences from the ureolytic culture based study and
from ureC DGGE from the PL pilot scale composting. Relevant Genbank results from BLASTp
searches are shown. Species or isolate names are given, and the Genbank accession numbers
were placed to the right of the names. Branches were collapsed with less than 50% support
(unlabeled branches). Isolates that were found to have a PLUP ureC sequence were identified.
3.3.5 16S rRNA gene analysis of ureolytic bacterial isolates
3.3.5.1 16S rRNA gene sequencing
Near full length sequencing of the 16S rRNA gene (1.5 kbp) was used for identification
of the cultured ureolytic bacterial isolates. From the Genbank BLASTn alignments, several of
the highest scoring results were recorded (Table 11).
Interestingly, several isolates were closely related to uncultured microorganisms,
including PL M5 col 27, PL M7 col 5, PL M7 col 8, NA M3 col 3, and Enrich col 48 and 49.
Considering the DGGE results, bands 5, 7, and 9 also matched uncultured organisms (Fig 22 and
table 11). These sequences might be indicative of novel species or subspecies that have yet to be
fully identified.
Furthermore, use of the near-full length 16S sequences enabled a better comparison of the
isolates to the 16S DGGE bands and thus to a more accurate identification. While this
phylogenetic information is unable to ascribe function to these microbes, the ureolytic activity
data accompanying this information can help in this regard.
98
Table 11: 16S sequences from the bacterial isolates cultured from composted PL, and closest
matches found on Genbank via BLASTn. Isolated colonies names are prefixed by the media they
were cultured on (PL, poultry litter extract agar; NA, nutrient agar; Enrich, urea enrichment
culture followed by NA with added urea) and the master plate number (M). All E-values were
0.0.
Isolate Genbank Accession # Identity
PL M1 col 33 Bacillus galactosidilyticus strain LMG
17892
NR_025580.1 1481/1486 (99%)
PL M1 col 42 Bacillus galactosidilyticus strain LMG
17892
NR_025580.1 1480/1486 (99%)
PL M2 col 5 Virgibacillus sp. CC-YMP-6 (V. soli
from paper)
EU213011.1 1524/1526 (99%)
PL M3 col 15 Bacillus galactosidilyticus strain LMG
17892
NR_025580.1 1482/1486 (99%)
PL M5 col 6 Bacillus galactosidilyticus strain LMG
17892
NR_025580.1 1484/1486 (99%)
PL M5 col 8 Oceanobacillus caeni strain S-11 NR_041533.1 1517/1524 (99%)
Bacillus sp. LMG 19636 AF329473.1 1493/1507 (99%)
PL M5 col 27 Uncultured compost bacterium
clone 0B37
DQ345490.1 1507/1517 (99%)
Sporosarcina sp. MBEEE392 AB733545.1 1487/1507 (99%)
PL M7 col 5 Uncultured bacterium clone GD58 GQ279243.1 1525/1539 (99%)
PL M7 col 8 Uncultured bacterium, clone A20 FR687520.1 1508/1510 (99%)
Sporosarcina sp. SS6.3 KC160876.1 1502/1510 (99%)
NA M1 col 2 Bacillus megaterium WSH-002 CP003017.1 1543/1544 (99%)
Bacillus aryabhattai HQ242770.1 1543/1544 (99%)
NA M1 col 6 Bacillus macroides strain 608 DQ350821.1 1538/1544 (99%)
Bacillus cereus strain ZQN6 GU384236.1 1538/1544 (99%)
Lysinibacillus xylanilyticus strain GT18 JQ677989.1 1536/1542 (99%)
Bacillus fusiformis strain S401 DQ350836.1 1537/1544 (99%)
NA M1 col 7 Bacillus megaterium WSH-002 CP003017.1 1543/1544 (99%)
Bacillus aryabhattai HQ242770.1 1543/1544 (99%)
NA M1 col 12 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1531/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1533/1545 (99%)
NA M1 col 16 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1530/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1532/1545 (99%)
NA M2 col 1 Bacillus amyloliquefaciens subsp. HG328254.1 1540/1542 (99%)
99
plantarum
Bacillus subtilis strain Em7 GU258545.1 1540/1542 (99%)
NA M2 col 2 Bacillus licheniformis 9945A CP005965.1 1540/1543 (99%)
Bacillus subtilis strain JM4 AY728013.1 1536/1541 (99%)
NA M2 col 5 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1526/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1528/1545 (99%)
NA M2 col 16 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1528/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1530/1545 (99%)
NA M3 col 3 Uncultured bacterium, clone A20 FR687520.1 1506/1510 (99%)
Sporosarcina luteola strain BP11_7A JN644559.1 1511/1524 (99%)
NA M3 col 4 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1529/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1531/1545 (99%)
NA M3 col 9 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1529/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1531/1546 (99%)
NA M3 col 12 Staphylococcus equorum subsp. linens
strain RP29
NR_041926.1 1529/1534 (99%)
Staphylococcus succinus subsp.
succinus strain AMG-D1
NR_028667.1 1531/1545 (99%)
NA M3 col 14 Bacillus macroides strain 608 DQ350821.1 1536/1544 (99%)
Bacillus cereus strain ZQN6 GU384236.1 1536/1544 (99%)
Lysinibacillus xylanilyticus strain GT18 JQ677989.1 1534/1542 (99%)
Bacillus fusiformis strain S401 DQ350836.1 1535/1544 (99%)
Enrich col 7 Sporosarcina newyorkensis strain R-
31323
AM910326.1 1511/1514 (99%)
Enrich col 8 Sporosarcina newyorkensis strain R-
31323
AM910326.1 1510/1514 (99%)
Enrich col 22 Sporosarcina newyorkensis strain R-
31323
AM910326.1 1510/1514 (99%)
Enrich col 48 Uncultured bacterium clone JF31 JX133320.1 1467/1526 (96%)
Enrich col 49 Uncultured Virgibacillus sp. clone
LZNX121
JX456425.1 1474/1528 (96%)
100
3.3.5.2 16S rRNA gene phylogenetic analysis
From the 16S dendrogram (Fig 22), it is observed that the phylogenetic diversity of the
isolates under these specific cultivation conditions was limited. All isolates were of the order
Bacillales (phylum Firmicutes), and about half are within the family Bacillaceae. The
staphylococci (family Staphylococcaceae) were the most numerous group - to which 7 out of 28
(25%) isolates belong. Other major groups with about four isolates each (4 out of 28, 14.3%) are
most closely related to Bacillus galactosidilyticus, or Sporosarcina.
101
102
Figure 22: Dendrogram of 16S sequences obtained from the ureolytic culture based experiment,
16S DGGE from the PL pilot scale composting, and relevant Genbank results from BLASTn
searches. Species, subspecies or isolate names were given when known, and the Genbank
accession numbers were placed to the right of the names. Branches were collapsed with less than
50% support (unlabeled branches). Isolates with a PLUP ureC sequence were identified by a
black arrow.
3.3.5.3 Comparing 16S rRNA gene identities to ureC BLASTn and BLASTp
Concurrent sequencing of both the 16S and ureC gene(s) from the isolated bacteria
enabled an interesting comparison of identities for active urease genes with corresponding 16S
sequences. A web diagram is used to illustrate the relationships (Fig 23). These results suggest
the 16S and ureC gene sequencing do not necessarily corroborate the same identity of the isolate,
and many different 16S identities are linked to a similar ureC identity. This finding, while not
definitive from this work, suggests the possibility of past horizontal gene transfer (HGT) events.
Five such alleles were found in this experiment that were considered candidates of past HGT
events (Fig 24). Without accounting for replicate isolates, the ureC protein sequence identified as
Bradyrhizobium sp. was found in 14 isolates (out of 28 total; 50%), Corynebacterium
ammoniagenes (PLUP; 3; 10.7%), Trichodesmium erythraeum et al. (4; 14%), Bacillus sp. et al.
(2; 7%), and Batrachochytrium dendrobatidis et al. (3; 11%). The translated PLUP allele,
identified as C. ammoniagenes, was found in bacterial isolates that do not identify as
Corynebacteria based on 16S rRNA. In fact, the bacterial species identified are not even found in
the same phylum as Corynebacterium (Actinobacteria). Similarly, the isolate NA M2 col 1
identified as Bacillus amyloliquefaciens based on 16S rRNA, does not share similarity to the
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ureC gene of B. amyloliquefaciens available on Genbank. Interestingly, the Genbank sequence
clusters on a separate main branch from the others, indicating a more significant sequence
divergence from the other isolates (Fig 22 above), one that is not present in the litter
environment. Isolate NA M3 col 12 was unique in that it shared the same identity of the
translated ureC sequence as Enrich cols 48 and 49, however the isolates differed in 16S rRNA
identities and ureC DNA, except for the ureC DNA sequence of Enrich col 48 which only
differed from NA M3 col 12 by a single nucleotide. Finally, it is clear that isolates identified as
Bacillus galactosidilyticus or Staphylococcus sp. are unique in that none have ureC genes
identified as such, but rather, are identified as being from a number of different species.
104
Figure 23: Web diagram of 16S rRNA and partial ureC gene sequence identities for the ureolytic
bacteria cultured from PL. The ureC nucleotide BLAST (BLASTn) and protein BLAST
(BLASTp) results are both shown, along with corresponding 16S identities (the placement of
lines on the ureC boxes is not consequential). A dotted line indicates the result is similar except
for the BLASTn result (placed above the dotted line).
