Phylogenetic Diversity and Ecophysiology Of
Phylogenetic Diversity and Ecophysiology of
Alphaproteobacterial Glycogen Accumulating Organisms in
Enhanced Biological Phosphorus Removal Activated Sludge
Systems
Submitted by
Simon Jon McIlroy
Bachelor of Applied Science (Honours)
La Trobe University
A thesis submitted in total fulfilment
of the requirements for the degree of
Doctor of Philosophy
School of Molecular Sciences
Faculty of Science Technology and Engineering
La Trobe University,
Bendigo, Victoria, 3552
Australia
December 2010
Table of contents
Abbreviations xiv Summary xvii Statement of authorship xix List of publications xx Acknowledgements xxiv 1.0 Introduction 1
1.1 The requirement for nutrient removal in the treatment of wastewater ...... 1 1.2 The application of activated sludge to the treatment of wastewater...... 2 1.2.1 Enhanced biological phosphorus removal ...... 2 1.2.2 Nitrogen removal ...... 6 1.3 The need for microbiological studies on P removal ...... 7 1.4 Biochemical models of EBPR and PAO metabolism...... 7 1.4.1 Anaerobic metabolism of the PAO...... 8 1.4.1.1 The uptake of volatile fatty acids (VFAs)...... 8 1.4.1.2 The source of anaerobic reducing equivalents...... 10 1.4.2 Aerobic metabolism of the PAO...... 11 1.4.3 The need for microbiological data in PAO modelling...... 11 1.5 How do we study the complex communities in EBPR systems?...... 12 1.5.1 Culture dependent methods...... 12 1.5.2 Culture independent methods: The ‘Full rRNA cycle’ approach ...... 14 1.5.2.1 Obtaining 16S rRNA gene sequence information from EBPR systems ...... 14 1.5.2.2 In situ analysis of populations...... 18 1.5.2.3 The functional phylogenetic unit: limitations of the 16S rRNA gene?...... 19 1.6 Which are the important PAO in EBPR systems? ...... 20 1.6.1 The putative gammaproteobacterial PAO...... 20 1.6.1.1 The acinetobacterial PAO...... 20 1.6.1.2 Stentrophomonas sp. EBPR-1...... 23 1.6.2 The betaproteobacterial PAO...... 23 1.6.2.1 Lampropedia hyalina...... 23 1.6.2.2 Candidatus ‘Accumulibacter phosphatis’...... 28 1.6.2.2.1 Intraphylotypic diversity among ‘Accumulibacter’ ...... 28 1.6.2.2.2. Anaerobic metabolism of ‘Accumulibacter’...... 30 1.6.2.2.3. Aerobic metabolism of ‘Accumulibacter’...... 32 1.6.2.2.4 Can ‘Accumulibacter’ denitrify?...... 33 1.6.2.3 Are members of the Dechloromonas PAO? ...... 35 i 1.6.3 Actinobacterial-related PAO...... 36 1.6.3.1 Microlunatus phosphovorus...... 36 1.6.3.2 Tetrasphaera-related PAO...... 37 1.6.3.2.1 Metabolism of the Tetrasphaera PAO...... 38 1.6.4 Other possible PAO ...... 39 1.7 The glycogen accumulating organisms (GAOs)...... 40 1.7.1 The anaerobic metabolism of the GAO ...... 41 1.7.2 Aerobic metabolism of the GAO ...... 42 1.8 The phylogenetic identity of the GAO...... 42 1.8.1 The gammaproteobacterial GAO - Candidatus ‘Competibacter phosphatis’...... 43 1.8.1.1 Anaerobic metabolism of the ‘Competibacter’...... 45 1.8.1.2 Aerobic/anoxic metabolism of the ‘Competibacter’...... 45 1.8.2 Alphaproteobacterial-related GAO...... 48 1.8.2.1 Amaricoccus sp...... 48 1.8.2.2 Sphingomonas-related GAO ...... 48 1.8.2.3 Defluviicoccus-related GAO...... 49 1.8.2.3.1 Anaerobic metabolism of the Defluviicoccus-related GAO ...... 50 1.8.2.3.2 Aerobic metabolism of the Defluviicoccus-related GAO ...... 51 1.8.3 Other putative GAO...... 52 1.9 Competition between the PAO and GAO populations in EBPR ...... 53 1.9.1 Carbon source ...... 53 1.9.2 pH...... 57 1.9.3 Temperature ...... 59 1.9.4 Carbon to phosphorus ratio (C:P)...... 60 1.9.5 Sludge retention time (SRT) ...... 60 1.9.6 Dissolved oxygen (DO) levels...... 61 1.10 Aims of this study ...... 62
2.0 Flow cytometry assisted identification of GAO in lab-scale EBPR systems...... 63
2.1 Introduction...... 63 2.2 Materials and methods ...... 65 2.2.1 Operation of the EPBR SBR reactors ...... 65 2.2.2 Cell fixation ...... 66
ii 2.2.3 Fluorescence in situ hybridisation (FISH) ...... 66 2.2.4 Fluorescence microscopy...... 67 2.2.5 Quantitative FISH (qFISH)...... 67 2.2.6 Flow cytometry ...... 69 2.2.6.1 FISH labelling of cells ...... 69 2.2.6.2 Sorting of Alphaproteobacteria ...... 70 2.2.6.3 Sorting of ‘Competibacter’...... 70 2.2.7 Extraction of DNA from FACS sorted biomass ...... 70 2.2.8 16S rRNA gene clone library construction...... 72 2.2.8.1 16S rRNA gene clone library PCR ...... 72 2.2.8.2 Ligation and transformation...... 72 2.2.8.3 Plasmid extraction ...... 74 2.2.8.4 Colony PCR ...... 74 2.2.8.5 DNA electrophoresis ...... 75 2.2.8.6 Sequencing and phylogenetic analysis ...... 75 2.3 Results and discussion ...... 76 2.3.1 SBR community compositions ...... 76 2.3.2 Flow cytometry sorting of the Alphaproteobacteria ...... 78 2.3.3 Clone library analysis of ALF968 FACS sorted cells ...... 78 2.3.4 Could FACS also assist in uncovering further diversity among the ‘Competibacter’? ...... 81 2.4 Conclusions ...... 84
3.0 Developing methods for nucleic acid extraction from activated sludge samples which reflect community population diversity ...... 86 3.1 Introduction...... 86 3.2 Materials and methods ...... 88 3.2.1 Sampling ...... 88 3.2.1.1 Pure cultures ...... 88 3.2.1.2 Lab-scale EBPR activated sludge samples ...... 88 3.2.1.3 Full-scale EBPR activated sludge samples ...... 89 3.2.2 Nucleic acid extractions ...... 89 3.2.2.1 NaTCA method (MI) ...... 90 3.2.2.2 Method of Corgié et al. (2006) (CR) ...... 91 3.2.2.3 Metho d of Costa et al. (2004) (CS) ...... 91
iii 3.2.2.4 Method of Griffiths et al. (2000) (GR) ...... 91 3.2.2.5 Method of McVeigh et al. (1996) (MV)...... 92 3.2.2.6 Method of Orsini and Romano-spica (2001) (OR)...... 92 3.2.2.7 Method of Tillett and Neilan (2000) (TN)...... 93 3.2.2.8 Method of Yu and Mohn (1999) (YM)...... 93 3.2.2.9 Method of MoBIO© Soil DNA Kit (MO)...... 93 3.2.3 Post-extraction biomass lysis assessment using SYBR Gold© staining ...... 94 3.2.3.1 In situ nuclease treatment of controls ...... 94 3.2.4 Electrophoresis...... 95 3.2.4.1 DNA electrophoresis...... 95 3.2.4.2 RNA electrophoresis...... 95 3.2.4.3 SDS-polyacrylamide gel electrophoresis (SDS-PAGE) ...... 95 3.2.4.4 Gel visualisation...... 96 3.2.5 Estimating nucleic acid concentration ...... 96 3.2.6 Spectrophotometry...... 96 3.2.7 Restriction digest inhibition assessment ...... 96 3.2.8 Denaturing gradient gel electrophoresis (DGGE)...... 97 3.2.8.1 PCR cycling conditions...... 97 3.2.8.2 Electrophoresis...... 97 3.2.9 Reverse transcription PCR (RT-PCR) ...... 98 3.2.10 PCR of marker population phylotypes...... 98 3.2.11 Real-time qPCR conditions...... 99 3.2.12 Clone library construction...... 101 3.2.12.1 16S rRNA gene clone library PCR ...... 101 3.2.12.2 Cloning, sequencing and phylogenetic analysis ...... 101 3.2.13 FISH...... 102 3.3 Results and discussion ...... 102 3.3.1 Optimisation of the NaTCA extraction method...... 102 3.3.2 Comparison of all methods for nucleic acid isolation ...... 105 3.3.2.1 Comparison of nucleic acid yields...... 105 3.3.2.2 Comparison of nucleic acid purity...... 106 3.3.2.3 Nucleic acid integrity...... 106 3.3.2.4 Effects of homogenisation on biomass integrity...... 110
iv 3.3.2.5 Assessing cell lysis efficiency using target populations ...... 111 3.3.2.6 Application of these observations...... 117 3.4 Conclusions...... 117
4.0 Elucidating further phylogenetic diversity among the Defluviicoccus-related GAO in activated sludge ...... 119
4.1 Introduction...... 119 4.2 Materials and methods ...... 121 4.2.1 Biomass samples...... 121 4.2.2 Clone library construction...... 121 4.2.3 Cell sorting-RT-PCR...... 123 4.2.4 FISH analyses ...... 123 4.2.5 FISH probe design ...... 123 4.2.6 Optimisation of hybridisation conditions for FISH probes...... 126 4.2.7 FISH-MAR ...... 126 4.2.7.1 Incubations...... 126 4.2.7.2 Fixation and storage...... 127 4.2.7.3 Scintillation counting of MAR incubated biomass...... 127 4.2.7.4 FISH analysis of MAR samples...... 128 4.2.8 Histochemical staining...... 128 4.2.8.1 DAPI staining for polyP granule inclusions ...... 128 4.2.8.2 Nile blue A staining for PHA granule inclusions ...... 128 4.2.9 PHA cycling experiments ...... 129 4.2.10 Enzyme-labelled fluorescence (ELF)...... 129 4.2.11 Microsphere adhesion to cells (MAC)...... 130 4.3 Results...... 130 4.3.1 FISH survey of Bendigo biomass ...... 130 4.3.2 16S rRNA sequence analyses of the Bendigo biomass ...... 130 4.3.3 Probe design against clones of interest ...... 136 4.3.4 Retrieval of 16S rRNA sequences by micromanipulation and RT-PCR of the dominant ‘Nostocoida limicola’ morphotype in the Bendigo WWTP ...... 136 4.3.5 Ecophysiology of cluster III Defluviicoccus in the Bendigo EBPR plant ...... 137 4.3.6 Ecophysiology of cluster III Defluviicoccus in other Australian EBPR plants. .. 138
v 4.3.7 Carbon cycling by cluster III Defluviicoccus...... 142 4.3.8 MAC and ELF analysis...... 142 4.3.9 Further phylogenetic diversity among Defluviicoccus-related organisms...... 143 4.3.10 Ecophysiology of cluster IV Defluviicoccus...... 145 4.3.11 Distribution of Defluviicoccus-related organisms in full-scale EBPR plants.... 145 4.3.12 The distribution of Amaricoccus sp. in wastewater treatment systems ...... 148 4.4 Discussion...... 152 4.4.1 Description of clusters III and IV of the Defluviicoccus-related organisms...... 152 4.4.2 The ecophysiology of clusters III and IV Defluviicoccus in EBPR systems...... 152 4.4.3 Distribution of Defluviicoccus GAO in full-scale systems...... 154 4.4.4 Other alphaproteobacterial GAO ...... 155
5.0 Resolving the identity of the ‘Sphingomonas-related’ putative GAO in EBPR...... 156
5.1 Introduction...... 156 5.2 Materials and methods ...... 158 5.2.1 Pure cultures...... 158 5.2.2 DNA extraction...... 158 5.2.3 Clone library construction...... 158 5.2.4 FISH...... 159 5.2.4.1 Pre-permeabilisation of cells for FISH ...... 159 5.2.4.2 In situ RNAse treatment...... 162 5.2.5 Dissociation temperature (Tm) determination ...... 162 5.2.6 Thermodynamic stability of single nucleotide insertions and deletions...... 162 5.2.7 Detecting non-target single base insertions and deletions sites in FISH probes.. 163 5.3 Results...... 163 5.3.1 The SBR9-1 putative GAO 16S rRNA clone sequence is a chimera ...... 163 5.3.2 Where does the SBR9-1a probe bind to this chimeric 16S rRNA sequence?...... 164 5.3.3 Is the SBR9-1a FISH probe binding to the RNA?...... 167 5.3.4 To what other 16S rRNA sequences does this SBR9-1a probe bind? ...... 167 5.3.5 Investigating the possibility that bulge FISH probes can still hybridise with their target sites ...... 171 5.3.6 The conundrum of the SBR9-1b probe...... 173 5.3.7 The absence of SBR9-1a positive cells in other studies ...... 173
vi 5.3.8 Does the problem of binding to non-target sites containing insertions or deletions occur with other FISH probes? ...... 173 5.3.9 Can the binding efficiency of FISH probes to non-target sites containing insertion or deletions be predicted from theoretical free energy calculations?...... 179 5.3.10 Are non-target sites containing insertions and deletions commonly found for existing FISH probes?...... 180 5.3.11 Screening for non-target sites containing single nucleotide insertions and deletions and predicting the likelihood of FISH probe binding...... 180 5.3.12 A case study of the presence of a non-target single nucleotide insertion site resulting in a FISH false positive...... 186 5.4 Discussion...... 188 5.5 Concluding remarks...... 190
6.0 The influence of pH on the microbial community present in a laboratory scale EBPR...... 191
6.1 Introduction...... 191 6.2 Material and methods...... 192 6.2.1 Reactor operation ...... 192 6.2.2 Chemical analysis ...... 193 6.2.3 FISH analyses ...... 194 6.2.4 Histochemical staining...... 194 6.2.5 FISH-MAR ...... 194 6.2.6 Electron microscopic analysis of granules...... 195 6.2.7 DNA extraction...... 195 6.2.8 DGGE analysis...... 195 6.3 Results...... 199 6.3.1 Effect of pH on EBPR Performance ...... 199 6.3.2 Effect of operating pH on reactor chemical profiles...... 200 6.3.3 Effect of operating pH on microbial community structure and ecophysiology... 202 6.3.4 Effect of operating pH on biomass organisation...... 220 6.4 Discussion...... 233 6.5 Concluding remarks...... 238
7.0 Conclusions and Future Directions ...... 240
vii 7.1 Conclusions...... 240 7.2 Future work...... 242
8.0 Appendices...... 244
Appendix 1: Assessment of probe defined ‘Competibacter’ phylotype diversity...... 244 Appendix 2: Defluviicoccus-related probe coverage assessment...... 251 Appendix 3: Preparation protocol for 4.5 M NaTCA ...... 257 Appendix 4: Cluster IV Defluviicoccus probe optimisation ...... 258 Appendix 5: Media composition...... 259 Appendix 6: Publications forming chapters in this thesis...... 260
9.0 References...... 261
viii List of figures
Figure 1.1: Schematic diagrams of activated sludge treatment systems...... 3 Figure 1.2: Diagrammatic representation of the structure and composition of the organic fraction of an activated sludge floc...... 5 Figure 1.3: Summary of basic ‘important’ transformations of EBPR sludge...... 9 Figure 1.4: Schematic of major anaerobic and aerobic features of the proposed metabolic models...... 10 Figure 1.5: Diagrammatic representation of the steps involved in the ‘Full-rRNA cycle’ approach to the identification of an organism in a mixed environmental communities...... 15 Figure 1.6: Maximum likelihood phylogenetic tree of sequences of putative PAO...... 22 Figure 1.7: Phylogenetic trees for ‘Accumulibacter’-related 16S rRNA gene and ppk gene sequences...... 31 Figure 1.8: Important membrane transport mechanisms in characterised PAO and GAO..... 34 Figure 1.9: Proposed alternate anaerobic metabolic pathways for ‘Accumulibacter’ ...... 35 Figure 1.10: Maximum likelihood phylogenetic tree of sequences of putative GAO ...... 44 Figure 2.1: FACS sorting plots for LS1 and LS2 ...... 71 Figure 2.2: Micrographs of FACS sorted biomass samples ...... 77 Figure 2.3: Maximum likelihood tree of the complete sequences obtained from the LS1 ALF968 FACS sorted community ...... 79 Figure 2.4: Maximum likelihood tree of the complete sequences obtained from the LS2 FACS sorted community...... 82 Figure 3.1: Gel electrophoresis of the total nucleic acid extracted from different organisms using the different extraction protocols ...... 105 Figure 3.2: Semi-quantitative comparison of total DNA and RNA yields from each sample source for each extraction method...... 107 Figure 3.3: Electrophoresis gel of total nucleic acid extracts from LS1 for all extraction methods...... 110 Figure 3.4: Micrographs of SYBR Gold© stained post-extraction LS1 biomass...... 112 Figure 3.5: Maximum likelihood tree of the complete sequences obtained from the LS2 using the MI and MV extraction methods...... 114
ix Figure 3.6: DGGE of DNA extracts from the FS1 sample using several extraction methods ...... 116 Figure 4.1: FISH CLSM micrographs of the ‘Nostocoida limicola’-like, cluster III ‘Defluviicoccus’ in the Bendigo EBPR plant...... 132 Figure 4.2: FISH CLSM micrographs showing the effect of the DF988 competitor on DF988 binding to the ‘Nostocoida limicola’-like organisms in the Bendigo EBPR plant...... 133 Figure 4.3: Maximum likelihood tree of all available complete Defluviicoccus vanus-related sequences...... 134 Figure 4.4: FISH-MAR micrographs measuring 14C labelled acetate assimilation of cluster III Defluviicoccus...... 139 Figure 4.5: FISH-MAR micrographs measuring 14C labelled propionate assimilation of cluster III Defluviicoccus...... 140 Figure 4.6: FISH-MAR micrographs measuring 3H labelled glutamte assimilation of cluster III Defluviicoccus...... 141 Figure 4.7: Micrographs of the cluster III Defluviicoccus-related organisms in the Bendigo EBPR plant...... 144 Figure 4.8: FISH CLSM micrographs of cluster IV Defluviicoccus-related organisms...... 146 Figure 4.9: FISH micrographs for ecophysiological studies of cluster IV Defluviicoccus- related organisms ...... 147 Figure 4.10: FISH micrographs of putative alphaproteobacterial GAO...... 151 Figure 5.1: Diagrammatic representation of the chimeric nature of the SBR9-1 16S rRNA clone sequence ...... 165 Figure 5.2: Maximum-likelihood phylogenetic tree of the SBR9-1 sequence ...... 166 Figure 5.3: Phase and corresponding FISH micrographs for competitor probe experiments with bulge probes...... 169 Figure 5.4: FISH micrographs of SBR9 biomass and Bendigo WWTP with the SBR91a and DF218 probes...... 172 Figure 5.5: Diagrammatic representation of the SBR9-1a FISH probe forming a loop out to hybridise to the Defluviicoccus-related sequences...... 172 Figure 5.6: FISH micrographs for bulge probe experiments with synthesised bulge probe variants...... 177 Figure 5.7: Formamide dissociation curves for selected FISH probes and their ‘bulge’ variants...... 181
x Figure 5.8: Box plot analysis of bulge mismatches for selected FISH probe sequences...... 185 Figure 5.9: FISH micrographs of Rhodococcus sp. J71 cells ...... 187 Figure 6.1: Daily effluent P concentrations at the end of anaerobic and aerobic stages of the SBR under different operational pH conditions...... 199 Figure 6.2: Typical profiles of the EBPR chemical transformations occurring in the SBR under different operational pH conditions ...... 201 Figure 6.3: Community composition profile with changing operational pH in the lab-scale reactor (LS2) ...... 204 Figure 6.4: Composite CLSM FISH micrographs of populations of interest in the LS2 lab- scale reactor...... 205 Figure 6.5: DGGE analysis of the lab-scale reactor EBPR community over time ...... 206 Figure 6.6: Maximum likelihood tree of the complete sequences obtained from the LS2 reactor community ...... 207 Figure 6.7: Linear regression analysis of operational pH against total cluster II Defluviicoccus, and ‘Chloroflexi’, population size determined by qFISH ...... 209 Figure 6.8: FISH-MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Accumulibacter’...... 210 Figure 6.9: Micrographs of Nile blue A stained ‘Accumulibacter’ PAO ...... 212 Figure 6.10: FISH-MAR micrographs for aerobic 33P phosphate uptake experiments ...... 213 Figure 6.11: Micrographs of DAPI stained ‘Accumulibacter’...... 215 Figure 6.12: Micrographs of DAPI stained populations of interest in LS2...... 216 Figure 6.13: Linear regression analysis of total ‘Accumulibacter’ cell biovolume against the concentration of supernatant P at the end of the anaerobic phase ...... 217 Figure 6.14: Linear regression analysis of operational pH against total ‘Accumulibacter’ cell biovolume and the concentration of supernatant P at the end of the anaerobic phase...... 218 Figure 6.15: Electron microscope micrographs of granules from LS2...... 218 Figure 6.16: Composite CLSM FISH micrographs of cluster II Defluviicoccus...... 219 Figure 6.17: Composite CLSM FISH micrographs of ‘Competibacter’...... 221 Figure 6.18: FISH-MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Defluviicoccus’ ...... 222 Figure 6.19: FISH-MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Competibacter’ ...... 224 Figure 6.20: Micrographs of Nile blue A stained cluster II ‘Defluviicoccus’...... 226
xi Figure 6.21: Micrographs of Nile blue A stained ‘Competibacter’...... 227 Figure 6.22: FISH micrographs of LS2 biomass structure...... 229 Figure 6.23: CLSM Fluorescent images of sections of granules produced in the LS2 reactor ...... 231 Figure 6.24: Comparison of the total community composition of the LS2 reactor with that present in the granules alone...... 233
xii List of tables
Table 1.1: Summary of suggested PAO/GAO populations selected biochemical properties. 24 Table 1.2: FISH probes and distribution summary for putative PAO populations...... 26 Table 1.3: FISH probes and distribution summary for putative GAO populations ...... 46 Table 1.4: FISH-MAR summary of available data for in situ uptake of different substrates. 56 Table 2.1: FISH probes applied in Chapter 2 ...... 68 Table 2.2: Required Na+ concentration for FISH wash buffer...... 69 Table 2.3: Filter sets for Nikon Eclipse 800 ...... 69 Table 2.4: Primer sequences used in Chapter 2 ...... 73 Table 2.5: Quantitative FISH results ...... 76 Table 3.1: Overview of the lysis step in each extraction method...... 90 Table 3.2: PCR primers applied in Chapter 3...... 100 Table 3.3: FISH probes applied in Chapter 3...... 103 Table 3.4: Summary of comparison data of extraction methods assessed in Chapter 3...... 108 Table 3.5: Comparison of FISH quantitative data for LS2 biomass...... 113 Table 4.1: Details of activated sludge plants sampled in Chapter 4 ...... 122 Table 4.2: FISH probes applied in Chapter 4...... 124 Table 4.3: Mismatches in target sites between FISH probes and selected 16S rRNA sequences ...... 135 Table 4.4: Summary of FISH-MAR data...... 138 Table 4.5: Distribution of GAO and PAO in Australian wastewater treatment plants...... 149 Table 5.1: Organisms and culture conditions used in Chapter 5 ...... 158 Table 5.2: Oligonucleotide sequences used in Chapter 5 ...... 160 Table 5.3: Mismatches in target sites between FISH probes and selected 16S rRNA sequences ...... 168 Table 5.4: Summary of data for variations in binding energies for bulge FISH probes...... 175 Table 5.5: Survey data showing the frequency of single base insertions/deletions for selected FISH probe sequences...... 182 Table 6.1: List of oligonucleotide FISH probes used in Chapter 6 ...... 196 Table 6.2: qFISH data for reactor ...... 202
xiii
Abbreviations
ADP Adenosine diphosphate ATP Adenosine triphosphate BOD Biochemical oxygen demand bp DNA base pair CLSM Confocal laser scanning microscope CoA Coenzyme A COD Chemical oxygen demand CRISPR Clustered regularly interspaced short palindromic repeats CTAB Hexadecyltrimethylammonium bromide Cy3 Indocarbocyanine Cy5 Indodicarbocyanine DAPI 4ƍ-Diamidino-2-phenylindole dihydrochloride DEPC Diethylpyrocarbonate DGGE Denaturing gradient gel electrophoresis DMSO Dimethyl sulphoxide DNA Deoxyribonucleic acid dNTP Deoxynucleotide triphosphate DO Dissolved oxygen DPAO Denitrifying polyphosphate accumulating organisms DTT Dithiothreitol EBPR Enhanced biological phosphorus removal ED Entner-Doudoroff EDTA Ethylenediaminetetraacetic acid ELF Enzyme linked fluorescence EMP Embden-Meyerhoff Parnas EPS Exopolysaccharide [FA] Formamide concentration [FA]m Melting formamide concentration FACS Fluorescence activated cell sorter
xiv FISH Fluorescence in situ hybridisation FLUOS 5(6)-carboxyfluorescein-N-hydroxy-succinimide ester GAO Glycogen accumulating organism HPLC High performance liquid chromatography IDEA Intermittently decanted extended aeration IPTG ,VRSURS\Oȕ-D-1-thiogalactopyranoside ITS Internally transcribed spacer region LB Luria-Bertani MAC Microsphere adhesion to cells MAR Microautoradiography MLSS Mixed liquor suspended solids mRNA Messenger ribosomal ribonucleic acid MUCT Modified University of Cape Town N Nitrogen NAD+ Nicotinamide adenine dinucleotide (oxidised form)
NADH2 Nicotinamide adenine dinucleotide (reduced form) NaTCA Sodium trichloroacetate NMR Nuclear magnetic resonance OTU Operational taxonomic unit P Phosphorus PAGE Polyacrylamide gel electrophoresis PAO Polyphosphate accumulating organism PBS Phosphate buffered saline P:C Phosphorus to carbon ratio PCR Polymerase chain reaction PFA Paraformaldehyde PHA Poly-ȕ-hydroxyalkanoate phaC Poly-ȕ-hydroxyalkanoate synthase gene PHB Poly-ȕ-hydroxybutyrate PHV Poly-ȕ-hydroxyvalerate Pit Phosphate inorganic transport pmf Proton motive force PolyP Polyphosphate
xv ppk Polyphosphate kinase gene Pst Phosphate specific transport PVP Polyvinylpyrrolidone PVPP Poly(vinylpolypyrrolidone) qFISH Quantitative fluorescence in situ hybridisation qPCR Quantitative polymerase chain reaction RBCOD Readily biodegradable chemical oxygen demand RDPII Ribosomal database project II RFU Relative fluorescence units RNA Ribonucleic acid rRNA Ribosomal ribonucleic acid RT-PCR Reverse transcriptase polymerase chain reaction SBR Sequencing batch reactor SDS Sodium dodecyl sulphate SEM Scanning electron microscope SIP Stable isotope probing smf Sodium membrane potential SRT Sludge retention time SS Suspended solids TAE Tris/sodium acetate/EDTA TCA Trichloroacetic acid TCA cycle Tricarboxylic acid cycle TE Tris/EDTA TEM Transmission electron microscope TEMED N’,N’,N’,N’-tetramethylethylenediamine TFO Tetrad forming organism Tm Melting temperature T-RFLP Terminal restriction fragment length polymorphism Tris Tris(hydroxymethyl)aminomethane WWTP Wastewater treatment plant VFA Volatile fatty acid X-Gal 5-Bromo-4-chloro-3-indolyl ȕ-D-galactopyranoside
xvi Summary
This study investigated the phylogenetic diversity among the alphaproteobacterial Defluviicoccus-related glycogen accumulating organisms (GAO), populations thought to compete with the phosphate accumulating organisms (PAO) in enhanced biological phosphorus (P) removal (EBPR) activated sludge systems for substrates in the anaerobic feed stage. As these GAO do not accumulate large amounts of P, their dominance in plants is thought to lead to decreases in P-removal capacity and even process failure. A summary of the major findings of this work are as follows:
x The apparent bias against representation of Defluviicoccus-related organisms in constructed clone libraries from communities where they are highly abundant arises partly from DNA extraction problems with them and can be overcome with novel extraction protocols and/or FISH assisted flow cytometry.
x 16S rRNA clone library data and fluorescence in situ hybridization (FISH) analysis was applied to identify two novel phylogenetic clusters of Defluviicoccus (clusters III and IV). Members of cluster III contained filamentous ‘Nostocoida limicola II’ like morphotypes, known to cause bulking/settling problems in activated sludge plants.
x Application of histochemical staining and microautoradiography (MAR) together with FISH indicated cluster III and IV members possessed the GAO phenotype in situ.
x The previously described Sphingomonas-related alphaproteobacterial GAO were revealed in this study as artifacts where their initial identification was based on a chimeric rRNA sequence. The FISH probe published for it in fact targets members of cluster I Defluviicoccus. Problems with interpretation of FISH data arising from bulge-out probes was also described for the first time in relation to these Defluviicoccus, and shown to be of much wider importance.
xvii x Contrary to many other studies, quantitative FISH (qFISH) analysis of an EBPR SBR system showed that ‘Accumulibacter’ PAO dominated numerically the GAO over the pH range 6.5-7.5, forming large granular aggregates, and apparently shifting their phenotype from a PAO closer to that of a GAO, at the lower pH (6.5).
xviii Statement of authorship
Except where reference is made in the text of the thesis, this thesis contains no material published elsewhere or extracted in whole or in part from a thesis for any other degree or diplomas.
No other person’s work has been used without due acknowledgement in the main text of this thesis.
This thesis has not been submitted for the award for any degree or diploma in any other tertiary institution.
Signed:
Date:
xix List of publications
Publications forming chapters of this thesis (included in Appendix 6)
McIlroy, S.J., Hoefel, D., Schroeder, S., Ahn, J., Tillett, D., Saint, C., and Seviour, R. (2008) FACS enrichment and identification of floc-associated alphaproteobacterial tetrad-forming organisms in an activated sludge community. FEMS Microbiol Lett 285: 130-135.
Seviour, R.J., and McIlroy, S.J. (2008) The microbiology of phosphorus removal in activated sludge processes- the current state of play. J Microb 46: 115-124.
McIlroy, S.J., Porter, K., Seviour, R.J., and Tillett, D. (2008) Simple and safe method for simultaneous isolation of microbial RNA and DNA from problematic populations. Appl Environ Microbiol 96: 593-605.
Ahn, J., McIlroy, S.J., Schroeder, S., and Seviour, R.J. (2009) Biomass granulation in an aerobic:anaerobic-enhanced biological phosphorus removal process in a sequencing batch reactor with varying pH. J Ind Microbiol Biotechnol 36: 885-893.
Nittami, T., McIlroy, S.J., Seviour, E.M., Schroeder, S., and Seviour, R.J. (2009) Candidatus Monilibacter spp., common bulking filaments in activated sludge, are members of Cluster III Defluviicoccus. Syst Appl Microbiol 32: 480-489.
McIlroy, S.J., Porter, K., Seviour, R.J., and Tillett, D. (2009) Extracting nucleic acids from activated sludge which reflect community population diversity. Antonie van Leeuwenhoek 96: 593-605.
McIlroy, S.J., and Seviour, R.J. (2009) Elucidating further phylogenetic diversity among the Defluviicoccus-related glycogen-accumulating organisms in activated sludge. Environ Microbiol Rep 1: 563-568.
xx McIlroy, S.J., Nittami, T., Seviour, E., and Seviour, R.J. (2010) Filamentous members of cluster III Defluviicoccus have the in situ phenotype expected of a glycogen accumulating organism in activated sludge. FEMS Microbiol Ecol 74: 248-256
McIlroy, S.J., Tillett, D., Petrovski, S., and Seviour, R.J. (In press) Non-target sites with single nucleotide insertions or deletions are frequently found in 16S rRNA sequences and can lead to false positives in fluorescence in situ hybridization (FISH). Environ Microbiol DOI: 10.1111/j.1462-2920.2010.02306.x
Additional publications
Ahn, J., Schroeder, S., Beer, M., McIlroy, S.J., Bayly, R.C., May, J.W. et al. (2007) Ecology of the microbial community removing phosphate from wastewater under continuously aerobic conditions in a sequencing batch reactor. Appl Environ Microbiol 73: 2257-2270.
Martin, K., McDougall, B.M., McIlroy, S.J., Chen, J., and Seviour, R.J. (2007) Biochemistry and molecular biology of exocellular fungal beta-(1,3)- and beta-(1,6)-glucanases. FEMS Microbiol Rev 31: 168-192.
Speirs, L., Nittami, T., McIlroy, S.J., Schroeder, S., and Seviour, R.J. (2009) Filamentous bacterium Eikelboom type 0092 in activated sludge plants in Australia is a member of the phylum Chloroflexi. Appl Environ Microbiol 75: 2446-2452.
Schroeder, S., Petrovski, S., Campbell, B., McIlroy, S.J., and Seviour, R.J. (2009) Phylogeny and in situ identification of a novel gammaproteobacterium in activated sludge. FEMS Microb Lett 297: 157-163.
Nielsen, P.H., Nguyen, H.T.T., McIlroy, S.J., Mielczarek, A.T., and Seviour, R.J. (2009) Identification of polyphosphate-accumulating and glycogen-accumulating organisms by FISH. In FISH handbook for biological wastewater treatment. Nielsen, P.H., Daims, H., and Lemmer, H. (eds). London: IWA Publishing, pp. 25-31.
xxi McIlroy, S.J., and Porter, K. (2010) DNA and RNA extraction. In Microbial ecology of activated sludge. Seviour, R.J., and Nielsen, P.H. (eds). London: IWA Publishing, pp. 343- 349.
Seviour, E.M., McIlroy, S.J., and Seviour, R.J. (2010) Descriptions of activated sludge organisms. In Microbial ecology of activated sludge. Seviour, R.J., and Nielsen, P.H. (eds). London: IWA Publishing, pp. 453-487.
Speirs, L., McIlroy, S.J., Petrovski, S., and Seviour, R.J. (In press) The activated sludge bulking filament Eikelboom Type 0914 is a member of the Chloroflexi. Environ Microbiol Rep DOI: 10.1111/j.1758-2229.2010.00201.x
Petrovski, S., Dyson, Z.A., Quill, E.S., McIlroy, S.J., Tillett, D., and Seviour, R.J. (Accepted) An examination of the mechanisms for stable foam formation in activated sludge systems. Water Res.
Conference presentations
McIlroy, S.J., Ahn, J., Schroeder, S., and Seviour, R.J. (2008) The long term influence of pH on the microbiological community of a lab-scale SBR EBPR system. Poster presentation. 12th
International Symposium on Microbial Ecology (ISME 12). Cairns, Australia.
Schroeder, S., McIlroy, S.J., and Seviour, R.J. (2008) Diversity of Candidatus ‘Accumulibacter phosphatis’ in aerated lab-scale and anaerobic: aerobic full-scale biological phosphorus removal systems by PCR-DGGE analysis. Poster presentation. 12th International Symposium on Microbial Ecology (ISME 12). Cairns, Australia.
McIlroy, S.J., Nittami, T., and Seviour, R.J. (2009) The extent of phylogenetic diversity amongst the Defluviicoccus related organisms in activated sludge. Poster presentation. 5th IWA Activated Sludge Population Dynamics Conference (ASPD5), Aalborg, Denmark.
xxii Speirs, L., Nittami, T., McIlroy, S.J., Schroeder, S., and Seviour R.J. (2009) Identification of the Eikelboom filamentous bacterium type 0092, found in activated sludge. Poster presentation. 5th IWA Activated Sludge Population Dynamics Conference (ASPD5), Aalborg, Denmark.
Schroeder S., Petrovski, S., Campbell, B., McIlroy, S.J., and Seviour, R.J. (2009) Phylogeny and in situ identification of a novel gammaproteobacterium in activated sludge. Platform presentation. 5th IWA Activated Sludge Population Dynamics Conference (ASPD5), Aalborg, Denmark.
McIlroy, S.J., Petrovski, S., Tillett, D., and Seviour, R.J. (2009) ‘Sphingomonas-related’ glycogen accumulating organisms in activated sludge processes removing phosphate - a reassessment of their identity. Platform presentation. 5th IWA Activated Sludge Population Dynamics Conference (ASPD5), Aalborg, Denmark.
xxiii Acknowledgements
For the privileged opportunity I have had to begin and complete PhD studies I have many people to express my appreciation to.
Firstly to my supervisor, Professor Robert Seviour, for the time that he invested into both my thesis work and my personal development as a research scientist. His genuine enthusiasm and passion for research, accompanied by his generosity and tenacious support for his students, has led to many opportunities for me during my PhD studies and likely many more.
Thanks also to my co-supervisor Dr Daniel Tillett for his advice and willingness to assist when I reached an impasse in my research. We were not always on the same page, but the discussion opened up some new and interesting directions for my project.
Thanks to Drs Barbara McDougall and Steve Petrovski, for very kindly proof reading my thesis and to Bianca Gifford for helping to put the document together.
Thanks to all the technical staff in the Departments of Pharmacy, Microbiology and Biochemistry for all their assistance, especially to David Osborne, Rod Lindrea, John Hamilton, Sue Mullins and Dot Andison.
An enormous thank you to Barb Udale for dealing with most of the logistical problems created with my working in Melbourne.
Thanks to Drs Chris Saint and Daniel Hoefel at South Australia Water, for allowing me to work in their laboratory and for helping me with the FACS sorting method. Thanks also to Kathryn Eales and Frank Schmid for accommodating me during this time.
Thank you to the people that I had the pleasure of working closely with. To Dr Johwan Ahn for operating the ‘pH reactor’, Dr Kate Porter for her assistance with the nucleic acid protocol validation and Dr Tadashi Nittami who worked with me on the cluster III work presented in this thesis.
xxiv Thank you also to Drs Sarah Schroeder, Kate Porter, Michael Beer and Kathryn Eales, and Ms Beth Seviour, for their time and patience in teaching me basic techniques when I first started in the lab.
Thank you to all members of the Pharmacy and Microbiology Departments of La Trobe University, especially the members of the Plasmid Lab and the Biotechnology Research Centre, for helping to create a pleasant working environment.
Thanks to Holly and the Loughran family for all of their support, and, along with Harry and Leanne Putting, for generously sharing their homes with me when I had limited options for accommodation during my time in Melbourne.
I would also like to express my appreciation to my wonderful family, including its newest members. Throughout my studies, despite the very difficult personal hardship the family has endured, your support never wavered. You always provided me with everything I needed to go in what ever direction I chose and it is that continued support that led me to the start, and now the end, of my PhD studies.
To my beautiful fiancée Bianca, thank you for all the love, support, patience you gave to me during this time, and for the personal sacrifices that it often involved for you.
xxv
For my sister Kristy, who endured so much yet, continues to give. Her inspirational courage and approach to life keeps everything in perspective.
1.0 Introduction 1.1 The requirement for nutrient removal in the treatment of wastewater Human population growth, pollution and global warming are increasingly impacting on water supply throughout the world. Estimates indicate that over half of the global population will have limited access to clean water by the year 2050, making its availability and therefore treatment efficacy for re use of paramount importance (Tchobanoglous et al., 2003; Jimenez and Asano, 2008). Massive volumes of wastewater are generated daily from domestic (municipal) and industrial sources. This waste is a complex, highly variable mixture of organic and inorganic materials, being rich in carbohydrates, proteins and lipids (Gray, 2005; Seviour, 2010). The primary role of treatment is to reduce suspended solids and the biochemical/chemical oxygen demand (BOD/COD) of the waste before its eventual release into a receiving water body, thus reducing its ecological impact on the environment and the potential spread of water borne disease (Theron and Cloete, 2002; Gray, 2005). Of additional concern is the removal of the nutrients nitrogen (N) and phosphorus (P), as high discharge levels have potentially severe ecological consequences, causing nutrient over enrichment (eutrophication) in receiving water bodies that can lead to the proliferation of Cyanobacteria (Phlips, 2002; Smith and Schindler, 2009). These outbreaks or ‘blooms’ reduce available oxygen and light in these systems. Furthermore, some Cyanobacteria produce carcinogenic hepato and neuro toxic compounds that present a serious health hazard to humans and other animals (Burkholder, 2002; Falconer, 2002). Limiting P release levels is thought to be more critical than controlling N discharges into freshwater systems, since some Cyanobacteria can fix atmospheric N. However, placing stringent requirements on both is considered the most reliable preventative measure against episodes of eutrophication (Conley, 2000; Burkholder, 2002; Paerl, 2008).
1 1.2 The application of activated sludge to the treatment of wastewater Conventional activated sludge systems consist of an aerated tank where organisms are kept in intimate contact with carbonaceous compounds by submerged aerators or mechanical mixers. In the presence of oxygen these organic compounds are oxidised by chemoorganoheterotrophic bacteria, releasing CO2 gas (Lindrea and Seviour, 2002) (Fig. 1.1). Sludge from the aeration tank passes into a clarifier where solids are allowed to settle, separating the biomass or sludge from the effluent liquid supernatant. At this stage microbes associated with flocs are advantaged as flocculation enhances biomass settleability (Toerien et al., 1990; Nielsen, 2002). These flocs are made up predominantly of organic material consisting of microbial cells and exopolymeric substances (EPS), the latter consisting mainly of protein is thought to act as an adhesive ‘glue’, with filamentous bacteria possibly providing a skeletal structural matrix (Fig. 1.2) (Nielsen, 2002; Jenkins et al., 2004; Seviour and Nielsen, 2010b). The principle of the activated sludge process is the return of some of the settled biomass, termed ‘activated sludge’, to re inoculate fresh untreated waste (Fig. 1.1) (Ardern and Lockett, 1914). This ensures that the specialist organisms suited to the breakdown of compounds coming into the plant are retained in the system and ensures more rapid treatment compared to the original treatment systems where all the biomass was discarded (Ardern and Lockett, 1914; Seviour, 2010). 1.2.1 Enhanced biological phosphorus removal (EBPR) In Australian municipal wastes influent P levels are typically around 10 15mg l 1, the primary sources being faeces and household detergents (Argaman, 1991; Blackall et al., 2002). The high ratio of P to carbon in these wastes means that the former, like N, is often present in excess of bacterial growth requirements, and so, only a fraction is removed by conventional treatment systems (Gray, 2005). Plants designed for the deliberate microbiological reduction of P are known as enhanced biological phosphorus removal (EBPR) systems and are claimed to achieve total P effluent levels as low as 0.1 0.2mg l 1 (Blackall et al., 2002). Limits imposed on P release vary with country and receiving water body type, but are becoming increasingly stringent from elevated environmental awareness, making treatment optimisation and efficiency a high priority (Barnard and Steichen, 2006). 2
3 Figure 1.1: Schematic diagrams of activated sludge treatment systems a. Schematic of a conventional activated sludge treatment plant (Seviour, 2010) and b. a simplified map of the conceptual nutrient flow through the aerobic plant. c. Basic schematic of a plant designed for N and P removal (EBPR) (Seviour et al., 2010); d. simplified map of the conceptual nutrient flow through the EBPR plant. ANA = Anaerobic; ANX = Anoxic; AER = Aerobic; N = Nitrogen; P = Phosphorus; C = Carbon. Pi = inorganic phosphate; PolyP = internally stored polyphosphate.
4
Figure 1.2: Diagrammatic representation of the a. structure and b. approximate composition of the organic fraction of an activated sludge floc (adapated from Seviour and Nielsen (2010b) and Nielsen (2002), respectively). The principal conditions for EBPR are the cycling of the biomass through alternating ‘FEAST:FAMINE’ conditions. In traditional EBPR this is achieved with an anaerobic zone where readily biodegradable COD (RBCOD) are available (the ‘FEAST’ phase) and subsequent aerobic conditions where RBCOD is limiting (the ‘FAMINE’ phase) (Barnard, 1974, 1975; Toerien et al., 1990). However, completely aerated systems have been operated stably for several months where the ‘FEAST:FAMINE’ regime is created by temporal separation of carbon and P additions (Ahn et al., 2007). In the ‘FEAST’ phase specialised organisms, known collectively as the polyphosphate accumulating organisms (PAO), use 5 energy obtained from the hydrolysis and release of stored polyphosphate (polyP) for the uptake and storage of substrates (RBCOD). Then in the subsequent ’FAMINE’ phase, the PAO accumulate excessive amounts (more than is required for their growth) of P as intracellular polyP granules. Net P removal is achieved when this polyP rich biomass is wasted from the system (Fig. 1.1c. d.) (Toerien et al., 1990; Mino et al., 1998). Chemical precipitation methods can also be used to remove P from wastewater and are often required to polish the effluent of inefficient or unreliable EBPR plants. However, these add expense and have their own environmental problems, which include increasing salinity of receiving water bodies and producing large amounts of chemical sludge that is difficult to dispose of (Kortstee et al., 1994; Blackall et al., 2002). Conversely the high settleability and nutrient content of EPBR sludge are ideal properties for application as a fertiliser, making biological removal a particularly environmentally attractive process (McMahon et al., 2010). 1.2.2 Nitrogen removal P removal is rarely addressed independently of N removal since plants designed to remove P also remove N. With the inclusion of an anoxic tank or ‘zone’1, the high levels of nitrate (NO3 ) released from the oxidation of ammonia (NH3) by the nitrifying bacteria in the aerobic zone, are reduced to dinitrogen gas by the denitrifying bacteria there, thus ensuring a net removal of N from the system and limiting the nitrate levels entering the anaerobic zone (see Fig 1.1.c. d.) (Argaman, 1991; Daims and Wagner, 2010). This is considered important for successful EBPR as denitrifiers in the anaerobic zone may use any nitrate present as a terminal electron acceptor, competing with the PAO for carbon, thus reducing their selective advantage (Tchobanoglous et al., 2003). Interestingly, some PAOs appear to be capable of using nitrate instead of oxygen as terminal electron acceptor and are known collectively as denitrifying PAOs (DPAOs). These allow a much more efficient use of the often limiting levels of available carbon, potentially reducing aeration costs and resulting in less sludge production for disposal (Kuba et al., 1996; McMahon et al., 2010) (see Fig 1.1.c. d.). 1 A term used to describe the condition of high levels of nitrate (NO3 ) with limited oxygen. 6 1.3 The need for microbiological studies on P removal While many advances have been made in the design of EBPR plants (reviewed by Toerien et al., 1990; van Loosdrecht et al., 1997; Tchobanoglous et al., 2003; Oehmen et al., 2007; Seviour et al., 2010), very little is known of the microbiological mechanisms involved in EBPR, with advances in process efficiency being largely empirical and engineering based (Seviour et al., 2003). These systems are often subject to failure or prolonged periods of operational inefficiency. A more complete knowledge of the complex microbiology of EBPR is likely to increase markedly our ability to control these systems, increasing their efficiency and perhaps paving the way for the design of more innovative treatment processes that maximise the metabolic potential of these organisms for treatment purposes (Seviour et al., 2003; McMahon et al., 2007a; McMahon et al., 2010). 1.4 Biochemical models of EBPR and PAO metabolism The construction of metabolic models for EBPR biomass has been claimed to lead to a better understanding of the biochemical transformations involved, and a potential improvement in process control, as well as providing an insight into the microbiology of the PAO (Oehmen et al., 2007; Oehmen et al., 2010b). In the absence of pure cultures of the PAO, earlier models were based on observations of gross biochemical transformations carried out by a complex mixed microbial community (see Fig. 1.3). The application of real time nuclear magnetic resonance (NMR) carbon tracer experiments and directed inhibition of postulated key PAO metabolic enzymes have also assisted in clarifying which are their important metabolic pathways (Liu et al., 1994; Pereira et al., 1996; Louie et al., 2000). However, with the latter approach, the specificity of some of these metabolic inhibitors is unclear, and it is feasible that alternative pathways may replace those being inhibited (Burow et al., 2007; Oehmen et al., 2010b; Zhou et al., 2010). The first convincing models to explain PAO metabolism were those of Comeau and Wentzel (Comeau et al., 1986; Wentzel et al., 1986) (Fig. 1.3a.) and Mino (Mino et al., 1987) (Fig. 1.3b.). Many modified versions of these models have since been proposed (Oehmen et al.,
7 2007). Although they share many core similarities, some aspects of PAO metabolism are still the subject of much debate and controversy. A brief overview of these models is presented next. 1.4.1 Anaerobic metabolism of the PAO 1.4.1.1 The uptake of volatile fatty acids (VFAs) Most PAO metabolic models are based on acetate as the sole carbon source for the PAO, given its abundance in activated sludge and common use in laboratory based experiments. However, a few studies have investigated EBPR based on other carbon sources and proposed models incorporating their metabolism (Oehmen et al., 2007). While the different models suggest either active transport or passive diffusion at the expense of a proton motive force (pmf) (Schuler and Jenkins, 2003a), there is an assumed universal reliance on polyP hydrolysis to supply the required energy or to directly maintain the required membrane potential (Comeau et al., 1986; Schuler and Jenkins, 2003b; McMahon et al., 2010). It is generally accepted that acetate is first activated to acetyl CoA, consuming ATP sourced from the hydrolysis of polyP, although the Mino model (Fig. 1.4b.) also assumes a partial ATP supply from glycolysis (Comeau et al., 1986; Wentzel et al., 1986; Mino et al., 1987; Oehmen et al., 2007). The role of polyP as an energy source for substrate uptake and synthesis as storage polymers represents the key metabolic feature of the PAO that enable them to outcompete other chemoorganoheterotrophic organisms anaerobically where influent carbon is available (Marais et al., 1983).
8
Figure 1.3: a. Summary of basic ‘important’ transformations of EBPR sludge (see Marais et al., 1983; Wentzel et al., 1991; Mino et al., 1998; McMahon et al., 2010). Anaerobic transformations: • In the presence of substrate, biomass polyP levels decrease with an increase in P in the bulk liquid • Short chain volatile fatty acids (VFA) in the bulk liquid decrease with an increase in biomass poly β hydroxyalkanoates (PHA) levels • Biomass polysaccharides (glycogen) levels decrease during VFA uptake Aerobic transformations: • P in the bulk liquid is assimilated as polyP • Biomass PHA levels decrease • Biomass polysaccharide (glycogen) levels increase b. Example of a typical chemical profile of an anaerobic: aerobic lab scale EBPR reactor. Pi = phosphorus; Ac = Acetate; PHB = poly β hydroxybutyrate.
9
Figure 1.4: Schematic of major anaerobic (left) and aerobic (right) features of the proposed metabolic models (McMahon et al., 2010): a. The Comeau Wentzel model (Comeau et al., 1986; Wentzel et al., 1986); b. Mino model (Mino et al., 1987; Arun et al., 1988). 1.4.1.2 The source of anaerobic reducing equivalents While there is general agreement that acetate is used to synthesise poly β hydroxyalkanoates (PHA), the suggested origin of the reducing equivalents required for its production is still a major point of debate (Zhou et al., 2010). The Comeau Wentzel model (Fig. 1.4a.) proposes that an anaerobic operation of the TCA cycle provides these, while the Mino based models (Fig. 1.4b.) suggest the hydrolysis of glycogen alone is the source, in order to explain their observed fluctuations of glycogen levels during anaerobic: aerobic biomass cycling (Mino et al., 1987). The additional ATP provided from anaerobic glycolysis also provides the
10 theoretical energy shortfall for the activation of acetate to acetyl CoA when polyP is the only energy source considered (Smolders et al., 1994a). Glycogen exhaustion has also been found to limit anaerobic substrate uptake, despite the availability still of biomass polyP stores (Brdjanovic et al., 1998d). Perriera et al. (1996) used 13C labelled acetate and NMR to trace carbon from acetate to PHA anaerobically, to glycogen in the subsequent aerobic phase, and then to PHA again in the following anaerobic phase, and confirmed a direct role for glycogen catabolism in ATP supply. However their model required an additional source of anaerobic reducing equivalents, and they concluded that at least some are supplied through the anaerobic operation of the TCA cycle. These outcomes suggest that a combination of the two popular metabolic models (Fig. 1.4) may best describe metabolism of the PAO, a suggestion supported by most of the later studies (Hesselmann et al., 2000; Schuler and Jenkins, 2003c, a; Oehmen et al., 2007). 1.4.2 Aerobic metabolism of the PAO There is general consensus on the major metabolic transformations by the PAO in the aerobic phase. Catabolism of PHA occurs through the TCA cycle where energy and carbon are used predominately for biomass growth and, in the case of Mino based models, for replenishing the glycogen stores depleted during the anaerobic stage. Aerobic respiration is also thought to provide the ATP for P uptake and its polymerisation as polyP (Fig. 1.4) (Mino et al., 1995; Smolders et al., 1995; Oehmen et al., 2007). 1.4.3 The need for microbiological data in PAO modelling Although this early modelling work based on whole community chemical profiling has been crucial in developing an understanding of EBPR systems, the models which emerged assumed that the characteristic EBPR transformations were performed by a single PAO population. In no case was any attempt made to analyse the composition of the communities responsible for these chemical transformations (Schuler and Jenkins, 2003c; Seviour et al., 2003). As discussed later, the PAO are now known to be phylogenetically and
11 physiologically diverse and more than one PAO population are likely to co exist in the same community. Characterisation of the microbial community, the use of highly enriched cultures of defined PAO populations and the ultimate attainment of pure cultures, will make interpretation of these profile data more meaningful (Seviour et al., 2003; Oehmen et al., 2007), leading to models that account for their physiological diversity now known to be present (Oehmen et al., 2010b). 1.5 How do we study the complex communities in EBPR systems? Because of the comparative simplicity of bacterial morphology, unequivocal identification of individual populations in complex microbial communties can not be based on morphology based characters, as is the case with higher plants and animals (Amann et al., 1995). For example, although microscopic characteristics are still widely used by plant operators for ‘identification’ of filamentous bacteria in bulking and foaming activated sludge (Eikelboom, 1975; Jenkins et al., 2004), members of a single filament morphotype can be phylogenetically very diverse (Nielsen et al., 2009a), and their morphology can change with environmental conditions (McKenzie et al., 2006). Therefore, a thorough understanding of microbial community composition must rely on more precise methods of identification (Kragelund et al., 2009; Nielsen et al., 2009a). 1.5.1 Culture dependent methods Early attempts to identify the PAO relied on isolating organisms from EBPR processes into pure culture and their subsequent phenotypic characterisation. Initially effort was directed at culturing isolates that then showed the same biological transformations as seen in EBPR biomass samples (Seviour et al., 2003). However, since less than one percent of the suggested bacterial diversity in nature is thought to be culturable, this approach is certain to generate a highly biased view of community composition, favouring those organisms able to grow best under the conditions chosen for isolation (Amann et al., 1995; Hugenholtz et al., 1998; Pace, 2009), as illustrated later (Section 1.6.1.1).
