Cassandra Trent Thesis
Culture independent analysis of greyback canegrub-associated microflora and microbial community comparision using subtractive hybridisation.
Cassandra Robyn Trent BSc(Hons)
School of Life Sciences
Queensland University of Technology
A thesis submitted to the Queensland University of Technology for the degree of Doctor of Philosophy 2010
i. ii. ABSTRACT
Greyback canegrubs cost the Australian sugarcane industry around $13 million per annum in damage and control. A novel and cost effective biocontrol bacterium could play an important role in the integrated pest management program currently in place to reduce damage and control associated costs. During the course of this project, terminal restriction fragment length polymorphism (TRFLP), 16-S rDNA cloning, suppressive subtractive hybridisation (SSH) and entomopathogen-specific PCR screening were used to investigate the little studied canegrub-associated microflora in an attempt to discover novel pathogens from putatively-diseased specimens.
Microflora associated with these soil-dwelling insects was found to be both highly diverse and divergent between individual specimens. Dominant members detected in live specimens were predominantly from taxa of known insect symbionts while dominant sequences amplified from dead grubs were homologous to putatively- saprophytic bacteria and bacteria able to grow during refrigeration. A number of entomopathogenic bacteria were identified such as Photorhabdus luminescens and Pseudomonas fluorescens . Dead canegrubs prior to decomposition need to be analysed if these bacteria are to be isolated. Novel strategies to enrich putative pathogen-associated sequences (SSH and PCR screening) were shown to be promising approaches for pathogen discovery and the investigation of canegrubs- associated microflora. However, due to inter- and intra-grub-associated community diversity, dead grub decomposition and PCR-specific methodological limitations (PCR bias, primer specificity, BLAST database restrictions, 16-S gene copy number and heterogeneity), recommendations have been made to improve the efficiency of such techniques. Improved specimen collection procedures and utilisation of emerging high-throughput sequencing technologies may be required to examine these complex communities in more detail.
This is the first study to perform a whole-grub analysis and comparison of greyback canegrub-associated microbial communities. This work also describes the development of a novel V3-PCR based SSH technique. This was the first SSH
iii. technique to use V3-PCR products as a starting material and specifically compare bacterial species present in a complex community.
iv. TABLE OF CONTENTS
ABSTRACT ...... iii TABLE OF CONTENTS...... v LIST OF FIGURES ...... ix LIST OF TABLES ...... xi LIST OF ABBREVIATIONS...... xiii STATEMENT OF ORIGINAL AUTHORSHIP...... xv ACKNOWLEDGEMENTS...... xvii Chapter 1: Introduction ...... 1 1.1 Sugarcane industry outline...... 3 1.1.1 Sugarcane crop...... 3 1.1.2 History of the industry...... 6 1.1.3 Economic importance...... 8 1.1.4 Sugarcane pests and diseases...... 9 1.2 Biological control...... 13 1.2.1 Forms of biocontrol...... 13 1.2.2 Environmental risks...... 16 1.2.3 Future of biocontrol...... 17 1.3 Canegrubs...... 18 1.3.1 Greyback canegrubs ...... 21 1.4 Methods for pathogen isolation...... 31 1.4.1 Traditional culturing methods...... 31 1.4.2 Molecular methods used for community analysis...... 31 1.4.3 Suppressive Subtractive Hybridisation...... 35 Project goals and objectives...... 40 Chapter 2: Identifying dominant bacterial members from living and dead greyback canegrubs by using 16-S rDNA cloning and sequencing...... 41 2.1 Introduction ...... 43 2.2 Materials and methods ...... 47 2.2.1 Canegrub specimens from the 2007 grub season...... 47
v. 2.2.2 16S PCR...... 48 2.2.3 Cloning...... 48 2.2.4 Sequencing analysis...... 49 2.3 Results...... 50 2.3.1 Living Grubs...... 50 2.3.2 Dead grubs...... 51 2.3.3 BLAST isolates...... 54 2.4 Discussion...... 56 2.4.1 Conclusions ...... 65 Chapter 3: Developing and testing a novel V3-PCR-based SSH technique for identifying pathogens from canegrubs ...... 67 3.1 Introduction...... 69 3.2 Materials and methods...... 72 3.2.1 2006 Grub Season...... 72 3.2.2 Control bacteria ...... 74 3.2.3 V3 PCR ...... 74 3.2.4 V3-PCR contamination...... 75 3.2.5 Adaptor PCR...... 75 3.2.6 SSH ...... 77 3.2.7 Sequencing analysis...... 81 3.3 Results...... 82 3.3.1 V3-PCR...... 82 3.3.2 Adaptor PCR...... 87 3.3.3 P1-PCR...... 96 3.3.4 Control SSH...... 100 3.3.5 Tester:driver ratio analysis...... 102 3.4 Discussion...... 103 3.4.1 V3-PCR...... 104 3.4.2 Adaptor PCR...... 113 3.4.3 P1-PCR...... 118 3.4.4 Control SSH...... 120 3.4.5 Tester: driver ratio study...... 121 3.5 Conclusions...... 122
vi. Chapter 4: Evaluation of metagenomic SSH analysis for pathogen detection in canegrubs ...... 125 4.1 Introduction ...... 127 4.2 Materials and methods ...... 129 4.2.1 Grub specimens...... 129 4.2.2 DNA extraction...... 129 4.2.3 Clontech kit...... 130 4.2.4 Cloning ...... 131 4.2.5 Minipreps...... 131 4.2.6 Dot blots ...... 131 4.2.7 Sequencing analysis ...... 131 4.3 Results...... 132 4.3.1 Control reactions...... 132 4.3.2 Dead grub reactions...... 134 4.4 Discussion...... 137 4.4.1 General conclusions ...... 146 Chapter 5: Comparing and profiling microbial communities associated with canegrubs from within and between different geographical locations...... 149 5.1 Introduction ...... 151 5.2 Materials and methods ...... 154 5.1.1 Grub processing ...... 154 5.1.2 Treatments ...... 155 5.1.3 TRFLP PCR...... 156 5.1.4 Chromatogram analysis...... 156 5.3 Results...... 158 5.1.5 Living grubs...... 158 5.1.6 Dead grubs...... 163 5.1.7 Peak identities...... 167 5.4 Discussion...... 170 5.5 Conclusions ...... 176
vii. Chapter 6: Pathogen identification in dead canegrub specimens via an entomopathogen-specific PCR-screening assay ...... 177 6.1 Introduction...... 179 6.2 Materials and methods...... 182 6.2.1 Canegrub specimens ...... 182 6.2.2 Genera-specific PCR...... 182 6.2.3 Cloning...... 185 6.2.4 Sequencing analysis...... 185 6.3 Results...... 186 6.3.1 Iridovirus ...... 187 6.3.2 Rickettsia...... 187 6.3.3 All other genera/genes...... 189 6.4 Discussion...... 190 6.4.1 Conclusions ...... 196 Chapter 7: General discussion...... 199 7.1 Conclusions...... 214 Chapter 8: References...... 217
viii. LIST OF FIGURES
Figure 1.1. Sugarcane growing regions of Australia as shown in green...... 4 Figure 1.2. The complete milling process of Queensland sugarcane from harvest to raw sugar production...... 5 Figure 1.3. Percent crop damage (hectares of damaged cane) caused by sugarcane pests from 1994 to 2003...... 11 Figure 1.4. Aerial view of sugarcane crop damage caused by canegrubs...... 18 Figure 1.5. Damage to sugarcane stools caused by feeding of greyback canegrubs ...... 19 Figure 1.6. Greyback canegrub larvae (A.) and adult beetle (B.)...... 21 Figure 1.7. Life-cycle of greyback canegrubs with respect to the sugarcane harvesting cycle...... 22 Figure 1.8. Greyback canegrubs infected with the entomopathogenic fungus, M. anisopliae ...... 23 Figure 1.9. Overview of the Suppressive Subtractive Hybridisation technique...... 36 Figure 2.1. Origins of bacteria with 16-S sequence homology to sequences amplified from dead and live greyback canegrubs...... 52 Figure 2.2. Phylogenetic tree of all cloned 16-S sequences from living and dead grub samples...... 55 Figure 3.1. Sectioning of healthy canegrubs for 16-S PCR based SSH. Canegrubs were sliced into head, mid- (M) and hind-sections (B)...... 73 Figure 3.2. Flow diagram of the steps involved in the V3-PCR SSH process...... 76 Figure 3.3. A flow diagram showing SSH hybridisation steps and sample tube arrangement (3.2.6)...... 77 Figure 3.4. V3-PCR contamination reduction after DNase pre-treatment of the Taq premix...... 82 Figure 3.5. Graphs comparing linear correlations between V3-PCR sequence separation results (Tables 3.5 and 3.7) and: (A.)Thermodynamic Tm; and (B.) GC-content based Tm...... 94
ix. Figure 3.6. Alignment of all V3 and adaptor-V3 PCR sequences amplified from soil and canegrub 59-M ...... 97 Figure 3.7. Alignment tree of the V3 regions amplified from V3 (G-V3-), Adaptor (GA, GB, SA and SB) and P1-PCR [Driver minus driver control (DD) and tester minus driver (DT)] reactions...... 98 Figure 3.8. Representative DHPLC chromatograms of tester sequences from control reactions one to five...... 100 Figure 4.1. Nested-PCR products of control SSH reactions one to four (Table 4.1) ...... 131 Figure 4.2. Representative dot blots showing control reaction one clones hybridised to (A.) E. coli and (B.) phi X174/ Hae III marker...... 132 Figure 5.1. Flow diagram of treatments for the 2007 collected canegrubs used in the TRFLP study outlining prefixes and suffixes applied to each canegrub specimen name in Table 5.1 ...... 153 Figure 5.2. Cluster analysis showing the relationship between all profiles digested with Rsa I from each location and with each treatment ...... 161 Figure 5.3. Comparison of Morisita indices within each of the three locations (Attard’s and Reed’s farms in Mackay and Tully) ...... 162 Figure 5.4. Comparison of mean Morisita indices both within each field or location. and between fields ...... 162 Figure 5.5. Mean Morisita indices for each treatment...... 162 Figure 5.6. Mean Morisita and Simpson’s dominance indices for different grub groups and treatments...... 163 Figure 5.7. Simpson’s dominance indices of canegrub subjected to different treatments collected from Attard’s farm...... 163 Figure 5.8. Averaged Morista indices comparing Attard’s farm TRFLP profiles of each canegrub treatment group digested with each of the three restriction enzymes...... 164 Figure 5.9 Representitive TRFLP profiles of one living grub and one dead grub sample...... 165 Figure 6.1. PCR-amplified sequences using the Iridovirus-specific primer set from dead and living grub samples as listed in Table 6.5...... 184 Figure 6.2. PCR-amplified sequences using the Rickettsia -specific primer set from dead and living grub samples as listed in Table 6.7...... 187 x. LIST OF TABLES
Table 1.1. Crop damage caused by major sugarcane pests...... 10 Table 1.2. Endemic canegrubs that cause damage to sugarcane crops...... 20 Table 2.1. BLAST matches to 16-S sequences amplified from living grubs collected in Mackay from Attard’s (A) and Reed’s (R) farms and from Tully (T) (section 2.2.1)...... 51 Table 2.2. BLAST matches to 16-S sequences amplified from dead grubs (section 2.2.1)...... 54 Table 3.1. Experimental design of SSH reactions with a known spiked “control-x” component ...... 78 Table 3.2. Tester and driver mixes used in tester and driver control samples ...... 79 Table 3.3. Control-V3 PCR sequences homology to BLAST entries ...... 84 Table 3.4. BLAST sequences homologous to clones amplified using V3- PCR primers from the mid-region of a living grub (59-M)...... 85 Table 3.5. Thermodynamic properties and separation values for V3-PCR clones...... 87 Table 3.6. Sequences amplified using the V3 Adaptor-A and B primer sets homologous to Genbank entries...... 89 Table 3.7. Thermodynamic properties and separation values for Adaptor V3-PCR clones amplified from soil and canegrubs...... 93 Table 3.8. Total percentage of canegrub-amplified V3 clones from each PCR reaction (V3, adaptor and P1-PCR) homologous to each taxonomic group...... 99 Table 4.1. Mixture of canegrub-associated DNA and control organism DNA in each SSH reaction...... 128 Table 4.2. Ratio of clones present in the tester sample and absent in driver sample determined by dot-blot hybridisations...... 130 Table 4.3. Dot-blot results for control reactions one to four...... 131 Table 4.4. BlastX matches from tester-specific sequences amplified from SSH reactions five, six, and seven...... 133 Table 5.1. Outline of origins of each canegrub specimen and treatments each specimen received...... 151
xi. Table 5.2. Dead grub and live grub specimens from Tully and Mackay (Attard’s and Reed’s farms) profiled using TRFLP and digested with Rsa I...... 157 Table 5.3. Live grub specimens from Mackay (Attard’s farm) profiled using TRFLP and digested with Rsa I...... 158 Table 5.4. Live grub specimens from Mackay (Attard’s farm) profiled using TRFLP and digested with Hha I...... 159 Table 5.5. Live grub specimens from Mackay (Attard’s farm) profiled using TRFLP and digested with Msp I...... 160 Table 5.6. Taxonomic identification of dominant peaks present in dead grubs and a subset of live grub samples...... 166 Table 6.1. Dead grub specimens used in 96-well plate PCR-screening assays...... 181 Table 6.2. Diagram of the 96-well plate setup as per samples in Table 6.1...... 182 Table 6.3. Live grubs used as “healthy” controls for comparison to dead grubs by PCR-screening...... 182 Table 6.4. Diagram of the 96-well plate setup of the living grubs as per samples in Table 6.3...... 183 Table 6.5. Iridovirus-amplified grub samples loaded in each lane of figure 6.1 and bands chosen for sequencing...... 184 Table 6.6. BLAST matches of sequenced bands from Iridovirus PCR- amplified grub samples (Table 6.5 and figure 6.1)...... 185 Table 6.7. PCR-amplified grub samples using the Rickettsial primer set were loaded in each lane of figure 6.2 and bands chosen for sequencing...... 186 Table 6.8. BLAST matches of sequenced bands from Rickettsia -amplified grub samples (Table 6.7 and figure 6.2) ...... 187
xii. LIST OF ABBREVIATIONS
+ve positive -ve negative AGRF Australian Genome Research Facility bp base pair(s) BSES Bureau of Sugar Experimental Stations cDNA complimentary DNA cm centimetre DGGE denaturing gradient gel electrophoresis DHPLC denaturing high performance liquid chromatography DNA deoxyribonucleic acid dNTP deoxynucleotide triphosphate EDTA ethylene diamine tetraacetic acid g gram h hour HPLC high performance liquid chromatography IPM Integrated Pest Management IPTG isopropyl-beta-D-thiogalactopyranoside L litres LB Luria Bertani M molar mA milliamp µg microgram µL microlitre µM micromolar mg milligram mL millilitre mm millimetre min minute mRNA messenger RNA ng nanogram nmol nanomole
xiii. NSW New South Wales PCR polymerase chain reaction PEG polyethylene glycol pmol picomole QLD Queensland rDNA ribosomal deoxynucleic acid RNA ribonucleic acid s second SDS sodium dodecyl sulphate SSH Suppressive subtractive hybridisation TAE 40 mM Tris base, 1mM EDTA to pH 7.5 with glacial acetic acid Tris-HCl tris (hydroxymethyl) aminomethane-hydrochloride TGGE temperature gradient gel electrophoresis TRFLP Terminal restriction fragment length polymorphism UV ultraviolet V volt w/v weight per volume v/v volume per volume X-gal 5-bromo-4-chloro-3-indolyl-BD-galactoside
xiv.
STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher degree education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by any other person except where due reference is made.
Signed:
Cassandra Trent BSc (Hons)
Date: 7th June 2010
xv.
xvi. ACKNOWLEDGEMENTS
I would sincerely like to thank my patient and understanding supervisors Dr James Smith, Dr Kerry Nutt, Dr Peter Allsopp, Professor Peter Timms and Dr Jason Geijskes. You have all taught me so much about science, life, patience, time management etc. It’s a whole other thesis just listing all that I have learnt. Thank you.
Thank you to the BSES entomology team, especially Mohammed Sallam and Peter Samson, for collecting canegrubs for use in this work. Without them there would be no thesis.
Thank you also to those who gave me access to their lab equipment and provided invaluable training and advice. To Peter Mather and David Hurwood (QUT), thank you for both training and use of your TGGE equipment. I am sincerely grateful for your time and patience. To Sue Healy at QIMR, thank you for sharing your DHPLC equipment and technical knowledge.
Many thanks to the CRC-SIIB for providing funding and support throughout the course of the project. In particular, thank you to Suzanne Morris for all your help and support. In addition, thank you to BSES Ltd for giving me access to all of your resources. To all my friends at BSES Ltd, you have kept me sane throughout this whole process. Thank you for your encouragement, fun antics and technical help. I could not have survived without you.
A big thank you to my lovely family. The last few years have been riddled with many personal and professional challenges. Thank you for all your support and encouragement. Your love and understanding has kept me going and helped me map my future path.
xvii. xviii.
I dedicate this thesis to my late grandfather, Morris Henry Russell
29 th May 1925 – 2 nd October 2009
Thank you for all you have taught me.
xix.
xx. Chapter 1: Introduction ______
Chapter 1: Introduction
1. Cassandra Trent ______
2. Chapter 1: Introduction ______
1.1 Sugarcane industry outline
1.1.1 Sugarcane crop
Sugarcane ( Saccharum spp.), is grown throughout Queensland and northern New South Wales (figure 1.1). It is a tall growing monocotyledous crop cultivated in tropical and subtropical regions (Cox et al , 2000). The genus Saccharum belongs to the tribe Andropogoneae and specifically the grass family, Poaceae , alongside the cereals Sorghum and Zea (corn). Most commercial canes are hybrids of S. officinarum and S. spontaneum with each species contributing 75-80% and 15-20% of chromosomes, respectively. The remaining 5% is made up of recombinant or translocated chromosomes from both species. S. officinarum is best known for its superior sucrose yield. However, it is susceptible to disease. In contrast, S. spontaneum has high disease resistance and vigorous growth. Hence, creating hybrids of the two species maximises sucrose while increasing disease resistance (Cox et al , 2000).
Each crop planted is grown over a three-to-four-yr cropping cycle. Initially, vegetative cuttings are planted and harvested after 12-18 months growth. Post stalk-harvest, the roots and stem still submerged in the soil (the stool) are left to reshoot (ratoon), grow, and the new crop is re-harvested a year later. Stools are ploughed out after two-to-three cycles and a new crop replanted within 2-12 months (Allsopp, 2004).
Harvested cane is transported to a central mill where the quality of cane is measured on arrival. The amount paid to the farmer is calculated taking into account an estimation of commercial cane sugar (CCS), a measure of how much pure sucrose can be extracted from the cane. During the milling process (figure 1.2), the cane stalks are shredded and the juice extracted by crushing. Impurities are removed by clarifying the cane juice. This is accomplished with lime (Ca(OH) 2) and heat. Lime complexes with phosphorus and precipitates as calcium phosphate from the juice. Flocculants are added to speed settling and other impurities are also removed via this process. Post-clarification, juice is evaporated to syrup before multiple rounds of crystallisation produce raw sugar. The syrup is boiled and the sucrose crystallises in
3. Cassandra Trent ______the molasses. Sucrose crystals are then separated from the molasses via centrifugation. In the Australian industry, three rounds of crystallisation are performed. The first two rounds (A and B) produce molasses and the sucrose is dried to make raw sugar. In the third round, the sucrose is mixed with water and used to seed the first two rounds, aiding the crystallisation process. The raw sugar produced can be sold and shipped for further refining (Mackintosh, 2000).
Figure 1.1. Sugarcane growing regions of Australia as shown in green. Courtesy of Canegrowers Ltd (http://www.canegrowers.com.au).
4. Chapter 1: Introduction ______
Figure 1.2. The complete milling process of Queensland sugarcane from harvest to raw sugar production. Courtesy of Maryborough Sugar (www.marysug.com.au/process.htm ).
There are two main by-products from the milling process, bagasse and molasses. Molasses (as described above) is produced after the boiling of treated cane juice and is used as a livestock feed supplement (Pate et al. , 1990) and in rum manufacturing. Bagasse is the fibrous plant material remaining after crushing. This includes cellulose in the rind, vascular tissue, and the pith of which equates to 55% of bagasse dry weight. Overall, bagasse is comprised of 50% cellulose, 25% hemicellulose and 25% lignin (Pandey et al. , 2000). The main use of bagasse in the Australian industry is energy production. It is burnt to produce steam to power turbines, generating electricity (also known as cogeneration). The electricity is used for mill operation and excess is sold to neighbouring energy providers. Ash produced from bagasse burning can be used as an additive to produce acid-resistant concrete (Singh et al. , 2000). Other uses include animal feed. However, digestibility of these fibres by livestock is low (Gandi et al. , 1997).
5. Cassandra Trent ______Bagasse can also be used to make the biofuel ethanol (Gellar et al ., 1985). However, in Australia the scale of bagasse production required outweighs the benefits, as alternative electrical and steam sources would be required to power the mills. In addition, large amounts of costly cellulase are presently required, rendering it economically non-viable (Pandey et al ., 2000). In other countries with limited oil reserves there may be an economic benefit. For example, in Brazil, ethanol was added to fuel as early as 1931, and by the 1960’s ethanol production was between 400-700 million litres per year (Geller et al. , 1985).
Unlike ethanol production, bagasse derived drug or enzyme production requires less substrate and would not impede on the bagasse requirements of the Australian industry for mill operation (Pandey et al. , 2000). An example of this is the application of mixed fungal culture fermentation of bagasse for Xylanase production (Gutierrez-Correa and Tengerdy, 1998). Bagasse is also used to remove heavy metals such as cadmium, zinc and chromium (Goncalves et al. , 2007, Krishnani et al. , 2007) and contaminants such as ammonia (Parimala et al. , 2007) from water.
1.1.2 History of the industry
Canegrubs have been a pest to Australian sugarcane since early cultivation. Much research has been undertaken throughout the history of the industry to reduce the damage associated with these pests. In order to provide an indication of the structure of today’s industry and the background to how initial research bodies were formed, a brief history has been included.
Sugarcane was introduced to Australia via the First Fleet in 1788 along with other exotic plants (Easterby, 1933). However, the first commercially grown sugarcane was in 1821 near Port Macquarie, New South Wales (600 acres harvesting approximately 70 tonnes of sugar). The production of commercial sugar in Queensland wasn’t recorded until 1859, harvesting out of the Botanical Gardens (Easterby, 1933). Within three years, Queensland sugar was commercially manufactured and by 1864, the first commercial sugar mill was operational. From here, the industry boomed. In 1867, 2000 acres of cane was harvested and sent to six mills producing 168 tonnes of sugar. An excess of cane was produced and the mills could not meet the demands of growers due to the labour intensive milling
6. Chapter 1: Introduction ______processes of the time. Cane stalks were individually crushed between ironbark timber rollers driven by horse or cattle. The resulting juice was boiled then cooled and crystallised. Yield was low, with 20 tonnes of cane producing only one tonne of sugar (Easterby, 1933).
By 1870, 28 mills were in operation and cane growing had spread throughout south east Queensland, up the north coast to Wide Bay, Bundaberg, Mackay, the Herbert and Johnston rivers and as far north as Cairns. The industry continued to boom and by 1872, 3.5 thousand acres of cane was planted in Mackay alone. Milling technologies were advancing and horse and cattle driven mills were being replaced by new steam powered machinery. By 1872 there were sixty-five mills producing 6266 tons of sugar and 16 thousand gallons of rum (Easterby, 1933).
The industry in northern New South Wales also started to develop during this period. In 1868 nine mills were in operation producing 60 tonnes of sugar. Due to the uncertainty of raw sugar supply to the Sydney Colonial Sugar Refinery (CSR), CEO Edward Knox proposed a way to bypass the traditional plantation sugar growing system by erecting central mills supplied by independent cane growers, referred to as the central milling system. The first central mill was built on the Macleay and Clarence rivers in 1869. The Condong mill was erected on the Tweed River in 1880 and the Broadwater mill on the Richmond River in 1881, both of which are still in operation today. Between 1885 and 1890, New South Wales reduced its mill numbers from 102 to 33 and by 1912, only three large CSR mills remained. The central milling system was introduced into Queensland in 1881 and again, small mills were forced to close due to hardships and competition. By 1898, 80 mills remained in Queensland producing 100 thousand tonnes of sugar (Easterby, 1933).
This central mill system is still in use today and the running of such a scheme is overseen by a number of industry bodies such as Canegrowers, BSES Ltd, Australian Sugar Milling Council, Australian Cane Farmers Association and CaneHarvesters. Sugar is sold internationally predominantly via Queensland Sugar Ltd who markets the sugar and manages ports owned by the Sugar Terminals Limited. In collaboration with universities and other research agencies, advances in industry technology have been the primary focus of many other industry bodies such
7. Cassandra Trent ______as BSES Ltd, Sugar Research Institute, Sugar Research and Development Corporation, Cooperative Research Centre for Sugar Industry Innovation through Biotechnology and CSIRO.
1.1.3 Economic importance
Australia is a major exporter and low cost sugar producer with the capacity to produce 4.5 to 5 mllion tonnes of raw sugar annually. Direct revenue from the industry is estimated at between AUD $1.5-2.5 billion, including AUD $1.2 billion in exports and 20% domestic sales. Within the industry, 5000 businesses supply 32- 35 million tonnes of cane to 25 mills in Queensland (94%), northern New South Wales (5%) and Western Australia (1%) (Canegrowers, 2010). The industry also provides employment and growth to these regions.
Crushing is scheduled with millers, farmers and harvesters so cane is crushed within an average of 12 hours post harvest. This ensures the best quality sugar and highest yield. Bulk port terminals can store up to two million tonnes of raw sugar, enabling Queensland Sugar Ltd to negotiate the best price (Canegrowers Annual Report, 2007). Major customers include Japan, Korea, Malaysia, New Zealand, Canada and the United States of America.
8. Chapter 1: Introduction ______
1.1.4 Sugarcane pests and diseases
There are many pests of sugarcane in Australia. Each pest causes different symptoms and damage to different parts of the plant (Table 1.1). The most economically important are canegrubs, soldier flies, nematodes, rats and pigs. Of these, the most significant losses are from canegrub control and crop damage (figure 1.3). It is estimated that the industry spends AUD $13 million on canegrub damage and control each year and that approximately 3.3% of the crop is lost due to these pests. In 2001, the cost of canegrubs to the industry reached a high of AUD $31 million (data collected by Cane Pest and Productivity Boards 1994-2003, compiled by Peter Samson, BSES Limited, Mackay).
There are 19 known canegrub species belonging to the genera Antitrogus, Rhopaea, Dermolepida and Lepidota that cause damage to sugarcane crops in Australia (Allsopp, 2004). Of these, the greyback canegrub (Dermolepida albohirtum ) is the most significant in North Queensland (Samson et al. , 2005). Larvae feed on sugarcane roots, causing stunted crop growth and poor sugar yields. The loss of roots deprives the plant of soil water and nutrients and affects the stability of the growing crop. Damaged stools are also unintentionally removed by machinery during harvesting, affecting the following season’s ratoon crop (Allsopp et al. , 2000).
Soldier flies ( Inopus rubriceps ) are a pest both in Queensland and New South Wales. Larvae live within the first 25 cm of the soil surface and suck sap from the roots causing poor growth and failed ratooning (Robertson 1984). Samson et al. , (1991) found a link between reduced damage and suSCon® Blue usage, indicating this canegrub-registered insecticide may provide protection against soldier fly as well. However, specific soldier fly-targeted pesticide treatments are also available (Samson et al. , 1991). In the 1996 and 1997 seasons, soldier flies cost the industry over a million dollars in damage and control (data collected by Cane Pest and Productivity Boards 1994-2003, compiled by Peter Samson, BSES Limited, Mackay).
9. Cassandra Trent ______Table 1.1. Crop damage caused by major sugarcane pests. Table has been adopted from pg 300 of the BSES Manual of Cane Growing (Allsopp et al. , 2000).
Symptom Pests Soldier flies, bud moth, wireworms, field crickets, mole crickets, Germination failure wart eye, termites, weevils Soldier flies, canegrubs, wireworms, cicadas, ground pearls, Ratoon failure butt weevil, stenocorynus weevil Wireworms, black beetles, rhyparida, butt weevil, stenocorynus Dead hearts leading to dead shoots weevil, large moth borer, ratoon shoot borer, bud moth Yellowing, poor growth and death in young Canegrubs (spring-summer feeding species), nematodes, cane ground pearls, cicadas, symphylans, weevils, funnel ants Yellowing and death of semi-mature or mature Canegrubs (summer-autumn feeding species), sugarcane scale cane Boring of large stalks Weevil borer, large moth borer, termites Rodents (ground and climbing rat), feral pig, wallabies, fox, Large-animal chewing of shoots or stalks eastern swamphen, cockatoo Chewing of large areas of leaf Armyworms and loppers, locusts Sooty mould Planthoppers, mealybugs, aphids, sugarcane scale Mottling or discolouration of leaves Planthoppers, froghopper, linear bug, aphids
Other damaging insects include wireworms ( Agrupnus variabilis ), armyworms (Leucania spp., Mythimna spp., Spodoptera spp., and Mocis fugalis ), ground pearls (Eumargarodes laingi , and Promargarodes australis ), weevil borers ( Rhabdoscelus obscures ), black beetles ( Heteronychnus arator ), funnel ants ( Aphaenogaster pythia ), cicadas ( Cicadetta crucifera , and Parnkalla muelleri ), symphylans (Hanseniella spp.), crickets ( Gryllotalpa sp., and Teleogryllus spp.), locusts (Chortoicetes terminifera , Locusta migratoria , Austracris guttulosa , and Gastrimargus musicus ), borers ( Bathytricha truncata , and Ephysteris promptella ), bud moths ( Opogona glycyphaga ), rhyparida ( Rhyparida nitida , and Rhyparida dimidiata ), aphids ( Melanaphis sacchari, Rhopalosiphym maidis , and Tetraneura nigriabdominalis ), weevils ( Leptopius maleficus , Naupactus leucoloma , and Stenocorynus spp.), wart eye, termites ( Mastotermes darwiniensis ), planthoppers (Perkinsiella saccharicida ), mealybugs ( Sacchariococcus sacchari ), linear bug (Phaenacantha australiae ), froghopper ( Eoscarta carnifex ), and sugarcane scale (Aulacaspis madiunensis ). Although not as economically important, these insects can cause damage to the roots, sets, stalks, leaves, shoots, and buds, or disrupt soil stability (Hogarth and Allsopp, 2000). These insects cost the industry a combined AUD $3.5 million dollars in control and damage in 1997 and averaged AUD $2 million annually between 1994 and 2003 (data collected by Cane Pest and
10. Chapter 1: Introduction ______Productivity Boards 1994-2003, compiled by Peter Samson, BSES Limited, Mackay) .
Figure 1.3. Percent crop damage (hectares of damaged cane) caused by sugarcane pests from 1994 to 2003 (data collected by Cane Pest and Productivity Boards 1994- 2003, compiled by Peter Samson, BSES Limited, Mackay).
Other soil-dwelling, root feeding pests are the nematodes. Root-knot nematodes are internal root parasites while reniform nematodes are only internal parasites during their adult stages. Root-lesion, spiral, stunt, stubby-root and ring nematodes all live in the surrounding soil and feed on the roots externally. All seven of these nematode types are found in Queensland sugarcane soils (Williams et al. , 1969, Allsopp 1990, Stirling et al. , 2001). In 1994, $3.36 million was spent on control and losses due to nematodes. In subsequent years (up until 2003), an average of AUD $300 thousand per annum was spent (data collected by Cane Pest and Productivity Boards 1994- 2003, compiled by Peter Samson, BSES Limited, Mackay).