Figure 24: Percent abundance of five common ureC alleles among the 28 ureolytic bacterial
isolates from PL, the ones most likely to have undergone HGT.
3.4 Microcosm incubation experiments
The PL microcosm incubation experiments sought to quantify the inhibitory effects of
BWW on the microbially mediated N-mineralization reactions, urease and uricase (urea
quantities were used as an indirect measure of uricase activity), during the initial period
105
following treatment application. The initial several days are associated with mesophilic
temperatures and rapid mineralizing activity which then leads to a spike in ammonia as the
temperature becomes thermophilic. Inoculation of the novel PLUP ureolytic bacteria into the
microcosms was done in an attempt to accentuate ureolytic activity, and in part to examine their
specific activity in the PL environment.
3.4.1 PLUP inoculants
The slower ureolytic PLUP, PL M2 col 5 (identified by 16S rRNA gene as Virgibacillus
soli), did not grow to the same concentration as the faster PLUP, NA M3 col 9 (Staphylococcus
sp.), so it did not meet the same 109 cells(g)
-1 target (Table 12). CFU plate counts for NA M3 col
9 were made using NA since it was observed that the PLA appeared to inhibit the growth of this
PLUP isolate by about 10 fold.
Table 12: PLUP cultures used to inoculate the PL for incubation microcosms. ODA600 and
CFU(mL)-1
counts shown are from the second replicate experiment. PL M2 col 5 was grown on
PLA, and NA M3 col 9 was grown on NA.
Isolate ODA600 CFU(mL)-1
CFU(g)-1
dry PLa
PL M2 col 5 0.29 1.33 x 108 3.15 x 10
8
NA M3 col 9 0.57 (1:10 diluted) 3.42 x 109 8.10 x 10
9
aCFU(g)
-1 calculated based on 600 mL culture volume; total weight of 220g wet wt PL in
microcosm (1 g wet wt PL = 0.575251 g dry); and inoculum divided in two, half for each of MW
PLUP and BWW PLUP
106
3.4.2 Moisture content (MC%) and pH
The MC% was within the range of 60-70%, and did not vary much (< 5%) from the start
till the end of the experiment (Table 13). However, despite having added identical quantities of
liquid treatment, the MC% was higher in MW microcosms than in BWW.
Table 13: Moisture content (MC%) averaged across three replicates for each treatment used in
microcosm incubation experiment.
Treatment MC % (± SD)
MW 67.55 (2.04)
MW PLUP 66.56 (2.53)
BWW 60.98 (1.12)
BWW PLUP 61.95 (0.41)
The MC and pH variability were higher in MW treated microcosms. Total average pH
standard deviation was 0.055 for BWW (including PLUP, and across the four sampling times),
versus 0.091 for MW. The pH was more variable with the PLUP inoculations, with the average
standard deviation (from all time points) increasing 56% and 76% over the average SD from
MW and BWW, respectively.
For both treatments and inoculations, the pH initially dropped to 8.6-8.7 on day one from
the starting pH of 8.96 ± 0.06, then throughout the incubation trended higher in MW treatments
to a final pH of about 8.9 and trended lower from BWW treatments to about 8.4 (Fig 25). By the
end of the experiment on day four, the pH of the PL was significantly lower (p < 0.001) in the
BWW and BWW PLUP microcosms than from the MW and MW PLUP microcosms. The
continual decrease in pH from BWW treatment suggests its effects on pH are not contained to
the initial application, but the effect of BWW on pH beyond four days is unknown from the
current microcosms.
107
Figure 25: Microcosm pH levels [1:10 w(v)-1
] in dH2O; Tiquia 2005) measured daily for each of
the four treatment types (BWW ± PLUP, and MW ± PLUP). An asterisk indicates a significant
difference (p < 0.05) between MW and BWW treatments (including PLUP).
3.4.3 Urea levels in the microcosm
The measured urea is quite variable between replicates (Fig 26). Importantly, the intra-
treatment variation is equal comparing the treatments MW and BWW including PLUP, with urea
deviations equaling 26% of the total for both MW and BWW. Important trends can be observed
comparing the MW and BWW treatments, and regarding PLUP inoculations, despite the lack of
significant differences in urea levels between treatments at any time point (p > 0.05). While urea
quantification between replicate experiments was variable, the data indicate that nitrogen
mineralization in the MW treated microcosms occurred rapidly under the prescribed conditions
108
(about 100% increase from time zero). In MW, the quick rise in urea generation followed by
rapid urea hydrolysis suggests very active mineralization processes are at work. The spike in
urea levels on day 2 in MW was not reflected in MW PLUP, which remained stable, suggesting
that the additional PLUP bacteria inoculated in the microcosm are active within the litter. Based
on a comparison of MW and MW PLUP between days 2 and 3, the PLUP bacteria have
potentially reduced the amount of urea by 2000 mg(kg PL)-1
in one day. Contrasting to MW, the
BWW treatment is characterized by a quite stable range of urea between 5-6000 mg(kg PL)-1
for
the four sampling days, and no spike in urea was seen (Fig 26). The smaller extent of urea
generation in BWW treated microcosms (about 50% increase from time zero) suggests that at
least uricolytic processes were inhibited (converting uric acid to urea), but perhaps both urea
generation by uricolytic microbes and urea hydrolysis by ureolytic microbes and PLUP were
inhibited based on the relatively stable levels of urea. Overall, the main difference in urea levels
appears to be due to the application of MW or BWW.
109
Figure 26: Urea levels measured in the incubated PL microcosms. The amount of urea is
calculated as mg per kg dry litter. Activity was averaged across three replicates and standard
deviation is shown by the error bars. No significant differences were observed between
treatments at any time point (p > 0.05).
3.4.4 Urease activity in the microcosms
The total urease activity of the resident microorganisms increased in about the same
proportion as urea from day zero (about 50% and 100%, for BWW and MW microcosms,
respectively; Fig 27). At day 1, the level of urease activity from the PLUP inoculated
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microcosms was only higher in the BWW treated microcosm and not MW. This was an
unexpected consequence of the PLUP inoculation; however, the urease activity in MW PLUP
increased quickly as the experiment proceeded (25% from day 1 to day 4) suggesting that the
bacterial inoculations are actively ureolytic. Conversely, the urease activity in BWW PLUP
continued to diverge from MW PLUP and trend lower, but remained above the uninoculated
BWW. Both PLUP inoculated microcosms ended on day 4 at similar levels of activity as the
uninoculated microcosms, and both the MW and MW PLUP having significantly higher urease
activity (p < 0.05) than BWW (66% and 64%, respectively). The urease activities of MW and
MW PLUP on day 4 were also higher than BWW PLUP (41% and 39%, respectively) but not
significantly (p > 0.05).
111
Figure 27: Urease activity in incubated PL microcosms (mg NH4+ per gram dry PL per hour).
The values presented are results from the samples that were incubated with additional urea.
Activity was averaged across the three experimental replicates and standard deviation is shown
by the error bars. Significant differences were observed on day 4 (*) between MW PLUP and
BWW (p < 0.05) and between MW and BWW (p < 0.05).
Net urease activity (Fig 28) was also evaluated for each treatment by subtracting the
urease activity of the water control incubation from the urease incubation with added urea. There
are no significant differences between treatments at any time point (p > 0.05), due to the
112
relatively high activity of the water controls. The range of activity is narrow and mostly within
the levels of the initial PL since the water controls were inconsistently lower in activity
throughout the experiment. The zero line on the y-axis indicates a baseline activity, meaning the
urease activity did not change following the addition of urea. The relatively stable urease activity
indicates that urea was not limiting in these microcosms, and that the urease activity within the
microcosms presented above (Fig 27) is in fact divergent between treatments, with reduced
activity observed following the addition of BWW compared to MW.
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Figure 28: Net urease activity in incubated PL microcosms (mg NH4+ per gram dry PL per hour).
The values presented are the net result from subtracting the activity from the samples that were
incubated with additional urea from those without added urea (water control). Activity was
averaged across the three experimental replicates and standard deviation is shown by the error
bars. No significant differences were observed between treatments at any time point (p > 0.05).
In the current study, the measured urea levels and urease activity are slightly correlative.
For example, the spike in urea from the MW microcosm on day 2 was potentially indicative of
the low urease activity, declining from day 1. Additionally, the higher net urease activity from
the MW PLUP inoculated microcosm supports this assertion (Fig 28), and urea levels remained
stable compared to uninoculated. The declining urease activity found in BWW and BWW PLUP
indicates an increasingly inhibiting environment, and may correspond to the continued decline in
pH or the inhibitory action of other components (i.e., soap and methanol) found in BWW
throughout the incubation.
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CHAPTER 4: DISCUSSION
The goal of this project was to explore the structure and abundance of the microbial
communities during PL composting and how the microbes and their activities were affected by
the addition of BWW. Overall, it was expected that the BWW treatment would be equally
effective as MW in controlling pathogens, and the BWW (about pH 1) was expected to acidify
the litter thereby increasing the dominance of fungi compared to MW. The rate at which
ammonia was produced via the microbially-mediated N-mineralizing reactions was an important
consideration, and BWW was hypothesized to reduce the abundance of N-mineralizing
microorganisms and concomitantly inhibit the uricolytic and ureolytic processes.