12 Conventional culture based approaches generally favour the faster growing organisms, and there is a recognised need now for more rational direct approaches to culturing organisms of interest (van Loosdrecht et al., 1997). In situ physiology data may assist, although this has been of limited value thus far (Seviour and Nielsen, 2010b). Flow cytometry and micromanipulation may aid in the enrichment of key populations for their subsequent isolation (Amann, 1995; Porter et al., 1996) and success with the latter approach, has led to the isolation of filamentous and tetrad forming morphotypes from activated sludge. Those obtained in pure culture include putative PAO and their potential competitors (Duncan et al., 1988; Beacham et al., 1990; Bradford et al., 1996; Maszenan et al., 1997; Christensson et al., 1998; Maszenan et al., 1999a; Maszenan et al., 1999b; Blackall et al., 2000; Maszenan et al., 2000b; Beer et al., 2002; Maszenan et al., 2002; Snaidr et al., 2002; Stratton et al., 2002; Levantesi et al., 2004; Maszenan et al., 2005; Levantesi et al., 2006; McKenzie et al., 2006; Kragelund et al., 2007a; Kragelund et al., 2008). Density centrifugation methods have also been applied to enrich for the PAO, based on their theoretically higher cell densities from their abilities to accumulate intracellular PHA and polyP inclusions (Suresh et al., 1985; Hung et al., 2002; Schuler et al., 2002; Zilles et al., 2002a). It is difficult to confirm or deny the true PAO status of isolates from pure culture/ex situ studies alone, given our lack of understanding of these complex systems and our inability to replicate strictly the conditions of EBPR and possible population interactions occurring there (Brodisch, 1985; Ye et al., 1988; van Loosdrecht et al., 1997; Mino et al., 1998; Chaffron et al., 2010; Slater et al., In press). The cultures isolated may also not represent those originally present in situ, slowly evolving to adapt to the very different axenic laboratory culture conditions they are maintained in (Caldwell et al., 1997). Therefore in situ analyses of putative PAO abundance and ecophysiology are essential. However they do not replace totally the advantages in being able to study pure cultures, as only a snapshot of their basic metabolic properties is gained with in situ methods like fluorescence in situ hybridisation microautoradiography (FISH MAR). Although whole genome sequence information has been obtained for the ‘Accumulibacter’ from a metagenomic study on enriched PAO cultures (García Martín et al., 2006), pure cultures are a much preferred way of obtaining this information, which then paves the way for genomic, transcriptomic, and proteomic studies. Together these are essential in uncovering all the metabolic capabilities of these organisms (Forbes et al., 2009; McMahon et al., 2010).
13 1.5.2 Culture independent methods: The ‘Full rRNA cycle’ approach The implementation of rRNA genes in bacterial taxonomy, particularly those encoding the 16S ribosomal subunit, has now resolved many of the phylogenetic relationships which exist between bacteria, and allowed a characterisation of the previously inaccessible uncultured majority (Woese et al., 1990; von Wintzingerode et al., 1997; Hugenholtz et al., 1998; Amann and Ludwig, 2000; Schleifer, 2004; Zengler, 2009; Cole et al., 2010). This gene is an attractive phylogenetic marker as it is ubiquitous in bacteria, highly conserved, independant of selection pressures and shows limited evidence of horizontal gene transfer (Amann et al., 1995; Kurland et al., 2003). Any variations in the levels of sequence conservation along the gene also permits the design of rRNA targeted probes and primers that can target groups at different phylogenetic levels (Amann et al., 1995; Head et al., 1998). The approach to studying complex environmental microbial community composition is known as the ‘Full rRNA cycle’ approach. This is summarised in Fig. 1.5 and its main components are briefly discussed in the following sections. A more detailed discussion of these techniques is given (where relevant) in later chapters and they have also frequently been critically reviewed (Amann et al., 1995; von Wintzingerode et al., 1997; Head et al., 1998; Theron and Cloete, 2000; Rochelle, 2001; Wagner et al., 2003; Kowalchuk et al., 2004; Osborn and Smith, 2005; Amann and Fuchs, 2008; McMahon et al., 2009; Prosser et al., 2010; Seviour and Nielsen, 2010a). 1.5.2.1 Obtaining 16S rRNA gene sequence information from EBPR systems Although eliminating much of the bias associated with the culture dependent approach, culture independent methods are themselves subject to considerable bias that can be introduced at almost every step in rRNA based community analysis (Hugenholtz and Goebel, 2001).
14 Figure 1.5: Diagrammatic representation of the steps involved in the ‘Full rRNA cycle’ approach to the identification of an organism in a mixed environmental community (taken from Seviour and Nielsen, 2010b). Genomic DNA is extracted from the environmental sample and a clone library of 16S rRNA sequences prepared. FISH probes are then designed against sequences of interest and applied to the original sample to study the organisms represented by these 16S rRNA genes in situ. In this example, different populations are represented by squares, triangles and circles and the FISH probe is designed for the ‘square’ population.
15 Critical to the success of culture independent analyses of activated sludge communities is the isolation of high quality nucleic acids. The extraction method used may result in considerable bias, where some populations yield up their nucleic acids more readily than others (von Wintzingerode et al., 1997; Roose Amsaleg et al., 2001). Selecting a suitable extraction method is essential as organisms resistant to the lysis method chosen will be either under represented in subsequent analyses or completely excluded from them, producing a skewed starting point for all further explorations (von Wintzingerode et al., 1997; Head et al., 1998). This susceptibility of different populations to lysis protocols (Frostegård et al., 1999; Krsek and Wellington, 1999; Moore et al., 2004) can be exacerbated in studies on activated sludge communities by the formation of heavily encapsulated cell aggregates or flocs (Watanabe et al., 1998; Bourrain et al., 1999; Yu and Mohn, 1999; Ahn et al., 2007) (see Chapter 3). The PCR amplification step is also a source of considerable bias (von Wintzingerode et al., 1997; Head et al., 1998). ‘Universal’ primer site mismatches, template concentrations, PCR cycling conditions and differences in template melting temperatures (Tm) reflecting differences in their G+Cmol %, may all contribute to differential amplification of heterogeneous 16S rRNA gene templates (Reysenbach et al., 1992; Chandler et al., 1997; Suzuki et al., 1998; Ishii and Fukui, 2001; Kurata et al., 2004; Sipos et al., 2007). Compounded by nucleic acid extraction biases, these probably explain the often reported absence or under representation of dominant populations in community profile studies of activated sludge (Snaidr et al., 1997; Wallner et al., 1997; Crocetti et al., 2000; Liu et al., 2001; Ahn et al., 2002; Nielsen et al., 2004; Meyer et al., 2006; Ahn et al., 2007; Wong and Liu, 2007). Consequently, methods involving DNA extraction and PCR based profiling are of questionable quantitative value. Further complicating rRNA based methods are the generation of sequence artifacts, predominantly the formation of chimeric sequences and incorporation of incorrect bases by the DNA polymerase during PCR amplification. Both can lead to an overestimation of community diversity (Acinas et al., 2005; Ashelford et al., 2005; Röling and Head, 2005). Chimeric sequences are produced when incomplete fragments of heterogeneous sequences anneal in shared regions of homology during the PCR annealing step, and are extended in all subsequent extension steps to form a complete ‘chimeric’ artificial 16S rRNA gene sequence
16 that was not present initially in the community (Liesack et al., 1991; Röling and Head, 2005). These chimeras are a common occurence, shown to constitute up to 30 % of the total cloned rRNA gene sequences from any single community study (Wang and Wang, 1997). A lack of adequate screening protocols has resulted in at least 9 % of such sequences held in the NCBI database being artifacts (Ashelford et al., 2006). Software programs are available for their detection, and these include Bellerophon (DeSantis et al., 2006), Mallard (Ashelford et al., 2006) and Pintail (Ashelford et al., 2005). However, these programs are only guides because chimeras are notoriously difficult to detect unequivocally, especially for closely related or novel sequences that show low similarities to all others held in public databases. Large databases of screened ‘clean’ sequences like Greengenes (DeSantis et al., 2006), SILVA (Pruesse et al., 2007) and the Ribosomal Database Project II (RDII) (Cole et al., 2009) are available, to ensure the impact of sequence artifacts on phylogenetic analysis, including probe and PCR primer design, is limited. Despite the pitfalls of such methods, their application has meant that advances in our understanding of the activated sludge community have been immense as clearly demonstrated in Section 1.6. Identifying the important bacterial members in activated sludge communities is made possible by the construction of PCR clone libraries of individual 16S rRNA gene sequences from DNA extracts, especially in detection of novel populations (Hugenholtz and Goebel, 2001; Wagner and Loy, 2002). These can serve to catalogue the phylogenetic diversity of an entire community (Bond et al., 1995; Wagner and Loy, 2002; Kong et al., 2007) or deliberately target sequences of groups of interest after prior population enrichments (Levantesi et al., 2004; Thomsen et al., 2004). Prior enrichment of PAO using density gradient separation (see Section 1.5.2.1) and targeting those staining positively for polyP storage with flow cytometry have both been used to generate clone libraries of putative PAO sequences (Kawaharasaki et al., 1999; Hung et al., 2002; Kawaharasaki et al., 2002; Zilles et al., 2002a; Zilles et al., 2002b; Günther et al., 2009). Other functionally important phenotypic groups can be identified with techniques like stable isotope probing (SIP), where selected stable isotope labelled substrates (ie. 13C and 15N) are tracked into nucleic acids under defined conditions, connecting population function with phylogenetic identity (Neufeld et al., 2007; Whiteley et al., 2007; Ginige, 2010).
17 However, clone library construction is slow and expensive and so other community profiling techniques, like PCR denaturing gradient gel electrophoresis (DGGE) (Muyzer et al., 1993) and PCR terminal restriction fragment length polymorphism (T RFLP) (Liu et al., 1997a) are often used to generate community fingerprints (Liu et al., 1997a; Watanabe et al., 1998; Nielsen et al., 1999; LaPara et al., 2000; Liu et al., 2000b; Boon et al., 2002; Eschenhagen et al., 2003; Okabe et al., 2003; Ahn et al., 2007; Schroeder et al., 2008). These allow many samples to be analysed simultaneously, and so are more suitable to study changes in community composition over time and/or in response to changing conditions (Muyzer and Smalla, 1998; Nocker et al., 2007; Smalla et al., 2007). Each band/peak generated in these profiles is considered to represent a single population, an assumption which is not always sustainable. Identifying individual bands/peaks is tedious and often unsuccessful, and making meaningful comparisons between different profiles is complicated (Nocker et al., 2007; Prosser et al., 2010). 1.5.2.2 In situ analysis of populations One technique that has contributed substantially to our understanding of microbial ecology, in both qualitative and quantitative terms, is fluorescence in situ hybridisation (FISH) (Amann, 1995; Wagner et al., 2003; Amann and Fuchs, 2008). Fluorescently labelled DNA oligonucleotides are hybridised in situ to target specific sequences contained within the rRNA of populations of interest, providing information about their morphology, relative abundance and spatial arrangement at the level of a single cell (DeLong et al., 1989; Amann, 1995; Amann and Fuchs, 2008). In combination with other techniques like histochemical staining (Ostle and Holt, 1982; Kawaharasaki et al., 1999; Serafim et al., 2002a) and MAR (Lee et al., 1999; Nielsen and Nielsen, 2005), FISH can allow the in situ ecophysiology of a population to be resolved at the resolution of a single cell and, by linking phylogeny to functions in situ, has been crucially important in allowing the identity of organisms participating in the microbiology of P removal in activated sludge (Lee et al., 1999; Wagner et al., 2003; Nielsen et al., 2010a; Nielsen et al., 2010b). FISH also avoids the biases of preferential nucleic acid extraction and PCR amplification protocols, making it suitable for semi quantitative analysis (Daims et al., 2001b; 2005; 2006).
18 However, as with other molecular ecological methods, well documented problems exist which need to be acknowledged. For example, cell permeability (Carr et al., 2005), rRNA levels (Pernthaler et al., 2002) and probe inaccessibility to regions of the target rRNA arising from its secondary structure (Fuchs et al., 1998) may lead to low fluorescence signal intensities, and often false negatives (Amann and Fuchs, 2008). False positives arising from a lack of FISH probe specificity are particularily challenging to address, and emphasise the need for careful probe design and validation of the hybridisation protocol (Yilmaz et al., 2008). The exponential growth of databases containing 16S rRNA sequences has encouraged the requirement for continuing FISH probe specificity reassessment (Amann et al., 2001). Software packages like PRIMROSE (Ashelford et al., 2002), PROBE (Pozhitkov and Tautz, 2002) and ARB (Ludwig et al., 2004) allow for the design and screening of potential FISH probe sequences. 1.5.2.3 The functional phylogenetic unit: limitations of the 16S rRNA gene? While the use of the 16S rRNA gene has revolutionised our understanding of bacterial ecology, there are many often ignored pitfalls in its use. A similarity level of <97 % is used frequently to delineate individual bacterial species (Stackebrandt and Goebel, 1994), although many believe this should be increased to >98.5 99 % (Stackebrandt and Ebers, 2006; Cole et al., 2010). One potential problem with increasing stringent species or ‘cluster’ cut off values is that interoperon diversity between 16S rRNA genes in the same cell may exceed these values (Pei et al., 2010). Such interoperon diversity can lead to an overestimation of species diversity in community profiling techniques (Pei et al., 2010). From metagenomic studies of habitats, it is becoming increasingly evident that an individual phylotype can embrace considerable genomic diversity (Wilmes et al., 2009). Organisms indistinguishable by rRNA based methods can occupy different ecological niches (Coleman et al., 2006; Ivars Martinez et al., 2008; Peña et al., 2010). It is unclear how and why similar organisms co inhabit the same niche, but this may result from either selection sweeps being insufficiently weak to purge ‘neutral’ diversity (Acinas et al., 2004), micro niches based on increasing micro partitioning of resources within the system (Hunt et al., 2008), or viral predation controlling ‘break away’ dominant strains (Thingstad and Lignell, 1997; Wilmes et al., 2009). For this reason the ‘ecotype’ is proposed to better define a bacterial species, with 19 the currently recognised species suggested to more likely represent individual genera (Cohan, 2002; Cohan and Koeppel, 2008). From a practical point of view how much of this diversity is functionally relevant needs to be determined, as does the most meaningful functional unit to study, with an understanding of the role and impact of this diversity being essential to our understanding of these systems (McMahon et al., 2007a; Wilmes et al., 2009; Oehmen et al., 2010b). No single gene has adequate information to reveal this diversity alone, and so a multi locus approach is required (Cohan, 2002; Pace, 2009; Cole et al., 2010), like the suggested use of the polyphosphate kinase (ppk) gene to resolve additional diversity among the ‘Accumulibacter’ PAO (He et al., 2007) (see Section 1.6.2.2.1). The challenge with the uncultured majority is linking the phylogeny of alternative gene candidates with that of the traditionally applied 16S rRNA gene (He et al., 2007). Metagenomics (Nelson et al., 2010), enrichments of target organisms with flow cytometry of rRNA FISH labelled cells (Miyauchi et al., 2007) or using FISH to simultaneously target the rRNA and the mRNA of candidate genes (Pernthaler and Amann, 2004; Moraru et al., In press) or the 16S 23S internally transcribed spacer region (ITS) (Schmid et al., 2001), may assist with this. 1.6 Which are the important PAO in EBPR systems? Many putative PAO have been described in the literature. The evidence to support their importance in P removal in EBPR systems is discussed in the following sections, and their phylogenetic diversity, distribution and metabolic characterisation are summarised in Fig. 1.6 and Tables 1.1 and 1.2. 1.6.1 The putative gammaproteobacterial PAO 1.6.1.1 The acinetobacterial PAO In early EBPR microbiological work a number of organisms with the ability to accumulate P aerobically could be isolated readily from EBPR communities. The majority of these were identified phenotypically as members of the genus Acinetobacter with A. johnsonii particularly common (Fuhs and Chen, 1975; Deinema et al., 1980; Lawson and Tonhazy, 1980; Buchan, 1983; Lötter and Murphy, 1985; Suresh et al., 1985; Duncan et al., 1988;
20 Beacham et al., 1990; Streichan et al., 1990; Bark et al., 1993; Knight et al., 1995; Kim et al., 1997) (Fig. 1.6). Their predominance among cultured EBPR isolates led many to believe that these were the important PAO there (Seviour et al., 2003). Although many of these isolates showed high levels of aerobic polyP and PHA accumulation, none could accumulate substrates like actetate and store them anaerobically as PHA (see Table 1.1). Anaerobic P release was also much lower than that reported for EBPR biomasses, and was not linked causally to the presence of substrate (Jenkins and Tandoi, 1991; Tandoi et al., 1998; Seviour et al., 2003) Subsequent application of culture independent methods to EBPR communities revealed that the dominance, and therefore importance of Acinetobacter in P removal had been overestimated substantially by these culture dependent approaches (Seviour et al., 2003). Direct comparison between FISH and culture dependent community profiling showed that the latter heavily favoured the Gammaproteobacteria (Wagner et al., 1993; Manz et al., 1994; Wagner et al., 1994b; Kämpfer et al., 1996). The first culture independent analysis of EBPR communities indicated that the presence of Acinetobacter populations was generally low, a view which has been confirmed often subsequently (Cloete and Steyn, 1988b, a; Hiraishi et al., 1989; Auling et al., 1991; Wagner et al., 1993; Manz et al., 1994; Bond et al., 1995; Kämpfer et al., 1996; Bond et al., 1998) (Table 1.2). These never exceeded 2 % of the total biomass in a full scale EBPR plant with high P removal efficiency (Wagner et al., 1994b). Cells responding to the ACA23a FISH probe targeting Acinetobacter (Wagner et al., 1994b), also stain negatively for polyP granules in situ (Wagner et al., 1994b; Kämpfer et al., 1996; Beer et al., 2006). Although Acinetobacter may be contributing to a small extent to P removal, and the coverage of the ACA23a probe may underestimate their true abundance, it is now abundantly clear that these Bacteria are at best not important PAO, and also that the phenotypically defined PAO group are much more diverse than initially thought (Mino et al., 1998; Seviour et al., 2003).
21
Figure 1.6: Maximum likelihood phylogenetic tree of sequences of putative PAO and related organisms. All sequences were aligned using the ARB program (Ludwig et al., 2004) and were at least 1200 bp long. * Indicates partial sequences (<1200 bp) added later using the ‘quick add’ function in ARB. Probe coverage is indicated by brackets, where broken lines indicate the absence of sequence information at the probe site. PAOmix(a) = PAO462, PAO651 and PAO846 (Crocetti et al., 2000); PAOmix(b) = PAO462b, PAO651 and PAO846b (Zilles et al., 2002a). See Table 1.2 for further probe details. Phylogenetic affiliations are indicated in the boxes to the far left and broad groups are also colour coded. The scale bar corresponds to 0.1 substitutions per nucleotide position.
22 1.6.1.2 Stentrophomonas sp. EBPR 1 Ghosh et al. (2005) described a gammaproteobacterial isolate, designated Stentrophomonas sp. EBPR 1, that does conform strictly to the proposed PAO models (Table 1.1), but may be the only PAO isolated from activated sludge so far that fits the metabolic model requirements discussed earlier (Ghosh et al., 2005). Applying a FISH probe designed for Stentrophomonas sp. (Sten445) showed that it made up 3 4 % of the total cell numbers in two full scale EBPR plant communities (Ghosh et al., 2005) and Zilles et al. (2002b) have detected its presence in a clone library prepared from a biomass sample enriched for polyP stained (DAPI) cells from a full scale plant. Further characterisation of this Stentrophomonas sp. EBPR 1 isolate, together with the application of the Sten445 probe (Table 1.2) to more full scale EBPR communities is clearly required. 1.6.2 The betaproteobacterial PAO While the data from culture independent methods questioned the role of Gammaproteobacteria in EBPR, much of the accumulating evidence favoured members of the Betaproteobacteria. For example, quinone profiling (Hiraishi et al., 1989; Hiraishi et al., 1998), clone library data (Bond et al., 1995; Snaidr et al., 1997) and early FISH based studies (Manz et al., 1994; Wagner et al., 1994b; Kämpfer et al., 1996; Snaidr et al., 1997; Bond et al., 1999a), all suggested that organisms in this sub phylum dominated EBPR communities. Lee et al. (1999; 2002) also applied FISH MAR to show that its members could assimilate P aerobically in EBPR systems. 1.6.2.1 Lampropedia hyalina Stante et al. (1997) isolated an organism, later identified as Lampropedia hyalina (Lee et al., 2004), that behaves according to the proposed PAO models (Table 1.1), but with much lower levels of P cycling than shown by an EBPR biomass. Although a FISH probe is now available for its detection (Lee et al., 2004), its in situ importance has not been confirmed.
23 Table 1.1: Summary of suggested PAO/GAO populations selected biochemical properties Aerobic Anaerobic b e Organism Morphology a c d C uptake PolyP ↑ PHA PolyP ↓ Glycogen Putative PAOsf + + + + + Gammaproteobacteria Acinetobacter spp.1 Rods/cocci/filaments + ND + –¥ – Stentrophomonas sp. EBPR 12 Rods + ND + + + Betaproteobacteria Lampropedia hyalina3 Cocci in sheets + ND + + + Malikia granosa4 Rods + ND ND – ND Quatrionicoccus australiensis5 Cocci in tetrads + ND ND –¥ ND Candidatus ‘Accumulibacter phosphatis’6 Rods/cocci + + + + + Bet135 PAO7 Cocci + ND ND + + Actinobacteria Tetrasphaera japonica; Tetrasphaera australiensis8 Cocci in tetrads + ND ND – ND Tetrasphaera elongata9 Coccibacilli/rods + ND + – + Tetrasphaera jenkinsii; Tetrasphaera vanveenii; Tetrasphaera. veronensis10 Filamentous + ND ND –¥ ND Microlunatus phosphovorus11 Cocci + – + + + Tessarococcus bendigoensis12 Cocci in tetrads + ND ND – ND Friedmanniella spumicola; Friedmanniella capsulata13 Cocci + ND ND – ND Actino 658; Actino 221 PAO14 Cocci in tetrads; rods + –* + – + Gemmatimonadetes Gemmatimonas aurantiaca15 Rods + ND ND ND ND
24 Putative GAOsg – + – + + Gammaproteobacteria Candidatus ‘Competibacter phosphatis’16 Coccibaccilli – + – + + Gam445 GAO7 Rods in clusters – ND – + + Betaproteobacteria Bet65 GAO7 Rods in clusters – ND – + + Alphaproteobacteria Amaricoccus sp.17 Cocci in tetrads – ND – –¥ – Sphingomonas related18 Cocci in tetrads – + – + + Defluviicoccus related19 Cocci in tetrads – + – + + Actinobacteria Microsphaera multipartita20 Cocci – + – ND ND Micropruinia glycogenica21 Cocci in clusters – + – + + Kineosphaera limosa22 Cocci – + – + + KSBR532 GAO23 Cocci in clusters – ND – + + Summary of the current understanding: based on often equivocal results obtained under specific conditions. a Excess polyP storage; b aerobic carbohydrate accumulation, ‘*’ indicates inference from inhibitor studies in situ; c anaerobic P release; d anaerobic PHA storage, ‘¥’ indicating aerobic storage ability; e ability to assimilate carbon anaerobically during anaerobic: aerobic cycling. f and g suggested by respective models. ND = Not determined. 1 22 References: 1 (Jenkins and Tandoi, 1991; van Loosdrecht et al., 1997; Tandoi et al., 1998; Seviour et al., 2003); 2 (Ghosh et al., 2005); 3 (Stante et al., 1997; Lee et al., 2004); 4 (Spring et al., 2005); 5 (Maszenan et al., 2002); 6 (Hesselmann et al., 1999; Crocetti et al., 2000; Kong et al., 2004); 7 (Kong et al., 2007); 8 (Kataoka et al., 1996; Maszenan et al., 2000b); 9 (Hanada et al., 2002; Onda and Takii, 2002); 10 (Blackall et al., 2000; McKenzie et al., 2006); 11 (Ubakata and Takii, 1994; Kawaharasaki and Nakamura, 1995; Nakamura et al., 1995a; 1995b; Ubakata and Takii, 1998; Santos et al., 1999; Akar et al., 2006); 12 (Maszenan et al., 1999b); 13 (Maszenan et al., 1999a); 14 (Kong et al., 2005); 15 (Zhang et al., 2003); 16 (Crocetti et al., 2002; Kong et al., 2002a; Kong et al., 2006); 17 (Blackall et al., 1997; Maszenan et al., 1997; Falvo et al., 2001); 18 (Beer et al., 2004); 19 (Maszenan et al., 2005; Meyer et al., 2006; Burow et al., 2007; Wong and Liu, 2007); 20 (Yoshimi et al., 1996); 21 (Shintani et al., 2000; Kong et al., 2001); 22 (Liu et al., 2000a; 2002); 23 (Kong et al., 2001).
25 Table 1.2: FISH probes and distribution summary for putative PAO populations Lab Full scale * Probe Target group Reference Scale No. plants Range Avg. (%)¶ analysed¥ (%) (%)§ Gammaproteobacteria ACA23A Acinetobacter sp. (Wagner et al., 1994b) <51 142,3 <9 2.8 Sten455 Stentrophomonas sp. EBPR 1 (Ghosh et al., 2005) <3.54 24 <4 <4 Betaproteobacteria LAMP444 Lampropedia hyalina (Lee et al., 2004) NA NA NA NA RHC439 Some Rhodocyclus inc. ‘Accumulibacter’ (Hesselmann et al., 1999) PAO462 Most ‘Accumulibacter’ (Crocetti et al., 2000) PAO462b Most ‘Accumulibacter’ (extended) (Zilles et al., 2002a) <964,5 1113,6 12 <24 7.5 PAO651 Most ‘Accumulibacter’ (Crocetti et al., 2000) PAO846 Most ‘Accumulibacter’ (Crocetti et al., 2000) PAO846b Most ‘Accumulibacter’ (extended) (Zilles et al., 2002a) Acc I 444 Some clade I members ‘Accumulibacter’ (Flowers et al., 2009) <7113 2512 <4.6 1.5 Acc II 444 Some clade II members ‘Accumulibacter’ (Flowers et al., 2009) <8214 2512 <3.2 1.1 Bet135 Putative PAO cluster (Kong et al., 2007) NA 2010 <3 1.3 Actinobacteria Actino_1011 Some Tetrasphaera sp., inc. T. japonica (Liu et al., 2001) <16.715 123,8 <10 1.5 TET63 Some Tetrasphaera sp., inc. T. japonica and T. elongata (cited in Kong et al., ND16 113,7,8 <8 1.6 2001) NLIMII 175 Some Tetrasphaera sp., inc. T. jenkinsii and T. veronensis (Liu and Seviour, 2001) NA NA NA NA Actino 221 Tetrasphaera related PAO (Kong et al., 2005) 723,7,9,10,12,18,19 <30 5.6 <4.117† Actino 658 Tetrasphaera related PAO (Kong et al., 2005) 633,7,9,10,12,19 <14 4.3 MIC179 Micolunatus phosphovorus (cited in Lee et al., 2003a) MIC429 Micolunatus phosphovorus (cited in Lee et al., 2003a) <320 93 <5 1.8 MP2 Micolunatus phosphovorus (Kawaharasaki et al., 1998)
26 * Probes are considered in sets where they are applied as a mix or an alternative to target the same group of organisms; ¶ Highest enrichment obtained in a lab scale EBPR reactor; ¥ Number of full scale plants contributing to the analyses, only values determined by quantitative FISH methods were included in the analysis; § Average from all studies. Where ranges are given the middle of these values is considered for calculations; † Combined total for Actino 221 and Actino 658 applied to aerobic granular sludge; NA = Not available; ND = Not detected; 1 20 References: 1 (Eschenhagen et al., 2003); 2 (Wagner et al., 1994b; Kämpfer et al., 1996); 3 (Beer et al., 2006); 4 (Ghosh et al., 2005); 5 (Perez Feito et al., 2006); 6 (Zilles et al., 2002a; 2002b; Saunders et al., 2003; Kong et al., 2004; Chua et al., 2006; He et al., 2006; Burow et al., 2007; Thomsen et al., 2007; He et al., 2008; Morgan Sagastume et al., 2008; Hesselsoe et al., 2009); 7 (Kong et al., 2005); 8 (Wong et al., 2005); 9 (Kong et al., 2006); 10 (Kong et al., 2007); 11 (Lopez Vazquez et al., 2008a); 12 (Nielsen et al., 2010b); 13 (Flowers et al., 2009); 14 (Oehmen et al., 2010a; 2010c); 15 (Liu et al., 2001); 16 (Kong et al., 2001; Kong et al., 2002b); 17 (Lemaire et al., 2008a); 18 (Kong et al., 2008); 19 (Xia et al., 2008); 20 (Kawaharasaki et al., 1999).
27 1.6.2.2 Candidatus ‘Accumulibacter phosphatis’ Bond et al. (1995; 1999a) applied clone library and FISH analysis to generate data suggesting that members related to the β 2 proteobacterial genus Rhodocyclus were possible important PAO, a verdict based on their higher representation in laboratory scale (lab scale) systems efficiently removing P. Later studies in Switzerland and Australia generated data which confirmed these suggestions (Hesselmann et al., 1999; Crocetti et al., 2000). Applying more specific FISH probes targeting the Rhodocyclus related group showed these were present in EBPR systems as large ovoid cells in clusters, typical of EBPR communities. They also combined polyP staining to show that they behaved as PAO in situ and that their presence correlated with high EBPR efficiency (Hesselmann et al., 1999). Hesselmann et al. (1999) named these putative PAO group Candidatus ‘Accumulibacter phosphatis’ (henceforth referred to as ‘Accumulibacter’). Crocetti et al. (2000) designed three FISH probes for their in situ identification – PAO462, PAO651 and PAO846, which revealed that ‘Accumulibacter’ are often prominent populations in EBPR systems (Table 1.2). PHA and polyP staining in combination with FISH MAR confirmed their phenotype also conformed to that expected of a PAO in the proposed EBPR models (Crocetti et al., 2000; Liu et al., 2001; Kong et al., 2004; Beer et al., 2006) (Tables 1.1 and 1.2). Consequently ‘Accumulibacter’ are now considered to be a major PAO globally in EBPR plants of widely different configurations (Seviour et al., 2003; Oehmen et al., 2007; McMahon et al., 2010). 1.6.2.2.1 Intraphylotypic diversity among ‘Accumulibacter’ Although the PAO probes of Crocetti et al. (2000) (Table 1.2) are applied frequently to study ‘Accumulibacter’ as a functional phylogenetic group, the conflicting findings of some studies indicate considerable phenotypic variation probably exists among them. These differences include their cell morphology (Kong et al., 2002b; Carvalho et al., 2007; Kim et al., 2010), their ability to use nitrate as an electron acceptor and denitrify (Carvalho et al., 2007; Flowers et al., 2009) and the source of reducing equivalents they can use for anaerobic PHA formation (Wexler et al., 2009) (also see Sections 1.6.2.2.2 1.6.2.2.4). The whole genome sequence studies of two enriched cultures of ‘Accumulibacter’ found that both had variant
28 strains with 15 % genome sequence divergence from the dominant one (García Martín et al., 2006). These would almost certainly translate into phenotypic differences (Carvalho et al., 2007) among them. Comparative analysis of the sequences of the ITS region of their rrn operons (He et al., 2006), and their PHA synthase genes (phaC) (Wang et al., 2008a) have revealed much more subtle scale differences between members of the ‘Accumulibacter’ PAO than emerge from 16S rRNA sequence data alone. Subsequently McMahon et al. (2007b) suggested that the gene encoding the polyphosphate kinase (ppk) involved in the production of polyP, is more useful as a marker for elucidating finer scale ‘Accumulibacter’ biodiversity. They showed that phylogenetic trees based on sequences of the ppk gene and the 16S rRNA were largely congruent (see Fig. 1.7), but the ppk gene could better define two broad groups I and II, comprising a total of 12 clades, designated IA E and IIA G (He et al., 2007; Peterson et al., 2008). This additional phylogenetic resolution provided by the ppk sequences has allowed appropriate primer sets to be designed which differentiate between members of each clade, and their quantification using real time quantitative PCR (qPCR). Members of clades IA C, IIA D and IIF have been detected in EPBR systems (García Martín et al., 2006; He et al., 2007; McMahon et al., 2007b; Wexler et al., 2009; Kim et al., 2010; Slater et al., In press) while the remaining clade members have been found in freshwater and sediment samples, which were suggested to represent their ‘natural’ habitat (Peterson et al., 2008). While the ecological importance of this phylogenetic diversity is not well understood, it appears the complexity of the habitat reflects the diversity of this group, with higher numbers of clades present in full scale plants compared to lab scale systems (He et al., 2007). Slater et al. (In press) have suggested that the clade IIA members are associated with high EBPR performance and IIC members with poor, and, together with He et al. (2010), have proposed that the composition of the ‘Accumulibacter’ community may be influenced by other flanking populations. However, these suggestions are based on limited evidence from lab scale reactors and further work on the influence of operating conditions in full scale EBPR plants on the presence of individual clades and overall ‘Accumulibacter’ diversity is required. Based on the metagenomic data of García Martin et al. (2006), Kunin et al. (2008) found differences between ‘Accumulibacter’ clades related to their phage defence mechanisms, which were associated with the genes encoding exopolysaccharides (EPS) production, and clustered regularly interspaced short palindromic repeats (CRISPR), suggesting that phage
29 predation probably contributes to the maintenance of ‘Accumulibacter’ diversity. For the nitrifiers, Maixner et al. (2006) found marked differences between the proximity of co existing sub lineages of the Nitrospira nitrite oxidisers and the ammonia oxidisers present. This was suggested to relate to niche differentiation based on sensitivity to nitrite concentration. Whether a similar niche differentiation also occurs among ‘Accumulibacter’ clades is an interesting question. 1.6.2.2.2. Anaerobic metabolism of ‘Accumulibacter’ FISH MAR and lab scale studies indicate that ‘Accumulibacter’ can utilise a narrow range of substrates, and has a high affinity for short chain VFAs including acetate (Kong et al., 2004) (Table 1.4). Proteomics and inhibitor studies with enriched cultures indicate that acetate is transported across the cell membrane using an acetate permease, energised by the pmf generated by the symport efflux of P and H+ through the phosphorus permease Pit (phosphate inorganic transport) system (García Martín et al., 2006; Saunders et al., 2007; Burow et al., 2008a; Wilmes et al., 2008a) (see Fig. 1.8a.). In ‘Accumulibacter’, the source of reducing equivalents used for reducing acetate to PHA is still the subject of much debate, although the most likely source is from glycogen degradation and anaerobic operation of the TCA cycle (Zhou et al., 2010); in other words a combination of the Mino (Mino et al., 1987) and Comeau Wentzel models (Comeau et al., 1986; Wentzel et al., 1986). Inhibitor studies on enriched cultures (Burow et al., 2008b) and in situ MAR studies of full scale plant communities (Kong et al., 2004) support an important role for anaerobic glycolysis. Whole genome sequence data indicate that glycolysis is mediated by the Embden Meyerhof pathway (EMP) and not the Entner Douderoff (ED) pathway, as the full complement of enzymes involved in each pathway were only present for the former (García Martín et al., 2006). This proposal is also supported by the transcriptomic (He et al., In press) and proteomic detection of key EMP enzymes (Wilmes et al., 2008a; Wilmes et al., 2008b; Wexler et al., 2009). However, Hesselmann et al. (2000) with 13C NMR had suggested an anaerobic operation of the ED pathway in an EBPR reactor assessed by FISH analysis probably dominated by ‘Accumulibacter’ (Hesselmann et al., 1999; 2000). 30 Figure 1.7: Phylogenetic trees for ‘Accumulibacter’ related a. 16S rRNA gene and b. ppk gene sequences (He et al., 2007). Clades are inferred by brackets. Posterior probabilities are shown next to each node. The first number in parenthesis indicates the number of sequences that contribute to the operational taxonomic unit (OTU), and the second number indicates how many ‘Accumulibacter’ sequences were found in the source community of these sequences. The scale bars indicate changes per site.
31 Transcriptomic, proteomic and inhibitor studies all support some level of anaerobic TCA cycle operation, and evidence suggests that all the enzymes required for its full, partial and split operation (pathway details summarised in Fig. 1.9) are encoded by genes located in the whole genome sequence (García Martín et al., 2006). A novel cytochrome b/b6 encoded by a gene detected in the genome sequence, and its anaerobic expression in the presence of acetate, is thought to play the key role in the operation of the full TCA cycle by re oxidising quinones reduced by reverse electron transport by the activity of a succinate dehydrogenase (García Martín et al., 2006; Burow et al., 2008b). Evidence for the split operation, with the reductive arm operating in connection with the methylmalonyl CoA pathway to re oxidise the accumulation of these reduced quinones (Wilmes et al., 2008a; He et al., In press; He and McMahon, In press), and partial operation through the glyoxylate shunt, maximising carbon storage by avoiding the loss as CO2, has also been published (see Fig. 1.9) (Burow et al., 2008b). The different sources of reducing power available to ‘Accumulibacter’ are thought to provide it with the metabolic flexibility needed to better cope with the changing conditions of EBPR, where reliance on each pathway may depend on the prevailing environmental conditions (Oehmen et al., 2007; Burow et al., 2008b; He et al., In press). Further evidence for such metabolic flexibility is provided by Zhou et al. (2009) who showed that when glycogen stores were exhausted, reducing equivalents were increasingly sourced from the operation of the TCA cycle. 1.6.2.2.3. Aerobic metabolism of ‘Accumulibacter’ In agreement with the Mino model (Mino et al., 1987), ‘Accumulibacter’ can oxidise its PHA stores aerobically through the TCA cycle, coupling the energy generated for growth, P uptake and its polymerisation into polyP and replenishment of glycogen stores. Transcriptomics and inhibitor studies suggest that aerobic carbon flux occurs partially through the TCA cycle for energy provisions, while gluconeogenesis and the glyoxylate pathway operate to replenish glycogen stores (Burow et al., 2008b; Wexler et al., 2009; He et al., In press; He and McMahon, In press) (Fig. 1.9). Aerobic P uptake occurs through both a low affinity Pit and high affinity Pst (phosphate specific transport) phosphate permeases (García Martín et al., 2006; Burow et al., 2008a; He et al., In press) (Fig. 1.8a.). Interestingly, when P levels become limiting, ‘Accumulibacter’ can shift its reliance from polyP to glycogen as its source 32 of the anaerobic energy required for acetate uptake, showing yet further metabolic diversity and adaptability (Zhou et al., 2008). 1.6.2.2.4 Can ‘Accumulibacter’ denitrify? Much effort has been devoted to determining whether the PAO and the DPAO are different populations (Dabert et al., 2001; Ahn et al., 2002; Zeng et al., 2003a; Freitas et al., 2005; Carvalho et al., 2007; Flowers et al., 2009; Guisasola et al., 2009). The whole genome sequence of an ‘Accumulibacter’ clade IIA member indicates that it may possibly use nitrite but not nitrate as a terminal electron acceptor for denitrification (García Martín et al., 2006). Miyauchi et al. (2007) also amplified a nitrite reductase gene (nirS) from a fluorescence activated cell sorted (FACS) enriched ‘Accumulibacter’ sample, and suggested that it most likely derived from this population. However, FISH MAR data show that some ‘Accumulibacter’ in full scale plant communities may use both nitrate and nitrite (Kong et al., 2004; Morgan Sagastume et al., 2008), and data from enriched cultures indicate that ‘Accumulibacter’ can couple P uptake with nitrate reduction (Ahn et al., 2002; Zeng et al., 2003a; Carvalho et al., 2007; Flowers et al., 2009). Carvalho et al. (2007) have demonstrated that two morphologically distinct PAO populations both responding to the ‘Accumulibacter’ FISH probes differed in their abilities to reduce nitrate, suggesting metabolic variants exist among them. FISH has since been applied to differentiate between clades IA and IIA members, where only the former could reduce nitrate (Flowers et al., 2009; Oehmen et al., 2010a; Oehmen et al., 2010c).
33
Figure 1.8: Important membrane transport mechanisms in characterised PAO and GAO (modified from Burow et al., 2008a; McMahon et al., 2010). a. ‘Accumulibacter’ PAO: i. Symport efflux of protons and P through the Pit transporter generate an electrical membrane potential. ii. Symport of acetate and protons through an acetate permease, driven by the electric membrane potential. iii. Symport uptake of P and protons through the low affinity Pit transporter at high external P concentration (early aerobic) iv. P uptake through the high affinity Pst transporter, at the expense of energy, when external P concentration is low (late aerobic). b. ‘Competibacter’ GAO: i. Efflux of H+ via the fumarate reductase system generates an electrical membrane potential. ii. Symport of acetate and protons through an acetate permease, driven by an electric membrane potential. iii. Export of H+ through an ATPase maintains the membrane potential at the expense of energy. c. Defluviicoccus related GAO: i. Efflux of protons via the fumarate reductase system generates an electrical membrane potential. ii. Symport of acetate and protons through an acetate permease, driven by electric membrane and sodium potentials. iii. Sodium potential maintained by methylmalonyl CoA decarboxylation.
34
Figure 1.9: Proposed alternate anaerobic metabolic pathways for ‘Accumulibacter’ adapted from Oehmen et al. (2007). Key varied steps in alternate pathways are colour coded: ED = green; EMP = blue; partial TCA/glyoxylate shunt = purple; split reductive branch of the TCA cycle = red. 1.6.2.3 Are members of the Dechloromonas PAO? Some evidence suggests that members of the genus Dechloromonas can behave as PAO (Zilles et al., 2002a; Kong et al., 2007; Günther et al., 2009), which is not surprising given their phylogenetic proximity to the ‘Accumulibacter’ (McMahon et al., 2010) (see Fig. 1.6). Thus, Zilles et al. (2002a) redesigned two of the original PAO probes (Crocetti et al., 2000) PAO651b and PAO846b, to include two Dechloromonas related sequences (see Fig. 1.6),
35 although their application to EBPR systems shows little observed difference in their coverage when compared to the original unmodified probes (Nielsen et al., 2009b). Applying the full cycle approach to an activated sludge system, Kong et al. (2007) also described a probe defined group (Bet135) related to the Dechloromonas that behaved in situ according to PAO models (Kong et al., 2007). Conversely, members of this genus have also been shown to behave as possible competitors of the ‘Accumulibacter’ PAO in a continuously aerated lab scale EBPR system where the FEAST:FAMINE conditions essential for EBPR were achieved by temporal separation of addition of the P and carbon source (Ahn et al., 2007; Schroeder, 2009). In this system they assimilated acetate as PHA during the ‘FEAST’ period without subsequent P accumulation during the cycle periods of ‘FAMINE’, a phenotype characteristic of the GAO (see Section 1.7). 1.6.3 Actinobacterial related PAO As with the Betaproteobacteria, members of the Gram positive high mol % G+C Actinobacteria are often abundant in EBPR systems and sufficient evidence exists to suggest they contribute to P removal (Wagner et al., 1994b; Kämpfer, 1997; Christensson et al., 1998; Hiraishi et al., 1998; Kawaharasaki et al., 1999; Crocetti et al., 2000; Lee et al., 2002; Zilles et al., 2002b; Eschenhagen et al., 2003; Beer et al., 2006). Examples of these putative actinobacterial PAO are discussed here. 1.6.3.1 Microlunatus phosphovorus Nakumura et al. (1991) isolated an actinobacterial species, later named Microlunatus phosphovorus, from activated sludge that accumulates large amounts of polyP aerobically, and releases it anaerobically with a concomitant uptake of glucose (Nakamura et al., 1991; 1995a; 1995b) and glutamate (Ubakata and Takii, 1994, 1998). These are the metabolic traits expected of a PAO (see Table 1.1). Then Santos et al. (1999) with 13C NMR showed the organism did not polymerise acetate or glutamate anaerobically, but instead glucose was fermented to acetate, which they claimed was accumulated intracellulary, together with
36 glutamate from the supplied yeast extract. Both were utilised in the subsequent aerobic phase (Santos et al., 1999). The fate of glutamate when supplied as the sole carbon source was similar (Ubakata, 1994). Although some unidentified storage polymer was postulated, neither PHA nor glycogen cycling occurred and P and carbon metabolism were not exclusively coupled (Santos et al., 1999). M. phosphovorus has since been shown to produce PHA anaerobically (Akar et al., 2006), although P metabolism was not monitored and the glucose levels required for PHA production were eight times higher than those used by Santos et al. (1999). In situ FISH studies show that this organism is observed in EBPR systems but only intermittently (Table 1.2). Where detected it stains positively in situ for polyP, indicating that it may participate as a minor player in P removal (Kawaharasaki et al., 1998; Kawaharasaki et al., 1999; Lee et al., 2002; Eschenhagen et al., 2003; Lee et al., 2003b; Beer et al., 2006). 1.6.3.2 Tetrasphaera related PAO Several Tetrasphaera isolates have been cultured from activated sludge, and all accumulate polyP aerobically, suggesting a possible involvement in P removal (Kataoka et al., 1996; Blackall et al., 2000; Maszenan et al., 2000b; Hanada et al., 2002; Onda and Takii, 2002; McKenzie et al., 2006) (see Table 1.1). Of these only Tetrasphaera elongata assimilates polyP aerobically after an anaerobic incubation with substrate. However, P cycling was lower than expected for an EBPR biomass, PHAs were not formed, and P uptake was only observed when casamino acids, and not acetate, were the anaerobic carbon source supplied (Onda and Takii, 2002). The TET63, actino_1011 and NLIMII175 FISH probes were designed to target defined clusters of sequences within the genus Tetrasphaera (Kong et al., 2001; Liu and Seviour, 2001; Liu et al., 2001), although it is now clear that these target much broader, less defined, clusters of 16S rRNA sequences within this genus. Their application has revealed the presence of Tetrasphaera related organisms in EBPR systems (Table 1.2) where they often stain positively for polyP (Kong et al., 2001; Liu and Seviour, 2001; Liu et al., 2001; Kong et al., 2002b; Eschenhagen et al., 2003; Wong et al., 2005; Beer et al., 2006). 37 Kong et al. (2005) applied the Actino_1011 FISH probe sequence as a PCR primer to enrich for Tetrasphaera related organisms in their clone library, which they then used to design the FISH probes, Actino 658 and Actino 221. Combining FISH with MAR revealed the identity of two Tetrasphaera related groups with PAO like phenotypes. These groups, one growing as cocci in tetrads and the other as rods, assimilated amino acids anaerobically in situ with the concomitant release of stored P, while under aerobic conditions the same cells accumulated P as polyP. However, short chain fatty acids like acetate were not assimilated anaerobically and PHA was not formed (agreeing with pure culture data). Instead storage polymer/s of unknown chemical composition was synthesised from the amino acids. The absence of PHA formation has also been reported for other putative actinobacterial PAO in EBPR systems (Liu et al., 2001; Beer et al., 2006). These Tetrasphaera related PAO sometimes dominate EBPR communities, often out numbering the ‘Accumulibacter’ PAO, showing that while not conforming strictly to the usual definition of a PAO, they are major contributors to EBPR (Kong et al., 2005). They occur more abundantly in EBPR plants treating industrial waste where protein, and hence amino acids, levels can be higher than in plants treating domestic wastes (Kong et al., 2005; Kong et al., 2006). A further phylogenetic diversity among the Tetrasphaera related PAO group of organisms has since been reported, emphasising their importance in EBPR (Nguyen et al., 2009). 1.6.3.2.1 Metabolism of the Tetrasphaera PAO FISH MAR studies after addition of inhibitors of the glycolytic pathway indicate that glycolysis is not important anaerobically in providing energy for substrate (amino acid) assimilation by them (Kong et al., 2005). Although they seem unable to assimilate anaerobically short chain fatty acids like acetate (see above) (Kong et al., 2005), populations responding to the Actino 221 probe could take up oleic acid anaerobically. However, this was not coupled to subsequent aerobic P uptake (Kong et al., 2005). These organisms may also make an important contribution to the hydrolysis of more complex carbon sources and their fermentation (Kong et al., 2008; Xia et al., 2008). Since not all organisms currently covered by the two available Tetrasphaera FISH probes show these metabolic capabilities, further physiological diversity probably exists among these sub groups (Kong et al., 2005; Kong et al., 2008; Xia et al., 2008). Kong et al. (2008) did speculate that, as with 38 Microlunatus phosphovorus (Santos et al., 1999), some Tetrasphaeras may store fermentation by products for subsequent use aerobically, which would explain their apparent lack of reliance on PHA storage. FISH MAR studies indicate that both the Actino 221 and Actino 658 probe defined populations can use nitrate but not nitrite as their terminal electron acceptor (Kong et al., 2005), consistent with data from the pure culture isolates except for T. japonica and T. australiensis, neither of which could use nitrate either (Kataoka et al., 1996; Blackall et al., 2000; Maszenan et al., 2000b; Hanada et al., 2002; Onda and Takii, 2002; McKenzie et al., 2006). 1.6.4 Other possible PAO Several other activated sludge isolates listed in Table 1.1, which can assimilate polyP aerobically await further characterisation and in situ analysis to clarify their possible roles in EBPR. It is evident from FISH MAR data, and/or staining based surveys of full scale plant samples that many more PAO not covered by existing FISH probes, and whose identities remain unknown are present in EBPR communities. These include members of the Alphaproteobacteria (Kawaharasaki et al., 1999; Kong et al., 2004; Beer et al., 2006), Gammaproteobacteria (Kong et al., 2004; Beer et al., 2006), Bacteroidetes (Kawaharasaki et al., 1999) and additional members of the Betaproteobacteria (Chua et al., 2004; Beer et al., 2006) and Actinobacteria (Beer et al., 2006). Eukaryotic PAO have also been suggested (Melasniemi and Hernesmaa, 2000), although the evidence presented in their support was indirect, and organisms with similar morphologies were shown to be novel Betaproteobacteria (Chua et al., 2004). Application of flow cytometry to enrich for cells stained positively for polyP, and clone library analysis also suggests a higher diversity of organisms involved in EBPR, and approaches like this may assist in documenting this diversity (Kawaharasaki et al., 2002; Zilles et al., 2002b; Günther et al., 2009). The continuing re appraisal of current FISH probes against the rapidly increasing public rRNA sequence databases, and the search for the identity of new groups of PAOs, coupled with ecophysiological studies, will reveal eventually the true extent of their diversity and function.
39 The ecophysiology of the Tetrasphaera related PAO suggests that it is more appropriate to use a wider and looser definition of a PAO than that fitting the phenotype suggested from the metabolic models discussed earlier (Section 1.4). Thus, any population which accumulates more P than it requires for growth and which stains positively in situ for polyP should be considered as a putative PAO, regardless of whether it synthesises PHA from acetate anaerobically. This need is emphasised by reports of anaerobic uptake of a wide range of substrates by lab scale EBPR biomass samples (Satoh et al., 1998; Carucci et al., 1999) and populations detected in full scale plants (Kong et al., 2004) where in situ PHA storage is not observed. These observations show that other survival strategies must exist to allow cells to cope with the ‘FEAST:FAMINE’ conditions of EBPR anaerobic: aerobic recycling, and more are likely to emerge as our understanding of EBPR microbiology increases. Distribution studies show that many of the proposed candidate PAO populations are often present at low numbers in these systems (Table 1.2), but this does not denote their potential importance, as the proportions of organisms staining positively for polyP inclusions in these systems is often lower than 20 % of the total biomass (Hung et al., 2002; Beer et al., 2006). These could still be making important contributions to EBPR, given P removal results from the combined activities of a wide range of different organisms (Mino et al., 1998; Seviour et al., 2003). The reason for such functional redundancy probably provides stability for these continuously fluctuating systems, where each group may occupy a slightly different niche (Wagner et al., 2002; McMahon et al., 2007a; Hesselsoe et al., 2009; Oehmen et al., 2010b). 1.7 The glycogen accumulating organisms (GAOs) The general field experience is that full scale EBPR processes dealing with complex and variable feeds are notoriously unreliable. There may be several sound explanations for poor EBPR capacity, including events of high rainfall, high levels of nitrate in the anaerobic zone encouraging denitrifying bacteria or viral predation of important PAO (Seviour et al., 2003; Barnard and Abraham, 2006; Oehmen et al., 2007; Barr et al., In press). One suggestion is the negative impact of non polyP accumulating competitors of the PAO, an idea which is based on early observations of anaerobic substrate uptake in the absence of substantial P release in low capacity EBPR lab scale systems (Fukase et al., 1984; Cech and Hartman, 1990; Satoh et al., 1992; Cech and Hartman, 1993; Liu et al., 1994; Satoh et al., 1994). 40 Because of complete reliance on accumulated glycogen and not polyP stores as an energy source for anaerobic uptake and storage of carbon (Liu et al., 1994), organisms possessing a phenotype suitable for outcompeting the PAO have been termed the glycogen accumulating organisms (GAO) (Mino et al., 1995). As they do not cycle polyP but compete with the PAO for substrates in the anaerobic feed stage, they may have a negative influence on the aerobic ‘FAMINE’ P removal stage, thus leading to possible eventual EBPR failure (Satoh et al., 1992; Mino et al., 1998; Seviour et al., 2003; Oehmen et al., 2007). Compared to the ‘Accumulibacter’ PAO, little data are available on the anaerobic metabolism of the GAO (Oehmen et al., 2007), although metabolic models have been proposed for them (Satoh et al., 1994; Mino et al., 1995; Filipe et al., 2001b; Zeng et al., 2003b; Oehmen et al., 2006b). However, it is now clear that these, like the PAO are phylogenetically diverse (see Fig. 1.10) and most of the models available have been constructed without resolving the GAO identity in the communities analysed. 1.7.1 The anaerobic metabolism of the GAO Like the PAO, the GAO are thought to sequester carbon in the anaerobic feed stage and use it to synthesise PHA, with the required energy and reducing equivalents sourced from the metabolism of glycogen stores (Liu et al., 1994; Satoh et al., 1994; Mino et al., 1995). The lack of ATP generated from the hydrolysis of polyP and subsequent P release, which occurs with the PAO, means that the GAO must compensate with an elevated reliance on anaerobic glycogen hydrolysis for their energy (Oehmen et al., 2007). To balance the resulting higher production of reducing equivalents, the GAO cells are thought to balance their redox by reducing pyruvate to propionyl CoA through the left reductive branch of the TCA cycle and the methylmalonyl CoA pathway (Fig 1.9). Consequently unlike the PAO, poly β hydroxyvalerate (PHV) in addition to poly β hydroxybutyrate (PHB) is synthesised by them when acetate is the carbon source (Fig. 1.9) (Liu et al., 1994; Satoh et al., 1994; Filipe et al., 2001b).