There are two common types of rat pests; the canefield rat, Rattus sordidus and the climbing rats, Melomys burtoni and Melomys cervinipes . The canefield rat chews from ground level at the base of the stalk while the climbing rats can damage both the base and above-ground stalk. Damage causes susceptibility of cane to bacterial
11. Cassandra Trent ______and fungal infections that reduce stalk weight and sugar content. Canefield rats are widely distributed along the Queensland and northern New South Wales coastline. However, they are mainly a pest in areas where other rat species are not as numerous (from Mackay to Mossman). M. burtoni is found in fields adjacent to grassland, forest or swampland while M. cervinipes is mainly found north of Mackay adjacent to dense forest. Rodenticides such as Racumin® (coumatetralyl) and Ratoff® (zinc phosphide) are available to farmers. However, a permit is required to minimise the effect of these rodenticides on native wildlife. Rats cost the industry an average of AUD $4 million annually between 1994 and 2003, and were responsible for a AUD $10 million loss in 1999 (data collected by Cane Pest and Productivity Boards 1994- 2003, compiled by Peter Samson, BSES Limited, Mackay). Another vertebrate that gnaws on the stalks are foxes, Vulpes vulpes , leaving the cane susceptible to rot. Foxes are found from central Queensland down to northern New South Wales (Allsopp et al ., 2000).
Feral pigs, Sus scrofa , are another vertebrate pest found in all cane areas. These pigs can be up to 80 cm tall at the shoulder and 135 kg. Colonies of pigs can invade whole blocks, starting undetected from the middle. They break, chew, uproot and create wallows/runs in the cane. They can be controlled either by hunting or baiting. Removal of an established colony can be costly and difficult (Allsopp et al ., 2000). Pigs cost the industry an average of AUD $1 million each year between 1994 and 2003 (data collected by Cane Pest and Productivity Boards 1994-2003, compiled by Peter Samson, BSES Limited, Mackay). Other vertebrates, such as wallabies, can also cause damage to cane in dry periods where native foods are scarce (Allsopp et al ., 2000).
Birds such as the eastern swamphen ( Porphyrio porphyrio ) and the sulphur crested cockatoo ( Cacatua galerita ) also cause damage by eating the pith of the stalk and shredding the stalks, respectively (Allsopp et al ., 2000).
12. Chapter 1: Introduction ______
1.2 Biological control
Biological control strategies have been used since early civilisation to reduce pest numbers from agriculture and control exotic and native weeds. From as early as 1200BC, the Chinese have used predatory ants for controlling caterpillars and wood boring beetles in citrus groves. Similar reports exist from other cultures such as the ancient Sumerians and Egyptians (Zhang et al. , 2008). In addition to traditional agriculture, organic farming is another growing industry with increased interest in biocontrol for pest management (Zehnder et al. , 2007). There are two forms of biocontrol, classical and augmentative. Classical biological control is the introduction of an exotic natural enemy to control an exotic pest. Augmentative biological control is the periodic release of natural or exotic bacteria to control native or exotic pests, particularly in agriculture (van Lanteren et al. , 2006). Both of which will be reported here with a focus on augmentative biological control of agricultural pests.
1.2.1 Forms of biocontrol
Biocontrol bacteria are present in many forms. Many successful pest-control strategies have been developed using arthropods, amphibians, mammals, nematodes, bacteria, fungi, protozoa and viruses. Of these, arthropods are one of the most commonly released. Around 2000 exotic arthropod species have been introduced more than 5000 times for control of arthropod pests in 196 countries (van Lanteren et al. , 2006). Examples of these include the control of forage weevils with Microtonus spp. in New Zealand, the control of fruit flies with parasitoids in Hawaii and the control of the gypsy moth ( Lymantria dispar ) with the parasitoid Compsilura concinnata in North America. Many of which attack the egg casings or pupae of arthropod pests and cause a reduction in pest numbers (reviewed in Louda et al. , 2003, Gray et al. , 2008).
Arthropods have also been used to control plant weeds such as the successful control of the introduced prickly pear ( Opuntia spp.) in Australia by the subsequent introduction of an Argentine pyralid moth ( Cactoblastis cactorum ). The introduction of this moth in 1926 provided quick control of the weed without known non-target effects. This is due to the high specificity of C. cactorum for Opuntia
13. Cassandra Trent ______spp. Due to its success in Australia, C. cactorum was introduced in the Caribbean islands from 1957 and the USA mainland by 1960 and provided adequate control of native Opuntia spp. pests (Louda et al. , 2003, Raghu et al ., 2007).
Another form of biocontrol bacterium includes the use of viruses (Lacey et al. , 2001, Gitau et al. , 2009). Viruses cause epizootics in insect populations and reduce insect numbers dramatically. A good example of this is the non-occluded virus that controls the palm rhinoceros beetle ( Oryctes rhinoceros ). Adult beetles attack the crown of coconut palms, oil palms and other species. Feeding of the adult on palm trees causes reduced yield, loss of seedlings and death to both young and old trees. Larvae then develop in rotting palm logs and the dead tops of standing palms. The non-occluded virus chronically infects adult beetles, providing a reservoir for the virus to infect the eggs and larvae. The disease is transmitted during mating or via contamination of communal food within the insect population. The incubation period in adults is long causing them to live shorter lives and lay fewer eggs. However, it is fatal in eggs and larvae. Mass production of this virus has enabled the introduction of this virus into larval habitats and following multiple generations, into the broader beetle community. Such a strategy provides long term effects and hence, is economically viable (reviewed in Lacey et al. , 2001, Gitau et al ., 2009, Moslim et al ., 2010). Such a strategy limits the need for constant application to the field as viral numbers are maintained within the beetle population.
Of all the microbial forms of insect biocontrol, Bacillus thuringiensis has proven to be the most well known. B. thuringiensis has been shown to cause larvicidal activity against Lepidoptera (moths), Coleoptera (beetles) and Diptera (flies). Most of the insecticidal activity is associated with insecticidal toxins ( δ-endotoxins) produced in the parasporal inclusion bodies or parasporal crystals. These are produced during sporulation and can account for up to 30% of the total protein in the bacterium. These toxins can cause larval death within several hours and have no known effect on non-target bacteria including vertebrates (Lacey et al. , 2001, Brar et al. , 2009, Raddadi et al. , 2009, Bailey et al ., 2010, Gentz et al ., 2010, Liu et al. , 2010).
14. Chapter 1: Introduction ______In addition to direct application of the bacterium to agricultural crops, the B. thuringiensis toxin (Bt) has been transgenically expressed in many crops for Lepidopteran pest management such as corn, cotton, potato broccoli and canola. In 2002 it was estimated that more than 14 million hectares of transgenic Bt-expressing crops were growing worldwide (James, 2002). These transgenic crops were effective against insect pests such as the European corn borer ( Ostrinia nubilalis ) (Huang et al. , 2002), the cotton bollworm ( Helicoverpa armigera ) (Fan et al. , 2000), the tobacco budworm ( Heliothis virescens ) (Gould et al. , 1995), the pink bollworm (Pectinophora gossypiella ) (Tabashnik et al. , 2000), and the diamondback moth (Plutella xylostella ) (Roush, 1994).
Fungi from genera such as Beaveria and Metarhizium have routinely been used to control insect pests (Ferron et al. , 1978, Shi et al. , 2005, Jin et al ., 2008, Bai et al. , 2010). The most common forms of fungal infection start from penetration of the cuticle and the first signs are melanisation at the sites of infection. This is done via both physical and enzymatic mechanisms that disturb the proteins, chitin, lipids and phenolitic compounds present in the insect integument (e.g. the enzyme chitinase). Post-penetration, the insect’s cellular defence mechanisms attempt to kill the fungi. Plasmatocytes in hemolymph accumulate and cause melanisation at these points. Hemocytic pseudotissue form visible lamellae within a few days. After this, either infection is overcome or the fungus penetrates this hemocytic layer to invade the insect. Many fungi produce toxins that cause host mortality and the cadaver is used for saprophytic development of fungal spores (Ferron et al. , 1978).
An example of a fungal biocontrol agent is M. anisopliae for control of greyback canegrubs. BioCane™ was registered for use in Australia in 2000 and gave sugarcane farmers a new alternative for greyback canegrub control. BioCane™ contains Metarhizium anisopliae var. anisopliae strain FI-1045 conidia grown on broken parboiled rice. It is applied during planting and gives a 50-60% reduction in greyback canegrub larvae in the first year of the crop cycle (Samson and Milner, 1999, Samson et al. , 2001, Milner et al. , 2002). This biocontrol formulation is used as part of an Integrated Pest Management solution in conjunction with chemical pesticides.
15. Cassandra Trent ______Protozoans have also been assessed for biocontrol suitability. However, they are generally host-specific, slow acting and require costly in-vivo production. Entomopathogenic protozoans usually produce chronic infections in insects. An example of this is the use of Nosema locustae for control of locust (Bomar et al ., 1993, Lange et al ., 1996, Lomer et al. , 2001). Infection occurs in the cytoplasm of adipose tissue cells in the fat bodies (Sokolova and Lange, 2002). Commercial preparations of solid pellets containing spores are available for domestic use. These include Semaspore Bait (Planet Natural, USA) and NoLo Bait™ (M&R Durango, Inc., USA). A Nosema sp. has also been identified that is pathogenic to canegrubs. However, the high cost of in-vivo production and low immediate mortality rendered this bacterium unsuitable for biocontrol development (Dall et al, 1995).
1.2.2 Environmental risks
When comparing the non-target effects associated with arthropod release, Louda et al. (2003) compared ten different arthropod biocontrol releases. Many of their findings are suitable for other forms of biocontrol as well. The most likely non- target bacteria to be attacked were relatives of the pest being controlled. In addition, host-specificity testing performed to determine the specific action on the pest and closely related species, only addresses physiological actions of the introduced species and not ecological effects such as downstream effects on other bacteria due to the reduction of the pest or closely related species. Therefore, non-target effects may be indirect and also agents may disperse from the agro-ecosystems from which they are released, hence the effect on surrounding ecosystems is also important. To predict complex ecological consequences, large amounts of population data for a large range of species would be required. Such information is hard to collect and is constantly shifting due to other factors in the environment. Rare native species can be reduced at an accelerated rate due to the introduction of an agent targeting a closely related pest (Louda et al. , 2003).
To overcome the aforementioned issues, Louda et al. (2003) made the following recommendations. The main way to reduce non-target effects is to avoid releasing generalist (ie not specific for host species) bacteria or adventive bacteria that adapt to the environment and develop ways to control non-target species. This can be overcome by expanding the host-specificity testing, collecting more ecological
16. Chapter 1: Introduction ______information prior to release, considering ecological risks, and by obtaining genetic data on possible adaptation strategies (i.e. genes involved in host selection) (Louda et al. , 2003). Even though this study was comparing arthropod introductions, the same principles can be applied to the release or augmentative enrichment of other bacteria in a range of environments such as sugarcane in North Queensland.
1.2.3 Future of biocontrol
The future of biocontrol bacteria depends on both economic and environmental concerns. Lacey et al. (2001) evaluated the future of biocontrol pathogens for insects and suggested the need for increased virulence, speed of kill, environmental performance (ie will perform at all temperatures and rainfall), increased production efficiency, easy application, more thorough understanding of IPM integration, appreciation of environmental advantages and increased acceptance by growers and the general public (Lacey et al. , 2001). Hence, development of a successful biocontrol pathogen relies greatly on the efficiency of the bacterium and the economics behind production and application. Once the efficiency and benefits of a suitable biocontrol bacterium can be demonstrated, the education of farmers and the general public would be much easier.
17. Cassandra Trent ______
1.3 Canegrubs
Of all Australian sugarcane pests, canegrubs consistently cause the highest percentage of sugarcane damage (figures 1.3 and 1.6) (data collected by Cane Pest and Productivity Boards 1994-2003, compiled by Peter Samson, BSES Limited, Mackay). In addition, there are 19 known canegrub species that cause damage to sugarcane crops in different growing regions of Australia (Table 1.2). To control these pests, a broad range of pesticides registered for canegrub control are currently on the market. In addition to high costs associated with application (Allsopp et al. , 2002, Hunt et al. , 2003), overuse poses a threat to the environment (Cavanagh et al. , 1999, Rayment, 2003) and may lead to canegrub resistance (Denholm et al. , 1998). To overcome these issues, production and application of a range of low-cost, efficient, environmentally-sound management plans has been the overall focus of canegrub research for the past decade. These plans are referred to as Integrated Pest Management (IPM), and incorporate a range of control methods such as modified farming practices, indigenous biocontrol agents, efficient use of pesticides and transgenic sugarcane (Dall et al. , 1995, Robertson et al. , 1995, Allsopp et al. , 1996, Samson et al. , 1998, Allsopp, 2001, Samson et al. , 2001, Horsfield et al. , 2002, Allsopp, 2004, Samson et al. , 2005). The main focus of this work is on the use of biocontrol agents.
Figure 1.4. Aerial view of sugarcane crop damage caused by canegrubs. Note that the middle field is heavily infested and demonstrates severe crop loss. Photo courtesy of BSES Ltd.
18. Chapter 1: Introduction ______The natural environment of the canegrub was originally amongst dispersed eucalypt trees, small shrubs and native grassland. The slow-growing native grasses provided adequate food for developing larvae, and soil contained little organic matter. The reduced food quality and limited quantity possibly maintained a smaller canegrub population. Neighbouring nectar sources presumably housed many natural predators, and undisturbed soils may have been more favourable for growth of canegrub microbial pathogens (Allsopp, 2004). Early documentation describes many canegrub predators including birds (hawks, owls, ibis, dollar birds, poultry, starlings, cuckoo pheasants, nightjars etc), mammals (bandicoots, bats and flying foxes), frogs, lizards (blue tongue and the iguana) and other insects (asilid larvae, elaterid larvae, scoliids and large centipedes) (Illingworth, 1921a).
Figure 1.5. Damage to sugarcane stools caused by feeding of greyback canegrubs.
Over the last 200 years native grasslands and forests have been replaced by grazing pastures, pineapples and sugarcane. All three crops have large, continuously present root systems and their farming causes an increase in soil organic matter. In place of native grasses, larvae feed beneath the soil surface on the roots and regenerative region of sugarcane (Figure 1.5). Significant feeding causes stunted crop growth, poor sugar yield and the damaged stools are unintentionally removed by machinery during harvesting. Hence, damage not only affects the first season but subsequent ratoon crops as well. Beetles lay their eggs over Spring/Summer. Therefore, damage is not visible until late summer or early autumn when the crop is too tall to apply soil-injected pesticides. Hence, current control using pesticides is difficult, as canegrub presence must be predicted if it is to be controlled (Robertson et al. , 1995).
19. Cassandra Trent ______The loss of trees has caused the loss of many of natural predators, and in combination with an increased and reliable food source, canegrub numbers have increased (Allsopp, 2004).
Table 1.2. Endemic canegrubs that cause damage to sugarcane crops. Note that grubs with a two year lifecycle can occur in the same field simultaneously a year out of phase. Information compiled from the BSES Manual of Canegrowing (Allsopp et al ., 2000).
Name Taxonomy Distribution Lifecycle Insecticides Greyback Dermolepida Northern and 1 year cycle, damage suSCon® Blue, canegrubs albohirtum central districts between February and Confidor®, BioCane™ July French’s Lepidiota frenchi Most areas north of 2 year cycle, damage in suSCon® Blue, canegrubs Bundaberg spring or early summer Mocap® Negatoria Lepidiota Proserpine south to 2 year cycle, damage in suSCon® Blue, canegrubs negatoria Beenleigh spring or early summer Mocap®, Rugby® Childers Antitrogus Childers, 2 year cycle, damage in suSCon® Blue, canegrubs parvulus Woongarra, spring and summer Mocap®, Rugby®, Bingera and Gin Confidor® Gin Consobrina Lepidota Mossman to Cairns 1 year and 2 year types suSCon® Blue, canegrubs consobrina depending on race Mocap®, Rugby® Southern 1-yr Antitrogus Bundaberg and 1 year cycle showing suSCon® Blue canegrubs consanguineus Maryborough areas damage in late summer and autumn The following canegrubs are less common yet still cause damage in specific regions. Note there are no specific pesticides for the control of these species. Name Taxonomy Distribution Lifecycle Planiceps Antitrogus Harwood and Lifecycle details unknown. Third instars are present canegrub planiceps Broadwater (NSW) in early summer Rhopaea Rhopaea Southern Mainly 1 year cycles but some individuals have a 2 canegrubs magnicornis Queensland and year cycle northern NSW Nambour Antitrogus Southern 1 year life cycle canegrubs rugulosus Queensland and northern NSW Noxia Lepidota noxia Bundaberg region, 2 year life cycle with damage appearing in late canegrubs Sunshine Coast summer and early autumn Bundaberg Lepidota crinita Only cause 2 year life cycle with damage in spring or early canegrubs damage at summer Bundaberg and Gin Gin Picticollis Lepidiota Bundaberg and Isis 2 year life cycle with damage evident late summer canegrubs picticollis areas and early autumn Squamulata Lepidiota Northern Australia 1 year life cycle with damage occurring between canegrub squamulata February and May Grata Lepidiota grata Throughout Both 1 and 2 year cycles depending on field canegrubs Queensland conditions with damage occurring between April to June (in a 1 year cycle) and addition feeding after August (in a 2 year cycle) Rothe’s Lepidiota rothei Northern 1 year life cycle and feed after winter until November canegrubs Queensland and Northern Territory Caudata Lepidiota North Queensland 1 and 2 year life cycles and damage begins mid- canegrubs caudate summer to autumn and continues until spring Froggatt’s Lepidiota Far north Suspected 2 year life cycle canegrubs froggatti Queensland Grisea Lepidiota grisea Near Mossman, 1 year life cycle canegrubs Gordonvale, Ayr and Ingham Sororia Lepidiota sororia Near Mossman 1 year life cycle but details are not known canegrubs and the Herbert Valley
20. Chapter 1: Introduction ______1.3.1 Greyback canegrubs
Of these, the greyback canegrub ( Dermolepida albohirtum ) (Figure 1.6) is the most significant (Samson et al. , 2005). Greyback canegrubs have a one-yr life cycle synchronised with sugarcane harvesting cycles (Figure 1.7) (Allsopp, 2004). Canegrub larvae feed on sugarcane roots beneath the soil and cause stunted crop growth, poor sugar yield and unintentional removal of damaged plants from soil by machinery during harvesting. Damage is noticeable only after considerable feeding and root damage by larvae from February to July. It is therefore not visible until the crop is too tall to apply soil-injected pesticides (Robertson et al. , 1995). Hence, current control using pesticides is difficult, as greyback canegrub presence must be predicted if it is to be controlled. Some other species of canegrubs, such as Antitrogus parvulus and Lepidota noxia , can more easily be controlled by pesticides. They have a two-yr life cycle where sugarcane feeding larvae occur from September to November and hence, the sugarcane has been recently harvested and pesticides can be applied.
A. B.
Figure 1.6. Greyback canegrub larvae (A.) and adult beetle (B.).
21. Cassandra Trent ______
Figure 1.7. Life-cycle of greyback canegrubs with respect to the sugarcane harvesting cycle. Beetles emerge in December or January to mate and lay eggs. Larvae develop and feed on sugarcane roots from January to May before burrowing into the soil to pupate. Note the most amount of damage occurs in March/April when third instar larvae are feeding.
Three main microbial pathogens of canegrubs have been isolated from greyback canegrubs. These are Metarhizium anisopliae (fungus) (Figure 1.8), Paenibacillus (prev. Bacillus ) popilliae (bacterium) and Adelina sp. (protozoan) (Dall et al. , 1995, Lai-Fook et al. , 1997). A few other, less common bacteria have been partially identified as being similar to Nosema (microsporidian) and Bacillus spaericus (bacterium) ( Dall et al., 1995 , Robertson et al., 1995, Lai-Fook et al., 1997 ). Enhancing these and other naturally occurring pathogen populations will minimise ecological disturbance and ideally maintain low populations of canegrubs. Past efforts involving foreign biocontrol organisms (eg. canetoad, Bufo marinus , 1935) have proven to be unsuccessful and releasing these organisms poses a considerable threat to surrounding environments (Land-Protection, 2003). Little is known about sugarcane soil and canegrub microbial communities. Recent developments in community molecular microbial analysis can shed light on which bacteria belonging to known insect pathogenic genera are present in canegrubs with undiagnosed diseased. Molecular methods are the most accurate, rapid, precise and broad way of discovering more about such complex communities.
22. Chapter 1: Introduction ______
Figure 1.8. Greyback canegrubs infected with the entomopathogenic fungus, M. anisopliae .
1.3.1.1 Current pathogens Limited information is available regarding indigenous microbial pathogens of the greyback canegrub ( Dermolepida albohirtum ). Of the three most common pathogens identified and studied to date, [ Metarhizium anisopliae (fungus), Paenibacillus (prev. Bacillus ) popilliae (bacterium) and Adelina sp. (protozoan)], only one has been successfully applied as a biocontrol agent (Dall et al. , 1995, Lai- Fook et al. , 1997). BioCane™ (viable conidia of Metarhizium anisopliae var. anisopliae strain FI-1045 packaged onto granules of parboiled rice) is the only commercially-available biocontrol agent for greyback canegrub control. A limited number of other pathogens have been partially identified such as the microsporidian similar to Nosema and a bacterium that resembled Bacillus spaericus (Dall et al., 1995, Lai-Fook et al., 1997, Robertson et al., 1997) . Other environmental pathogens likely exist that produce high greyback mortality. Disease has been observed which is not caused by the above known pathogens (Dall et al. , 1995). These have either not been pursued as biocontrol agents, mortality was not associated with microbial pathogenesis, or the causative microbial pathogen(s) remain unknown.
In a field study of canegrub mortality in 1994 reported by Dall et al. (1995), 46% of randomly collected larvae died and of those, only 65% were diagnosed with identifiable infections. Cause of death of the remaining 35% remains unknown. No definitive studies were carried out to determine actual causes of death amongst
23. Cassandra Trent ______larvae . Therefore, currently unknown grub-pathogenic bacteria may have contributed to the death of some percentage of these grubs. It is also possible that genetic disorders or injuries and stress sustained during collection may have contributed to the mortality rate. However, the possibility of pathologies related to undetected bacteria is still feasible and should be explored.
A study by Dall et al. (1995) found that Metarhizium anisopliae occurs naturally in the soil and accounted for 31% of larval mortality in 1994. It invades the cuticle and replicates within the cangrub larvae. Here it excretes destruxins which are cyclic depsipeptide toxins that cause tetanic contraction of body wall muscles followed by paralysis (Samuels et al. , 1988). It was observed that these diseased grubs migrate to the soil surface before death making them susceptible to predators (Illingworth, 1921b). The presence of this fungus in canegrubs has been reported since 1897. Even then, the potential for biocontrol was realised.
Adelina causes mortality in larvae roughly nine months after egg laying. It is contracted by ingestion of oocysts from soil. Each oocyst contains about eight sporocysts and releases them into the hemolymph. Each sporocyst releases multiple sporozoites that are the motile and infectious stage of the disease. Feeding ceases and grubs become flaccid and lethargic after this stage. One or more generations of sporozoites cycle before oocysts develop. Oocysts are released into soil and remain in the decaying cadaver ready to be ingested by grubs. Mortality caused by Adelina can be identified by a pungent odour and a black, soggy and watery appearance (Sallam et al. , 2003). This disease contributed to 27% of greyback death in the 1994 season (Dall et al. , 1995). It is also thought to be the main contributor to greyback population cycles. Without pesticide use, greyback canegrub outbreaks have occurred approximately every 10–13 years, followed by a rapid population decline in following seasons. The decline corresponds to an increase in Adelina mortality (Robertson et al. , 1997). Application for biocontrol is limited as it is currently unculturable in vitro . Hence all current production is in vivo . This process is unsuitable for practical, large-scale biocontrol application (Sallam et al. , 2003).
Paenibacillus popilliae is one of the causes of milky disease in canegrubs (Tanada and Kaya, 1993). Sporulated cells are ingested from the soil, and germination
24. Chapter 1: Introduction ______occurs in the gut lumen. It passes through the gut epithelial cells via phagocytosis before penetrating and multiplying in the basement membrane (Splittstoesser et al. , 1973, Splittstoesser et al. , 1978). Once in the hemocoele it multiplies and sporulates to such a degree that the hemolymph becomes turbid, and the region proximal to the anus turns opaque and milky white (Tanada and Kaya, 1993). A 1994 study estimated this disease accounted for 7% of greyback mortality in the field (Dall et al. , 1995). However, currently published in vitro culturing methods cannot induce sufficient sporulation for practical biocontrol application (Koppenhofer and Fuzy, 2002, Potter and Held, 2002, Stahly and Klein, 1992, Steinkraus et al. , 1998). A series of Japanese patents claim to have developed methods to increase sporulation, but specific data or results have not been publicly released (Masatoshi et al. , 2001, Takeshi et al. , 2002a, Takeshi et al. , 2002b, Takeshi et al. , 2002c, Takeshi et al. , 2003, Masatoshi and Hideji, 2004, Takeshi et al. , 2004a, Takeshi et al. , 2004b, Takeshi et al. , 2004c).
1.3.1.2 Methods of control Integrated Pest Management (IPM) programs incorporate a range of control practices from alternative farming methods such as trap cropping (section 1.3.1.2.2), managed pesticide application, light-trapping of beetles, biocontrol agents and use of canegrub resistant sugarcane clones. The aim is to reduce pesticide use and utilise other naturally occurring canegrub control mechanisms to manage populations of canegrubs rather than fully eradicating them. Used individually, these techniques and alternative methods may not be effective enough to provide complete protection, but a combination of methods managed correctly can eliminate canegrub damage by maintaining low canegrub populations without sacrificing the environment or the farmer’s budget. In order to develop successful IPM plans, an improved knowledge of canegrub physiology, behaviour, diseases etc is required. The control methods listed in the following sections have been used in the past and have been incorporated into the IPM program (Dall et al. , 1995, Robertson et al. , 1995, Allsopp et al. , 1996, Samson et al. , 1998, Allsopp, 2001, Samson et al. , 2001, Horsfield et al. , 2002, Allsopp, 2004, Samson et al. , 2005).
1.3.1.2.1 Insecticides
25. Cassandra Trent ______An average of AUD $7 million is spent on canegrub insecticides each year. Since organochloride based pesticides were developed in the 1940s, the first action of farmers in response to canegrub presence has been application of chemical pesticides. Organochloride pesticides were a cheap and relatively effective method of eradicating insect pests (Samson et al. , 1998). After around ten years of widespread use, canegrub populations were substantially reduced. However, canegrub populations increased in surrounding pineapple fields and grassland (Skinner, 1961). These products were banned by the Australian government in 1987, leaving non-organochloride-based suSCon® Blue (introduced 1986) as the only available insecticide to the sugarcane industry. It still remains the most commonly used canegrub control method, even after many reports of ineffectiveness in the Burdekin region (Robertson et al. , 1998, Samson et al. , 2005). Four insecticides are currently available for canegrub control, three of which are chemical pesticides. These are suSCon® Blue/Plus, Confidor® and Rugby® (Samson et al. , 2005).
The active ingredient of suSCon® Blue is chlorpyrifos in the form of a slow-release granule. Application of this pesticide can only occur whilst planting since the soil is inaccessible after this time. Therefore, one application is used to provide protection for a three-to-four year crop cycle. In the Burdekin region the pesticide has failed to provide continuous protection. Analysis of product granules after one year of soil placement shows the concentration of chlorpyrifos is reduced when compared to its use in other geographic areas. The same increased degradation occurs when applied to previously-treated fields compared to non-treated fields and fumigated soils, indicating that soil micro-bacteria are partly responsible for the reduced efficiency (Robertson et al. , 1998). The other degrading factor is increased pH.
Imidacloprid is currently registered as two formulations. Confidor® Guard, a liquid formulation, is applied to the soil of ratoon crops, becomes incorporated into plant tissues and grubs consume the roots containing the toxins. It has a short lifespan of only four-to-five months and costs between AUD $300 to $416 per hectare to apply (Hunt et al. , 2003). A controlled-release formulation, sold as suSCon® Maxi or Confidor® CR, can be applied at or soon after planting and can give at least two
26. Chapter 1: Introduction ______years control. The insecticide is not degraded rapidly in alkaline soils, so is useful in areas such as the Burdekin.
Rugby® is registered for the control of Southern one-yr, Childers and Negatoria canegrubs at around AUD $200 to $250 per hectare. The active ingredient is cadusafos and it is applied only when canegrub species are found in the soil. Rugby® persists effectively in the soil for around six to eight weeks (Allsopp et al. , 2002).
Pesticides raise many environmental concerns. Pesticide residues and fertiliser runoff have affected water quality of surrounding lakes and rivers that feed into the Great Barrier Reef, causing significant concern and media attention (Cavanagh et al. , 1999, Rayment, 2003). Non-target microbial populations can be directly or indirectly (e.g. change in pH) affected by pesticide use. Many of these non-target microorganisms may contribute to natural control of pest populations. Profiling soil microbial-communities pre- and post-pesticide application may highlight possible changes within the community caused by the particular pesticide (Johnsen et al. , 2001). These factors further illustrate the need for alternative control agents and reduced application of chemical pesticides.
1.3.1.2.2 Sustainable farming practises
Green-cane trash blanketing is the retention and distribution of post-harvest leaves and husks, providing a “trash blanket” over the soil (Dawson, 2002). Higher cane yields and fewer canegrubs were observed when this technique was used in fields with no grass or weeds when compared to traditional cane burning (Robertson and Walker, 1996).
Another modified farming technique is the incorporation of “trap crops” to concentrate grubs to one area and use all available methods to eradicate them. Greyback beetles prefer to oviposit in fields with the tallest cane (Illingworth, 1918, Ward, 2003). Therefore, if one field is planted or ratooned earlier than adjacent fields, the cane will be much taller at time of beetle flight and most eggs will be laid in this field. To prepare for this large larval population, the field can be prepared
27. Cassandra Trent ______with synthetic or biological insecticides at planting and grubs can be contained and controlled (Ward and Cook, 1996, Horsfield et al. , 2002).
28. Chapter 1: Introduction ______
1.3.1.2.3 Plant Resistance
An alternative approach to canegrub control is the creation of sugarcane that is resistant to canegrubs. Transgenic crops have been created that contained genes known to express larval growth inhibiting substances such as the snowdrop lectin and the potato proteinase inhibitor II genes (Allsopp et al. , 1996, Allsopp and McGhie, 1996, Nutt et al. , 1999). Glasshouse trials of transgenic sugarcane expressing potato proteinase inhibitor II protein showed that after six weeks, larvae feeding on this transgenic line gained 4.2% of the weight of control grubs which fed on non-transgenic clones (Nutt et al. , 1999). Current media controversy over the use of genetically modified foods has prolonged further research into these transgenic lines (Keatly, 2000, Mayer and Stirling, 2002).
Many clones produced via breeding programs have shown increased resistance to canegrubs. Through further breeding, a clone with higher canegrub resistance and sugar yield may be possible (Allsopp et al. , 1996, Allsopp and Cox, 2002).
1.3.1.2.4 Biocontrol
In 1921, before the discovery of organochloride pesticides, Illingworth isolated and cultured Metarhizium anisopliae , a fungus that infects and kills canegrubs. Several field trials were conducted. However, results were inconclusive (Illingworth, 1921b). In 1935, pressure was exerted on BSES to import and release the cane toad (Bufo marinus ). After the failure of the cane toad to control canegrubs (Land- Protection, 2003), and the discovery of effective pesticides, the hunt for a biocontrol agent ceased until the 1980s. Further isolation of different strains of Metarhizium anisopliae were tested, and pathogenicity trials took place in this decade (Samuels et al. , 1990). Some attention was focused on testing parasitic nematodes for canegrub biocontrol. However, the high cost and limited effectiveness discouraged further research (Allsopp, 2001).
The registration of BioCane™ in 2000 gave sugarcane farmers a new alternative for greyback canegrub control. BioCane™ contains Metarhizium anisopliae var. anisopliae strain FI-1045 conidia grown on broken parboiled rice. It is applied during planting and gives a 50-60% reduction in greyback canegrub larvae in the
29. Cassandra Trent ______first year of the crop cycle (Samson and Milner, 1999, Samson et al. , 2001, Milner et al. , 2002). Viable conidia remain in the soil and give some protection for up to three years, after which the product can only provide 10-20% infection rates. The sporulating third-instar cadavers maintain the conidia throughout the soil, but its aggregated distribution leaves most of the crop unprotected (Milner et al. , 2003). Application costs around AUD $290 per hectare and is used when the grub risk is medium to moderate. This FI-1045 strain is effective for greyback canegrubs, but is not effective for control of other grub species such as Lepidiota negatoria or Lepidiota frenchi . Another isolate, Metarhizium anisopliae var. lepidiotum strain FI-147, is more pathogenic to these species and treatment of crops with a combination of Metarhizium strains may provide better control against a range of canegrub species (Samson and Milner, 1999, Samson et al. , 2001).