4.1 Large scale composting
4.1.1 Differences in compost process between BWW and MW treatments
The differences in PL compost process were not expected between treatments BWW and
MW. The channels receiving BWW treatment were generally slower to increase to thermophilic
temperatures of > 55°C for several days, the requirement outlined for safe compost handling
(CCME 2005). The acidic BWW was expected to reduce the PL compost pH; and a reduced
compost pH (< 6) during the mesophilic stage will have lower biological activity (Smårs et al.
2002), thus extending the length of the process, which was observed in the large-scale
experiment. Fungi are able to tolerate a lower range of pH conditions than bacteria and
proliferate under low pH conditions in PL. For example, Rothrock et al. (2010) observed in an
incubation of acidified PL that N-mineralization still occurred but was delayed during the
transition from a bacteria dominant litter to one that is fungi dominant. This delay may provide
115
sufficient time for immobilization and assimilation of NH3. However, the use of BWW during
the large-scale composting did not produce an immediate or sustained decrease in pH for the
duration of the study, averaging 6.53 (± 0.32) compared to 7.01 (± 0.23) from MW. This was
corroborated in the PL microcosm incubations of the current study, which indicated that the pH
did not decrease lower than pH 8 after the addition of BWW from a starting pH of around 9.
Therefore, acidification of PL by BWW is not likely to be the most important factor impacting
the microbial communities and N-mineralization. The slightly higher pH in the MW treated PL
would not be sufficient to explain a higher bacterial abundance in the litter, and a faster increase
in biodegradation and thus temperature.
Contaminants from biodiesel purification end up in the BWW and have a negative, but
uncharacterized, impact on microbial growth and activity (Suehara et al. 2005; Lamers 2010).
Bioremediation of BWW is difficult and requires pre-treatment and dilution in order for fungi to
grow (Lamers, 2010). The components found in the BWW such as methanol and soaps [8k-800k
mg(L)-1
; Lamers, 2010], may be inhibiting microbial activity in the compost and thus responsible
for the reduced temperature generation.
A lower pH results in greater NH4+
in the NH4+/NH3 equilibrium (Shuler et al. 1979), but
there was no great difference in pH between BWW and MW treatments. Alternatively, a greater
amount of organic compounds in BWW would increase the level of fixation of ammoniacal-N
(Nyborg 1969), thereby reducing the amount that is volatilized from the compost (Reece et al.
1979; Elliott and Collins 1982).
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Furthermore, while low C:N ratios increase the amount of NH3 volatilization (Subair et
al. 1999), the relationship between N retention in compost and C:N is nonlinear (Larsen and
McCartney 2000). Poultry manure has a C:N ~6 (Kithome et al. 1999) whereas PL, which has
additional bulk bedding materials, has a C:N ~10 or above (Brinson et al. 1994; Edwards and
Daniel, 1992). The high carbon content of BWW [COD of 150k-750k mg(L)-1
; Lamers, 2010]
and the addition of crude glycerol was expected to increase the C:N thereby reducing NH3
volatilization, but the difference was negligible between treatments (9.7 ± 0.24 and 9.4 ± 0.19 for
BWW and MW, respectively).
4.1.2 Conclusions
This preliminary work with the BWW treatment is an important starting point regarding
the nitrogen dynamics of PL composting. While there appears to be a delay in biodegradation
and temperature generation following the addition of BWW, the cause is uncertain. The
reduction in compost efficiency at the early mesophilic stages is perhaps due to methanol and
soap contaminants affecting microbial activity. A delay of N-mineralization would provide a
longer period of time for NH3 fixation onto organic matter and assimilation by microbes thereby
reducing total N loss via NH3 once temperatures become thermophilic.
4.2 Molecular analysis of microorganisms during PL composting
4.2.1 Sampling procedure
The composite sampling design used in this study was appropriate to minimize biases
(Gilbert and Pulsipher, 2005). Often for environmental samples, composite sampling is
appropriate due to the large spatial scale and potential variance between samples. Additionally,
117
the use of a small sample size (100 mg) limits the breadth of molecular analysis for examining
large scale composting, however it was necessary for extraction by the commercial kits, and to
provide a clean nucleic extraction.
4.2.2 Optimization of DNA extraction and detection by qPCR
4.2.2.1 Extraction efficiency
Commercial extraction kits are commonly used in research and often preferred over
manual methods, but extraction purities may differ depending on the environmental matrix used
in the extraction. The FASTDNA kit for soil used in this study, was also successfully used in
prior studies of PL (Cook et al. 2008; Rothrock et al. 2008a; Rothrock et al. 2010), feces, and
various soils and sediments (Mumy and Findlay 2004; Pontiroli et al. 2011). In the current study,
the initial PL extraction using the FASTDNA kit without additional wash/lysis steps provided an
efficiency of 37.2% ± 4.4%, comparable to the previous studies. From two additional wash/lysis
steps, the efficiency increased by almost 3-fold (p < 0.05) to 95% ± 5.8%, which is considerably
higher than other procedures as outlined below.
Other studies have also examined optimizing extraction and purification of DNA
specifically from composts (Arbeli and Fuentes 2007; Yang et al. 2007), including a number of
commercially available kits (Tian et al. 2013b), as many protocols can produce pure DNA/RNA
extractions from composts that are adequate for molecular work. Previous work by Mumy and
Findlay (2004) showed that the FASTDNA Extraction Kit for Soil provides a very high quantity
of DNA, albeit with shearing due to the bead beating process. Additionally, for the kits they
tested (FASTDNA, SoilMaster, and UltraClean) they spiked sediment samples with E. coli
118
harbouring lambda-phage target DNA, and measured the extraction efficiencies and sensitivity
of detection by qPCR. The researchers also used additional wash steps; however, the additional
yield was shown to be minimal. The extraction efficiency for the FASTDNA kit was 28.3% ±
10.5% without additional washes, similarly found by other researchers and by other extraction
methods (Pontiroli et al. 2011). The extraction efficiency was even lower for the SoilMaster
extracts, averaging 2.4% ± 0.1%, supporting their decision not to proceed with that kit. One
aspect to note is that these researchers were comparing to a plasmid extraction kit (Qiagen
Plasmid Mini kit) and not to the same soil kit for their pure culture extractions, which might alter
the comparison.
4.2.2.2 Sensitivity of detection by qPCR
A larger template volume (9.5 µL) was initially used to increase the detection potential,
as per the TDL equation (Pontiroli et al. 2011); however, despite numerous attempts the PCR
inhibition was very high and unable to be alleviated by BSA (data not shown). Quantification of
gene copies was thus reported based on a template volume of 1 µL, along with the addition of
BSA [800 ng(µL)-1
] to alleviate the variable inhibition between samples, which resulted in no
measurable PCR inhibition. The use of BSA as a PCR additive is common for environmental
samples to alleviate inhibition resulting from co-extraction of humic acids (Kreader, 1996) and
was beneficial in the current work with compost as well.
Mumy and Findlay (2004) also examined the sensitivity of detection from the extraction
kits, and discovered a detection limit of 1x103 cells per 0.5 g of sediment [2 x 10
3 cells(g)
-1].
Without considering differences in the qPCR assay, the 1:100 diluted sample from the direct
119
inoculation of the current work at about 7.53 ± 3.73 x 102 cells(g)
-1 indicates the sensitivity of
detection was improved between 2 and 4 fold. The second sensitivity test supported these results,
with an indicated detection limit of 6.67 ± 1.72 x 102 cells(g)
-1. Other researchers discovered an
analytical sensitivity of detection of 4.25 x 105 cells(g)
-1 after reducing the extraction size to 100
mg for the FASTDNA kit (Pontiroli et al. 2011). The level of detection from the current study
would thus correspond to an about 800-fold improvement in sensitivity. It is clear that the type of
environmental sample and handling can be variable, thus highlighting the power of extraction kit
optimization to improve the sensitivity for detecting low-copy abundance gene targets.
The theoretical detection limit outlined by Pontiroli et al. (2011) may underestimate the
improvements that can be made in the extraction and detection of gene copies. In the equation
outlined earlier (section 3.1.3), TDL = 1/TV x 1/w x D x E, the assumed extraction efficiency is
100% which is not true for most extraction methodologies. A high efficiency is necessary to
detect gene targets that are in low abundance (e.g., pathogens) in the sample where other
parameters cannot be easily improved (e.g., increasing PCR template volume due to inhibitors).
While efficiency may not affect the detection of high copy targets, it can impact their accurate
quantification. A modification to the equation is proposed, to include extraction efficiency (E.E,
as a decimal out of 1):
TDL = 1/TV x 1/w x D x E x (1/E.E)
For example, an extraction efficiency of 10% would require the latent cell abundance to
be 1log (i.e., 10 fold) higher per gram in the sample than from an efficiency of 100% in order to
detect one cell by PCR.