41 1.7.2 Aerobic metabolism of the GAO As with the PAO, under aerobic famine conditions, degradation of the stored PHA occurs through the TCA cycle, and the energy and carbon are predominately made available for biomass production, cell maintenance and for replenishing glycogen stores, but without P assimilation and synthesis of polyP (Zeng et al., 2003b). 1.8 The phylogenetic identity of the GAO The frequent association of EBPR inefficiency with the proliferation of tetrad forming organisms (TFO) (Cech and Hartman, 1993; Liu et al., 1996b; Liu et al., 2000a; Filipe et al., 2001c; Tsai and Liu, 2002; Whang and Park, 2002) led early studies to concentrate on the isolation and identification of what were initially termed the ‘G bacteria’, named for their suggested affinity for glucose assimilation and defined by their TFO morphology (Cech and Hartman, 1993; Seviour et al., 2000). It was soon realised that this morphologically defined ‘G bacteria’ group contained several phylogenetically and phenotypically diverse populations (Seviour et al., 2000), some of which accumulated polyP (see Table 1.1), and equally importantly that the GAO phenotype was possessed by a morphologically diverse group of organisms (see Table 1.1). In the absence of being able to directly identify glycogen accumulating cells by staining (Serafim et al., 2002a), indirect methods have been used for GAO identification based on applying a range of molecular methods to identify the dominating populations in systems with low EBPR capacity. Whether these possess the required GAO phenotype can be determined partly by FISH MAR and histochemical staining (Kong et al., 2006; Burow et al., 2007; Wong and Liu, 2007), supported by the overall chemical profile data from enriched lab scale reactor communities (Crocetti et al., 2002; Wong et al., 2004; Meyer et al., 2006; Oehmen et al., 2007). As with the PAO, the putative GAO members are clearly phylogenetically diverse, and two main groups are now recognised; those within the Gammaproteobacteria and Alphaproteobacteria. Their phylogenetic affiliations are shown in Fig. 1.10. and data on their ecophysiology and distribution summarised in Tables 1.1 and 1.4, respectively.
42 1.8.1 The gammaproteobacterial GAO Candidatus ‘Competibacter phosphatis’ Nielsen et al. (1999) were the first to recognise a putative GAO population in a deteriorated EBPR system using the molecular approaches of 16S rRNA DGGE community analysis and from the data generated, designing two FISH probes (Gam1278 and Gam1019) to cover the dominant sequences they found. These probes hybridised with large coccobacilli not accumulating polyP. Crocetti et al. (2002) who named this group Candidatus ‘Competibacter phosphatis’ (henceforth referred to as ‘Competibacter’) designed new probes, GAOQ989 and GAOQ431 to cover all the then known sequences from their 16S rRNA clone libraries. Finding further sequence diversity encouraged Kong et al. (2002a) to design a set of FISH probes now defining two groups which were further divided into a total of seven sub groups (Fig 1.10 and Table 1.3). Importantly, Kong et al. (2006) and Wong and Liu (2006) both reported that some ‘Competibacter’ cells responding to the universal GB FISH probe were not covered by any of these sub group FISH probes (Fig 1.10 and Table 1.3) and suggested the existence of further sub groups and increased biodiversity. FISH MAR and histochemical staining reveal that these organisms have the phenotype appropriate for a GAO, in assimilating substrates anaerobically and storing them as PHA but without subsequent aerobic storage of excess P as polyP (Crocetti et al., 2002; Kong et al., 2002a; Kong et al., 2006) (Table 1.1). These ‘Competibacter’ have since been reported in lab and full scale systems, making up to 98 % and 31 % of the total biomass, respectively (Table 1.3). Their presence has also been correlated with poor EBPR capacity in full scale systems (Saunders et al., 2003). No link has been found between in situ physiology of the different sub groups and their distribution from a survey involving 12 full scale EBPR plants in Denmark (Kong et al., 2006). Understanding how individual sub groups behave in response to different operational conditions is likely to be vital in attempts to understand and ultimately control their undesirable proliferation.
43
Figure 1.10: Maximum likelihood phylogenetic tree of sequences of putative GAO and related organisms. All sequences were aligned using the ARB program and were at least 1200 bp long. ‘*’ Indicates partial sequences (<1200 bp) added later using the ‘quick add’ function in ARB. Probe coverage is indicated by brackets, where broken lines indicate the absence of sequence information at the probe site. DF1mix = TFO_DF218, TFO_DF618 and TFO_DF776; DF2mix = DF988 and DF1020. See Table 1.3 for further probe details. Phylogenetic affiliations are indicated in the boxes to the far left and some groups are also colour coded. The scale bar corresponds to 0.1 substitutions per nucleotide position.
44 1.8.1.1 Anaerobic metabolism of the ‘Competibacter’ Very few differences in substrate affinities have been noted for members of the different ‘Competibacter’ sub groups, with all having a high affinity for VFAs like acetate (Kong et al., 2006) (Table 1.4). Inhibitor studies with enriched cultures indicate that a pmf supplies the energy for acetate uptake, and is thought to be maintained by an efflux of protons involving the activity of F1F0 ATPase, at the expense of ATP and fumarate reductase (Saunders et al., 2007) (see Fig. 1.8b.). The observed inhibition of in situ anaerobic acetate uptake after addition of the glycolysis inhibitor iodoacetate, indicates a reliance on glycogen utilisation for energy for substrate assimilation (Kong et al., 2006). 13C NMR based studies on cultures enriched with both ‘Competibacter’ and Defluviicoccus related GAO (see later) indicate that their cells use the ED pathway for glycogen degradation (Lemos et al., 2007) supported by the profiles of another temperature based study with a ‘Competibacter’ enriched biomass also using an incubation temperature of 30 °C (Lopez Vazquez et al., 2009a). However, the profile data for studies at lower temperatures with enrichments of GAO, including ‘Competibacter’ and the Alphaproteobacterial TFO, seem to support the involvement of the EMP pathway due to a higher glycogen utilisation and PHA production (see Oehmen et al., 2010b). It has been suggested that either these GAO possess both pathways that are temperature dependant, or different phylotypes of these groups possess either pathway (Oehmen et al., 2010b). 1.8.1.2 Aerobic/anoxic metabolism of the ‘Competibacter’ Enriched culture studies indicate that stored PHA is respired through the TCA cycle with gluconeogenesis and glycolysis involved in the replenishment of glycogen levels (Lemos et al., 2007). While lab scale data suggest an ability of these cells to denitrify (Zeng et al., 2003c; Wang et al., 2008b), FISH MAR studies using the ‘Competibacter’ sub group probes (Fig 1.10 and Table 1.3) revealed that only members of sub group 6 could denitrify while those of sub groups 1, 4 and 5 reduced nitrate but only to nitrite (Kong et al., 2006). Interestingly, organisms responding to the sub group 6 probe (GB_6/Gam1019) appear to accumulate small amounts of polyP (Liu et al., 2001), and although not reported since, this suggests that they, like the PAO, can adjust their metabolisms according to conditions.
45 Table 1.3: FISH probes and distribution summary for putative GAO populations Lab Full scale * Probe Target group Reference Scale No. plants Range Avg. (%)¶ analysed¥ (%) (%)§ Gammaproteobacteria Gam1278 Some ‘Competibacter’ (Nielsen et al., 1999) GB ‘Competibacter’ (Kong et al., 2002a) GB_G1 (GAOQ989) Group 1 ‘Competibacter’ (Crocetti et al., 2002) <981 652 8 <31 4.5 GB_G2 Group 2 ‘Competibacter’ (Kong et al., 2002a) GAOQ439 Some ‘Competibacter’ (Crocetti et al., 2002) GB_1&2 Sub groups 1 and 2 ‘Competibacter’ (Kong et al., 2002a) <109 124 <5.8 0.8 GB_2 Sub group 2 ‘Competibacter’ (Kong et al., 2002a) ND9 133,4 <12.5 1 GB_3 Sub group 3 ‘Competibacter’ (Kong et al., 2002a) ND9 124 <1.8 0.3 GB_4 Sub group 4 ‘Competibacter’ (Kong et al., 2002a) <509 223,4,10 <2.5 1.2 GB_5 Sub group 5 ‘Competibacter’ (Kong et al., 2002a) ND9 124 <2 0.4 GB_6 (Gam1019) Sub group 6 ‘Competibacter’ (Nielsen et al., 1999) <109 133,4 <11.5 1.3 GB_7 Sub group 7 ‘Competibacter’ (Kong et al., 2002a) <209 124 <2.3 0.6 Gam455 Putative GAO clone (Kong et al., 2007) NA 456,8 <4 0.6 Betaproteobacteria Bet65 Putative GAO clone (Kong et al., 2007) NA 206 <6 1.7 Alphaproteobacteria AMAR839 Amaricoccus sp. (Maszenan et al., 2000a) <111 410 <9 2.6 SBR9 1a Sphingomonas related GAO (Beer et al., 2004) <71 365,7,10,12 <24 1.9 DEF438 Defluviicoccus vanus (cited in Kong et al., 2001) TFO_DF862 Defluviicoccus vanus (Wong et al., 2004) TFO_DF218 Cluster I Defluviicoccus related GAO (Wong et al., 2004) <9513 583,5 8 <1.5 0.1 TFO_DF618 Some cluster I Defluviicoccus related GAO (Wong et al., 2004) TFO_DF776 Cluster I Defluviicoccus related GAO (Wong and Liu, 2007) TFO_DF629 Cluster II Defluviicoccus related GAO (Wong and Liu, 2007) DF988 Cluster II Defluviicoccus related GAO (Meyer et al., 2006) DF1020 Cluster II Defluviicoccus related GAO (Meyer et al., 2006) <5514 455 8 <9 0.6 DEF636 Some cluster II Defluviicoccus related GAO (Kondo et al., 2007) DEF827 Some cluster II Defluviicoccus related GAO (Kondo et al., 2007) 46 Actinobacteria MIC184 Micropruina glycogenica (cited in Kong et al., 2001) <2215 223,10 <8 0.8 KSBR531 Putative GAO clone (cited in Kong et al., 2001) <715 910 <1 <1 * Probes are considered in sets where they are applied as a mix or an alternative to target the same group of organisms; ¶ Highest enrichment obtained in a lab scale ‘EBPR’ reactor; ¥ Number of full scale plants contributing to the analyses, only values determined by quantitative FISH methods were included in the analysis; § Average from all studies. Where ranges are given the middle of these values is considered for calculations. ND = Not determined. 1 15 References: 1 (Saunders et al., 2007); 2 (Saunders et al., 2003); 3 (Wong et al., 2005); 4 (Kong et al., 2006); 5 (Burow et al., 2007); 6 (Kong et al., 2007); 7 (Lopez Vazquez et al., 2008a); 8 (Nielsen et al., 2010b); 9 (Kong et al., 2002a); 10 (Beer et al., 2006); 11 (Kong et al., 2002b); 12 (Beer et al., 2004); 13 (Dai et al., 2007); 14 (Meyer et al., 2006); 15 (Kong et al., 2001).
47 1.8.2 Alphaproteobacterial related GAO Much less is known of the phylogenetic diversity among the Alphaproteobacterial GAO. This arises partly from difficulties in acquiring 16S rRNA sequence information from them in clone libraries generated from EBPR communities, even where FISH suggests they are abundant there (Beer et al., 2004; Meyer et al., 2006). Some success in overcoming this has been achieved by prior population enrichment steps (Wong et al., 2004; Meyer et al., 2006; Wong and Liu, 2007). Much higher diversity among these than discussed here is likely, especially since Oehmen et al. (2006b) showed that only 16 % of their lab scale reactor community, highly enriched in alphaproteobacterial TFO, hybridised with the existing FISH probes designed to target the then known GAO members of this group (Fig 1.10; Table 1.3). Examples of alphaproteobacterial GAO members are discussed next. 1.8.2.1 Amaricoccus sp. The original ‘G bacteria’ isolate of Cech and Hartman (1993) was later identified as Amaricoccus kaplicensis, a member of a novel genus that now includes isolates from geographically diverse activated sludge systems (Maszenan et al., 1997) (Fig. 1.10). Although Amaricoccus spp. are abundant by FISH in some EBPR plants in Australia (Maszenan et al., 2000a), A. kaplicensis strains show no ability to assimilate substrates anaerobically in pure culture, which questions their capability to compete with the PAO for substrates under anaerobic conditions (Falvo et al., 2001). As with the Acinetobacterium PAO, they appear to be laboratory weeds of historical interest only in EBPR research. 1.8.2.2 Sphingomonas related GAO Beer et al. (2004) also identified putative members of the GAO in situ in a lab scale reactor dominated by FISH probed alphaproteobacterial TFO and with poor EBPR capacity. Applying DGGE and clone library analysis they identified the dominant alphaproteobacterial sequence as a member of the genus Sphingomonas (Fig. 1.10). Application of FISH probes (SBR9 1a and SBR9 1b) designed against this sequence gave positive hybridisation with the
48 dominant TFO, and histochemical staining and chemical profiles showed that these organisms behaved according to the proposed GAO models (Table 1.1). In a later survey of Australian full scale EBPR plants by Beer et al. (2006), these populations were observed commonly making up to 24 % of the total biovolume, but they have been conspicuously absent from communities of full and lab scale EBPR systems analysed elsewhere (Oehmen et al., 2005a; Meyer et al., 2006; Oehmen et al., 2006a; Oehmen et al., 2006b; Burow et al., 2007; Saunders et al., 2007; Burow et al., 2008a; Lopez Vazquez et al., 2008b; Pijuan et al., 2008). Of additional concern are the many inconsistencies in the original study that question the validity of this group (Saunders, 2005) and these are discussed in depth in Chapter 5. 1.8.2.3 Defluviicoccus related GAO Wong et al. (2004) proposed that members of the putative alphaproteobacterial GAO recovered from a deteriorated lab scale membrane EBPR bioreactor may be related to the activated sludge isolate Defluviicoccus vanus (Maszenan et al., 2005), from sequences in a 16S rRNA clone library constructed with alphaproteobacterial targeted PCR primers. These sequences formed a monophyletic group including D. vanus. The two FISH probes they designed from them (TFO_DF218 and TFO_DF618) targeted members of this entire cluster (cluster I). Then Meyer et al. (2006) obtained further 16S rRNA sequences also related to D. vanus, but these formed a distinct second cluster with 97 % similarity between its members (cluster II), and shared only 90 % similarity to members of the cluster I of Wong et al. (2004) (Fig. 1.10). The probes Meyer et al. (2006) published (DF988 and DF1020) were also designed so that when used together they targeted the whole cluster (Fig 1.10 and Table 1.3). A third Defluviicoccus ‘cluster’ (cluster III) suggested by Wong and Liu (2007) was based by them on a single 16S rRNA sequence generated from a non EBPR system (see Fig. 1.10), but no FISH probes were designed to detect its members. Thus their potential importance in EBPR systems is unknown. Both cluster I and II members are abundant in lab scale reactors, with successful enrichments of up to 95 and 55 %, respectively, of the total biovolume being achieved for each (Meyer et al., 2006; Dai et al., 2007) (Table 1.3). In full scale plants cluster I members are rarely found (Wong et al., 2005; Burow et al., 2007; Nielsen et al., 2010b) while cluster II members can constitute up to 9 % of the total biomass in those examined so far, and are sometimes in 49 higher abundance than the ‘Competibacter’ GAO and Accumulibacter PAO, indicating thier potential importance as competitors of the PAO in these systems (Meyer et al., 2006; Burow et al., 2007; Nielsen et al., 2010b) (see Table 1.3). Whether this Defluviicoccus group is as diverse phylogenetically as the ‘Competibacter’ GAO is not known. With the exception of the D. vanus sequence all the 16S rRNA data available for clusters I and II Defluviicoccus members were obtained from lab scale reactors fed simple fatty acids, alone or substituted with casamino acids as substrates (McMahon et al., 2002; Wong et al., 2004; Zhang et al., 2005; Meyer et al., 2006; Wong and Liu, 2007). Studies also suggest that morphological and physiological variation exists among the members of each of these clusters (Wong et al., 2004; Burow et al., 2007; Kondo et al., 2007; Wong and Liu, 2007; Schroeder et al., 2008). For example the aerobic substrate uptake profiles for cluster II members in an aerated novel EBPR process (Schroeder et al., 2008) differed to those reported by Burow et al. (2007) for their Defluviicoccus from full scale anaerobic: aerobic EBPR processes. It was especially noticeable with propionate assimilation, which was very slow in the Defluviicoccus identified by Burow et al. (2007). This is a surprising result, considering that their high apparent affinity for propionate had been exploited to enrich them for RNA SIP (Meyer et al., 2006). These published data suggest that the FISH probes currently available do not describe the full phylogenetic diversity within this group. 1.8.2.3.1 Anaerobic metabolism of the Defluviicoccus related GAO Application of the Defluviicoccus FISH probes, in combination with MAR and histochemical staining, has shown that members of both clusters I and II conform to the proposed models for the GAO (Table 1.1), and both have similar in situ physiologies and anaerobic substrate uptake profiles to both the ‘Accumulibacter’ and ‘Competibacter’ (Wong et al., 2004; Meyer et al., 2006; Burow et al., 2007; Wong and Liu, 2007) (see Table 1.4). As with ‘Accumulibacter’, inhibitor studies with enriched cultures of cluster I members indicate that acetate uptake is mediated by an acetate permease involving a pmf (Burow et al., 2008a). However, this pmf is generated by a H+ and/or Na+ efflux mediated by the activity of a
50 fumarate reductase and the decarboxylation of methylmalonyl CoA respectively, with ATP generated by proton influx through ATPases (Burow et al., 2008a) (see Fig. 1.8c). Enriched culture analysis also shows a reliance on glycolysis as an energy source for acetate uptake anaerobically for both cluster I (Dai et al., 2007; Burow et al., 2008a; Burow et al., 2009) and II members (Burow et al., 2007). Inhibition of key enzymes in the TCA cycle also reveal with cluster I members that carbon flux occurs through the glyoxylate pathway. Oxidation of excess reducing equivalents seems to involve the reductive branch (Fig 1.9) of the TCA cycle (Burow et al., 2009), an outcome agreeing with that from 13C NMR studies using enriched cultures of members of the same cluster (Lemos et al., 2007). Inhibition of aconitase, an enzyme involved in the oxidative branch of the TCA cycle (Fig. 1.9), did not reduce noticeably in situ acetate uptake and storage of PHA in cluster II members, suggesting that these do not rely on the glyoxylate pathway or full anaerobic operation of the TCA cycle for energy or reducing equivalents (Burow et al., 2007) (see Fig. 1.9). 1.8.2.3.2 Aerobic metabolism of the Defluviicoccus related GAO The aerobic metabolism of cluster I Defluviicoccus members appears to be very similar to that reported for ‘Competibacter’, with gluconeogenisis and the oxidation of PHA through the TCA cycle responsible for restoring their glycogen stores (Dai et al., 2007; Lemos et al., 2007; Wong and Liu, 2007). Glycogen cycling has been inferred from data from chemical profiles of enriched cultures of cluster I (<95 % of the total biomass by FISH) (Dai et al., 2007) and cluster II members (<33 % of total biomass by FISH) (Meyer et al., 2006) and also shown for an axenic culture of Defluviicoccus vanus (Wong and Liu, 2007). Defluviicoccus vanus reduces nitrate in pure culture (Maszenan et al., 2005). Enriched culture studies with cluster I members suggest they too can reduce nitrate but not nitrite (Wang et al., 2008b), while in situ FISH MAR studies indicate that cluster II members can use neither as an electron acceptor in the absence of oxygen (Burow et al., 2007).
51 1.8.3 Other putative GAO Other potential competitors of the PAO have been suggested, including those isolated in attempts to identify the TFO ‘G bacteria’ (Seviour et al., 2000). These include Microsphaera multipartita (Yoshimi et al., 1996), Micropruina glycogenica (Shintani et al., 2000) and Kineosphaera limosa (Liu et al., 2000a; Liu et al., 2002), all shown to accumulate carbohydrates aerobically without any polyP storage (Table 1.1). Of these, only Micropruinia glycogenica has been reported in EBPR systems in situ (Kong et al., 2001; Wong et al., 2005). Kong et al. (2001) applied FISH probes designed against Micropruinia glycogenica (MIC184), and an unidentified actinobacterial sequence (KSBR 531: see Fig 1.10 and Table 1.3), to the biomass of a lab scale system with no P removal and a high glycogen content, reporting that these were present in this system at 22 and 7 % of the total biomass, respectively. FISH MAR/staining revealed that both had the ability to accumulate anaerobically acetate and glucose and produce PHA, but without polyP cycling, a metabolism consistent with the GAO phenotype (see Table 1.1). While neither population appears common in full scale EBPR communities, Micropruinia glycogenica was present at 8 % of the community biovolume in a plant community located in Japan (Wong et al., 2005) (see Table 1.3). Kong et al. (2007) also used the full cycle approach with FISH MAR to identify two other putative GAO populations, defined by the FISH probes Bet65 and Gam445, within the Betaproteobacteria and Gammaproteobacteria respectively (Table 1.3 and Fig. 1.10). These assimilated substrates anaerobically and stored them as PHA but without any subsequent aerobic polyP storage (Table 1.1), and made up to 3 to 6 % respectively of the total populations of surveyed Danish full scale plant biomass samples (Kong et al., 2007; Nielsen et al., 2010b) (Table 1.3). Tsai and Liu (2002) also noted the presence of a betaproteobacterial TFO population not assimilating polyP in their failed lab scale reactor and other putative GAO within this class have dominated other failed reactors, as determined by 16S rRNA clone library construction (Bond et al., 1995) and FISH (Bond et al., 1999a) analyses. However, any in situ dominance of the former has not been confirmed with FISH and the unequivocal identity of the latter was not determined. 52 Given the current practical difficulties in identifying ‘true’ GAO, more diversity within this group is likely to be revealed as other experimental approaches are used. Also, as suggested with the PAO, we should not confine ourselves to the current definition of GAO phenotype when seeking to identify PAO competitors, since competition between them may involve different strategies. 1.9 Competition between the PAO and GAO populations in EBPR Information on understanding the ecophysiology of the different PAO and GAO populations should provide useful insight into how operational parameters may influence the competition between them in EBPR systems. Much of our current understanding of the PAO and GAO comes from lab scale systems, with the advantage that these can be operated with higher control than full scale plants, where it is difficult to correlate population or activity shifts with individual operating parameters. The data from such studies suggest that certain conditions may shift the balance between them to where one is competitively favoured over the other. Unfortunately few identify which PAO or GAO populations are present in their systems, and in fact little or no microbiological community analyses are often performed (Seviour et al., 2003; Oehmen et al., 2007). Instead whether the PAO or GAO dominate is deduced indirectly from chemical profile data, which reveal key metabolic steps assumed from the metabolic models discussed earlier to be performed exclusively by each. For example the anaerobic acetate uptake to P release, or anaerobic PHB:PHV ratios, indirectly relate to the PAO/GAO population balance (Seviour et al., 2003; Oehmen et al., 2007; McMahon et al., 2010; Oehmen et al., 2010b). What might determine the outcomes of PAO GAO competition and subsequent community composition is discussed now. 1.9.1 Carbon source Since the main basis for the competition between the PAO and GAO is for available carbon substrates in the anaerobic feed stage in EBPR, differences in uptake rates of the supplied carbon source may be crucial. While acetate is thought to be the most abundant VFA present
53 in the influent, propionate levels can also be high in plants that incorporate pre fermentation tanks, and other VFAs, sugars and amino acids may also be present (Oehmen et al., 2007). Examination of available FISH MAR data indicate that ‘Accumulibacter’, ‘Competibacter’ and Defluviicoccus all have a high affinity for short chain VFAs (Table 1.4), ensuring a direct competition for these substrates in situ (Kong et al., 2004; Kong et al., 2006; Burow et al., 2007; Wong and Liu, 2007; Schroeder et al., 2008). Lab scale reactor studies suggest that PAO are advantaged when propionate is provided as carbon source, compared to when acetate alone is available (Hood and Randall, 2001; Chen et al., 2004; Oehmen et al., 2004; Pijuan et al., 2004a; Pijuan et al., 2004b; Chen et al., 2005; Oehmen et al., 2005b; Oehmen et al., 2006a). The improved performance of a full scale EBPR system in Australia has been credited to an increase in the propionate level from a pre fermenter feed, which replaced a direct addition of acetate (Thomas et al., 2003; Thomas, 2008). While mixed cultures enriched with ‘Acumulibacter’ appear to show similar uptake rates for both propionate and acetate (Pijuan et al., 2004b; Oehmen et al., 2005b), a ‘Competibacter’ enriched biomass showed slow uptake rates for propionate after switching from an acetate feed (Oehmen et al., 2005b). Furthermore, a low abundance of ‘Competibacter’ was reported in propionate fed lab scale communities (Oehmen et al., 2004; Pijuan et al., 2004a; Pijuan et al., 2004b; Oehmen et al., 2005b; Oehmen et al., 2006a). However, FISH MAR studies on full scale plant biomass samples suggest that ‘Competibacter’ assimilates propionate at a rate similar to ‘Accumulibacter’ based on subjective MAR signal intensities. The ‘Competibacter’ also showed no preference for either, as they assimilated propionate in the presence of acetate (Kong et al., 2006). Although MAR signal strengths varied between individual ‘Competibacter’ cells, within and between FISH MAR experiments (Kong et al., 2006), which could be taken to suggest a low affinity for propionate uptake by some members of this group, these data may reflect different physiological states of individual cells and not substantial phenotypic differences between them. However, as bulk liquid acetate or propionate are not measured, it can not be unequivocally stated that they are taken up simultaneously, as acetate may have been depleted before propionate uptake began. None of the lab scale studies have applied the sub group level ‘Competibacter’ FISH probes, leaving
54 open the possibility that other sub groups different to those reported so far from the full scale EBPR studies of Kong et al. (2006) may have been present. The literature suggests that the alphaproteobacterial GAO can better compete with the ‘Accumulibacter’ PAO in propionate fed systems than with ‘Competibacter’ populations. Enriched culture studies with ‘Defluviicoccus’ cluster I (Dai et al., 2007), and an unidentified alphaproteobacterial GAO (Oehmen et al., 2005b), suggests that these may prefer propionate over acetate, and cluster II ‘Defluviicoccus’ dominated a lab scale system fed propionate as the sole carbon source (Meyer et al., 2006). However, cluster I Defluviicoccus related GAO have become highly enriched in systems fed acetate (Wong et al., 2004; Wong and Liu, 2006, 2007), and cluster II members take up acetate in the presence of propionate in situ, also generating a higher MAR signal in the case of the former (Burow et al., 2007). Again these trends may relate to the different conditions imposed in these studies, or to basic physiological differences between and within the currently known phylotypes of the alphaproteobacterial GAO. Nevertheless, Lu et al. (2006) applied these experiences to generate an enriched culture of ‘Accumulibacter’ at >90 % of the total population by alternating between propionate, when they were presumably out competing the ‘Competibacter’, and acetate, when they may have had a selective advantage over the alphaproteobacterial GAO. Although the anaerobic supply of glucose was thought to promote the growth of PAO competitors (the ‘G Bacteria’), leading to EBPR failure (Cech and Hartman, 1990; 1993; Satoh et al., 1994), successful P removal in its presence has been reported (Liu, 1998; Carucci et al., 1999; Sudiana et al., 1999; Jeon and Park, 2000; Lee et al., 2002; Wang et al., 2002). While ‘Accumulibacter’ PAO and ‘Competibacter’ GAO appear unable to assimilate glucose directly anaerobically (Kong et al., 2004; 2006), Defluviicoccus GAO and some actinobacterial PAO can (Burow et al., 2007; Kong et al., 2008) (Table 1.4). However, it seems probable that this glucose is first fermented under anaerobic conditions to generate VFA fermentation end products and these are the substrates actually assimilated by the PAO (Wentzel et al., 1985; Sudiana et al., 1999; Jeon and Park, 2000; Wang et al., 2002; Kong et al., 2004; 2008; Nielsen et al., 2010b).
55
Table 1.4: FISH MAR summary of available data for in situ uptake of different substrates GAO Incubation D. vanus PAO Substrate ‘Competibacter’ condition related I II GB 1 3 4 5 6 7 RPAOa A221b A658c Formic acid Anaerobic –7 –7 +4 –4 –4 –4 –4 +4 –4 –2 –3 –3 Acetate Anaerobic +6,7 +7 +4 +4 +4 +4 +4 +4 +4 +1,2,5 –3 –3 Butyrate Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 –2 –3 –3 Propionate Anaerobic +6,7 +7 +4 +4 +4 +4 +4 +4 +4 +2 –3 –3 Pyruvate Anaerobic +6,7 +7 +4 +4 +4 +4 +4 +4 +4 +2 –3 –3 Lactate Anaerobic +6 ND ND ND ND ND ND ND ND ND –3 –3 Ethanol Anaerobic –7 –7 ND –4 –4 –4 –4 –4 –4 –2 –3 –3 Glycerol Aerobic –8 +8 –8 ND ND ND ND ND ND –8 ND ND Glucose Aerobic –8/+7 –8/+7 –4,8 –4 –4 –4 –4 –4 –4 –1,2,8 –3/+9 –3 Anaerobic –6/+7 +7 +4 +4 +4 +4 +4 +4 +4 +2 –3/+9 –3 Galactose Aerobic ND ND ND ND ND ND ND ND ND ND –9 ND Mannose Aerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 ND –9 ND Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 ND ND ND Oleic acid Aerobic –7,8 –7,8/+8 –4 –4 –4 –4 –4 –4 –4 –2,8 +3 –3 Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 –2 +3 –3 Palmitic acid Aerobic –8 –8 –8 ND ND ND ND ND ND –5,8 ND ND Aspartic acid Aerobic –7/+8 –7/+8 –4/+8 –4 –4 –4 –4 –4 –4 –2/+8 –3 –3 Anaerobic –6,7 –7 –4 –4 –4 –4 –4 –4 –4 –2/+5 –3 –3 Glutamic acid Aerobic –7,8 –7/+8 –4,8 –4 –4 –4 –4 –4 –4 +2,8 –3 –3 Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 +2,8 –3 –3 Leucine Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 –2 –3 –3 Glycine Anaerobic –7 –7 –4 –4 –4 –4 –4 –4 –4 –2 –3 –3 Thymidine Anaerobic –7 –7 +4 +4 –4 –4 –4 +4 –4 –2 –3 –3 Amino acidsd Anaerobic ND +7 +4 ND ND ND ND ND ND ND +3 +3 a RPAO = Rhodocyclus related ‘Accumulibacter’ PAO; b c Tetrasphaera related PAO, defined by probes bActino 221 and cActino 658 (Kong et al., 2005); d defined mixture of 15 amino acids (Kong et al., 2005). ‘–‘ = No uptake; ‘+’ = positive uptake; ND = No data. GB = data for general GB group probe (Kong et al., 2002a). Aerobic data is included for substrates where anaerobic data is unavailable or different. e acc = electron acceptor present: O2 = aerobic (oxygen); No = anaerobic. 1 9References: 1(Kong et al., 2002b) 2(Kong et al., 2004); 3(Kong et al., 2005); 4(Kong et al., 2006); 5(Chua et al., 2006); 6(Wong and Liu, 2007); 7(Burow et al., 2007); 8(Schroeder et al., 2008); 9(Kong et al., 2008). 56 Unlike the proteobacterial PAO and GAO, the actinobacterial PAO from early FISH MAR data do not take up VFAs, but instead assimilate amino acids under anaerobic conditions (Kong et al., 2005). However, from more recent experiments, some of these sub groups appear to assimilate acetate (see Nielsen et al., 2010b). ‘Competibacter’ and Defluviicoccus related GAO also assimilate amino acids anaerobically (Table 1.4), although whether this leads to any direct competition for substrates with the actinobacterial PAO is unclear since the FISH MAR experimental protocol used applied an amino acid mix and not individual substrates (Kong et al., 2006; Burow et al., 2007). Together with substrate type, how it is supplied may also impact on PAO GAO competition. For example, Burow et al. (2008a) suggest that ‘Accumulibacter’ may better scavenge acetate at low concentrations than Defluviicoccus because of its higher acetate permease activity. Although pre fermenters improve EBPR capacity by supplementing VFAs in the feed (Thomas et al., 2003), full scale surveys indicate that sometimes their use may discourage ‘Accumulibacter’ (Beer et al., 2006; He et al., 2008) since the VFA fermentation end products are provided as a dump feed, and not released slowly removing any scavenging advantage the ‘Accumulibacter’ might have (He et al., 2008). 1.9.2 pH Increased EBPR capacity has been reported in lab scale systems operating at higher pH (Bond et al., 1998; Bond et al., 1999b; Filipe et al., 2001c; Jeon et al., 2001; Levantesi et al., 2002; Schuler and Jenkins, 2002; Serafim et al., 2002b; Oehmen et al., 2005a; Zhang et al., 2005), and a full scale EBPR plant (Thomas, 2008), which is thought to reflect an increased advantage the PAO have at operating pH >7.0. A linear relationship between anaerobic P release and increased pH has been noted on several occasions (Smolders et al., 1994a; Liu et al., 1996a; Romanski et al., 1997; Bond et al., 1999a; Bond et al., 1999b; Pijuan et al., 2004b; Oehmen et al., 2005a; Liu et al., 2007b). Smolders et al. (1994a) suggested an elevated polyP hydrolysis is required to meet the increased energy demand for acetate transport into the cell at a higher pH, as a result of a greater pH gradient across the membrane. Serafim et al. (2002b) suggest that it is better handled by the PAO, which have both polyP and glycogen as energy sources, whereas the 57 GAO rely totally on the latter. Anaerobic substrate uptake studies carried out with enriched cultures of unknown GAO/PAO composition at pH of 6.5 8.0, generated data which suggested that the GAO increase their glycogen consumption at the higher pH (Filipe et al., 2001b) to compensate. However, while acetate uptake by the PAO seemed to be independent of pH (Filipe et al., 2001d), it is reduced in the GAO, thus giving the PAO an advantage under these conditions (Filipe et al., 2001b). Under aerobic conditions P uptake and biomass growth in the PAO were postulated to be inhibited at low pH (<7.0), while GAO growth was largely unaffected over the same pH range (Filipe et al., 2001a), further emphasising the importance of pH in deciding the outcome of competition between the two. Performing anaerobic polyP break down was also thought to advantage the unidentified PAO by assisting in internal pH regulation (Bond et al., 1999b). A few studies have been carried out where the identity of both the PAO and GAO populations has been dermined, and these are discussed now. Zhang et al. (2005) reported EBPR failure in a reactor when the pH shifted from 7.0 to 6.5. Clone library analyses of their communities before and after failure showed the ‘Accumulibacter’ 16S rRNA related sequences had disappeared. Sequences for cluster I ‘Defluviicoccus’ GAO members had also disappeared, although more direct quantitative FISH analyses would have been more revealing of such population shifts. Bond et al. (1999a) did use FISH to show that corresponding relationship between increases in pH and EBPR efficiency were associated with increased numbers of the β 2 proteobacteria, including ‘Accumulibacter’ members, and actinobacterial groups. Applying FISH to communities in two reactors, one dominated by the alphaproteobacterial GAO and the other ‘Competibacter’, Oehmen et al. (2005a) reported a clear community shift in favour of ‘Accumulibacter’ members, and a subsequent improvement in EBPR efficiency in both cases with a shift in operating pH from 7.0 to 8.0. A higher operating pH seemed to correspond to higher ‘Accumulibacter’ population sizes in full scale EBPR systems although Defluviicoccus and ‘Competibacter’ GAO populations in those analysed were barely detectable (<3.2 %) (Lopez Vazquez et al., 2008a).
58 1.9.3 Temperature Many studies have suggested that temperature can also influence EBPR efficiency, although the published data are often in conflict, with both increases and decreases in temperature being claimed to lead to improved EBPR capacity (see Brdjanovic et al., 1997). The general belief is that temperatures of 5 20 °C favour the PAO but above 20 °C chemical profile data suggest the advantage switches to the GAO phenotype, which may explain the frequent reports of EBPR inefficiency at such temperatures (Whang and Park, 2002; Erdal et al., 2003; Panswad et al., 2003; Whang and Park, 2006; Erdal et al., 2008). Unfortunately, as with the pH studies, the composition of the communities showing these trends has rarely been determined, and so without knowing the identity of the PAO and GAO drawing comparisons between the different studies become problematic. Enriched cultures have shown that the chemical transformations typical of ‘Competibacter’ change little in their stoichiometry between 15 30 °C although at 10 °C glycolysis appears to be repressed and at 40 °C these cells do not survive (Lopez Vazquez et al., 2007; Lopez Vazquez et al., 2008b; Lopez Vazquez et al., 2009a). Short term temperature response studies indicate that ‘Accumulibacter’ and ‘Competibacter’ share similar acetate uptake rates below 20 °C, although in long term acclimatised enrichments, such rates were lower for ‘Competibacter’ than those for an unidentified PAO enriched mixed culture (Brdjanovic et al., 1998a; Lopez Vazquez et al., 2009a). The lower energy requirements for anaerobic maintenance below 30 °C are thought to advantage ‘Accumulibacter’ (Lopez Vazquez et al., 2007), while higher acetate uptake rates of the ‘Competibacter’ advantage them above 20 °C (Lopez Vazquez et al., 2007; Lopez Vazquez et al., 2009a). Compared to the data from an undefined PAO enrichment culture (Brdjanovic et al., 1998c), ‘Competibacter’ had a lower aerobic growth rate, increasingly favouring glycogen replenishment at temperatures below 20 °C. Thus, the PAO are advantaged further (Lopez Vazquez et al., 2009a). These supposed advantages were used to explain the community shift from ‘Competibacter’ to ‘Accumulibacter’ dominance in a lab scale EBPR reactor run at 10 °C (Lopez Vazquez et al., 2009a). Whether similar advantages at low temperatures exist for the alphaproteobacterial GAO is unknown, although Whang and Park (2002) noted the proliferation of an unidentified TFO 59 when temperature increased in their community. Based on several untested assumptions, a model incorporating the reported influence of carbon source, pH and temperature on ‘Accumulibacter’ PAO and ‘Competibacter’ and alphaproteobacterial GAO, suggests an overall advantage to ‘Accumulibacter’ at 10 °C, even at a pH of 6.5 and regardless of carbon source (although only acetate and propionate are considered) (Lopez Vazquez et al., 2009b). The low abundance of ‘Competibacter’ and Defluviicoccus GAO in full scale plants in Denmark and the Netherlands has also been considered to reflect the generally low recorded temperatures in these two countries (Lopez Vazquez et al., 2008a; Nielsen et al., 2010b). 1.9.4 Carbon to phosphorus ratio (C:P) It is thought that a high C:P ratio (>50 mg COD/ mg P) favours the proliferation of the GAO at the expense of the PAO (Oehmen et al. 2007), and this ratio has often been manipulated to obtain high enrichments of the former (Liu et al., 1994; 1996b; 1997b; Mino et al., 1998; Sudiana et al., 1999; Kong et al., 2002b; Schuler and Jenkins, 2003a; Zeng et al., 2003c; Oehmen et al., 2004; Oehmen et al., 2005b; Oehmen et al., 2006b; Dai et al., 2007; Lopez Vazquez et al., 2007; Saunders et al., 2007; Burow et al., 2009). He et al. (2006) also related the abundance of ‘Accumulibacter’ when P:C ratios were increased in the feeds to full scale EBPR plants. Even though ‘Accumulibacter’ can increase its reliance on glycogen stores for energy when P levels are depleted, their efficiency of acetate uptake is decreased substantially, as is their ability to compete with the GAO (Zhou et al., 2008). 1.9.5 Sludge retention time (SRT) The SRT is thought to influence the competition between the PAO and GAO. As with anaerobic carbon uptake, the aerobic growth phase is important in determining whether an organism will survive in EBPR systems. Interestingly Whang and Park (2006) reported high EBPR capacity at 30 °C, a temperature thought to favour the GAO, when sludge age was reduced from 5 to 3 days. Later Whang et al. (2007) proposed that a lower conversion efficiency of aerobic PHA catabolism to biomass in the GAO could explain the advantage the PAO seem to have at the shorter SRT. The minimum calculated aerobic SRT for ‘Competibacter’ (1.9 days) was larger than that of an unidentified PAO enrichment culture (1.25 days), which also suggested a lower growth rate for the former (Brdjanovic et al.,
60 1998c; Lopez Vazquez et al., 2009a). He et al. (2008) also showed a relationship between higher levels of ‘Accumulibacter’ in communities of full scale EBPR plants with shorter SRT, which is supported by indirect evidence from other studies (Zilles et al., 2002a; Wong et al., 2005; Beer et al., 2006; He et al., 2008). It may be that Defluviicoccus related GAO are disadvantaged at reduced SRT. Wong and Liu (2006) noted a community shift in their lab scale reactor where ‘Competibacter’ GAO (abundant in the seed) were replaced by cluster I ‘Defluviicoccus’, after SRT was increased to 15 days from 7 8 days. Despite the proposed advantages for the PAO at shorter SRT the challenge in implementing this as a control strategy in full scale plants may be at the expense of N removal, with a potential wash out of the slower growing nitrifiers (Lopez Vazquez et al., 2009a). 1.9.6 Dissolved oxygen (DO) levels The few studies carried out are compromised by the lack of microbiological data to support their findings and in some cases are anecdotal only. For example Griffiths et al. (2002) monitored several full scale EBPR plants in Australia and concluded that the PAO, ‘identified’ solely by their polyP positive staining, required an optimum DO of 2.5 3.0 mg/l. Higher DO levels of 4.5 5 mg/l had a negative impact on EBPR and instead appeared to promote the growth of unidentified TFO (probably Defluviicoccus; R.J. Seviour, unpublished). Brdjanovic et al. (1998b) speculated that over aeration decreases EBPR capacity because the supply of PHA required for aerobic P uptake is exhausted then. Dai et al. (2007) suggested that Defluviicoccus is more competitive than ‘Competibacter’ at lower DO level. The possible impact of DO on PAO GAO competition is still far from clear since PAO levels were probably negatively affected at the low P:C ratio used in the GAO enrichment studies of Dai et al. (2007), although Lemaire et al. (2006) have shown that ‘Accumulibacter’ dominated a reactor run at an even lower DO concentration of 0.5 mg/l, suggesting they are competitive with the GAO there.
61 1.10 Aims of this study It is clear from the review of the literature given in this chapter, that much still needs to be learned about the microbiology of EBPR systems including the biodiversity and ecophysiology of the Defluviicoccus GAO. Only then will the bases for competition between these populations and the PAO become clear. This thesis describes work which attempts to contribute to this by;
• Examining systematically the experimental problems mentioned in the literature inherent in identifying these populations in EBPR communities, by looking at more suitable enrichment recovery methods and possible biases in the methods used in
earlier clone library studies for DNA/RNA extraction from activated sludge • Applying these protocols to determine whether further biodiversity exists among the Defluviicoccus and develop methods for their in situ identification and for resolving their ecophysiologies • Applying these methods to resolve some of the inconsistencies in the literature concerning the identification of the alphaproteobacterial GAO • Attempting to understand the physiological bases for the competition between the GAO and PAO in response to changes in culture pH.
62 2.0 Flow cytometry assisted identification of GAO in lab scale EBPR systems 2.1 Introduction EBPR activated sludge systems often behave unreliably because the PAO are out competed for substrates in the anaerobic zone by GAO (Seviour et al., 2003; Oehmen et al., 2007). Both occur commonly in heavily capsulated clusters in the flocs. Instead of assimilating P aerobically into intracellular polyP as the PAO do, the GAO store glycogen instead. Invariably these are found with the PAO in EBPR systems, but occur often in higher numbers in those with reduced EBPR capacity. Since the glycogen accumulating cells are not detectable by staining (Serafim et al., 2002a), and as most are yet to be cultured, their identity has been sought by constructing 16S rRNA based clone libraries and PCR DGGE profiling (Crocetti et al., 2002; Beer et al., 2004; Wong et al., 2004; Meyer et al., 2006; Kong et al., 2007). These methods have identified the gammaproteobacterial GAO as members of the GB or ‘Competibacter’ group. At present these are divided into seven distinct sub groups with FISH probes designed to cover each (Kong et al., 2002a). However, Wong and Liu (2006) and Kong et al. (2006) have hinted at further diversity, with both studies reporting the existence of cells in their EBPR systems responding to the broad group coverage FISH probes for the ‘Competibacter’ but none of the sub cluster probes. Greater difficulties have been encountered in identifying the alphaproteobacterial GAO. Wong et al. (2004) and Wong and Liu (2007) used alphaproteobacterial targeted primers to construct 16S rRNA clone libraries from a membrane EBPR process in which sequences closely related to Defluviicoccus vanus were well represented. However, when 16S rRNA clone libraries were prepared for samples isolated from a lab scale sequencing batch reactor (SBR) anaerobic: aerobic EBPR process which contained high numbers of TFO targeted by alphaproteobacterial FISH probes, no sequences associated with them could be detected (Meyer et al., 2006). Identification of these TFO as Defluviicoccus spp. only proved possible after Meyer et al. (2006) applied RNA SIP enrichment before clone library construction.
63 Prior studies have suggested that the Defluviicoccus related TFO are phylogenetically diverse and can be divided into three distinct clusters, although the third cluster is only based on a single rRNA sequence (Wong and Liu, 2007). A series of FISH probes have been designed for clusters I and II of the Defluviicoccus related organisms (Wong et al., 2004; Meyer et al., 2006; Wong and Liu, 2007). Wong and Liu (2007) extended the phylogenetic diversity observed in these clusters with additional rRNA sequences isolated from their community and designed new probes for each cluster. Whether the original cluster II probes of Meyer et al. (2006) cover these new sequences is unclear since the 16S rRNA region they target was not revealed in the clone sequences obtained by Wong and Liu (2007) with their alphaproteobacterial primers. Ahn et al. (2007) described a novel continuously aerated EBPR process where the supply of carbon (‘FEAST’ stage) and supply of P (‘FAMINE’ stage) are temporally separate. This is quite unlike conventional EBPR systems, where both are added simultaneously (Seviour et al., 2003), and the chemical profiles suggest little change in glycogen biomass levels across the reactor cycle (Ahn et al., 2007). As Meyer et al. (2006) experienced earlier, when 16S rRNA based clone libraries were generated from samples taken from this community containing alphaproteobacterial TFO, no Defluviicoccus related sequences were able to be identified. FISH analyses showed that the TFO were all members of the cluster II Defluviicoccus (Ahn et al., 2007). However, these probes were not designed against individual strains but clusters of 16S rRNA sequences all sharing the probe target site, and hence they may not discriminate sufficiently to reveal any additional phylogenetic (and hence ecophysiological) diversity which might exist. Since the aerated EBPR process is quite different to previously studied EBPR systems it was hypothesised that the Defluviicoccus GAO present there may be phylogenetically distinct. This hypothesis was supported by FISH MAR studies (Schroeder et al., 2008) which found their ecophysiology was markedly different to that described by Burow et al. (2007). Consequently a more precise phylogenetic identity of these Defluviicoccus GAO was sought. Neither of the previously published approaches for identifying Defluviicoccus GAO were suitable for use in the aerated EBPR process. With the method of Wong et al. (2004), only partial sequences are generated, restricting opportunities for FISH probe design (Nielsen et al., 2004). RNA SIP is complex, and requires a priori information about the targeted
64 organisms’ substrates preferences (Meyer et al., 2006). Where the targeted population numbers are low, the probability of success with either approach is reduced since screening of clone libraries for rare sequences is unreliable (Nielsen et al., 2004). The approach selected here to identify the Defluviicoccus TFO was flow cytometry sorting of FISH targeted cells (fluorescent activated cell sorting (FACS)). This method has been applied to conventional and EBPR activated sludge samples for similar purposes, predominantly for the enrichment of PAO based on positive staining for polyP inclusions with the fluorescent DAPI (Porter et al., 1993; Wallner et al., 1997; Kawaharasaki et al., 1999; Snaidr et al., 1999; Hung et al., 2002; Kawaharasaki et al., 2002; Zilles et al., 2002a; Zilles et al., 2002b; Miyauchi et al., 2007; Günther et al., 2009). The same approach was also applied to another conventional anaerobic (‘FEAST’): aerobic (’FAMINE’) EBPR SBR system where again cluster II Defluviicoccus sequences were absent from clone library analysis despite FISH indicating their high abundance in the same source community (see Chapters 3 and 6). 2.2 Materials and methods 2.2.1 Operation of the EPBR SBR reactors LS1 was a SBR operated under continuously aerated conditions and fed with clarified effluent containing 10 12 mg/l P and supplemented with acetate (120 mg/l carbon) from a non EBPR treatment plant in Melton, Australia. Other conditions are detailed in Ahn et al. (2007). LS2 was a SBR operated under alternating aerobic: anaerobic conditions and also fed with acetate as the sole carbon source (75mg/l carbon). Further operational conditions can be found in Chapter 6. Both reactors were initially seeded from a full scale anaerobic: aerobic cycling modified University of Cape Town (MUCT) EBPR plant located in Castlemaine, Victoria.
65 2.2.2 Cell fixation Samples for flow assisted sorting were taken after 97 and 387 days operation from LS1 and LS2, respectively. For analysis of Gram negatives biomass was fixed with 3 volumes of freshly prepared (≤ 48 h old) 4 % [w/v] paraformaldehyde (PFA) in 1 × phosphate buffered saline (PBS) (137 mM NaCl, 10 mM Na2HPO4.7H2O, 2.7 mM KCl, 1.47 mM KH2PO4) solution for 2 h at 4 °C. For analysis of Gram positives (later chapters) biomass was fixed with 50 % [v/v] ethanol at 4 °C overnight. After the fixation step, samples were washed twice in 1 × PBS, re suspended in 50 % [v/v] ethanol in 1 × PBS solution and stored at 20 °C until required. 2.2.3 Fluorescence in situ hybridisation (FISH) FISH was performed as detailed by Daims et al. (2005). All probes were purchased from ProOligo (Sigma Aldrich, Australia) containing either 5(6) carboxyfluorescein N hydroxy succinimide ester (FLOUS), indocarbocyanine (CY3) or indodicarbocyanine (CY5) fluorochromes attached to the 5’ end of the oligonucleotide. Probe sequences are given in Table 2.1. Initially 8 l of fixed cell suspension was added to 6 well Teflon coated microscope slides that had been previously coated in Vectabond (Vector Laboratories). Slides were then dried at 46 °C for 10 min and dehydrated by sequentially immersing the slides in a series of ethanol concentrations, 50 % [v/v], 80 % [v/v] and 95 % [v/v] for 3 min each and allowed to air dry. Hybridisation buffer was added to each well (0.9 M NaCl, 20 mM Tris HCl (pH 8.0), 0.01 % [w/v] sodium dodecyl sulphate (SDS), 0 70 % [v/v] formamide and 5 ng/ l of each probe, with a total volume of 10 l with sterile milli Q water). The formamide concentration used for each probe is given in Table 2.1. Slides were immediately placed into a horizontal 50 ml centrifuge tube, containing paper towel moistened with 1 ml hybridisation buffer, and incubated for 1.5 h at 46 °C in a BFD 53 hybridisation oven (Binder Scientific). Immediately following hybridisation the probe was rinsed off with wash buffer of the appropriate stringency (20 mM Tris HCl (pH 8.0), x mM Na+ (adjusted with 5 M NaCl) (see Table 2.2), 5 Mm ethylenediaminetetraacetic acid (EDTA) (pH 8.0), 0.01 % SDS) and immersed in 50 ml of pre warmed wash buffer in a 50ml centrifuge tube at 48 °C for 10 min. 66 Slides were then carefully removed and immersed three times in chilled distilled water and immediately air dried. If slides were not viewed immediately, they were stored at 20 °C with silica gel until required. 2.2.4 Fluorescence microscopy Sample wells were mounted with 5 l of Vectashield (Vector Laboratories) and a glass cover slip. Slides were viewed using a Nikon Eclipse 800 epifluorescence microscope. The filter sets for each fluorochrome are given in Table 2.3. Images were captured using a Nikon DMX 1200 CCD camera driven by Nikon ACT 1 v.3 software. Images were alternatively taken using a Leica TCS SP2 confocal scanning laser microscope (CLSM) (model DMRE2) equipped with an Argon, HeNe (red and green) lasers driven by Leica Confocal Software v. 2.61. To assist in the identification of false positives all experiments included a negative control well where the NON EUB probe (Table 2.1) was applied to the same biomass. 2.2.5 Quantitative FISH (qFISH) Estimation of target populations using qFISH was performed as described by Daims et al. (2005) and values calculated by DAIME software (Daims et al., 2006) based on at least 40 fields of view taken at 630 x magnification on a confocal laser microscope. The abundance is represented as the percentage of the area fluorescing with the EUBmix probes (FLOUS) that also fluoresced with the target probe (CY5). Biomass was applied to each slide well in three successive 5 L aliquots of the same sample, with each aliquot allowed to dry before the addition of the next. Slides were then horizontally immersed in 0.5 % [w/v] agarose which was allowed to set on ice. Excess agarose was removed from around the wells with a razor blade and the slides dried at 46 °C for 20 min. FISH was then performed as detailed in Section 2.2.3. The qFISH analysis for LS1 in this chapter was performed by Dr Sarah Schroeder (La Trobe University, Bendigo).