30. Chapter 1: Introduction ______
1.4 Methods for pathogen isolation
1.4.1 Traditional culturing methods
Many studies have been performed on insect guts analysing the culturable bacteria present. In a range of different insects such as grasshoppers, ticks, flies, termites, fire ants and beetles, the fast growing members of the Enterobacteriaceae, pseudomonads, bacilli or actinobacteria dominated (Eutick et al. , 1978, Fitt et al. , 1985, Mead et al. , 1988, Toth et al. , 2004, Buresova et al. , 2006, Yilmaz et al. , 2006, Behar et al. , 2007, Cook et al. , 2007). These bacteria were cultured from homogenised gut tissue on standard medium (such as nutrient agar) over 1-5 days. Isolates were identified via a combination of biochemical, morphological, metabolic, physiological and molecular tests. However, it has been estimated that less than 0.5% of soil bacteria are culturable, hence the need for more suitable detection methods (Amann et al. , 1995). As Cook et al. , 2007 demonstrated, cultured isolates and 16-S amplified clones from the same specimens of crane fly produced very different results. As canegrubs ingest soil, they are expected to have a higher level of microbial diversity and hence an alternate approach is required to adequately screen these bacteria for putative pathogens.
1.4.2 Molecular methods used for community analysis
Environmental microbial diversity is an area of research which has expanded significantly with the development and application of molecular biological techniques. Molecular phylogenetic methods allow the detection and analysis of non-culturable and culturable bacteria. Many different techniques have been developed based on whole genomic sequences (metagenomics), ribosomal RNA gene sequences and DNA or RNA hybridisation techniques. All have varying taxonomic specificity or community fingerprinting capabilities. The most commonly used are briefly described below.
Genomic DNA analysis or metagenomics is based around whole genome extraction from the sample community. The DNA is commonly cloned in vectors, transformed into a host (eg. Escherichia coli ), and filed in a bacterial artificial chromosome (BAC) library. Individual clones can be identified via sequencing, and taxonomic or
31. Cassandra Trent ______gene specific hybridisation experiments (Amann et al. , 1995, Matheson et al. , 1997, Rondon et al. , 2000, Liles et al. , 2003, Schloss and Handelsman, 2003, Yun et al. , 2004). Community profiles can be compared by analysing the molar percent of guanine and cytosine in the extracted DNA based on the kinetics of thermal denaturation and reassociation (Torsvik et al. , 1990, Ritz et al. , 1997). The main limitation for genomic analysis is the extraction of DNA from the original sample. It is difficult to extract purified DNA at high yield without shearing or breaking the genomic sequences.
Ribosomal RNA (rRNA) analysis avoids the need for intact DNA post DNA extraction. A variable region of the 16S ribosomal subunit gene is PCR-amplified using specific primers (Edwards et al. , 1989, Mau and Timmis, 1998, Furlong et al. , 2002, Egert et al. , 2003). These PCR fragments are often cloned into vectors, transformed into E. coli, cloned and sequenced, enabling the dominant members of the bacterial community to be identified (Franke-Whittle et al. , 2004, Egert et al. , 2005, Nakajima et al. , 2005, Xiang et al. , 2006). Amplicons can also be analysed via restriction analysis [terminal restriction fragment length polymorphism (T- RFLP) or amplified ribosomal DNA restriction analysis (ARDRA)] or gel separation via denaturing or conformational properties [denaturing/thermal gradient gel electrophoresis (DGGE/TGGE), single strand conformational polymorphism (SSCP) or ribosomal intergenic spacer region analysis (RISA)]. Specific bacteria can be visualised within samples via fluorescent in situ hybridisation (FISH).
T-RFLP involves adding a fluorescent tag the terminal end of the rRNA amplicon during PCR. Digestion with specific restriction enzymes such as Rsa I, Msp I, Hha I, Hae III, and Alu I, creates fluorescent sequences with species specific lengths sorted via gel electophoresis and identified through database searching (Avaniss-Aghajani et al. , 1994, Bruce, 1997, Osborn et al. , 2000, Egert et al. , 2003, Broderick et al. , 2004). This technique has been used to profile the microflora associated with many insects such as aphids, scarabs, termites, beetles and honeybees (Egert et al. , 2003, Haynes et al. , 2003, Schmitt-Wagner et al. , 2003, Thongaram et al. , 2005, Babendreier et al. , 2007, Miyata et al. , 2007, Lehman et al. , 2008, Kohler et al. , 2008, Yu et al. , 2008). T-RFLP analysis is most often confirmed with 16-S cloning
32. Chapter 1: Introduction ______and sequencing (Egert et al. , 2003, Thongaram et al. , 2004, Yang et al. , 2005, Nakajima et al. , 2006,Lehman et al. , 2008).
ARDRA involves PCR incorporated fluorescent dUTP, restriction enzyme digestion and sequence separation on an automated DNA sequencing gel. Patterns are compared to those of known bacteria to deduce identity (Smit et al. , 1997, Pukall et al. , 1998). Bacterial colonies isolated from the southern pine beetle ( Dendroctonus frontalis ), the wood borer ( Saperda vestita ) and bark beetles ( Ips pini ) have been screened using this method to verify colonies belonging to the same species and reducing the amount of 16-S sequencing required (Delalibera et al. , 2005, Vasanthakumar et al. , 2006).
DGGE and TGGE separate amplicons due to size and sequence denaturation properties. Patterns produced are from a range of different 16-S regions used for community profiling and bands of interest can be isolated, cloned and sequenced (Muyzer et al. , 1993, Heuer and Smalla, 1997, Schabereiter-Gurtner et al. , 2003, Jensen et al. , 2004). These techniques have been used to analyse many insect microbial communities such as from mealybugs, aphids, greyback canegrubs, and cotton bollworms (Haynes et al. , 2003, Franke-Whittle et al. , 2004, Xiang et al. , 2006, Pittman et al. , 2008).
SSCP is another profiling technique that separates single-stranded amplicons due to size and DNA secondary structure (Lee et al. , 1996, Schwieger and Tebbe, 1998, Stach et al. , 2001). It has previously been used to compare the microbial communities of three bee species (Mohr et al. , 2006). Bands were cloned and sequenced to determine individual members.
RISA separates different bacterium DNA by PCR-amplifying the intergenic spacer region between the 16-S and 23-S (prokaryotic) and 18-S and 28-S (eukaryotic) ribosomal genes. The length is highly variable, is determined by gel electrophoresis and can be used to putatively identify bacteria (Borneman and Triplett, 1997, Ranjard et al. , 2000). This technique has been used to analyse soil associated with a range of termites. Both bacterial and fungal communities can be analysed using different primer sets (Jouquet et al. , 2005, Kumari et al ., 2009, Patreze et al ., 2009).
33. Cassandra Trent ______
Jones et al. (2007) developed a different approach to analyse ribosomal internal transcribed spacer regions (ITS). From each community, ribosomal ITS were PCR- amplified and separated via 2-dimentional polyacrylamide gel electrophoresis (2D- PAGE). The first dimension of the gel separates products based on size. The second dimension separated the sized bands via a urea/formamide denaturing gradient. When comparing the analysis of the same soil samples, this technique was shown to increase the apparent diversity when compared to TRFLP, ARISA or DGGE (Jones et al. , 2007).
FISH is the hybridisation of a fluorescent probe to a mounted specimen and the location of the target bacteria can be visualised. This technique has been used to visualise bacteria in the guts of many insects such as termites, scarabs, ant lions, and mealybugs (Ohkuma et al. , 1998, Dunn et al. , 2005, Egert et al. , 2005, Franke- Whittle et al. , 2005, Nakajima et al. , 2005).
A relatively new method being applied in the field is 454 pyrosequencing. This technology allows high throughput analysis of DNA and produces reads of around 100 bp (Margulies et al. , 2005). In a single run, 96% of the Mycoplasma genitalium genome was determined with 99.96% accuracy. The system works by shearing the DNA and attaching adaptors. Each single adaptor-ligated molecule immobilises onto a single bead that is clonally PCR-amplified within its own emulsion. Beads are transferred into picolitre reaction vessels where sequencing occurs. Pyrophosphate sequencing detects nucleotide incorporation upon amplification with a sequencing primer. When each nucleotide is incorporated, pyrophosphate and photons are generated and photons are detected using a CCD camera-based imaging system. Software then deciphers and adjusts the data to provide single, short (~100bp) reads per individual bead (Margulies et al. , 2005). With the development of this technology, 1,200,000 reads are now achievable per run with average read lengths of 400 bp (Lundin et al., 2010).
This 454 pyrosequencing technique has been applied to microbial community analysis as well (Frias-lopez et al. , 2008, Middelbos et al. 2010). Frias-lopez et al. , (2008) used the 454 pyrosequencing technique to analyse pooled DNA and
34. Chapter 1: Introduction ______amplified cDNA from an ocean-associated microbial community. From the sheared genomic DNA, 400 thousand reads provided 200 thousand BLAST nucleotide hits and 300 thousand peptide hits. From cDNA, 130 thousand reads provided 7300 and 23 thousand nucleotide and peptide hits, respectively. These BLAST matches not only provided taxonomic information, but also gave a broad functional analysis of what genes are being expressed within the microbial communities of ocean water (Frias-lopez et al. , 2008).
Other variations to the method include amplification of 16-S DNA regions from a community and the addition of adaptors during PCR to enable bead immobilisation, PCR and pyrosequencing. This method was used by Dowd et al. (2008) to analyse microbial communities associated with cow faeces. The result was around 3200 sequences per cow specimen and a sample size of 20 cows (Dowd et al. , 2008).
Larval microbial communities are complex and hence a set of compatible techniques are required that ultimately isolate and identify bacteria unique to diseased canegrub larvae. Apart from 454 pyrosequencing (which was beyond the budget of this project at the time of conception), the above listed techniques do not highlight unknown species differences between two samples without the laborious and costly task of individual clone sequencing. Hybridisation can provide species determination, but prior knowledge of the species of interest is required. Hence, a new methodological alternative must be developed to fulfil project goals.
1.4.3 Suppressive Subtractive Hybridisation
While individual 16-S sequences can provide phylogenetic information about each bacterial species, a molecular method that can compare individual species differences between living and diseased grubs would be ideal to help identify putative pathogens. One such technique, suppressive subtractive hybridisation (SSH), selectively isolates sequences present in one sample but absent in another (Diatchenko et al. , 1996).
During the SSH process, one sample is treated as the “tester” sample. This is the sample that contains the unique sequences with which to be isolated. The “driver” sample contains presumptively common sequences to the tester sample but does not
35. Cassandra Trent ______contain the sequences of interest. The method is based on separation of double- stranded DNA followed by common sequences found in the tester and driver samples hybridising together. Using molecular adapters (Figure 1.9), sequences unique to the tester sample are PCR-amplified and can be isolated. The enrichment of tester-specific sequences is performed over three stages.
1.4.3.1 Stage I “Tester” DNA is the sample with which you want to amplify the unique sequences. “Driver” samples are samples that are subtracted from the “tester”. The “tester” fragments are divided into two tubes, “Tester A” and “Tester B” and ligated with adapter A or B accordingly. Adapter sequences contain no phosphate groups on the ends and are designed to covalently bind to the 5’-end of DNA sequences (Figure 1.9, Stage I). After ligation, an excess of driver sequences are added to tester A and B solutions and the samples are heat denatured. Note that driver sequences do not contain adapters. “Normalisation” of sequences occurs in this step, i.e. the single- stranded tester sequences disperse throughout the high concentration of single- stranded driver. If a tester sequence encounters a complementary driver sequence they anneal to form a tester-driver heterohybrid (Figure 1.9, Stage I). A heterohybrid sequence refers to the hybridisation of two differing sequences, in this case one sequence that contains an adaptor and one that does not. Another annealing reaction is the formation of homohybrid sequences (tester-tester) where, if a tester sequence is in higher concentrations than in the driver sample, there is a higher chance that it will encounter its complementary tester sequence from the same tester solution (A or B). These sequences are referred to as homohybrids as they contain two identical sequences that both contain the same adaptor. This occurs even when compatible driver sequences are available, however, at such low concentrations that these complimentary tester sequences can hybridise even in the presence of excess driver (Diatchenko et al. , 1996, Gurskaya et al. , 1996) (Figure 1.9, Stage I).
36. Chapter 1: Introduction ______
Figure 1.9. Overview of the Suppressive Subtractive Hybridisation technique. Based on original diagram by Gurskaya et al. (1996) (Gurskaya et al., 1996). The method is comprised of three stages; melting hybridisation, mixing hybridisation and PCR amplification. Tester-specific sequences are preferentially and exponentially amplified.
37. Cassandra Trent ______1.4.3.2 Stage II The second hybridisation step is termed mixing hybridisation and involves combining “tester A”, “tester B” and additional single-stranded driver (Figure 1.9, Stage II). Only the remaining single-stranded tester sequences can reassociate and form hybrids with tester sequences containing a different adapter (Figure 1.9, Stage II). These sequences are predominantly unique to the tester sample and are the sequences of interest.
1.4.3.3 Stage III To amplify subtracted tester-tester sequences containing both adapter A and B, the sticky ends of all double stranded sequences must be filled in. Sticky ends occur due to the adapter only ligating to the 5’-end of each strand. PCR amplification is conducted using primers that anneal to the 5´ end of the adapter sequences. These primers do not allow amplification of double-stranded driver sequences or single- stranded sequences as single-stranded sequences cannot be amplified due to the absence of primer binding sites (Figure 1.9, Stage III). A primer binding site is only created upon filling in the ends of adaptors as primer sequences are complimentary to the filled-in sequence. Exponential amplification of sequences only occurs in tester:tester heterohybrids (i.e. sequence containing both adaptors) as these are the only sequences to contain both primer binding sites (Figure 1.9, Stage III).
Adapters contain inverted repeats and if a double-stranded tester sequence contains only one variation of adapter, the filled-in ends are attracted and form a pan-like secondary structure that inhibits PCR amplification (Figure 1.9, Stage III). Linear amplification occurs where sequences contain one tester and one driver strand. Since only one strand contains an adapter sequence, only one strand can anneal to the primer and be amplified. Hence, each amplicon creates only one new strand per cycle compared with two in exponential amplification. Therefore, exponential amplification only occurs in sequences containing both adapters and hence the subtracted sequences are highly amplified and can be separated for further sequencing and analysis (Diatchenko et al. , 1996, Gurskaya et al. , 1996).
To isolate tester-specific sequences, resulting amplicons are cloned. Clones are screened via southern blotting and dot blots to confirm that the amplified sequence is
38. Chapter 1: Introduction ______not present in driver. In both hybridisation techniques, original tester and driver are hybridised to each clone to confirm that the clones of interest are present in tester and not in driver. Clones only hybridising to tester probes are considered to be tester-specific (Diatchenko et al. , 1996, Gurskaya et al. , 1996).
This technique was originally reported using transcribed cDNA to compare gene expression in different mouse tissues (Diatchenko et al. , 1996). Applied from this technology, the PCR-Select Bacterial Genome Subtraction Kit (BGSK) (Clontech, USA) is a commercial kit that compares genomic DNA from bacterial isolates. The kit was originally designed to compare single bacterial genomes and isolate sequences unique to one bacterium (Janke et al. , 2001, Nielsen et al. , 2002, Parsons et al. , 2003, Dwyer et al. , 2004, Bae et al. , 2005, Mokady et al. , 2005,). It has been particularly useful in determining pathogenicity factors (Smoot et al. , 2002, Nesbo et al. , 2002, Mavrodi et al. , 2002). The BGSK is based on the “Suppressive Subtractive Hybridisation” (SSH) methods of Datchenko et al. , (1996) and Gurskaya et al. , (1996) (Diatchenko et al. , 1996, Gurskaya et al. , 1996, Anonymous, 2001).
Galbraith et al. , (2004) successfully used the BGSK to isolate DNA fragments present in one rumen sample, but absent in another. Results were supported by dot blot hybridisation experiments and proved that the isolated genomic fragments were only present in one sample (Galbraith et al. , 2004). Application of this method to compare the “metagenome” of a community raised a new area of research that focuses on individual and specific differences between two communities. The one approach not only gives a snapshot of the species differences but also compares the functional genes present in each sample. The main limiting factor is the knowledge of bacterium genomes as 48.1% of clones were not homologous to GenBank entries available at the time of publishing (Galbraith et al. , 2004). According to the NCBI website, around 220000 sequences are submitted each month. The addition of these extra sequences increases the chance of higher homology and in turn increases the usefulness of this approach. This project will base its protocol on these early SSH methods, but will compare extracted and PCR-amplified 16-S rDNA sequences as well as whole genomic DNA fragments. By comparing diseased-grub-associated communities to living-grub-associated communities, putative pathogens may be identified.
39. Cassandra Trent ______Project goals and objectives
Canegrubs are an important economic pest of sugarcane and cost-effective control methods are a valuable part of the industry. Therefore, the main goal of this project was to investigate canegrub-associated microbial communities and attempt to identify putative, novel pathogens that may be causing unexplained deaths found in field-collected grubs. Any putative pathogens identified and isolated may subsequently be evaluated for biocontrol efficacy. The specific goals were divided into the following objectives:
Goal 1: Identify dominant putatively-pathogenic members of dead canegrub microbial communities.
Objective 1.1: Amplify 16-S rDNA from grub microbial communities and identify dominant bacteria present through sequencing and homology.
Objective 1.2: Use TRFLP as a tool to profile and compare each specimen’s microflora and assess microbial diversity.
Objective 1.3: Amplify and specifically confirm (via PCR and sequencing) if known entomopathogenic bacteria are present in dead or live grub samples.
Goal 2: Amplify pathogen-specific sequences from a complex microbial community.
Objective 2.1: Specifically-amplified tester-specific 16-S sequences from complex canegrub communities using a novel 16-S PCR-based SSH approach.
Objective 2.2: Selectively-amplify sequences unique to dead grub specimens using the Clontech PCR-Select™ Bacterial Genome Subtraction Kit on genomic DNA extracted from grubs and their associated microflora.
40. Chapter 2: Canegrub dominant bacterial members ______
Chapter 2: Identifying dominant bacterial members from living and dead greyback canegrubs by using 16-S rDNA cloning and sequencing.
41. Cassandra Trent ______
42. Chapter 2: Canegrub dominant bacterial members ______
2.1 Introduction To date, only one study of canegrub gut-associated microflora has been published and in this study, focus was placed on putative symbionts present in the hindgut wall of living canegrub larvae (Pittman et al., 2008). There has been no reported work on microbial-communities associated with canegrub cadavers that do not show symptoms of previously known disease. These specimens may be infected with agents suitable for biocontrol development. Pittman et al. (2008) profiled the hindgut wall-associated microflora from greyback canegrubs ( Dermolepida albohirtum ) through 16-S sequences using denaturing gradient gel electrophoresis (DGGE). Dominant bands common to a number of individual specimens were homologous to the Clostridiales, Deltaproteobacteria , Actinobacteria , Bacteroides and Betaproteobacteria . In addition, DGGE profiles showed significant diversity within the microbial communities present in the gut wall, and also differences between the communities of individual specimens (Pittman et al., 2008). The hindgut wall represents only a fraction of the grub microbial community. Therefore, to determine the difference between dominant members present in living and dead canegrubs, the bacteria present in the whole grub extractions of both living and putatively diseased grubs need to be investigated.
Dominant community members associated with other scarabs have previously been investigated. The intestinal tract of the European cockchafer ( Melalontha melalontha ) has been studied in detail using a range of methods (Egert et al., 2005). The physiochemical gut conditions (pH, oxygen, hydrogen and redox potential) were analysed in three individuals and showed that both the midgut and hindgut were alkaline and anoxic. Hindgut microflora of an individual specimen was assessed via 16-S cloning and sequencing. The hindgut wall and lumen both contained Clostridiales, Turicibacter , and Bacteroides . This was supplemented with community profiles of the different gut regions using terminal restriction fragment length polymorphism (T-RFLP) (Egert et al., 2005). Therefore, it is likely that both scarabs ( M. melalontha and D. albohirtum ) contain similar gut microbial composition as they both contain Clostridiales and Bacteroides members within the hindgut. In addition, the dominant microbial community members present in the
43. Cassandra Trent ______midgut and the rest of the intestinal tract were not explored in this work and have yet to be investigated.
Clostridiales and Bacteroides have also been identified from another scarab hindgut. The hindgut-associated microbial community of the New Zealand grass grub (Costelytra zealandica ) was analysed using DGGE analysis and sequencing (Zhang et al., 2008). Different diets were compared and while diversity was similar in the hindgut of all three 14 day treatments, grubs fed carrot or no food showed reduced diversity. Common bands from DGGE analyses were isolated and sequenced. These sequences were homologous to Clostridia , Deltaproteobacteria , Betaproteobacteria and Bacteroidetes (Zhang et al., 2008). Again, only the dominant members of the hindgut were identified, dominant members were chosen via DGGE separation of 16-S sequences and grubs were not analysed straight from the field. Therefore, little has been done on whole-larvae analysis of the microbial communities associated with scarabs.
Culturing studies of scarab gut flora have also been reported. The rose chafer, Pachnoda marginata , was screened for culturable celluolytic and hemicelluolytic bacteria involved in food metabolism (Cazemier et al., 2003). The bacteria were isolated from the intestinal tract using selective media and identified using morphological, physiological, biochemical, molecular, scanning electron microscopy and fatty acid compositional techniques. The resulting isolate was named Promicronospora pachnodae and is phylogenetically similar to P. sukumoe and P. citrea (Cazemier et al., 2003). However, due to the diversity found from the DGGE study of Pittman et al. (2008) and the current knowledge gap of the basic dominant members, an initial molecular screen of canegrubs was chosen in preference to a culturing analysis based on gaining an initial snapshot of the entire community.
Little is known regarding scarab-associated microflora, particularly in the greyback canegrub. To date only three main microbial pathogens have been isolated from greyback canegrubs. These are Metarhizium anisopliae (fungus), Paenibacillus (prev. Bacillus ) popilliae (bacterium) and Adelina sp . (protozoan) (Dall et al., 1995, Lai-Fook et al., 1997). A few other, less common bacteria have been partially identified as being similar to Nosema (microsporidian) and Bacillus spaericus
44. Chapter 2: Canegrub dominant bacterial members ______(bacterium) (Dall et al., 1995, Lai-Fook et al., 1997, Robertson et al., 1997). Of these agents, only M. anisopliae has been suitable for development as a biocontrol agent with limited success (Samson and Milner., 1999, Samson et al., 2001, Milner et al., 2002).
As mentioned above, DGGE and TRFLP have both previously been used to assess microbial diversity within scarabs. Pittman et al. (2008) found the hindgut wall- associated community to be so diverse that DGGE profiles produced smears. The microflora associated with the entire grub was suspected to be even more diverse and therefore, DGGE separation may not be suitable. TRFLP profiles provide a broad view of microbial diversity however, specific identification of single sequences is not possible without individual 16-S rDNA cloning and sequencing (Egert et al., 2005).
In isolation, the 16-S PCR approach can provide a broad view of dominant members within a community and has been used to compare many diverse microbial- communities associated with insects. Some of these include the microflora associated with flies (Corby-Harris et al., 2007), beetles (Schloss et al., 2006), ticks (Benson et al., 2004), termites (Hongoh et al., 2003) and environmental samples (Huang et al., 2005). The approach is based on the amplification, cloning and sequencing of 16-S rDNA from bacterial community members. As this gene is evolutionally conserved, bacterial members can be taxonomically identified via this sequence (Weisburg et al., 1991). This allows the identification of the dominant bacterial members present within the community.
The goal of this work was to identify a dominant bacterial pathogen from dead grubs and to assess the dominant bacterial members associated with living canegrub larvae. Little is known of the bacterial flora associated with either healthy or dead specimens. Determining the dominant members associated with both types of specimen would be beneficial to identify if a dominant pathogen is present. In order to achieve this, 16-S PCR was performed to amplify and sequence dominant members of both live and dead grub microbial communities. Resulting sequences were phylogenetically compared and assessed for possible relation to known
45. Cassandra Trent ______entomopathogens. This technique was chosen to give broad knowledge of canegrub microflora and establish a solid foundation for further molecular analysis.
46. Chapter 2: Canegrub dominant bacterial members ______
2.2 Materials and methods
2.2.1 Canegrub specimens from the 2007 grub season
Grubs were collected from two locations in Mackay (by Peter Samson) and one farm from Tully (by Nader Sallam). Grubs were dug up under infected stools. Stools were removed from soil and a 30 cm 3 volume of soil was dug out. Both the stool and associated soil were searched for grubs. Each stool was also split with a shovel and shaken to recover additional grubs. Live grubs were freighted to Brisbane within two days of collection. Specimens were air-freighted under ambient temperature in small tubes containing field collected soil. Dead grubs were collected as above, live from Mackay and Tully. Specimens were maintained in tubs containing soil at BSES Ltd in Meringa. Individuals were monitored for disease symptoms. Once deceased, specimens were refrigerated and posted to Indooroopilly en-mass.
2.2.1.1 Grub processing
Grubs were washed in a 5% sodium hypochlorite solution for three min and rinsed in three separate washes of sterile distilled water before being placed in a sterile seven ounce Whirl-pak bag (180 x 95 mm) (Nasco, USA) containing 3 mL of a 10 mM phosphate buffer solution. The bag was placed in a stomacher bag and processed in a stomacher for 2.5 min. The content was aseptically poured into sterile 15 mL falcon tubes, leaving behind the solid debris. Samples were cryopreserved in a 5% BSA (Sigma, USA), 15% glycerol (Unilab) solution and stored at -80 ºC. DNA was extracted using the Powersoil DNA Extraction Kit (MOBIO, USA) as per manufacturers’ instructions. The optional 70ºC incubation step for 10 min was also performed after the addition of solution 1.
Dead grubs were processed as per above, omitting the initial washing step. DNA was extracted using the Powersoil DNA Extraction Kit (MOBIO, USA) as per manufacturers’ instructions and optional heating step above.
47. Cassandra Trent ______2.2.1.2 Live grubs
One live grub from each geographic location was randomly chosen for analysis. Whole grub extracts were used for analysis. Specimens analysed were from Attard’s farm Mackay (A5R), Reeve’s farm Mackay (R7R) and a grub infested field from Tully (T8R).
2.2.1.3 Dead grubs
The following dead grubs from Mackay were chosen for analysis: 6P, 18P, 11S, 39S and M1-37. The M1-37 grub was suspected of having milky disease (caused by Paenibacillus popilliae ) by visual and microscopic inspection performed by Meringa laboratory staff. Whole grub extracts were used for analysis. Samples 6P and 18P were transferred to fresh peat after collection whilst 11S and 39S were kept in their original soil.
2.2.2 16S PCR
Eubacterial primer 27-F (5´-AGAGTTTGATCMTGGCTCAG-3´) (Edwards et al., 1989) and universal primer 1392-R (5´- ACGGGCGGTGTGTRC-3´) (Lane et al., 1985) were used to amplify the 16-S region of bacterial DNA from living and dead grubs. Each 50 µL reaction contained 1x Premix Ex Taq™ Hot Start Version (Takara, Japan) and 0.5µM of each primer. Cycle conditions were as follows: initial denaturation at 94°C for 5 minutes, 10 cycles of 94°C for 30 seconds, 65-55°C for 30 seconds (- 1°C per cycle), and 72°C for 2 minutes followed by 15 cycles of 94°C for 30 seconds, 55°C for 30 seconds, 72°C for 2 minutes and a final extension at 72°C for 7 minutes.
2.2.3 Cloning
Sequences were ligated into p-GEM-T Easy (Promega, USA) and transformed into JM109 competent E. coli cells (Promega, USA) as per manufacturer’s instructions. Cells were selected using LB/Amp/X-Gal/IPTG agar (2.2.3.2), and incubated at 37ºC for 12-16 h. White colonies (n=20 per specimen) were incubated in LB broth (2.2.3.1) at 37ºC for 12-16 h and plasmids purified using the Wizard SV Plus Miniprep Kit (Promega, USA) as per manufacturers’ instructions.
48. Chapter 2: Canegrub dominant bacterial members ______Purified DNA (PD) sequencing reactions were submitted to AGRF with 1-6 µL of purified plasmid and 6.4 pmol of universal M13 forward (5´- dCGCCAGGGTTTTCCCAGTCACGAC-3´) and reverse (5´- dTCACACAGGAAACAGCTATGAC-3´) primers (Promega, USA or Geneworks, Australia).
2.2.3.1 Luria Bertani (LB) broth and agar
LB broth contained 1% tryptone, 0.5% yeast extract and 1% sodium chloride dissolved in deionised water and autoclaved at 121ºC for 20 min (pH 7.0). For solid medium, 2% agar (BD, USA) was added before autoclaving (pH 7.0).
2.2.3.2 LB agar with Ampicillin, X-gal and IPTG (LB/Amp/X- gal/IPTG)
Agar was made as per 2.2.2. Once cooled to below 55ºC, 100 mg/L Ampicillin (ICN Biomedicals, USA), 60 mg/L IPTG (Progen, Australia) and 100 mg/L X-Gal (Progen USA) dissolved in N-N dimethylformamide (Sigma, USA) were filter sterilised and added to tempered agar. Plates were poured on day of use.
2.2.3.3 SOC medium
SOC medium contained 2% tryptone, 0.5% yeast extract, 0.05% sodium chloride and 2.5 mM potassium chloride dissolved in deionised water and autoclaved at 121ºC for 20 min. Prior to pouring add 10 mM magnesium chloride and 20 mM D- glucose (filter sterilised).
2.2.4 Sequencing analysis
Plasmid and primer sequences were removed using the ContigExpress application in the Vector NTI Advance 10.3.0 Suite (Invitrogen, USA). Sequences were cross referenced to the NCBI nucleotide database via a blastn search. Blastn neighbour- joining trees displayed Genbank sequences with the highest homology to those in this study. The phylogenetic tree was created using the Align-X program from the Vector NTI Advance 10.3.0 Suite.
49. Cassandra Trent ______
2.3 Results
PCR for each sample was successful and amplified sequences of the expected size (~1500 bp). Each specimen produced 20 individual clones (160 total) and of these, five differing sequences were amplified from A-5-R, six from R-7-R, eight from T- 8-R, seven from 6-P, three from 18-P, three from 11-S, two from 39-S and six from M1-37 (Table 2.1). Sequences showed between 81 to 99% homology to previously reported Genbank sequences.
2.3.1 Living Grubs
BLAST searches indicated that A-5-R sequences were most closely related to uncultured bacteria, a Chryseobacterium sp. , an uncultured Clostridiaceae, an uncultured Eubacterium sp. , a Clostridium sp. , and Melissococcus plutonius (Table 2.1). Sequences from R-7-R were most closely related to uncultured bacteria and uncultured Clostridiaceae, while sequences from T-8-R were most closely related to uncultured bacteria, uncultured Eubacteriaceae and uncultured Clostridiaceae. Homology ranged between 81 to 98% and all E values were 0 apart from sequences 4 and 5 from sample A-5-R (Table 2.1).
The most closely aligned BLAST sequences were isolated from differing environments. Sequences showing homology to A-5-R clones were isolated from mammalian guts (human and mouse), insects (midge, Protaetia brevitarsis , scarabs and termites), an ulcerated snakehead and a lake sediment. Homologous sequences to R-7-R were isolated from mammalian guts (mouse, bovine, swine and human), insects (termites, Protaetia brevitarsis and scarabs) and the turkey gut, while T-8-R homologous sequences were isolated from mammalian guts (gazelle, swine, reindeer, bovine, mouse, rabbit and human), insects (termites and scarabs), soil and a spacecraft manufacturing clean room (Table 2.1).