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4.2.2.3 Conclusions
The FASTDNA kit may be superior to the other kits for gDNA extractions of PL and
other environmental samples. It has been used successfully by many other researchers (Lovanh et
al. 2007; Rothrock et al. 2010; Töwe et al. 2010; Pontiroli et al. 2011). The extraction sample
size (100 mg) helped to minimize co-extracted humic acid PCR inhibitors. Other researchers
have also used similarly sized samples for extractions from soil or manures (Asano et al. 2010;
Pontiroli et al. 2011). The modified extraction protocol with additional wash/lysis steps was able
to improve the detection of cells approaching the TDL, while also minimizing the high levels of
PCR inhibitors (humics) present in PL compost.
4.2.3 Determining the abundance of microbial groups by qPCR
The quantification of microbial cells based on DNA detection can indicate important
differences relevant to their abundance in environments more sensitively than culture based
approaches (Klein et al. 2011). This is most apparent in detecting specific groups within the total
community or microbes that are not culturable. The use of DNA can sometimes be limiting,
especially in environments where persistence of these molecules can interfere with the
correlation to total cell counts (England et al. 1998; Levy-Booth et al. 2007). Studies on the
persistence of DNA in PL composts have not been performed, but a preliminary test conducted
as part of this study indicated extracellular DNA added to PL resulted in a loss of about 80% of
detectable extracellular DNA after a 24h incubation at room temperature. A rapid turnover of
cells is normally found in composts due to the high rate of degradation (Partanen et al. 2010),
providing greater confidence that the quantified gene targets from qPCR are viable cells.
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4.2.3.1 Pathogens
The pathogens (i.e., Salmonella, Campylobacter jejuni) and pathogen proxy targets (i.e.,
E. coli and Enterococcus) were reduced as expected in the final compost, but Enterococcus spp.
and C. jejuni were reduced moreso in MW. A lack of detection of potential pathogens such as
Salmonella in the final composted PL are important indicators that composting proceeded
normally; however, PCR-based methods have not yet been adopted as measures of pathogen
inactivation in composts and only culture-based methods are currently used for this purpose
(CCME 2005). Similar results have been observed previously, where certain pathogens in
uncomposted PL (Salmonella, Campylobacter, or pathogenic E. coli), detected by PCR (Lu et al.
2003; Rothrock et al. 2008b) or by culturing (Martin et al. 1998), were not found. Furthermore,
Salmonella was not culturable during PL composting (Mohee et al. 2008), suggesting these
pathogenic microbes are not consistently found in PL. In this study, differences in compost
temperatures between replicate channels or possible hotspots of certain microorganisms may
explain the variable presence of Campylobacter and sometimes higher cell counts seen in BWW.
Inactivation of pathogens is time-, temperature-, and substrate-dependent (Larney et al. 2003;
Asano et al. 2010; McCarthy et al. 2011), and the presence of non-uniform or insufficient heating
within the compost can produce refuges of protection for these microbes. The detection of these
microorganisms by qPCR is not conclusive evidence they are viable and infective in the
composted litter. Consequently, this should be explored further either by attempts to culture
these pathogens or to use additional molecular methods capable of evaluating the viability of
these microorganisms, such as reverse transcription qPCR.
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The enterococci are common intestinal bacteria in poultry, and found at high levels
within the litter [7-8log CFU(g)-1
; Schefferle, 1965a]. Enterococcus abundance as determined by
qPCR in this study were 2 to 1000 fold higher than found by culturing from uncomposted PL
(Schefferle 1965a; Lu et al. 2003). Furthermore, composting of PL was found to decrease viable
fecal enterocci by 5.96log and E. coli by 8.18log (Mohee et al. 2008), a much larger reduction
than seen by qPCR in this study (in MW between 1-2log, and 6log, respectively), but again
potentially biased by cells entering the viable but not culturable (VBNC) state. Viable counts are
often much lower than qPCR (van Frankenhuyzen, 2010; Klein et al. 2011), and would perhaps
become more divergent using the optimized extraction outlined in the current work.
While E. coli is routinely used as an indicator organism for the presence of pathogenic
species, other researchers have found that the enterococci abundance may be more representative
(Mohee et al. 2008; Elving et al. 2010). From the current work, the enterococci and E. coli
decreased abundances in the final PL compost correspond to pathogen control and proper
composting for both treatments, but enterococci were significantly lower (about 4-fold) in the
final MW compost indicating a potential discrepancy between indicator organisms and
treatments. The non-significant reduction in E. coli by the final BWW treated compost is likely
due to the high variability in E. coli counts from the initial time point and not representative of a
difference between the treatments. The high variability observed in E. coli counts [31.5 ± 33.5 x
105 cells(g)
-1] was also reflected in the C. jejuni counts [47.7 ± 21.0 x 10
5 cells(g)
-1].
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4.2.3.2 Nitrogen cycling microbes
Previous work examining nitrogen cycling microbes in PL used a variety of litter
amendments to monitor changes in organic N mineralization, and correlate the changes to
microbial cell abundances (Kim and Patterson, 2003; Cook et al. 2008; Rothrock et al. 2008b;
Rothrock et al. 2010; Cook et al. 2011). These microorganisms were identified to be
representative of the microbes involved in uric acid (uricolytic) or urea (ureolytic) degradation.
Some of the microbes can perform both enzymatic reactions (Schefferle, 1965b). While an
examination of available literature yielded no studies that have examined these microbes in
composted PL or PL amended with BWW, a comparison to acidic treatments of incubated PL
may be similar in regards to the changes in the microbial communities. Previously, researchers
found that the mineralization of uric acid and urea was rapid during the period when the litter
microbial population was dominated by bacteria. Following acid treatment, N mineralization due
to bacteria decreased or ceased at which point the fungal population became dominant, and
mineralization resumed (Rothrock et al. 2010). Despite the lack of acidification in the BWW
treated PL compost of the current study, the fungal population was similarly found to increase by
the final compost, but remained about 4log lower in total abundance compared to bacteria. The
final MW treated compost remained bacteria dominant, with about 5log higher abundance of
bacteria than fungi.
Total bacteria quantified from the initial PL [7 x 1011
cells(g)-1
] corresponded to about a
20-fold increase [4 x 1010
cells(g)-1
] over previous findings (Rothrock et al. 2010), perhaps due
to variability in PL samples and differences in gDNA extraction protocols. Arthrobacter-uric of
the current study was found at similar levels [9log cells(g)-1
] as found in previous research
124
(Rothrock et al. 2010); however, Bacillus-uric [about 10log cells(g)-1
] was found to be about
200-fold higher than found previously (Rothrock et al. 2010). These two groups were previously
combined to provide an estimate of total uricolytic bacteria counts (Rothrock et al. 2010), but
they appeared to respond more uniquely to BWW in this research. The differences found in the
current study when compared to acidified incubated litters are likely due to the compost process
itself. The significant decrease in Arthrobacter (61%) in BWW treated PL but not Bacillus spp.
is intriguing since Arthrobacter spp. are known dominant bacteria in PL, and are quite resistant
to dessication despite their lack of sporulation (Lovanh et al. 2007). The Bacilli were more
sensitive to conditions found in MW than BWW, since they decreased (81%) significantly from
the initial to final compost samples. While Bacillus spp. are very common in composts (Strom
1985; Petric and Selimbašić 2008), the decline in their cell abundance but general increase in the
bacterial population may indicate other bacterial groups are proliferating, or potentially Bacilli
which do not contain the uricase gene, the target of qPCR in this study.
The ureolytic bacteria (PLUP) were found in similar proportions, 0.1-1.4% of the total
bacteria, but 10-1000 fold greater abundance [9log(g)-1
] compared to their abundance in litter
where they were originally discovery (Rothrock et al. 2008a). PLUP cell counts decreased
significantly in the final compost of the MW treated PL but not BWW. Perhaps since the
acidification by BWW was not as effective as dry acid treatments, this enabled the persistence of
these bacterial groups longer than hypothesized. Rothrock et al. (2010) reported a reduction in
pH to < 5 using dry acids; however, as mentioned earlier the BWW treatment has only a minor
effect on pH (from pH 9 to 8).
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Therefore the addition of BWW containing methanol and soap contaminants may not be
effective in reducing the abundance of all N-mineralizing groups, but the qPCR counts at the
current sampling time points may not fully reflect changes in activity affecting the rate of N-
mineralization.
4.2.4 Community analysis by DGGE
DGGE has been used extensively for profiling microbial communities in diverse
environments and composting (Haruta et al. 2005; Sasaki et al. 2005; Novinscak et al. 2009;
Xiao et al. 2011a). A comparison to litter amendments is difficult since the BWW has not yet
been examined by other researchers, nor have the microbial communities in general been
profiled in composted PL. However, similarities may be found by examining uncomposted PL
treated with amendments, or with compost systems in general since the conditions will affect the
microbial communities in a similar way. Community differences may exist between MW and
BWW treatments as a result of the length of time taken to achieve thermophilic state; however,
overall the changes in the bacterial and fungal communities were directed moreso by compost
process than treatment type, which is not always found (Zeng et al. 2011a). In cow and horse
manure compost, bacterial diversity was increasingly higher than fungal diversity until after the
thermophilic stage, at which point a reversal occurred and fungal diversity began to increase
(Tiquia, 2005). The temperature changes from mesophilic to thermophilic and then cooling
creates large disturbances for these microorganisms.
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Intra-treatment differences were found between replicates; however, the dominant
microbial community members are presented since they are often found in an even spatial
distribution as outlined previously in compost research (Novinscak et al. 2009).