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Table 2.1: FISH probes applied in this chapter Probe name Sequence (5´ 3´) Target [FA]1 (%) Reference EUB338 I2 GCTGCCTCCCGTAGGAGT Most Bacteria 35 (Amann et al., 1990) EUB338 II2 GCAGCCACCCGTAGGTGT Planctomycetales 35 (Daims et al., 1999) EUB338 III2 GCTGCCACCCGTAGGTGT Verrucomicrobiales 35 (Daims et al., 1999) NON EUB ACTCCTACGGGAGGCAGC Control probe complementary to EUB338 n/a (Wallner et al., 1993) GB3 CGATCCTCTAGCCCACT ‘Competibacter’ group GB 35 (Kong et al., 2002a) GB_G1(GAOQ989)3 TTCCCCGGATGTCAAGGC ‘Competibacter’ subgroup G1 in Group GB 35 (Crocetti et al., 2002) GB_G23 TTCCCCAGATGTCAAGGC ‘Competibacter’ Subgroup G2 in Group GB 35 (Kong et al., 2002a) GB_2 GGCATCGCTGCCCTCGTT ‘Competibacter’ Subgroup 2 (Kong et al., 2002a)
ALF968 GGTAAGGTTCTGCGCGTT Alphaproteobacteria, except Rickettsiales 20 (Neef et al., 1999) TFO_DF862 AGCTAAGCTCCCCGACAT Defluviicoccus vanus 35 (Wong et al., 2004) TFO_DF2184 GAAGCCTTTGCCCCTCAG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) TFO_DF6184 GCCTCACTTGTCTAACCG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) TFO_DF776 GCTATAGCGTCAGTTACGG Cluster I Defluviicoccus related TFO 30 (Wong and Liu, 2007) TFO_DF629 AGGACTTTCACGCCTCAC Cluster II Defluviicoccus related TFO nd (Wong and Liu, 2007) DF9885 GATACGACGCCCATGTCAAGGG Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006) DF10205 CCGGCCGAACCGACTCCC Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006) H966 CTGGTAAGGTTCTGCGCGTTGC Helper probe for DF988 n/a (Meyer et al., 2006) H1038 AGCAGCCATGCAGCACCTGTGTGGCGT Helper probe for DF988 and DF 1020 n/a (Meyer et al., 2006) SBR9 1a AAGCGCAAGTTCCCAGGTTG Putative SBR GAO 30 (Beer et al., 2004) AMAR839 CTGCGACACCGAACGGCAAGCC Amaricoccus spp. 20 (Maszenan et al., 2000a) nd = not determined; n/a = not applicable; 1[FA] = formamide concentration in hybridisation buffer; 2 5 applied in equimolar amounts as: 2 EUBmix, 3 GBmix, 4 DF1mix, 5 DF2mix 68 Table 2.2: Required Na+ concentration for FISH wash buffer. [FA] 1 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 [Na+]2 900 636 450 318 225 159 112 80 56 40 28 20 14 10 7 1 Formamide concentration (% [v/v]) in the hybridisation buffer. 2 Salt concentration (mM) in the wash buffer. Calculations are based on the equation of Lathe (1985): [Na+] = 10( 0.5 [% FAHyb] – 0.76) / 16.61 where an increase in 1 % [v/v] formamide (FA) is considered to be equivalent to an increase of 0.5 °C. The addition of EDTA to the wash buffer contributes 0.01M Na+ (Daims et al., 2005). Table 2.3: Filter sets for Nikon Eclipse 800 Filter Set Excitation /Emission λ (nm) Fluorochrome/Stain UV 2A 330 380/420 DAPI FITC 465 495 /505 FLOUS/SYBR Gold G 2A 510 560 /575 CY3/Nile blue A CY5/HYQ 620/60 /660 CY5 2.2.6 Flow cytometry 2.2.6.1 FISH labelling of cells Prior to flow cytometry PFA fixed biomass was homogenised in a 15 ml dounce tissue homogeniser to disrupt cellular aggregates, and filtered through 40 m nylon mesh (353335 Falcon). 100 l of homogenised biomass was added to a 1.5 ml centrifuge tube and centrifuged (12, 000 x g, 4 °C, 5 min), the supernatant discarded and the pellet washed once with 250 l hybridisation buffer (0.9 M NaCl, 20 mM Tris HCl (pH 8.0), 0.01 % [w/v] SDS and 35 % [v/v] formamide. The pellet was then resuspended in 100 l of hybridisation buffer containing the FLUOS labelled FISH probe (5 ng/ l) and incubated at 46 °C for 2 h. The tube was then centrifuged (12, 000 x g, room temperature, 4 min) and the pellet resuspended in 600 l of wash buffer (see Section 2.2.3) before incubation for 10 min at 48 °C (for the sorting of ‘Competibacter’ the wash step was omitted and instead 600 l of ice cold 1 × PBS
(137 mM NaCl, 10 mM Na2HPO4.7H2O, 2.7 mM KCl, 1.47 mM KH2PO4) (pH 8.0) was added). The cells were again centrifuged, the supernatant discarded and the pellet resuspended in 1 × PBS (pH 8.0) before sorting. Successful labelling of target organisms was then assessed by fluorescence microscopy.
69 2.2.6.2 Sorting of Alphaproteobacteria Cell sorting was performed with the assistance of Dr Daniel Hoefel (SA Water, Adelaide). Cells labelled with the ALF968 probe (Table 2.1) were sorted on a FACS Calibur flow cytometer (Becton Dickinson), using a 530/30 band pass filter, as previously described by Hoefel et al. (2005). Cells were sorted using the ‘single sort mode’ and the concentration of the biomass kept below a level that gave higher than 300 events s 1. Approximately 5 × 106, cells were sorted into 50 ml centrifuge tubes at a concentration of approx. 2 × 103 cells ml 1. Cells were concentrated by sequential centrifugation (10, 000 x g, 4 °C, 20 min). Escherichia coli (a Gammaproteobacteria) and Novosphingobium sp. Geo25 (DQ137853) (an Alphaproteobacteria) were used as negative and positive controls respectively. Sort window parameters are given in Fig. 2.1a and b. 2.2.6.3 Sorting of ‘Competibacter’ Cells labelled with the GBmix probe set (GB, GB_G1 and GB_G2: Table 2.1) were sorted with a FACS Aria (BD Biosciences), using a 530/30 band pass filter by Ken Field (Melbourne University, Melbourne). Sorting parameters were set by the FACSDiva software package. Approximately 1 × 106 cells were sorted in 1 ml. Sorted cells were concentrated by centrifugation (20, 800g, 4 °C, 20 min). Biomass labelled with NON EUB (Table 2.1) was used as a negative control and biomass samples initially hybridised with each ‘Competibacter’ probe (GB, GB_G1 and GB_G2) individually to ensure sort parameters did not exclude cells because one probe had a lower fluorescence than the others, or the cell only hybridised one of the probes. Sort window parameters are given in Fig. 2.1c. 2.2.7 Extraction of DNA from FACS sorted biomass The DNA was extracted by addition of extraction buffer (1 mM EDTA (pH 8.0), 10 mM Tris HCl (pH 8.0), 0.2 % Triton X 100 [v/v] and 0.4 mg ml 1 proteinase K), incubated at 55 °C for 2 h and then heated to 95 °C for 10 min. The extract was mixed using a vortex mixer for 5 s and stored at 70 °C until required.
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Figure 2.1: FACS sorting plots for a. LS1 ALF968 positive cells; b. LS2 ALF968 positive cells; c. LS2 GBmix positive cells, other gates not shown here were also applied to minimise cell aggregates; All sort window boundaries are shown in red.
71 2.2.8 16S rRNA gene clone library construction 2.2.8.1 16S rRNA gene clone library PCR PCR reactions were performed in 200 l thin walled PCR tubes, with a reaction volume of 20 l, on an iCycler IQTM Multicolor Real Time Detection System (BIO RAD). Primer sequences are given in Table 2.4 and were synthesised by Geneworks (Adelaide, Australia). All 16S rRNA genes were amplified using universal primers, 27F and 1525R. PCR reactions contained 1 U Amplitaq Gold DNA polymerase (Applied Biosystems), 1 × Amplitaq Gold reaction buffer, 2.5 mM MgCl2, 400 nM of each primer, 200 M deoxynucleotide triphosphates (dNTPs) (Roche), 5 % [v/v] dimethyl sulphoxide (DMSO) (Sigma Aldrich). The PCR cycle used was: 96 °C for 10 min followed by 35 cycles of 96 °C for 1 min, 52 °C for 30 s and 72 °C for 2 min with a final 7 min extension at 72 °C. 2.2.8.2 Ligation and transformation PCR products were purified using QIAquick Gel Extraction kits (Qiagen) and cloned into the pGEM T Easy Vector System II (Promega). Template was ligated into the pGEM® T Easy vector (Promega). Ligation reactions contained 5 l of 2 × Rapid Ligation Buffer (Promega), 1 l vector DNA, 3 l of PCR product and 1 l of T4 DNA ligase (Promega). The reactions were gently mixed and incubated at 4 °C overnight. Plasmids transformed into JM109 competent E. coli cells (Promega). A 2 l aliquot of the ligation reaction was added to 50 l of JM109 E. coli chemical competent cells (Promega), mixed by gently flicking the tube and incubated on ice for 20 min. The cells were then heat shocked at 42 °C for 45 s and placed on ice for 2 min.
72
Table 2.4: Primer sequences used in this chapter Primer Name Sequence (5’ 3’)* Target Target Organism Reference Gene 27F GAG TTT GAT CMT GGC TCA G 16S rDNA Bacteria (Modified from Lane, 1991) 357F CTC CTA CGG GAG GCA GCA G 16S rDNA Bacteria (Lane, 1991) 519R GWA TTA CCG CGG CKG CTG 16S rDNA Bacteria (Lane, 1991) 530F GTG CCA GCM GCC GCG G 16S rDNA Bacteria (Lane, 1991) 534R ATT ACC GCG GCT GCT GG 16S rDNA Bacteria (Muyzer et al., 1993) 907R CCG TCA ATT CMT TTR AGT TT 16S rDNA Bacteria (Lane, 1991) 926F AAA CTY AAA KGA ATT GAC GG 16S rDNA Bacteria (Lane, 1991) 1100R GGG TTG CGC TCG TTG 16S rDNA Bacteria (Lane, 1991) 1114F GCA ACG AGC GCA ACC C 16S rDNA Bacteria (Lane, 1991) 1392R ACG GGC GGT GTG TRC 16S rDNA Bacteria (Lane, 1991) 1369F CGG TGA ATA CGT TCY CGG 16S rDNA Bacteria (Suzuki et al., 2000) 1492R GGY TAC CTT GTT ACG ACT T 16S rDNA Bacteria (Lane, 1991) 1525R AAG GAG GTG WTC CAR CC 16S rDNA Bacteria (Lane, 1991) M13F GCGCAGGGTTTTCCCAGTCACGAC Plasmid pGEM® T Easy Vector (Messing, 1983) M13R TCACACAGGAAACAGCTATGAC Plasmid pGEM® T Easy Vector (Messing, 1983) * R= AG; Y=CT; M=AC; K=GT; S=GC; W=AT; H=ACT; B=GCT; V=AGC; D=AGT; N=AGCT
73 950 l of SOC medium (0.2 g l 1 tryptone, 50 mg l 1 yeast extract, 10 mM NaCl, 2.5 mM 2+ 1 1 KCl, 20 mM Mg (203.3 g l MgCl2.6H20, 246.5 g l MgSO4.7H20), 20 mM glucose) was then added and the tube incubated at 37 °C for 1.5 h whilst shaking at 150 rpm. 100 l aliquots of transformed cells were then plated onto pre warmed LB (Luria Bertani) plates (15 g l 1 agar, 10 g l 1 tryptone, 5 g l 1 yeast extract, 5 g l 1 NaCl, 100 g ml 1 ampicillin (Sigma)) supplemented with 0.5 mM isopropyl β D 1 thiogalactopyranoside (IPTG) and 80 g ml 1 5 bromo 4 chloro 3 indolyl β D galactopyranoside (X Gal) and the plates incubated overnight at 37 °C. Selection of colonies with inserts was based on blue white screening. 2.2.8.3 Plasmid extraction Colonies generated in Section 2.2.8.2 were touched lightly with a 200 l pipette tip that was then added to 5 ml of LB broth in a 50 ml screw cap centrifuge tube which was incubated in a shaking water bath at 37 °C overnight. Plasmids were then extracted using QIAprep Spin Miniprep Kit (Qiagen) according the manufacturer’s instructions. To estimate concentration and verify insert size, plasmids were digested with 10 U of EcoRI (Roche Diagnostics), at 37 °C for 30 min and fragments separated on an agarose gel (see Section 2.2.8.5). 2.2.8.4 Colony PCR Alternatively inserts were amplified directly, without plasmid isolation (as in Section 2.2.8.3), with colony PCR. Colonies were touched lightly with a 200 l pipette tip that was then dipped in a 1.5 ml tube containing lysis buffer (1 mM EDTA (pH 8.0), 10 mM Tris HCl (pH 8.5), 0.2 % [v/v] Triton X 100). The tube was then incubated at 90 °C for 5 min. 1 l of extract was used in a PCR reaction containing 1 U Amplitaq Gold DNA polymerase (Applied
Biosystems), 1 × Amplitaq Gold reaction buffer, 2.5 mM MgCl2, 400 nM of M13F and M13R primers (Table 2.4), 200 M dNTPs (Roche) and 5 % [v/v] DMSO. The PCR cycle was: 95 °C for 5 min followed by 20 cycles of 95 °C for 30 s, 60 °C (decreased by 0.5 °C every cycle) for 30 s and 72 °C for 70 s followed by 15 cycles of 95 °C for 30 s, 50 °C for 30 s and 72 °C for 70 s with a final 7 min extension at 72 °C. PCR products of the correct size (see Section 2.2.8.5) were purified using QIAquick Gel Extraction kits (Qiagen).
74 2.2.8.5 DNA electrophoresis Samples were mixed with loading dye (6 × stock: 0.25 % [w/v] bromophenol blue and 0.25 % [w/v] xylene cyanol, 30 % [v/v] glycerol in distilled water) before being loaded into an agarose gel (prepared in 1 × TAE buffer (40 mM Tris HCl, 20 mM glacial acetic acid, 1 mM EDTA (pH 8.0)), electrophoresed at 90 V in 1 × TAE buffer and post stained with 100 g/ml ethidium bromide or SYBR© GOLD nucleic acid stain (Invitrogen) in 1 × TAE buffer. Size of DNA was estimated against 2 log ladder (New England Biolabs) or a 1kb ladder (Promega). 2.2.8.6 Sequencing and phylogenetic analysis Either plasmids (Section 2.2.8.3) or PCR products (Section 2.2.8.4) were sent to the Australian Genome Research Facility (AGRF, Brisbane, Australia) for sequencing. Primers used in sequencing reactions are given in Table 2.4. Sequences were arranged into operational taxonomic units (OTUs) based on a mutual 99 % similarity between their partial sequences (≥500 bp) and a representative sequence of each OTU fully sequenced in both directions. As OTU I149 was of particular interest, both clone sequence variants, I25 and I149, were included despite being 99.3 % similar to each other. Contigs were assembled using Geneious Pro 3.0.6 (Biomatters Ltd.). If necessary partial sequence reads were processed with the LongTrace program (www.nucleics.com.au). Complete sequences were checked for chimeras using Bellerophon v3 (DeSantis et al., 2006) and Mallard (Ashelford et al., 2006). Suspect sequences were individually checked using Pintail (Ashelford et al., 2005) against their closest matches in the NCBI database and divergent regions individually used to screen the NCBI database. Sequences assessed as being ‘clean’ were then added to ARB (Ludwig et al., 2004) into the Greengenes ARB database (DeSantis et al., 2006) updated with new sequences from the NCBI databases that were close matches to sequences of interest. These sequences were then aligned and phylogenetic trees constructed.
75 2.3 Results and discussion 2.3.1 SBR community compositions Biomass taken from SBRs LS1 and LS2 contained alphaproteobacterial TFO organised mainly in clusters, with few cells dispersed as single tetrads (Fig. 2.2). In LS2 all the observed tetrads as well as some large cocci, gave a positive hybridisation signal with the ALF968 probe (Fig 2.2b i. ii.), while in LS1 very few ALF968 positive cells did not exhibit the TFO morphology (Fig 2.2a i. ii.). A small number of these TFO responded to the AMAR839 probe and those designed to target cluster I Defluviicoccus (Table 2.1) in either SBR community. Although cluster II Defluviicoccus had not been described at the commencement of this study, application of the later reported probes of Meyer et al. (2006) revealed that most of the TFO in both these reactors were positive with the DF988 probe and about 60 % and 75 % of these also fluoresced with the DF1020 probe for LS1 and LS2 respectively (Table 2.5). No cells fluoresced with the TFO_DF629 FISH probe in either SBR, even though it was designed to cover all the cluster II Defluviicoccus related TFO as defined by Wong and Liu. (2007). Cells responding to the GBmix probe set, for the ‘Competibacter’ GAO, were abundant in the LS2 community but were rarely observed in LS1 (Table 2.5). Interestingly, the GBmix probe hybridised with the large cocci also responding to the ALF968 probe (Fig. 2.2b), even though ‘Competibacter’ are members of the Gammaproteobacteria which was also reported by Oehmen et al. (2006a) prompting the design of the ALF969 probe with a competitor for some of these ‘Competibacter’ sequences. Such specificity problems with the broad coverage proteobacterial sub phylum probes are well documented (Amann and Fuchs, 2008). Table 2.5: Quantitative FISH results Probe(s) LS1 LS2 AMAR839 <1 % <1 % SBR9 1a ND ND DF1mix <1 % <1 % DF988 17.6 ± 1.2 % 7.4 ± 0.6 % DF1020 10.9 ± 1.0 % 5.6 ± 0.6 % GBmix <1 % 9.5 ± 1.2 % ND = None detected. Standard error is given. 76
Figure 2.2: Micrographs of biomass samples: a.i. Phase contrast image of the LS1 biomass; ii. corresponding FISH image of cells hybridised with the ALF968 probe (CY3: red); iii. Phase contrast image of the ALF968 FACS sorted biomass. b.i. Phase contrast image of the LS2 biomass; ii. corresponding FISH image of cells hybridised with the ALF968 probe (CY3), some large positive cocci cells are indicated with blue arrows; iii. and iv. Phase contrast images of the ALF968 FACS and GBmix FACS sorted biomass, respectively. Scale bars represent 10 m. 77 2.3.2 Flow cytometry sorting of the Alphaproteobacteria The results of the flow cytometry analyses confirmed earlier reports that breaking up activated sludge flocs and aggregates to encourage cells to go into suspension, is problematic (Wallner et al., 1995; Perez Feito et al., 2006; Miyauchi et al., 2007). This proved especially so with the heavily capsulated biomass in the SBRs analysed here. However, when the ALF968 sorted LS1 community was examined microscopically, it contained almost exclusively TFO, and cells exhibiting other morphologies as in the original sample, were rarely present (Fig. 2.2a iii.). The ALF968 sorted population for LS2 also contained some TFO, although the majority of the cells were large coccobacilli (Fig. 2.2b iii), which were much more easily dispersed with the methods applied here. 2.3.3 Clone library analysis of the ALF968 FACS sorted cells A library of the 16S rRNA sequences from the ALF968 positive sorted cells from LS1 and LS2 were constructed and phylogenetic trees generated for the clones obtained for each reactor community (Figs. 2.3 and 2.4). Of the clones in the LS1 library, almost a third (17 of 55) (representing OTU I149) showed > 99 % sequence homology to the alphaproteobacterial TFO clone (DQ145465) reported by Meyer et al. (2006). This belongs to cluster II of the Defluviicoccus vanus related TFO, sharing 92 % sequence homology to Defluviicoccus vanus (AF179678). The OTU I149 clones possessed the binding site for the Wong et al. (2007) TFO_DF629 probe, so the negative FISH result reported above confirms that it is probably unable to access its target site. None of the clones contained the target for the DF988 probe without also containing the site for the DF1020 probe. Only two other Alphaproteobacterial clones appeared in this library, OTU I78 and OTU I159, only distantly related to Phenylobacterium koreensis (89 %) and Mesorhizobium septentrionale (98 %), respectively (Fig. 2.3).
78
Figure 2.3: Maximum likelihood tree of the complete sequences obtained from the LS1 ALF968 FACS sorted community and related sequences (all sequences were at least 1250 bp long). OTUs from this study are represented in red and bold text. ALF968 probe coverage is indicated with a red bracket. Broad phylogenetic affiliations are indicated with black brackets. Clone frequency for each OTU is indicated in parenthesis. Parsimony bootstrap values are calculated as a percentage of 1000 analyses and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position.
79 In the library prepared from the ALF968 sorted cells of LS2, no Defluviicoccus related sequences were found, nor did any of the sequences contain the DF988 or DF1020 FISH probe sites, indicating that the abundant TFO responding to these probes were not represented. Of concern was that only one sequence in the library, related to Brevundimonas diminuta (99 %), was a member of the targeted Alphaproteobacteria (Fig. 2.4). However, 46 % of these clones had the ALF968 probe site, the majority of which were members of the gammaproteobacterial ‘Competibacter’ GAO. Therefore the application of this approach to identify organisms recalcitrant to clone library analysis is compromised if other populations from which sequence information is readily obtainable are also targeted by the FISH probe used for the FACS enrichment. The high prevalence of clones in both libraries (Figs. 2.3 and 2.4) that lacked the 16S rRNA target site for the ALF968 probe may arise from probe mis hybridisation (Nielsen et al., 2004), or from non target cells physically attaching to the target cells during FACS. The less than expected representation of the Defluviicoccus may reflect the same biases in clone library generation to those responsible for their omission from the clone library of Ahn et al. (2007). DNA/RNA extraction bias against some populations in environmental systems is well known (see Chapter 3), and if this is the case here, a cross linking of the cell wall during fixation is likely to have made nucleic acid extraction from these cells even more difficult (Wallner et al., 1997). FACS with FISH labelled cells has been performed successfully without fixing the biomass, to avoid any cross linkage of the nucleic acids during fixation that can affect subsequent analyses (Yilmaz et al., 2010). It may have overcome the problems encountered here if DNA extraction inefficiency was a contributing factor to the under representation of Defluviicoccus related sequences in the clone libraries. It is important to note that no novel Defluviicoccus related sequences were detected in the aerated EBPR process clone library despite its novel principle of operation. This may reflect an adaptability of the Defluviicoccus related GAO, but may also be attributed to a lack of resolving power of the 16S rRNA gene. He et al. (2007) also found that Accumulibacter PAO 16S rRNA sequences obtained from geographical and operationally distinct systems shared a high similarity. They had to use the ppk gene to resolve the full extent of phylogenetic diversity among these populations. If an alternative gene marker is required to
80 resolve the full diversity of the Defluviicoccus related organisms, then FACS seems an ideal enrichment method as the whole cell (and its genome) is isolated (Miyauchi et al., 2007). 2.3.4 Could FACS also assist in uncovering further diversity among the ‘Competibacter’? Analysis of ‘Competibacter’ related sequences indicates that further diversity, not covered by the existing sub group FISH probes, exists for this group of important organisms (Fig. 2.4). As previously mentioned, this is also suggested by reports of cells responding to the broad GB group probe, targeting the whole group, but none of the 7 sub group probes (Kong et al., 2006; Wong and Liu, 2006). Definition of the sub groups, as practically determined by the currently available FISH probes, clearly requires attention. This should include the design of additional probes for new, potentially important sub groups, and the reassessment of the present FISH probes. As with the alphaproteobacterial GAO, the majority of sequences available for the ‘Competibacter’ are from lab scale communities fed a single simple carbon source. There is therefore a need to obtain more sequence information from these groups from more complex full scale systems where additional undescribed diversity is known to exist (Kong et al., 2006). As the FACS method seemed suited to the enrichment ‘Competibacter’ GAO in the ALF968 enrichment, its success in enriching these organisms from EBPR systems was assessed. Unlike the situation with the alphaproteobacterial GAO, no reported bias is reported for obtaining ‘Competibacter’ sequences in clone libraries, but they are often present in full scale systems in low abundances (Lopez Vazquez et al., 2008a; Nielsen et al., 2010b). Even though ‘Competibacter’ sequences contributed 9.5 % to the FISH probed total bacterial biovolume, an enrichment step would increase the likelihood of obtaining sequences from members of each of the sub groups, some of which may be present at low abundance.
81
82 Figure 2.4: Maximum likelihood tree of the complete sequences obtained from the LS2 FACS sorted community and selected related sequences (all sequences were at least 1250 bp long). OTUs from this study are represented in bold text and sequences from the ALF968 and GBmix sorted communities are represented in red and blue, respectively. Probe coverage is indicated with brackets and those used for FACS enrichment are coloured. ‘Competibacter’ probe details are reported by Kong et al. (2002). Broad phylogenetic affiliations are indicated with the brackets to the far right. Clone frequency for each OTU is indicated in parenthesis. Parsimony bootstrap values are calculated as a percentage of 1000 analyses and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position. A more comprehensive tree of ‘Competibacter’ related sequences and probe coverage is given in Appendix 1.
83 Therefore the GBmix probe primer set was applied to enrich for all ‘Competibacter’ sequences present in the LS2 community. Of the 61 clones in the clone library, 95 % were ‘Competibacter’ (Fig. 2.4). However, >30 % (19 of 61) of these were assessed as being chimeric artefacts. As chimeras appear more frequently between closely related sequences, the high incidence of this artefact here is probably a consequence of enriching for close phylogenetic groups. This is of particular concern for ‘Competibacter’ enrichments given multiple sub groups often co exist (Kong et al., 2006). OTU G115 is a member of sub group 2 of the ‘Competibacter’ and makes up 64 % of the total clones, yet constitutes <15 % of the GBmix positive biomass by quantitative FISH analysis with the GB_2 probe of Kong et al. (2002). Although based on limited data, and at least some sub group 2 members contain a single mismatch to the GB_2 FISH probe (see Fig 2.4), this might suggest a biased preference for this sub group in clone library construction, providing yet another example of the failings of clone library analyses as a semi quantitative tool for community analyses. All 16S rRNA sequences retrieved here fall into the seven previously described clusters. However, a major limitation with applying the GBmix probes is that it is limited to organisms with these sequences, and will miss any closely related sequences from groups not covered by these probes (see Fig. 2.4). 2.4 Conclusions On the basis of these data, FACS provides an attractive method for recovering molecular information from important EBPR populations including the clustered capsulated PAO, which are often under represented in clone libraries (Hesselmann et al., 1999; Crocetti et al., 2000; Ahn et al., 2007; Miyauchi et al., 2007), regardless of their in situ activity or abundance. It seems especially suited to full scale EBPR processes where these populations often contribute only a small percentage of the total cell biovolume (Burow et al., 2007; Nielsen et al., 2010b). However, the appropriateness of the method for this purpose is conditional on the availability of suitably targeted FISH probes and the community composition, and may lead to a higher incidence of PCR artefacts.
84 As well as being useful for uncovering the diversity of these organisms (based on their 16S rRNA), the demonstrated successful enrichment of sub populations for metagenomic analysis (Podar et al., 2007; Mary et al., 2010; Müller and Nebe von Caron, 2010; Yilmaz et al., 2010) is another potentially useful application for flow cytometry based enrichment of populations important to EBPR, especially since the majority are recalcitrant to isolation.
85 3.0 Developing methods for nucleic acid extraction from activated sludge samples which reflect community population diversity
3.1 Introduction Community profiling of EBPR communities using techniques like 16S rRNA clone library construction, often suggest an under representation of functionally important populations, which have included the alphaproteobacterial GAO (Hesselmann et al., 1999; Crocetti et al., 2000; Wong et al., 2004; Meyer et al., 2006; Ahn et al., 2007; Miyauchi et al., 2007). The previous chapter applied FACS enrichment to target underrepresented 16S rRNA cloned sequences, but this relies on an a priori knowledge of the community composition and does not address the issue of population underrepresentation in popular community profiling techniques. While many possible reasons exist for this, often neglected is the choice of an adequate DNA extraction method. The isolation of high quality nucleic acids reflecting all community members being crucially important to all PCR based culture independent analyses (von Wintzingerode et al., 1997). Compared to the extensive literature assessing the isolation of nucleic acid from soils and sediments, relatively few reports have assessed extraction protocols for their suitability for application to activated sludge biomass (Bourrain et al., 1999; Yu and Mohn, 1999; Orsini and Romano Spica, 2001; Gabor et al., 2003; Purohit et al., 2003; Lemarchand et al., 2005; Roh et al., 2006; Guobin et al., 2008; Bonot et al., 2010; Vanysacker et al., 2010). Unfortunately, most have adapted protocols originally developed for extracting nucleic acids from cells from habitats like soil without providing justification for their selection. Activated sludge is markedly different to these environmental samples in that the biomass is organised typically as cellular aggregates or flocs (Yu and Mohn, 1999; Nielsen, 2002). These flocs can be robust and resistant to extraction methods (Watanabe et al., 1998; Bourrain et al., 1999; Yu and Mohn, 1999; Vanysacker et al., 2010).
86 The protocols used to isolate nucleic acids from environmental samples incorporate chemical (see Zhou et al., 1996), enzymatic (Porteous et al., 1994), mechanical (Ogram et al., 1987), heat (Picard et al., 1992), and freeze thaw (Tsai and Olson, 1991) treatments, often in combination. Mechanical lysis is popular as it provides high nucleic acid yields and can facilitate the disruption of aggregated biomass (Moré et al., 1994; Watanabe et al., 1998; Bourrain et al., 1999; Frostegård et al., 1999; Yu and Mohn, 1999; Bürgmann et al., 2001; Stach et al., 2001; Lakay et al., 2007; Guobin et al., 2008). While most protocols are dedicated to DNA isolation, those able to extract RNA and DNA simultaneously provide the opportunity to obtain additional valuable information on the metabolic activities of populations in the community (von Wintzingerode et al., 1997; Hurt et al., 2001). In this study a method for nucleic acid isolation from activated sludge was developed. This method employs the strongest known chaotrophic agent, sodium trichloroacetate (NaTCA) (Hamaguchi and Geiduschek, 1962). NaTCA was used by Summerton et al. (1983) to isolate plasmid DNA, but has never been used to extract nucleic acids from natural microbial communities. It was hypothesised that NaTCA in combination with physical disruption may provide high quality DNA and RNA from recalcitrant environmental samples, especially those containing the alphaproteobacterial GAO, without any need for using potentially hazardous chemicals like phenol or chloroform. Having optimised the protocol, it was then compared with eight different published methods for its ability to extract simultaneously high quality nucleic acids from activated sludge samples. These methods were chosen for their demonstrated ability to extract both RNA and DNA, and their suitability for high sample throughput processing at minimal cost. The effectiveness of each with activated sludge was compared using 16S rRNA group specific PCR of selected marker bacterial populations which had been well represented, under represented or absent from 16S rRNA gene clone libraries generated from activated sludge. Special attention was given to Defluviicoccus related organisms, for the reasons discussed in Chapter 2. FISH was used to assess the relative abilities of each method to recover nucleic acids from the selected marker populations independently of the extraction process, and to allow a rational basis for nucleic acid extraction method selection.
87 3.2 Materials and methods 3.2.1 Sampling 3.2.1.1 Pure cultures Pure cultures of Escherichia coli, Pseudomonas aeruginosa, Micrococcus luteus, Bacillus subtilis, Mycobacterium phlei, and Mycobacterium smegmatis were obtained from the La Trobe University culture collection. These cultures were selected to represent a range of both Gram positive and negative organisms with assumed differing susceptibilities to cell lysis. For nucleic acid extraction, liquid cultures were sampled in their exponential phases of growth (determined by UV Vis Spectrophotometry at 600 nm in a SmartSpecTM Plus spetrophotometer (BIO RAD)). These cultures were grown in 150 ml of nutrient broth (Oxoid) in 250 ml conical flasks that were inoculated with 10 ml of a stationary phase culture. P. aeruginosa and E. coli were grown at 37 °C and all others at 30 °C. 3.2.1.2 Lab scale EBPR activated sludge samples Activated sludge biomass samples were obtained from four EBPR plants. Plant LS1 was a laboratory scale EBPR SBR that was continuously aerated and fed with clarified effluent from a non EBPR treatment plant located in Melton, Victoria, Australia. Operational conditions of this reactor are given in Ahn et al. (2007). Plant LS2 was also a lab scale EBPR SBR, but was operated under alternating aerobic: anaerobic conditions and also fed with acetate as the sole carbon source (75 mg/l carbon). Further operational conditions can be found in Chapter 6. Samples from both were taken at the end of the SBR cycle and stored at 70 °C. Biomass samples used for FISH analyses were removed at the same time, washed twice in 1 × PBS
(137 mM NaCl, 10 mM Na2HPO4.7H2O, 2.7 mM KCl, 1.47 mM KH2PO4) and fixed in 4 % [w/v] PFA and 1 × PBS at 4 °C for 3 h, before being stored in 50 % [v/v] ethanol and 1 × PBS at 20 °C until further use.
88 3.2.1.3 Full scale EBPR activated sludge samples Plants FS1 and FS2 were two full scale EBPR waste water treatment plants. Both were MUCT configured systems located at Castlemaine and Kyneton (both in Victoria, Australia) respectively. Samples (100 ml) were taken from their aerobic reactors and, while continuously mixed, 200 l aliquots of each were immediately dispensed into 1.5 ml microcentrifuge tubes using wide bore pipette tips. These were transported on dry ice to the laboratory for storage at 70 °C. 3.2.2 Nucleic acid extractions Aliquots of 200 l of biomass (approx 8 14 mg wet weight), which were pelleted by centrifugation (6, 800 x g, for 5 min, 4°C) and nucleic acids were extracted using adaptations of the methods of Corgié et al. (2006) (abbreviated to CR) (Section 3.2.2.2), Costa et al. (2004) (CS) (Section 3.2.2.3), Griffiths et al. (2000) (GR) (Section 3.2.2.4), McVeigh et al. (1996) (MV) (Section 3.2.2.5), Orsini and Romana Spica (2001) (OR) (Section 3.2.2.6), Tillet and Neilan (2000) (TN) (Section 3.2.2.7), and Yu and Mohn (1999) (YM) (Section 3.2.2.8) and the NaTCA method developed in this study (MI) (Section 3.2.2.1). These methods were modified to make the non lysis steps in each as uniform as possible. Following their extraction (see Sections 3.2.2.1 3.2.2.8), nucleic acids were precipitated by the addition of 0.6 volumes of 2 propanol on ice, for 15 min and pelleted, at 20, 800 x g for 15 min at 4 °C, washed twice in 70 % (w/v) ethanol, air dried and re suspended in 50 l TE buffer (10 mM Tris HCl, 1 mM EDTA, pH 8.0). An overview of the principle differences between the lysis steps in these protocols are summarised in Table 3.1. Extractions using the CR, CS, GR, MV and YM methods were performed by Dr Kate Porter (La Trobe University, Bendigo).
89 Table 3.1: Overview of the lysis step in each extraction method. a Chemical lysis Mechanical lysis CRb SDS Bead beat 1 min SDS + phenol chloroform Bead beat 0.5 min CSb Bead beat 0.5 min Cetyl trimethylammonium bromide (CTAB) + SDS + heat (65 °C/30 min) GR CTAB + phenol chloroform Bead beat 0.5 min MI Sodium trichloroacetate (NaTCA) + Sarkosyl Bead beat 3 min MVb Phenol chloroform Bead beat 1 min Phenol chloroform Bead beat 0.5 min ORb SDS + Microwave (800 W/45 s) Guanidine thiocyanate + phenol chloroform TN Potassium ethyl xanthogenate + SDS + phenol chloroform + heat (65 °C/5 min) YMb SDS Bead beat 5 min SDS Bead beat 5 min MO Commercial buffer Bead 10 min vortex a Method; b Additional lines indicate a separate step 3.2.2.1 NaTCA method (MI) The optimised protocol to extract nucleic acids using the NaTCA method (MI) is detailed here. The biomass pellet was re suspended in 1500 l of lysis buffer (3 M NaTCA (4.5 M stock: Appendix 3), 50 mM Tris HCl (pH 8.0), 15 mM EDTA pH 8.0, 1 % [w/v] N Lauroylsarcosine, 1 % [w/v] polyvinylpyrrolidone (PVP), 10mM dithiothreitol (DTT), 1.67 % [v/v] antifoam (Dow Corning 1520 US)) and 0.6 g of 0.1 mm diameter glass beads (Biospec), and homogenised in a mini bead beater (Biospec) at maximum speed for 3 min. After centrifugation (20,800 x g, 5 min, 4 °C), the supernatant was retained. After precipitation and re suspension, necessary because of the presence of an insoluble ‘gelatinous’ pellet, samples were incubated for 20 min at 37 °C, mixed by pipetting up and down three times, centrifuged (20, 800 x g, 30 s) and the resulting supernatant transferred to a fresh 1.5 ml tube.
90 3.2.2.2 Method of Corgié et al. (2006) (CR) With the method of Corgié et al. (2006) (CR), the biomass pellet was re suspended in 800 l of lysis solution (100 mM Tris HCl (pH 8.0), 100 mM EDTA, 100 mM NaCl, 1 % [w/v] polyvinylpolypyrrolidone (PVPP), 2 % [w/v] SDS) with 0.6 g of 0.1 mm diameter glass beads, and homogenised in a mini bead beater at maximum speed for 1 min. 800 l phenol chloroform isoamyl alcohol (25:24:1 v:v:v) was added and the mixture was homogenised again for 30 s. After centrifugation (20, 800 x g, 2 min, 4 °C) the aqueous phase was combined with 800 l chloroform isoamyl alcohol (24:1 v:v). The mixture was homogenised briefly in a mini bead beater, centrifuged (20, 800 x g, 5 min, 4 °C) and the aqueous phase saved. This process was repeated twice. 3.2.2.3 Method of Costa et al. (2004) (CS) For the method of Costa et al. (2004) (CS), the biomass pellet was re suspended in 500 l ethanol with 0.6 g of 0.1 mm diameter glass beads, and homogenised in a mini bead beater at maximum speed for 30 s. After centrifugation (20, 800 x g, 5 min, 4°C), the supernatant was discarded. 1.2 ml of lysis solution (100 mM sodium phosphate buffer, 100 mM Tris Cl (pH 8.0), 100 mM EDTA (pH 8.0), 1.5 M NaCl, 1 % [w/v] cetyl trimethylammonium bromide (CTAB), 2 % [w/v] SDS)) was added and the solution was incubated at 65 °C for 30 min while being inverted every 10 min. After centrifugation (20, 800 x g, 5 min, 4 °C) the supernatant was retained and 1 ml chloroform isoamyl alcohol (24:1 v:v) was added, the tube was agitated, centrifuged (20, 800 x g, 5 min, 4 °C) and the aqueous phase saved. 3.2.2.4 Method of Griffiths et al. (2000) (GR) For the method of Griffiths et al. (2000) (GR), the biomass pellet was re suspended in 250 l of lysis buffer (0.7 M NaCl, 240 mM potassium phosphate buffer, 250 l CTAB (10 % [w/v])) and 500 l phenol chloroform isoamyl alcohol (25:24:1 v:v:v) with 0.6 g of 0.1 mm diameter glass beads, and homogenised in a mini bead beater at maximum speed for 30 s. After centrifugation (20, 800 x g, 5 min, 4 °C) the supernatant was retained and 1 volume
91 chloroform isoamyl alcohol (24:1 v:v) was added to it, the tube was centrifuged (20, 800 x g, 5 min, 4 °C) and the aqueous phase saved. 3.2.2.5 Method of McVeigh et al. (1996) (MV) In the McVeigh et al. (1996) (MV) protocol the biomass pellet was re suspended in 500 l phenol chloroform isoamyl alcohol (25:24:1 v:v:v) and 750 l 120 mM sodium phosphate buffer (pH 8.0), with 0.6 g of 0.1 mm diameter glass beads, and homogenised in a mini bead beater at maximum speed for 60 s. After centrifugation (10, 600 x g, 10 min, 4 °C), the aqueous phase was saved on ice and 750 l 0.12 M sodium phosphate buffer (pH 8.0) was added to the pellet. The mixture was homogenised again at maximum speed for 30 s. After centrifugation (10, 600 x g, 10 min, 4 °C), the aqueous phase was saved on ice and the pooled supernatant was made up to 1,000 l in 2 ml centrifuge tube, using 0.12 M sodium phosphate buffer (pH 8.0). 1,000 l phenol chloroform isoamyl alcohol (25:24:1 v:v:v) was added and mixed by inverting approximately 20 times to form an emulsion. The mixture was centrifuged (20,800 x g, 5 min, 4 °C) and the aqueous phase saved. 3.2.2.6 Method of Orsini and Romano spica (2001) (OR) For this method the biomass pellet was washed with 1 ml of a wash solution (50 mM Tris HCl (pH 8.0), (pH 8.0), 25 mM EDTA (pH 8.0), 0.1 % [w/v] SDS and 0.1 % [w/v] PVP), re suspended in 35 l of a lysis solution (50 mM Tris HCl (pH 8.0), 25 mM EDTA (pH 8.0), 3 % [w/v] SDS, 1.2 % [w/v] PVP) and heated in a microwave oven for 1 min at 800 W. After the microwave disruption step 400 l of a preheated (65 °C), freshly prepared lysis solution (1 M guanidinium thiocyanate, 7 mM sodium citrate (pH 7.0), 0.025 M 2 mercaptoethanol, 0.3 M sodium acetate, 1.2 % [w/v] PVP) was added and the mixture vortexed for 30 s. The mixture was added to 450 l of phenol:chloroform:isoamyl alcohol (25:24:1 v:v:v) inverted several times, centrifuged (20,800 x g, room temperature, 5 min) and the aqueous phase saved.
92 3.2.2.7 Method of Tillett and Neilan (2000) (TN) In this protocol, the biomass pellet was re suspended in 50 l TE buffer and added to 1300 l of preheated, freshly prepared lysis buffer (1 % [w/v] potassium ethyl xanthogenate, 100 mM Tris HCl pH 7.4, 20 mM EDTA pH 8.0, 1 % [w/v] SDS, 800 mM ammonium acetate) and incubated at 65 °C for 5 min. During this period the tubes were periodically mixed by inversion. The mixture was vortexed and 200 l chloroform: isoamyl alcohol (24:1 v:v) was added. After centrifugation (20,800 x g, room temperature, 4 min) the aqueous phase was transferred to a fresh tube containing 500 l phenol:chloroform:isoamyl alcohol (25:24:1 v:v:v), incubated at 65 °C for 2 min and centrifuged (20,800 x g, room temperature, 4 min). Phenol chloroform extractions were repeated until no opaque interphase was visible (repeated 2 4 times). 3.2.2.8 Method of Yu and Mohn (1999) (YM) The biomass pellet in this method was re suspended in 1 ml lysis buffer (50 mM Tris HCl (pH 8.0); 5 mM EDTA (pH 8.0); 3 % [w/v] SDS) and 30 l diethylpyrocarbonate (DEPC), with 0.6 g of 0.1 mm diameter glass beads, and homogenised in a mini bead beater at maximum speed for 2.5 min. The mixture was incubated on ice for 1 min and then homogenised again at maximum speed for 2.5 min. After centrifugation (20, 800 x g, 3 min, 4 °C), the supernatant was saved on ice. The pellet was re suspended in 1 ml lysis buffer and 30 l DEPC and homogenised in a mini bead beater at maximum speed for 2.5 min. The mixture was incubated on ice for 1 min and then homogenised again at maximum speed for 2.5 min. After centrifugation (20, 800 x g, 3 min, 4 °C), the supernatants were combined and ammonium acetate was added to 2 M. The mixture was incubated on ice for 5 min and then centrifuged (20, 800 x g, 3 min, 4 °C) and the supernatants combined. 3.2.2.9 Method of MoBIO© Soil DNA Kit (MO) Nucleic acid was extracted with the MoBIO© Soil DNA kit (MO) using the manufacturer’s instructions for maximum yield. This method was included in the comparison as both ‘Accumulibacter’ and Defluviicoccus related organisms were markedly underrepresented
93 (when compared to the corresponding FISH data) in previous clone libraries prepared using it from the LS1 (Ahn et al., 2007) and LS2 (Michael Beer, unpublished data) communities. 3.2.3 Post extraction biomass lysis assessment using SYBR Gold© staining After the lysis step for each extraction method the pelleted biomass debris were washed three times in 1 × PBS and fixed in PFA as with samples for FISH (see Section 2.2.2). 10 l of each sample was applied to Vectabond© (Vector Laboratories) coated slides and stained with 1 × SYBR Gold© Nucleic acid stain (Invitrogen) for 10 min, rinsed briefly with distilled water and air dried with compressed air. Slides were mounted in VectaShield (Vector Laboratories) and examined with an epifluorescence microscope (Eclipse 800, Nikon). Fixed sludge biomass, treated with DNAse and RNAse (see Section 3.2.3.1) and untreated controls, were used as positive and negative controls respectively. 3.2.3.1 In situ nuclease treatment of controls PFA fixed biomass samples were applied to slides coated with Vectabond (Vector Laboratories, USA) and allowed to air dry. Slides were transferred through an ethanol series of 50 % [v/v], 80 % [v/v] and 96 % [v/v] for 3 minutes at each concentration and allowed to air dry. Initially 25 l of lysozyme was added (10 mg ml 1 lysozyme (0.5 M EDTA (pH 8.0), pH 8, 1 M Tris HCl (pH 7.5)) to each well and incubated on ice for 30 min. Slides were immersed in distilled water, followed by ethanol and allowed to air dry. RNAse and DNAse treatments were performed as detailed by Weichart et al. (1997). To digest DNA, 25 l of
DNAse I (1 U in 5 % [v/v] glycerol, 1 mM CaCl2, 20 mM Tris HCl (pH 8.3) 2 mM MgCl2) (Sigma Aldrich) was added to each well and the slides incubated horizontally in a 50 ml centrifuge tube at 37 °C for 1 h, then carefully rinsed with distilled water and air dried with compressed air. For RNA digestion RNAse (100 mg of RNase A ml 1 (Sigma Aldrich) in 50 mM Tris HCl, 10 mM EDTA, (pH 7.5)) was applied to each well and the slides incubated at room temperature (approx. 22 °C) for 30 min. Slides were then rinsed with distilled water and air dried with compressed air.
94 3.2.4 Electrophoresis 3.2.4.1 DNA electrophoresis Before electrophoresis, extracts were treated with 10 ng Ribonuclease A (Sigma Alrich) at room temperature for 30 min (if applicable). Electrophoresis was then performed as described in Section 2.2.8.5. 3.2.4.2 RNA electrophoresis Before electrophoresis extracts were treated with DNAse I (1 U in 5 % glycerol, 1 mM
CaCl2, 20 mM Tris HCl (pH 8.3) 2 mM MgCl2) (Sigma Aldrich) at room temperature for 30 min, and inactivated at 75°C for 10 min. The method for RNA gel electrophoresis was adapted from that of Goda and Minton (1995). Agarose gels were prepared by adding 20 mM guanidine thiocyanate and 100 g/ml ethidium bromide to molten agarose in 1 × TBE buffer (10.8 g l 1 Tris HCl, 5.5 g l 1 boric acid, 9.3 g l 1 EDTA). RNA was denatured by adding RNA agarose gel loading buffer (6 × sterile filtered (0.2 m pore size) stock: 0.25 % [w/v] bromophenol blue and 0.25 % [w/v] xylene cyanol, 30 % [v/v] glycerol, 1.2 % [w/v] SDS, 60 mM sodium phosphate buffer (pH 6.8) in DEPC treated distilled water) and heating at 75 °C for 5 min. Samples were immediately loaded into the wells. The gels were electrophoresed at 65 V in 1 × TBE buffer. 3.2.4.3 SDS polyacrylamide gel electrophoresis (SDS PAGE) This was carried out by Dr Kate Porter (La Trobe University, Bendigo). SDS polyacrylamide gel electrophoresis (PAGE) was performed according to the method of Laemmli (Laemmli, 1970). The gels were electrophoresed at 200 V in 1 × running buffer (pH 8.3) and stained with Coomassie blue R 250.
95 3.2.4.4 Gel visualisation Gels were visualised on a AITM 26 Transilluminator (Quantum Scientific) and captured using an Alpha Digidoc system (Quantum Scientific) equipped with a C 5060 Wide Zoom digital camera (Olympus). 3.2.5 Estimating nucleic acid concentration PCR product DNA concentrations were estimated by comparing bands against bands of 2 log ladder (BioLabs) of known concentration, using Adobe Photoshop 7. DNA and RNA levels in crude extracts were quantified separately after capturing an image of each agarose gel (Sections 2.2.8.5, 3.2.4.2 and 3.2.4.4). For nucleic acid extracts, Adobe Photoshop 7 (Adobe, San Jose, CA, USA) was used to measure the integrated fluorescence intensity volume of each sample lane. Nucleic acid concentrations were calculated after comparing fluorescence intensity volumes of a three point standard curve prepared from DNA/RNA standards of known concentrations. Duplicate values were obtained from duplicate extractions for each method, and for each point of the standard curve. The error represents the range of values obtained. It should be noted that values given do not indicate the integrity of the nucleic acid, and may be overestimations for sheared samples. 3.2.6 Spectrophotometry Absorption levels of extracts were obtained by Dr Kate Porter (La Trobe University, Bendigo) using a ND 1000 NanoDrop Spectrophotometer (Thermo Scientific). 3.2.7 Restriction digest inhibition assessment Restriction enzyme digestion reactions were performed with 2.5 l of RNAse treated (Section 3.2.4.1) crude DNA from each extraction, 60 ng (approx) of uncut pGEM T plasmid (Promega), 10 U of EcoRI and 1 × EcoRI buffer (Roche Diagnostics), and were incubated at
96 37 °C for 30 min. Successful DNA digestion was assessed using agarose electrophoresis (Section 2.2.8.5). 3.2.8 Denaturing gradient gel electrophoresis (DGGE) 3.2.8.1 PCR cycling conditions Amplification of 16S rRNA gene fragments spanning the v3 region was achieved with the primers 341F and 534R with the addition of a GC clamp to the forward primer (Table 3.2), as detailed by Muyzer et al. (1993). Reactions contained 1 × GoTaq® Green Master Mix and 0.5 M primers, and approximately 1000 nM template DNA. PCR cycle parameters were 5 min initial denaturation at 95 °C, followed by 30 cycles of 30 s denaturation at 95 °C, 30 s annealing at 53 °C and 30 s elongation at 72 °C, with a final elongation of 10 min. 3.2.8.2 Electrophoresis DGGE gels were cast and run essentially as described by Kong et al. (2001) using the DCode universal mutation detection system (Bio Rad, Sydney, Australia) by Dr Sarah Schroeder (La Trobe University, Bendigo). Gels had a denaturation potential of 40–80 % (100 % denaturation 10 mol l 1 urea and 40 % [v/v] formamide) with a 10 % acrylamide solution (Sigma). Polymerising agent (0.09 % [v/v] ammonium persulphate, 0.09 % N,N,N’,N’ tetramethylethylenediamine (TEMED)) was added to each solution, and 100 l of DCode dye the higher percentage solution, added immediately before pouring. Gels were cast using a DCode Gradient Delivery System (BIORAD) at 1 mm x 16 cm x 10 cm and allowed to set for 3 h. The comb region was then poured with 0 % denaturation solution and allowed to set for 1.5 h. Approximately 350 ng of DNA was loaded for each sample in 1 × DCode loading dye (0.05 % [w/v] bromophenol blue, 0.05 % [w/v] xylene cyanol, 70 % [v/v] glycerol). Gels were electrophoresed for 16 h at 70 V and stained for 20 min in 1 × TAE containing a 1 × SYBR Gold (Invitrogen) on an orbital shaker.
97 3.2.9 Reverse transcription PCR (RT PCR) RT PCR reactions were performed by Dr Kate Porter (La Trobe University, Bendigo) in 200 l thin walled PCR tubes, with a reaction volume of 25 l, on an iCycler IQTM Multicolor Real Time Detection System (BIO RAD). RNA was combined with 0.5 g primer 27F per g RNA and incubated at 70 °C for 5 min, and then incubated on ice for 5 min. Reactions contained 1 × AMV Reverse Transcriptase Reaction Buffer (Promega), 40 U Recombinant RNasin® Ribonuclease Inhibitor, 1 mM dNTP mix and 40 U AMV Reverse Transcriptase (Promega), and were made up to volume with DEPC treated distilled water. Reactions were heated at 42 °C for 1 h, and then screened by PCR for members of the Domain Bacteria with the primers 1396F and 1492R. For all primer sequences see Table 3.2. 3.2.10 PCR of marker population phylotypes PCR detection of marker populations was performed with the assistance of Dr Kate Porter (La Trobe University, Bendigo). PCR reactions were performed in 200 l thin walled PCR tubes, using an iCycler IQTM Multicolor Real Time Detection System (BIO RAD, Hercules, CA, USA). Reactions contained 1 × GoTaq® Green Master Mix (Promega, Madison, WI, USA), 200 nM primers, 3 l of a 1:10 or a 1:100 dilution of nucleic acid extract and distilled water to a final volume of 25 l. Reactions were heated to 95 °C for 5 min followed by 40 cycles of 95 °C for 1 min, annealing at the specified temperature for 30 s, and extension at 72 °C for 30 s, before a final extension at 72 °C for 10 min. A no template negative control was included for every PCR reaction. PCR reactions targeting the 16S rRNA genes of Dechloromonas related organisms used primers Dech454F and BTW0663R at an annealing temperature of 62.8 °C. Those targeting 16S rRNA genes of cluster II Defluviicoccus related populations used primers DF988F and 1492R, at an annealing temperature of 56.5 °C, while those targeting the 16S rRNA genes of Rhodocyclus related populations were performed using primers PAO462F and PAO651R, at an annealing temperature of 62.8 °C. To ensure that negative results were not the result of insufficient DNA, the amount added to each PCR reaction was normalised down for all samples giving an initial positive result with the DF988F and 1492R primers to 13 ng and 3
98 ng for the LS1 and FS1 samples respectively. These DNA concentrations were chosen to be equivalent to those in the sample giving the lowest nucleic acid yield. PCR products were detected by agarose gel electrophoresis using 5 l of the final PCR reaction mix. Primers applied in this chapter were synthesised by Geneworks (Adelaide, Australia) and details are given in Table 3.2. 3.2.11 Real time qPCR conditions The qPCR experiments were performed by Dr Kate Porter (La Trobe University, Bendigo). Reactions were performed in 96 well semi skirted PCR plates (Thermo Scientific), sealed with microseal B adhesive seal (BIO RAD), with a reaction volume of 25 l, on an iCycler IQTM Multicolor Real Time Detection System (BIO RAD). Reactions contained × 1 FailSafe GREEN Real Time PCR PreMix (Epicentre Biotechnologies), 200 nM primers and 1 l FailSafe Enzyme Blend (Biotechnologies), and were made up to volume with distilled water. Reactions were heated at 95 °C for 5 min; followed by 40 cycles of denaturing at 95 °C for 1 min, annealing for 30 s, and extension at 72 °C for 30 s; and a melt curve. Control plasmids were generated from clones of the 16S rRNA gene obtained from previous EBPR studies and were linearised and purified, and the copy number calculated. For routine use, four point calibration curves for qPCR were produced by ten fold serial dilution of positive controls in triplicate within each assay, from 1 × 109 to 1 × 106 copies per reaction. Valid experiments had a regression of ≥ 0.99 and an efficiency between 90 – 110 %. PCR reactions specific for the 16S rRNA gene of members of the Domain Bacteria were performed using primers 1369F and 1492R, at an annealing temperature of 42.8°C, with buffer D (Epicentre Biotechnologies). PCR reactions specific for the 16S rRNA gene of organisms closely related to Defluviicoccus spp. cluster II were run using primers 518F and DF1020R, at an annealing temperature of 69 °C, with buffer C (Epicentre Biotechnologies). PCR reactions targeting Rhodocyclus spp. were performed with primers PAO462F and PAO651R, at an annealing temperature of 62.8 °C, with buffer F (Epicentre Biotechnologies).