50. Chapter 2: Canegrub dominant bacterial members ______’s (A) ’sReed’s (A) and (R) and from farms below detailsalong with of where insectsanimal from guts.or d from living d collected grubs in Mackay from Attard ches ches showing highest sequence homology recordedare ies ies were of previously uncultured bacteria isolated they were isolated 2.2.4). (section Note-many entr Tully (section The (T) three 2.2.1). BLAST best mat Table BLAST 2.1. matches to 16-S sequences amplifie
2.3.2 Dead grubs 51. Cassandra Trent ______Amplified sequences from 18-P were homologous to uncultured bacteria, Klebsiella pneumoniae , Morganella morganii , Sphingomonas sp. , Pseudomonas sp. , Pseudomonas palleroniana and Pseudomonas stutzeri (Table 2.2). Sequences from 6-P were homologous to uncultured bacteria, Alcaligenes sp ., Achromobacter xylosoxidans , Dysgonomonas gadei , uncultured Dysgonomonas , uncultured Bacteroidales, Bordetella sp ., Pseudomonas fluorescens , Pseudomonas sp. and Pseudomonas veronii . Amplified 11-S sequences were homologous to Myroides odoratus , uncultured bacteria, Klebsiella pneumoniae and Morganella morganii while sequences from 39-S were homologous to uncultured bacteria, Klebsiella sp . and Klebsiella pneumoniae . M1-37 sequences showed homology to uncultured bacteria, Morganella morganii , Elizabethkingia meningoseptica , an Enterobacterium, Comamonas sp ., an uncultured Bacterioidetes bacterium and an uncultured Dysgonomonas sp . Homology ranged from 90 to 99% and all E values were 0 (Table 2.2).
120
100
80 Dead grubs 60 Live Grubs 40
20
0 Insect mammal soil water plant guts
Figure 2.1. Origins of bacteria with 16-S sequence homology to sequences amplified from dead and live greyback canegrubs. Each dead or live-canegrub-associated sequence was homologous to a known entry in the Genbank database. The source of each entry was categorised into insect, mammalian guts, soil, water and plants (See Tables .2.1 and 2.2). A percentage of how many sequences were categorised into these groups were tallied for dead and live grubs. Error bars refer to one standard deviation.
52. Chapter 2: Canegrub dominant bacterial members ______many many entries were .2.4). Note- 1). The three best BLAST matches showing with details of where they were isolated (section 2 environment. S S sequences amplified from dead grubs (section 2.2. Table 2.2. BLAST matches to 16- highest sequence homology are recorded below along of previouslybacteria uncultured isolated from the
53. Cassandra Trent ______Again, dead grub sequences were homologous to a range of bacteria isolated from differing environments. Homologous 18-P sequences were isolated from insects (ant lion), a human pathology specimen (cystic fibrosis patients and haemodialysis water), soil, biofilter, type strains and marine water, while 6-P homologous sequences were isolated from a human pathology specimen (gall bladder), insects (termites and scarabs), plants (bush lily and alpine plants), soil (forest soil and manure plot), industry (activated sludge, lab contaminate and dye-wastewater), mushrooms and groundwater. Closely aligning sequences to 11-S were isolated from water (marine and lagoon), bacterial type strains, insects (fruit fly and ant lion) and human pathology (cystic fibrosis patients). Homologous sequences to 39-S were isolated from insects (ant lion, stink bug and termites) and human pathology specimens (cystic fibrosis patients). M1-37 homologous sequences were isolated from insects (fire ant, aphid, ant lion and termites), bacterial type strains, plants (rice), mouse, mudfish, industry (activated sludge, olive mill wastewater), water (lake) and a cave (Table 2.2).
2.3.3 BLAST isolates
When the origins of the matching blast sequences were compared, a difference was noted between living and dead grub specimens. Dead grub sequences were more homologous to sequences isolated from insects whilst living grub BLAST matches were isolated from both insect samples and mammalian guts (figure 2.1). All dead grub and living grub sequences cluster independently when compared on a phylogenetic tree (figure 2.2).
54. Chapter 2: Canegrub dominant bacterial members ______
Figure 2.2. Phylogenetic tree of all cloned 16-S sequences from living and dead grub samples. This tree shows the relationship between each sequence and compares sequences amplified from dead and live grubs. Note sequences from dead and live grubs clustered independently. Distances to the closest branch are in brackets at the end of each sequence. The tree was constructed using the Align-X program from the Vector NTI Advance 10.3.0 Suite. Sequence distances were calculated using the Neighbour Joining algorithms of Saitou and Nei (1987). Similar sequences cluster closely together with smaller distances between branches. Living grub associated sequences. Dead grub associated sequences.
55. Cassandra Trent ______
2.4 Discussion
Little is known regarding the bacterial community associated with greyback canegrubs. This was the first study to examine dominant members from live specimens and decomposed dead specimens. From the 16-S clones analysed, dominant members amplified from dead grubs differed to those amplified from live grubs. Known insect symbionts from uncultured and cultured Clostridiales dominated live grub samples whilst a range of saprophytic and psychrotrophic bacteria dominated dead grub-associated communities. This suggests that decomposition occurred during refrigeration of the dead specimens. During this time, the bacterial community associated with the dead grub may have also altered. Consequently, pathogen isolation and detection may be restricted to freshly collected and preferably living diseased specimens, or alternate collection and storage conditions may be required. For example, portions of diseased specimens may be frozen for future analysis, or putative pathogens may be cultured from hemolymph prior to death.
There is currently limited knowledge regarding the microflora associated with field- collected dead-insect specimens and saprophytes associated with the decay of insects in the environment. In canegrubs, some field-collected dead-specimens have previously been studied. However, these specimens were only examined for parisitoids and the presence of any other entomopathogen was not explored (Logan et al., 1999). Most studies analysing dead insect material are of lab-reared insects artificially infected with known entomopathogens such as nematodes (Gouge et al., 2006, Pechy-Tarr et al., 2008, Blackburn et al., 2008) or entomopathogenic fungi (Barker et al., 1998, Majumdar et al., 2008). Therefore, this study is one of the first to analyse the microbial community associated with decaying insects with unknown causes of death and the first molecular study of dead canegrubs. Results provide insights into dead insect associated communities and not surprisingly, many putatively saprophytic bacteria were identified from these specimens.
Little is currently known about insect decomposition in the environment and which bacteria are involved. This study, via 16-S community analysis, showed that some dominant members associated with decomposing specimens are suspected to be
56. Chapter 2: Canegrub dominant bacterial members ______saprophytes. These bacteria may play a role in tissue decomposition. Other species of bacteria may have caused death to these specimens and may have been dominant at the time of death. Upon decomposition, the microbial community may change and conditions may favour the growth of the saprophytic bacteria identified in this study. Hall et al., (1957) examined brittle, decomposed insect cadavers infected with rickettsial cells and upon microscopic examination, showed that the rickettsial cells were no longer intact and were widely scattered. Therefore, the rickettsial infection within the intact cadaver was no longer culturable after the specimen continued to decompose and dry out. This demonstrates the need for freshly dead or live-diseased specimens if putatively pathogenic bacteria are to be isolated and cultured from canegrub specimens.
Many known entomopathogens produce antimicrobial and antifungal substances that limit the growth of competing bacteria such as saprophytes. Some of these include the bacterial nematode symbionts Photorhabdus spp. (Paul et al., 1981, Boemare et al., 2002, Duchaud et al., 2003, Blackburn et al., 2008) and Xenorhabdus spp. (Gouge et al., 2006), the spore-forming bacterium Bacillus thuringiensis (Bode et al., 2009), and the entomopathogenic fungi Beuveria bassiana (Blackburn et al., 2008). Therefore, the most commonly isolated and identified entomopathogens are generally those which use the cadaver as a food source and vessel for replication. Entomopathogens that replicate within the dead insect may be more readily discovered as these bacteria dominate the microbial population in the cadaver and can be viewed via microscopy. Entomopathogens may exist with different infection strategies that do not require the cadaver for transmitting the disease and therefore, may not produce antimicrobial compounds, and may not dominate the microbial population in the cadaver. Therefore, if the dead specimens analysed in this chapter contained a bacterial pathogen, it may not be the most dominant member of the community. In addition, if the putative pathogen was fungal or of eukaryotic origin, it would not be detected using 16-S PCR. Such an organism may be discovered using alternate molecular methods that allow amplification of less dominant sequences.
In this study, no bacterial genera associated with the dead grub amplified sequences, were detected in the live grub samples (figure 2.2). Of these, sequences matching
57. Cassandra Trent ______isolates from the family Enterobacteriaceae were amplified from all dead grub samples (apart from 6-P, where they were not detected) and attributed for the majority of the dead grub clones sequenced. Sample 6-P produced amplified sequences homologous to Pseudomonas (8 of 20 clones) and Dysgonomonas (9 of 20 clones). These genera are discussed further below. Bacteria from the genus Klebsiella have been associated with many insects such as aphids (Nakabachi et al., 2003), fire ants (Lee et al., 2008), beetles (Blackburn et al., 2007), flies (Behar et al., 2005), locusts (Dillon et al., 2002) and ant lions (Dunn et al., 2005, Nishiwaki et al., 2007). The association with these insects appears to be symbiotic and there is no evidence to suggest that they may be entomopathogenic. In addition, Klebsiella is also linked to nitrogen-fixation in flies (Behar et al., 2005) and the chemical production of an aggregation-causing pheromone in locusts (Dillon et al., 2002). Therefore, some species of Klebsiella may aid in the metabolism of organic compunds within the insect gut and have become dominant after the decomposition of the gut wall post death.
Members of the genus Klebsiella were not detected in the living canegrub specimens, and symbiotic associations with other scarabs remain unknown. Therefore, if these Klebsiella are canegrub symbionts, they do not appear to be dominant members of the live grub-associated communities. Another explanation for the dominance of Klebsiella in dead specimens is the observation that many of these bacteria have the ability to grow at low temperatures (below 7ºC in dairy products) (Juven et al., 1981). Therefore, these bacteria may have become more prominent within the community during the period when dead specimens were stored at 4°C.
Another genus identified from dead grubs was Sphingomonas . A sequence related to Sphingomonas sp . was isolated from one clone in sample 18-P. Members of the genus Sphingomonas are important biodegraders of organic chemicals such as insecticides (for example chloropyrifos, the active ingredient in suSCon® and suSCon® Blue (Li et al., 2007)) and herbicides (Keum et al., 2008). This suggests that it is commonly present in soil, may play a role in the breakdown of currently applied canegrub insecticides, and may be transiently present in grubs. Some
58. Chapter 2: Canegrub dominant bacterial members ______species are also known to excrete chitinase to break down chitin from insect and shellfish remains in soil (Zhu et al., 2007). This suggests it may either be able to penetrate the outer cuticle and cause pathogenesis or have a saprophytic role in the degradation of the canegrub cadaver. Other species of Sphingomonas are pathogenic to plants such as melons (Buonaurio et al., 2002) or are human opportunistic pathogens associated with hospital infections (Ammendolia et al., 2004). Therefore, if a promising entomopathogenic Sphingomonas was discovered, it may be considered unsuitable for biocontrol for fear of disease transmission to the sugarcane crop or humans.
In addition to their association with plants and environmental soils, sphingomonads have been previously associated with live tick ( Ixodes scapularis ) specimens and accounted for 11% of total clones amplified from seven individual insects (Benson et al., 2004). A Sphingomonas -like sequence has also been amplified from field collected cotton bollworms ( Helicoverpa armigera ) (Xiang et al., 2006) and pea aphids ( Acyrthosiphon pisum ) (Nakabachi et al., 2003). Of the insect associations, no Sphingomonas isolated in these studies appeared pathogenic. Therefore, it is unlikely that this bacterium is an entomopathogen. More likely the Sphingomonas - related bacterium may have been consumed from the soil and may have contributed to the degradation of dead grub tissue via chitinase production.
Another genus identified from dead grub specimens was Pseudomonas . Sequences related to Pseudomonas were amplified from dead grub samples stored in peat (18-P and 6-P). Fluorescent pseudomonads are widely known to be saprophytes associated with plants and soil (Godfrey et al., 2001, Reiter et al., 2003, Caesar-TonThat et al., 2007). In addition, many Pseudomonas spp. are psychrotrophic and can grow at 4ºC (Hinton et al., 2004). Therefore, these bacteria may have become more prevalent during specimen refrigeration. In addition, pseudomonads were only amplified from grubs stored in peat. Therefore, it is also likely that they were introduced to the grub during storage in the peat and began to dominate the microbial population post- death.
Pseudomonads have also been associated-with or isolated from, many insects such as bees (Mohr et al., 2005), moths (Indiragandhi et al., 2007), flies (Fitt et al., 1985)
59. Cassandra Trent ______and beetles (Bahar et al., 2007, Lehman et al., 2008). P. palleroniana (Gardan et al., 2002), P. fluorescens (Ceasar-ThonThat et al., 2006), P. veronii (Adhikhari et al., 2001) and P. stutzeri (Meier et al., 2003, Espinosa-Urgel et al., 2004) have been commonly associated with plants or agricultural soils and hence were most likely present in the peat or the canegrub gut. However, P. fluorescens has also been shown to produce an insecticidal toxin that causes death in the greater wax moth (Galleria mellonella ), tobacco hornworm ( Manduca sexta ) and three species of mosquito ( Anopheles stephensi, Culex quinquefasciatus and Aedes aegypti ) (Prabakaran et al., 2002, Pechy-Tarr et al., 2008). P. fluorescens is also a pathogen of leaf folder larvae ( Cnaphalocrocis medinalis ) and has been isolated from paralysed larvae (Dangar et al., 2008).
Further investigation of P. fluorescens present in canegrubs is important to determine if these strains present are pathogenic or merely soil-dwelling saprophytes. Freshly-deceased samples are required to isolate and culture putatively entomopathogenic strains of this bacterium from canegrubs. Media selective for the isolation of Pseudomonas spp. are readily available (such as Pseudomonas CN agar) (Atlas et al., 2004) and can be used to isolate Pseudomonas spp. from freshly- deceased grubs. Both canegrub-specific entomopathogenic strains of the bacterium and also the exotoxins produced by such P. fluorescens strains are both potential agents for biocontrol. Fulfilling Koch’s postulates via larval feeding trials would be required to determine the dosage required for infection, host-specificity, mortality rate, effects on feeding, length of disease incubation and which stages of the canegrub lifecycle were affected. Such parameters along with costs associated with commercial production and soil delivery are needed to help determine the viability of commercial biocontrol development.
Remaining sequences amplified from the dead grubs were homologous to bacteria that have been previously associated with insects, yet none were reported as entomopathogenic. Myroides spp. have previously been reported in fruit flies (Drosophila melanogaster ) (Corby-Harris et al., 2007), and flesh flies ( Sarcophaga sp.) (Dharne et al., 2008). A Myroides sp. was also isolated from dead Lepidopteran larvae ( Hylesia metabus ). However, a feeding study showed that this strain did not cause mortality to fifth instar larvae and is not likely to be a pathogen (Osborn et al.,
60. Chapter 2: Canegrub dominant bacterial members ______2002). Some strains also have clinical significance especially due to antibiotic resistance mechanisms. A tetracycline-resistant strain has been isolated from a swine lagoon (Macauley et al., 2007) and a Myroides sp . from flesh flies was shown to be resistant to penicillin-G, erythromycin, streptomycin, amikacin, kanamycin, gentamycin, ampicillin, trimethoprim and tobramycin (Dharne et al., 2008). Myroides spp. have also previously been reported as human opportunistic pathogens in immunocompromised patients (Green et al., 2001, Kallman et al., 2006). Hence, antibiotic resistance can pose a problem when determining treatment of these infections. Therefore, even if pathogenic, may not be a suitable choice for biocontrol development. There is no current evidence to suggest Myroides have caused pathogenesis in insects. Therefore, this bacterium is not thought likely to be a canegrub pathogen. As with Klebsiella and Pseudomonas , Myroides odoratus has been reported to grow at 4 ºC (Nichols et al., 2005) and may have become more prevalent throughout the refrigeration of the dead grub specimens.
Of the other bacterial genera amplified from dead grub specimens, the genus Dysgonomonas has previously been detected in scarabs (Egert et al., 2003) and termites (Noda et al., 2003, Shinzato et al., 2007) and is thought to be symbiotic. The genus Elizabethkingia has previously been isolated from mosquitoes (Lindh et al., 2008) and diseased frogs and fish (Bernadet et al., 2004). However, it is more commonly isolated as an opportunistic pathogen of newborn children (Ceyhan et al., 2008). Bacteria from the genus Bordetella have been detected in the larval cotton bollworm (Xiang et al., 2006) and flies (Corby-Harris et al., 2007). They are primarily isolated from the environment (Kapley et al., 2007) and some species can cause respiratory diseases in mammals (Musser et al., 1986, Spilker et al., 2008). Comamonas related sequences have been isolated from cotton bollworm larvae (Xiang et al., 2006), beetles (Schloss et al., 2006), bees (Mohr et al., 2005), and fly pupae (Toth et al., 2004). However, there was no evidence of pathogenisis in these insects. The above mentioned bacteria are not likely to be pathogenic to canegrubs. Even if found to be pathogenic, due to the clinical significance of some species, may not be suitable for biocontrol due to the risk of human transmission.
No dominant bacterial-community members amplified from dead grubs were identified from live grub specimens. Most 16-S sequences amplified from live grubs
61. Cassandra Trent ______were homologous to uncultured bacteria isolated from the environment. Of these, many clones were also highly homologous to cultured bacteria such as members of the Clostridiales, or a Chryseobacterium spp . Due to culture-independent analysis of microbial communities, many Genbank entries are reported as being uncultured. However, this does not confirm these bacteria as non-culturable. Using the most closely related taxonomic information, selective media and growth conditions may be chosen to selectively isolate such bacteria.
All of the living grub samples (apart from R-7-R-6) clustered together in two separate, uncultured (Clostridiales) groups (figure 2.2). The Clostridiales were the most commonly amplified sequences from living specimens and may play a role in canegrub metabolism. Clostridiales have been previously reported as abundant in many arthropod and mammalian guts. In insects, they have been commonly isolated from termites (Hongoh et al., 2003, Thongaram et al., 2005, Hongoh et al., 2006, Nakajima et al., 2006), flies (Cook et al., 2007), scarabs (Tokuda et al., 2000, Egert et al., 2003, Egert et al., 2005) and other beetles (Lehman et al., 2008). However, little is known about their precise role in arthropod metabolism. They are thought to play a role in digestion as they are fermentative and are particularly associated with the hindgut wall and lumen of scarabs (Egert et al., 2005). In humans, Clostridiales ferment cellulose, pectin and starch not degraded in the small intestine, and are more abundant when these compounds are present (Sharp et al., 2000, Chinda et al., 2004). In this study, all but three living grub clones were related to the Clostridiales. This dominance may be due to the consumption of cellulose from sugarcane roots (Campbell et al., 1999) and the role of these bacteria may be to ferment these fibres in the hindgut.
A number of these uncultured Clostridiales-like bacteria were linked to obesity in mice and humans, in particular fat storage (Bakhed et al., 2004, Turnbaugh et al., 2006, Ley et al., 2006). The introduction of obese-mouse microflora to germ-free mice caused increased body fat storage despite a decrease in food consumption when compared to transplants from normal (lean) mice (Bakhed et al., 2004, Turnbaugh et al., 2006). These studies indicated that the microrflora may play a substantial role in mammalian metabolism and fat storage. Firmicutes are the bacterial phylum containing the genera Clostridium and Bacillus . In obese individuals, there is also
62. Chapter 2: Canegrub dominant bacterial members ______an increased firmicute-to-bacterioides ratio which is decreased when the individual is placed on a calorie restricted diet (Ley et al., 2006). This indicates that the microflora may be important in fat storage, may be continually changing in response to diet and may play an important part in controlling human metabolism. Such roles may be true for canegrub larvae as well.
Experiments investigating the effect of gut bacteria on canegrub appetite would be useful to determine if the microflora plays a part in feeding behaviour. Gut microfloral feeding studies could be done under lab conditions using lab-reared first and second instar canegrubs inoculated with firmicutes from a third instar. Quantifying feeding and fat storage in these early instars containing third-instar-flora compared to control first and second instar grubs may indicate if these bacteria increase feeding behaviour or promote fat storage. Understanding any links between microflora compositions and feeding behaviour may provide clues on alternative biocontrol methods that may reduce feeding and can be incorporated into the integrated pest management plan.
In the parasitic fly, Wohlfahrtia magnifica , the gut microbiota changes dramatically throughout different developmental stages (Toth et al., 2004). In canegrubs, further study regarding the gut microflora at different larval developmental stages would be useful in determining which bacteria play vital roles in canegrub development, and also if there is a change in gut microbiota prior to the commencement of late third instar fat storage (as discussed above in humans). Firmicute dominance within gut microflora would need to be compared during different life stages and under different feeding conditions to determine if they play such a role in canegrub larval fat storage. For example, the presence of the firmicutes from first, second and third instars could be compared via profiling techniques such as TRFLP or by culturing isolates present within the community such as a firmicute-specific most probable number test. Transplants of third instar microflora into earlier instars and adipose tissue measurements may also help determine if gut microflora play a role in feeding behaviours, and the metabolism of sugarcane roots and surrounding soil.
Sequences similar to a Chryseobacterium spp. (20% of clones) were only amplified from living grub sample A-5-R, collected from Reed’s farm in Mackay. This genus
63. Cassandra Trent ______has been isolated from diseased fish, frogs, shellfish and snakes (Bernadet et al., 2005). It has rarely been detected in insects suggesting it may either be transiently present in the canegrub or may not play an important symbiotic role. A bacterium similar to C. gleum was isolated from the midgut and hindgut of the cockroach (Periplaneta americana ) (Dugas et al., 2001) and an unknown Chryseobacterium spp. was isolated from the Australian termite ( Mastotermitidae darwiniensis ) (Eutick et al., 1978). In both cases it was suggested as a symbiont.
Another species from the Chryseobacterium genera, C. indologenes has been shown to be pathogenic to laboratory-reared colonies of the soft tick ( Ornithodoros moubata ). Inoculation of ticks from a C. indologenes contaminated blood-meal (10 6 cells/mL) caused 100% tick mortality in less than seven days. However, the effect of this pathogen in the wild is not known (Buresova et al., 2006). This study also demonstrated the ability of this bacterium to be routinely cultured, penetrate the gut wall and remain persistent in the gut when administered at a sub-lethal dose (Buresova et al., 2006). The living grub from which this Chryseobacterium was isolated did not show obvious symptoms of disease. However, the grub may have been infected with an entomopathogenic Chryseobacterium and was still within an initial incubation period.
Many species of Chryseobacterium are also clinically important, causing a range of human diseases (Hsueh et al., 1997) hence, even if an isolated Chryseobacterium spp. was pathogenic, it may be unsuitable for biocontrol purposes due to the risk of transmission to humans. They are also important contaminants of food products such as dairy (Hugo et al., 1997). Chryseobacterium spp. have also been isolated from agricultural soil (Caesar-TonThat et al., 2007) and hence, may naturally be present in the soil consumed by canegrubs. The likelihood of a Chryseobacterium spp. being pathogenic to the greyback canegrub is low as this sequence was amplified from a living specimen. Alternatively, the required dosage to cause mortality in canegrubs may be high and hence, it would be unsuitable for biocontrol purposes. In general, symptoms of disease may be difficult to detect in live third instar larvae due to the large fat deposits present under the cuticle and further investigation is required to verify if a Chryseobacterium is present and establish its potential pathogenicity.
64. Chapter 2: Canegrub dominant bacterial members ______
Another live grub associated sequence was homologous to Melissococcus pluton , the causal agent of European Foulbrood in the honey bee ( Apis mellifera ). Many strains of M. pluton have been isolated across Australia in bee populations (Djordjevic et al., 1999). This culturable, non-sporeforming bacterium multiples within the gut of the bee larvae and is ejected with gut contents before pupation. During this phase, the pathogen infects younger larvae and completes the cycle of infection. Most infected larvae die before pupation (Bailey et al., 1983). M. pluton has rarely been isolated in other insects. However, 21% (n=100) of 16S rDNA clones amplified from the Asian long-horned beetle ( Anoplophora glabripennis ) were homologous to Melissococcus (Schloss et al., 2006) compared to 15% in this study of canegrubs. To date, Melissococcus bacterial colonies have not been isolated and cultured from insects other than bees and pathogenicity within other arthropods remains unknown.
Sequences homologous to M. pluton were amplified only from the live grub A-5-R collected in Mackay. Further investigation and culturing of this bacterium is required to determine if it is pathogenic to scarabs. However, transmission of this bacterium is more efficient amongst more social insects such as the honey bee and due to the solitary nature of canegrubs, may not be easily transmitted between individuals. In addition, canegrub damage has already occurred before pupation; hence the bacterium may only reduce the amount of beetles emerging yet may not heavily impact on the population due to the solitary nature of canegrub pupation. Viability of the bacterium may not stay present at a lethal dose within the soil to control future generations. Therefore, even if the bacterium showed pathogenicity, it may not be suitable for biocontrol purposes. A thorough feeding trial would be required to verify pathogenicity.
2.4.1 Conclusions
This was the first cited 16-S cloning study of dominant bacterial members associated with canegrubs. In particular, the first cited whole-grub analysis and the first time dead grub cadavers have been explored for associated microflora and putative pathogens using molecular techniques. Bacteria associated with live specimens showed associations with known insect symbionts that may have metabolic roles within canegrub guts. Many of the predominant species amplified from dead grub
65. Cassandra Trent ______specimens may be the result of long term refrigeration post-death. The decomposition of the dead grub specimens was rapid and many of the amplified sequences from these samples appear to be associated with either saprophytic, or psychrotrophic bacteria that may have been able to dominate the community at low temperatures. In addition, dead grub and live grub microflora clustered separately phylogenetically (figure 2.2), suggesting that these dead grub samples harboured a different set of dominant community members. This may be due to the saprophytic and psychrotrophic domination post-death or putative pathogens masking any healthy gut flora. In order to investigate putatively-pathogenic bacteria, the use of alternate molecular methods to enrich these pathogen-associated sequences is required. The 16-S cloning method was useful to gain insight into canegrub- associated microbial communities. However, thorough analysis using this method would not only be time consuming due to the labour required for sequencing thousands of clones, but also outside of the funding limitations of this work. Molecular tools that determine sequences unique to one sample and absent in another (such as subtractive hybridisation or SSH) (chapters 3 and 4) may be useful in screening for bacteria unique to dead grub samples and filtering out unwanted saprophyte-related sequences. Bacteria may then be identified via SSH, isolated and tested for pathogenicity and biocontrol suitability.
66. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Chapter 3: Developing and testing a novel V3-PCR- based SSH technique for identifying pathogens from canegrubs
67. Cassandra Trent ______
68. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
3.1 Introduction
Greyback canegrubs with unexplainable mortality have been collected from sugarcane fields in Mackay and north Queensland. Dominant members of a subset of these dead specimens were analysed using basic 16-S PCR, cloning and sequencing (Chapter 2). This is the first reported study of whole-canegrub- associated bacteria from both living and dead specimens. These experiments showed that the dominant members of each dead grub may be either secondary infections or saprophytic bacteria. Putative-pathogen associated sequences could not conclusively be determined from these initial dominant members. To overcome this, a method is required to selectively amplify sequences from bacteria unique to these dead specimens. Such a technique may selectively amplify pathogen-associated sequences and putative pathogens may be identified.
Dominant bacteria associated with canegrubs were not only diverse, but also highly varied between three living and five dead canegrub specimens. Many of the amplified sequences from Chapter 2 were associated with symbiotic bacteria and general soil bacteria. Some were also able to grow at refrigeration temperature and were possibly enriched within the community post-grub-death. Dead-grub- associated sequences were homologous to a number of genera including Myroides , Klebsiella , Bordetella , Elizabethkingia , Pseudomonas , Dysgonomonas , Comamonas , and Sphingomonas . None of these genera were amplified from the live specimens. Putative-pathogens likely exist that were not detected using this method. A method needs to be chosen that selectively enriches sequences associated with these bacteria.
Many different ribosomal RNA gene techniques have been developed and each has varying taxonomic specificity and community fingerprinting capability (see section 1.3.2). A set of compatible techniques are required that ultimately isolate and identify individual bacteria unique to individual diseased specimens. Many currently used community molecular biological techniques do not highlight unknown species differences between two community samples without the laborious and costly task of extensive individual clone sequencing. Hence, a new methodological alternative is being explored to help discover novel canegrub pathogens.
69. Cassandra Trent ______
Suppressive subtractive hybridisation (SSH) selectively isolates sequences present in one sample, yet absent in another. The method is based on separation of double- stranded DNA followed by re-hybridisation of sequences with and without molecular adaptors (figure 3.2, for a detailed explanation see section 1.3.3). Only sequences unique to the sample of interest are PCR amplified and subsequently cloned and sequenced. To date, SSH has seldom been used for analysing microbial community differences. Galbraith et al ., (2004) examined differences between the microbial metagenome of two cow rumen samples using the SSH technique. This was the first application of SSH to compare microbial communities and almost half (48.1%) of the resulting cloned sequences did not match any proteins in the Genbank database.
To rectify this problem, a new application of SSH is proposed in this chapter. Using canegrub-associated genomic DNA, a ~200bp variable region of the 16-S ribosomal DNA, the V3 region, was chosen to be amplified and used as SSH starting material (figure 3.2). The V3 region has been shown to provide the highest amount of separation in denaturing gradient gel electrophoresis (DGGE) profiles of sheep rumen communities (Yu et al ., 2004). The V3 PCR-products produced more bands when compared to other amplified regions and also showed more intense banding (Yu et al ., 2004). The V3 region has also been amplified from the larval midgut community of the cotton bollworm, Helicoverpa armigera . Fragments were separated via DGGE and highlighted that lab-reared larvae showed less community diversity when compared to the field-collected counterparts (Xiang et al ., 2006).
As amplicons were projected to be of similar size, two denaturing-dependent separation techniques were chosen to help screen and determine the efficiency of the V3-PCR SSH technique. Temperature gradient gel electrophoresis (TGGE) is an acrylamide gel electrophoresis run along an increasing temperature gradient. As the DNA denatures, the migration rate slows. Therefore, sequences of the same size with differing denaturing properties migrate at different rates. This technique has previously been used to separate community V3-PCR in activated sludge (Watanabe et al ., 1999) and was chosen to screen the V3-PCR SSH amplicons. In addition to TGGE, denaturing high performance liquid chromatography (DHPLC) was also
70. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______chosen for screening. The technique has been shown to separate 16-S-PCR amplicons from different individual and pooled isolates (Hurtle et al ., 2002, Domann et al ., 2003, Jacinto et al ., 2007). From a study of 39 individual bacterial isolates from various taxonomic lineages, 36 isolates were reproducibly determined with unique retention times and peak patterns using this technique (Hurtle et al ., 2002).
This chapter reports the use and efficiency of a 16-S-PCR based SSH as a tool to compare the bacterial communities of canegrubs and its possible use for pathogen detection. By using the V3-PCR approach, the aim was to reduce the complexity of the metagenomic sequences and increase the likelihood of Genbank matches by limiting the amount of contributing sequences from each bacterial species. Each step of the V3-PCR SSH process was confirmed by cloning and sequencing all products. As all resulting SSH end-products were of similar size, temperature gradient gel electrophoresis (TGGE) and denaturing high-performance liquid chromatography (DHPLC) were used to determine efficiency and provide rapid screening of tester-specific SSH amplicons.
71. Cassandra Trent ______
3.2 Materials and methods
3.2.1 2006 Grub Season
Canegrubs were collected from beneath stools of mature cane in the Mulgrave region near Cairns, Qld. A 30 cm 3 area of soil was dug out surrounding the roots of the cane. Canegrubs were collected from field-grown sugarcane by hand from both within the root system and in the surrounding soil. Individual grubs were placed into small cylindrical collection tubes containing aeration holes. Soil from the stool was included for cushioning and transport. Specimens were air-freighted in domestic luggage to Brisbane. Upon arrival, live canegrubs were transferred to larger 10 cm diameter tubs containing autoclaved peat and sand. Grubs were maintained at room temperature in the dark until processing.
Prior to processing, live canegrubs were photographed, incubated at 4ºC for 30 minutes and surface sterilised with 2 x 70% ethanol washes. Each canegrub was cut into three sections using sterilised scissors (as per figure 3.1). The head was discarded and the mid- and hind-sections homogenised using an Ultra-turrax® homogeniser at full speed, on ice in the presence of 900 µL BHISE broth. DNA was extracted directly from this homogenate using the Powersoil DNA Extraction Kit (MOBIO, USA) as per manufacturers’ instructions. The optional 70ºC incubation step for 10 min was also performed after the addition of solution 1. The mid-section from grub 59 (labelled 59-M) was used throughout this chapter. DNA from the surrounding soil in the maintenance tub was also extracted using the Powersoil DNA Extraction Kit (MOBIO, USA) as above.
72. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Mid -section Head
Hind -section
Figure 3.1. Sectioning of healthy canegrubs for 16-S PCR based SSH. Canegrubs were sliced into head, mid- (M) and hind-sections (B).
3.2.1.1 Brain heart infusion soil extract broth (BHISE broth)
3.2.1.1.1 Yeast extract solution
Yeast extract solution contained 10% yeast extract and 1% glucose (Sigma, USA) (filter sterilised).
3.2.1.1.2 Vitamin B12 solution
Vitamin B12 (Natures Own, Australia) was dissolved in deionised water (4000 µg/L), filter sterilised and used immediately.
3.2.1.1.3 Soil extract
Soil extract contained 0.25% wt/vol soil (from canegrub holding container) and 0.25% sodium carbonate dissolved in 1 L deionised water and autoclaved at 121ºC for 60 minutes. The solution was vacuum filtered before use.
3.2.1.1.4 Medium preparation
Contained 1.6% pancreatic digest of casein (Merck, Germany), 0.8% brain heart solids from infusion (BD, USA), 0.5% peptic digest of animal tissue (BD, USA), 0.5% sodium chloride , 0.25% disodium phosphate (Merck, Australia) and 250 mL soil extract (as per 3.2.1.1.3) dissolved in up to 1 L deionised water. The pH was adjusted to 7.2 ± 0.2 and the solution was autoclaved at 121ºC for 20 min. When
73. Cassandra Trent ______cooled to below 55ºC, 200 mL of yeast extract solution (3.2.1.1.1), 1.0 mL Vitamin B12 solution (3.2.1.1.2) and 100 mg/L cyclohexamide (Sigma, USA) were added.
3.2.2 Control bacteria
Control bacteria Escherichia coli , Vibrio harveyi and presumptive Vibrio fischeri were grown up in 15 mL of LB-broth (E.coli) (2.2.2) and Photobacterium broth ( V. fischeri and V. harveyi ) (2.2.4) for 24 h at 37 ºC and 28 ºC respectively. Bacterial cells were centrifuged for 5 min at 25ºC and 3000 rpm prior to DNA extraction. DNA was extracted using the Powersoil DNA Extraction Kit (MOBIO, USA) as per section 3.2.1.1.
3.2.2.1 Photobacterium medium
Photobacterium medium contained 3.3% seawater aquarium salt (Aquarium pharmaceuticals Inc, Canada), 0.5% yeast extract, 0.5% tryptone, 0.3% glycerol, 0.05 M Tris-base, and 0.09 M ammonium chloride (Ajax Chemicals, Australia) dissolved in distilled water. For agar, 0.1% calcium carbonate (May and Baker, UK) and 2% agar were added. Agar was then autoclaved at 121ºC for 20 min (pH 7.35 ± 0.15).
3.2.3 V3 PCR
V3-PCRs were performed on the 59-M grub sample (twice to test reproducibility) and each control bacterium ( Escherichia coli , control-x and Vibrio harveyi ). Each 50 µL reaction contained 1 X GoTaq master mix (Promega), 1 µM each primer (357F (5´- CCT ACG GGA GGC AGC AG- 3´) and 518R (5´- ATT ACC GCG GCT GCT GG – 3´))(Tannock et al ., 2000) and 2 µL template. Cycle conditions were 95 ºC for 2 mins, 10 cycles of touchdown PCR (95ºC for 30 s, 61ºC-56ºC for 30 s, reducing by 1ºC every second cycle, 72 ºC for 1 min), 15 cycles of regular PCR (95ºC for 30 s, 56ºC for 30 s, 72 ºC for 1 min), and final extension at 72 ºC for 5 min. Products were run on 2% agarose gels containing 0.25X SYBR SAFE (Invitrogen) at 140 V for 50 min and visualised using the GelDoc system (BIORAD). Bands were excised and DNA purified using the Wizard SV Gel and PCR Purification Kit (Promega) according to the manufacturer’s instructions. All abovementioned samples and a contaminant from the template-free control were cloned. Post cloning, 12 clones per 59-M PCR reaction and 4 clones per control
74. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______were chosen for sequencing. Cloning and sequencing was performed as per sections 2.2.3 and 2.2.4.
3.2.4 V3-PCR contamination
To investigate a Taq -associated contamination, a series of 0, 1, 3 and 5 U of RQ1 RNase-Free DNase (Promega) was added to 1X GoTaq Green Mastermix (Promega) and incubated at 37 ºC for 30 min. The reaction was heated to 95 ºC for 10 min to denature the DNase and cooled to 4 ºC in a Gradient Palm Cycler (Corbett Research). V3-primers (as per 3.2.3) and template were added post DNase treatment. Template was only added to 4 of the 8 reactions as a positive control. PCR cycles were as described in section 3.2.3. Products were visualised on a 2% agarose gel containing 0.25% SYBR-Safe (Invitrogen) and run at 140 V for 50 min.
3.2.5 Adaptor PCR
Adaptor PCR was used to amplify the V3 region as well as add adaptor sequences to the 5´ end of the PCR products. Soil and grub DNA extracts from the Grub 59 specimen were individually used as template in Adaptor-A PCR and Adaptor-B PCR. PCR conditions were as V3-PCR in section 3.2.3 with the following sets of primers: Adaptor A [Adaptor A F (5´-GTA ATA CGA CTC ACT ATA GGG CTC GAG CGG CCG CCC GGG CAG GTC CTA CGG GAG GCA GCA G -3´) and 518R (5´- ATT ACC GCG GCT GCT GG – 3´)], and Adaptor B [357F (5´- CCT ACG GGA GGC AGC AG- 3´) and Adaptor B R ( 5´- TGT AGC GTG AAG ACG ACA GAA AGG GCG TGG TGC GGA GGG CGG TAT TAC CGC GGC TGC TGG -3´)]. Detection, cloning and sequencing (24 clones per sample) were as per sections 3.2.3, 2.2.3 and 2.2.4.
75. Cassandra Trent ______
Figure 3.2. Flow diagram of the steps involved in the V3-PCR SSH process. Panel A is a general representation of the SSH concept, where one sample is subtracted from another to produce sequences unique to the tester sample. The V3-PCR SSH process was then broken down into each main step in the flow diagram (panel B). After V3 amplification, adaptor-containing tester were hybridised over two steps to driver-, and tester-unique sequences were PCR-amplified using primers specific for adaptor sequences.
76. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Figure 3.3. A flow diagram showing SSH hybridisation steps and sample tube arrangement (3.2.6). A total of three tubes per-reaction were set up initially. Each reaction was overlayed with mineral oil to prevent evaporation during heating. After hybridisation steps in tubes 1 and 2, all three tubes were combined to create the final subtracted product.
3.2.6 SSH
SSH was performed as described by Diatchenko et al . (1996). Figures 3.2 and 3.3 are flow diagrams of the V3-SSH process. Tester-A and Tester-B (20 ng in tubes 1 and 2, respectively) were combined with 600 ng driver and hybridisation buffer (50 mM Hepes, pH 8.3, 0.5 M NaCl, 0.02 mM EDTA, pH 8.0, 10% (wt/vol) PEG 8000) to make a final volume of 10 µL (figure 3.3). After mixing, each component was overlayed with a drop of sterile mineral oil, denatured at 98 ˚C for 1.5 min and incubated at 63˚C for 10 h. Excess driver (300ng in tube 3) in hybridisation buffer overlayed with mineral oil was denatured at 98 ˚C for 1.5 min. Components in tubes 1 to 3 were mixed by setting a pipette to 50 µL, aspirating tube 3 contents, taking up a small air pocket, aspirating tube 2 contents and mixing with tube 1 by pipetting up and down (figure 3.3). The second hybridisation step was incubated at 63˚C overnight for 18 h. Samples were diluted to 200 µL with dilution buffer [20 mM HEPES (pH 8.3), 50 mM NaCl, 0.2 mM EDTA (pH 8.0)].
77. Cassandra Trent ______Table 3.1. Experimental design of SSH reactions with a known spiked “control-x” component. Each tester reaction contained 10% (wt/wt) “control-x.”
SSH sample- DNA added from each PCR reaction (ng) DNA SSH component PCR-reaction source Tester:driver (TD) Driver:driver (DD)
Driver V3-PCR 59-M 600 600
59-M 18 20 Adaptor A- Tester-A PCR products Control-x 2 0
Adaptor B- 59-M 18 20 Tester-B PCR products Control-x 2 0
For the first SSH PCR, P1-PCR, each 20 µL reaction contained 1x GoTaq® Green Master Mix (Promega, USA), 1 µM each primer [P1-F (5´- GTAATACGACTCACTATAGGGC-3´) and P2-R (5´- TGTAGCGTGAAGACGACAGAA-3´)] and 0.8 µL template. Cycle conditions were as follows: initial denaturation at 72°C for 6 min (to fill in the ends of adaptor sequences), 25 cycles of 95°C for 30 s, 66°C for 30 s, and 72°C for 60 s followed by final extension at 72°C for 5 min.
For the nested-PCR, P1-PCR products were diluted (1:39) in MBG water. Each 20 µL reaction contained 1x GoTaq® Green Master Mix (Promega, USA), 1 µM each primer [N-F (5´-TCGAGCGGCCGCCCGGGCAGGT-3´) and N-R (5´- AGGGCGTGGTGCGGAGGGCGGT-3´)] and 0.8 µL diluted P1-PCR product. Cycle conditions were as follows: initial denaturation at 95°C for 2 mins, 15 cycles of 95°C for 30 s, 66°C for 30 s, and 72°C for 60 s followed by final extension at 72°C for 5 min.
3.2.6.1 SSH P1-PCR
PCR products from 3.2.3 and 3.2.5 were quantified (ng/µL) using the Qubit fluorometer (Invitrogen). Hybridisation 1 was prepared as described in Table 3.1 and figure 3.3. Both samples were similar, the only difference being that the tester:driver sample contained 2 ng of control-x in the tester (Table 3.1 and figure 3.2). In the driver:driver sample, tester and driver were the same. This was to determine the efficiency of driver subtraction from tester. P1-PCR’s, 2 per SSH reaction, were performed as per above. PCR reactions were run on a 2% agarose gel containing 0.25X SYBR SAFE (Invitrogen). Gels were run at 140 V for 50 min and
78. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______visualised using the GelDoc system (BIORAD). Bands were excised and DNA purified using the Wizard SV Gel and PCR Purification Kit (Promega) according to the manufacturer’s instructions. Cloning and sequencing was performed as per sections 2.2.3 and 2.2.4, and 12 clones from each PCR reaction were chosen for sequencing.
Table 3.2. Tester and driver mixes used in tester and driver control samples. The “mix” refers to a group of five different V3-PCR sequences amplified from live grubs.
SSH # Tester Sample Driver Sample
1 E. coli E. coli
2 V. harveyi V. harveyi
3 E. coli - V. harveyi E. coli
4 E. coli - V. harveyi V. harveyi
5 E. coli - V. harveyi E. coli - V. harveyi
6 Mix Mix
7 V. harveyi - Mix Mix
8 V. harveyi - Mix V. harveyi - Mix
9 E. coli - Mix Mix
10 E. coli - Mix E. coli - Mix
3.2.6.2 Control SSH
Control bacterium V3 DNA was subtracted from a known mix of sequences homologous to a broad range of taxonomic groups. Tester and driver were PCR amplified for these SSH reactions from the following samples: V. harveyi genomic DNA, E. coli genomic DNA and representative clones GA-7 (group 3 Enterobacteriaceae), GB-3 ( Clostridium ), and GB-7 ( Lactococcus ) from section 2.2.4; and G-V3-1-3 (uncultured Actinobacterium ) and G-V3-2-3 (group 2 Enterobacteriaceae) from section 3.2.3. The clones were combined and amplified as a mix of plasmids for both adaptor PCR reactions and V3 PCR (referred to as “mix”). The following SSH reactions were set up as per Table 3.2 using equal amounts of each at a tester:driver ratio of 1:30.
3.2.6.3 SSH Ratio Study
3.2.6.3.1 Tester:driver ratio study
79. Cassandra Trent ______V3 amplicons and adaptor A and B amplicons from grub 59-M and 54-M (prepared as per sections 3.2.3 and 3.2.4) were used as tester and driver respectively. SSH was performed as per the introduction to section 3.2.6. The total DNA content of 620 ng remained constant but the ratio of tester and driver (w:w) was altered. The following tester:driver DNA ratios were tested: 1:30, 1:40, 1:50, 1:60, 1:70, 1:80, 1:90, 1:100, 1:110, 1:120, 1:130 and 1:150. P1-PCR and nested-PCR were performed as per section 3.2.6.
3.2.6.3.2 Spike:tester ratio study
V3-PCR, adaptor A and adaptor B amplicons from grub 59-M were used as tester and driver in this study. Tester was also spiked with adaptor A and B PCR- amplified V. harveyi (prepared as per sections 3.2.3 and 3.2.5). SSH was setup as per the introduction to section 3.2.6 but within each adaptor A and adaptor B component, the following V. harveyi spike to 59-M adaptor PCR ratios were tested; 0:1 (unspiked control), 1:19, 1:39, and 1:79. Each spike:tester ratio was performed at the following tester:driver ratios; 1:30, 1:80 and 1:130. P1-PCR and nested-PCR were performed as per the section 3.2.6 introduction.
3.2.6.4 TGGE
Samples were run on a denaturing polyacrylamide gel (8M urea, 2% glycerol, 30: 0.5 acrylamide:bis, 1X ME buffer) cast onto Pagebond film. Gels were run at a gradient of 43ºC to 60ºC in 1 X ME buffer at 300 V for 2.5 h. Gels were silver- stained [2 x 3 min in buffer A (0.5% acetic acid, 10% ethanol), 10 min in buffer B (0.06M silver nitrate), up to 20 min in buffer C (1% formaldehyde, sodium borohydride), 10 min buffer D (0.07M sodium carbonate), rinse with distilled water between solutions].
3.2.6.5 DHPLC
Methods for control-x and E. coli control species V3-amplicon separation via DHPLC were trialled based on temperatures used in previously published methods by Barlaan et al . (2005) (64 ºC) and Domann et al . (2003) (62 ºC). Original trials were performed on a DVA column for the Varian Helix system and the manufacturer-provided short methods. Short methods were as follows: 1 s at 52% buffer B, 59 s at 57%, 1 min at 66%, 1 min at 52% and a flow rate of 0.5mL/min.
80. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Small methods were trialled at 62, 63 and 64 ºC to determine which temperature gave the best separation and 64 ºC was chosen for all further analysis.
3.2.7 Sequencing analysis
All sequences obtained from AGRF (see sections 2.2.3 and 2.2.4) were processed using Vector NTI 10.3.0 (Invitrogen Corporation). Raw sequences were aligned in ContigExpress. Plasmid and primer sequences were removed and sequences re- aligned to within 98% sequence identity. Each group of identical clones were saved as Vector NTI sequences. All resultant sequences were aligned in AlignX and sequence trees were created for all clones described in this chapter (figure 3.7). Phylogenetic analysis was performed by comparing these sequences to the BLAST database (NCBI).
81. Cassandra Trent ______
3.3 Results
3.3.1 V3-PCR
3.3.1.1 Taq contamination
V3-community PCR is the backbone of the proposed 16-S-PCR-based SSH technique. Non-specific eubacterial primers are required for amplifying this region of the 16-S rRNA gene. The non-specific nature of these primers increases the risk of PCR contamination as microbial DNA has previously been reported in commercial Taq DNA polymerase preparations (Altwegg et al ., 1995, Carroll et al ., 1999, Heininger et al ., 2003). Upon cloning and sequencing of the V3-PCR positive blank reaction (containing no template), the detected V3-PCR contamination showed 100% homology to a Halomonas sp. and uncultured bacterial species (Table 3.3) (Gophna et al ., 2006, Yoshie et al ., 2006). To reduce contamination, a pre-PCR DNase treatment (Heininger et al ., 2003) of the GoTaq PCR mastermix was performed and 1U DNase reduced contamination without causing a visual reduction in the positive control V3-amplicons. Higher concentrations of DNase ( ≥ 3 U) caused a significant visual reduction in contaminant band size, however, greatly reduced product yield from the template-containing PCR (figure 3.4).
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Figure 3.4. V3-PCR contamination reduction after DNase pre-treatment of the Taq premix. Lanes 1 and 10, 50 bp ladder (Promega, USA). Lanes 2, 3, 4 and 5 contained positive control (grub 59-M) template. Lanes 6, 7, 8 and 9 had no template added. Lanes 2 and 6 had no DNase added, lanes 3 and 7 were treated with 1 U DNase, lanes 4 and 8 were treated with 3 U DNase, and lanes 5 and 9 were treated with 5 U DNase. Note how DNase appeared to reduce the efficiency of the PCR containing template.
82. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______3.3.1.2 Confirmation of control isolates
To confirm the identity the 16-S rDNA V3-regions were amplified, cloned and sequenced from each isolate. All cloned sequences aligned to eubacterial 16-S rRNA genes. E. coli showed 100% sequence homology to the V3-regions of E. coli , cultured and uncultured Escherichia sp. , uncultured bacteria, uncultured Shigella sp. , Shigella sonnei strain AU65 and Acinetobacter sp. SF6 (Table 3.3). Therefore, it is likely the cultured E. coli isolate is closely related to E. coli however, additional species contain the same V3-region sequence. Classical tests were not used to confirm the V3 data as the V3 region sequence was the only identification required for future experiments. The V. harveyi isolate showed 100% sequence homology to Vibrio vulnificus , Vibrio harveyi , Vibrio sp. , Vibrio campbellii , Vibrio parahaemolyticus , Vibrio alginolyticus , Vibrio agarivorans , Shewanella sp. and previously-uncultured bacteria. It is therefore likely that the V. harveyi control isolate is closely related to a Vibrio sp. , in particular Vibrio harveyi , however a number of other Vibrio sp. have 100% homology to the same 16-S rDNA region.
In contrast to the above confirmation of identity, the V. fischeri isolate (control-x) showed 100% sequence homology to Bacterium sp. , Bacterium oxydans , Bacterium maritypicum and previously-uncultured bacteria. This suggests the isolate was not V. fischeri, and was a contaminant isolated when culturing V. fischeri from a freeze- dried culture.
3.3.1.3 Cloning and sequencing of grub V3 amplicons
Cloning and sequencing V3-PCR products from a grub microbial population was performed to determine grub-community diversity and the range of sequence divergence between V3-sequences amplified. This information may help determine suitability of SSH for pathogen isolation from canegrubs and identify dominant members of the amplified community. All clones showed homology to 16-S rRNA gene sequences. Cloned sequences were aligned into groups and named according to bacterium, amplified region, PCR number and clone number, respectively. For example, G-V3-1-2 refers to a grub sample (G-) V3-PCR (V3-) clone, from the first PCR reaction (1-) belonging to clone number 2 (see Table 3.4 for a list of clones).
83. Cassandra Trent ______Table 3.3. Control-V3 PCR sequences homology to BLAST entries. Each set of BLAST matches confirmed the identity of each bacterial control organism and determined the specificity of the V3 region for putative bacterial identification.
Homologous V3 sequences Control Blast sequence % Accession numbers E value identity homology Vibrio vulnificus AJ885045.1 EF187016.1, DQ991224.1, DQ521082.1, DQ868673.1, Vibrio sp. DQ317691.1, DQ317685.1 EF193023.1, EF089460.1, DQ269050.1, AM157530.1, Uncultured bacteria DQ985877.1, DQ831120.1, DQ317686.1, DQ416597.1, AY911163.1, AM183768.1 Vibrio campbellii DQ980029.1 EF011651.1, DQ995520.1, DQ995253.1, DQ991206.1DQ530549.1, DQ868990.1, DQ842240.1, -85 DQ005911.1, DQ068936.1, DQ304557.1, EF011651.1, 7 x 10 100 Vibrio harveyi DQ995520.1, DQ995253.1, DQ991206.1, DQ530549.1,
Vibrio harveyi Vibrioharveyi DQ868990.1, DQ842240.1, DQ005911.1, DQ068936.1, DQ304557.1 Vibrio DQ991216.1, DQ026024.1, DQ068942.1 parahaemolyticus Shewanella sp. DQ521064.1 Vibrio agarivorans DQ462574.1 Vibrio alginolyticus DQ173157.1 EF365042.1, EF192868.1, EF191172.1, DQ806382.1, AM421676.1, DQ538070.1, DQ342901.1, DQ420287.1, DQ460809.1, AM087506.1, EF072536.1, EF071401.1, Uncultured bacteria EF071299.1, EF074307.1, DQ441386.1, EF025263.1, AB279983.1, DQ990036.1, DQ857237.1, DQ857233.1, DQ857009.1 -78 1 x 10 100 Escherichia coli EF191171.1, AB272358.1 Escherichia sp. EF127652.1, EF121968.1, DQ975237.1
Escherichiacoli Shigella sonnei EF032687.1 Shigella sp. EF016514.1 Acinetobacter sp. AB272345.1 EF204427.1, EF028128.1, EF061897.1, AM403723.1, AM396495.1, DQ822774.1, DQ664254.1, AY779522.1, AY730719.1, AF409017.1, AF287752.1, DQ131846.1, Microbacterium sp. AB242732.1, DQ357778.1, AJ879104.1, AJ876678.1, AJ864858.1, DQ083505.1, AJ969168.1, DQ067267.1, DQ347555.1, AY571814.1, AY017057.1, AY368525.1 AB176173.1, DQ989491.1, DQ922933.1, AB255091.1, -73 5 x 10 100 DQ188461.1, DQ001653.1, DQ228761.1, DQ191237.1,
Control-x Control-x Uncultured bacteria AY038632.1, AY537789.1, AF544326.1, AY362831.1, AY039447.1, AF441321.1, AF507934.1, AY937056.1, DQ211417.2 AY785738.1, DQ403811.1, DQ417333.1, DQ105974.1, Microbacterium AJ717357.1, AB193267.1, AB193263.1, Y17227.1, oxydans AM234158.1 AM490139.1, DQ489548.1, AY347310.1, DQ357033.1, Halomonas sp. Blank DQ356999.1, AJ309560.1, DQ202277.1 1 x 10 -83 100 Uncultured bacteria DQ441377.1, AB205638.1
The majority of clones (96%, n = 24) showed sequence homology ( ≥ 99%) to members of the family Enterobacteriaceae and can be represented by three groups. Group 1 contained sequences from Erwinia sp. , Pantoea dispersa , Enterobacter gergoviae, Klebsiella oxytoca and uncultured bacteria. Clone G-V3-1-1 showed 100% homology to this group while G-V3-1-5 and G-V3-2-2 showed 99% homology. Clones from G-V3-2-2 were also 100% homologous to an uncultured
84. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______bacterium (DQ342682.1). This group contained 33% and 17% of PCR 1 and 2 clones, respectively (n = 12 per PCR reaction). Enterobacteriaceae group 2 contained an uncultured bacterium that showed 99% homology (E = 2 x 10 -82 ) to Enterobacter cloacae strain JA55. Clones G-V3-1-2 and G-V3-23 showed 100% homology to this group while G-V3-2-4 showed 99% homology. Group 2 contained 42% and 58% of PCR 1 and 2 clones, respectively. Enterobacteriaceae group 3 contained Citrobacter sp., Citrobacter amalonaticus, Salmonella sp., unidentified familial bacteria and uncultured bacteria. Clones G-V3-1-4 and G-V3-2-1 showed 100% homology. Group 3 contained 42% and 58% of PCR 1 and 2 clones, respectively. The remaining 8% of clones from PCR reaction 1 (G-V3-1-3) did not align to the family Enterobacteriaceae, but showed 91% homology to an uncultured soil Actinobacterium (Stach et al ., 2003).
Table 3.4. BLAST sequences homologous to clones amplified using V3-PCR primers from the mid-region of a living grub (59-M). These amplified sequences were also used as driver in grub-associated SSH experiments (section 3.2.6).
Homologous % of clones per BLAST sequence Sample E value % homology BLAST sequence identity sample (n=12) group Group 1 G-V3-1-1 25 7 x 10 -85 100 Erwinia sp. (EF088378.1), Pantoea dispersa (AY227805.1), Enterobacter gergoviae -82 G-V3-2-2 17 ≤ 2 x 10 99 (AB004748.1) , Klebsiella oxytoca (AB118219.1, AF129440.1, U78184.1, AJ871858.1, Y17655.1) and uncultured G-V3-1-5 8 ≤ 2 x 10 -82 99
bacteria (DQ441385.1, DQ170459.1, AY770416.1, AJ487024.1) Group 2 G-V3-1-2 42 7 x 10 -85 100 uncultured bacterium -85 (DQ221308.1) with 99% G-V3-2-3 25 7 x 10 100 homology (E = 2 x 10 -82 ) to -82 Enterobacter cloacae strain JA55 G-V3-2-4 17 2 x 10 99 (EF185910.1) Enterobacteriaceae Group 3 G-V3-1-4 42 7 x 10 -85 100 Citrobacter sp. (AM401577.1, DQ083977.1) Citrobacter amalonaticus (AF025370.1, AJ415574.1) , Salmonella sp. ( G-V3-2-1 58 7 x 10 -85 100 AY379978.1) unidentified familial bacteria (EF212951.1) and uncultured bacteria (AY563465.1, DQ083987.1) Uncultured G-V3-1-3 8 91 uncultured soil Actinobacterium Actinobacterium clone (AY124413.1)
85. Cassandra Trent ______3.3.1.4 Comparison of sequence group properties from control and community-V3 sequences
All control and V3 clones were analysed via TGGE and DHPLC and their thermodynamic properties were calculated (Table 3.5). The range of GC and T m values for the control samples are divergent enough that they can be effectively separated via DHPLC (retention time) and TGGE (% migration in the gel compared to the migration of E. coli V3-sequence) (Table 3.5).
Sequences from each Enterobacteriaceae group described in section 3.3.1.3 were aligned independently to determine similarity between groups. There was 94% homology (E = 2 x 10 -65 ) between groups 1 and 2 compared to 95% (E = 2 x 10 -69 ) between groups 2 and 3. Greatest homology occurred between groups 1 and 3 (96%, E = 2 x 10 -72 ), however sequence similarity did not reflect sequence separation properties (Table 3.5). Divergent sequences within the same Enterobacteriaceae group (ie, with 99% homology) showed better separation (TGGE and DHPLC) when compared to each other than to sequences with less homology. For example, the difference in separation values (retention time and percent migration) is greater between G-V3-1-1 and G-V3-2-2 in group 1 than between G-V3-1-1 and G-V3-2-4 in groups 1 and 2, respectively.
All Enterobacteriaceae-homologous sequences produced shouldering on DHPLC chromatograms. This refers to the chromatographic peak showing a “lumpy” appearance instead of a smooth symmetrical shape. The sequences aligning to this family with a GC content of 52.5% (G-V3-1-2, G-V3-1-4, G-V3-1-5, G-V3-2-1, G- V3-2-2 and G-V3-2-3) also show multiple-peak patterns, as did the Vibrio harveyi control sequence.
The clone aligning to the uncultured Actinobacterium shows the highest GC content,
Tm and separation values. In general, the larger the GC content divergence between sequences, the better the separation (Table 3.5).
86. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Table 3.5. Thermodynamic properties and separation values for V3-PCR clones. Column 7 refers to the ratio of migration in a TGGE gel when compared to the E. coli control sample (length:length) displayed as a percentage. Retention time refers to the time retained in the HPLC column.
Thermodynamic Retention % Migration Group Sample % GC GC-Tm Tm time to E. coli Escherichia coli 51.9 77 96.7 2.854 1 Control Vibrio harveyi 50.6 76.4 96.4 2.288 0.667 Control-x 55.7 77.9 98.3 3.881 1.160 Data range 5.1 1.5 1.9 1.593 0.493 G-V3-1-1 51.9 77 96.8 3.434 1.035 Enterobacteriaeceae G-V3-2-2 52.5 77.2 97.4 3.045 1.010 Group 1 G-V3-1-5 52.5 77.2 97.4 3.045 1.010 G-V3-1-2 52.5 77.2 97.9 3.036 1.012 Enterobacteriaeceae G-V3-2-3 52.5 77.2 97.9 3.036 1.012 Group 2 G-V3-2-4 51.9 77 97.7 3.437 1.033 Enterobacteriaeceae G-V3-1-4 52.5 77.2 96.8 2.942 1.005 Group 3 G-V3-2-1 52.5 77.2 96.8 2.942 1.005 Data range 0.6 0.2 1.1 0.495 0.03 Uncultured G-V3-1-3 59.8 79.1 100 4.354 1.165 actinobacteria
Total data range 9.2 2.7 3.6 2.066 0.498
3.3.2 Adaptor PCR
Addition of adaptors to the tester sequences is crucial in the SSH technique and is performed during the initial V3-PCR step. Community adaptor-PCR from both grub and soil samples were cloned and sequenced to determine: if adaptors were being added correctly, determine if the PCR bias is different to regular V3-PCR, and identify key community members of a grub and its associated soil. All sequences contained their respective adaptor sequences in the correct orientation (adaptor A on the 5´ and adaptor B on the 3´). A subset (8.3%) of adaptor B clones from both soil and grub communities (n = 48) showed truncation of the adaptor primer sequence (>25%). This was not seen in adaptor A sequences (n = 48).
Clones were named as follows. The first letter refers to the sample origin, either grub (G) or soil (S), the second letter refers to the adaptor sequence (A or B), and the number refers to the clone number.
87. Cassandra Trent ______3.3.2.1 Adaptor-A
Of the adaptor A clones from the grub-PCR sample (n = 24), 21% of the V3 sequences (primers removed) aligned to Group 1 Enterobacteriaceae BLAST sequences (Table 3.6). Clone GA-1 (16.7% of GA clones) showed 100% homology (E = 7 x 10 -85 ) to G-V3-1-5 and G-V3-2-2, and 99% homology (E = ≤ 2 x 10 -82 ) to group 1 sequences. Clone GA-3 (33.3%) showed 100% homology (E = 7 x 10 -85 ) to clones G-V3-1-2, G-V3-2-3 and Group 2 Enterobacteriaceae BLAST sequences. Clone GA-5 (42%) showed 97% homology (E = 3 x 7 -75 ) to uncultured bacteria, Citrobacter sp. , Salmonella sp. , Klebsiella oxytoca and Citrobacter amalonaticus .
Of grub-PCR adaptor A clones, 41.6% of V3-sequences aligned to Group 3 Enterobacteriaceae BLAST sequences (Table 3.6). Clone GA-2 (33.3%) showed 100% homology to G-V3-1-4, G-V3-2-1, and Group 3 sequences; while GA-4 (8.3%) showed 99% homology to these sequences. Clone GA-7 (4.2%), however did not align with the above familial groups but showed 95% homology (7 x 10 -66 ) to an uncultured bacterium (DQ083963.1) and 93% homology (E = 3 x 10 -59 ) to Enterobacter sp . (EF471230.1).
Adaptor A clones from the soil-sample PCR (n = 24) aligned to distinctively different BLAST groups when compared to the grub-sample associated with that soil (Table 3.6). Clones SA-1 (4.2% of adaptor A soil clones) and SA-5 (4.2%) both showed 98% homology, while SA-9 (4.2%) showed 97% homology to Halomonas sp . and uncultured bacteria. Additionally, SA-5 showed 98% homology to Halomonas campisalis , Halomonas gudaoense , uncultured bacteria and an additional Halomonas sp . SA-9 also showed 100% homology to Salinomonas halophila , Shewanella putrefaciens , Halomonas cf. campisalis 'campaniae' , Deleya pacifica and uncultured bacteria.
88. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Table 3.6. Sequences amplified using the V3 Adaptor-A and B primer sets homologous to Genbank entries. Canegrub (GA or GB) and soil (SA or SB) samples were amplified, sequenced and BLAST searched.