The dominant species and those seemingly most affected by the treatments were
attempted to be isolated from the DGGE gel. Extracting pure single bands was difficult and
required a number of repeated attempts at purification. This is a common problem associated
with DGGE (Sekiguchi et al. 2001). After work on the 18S DGGE, it was realized that a
procedure involving the cloning of PCR amplicons, followed by screening would be most
suitable for collecting single isolated bands. High-throughput clone screening in the same lane
has also been reported to increase the efficiency of this process (Gonzalez et al. 2003).
4.2.4.1 Bacteria
Use of the conserved 16S rRNA gene sequence for analysis by DGGE functioned well to
profile the bacterial communities by providing clear banding patterns. Other researchers have
used phyla-specific (Rothrock et al. 2010) or gene specific (Kowalchuk et al. 1998) primer sets
for DGGE to focus on particular groups since extensive banding can be problematic.
The reduced diversity of the final litter samples, but similar quantity of DNA, suggests
that the remaining bands (Bacillaceae family) had become dominant in the composted litter, and
especially in MW. These species are therefore connected to the increase in quantified bacteria at
this time point. Bacillus spp., and the now reported Bacillus-like genera (such as Virgibacillus,
Lentibacillus), are commonly found compost bacteria since they can produce endospores which
are resistant to the high temperature and pH conditions (Strom 1985; Petric and Selimbašić
127
2008). Bacillus spp., are expected to be dominant in the bacterial communities of compost, and
as such suggests the MW treated PL has proceeded through proper composting (Partanen et al.
2010; Xiao et al. 2011b). However, the presence of bands similar to the anaerobic Clostridia in
BWW and lack thereof in MW suggest that differences in oxygen availability were present
during composting, potentially as a result of high moisture or poor aeration in BWW treated PL.
The decline in band presence relating to Staphylococcus spp. and Clostridium spp.
indicates a general removal of potential pathogens found in PL (Lu et al. 2003). These bands
were in low abundance but more clearly found in BWW treated PL compared to MW. The
continued presence of E. coli throughout the composting process, and especially in MW, is
potentially problematic since this organism is commonly used as an indicator species for
pathogen removal. In addition, the presence of E. coli did not appear to correspond with the
pathogens in PL based on qPCR since none were detected.
The Corynebacteria are also members of the Actinomycetes, and similar to Arthrobacter
are found in high abundance in PL (Lovanh et al. 2007). As well, similar to the genus
Arthrobacter (Ramesh 2013; Schneider 1984), many Corynebacteria are ureolytic (Nakano et al.
1984). Phylotypes of ureolytic microbes such as the Corynebacterium spp. and Staphylococcus
spp. were less abundant in MW treated PL; however, there was an increased abundance of
Bacillus-like groups (Bacillaceae family), also known to be ureolytic and uricolytic, which may
have replaced the activity of other NH3 producing groups in MW treated PL. Further work will
be needed to explore the relationship between these microbial groups and the mineralization
activity during PL composting.
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4.2.4.2 Fungi
Typically fungal richness declines during composting and is lower compared to bacteria
in composts (Tiquia, 2005; Novinscak et al. 2009). Previous compost studies found that the
dominant mesophilic fungi were yeasts, the dominant thermophilic fungi were from the genera
Penicillium, Aspergillus, and other yeasts (Petric and Selimbašić 2008). Differences were noted
in the current study, since bands likely related to Aspergillus and Penicillium were not found in
high abundance in the mature compost. While Aspergillus is a common fungus often found in
urea rich environments (Hasan 2000) such as PL (Rothrock et al. 2008b; Rothrock et al. 2010), it
is not always a dominant fungus found in all PLs (Cook et al. 2011).
Penicillium, Aspergillus, and certain Candida spp. are known uricase (Anderson and
Vijayakumar 2012), and urease producers (Hasan 2000). Their reduced band intensity in the final
compost indicates that their overall presence is diminished and provides evidence to support that
the earlier stages of composting are more conducive for N mineralization.
Scopulariopsis brevicaulis is a common soil saprotroph but also a known poultry, and
opportunistic plant, pathogen (Abbott et al. 1998). The presence of the phylotype closely
associated with this fungus, along with the results from bacterial sequencing, may suggest that
incomplete composting occurred in BWW treated PL. The presence of the band identified as
Candida rugosa (DGGE band A; section 3.2.3.1) is intriguing since it is found only in the third
replicate for each treatment. Its presence suggests a difference in the environment of channel
three to enable its proliferation in the final compost despite not being found at the earlier time
points. Other researchers have found high concentrations of lactic-acid producing yeasts such as
129
Candida when oxygen limited conditions are present (Partanen et al. 2010), which might explain
the observations from channel three.
4.2.4.3 ureC DGGE
Analysis of the diversity of functional genes is an important addition to the robust
phylogenetic markers such as the 16S rRNA gene in understanding microbial processes
performed by the environmental community.
Despite the heavy conservation at the protein sequence level, the search for primers to
optimally amplify general ureC genomic sequences is an ongoing challenge (Reed 2001; Koper
et al. 2004; Gresham et al. 2007). Use of the ureC1F/2R primer pair (Koper et al. 2004) for
DGGE was novel to this study. To the best of the author’s knowledge, there has yet to be any
reported use of a ureC PCR-DGGE protocol to examine an environmental sample. An older
primer set (Reed 2001) had been used to identify multiple isoforms of ureC found in specific
bacterial isolates (Hammes et al. 2003b), which will be discussed in more depth later. The
singularly dominant presence of the PLUP ureC sequence in the DGGE profile was a very
interesting result, and confirmed the presence and dominance of this allele in PL from Ontario,
Canada, a similar finding to that found in PL from the U.S (Rothrock et al. 2008a). While several
additional bands were noted, the relative fluorescences compared to the PLUP phylotype indicate
they are less common in the community but still make up at least 1% of the total (Muyzer et al.
1993). A larger set of ureC phylotypes were expected, considering the prevalence of bacterial
species that contain two or more ureC isoforms (Hammes et al. 2003b; Koper et al. 2004);
however it has also been noted that environmental conditions may direct a convergence of
130
function resulting in a small subset becoming the dominant group (Hammes et al. 2003a). It is
also possible that the denaturant range used was both too broad to resolve ureC sequences with
similar GC% content, and too narrow to demonstrate the full diversity of ureC sequences found
in the PL compost environment.
4.3 The isolation and analysis of ureolytic microorganisms from PL
4.3.1 Culturing of ureolytic microbes
There remains a gap in our knowledge of ureolytic microbes found in PL (Rothrock et al.
2008a) from the initial work started decades ago (Schefferle 1965b). Thus the current
examination of culturable ureolytic microbes will be a significant follow up and unique for
composted PL. Prior studies of PL have looked at culturable uricolytic microbes (Schefferle
1965b; Kim and Patterson 2003) or DNA based measures of ureolytic and uricolytic microbes
(Cook et al. 2008; Rothrock et al. 2008a; Rothrock et al. 2010).
In a sample of 6 different soils, 17-30% of culturable bacteria were ureolytic (Lloyd and
Sheaffe 1973). This was observed when ureolytic bacteria were cultured on NA (26%) and
enrichment media (16%), with the exception of the PLA plates (4%). It could be hypothesized
that the proportion of ureolytic bacteria be higher in a urea rich environment such as PL, but a
study of urea amendment to soil has indicated otherwise (Lloyd and Sheaffe 1973). Lloyd and
Sheaffe (1973) suggested that extracellular ureases, released from dead cells, provided urea-
derived nitrogen to non-ureolytic bacteria. It was hypothesized that the novel poultry litter
extract agar (PLA) plates would allow the greatest number of ureolytic bacterial species to
proliferate since the nutrient profile should be more attuned to the environment from which the
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microbes originated. While the PLA was able to culture a wide diversity of bacterial isolates,
nutrient agar (NA) produced the greatest percent which were ureolytic. Furthermore, the
additional enrichment media used by other researchers to specifically enrich and culture ureolytic
bacteria (Al-Thawadi and Cord-Ruwisch 2012) was also second to NA in this respect, and
additionally showed limited morphological diversity of isolates biasing colony selection. Slower
growing ureolytic bacteria may also be under represented by the enrichment media because the
fastest growing colonies are more commonly selected. This might in part explain the lack of
PLUP ureC isolates discovered in this media, which were found on both PLA and NA plates.
4.3.2 Phylogenetic analysis of isolates
DGGE band sequencing and phylogenetic comparisons with the amplified 16S rRNA
gene sequences from the ureolytic isolation study was important in understanding the identities
of the bacterial community members involved in N cycling. Certain isolates and 16S DGGE
bands closely matched uncultured organisms, and this is not surprising considering the low
percentage of cultured microbes in the environment (Amann et al. 1995). This further highlights
the general knowledge gap that remains to be filled. The cultured isolates can now be
characterized further to produce a more definitive identity.