99
Table 3.2: PCR primers applied in this chapter. PCR primer Sequence (5’ 3’)a Target Reference 27F GAGTTTGATCMTGGCTCAG Bacteria (Modified from Lane, 1991) 341F + GC CGCCCGCCGCGCGCGGCGGGCGGGGCGGG Bacteria (Muyzer et al., 1993) clamp GGCACGGGGGGCCTACGGGAGGCAGCAG 518F CCAGCAGCCGCGGTAAT Bacteria (Muyzer et al., 1993) 534R ATTACCGCGGCTGCTGG Bacteria (Muyzer et al., 1993) 1369F CGGTGAATACGTTCYCGG Bacteria (Suzuki et al., 2000) 1492R GGYTACCTTGTTACGACTT Bacteria (Lane, 1991) 1525R AAGGAGGTGWTCCARCC Bacteria (Lane, 1991) BTW0663R GGAATTCCACCCCCCTCT Most Rhodocyclales (Adapted from Loy et al., 2005) Dech454F CCCTGTGCGGATGACGGT Some Dechloromonas (Adapted from Ahn et al., 2007) DF988F CCCTTGACATGGGCGTCGTATC Cluster II Defluviicoccus related organisms (Adapted from Meyer et al., 2006) DF1020R CCGGCCGAACCGACTCCC Cluster II Defluviicoccus related organisms (Adapted from Meyer et al., 2006) PAO462F GTTAATACCCTGWGTAGATGACGG ‘Accumulibacter’ (Adapted from Crocetti et al., 2000) PAO651R CCCTCTGCCAAACTCCAG ‘Accumulibacter’ (Adapted from Crocetti et al., 2000) PAO846R GTTAGCTACGGCACTAAAAGG ‘Accumulibacter’ (Adapted from Crocetti et al., 2000) a W = A:T, Y=C:T, M=C:A, R=A:G
100 3.2.12 Clone library construction 3.2.12.1 16S rRNA gene clone library PCR 16S rRNA genes were amplified using the Bacterial universal primers, 27F and 1525R. PCR reactions contained 1 U Amplitaq Gold DNA polymerase (Applied Biosystems) (activated prior to addition at 96 °C for 3 min), 0.1 U Pfu (Promega), 1 × Amplitaq Gold reaction buffer, 2.5 mM MgCl2, 400 nM of each primer, 200 M dNTPs (Roche), 5 % [v/v] DMSO (Sigma Aldrich). The PCR cycle used was: 96 °C for 2 min followed by 35 cycles of 96 °C for 1 min, 52 °C for 30 s and 72 °C for 2 min followed by a final 7 min extension at 72 °C. 3.2.12.2 Cloning, sequencing and phylogenetic analysis Clone libraries were constructed using the TOPO (Invitrogen) cloning kit. PCR products were purified initially with QIAquick Gel Extraction kits (Qiagen). Replacement of A overhangs was performed based on the protocol in the TOPO TA Cloning Kit (Invitrogen, 2006). Template was incubated with 1 U Amplitaq Gold DNA polymerase (Applied Biosystems) (activated prior to its addition at 96 °C for 6 min) 1 × Amplitaq Gold reaction buffer, 2.5 mM MgCl2, 100 M deoxyadenosine triphosphate (dATP) (Promega) for 15 min at 72 °C. Template was then ligated into the pCR®4 TOPO® vector. The ligation contained
4 l PCR template, 1 l of salt solution (1.2 M NaCl, 0.06 M MgCl2) and 1 l vector DNA. The reaction was run at room temperature (approx. 28 °C) for 30 min. 2 l of ligation mixture was added to a vial of One Shot® Mach1TM T1R competent cells, mixed gently and incubated on ice for 15 min. The cells were then heat shocked at 42 °C for 30 s and immediately returned to ice. 250 l of SOC medium (Invitrogen) was added and the vial shaken at 250 rpm at 37 °C for 1 h. 50 l of the transformed cells were plated on pre warmed LB plates (15 g l 1 agar, 10 g l 1 tryptone, 5 g l 1 yeast extract, 5 g l 1 NaCl, 50 g ml 1 ampicillin (Sigma)) and incubated for 8 h at 37 °C. Methods for plasmid extraction from selected colonies, insert sequencing of these and phylogenetic analysis were described earlier in Sections 2.2.8.3 and 2.2.8.6.
101 3.2.13 FISH FISH and qFISH were performed as detailed in Sections 2.2.3 2.2.5. Probes were purchased from Sigma Genosys (Castle Hill, NSW, Australia) and those applied in this chapter are listed in Table 3.3. 3.3 Results and discussion 3.3.1 Optimisation of the NaTCA extraction method An optimal concentration of 3 M NaTCA was selected, as lower concentrations (≤ 2.5 M) gave a lower total yield of DNA, while at higher concentrations (≥ 3.5 M), a white precipitate appeared following the 2 proponal precipitation step, and DNA yields were severely reduced (data not shown). Stable foam produced during the bead beating step, arising from the presence of SDS in the lysis buffer, appeared to reduce cell lysis efficiency (as judged subjectively microscopically). The inclusion of an antifoam substantially improved floc disruption and DNA/RNA yield. Three commercially available antifoams were screened and the 1520 US Antifoam (Dow Corning) was selected, as it was effective at a lower concentration and it still worked after autoclave sterilisation, compared to the other two (Food Grade AF Emulsion and Antifoam Compound A (Dow Corning)) (data not shown). The optimal beating time chosen was based on the yield and integrity of nucleic acids, as assessed by their appearance on agarose electrophoresis gels (Fig. 3.1). A time of 3 min was selected for activated sludge samples, based on that required for successful isolation and PCR of DNA from cluster II Defluviicoccus cells, as determined subjectively by electrophoretic analysis of PCR products.
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Table 3.3: FISH probes applied in this chapter. FISH probe ID Sequence (5’ 3’)d Target [FA]e Reference EUB338 Ia GCTGCCTCCCGTAGGAGT Bacteria 35 (Amann et al., 1990) EUB338 IIa GCAGCCACCCGTAGGTGT Planctomycetales 35 (Daims et al., 1999) EUB338 IIIa GCTGCCACCCGTAGGTGT Verrucomicrobiales 35 (Daims et al., 1999) Non EUB ACTCCTACGGGAGGCAGC Control probe complementary to EUB338 NA (Wallner et al., 1993) PAO462bb CCGTCATCTRCWCAGGGTATTAAC ‘Accumulibacter’ 35 (Zilles et al., 2002a) PAO651b CCCTCTGCCAAACTCCAG ‘Accumulibacter’ 35 (Crocetti et al., 2000) PAO846bb GTTAGCTACGGYACTAAAAGG ‘Accumulibacter’ 35 (Zilles et al., 2002a) Dech454 ACCGTCATCCGCACAGGG Some Dechloromonas 35 (Ahn et al., 2007) DF988 GATACGACGCCCATGTCAAGGG Defluviicoccus related organisms, cluster II 35 (Meyer et al., 2006) DF1020 CCGGCCGAACCGACTCCC Defluviicoccus related organisms, cluster II 35 (Meyer et al., 2006) H966 CTGGTAAGGTTCTGCGCGTTGC Helper probe for DEF988 35 (Meyer et al., 2006) H1038 AGCAGCCATGCAGCACCTGTGTGGCGT Helper probe for DEF988 and DEF1020 35 (Meyer et al., 2006) DF181A CTTTCCCTCACAAGGCAC Clones K42 and L24 within cluster IV 30 Chapter 4 ‘Defluviicoccus’ related TFO CFX1223c CCATTGTAGCGTGTGTGTMG phylum ‘Chloroflexi’ (green nonsulfur bacteria) 35 (Björnsson et al., 2002) GNSB 941c AAACCACACGCTCCGCT phylum ‘Chloroflexi’ (green nonsulfur bacteria) 35 (Gich et al., 2001) a b c d Used together as EUBmix to cover most bacteria; Used together as PAOmix to cover whole group; Used together as a CFXmix to cover whole group; W = A:T, Y=C:T, R=A:G; e applied formamide concentration; NA = not applicable.
103 Some DNA pellets were gelatinous; some of this material was precipitated antifoam, although the rest was possibly from the high levels of polymeric material present in some biomass samples (Nielsen, 2002; Moore et al., 2004). Attempts to selectively precipitate nucleic acids in the presence of any polysaccharides with the addition of either 2 M LiCl (Su and Gibor, 1988) or 1 % [w/v] potassium ethyl xanthogenate (Tillett and Neilan, 2000) did not noticeably reduce the size of the gelatinous pellet formed during precipitation. Also, these additional buffer components did not stay in solution, forming an additional precipitate during the 2 propanol precipitation step. Attempts to selectively remove polysaccharides prior to the precipitation of nucleic acids were also undertaken. Strategies attempted included addition of 1.5 M CTAB to the extraction buffer (Moore et al., 2004), fractionation of the precipitation over a range of alcohol concentrations (using either ethanol, 2 butanol or 2 propanol) and additional precipitation steps at 20 % [v/v] ethanol in the presence of 0.5 M potassium acetate (Wilkins and Lawrence, 1996). Despite these efforts attempted separation of the nucleic acids and gelatinous material was unsuccessful as they always co precipitated. Inclusion of chloroform isoamyl alcohol purification steps and extensive washing of the final pellet with 3 M sodium acetate (Logemann et al., 1987) were also unsuccessful. Ultimately, this gelatinous material was removed successfully by resuspending the nucleic acids (20 min at 37°C or 4°C overnight), centrifuging (20, 800 x g, 30 s), and transferring supernatants to fresh tubes. The optimised extraction method successfully isolated DNA and RNA simultaneously from a range of Gram negative and positive bacteria, including Escherichia coli, Pseudomonas aeruginosa, Micrococcus luteus, Bacillus subtilis, Mycobacterium phlei, and Mycobacterium smegmatis. The latter two were chosen to be relatively recalcitrant acid fast organisms. Bead beating was essential for Gram positive cells, but proved optional for Gram negative cells (see Fig. 3.1). Nucleic acids isolated by this method proved sufficiently free of inhibitory substances to allow successful PCR, RT PCR, real time PCR and restriction digestion (data not shown). When incubated for 2 h at 37°C in 1 × EcoR1 restriction enzyme buffer (Roche) no DNase activity was detected (data not shown).
104
Figure 3.1: Gel electrophoresis (1 % [w/v] agarose, 1 x TAE) of the total nucleic acid extracted from different organisms using the NaTCA method (2.5 l load). Lanes: M, 1 kb DNA ladder (Promega, G5711); 1. E. coli (no bead beating); 2. P. aeruginosa (no bead beating); 3. M. luteus (1 min bead beating); 4. B. subtilis (1.5 min bead beating); 5. M. phlei (1 min bead beating); 6. M. smegmatis (1 min bead beating); 7. Aerobic laboratory scale reactor (LS1) (3 min bead beating). 3.3.2 Comparison of all methods for nucleic acid isolation 3.3.2.1 Comparison of nucleic acid yields The final nucleic acid yields from each of the extraction methods screened were quantified by gel electrophoresis (Fig. 3.2). In general, higher DNA yields were obtained with those incorporating bead beating as the mechanical lysis step than those employing a milder vortexing step in the presence of beads (i.e. the MO method), or relying totally on chemical lysis (i.e. the TN and OR methods). These findings agree with others from similar studies (Moré et al., 1994; Watanabe et al., 1998; Bourrain et al., 1999; Frostegård et al., 1999; Yu and Mohn, 1999; Bürgmann et al., 2001; Stach et al., 2001; Lakay et al., 2007; Guobin et al., 2008; Vanysacker et al., 2010). Of those where bead beating was incorporated, lowest DNA yields were obtained with the YM extraction method despite it having the longest beating time (Fig. 3.2). This may have resulted from the presence of SDS in the lysis buffer
105 contributing to the formation of stable foam that reduced the effectiveness of the bead beating process. Inclusion of silicone antifoam in this lysis buffer, as included in the MI method lysis buffer, may assist here. 3.3.2.2 Comparison of nucleic acid purity The purity of the nucleic acids obtained with each method was assessed spectrophotometrically (Table 3.4). It is important to note that purification steps were standardised for each method, mainly to ensure that comparisons of the efficiency of the cell lysis step could be made, but also to remove superfluous steps from the original protocols. The purity of DNA extracted with the CR, GR, MV and OR methods was higher than that obtained with the other extraction protocols. However, all nine methods yielded DNA sufficiently free of inhibitors to allow subsequent PCR and restriction enzyme digestion (Table 3.4, data not shown). Equally, the RNA obtained was suitable for use in RT PCR reactions (Table 3.4). The only exception was the CS extract from FS1, where an unidentified inhibitor of DNase I prevented removal of genomic DNA during RNA purification (data not shown). All extracts appeared to be free of high levels of proteins as none of the extracts gave visible bands on SDS PAGE protein gels (data not shown). 3.3.2.3 Nucleic acid integrity In addition to total yield, the physical integrity of the nucleic acid is a crucial factor in selecting an extraction method, as excessive shearing can limit the ability to amplify large gene regions. Furthermore, excessive DNA shearing may lead to the generation of PCR artefacts, especially chimeras (Liesack et al., 1991). Using LS1 biomass as the source, all non bead beating methods produced high molecular weight DNA (Fig. 3.3) and extracts with 23S:16S rRNA ratios closer to the expected value (Table 3.4). The bead beating methods all resulted in some DNA shearing (Fig. 3.3). Of these protocols, DNA integrity was best with the CS and YM methods, while the other methods showed higher levels of shear damage (Fig. 3.3). It should be noted that even methods with higher levels of damage did not prevent the successful later use of their DNA in PCR, as shown in Table 3.4. 106
Figure 3.2: Semi quantitative comparison of total DNA ■ and RNA ■ yield from each sample source (indicated on x axis) for each extraction method (indicated below the graph). Errors indicate the range obtained with duplicate readings of duplicate samples. LS = LS1.
107
Table 3.4: Summary of comparison data of extraction methods assessed in this chapter CR CS GR MI MV OR TN YM MO General characteristics: Nucleic acid pellet No White White Large clear White Small, Small, white No NA discernable pellet, pellet, solid pellet pellet white pellet pellet discernable pellet small, tight pellet Macroscopic biomass White Brown White Brown White Brown White Brown pellet, Brown after extraction interface pellet interface pellet interface pellet, interface visible flocks pellet, flocks flocks Microscopic biomass Cells Cells Cells Cells Cells Cells Cells Cells Cells after extraction containing containing containing containing containing containing containing containing containing nucleic acid nucleic acid nucleic nucleic nucleic nucleic nucleic acid nucleic acid nucleic acid acid acid acid acid Estimated Riska High Moderate High Moderate High Moderate Very high High Low Estimated time to complete 1h 30m 1h 50m 1h 20m 1h 40m 1h 40m 1h 10m 1h 40m 1h 40m 40m Lab scale sample (LS1): 260/280 nm 2.05 9.26 1.9 2.02 1.7 2.11 2.32 2.06 2.44 (SD 0.01) (SD 10.10) (SD 0.03) (SD 0.02) (SD 0.03) (SD 0.01) (SD 0.07) (SD 0.02) (SD 0.02) 260/230 nm 1.6 0.48 1.69 0.54 1.45 2.14 0.89 1.33 0.78 (SD 0.11) (SD 0.63) (SD 0.04) (SD 0.04) (SD 0.12) (SD 0.03) (SD 1.45) (SD 0.31) (SD 0.83) Ratio 23S:16S 0.67 0.63 0.38 0.61 0.95 1 1.24 1.07 NA (0.54 0.74) (0.53 0.82) (0.26 0.62) (0.49 0.84) (0.78 1.06) (0.97 1.02) (1.2 1.3) (1.02 1.18) PCRb – Dechloromonas + + + + + + + + + (Dech454: 8.9±0.6 %c) PCRb – Defluviicoccus – + – + + – – + – (DF988: 4.3±0.8 %c) PCRb – Accumulibacter + + + + + + + + + (PAOmix: 11.8±1.0 %c) RT PCR + + + + + + + + NA
108
Castlemaine sample WWTP (FS1): PCRb – Defluviicoccus + + + + + – – – – (2.9±0.4 %c) PCRb – Accumulibacter + + + + + + + + + (1.4±0.3 %c) RT PCR + NDd + + + + + – NA Kyneton sample WWTP (FS2): PCRb – Accumulibacter + + + + + + + + + (2.3±0.2 %c) a Risk criteria: Very high = heating toxic chemicals (i.e. phenol, CTAB, DEPC), High = bead beating with toxic chemicals, Moderate = using toxic chemicals, Low = some hazards; b PCR Evaluation: + = band visible, – = no band visible; c qFISH values for target population sample. Error is given as standard error (SE); d Unable to remove DNA; SD = Standard deviation; ND = No data; NA = Not applicable.
109 Detected chimeric sequences contributed 5 and 6 % of the total sequences of clones retrieved in libraries generated from MI and MV DNA extracts, respectively (Fig. 3.5). This proportion is similar to the average proportion of suspected 16S rRNA gene artefact sequences per clone library deposited in the publically accessible NCBI database (9 %) (Ashelford et al., 2006). However, DNA from both the MI and MV methods was severely sheared and no comparison was made with methods showing lower DNA shear damage (Fig. 3.3).
Figure 3.3: Electrophoresis gel of total nucleic acid extracts from LS1 for all extraction methods. Features including genomic DNA and rRNA are indicated. M = molecular weight marker. 3.3.2.4 Effects of homogenisation on biomass integrity Nucleic acid stained biomass was macro and microscopically examined before and after each extraction process to assess subjectively the overall extent of cell lysis. The MO, OR and YM methods each contained visible macroscopic intact flocs after the cell lysis step (Table 3.4). This problem has been reported with other activated sludge samples, suggesting that harsh mechanical lysis may be required when working with such biomass organisation 110 (Watanabe et al., 1998; Bourrain et al., 1999; Yu and Mohn, 1999; Vanysacker et al., 2010). Even methods incorporating mechanical lysis still yielded suspensions containing small numbers of microscopic flocs and small cell aggregates following the lysis stage (examples of post extraction biomass are shown in (Fig. 3.4)). It may be that complete cell lysis is unattainable for nucleic acid extraction from activated sludge samples without unacceptable mechanical shear damage to the nucleic acids, and a compromise between the two is required. A similar problem has been reported for soil and compost samples where some cells appear to escape lysis (Moré et al., 1994; Zhou et al., 1996; Yang et al., 2007; Wu et al., 2009). 3.3.2.5 Assessing cell lysis efficiency using target populations Although comparing DNA yields provides an approximate estimate of relative cell lysis efficiency, yield alone can not be assumed to correlate necessarily with, or reflect meaningfully the biodiversity of the individual populations in any sample (Stach et al., 2001; Gabor et al., 2003; Luna et al., 2006). As intact biomass always remained after the lysis step, the representative nature of the DNA and RNA in the extract was estimated using PCR to target the selected marker populations, whose presence in the sample was then assessed by qFISH (Table 3.4). Three marker populations were used to assess nucleic extraction efficiency. These were Dechloromonas , Defluviicoccus and ‘Accumulibacter’ related groups. Earlier studies had shown that Defluviicoccus and ‘Accumulibacter’ related populations were conspicuously absent, or severely underrepresented in clone libraries generated from DNA extracted from several activated sludge communities, despite FISH data suggesting they were among the numerically dominant populations there (Hesselmann et al., 1999; Crocetti et al., 2000; Wong et al., 2004; Meyer et al., 2006; Ahn et al., 2007; Miyauchi et al., 2007). These two groups were therefore selected as marker populations representing populations in activated sludge from which nucleic acids were more difficult to extract.
111
Figure 3.4: Micrographs of SYBR Gold© stained post extraction LS1 biomass. a. Phase contrast image post CR b. Corresponding field of view fluorescent image. c. Phase contrast of post CS d. Corresponding field of view fluorescent image. e. Phase contrast post GR f. Corresponding field of view fluorescent image. g. Phase contrast of MI h. Corresponding field of view fluorescent image. i. Phase contrast post MV j. Corresponding field of view fluorescent image. k. Phase contrast post OR l. Corresponding field of view fluorescent image. m. Phase contrast post TN n. Corresponding field of view fluorescent image. o. Phase contrast post YM p. Corresponding field of view fluorescent image. q. Phase contrast post MO r. Corresponding field of view fluorescent image. All scale bars represent 10 m.
112 The ‘Accumulibacter’ related organisms were present in all three activated sludge communities examined in this study, contributing 1.4 to 11.8 % of the total biovolume as determined by qFISH. Similarly, the cluster II Defluviicoccus related organisms were found in all biomass samples except FS2, where they contributed up to 4.3 % of the total cell biovolume (Table 3.4). The PCR data showed that only the CS, MI and MV extraction methods were able to extract DNA consistently from the Defluviicoccus related cells in each of the two sludge communities where their presence was confirmed by FISH analysis (Table 3.4). Defluviiccoccus related populations were never detected in DNA extracted with the OR, TN or MO protocols. These results may explain why these dominating populations were not observed in 16S rRNA gene clone libraries generated from DNA extracted using the MO method from the EBPR communities present in LS1 (Ahn et al., 2007) and LS2 (Michael Beer, unpublished data) . When clone libraries were prepared from the LS2 community, with DNA extracts from the MI and MV methods, the extent of representation of ‘Accumulibacter’ was in much closer agreement with the qFISH data (Fig. 3.5. and Table 3.5) than for previous libraries prepared from LS2 with the MO method (never exceeding 20 % (Michael Beer, unpublished data) in communities dominated by ‘Accumulibacter’ (Chapter 6)). A single cluster II Defluviicoccus sequence only was present in the MV DNA clone library, although qPCR analysis suggested that both the MI and MV methods gave similar extraction efficiencies for DNA from these organisms (Table 3.5). Clone libraries generated with DNA obtained with these two methods also revealed a further diversity among the Defluviicoccus related organisms, both having related sequences (Fig. 3.5) that did not belong to any of the three previously described clusters (Wong et al., 2004; Meyer et al., 2006; Wong and Liu, 2007). Their identity is investigated in Chapter 4. Table 3.5: Comparison of FISH quantitative data for LS2 biomass ‘Accumulibacter’ Cluster II Defluviicoccus Methods (PAOmix: 67.7±0.6 %)a (DF1020b: 4.1±0.6 %)a MI MV MI MV Clone library (%) 79c 58d 0c 1.5d qPCR (%) 66±14 72±16 11±3.0 10±2.5 a % of biovolume as determined by FISH; b Selected over the DF988 probe to match qPCR primer coverage; c of 71 clones; d of 65 clones; NA = not assessed. Errors represent SE.
113
Figure 3.5: Maximum likelihood tree of the complete sequences obtained from the LS2, using the MI (red boldface font) and MV (blue boldface font) methods, and selected related sequences (all sequences were at least 1250 bp long). Probe coverage and qFISH data is shown in green and broad phylogenetic allocation is given in purple. For probe details see Table 3.3. Clone frequency for each OTU is indicated in parenthesis. Parsimony bootstrap values are calculated as a percentage of 1000 analysis and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position.
114 In earlier clone libraries prepared from the LS1 biomass, Dechloromonas related organisms were much more frequently seen in the same 16S rRNA gene library than the other two marker populations, both of which were under represented (Ahn et al., 2007). This suggests that Dechloromonas related organisms may be suitable markers for more readily extractable bacterial populations. The qFISH data showed these dominated the LS1 community contributing approximately 8.9 % to the total cell biovolume (Table 3.4). All the methods examined in this current study extracted PCR amplifiable levels of DNA from Dechloromonas related organisms (Table 3.4). Tracking DNA from such easily lysed organisms may indicate if DNA released early in the cell lysis step is damaged subsequently beyond detection (eg. if lysis process is too harsh), or lost through binding to the sample matrix (Frostegård et al., 1999). FISH is shown here to be an attractive method for assessing DNA/RNA extraction efficiency, as the population abundance data it provides is independent of the nucleic acid extraction process. It allows any chosen marker populations in a sample to be quantified directly, and thus avoids the need to use broad marker groups, or to make untested assumptions as to which populations might be present, as has been adopted in other community based studies (Kuske et al., 1998; Yeates et al., 1998; Bürgmann et al., 2001; Gabor et al., 2003; Roh et al., 2006). Monitoring indigenous populations is also considered to be superior to the more common practice of monitoring exogenous organisms ‘spiked’ into samples (Picard et al., 1992; Kuske et al., 1998; Frostegård et al., 1999; Schneegurt et al., 2003; Kauffmann et al., 2004; Klerks et al., 2006; Lakay et al., 2007; Herthnek et al., 2008) as the latter would not be associated with the ‘local’ sample matrix (Zhou et al., 1996) which in this case is heavily encapsulated activated sludge flocs. The benefit of FISH is also emphasised when DGGE profiles were used to compare DNA extraction methods. For example, the MI method extracted DNA from the important cluster II Defluviicoccus GAO, while the MO method could not. Yet a visual comparison of the DGGE profiles for all these DNA extracts show few obvious qualitative differences (Fig. 3.6).
115
Figure 3.6: DGGE of DNA extracts from the FS1 sample using several methods. See Section 3.3.2 for explanations of abbreviations.
116 Using profiling techniques as bases for comparing outcomes of different nucleic acids extraction protocols to a community with low diversity, dominated by a single phylotype that is recalcitrant to extraction (ie. ‘Accumulibacter’ in LS2), may also imply misleadingly, that a higher diversity than is actually present is obtained if there is a high diversity among the less dominant over represented populations present. Thus such a method might be chosen over those achieving more representative DNA/RNA extraction. These community fingerprint profiling methods are applied frequently in assessing the relative merits of different DNA extraction methods (Duarte et al., 1998; Krsek and Wellington, 1999; Griffiths et al., 2000; Niemi et al., 2001; Gabor et al., 2003; de Lipthay et al., 2004; Fortin et al., 2004; Desai and Madamwar, 2006; Dong et al., 2006; Yang et al., 2007; Guobin et al., 2008; Mitchell and Takacs Vesbach, 2008; Sheu et al., 2008; Feinstein et al., 2009; Tang et al., 2009; Thakuria et al., 2009; Ariefdjohan et al., 2010; Bey et al., 2010; Cabrol et al., 2010; Đnceoğlu et al., 2010; Vanysacker et al., 2010), but data presented here again emphasise the dangers in relying completely on this approach for assessing extraction method suitability. 3.3.2.6 Application of these observations The duration of, and environmental controls associated with each extraction method were also assessed (Table 3.4), as both are important in selecting a protocol suited for high throughput analysis. Extraction time is also critical especially with RNA extractions, given the typically short half life of bacterial mRNA (Kaberdin and Bläsi, 2006). All protocols in the study with the exception of the MO method, take approximately 100 min to complete. Both the CR and MV methods incorporate bead beating with phenol chloroform, while the TN method requires the use of hot phenol chloroform, which increase their OH&S risks, given their toxicity. 3.4 Conclusions The results presented here suggest that the best methods for obtaining the most representative nucleic acids from activated sludge bacterial communities are the MI and MV protocols. The MV method generates nucleic acid of higher purity, yield and integrity than the MI method, but uses phenol in the bead beating step. The lower quality of the nucleic acids obtained with the MI method does not appear to prevent their use in downstream molecular applications, and it may be better suited for high throughput
117 processing. Because of variations in floc size, density, and populations occurring in different activated sludge communities, it is recommended that several different validated methods are used in parallel and the extracted nucleic acids pooled if optimal biodiversity is to be captured, as has been recommended by Juretchsko et al. (2002). The failure of most of the methods applied here to isolate DNA consistently from the marker populations in these samples suggests a need to validate the nucleic acid isolation protocols used in all molecular ecological studies. The lack of sequence data currently available for the alphaproteobacterial GAO is due in part from choosing inappropriate DNA isolation methods. Application of the efficient methods described here will help in extending the known phylogenetic diversity of this important group, as Chapter 4 shows.
118 4.0 Elucidating further phylogenetic diversity among the Defluviicoccus related GAO in activated sludge
4.1 Introduction The microbial ecology of activated sludge communities removing P by the process of EBPR has become clearer with the application of molecular methods (Wagner and Loy, 2002; McMahon et al., 2010). There is considerable experimental data now available to support the proposition that these processes fail because the organisms responsible, known collectively as the PAO, are out competed under some operational conditions by populations possessing the GAO phenotype (Oehmen et al., 2007). The GAO accumulate substrates in the anaerobic feed stage of EBPR systems, which, as with the PAO, are used for synthesis of PHA. Then in the aerobic famine stage, while the PAO respire their PHA stores using the energy to assimilate P and synthesise intracellular polyP, the GAO use their stored PHA for glycogen production (Seviour et al., 2003; Oehmen et al., 2007). At least two main groups of GAO are now thought to exist in EBPR communities, the gammaproteobacterial ‘Competibacter’ (Crocetti et al., 2002) and the alphaproteobacterial GAO (Beer et al., 2004; Wong et al., 2004; Meyer et al., 2006). Kong et al. (2002) recognised seven ‘Competibacter’ sub groups, and by FISH MAR, revealed physiological differences among them (Kong et al., 2006). Analysis of the currently available sequences reveals further phylogenetic diversity, not covered by these existing probes (Chapter 2) from their earlier application to EBPR communities (Kong et al., 2006; Wong and Liu, 2006). Much less is known of the extent of phylogenetic diversity among the alphaproteobacterial GAO. Amaricoccus sp. were the first suggested competitors of the PAO proposed (Maszenan et al., 1997), although they were shown not to accumulate substrates like acetate anaerobically in pure culture (Falvo et al., 2001), an essential metabolic feature of populations which might compete with the PAO for substrates in the feed stage of EBPR systems (Seviour et al., 2003). Beer et al. (2004) applied the full 16S rRNA cycle approach to identify putative members of the Sphingomonas related GAO in situ in a lab scale reactor dominated by FISH probed alphaproteobacterial TFO and with poor EBPR capacity. Application of probes designed against this sequence gave a positive hybridisation with the dominant TFO, and histochemical staining and chemical profiles
119 showed that these organisms behaved according to the proposed GAO models (Beer et al., 2004). Wong et al. (2004) also proposed from their 16S rRNA clone library data that putative alphaproteobacterial GAO were related to Defluviicoccus vanus, with which they formed a monophyletic group (cluster I). Then Meyer et al. (2006) obtained additional 16S rRNA sequences also related to D. vanus, forming a second cluster (II) with 97 % similarity between its members and 90 % similar to the cluster I members of Wong et al. (2004). A third Defluviicoccus ‘cluster’ was proposed (Wong and Liu, 2007) based on a single 16S rRNA sequence only from a non EBPR system treating pharmaceutical waste. However, a FISH probe targeting this sequence is currently not available and so the importance of members of this cluster in EBPR systems is unknown. Further phylogenetic diversity probably exists among the alphaproteobacterial GAO (Kong et al., 2006; Wong and Liu, 2007), since Oehmen et al. (2006b) showed only 16 % of the total biomass community of an EBPR SBR reactor, almost all of which consisted of alphaproteobacterial TFO, hybridised with the existing FISH probes designed to target known TFO Defluviicoccus members. A cluster of sequences related to Defluviicoccus vanus, but forming their own phylogenetic cluster separate from the three previously described clusters (Wong and Liu, 2007), was also reported in Chapter 3. Unexpectedly the DF988 probe, designed to target the cluster II Defluviicoccus members also targeted a filamentous population with the ‘Nostocoida limicola II’ morphotype in biomass from a full scale EBPR plant in Bendigo Australia (unpublished data). Excessive proliferation of organisms with this morphotype is associated with settling or ‘bulking’ problems in activated sludge (reviewed by Wanner et al., 2010), and not them behaving as GAO competitors of the PAO. As mentioned previously, this DF988 FISH probe was designed to target organisms with the TFO morphotype, and its binding to filamentous organisms has never been reported previously. This study sought to resolve whether binding of the DF988 FISH probe to the ‘Nostocoida limicola II’ filaments reflected a further unknown phylogenetic diversity among cluster II of the Defluviicoccus related GAO and/or if this was a problem of DF988 probe specificity. Furthermore, all currently available Defluviicoccus related 16S rRNA sequences, including those obtained in the work described in Chapters 2 and 3,
120 were analysed to determine whether they might reveal any previously unsuspected biodiversity among this group. New FISH probes could then be designed and applied to determine their distribution and ecophysiology in full scale EBPR systems and thus their potential operational importance there. 4.2 Materials and methods 4.2.1 Biomass samples Activated sludge samples were taken from a number of full scale systems located on the east coast of Australia, whose details are given in Table 4.1. These were sent by overnight courier and fixed for FISH as soon as possible in either 4 % PFA [w/v] or 50 % ethanol [v/v] as described in Section 2.2.2 and stored at 20 °C. The LS2 sample was taken from a SBR operated under alternating aerobic: anaerobic conditions and fed with acetate as the sole carbon source (75 mg l 1 carbon). Further operational details can be found in Chapter 6. Aurantimonas ureilytica 5KACC 11607T was supplied by Soon Wo Kwon (Korean Agricultural Culture Collection (KACC)) and grown on R2A media (Appendix 5) at 28 °C by Dr Steve Petrovski (La Trobe University, Bendigo). 4.2.2 Clone library construction The clone libraries in this chapter were constructed by Dr Tadashi Nittami (La Trobe University). DNA was extracted from ethanol fixed biomass, from the Bendigo EBPR plant, using the MI (Section 3.2.2.1) and MV methods (Section 3.2.2.5) described in Chapter 3, and the commercially available FastDNA SPIN Kit (Qbiogene). The DNA extracts were stored at 20 °C.
121 Table 4.1: Details of activated sludge plants sampled in this study Plant location1 Configuration2 Wastewater type EBPR plants Ballarat, VIC Johannesburg Domestic Bendigo, VIC MUCT Domestic Castlemaine, VIC MUCT Domestic/industrial (meat processing plant) Coolum, QLD Oxidation ditch Domestic Dalby. QLD Multimodal Domestic/industrial Kyneton, VIC MUCT Lutzack Ettinger Domestic Maroochydore, QLD 5 stage Bardenpho Domestic (95 %)/industrial Merrimac, QLD MUCT Domestic Morpeth, NSW Modified Johannesburg Domestic Nambour, QLD Johannesburg Domestic (80 %)/industrial Non– EBPR plants ATP City West, VIC Modified Lutzack Ettinger Domestic/industrial Boulder Bay, NSW Modified Lutzack Ettinger Industrial Branxton, NSW Oxidation carousal/IDEA Domestic Dora Creek, NSW Conventional extended aeration Domestic Edgeworth, NSW Modified Lutzack Ettinger Domestic/industrial Karuah, NSW IDEA Domestic Port Fairy, VIC Industrial (opium processing waste) Raymond Terrace, NSW Modified Lutzack Ettinger Domestic/industrial Shortland, NSW Modified Lutzack Ettinger Domestic/industrial Tanilba Bay, NSW Modified Lutzack Ettinger Domestic Toronto, NSW Modified Lutzack Ettinger Domestic Paper mill wastewater treatment plants (non EBPR) Tasmania, TAS Custom Industrial Albury, NSW Custom Industrial Montreal, Canada Industrial Winery wastewater treatment plants (non EBPR) Wickham Hill Aerated SBR Winery waste (non vintage) Orlando Wines Aerated SBR Winery waste (non vintage) Tasmania Aerated SBR Winery waste (non vintage) 1VIC = Victoria; NSW = New South Wales; QLD = Queensland. 2MUCT = Modified University of Cape Town; IDEA = Intermittently decanted extended aeration; SBR = Sequencing batch reactor. Five PCR reactions were performed with each DNA extract to minimise PCR associated biases. 16S rRNA genes were amplified with primers 27F (5’ GAGTTTGATCMTGGCTCAG 3’) and 1492R (5’ GGYTACCTTGTTACGACTT 3’) (Lane, 1991), under the following conditions, 1 cycle 10 min 95 ºC, 30 cycles (30 s 94 ºC, 30 s 50 ºC, 2 min 72 ºC) and 1 cycle 10 min 72 ºC. Each PCR reaction mixture (50 l) contained 1 l of template DNA, 200 nM of each primer, 200 M of each dNTP, 1 × PCR buffer, 2.5 mM of MgCl2, and 0.025 U/ l of AmpliTaq Gold (ABI). The combined PCR products were purified using QIAquick Gel Extraction kits (Qiagen) and cloned into the pGEM T Easy Vector System II (Promega) as described in Sections 2.2.8.2 2.2.8.5 and insert sequencing and phylogenetic analysis performed as described in Section 2.2.8.6.
122 Only inserts of interest, as determined by their partial sequences (>500 bp), were fully sequenced (see results for criteria). 4.2.3 Cell sorting RT PCR. The RT PCR method described by Levantesi et al. (2004) was used to recover and amplify the 16S rRNA gene from the ‘Nostocoida limicola II’ like filamentous bacteria from a fresh biomass sample from the Bendigo plant by Dr Tadashi Nittami (La Trobe University, Bendigo). More than 30 target filaments of this morphotype were micromanipulated with a Skerman (Skerman, 1968) micromanipulator onto agar plates by Ms Beth Seviour (La Trobe University, Bendigo). These filaments were resuspended in 10 l of sterilised milliQ water and subjected to RT PCR with the Invitrogen SuperScriptTM III One Step RT PCR System with Platinum Taq DNA Polymerase (Invitrogen), exactly as described by Levantesi et al. (2004). Preparation of the resulting 16S rRNA gene clone library was then performed as described above (Section 4.2.2). 4.2.4 FISH analyses FISH was performed as detailed in Section 2.2.3 and the details of probes applied in this chapter are given in Table 4.2. For FISH experiments with the Actino 221 and Actino 658 probes, biomass samples were pre treated with lysozyme (10 mg ml 1 in 0.5M EDTA (pH 8), 1M Tris HCl, (pH 7.5)) at 37 °C for 30 min (Nielsen and Nielsen, 2009). 4.2.5 FISH probe design Probes were designed using the ‘Probe design’ function of ARB (Ludwig et al., 2004). The online software programs Probe Match (Cole et al., 2009) and probeCheck (Loy et al., 2008) were used to assess potential probe 16S rRNA target sites against the RDPII (Cole et al., 2009), SILVA (Pruesse et al., 2007) and Greengenes (DeSantis et al., 2006) databases. BLAST searches using the NCBI database were also performed to screen for the probe sequence in new or partial sequences not yet added to ribosomal RNA gene sequence databases.
123
Table 4.2: FISH probes applied in this chapter. [FA]1 Probe name Sequence (5´ 3´) Target Reference (%) EUB338 I2 GCTGCCTCCCGTAGGAGT Most Bacteria 35 (Amann et al., 1990) EUB338 II2 GCAGCCACCCGTAGGTGT Planctomycetales 35 (Daims et al., 1999) EUB338 III2 GCTGCCACCCGTAGGTGT Verrucomicrobiales 35 (Daims et al., 1999) Non EUB ACTCCTACGGGAGGCAGC Control probe complementary to EUB338 n/a (Wallner et al., 1993) PAO462b3 CCGTCATCTRCWCAGGGTATTAAC ‘Accumulibacter’ 35 (Zilles et al., 2002a) PAO6513 CCCTCTGCCAAACTCCAG Most ’Accumulibacter’ members 35 (Crocetti et al., 2000) PAO846b3 GTTAGCTACGGYACTAAAAGG ‘Accumulibacter’ 35 (Zilles et al., 2002a) GB4 CGATCCTCTAGCCCACT ‘Competibacter’ Group (GB) 35 (Kong et al., 2002a) GB_G1(GAOQ989) 4 TTCCCCGGATGTCAAGGC ‘Competibacter’ Subgroup G1 35 (Crocetti et al., 2002) GB_G24 TTCCCCAGATGTCAAGGC ‘Competibacter’ Subgroup G2 35 (Kong et al., 2002a) ALF968 GGTAAGGTTCTGCGCGTT Alphaproteobacteria, except Rickettsiales 20 (Neef et al., 1999) TFO_DF2185 GAAGCCTTTGCCCCTCAG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) TFO_DF6185 GCCTCACTTGTCTAACCG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) DF9886 GATACGACGCCCATGTCAAGGG Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006) DF10206 CCGGCCGAACCGACTCCC Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006) H966 CTGGTAAGGTTCTGCGCGTTGC Helper probe for DF988 n/a (Meyer et al., 2006) H1038 AGCAGCCATGCAGCACCTGTGTGGCGT Helper probe for DF988 and DF1020 n/a (Meyer et al., 2006) DF988c GCCGCGACGCCCATGTCAAGGG Competitor for DEF988 This study MC2 649 CTCTCCCGGACTCGAGCC Candidatus ‘Monilibacter batavus’ 35 (Snaidr et al., 2002) DF198 ATCCCAGGGCAACATAGTCT Cluster III Defluviicoccus related 35 This study Sub group within cluster III Defluviicoccus DF1004 TAAGTTTCCTCAAGCCGC 35 Dr Tadashi Nittami related DF1004c TAACTTTCCTCAAGCCGC Competitor for DEF1004 Dr Tadashi Nittami DF987H GACGCCCATGTCAAGGGC Helper for DEF1004 Dr Tadashi Nittami DF1021H CCAGCCGAACTGAAGGCT Helper for DEF1004 Dr Tadashi Nittami Sub group within cluster III ‘Defluviicoccus’ DF1013 GAACTGAAGGCTCGAGTTTC 35 50 This study related DF1013c GAACTGAAGGCTTGAGTTTC Competitor for DEF1013 This study DF997H CCCAAGCCGCGACGCCCA Helper for DEF1013 This study DF1032H CCTGTGTGGCGTCCAGCC Helper for DEF1013 This study
124
DF181A CTTTCCCTCACAAGGCAC Clone K42 within cluster IV ‘Defluviicoccus’ 30 This study related DF181B CTTTGCCCCTCAAGGCAC Some cluster IV ‘Defluviicoccus’ related TFO 30 This study AMAR839 CTGCGACACCGAACGGCAAGCC Amaricoccus spp. 20 (Maszenan et al., 2000a) Actino 221 CGCAGGTCCATCCCAGAC Actinobacterial PAO 30 (Kong et al., 2005) C1 Actino 221 CGCAGGTCCATCCCATAC Competitor 1 for Actino 221 n/a (Kong et al., 2005) C2 Actino 221 CGCAGGTCCATCCCAGAG Competitor 2 for Actino 221 n/a (Kong et al., 2005) Actino 658 TCCGGTCTCCCCTACCAT Actinobacterial PAO 40 (Kong et al., 2005) C1 Actino 658 TCCGGTCTCCCCTACCAC Competitor probe 1 for Actino 658 n/a (Kong et al., 2005) C2 Actino 658 ATTCCAGTCTCCCCTACCAT Competitor probe 2 for Actino 658 n/a (Kong et al., 2005) 1[FA] = formamide concentration in hybridisation buffer, 2 6 applied in equimolar amounts as: 2 EUBmix for most Bacteria, 3 PAOmix for ‘Accumulibacter’, 4 GBmix for ‘Competibacter’, 5 DF1mix for cluster I Defluviicoccus members, 6 DF2mix for cluster II Defluviicoccus members; n/a = not applicable
125 4.2.6 Optimisation of hybridisation conditions for FISH probes Optimal hybridisation conditions for newly designed FISH probes were determined as described by Daims et al. (2005). Potential probes were validated by performing hybridisations, with the biomass containing the target population, at different formamide concentrations, which were increased in 5 % steps from 0 to 60 %. Optimal formamide concentration was selected as the concentration just after fluorescence of the target population started to decrease. For the Defluviicoccus cluster III probes (see later) this was determined subjectively by visual assessment by Dr Tadashi Nittami (La Trobe University, Bendigo). For the Defluviicoccus cluster IV probes (see later), images were captured at each formamide concentration used and the average relative fluorescence determined with Adobe Photoshop 7, based on the fluorescence of at least 100 cells, as described in Schroeder et al. (2009). As pure cultures were not available for this purpose for either cluster III or IV Defluviicoccus members, all cluster III probes were validated using biomass from the Bendigo EBPR plant. The DF181A probe was validated against LS2 reactor biomass (see Chapter 6) and DF181B probe against biomass from the Morpeth (NSW) EBPR plant. 4.2.7 FISH MAR 4.2.7.1 Incubations MAR incubations were performed with the assistance of Dr Tadashi Nittami and Lachlan Speirs (La Trobe University, Bendigo). Samples were taken from the aerobic tanks of three EBPR wastewater treatment plants (WWTPs) on the east coast of Australia: Bendigo, Victoria; Morpeth, New South Wales; Nambour, Queensland. Biomass samples were sent by courier and stored at 4 °C until required. MAR incubations were performed within 48 h of sampling. The MAR protocol was based on the methods of Lee et al. (1999) and Kong et al. (2004), but with some modifications. Each biomass sample was aerated for 1 h at room temperature to remove any residual substrates present and diluted with filtered sludge water from the same plant to give a biomass concentration of 1 mgSS ml 1. Radiolabelled substrates were added to give a total radioactivity of 10 Ci mgSS 1. The following were used: 1 [2,3 3H] aspartic acid, [1(3) 3H] glycerol (MPA), [1 14C]
126 propionic acid (American Radiochemicals Company (ARC)), [1 14C] acetic acid, D [6 3H] glucose, 1 [G 3H] glutamic acid, [9,10(n) 3H] oleic acid and [9,10(n) 3H] palmitic acid (GE Healthcare). Cold substrate was then added to give a total concentration of 2 mM carbon. For incubations under anaerobic or anoxic conditions an additional 2 h pre incubation was included under anaerobic conditions with cold substrate to saturate those capable of anaerobic substrate uptake but unable to store this compound or utilise it for growth. Thus only those with the latter abilities should give a positive MAR signal (Andreasen and Nielsen, 2000). To obtain anaerobic or anoxic conditions, vials were capped and sealed before being flushed sequentially with nitrogen (N2) gas for 10 s and placed under vacuum for 1 min, a process that was repeated three times. Samples were incubated with labelled substrate for 3 h at room temperature (approx. 22 °C) on an orbital shaker at 250 rpm. For anoxic conditions either nitrite (0.5 mM NaNO2) or nitrate
(2 mM NaNO3) was included in the incubations with labelled substrate. Each incubation was repeated in duplicate with two negative controls to ensure silver grain formation was not from chemography: one control excluded labelled substrate, and with the other, biomass was pasteurised by heating to 70 °C for 10 min prior to substrate addition (Lee et al., 1999; Nielsen and Nielsen, 2005). 4.2.7.2 Fixation and storage To terminate the MAR reaction 4 % [w/v] PFA in 1 × PBS was added to the biomass. For anaerobic and anoxic conditions this was injected through the neoprene stopper. After 1.5 h, samples were washed three times with 1 × PBS. An additional two washes with ethanol were necessary for biomass samples incubated with long chain fatty acids (oleic and palmitic acids) given their low aqueous solubility. Following these washes, the biomass was resuspended in 50 % [v/v] ethanol in 1 × PBS and stored at 20 °C until required. 4.2.7.3 Scintillation counting of MAR incubated biomass To assess the adequacy of sample washing during MAR, uptake of substrates in sample incubations and the lack of uptake in negative controls, radioactivity of the samples was assessed using a liquid scintillation counter (Wallac 1450 Microbeta Plus). 100 l of biomass was centrifuged (10 000 x g, 5 min, 22 °C) and the supernatant removed and added to a 96 well microtitre plate (Wallac). The biomass pellet was resuspended in 100 l of sterile distilled water and also added to the plate. An equal volume of Supermix
127 scintillation cocktail (Wallac) was added and the plate shaken for 30 min on a Wallac Delfia Plate Shake mixer (Wallac) before counting. 4.2.7.4 FISH analysis of MAR samples A 40 l aliquot of sample after MAR incubation was placed on a glass cover slip and homogenised gently by pressing against a glass slide. FISH and MAR exposure, using LM 1 Emulsion (GE Healthcare), was then performed as described in Lee et al. (1999), for periods of between 4 and 14 days. A negative MAR signal was recorded if there was no observed formation of silver granules associated with the probe defined group of organisms after a 14 day exposure time. If a substrate was not taken up aerobically it was assumed it probably would not be taken up under the other conditions. Likewise, if anaerobic uptake was demonstrated for a substrate then anoxic uptake was not determined, as it would then be difficult to link any substrate uptake to cells using nitrate or nitrite as their electron acceptor. 4.2.8 Histochemical staining 4.2.8.1 DAPI staining for polyP granule inclusions Intracellular polyP granules were detected using 4’,6 diamidino 2 phenylindole (DAPI) (Kawaharasaki et al., 1999) staining as described essentially in Ahn et al. (2007). FISH was performed as described in Section 2.2.3. Then 20 l of DAPI solution (50 g ml 1) was applied to each well. After 10 min the slide was rinsed in distilled water and dried with compressed air. These slides were then viewed by epifluoresence microscopy (Section 2.2.4). 4.2.8.2 Nile blue A staining for PHA granule inclusions Intracellular PHA granules were detected using Nile blue A (Ostle and Holt, 1982) as described essentially in Ahn et al. (2007). Initially 8 l of fixed cell suspension was added to 6 well Teflon coated microscope slides that had been previously coated in Vectabond (Vector Laboratories) and air dried. These slides were then immersed in Nile blue A solution (100 mg l 1 in 96 % [v/v] ethanol) for 10 min at 55 °C, rinsed in 8 % [v/v] acetic acid (in absolute ethanol) for 1 min and then rinsed in distilled water and dried with 128 compressed air. They were then viewed with epifluoresence microscopy (Section 2.2.4) using the G2 A filter set and the locations of cells of interest recorded. Following microscopy, cells were de stained by immersing the slides in 96 % [v/v] ethanol overnight. FISH was then performed as described in Section 2.2.3. 4.2.9 PHA cycling experiments To determine the ability of Defluviicoccus members to carry out aerobic/anaerobic cycling of polyhydroxyalkanoates (PHAs), a fresh sample of biomass was taken from the aerobic tank of the EBPR treatment plant in Bendigo, Victoria, Australia. The biomass was aerated overnight to exhaust any residual carbon sources and stored PHA. Then 800 ml of biomass was washed twice, resuspended in the same volume of a synthetic wastewater (Ahn et al., 2009) and stirred in a 1 l Schott bottle sparged with N2 (to create an anaerobic environment) for 1 h. Sodium acetate was then added to give a total 1 concentration of 75 mg l carbon. After 4 h, N2 sparging was replaced with compressed air, and the sample incubated for a further 7 h. Samples were taken every hour and any PHA storage determined by Nile blue A staining (Section 4.2.8.2) and measurement of acetate uptake. Samples for residual acetate determination were sent to the Advanced Wastewater Management Centre (University of Queensland, Australia) for analysis by high performance liquid chromatography (HPLC). 4.2.10 Enzyme labelled fluorescence (ELF) ELF was used to visualise exoenzymic activity as described by (Nielsen et al., 2010a) with up to a 4 h incubation used for each substrate. These analyses were performed on fresh biomass samples from the aerobic tank of the Bendigo EBPR plant. The following enzymic activities were evaluated using ELF 97 substrates (Molecular Probes): ELF 97 N acetylglucosaminide for chitinase/N acetylglucosaminidase; ELF 97 acetate for esterase; ELF 97 β D galactopyranoside for β D galactosidase; ELF 97 β D glucuronide for β D glucuronidase; ELF 97 palmitate for lipase; ELF 97 phosphate for phosphatase. In theory, hydrolysis of these commercially available fluorescent substrates releases an attached fluorescence quencher molecule and fluorescent breakdown products precipitate on the cell surface that are visualised with fluorescence microscopy (Nielsen et al., 2010a) (see Section 2.2.4).
129 4.2.11 Microsphere adhesion to cells (MAC) MAC assays, for determination of cellular hydrophobicity, were performed on samples from the Bendigo EBPR plant essentially as described by Kragelund et al. (2005), except 2.5 l of the 0.02 % [w/v] sonicated solution of FluoSpheres© fluorescent sulphate microspheres (Molecular Probes) was mixed with 10 l of sludge and 100 l of distilled water. 4.3 Results 4.3.1 FISH survey of Bendigo biomass When samples of biomass from the Bendigo EBPR plant were surveyed for the presence of the alphaproteobacterial Defluviicoccus TFO cluster II members, with the DF988 probe originally designed to target these (Meyer et al., 2006), two Neisser negative ‘Nostocoida limicola II’ filament morphotypes (see Fig. 4.1), differing slightly in their trichome diameters and regularity of cell shape, also fluoresced with it (Fig. 4.2a) and also with the ALF968 probe. However, neither fluoresced with the DF1020 FISH probe designed to target the same Defluviicoccus populations (Meyer et al., 2006), nor the DF218 and DF618 FISH probes for members of the cluster I Defluviicoccus of Wong et al., (Wong et al., 2004). 4.3.2 16S rRNA sequence analyses of the Bendigo biomass A clone library of 75 partial sequences (the first 500 bp) of bacterial 16S rRNA genes from the Bendigo EBPR plant biomass contained nine alphaproteobacterial sequences (data not shown). These fell into two OTUs from which one representative clone of each (A40 and B29) was selected for complete sequencing, and the resulting sequences are included in the phylogenetic tree given in Fig. 4.3. Both sequences were most closely related (98.8 % similarity) to an uncultured bacterial clone mle1 13 (AF280850) recovered from an industrial activated sludge treating pharmaceutical waste (LaPara et al., 2000) and designated as a member of ‘cluster’ III Defluviicoccus by Wong and Liu (2007). These sequences also clustered with clones MC2 (AY428763), Candidatus ‘Monilibacter batavus’ (AY590701) and MU073 (AM157605) (Fig. 4.3). 130 Sequences of A40 and B29 clones were then screened for the presence of the target sequences for the DF988 probe. The MC2 649 probe, designed by Levantesi et al. (2004) against a related partial MC2 sequence (AY428763) and also covering Candidatus ‘Monilibacter batavus’ (AY590701), has a single central mismatch with the 16S rRNA sequences of the A40 and B29 clones (Table 4.3), but the recommended hybridisation stringency (Snaidr et al., 2002) was clearly adequate to ensure these ‘N. limicola’ filament morphotypes did not fluoresce with this probe (data not shown). The 16S rRNA from both A40 and B29 clones had three mismatches with the DF988 probe, as shown in Table 4.3, but these were all terminal of the 22 base oligonucleotide probe. This meant that total complementarity existed with the other 18 nucleotides, and would explain why these filaments fluoresced with it. Thus, a competitor probe (Manz et al., 1992) was designed (DF988c) with a perfect match with the A40 and B29 target sites. Inclusion of this competitor probe in FISH hybridisation with the DF988 probe eliminated probe conferred fluorescence in all ‘Nostocoida limicola’ like filaments in the Bendigo EBPR biomass community (Fig. 4.2). This strongly suggests that the A40 and B29 clone sequences were derived from at least some of these filaments and that DF988 probe specificity problems would explain why these fluoresced. This competitor probe also eliminated DF988 probe conferred fluorescence of all alphaproteobacterial ‘Nostocoida limicola’ like organisms observed in the full scale plant biomass samples surveyed here (see later: Table 4.5), Thus, it is recommended that the DF988c probe always be used in combination with the DF988 probe.
131
Figure 4.1: FISH CLSM micrographs of the ‘Nostocoida limicola’ like, cluster III ‘Defluviicoccus’ (see later), variants present in the Bendigo EBPR plant. a. c. Composite FISH micrographs of the Bendigo EBPR biomass: EUBmix (FLUOS: green) alone = green; DF198 (CY5: blue) + EUBmix (FLUOS) = light blue; DF1013 (CY3: red) + DF198 (CY5:blue) + EUBmix (FLUOS) = white/lavender. d. and e. Composite FISH micrographs of the Bendigo EBPR biomass: EUBmix (FLUOS) alone = green; DF1004 (CY3) + DF198 (CY5) + EUBmix (FLUOS) = white/lavender. e. Includes the corresponding phase contrast image. All scale bars represent 10 m. In these images the thicker more abundant ‘Nostocoida limicola’ variant is indicated with white arrows and the thinner variant with the irregular trichome is indicated with the red arrows. 132
Figure 4.2: FISH CLSM micrographs showing the effect of the DF988 competitor on DF988 binding to the ‘Nostocoida limicola’ like organisms in the Bendigo EBPR plant. a. and b. Phase contrast image and the corresponding FISH image after hybridisation with: a. the DF988 probe (CY3: red) alone and b. the DF988 probe (CY3: red) with the DF988c (unlabelled) competitor probe.