% of clones Homologous BLAST % Sample per E value BLAST sequence identity sequence group homology sample (n=24)
-82 Erwinia sp. (EF088378.1), Pantoea GA-1 16.7 ≤ 2 x 10 99 dispersa (AY227805.1), Enterobacter gergoviae (AB004748.1), Klebsiella Group 1 oxytoca (AB118219.1, AF129440.1, GB-1 12.5 7x 10 -85 100 U78184.1, AJ871858.1, Y17655.1) and uncultured bacteria (DQ441385.1, DQ170459.1, AY770416.1, AJ487024.1 GA-3 33.3 7 x 10 -85 100 uncultured bacterium (DQ221308.1) with 99% homology (E = 2 x 10 -82 ) to Group 2 GB-6 25 7 x 10 -85 100 Enterobacter cloacae strain JA55 (EF185910.1) GA-2 33.3 7 x 10 -85 100 Citrobacter sp. (AM401577.1, GA-4 8.3 2 x 10 -82 99 DQ083977.1) Citrobacter amalonaticus (AF025370.1, AJ415574.1), Salmonella Group 3 -85 sp. ( AY379978.1) unidentified familial GB-2 12.5 7 x 10 100 bacteria (EF212951.1) and uncultured bacteria (AY563465.1, DQ083987.1) Enterobacter sakazakii (EF088361.1 and
Enterobacteriaceae Enterobacter -85 AY803192.1) and an uncultured GB-8 4.2 7 x 10 100 sakazakii bacterium (DQ211457.2 and DQ211359.2) uncultured bacteria (EF212951.1, DQ342682.1, AF231490.1, AY563465.1, DQ083987.1, AM113929.1 and Citrobacter / DQ447496.1), Citrobacter sp. Salmonella / GA-5 42 3 x 7 -75 97 (AM401577.1 and DQ083977.1), Klebsiella Salmonella sp . (AY379978.1), Klebsiella oxytoca (AY823620.1 and DQ294284.1) and Citrobacter amalonaticus (AF025370.1 and AJ415574.1) Actinomadura sp. (EF216358.1 and SA-2 33.3 1 x 10 -70 99 AF131331.1), Actinomadura meyerii (AY273787.1), Actinomadura nitritigenes (AY035999.1), Actinomadura pelletieri (AF163119.1), Actinomadura Group 1 SB-2 33.3 1 x 10 -70 99 formosensis (AJ420140.1), Actinomadura fulvescens (AJ420137.1), Actinomadura atramentaria (AJ420138.1), Thermomonospora formosensis SB-8 4.2 5 x 10 -64 97 (AF002263.1) and Actinomadura rudentiformis (DQ285420.1) uncultured bacteria (AF544325.1,
Actinomadura Actinomadura -63 SA-3 8.3 2 x 10 97 AY917875.1, DQ530728.1, DQ129009.1, AY827007.1, AY330267.1, AY913402.1 and DQ154512.1), Actinomadura sp. Group 2 SB-1 12.5 2 x 10 -63 97 (AB123688.1 and AF131327.1), Actinomadura oligospora (AJ293709.1 SA-6 8.3 2 x 10 -63 97 and AF163118.1), Pseudonocardiaceae str. PA123 (AF223348.1) Enterobacter sp. 95% homology (7 x 10 -66 ) to an uncultured bacterium (DQ083963.1) GA-7 4.2 (93% homology) and 93% homology (E = 3 x 10 -59 ) to Enterobacter sp . (EF471230.1).
-52 Uncultured SB-5 12.5 5 x 10 91 uncultured actinobacterium (AY124413.1) Actinobacterium (90% homology) -52 SA-4 33.3 5 x 10 91 uncultured actinobacterium (AY124413.1)
Uncultured
Uncultured bacteria bacteria Uncultured uncultured actinobacterium Actinobacterium SB-3 12.5 1 x 10 -67 99 (AJ232686.1). (99% homology)
89. Cassandra Trent ______
uncultured actinomycete (AJ427655.1) and 95% homology (E = 3 x 10 -56 ) to Actinomadura sp . (EF216355.1), -63 Actinocorallia sp. (DQ460468.1), SB-4 12.5 3 x 10 99 Actinocorallia aurantiaca (D50669.2), Actinomadura aurantiaca (AF134066.1) and Sarraceniospora aurea Actinocorallia / (AB006160.1)
Actinomadura uncultured bacterium clone (AY827008.1), and 95% homology (E= 3 SB-7 4.2 7 x 10 -57 95 x 10 -56 ) to uncultured bacteria (AY387338.1 and AY675979.1) and an Actinomycete sp. (Z73395.1). uncultured antinomycete (AJ427655.1) SB-9 4.2 3 x 10 -68 99 and 95% homology (E = 3 x 10 -56 ) to the remaining group as per SB-4 Clostridium botulinum (EF051574.1, Clostridium / -70 GB-3 33.3 5 x 10 100 X68186.1 and L37593.1) and Eubacterium Eubacterium combesii (AY533380.1) Lactococcus gariveae (AB267905.1, DQ485325.1, AY430483.1, AY699289.1, AY438044.1, AY946285.1, AF283499.1, AB120031.1, AF352166.1 and Lactococcus / AB012306.1), a Lactobacillales bacterium Lactobacillales / GB-7 8.3 2 x 10 -85 100 (DQ822778.1), an uncultured bacterium Enterococcus (AY119434.1), Lactococcus sp. (DQ376921.1), Enterococcus sp. (AB079371.1) and Enterococcus seriolicida (AF061005.1 and L32813.1) GB-4 4.2 2 x 10 -82 99 Halomonas phoceae (AY922995.1) SA-1 4.2 1 x 10 -77 98 Halomonas sp . (AM490139.1, AM490135.1, DQ489548.1, AY347310.1, DQ357033.1, DQ356999.1, DQ356998.1 SA-5 4.2 4 x 10 -80 98 and AJ309560.1) and uncultured bacteria (DQ441377.1 and AB205638.1) Salinomonas halophila (AJ427627.1) (E = 2 x 10 -40 ), Shewanella putrefaciens (U91549.1) (E = 2 x 10 -40 ), Halomonas cf. Halomonas campisalis 'campaniae' (DQ289061.1) (E = 2 x 10 -40 ), Deleya pacifica (L42616.1)
Other Other -40 -38 (E = 2 x10 ) and uncultured bacteria 1 x 10 to -39 SA-9 4.2 -40 100 [AY509454.1 (E = 3 x 10 ), DQ824699.1 2 x 10 -38 - (E = 1 x 10 ), DQ824588.1 (E = 1 x 10 38 ), DQ469203.1 (E = 1 x 10 -38 ), AY528788.1 (E = 1 x 10 -38 ), AY894954.1(E = 1 x 10 -38 ) , DQ354722.1 (E = 1 x 10 -38 ) and AM158419.1 (E = 1 x 10 -38 )] Nocardia rhamnosiphila (EF418604.1), Nocardia sp. (EF216365.1, DQ462650.1, AB123710.2, AF430022.1, AF430021.1 and AB123596.1), Nocardia alboflava (AM295159.1), Nocardia pigrifrangens (AF219974.1), Nocardia asteroides (AY262327.1 and Z82230.1), Nocardia SA-7 4.2 8 x 10 -66 97 uncultured actinobacteria (AF544262.1), Nocardia flavorosea (AY756552.1 and AF430048.1), Nocardia carnea (AY756546.1, AF430037.1, X80602.1 and Z36929.1), Nocardia sienata (AB121770.1) Streptomyces sp. (AB124305.1) and Nocardia flavorozea (Z46754.1)
90. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
uncultured bacteria (AM423155.1, DQ801310.1, AB290344.1, DQ847132.1, DQ339478.1, DQ322199.2, EF033504.1, EF033501.1, AB252902.1, AY758376.1, DQ462292.1, DQ451459.1, AY838519.1, DQ228381.1, AY753395.1, AY957724.1, AY081980.1, AY133078.1, AB074610.1, DQ066602.1, AY162035.1, AJ536683.1, AJ295350.1, AJ534658.1 and DQ221655.1), Delftia tsuruhatensis Delftia SB-6 8.3 3 x 10 -84 100 (DQ864991.2, AY738262.1, AY684787.1, DQ131838.1, DQ356901.1, AJ606337.1, AJ852059.1, AB075017.1, AY838308.1 and AY302438.1), Delftia sp. (EF061135.1, DQ140182.1, DQ131817.1, DQ168428.1, AY052781.1, AJ237966.1 and DQ301783.1), Comamonas sp. (AY965248.1), Delftia acidovorans (AF526915.1) and Rhodobacter sphaeroides (DQ001153.1) Clone SA-7 (4.2%) showed 97% homology to Nocardia rhamnosiphila , Nocardia sp ., Nocardia alboflava , Nocardia pigrifrangens , Nocardia asteroides , uncultured actinobacteria, Nocardia flavorosea , Nocardia carnea , Nocardia sienata , Streptomyces sp. and Nocardia flavorozea (Table 3.6). SA-7 also showed 97% homology (E = 1 x 10-63) to Nocardia asteroides (AF163818.2, AY262325.1, Z82231.1), uncultured bacteria (DQ413074.1 and AB196106.1), Gordonia aichiensis (AY262332.1, X80633.1, X81925.1), Gordonia sputi (AY262330.1, X81927.1 and X80627.1), Gordonia otitidis (AB122026.1), Gordonia jacobaea (AF251791.1), Nocardia testacea (AY903612.1, AB121769.1 and AB192415.1), Mycobacterium sp . (AY367023.1 and DQ249999.1), Nocardia brasiliensis (AB201298.2), Nocardia otitidiscaviarum (AB201303.1), Nocardia transvalensis (AB201300.1), Nocardia sp . (AF430024.1, AB181514.1 and AB181512.1), Gordona sp. (X92481.1 and AF150493.1) and Nocardia harenosa (DQ282122.1).
Clone SA-4 (33.3%), showed 91% homology to an uncultured actinobacterium (Table 3.6). A large group of clones aligned to members of the genera Actinomadura and are grouped accordingly. Group 1 contains Actinomadura sp ., Actinomadura meyerii , Actinomadura nitritigenes , Actinomadura pelletieri , Actinomadura formosensis , Actinomadura fulvescens , Actinomadura atramentaria , Thermomonospora formosensis and Actinomadura rudentiformis . Clone SA-2 (33.3%) showed 99% homology to this group.
Clones SA-3 (8.3%) and SA-6 (8.3%) showed 98% homology to an uncultured bacterium and 97% homology (E = 2 x 10 -63 ) to the following BLAST sequences
91. Cassandra Trent ______that form group 2: uncultured bacteria, Actinomadura sp ., Actinomadura oligospora , Pseudonocardiaceae strain PA123 (Table 3.6).
3.3.2.2 Adaptor-B
The V3 region of grub-PCR clone GB-1 (12.5%), showed 100% homology to G-V3- 1-1 and group 1 Enterobacteriaceae BLAST sequences while clone GB-6 (25%) showed 100% homology to G-V3-1-2, G-V3-2-3 and group 2 Enterobacteriaceae BLAST sequences (Table 3.6). Clone GB-2 (12.5%) showed 100% homology to G- V3-1-4, G-V3-2-1 and group 3 Enterobacteriaceae sequences. Clone GB-8 (4.2%) aligned to members of the Enterobacteriaceae family outside of the above groups and showed 100% homology to Enterobacter sakazakii and an uncultured bacterium.
The remaining 45.8% of clones aligned to a range of sequences outside the Enterobacteriaceae family (Table 3.6). Clone GB-3 (33.3%) showed 100% homology to Clostridium botulinum and Eubacterium combesii . Clone GB-4 (4.2%) showed 99% homology to Halomonas phoceae . GB-7 (8.3%) showed 100% homology to Lactococcus gariveae , a Lactobacillales bacterium, an uncultured bacterium, Lactococcus sp ., Enterococcus sp . and Enterococcus seriolicida .
Of the soil-PCR adaptor B clones, SB-5 (12.5%) showed 100% homology to SA-4 and 91% homology to uncultured actinobacterial clones (Table 3.6). Clone SB-6 (8.3%) showed 100% homology to uncultured bacteria, Delftia tsuruhatensis , Delftia sp ., Comamonas sp ., Delftia acidovorans and Rhodobacter sphaeroides .
Clone SB-3 (12.5%) showed 99% homology to an uncultured actinobacterium (Table 3.6). Clone SB-4 (12.5%) showed 99% homology to an uncultured actinomycete and 95% homology to Actinomadura sp. , Actinocorallia sp. , Actinocorallia aurantiaca , Actinomadura aurantiaca and Sarraceniospora aurea . Clone SB-9 (4.2%) showed 99% homology to the uncultured antinomycete and 95% homology (E = 3 x 10 -56 ) to the remaining group as per SB-4. Clone SB-7 (4.2%) showed 95% homology to an uncultured bacterium clone, and 95% homology to uncultured bacteria and an Actinomycete sp .
92. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Table 3.7. Thermodynamic properties and separation values for Adaptor V3-PCR clones amplified from soil and canegrubs. Column seven refers to the ratio of migration in a TGGE gel when compared to the E. coli control sample (length:length) displayed as a percentage. Retention time refers to the time retained in the HPLC column.
GC- Ratio of % Thermodynamic Retention Homologous BLAST sequence group Sample Tm Migration GC Tm time to E. coli GA-1 52.5 77.2 96.8 3.045 1.010 Group 1 GB-1 51.9 77 96.8 3.434 1.035 GA-3 52.5 77.2 97.9 3.036 1.012 Group 2 GB-6 52.5 77.2 97.9 3.036 1.012 GA-2 52.5 77.2 97.9 2.942 1.005 Enterobacteriaceae Group 3 GA-4 52.5 77.2 97.4 3.335 1.029 GB-2 52.5 77.2 96.8 2.942 1.005 Enterobacter GB-8 53.8 77.7 98.4 3.382 1.029 sakazakii Citrobacter / Salmonella / GA-5 52.5 77.2 97.1 2.131 0.994 Klebsiella SA-2 60.7 80 100 4.024 1.191 Group 1 SB-2 60.7 80 100 4.024 1.191 SB-8 60.3 79.8 100 4.011 1.180 Actinomadura SA-3 60.7 80 100 3.929 1.171 Group 2 SB-1 60.7 80 100 3.929 1.171 SA-6 60.7 80 100 4.045 1.169 Enterobacter sp. GA-7 53.4 77.6 96.8 3.382 1.012 (93% homology)
Uncultured SB-5 59.7 80 100 4.029 1.170 Actinobacterium (90% homology) SA-4 59.7 80 100 4.27 1.149 Uncultured bacteria Uncultured Actinobacterium SB-3 54.8 77.4 97.3 2.797 0.972 (99% homology) SB-4 61 80 100 4.034 1.171 Actinocorallia / SB-7 60 79.7 100 4.055 1.173 Actinomadura SB-9 59.6 79.4 100 3.382 1.169 Clostridium / GB-3 53.3 76.8 95.9 1.87 0.674 Eubacterium Lactococcus / Lactobacillales / GB-7 47.2 75.1 92.9 3.255 1.014 Enterococcus GB-4 55.6 78.5 99.5 3.561 1.082 Other SA-1 56.3 78.7 99.3 4.044 1.188 Halomonas SA-5 54.4 78 98.3 4.055 1.179 SA-9 60.7 80 100 4.053 1.178 Nocardia / SA-7 57.1 78.5 98.9 3.892 1.090 Gordonia Delftia SB-6 51.6 76.8 96.3 3.303 1.159
Total data range 13.5 4.9 7.1 2.185 0.517
93. Cassandra Trent ______
5
4.5 y = 0.2673x - 22.819 R2 = 0.5169 4
3.5
3 E. coli E.
2.5
2 compared to compared 1.5
1
Retension time (mins) and % Distance travelled travelled Distance % and (mins) time Retension 0.5 y = 0.0507x - 3.913 R2 = 0.4746 0 92 93 94 95 96 97 98 99 100 101 Temperature-based Tm (ºC)
Retension time Peak 1 % of E.coli
Linear (Retension time Peak 1) Linear (% of E.coli)
5
y = 0.3433x - 23.388 4.5 R2 = 0.5848
4
3.5
3 E. coli E.
2.5
2 compared to compared 1.5
1
y = 0.0641x - 3.937 Retension time (mins) and % Distance travelled travelled Distance % and (mins) time Retension 0.5 R2 = 0.5193 0 74 75 76 77 78 79 80 81 GC-content Tm (ºC)
Retension time Peak 1 % of E.coli
Linear (Retension time Peak 1) Linear (% of E.coli)
Figure 3.5. Graphs comparing linear correlations between V3-PCR sequence separation results (Tables 3.5 and 3.7) and: (A.)Thermodynamic Tm; and (B.) GC- content based Tm. Circles indicate datapoint belonging to clone GB-7.
94. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Soil-PCR adaptor B clones also aligned with sequences from the 2 Actinomadura sequence groups (Table 3.6). Clone SB-2 (33.3%) showed 100% homology to SA-2 and 99% homology to Group 1 sequences while clone SB-8 (4.2%) showed 97% homology to these Group 1 sequences. Clone SB-1 (12.5%) showed 100% homology to SA-3, 98% homology to an uncultured bacterium and 97% homology (E = 2 x 10 -63 ) to Group 2 BLAST sequences.
3.3.2.3 Comparison of sequence group properties from soil and grub Adaptor-PCR sequences
When comparing DHPLC and TGGE data for each homology-linked sequence group (Table 3.7), there is increased separation within each highly homologous group when compared to other groups of sequences within the same family (Enterobacteriaceae) or genus ( Actinomadura ) as demonstrated in the grub V3 data. As a whole, the DHPLC and TGGE data showed linear relationships to Tm values (figure 3.5), however there is one main outlier (especially within DHPLC data) (circled). This belongs to clone GB-7, which aligns to BLAST sequences from Lactococcus , Lactobacillales and Enterococcus . A sequence alignment showed that this sequence has a very different motif to all other clones from positions 40 to 65 bp of the V3 region (5´-TAAGTAATTTTCCACTCTACTTAA -3´) (figure 3.6). The motif contains reduced GC-content (25%) compared to the consensus sequence (38.5%) and a BLAST search of this motif resulted in a limited number of bacteria showing a 100% match (E = 3 x 10 -4). These were Lactococcus gariveae (AB300219.1, AB267905.1, DQ485325.1, AY430484.1, AY699289.1, AY438044.1, AY946285.1, AF283499.1, AJ878010.1, AB120031.1, AF352166.1, AY669388.1, X54262.1, AB026843.1, AB018211.1 and AB012306.1), a Lactobacillales bacterium (DQ822778.1), primary endosymbiont of booklice (Liposcelis decolor ) (DQ407747.1), mosquito ( Culex pipiens quinquefasciatus ) midgut community member (AY332754.1), Lactococcus sp. (DQ376923.1 and DQ376921.1), Enterococcus sp. (AB079371.1), Enterococcus seriolicida (AF061005.1 and L32813.1), Enterococcus malodoratus (AJ878014.1) and uncultured bacterial clones (AY512303.1 and AM040059.1). Size differences in all other clones were due to deletions/insertions in this region and can account for up to 25 bp size differences between sequences (135 to 160 bp)(figure 3.6).
95. Cassandra Trent ______3.3.3 P1-PCR
All cloned sequences contained the full-length 3´ adaptor A and 5´ adaptor B sequences. Only 1 clone (n = 46) was not homologous to grub adaptor-V3-PCR clones. Clone DT2-1 showed 93% homology with differing E values to uncultured bacteria [AB288922.1 (E = 4 x 10 -65 ), EF428984.1, AB277374.1, EF121342.1, AB291818.1, EF212951.1 and AB288903.1 (E = 9 x 10 -63 )], Enterobacter sp. [AY880198.1 (E = 2 x 10 -63 ), EF429007.1, EF419181.1 and EF175731.1 (E = 9 x 10 -63 )] and Enterobacter cloacae [EF219421.1 (E = 9 x 10 -63 )].
Of the Enterobacteriaceae -grouped sequences, 52% of DT (PCR reactions 1 and 2) and 74% of DD clones were homologous to these groups. Clones DD1-1 (27% of DD1 clones), DD2-1 (41.6% of DD2 clones) and DT2-3 (27.3% of DT2 clones) showed 99% homology (E = 2 x 10-82) to group 1 sequences and 100% homology (E = 7 x 10 -85 ) to GA-1 (see figure 3.6 for alignment). Clone DT1-6 (8.3%) showed 100% homology (E = 7 x 10 -85 ) to group 1 and GB-1. Clones DD1-2 (36.4%), DD2-2 (25%) and DT1-1 (33.3%) showed 100% homology (E = 7 x 10 -85 ) to group 2 sequences including GA-3 and GB-6. Clones DD1-6 (9.1%), DD2-3 (8.3%), DT1- 3 (16.7%) and DT2-5 (18.2%) showed 100% homology (E = 7 x 10 -85 ) to group 3 sequences, as did GA-2 and GB-2 (figure 3.7).
Sequences homologous to Clostridium botulinum and Eubacterium combesii accounted for a total 21.7% of both DD and DT P1-PCR clones. Clones DD1-3 (9.1%), DD2-4 (8.3%) and DT1-5 (8.3%) showed 100% homology (E = 5 x 10 -70 ) to this group and to adaptor-PCR clone GB-3 (figure 3.6). Clones DD1-4 and DT1-2 showed 99% homology (E = 1 x 10 -67 ), and DT2-4 showed 98% homology (E = 3 x 10 -65 ) to this BLAST sequence group (note that all three clones differ in sequence) (figure 3.6).
Only one clone was homologous to the Lactococcus, Lactobacillales and Enterococcus sequence group. DD1-5 (9.1%) showed 100% homology (E = 2 x 10 - 85 ) to this group and to clone GB-7.
96. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Figure 3.6. Alignment of all V3 and adaptor-V3 PCR sequences amplified from soil and canegrub 59-M. Purple shaded region denotes the region containing the Lactococcus gariveae and Enterococcus seriolicida specific motif. Yellow shading refers to bases that are identical to all clones; blue shading denotes regions that are highly conserved. Note how all sequences have regions of high universal homology.
97. Cassandra Trent ______The control-x spike was unique to the DT-SSH reaction and homologous sequences were only found in DT clones (21.7% total). Clones DT1-7 (8.3% of DT1-PCR) and DT2-2 (27.3% of DT2-PCR) showed 100% homology to the control-x sequence and 99% homology (E = 8 x 10 -69 ) to the Bacterium sp. sequence group. DT1-4 (8.3%) showed 97% (E = 8 x 10 -66 ) homology to this group.
98. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Figure 3.7. Alignment tree of the V3 regions amplified from V3 (G-V3-), Adaptor (GA, GB, SA and SB) and P1-PCR [Driver minus driver control (DD) and tester minus driver (DT)] reactions. Note the short distance between the Enterobacteriaceae groups and their dominance in each canegrub PCR reaction. The tree was constructed using the Align-X program from the Vector NTI Advance 10.3.0 Suite. Sequence distances were calculated using the Neighbour-Joining algorithms of Saitou and Nei (1987). Distances (in brackets) are between each sequence and the closest branch.
The overall percentage of clones from each set of PCR reactions (grub-V3, grub adaptor-V3, DD P1-PCR and DT P1-PCR) were tallied and are presented in Table 3.8 for comparison. The proportion of clones from adaptor V3-PCR (representative of tester) differs to the non-spiked SSH P1-PCR (DD clones that have undergone SSH), indicating some sequences may be subtracted more efficiently than others. For example, the percentage of clones aligning to group 1 Enterobacteriaceae
99. Cassandra Trent ______doubled in the DD clones, group two clones were stable and group 3 had a three fold decrease when compared to tester (adaptor PCR) clones. Percentages for the other sequence groups remained consistent (Table 3.8).
Table 3.8. Total percentage of canegrub-amplified V3 clones from each PCR reaction (V3, adaptor and P1-PCR) homologous to each taxonomic group. Percentages do not add up to 100% as several outliers were homologous to other organisms.
Total percentage of clones homologous to each group BLAST sequence groups V3-clones Adaptor-clones Non-spiked SSH Spiked SSH
Group 1 25.0 14.6 34.8 17.4 Enterobacteriaceae Group 2 45.8 29.2 30.4 17.4 Group 3 20.8 27.1 8.7 17.4 Control-x 0.0 0.0 0.0 21.7 Clostridium 0.0 16.7 21.7 21.7 Lactococcus 0.0 4.2 4.3 0.0 TOTAL 91.7 91.7 100.0 95.7
3.3.4 Control SSH
The first two SSH experiments were intended to subtract single species V3- amplicons from themselves (representative chromatograms as per figure 3.8). Control SSH reaction 1 ( E. coli - E. coli ) produced a single large shouldered E. coli peak in both the P1-PCR and nested PCR samples, while SSH reaction 2 ( V. harveyi - V. harveyi ) produced two large dominant peaks in the P1-PCR sample and one large peak and a doublet in the nested-PCR sample. In addition, combinations of these two controls were subtracted against each other. In SSH reaction 3 ( E. coli and V. harveyi - E. coli ), no peaks were detected in either P1 or nested PCR products apart from the injection peak. In reactions 4 ( E. coli and V. harveyi - V. harveyi ) and 5 ( E. coli and V. harveyi - E. coli and V. harveyi ) P1-PCR, 3 similar dominant peaks were present. In nested-PCR samples, both experiments showed 2 dominant peaks, but differing smaller peaks in-between the dominant peaks between the two samples.
100. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
Figure 3.8. Representative DHPLC chromatograms of tester sequences from control reactions one to five. Note how each Adaptor PCR reaction from each control produced more than one peak, and how each peak pattern changed when a different adaptor was added. (A.) Adaptor-A PCR reactions. (B.) Adaptor-B PCR reactions.
101. Cassandra Trent ______
All mix samples [SSH 6 (Mix – Mix), 7 ( V. harveyi and Mix – Mix), 8 ( V. harveyi and Mix - V. harveyi and Mix), 9 ( E. coli and Mix – Mix) and 10 ( E. coli and Mix - E. coli and Mix)] showed similar profiles for both P1-PCR and nested PCR. P1 - 8 also showed another peak in a similar position to the V. harveyi doublet described in the single controls.
3.3.5 Tester:driver ratio analysis
The aim of this experiment was to find the tester:driver ratio at which the tester was completely subtracted and no nested-PCR products could be detected on an agarose gel. Ratios from 1:30 (as used by Diatchenko et al ., 1996) to 1:150 produced large bands when visualised on agarose. P1-products ranged in intensity, but nested products showed consistently strong bands in agarose gels suggesting that clean subtraction of all products may not be possible. When separated via DHPLC and TGGE, the only difference between ratios was a decrease in peak or band intensities when the ratio was increased.
102. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
3.4 Discussion
To date, only one study has used a PCR product as SSH starting material (Zhang et al , 2008). Most reports having focused primarily on genomic DNA or cDNA. Zhang et al . (2008) used a 16-S PCR based SSH to develop primers specific for bacteria indicative of human faecal matter. In contrast to the work in this chapter, Another difference between works is that this paper performed adaptor ligation with ligase, and a larger ~900 bp region of the 16-S was amplified. In addition, resulting clones were not screened for tester specificity; hence the efficiency of this approach was not determined (Zhang et al ., 2008). The novel V3-PCR-based approach reported in this chapter has proven to be effective through these preliminary verifications and showed potential as a useful tool for comparing species differences between microbial communities. The primary advantage of this V3-PCR-SSH technique over previous approaches was the generation of taxonomically-identifiable sequences. The metagenomic SSH approach originally described by Galbraith et al . (2004) (discussed in Chapter 4), provided random genomic DNA that showed low homology to Genbank entries. However, the V3-PCR approach provided short, taxonomically-diverse, well-studied sequences that could be associated with a large range of known bacterial species. In addition this is the first study of the microflora associated with the mid-region of living canegrubs.
There are a number of other uses for this novel SSH approach. Alternative primer sets may be used to identify differences in other taxa such as fungi. Specific functional groups of bacteria such as methanogens could be targeted and compared using methanogen-specific primers. This technique may also prove applicable to compare the function of microbial communities; not just bacterial community members. Specific primers may be used to target functional genes associated with nitrogen fixation (eg. dinitrogenase or dinitrogenase reductase) or cellulose fermentation (eg. cellulase) (Nelson and Cox, 2000) in soils, or determine metabolic differences between the gut flora of obese and healthy humans. As long as there is a primer set specific for a group of bacteria or genes, the presence of these bacteria or genes may be compared between two or more communities.
103. Cassandra Trent ______The percentage of tester-unique clones or the overall efficiency of the technique could be improved by optimising the PCR reactions associated with sample preparation, P1- and nested-PCR. Previous studies examining traditional SSH have not investigated the dominant sequences present at each SSH stage. This is the first study to look at dominant members in a population at each SSH step. Previously, overall efficiency has only been assessed using dot blots and southern analysis (Diatchenko et al ., 1996, Gurskaya et al ., 1996). Sequence composition throughout each SSH process could be analysed using high-throughput sequencing techniques (discussed later) to determine what sequences are present at each stage. Recommendations may be made based on this data to improve the efficiency of the technique and alternate approaches for a range of applications could be developed (as discussed above). Careful optimisation of temperature, cycle number and reagent concentrations could increase the efficiency of and reduce biases associated with individual PCR stages. For example, by performing both V3 and adaptor-V3 PCR using the same template and adjusting the aforementioned parameters, specific PCR conditions may be achieved that minimise the differences in bias seen in this study. This would be required for each PCR reaction and primer set used. In addition, the hybridisation temperature during SSH hybridisations one and two could be optimised to increase hybridisation specificity, reducing the occurrence of mismatched hybridisations. However, specificity of temperature may vary depending on the structure of each microbial community so it would be specific for canegrub-associated microflora.
3.4.1 V3-PCR
The V3 hypervariable region of 16-S ribosomal DNA was chosen as the starting material for the novel 16-S-based SSH designed in this study. This region was chosen due to its diverse thermodynamic properties, small size (200 bp), and ability to identify bacterial members from the grub microbial community. Cloning and sequencing of these V3-PCR products was performed to determine if the correct region was being amplified, investigate the microbial community associated with a live grub mid-section, confirm the sequences present in SSH driver, confirm identities of controls, and investigate the DHPLC and TGGE separation of sequences present in driver.
104. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______Initial amplifications using the V3-primers and GoTaq® Green Master Mix (Promega) detected a contaminant in the blank control. Many commercial Taq preparations contain contaminant bacterial DNA (Hughes et al ., 1994, Newsome et al ., 2004, Chiang et al ., 2005). With regards to the SSH process, V3-PCR contamination may cause false-positive isolation of such contaminant DNA. The effect on the isolated clones is minimal as contamination from the initial Taq containing master-mix (excluding primers) will be present in both tester and driver, and should be subtracted during the SSH process. The presence of this contaminate should not hinder subtraction of other sequences. Techniques used to reduce Taq contamination such as the degradation of DNA using DNase or UV treatment, also reduce PCR-efficiency (Heinringer et al ., 2003). As a result, these techniques are not practical for this application as high PCR amplicon concentrations (300 ng per µL) are required for V3-PCR SSH. The absence of the contaminant sequence in V3- community PCR reactions suggests it does not dominate the driver PCR reaction (present in more than eight percent of clones per reaction); however a larger sample size (i.e. analysis of more clones) is required to support this conclusion. The contaminant was therefore thought to be present in very low concentration within the PCR mix and hence was not dominant in the PCR-amplified sequence pool, since added template is present in much higher concentration. Contamination issues can be avoided by purchasing highly purified Taq preparations preferably with guarantees of purity.
Primers used in this study for V3-PCR (357-F and 518-R) successfully amplified the correct 16-S rDNA region in agreeance with previous reports (Tannock et al ., 2000, Dilly et al ., 2004, Yu and Morrison 2004). Verifying that the correct 16-S rDNA region was being amplified was important to ensure bacteria could subsequently be identified from aligning the sequences amplified to Genbank entries. This region was chosen based on the high sequence and Tm variability seen when comparing this region in known bacterial sequences (Yu and Morrison 2004). High sequence variation was important to distinguish between bacterial species. In addition, high Tm value variation was important for sequence separation via DHPLC and TGGE. The V3-region showed high values in both criteria when compared to other hypervariable regions. Due to problems with sequence separation and species resolution, a larger 16-S region may be required such as the 900 bp region used by
105. Cassandra Trent ______Zheng et al ., 2008 in their 16-S PCR SSH approach. A larger sequence may help to provide species-specific sequences that are easily separated via denaturing gels such as TGGE or DHPLC columns (discussed in further detail in section 3.4.2).