4.3.3 Discovery of novel ureolytic bacteria
Ureolytic bacteria are continually being discovered in diverse environments such as
groundwater (Gresham et al. 2007) and soils (Burbank et al. 2012). Many species from the
current study were previously known to be ureolytic such as those belonging to the Bacillus
genus: B. cereus, B. megaterium, B. subtilis, and B. aryabhattai, (Jahns and Kaltwasser 1989;
132
Rasko et al. 2004; Kim et al. 2005; Ray et al. 2012), as are species from closely related genera
such as Lysinibacillus (L. sphaericus and L. boronitolerans) (Yang et al. 2012) and Sporosarcina
(S. ureae, S. newyorkensis) (Gruninger and Goldman 1988; Kämpfer et al. 2010; Wolfgang et al.
2012). Many Staphylococcus spp. are common in PL (Fries et al. 2005; Lovanh et al. 2007), and
known to be ureolytic (Staphylococcus equorum subsp. linens, S. succinus subsp. succinus)
(Lambert et al. 1998; Place et al. 2003).
In contrast, certain species which were originally described as non-ureolytic or not
containing urease genes have recently been observed to be ureolytic. As an example, urease was
not found from a genome survey of Bacillus licheniformis (Rey and Ramaiya 2004), but was
identified as ureolytic in the current study and by another researcher group (Gresham et al.
2007). This organism was previously found in PL (Schefferle 1965a).
Novel ureolytic activity was also discovered in species previously identified as non-
ureolytic: isolate NA M2 col 1 (Bacillus amyloliquefaciens, Singh et al. 2013), PL M5 col 8
(Oceanobacillus caeni, (Nam et al. 2008), and NA M3 col 3 (Sporosarcina luteola, (Tominaga et
al. 2009). Isolate NA M3 col 14 matched several different species which, except for B. cereus,
are non-ureolytic (Yang et al. 2012): Lysinibacillus xylanilyticus, and Bacillus macroides
(Coorevits et al. 2012) and Bacillus fusiformis (Ahmed et al. 2007) which were recently
transferred to the genus Lysinibacillus.
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4.3.4 Variable ureolytic activity of isolates
Variable urease activity has been observed in a number of species, such as Bacillus
galactosidilyticus (Heyndrickx 2004) which corresponds to PL isolates found. The final
expression of urease is dependent on the species and conditions surrounding the cell (Mobley et
al. 1995; Dupuy et al. 1997). The presence or lack of urea, nickel, or alternative nitrogen sources
will influence the bacterium to produce urease. Most surprising was the discovery of
Virgibacillus soli in this study, shown to contain the PLUP ureC gene, but was originally
characterized as a non-ureolytic soil bacterium (Kämpfer et al. 2011) as were other Virgibacillus
spp. (V. arcticus, V. kekensis) (Chen et al. 2008; Niederberger et al. 2009). The V. soli isolate
from PL is a slow ureolytic bacterium, taking four days for the distinctive pink colour to appear
on the urea agar. The urease indicator test is often limited to two days in length (Lloyd and
Sheaffe 1973) due to the possibility of spontaneous protein hydrolysis resulting in a false
positive reaction. However, this was not observed in the negative controls of this study or
observed by researchers who incubated urea plates for several weeks (Gresham et al. 2007). As
such, a longer incubation time allowed the slower ureolytic microbes such as V. soli to be
discovered.
Chromosomal rearrangement or the presence of an active plasmid-borne urease operon
may explain both the unstable and variable activity of certain isolates and the presence of activity
in V. soli (Wachsmuth et al. 1979; Collins and Falkow 1988; Collins and Falkow 1990; D’Orazio
and Collins 1993; Mobley et al. 1995; Dupuy et al. 1997). In environmental strains, it is highly
likely that plasmid-borne ureases would play an advantageous role in urea rich environments,
such as PL. An interesting finding from transformations of E. coli with multi-copy urease
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plasmids was that the resultant urease activity did not exceed the activity of the wild-type species
(Mobley et al. 1995). This might imply that a larger advantage exists for the transformation of
non-ureolytic strains than those already capably ureolytic. Further work with these cultured
isolates will elucidate the presence and activity of multiple urease isoforms and isozymes.
4.3.5 Identification of PLUP ureC in several isolates
The discovery of three unique isolates each containing the PLUP ureC sequence reveals a
great deal of information regarding the nature of the PLUP isoform. Previous work with PLUP
did not ascribe bacterial identity to this ureC (Rothrock et al. 2008a), thus is it unclear how many
bacteria may contain this operon, only that it is a dominant allele found within PL. Based on the
rate of growth of the PLUP isolates, the function of this urease operon is likely correlated to its
growth. This contrasts with other isolates which were noted to either proliferate quickly and
produce colour slowly (NA M1 cols 2 and 7), or vice versa (NA M1 cols 6, 12, 16).
The Corynebacterium ammoniagenes ureC amino acid sequence (or PLUP, by its
nucleotide sequence) was one of the first isolated ureases that were sequenced (Nakano et al.
1984), hence the species name. The species was originally found in the genus Brevibacterium,
but was later reclassified as C. ammoniagenes (Collins 1987). While this bacterium was not
cultured from the PL sample, this is likely due to the high temperatures at the stage of
composting, which is not conducive to its growth. Other Corynebacteria, including C.
ammoniagenes, have previously been found in PLs (Shefferle 1965b; Lu et al. 2003; Lovanh et
al. 2007) but the connection to PLUP ureC had not been made. Once PLUP was discovered, the
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researchers observed specificity of the target gene to PL and PLUP was not found outside of
broiler houses (Rothrock et al. 2008a).
The high stability at thermophilic temperatures and neutral to basic pH indicates that the
urease from C. ammoniagenes would be ideal in compost environments. Further characterization
of the PLUP ureC would be necessary to determine if the properties are the same.
4.3.6 Evidence of horizontal gene transfer (HGT) among PL bacteria
While the identification of urease genes is limited due to the minimal number of ureC
genes that have been sequenced and fewer complete urease operons (Mobley et al. 1995), the
presence of similarly identified ureC partial gene sequences in diverse bacterial isolates indicates
a degree of convergence and thus horizontal gene transfer (HGT) in regards to ureolytic function
within PL.
HGT is an important phenomenon associated with prokaryote evolution (Ochman et al.
2000; Koonin et al. 2001). Selective advantages exist for bacteria that can incorporate functional
genes such as urease that increase their nitrogen acquisition, or ureases that are more stable at
higher temperatures (Magana-Plaza et al. 1971; Nakano et al. 1984). These genes may be co-
selected along with resistance genes (e.g., antibiotic resistance) which are commonly transferred
on mobile genetic elements (Wachsmuth et al. 1979; Dupuy et al. 1997; Furuya and Lowy 2006).
Successful PCR amplification of the ureC genes from gDNA extracts of the isolates of the
current study indicates either a co-extraction of plasmid DNA containing the ureC gene, or ureC
may be located within the bacterial genome but transferred via gene transfer agents (GTA) which
136
are remnants of virus particles that encapsulate fragments of DNA and enable their transfer
between bacterial cells (McDaniel et al. 2010; Lang et al. 2012).
Gene and genome sequencing has expanded the phylogenetic information of urease
evolution. Multiple phylogenetically divergent ureC genes were previously found in bacteria via
sequencing efforts (Koper et al. 2004; Gresham et al. 2007). The observed congruency between
the 16S and partial ureC gene sequence identities for B. licheniformis, B. megaterium and
possibly Lysinibacillus spp., was not common among the other isolates. Isolates identified as
Staphylococcus spp. and B. galactosidilyticus do not have ureases identified as such. Certain
urease isoforms would necessarily be more active than others in PL, and likely provide an
advantage in the rich nitrogen environment of PL. While most microbes utilize the products of
urea hydrolysis, some work has been done to characterize urease for alternative purposes, such as
a storage protein during times of limiting nutrients (Zawada and Sutcliffe 1981), or as a source of
ATP energy which was uniquely found in Ureaplasma ureolyticum (Romano et al. 1980; Smith
et al. 1993). It is unclear what the effects are of the various common isoforms of ureC found in
the PL of this study, such as the dominant ureC identified as Bradyrhizobium sp., but further
work should necessarily be undertaken to understand the rich diversity of these, most likely
functional, ureases.
Caution may be warranted in the phylogenetic analysis of these isolates considering the
general ureC primers have targeted one section of one gene of the urease operon. Previous
studies also only found partial congruency between 16S and ureC genes, concluding that urease
(ureC) was not a robust phylogenetic marker due to HGT (Gresham et al. 2007). It is unclear at
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this time how the phylogenetic comparisons might differ between isolates once more genetic
information is available, and particularly for distinguishing between urease isoforms.
Additionally, given the limited number of urease sequences available, the order they were
discovered will impact the potential identity of novel ureases, unless the full length gene is
sequenced. An interesting point to note is that more complete congruency was noted between
16S rRNA and the uricase gene sequences than urease, indicating a much more conserved
evolution of uricase alongside rRNA (Gresham et al. 2007).
4.3.7 Conclusions
By studying urease activity in diverse environments, researchers can try to understand the
total diversity of its genetic structure, regulation, functions and activities. By performing both
phylogenetic analysis and functional activity of bacterial isolates, knowledge of the types and
characteristics of the ureolytic microbes in PL was expanded. Partial gene sequencing of the
urease ureC subunit revealed that a significant gap was present in our understanding of urease
and adaptation in PL. Along with almost full-length 16S rRNA gene sequencing, this experiment
has been pivotal in determining the identities of many culturable, and previously uncultured,
ureolytic microorganisms found in composting PL and likely novel species or strains including
the PLUP group which is unique to PL (Rothrock et al. 2008a).