133
Figure 4.3: Maximum likelihood tree of all available complete Defluviicoccus vanus related sequences (>1200 bp) using the ARB software (Ludwig et al., 2004). Sequences obtained in this study are presented in boldface type. Brackets to the far right indicate proposed probe defined phylogenetic clusters. Inner brackets indicate probe coverage (a broken line indicates the absence of sequence information at the probe site). For probe details see Table 4.2. ‘*’ Indicates selected partial sequences (< 1200bp) added using the ‘quick add’ function in ARB after tree construction. Parsimony bootstrap values are calculated as a percentage of 1000 analysis and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position. A more thorough assessment of available probe coverage and specificity is provided in Appendix 2.
134
Table 4.3: Mismatches in target sites between FISH probes and selected 16S rRNA sequences MC2 649 probe 5’ C T C T C C C G G A C T C G A G C C 3’ MC2 649 target 3’ G A G A G G G C C U G A G C U C G G 5’ Clones A40, B29, C17 and C23 3’ G A G A G G G C C U G A G U U C G G 5’ DF988 probe 5’ G A T A C G A C G C C C A T G T C A A G G G 3’ DF988 target 3’ C U A U G C U G C G G G U A C A G U U C C C 5’ Clones A40, B29, C17 and C23 3’ C G G C G C U G C G G G U A C A G U U C C C 5’ DF1013 probe 5’ G A A C T G A A G G C T C G A G T T T C 3’ DF1013 target 3’ C U U G A C U U C C G A G C U C A A A G 5’ Clone mle1 13 (AF280850) 3’ C U U G A C U U C C G A A C U C A A A G 5’ Clones C17 and C23 3’ C U U G A C U U C C G A A U U C A A A G 5’ DF1004 probe 5’ T A A G T T T C C T C A A G C C G C 3’ DF1004 target 3’ A U U C A A A G G A G U U C G G C G 5’ ‘Monilibacter’ (AY590701) 3’ A U U G A A A G G A G U U C G G C G 5’ DF181A probe 5’ C T T T C C C T C A C A A G G C A C 3’ DF181A target 3’ G A A A G G G A G U G U U C C G U G 5’ Clone ZW 19 (GU390332) 3’ G A A A G G G A G U A U U C C G U G 5’ Other cluster IV members 3’ G A A A C G G G G A G U U C C G U G 5’ DF181B probe 5’ C T T T G C C C C T C A A G G C A C 3’ DF181B target 3’ G A A A C G G G G A G U U C C G U G 5’ Aurantimonas ureilyticaT 3’ G A A A C G G G G A G U C C C G U G 5’ Clone ZW 19 (GU390332) 3’ G A A A G G G A G U A U U C C G U G 5’ AMAR 839 probe 5’ C T G C G A C A C C G A A C G G C A A G C C 3’ AMAR 839 target 3’ G A C G C U G U G G C U U G C C G U U C G G 5’ Clone K42 3’ G A C G C U G U G G C U U C C C G U U C G G 5’ Target sequences mismatches are in grey boxes and italicised.
135 4.3.3 Probe design against clones of interest Two new probes (DF198 and DF1013) were designed against the A40 and B29 clones. Probe DF198 targets the 16S rRNA sequences of all members of the cluster III Defluviicoccus including clones A40 and B29, while probe DF1013 targets only the A40 and B29 clone sequences (Fig. 4.3). Two helper probes (Fuchs et al., 2000), DF997H and DF1032H were designed to use with the DF1013 probe as it targeted a region thought to be relatively inaccessible to FISH probing (brightness region class VI (Fuchs et al., 1998)). Of these helpers, DF997H increased the fluorescence signal strength of the DF1013 probe as assessed subjectively by eye, although the addition of the DF1032H did not noticeably improve it. A competitor probe, DF1013c, was also designed to reduce the possibility of the DEF1013 probe binding to the mle1 13 (AF280850) clone 16S rRNA sequence, which has a single mismatch with it (Table 4.3). FISH analysis with the DF198 probe and Bendigo EBPR biomass sample showed that both morphological variants of this ‘Nostocoida limicola’ filament fluoresced with it (Fig. 4.1). On the other hand, only the thinner and less regular ‘Nostocoida limicola’ filaments present in much smaller numbers fluoresced with the DF1013 probe (Figs. 4.1a c.). These results suggest that while the dominant ‘Nostocoida limicola’ in Bendigo samples is a member of the same cluster, it is phylogenetically distinct from the A40 and B29 clones. A corresponding sequence was not recovered in the clone library, suggesting either DNA extraction problems, despite efforts to avoid this commonly encountered problem (Chapter 3), or PCR associated biases (von Wintzingerode et al., 1997). Hence its identity was sought by a different approach. 4.3.4 Retrieval of 16S rRNA sequences by micromanipulation and RT PCR of the dominant ‘Nostocoida limicola’ morphotype in the Bendigo WWTP The flow cytometry method applied in Chapter 2 was thought inappropriate given the probable difficulties in sorting filamentous organisms by FACS (Müller and Nebe von Caron, 2010). Therefore the micromanipulation cell sorting RT PCR method was applied to recover the dominant ‘Nostocoida limicola’ filaments from a Bendigo biomass sample, and another clone library was generated. Partial sequences (> 500 bp) of 13 clones revealed that six (designated C17, C23, C38, C41, C43, and C49) had the DF988 probe target
136 sequence, all with the same terminal three nucleotide mismatches (Table 4.3). Five of these (C17, C23, C38, C41 and C49) had two mismatches with the DF1013 probe target site (Table 4.3) while C43 was 99.6 % similar to the clone A40 sequence obtained earlier (see Section 4.3.2). These features were consistent with the FISH data for the dominant ‘Nostocoida limicola’ morphotype. As four of the five clones (C17, C38, C41 and C49) had identical partial sequences, only clones C17 and C23 were fully sequenced. Both fell into the same cluster as clones A40 and B29 (Fig. 4.3) (sharing 98.6 98.9 % sequence similarity). They share the same marker sequences with the A40 and B29 clones at the target sites for the DF988, MC2 649 and DF198 FISH probes. Thus, a new probe, DF1004 that targeted only the C17 and C23 sequences was designed. Because of possible accessibility problems again with the target site (Fuchs et al., 1998), helper probes DF987H and DF1021H were also designed. Of these, DF987H improved the fluorescence signal of the DF1004 probe but application of the DF1021H probe seemed unnecessary. A competitor probe, DF1004c, was also designed to reduce any likelihood of the DF1004 probe binding to the Candidatus ‘M. batavus’ clone (AY590701), which contains just a single mismatch with it (Table 4.3). When this DF1004 FISH probe was applied together with the helpers and competitor to a sample from the Bendigo plant, only the dominant ‘Nostocoida limicola’ filaments hybridised with it (Fig. 4.1d and e.), suggesting that the C17 and C23 clones contained the sequences from this filament. 4.3.5 Ecophysiology of cluster III Defluviicoccus in the Bendigo EBPR plant The ecophysiology of both morphological filamentous variants of the cluster III ‘Nostocoida limicola’ Defluviicoccus, shown to dominate the Bendigo EBPR biomass community, was examined with FISH MAR. The co dominant filament in this plant was the ‘Chloroflexi’ Eikelboom type 0092 (Speirs et al., 2009), and no filaments in the sample responded positively to the MC2 649 probe designed to cover the group containing Candidatus ‘M. batavus’ (Snaidr et al., 2002; Levantesi et al., 2004). The data obtained are presented in Table 4.4. Both filament variants, targeted by FISH probes DF1004 and DF1013 respectively, showed qualitatively identical substrate assimilation patterns, although slight quantitative differences between them were apparent (Table 4.4). The results show that these filaments assimilated acetate and propionate strongly under both aerobic and anaerobic conditions (Figs. 4.4a d. and 4.5a d.; Table 4.4), while weak
137 glutamate assimilation was also apparent in some Defluviicoccus filaments under these conditions (Fig. 4.6a. d.; Table 4.4). No aerobic uptake of glucose, glycerol, aspartate, glycerol, oleate and palmitate was detected (Table 4.4). Consequently, assimilation of these substrates under the other conditions was not examined. 4.3.6 Ecophysiology of cluster III Defluviicoccus in other Australian EBPR plants. FISH MAR data were also obtained for FISH probed Defluviicoccus cluster III filaments covered by the DF1013 probe, in two other geographically separate EBPR plants, one in Morpeth, NSW and the other in Nambour, Queensland (Table 4.4). Even though it was highly abundant in the Nambour plant in the survey work carried out earlier in this study (presented later: Table 4.5), the low abundance of the DF1004 defined phylotype in biomass samples taken for the MAR analyses and heavy attached epiphytic growth meant unequivocal substrate assimilation data for this phylotype was difficult to obtain. Table 4.4: Summary of FISH MAR data Biomass Morpeth Nambour Bendigo WWTP Source WWTP WWTP Probe DF1004 DF1013 DF1013 e acceptor O2 none O2 none O2 none O2 none Acetate + + + + + + + + Aspartate – n/a – n/a – n/a – n/a Glucose – n/a – n/a – n/a – n/a Glutamate + +/– + +/– +/– – +/– – Glycerol – n/a – n/a – n/a – n/a Oleate – n/a – n/a – n/a – n/a Palmitate – n/a – n/a – n/a – n/a Propionate + + + + + +/– + +/– + = Strong positive MAR signal; +/– = Weak positive MAR signal; – = Negative MAR signal; n/a = Not assessed; e acceptor = Electron acceptor; O2 = aerobic; none = anaerobic Generally the MAR data from both plants for the DF1013 filament phylotype agreed with those obtained for both phylotypes in the Bendigo biomass samples. Thus, acetate and propionate were taken up under both aerobic and anaerobic conditions (Figs 4.4e. h. and 4.5 e. h.; Table 4.4), and again there was no aerobic assimilation of glucose, glycerol, aspartate, glycine, oleate or palmitate (Table 4.4).
138
Figure 4.4: FISH MAR micrographs measuring 14C labelled acetate assimilation of cluster III Defluviicoccus. a. and b. Composite FISH images, and corresponding brightfield MAR images, with the EUBmix probe (FLUOS: green) and the DF1004 probe (CY3: red). EUBmix alone = green, EUBmix (FLUOS) + DF1004 (CY3) = yellow. c. and h. FISH MAR images with the EUBmix probe (FLUOS: green) and the DF1013 probe (CY3). EUBmix alone = green, EUBmix (FLUOS) + DF1013 (CY3) = yellow. a., c., e. and g. Aerobic conditions; b., d., f. and h. Anaerobic conditions. a. and b. Bendigo EBPR plant; e. and f. Morpeth EBPR plant; g. and h. Nambour EBPR plant. Silver grain deposition (bright field images (to the right of FISH image)) indicates positive uptake for associated cells. All scale bars represent 10 m.
139
Figure 4.5: FISH MAR micrographs measuring 14C labelled propionate assimilation of cluster III Defluviicoccus. a. and b. Composite FISH images, and corresponding brightfield MAR images, with the EUBmix probe (FLUOS: green) and the DF1004 probe (CY3: red). EUBmix alone = green, EUBmix (FLUOS) + DF1004 (CY3) = yellow. c. and h. FISH MAR images with the EUBmix probe (FLUOS: green) and the DF1013 probe (CY3). EUBmix alone = green, EUBmix (FLUOS) + DF1013 (CY3) = yellow. a., c., e. and g. Aerobic conditions; b., d., f. and h. Anaerobic conditions. a. and b. Bendigo EBPR plant; e. and f. Morpeth EBPR plant; g. and h. Nambour EBPR plant. Silver grain deposition (bright field images (to the right of FISH image)) indicates positive uptake for associated cells. All scale bars represent 10 m.
140
Figure 4.6: FISH MAR micrographs measuring 3H labelled glutamte assimilation of cluster III Defluviicoccus. a. and b. Composite FISH images, and corresponding brightfield MAR images, with the EUBmix probe (FLUOS: green) and the DF1004 probe (CY3: red). EUBmix alone = green, EUBmix (FLUOS) + DF1004 (CY3) = yellow. c. and h. FISH MAR images with the EUBmix probe (FLUOS: green) and the DF1013 probe (CY3: red). EUBmix alone = green, EUBmix (FLUOS) + DF1013 (CY3) = yellow. a., c., e. and g. Aerobic conditions; b., d., f. and h. Anaerobic conditions. a. and b. Bendigo EBPR plant; e. and f. Morpeth EBPR plant; g. and h. Nambour EBPR plant. Silver grain deposition (bright field images (to the right of FISH image)) indicates positive uptake for associated cells. All scale bars represent 10 m.
141 While aerobic glutamate uptake was positive for the DF1013 probed filaments in samples from both plants, it was not assimilated anaerobically, and subsequently anoxically (Fig 4.6e. h.; Table 4.4). This discrepancy can probably be attributed to the generally lower metabolic activity of filaments in these samples compared to those from the Bendigo plant, rather than basic physiological differences, since both acetate and propionate activity appeared lower by comparison as adjudged by intensity of silver grain deposition on the cells. Furthermore, detection of any anaerobic uptake of glutamate in the Bendigo sample required 14 days exposure. 4.3.7 Carbon cycling by cluster III Defluviicoccus The irregular positive staining reaction for PHA production by these filaments in full scale plants (approx. 20 % only of the FISH positive filaments: data not shown) was examined further. The data obtained clearly showed that acetate was taken up by the filaments anaerobically with a subsequent increase in their PHA content, as judged by Nile blue A staining intensity (Fig 4.7a and b.) in filaments responding to the cluster III Defluviicoccus probe DF1004. Then during subsequent incubation under aerobic conditions, cellular Nile blue A fluorescence intensity gradually decreased (Fig. 4.7 b and c.), consistent with PHA levels within the cells decreasing. Intracellular polyP granules were never observed in cells examined throughout the profile or in any full scale EBPR communities (data not shown). 4.3.8 MAC and ELF analysis MAC analysis showed the filament had relatively low cell surface hydrophobicities, with very few of the microspheres attaching to the filament, and mainly associated with their surface epiphytic growth (Fig 4.7d.). Application of the ELF substrates to a fresh biomass sample from the Bendigo EBPR plant also showed that none of the substrates gave a positive result associated with the dominant ‘Nostocoida limicola’ filaments responding to the Defluviicoccus DF1004 probe (Fig. 4.7e.).
142 4.3.9 Further phylogenetic diversity among Defluviicoccus related organisms Phylogenetic analysis of the 16S rRNA gene sequences related to Defluviicoccus revealed some of these, including those obtained from the work described in Chapter 3, failed to group with any existing sequences of the three previously described Defluviicoccus clusters (Fig. 4.3). These were clones, embraced by OTU K42 (EU834757), which together formed a separate fourth cluster (cluster IV) grouping with 16S rRNA sequences retrieved from a range of environments, including activated sludge, urban aerosols, sediments and oral plaques (Fig. 4.3). All members of the cluster shared at least 96.7 % sequence similarities. To help elucidate the occurrence and function of these novel members of cluster IV Defluviicoccus in EBPR communities, two FISH probes were designed to cover this group; probe DF181A covering the K42 clone, and probe DF181B embracing the remaining members (showing at least 98.6 % sequence similarity to each other). The closest non target matches to the DF181A probe all contain three internal mismatches to it. With no pure cultures available, the probe was validated against the EBPR SBR community from where the K42 sequence originated (Chapter 3), where it hybridised to small cocci forming tight clusters within the flocs (Fig. 4.8a and b.). The DF181B probe was validated against biomass from the Morpeth (NSW) EBPR plant biomass and Aurantimonas ureilytica 5KACC 11607T (DQ833810). The latter, and several other sequences available in public databases, contain a single base mismatch with the target sequence of this probe (see Table 4.3). A competitor probe (DF181Bc) was designed to preclude cells with this mismatch, although at 30 % [v/v] formamide level, the fluorescence signal from Aurantimonas ureilytica cells was barely detectable (Appendix 4). When the DF181A and DF181B probes were applied to full scale plant biomass samples they always hybridised with cells arranged in distinctive tetrads (Fig. 4.8c and d.).
143
Figure 4.7: Micrographs of the cluster III Defluviicoccus related organisms in the Bendigo EBPR plant. a. c.: Phase contrast images of biomass from the PHA cycling experiment with corresponding Nile blue A stained images: a. Before anaerobic phase; b. End of the anaerobic phase; c. End of aerobic phase. d. Phase contrast image of the biomass and corresponding fluorescence image after MAC bead incubation. e. Phase contrast image and corresponding fluorescence image after ELF 97 acetate for esterase. Target organisms are indicated with an arrow. Scale bars represent 10 m. 144 After completion and publication of this work an additional partial sequence (741 bp) was published in the database. This sequence, obtained from soil (GU390332), grouped with cluster IV members (Fig 4.3), being 97.9 % similar to K42, but has one central mismatch with the DF181A probe target site and four with the DF181B probe (see Table 4.3). 4.3.10 Ecophysiology of cluster IV Defluviicoccus Unfortunately, the cluster IV tetrad forming Defluviicoccus were present at such low abundances in the three plant samples examined for cluster III members (see above) and were always intimately associated with the flocs, that their substrate uptake patterns were not revealed readily by FISH MAR. However, evidence for anaerobic uptake of acetate was demonstrated in the Bendigo sample (Fig. 4.9g and h.), where these organisms were more abundant. Their low abundance also made PHA staining difficult, although cells responding to the DF181A probe in the SBR community (reactor LS2) stained positively for anaerobic PHA storage (Fig. 4.9d f.). PolyP was not detected by staining in DF181A or DF181B positive cells in any full or lab scale EBPR community (Fig. 4.9a c.). 4.3.11 Distribution of Defluviicoccus related organisms in full scale EBPR plants Members of all four Defluviicoccus clusters were common in the full scale activated sludge plant communities analysed here although generally as minor populations only. The clear exception was with the filamentous cluster III members, which dominated communities of two of the EBPR plants (Table 4.5). The two ‘Nostocoida limicola’ filament morphotype variants of cluster III fluoresced with either DF1004 or DF1013 probes, as in the Bendigo plant sample, although their numbers varied, ranging from some in a few plants to very common and excessive in others (Table 4.5). Cluster IV Defluviicoccus members responding to the DF181A probe, designed against a single sequence from a lab scale system, were detected in small numbers only in the Kyneton EBPR plant.
145
Figure 4.8: FISH CLSM micrographs of cluster IV Defluviicoccus related organisms. a. and b. FISH image of the LS2 biomass with the EUBmix, (FLUOS: green), DF181A (CY3: red) and PAOmix (CY5: blue) probes. EUBmix alone (FLUOS) = green; EUBmix (FLUOS) + DF181A (CY3) = yellow/orange; EUBmix (FLUOS) + PAOmix (CY5) = light blue. c. and d. FISH image of the Branxton full scale EBPR plant with the EUBmix, (FLUOS: green), DF181B (CY3) and PAOmix (CY5) probes. EUBmix alone (FLUOS) = green; EUBmix (FLUOS) + DF181B (CY3) = yellow/orange; EUBmix (FLUOS) + PAOmix (CY5) = light blue. The corresponding phase contrast image is included for a. and d. All scale bars represent 10 m.
146
Figure 4.9: FISH micrographs for ecophysiological studies of cluster IV Defluviicoccus related organisms. a. c. Kyneton full scale EBPR plant biomass stained with DAPI: a. Phase contrast image; b. FISH image with the DF181A probe (CY3: red); c. DAPI stained biomass image where polyP granules are stained bright yellow. d. f. LS2 biomass stained with Nile blue A: d. Phase contrast image; e. corresponding Nile blue A stained image where PHA granules stain red; f. corresponding FISH image with the DF181A probe (CY5: blue). g. and h. FISH MAR images of Bendigo full scale EBPR plant: g. FISH image with the EUBmix (FLUOS: green) and DF181B (CY3: red) probes. EUBmix (FLUOS) alone = green, EUBmix (FLUOS) + DF181B (CY3) = yellow; h. Corresponding bright field MAR image. All scale bars represent 10 m. 147 Cluster II and III members also dominated in some paper mill waste treatment plants community samples and Defluviicoccus cluster I and II members were abundant in treatment systems treating winery waste (Fig. 4.10a and b.; Table 4.5). Both deal with influents low in P. This suggests that they are not restricted exclusively to EBPR processes, but clearly have other survival strategies allowing them to survive P limiting conditions. 4.3.12 The distribution of Amaricoccus sp. in wastewater treatment systems As the FISH probe (AMAR838) designed to cover members of the genus Amaricoccus (Maszenan et al., 2000b) has only a single base mismatch with Defluviicoccus cluster IV 16S rRNA sequences (clone K24) (Table 4.3), some concerns exist about its specificity. However co application of the probe with the DF181A probe to the LS2 biomass showed that the single base mismatch with Defluviicoccus cluster IV sequences is sufficient to discriminate these populations at the recommended formamide concentration of 20 % [v/v] (Fig. 4.10e.). The AMAR839 probe was also applied to the full scale EBPR plant communities surveyed (Table 4.5) where Amaricoccus, like Defluviicoccus related cluster members, were common, but never dominant. These organisms were also abundant in a system treating paper mill waste and dominated another treating winery waste (Fig. 4.10c and d.; Table 4.5), which suggests they might share a similar ecological niche to the Defluviicoccus.
148
Table 4.5: Distribution of GAO and PAO in Australian wastewater treatment plants ’ 4 ’
Defluviicoccus– related GAO2 sp. Plant Location1 5 6 7
2 3 III IV Competibacter I II Accumulibacter ‘ GAO Actinobacterial PAO PAO ‘ DF1013 DF1004 DF198 DF181A DEF18B Amaricoccus EBPR Ballarat, VIC – + – – – – – + ++ ++ ++ Bendigo, VIC + + ++ +++ +++ – + – + ++ + Castlemaine, VIC + ++ – – – – + + + ++ + Coolum, QLD + + ++ ++ ++ – + + + ++ – Dalby. QLD + + ++ ++ ++ – + – + ++ – Kyneton, VIC ++ ++ – – – + + – ++ + + Maroochydore, QLD + + + + + – – + + ++ + Merrimac, QLD + + + + + – – + ++ ++ + Morpeth, NSW + + + + + – + + ++ ++ + Nambour, QLD + + ++ +++ +++ – – – ++ ++ ++ Non– EBPR ATP City West, VIC – + + – + – + – ++ ++ + Boulder Bay, NSW – ++ – – – – + + + ++ + Branxton, NSW – + – – – – + + – – – Dora Creek, NSW + + + – + – – + + + + Edgeworth, NSW + + + + + – – + + + – Karuah, NSW + + + – + – – + + – – Port Fairy, VIC – ++ – – – – + – – – – Raymond Terrace, NSW – + – – – – – + + + – Shortland, NSW + + + + + – – – + + – Tanilba Bay, NSW – + ++ + ++ – + + + ++ + Toronto, NSW – – ++ ++ ++ – + + + ++ + Paper Mill
149
Tasmania, TAS – +++ – – – – – ++ – – – Albury, NSW – ++ – – – – – – – – – Montreal, Canada – – +++ – +++ – + + + – – Winery Waste Wickham Hill ++ + – – – – – – – – – Orlando Wines ++ ++ – – – – – – – – – Tasmania – – – – – – – +++ – – – 1 VIC = Victoria; NSW = New South Wales; QLD = Queensland; 2– 7 FISH defined groups targeted by probes: 2 TFO_DF218/TFO_DF618 (Wong et al., 2004); 3 DF988/DF1020 (Meyer et al., 2006); 4 AMAR 839; 5 GB/GB_G1/GB_G2 (Crocetti et al., 2002; Kong et al., 2002a); 6 PAO46b/PAO651/PAO846b (Crocetti et al., 2000; Zilles et al., 2002a); 7 Actino– 221/Actino– 658 (applied separately) (Kong et al., 2005). Subjective quantification criteria: +++ = very abundant (approx. >25 % of biovolume); ++ = common (approx. 5– 25 % of biovolume); + = some to few (approx. <5 % of biovolume); – = not detected.
150
Figure 4.10: FISH micrographs of putative alphaproteobacterial GAO. a. Phase contrast image of biomass from the Wickham Hill Winery wastewater treatment plant (WWTP) and corresponding composite FISH image: EUBmix (FLOUS: green) alone = green; EUBmix (FLUOS) + DF1mix (CY3: red) = yellow. b. Phase contrast image of the biomass from the Tasmanian paper mill WWTP and corresponding composite FISH image: EUBmix (FLUOS) alone = green; EUBmix (FLUOS) + DF2mix (CY3) = yellow. c. Phase contrast image of biomass from the Tasmanian winery WWTP and corresponding composite FISH image: EUBmix (FLUOS) alone = green; EUBmix (FLUOS) + AMAR839 (CY3) = yellow. d. Biomass from the Tasmanian winery WWTP stained with Nile blue A for PHA inclusions. e. Composite FISH image of the LS2 reactor biomass: EUBmix alone (FLUOS: green) = green; EUBmix (FLUOS) + AMAR839 (CY3) = yellow; EUBmix (FLUOS) + DF181A (CY5) = light blue. All scale bars represent 10 m. 151 4.4 Discussion 4.4.1 Description of clusters III and IV of the Defluviicoccus related organisms This chapter shows that two variants of a Neisser negative alphaproteobacterial ‘Nostocoida limicola’ morphotype dominating the community of EBPR activated sludge biomass are members of the cluster III Defluviicoccus proposed by Wong and Liu (Wong and Liu, 2007). This is the first report of a filamentous member of this group, all of whose earlier members grew as TFOs. Clones possessing or lacking marker sequences elucidated from preliminary FISH analyses could be identified in the generated clone library from the Bendigo WWTP and selected for further examination. By this approach FISH probes could be designed confidently to target both filamentous Defluviicoccus cluster III morphotype variants, and those described here embrace all currently known members of the cluster III Defluviicoccus. Members of cluster III are all phylogenetically very closely related (98.1 to 98.8 % 16S rRNA shared similarity) to the ‘Nostocoida limicola’ filament morphotype Candidatus ‘M. batavus’ which, from the data presented here, must now also be considered a member of the genus Defluviicoccus. Further analysis of available Defluviicoccus sequences also revealed a fourth cluster of this group. FISH probes designed here were applied to show this group exists in EBPR communities as small cocci, often arranged in tetrads, which is similar to the cell arrangement of cluster I and II members (Wong et al., 2004; Meyer et al., 2006). 4.4.2 The ecophysiology of clusters III and IV Defluviicoccus in EBPR systems This study has also shown that members of cluster III Defluviicoccus filaments can assimilate acetate and propionate and accumulate PHA anaerobically, subsequently utilising it aerobically, without any synthesis of polyP. Although comprehensive data could not be obtained for members of cluster IV Defluviicoccus, because of their low abundances in the plant examined, evidence for anaerobic acetate assimilation was also
152 found. These observations, combined with their ability to accumulate PHA and not polyP, support the view that members of both these phylogenetic clusters behave like other Defluviicoccus related organisms (Wong et al., 2004; Meyer et al., 2006; Burow et al., 2007; Wong and Liu, 2007), in having the in situ phenotype expected of GAO. Like other Defluviicoccus related organisms (Wong et al., 2004; Meyer et al., 2006; Ahn et al., 2007; Burow et al., 2007; Wong and Liu, 2007; Schroeder et al., 2008), these FISH probed populations possessed a high affinity for acetate and propionate under both aerobic and anaerobic conditions. None of the other substrates supplied, with the possible exception of glutamate which was taken up at very low rates, could be utilised either aerobically or anaerobically, implying that the Defluviicoccus in activated sludge communities are highly specialised feeders. The lack of any ectoenzyme activity in Defluviicoccus supports this view, since it suggests that these populations are unable to use high molecular weight polymeric substrates. These results are in marked contrast to those reported by Kragelund et al. (2006) for their filamentous Candidatus ‘Monilibacter batavus’ now also shown to be members of cluster III Defluviicoccus. Although they showed similar aerobic substrate assimilation patterns to the cluster III members in this study, ‘Monilibacter’ were reported as having no ability for anaerobic substrate uptake. Consequently its phenotype could not be that of a GAO. A number of other phenotypic differences exist between these. For example, contrary to the data of Kragelund et al. (2006), the filaments in this present study showed no lipase activity with ELF, and stained Neisser negatively. Slight differences in substrate uptake profiles have been reported previously for members of clusters I and II Defluviicoccus related organisms in different activated sludge samples (Burow et al., 2007; Wong and Liu, 2007; Schroeder et al., 2008), but where tested, never a complete inability for anaerobic substrate assimilation. These differences may reflect strain variations, and Kragelund et al. (2006) used FISH MAR on a single biomass sample only. Equally, the MC2 649 probe was designed from a partial sequence from clone MC2 (581 bp) (Snaidr et al., 2002) and has low specificity (Appendix 2). It also targets a number of other alphaproteobacterial sequences and members of the Acidobacteria and Candidatus division SBR1093. Furthermore, many deposited 16S rRNA sequences in the Greengenes database (DeSantis et al., 2006) contain a single, often terminal, mismatch with it. Therefore, the suitability of the MC2 649 probe needs re evaluating before it can be confirmed that such marked differences in ecophysiology exist between such closely related phylotypes.
153 Survey studies also show that the Defluviicoccus related organisms are as prevalent in EBPR and non EBPR plants as other known PAO and GAO (Table 4.5), and can be present in larger numbers, suggesting that they do compete effectively with the PAO. Hence they should not be considered as laboratory curiosities (Burow et al., 2009), but as organisms potentially impacting on both floc settlability and P removal. Their ability to assimilate substrates anaerobically means that the proposal to use anaerobic or anoxic selectors for controlling these alphaproteobacterial ‘Nostocoida limicola’ filaments, based on the data of Kragelund et al. (2006) for their ‘Monilibacter’ MC2 649 defined phylotype, would be unsuitable for both the Defluviicoccus phylotypes studied here. 4.4.3 Distribution of Defluviicoccus GAO in full scale systems Members of all four Defluviicoccus related clusters were common in the full scale plant communities surveyed here, although generally as minor populations only, as reported elsewhere for members of cluster I and II Defluviicoccus in European full scale treatment plants (Burow et al., 2007; Lopez Vazquez et al., 2008a; Nielsen et al., 2010b). The clear exception was with the filamentous cluster III members, which dominated communities of two of the EBPR plants (Table 4.5), and were possibly responsible for the settling/bulking problems in one of these. Interestingly, unlike many experiences with lab scale EBPR reactors (Oehmen et al., 2007), most plant communities contained co existing members of more than one Defluviicoccus cluster (Table 4.5). A similar situation has been reported in a survey of full scale plants for Competibacter GAO phylotypes (Kong et al., 2006), and Accumulibacter PAO (He et al., 2007). This might suggest that each phylotype may be occupying an individual niche, whose numbers are likely to be greater in full scale systems (He et al., 2007). The presence of additional sequences flanking the ‘probed covered’ cluster II sequences (Fig. 4.3) indicates that further diversity among the Defluviicoccus group may exist in EBPR systems. Dominance of Defluviicoccus in samples from all the plants examined treating paper mill and winery wastes (Table 4.5) probably reflects the low P:C ratios of their feeds (Oehmen et al., 2007). The ability of paper mill waste to support the growth of Defluviicoccus related organisms has been reported by Bengtsson et al.(2008) where their potential for producing large amounts of PHA for possible manufacture of bioplastics has attracted interest (Bengtsson et al., 2008; Pisco et al., 2009).
154 4.4.4 Other alphaproteobacterial GAO Amaricoccus spp. were also common but never abundant in full scale systems, probably scavenging the small amount of available carbon in the aerobic zone, given their inability to assimilate carbon anaerobically. They dominated the community of an aerobic winery waste treatment system, where they appeared to accumulate large amounts of PHA (Fig. 4.10d.). In pure culture, prolonged periods of starvation is known to lead to higher levels of PHA storage (Aulenta et al., 2003). This high PHA storage capacity probably provides them with an advantage over most other organisms in being able to survive long starvation periods imposed by the intermittent supply of carbon in this treatment system. This is also clearly a feature of Defluviicoccus metabolism (Wong et al., 2004; Meyer et al., 2006; Dai et al., 2007; Pisco et al., 2009), which may explain their joint presence in these communities. In an earlier survey of Australian full scale EBPR plants by Beer et al. (2006), the Sphingomonas related putative GAO related organisms were observed with the FISH probes designed against them at up to 24 % of the total biovolume. However, their conspicuous absence from communities of full and lab scale EBPR systems examined here and elsewhere (Oehmen et al., 2005a; Meyer et al., 2006; Oehmen et al., 2006a; Oehmen et al., 2006b; Burow et al., 2007; Saunders et al., 2007; Burow et al., 2008a; Lopez Vazquez et al., 2008b; Pijuan et al., 2008), and some of the inconsistencies in the original publication of Beer et al. (2004) (Saunders, 2005) prompted further investigation into this group of organisms. These findings are reported next in Chapter 5.
155 5.0 Resolving the identity of the ‘Sphingomonas related’ putative GAO in EBPR
5.1 Introduction Given the difficulties in culturing most of the microbial biodiversity present on this planet, our understanding of the composition of natural communities has been enhanced considerably by applying rRNA based methods (von Wintzingerode et al., 1997; Hugenholtz et al., 1998; Amann and Ludwig, 2000; Schleifer, 2004; Amann and Fuchs, 2008; Zengler, 2009). One technique that has contributed substantially to this, in both qualitative and quantitative terms, is FISH (Amann, 1995; Amann and Fuchs, 2008). Fluorescently labelled DNA oligonucleotides are hybridised in situ to target sequences contained within the rRNA of the microbial populations of interest, providing information about their morphology, relative abundance and spatial arrangement (DeLong et al., 1989; Amann, 1995; Amann and Fuchs, 2008). In combination with other techniques like MAR, FISH can allow the in situ ecophysiology of a population to be resolved at the resolution of a single cell (Lee et al., 1999; Wagner et al., 2003; Nielsen and Nielsen, 2005). One such group of organisms examined in this way are the GAO, which store glycogen but not polyP, and appear to out compete those thought responsible for EBPR, known generically as the PAO, under certain still poorly understood operational conditions (Oehmen et al., 2007; McMahon et al., 2010). Crocetti et al. (2002) suggested that the phylogenetically diverse gammaproteobacterial ‘Competibacter’ were putative GAO, and 16S rRNA targeted FISH probes in combination with staining for polyP and PHA showed they possessed an in situ phenotype consistent with that of a GAO (Kong et al., 2006). Beer et al. (2004) working in this laboratory also demonstrated by FISH that an alphaproteobacterial population dominated a wastewater community showing poor P removal, and by cloning 16S rRNA PCR DGGE analyses, identified these TFO as members of the genus Sphingomonas. They designed two FISH probes, SBR9 1a and SBR9 1b, to target this population (Beer et al., 2004), and by similar methods to those of Crocetti et al. (2002) showed it too possessed a GAO phenotype in situ.
156 The FISH probe SBR9 1a designed against the 16S rRNA sequence of clone SBR9 1 (AY254694) has since been used in similar studies, but no populations responding to it have been detected in any other EBPR communities (Oehmen et al., 2005a; Meyer et al., 2006; Oehmen et al., 2006a; Oehmen et al., 2006b; Burow et al., 2007; Saunders et al., 2007; Burow et al., 2008a; Lopez Vazquez et al., 2008b; Pijuan et al., 2008). Furthermore, no sequences closely related to this SBR9 1 clone have ever been recovered in clone libraries from other EBPR communities dominated by alphaproteobacterial TFO (Wong et al., 2004; Meyer et al., 2006). Interestingly, Beer et al. (2004) reported that the SPH120 probe, designed to target Sphingomonas spp. (Neef et al., 1999) failed to hybridise to cells responding to the SBR9 1a probe, despite its target site being present in the SBR9 1 16S rRNA clone sequence. This has raised doubts about the probe and/or sequence from which it was designed, and therefore the identity of the alphaproteobacterial TFO present in the communities they analysed (Saunders, 2005). Other Alphaproteobacteria with a GAO phenotype have been identified subsequently. These are all closely related to members of the genus Defluviicoccus, and four phylogenetically different clusters, I, II, III and IV, are currently recognised (Wong et al., 2004; Meyer et al., 2006; Wong and Liu, 2007) (Chapter 4). FISH probes are available for members of all four clusters, and these have revealed repeatedly their presence in communities from laboratory scale and full scale EBPR systems around the world (Wong et al., 2004; 2005a; Meyer et al., 2006; Wong and Liu, 2006; Ahn et al., 2007; Burow et al., 2007; Dai et al., 2007; Lemos et al., 2007; Wong and Liu, 2007; Burow et al., 2008a; Lopez Vazquez et al., 2008a; Burow et al., 2009; Nielsen et al., 2010b) (Chapter 4). The apparent repeated absence of these ‘Sphingomonas related GAO’ in EBPR communities suggested the need to explain this anomaly by clarifying their identity and thus the phylogenetic diversity among the Alphaproteobacteria possessing the GAO phenotype.
157 5.2 Materials and methods 5.2.1 Pure cultures Pure cultures used in this study were obtained from the La Trobe University culture collection, with the exception of Thermus thermophilus which was supplied by Professor Huw Morgan (Waikato University, New Zealand). Cultures were maintained by Dr Steve Petrovski (La Trobe University, Bendigo). Organisms and culture conditions are given in Table 5.1. Table 5.1: Organisms and culture conditions used in this study Culture conditions Organism Media1 Temp Acinetobacter baumannii str. ATCC 19606T R2A 30 °C Gordonia amarae str. DSMZ43392T PYCa 30 °C Gordonia terrae str. DSMZ43249T PYCa 30 °C Rhodococcus sp. J71 PYCa 30 °C Thermus thermophilus str. ATCC 27634T TM2 55 °C Tetrasphaera jenkinsii str. Ben74T R2A 25 °C 1 Media compositions are described in Appendix 5. 2 Thermus media. 5.2.2 DNA extraction DNA was isolated using the MI (Section 3.2.2.1) and MV (Section 3.2.2.5) methods from biomass samples from the same SBR9 reactor community used originally by Beer et al. (2004). 5.2.3 Clone library construction 16S rRNA genes were amplified with universal primers, 27F and 1525R (Table 5.2) as described in Section 2.2.8.1 by Dr Steve Petrovski (La Trobe University, Bendigo). Five PCR reaction products from each extraction method were purified using QIAquick Gel Extraction kits (Qiagen), mixed together and cloned into the pGEM T Easy Vector System II (Promega) as detailed in Section 2.2.8. Ten plasmid inserts were sequenced by AGRF (Brisbane, Australia), and clones arranged into OTUs based on a mutual 99 % similarity of their partial sequences (>500 bp). A representative 16S rRNA sequence of the single alphaproteobacterial OTU representing 9 of these sequences was then fully sequenced. The OTU Contig was assembled using Geneious Pro 3.0.6. The nucleotide
158 sequence reported in this chapter was deposited in the DDBJ/EMBL/GenBank nucleotide sequence databases with the accession number FJ356059. 5.2.4 FISH Cells were fixed in either 4 % [w/v] PFA or 50 % [v/v] ethanol and FISH was carried out as described in Sections 2.2.2 and 2.2.3. The FISH probes applied in this study are detailed in Table 5.2. Formamide dissociation profiles were constructed by measuring the fluorescence intensity of positive cells with Adobe Photoshop™ as detailed in (Schroeder et al., 2009) and curves (base line values set to = 0) and statistics calculated using GraphPad Prism 4. Values were corrected for each profile using a correction factor where the theoretical fitted curve would intersect the Y axis at 100 relative fluorescence units (RFU). Base line fluorescence was set based on the autofluorescence of the same biomass samples hybridised with the NON EUB probe. 5.2.4.1 Pre permeabilisation of cells for FISH Rhodococcus sp. J71 and Gordonia sp. cells were pretreated with lysozyme, achromopeptidase and mild acid hydrolysis, and the FISH hybridisation period extended to 16 h, as detailed in the protocol of Kragelund et al. (2007b), to avoid any problems of false negative FISH results arising from cell wall impermeability. Slides were transferred sequentially through an ethanol series of 50 % [v/v], 80 % [v/v] and 96 % [v/v] for 3 min at each concentration, and allowed to air dry. Lysozyme (20 l: 10 mg ml 1 lysozyme (0.5 M EDTA (pH 8), 1M Tris HCl, (pH 7.5)) was added to each well and slides incubated horizontally in a 50 ml centrifuge tube containing damp paper towel to retain high humidity at 37 °C for 30 min. Slides were immersed in distilled water, followed by ethanol, and allowed to air dry. 20 l of achromopeptidase (60 U ml 1 in 0.01 M NaCl, 0.01 M Tris HCl, (pH 8)) was added to each well and slides again incubated horizontally as above. Slides were again immersed in ethanol and allowed to air dry before 20 l of 0.1 M HCl was added to each well and the slides then incubated at room temperature for 10 min, before being immersed in 96 % [v/v] ethanol for 2 min and allowed to air dry. The FISH hybridisation step was then performed as detailed in Section 2.2.3.
159
Table 5.2: Oligonucleotide sequences used in this study. Oligonucleotide name Sequence (5’ 3’)a Target Organisms/Description Form (%)b Reference 27F GAGTTTGATCMTGGCTCAG Most Bacteria N/A (Lane, 1991) 1525R AAGGAGGTGWTCCARCC Most Bacteria N/A (Lane, 1991) EUB 338 Ic GCTGCCTCCCGTAGGAGT Most Bacteria 35 (Amann et al., 1990) EUB 338 IIc GCAGCCACCCGTAGGTGT Planctomycetales 35 (Daims et al., 1999) EUB 338 IIIc GCTGCCACCCGTAGGTGT Verrucomicrobiales 35 (Daims et al., 1999) NON EUB ACTCCTACGGGAGGCAGC Control probe complementary to EUB338 N/A (Wallner et al., 1993) TFO_DF218 GAAGCCTTTGCCCCTCAG Cluster I Defluviicoccus related organisms 35 (Wong et al., 2004) DF198 ATCCCAGGGCAACATAGTCT Cluster III Defluviicoccus related organisms 35 (Nittami et al., 2009) SBR9 1a AAGCGCAAGTTCCCAGGTTG ‘Sphingomonas related GAO’ 30 (Beer et al., 2004)
SBR9 1a RC CAACCTGGGAACTTGCGCTT ‘Sphingomonas related GAO’ 30 (Beer et al., 2004: erratum) SBR9 1a Comp(a) AAGCGCARTTCCCAGGTTG Competitor for clusters I, III and IV Defluviicoccus related N/A This study organisms SBR9 1a Comp(b) AAGCGCAATTCCCAGGTTG Competitor for cluster I Defluviicoccus related organisms N/A This study SBR9 1a Ins Bulge AAGCGCAATCCCAGGTTG SBR9 1a probe minus an internal base N/A (adapted from Beer et al., 2004) SBR9 1a Comp CAACCTGGGAATTGCGCTT Proposed target for SBR9 1a within cluster I Defluviicoccus N/A (adapted from Beer et Targete related TFO al., 2004) ACA23A ATCCTCTCCCATACTCTA Acinetobacter 35 (Wagner et al., 1994b) ACA23A Del Bulged ATCCTCTCCCCATACTCTA ACA23A probe with additional internal base 35 (adapted from Wagner et al., 1994b) ACA23A Ins Bulge A ATCCTCTCCATACTCTA ACA23A probe minus an internal base (adapted from Wagner et al., 1994b) ACA23A Ins Bulge B ATCCTCTCCCTACTCTAG ACA23A probe minus an internal base extended one base (3’) (adapted from Wagner et al., 1994b) ACA23A Targete TAGAGTATGGGAGAGGAT Target sequence for ACA23A probe N/A (adapted from
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Wagner et al., 1994b) HGC69A TATAGTTACCACCGCCGT Actinobacteria (high G+C Gram positive bacteria) 25 (Roller et al., 1994) HGC69A Del Bulged TATAGTTACTCACCGCCGT HGC69A probe with an additional internal base 25 (adapted from Roller et al., 1994) HGC69A Ins Bulge TATAGTTCCACCGCCGT HGC69A probe minus an internal base 25 (adapted from Roller et al., 1994) HGC69A Targete ACGGCGGTGGTAACTATA Target sequence for HGC69A N/A (adapted from Roller et al., 1994) Gor596 TGCAGAATTTCACAGACGACGC Gordonia 20 (de los Reyes et al., 1997) Gor596 Del Bulged TGCAGAATTTCTACAGACGACGC Gor596 probe with an additional internal base 20 (adapted from de los Reyes et al., 1997) Gor596 Target GCGTCGTCTGTGAAATTCTGCA Target sequence for Gor596 N/A (adapted from de los Reyes et al., 1997) Tth1248 TCCCCGTTGCCGGGTGGC Thermus thermophilus 70 (Byers et al., 1997; Hayes et al., In press) Tth1248 Del Bulged TCCCCGTTGCCCGGGTGGC Tth1248 probe with an additional internal base 70 (adapted from Byers et al., 1997) Tth1248 Targete GCCACCCGGCAACGGGGA Target sequence for Tth1248 N/A (adapted from Byers et al., 1997) Goam192 CACCCACCCCCATGCAGG Gordonia amarae 30 (de los Reyes et al., 1997) a. All oligonucleotides used for FISH (see text) were labelled with either FLOUS (5(6) carboxyfluorescein N hydroxy succinimide ester), CY3 or CY5 (except when included as a competitor, or helper probe) and supplied by Sigma Genosys (Castle Hill, NSW, Australia); b. Concentration of formamide when applied in FISH, N/A = Not applicable; c. Applied in equimolar amounts as EUBmix; d. Additional base is in bold and underlined. e. used for Tm determination experiments (Section 5.2.5).
161 5.2.4.2 In situ RNAse treatment PFA fixed biomass was applied to slides coated with Vectabond (Vector Laboratories, USA) and allowed to air dry. RNAse treatment was performed as detailed in Section 3.2.3.1. FISH was then carried out as in Section 2.2.3. 5.2.5 Dissociation temperature (Tm) determination The melting points (Tm) of sets of DNA/DNA oligonucleotide duplexes were determined from melt curve analysis using an iCycler IQTM Multicolor Real Time Detection System (BIO RAD, Hercules, CA, USA). Each reaction contained 0.9 M NaCl, 20 mM Tris HCl (pH 8.0), 5 × SYBR Green and 8 M of each DNA oligonucleotide in 96 well semi skirted PCR plates (Thermo Scientific), sealed with microseal B adhesive seal (BIO RAD), with a reaction volume of 25 l. Reactions were held at 90 °C for 2 min and then at 40 °C for 1 min. The temperature then increased by 0.5 °C every 10 s until reaching 95 °C. Each oligonucleotide was also tested alone as a control against self association. Each reaction and control run was performed in triplicate. 5.2.6 Thermodynamic stability of single nucleotide insertions and deletions Free energy values (∆G°) for the RNA/DNA duplexes were calculated using the conditions of 1M [Na+] and 37 °C and the nearest neighbour values of Sugimoto et al. (1995). Although these salt and temperature values differ from those typically used in FISH (0.9M Na+, 46 °C), they are within the range of reasonable error for the nearest neighbour model (Lang and Schwarz, 2007). Since no ∆G° values for single nucleotide insertions or deletions in RNA/DNA duplexes have been reported in the literature, the free energy values for RNA/RNA duplexes were used (Znosko et al., 2002), as single nucleotide insertions and deletions display similar free energy penalties for both RNA/RNA and DNA/DNA duplexes (Zhu and Wartell, 1999). Although a free energy model based on empirical values is desirable, given the wide range of hybridisation efficiencies observed in FISH arising from targeted rRNA secondary structure (Fuchs et al., 1998), any deviation resulting from these assumptions should fall within the expected error.
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The ∆G°bulge values were calculated using Equation 1 and 2 for pyrimidine and purine bases, respectively (Znosko et al., 2002).
∆G°bulge = 3.9 + 0.1∆G°nn kcal/mol (1)
∆G°bulge = 3.3 0.3∆G°nn kcal/mol (2)
where ∆G°nn is the calculated free energy of the neighbouring base pair of the two bases flanking the bulge A correction factor of 0.8 kcal/mol was applied for the so called Group II bulges (i.e. those where one of the neighbouring bases is identical to the inserted base) (Znosko et al.,
2002). An additional non nearest neighbour correction factor was applied to the ∆G°bulge to account for the empirically observed lower disruption to the duplex free energy resulting from mismatched and bulged bases at duplex ends (Carlon and Heim, 2006; Pozhitkov et al., 2006). This correction factor was 0.9, 0.8 and 0.7 for bulges located 3, 2 and 1 nucleotides from either end of the duplex respectively. 5.2.7 Detecting non target single base insertions and deletions sites in FISH probes To enable the detection of non target sites containing single nucleotide insertions and deletions, a simple command line program (LOOPOUT) was written in ANSI C. This program (conceived and constructed by Dr Daniel Tillett (La Trobe University, Bendigo) generates all possible insertion/deletion variants of a selected FISH probe and outputs a FASTA formatted text file. This file contains each possible probe along with the binding ° free energy at each non target site and the ∆G bulge of each possible bulged base. The software is available in Windows, Apple Mac OS X, and Linux x86 binary versions from http://www.ribosome.org/loopout/. 5.3 Results 5.3.1 The SBR9 1 putative GAO 16S rRNA clone sequence is a chimera The original SBR9 1 clone 16S rRNA sequence (AY254694) used to design the SBR9 1a and SBR9 1b FISH probes had been found not to be a chimera by Beer et al., (2004) after checks with the Chimera Check program (Ribosomal Database Project II) (Cole et al., 163 2003). It was included subsequently in the Greengenes database (DeSantis et al., 2006), after the Bellerophon software also failed to recognise it as a putative chimera. However, subsequent screening of short (approx. 100 bp) sections of the sequence against the NCBI GenBank database in this study revealed that the SBR9 1 clone sequence is indeed chimeric. Thus, the first 170 bp are in the reverse orientation to the rest of the sequence (Fig. 5.1), an artefact seen in several other 16S rRNA sequences still available in public data bases (Ashelford et al., 2005). The following sequence region is 96 % identical to an uncultured gammaproteobacterial clone 16S rRNA sequence (AY741401), while the remainder shares 96 % identity with an uncultured Sphingopyxis related 16S rRNA sequence (EF157083). The entire sequence is 89 % identical to that of Sandaracinobacter sibiricus (Fig. 5.1). The chimeric break point of the SBR9 1 clone sequence is estimated to be at around Eschericia coli position 725 as determined by Pintail software (Ashelford et al., 2005). Phylogenetic analysis of individual fragments of the SBR9 1 sequence clearly reveals the chimeric nature of the clone, with fragments clustering within both the alphaproteobacterial and gammaproteobacterial Class divisions (Fig. 5.2). 5.3.2 Where does the SBR9 1a probe bind to this chimeric 16S rRNA sequence? Beer et al. (2004) reported that their SBR9 1a probe targeted cocci that also responded to the ALF968 probe designed against the Alphaproteobacteria (Neef et al., 1999). However, both their SBR9 1a and SBR9 1b probe target sites fall within the gammaproteobacterial section of the SBR9 1 clone sequence (see Fig. 5.1). This sequence was reported to contain the target site for the SPH120 probe for Sphingomonas spp. (Neef et al., 1999), yet the latter probe failed to light up the SBR9 1a FISH positive cells (Beer et al., 2004). In their paper Beer et al. (2004) detailed the target sequence of the SBR9 1a probe rather than its reverse complement (corrected in a later erratum (Beer et al., 2004: erratum)). However, when a FISH probe with this reverse complement sequence (SBR9 1a RC, as published in the erratum: Table 5.2) was applied to biomass samples from the original SBR9 reactor biomass in this study, no fluorescent cells were observed (data not shown), confirming the probe was used in the original published orientation included in the Beer et al. (2004) study.
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Figure 5.1: Diagrammatic representation of the chimeric nature of the SBR9 1 16S rRNA clone sequence. Base numbering is based on the SBR9 1 sequence as submitted to the NCBI database (not E. coli position numbering). i.= intended binding site for the non published SBR9 1a RC probe sequence (Beer et al., 2004: erratum); ii = potential binding site for published SBR9 1a probe (Beer et al., 2004); iii.= intended binding site for the non published SBR9 1b RC probe (Beer et al., 2004: erratum); iv.= binding sites for the SPH120 probe; v. Estimated chimeric break point (Pintail).
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Figure 5.2: Maximum likelihood phylogenetic tree of the SBR9 1 sequence, and selected related sequences, constructed using the ARB software package (Ludwig et al., 2004). a. Includes the ‘complete’ SBR9 1 sequence. b. Includes the three chimera fragments of the SBR9 1 sequence based on the theoretical chimeric breakpoints (see Fig. 5.1). SBR9 1 sequences were added after tree construction with the ‘quick add’ function of ARB and indicated in red and bold font. The SBR9 S1 clone obtained in this study is given in blue and bold font. Probe coverage of the SBR9 1a probe is shown with red brackets. Defluviicoccus related sequences with the exact SBR9 1a probe site but a single missing internal base are indicated by the purple brackets. Phylogenetic affiliations are indicated with brackets to the left.
166 A potential binding site, with two terminal mismatches with the published SBR9 1a probe is located at E. coli position 654 in the SBR9 1 clone sequence (Fig. 5.1; Table 5.3). The terminal mismatches make little difference to the calculated ∆G of the probe target hybrid ( 22.61 to 20.96 kcal/mol: as estimated with default settings for probeCheck (Loy et al., 2008)). However, this site is within the Sphingopyxis related region of the chimeric sequence that also contains the SPH120 probe target (Fig. 5.1), which as mentioned earlier, does not bind to SBR9 1a FISH positive cells. 5.3.3 Is the SBR9 1a FISH probe binding to the RNA? Remarkably the TFO dominating the frozen SBR9 reactor biomass sample which had been kept at 20 °C for eight years still gave strong FISH signals with the published version of the SBR9 1a probe after PFA fixation (Fig. 5.3a). Treating this sample with RNAse before attempting hybridisation with this SBR9 1a probe led to a complete loss of cell fluorescence (Fig. 5.3b.), indicating that it was binding specifically to the RNA within these cells. 5.3.4 To what other 16S rRNA sequences does this SBR9 1a probe bind? Beer et al. (2004) reported difficulties in generating 16S rRNA clone library data from DNA extracted using a MoBio extraction kit which agreed with their FISH data showing that the alphaproteobacterial TFO dominated the community. Therefore two other DNA extraction methods, shown in an earlier study to extract DNA efficiently from the PAO and GAO populations (Chapter 3), were applied to the original frozen biomass sample. A small 16S rRNA gene clone library constructed using this extracted DNA as templates showed that of ten 16S rRNA gene clones sequenced, nine were 97 % identical to the 16S rRNA sequence of Defluviicoccus vanus (AF179678), and identical to the sequence of clone TFOa44 (AY351640) designated by Meyer et al. (2006) as a member of cluster I Defluviicoccus vanus related TFO (see Fig. 5.2). Furthermore, applying the FISH probes available for the Defluviicoccus related members to this SBR9 reactor biomass showed the majority of the TFO hybridised with the TFO_DF218 probe for cluster I Defluviicocus related sequences. These TFO_DF218 FISH positive cells were largely congruent with those responding to the published version of the SBR9 1a FISH probe (Fig. 5.4a).