In the present study, all three control and 24 V3-community sequences (apart from one clone), showed at least 99% sequence homology to sequences in the BLAST database (Tables 4.4 and 4.5). This may be due to both the ever-increasing number of 16-S rRNA sequences in the Genbank database and the short region size (~200 bp). In addition, 23 from 24 V3 sequences analysed were homologous to well- described and culturable Enterobacteriaceae. Many unculturable bacteria likely exist within complex communities that cannot be identified via this region. Most bacteria represented by the database have previously been isolated, cultured and identified using classical taxonomic tests. Bacteria that cannot be identified in this manner were likely overlooked in most experiments and remain unclassified and labelled as uncultured bacteria. However, the focus of this project was those bacteria that were economically culturable, putatively pathogenic and suitable for biocontrol. Bacteria with these characteristics should be highly represented within the Genbank database as they have been previously cultured, identified and sequenced.
High V3-sequence homology (98-100%) to known bacteria increases the chance of identifying bacterial species present within a community when compared to the lower homologies seen in the genomic SSH approach (Chapter 4). This is due to the higher database homology found for the well studied V3 region when compared to random genomic DNA fragments amplified while using the metagenomic approach. This increase in identification is possibly due to less bacterial species having a whole genome sequence submitted to Genbank compared to 16-S sequences. However, use of the 200 bp V3 sequence is at the cost of species resolution. Different species may share the same V3-sequence, as demonstrated by each clone showing 100% homology to a number of differently bacterial species within the database. Numerous species containing the same V3 sequence may result in false negatives in the SSH technique and poor product separation. These factors may reduce the probability of identifying all tester-specific bacteria. In addition, numerous 16-S rRNA gene copies have been documented (Fegatella et al ., 1998, Crosby et al ., 2003), as well as heterogeneity of these copies in single isolates (Ueda
106. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______et al ., 1999, Moreno et al ., 2002, Acinas et al ., 2004). Multiple gene copies enables a bias towards amplification of bacteria with more copies. Bacteria with large amounts of copies and with gene heterogeneity will contribute more than one sequence to the SSH pool. Thus, an individual bacterial species may share a V3- sequence with other species while also contributing numerous heterologous V3- sequences to the community-PCR. This bias effect influences all 16-S PCR methods. However, it should be negligible as the SSH subtraction process enriches rare sequences and subtracts sequences common to tester and driver.
Poor taxonomic classification of BLAST entries may also contribute to multiple- species BLAST homologies i.e. multiple species sharing the same V3 sequence. However, sequences associated with putative pathogens, as identified via a healthy vs diseased grub SSH, could be used to choose selective and/or differential media. Freshly dead/diseased specimens could be used to isolate the putative pathogen by culturing the whole grub extract on media selective for the identified genera, limiting the need for species specificity. For example, to isolate an unknown Enterobacter spp. identified via dead vs living grub SSH, the Enterobacteriaceae could be enriched in a broth containing bile salts and brilliant green then cultured on violet red bile glucose agar to select for Enterobacter spp.. Presumptive Enterobacter colonies may be confirmed using a classical taxonomic biochemical tests such as API 20 E strips (bioMerieux, France) (Shaker et al ., 2006). Final identification may also be confirmed by amplifying the full length 16-S PCR from the isolated Enterobacter sp.. The V3 region from the full length 16-S amplicon can then be cross referenced to the Enterobacter spp. sequence identified from SSH.
Use of SSH screening and selective culturing methods to isolate target bacteria is advantageous over non-specific colony isolation. This is due to the possible presence of relatively faster growing non-target bacteria in canegrubs. These non- target bacteria may dominate a culture reducing the probability of isolating the target bacteria. Hence, a molecular method is required to screen the sample before a putatively pathogenic bacterium can be selectively cultured. An example of where selective culturing would be advantageous is the Enterobacteriaceae identified from the healthy grub mid-section in this chapter. This approach is discussed in detail above. In addition, the 16-S cloning study of whole dead grubs (Chapter 2) showed
107. Cassandra Trent ______that many saprophytic and psychrotrophic bacteria dominated these specimens. Therefore, media selective for putative pathogens may be required to specifically isolate these bacteria that may be less dominant in the decomposed carcass.
While verifying the V3-PCR for use as driver, the living grub mid-section analysed (59-M) amplified only six different V3-PCR sequences. From these, five showed ≥ 94% sequence homology to each other and were homologous to the Enterobacteriaceae (figure 3.7). High taxonomic similarity ( ≥ 94%) of dominant bacteria may be attributed to the transient nature of the midgut (compared to hindgut) and the time the grub spent in autoclaved peat prior to analysis. The Enterobacteriaceae may either be symbionts of the midgut or may thrive in autoclaved peat after being introduced by bacteria present on the grub’s cuticle or in faeces. As grubs were not fed in captivity, the bacteria present in the midgut may have been associated with the midgut wall and lumen and may be thus symbionts. The Enterobacteriaceae family has been previously reported in other insect species and members are thought to aid digestion and metabolism of plant matter (Lukwinski et al ., 2006). Of all sequences amplified from the mid-section of live grub 59-M, 50% were homologous to Enterobacter cloacae (group 2); a well studied insect symbiont (Armstrong et al ., 1990, Watanabe et al ., 1998, Tatfeng et al ., 2005, Yilmaz et al ., 2006). Therefore, it is likely that the amplified clones may be associated with canegrub midgut symbionts.
Of the five different Enterobacteriaceae sequences, three separate homologous groups were formed (figure 3.7). All of these three groups of Enterobacteriaceae sequences amplified from living grub (59-M) V3-PCR (SSH driver) have previously been associated with insects (Table 3.4, figure 3.6). This provides further evidence (as mentioned above) to suggest these bacteria may be symbionts. From group one Enterobacteriaceae matches, all bacteria were reported to be associated with insects such as the pink bollworm ( Pectinophora gossypiella ) (Kuzina et al ., 2002), Hylesia metabus (Osborn et al ., 2002), thrips ( Frankliniella occidentalis ) (de Vries et al ., 2004), and wireworms ( Limonicus canus ) (Lacey et al ., 2007). Enterobacter gergoviae from group two was associated with entomopathogenic nematodes that invade Galleria mellonella (Gouge et al ., 2006). However, since no symptoms of nematode infection were seen, either the infection was in it’s early stages, there was
108. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______no nematode infection or the E. gergoviae was associated with a non-pathogenic nematode naturally occurring in the soil. Group 3 Enterobacteriaceae showed homology to Citrobacter sp. which has previously been reported in the whitefly Bemisia argentifollii (Davidson et al ., 2000). All of these insect associations appeared to be symbiotic. No adaptor-V3 clones (V3-PCR containing adaptor sequences that forms SSH tester) amplified from the canegrub-associated soil were homologous to members of the Enterobacteriaceae providing additional evidence that bacteria from this family present in canegrubs may be symbionts and may be associated with the midgut wall.
In the V3-PCR reactions, the only non-Enterobacteriaceae homologous sequence was homologous to an Actinobacterium sp.. These bacteria have been isolated from other soil dwelling insects, particularly scarabs (Egert et al ., 2003), so the presence of a homologous sequence from grub-V3 PCR is not unexpected. As discussed above, an unexpectedly small range of sequences (six) were amplified from the mid- section live grub V3-PCR. In the midgut of the scarab Pachnoda ephippiata , clones (n=56) were associated with ten phylogenetic groups with the most dominant sequences being homologous to the Actinobacteria , Clostridiales, Lactobacillales and Bacillales (Egert et al ., 2003). In the midgut of the gypsy moth larvae (Lymantria dispar ), nine different phylotypes were amplified including members from Actinobacterium and Enterobacter (Broderick et al ., 2004). Therefore, the six phylotypes amplified in this study is low in comparison.
In addition to gut clearance, the relatively low sequence diversity (many clones sharing the same sequence) in this study, may be influenced by low clone number (n = 24), possible PCR bias (Hansen et al ., 1998, Lueders et al ., 2003, Kurata et al ., 2004), inadequate V3-sequence divergence, multiple 16-S rRNA gene copy and heterogeneity, and DNA extraction bias (Krsek et al ., 1999). Hence, 16-S rRNA gene PCR may not adequately represent an entire grub microbial-community. However, a larger region of hypervariable 16-S may provide 16-S SSH analysis with more suitable source for starting material for denaturing-based sequence separation and taxonomic classification. With regards to clone number (n=24), the number of sequences generated may be greatly increased using high-throughput sequencing
109. Cassandra Trent ______techniques such as 454 sequencing (Roesh et al ., 2007, Andersson et al ., 2008, Hamady et al ., 2008, Lundin et al ., 2010, Midelboss et al ., 2010).
The 454 pyrosequencing method uses an emulsion method to prepare single- sequence containing beads that are read on a fibre-optic slide (Margulies et al ., 2005). Each slide contains ~1.6 million wells where around 25 million bases can be read at an accuracy of >99% in four hours. This is 100 times faster than the current Sanger methods. In 454 pyrosequencing, the genomic DNA is sheared and diluted. Adaptor sequences are ligated to the fragments and individual sequences are captured on individual beads. These sequences are clonally amplified within an oil emulsion containing the PCR reaction mix. Beads are loaded into individual wells on the fibre-optic slide. A flow chamber is attached that creates 300 µm channels that deliver sequencing reagents to each well. The bottom of the slide is in contact with an optical sensor that records photons emitted during the pyrosequencing reaction and determines the sequence. Each nucleotide is read via PCR, as photons are emitted when inorganic pyrophosphates are released after each individual nucleotide is incorporated (Margulies et al ., 2005).
This technology has been used to analyse both genomic DNA and also microbial community metagenomes and 16-S sequences. Turnbaugh et al (2006) analysed the metagenome of mouse-gut microflora associated with obesity. This study used a combination of both 454 pyrosequencing and Sanger sequencing as both techniques have their advantages. The main advantage of 454 pyrosequencing is the increased sequence coverage, increased output and cloning bias elimination. However, only short reads are produced when compared to the traditional Sanger method. The resulting sequence data suggested that obese-associated microflora had an increased ability to harvest energy from the diet when compared to the lean-mouse associated microflora. This was determined by identifying sequences homologous to metabolic genes (Turnbaugh et al ., 2006).
The meta-transcriptome of soil has also been investigated using a modified 454 pyrosequencing protocol. The total soil RNA was randomly reverse-transcribed to cDNA and analysed via the 454 pyrosequencing technique. From this, 258,411 tags of ~98 bp were produced that provided a snapshot of what genes were being
110. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______transcribed within the soil at the time of sampling (Urich et al ., 2008). Such a technique could prove very useful in determining metabolic roles of microflora in the guts of higher bacteria under different dietary conditions, or determining the effect of a pathogen, especially one that causes feeding cessation, on the metabolism of the host.
In addition to metagenomic analysis of microbial communities, hypervariable regions of 16-S have also been amplified and analysed using this 454 pyrosequencing technique. The V6 region (~280 bp) was amplified from human gut microflora to establish genus level taxonomic information about the community. Of the 56,382 reads, 88% showed homology to a Ribosomal Database Project (RDP) entry. From the individual analysis of throat, stomach and faecal microflora, 911 different RDP homologous sequences were identified showing 609 individual phylotypes (Andersson et al ., 2008). This 16-S approach has also been used to estimate the microbial diversity present in 1g of both forest and agricultural soils. The highest number of unique sequences did not exceed 52,000 in any of the four samples. Previous diversity estimates of up to 8.3 million species per gram of soil, as calculated via Sanger sequencing, may be questioned based on this 454 pyrosequencing data (Roesch et al ., 2007). However, the Sanger estimates were determined by comparing a different population and with different DNA extraction techniques etc. Therefore, a direct comparison cannot be determined.
As investigated by Liu et al (2007), short 16-S hypervariable region sequences of 100 bp can still provide genus and species level resolution. As the 454 pyrosequencing technique is ten fold more cost efficient (per base pair) than traditional Sanger services and is increasingly becoming more affordable (Liu et al ., 2007), it is becoming a more attractive option for analysing large, complex microbial communities. One such potential application would be the screening of sequences produced from SSH final PCR. Alternatively, the extensive data produced from thorough 454 analyses of two populations, could be subtracted computationally to identify sample unique sequences without the complex hybridisation processes.
The 454 method produces thousands of short sequences per reaction and could be used to generate thousands of 16-S sequences per run. Hence, can more thoroughly
111. Cassandra Trent ______analyse the bacteria present within the canegrub-associated communities. In addition, the high-throughput technique would be useful to determine the efficiency of the V3-SSH method and better understand what sequences are present at each stage. A large database of tester-specific sequences could be compiled using this method. To date, the SSH method has not been analysed in such detail and understanding the actual sequences present at each stage would help to determine ways to increase the efficiency.
Without the use of such technology, DHPLC analysis was used to separate and visualise SSH sequences at each stage. Multiple-peak patterns and shouldering was seen when single control and V3 amplified clones were initially thought to be as a result of multiple heterogenic 16-S rRNA gene copy number. However, sequences for separation analysis were amplified from clones and hence should be uniform in each reaction (figure 3.7). Shouldering of peaks and multiple-peaking patterns has been previously reported to be due to heteroduplex formation (Hannachi-M’Zali et al ., 2002, Hurtle et al ., 2003). The occurrence of such heteroduplexes in PCR- amplified samples may be due to Taq DNA polymerase misincorporations (Saiki et al , 1988) or misincorporations in plasmid replication during the cloning process. According to Hurtle et al ., (2002), heteroduplex formation from 16-S rRNA PCR amplification was observed to be reproducible and sequence-specific. Hence, some sequences may be more prone to mutation (or Taq polymerase misincorporation) than others. Further research in this area is required as the current literature indicates Taq misincorporations are random (Saiki et al ., 1988, Kaur et al ., 2003).
Sequence separation via TGGE or DHPLC did not reflect sequence divergence within the Enterobacteriaceae-homologous sequences as determined by the sequencing data. Individuals from a known mixture of V3-sequences could not be resolved using these techniques in the present study. As suggested above, a larger hypervariable region may be required for more efficient separation. When comparing V3-sequences within certain taxa, GC content and Tm values were too homologous to distinguish sequence divergence via denaturing-based separation techniques. Therefore, better separation may be achieved with a larger PCR product showing more regions of higher sequence divergence. Sequence similarity was demonstrated in figure 3.6.
112. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______3.4.2 Adaptor PCR
Another novel aspect of the 16-S PCR approach reported in this chapter is the addition of adapters using PCR and primers containing the adaptor sequences. Adaptor sequences are required for the preferential amplification of the tester unique sequences and are the basis of the SSH technique (Diatchenko et al ., 1996). In traditional SSH, adaptors are ligated to restriction digested DNA (or cDNA) using ligase (Diatchenko et al ., 1996, Gurskaya et al ., 1996, Galbraith et al ., 2004). In this study, adaptor sequences were successfully amplified to the tester sequences during V3-PCR at the cost of a PCR bias. The suggested bias is based on differences in the amplicons generate from the same template using regular V3-PCR and adaptor-V3- PCR. However, in traditional SSH, possible bias during ligation of adaptors has not been explored and hence cannot be compared. Further analysis of these potential biases would be useful to develop ways to improve the efficiency of the technique. Use of high-throughput 454 sequencing (Roesh et al ., 2007, Andersson et al ., 2008, Hamady et al ., 2008) (as discussed above) before and after ligation may help determine the efficiency of ligation and any associated bias. In addition, 454 sequencing could also help to compare the broader community associated with both V3-PCR and adaptor-V3-PCR. A better calculation of PCR would be determined from these findings.
In addition to differences in tester and driver PCR-bias, truncated adaptor sequences (>50% of adaptor sequence truncated) may affect amplification at the P1- and nested-PCR stages and increase false-negative SSH results. During the final PCR stages, primers anneal to filled-in adaptor sequences and the binding site for the P1- PCR primers is within the first 20 bp at the 5´ end. As a result of truncation, the probability of amplifying sequences from putatively pathogenic bacteria would be reduced due to the absence of the P1-PCR reverse primer binding site in truncated samples. Therefore, the amount of tester-specific clones would be reduced as a result of the truncation. To rectify this problem, HPLC grade adaptor-PCR primers (Geneworks, Australia) were used as opposed to sequencing/PCR grade primers to ensure full-length adaptor sequences in adaptor-PCR, and the P1-primer binding site was present on each tester sequence. Therefore, in combination with high-purity Taq polymerase (as discussed previously), high quality primer and adaptor
113. Cassandra Trent ______sequences are required to ensure that the correct sequences are amplified and improve the enrichment of tester-specific sequences.
When comparing V3-sequences amplified from the living grub mid-section, addition of the adaptor sequences to the V3 primers changed the PCR reaction bias. In addition to previously described taxa, sequences homologous to Clostridium and Eubacterium were amplified from the healthy grub mid-section adaptor-V3 PCR when compared to the original V3-PCR amplification from the same template. All other conditions within the PCR remained identical. Changes in clone composition between the two reactions may be attributed to the addition of adaptor sequences to the V3-primers. Another explanation is the low clone number as discussed above. Changes in bias may cause false-positive tester-specific amplification as the tester and driver PCR reactions produce different products. Afonina et al ., (2007) showed that by adding a non-complementary 5´ adaptor to a PCR primer, the efficiency of the PCR is increased. This was measured using real time PCR and comparing relative fluorescence. A 9-mer adaptor produced a 35% increase in PCR product, a 12-mer adaptor produced a 61% increase and a 15-mer adaptor produced a 39% increase in product. These adaptors particularly increase the amplification of difficult to amplify sequences (Afonina et al ., 2007). This phenomenon may also be true for the adaptor-PCR performed in this novel SSH application. Presence of the adaptors may have improved the efficiency of the PCR and increased the number of different sequences amplified, reducing the PCR bias towards increased sequence diversity.
Of the additional sequences amplified, Clostridiales are found in many different insects (Tokuda et al ., 2000, Hongoh et al ., 2003, Cook et al ., 2007, and Lehman et al ., 2008) and represented a large percentage of clones in whole-canegrub specimens as discussed and amplified from whole live grubs in Chapter 2. In scarabs, they have predominantly been found in the hindgut (Egert et al ., 2003, Egert et al ., 2005) and are thought to be involved in fermentative metabolism of starch and cellulose (Sharp et al ., 2000, Chinda et al ., 2004). The live grub specimen used in this chapter had the head and hindgut removed during processing. These Clostridiales homologous sequences may have been transiently present in the midgut or may be a hindgut-flora contaminant. Clostridium represented 33.3% of adaptor B canegrub
114. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______clones. However, this bacterium was not detected in any other 59-M PCR (V3-PCR or Adaptor A-V3-PCR) further demonstrating the influence of PCR bias on each of the three reactions (V3-PCR, Adaptor A-V3-PCR and Adaptor B-V3-PCR).
In addition to Clostridiales, Lactococcus and Enterococcus homologous sequences have also been associated with insects. These bacteria have been amplified from termites (Hongoh et al ., 2003, Nakajima et al ., 2005, Yang et al ., 2005), ants (Lee et al ., 2008), beetles (Schloss et al ., 2006, Lehman et al ., 2008) and bollworms (Xiang et al ., 2006). All associations were suggested to be symbiotic, and hence these Lactococcus and Enterococcus sequences may be associated with putative canegrub symbionts. This is highly likely as sequences homologous to these genera were only isolated from the one adaptor B PCR reaction of live grub mid-section material. As discussed above, the addition of adaptor B may have increased the efficiency of the PCR reaction and enabled the preferential amplification of sequences homologous to these genera. Alternatively, selection of clones and ligation of amplicons into vectors is random. Therefore, the small sample size of 24 clones per V3-PCR and Adaptor-V3-PCR reaction may have affected the variability observed in the chosen clones. More clones would need to be sequenced or high-throughput techniques used (such as 454 sequencing discussed above) to verify the changes in PCR bias. However, current data suggests that such a bias exists.
Canegrub-associated soil was also analysed to determine its possible effect on the mid-region flora. The homology of 79.1% of soil-associated clones to Actinobacteridae was expected as these bacteria are known to dominate soil and peat 16-S bacterial sequences (Janssen et al ., 2006). The soil in which the living grub was housed was autoclaved before exposure to the canegrub larvae. The bacteria may have been introduced into the soil from the canegrub excrement. In addition, a number of the Actinobacteridae may be thermotolerant and may have survived the autoclaving process (Fergus et al ., 1964).
The only soil-PCR isolates not homologous to sequences of the sub-order Actinobacteridae were homologous to the genera Delftia, Comamonas and Rhodobacter (8.3%); and Halomonas (12.6%). Sequences from genera Delftia and Comamonas have commonly been isolated from soil (Castaldini et al ., 2005) and
115. Cassandra Trent ______were likely present in the soil or introduced from grub excrement. Grub and soil clones with homology to Halomonas are most likely the PCR contaminant as discussed in section 3.4.1 as these experiments were performed prior to resolving the Taq contamination problem. Absence of these soil bacteria in the live grub clones suggests that the dominant members of the canegrub midsection may be associated with putatively symbiotic bacteria or bacteria that can colonise the midgut wall or lumen. Alternatively, these soil bacteria may be transiently present in the midgut but do not dominate the community.
Sequence divergence within each Adaptor-V3-PCR reaction (n=24) was low and amplification of specific taxa was favoured. The resulting bias was also different between adaptor A and B V3-PCR reactions, particularly in the grub sample. The number of Enterobacteriaceae-homologous clones was reduced from 100% in adaptor A V3-PCR to 54.2% for adaptor B (table VI). Clone sample size was small (n = 24) and the primer-annealing sequences were the same, but the adaptor sequences appeared to play a role in shifting the preferential amplification of sequences homologous to different taxa. As the SSH technique was designed to compare gene expression profiles (Diatchenko et al ., 1996), not only does SSH amplify sequences that are unique to the tester, it also amplifies tester sequences in greater excess to driver (Diatchenko et al ., 1996). Random bias differences shown in driver V3-PCR, adaptor A PCR and adaptor B PCR have been demonstrated using cloning and one live canegrub mid-section sample. The high variation of products and sequence ratios cloned from each reaction will likely affect the efficiency of the V3-PCR SSH. The limited number of clones sequenced (24 clones per PCR reaction) may contribute to some of the differences seen and hence, more clones would be required to statistically determine specific differences in bias between the three reaction types (driver V3-PCR, Adaptor A-V3-PCR and Adaptor B-V3-PCR). However, performing adaptor PCR after an initial V3-PCR may reduce the apparent PCR-bias difference by reducing the effect of the adaptors on the preferential amplification of different sequences (Afonina et al ., 2007). Therefore, the V3- sequences amplified from the initial V3-PCR would be re-amplified with the primers containing adaptors.
116. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______In addition to the PCR variability seen in this study, DNA extraction bias (Frostegard et al ., 1999, Martin-Laurent et al ., 2001) may influence the sequences amplified from canegrub samples. However the bias should be similar for all samples as the same extraction method is being used (Martin-Laurent et al ., 2001) and hence, would be negligible during the SSH process as these differences would be subtracted.
As per grub-V3 sequences, separation via TGGE and DHPLC was insufficient for use with community V3-PCR. Separation techniques have been developed to separate highly-homologous sequences and single-nucleotide polymorphisms (SNPs) (Wartell et al ., 1990, Xiao et al ., 2001). Within the Enterobacteriaceae, sequences with higher homology showed broader separation values than less closely related sequences. For example, if you look at the rate of migration ratio (TGGE) and retention times (DHPLC) within each different sequence of each Enterobacteriaceae group, there is a greater separation difference between different sequences within each group than between groups.
The region of V3-sequence identified as unique to Lactococcus gariveae and Enterococcus seriolicida was highly variable amongst other bacteria and may play a crucial role in determining the separation of these V3 amplicons. This motif may be a key melting domain that determines how the sequence separates in both techniques, and thus may explain why homologous sequence retention time correlated differently with Tm values than the other Tm vs retention time data points (figure 3.3). The poor correlation between sequences with a GC-content Tm below 77 ºC and a thermodynamic Tm below 96.5 ºC may be due to the temperature range or column temperature being too high for optimal separation of these sequences. Temperature in both techniques was optimised. However, since the sequences amplified have differing denaturing properties, the optimal range is not specific enough for all sequences amplified from canegrubs. A broad temperature range is required in TGGE to separate the broad range of sequences present in the community, but at the cost of band resolution. A temperature range was chosen based on the two clones that showed the shortest and longest migratory rates. However, such a broad range was not suitable for well-defined separation of closely related Enterobacteriaceae. For DHPLC, the higher temperature required to
117. Cassandra Trent ______denature the most thermo-stable sequences may distort the retention time resolution of the less stable sequences at this higher temperature. Since only one temperature is chosen for such a broad range of sequences, resolution is reduced.
3.4.3 P1-PCR
The P1-PCR reaction is the first amplification of tester-specific sequences within the SSH process. This reaction is crucial in determining the amplification efficiency of tester-specific sequences (figure 3.2). All clones amplified from the control SSH reaction [(live grub mid-section 59-M + control-x) – live grub mid-section 59-M] contained both adaptor A and B sequences. This was due to the exponential amplification of these sequences, as they contain both forward and reverse primer binding sites. As described by Diatchenko et al ., (1996), all linearly-amplified sequences containing one adaptor sequence are both rare and single-stranded. This is because they only contain one primer binding site, not two as per the exponentially amplified tester-specific sequences. Additionally, cloning is random and requires double-stranded sequences to circularise the cloning vector plasmid. Hence, these single-stranded sequences were not cloned and sequenced at the P1- PCR stage as they could not be ligated into the vector and could not be cloned. However, they may still have been present at low concentrations as suggested by Diatchenko et al (1996).
Tester sequences (live grub mid-section 59-M and control –x adaptor-V3-PCR) homologous to driver sequences (live grub mid-section 59-M V3-PCR) were not fully subtracted during the SSH process (figure 3.2). Due to PCR-bias differences (discussed in sections 3.4.1 and 3.4.2), dominant members of the tester and driver amplicon pools may differ even though the template is the same. Therefore, the rate of false-positives may increase as non-tester-unique sequences may be in excess in tester due to this bias difference. This can be demonstrated in Table 3.8 where the proportion of cloned sequences present in each PCR amplification step is compared (driver V3-PCR, Adaptor A and B-V3-PCR, and P1-PCR of a spiked and non-spiked control SSH reaction). Even though clone number was small (n=24), there was a difference between the proportions of taxonomically diverse sequences amplified from the four groups.
118. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______There was a small range of sequences within the initial SSH tester and driver (due to possible V3-PCR bias). This high relative abundance of similar sequences may reduce the probability of a low abundance sequence hybridising with its opposite tester counterpart. It may also reduce the detection of rare sequences after P1-PCR, as highly abundant sequences may produce false-positive PCR-template. In a more complex sequence pool, there may be less chance of driver:driver homohybrids forming, thus reducing the amount of driver available to hybridise to tester. Only one clone was not homologous to previously amplified sequences from the tester (adaptor-PCR) and driver (V3-PCR) reactions, suggesting less abundant tester sequences may have been isolated during SSH and amplified during the P1-PCR stage, however the majority of clones were the same as those amplified from the dead grub sample suggesting that subtraction did not specifically amplify rare sequences.
The control-x ( Bacterium sp .) spiked SSH reaction was performed to demonstrate the ability to identify a single tester-specific sequence from a complex, mixed population. In this experiment, P1-PCR products showed a two-fold increase in control-x concentration when compared to the concentration of control-x within the tester pool (10% in tester to 21.7% in P1-PCR products). Diatchenko et al ., (1996) and Gurskaya et al ., (1996) reported that tester unique sequences (target) made up 92 (n = 62) and 40% (n = 15), respectively, of cloned nested-PCR products when cDNA was used as starting material. This indicated that the observed 21.7% of unique clones from the spiked sample is low compared to their findings. The reduction in efficiency may be attributable to the V3-PCR bias problems as described in section 3.4.2. However, this is the first reported study of V3-PCR amplicons being used as starting material and this experiment has indicated that tester-unique sequences were enriched during subtraction.
Efficiencies of this study cannot be directly compared to those seen in previous reports as the sequence pools used in these studies were highly varied. Diatchenko et al ., (1996) and Gurskaya et al ., (1996) used cDNA amplified from mouse tissue to compare gene expression. In this chapter, the PCR-amplified V3 fragments cloned were all highly similar in sequence composition and size (figure 3.5), reducing the complexity of the sequence pool. This may reduce the amount of repulsion from
119. Cassandra Trent ______sequences during hybridisation as long sections of each individual sequence are highly similar, and sequence dissimilarity is required for the normalisation process to be effective (Diatchenko et al ., 1996). The high-sequence similarity within the starting material increases the occurrence of false-positives, and as a result, the subtraction efficiency is reduced. However, tester-specific sequences isolated from 16-S PCR SSH could be identified using online database tools such as BLAST and the Ribosomal Database Project, hence each sequence isolated was useful in determining which bacteria were present. Compared to the genomic-DNA alternative (Galbraith et al ., 2004), where over half of the resulting sequences did not match any Genbank bacteria, the 16-S approach produced less tester-specific clones yet provided more information regarding sequence homology to known bacterial taxa.
3.4.4 Control SSH
Subtracting known sequences from a constructed and simple mixed population was used to determine the efficiency of the 16-S based technique at subtracting driver sequences from tester. Insufficient subtraction of controls from a mix of known sequences occurred since subtraction was not 100% effective (Diatchenko et al ., 1996, Gurskaya et al ., 1996). Calculations describing the kinetics of hybridisation and sequence normalisation were determined comparing sequence pools that contained a higher level of complexity with regards to size, sequence variation and the proportions of dominant sequences present (Gurskaya et al ., 1996). Gurskaya et al ., (1996) based the technique on mRNA present in cells and an estimate of the abundance of different sequences (ca. 6742 different species of 500 bp across three logarithmically-spaced abundance groups). The assumptions from the model were that the single-stranded copies of the highly-abundant sequences present in both tester and driver would “normalise.” This would mean that the concentrations of single-stranded sequences from this group would equal the concentration of single- stranded sequences from the low abundance group. This is due to the increased presence of these highly-abundant sequences in driver and hence, the preferential hybridisation of these high-abundance tester-sequences to the corresponding high- abundance driver. After the addition of more single-stranded driver, the single- stranded tester-target sequences would be enriched, and upon mixing, would hybridise together to form the PCR-amplifiable heterohybrid product (Gurskaya et
120. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______al ., 1996). This heterohybrid product contains an adaptor sequence from both adaptor A and B and is exponentially amplified during P1- and nested- PCR reactions. The lack of complexity within the control tester and driver pools in the present work may have affected the rate of hybridisation of such high-abundance sequences, and increased the chance of false-positives. Once the normalisation equilibrium is reached, there may still be sufficient amounts of each single-stranded tester (particularly non-tester-specific) to create false-positives in the mixing hybridisation step. In addition, each V3-sequence shows high sequence similarity so the genetic diversity in the mix is much less than with amplified cDNA. The effect of sequence population diversity on SSH could be further explored using a mixed population of sequences and comparing the efficiency when differing numbers of unique sequences are present in the tester and driver. The sequences used may be up to 100 different known sequences of varied length, sequence similarity and Tm variation. By comparing the effect of sequence combinations on SSH subtraction efficiency, the dynamics of SSH may be better understood. Using this information, a suitable, phylogenetically identifiable region of bacterial DNA may then be chosen that may increase the efficiency of this SSH application.
Another factor contributing to the reduced SSH efficiency is the differing V3-PCR biases of numerous 16-S rRNA gene copies for each control. Numerous 16-S gene copies with slight genetic variation may be present within the bacterial genome as reported in many Vibrio and E. coli strains. This may cause multiple peaks and banding patterns for each control (figure 3.8). A preferentially-amplified gene copy in an adaptor-V3 PCR step may appear as a false-positive in subsequent P1-PCR products. However, due to the nature of subtraction (i.e. all copies are present in both tester and driver) and the driver being in excess, such a phenomenon is unlikely to reduce subtraction efficiency.
3.4.5 Tester: driver ratio study
Changing the tester:driver ratio between a range of 1:30 (as determined by Diatchenko et al ., 1996) to 1:130, did not appear to increase subtraction efficiency. Within this tester:driver ratio range, the amount of tester-spiked Vibrio harveyi was increased from 1:19 to 1:79 within the tester sample. Increasing the tester-unique sequence of interest like this did not increase efficiency either. Each reaction
121. Cassandra Trent ______showed a similar DHPLC profile. Therefore, preparation PCR-bias, DNA extraction bias, and multiple rDNA copies per bacterium (as discussed above) still affected the subtraction efficiency at all tester:driver ratios.