4.4 Microcosm incubation experiment comparing BWW and MW treatments for N-
mineralization and microbial ureolytic activity
As the compost transitions out of the mesophilic stage into the thermophilic, much of the
remaining ammonia that is not assimilated by microbes or immobilized has the potential to
138
volatilize and can be lost to the environment (Ryckeboer et al. 2003; Liang et al. 2006). An
incubation experiment was used to examine nitrogen mineralization in PL, as performed by other
researchers who looked at uric acid and urea levels in PL (Rothrock et al. 2010; Murakami et al.
2011). From a preliminary test, it was concluded that four days appeared sufficient to capture
important changes in nitrogen dynamics in the beginning mesophilic stages of compost (37°C),
and sampling methods for the urea extraction and urease assay were adequate. Differences in
urea and urease activity between BWW and MW treatments were observed at this time. In the
follow up experiment, MW and BWW treatments with and without inoculations with the recently
cultured and identified ureolytic PLUP microbes (in this study) were performed to emphasize
ureolytic activity between treatments.
4.4.1 Moisture content (MC%) and aerobicity of microcosms
Maintaining complete aerobicity during composting is likely not achievable (Atkinson et
al. 1996). While periodic turning was used in the current study and by other researchers, it is
perhaps not as effective as passive aeration (Fernandes et al. 1994; Jackson and Line 1998).
Larsen and McCartney (2000) reports that higher levels of aeration would occur should more
bulking materials be used. Fibrous bulking materials such as wood chips, as found in PL, are
thus able to maintain a higher total MC% while retaining free air space.
The MC% commonly achieved for composting ranges between 50-60% (Larsen and
McCartney 2000; Guan et al. 2007; Amanullah et al. 2010). The MC% was hypothesized to be
similar between treatment types, since the same volume to weight ratio was used for MW and
BWW microcosms. The dissimilarity persisted regardless of PLUP inoculation, and may have
139
been due to the high solids content in BWW (Lamers 2010). Low levels of moisture will hinder
microbial growth and the activity of urease (Nahm 2003) and uricase which leads to the
accumulation of uric acid (Murakami et al. 2011). Higher levels of moisture increase the
availability of substrates and activity of microbes leading to mineralization. Very high moisture
leads to anaerobic conditions, slowing composting, but also reduced NH3 volatilization
(Mahimairaja et al. 1994; Nahm 2003; Amanullah et al. 2010). While uricolytic and ureolytic
microbial activities occur aerobically and anaerobically (Schefferle 1965b; Lloyd and Sheaffe
1973), aerobic activity is more prevalent in PL composting. Since no anaerobically growing
ureolytic bacterial isolates were cultured (see section 3.3.2), the total contribution towards NH3
production would be severely reduced in anaerobic conditions.
4.4.2 pH and enzymatic activity within microcosms
The immediate decline in PL pH was expected from BWW treatment since it is acidic
(pH of 1); however, the similar acidity following MW treatment suggests the decline is perhaps
due moreso to the dilutive effect of water addition. For the remainder of the incubation, the pH
levels diverged between treatments (with no change from PLUP inoculation) indicating the
BWW has a continual but minor effect and increases the acidity of the PL, while MW proceeded
to become more basic as expected (Schefferle 1965a). Given the high organic content of BWW,
it is possible that acidic intermediate breakdown products compounds are also being produced
thus lowering the pH of the compost (Liang et al. 2006). The fluctuation of pH within a fairly
narrow range (between pH 8 and 9), despite the addition of the highly acidic BWW, was more
conclusive that BWW should not hinder the compost process on the basis of pH. This would
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support the conclusion that alternative characteristics of the BWW, such as the methanol or
soaps, are reducing microbial activity and thus also reducing N mineralization.
4.4.3 N mineralization to urea
Rapid mineralization was observed in this study as a result of the beneficial conditions
provided for the uricolytic and ureolytic microbes. In the microcosms, the temperature of 37°C
and pH range between 8 and 9 was conducive to ureolytic activity for many known ureases
(Magana-Plaza et al. 1971; Breitenbach and Hausinger 1988; Mobley and Hausinger 1989;
Mobley et al. 1995), and in fact are conditions used to incubate samples during the urease assay
(Douglas and Bremner 1970).
Previous work on incubated PL with and without dry acid treatment observed a slower
rate of urea mineralization (over a 16 week period), with an initial measured increase in urea
content only after two weeks, but urea levels between their untreated control and MW of the
current study were comparable at about 7000 mg(kg)-1
and peaking at around 16, 000 mg(kg)-1
(Rothrock et al. 2010). Rothrock et al. (2010) used a lower temperature (25°C) and water content
[35% w(w)-1
] which was similar to studies of land applied PL which showed similar rates of
mineralization (Edwards and Daniel 1992). Another study incubated PL at 25°C for seven days,
and examined the correlation of MC% to uric acid degradation (Murakami et al. 2011). At
moisture levels of the current experiment (> 55%), they found a rapid decline in total uric acid
within two days, which corresponds well with the rapid increase in urea found in this work for
MW. The reduced urea generation in the BWW microcosm is thus suggestive of inhibitory
effects of BWW on N-mineralization.
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From the work of Rothrock et al. (2010), it was observed that the mineralization of uric
acid and urea was rapid during the period when the litter was bacteria dominant, and decreased
or ceased after an acidic treatment at which point fungi became dominant, and then
mineralization resumed. The considerable inoculation of active bacteria (PLUP) undoubtedly
played a large role in the ureolytic capacity of these microcosms. Based on a comparison of MW
and MW PLUP between days 2 and 3, the PLUP bacteria had effectively reduced the amount of
urea by 2000 mg(kg)-1
in one day. In a fairly standard sized broiler house, there are 2 x 104 birds
each producing 1.5 kg of litter over a 10 week growing period (Perkins et al. 1964). A 2000
mg(kg)-1
per day rate of urease activity by PLUP thus corresponds to 4.86 x 105 mg of NH3 per
day per house, which is below the range of estimates (1.5 x 106 to 5.9 x 10
7 mg of NH3)
calculated for PLUP following its discovery (Rothrock et al. 2008a). However, the value is
perhaps even lower than expected considering the higher PLUP concentration both in the
inoculum [about 8.4 x 109 CFU(g)
-1] and found by qPCR of the raw litter [about 5 x 10
9 cells(g)
-
1] of the current study compared to the previous published work (Rothrock et al. 2008a). Overall,
this work encouragingly supports the prior conclusion that the PLUP group likely has a
significant role in the generation of NH3 from PL.
Two of the three dry acid treatments used by Rothrock et al. (2010), Al+Clear and
Poultry Guard, most closely resemble the conditions of BWW treatment since the final pH
reached above 7. Total urea for these treatments was measured at between 2-9000 mg(kg)-1
for
the early stages of the incubation, and proceeded to increase above 10, 000 mg(kg)-1
which was
not reflected in the current BWW treated microcosms, but likely due to the shorter time scale. An
extended time scale for this experiment may indicate a point at which the mineralization resumes
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and increases, should the microbial populations follow the trends indicated by Rothrock et al.
(2010) from acidic treatment of PL.
4.4.4 Explanations for the minimal accumulation of urea in BWW treated PL microcosms
The reduced urea generation suggested that BWW was inhibitory to uricase since it is the
first committed step leading to urea accumulation, but potentially inhibiting intermediate steps as
well. Using uricase as a proxy for the preliminary steps prior to urea formation, the effects of the
BWW treatment may be explained by several hypotheses relating to characteristics of BWW;
acidity, aerobicity of the litter post liquid treatment, carbon content, and specific components
such as methanol and soaps.
The slightly reduced pH (still alkaline) conditions found in the BWW treated microcosms
was not sufficient to explain reduced uric acid catabolism, as outlined earlier. Secondly, should
the free air space and thus oxygen content be reduced following BWW treatment, this might
explain the reduced uricase activity; however, the mechanism by which BWW reduced total
oxygen in the microcosms was not apparent and not likely since the MC% was even lower than
from MW. Alternatively, it is plausible the addition of BWW which contains a high
concentration of organic carbon might induce catabolite repression of uricase, as observed from
the addition of glucose and other readily assimilable carbon sources (Shuler et al. 1979).
Similarly, methanol and soaps inhibiting the growth of microbes would thus also affect uricolytic
processes. These compounds were likely involved in the slower transition to thermophilic
conditions in the large scale composting. Follow up work may be considered to evaluate the
direct influence of these components on N mineralization.
143
The intermediate enzymes leading to urea degradation by urease (Vogels and van der
Drift 1976; Kim and Patterson 2003), may also be affected by the BWW treatment and thus
intermediate substrates accumulated instead of urea. Previous work with poultry manure found
that zinc and copper greatly inhibited uricase but not enough to explain the retention of total N,
and thus postulating that the intermediate enzymes to producing NH3 may also be affected by the
metals (Kim and Patterson 2003). The impact on these processes upstream of urease was not the
focus of this work, and the lack of knowledge surrounding the intermediate steps would first
need to be remedied prior to experiments looking to fully elucidate how they are affected by
BWW.