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Table 5.3: Mismatches in target sites between FISH probes and selected 16S rRNA sequences E. coli Sequence origin Sequencec pos.a SBR9 1a Probe 5’ A A G C G C A A G T T C C C A G G T T G 3’ SBR9 1 (AY254694) 932 3’ U U C G C G U U C A A G G G U C C A A C 5’ SBR9 1 (AY254694) 654 3’ G U G G C G U U C A A G G G U C C A A C 5’ All cluster I Defluviicoccus 620 3’ C U U C G C G U C A A G G G U C C A A C 5’ 620b 3’ U U C G C G U U – A A G G G U C C A A C 5’ Clusters III and IV Defluviicoccus 620 3’ U U C G C G U – C A A G G G U C C A A C 5’ a Eschericia coli position on the 16S rRNA sequence; b. Alignment allowing for the missing base; c Target sequences mismatches are in grey and italicised.
168
169 Figure 5.3: Phase and corresponding FISH micrographs for competitor probe experiments with bulge probes. a. f. SBR9 biomass: a. SBR9 1a (CY3) (no pre treatment of biomass); b. SBR9 1a (CY3) (biomass pre treated with RNAse); c. NONEUB (CY3); d. SBR9 1a (CY3); e. SBR9 1a (CY3) + SBR9 1a Comp(a) (unlabelled); f. NONEUB (CY3). g. i. Biomass from Bendigo WWTP: g. SBR9 1a (CY3); h. SBR9 1a (CY3) + SBR9 1a Comp(a) (unlabelled); i. NONEUB (CY3). j. l. Acinetobacter baumannii str. ATCC 19606: j. ACA23a Bulge (CY3) + NONEUB (CY5); k. ACA23a Bulge (CY3) + ACA23a (CY5); l. ACA23a (CY5) + NONEUB (CY3). Micrographs for each fluorochrome, in each row, were taken at the same exposure time for comparison. Where multiple fluorochromes are reported the FISH image is split. All scale bars represent 10 m.
170 Analysis of all available Defluviicoccus related 16S rRNA clone sequences with ARB including those obtained in this present study, showed that the binding site for the published SBR9 1a FISH probe sequence was missing from them. However ‘Probe Match’ (Cole et al., 2009) revealed that some Defluviicoccus related sequences contained the perfect binding site for this SBR9 1a probe, except that an additional internal nucleotide was missing from its target site. Subsequent analysis revealed that this same site occurred in almost all available 16S rRNA sequences belonging to cluster I, III and IV Defluviicoccus members but not those in cluster II members (Table 5.3; Fig. 5.2), which also contained other base mismatches. When the SBR9 1a FISH probe with the sequence as published (Beer et al., 2004) was applied to biomass from the Bendigo EBPR plant known to contain filamentous cluster III Defluviicoccus members (the source of the majority of available sequences for this cluster; Chapter 4), these filaments also gave a strong positive fluorescence. This outcome was confirmed by double probing with the DF198 probe for cluster III Defluviicoccus members (Fig. 5.4b.). 5.3.5 Investigating the possibility that bulge FISH probes can still hybridise with their target sites To determine whether the published version of the SBR9 1a FISH probe was binding to the complementary target regions in the Defluviicoccus related sequences, and looping out from the missing base (Fig. 5.5a.) a competitor probe was designed (SBR9 1a Comp(a)), which was a perfect match for the suspected target region (Table 5.3; Fig. 5.5b). When this competitor probe was applied together with the SBR9 1a probe to biomass samples from the SBR9 reactor and the Bendigo EBPR community, no fluorescence was evident from the cells which had fluoresced earlier when the same SBR9 1a probe was applied alone (Fig. 5.3e and h.). This supports further the claim that the SBR9 1a FISH probe is binding to the 16S rRNA of Defluviicoccus related sequences in this region.
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Figure 5.4: FISH micrographs of a. SBR9 biomass and b. Bendigo WWTP using the following probes: EUBmix (Fluos = green); DF218 (CY5 = blue); SBR9 1a (CY3 = red). Green + blue + red = white; green + blue = light blue; green + red = yellow. Scale bars represent 10 m.
Figure 5.5: a. Diagrammatic representation of the SBR9 1a FISH probe forming a loop out to hybridise to the Defluviicoccus related sequences. b. Diagrammatic representation of the proposed action of the competitor sequence to the SBR9 1a probe.
172 5.3.6 The conundrum of the SBR9 1b probe An additional probe, SBR9 1b, was designed against the SBR9 1 sequence and applied to the SBR9 biomass to confirm the positive result obtained with the SBR9 1a probe (Beer et al., 2004). However, its application to the SBR9 biomass failed to give a fluorescence signal from the TFO above background levels as determined with the NON EUB negative control FISH probe. The original paper mentioned that the fluorescence conferred by this probe was weak, and the recorded high background fluorescence of the Defluviicoccus clusters was probably misinterpreted by the authors as a positive FISH signal. 5.3.7 The absence of SBR9 1a positive cells in other studies The failure of the published version of the SBR9 1a probe to hybridise to cells in EBPR communities which contained cluster I Defluviicoccus populations and analysed elsewhere, may arise because the reverse complement of its published sequence in an on line erratum in Microbiology (Beer et al., 2004: erratum) was used instead. Alternatively, cells responding to the cluster I Defluviicoccus FISH probe may not all respond to this SBR9 1a probe in its original published orientation, as the data shown in Fig. 5.4a would suggest. 5.3.8 Does the problem of binding to non target sites containing insertions or deletions occur with other FISH probes? The results obtained with the SBR9 1a FISH probe led to an investigation to see if the ability of this probe to bind to non target sites containing nucleotide insertions or deletions is unique to it, or is a more general problem and found with other probes. Revealing other similar examples would also provide further evidence to support the view that ‘bulge’ binding was actually responsible for the hybridisation of the SBR9 1a probe to ‘Defluviicoccus’ members. Consequently, FISH probes were synthesised to create ‘insertion’ and ‘deletion’ variants of four FISH probes (ACA23A, HGC69A, Gor596 and Tth1238), all whose target organisms are found frequently in activated sludge communities and are available as pure cultures (Tables 5.1 and 5.4). These possess either an additional nucleotide, referred to here as ‘deletion bulge probes’ since they mimic the effect of a single base deletion in 173 the rRNA template, or have a single nucleotide deleted, while maintaining the same target site. The latter are referred to here as ‘insertion bulge probes’, since they mimic the effect of a single base insertion in the template. An ‘insertion bulge probe’ version of the SBR9 1a probe was also synthesised. All deleted and inserted bases were centralised since microarray hybridisation studies suggest that this location is the most disruptive to the formation of a stable hybridised duplex (Naiser et al., 2008). When applied as FISH probes to pure cultures of their respective target organisms, positive fluorescence signals were obtained in all cases with the deletion bulge probes, despite the additional central nucleotide (Fig. 5.6b,f and k.). The single exception was the Gor596 Del Bulge for Gordonia sp. (Fig. 5.6p and q.). However, it should be noted that the fluorescence signal intensity with the normal Gor596 probe was weak, requiring extended exposure times. Therefore, even a slight drop in intensity with the bulge probe may have been sufficient to eliminate any such faint fluorescence. To confirm that the deletion probes were binding to the same site as the normal probes, the ACA23A probe and the ACA23A Del Bulge probe, labelled with different fluorophores, were applied to cells of Acinetobacter baumanii. Both generated a reduced cell fluorescence compared to that obtained when each probe was applied individually (Fig 5.3j. l.). These data with the deletion probes agree with earlier studies on nucleotide duplexes, where single base insertions/deletions had only a minor destabilising effect on the duplex free energy values (Zhu and Wartell, 1999; Znosko et al., 2002).
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Table 5.4: Summary of data for variations in binding energies for bulge FISH probes. Tm (°C) c [FA]md ∆G°e Oligonucleotide Hybrid sequencea Typeb f f ° f (∆Tm (°C)) (∆ [FA]m) (∆G bulge) SBR9 1a Comp(b) 82.2±0.7 40±2 24.5 SBR9 1a I 76.0±1.2 37±3 20.9 ( 6.2) ( 3) (3.6) SBR9 1a Ins Bulge II 76.5±0.0 25±2 20.7 ( 5.7) ( 15) (3.8) ACA23A 75.3±0.7 60±2 23.7 ACA23A Del Bulge II 71.7±0.7 53±2 20.9 ( 3.6) ( 7) (2.8) ACA23A Ins Bulge(a) II 68.5±1.2 19±2 17.9 ( 6.8) ( 41) (5.8) ACA23A Ins Bulge(b) I ND 41±3 20.2 ( 19) (3.5) HGC69A 79.3±0.7 32±4 24.3 HGC69A Del Bulge I 73.0±1.2 <11±3 20.7 ( 6.3) (> 21) (3.6) HGC69A Ins Bulge I 73.5±2.2 <16±7 20.1 ( 5.8) (> 16) (4.2) Gor596 83.3±0.7 ND 26.2
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Gor596 Del Bulge I 79.7±0.7 ND 21.7 ( 3.6) (4.5) Tth1248 84.0±2.8 ND 34.8 Tth1248 Del Bulge II 80.7±1.9 ND 29.1 ( 3.2) (5.7) Goam192 ND ND 30.4 Goam192 (against Rhodococcus I ND ND 27.4 sp. J71) (3.0) Errors are presented as 95 % confidence intervals. ND = Not determined. a. Proposed rRNA target sequences are given in bold. b. The ‘bulged’ base in type I is not neighboured by a like base where in type II it is. c. Tm = Experimentally determined dissociation temperature (∆Tm is given in parenthesis) d. [FA]m = Formamide concentration melting point (% [v/v]) (∆[FA]m is given in parenthesis). ° 1 ° e. ∆G values (kcal mol estimated using the ‘LOOPOUT’ program (version 0.52) (‘∆G bulge’). f. Differences are related to the exact match duplexes to take into account the differences in probe length.
176
177 Figure 5.6: FISH micrographs for bulge probe experiments. a. d. SBR9 biomass: a. SBR9 1a Comp(b) (CY3); b. SBR9 1a (CY3); c. SBR9 Ins Bulge (CY3); d. NONEUB (CY3); e. i. Acinetobacter baumannii: e. ACA23A (CY3); f. ACA23A Del Bulge (CY3); g. ACA23A Ins Bulge(a) (CY3); h. ACA23A Ins Bulge(b) (CY3); i. NONEUB (CY3); j. m. Tetrasphaera jenkinsii: j. HGC69A (CY3); k. HGC69A Del Bulge (CY3); l. HGC69A Ins Bulge (CY3); m. NONEUB (CY3). n. s. Gordonia sp. (n., p. and r. Gordonia terrae and o. q. and s. Gordonia amarae): n. and o. Gor596 (CY3); p. and q. Gor596 Del Bulge (CY3) r. and s. NONEUB (CY3). t. v. Thermus thermophilus: t. Tth1248 (CY3); u. Tth1248 Del Bulge (CY3); v. NONEUB (CY3). Micrographs for each fluorochrome, in each row, were taken at the same exposure time for comparison. All scale bars represent 10 m.
178 A more pronounced effect was seen with those probes containing a deleted base (simulating insertions in the rRNA). Applying the ACA23A Ins Bulge probe, which lacks a C nucleotide at position 9 (Table 5.4), led to a reduction in the fluorescence signal intensity compared to that obtained with the ACA23A probe (Fig. 5.6g). This probably arises from an additional large duplex free energy penalty of the probe from having one less base (Znosko et al., 2002), which could be confirmed by designing a ‘Insertion’ probe with an additional base added to the 3’ end of the ACA23A probe (ACA23A Bulge Ins(b)). This probe was designed to give a theoretical ∆G° with its perfect match (23.9 kcal mol 1) as close as possible to that of the ACA23A probe (23.7 kcal mol 1) (Table 5.4). In this case FISH hybridisation imparted a strong positive signal to the A. baumannii cells at the recommended formamide hybridisation concentration (Fig. 5.6h.). The HGC69A Ins Bulge probe affected the fluorescence signal intensity in a similar way to that observed with the corresponding insertion duplex probe (HGC69A Del Bulge) ° (Fig. 5.6l.), which reflects the similarity of the calculated ∆G bulge values for these two probes (Table 5.4). 5.3.9 Can the binding efficiency of FISH probes to non target sites containing insertion or deletions be predicted from theoretical free energy calculations? Since the process of FISH probe binding is complicated by several factors, including rRNA template secondary structure (Fuchs et al., 1998; Yilmaz et al., 2006), possible effects of insertions and deletions in the FISH target site on probe binding were investigated by carrying out a series of formamide titration experiments (Fig. 5.7). Each formamide dissociation curve was then compared to the theoretical free energy calculations and the experimentally determined ∆Tm for the same probe set (Table 5.4). ° The ∆Tm and ∆G bulge values for the insertion and deletion duplexes corresponded well, as might be expected given that these theoretical values are calculated from a model based on free solution duplex formation (Table 5.4). The outcomes of the formamide ° dissociation experiments were more complex. Nevertheless, duplexes having ∆G bulge of less than 3.8 kcal mol 1 all gave ∆[FA]m values of less than 20 %, a level which can make selective probe binding to the target site over the non target sites difficult or impossible to achieve by experimental hybridisation optimisation (Yilmaz et al., 2008).
179 ° 1 These data suggest that a tentative ∆G bulge threshold of at least 4 kcal mol can be used to screen FISH probes in silico for possible problematic non target binding. Again, as FISH probe binding is influenced by factors like rRNA secondary structure (Fuchs et al., 1998), any theoretically calculated threshold can only be an estimate of the potential for non target binding, and possible exclusion requires experimental confirmation (Yilmaz et al., 2008). 5.3.10 Are non target sites containing insertions and deletions commonly found for existing FISH probes? A survey against the Ribosomal Database Project II (RDPII) 16S rRNA sequence database of 50 published FISH probes revealed that 70 % matched non target sequences with single base insertions or deletions (Table 5.5). While the frequency of occurrence of these sites is much lower than for single base mismatches (approx. 26 times), on average there were 55.7 sequences with insertion/deletion sites detected for each probe. Since the continually expanding RDPII database contains only a very small subset of the 16S rRNA sequence diversity existing in nature (Amann and Ludwig, 2000; Amann and Fuchs, 2008), it is likely that many more as yet undetectable, but potentially problematic, ‘insertion/deletion’ non target sequences exist in natural communities. 5.3.11 Screening for non target sites containing single nucleotide insertions and deletions and predicting the likelihood of FISH probe binding. Since the most commonly used FISH probe design software packages do not take into account the possibility of non target insertion/deletion sites, a software tool (LOOPOUT) is described here to screen for these in current rRNA gene sequence databases. The software generates all possible single nucleotide insertion/deletion combinations for a ° ° given FISH probe and the corresponding theoretical ∆G and ∆G bulge values for each variant. LOOPOUT generates a FASTA format file compatible with the ProbeCheck program (Loy et al., 2008), which enables any probe to be screened against 16S rRNA gene sequence databases and its possibility of binding to non target insertion/deletion sites determined.
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Figure 5.7: Formamide dissociation curves for selected FISH probes and their ‘bulge’ variants. Fluorescence is represented as corrected RFU for each probe, and error bars represent 95 % confidence intervals based on fifty individual readings for each point. a. SBR9 1a probes applied to the SBR9 biomass of Beer et al. (2004); b. ACA23A probes applied to Acinetobacter baumannii; c. HGC69A probes applied to Tetrasphaera jenkinsii.
181
Table 5.5: Survey data showing the frequency of single base insertions/deletions for selected FISH probe sequences. Bulgesb,c Single ∆G° Probe Target Group E. coli position Hits a bu Reference Mis Ins Del Tot lge <4.0d 21N Eikelboom type 021N strain II 26 652 669 17 216 10(3) 0 10(3) 10 (Wagner et al., 1994a) ACA652 Acinetobacter 652 669 4118 1975 70(14) 619(4) 689(18) 71 (Wagner et al., (ACA23A) 1994b) actino 1011 Tetrasphaera japonica, EBPR clone Ebpr19, Ebpr20 1011 1029 142 197 0 2(2) 2(2) 1 (Liu et al., 2001) (Actinobacteria) Actino 221 Actinobacterial PAO 221 238 5 984 0 0 0 0 (Kong et al., 2005) Actino 658 Actinobacterial PAO 658 675 27 134 1 3(3) 4(4) 1 (Kong et al., 2005) ALBO577 Alcaligenes, Bordetella and close relatives 577 593 1466 4040 25(7) 86(3) 111(10) 34 (Friedrich et al., 2003) AMAR 839 Amaricoccus spp. 839 860 52 76 0 3(2) 3(2) 0 (Maszenan et al., 2000a) Amx820 Anaerobic ammonium oxidising bacteria, Candidatus ‘Brocadia 820 841 212 16 1 4(1) 5(2) 5 (Schmid et al., anammoxidans’ and Candidatus ‘Kuenenia stuttgartiensis’ 2001) Apr820 Candidatus ‘Anammoxoglobus propionicus’ 820 840 44 4 0 0 0 0 (Kartal et al., 2007) Ban162 (B. Candidatus ‘Brocadia anammoxidans’ 162 179 20 33 0 0 0 0 (Schmid et al., anam.) 2001) Bfu613 Candidatus ‘Brocadia fulgida’ 613 636 11 56 0 0 0 0 (Kartal et al., 2008) Cate 1013 Candidatus ‘Catenimonas italica’ 1013 1032 1 0 0 0 0 0 (Levantesi et al., 2004) CHL 1851 Filamentous bacterium Eikelboom Type 1851 592 611 31 26 0 0 0 0 (Beer et al., 2002) Combo Candidatus ‘Combothrix italica’ 1031 1050 3 0 0 0 0 0 (Levantesi et al., 1031 2004) CteA Comamonas testosterone 462 481 352 54 3(3) 1 4(4) 3 (Bathe and Hausner, 2006) DEN124 Acetate denitrifying cluster 124 143 1 259 0 10(1) 10(1) 0 (Ginige et al., 2005) DEN581 Acetate denitrifying cluster 581 600 107 7050 1 6(2) 7(3) 1 (Ginige et al., 2005) DEN67 Methanol utilising denitrifying cluster 67 84 38 2545 3(1) 0 3(1) 3 (Ginige et al., 2005) DLP Nocardioform actinomycete sp. 182 202 88 146 0 0 0 0 (Schuppler et al., 1998) EU25 1238 Filamentous ‘Chloroflexi’ 1238 1257 12 0 0 0 0 0 (Kragelund et al., 2007a) 182
G123T Thiothrix eikelboomii, T. nivea, T. unzii, T. fructosivorans, T. 697 714 346 4369 2(1) 1 3(2) 2 (Kanagawa et al., defluvii, Eikelboom type 021N group I, II, III 2000) G1B Eikelboom type 021N group I 1029 1046 24 54 26(1) 4(1) 30(2) 26 (Kanagawa et al., 2000) G2M Eikelboom type 021N group II 842 859 33 7 0 2(2) 2(2) 1 (Kanagawa et al., 2000) G3M Eikelboom type 021N group III 996 1013 27 135 0 0 0 0 (Kanagawa et al., 2000) Gam1019 Some ‘Competibacter’ 1019 1036 6 113 8(3) 192(2) 200(5) 8 (Nielsen et al., 1999) Gam1278 Some ‘Competibacter’ 1278 1297 58 447 3(1) 1159(2) 1162(3) 3 (Nielsen et al., 1999) GAOQ431 ‘Competibacter’ 431 448 98 1644 1 0 1 1 (Crocetti et al., 2002) GAOQ989 ‘Competibacter’ 989 1006 63 232 4(2) 0 4(2) 4 (Crocetti et al., 2002) GB Novel gammaproteobacterial group 612 628 124 13 0 0 0 0 (Kong et al., 2002a) GB_4 Novel gammaproteobacterial group 1020 1037 18 29 0 3(1) 3(1) 0 (Kong et al., 2002a) GB_7 Novel gammaproteobacterial group 1000 1017 14 27 6(1) 0 6(1) 6 (Kong et al., 2002a) GB_G2 Novel gammaproteobacterial group 989 1006 36 331 27(1) 0 27(1) 27 (Kong et al., 2002a) GLP1 Gordonia sp. SMKN27, Gordonia sp. SMKN12, Gordonia sp. 174 193 4 47 0 0 0 0 (Schuppler et al., SMKN17, Gordona sp. SMKN14, (nocardioform 1998) actinomycetes) GLP2 Gordonia sp. SMKN29, Gordonia sp. SMKN15, (nocardioform 178 197 8 3 0 4(3) 4(3) 0 (Schuppler et al., actinomycetes) 1998) GLP3 Nocardioform actinomycete sp. 182 202 1 86 0 0 0 0 (Schuppler et al., 1998) Goam192 Gordonia amarae 192 209 34 166 21(1) 44(2) 65(3) 62 (de los Reyes et al., 1997) Gor596 Gordonia (Gordona) 596 617 271 15 1 1 2(2) 1 (de los Reyes et al., 1997) H7 1014 Activated sludge clone H7 1014 1031 41 135 2(2) 0 2(2) 2 (Juretschko et al., 2002) HHY Haliscomenobacter hydrossis 655 672 42 120 0 5(1) 5(1) 0 (Wagner et al., 1994a) HoAc1402 Acidobacteria, activated sludge clones A22, A34, H3, H44, S6 1402 1419 6173 26100 11(7) 8(4) 19(11) 14 (Juretschko et al., 2002)
183
Kst157 Candidatus ‘Kuenenia stuttgartiensis’ 157 174 40 3 0 0 0 0 (Schmid et al., 2001) LDI Leptothrix discophora 649 666 909 5322 30(5) 68(6) 98(11) 29 (Wagner et al., 1994a) LMU Leucothrix mucor 652 669 11 852 19(2) 0 19(2) 19 (Wagner et al., 1994a) MLP Mycobacterium sp. SMKN23, Mycobacterium sp. SMKN22, 182 202 27 25 0 0 0 0 (Schuppler et al., (nocardioform actinomycetes) 1998) MNP1 Nocardioform actinomycetes 152 172 15202 5971 28(20) 23(8) 51(27) 38 (Schuppler et al., 1998) MPA Candidatus ‘Microthrix’ 645 662 19 213 0 9(3) 9(3) 3 (Erhart et al., 1997) Myc657 Mycobacterium subdivision (mycolic acid containing 657 672 6398 1178 21(13) 12(5) 33(18) 16 (Davenport et al., actinomycetes) 2000) MZ1 Thauera spp. mzt1t 646 664 1600 2791 7(7) 114(4) 121(11) 7 (Lajoie et al., 2000) NEU Most halophilic and halotolerant Nitrosomonas spp. 653 670 205 2434 43(3) 25(3) 68(6) 43 (Wagner et al., 1995) NIT3 Nitrobacter spp. 1035 1052 102 592 1 2(1) 3(2) 1 (Wagner et al., 1996) The probes screened were the first 50 on the list ‘bacteria of relevance to wastewater treatment’ as of July 2009 on probeBase (Loy et al., 2007). Only those probes validated in situ were used. Probes were screened with Probe Match (Cole et al., 2009) set to detect up to a single error. a. Probe site with one single mismatch. b. Probe site with a single missing (Del) or additional target site base (Ins). c. The number of different bulge sequence variations is given in parenthesis. d. Calculated as kcal mol 1 with ‘LOOPOUT’(Section 5.3.11).
184
Figure 5.8: Box plot analysis of bulge mismatches for selected FISH probe sequences. The probes screened were the first 50 on the list ‘bacteria of relevance to ° wastewater treatment’ as of July 2009 on probeBase (Loy et al., 2007) and are summarised in Table 5.5. ∆G bulge values are calculated using LOOPOUT. The number ° of bulge mismatches contributing to the analysis for each probe are given in parenthesis after the probe name. The dotted line marks the ∆G bulge <4.0 threshold.
185 The distribution of ∆G°bulge values for the 50 probes screened earlier is shown in Fig. 5.8. Using the ∆G°bulge threshold of 4 kcal mol 1 as predictive of sites where hybridisation may be difficult to separate by formamide concentration manipulation, 29 of these 50 probes had non target sites below the 4 kcal mol 1 threshold, with an average of 9.4 sites for each probe (Table 5.5). Our data suggest that ∆G°bulge values below the 4 kcal mol 1 threshold may result in false positives and that caution needs to be exercised when it is not possible to determine experimentally if probe binding at such sites can be eliminated by formamide concentration optimisation. 5.3.12 A case study of the presence of a non target single nucleotide insertion site resulting in a FISH false positive The Goam192 probe was used to demonstrate the potential implications of this problem in FISH analyses. This probe was originally designed to target Gordonia amarae, an organism frequently observed in foaming activated sludge plants (de los Reyes et al., 1997). In silico analysis shows that non target sites for this probe are also present in the 16S rRNA sequence of four cultured activated sludge isolates, Rhodococcus strains J27, J71 and J72 (respective accession numbers: X85240; X85241; X85242) (Soddell et al., 1998), and Cellulosimicrobium sp. HPC22 (AY838324). These sites all have a single nucleotide insertion between positions 3 and 4 of the Goam192 probe (Table 5.4), with a ° 1 predicted ∆G bulge of 3.0 kcal mol . When the Goam192 probe was applied to a pure culture of Rhodococcus sp. J71, a positive FISH fluorescence signal was obtained after the hybridisation conditions described by de los Reyes et al. (1997) were used (Fig. 5.9). This observation is important since both the intended target and the organisms containing the non target sequence are found in activated sludge.
186
Figure 5.9: FISH micrographs of Rhodococcus sp. J71 cells. a. Phase contrast and corresponding FISH image with the Goam192 probe (CY3). b. Phase contrast and corresponding FISH image with the NON EUB probe (CY3). Scale bars represent 10 m.
187 5.4 Discussion This study has revealed that the SBR9 1 sequence used to design FISH probes for the ‘Sphingomonas related GAO’ (Beer et al., 2004) was in fact a complex chimera, made up of sequence from at least three sources. This paper has now been retracted (Beer et al., 2009). Chimeras are frequently comprised of sequences from two separate DNA template sources, although more complex chimeras are held in public databases (Ashelford et al., 2005). A survey of these databases has estimated that about 9 % of all sequences submitted in clone library sets are chimeric, which indicates the extent of the problems with the chimera check protocols currently available, which is exacerbated by an apparent lack of their imperative implementation (Ashelford et al., 2006). One suggestion on how their reliability might be improved is the use of multiple check programs, as chimeras missed by one are often detected by another (Ashelford et al., 2006). In the case of the SBR9 1 chimeric clone sequence, the Pintail software (Ashelford et al., 2005) would have detected its sequence anomalies. The manual assessment completed in this study should also have revealed the anomalies, showing the dangers in relying on available software programs alone and their self professed role as advisory tools only. This study has revealed that the Sphingomonas related GAO are in fact members of cluster I Defluviicoccus related organisms. We suggest that the SBR9 1a probe is able to bind to this Defluviicoccus sequence by forming a bulge out over a missing base. It has been demonstrated experimentally here that successful binding of FISH probes to their rRNA target sites may occur even when an additional nucleotide is present in either the probe or its target sequence. Furthermore, in some cases, FISH hybridisation conditions could not be optimised to eliminate such non target binding, posing a similar problem to that reported with duplex mismatches (Yilmaz et al., 2008). The possibility of false positives resulting from this situation is a major concern for all quantitative and qualitative studies where FISH is employed. Current FISH probe design software including ARB (Ludwig et al., 2004) and probeCheck (Loy et al., 2008) make no allowance for such an effect, as they do not take into account the frame shift resulting from single base deletion/insertions, and instead assess these sequences as having multiple mismatches (Pozhitkov and Tautz, 2002) (see Table 5.3). Microarray data from RNA/DNA hybrids suggests that single base insertions and deletions are less destabilising
188 than single base pair mismatches (Naiser et al., 2008). This is especially true for what are known as ‘Group II’ insertions, where the additional nucleotide is flanked by a like base (Zhu and Wartell, 1999). These have a higher binding affinity than ‘Group I’ insertions (ie. those with non identical flanking nucleotides), resulting from a ‘zipping’ of the probe during hybridisation (Naiser et al., 2008). Microarray data on single nucleotide insertions and deletions duplexes has revealed that terminal ‘Group II’ bulge loops can have similar fluorescence intensities to the actual complementary non bulge duplex (Naiser et al., 2008). While systematic thermodynamic studies similar to those performed for DNA:RNA hybridisations in solution (Ke and Wartell, 1995; Zhu and Wartell, 1999; Znosko et al., 2002), or for microarrays (Naiser et al., 2008) are clearly required, the data presented here suggest that the presence of non target sites containing a nucleotide insertion/deletion can have important consequences for FISH analyses. A search of the 16S rRNA sequence databases has revealed that these sites are not unusual among existing FISH probes. An in silico examination of 50 existing probe sequences showed the existence of such non target sites for the majority. Furthermore, based on the findings of this study, many of these non target sites have theoretical free energy differences below the level where binding discrimination from the correct site by optimising the hybridisation stringency conditions becomes difficult. All the single base insertions and deletions used here were located in the middle of the probe sequence where they exert the highest potential destabilising effect (Naiser et al. 2008). Since there is a high probability that many insertions or deletions will be located close to the terminal ends of the probes, and that current databases contain only a fraction of the estimated total sequence diversity (Amann and Ludwig, 2000), the potential for false FISH positives must be considerable. To the best of our knowledge, insertions/deletions in non target sequences can only be detected by Probe Match (Cole et al., 2009) for sequences in the RDPII or by using the PROBE program (Pozhitkov and Tautz, 2002). Unlike PROBE, LOOPOUT provides an ° ° estimate of the ∆G and ∆G bulge values of probes binding to non target sites that contain insertions and deletions. Therefore it can be used to screen for possible problematic probe sequences. The ∆G° values generated also make the software compatible with programs developed for base mismatch detection, where the impact of rRNA secondary structure and probe probe interactions is taken into account (Yilmaz and Noguera, 2007; Yilmaz et al., 2008). Furthermore, LOOPOUT allows access to other sequence databases through its
189 compatibility with probeCheck. While it is recognised that theoretical free energy calculations are not always predictive in FISH, reflecting complexity of the template, such data should assist in the probe design process by identifying those probes with the potential for non target binding before undertaking expensive and time consuming laboratory based validation (Amann and Ludwig, 2000; Yilmaz et al., 2008). If it is not possible to design probes for a desired population after screening, then competitor probes, which have been applied successfully to eliminate false positive signals in FISH for mismatch sites (Manz et al., 1992), were also shown in this work to be effective in discriminating target sites from non target sites containing insertions and deletions. It is also advisable to adopt a multiple FISH probe approach and apply a hierarchical set of probes, to minimise the risk of false positive identification (Daims et al., 2005). 5.5 Concluding remarks This study has shown that the Sphingomonas related GAO described by Beer et al. (2004) are in fact members of cluster I Defluviicoccus related organisms. Furthermore, non target FISH probe sites containing single base insertions or deletions can lead to false positives in FISH analysis and these are therefore a general problem with the technique. Given the frequency of occurrence of these sites among existing probes, this finding is of concern to all FISH users. It is hoped that this work will lead to more general screening for potential non target single base insertion and deletions sites during the FISH probe design process.
190 6.0 The influence of pH on the microbial community present in a laboratory scale EBPR 6.1 Introduction Understanding the conditions that competitively favor the PAO over the GAO will allow better control and operation of EBPR systems. Unfortunately few investigations into the influence of operational parameters on PAO: GAO competition have characterised the PAO/GAO populations present in their systems (Seviour et al., 2003; Oehmen et al., 2007). Instead the prevalence of either phenotype is extrapolated from chemical profile data, based on measurements like the anaerobic carbon uptake to P release ratio (Seviour et al., 2003; Oehmen et al., 2007; McMahon et al., 2010; Oehmen et al., 2010b), since there is no anaerobic P release by the GAO. Operational pH is one such parameter that has attracted attention. Increased EBPR efficiency has been reported in lab scale systems operating at higher pH (>7.0) (Bond et al., 1998; Bond et al., 1999b; Filipe et al., 2001c; Jeon et al., 2001; Levantesi et al., 2002; Schuler and Jenkins, 2002; Serafim et al., 2002b; Oehmen et al., 2005a; Zhang et al., 2005) and is thought to result from an advantage the PAO have over the GAO at pH >7.0. Serafim et al. (2002b) suggest that the elevated demand for energy at higher pH is better met by the PAO which rely on both polyP and glycogen as energy sources, unlike the GAO which rely totally on the latter. Anaerobic substrate uptake studies over a pH range of 6.5 to 8.0, with undefined enriched cultures have generated data suggesting that the GAO increase glycogen consumption at higher pH (Filipe et al., 2001b) to satisfy their energy requirements. However, while acetate uptake by the PAO appears independent of pH (Filipe et al., 2001d), it is reduced with the GAO, thus giving the PAO an advantage at higher pH (Filipe et al., 2001b). However, the value of such data is compromised by the lack of accompanying microbiological data on the identity of the PAO and GAO. In the small number of laboratory studies where community responses to pH changes have been monitored, higher pH values appear to favour ‘Accumulibacter’ (Bond et al., 1999a; Oehmen et al., 2005a; Zhang et al., 2005). Some correlation was also observed between increased abundance of ‘Accumulibacter’ and elevated pH in full scale EBPR
191 systems in the Netherlands (Lopez Vazquez et al., 2008a). Very little is known of the impact of pH on individual GAO populations. In perhaps the only comprehensive study to date, Oehmen et al. (2005a) applied FISH to communities in two reactors, one dominated by an unknown alphaproteobacterial TFO and the other ‘Competibacter’, to show a clear community shift away from these GAO populations in favor of ‘Accumulibacter’ members when pH increased from 7 to 8. Unfortunately the identity of the main alphaproteobacterial GAO in their reactor could not be identified further as the FISH probes available then did not cover the majority of the TFO community. This illustrates the importance of continued efforts to identify these important populations in EBPR systems and constant re appraisal and update of the tools necessary, which has been the focus of the work reported in this thesis. Thus, in work described in this chapter, a SBR EBPR process was run, operating under different defined pH conditions, to obtain a better insight into how the microbial community composition might respond to changes in pH over a long term. Known PAO and GAO populations, including those newly described in Chapter 4, were monitored with FISH. 6.2 Material and methods 6.2.1 Reactor operation The reactor was operated and maintained by Dr Johwan Ahn (La Trobe University, Bendigo). The SBR (referred to as LS2 in Chapters 2, 3 and 4) with a 1.0 liter working volume (B. Braun) was operated at about 20 oC. It was fed with a synthetic wastewater of the following composition (per liter): 512.5 mg of CH3COO Na, 85.3 mg of
NaH2PO4 2H2O, 100.8 mg of NH4Cl, 180 mg of MgSO4 7H2O, 72 mg of KCl, 14 mg of
CaCl2 2H2O, 2 mg of n allylthiourea, 5 mg of yeast extract, 5 mg of peptone and 0.3 ml 1 1 1 of trace elements (1.5 g l FeCl3.6H2O, 0.15 g l H3BO3, 0.03 g l CuSO4.5H2O, 0.18 g l 1 1 1 1 1 KI, 0.12 g l MnC12.4H2O, 0.06 g 1 Na2MoO4.2H2O, 0.12 g l ZnSO4.7H2O, 0.15 g l 1 CoCl2.6H2O and 10 g l EDTA (Smolders et al., 1994b). n allylthiourea was included in the media to inhibit nitrification. This is common practice in EBPR studies with lab scale reactors to make the resulting chemical profiles easier to determine. Mixed liquor taken from the aerobic reactor of a MUCT full scale EBPR process in Castlemaine, Victoria, Australia was washed and 500 ml settled biomass was used as the seed to inoculate the
192 SBR. The reactor was operated on a cycle of 6 h, consisting of a 5 min filling phase, a 115 min anaerobic phase, a 195 min aerobic phase, a 30 min settling phase, and a 15 min withdraw phase. Biomass was mixed during the anaerobic and aerobic phases by a single agitator with 6 Rushton turbine blades (6 cm diam.) at 300 rev. min 1. To maintain aerobic conditions about 450 ml min 1 of air was bubbled through a stainless steel diffuser. Reactor pH was strictly controlled with an Ingold pH electrode and pH controller (LH505, LH Fermentation) by adding 0.25 N HCl or 0.25 N NaOH when required. The reactor was operated at pH 7.5 for the first 27 days, then pH 7.0 until day 77, 6.5 until day 184, pH 7.5 until day 256, pH 7.0 until day 351, pH 6.5 until day 425 and finally switched back to pH 7.5 until day 447. At the end of the aerobic phase, about 35.7 ml of excess sludge was discarded to maintain a mean cell retention time (sludge age) of 7 days. After settling, about 464.3 ml of the effluent was discarded. 6.2.2 Chemical analysis All chemical analyses were performed by Dr Johwan Ahn (La Trobe University, Bendigo). Analysis of P, biomass polyP and total suspended solids were performed according to Standard Methods (APHA et al., 1998). Acetate was determined with a HPLC (LC 10Ai, Shimadzu) equipped with an anion column (Shodex kC 811, Shodex Denko). To determine intracellular levels of PHA and glycogen, biomass samples were collected and immediately frozen in a mixture of dry ice and methanol, followed by lyophilisation. For PHA, biomass was incubated at 100 oC for 24 h with chloroform and acidified methanol (10 % [v/v] sulfuric acid) (Ahn et al., 2007). Identification and quantification of PHA methyl esters were conducted by a gas chromatograph (3900, Varian) equipped with a chrompack capillary column (CP Sil5CB, Varian) and a flame ionisation detector. Poly (3 hydroxybutyric acid co 3 hydroxyvaleric) acid (Aldrich) and sodium 3 hydroxybutyrate (Lancaster) were used as standards. No poly β hydrohyvalerate (PHV) was detected in any of the reactor samples. For glycogen, sludge samples were autoclaved with 0.6 N HCl at 121 oC for 1 h. After cooling to room temperature, glycogen concentrations were measured as glucose equivalents using a hexokinase enzymatic glucose kit (Thermo).
193 6.2.3 FISH analyses Biomass samples were collected at the end of the aerobic phase (unless otherwise stated) for FISH analysis and immediately fixed in 4 % [w/v] formaldehyde for Gram negative and 50 % [v/v] ethanol for Gram positive cells as detailed in Section 2.2.2. All samples before day 100 were collected by Drs Michael Beer and Sarah Schroeder (La Trobe University, Bendigo). Granulated samples were homogenised with a pellet pestle prior to FISH, which was carried out as described in Sections 2.2.3 with the probes listed in Table 6.1. For FISH analysis on granule sections, 30 m thick sections were prepared from fixed granules embedded in Tissue Tek OCT Compound (4583, ProSciTech) using a cryostat (CM1850, Leica), and placed on gelatin coated microscopic slides. The OCT compound was removed by soaking the slides in distilled water and FISH performed as described earlier (Section 2.2.3.). Populations were quantified (qFISH) as described in Section 2.2.5. Values are expressed for each probe as a percentage of the total cell area fluorescing with the EUBmix probes, and all qFISH errors are represented as standard errors. 6.2.4 Histochemical staining To detect polyP and PHA in the cells, DAPI (Kawaharasaki et al., 1999) and Nile blue A (Ostle and Holt, 1982) respectively were used as described in Sections 4.2.8. 6.2.5 FISH MAR The protocol used was based on the methods of Lee et al. (1999) and Kong et al. (2004). Samples for anaerobic acetate, and aerobic P, uptake determination were taken from the end of the aerobic and anaerobic cycles respectively and used undiluted. Radioactively labeled substrates were added to give a total radioactivity of 12.5 Ci ml 1 and 0.25 Ci ml 1 for P (33P Phosphate (GE Healthcare) and acetate ([l 14C] Acetic acid (GE Healthcare)), respectively. Cold substrates were added to give a total concentration identical to those in the lab scale reactor. Incubation conditions are as in Section 4.2.7.1. Acetate and P incubations were 1 h (anaerobic) and 2 h (aerobic) respectively (based on their uptake profiles for the reactor). Each incubation was repeated with two negative
194 controls to ensure any silver grain formation was not due to chemography (Nielsen and Nielsen, 2005) (Section 4.2.7.1). Then FISH MAR was carried out as described in Sections 4.2.7.2 4.2.7.4 except that 1 × PBS was substituted with 0.1 M sodium citrate HCl buffered (pH 2.0) for the wash steps involving the labeled P substrate experiments (Kong et al., 2004). 6.2.6 Electron microscopic analysis of granules For scanning electron microscopy (SEM) and transmission electron microscopy (TEM) (performed by Dr Johwan Ahn (La Trobe University, Bendigo), granules were fixed in 2.5 % [v/v] glutaraldehyde solution and 1 % [w/v] osmium tetroxide for 2 h each at room temperature, and then dehydrated through an acetone series (0 100 % [v/v]). For SEM, granules were dried with a critical point dryer (CPD 030, Bal Tec), and gold/palladium coated using a sputter coater (E5100, Polaron). Coated specimens were examined in a SEM microscope (S150, Cambridge) at 10 20 kV. For TEM, granules were infiltrated with Spurr’s epoxy resin, and left to harden overnight at about 65 70 oC. They were sectioned with a diamond knife (DDK) on an LKB Bromma ultramicrotome, and then stained with 2 % [w/v] uranyl acetate and 2 % [w/v] lead citrate for 15 min each. Specimens were examined by a Jeol JEM100CX electron microscope at 100 kV. 6.2.7 DNA extraction Total DNA was extracted from ethanol fixed biomass samples using the MI and MV extraction methods described in Sections 3.2.2.1 and 3.3.3.5, respectively. 6.2.8 DGGE analysis DGGE analysis was performed as detailed in Section 3.2.8. DNA extracts obtained with each extraction method (MV and MI) were amplified separately and then equal amounts pooled for the electrophoresis step. Standards were prepared by separate amplification and DGGE of cloned 16S rDNA inserts to determine their migration positions for comparison with whole community DGGE profiles.
195
Table 6.1: List of oligonucleotide FISH probes used in this chapter [FA]1 Probe name Sequence (5´ 3´) Target Reference (%) EUB338 I2 GCTGCCTCCCGTAGGAGT Most Bacteria 35 (Amann et al., 1990) EUB338 II2 GCAGCCACCCGTAGGTGT Planctomycetales 35 (Daims et al., 1999) EUB338 III2 GCTGCCACCCGTAGGTGT Verrucomicrobiales 35 (Daims et al., 1999) NON EUB ACTCCTACGGGAGGCAGC Control probe complementary to EUB338 n/a (Wallner et al., 1993) BET42a GCCTTCCCACTTCGTTT Betaproteobacteria 35 (Manz et al., 1992) BET42a competitor GCCTTCCCACATCGTTT Competitor for BET42a n/a (Manz et al., 1992) PAO462b3 CCGTCATCTRCWCAGGGTATTAAC ‘Accumulibacter’ 35 (Zilles et al., 2002a) PAO6513 CCCTCTGCCAAACTCCAG ‘Accumulibacter’ 35 (Crocetti et al., 2000) PAO846b3 GTTAGCTACGGYACTAAAAGG ‘Accumulibacter’ 35 (Zilles et al., 2002a) RHC439 CNATTTCTTCCCCGCCGA Rhodocyclus spp., most members of the 30 (Hesselmann et al., 1999) ‘Accumulibacter’ cluster, Azospira lineage ZRA23a CTGCCGTACTCTAGTTAT Most members of the Zoogloea lineage, not Z. 35 (Rossello Mora and resiniphila Wagner, 1995) GAM42a4 GCCTTCCCACATCGTTT Gammaproteobacteria 35 (Manz et al., 1992) GAM42a_T1038_G10314 GCCTTTCCACATGGTTT Representatives of the Xanthomonas group 35 (Siyambalapitiya and Blackall, 2005) GAM42a_T10384 GCCTTTCCACATCGTTT Representatives of the Xanthomonas group 35 (Siyambalapitiya and Blackall, 2005) GAM42a_A1041_A10404 GCAATCCCACATCGTTT Representatives of the Xanthomonas group 35 (Siyambalapitiya and Blackall, 2005) GAM42a competitor4 GCCTTCCCACTTCGTTT Competitor probe for GAM42a n/a (Manz et al., 1992) GB5 CGATCCTCTAGCCCACT ’Competibacter’ 35 (Kong et al., 2002a) GB_G15 CGATCCTCTAGCCCACT ’Competibacter’ group G1 35 (Kong et al., 2002a) GB_G25 TTCCCCGGATGTCAAGGC ’Competibacter’ group G2 35 (Kong et al., 2002a) ACA23a ATCCTCTCCCATACTCTA Acinetobacter 35 (Wagner et al., 1994b) ALF968 GGTAAGGTTCTGCGCGTT Alphaproteobacteria, except Rickettsiales 20 (Neef et al., 1999) TFO_DF862 AGCTAAGCTCCCCGACAT Defluviicoccus vanus 35 (Wong et al., 2004) TFO_DF2186 GAAGCCTTTGCCCCTCAG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) TFO_DF6186 GCCTCACTTGTCTAACCG Cluster I Defluviicoccus related TFO 35 (Wong et al., 2004) DF9887 GATACGACGCCCATGTCAAGGG Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006)
196
DF10207 CCGGCCGAACCGACTCCC Cluster II Defluviicoccus related TFO 35 (Meyer et al., 2006) H966 CTGGTAAGGTTCTGCGCGTTGC Helper probe for DF988 n/a (Meyer et al., 2006) H1038 AGCAGCCATGCAGCACCTGTGTGGCGT Helper probe for DF988 and DF 1020 n/a (Meyer et al., 2006) DF988c GCCGCGACGCCCATGTCAAGGG Competitor for DEF988 Chapter 4 DF198 ATCCCAGGGCAACATAGTCT Cluster III Defluviicoccus related ‘Nostocoida 35 Chapter 4 limicola’ DF1004 TAAGTTTCCTCAAGCCGC Sub group within cluster III Defluviicoccus 35 Chapter 4 related ‘Nostocoida limicola’ DF1004c TAACTTTCCTCAAGCCGC Competitor for DEF1004 Chapter 4 DF987H GACGCCCATGTCAAGGGC Helper for DEF1004 Chapter 4 DF1013 GAACTGAAGGCTCGAGTTTC Sub group within cluster III Defluviicoccus 35 50 Chapter 4 related ‘Nostocoida limicola’ DF1013c GAACTGAAGGCTTGAGTTTC Competitor for DEF1013 Chapter 4 DF997H CCCAAGCCGCGACGCCCA Helper for DEF1013 Chapter 4 DF181A CTTTCCCTCACAAGGCAC Clone K42 within cluster IV Defluviicoccus 30 Chapter 4 related TFO DF181B CTTTGCCCCTCAAGGCAC Some cluster IV Defluviicoccus related TFO 30 Chapter 4 AMAR839 CTGCGACACCGAACGGCAAGCC Amaricoccus spp. 20 (Maszenan et al., 2000a) Nso1225 CGCCATTGTATTACGTGTGA Betaproteobacterial ammonia oxidising 35 (Mobarry et al., 1996) bacteria Ntspa662 GGAATTCCGCGCTCCTCT genus Nitrospira 35 (Daims et al., 2001a) Ntspa662 comp GGAATTCCGCTCTCCTCT Competitor for Ntspa662 n/a (Daims et al., 2001a) HGC69a TATAGTTACCACCGCCGT Actinobacteria (high G+C Gram positive 25 (Roller et al., 1994) bacteria) Actino_1011 TTGCGGGGCACCCATCTCT Some Tetrasphaera spp. 30 (Liu et al., 2001) Actino 221 CGCAGGTCCATCCCAGAC Actinobacterial PAO 30 (Kong et al., 2005) C1 Actino 221 CGCAGGTCCATCCCATAC Competitor 1 for Actino 221 n/a (Kong et al., 2005) C2 Actino 221 CGCAGGTCCATCCCAGAG Competitor 2 for Actino 221 n/a (Kong et al., 2005) Actino 658 TCCGGTCTCCCCTACCAT Actinobacterial PAO 40 (Kong et al., 2005) C1 Actino 658 TCCGGTCTCCCCTACCAC Competitor probe 1 for Actino 658 n/a (Kong et al., 2005) C2 Actino 658 ATTCCAGTCTCCCCTACCAT Competitor probe 2 for Actino 658 n/a (Kong et al., 2005) LGC354A 8 TGGAAGATTCCCTACTGC Most Firmicutes (Gram positive bacteria with 35 (Meier et al., 1999) low G+C content) LGC354B 8 CGGAAGATTCCCTACTGC Most Firmicutes (Gram positive bacteria with 35 (Meier et al., 1999) 197
low G+C content) LGC354C 8 CCGAAGATTCCCTACTGC Most Firmicutes (Gram positive bacteria with 35 (Meier et al., 1999) low G+C content) CFX12239 CCATTGTAGCGTGTGTGTMG phylum Chloroflexi (green nonsulfur bacteria) 35 (Björnsson et al., 2002) GNSB 9419 AAACCACACGCTCCGCT phylum Chloroflexi (green nonsulfur bacteria) 35 (Gich et al., 2001) CHL1851 AATTCCACAACCTCTCCA Type 1851 filamentous bacterium 35 (Beer et al., 2002) PLA46 GACTTGCATGCCTAATCC Planctomycetales 30 (Neef et al., 1998) PLA886 GCCTTGCGACCATACTCCC Planctomycetales 35 (Neef et al., 1998) PLA886 comp GCCTTGCGACCGTACTCCC Competitor probe for PLA886 n/a (Neef et al., 1998) CF319a TGGTCCGTGTCTCAGTAC Most Flavobacteria, some Bacteroidetes, some 35 (Manz et al., 1996) Sphingobacteria CF319b TGGTCCGTATCTCAGTAC some Flavobacteria and Sphingobacteria 35 (Manz et al., 1996) CFB719 AGCTGCCTTCGCAATCGG Most members of the class Bacteriodetes, some 30 (Weller et al., 2000) Flavobacteria and Sphingobacteria CFB286 TCCTCTCAGAACCCCTAC Most members of the genus Tannerella and the 50 (Weller et al., 2000) genus Prevotella of the class Bacteriodetes 1[FA] = formamide concentration in hybridisation buffer, 2 9 applied in equimolar amounts as: 2 EUBmix, 3 PAOmix, 4 Gam42a_mix, 5 GBmix, 6 DF1mix, 7 DF2mix, 8 LGCmix, 9 CFXmix; n/a = not applicable; Note R = A:G, Y = C:T, M = A:C, K = G:T, S = G:C, W = A:T
198 6.3 Results 6.3.1 Effect of pH on EBPR Performance The daily variation in final effluent P levels in samples taken at the ends of the anaerobic and aerobic stages of the SBR operating at pH 7.5, 7.0 and 6.5 are given in Fig. 6.1. Initially the SBR was operated at pH 7.5. The amount of anaerobic P release increased steeply over the first 20 days, to about 110.7 mg P l 1, and the polyP biomass content reached about 9.7 % [w/w] after day 27. These values are typical for EBPR processes with acetate as the carbon source and consistent with a microbial community dominated by PAO (Oehmen et al., 2007). When the pH was changed from 7.5 to 7.0 on day 28, the P released under anaerobic conditions fell initially to about 62.1 mg P l 1 on day 47, but then gradually increased to 82.6 mg P l 1. Biomass polyP content also decreased to about 6.8 % [w/w]. On day 77, operating pH was again lowered from 7.0 to 6.5, a shift which led to a slight decrease in the level of P released anaerobically to 71.1 mg P l 1. Biomass P content had also fallen to 6.0 % [w/w] at pH 6.5. This trend is consistent with an increasing presence of GAO not accumulating polyP in their cells, and agrees with those seen by Filipe et al. (2001a) and Jeon et al. (2001). On the other hand, a marked improvement in EBPR capacity was observed when the pH was changed back from 6.5 to 7.5, where 90.9 mg P l 1 of anaerobic P release and 7.6 % [w/w] biomass P content were measured, increasing from 6 % [w/w] at pH 6.5. Despite further changes to the pH, efficient P removal was achieved for the remainder of the reactor operation (data not shown).
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Figure 6.1: Daily effluent P concentrations at the end of anaerobic and aerobic stages of the SBR under different operational pH conditions. Data provided by Dr Johwan Ahn. 6.3.2 Effect of operating pH on reactor chemical profiles The chemical profiles exhibited by the communities grown at these different pH values are given in Fig. 6.2. They show the typical patterns for EBPR processes carried out in SBR systems fed acetate (Mino et al., 1998; Oehmen et al., 2007). Thus, under anaerobic conditions, P release corresponded to acetate assimilation and an increase in biomass PHA content and glycogen utilisation, while complete P uptake and glycogen replenishment paralleled PHA utilisation in the subsequent aerobic stage. These observations are consistent with the existing EBPR activated sludge models. When operational pH was decreased, anaerobic biomass PHA production fell from 4.0 mM C at pH 7.5 to 2.2 mM C at pH 7.0, and 2.0 mM C at pH 6.5. On the other hand, glycogen utilisation levels increased markedly from 1.9 mM at pH 7.5 to 3.1 mM C at pH 7.0 and then 8.1 mM C at pH 6.5. These data again support the view that the GAO phenotype had become increasingly dominant in the community as operational pH decreased.
200
Figure 6.2: Typical profiles of the EBPR chemical transformations occurring in the SBR under different operational pH conditions. a. pH 7.5; b. pH 7.0; c. pH 6.5. Samples were taken after 27 days (pH 7.5), 76 days (pH 7.0) and 133 days (pH 6.5). Glycogen, PHA and acetate all expressed as mM carbon equivalents. Data provided by Dr Johwan Ahn.