4.4.5 Urease activity
The urease assay provides an estimate of the activity of the ureolytic potential of an
environmental sample, and includes all available ureases (intracellular and extracellular). Urease
activity has been measured in soils amended with poultry manure (Mian and Rodriguez-Kabana
1982; Martens et al. 1992; Tejada et al. 2006; Tejada et al. 2007; Tejada and Masciandaro 2011);
however, there is seemingly no literature regarding the direct measurement of urease activity in
PL or during PL composting despite the more considerable research on N-mineralization and
NH3 production which is influenced by ureolytic activity.
The methods used in this study to measure urease activity were modeled from soil studies
(Tabatabai and Bremner 1972, Kandeler and Gerber 1988), of which many variations exist. The
water controls introduced variability to the urease measurements per microcosm, as they were
not consistently lower than the urea inoculated samples. The samples were already maximizing
144
the inherent urease activity since there was no increase in urease activity following urea
application (Figs 27 and 28; section 3.4.4). Lloyd and Sheaffe (1973) similarly examined urease
activity in soils, and also found a lack of difference between the water control and the urea
amended sample.
A directly timed correlation may not exist between urea levels and urease activity since
urease can make up a considerable percentage of the total cell dry weight of microbes (Mobley
and Hausinger 1989) and may be used as storage proteins, thus not all ureases are normally
active at a particular time. However, in this study, urease appears to correspond with the urea
measurements. A calculation of the amount of ammonia produced on day 2 from PLUP (MW
PLUP minus MW; Fig 28 section 3.4.4), yields 0.69194 mg of NH4+(h)
-1(g)
-1, which is equal to
6.92 x 102 mg NH4
+(kg)
-1(h)
-1. Following the calculation as laid out for urea, the ureolytic rate of
NH3 generation is therefore equal to 7.12 x 106 mg NH4
+(kg)
-1(day)
-1, which is within the range
estimated for PLUP previously (Rothrock et al. 2008a) and almost 10-fold higher than from the
urea data (perhaps more reflective of the higher cell abundance). In the characterization of the
urease from C. ammoniagenes, it was found that the enzyme was stable but less active at
temperatures below 50°C (Nakano et al. 1984), and at 37°C the urease activity was about 50% of
that measured at 65°C. Should the PLUP urease be active at thermophilic temperatures, as is the
urease from C. ammoniagenes, the activity may in fact be much higher during PL composting
than observed from the mesophilic incubated microcosms of this study.
145
4.4.6 Conclusions
The measured reduction in urea production by uricase (from uric acid) and urease activity
in the BWW treated PL continues to expand the experimental evidence that pertains to the
inhibitory effect BWW has on nitrogen mineralization and subsequent NH3 volatilization.
The rapid mineralization of organic nitrogen forms observed in the incubated PL
microcosms supports the view that these systems represent a model for future work on the
nitrogen cycle, including the uricolytic/ureolytic reactions and downstream processes (such as
ammonia assimilation and oxidation). The more pronounced and short time period of the current
microcosm experiment was suitable in revealing a major difference in mineralization due to the
BWW treatment compared to MW. A longer time scale experiment would be necessary to model
total N-mineralization rates in PL composts, as done for soils (Stanford and Smith 1972).
146
CHAPTER 5: SUMMARY AND CONCLUSIONS
This study was undertaken as a preliminary assessment of the microbiology of poultry
litter (PL) compost, with a focus on the microbially-mediated N-mineralization leading to NH3
volatilization and thus N-loss, and pathogen presence in the final compost. Inorganic nitrogen
forms (e.g., NO3-, NH4
+) are necessary for plant uptake, but the rapid mineralization observed
during composting is not ideal for retaining high amounts of N in the PL. A higher total N-
retention requires that mineralization is slowly completed over time to allow immobilization into
the soil, and assimilation by microbes. To reduce N-loss from this system and to maximize its
downstream utility as an organic soil amendment, biodiesel wash water (BWW) was used as an
amendment during PL composting. A large scale composting trial was completed in 2011, and
the results indicated the BWW (with glycerol) treatment was as successful as the municipal
water (MW) control (with glycerol) at reducing NH3 levels compared to a previous compost trial
with only MW. A reduction in microbial activity due to BWW is an important factor relating to
the reduced N-loss via NH3. A delay in N-mineralization would provide a longer period of time
for NH3 immobilization by organic matter and assimilation by microorganisms. Molecular
analysis was performed in conjunction with an optimized DNA extraction protocol to investigate
potential changes in the microbial communities via DGGE and abundance of microbial groups
via qPCR. This approach indicated slight differences in bacterial or fungal community structure
between the treatment types, which supports the use of BWW. Additionally, a lower abundance
of total bacteria, including family Bacillaceae phylotypes and Arthrobacter spp., in the final
compost support the conclusion that the BWW treated PL was less suitable for many known
ureolytic bacteria than MW. The greater abundance of Bacillus spp., and potentially
147
Staphylococcus and Corynebacterium spp. as well, in the final BWW treated PL compost may
reflect an increased ureolytic activity but this is not currently unknown. While total fungal
populations increased in BWW, the known ureolytic group tested (Aspergillus) did not, thus it is
plausible that total ureolytic activity was lower as well. Further work will be needed to explore
the relationship between these microbial groups and the mineralization activity during PL
composting. Finally, significantly lower enterococci abundances in the MW treated compost,
along with the continued presence of C. jejuni in the BWW final compost samples suggested
reduced pathogen control in the final BWW compost, potentially due to the slower rise to
thermophilic temperatures. This aspect requires additional research and demonstration of
pathogen viability. Future trials also looking to optimize the composting process may attempt to
use various dilutions of BWW to reduce the impact on the microbial activity thereby increasing
the rate of temperature development.
The use of a culture-based approach for identifying the PLUP group of ureolytic bacteria
in composted PL was unique, and important for environmental N cycling research as none had
been cultured previously. Consequently, knowledge has been gained in terms of the types of
ureolytic microorganisms found in PL, the identities of previously unknown ureolytic bacterial
species, urease phylogeny and the possible impacts of gene transfer. Work in this area will
garner insights into understanding nitrogen dynamics in PL with potential future applications
towards understanding microbially mediated N loss through NH3 volatilization. From the ureC
DGGE results, and work by previous researchers, the PLUP bacterial group is dominant in PL
systems and thereby confirming its utility as a target for testing treatments affecting the broader
urease producing community. Now, with our recently cultured isolates and a preliminary
148
understanding of the types of microbes carrying the PLUP ureC isoform, further studies can be
performed using these cultured isolates. The use of genome sequencing of the ureolytic isolates
from this study, and PLUP in particular, would expand our understanding of the genetic
regulation, horizontal gene flow and evolution of urease. For example, one immediate question
that arises is whether PLUP is plasmid-borne or genomic. A repertoire of highly active urease
producers can also lead to other applications such as in microbially induced carbonate
precipitation with wide ranging applications. The carbonate released during urea hydrolysis can
combine with calcium ions to form calcium carbonate and its polymorph calcite, which may be
used as ‘biocement’ for geoengineering projects, or for the co-precipitation of divalent metals or
actinides for bioremediation projects (Burbank et al. 2012; Connolly et al. 2013; Cheng and
Cord-Ruwisch 2013).
Information gathered from the microcosm work further supported the use of BWW to
reduce NH3 volatilization by inhibiting microbially-mediated N-mineralization processes;
including urease activity and upstream enzymes converting uric acid to urea (uricase) as well.
The inoculated PLUP bacteria were able to degrade urea within the PL, and while the BWW may
not influence the total abundance of the PLUP bacteria, it affects their ureolytic activity.
Overall, even a similar urease activity found between BWW and MW treatments but higher urea
levels in MW would culminate to greater amounts of NH3 loss. Furthermore, the fluctuation of
pH within a fairly narrow range (between pH 8-9) following the addition of the highly acidic
BWW, more conclusively demonstrated that BWW does not hinder the compost process via pH,
but by alternative characteristics of the BWW such as the soaps or methanol also present.
149
Overall, the microcosms represent a strong model for future work on the nitrogen
dynamics of PL such as the uricolytic and ureolytic reactions. The more pronounced and short
time period of the current microcosm experiment was suitable in revealing a major difference in
the early stages of N-mineralization due to the BWW treatment compared to MW. A longer time
scale experiment would be necessary to model total N-mineralization rates and changes in pH in
PL composts as done for soils.
The biodiesel wash water (BWW) amendment for PL composting is a promising addition
to the repertoire of composting facilities and biodiesel producers as a means to improve the
sustainability of both industries. The inhibitory impact of BWW on the abundance and activity of
N-mineralizing microbes, thereby improving N retention and reducing N-loss via NH3, is likely
related to characteristics alternative to its low pH, which will need to be assessed along with
confirming pathogen safety. Poultry litter is a microbially-rich environment which represents an
important substrate for research on nitrogen dynamics and the production of ammonia. While
much is known of the enzymes uricase and urease, the dominant ureolytic and uricolytic
microbial groups and their impact on N-mineralization in high uric acid substrates such as PL are
not well known. From the current study, an important ureolytic bacterial group was discovered
(PLUP) and is likely to provide further insights into N-mineralization and urease regulation in
the environment.
150
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