201 6.3.3 Effect of operating pH on microbial community structure and ecophysiology FISH analysis with general broad target group probes (see Table 6.1) revealed that the community was always dominated by members of the Betaproteobacteria, and that, during the different periods of operation, alphaproteobacterial TFO, Gammaproteobacteria, ‘Chloroflexi’ and members of the Bacteroidetes group were also abundant (Table 6.2; Fig. 6.3). After the initial drop in pH, DGGE and FISH analysis suggested a drop in population diversity within the reactor community (Figs. 6.5 and 6.3). The Bacteroidetes group, present as small rods and thin filaments (Fig. 6.4a) at 10 % of the total biomass community on day 17, slowly disappeared from the system over time (Table 6.2; Fig. 6.3). The filamentous ‘Chloroflexi’, including the type 1851 morphotype responding to the CHL1851 probe (Fig. 6.4b and c.), persisted in the reactor where their abundance showed a statistically significant positive correlation with increasing operating pH (Fig. 6.7). Table 6.2: qFISH data for reactor Sample1 qFISH probe(s) pH Day PAOmix2 DF9883 DF10204 DF181A5 GBmix6 CFXmix7 CFB319a8 7.5 27 40.4±1.6% 2.0±0.2% 1.2±0.1% 1.0±0.1% <1.0% 10.0±0.8% 11.7±0.6% 7.0 72 67.7±3.5% 6.0±0.7% 5.0±0.5% 2.1±0.4% <1.0% 3.6±0.6% 3.4±0.4% 6.5 96 65.2±3.0% 5.7±0.6% 4.1±0.6% <1.0% <1.0% 1.9±0.4% 3.1±0.7% 6.5 179 68.7±2.7% 4.7±0.5% 3.2±0.4% 1.3±0.5% <1.0% 3.0±0.5% <1.0% 6.5 180 75.9±2.6% 4.5±0.9% 4.0±0.6% 2.3±0.6% 2.0±1.0% 3.4±0.7% 1.1±0. 4% 7.5 218 78.3±2.0% 2.0±0.2% 2.4±0.3% <1.0% <1.0% 7.4±0.2% <1.0% 7.0 303 62.3±2.6% 4.1±0.8% 2.6±0.3% <1.0% 6.5±1.7% 1.5±1.1% <1.0% 6.5 387 56.6±2.7% 7.4±0.6% 5.6±0.6% <1.0% 9.5±1.2% 1.9±0.2% <1.0% 1Sample details: including the day of operation and the operating pH at the time of sampling. 2 8 FISH probes used in qFISH analysis to target: 2 ‘Accumulibacter’; 3 4 Cluster II Defluviicoccus; 5 Cluster IV Defluviicoccus; 6 ‘Competibacter’; 7 ‘Chloroflexi’; 8 some Flavobacteria, Bacteroidetes and Sphingobacteria. FISH analysis revealed that the betaproteobacterial cells were almost exclusively ‘Accumulibacter’, which were the dominant population throughout the operation of the reactor irrespective of the pH conditions (Table 6.2; Figs. 6.3 and 6.5). Ecophysiology data revealed that members of this group behaved according to the PAO models at all three pH conditions, accumulating acetate (Fig. 6.8) as PHA anaerobically (Fig. 6.9) and utilising the stored PHA aerobically with P assimilation (Fig. 6.10) and storage (Fig. 6.11). The only other population observed to accumulate polyP aerobically were the actinobacterial PAO
202 responding to the Actino 658 probe (Fig. 6.12 g. i.). However these were detected in the early pH 7.5 FISH sample but rarely seen in subsequent samples, probably reflecting their inability to utilise acetate as a carbon source (Kong et al., 2005). When the pH was decreased from 7.5 to 7.0 the ‘Accumulibacter’ PAO levels increased and remained relatively unchanged following a subsequent decrease in pH to 6.5 before increasing when the pH was changed back to 7.5 and reducing again with subsequent drops to 6.5 via 7.0 (Table 6.2; Fig. 6.3). However, it should be noted that the initial increase in the ‘Accumulibacter’, after the initial drop in pH from 7.5 to 7.0, may have simply been due to continued acclimatisation of the biomass to the reactor, given the short 27 day pH 7.5 period, and the fact that no FISH analysis was completed on the initial ‘seed’ biomass. Despite being the only PAO present in apparent abundance, ‘Accumulibacter’ population levels did not show any statistically significant correlation with either the anaerobic P release levels or operating pH (Fig. 6.13 and 6.14). However, there was a high correlation between pH and anaerobic P release (Fig. 6.14).
203
Figure 6.3: Community composition profile with changing operational pH in the lab scale reactor (LS2). Operational pH at the time of sampling is indicated with a broken red line. Raw data is presented in Table 6.2.
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Figure 6.4: Composite CLSM FISH micrographs of populations of interest in the lab scale reactor. a. Phase contrast image and corresponding composite FISH image: EUBmix alone (FLUOS: green), CF319a (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue. b. and c. Composite FISH images: EUBmix alone (FLUOS: green), CHL1851 (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY3: blue) + EUBmix (FLUOS: green) = light blue. d. Phase contrast image and corresponding composite FISH image of the same field of view: EUBmix alone (FLUOS: green), DF181A (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS green) = light blue. All scale bars represent 10 m.
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Figure 6.5: DGGE analysis of the lab scale reactor EBPR community over time. Lanes: S = Standards prepared from a mixture of selected 16S rDNA clone inserts (see Fig. 6.6) with pre determined migration positions. These include: K90 = Legionella sp. related clone (EU834765); K8 and K60 = ‘Accumulibacter’ related clones (EU834753 and EU834760); K42 and K85 = Defluviicoccus related clones (EU834757 and EU834764); G54, G5, G18, G6 and G112 = ‘Competibacter’ related clones (FJ356053, FJ356049, FJ356052, FJ356050 and FJ356056); 1 7 = Reactor community samples. Times and the operational pH at the time of sampling were: 1 = day 17, pH 7.5; 2 = day 72, pH 7.0; 3 = day 96, pH 6.5; 4 = day 179, pH 6.5; 5 = day 218, pH 7.5; 6 = day 303, pH 7.0; 7 = day 387, pH 6.5.
206
207 Figure 6.6: Maximum likelihood tree of the complete sequences obtained from the LS2 reactor community from day 96 (data from Chapter 3) and selected related sequences (all sequences were at least 1250 bp long) using the ARB software package (Ludwig et al., 2004). Sequences obtained from the day 96 clone library are in bold typeface and sequences obtained from the LS2 reactor are all given in red. Clone frequency for each OTU is indicated in parenthesis. FISH probe coverage is shown in green and broad phylogenetic allocation is given in purple. For probe details see Table 6.1. Parsimony bootstrap values are calculated as a percentage of 1000 analysis and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position.
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Figure 6.7: Linear regression analysis of operational pH against total cluster II Defluviicoccus, and ‘Chloroflexi’, population size determined by qFISH. The plot and statistical analysis were completed using the GraphPad Prism 4 software program. ‘*’ Denoted statistical significance of R2 values. TEM of ‘Accumulibacter’ cells, during the first pH 6.5 period, revealed they contained sparse numbers of small electron dense polyP granules, but instead most of the cell was filled with electron transparent material (Fig. 6.15b). Occasional larger polyP granules appeared to have become dislodged during sectioning, resulting in obvious holes in the sections. Subjectively DAPI staining suggested that aerobic P storage within cells appeared generally lower for members of this group after the initial drop from pH 7.5 at day 27, where the dense microcolonies present at pH 7.5 and 7.0 gave a much brighter positive fluorescence (Fig. 6.11).
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Figure 6.8: FISH MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Accumulibacter’. a. d. pH 6.5 (day 422); e. h. pH 7.0 (day 341); i. l. pH 7.5 (day 446). a., c., e., g., i. and k. Composite FISH images: EUBmix alone (FLUOS: green) and PAOmix (CY3: red) + EUBmix (FLUOS: green) = yellow; b., d., f., h., j. and l. Corresponding positive MAR bright field images for adjacent (left) FISH images. Silver grain deposition indicates positive uptake for associated cells. All scale bars represent 10 m.
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Figure 6.9: Micrographs of Nile blue A stained ‘Accumulibacter’ PAO. a. f. pH 6.5 (day 387); g. l. pH 7.0 (day 303); m. r. pH 7.5 (day 446). a., d., g., j., m. and p. Phase contrast images of the reactor biomass; b., e., h., k., n., and q. corresponding FISH images: PAOmix (FLUOS: green); c., f., i., l., o. and r. Corresponding Nile blue A stained fluorescence. All scale bars represent 10 m.
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Figure 6.10: FISH MAR micrographs for aerobic 33P phosphate uptake experiments. a. d. pH 6.5 (day 422): a. and c. Composite FISH images for the ‘Accumulibacter’: PAOmix (CY3: red) + EUBmix (FLUOS: green) = yellow. b. and d. Corresponding bright field MAR positive images; e. j. pH 7.0 (day 341): e., g. and i. Composite FISH image for e. and g. alphaproteobacterial TFO: ALF968 (CY3: red) + EUBmix (Flous: green) = yellow and i. ‘Competibacter’: GB_G1 (CY3: red) + EUBmix (FLUOS: green) = yellow. f., h. and j. Corresponding bright field MAR negative images for the target organisms; k. n. pH 7.5 (day 446): k. and m. Composite FISH images for the ‘Accumulibacter’: (PAOmix (CY3: red) + EUBmix (Flous: green) = yellow). l. and n. Corresponding bright field MAR positive images. All scale bars represent 10 m.
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Figure 6.11: Micrographs of DAPI stained ‘Accumulibacter’: a. c. pH 7.5 (day 27); d. f. pH 7.0 (day 72); g. i. pH 6.5 (day 96); j. l. pH 7.0 (day 341); m. o. pH 6.5 (day 422); p. r. pH 7.5 (day 446); a., d., g., j., m. and p. Phase contrast images of the reactor biomass; b., e., h., k., n. and q.; Corresponding field of view composite FISH images with EUBmix alone (FLUOS: green) and PAOmix (CY3: red) + EUBmix (FLUOS: green) = yellow; c., f., i., l., o. and r. Corresponding DAPI stained images. DNA stains light blue and polyP granules stain light yellow. All scale bars represent 10 m.
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Figure 6.12: Micrographs of DAPI stained populations of interest: a. c. pH 7.5 (day 27) for the Flavobacteria Bacteroidetes group (CFB319a); d. f. pH 7.0 (day 72), for the cluster IV Defluviicoccus (DF181A); g. i. pH 6.5 (day 96), for the Actinobacteria (HGC69A); j. l. pH 7.0 (day 341), for the cluster II Defluviicoccus (DF2mix); m. o. pH 6.5 (day 422), for the ‘Chloroflexi’ (CFXmix); p. r. pH 7.5 (day 446) for the ‘Competibacter’ (GBmix); a., d., g., j., m. and p. Phase contrast images of the reactor biomass; b., e., h., k., n. and q.; Corresponding field of view composite FISH images with EUBmix (Flous = green) and target group probe (CY3 = red) (red + green = yellow/orange); c., f., i., l., o. and r. Corresponding DAPI stained images. DNA stains light blue and polyP granules stain light yellow. Arrows highlight cells of interest. All scale bars represent 10 m.
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Figure 6.13: Linear regression analysis of total ‘Accumulibacter’ cell biovolume against the concentration of supernatant P at the end of the anaerobic phase. The plot and statistical analysis was completed using the GraphPad Prism 4 software program. The deviation of the R2 value from 0 was determined to be statistically insignificant. The Amaricoccus, and cluster I Defluviicoccus FISH probes bound to some TFO present in these communities, but very rarely, and cluster III Defluviicoccus members were never detected. Of the probes targeting cluster IV Defluviicoccus, the DF181A probe bound to freely dispersed TFO as well as clustered cocci within the biomass (Fig. 6.4d), but these cells were always low in abundance and eventually disappeared from the community (Table 6.2; Fig. 6.3). The DF1020 probe, targeting cluster II Defluviicoccus bound to the majority of the TFO present. The DF988 probe hybridised with the same TFO, but also with small cocci arranged in clusters (Fig. 6.16), indicating morphological and phylogenetic diversity exists among cluster II members in this system. Unlike the ‘Accumulibacter’, DF988 FISH positive cells, their abundance appeared to be statistically significantly related to pH, showing a negative correlation with increasing operating pH over the range applied here (Table 6.2; Fig. 6.3 and 6.7).
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Figure 6.14: Linear regression analysis of operational pH against total ‘Accumulibacter’ cell biovolume and the concentration of supernatant P at the end of the anaerobic phase. The plot and statistical analysis was completed using the GraphPad Prism 4 software program. Statistical significance of R2 values are denoted by a ‘*’.
Figure 6.15: Electron microscope micrographs: a. SEM of a granule produced at pH 7.0; b. TEM of a PAO cell in a sample taken at the end of the aerobic stage at pH 6.5 (Dr Johwan Ahn).
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Figure 6.16: Composite CLSM FISH micrographs of cluster II Defluviicoccus. a. Phase contrast of the reactor biomass and the corresponding composite FISH image: EUBmix alone (FLUOS: green), DF988 (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue . b. and d. Composite FISH image: EUBmix alone (FLUOS: green), DF988 (CY3: red) + EUBmix (FLUOS: green) = yellow and DF1020 (CY5: blue) + DF988 (CY3: red) + EUBmix (FLUOS: green) = white/lavender. c. and e. Composite FISH image: EUBmix alone (FLUOS: green), DF2mix (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue. All scale bars represent 10 m.
219 ‘Competibacter’ were rarely detected until the sample taken at day 303 of operation, where they increased after a drop in pH from 7.5 to 7.0. Their numbers then further increased when pH was dropped further to 6.5 (Table 6.2; Fig. 6.3). The FISH positive cells appeared as characteristic ‘Competibacter’ large coccobacilli (Fig. 6.17). Interestingly, their population increase appeared to be at the expense of the ‘Accumulibacter’ population, suggesting some direct competition, while cluster II ‘Defluviicoccus’ abundance also increased during this period (Table 6.2; Fig. 6.3.). Both the ‘Competibacter’ and cluster II Defluviicoccus cells assimilated acetate and accumulated PHA anaerobically (Figs. 6.18 6.21), without any subsequent aerobic P assimilation or polyP storage (Fig 6.10e j., 6.12j.,l.,p. and r.) indicating they conformed to the proposed GAO phenotype. 6.3.4 Effect of operating pH on biomass organisation Changing pH also had a marked influence on the organisation of the biomass in the SBR (Fig. 6.22). Thus, at pH 7.5 and the early stages at pH 7.0, biomass appeared as loosely associated flocs with a visible filamentous matrix (Fig. 6.22a c). FISH analyses showed they contained large numbers of often spherical and densely packed microcolonies of ‘Accumulibacter’ cells (Fig. 6.22a c). Then at pH 7.0, the biomass changed and visually gradually became granulated, a transformation coinciding with the recovery of EBPR performance following the pH change (Fig. 6.1). Far fewer flocs were also present. These granules (up to 1 mm in diameter) were regularly spherical with smooth outer surfaces (Fig. 6.15a), and FISH analysis at pH 7.0 and 6.5 showed that they contained much larger aggregates of ‘Accumulibacter’ cells (Figs. 6.23d. g. and 6.23) and a few ‘Chloroflexi’ filamentous bacteria (Fig. 6.23d). Some of the larger clusters in individual granules appeared to be aggregates of smaller microcolonies of ‘Accumulibacter’, indicating a possible starting point for their formation (Fig. 6.22d g). At pH 6.5 the granules increased in size to about 2 3 mm in diameter, and FISH of thin sections revealed they consisted almost entirely of ‘Accumulibacter’ cells (Fig. 6.23). Only occasional small clusters of ‘Competibacter’ (Fig. 6.23c) and cluster II Defluviicoccus (Fig. 6.23e) were observed, but these were randomly distributed with no evidence of spatial population stratification.
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Figure 6.17: Composite CLSM FISH micrographs of ‘Competibacter’. a., d. and e. Composite FISH image: EUBmix alone (FLUOS: green), GB_G1 (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue. b. Phase contrast image of the reactor biomass showing the distinctive coccobacilli morphology of ‘Competibacter’ cells. c. Phase contrast image of the reactor biomass and corresponding composite FISH image of the same field of view: EUBmix alone (FLUOS: green), GB_G1 (CY3: red) + EUBmix (FLUOS: green) = yellow and PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue. All scale bars represent 10 m. 221
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Figure 6.18: FISH MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Defluviicoccus’. a. d. pH 6.5 (day 422); e. h. pH 7.0 (day 341); i. l. pH 7.5 (day 446). a., c., e., g., i. and k. Composite FISH images with EUBmix alone (FLUOS: green) and DF2mix (CY3: red) + EUBmix (FLUOS: green) = yellow; b., d., f., h., j. and l. Corresponding positive MAR bright field images for adjacent (left) FISH images. All scale bars represent 10 m.
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Figure 6.19: FISH MAR micrographs for anaerobic 14C acetate uptake experiments for the ‘Competibacter’. a. d. pH 6.5 (day 422); e. h. pH 7.0 (day 341); i. l. pH 7.5 (day 446). a., c., e., g., i. and k. Composite FISH images: EUBmix alone (FLUOS: green) and GBmix (CY3: red) + EUBmix (FLUOS: green) = yellow; b., d., f., h., j. and l. Corresponding positive MAR bright field images for adjacent (left) FISH images. All scale bars represent 10 m.
225
Figure 6.20: Micrographs of Nile blue A stained cluster II ‘Defluviicoccus’. a. f. pH 6.5 (day 387); g. l. pH 7.0 (day 303); m. r. pH 7.5 (day 446). a., d., g., j., m. and p. Phase contrast images of the reactor biomass; b., e., h., k., n., and q. corresponding FISH images: DF2mix (Flous = green); c., f., i., l., o. and r. Corresponding Nile blue A stained fluorescence. All scale bars represent 10 m.
226
Figure 6.21: Micrographs of Nile blue A stained ‘Competibacter’. a. f. pH 6.5 (day 387); g. l. pH 7.0 (day 303); m. r. pH 7.5 (day 446). a., d., g., j., m. and p. Phase contrast images of the reactor biomass; b., e., h., k., n., and q. corresponding FISH images with GBmix (Flous = green); c., f., i., l., o. and r. Corresponding Nile blue A stained fluorescence. All scale bars represent 10 m.
227 qFISH analysis of a biomass sample manually enriched with granules (approximately > 2 mm in diameter) indicated that the ‘Accumulibacter’ were at a higher abundance in granules when compared to the total biomass at day 257 (Fig. 6.24). This difference is likely to be even more substantial given that this quantitative comparison was made against the total biomass community (Fig 6.24) which was also dominated by granulated biomass, albeit to a smaller extent. When the pH was switched back to 7.5 again after day 184, the granules appeared to become less stable and began to disintegrate within 24 h, so that they gradually were seen much less commonly. They had been largely replaced after several days with loosely aggregated biomass with a higher level of observed filamentous bacteria. These were restricted largely to members of the ‘Chloroflexi’. These changes in aggregate organisation were reflected in biomass mixed liquor suspended solids (MLSS) values that changed from 2900 (±25) mg l 1 at initial pH 7.5 (day 27) rising to 4500 (±50) mg l 1 at pH 7 (day 76) and 5600 (±50) mg l 1 at pH 6.5 (day 183), before falling to 3570 (±30) mg l 1 when pH was increased to 7.5 (day 218). When the pH was then dropped to pH 7.0 at day 351, granulation was again observed, with individual granules increasing in size following a further drop to 6.5 at day 425. These granules became even larger (some approaching 5 mm in diameter) and appeared less dense and more loosely associated than the granules observed earlier. Sectioning revealed the presence of visible internal cavities, although, because of their fragile nature, repeated attempts at FISH analysis to determine population arrangement within them were unsuccessful (data not shown).
228
229
Figure 6.22: FISH micrographs of biomass: a c. (provided by Dr Sarah Schroeder) Sample taken at pH 7.5 (day 17). a. Phase constrast; b. after FISH with RHC439 probe (CY3: red); c. after DAPI staining showing polyP fluorescence (yellow); d. and f. Phase contrast images of the LS2 biomass taken pH pH 7.0 (day 72); e. and g. Corresponding fields of view after FISH probing with PAOmix probes (CY3). All scale bars represent 20 m..
230
231
Figure 6.23: CLSM Fluorescent images of sections of granules produced at pH 6.5 (day 180). a. Phase contrast micrograph of granule section and corresponding FISH image (below) overlay showing bacteria hybridised with: EUBmix only (FLUOS: green); PAOmix (CY5: blue) and EUBmix (FLUOS: green) = light blue; b. e. Composite FISH images with: EUBmix only (FLUOS: green); PAOmix (CY5: blue) + EUBmix (FLUOS: green) = light blue; c. GBmix (CY3: red) + EUBmix (FLUOS: green) = yellow d. CFX1223/GNSB 941 (CY3: red) + EUBmix (FLUOS: green) = yellow orange; e. DF988 (CY3: red) + EUBmix (FLUOS: green) = yellow. All scale bars represent 40 m. Arrows are used to highlight cells of interest.
232
Figure 6.24: Comparison of the total community composition with that present in the granules alone. For the granule sample, granules greater than approx. >2 mm were manually selected and processed as described in Section 6.2.3. Raw data is presented in Table 6.2. 6.4 Discussion The influences of culture pH on long term community structure, and competition between organisms with PAO and GAO phenotypes in a lab scale EBPR SBR were investigated with the suite of probes now available for known PAO and GAO. These included the probes designed in Chapter 4. Unlike the few studies applying molecular community profiling (Oehmen et al., 2005a; Zhang et al., 2005; Lopez Vazquez et al., 2008a), a shift away from the ‘Accumulibacter’ PAO was not observed as the pH was lowered. Instead, ‘Accumulibacter’ population size
233 increased and they continued to dominate the community in the reactor run for more than 15 months. Few or no Defluviicoccus or ‘Competibacter’ GAO cells were seen. The cluster II ‘Defluviicoccus’ population did show a linear relationship between its relative abundance and decreasing operational pH, agreeing with findings for the unidentified alphaproteobacterial GAO of Oehmen et al. (2005a). However, the failure of these alphaproteobacterial GAO to become dominant over ‘Accumulibacter’ at lower pH (≤7.0) contradicts the findings of the previous study, where in two separate reactors, the alphaproteobacterial and ‘Competibacter’ GAO populations dominated at a pH of 7.0 with a shift to ‘Accumulibacter’ dominance only after a shift in the pH to 8.0 (Oehmen et al., 2005a). However, contrary to the previous study, evidence presented here suggests that pH also influenced biomass structure, where at lower pH conditions (≤7.0), and relatively higher shear, the structure of the biomass changed from a floccular to a granular organisation. Apparent enrichment of the ‘Accumulibacter’ in these granules allows their continued dominance, due to their superior settleability to standard activated sludge flocs, despite exposure to pH conditions thought to favor ‘Competibacter’ and the alphaproteobacterial GAO (Oehmen et al., 2005a; Oehmen et al., 2007). The late appearance in these granules of ‘Competibacter’ probably reflects their low abundance in the seed inoculum. A switch from ‘Competibacter’ dominance to dominance by the Defluviicoccus related GAO was recorded after 85 days operation of an anaerobic: aerobic membrane bio reactor (Wong and Liu, 2006). This marked change was also attributed to the low level of Defluviicoccus in the initial seed culture used. It eventually dominated because it was thought to be slightly better adapted to the long sludge age used in their study (Wong et al., 2006). The prolonged operational period at pH 6.5 in this study probably gave the ‘Competibacter’ cells an opportunity to eventually become established. Increasing the reactor pH to 7.5 led to the disintegration of the firm spherical granules, and as the ‘Competibacter’ are reportedly favoured at lower pH (Oehmen et al., 2005a) their population size as expected, increased dramatically when operating pH dropped apparently at the expense of the ‘Accumulibacter’ (Table 6.3). Whether ‘Accumulibacter’ would still have dominated as they did here if the seed had contained higher initial levels of ‘Competibacter’ is an interesting question. Only one other report is known where pH has been reported to influence EBPR granule stability (Lemaire et al., 2008a) in a reactor treating an abbatoir waste feed, granules
234 became less stable at pH 6.5 than at pH 7.5. These trends are the opposite to those found here, although any possible influences of long term exposures on granular stability to pH shifts were not followed by them (Lemaire et al., 2008a). The flocs forming in this present study were also much more regularly spherical and smoother than theirs (Lemaire et al., 2008a) (Fig. 6.15a), and no surface attached protozoa were seen, possibly because of the higher shear used. This example clearly illustrates the problems of assuming that a single mechanism is responsible for all biomass granulation processes. Thus, biomass in the reactor operating at pH 7.5 and the early stages of pH 7.0 was organised almost entirely as small flocs containing mostly microcolonies of densely packed ‘Accumulibacter’ cells staining positively for polyP. These occupied a large portion of the floc volume (Fig. 6.22a c), suggesting that they could persist there under the imposed shear forces better than most other floc associated populations, which may have become physically dislodged as flocs eroded. The ability of PAO microcolonies to withstand high shear and tolerate physical stress better than other floc populations has been documented (Larsen et al., 2006). Consequently, under the conditions applied here it may be that the persisting flocs eventually became highly enriched with ‘Accumulibacter’ microcolonies, and the granules then formed, possibly by subsequent microcolony co aggregation (Fig. 6.22d g). Such a mechanism is not inconsistent with the TEM data of Lemaire et al. (2008a), which show clearly delineated and spatially related heterogeneities in cell density and exocellular polymeric substances (EPS) matrix appearance within individual granules. This is exactly what might be expected if aggregation of different individual microcolonies was involved in their formation. It might also explain the presence of regular cell free channels, which were also seen in these EBPR granules (Fig. 6.23). Furthermore this proposal provides a mechanism for the reported increases in their diameter, which may also involve increases in sizes of individual co aggregated microcolonies from PAO replication. Because of their operational attractions, interest in using granulated biomass instead of flocs in SBR systems is increasing. Their rapid settling rates provide an opportunity to generate and sustain very high biomass levels (de Kreuk and de Bruijn, 2004; de Kreuk et al., 2005a), allowing shorter treatment times in smaller reactors. Whether granules always arise from pre existing flocs is unclear from the literature because the early stages in
235 granule formation have not always been followed. Encouraging slow growth rates of populations with pre existing high cell co aggregation propensities may assist (de Kreuk and van Loosdrecht, 2004; Liu and Tay, 2004; Li et al., 2006), but less certain is whether imposed cell starvation (Liu and Tay, 2006) leading to increased EPS hydrophobicity in the floc matrix does (Liu et al., 2007a). Short SBR settling times (Ivanov et al., 2006), where more loosely aggregated biomass is lost from the reactor may also influence biomass aggregation (McSwain et al., 2005) and operating temperature (de Kreuk et al., 2005b), shear (Chen et al., 2007) and dissolved oxygen concentrations (Mosquera Corral et al., 2005) all seem to determine granule stability. However, explanations for these events, where given, are not always persuasive, and there may be several quite different mechanisms involved in granule production. For example the observations of Weber et al. (2007) suggesting that protozoa and fungi were crucial initially in providing the matrix for bacterial growth and EPS production in their granules is quite different to the situation with those examined here. Consequently, as discussed above, it may not be possible to sustain a unified mechanistic theory for granulation, as its one common feature is that it seems to occur mainly in SBR systems (Liu et al., 2005). Interestingly, despite the observed trends in the microbial population, the chemical profile data indicated a clear shift in the dominant phenotype of the community over this period and an observed linear relationship between levels of anaerobic P release, reflecting higher PAO activity, and increasing operational pH. This finding agrees with earlier observations (Smolders et al., 1994a; Liu et al., 1996a; Romanski et al., 1997; Bond et al., 1999a; Bond et al., 1999b; Pijuan et al., 2004b; Oehmen et al., 2005a; Liu et al., 2007b). Smolders et al. (1994a) suggested that elevated polyP hydrolysis is required to meet the increased energy demand for acetate transport into the PAO cells at a higher pH, because of the larger pH gradient across the cell membrane. Chemical data (Figs. 6.1 and 6.2) obtained from the SBR reactor in this study also showed that P levels in cells taken at the end of the aerobic stages from the initial pH 7.0 and 6.5 periods fell, while their glycogen contents increased. Such observations are consistent with a population shift from a PAO to a GAO dominated community (Oehmen et al., 2007). Serafim et al. (Serafim et al., 2002b) suggest that the increase in energy required for acetate uptake at a high pH is better met by the PAO who have polyP as an additional energy source. Less apparent is the advantage the GAO phenotype has at lower pHs. Filipe et al. (Filipe et al., 2001a) showed that under these conditions aerobic P
236 assimilation and growth were inhibited in their PAO enriched culture, but not in the GAO enrichment, at pH <7.0. However, the reasons for this are not clear, especially since the identities of their PAO and GAO populations were not determined. Complicating any interpretation of the chemical transformations presented here is the potential impact on organism behaviour of substrate diffusion gradients into granules of increasing size (Meyer et al., 2003). Falkentoft et al. (2001) suggested that increases in biofilm thickness may create limitations in P diffusion rates. Furthermore, microsensors could reveal that during the aerobic EBPR phase, oxygen did not penetrate beyond the outer 100 200 m of granules, thus creating a central anoxic zone (Meyer et al., 2003; Kishida et al., 2006). This effect may explain why ‘Accumulibacter’ have been seen restricted to granule outermost layers (Lemaire et al., 2008b). Nile blue A staining of granule sections from an aerobic:anaerobic SBR community dominated by ‘Competibacter’ showed that microbial activity did not occur beyond the aerobic outer layer, even though inactive GAO were detected further in Meyer et al. (2003). Microsensors would be valuable in determining the distribution of active cells in the granules generated in the present study. However, any inactivity of layered ‘Accumulibacter’ cells does not explain the changes noted here at the different operating pH in the levels of the glycogen transformations in these granules. TEM micrographs of individual tentatively identified ‘Accumulibacter’ cells in samples taken from the initial pH 6.5 reactor period showed they always contained large amounts of electron transparent material (Fig. 6.15b). This is unlikely to be PHA, since none could be detected chemically by GC or after Nile blue A staining of cells taken at the end of the aerobic stage (Fig. 6.2), which showed only a few contained PHA granules (data not shown). Therefore it seems probable that this pH shift does not result in major increases in GAO populations, but that the ‘Accumulibacter’ there rely increasingly under these conditions on intracellular glycogen cycling for the supply of anaerobic reducing equivalents (Fig. 6.2). This suggestion is supported by the observation of generally lower polyP storage levels in ‘Accumulibacter’ cells at lower pH values (data not shown). The ability of ‘Accumulibacter’ PAO to adopt the GAO phenotype has also been shown to occur under P limitation (Zhou et al., 2008) and high Ca2+ concentrations (Barat et al., 2006; Barat et al., 2008). The presence of ‘Accumulibacter’ cells in full scale EBPR plants not accumulating polyP has also been reported widely (Zilles et al., 2002a; Zilles et al., 2002b; Kong et al., 2004; Wong et al., 2005; Beer et al., 2006).
237 While some may have been inactive, Kong et al. (2004) did show with FISH MAR that cells responding to the ‘Accumulibacter’ probes would take up acetate anaerobically without aerobic polyP cycling. The possibility that different PAO phylotypes exist in EBPR systems with different P storage capacities or tolerances to pH also deserves attention. DGGE profiles of the 16S rRNA genes present in the communities examined here, with the exception of the initial pH 7.5 sample (taken on day 17), all possessed the same dominant band, which was identified putatively as ‘Accumulibacter’ (Fig. 6.5). However the resolving power of the 16S rRNA gene in revealing diversity within this group has been questioned (see Chapter 1) and the ppk gene is thought to provide a higher phylogenetic resolution (He et al., 2006; McMahon et al., 2007b). The inference of dominance based on PCR DGGE band fluorescence intensity is also questionable, bearing in mind the biases associated with all PCR based methods (von Wintzingerode et al., 1997; Nocker et al., 2007; de Araújo and Schneider, 2008). Application of the ppk gene based quantitative real time PCR method (He et al., 2006) to this reactor communities may have elucidated whether different ‘Accumulibacter’ phylotypes occurred at the different pH conditions used. 6.5 Concluding remarks Smooth regular granules consisting almost exclusively of ‘Accumulibacter’ were generated in an anaerobic: aerobic EBPR SBR operating at high shear rate, coinciding with a drop in operating pH from 7.5 to 7.0 and then 6.5. Increasing pH back up to 7.5 saw them disintegrate partially. It is proposed that these granules form from existing flocs, consisting mainly of ‘Accumulibacter’ PAO microcolonies, which co aggregate initially into small spherical granules that increase in size, probably by the same process. Chemical profiles and cell staining suggest that the GAO phenotype becomes more dominant as pH falls. The recorded increases in consumption of glycogen to replace polyP (Fig. 6.2) as an anaerobic energy source as seen by decreases in level of P release, are consistent with this proposal (Oehmen et al., 2007), but FISH data reveal little or no change in granule community composition. Instead, the PAO store aerobically little intracellular polyP but considerable glycogen, consistent with a partial switch in phenotype from a PAO to GAO. This study illustrates the importance of detailed semi quantitative community composition studies, since at the commencement of this study
238 only cluster I ‘Defluviicoccus’ members had been described and consequently FISH probes were available only for their detection.
239 7.0 Conclusions and Future Directions
7.1 Conclusions The work presented in this thesis sought to clarify the true extent of the phylogenetic and physiological diversity among the GAO present in EBPR systems where their presence is thought to reduce P removal efficiency. Particular attention was paid to the alphaproteobacterial related GAO and overcoming the difficulties reported in the literature which had hampered their experimental identification. The major findings of this study are: • The effectiveness of different DNA extraction methods for the Defluviicoccus related organsisms was addressed, and the reported problems obtaining DNA of a suitable quality for PCR were probably the consequence of their propensity to form encapsulated clusters of cells within flocs resistant to lysis. Flow cytometric and micromanipulation based enrichment techniques partially overcame these biases. Such approaches may be especially appropriate where GAO populations are present at very low abundances, as is often the case with full scale EBPR systems (Kong et al., 2006; Burow et al., 2007; Nielsen et al., 2010b). • The application and optimisation of such extraction and enrichment techniques led to the identification of two additional phylogenetic clusters of Defluviicoccus related organisms, and ecophysiological studies with FISH MAR revealed that they, like members of the two previously described clusters, possessed the GAO phenotype. The ability to assimilate acetate and synthesise PHA anaerobically would suggest these could compete for substrates with the PAO in EBPR systems. • Unexpectedly the cluster III members identified were not the characteristic tetrad shaped cells distinctive of all previous Defluviicoccus populations, but instead exhibited a filamentous ‘Nostocoida limicola II’ morphotype and embraced the bulking filament Candidatus ‘Monilibacter batavus’. Thus, these GAO may also be important in settling problems in full scale systems.
240 • Distribution studies found that these Defluviicoccus related organisms and cluster III members in particular, dominated some large scale EBPR plant communities. Therefore, they should be considered as important populations in EBPR communities, and not as has often been the case in the past, laboratory curiosities only. • Of other putative alphaproteobacterial GAO, Amaricoccus were confirmed to be unimportant in anaerobic: aerobic EBPR systems, reflecting their inability to assimilate substrates anaerobically. However, these may be important members of aerobic industrial treatment plants where high C: P feed ratios are present, and may share a similar ecological niche under such conditions with Defluviicoccus related GAO. • The putative Sphingomonas related alphaproteobacterial GAO were identified as members of cluster I Defluviicoccus. The evidence presented here reveals that the original SBR9 1a FISH probe, designed against a chimeric artificial 16S rRNA sequence and applied in the incorrect orientation, hybridised to Defluviicoccus cluster I members by forming a bulge over a ‘missing’ base in an otherwise perfectly matched sequence, leading to false positive FISH results. Such a possibility has never been demonstrated previously but requires attention in any FISH probe design exercise. • Detailed examination of all the ‘Competibacter’ related 16S rRNA sequences currently available also revealed further phylogenetic diversity beyond the currently described seven sub groups for this GAO group. • The application of the updated PAO and GAO FISH probe sets to a lab scale EBPR SBR community revealed that it was always dominated by ‘Accumulibacter’. There appeared to be a partial shift within ‘Accumulibacter’ from a PAO to a GAO phenotype with decreasing operating pH. The granular structure of this reactor biomass was thought to ensure that ‘Accumulibacter’ continued to dominate in the system.
241 7.2 Future work The increase in the known phylogenetic diversity among the GAO groups revealed in this study suggests that further biodiversity is almost certain to exist among them, and that probability justifies the need for continued studies of this kind. There is also a need to direct these studies at more full scale plant communities of higher complexity than those in lab scale reactors where most of the current 16S rRNA sequence information derives. The marked phenotypic differences among the FISH probe defined GAO phylotypes reported from different studies suggest that a gene giving higher resolution may help in niche discrimination among them. The FACS enrichment technique applied in the work described in this thesis may allow the difficult link between the phylogeny of any candidate genes to the conventional 16S rRNA gene to be resolved The ppk gene is one candidate gene that has been successfully applied to resolve finer scale phylogenetic diversity among the ‘Accumulibacter’ PAO. The possibility from this study that different clades contribute to the suggested metabolic flexibility of the ‘Accumulibacter’ to cope with changing operating conditions observed in this study deserves further attention. As with the ‘Accumulibacter’ (He et al., 2007) and ‘Competibacter’ (Kong et al., 2006), members of the different sub groups of the Defluviicoccus related GAO also appear to co exist in full scale systems. Further in situ studies, and comparative genomic analysis will help to reveal if major functional differences exist between these phylogenetic sub groups and increase our understanding of what role this diversity might play in their ecology The application of FISH MAR has been invaluable in the in situ study of these organisms, but it only provides a snap shot of an organism’s ecophysiology, and does not generate readily high quality quantitative data on substrate uptake kinetics. Raman FISH microscopy (Huang et al., 2007) and NanoSIMS (Li et al., 2008) should be applied to answer the critically important question of whether the PAO or GAO populations assimilate substrates faster anaerobically, and under what operating conditions is each favoured in this way. One outcome would be to resolve the influence of pH on PAO:GAO competition, particularly its influence on the key metabolic features thought to provide the GAO with an advantage at low operating pH.
242 A comprehensive understanding of GAO physiology will come most readily from pure culture studies of these. With next generation DNA sequencing protocols now becoming cheaper and more reliable, it becomes a comparatively simple task to sequence all their genomic DNA, where all their metabolic potential will be revealed. Defluviicoccus vanus is currently the only population with a demonstrated GAO phenotype available in pure culture. Having such information will provide detailed insight into how this organism behaves in EBPR systems providing subsequent knowledge about how their growth might be controlled and to provide the PAO with an advantage. Genome annotation has been invaluable to our understanding of the ‘Accumulibacter’ and with the associated proteomic and transcriptomic studies of these organisms in situ will address many of the unanswered questions in EBPR microbiology. The application of this approach to the GAO will no doubt be just as fruitful.
243 8.0 Appendices Appendix 1: Assessment of probe defined ‘Competibacter’ phylotype diversity
244
245 Figure A1.1: Maximum likelihood tree of all available complete ‘Competibacter’ related sequences (>1200 bp) using the ARB software (Ludwig et al., 2004). Brackets to the far right indicate proposed probe defined phylogenetic clusters. Inner brackets indicate probe coverage (a broken line indicates the absence of sequence information at the probe site). For probe details see Table A1.1. ‘*’ Indicates selected partial sequences (< 1200 bp) added using the ‘quick add’ function in ARB after tree construction. Parsimony bootstrap values are calculated as a percentage of 1000 analysis and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position.
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Table A1.1: Assessment of probes available for the ‘Competibacter’ GAO Perfect match Mismatch Name Proposed target Notes Ref (E coli. Pos) [FA] Target Non Single Weak Accounted % group target Mismatch for‡ GB All ‘Competibacter’ 35 64/69 5* 5* 2* 0 • Perfectly matches another (Kong et al., (655 671) gammaproteobacterial sequence isolated from 2002a) soil (FM242308) and 4 ‘Chloroflexi’ sequences isolated from wastewater • Single weak mismatches with two members of the Gammaproteobacteria isolated from activated sludge (AB247497 and DQ232432) • Conclusion: Currently the best option to cover the GB group. Provides better coverage and specificity than GBmix (GB_G1 and GB_G2) probes GAOQ989 All Group 1 of GB 35 46/56 9* 127** 79** 25 • Also applied as a mix with GB_G2 to cover (Crocetti et al., (GB_G1) 55 whole GB group 2002) (988 1005) • Perfectly targets another GB_G1comp Competitor for gammaproteobacterial sequence from soil (GB_G2) GAOQ989 (FM242308) (988 1005) • Weak mismatch to members of Group 2 covered by GB_G1 competitor probe • Also has weak mismatch with closely related sequences (FJ623321 and FJ623304) • Conclusion: Can be used to exclude GB_G2 group sequences. GB_G2 All Group 1 of GB 35 13/13 9* 230** 160** 53 • Used as a mix with GB_G1 probes to cover (Kong et al., (988 1005) 55 whole GB group 2002a) GB_G2 comp Competitor for • Perfectly matches 9 non target sequences (GB_G1) GAOQ989 including 4 clustering within the (988 1005) Gammaproteobacteria (DQ499324,
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DQ499296, DQ499304 and DQ499327) from a biofilm). • Mismatch to members of Group 1 members covered by GB_G2 competitor probe • Has weak mismatches to 3 sequences clustering within the GB group (AB255053, AJ504486 and FJ438010). • Conclusion: Can be used (with caution) to exclude most members of the GB_G1 group GB_1 and 2 All members of GB 20 18/19 5** 0 0 0 • Must be used with GB_2 to ‘confirm’ ‘sub (Kong et al., (587 603) sub group 1 and 2 group 1’ sequences 2002a) • Does not cover adequately any phylogenetically defined cluster within the GB • Conclusion: Not useful GB_2 All sub group 2 ND 14/15 0 21** 21** 0 • Weak mismatch to a number of closely related (Kong et al., (65 98) non target sequences 2002a) • Conclusion: Use with caution to cover sub group 2 members GB_3 All sub group 3 ND 6/6 0 0 0 0 • Conclusion: Use to cover sub group members (Kong et al., (642 659) 2002a) GB_4 All sub group 4 35 10/12 0 24** 24** 16 • Weak terminal mismatch to most sub group 2 (1020 1038) members covered by GB_4 for which an un GB_4 Comp Competitor for validated competitor probe is available (1020 1038) GB_4 • Has weak mismatches to other sequences clustering within the GB group (CU922396, CU921394, DQ413125 and CU927451) • Conclusion: Use with caution to cover sub group 4 members 248
GB_5 All sub group 5 ND 7/7 0 1 1 0 • The only weak mismatching sequence is from (Kong et al., (69 102) a eukaryotic organism 2002a) • Conclusion: Use to cover sub group 5 members Gam1019 All sub group 6 35 3/3 1 49** 49** 0 • Perfectly matches a single unidentified (Nielsen et al., (GB_6) bacterial sequence from sediment 1999) (1020 1038) • Weak mismatches with 49 sequences included 1 within the GB cluster (EF203182) and two partial sequences within sub group 4. • Conclusion: Use with caution to cover sub group 6 members GB_7 All sub group 7 35 5/6 7** 20** 0 18 • Perfectly matches 6 non target sequences (Kong et al., (1000 1018) within the GB (DQ413125, CU927451, 2002a) GB_7 Comp Competitor for FJ623329, FJ623304, FJ623321 and (1000 1018) GB_7 FJ517040) • Single mismatches with most sequences in sub groups 4 and 5 that are covered by GB_7 competitor probe • Single mismatches with two other sequences within the GB group that are not covered by the competitor probe • Conclusion: Not useful Gam1278 DGGE Bands 33 43/69 4** 293* 64* 0 • Originally designed to cover 3 DGGE 16S (Nielsen et al., (1279 1298) rRNA gene fragments (AF093777, AF093780 1999) and AF093781) clustering within the GB group. However, it only covers 2 of these. Assessed here for coverage of the entire GB group. • Does not target a defined phylogenetic group within the GB group, and matches 43 GB sequences including members of sub group 1, 249
2, 4, 6 and 7. • Conclusion: Not useful GAOQ431 Some 35 43/69 18** 705** 113** 0 • Matches 14 non target gammaproteobacterial (Crocetti et al., (431 448) ‘Competibacter’ sequences. 2002) • Perfect match to a sequence clustering within the ‘new’ cluster within the GB (FJ516904) and weak mismatches to the other members. Can not be used to exclude this cluster. • Conclusion: Not useful Probes were validated against the SILVA database (Version 102: February 2010) updated with Competibacter related sequences. Partial sequences (< 1200) were considered but were not included in analyses summary. * Indicates that some of these sequences are members of the Gammaproteobacteria potentially making them difficult to eliminate using the hierarchal probe approach (Daims et al. 2005). ** Indicate that some of these sequences also cluster within the Competibacter related sequences. ‡ These single base mismatches have been tested, often with a competitor probe, for their ability to prevent non target hybridisation.
250 Appendix 2: Defluviicoccus related probe coverage assessment.
Fig. A2.1: Maximum likelihood tree of all available complete Defluviicoccus related sequences (>1200 bp) using the ARB software (Ludwig et al., 2004). Brackets to the far right indicate proposed probe defined phylogenetic clusters. Inner brackets indicate probe coverage (a broken line indicates the absence of sequence information at the probe site). For probe details see Table A2.1. ‘*’ Indicates selected partial sequences (< 1200bp) added using the ‘quick add’ function in ARB after tree construction. Parsimony bootstrap values are calculated as a percentage of 1000 analysis and are only indicated for values ≥ 75 %. ○ Indicates a bootstrap value of ≥ 75 % and ● indicates a bootstrap value of ≥ 95 %. The scale bar corresponds to substitutions per nucleotide position.
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Table A2.1: Assessment of probes available for the Defluviicoccus related GAO Perfect match Mismatches Name Proposed target [FA] Target Non Single Notes Reference (E coli. Pos) ‘Weak’ % group target Mismatch DEF438 All cluster I 20 6/6 0 20** 17** • Originally designed to target the D. vanus (Kong et al., 2001) (440 483) sequence (AF179678) • Published in the incorrect orientation • Targets all cluster I members except one partial sequence (although only a single weak mismatch) • Contains weak mismatches to sequences within cluster II and peripheral (partials) to clusters I and II. • A competitor probe (5’ GTCATCATCGTCRCAGGCG 3’) would cover all 17 weak mismatches but requires empirical validation. • Conclusion: Superseded by TFO_DF776 TFO_DF862 Defluviicoccus vanus 35 1/1 4* 7** 5** • Has four non target hits, two of which are (Wong et al., 2004) (830 847) members of cluster III (AB445107 and AB445108) as well as two other alphaproteobacterial sequences isolated from the ocean (GQ350596 and GQ350586) • Weak mismatches with sequences within clusters I (partial) and III and others peripheral to clusters II (partial) and III. • Conclusion: Useful only to confirm the absence of D. vanus in a sample TFO_DF218 All cluster I 25 35 6/6 0 44** 14** • Weak mismatches include sequences peripheral to (Wong et al., 2004) (186 210) cluster I • Conclusion: Superseded by TFO_DF776
252
TFO_DF618 Most of cluster I 25 35 5/6 0 2 0 • Targets most of cluster I sequences with a single (Wong et al., 2004) (586 603) weak mismatch to D. vanus • Conclusion: Apply in a mix with TFO_DF776 to cover all cluster I although no evidence to suggest it is required TFO_DF776 All of cluster I 30 6/6 0 3 0 • Conclusion: Currently the best option to cover all (Wong and Liu, (744 762) cluster I sequences can also be applied with 2007) TFO_DF618 as a mix to cover the whole cluster TFO_DF629 All of cluster II ND 12/12 1** 51** 11** • Hits one sequence peripheral to cluster II (Wong and Liu, (597 614) (EU133261) 2007) • Has a single weak mismatch with 1 complete and 12 partial sequences peripheral to cluster II • Has single mismatches with almost all members of all four clusters • Does not impart fluorescence on communities known to contain organisms with a perfect match (Chapter 2). Possibly due to inaccessibility of target site (region IV (Fuchs et al., 1998)) • Conclusion: Not useful. DF988 All of cluster II 35 12/12 0 3** 3** • Weak mismatch with 2 sequences and 1 partial (Meyer et al., 2006) (988 1008) sequence peripheral to cluster II. DF988c Competitor for • Three terminal mismatches with 25 sequences (988 1008) DF988 including most cluster III members. Shown to H966 Helper for DF988 require a competitor probe to prevent binding (966 987) (DF988c) (Chapter 4) H1038 Helper for DF988 • Conclusion: Best available probe for cluster II but (1037 1063) use with additional caution DF1020 All of cluster II 35 12/12 3 9** 9** • Although covering the known sequence diversity (Meyer et al., 2006) 253
(1020 1036) of cluster II, it does not have the same coverage as H1038 Helper for DF1020 DF988. (1037 1063) • Like DF988 has a single weak mismatch with AY921916, a sequence peripheral to cluster II. • Perfect match with 3 Verrucomicrobia sequences (EU652671, AM040118 and AY907804) isolated from sediment and seawater. • Conclusion: Used with DF988 as a mix may only serve to lower probe specificity. Can be applied singly to target currently unknown diversity within cluster II. DEF827 Sequences within 20 1/1 4 66** 55** • Covers all partial sequences it was designed (Kondo et al., 2007) (826 843) sub group I of cluster against plus a closely related ‘complete’ sequence II • Perfectly matches four non target sequences, 2 members of the Alphaproteobacteria (EF092190 and GQ348482) (both have central mismatches to the ALF968 probe), one member of the Gammaproteobacteria (EU800829) and one from the Firmicutes (GQ502547). • Has weak mismatches with 7 other cluster II sequences • Conclusion: Not useful DEF636 Sub group III of 20 1/1 0 8** 1** • Covers all partial sequences it was designed (Kondo et al., 2007) (636 653) cluster II against, plus a closely related ‘complete’ sequence • Single mismatches with 7 cluster II sequences • Weak mismatch with a cluster II partial outside proposed sub group (DQ250537) • Conclusion: Use with extra caution DF198 All of cluster III 35 5/5 0 0 0 • Conclusion: Use to cover all cluster III members (Chapter 4) (197 229) 254
MC2 649 Candidatus 35 1/1 62* 2009** 615** • Designed to cover a partial sequence (AY428763), (Snaidr et al., 2002) (649 666) ‘Monilibacter later used to cover Candidatus ‘Monilibacter batavus’ batavus’ (Levantesi et al., 2004). • Matches 62 non target sequences from the Alpha and Delta proteobacteria, Veruccomicrobia, Acidobacteria, Actinobacteria and the Archaea • The 615 weakly mismatched sequences include sequences within cluster IV and peripheral to clusters I and II members • 42 of the 615 mismatches provide adequate discrimination to prevent binding. These include other members of cluster III (Chapter 4) • Conclusion: New probe set required for this sub cluster DF1004 Sub group of cluster 35 2/2 0 1** 1** • Competitor probe (DF1004c) designed to cover (Chapter 4) (1004 1020) III weak mismatch to Candidatus ‘Monilibacter DF1004c Competitor for batavus’ sequence (AY590701), but not tested (1004 1020) DF1004 empirically DF987H Helper for DF1004 • Conclusion: Apply with caution (987 1004) DF1013 Sub group of cluster 35 50 2/2 0 1** 1** • Competitor probe (DF1013c) designed to cover (Chapter 4) (1013 1031) III weak mismatch to non target member of cluster III DF1013c Competitor for (AF280850), but not tested empirically (1013 1031) DF1013 • Conclusion: Apply with caution DF997H Helper for DF1013 (997 1012) DF181A Sub group of cluster 30 2/2 0 0 0 • Weak terminal mismatch with two ‘Chloroflexi’ (Chapter 4) (181 198) IV sequences (FJ623314 and FJ623367), both identified as putative chimeras • Weak mismatch to a partial sequence clustering with cluster IV, not covered by DF181B, isolated from soil (GU390332)
255
• Conclusion: Apply separately to cover the sub cluster or in combination with DF181B for whole cluster. DF181B Sub group of cluster 30 4/4 0 13** 0 • All sequences with single base mismatch covered (Chapter 4) (181 198) IV by DF181Bc and validated empirically • Conclusion: Apply separately to cover the sub DF181Bc Competitor for cluster or in combination with DF181A for whole (181 198) DF181B cluster. Additional partial sequence not covered may require additional/new probes for this cluster Probes were validated against the SILVA database (Version 102: February 2010) (Pruesse et al., 2007) updated with Defluviicoccus related sequences. Partial sequences (< 1200) were considered but were not included in analyses summary. * Indicates that some of these sequences are also targeted by the general ALF968 probe for Alphaproteobacteria (Neef et al., 1999) making them difficult to eliminate using the hierarchal probe approach (Daims et al., 2005). ** Indicate that some of these sequences also cluster within the Defluviicoccus related sequences.
256 Appendix 3: Preparation protocol for 4.5 M NaTCA 1. Trichloroacetic acid (TCA) was continuously mixed, with a magnetic stirrer, in a sterile beaker on ice with a small volume of sterile distilled water (only enough water to allow stirring as a 4.5 M is close to saturation and NaOH solution is also added). 2. 10 M NaOH was slowly added with a dropping funnel, never allowing the solution to exceed 50 °C, until the TCA is almost neutralised (based on a pre calculated amount). 3. The pH of the solution was adjusted to 6.7 7.3 using 2 M NaOH solution and the final volume made up to give a final concentration of 4.5 M NaTCA. 4. The final solution is stored at 20 °C as breakdown of the NaTCA occurs at 4 °C precipitating out during the 2 propanol step as a brown pellet.
257 Appendix 4: Cluster IV Defluviicoccus probe optimisation
Fig. A4.1: Formamide dissociation curve for cluster IV Defluviicoccus probes. Formamide titration was performed as described by Schroeder et al. (2009). RFU = relative fluoresecent units calculated as the highest fluorescent value for each curve.
258 Appendix 5: Media composition Components Concentration R2A Peptone 0.5 g l 1 Casamino acids 0.5 g l 1 Yeast extract 0.5 g l 1 Dextrose 0.5 g l 1 Starch 0.5 g l 1 1 K2HPO4 0.3 g l 1 MgSO4 0.05 g l Sodium pyruvate 0.3 g l 1 PYCa Peptone 5 g l 1 Yeast extract 3 g l 1 Calcium chloride 1 g l 1 Glucose 1 g l 1 Thermus medium (CMD) (Sharp et al., 1995) Tryptone 1 g l 1 Yeast extract 1 g l 1 Castenholtz salts (10 ×) 100 ml l 1 Cantenholtz basal salts 10 × Concentrate Nitrilo triacetic acid 1 g CaSO4.2H2O 0.6 g MgSO4.H2O 1 g NaCl 0.08 g KNO3 1.03 g NaNO3 6.89 g Na2HPO4 1.11 g 1 FeCl3 solution (0.28g l ) 10 ml Nitch’s trace elements solution 10 ml Nitch’s trace elements solution 1 H2SO4 0.5 ml l 1 MnSO4.H2O 2.2 g l 1 ZnSO4.7H2O 0.5 g l H3BO3 0.5 g l 1 1 CuSO4 0.016 g l 1 Na2MoO4.2H2O 0.025 g l 1 CoCl2.6H2O 0.046 g l
259 Appendix 6: Publications forming chapters in this thesis
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