3.5 Conclusions
The novel application of community 16-S rRNA-PCR to SSH, has proved to be a promising new tool for comparing complex microbial communities. This new technique enables the amplification of taxonomically-identifiable sequences unique to a chosen microbial population. Previous attempts to analyse microbial communities using SSH have used genomic DNA which is useful for functional metagenomic analysis but impractical at describing species level microbial differences between two populations (Galbraith et al ., 1996). Further optimisation of the method is required (as discussed in section 3.4) to reduce the effects of DNA extraction bias, PCR-bias, V3-sequence similarity and multiple rDNA copies per bacterium. Adaptor A, adaptor B and V3-driver PCR reactions amplified different proportions of V3-PCR sequences from the same template. These differences may have reduced the SSH efficiency. TGGE and DHPLC did not adequately separate sequences from closely related taxa using the highly conserved V3-region, and are not broadly sensitive enough to separate such a large diversity of sequences amplified from canegrub microflora. However, a larger sized fragment may increase the separation. Commercial SSH-kits (PCR-Select™ Bacterial Genome Subtraction Kit, Clontech, USA) have been frequently used to show genomic differences between two microbes (Zhang et al ., 2000, Mavrodi et al ., 2002, Parsons et al ., 2002, Bae et al ., 2005) and between microbial communities (rumen) (Galbraith et al ., 2004). This option needs to be explored using canegrub samples so the efficiency and usefulness of the V3-PCR method can be compared to the previously reported genomic approach (Chapter 4). Canegrub microbial diversity may influence the amount of false positives produced when applying the V3-PCR-SSH technique to screen putative pathogens within dead specimens. Therefore, if there is generally large community diversity between grubs, SSH may not be the best choice for pathogen detection. Hence, the diversity between different grub microbial communities will also be investigated (Chapter 5).
122. Chapter 3: PCR-based SSH for identifying canegrub pathogens ______
123.
Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______
Chapter 4: Evaluation of metagenomic SSH analysis for pathogen detection in canegrubs
125. Cassandra Trent ______
126. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______
4.1 Introduction
Pathogen detection from dead canegrub specimens has proven challenging due to the presence of many saprophytic and psychrotrophic bacteria. Identifying dominant members of the community via 16-S sequencing (Chapter 2) showed that many other secondary pathogens and saprophytes may also thrive in these specimens while in refrigerated storage. To overcome the presence of these bacteria, Chapter 3 discussed the use and development of a 16S-PCR based SSH technique. This technique was used to investigate microbial community differences between canegrub specimens with intention to use the technique to identify putative pathogens from dead grub samples. However, this method showed lowered efficiency when compared to SSH using genomic DNA (Galbraith et al., 2004) or cDNA (Diatchenko et al., 1996).
Traditional SSH-based techniques have previously been used to compare complex microbial communities. A commercially available kit from Clontech (PCR-Select™ Bacterial Genome Subtraction Kit) has reportedly been used to compare cow rumen microflora (Galbraith et al., 2004). Isolation of tester-specific clones was highly efficient (99 %, n=96) and cloned sequences from bacterial, eukaryotic and Achaean origins were isolated (Galbraith et al., 2004).
Application of this method to compare the “metagenome” of a community raised a new area of research that focuses on individual and specific differences between two communities. The one approach not only gives a snapshot of the species differences but also compares the functional genes present in each sample. Current knowledge of microbial genomes is limited and as a result, 48.1% of clones were not homologous to GenBank entries available at the time of publishing (Galbraith et al., 2004). According to the NCBI website, around 220000 sequences are submitted each month. The addition of these extra sequences increases the chance of higher homology and in turn increases the usefulness of this approach.
The kit was originally designed to compare single bacterial genomes and isolate sequences unique to one bacterium (Janke et al., 2001, Nielsen et al., 2002, Parsons et al., 2003, Dwyer et al., 2004, Bae et al., 2005, Mokady et al., 2005). In
127. Cassandra Trent ______particular, the kit has been used to identify virulence factors in Aeromonas hydrophila , an aquatic opportunistic pathogen (Zhang et al., 2000). The SSH kit has also been used to investigate virulence genes in Pseudomonas aeruginosa isolated from a cystic fibrosis patient. Resulting tester-specific clones were used to create a diagnostic probe to screen patients for this specific P. aeruginosa strain (Parsons et al., 2002). It has also been applied to find factors affecting rhizosphere colonisation of a strain of Pseudomonas fluorescens (Mavrodi et al., 2002).
The aim in this study was to evaluate use of the PCR-Select™ Bacterial Genome Subtraction Kit to identify putative pathogens from dead greyback canegrubs. Dead grub specimens were compared to determine if putative pathogens could be isolated from the associated metagenomes. Controls were performed using live grub specimens and control bacteria to determine the efficiency of subtraction for comparison to the V3-PCR-SSH method developed in Chapter 3.
128. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______
4.2 Materials and methods
4.2.1 Grub specimens
Grub specimens used were from the 2007 grub season and prepared for DNA extraction as described in section 2.2.1. DNA was extracted using an alternate method (section 4.2.4) to increase the yield. Specimens analysed in this chapter were T-8-R (Tully live grub), Mk1-37 (Mackay dead grub suspected of having milky disease), 21-S (Mackay dead grub kept in soil) and 48-P (Mackay dead grub kept in peat).
4.2.2 DNA extraction
DNA was extracted as described by Tsai and Olson (1991). Briefly, samples were pelleted by centrifugation (6000 x g for 10 min) and washed in 2 mL of 120 mM sodium phosphate buffer (pH 8.0). Each sample was centrifuged (6000 x g for 10 min), resuspended in 2 mL of lysis solution (0.15 M NaCl, 0.1 M Na2EDTA [pH 8.0], 15 mg/mL lysozyme), and incubated at 37°C for 2 h. Following addition of 2 mL of 0.1 M NaCl-0.5 M Tris-HCl (pH 8.0)-10% sodium dodecyl sulfate, samples were subjected to three freeze-thaw cycles using a -80ºC freezer and a 65°C waterbath. After cell lysis, 2 mL of 0.1 M Tris-HCl (pH 8.0) saturated phenol was added; samples were briefly vortexed and centrifuged (6000 x g for 10 min). The 3 mL top aqueous layer was added to 1.5 mL of phenol and 1.5 mL chloroform/isoamyl alcohol (ratio 24:1). The 2.5 mL upper aqueous layer was added to 2.5 mL chloroform/isoamyl alcohol (ratio 24:1). The 2 mL upper aqueous layer was added to 2 mL cold isopropanol and precipitated at -20ºC overnight. Samples were centrifuged (10000 x g for 10 min) and resuspended in 100 µL MBG water. In order to increase the purity of the resulting extracted DNA a further purification step was added. Each sample was precipitated for 20 min by adding 12 µL of 0.1M spermine and 12 µL 3M sodium acetate (Buldewo et al., 2002). Samples were centrifuged (13000 x g; 20 min 4ºC) and pellets washed twice in 500 µL wash buffer (95% ethanol; 3M sodium acetate; 1M magnesium chloride). Pellets were dissolved in MBG water and measured using a Qubit fluorometer (Invitrogen).
129. Cassandra Trent ______4.2.3 Clontech kit
In total, seven SSH reactions were performed using the PCR-Select™ Bacterial Genome Subtraction Kit (Clontech, USA) as per the manufacturer’s instructions. Reactions 1-4 were control reactions to determine the efficiency of the technique. Reaction one was the control reaction using E. coli genomic DNA and phi x 174 marker (Table 4.1) as directed by the manufacturers’ instructions. Reactions 2-4 contained the live grub T-8-R sample and genomic DNA from E. coli and V. harveyi . These samples were to determine efficiency of subtraction from a live grub sample. Reactions 5-7 were subtracting dead grub samples from each other to determine sequence differences between these samples.
In brief, genomic DNA was digested with RsaI , adaptors were ligated to tester samples, hybridisations were performed, and tester-specific sequences were PCR amplified over two reactions. Each step was validated with PCR. Each PCR was set up according to the instructions using Premix ExTaq™ Hot Start Version (Takara, Japan). Samples were setup as per Table 4.1.
Table 4.1. Mixture of canegrub-associated DNA and control organism DNA in each SSH reaction. Reactions one to four are control reactions comprised of live grub- associated DNA and control bacterial DNA to determine subtraction efficiency. Reactions five to seven compare dead grub-associated sequences from three different dead grub specimens.
Expected SSH SSH # Tester Driver sequences E. coli control DNA + phi X174/ Hae III 1 E. coli control phi X174/ Hae III marker marker 2 T-8-R T-8-R Minimal PCR product 3 T-8-R + V. harveyi + E. coli T-8-R + E. coli V. harveyi T-8-R + V. harveyi + E. 4 T-8-R + V. harveyi + E. coli Minimal PCR product coli 5 Mk1-37 21-S + 48-P Mk1-37 specific sequences 6 21-S Mk1-37 21-S specific sequences 7 48-P Mk1-37 48-P specific sequences
130. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______4.2.4 Cloning
Sequences were ligated into p-GEM-T Easy (Promega, USA) and transformed into JM109 competent E. coli cells (Promega, USA) as per manufacturer’s instructions. Cells were selected using LB/Amp/X-Gal/IPTG agar (2.2.3.2), and incubated at 37ºC for 12-16 h. White colonies (n=20 per specimen) were incubated in LB broth (2.2.3.1) at 37ºC for 12-16 h.
4.2.5 Minipreps
Plasmid purification was performed using the UltraClean-htp™ 96 Well Plasmid Prep Kit (MOBIO, USA) as per the manufacturer’s instructions.
4.2.6 Dot blots
Each clone was PCR amplified as per the manufacturers’ instructions from the Clontech kit. Sodium hydroxide was added (0.3N) and each reaction was spotted onto positively charged nylon membranes (Roche, Germany) and neutralised in 0.5M Tris-HCl (pH 7.5) before baking at 120ºC for 30 min. Hybridisations were performed using the DIG High Prime DNA Labeling and Detection Starter Kit II (Roche, Germany) as per the manufacturer’s instructions. Membranes were exposed to X-ray film for 4-6 h.
4.2.7 Sequencing analysis
Sequencing of tester specific clones was performed as per section 2.3.7. Plasmid and primer sequences were removed using the ContigExpress application in the Vector NTI Advance 10.3.0 Suite (Invitrogen, USA). Sequences were cross referenced to the NCBI nucleotide database via a blastx search after blastn produced limited matches.
131. Cassandra Trent ______
4.3 Results
Genomic SSH reaction was applied to both dead and live grub specimens. Reactions 2-4 were control reactions setup to test the efficiency of the technique using a healthy grub microbial community. Reactions 5-7 were to compare three dead grub samples against each other in an effort to amplify putatively pathogenic sequences from each. Sample Mk1-37 was suspected of having milky disease (caused by Paenibacillus popilliae ) and samples 21-S and 48-P died from unexplained causes.
4.3.1 Control reactions
Reaction 1 was the efficiency control as recommended by the Clontech instructions. E.coli genomic DNA was subtracted from a mix of E.coli genomic DNA and a phi X174/ Hae III marker. The marker was amplified from the resulting reaction and 25 % of clones (n=32) (Table 4.2) were shown to be tester specific via dot blots (figure 4.2). In the nested-PCR, a banding pattern similar to the marker could be seen (figure 4.1).
Table 4.2. Ratio of clones present in the tester sample and absent in driver sample determined by dot-blot hybridisations. SSH reactions one to four contain bacterial control organisms and reactions; five to seven compare two dead grub-associated communities.
SSH # % tester specific clones 1 25 2 0 Control 3 40.6 4 0 5 44.8 Dead grubs 6 29.2 7 19.8
Reaction 3 subtracted V. harveyi from grub-associated genomic DNA and was used to test the efficiency of subtraction using grub samples. Clones analysed via dot blots showed that 40.6% (n=32) were tester specific. There was more nested-PCR product when compared to the fully subtracted controls, reactions 2 and 4 (figure 4.1).
132. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______Table 4.3. Dot-blot results for control reactions one to four. Reaction one was expected to preferentially amplify the phi X174/ Hae III marker, while reaction two was expected to preferentially amplify V. harveyi -associated sequences . Tester and driver in reactions two and four were the same to test subtraction of all sequences from a single reaction.
# of clones hybridising to each probe* Control # n Did not hybridise E.coli phi X174/ Hae III marker T-8-R V. harveyi 1 32 4 20 8 - - 2 16 3 - - 13 - 3 32 1 14 - 12 13 4 16 5 7 - 5 5 *Note that some clones hybridised to more than one probe.
Reactions two and four were designed to test if sequences could be fully subtracted. The tester and driver in both reactions were the same. Both reactions showed reduced band intensity of nested-PCR products when compared to all other reactions (figure 1). As tester and driver are the same, a tester-specific value could not be calculated. In reaction two, three clones did not show a positive result when hybridised with T-8-R (n = 16). In reaction four, five clones hybridised to T-8-R, seven to E. coli, five to V. harveyi (n = 16) and five did not hybridise to any probe. Of these, one clone hybridised to E. coli , V. harveyi and T-8-R, three hybridised to T-8-R and E. coli , two to V. harveyi and E. coli , three were specific to V. harveyi , one was specific to T-8-R, and one was specific to E. coli .
Figure 4.1. Nested-PCR products of control SSH reactions one to four (Table 4.1). Lanes 1-4: control reactions 1-4 respectively. Lane 5: PCR no-template control. Lane 6: blank lane. Lane 7: 1kb DNA marker. Note how reactions two and four show reduced amount of PCR product. This was due to tester and driver being the same and all sequences being theoretically subtractable.
133. Cassandra Trent ______
Duplicate dot blots from a single clone that has hybridised to phi X174/HaeIII marker
Duplicate dot blots from a single clone that has hybridised to E. coli genomic DNA
Figure 4.2. Representative dot blots showing control reaction one clones hybridised to (A.) E. coli and (B.) phi X174/ Hae III marker. Figure (A.) shows the clones that were present in both tester and driver while figure (B.) shows clones specific to tester. Each black dot represents a clone that hybridised to the labelled probe. Each clone is spotted twice to ensure reproducibility.
4.3.2 Dead grub reactions
In reaction 5, 44.8 % (n=96) of clones were specific to the Mk1-37 grub tester sequences (Table 4.2). Of the ten clones sequenced, clones were homologous to bacterial proteins isolated from Salmonella enterica, Gramella forsetii, Pseudomonas fluorescens, P. mendocina, Eschericia coli, Providencia stuartii, Salmonella typhimurium, Erwinia amylovora, Photorhabdus luminescens, and Stenotrophomonas maltophilia, and to the protest, Plasmodium chabaudi (Table 4.4) . E values of these homologies ranged from 3 x 10 -135 to 3 x 10 -14 . Of the homologous proteins reported, a putative virulence gene from E. coli may be of interest.
134. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______Table 4.4. BlastX matches from tester-specific sequences amplified from SSH reactions five, six, and seven. Matches may be associated with putative pathogens as the aim was to subtract sequences associated with common saprophytic organisms from the dead grub sequence pool.
SSH Clone # of Accession # Protein Bacterium E value # # clones Acyl-CoA thioester 1 1 ZP_02700233.1 Salmonella enterica 2E-26 hydrolase YbgC sulfate transporter family 2 1 YP_863005.1 Gramella forsetii 7E-62 protein Pseudomonas 3 1 YP_258200.1 GTP-binding protein LepA 6E-60 fluorescens glycosyl transferase family Pseudomonas 4 1 YP_001187363.1 6E-23 protein mendocina putative siderophore 5 1 AAK49483.1 receptor IreA-virulence Escherichia coli 3E-135 gene
5 6 1 ZP_02961595.1 hypothetical protein Providencia stuartii 5E-47 YP_194804.1, Salmonella typhimurium, YP_355421.1, hypothetical protein- Escherichia coli, Erwinia 7 1 3E-14 NP_758764.1, antibiotic resistance amylovora, Plasmodium XP_731877.1, chabaudi Photorhabdus 8 1 NP_931805.1 adenylate cyclase 2E-42 luminescens putative Stenotrophomonas 9 1 YP_001970853.1 1E-95 exopolyphosphatase maltophilia
nucleotide sugar Pseudomonas 10 1 YP_261397.1 4E-51 epimerase/dehydratase fluorescens putative 1 2 YP_001338267.1 phosphotransferase Klebsiella pneumoniae 9E-46 protein phosphatidylserine 2 1 ZP_03086283.1 Escherichia coli 8E-56 decarboxylase putative low-affinity YP_002228594.1, Salmonella enterica, 3 1 inorganic phosphate 4E-31 YP_001456421.1 Citrobacter koseri transporter 6 4 1 YP_001452312.1 tyrosine phosphatase Citrobacter koseri 2E-52 pyridine nucleotide- 5 1 YP_001745031.1 disulfide family Escherichia coli 2E-79 oxidoreductase 6 1 NP_758764.1 hypothetical protein Erwinia amylovora 2E-70 putative Stenotrophomonas 7 1 YP_001970853.1 1E-95 exopolyphosphatase maltophilia putative puroindoline b 8 2 CAQ43070.2 Triticum aestivum 5E-33 protein 7 1 1 YP_824793.1 hypothetical protein Solibacter usitatus 0.001 Burkholderia 2 1 YP_001109975.1 TraU protein 1E-32 vietnamiensis
135. Cassandra Trent ______
putative cysteine 3 1 YP_001334091.1 synthase/cystathionine Klebsiella pneumoniae 6E-45 beta-synthase transcriptional activator 4 1 YP_001748732.1 Pseudomonas putida 4E-29 Ogr/delta putative sucrose specific 5 1 AAW51730.1 transcriptional regulator Escherichia coli 7E-54 (Aec47) Enterobacter 6 1 ZP_03284296.1 hypothetical protein 4E-27 cancerogenus 7 1 ZP_00829036.1 Integrase Yersinia frederiksenii 5E-17 Pseudomonas 8 1 YP_346177.1| hypothetical protein 2E-59 fluorescens 9 1 NP_758764.1 hypothetical protein Erwinia amylovora 2E-70 Photorhabdus 10 1 NP_931805.1 adenylate cyclase 2E-42 luminescens
Of the reaction six clones, 29.2 % (n = 96) were tester specific and unique to 21-S (Table 4.2). The 10 sequenced clones were homologous to bacterial proteins isolated from Klebsiella pneumoniae, E. coli, Salmonella enterica, Citrobacter koseri, Erwinia amylovora, and Stentrophomonas maltophilia and a putative puroindoline b protein found in wheat (Triticum aestivum ) (Table 4.4). E values of these homologies ranged from 4 x 10 -31 to 1 x 10 -95 .
Only 19.8 % (n=96) of reaction seven clones were tester specific to 48-P. Clones were homologous to bacterial proteins isolated from Solibacter usitatus, Burkholderia vietnamiensis, Klebsiella pneumoniae, Pseudomonas putida, P. fluorescens, E. coli, Enterobacter cancerogenus, Yersinia frederiksenii, Erwinia amylovora and Photorabdus luminescens (Table 4.4). The E values of these homologies ranged from 1 x 10 -3 to 2 x 10 -70 .
136. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______
4.4 Discussion
The genomic SSH approach provided a range of genomic fragments from a broad group of bacteria. This is the first study to use the PCR-Select™ Bacterial Genome Subtraction Kit (Clontech) to compare metagenomes (both eukaryotic and prokaryotic) associated with insects and in particular, combinations of dead grubs have not previously been used as tester and driver in SSH. This is the first attempt to isolate putative pathogens from an insect host by using SSH. This genomic SSH approach was chosen in an attempt to reduce the saprophytic and psychrotrophic bacteria identified from dead specimens in Chapter 2. These bacteria dominate the dead grub-associated microbial community, masking possible putative pathogens. SSH enables the theoretical reduction of such sequences.
In contrast to the highly conserved V3-region used in the previously described V3- PCR-SSH approach of Chapter 3, bacterial genomic DNA is known to be highly variable within species (Lan and Reeves, 2000, Zhang et al., 2000, Mavrodi et al., 2002, Parsons et al., 2002, Bae et al., 2005). Genomic DNA from strains of E. coli or Salmonella enterica is known to differ in size up to 20%. In addition, S. enterica has more than 2000 known serovars of which all have differing genomic sequence composition (Lan and Reeves, 2000). Many of the genomic differences between strains can account for pathogenicity islands (genetic material that encodes specific aspects of pathogenicity) or differing metabolic genes that allow the metabolism of alternate sugars or substrates (Lan and Reeves, 2000). A number of these strain- specific genetic differences have been identified using the Clontech SSH kit (Zhang et al., 2000, Mavrodi et al., 2002, Parsons et al., 2002, Bae et al., 2005). Such high genomic diversity increases the amount of genetic material that differs between two populations when compared to using 16-S PCR (discussed below). Using genomic SSH to screen two differing microbial communities may not resolve taxonomic differences due to the limitations of currently sequenced bacteria and high genetic diversity. Both communities may contain the same species yet have differing genetic material due to the substantial genetic diversity within species. In addition, many sequences identified from a previous metagenomic SSH study, showed no homology to BLAST entries (Galbraith et al., 2004), indicating that this highly diverse and unreported genetic material may not be identifiable.
137. Cassandra Trent ______
Unidentifiable genetic material has also been isolated when comparing strains of the same bacterial species using this genomic SSH kit. Pseudomonas fluorescens strains with high and low rhizosphere colonising ability were compared to find factors that assist in rhizosphere colonisation (Mavrodi et al., 2002). Of the 32 tester-specific clones isolated, 28 showed no homology to known sequences. When used as a probe, seven of the tester-specific sequences hybridised to known efficient- colonisers, suggesting that even though the material is currently unidentified, it may still be linked to the colonising factor of interest. In the context of this work, sequences showing low homology to BLAST entries may be linked to currently unstudied virulence factors from unstudied entomopathogens. So little is known regarding these bacteria that random genomic material is unlikely to match known sequences belonging to current entomopathogenic bacteria.
When comparing such diverse complex communities, the likelihood of isolating all genetic differences between them is unlikely. Harakava and Gabriel (2003) estimated that around 4,520 SSH clones would need to be analysed to successfully determine the 152 gene differences between two strains of Xylella fastidiosa . This is taking into account that 50% of clones are not tester-specific and that all tester- specific clones would contain different fragments (Harakava and Gabriel, 2003). In addition to these calculations, the preferential amplification of certain tester-specific sequences may be attributed to hybridisation temperature and PCR bias (as discussed below). Therefore, even more clones would need to be analysed to obtain a full dataset of sequences unique to this strain. In relation to pathogen detection, there are so many genetic differences between two populations that the likelihood of amplifying a piece of genomic DNA specific to a pathogen is not likely unless a large number of clones (20,000 +) are analysed. This may be achieved with the use of high throughput sequencing methods such as 454 pyrosequencing (Margulies et al., 2005, Lundin et al ., 2010, Midelboss et al ., 2010).
The 454 pyrosequencing method (as discussed in detail in Chapter 3), uses an emulsion method to prepare single-sequence containing beads that are read on a fibre-optic slide (Margulies et al., 2005). This technology has been used to analyse not only genomic DNA, but also microbial community metagenomes and 16-S
138. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______sequences. Turnbaugh et al (2006) showed that obese-associated microflora had an increased ability to harvest energy from the diet when compared to the lean-mouse associated microflora via metagenomic 454 pyrosequencing and identifying sequences homologous to metabolic genes (Turbaugh et al., 2006). The meta- transcriptome of soil has also been investigated using a modified 454 pyrosequencing protocol (Urich et al., 2008). In addition to metagenomic analysis of microbial communities, hypervariable regions of 16-S have also been amplified and analysed using this 454 pyrosequencing technique. The V6 region (~280 bp) was amplified from throat, stomach and faecal associated microflora to provide 911 different RDP homologous sequences showing 609 individual phylotypes (Andersson et al., 2008). This 16-S approach has also been used to estimate the microbial diversity present in 1g of both forest and agricultural soils (Roesch et al., 2007). One such application would be to screen sequences produced from SSH final PCR. Alternatively, extensive data produced from thorough 454 analyses of two populations could be digitally subtracted to identify sample unique sequences without the complex hybridisation processes.
Pyrosequencing, however, does not address problems associated with tester and driver preparation and the SSH process. Inefficient digestion of genomic DNA by Rsa I may reduce the efficiency of SSH by limiting the amount of sequences available to ligate to adaptors. This in turn may reduce the probability of isolating rare sequences in the population. RsaI digestion is sensitive to both impurites and to eukaryotic DNA methylation (McClelland et al., 1994). Hence, some putatively pathogenic eukaryotes (such as fungi, nematodes and protozoans) may not be represented in the final SSH PCR. Impurities present in the genomic DNA may inhibit not only restriction digestion, but also adaptor ligation, hybridisation and PCR. Eukaryotic CpA and CpT methylation has been shown to occur in insects (Lyko et al ., 2000, Mandrioli et al ., 2003, Ying Wang et al ., 2006, Garcia et al ., 2007), protozoans (Lavi et al., 2006) and fungi (Antequera et al., 1984) all of which were not represented amongst the cloned sequences in this chapter. However, these bacteria may have been present in similar proportions in tester and driver and hence, were subtracted. The effects of temperature are also discussed below.
139. Cassandra Trent ______In addition to sample preparation, temperature selection for SSH hybridisations would likely influence the sequences detected during SSH. The digested genomic sequences isolated from a grub sample would be from both prokaryotic and eukaryotic origins and have differing GC content and Tm values. In order to reduce the amount of mis-matched pairing and false-positive hybridisations, a temperature of 68°C was chosen based on the knowledge that high GC-content bacteria are prevalent in grub samples. The 68°C temperature may have biased the reaction towards sequences with a higher Tm value.
Dot blots were performed at a higher temperature to reduce false-hybridisations. Any sequences with a lower Tm that may have been amplified may not have hybridised to tester or driver probes. This may have caused there to be little to no signal on these dots for both tester and driver hybridisations. A reduction in temperature may have compromised specificity and introduced false-positive hybridisations.
In addition to temperature, dot blots have a detection limit. Rare tester-specific sequences are enriched during the “normalisation” process of SSH. Such rare sequences may not produce sufficient signal to be detected. A number of dots did not hybridise to either tester or driver. These may be sequences that were rare in tester, enriched during SSH and undetectable throughout dot blot screening. In the case of tester-specific sequences associate with a putative pathogen, these should be in higher abundance and should be detected. Absence of a signal may also be due to the high hybridisation temperature (as discussed above).
Despite the above technical considerations, SSH did produce tester-specific sequences. SSH amplified sequences varied greatly in length due to the restriction digestion and were of both prokaryotic and eukaryotic origin. In addition, thermodynamic properties of the starting material would expectedly be substantially diverse. This suggests that an appropriate hybridisation temperature that limits non- specific hybridisations while simultaneously enriching sequences with substantially lower Tm values is unlikely. By comparison, the 16-S PCR method (Chapter 3) uses a small number of highly similar sequences per species. However, closely related species may share these 16-S regions. In Chapter 3, cloned sequences were all
140. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______similar in size (within 25 bp) and showed between 69 and 100% homology to each other. Even at such high homology, the thermodynamic (Tm) values for all grub associated sequences ranged over 7.1 °C.
When using genomic DNA, each individual species is represented by a multitude of different sequences, many of which are shared with other species in the population. In addition to increased variation within the bacteria included in the starting material, the starting material is more complex for each individual species. In the 16-S PCR method, each bacterial species was represented by a small number of different sequences, while using the genomic approach, each species is represented by its entire genome, digested into fragments. To further increase the complexity of the genomic approach, whole grub extracts from the 2007 season were used in this chapter compared to mid-region extracts from 2006 in Chapter 3. In addition, dead grubs were processed whole due to their decomposition upon processing.
As for efficiency of both methods, the genomic method was more efficient (40.6% tester specific) compared to the V3-PCR-SSH (21.7%) when a single control bacterium was subtracted from a grub sample containing two known controls. This increase in efficiency may be due to the increase in genetic complexity (as discussed above and in Chapter 3). In the absence of a similar control, Galbraith et al. (2004) reported 99% tester-specific efficiency (n=96) when comparing cow rumen microflora. However, of these, 48.1% of these were not homologous to any known GenBank sequence (Galbraith et al., 2004). This may be due to the limitations of the GenBank database or may be due to a hybridisation temperature that was too low and created mis-matched pairs. These hybrids may not match any known database sequence as they are not true genomic sequences but hybrids of two or more sequences. SSH hybridisation temperature was not reported in this study. Therefore, inferences on low hybridisation temperature cannot be determined but the possibility of non-specific hybridisations is likely. In addition, probes for dot blots are randomly primed therefore; fluorescent probes may still bind to mis-matched hybrids and cause false-positive results. However, this phenomenon should be negligible.
141. Cassandra Trent ______Despite lower tester-specific amplification efficiency compared to other studies and low homology to known Genbank sequences, bacteria identified from the dead grub samples were entirely different to that of the 16-S amplified sequences in Chapter 2. This suggests that the subtraction process was successful and useful data can be obtained from these degraded dead grub samples. The one specimen analysed using both 16-S rDNA cloning and genomic SSH was M1-37; the dead grub displaying symptoms of milky disease. In Chapter 2, when full length 16-S sequences were PCR amplified from raw template, sequences homologous to saprophytic bacteria from genera such as Morganella , Elizabethkingia , Comamonas , Dysgonomonas and Citrobacter were amplified. Upon subtraction of two pooled dead grub samples, the resulting SSH clones were homologous to range of different bacteria: Salmonella , Gramella , Pseudomonas , Photorhabdus , Escherichia and Stenotrophomonas . Of these, sequences homologous to the known entomopathogens Pseudomonas fluorescens and Photorhabdus luminescens were present (discussed below). Such a finding suggests that the saprophytic bacteria that dominated the dead grub 16-S PCR analysis in Chapter 2, were in fact subtracted allowing the identification of putatively pathogenic bacteria.
P. fluorescens has been reported in many different insects including flies (Fitt et al., 1985, Corby-Harris et al., 2007), mosquitoes (Prabakaran et al., 2002), ants (Lee et al., 2008), moths (Dangar et al., 2008, Pechy-Tarr et al., 2008) and grasshoppers (Mead et al., 1988). P. fluorescens also produces an insecticidal toxin known to cause mortality in the tobacco hornworm ( Manduca sexta ) (Pechy-Tarr et al., 2008), greater wax moth ( Galleria mellonella ) (Pechy-Tarr et al., 2008), mosquitoes (Anopheles stephensi , Culex quinquefasciatus and Aedes aegypti) ( Prabakaran et al., 2002) and the rice leaf folder ( Cnaphalocrocis medinalis ) (Dangar et al., 2008). P. fluorescens homologous sequences were amplified from two of the three dead grub samples suggesting it may be present in many dead grub specimens. Isolation of this bacterium from living specimens showing symptoms of disease is essential in order to investigate possible pathogenesis. The dead specimens from the 2007 season were too degraded to be able to culture the bacteria present at the time of death. Therefore, putatively-diseased specimens from future canegrub seasons are required to enable the isolation of P. fluorescens using selective media for Pseudomonas . The closely related P. mendocina has also been associated with insects. This
142. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______bacterium has been isolated from the citrus leafminer ( Phyllocnistis citrella ) and caused 66.7% larval mortality after six days of exposure (Campos et al., 2007). Isolation and characterisation of this species would be beneficial as well.
Photorhabdus luminescens is a symbiont of the entomopathogenic nematode, Heterhabditis bacteriophora, known to invade many insect species such as the greater wax moth ( Galleria mellonella ) (Gouge et al., 2006), and the diamondback moth ( Plutella xylostella ) (Abdel-Razek et al., 2003). They are also pathogenic to scarabs such as the June beetle ( Hoplia philanthus ) (Ansari et al., 2003). P. luminescens has also shown insecticidal activity in both feeding and injection studies (Abdel-Razek et al., 2003, Ansari et al., 2003, Abdel-Razek et al., 2006, Gouge et al., 2006), with reduced efficiency. The presence of entomopathogenic nematodes in the cadavers was not visually verified. Due to the presence of P. luminescens , a nematode infection is likely. However, little is currently know regarding entomopathogenic nematodes that infect Australian canegrubs and the use of nematodes as a biocontrol option is limited due to previous reports of low killing efficiency and low persistence in soil (Ehlers, 1996). A further study of the nematodes present within canegrubs may establish if there are novel entomopathogenic nematodes suitable for biocontrol application.
Of the other tester-specific sequences amplified from dead grubs, resulting clones were homologous to protein-encoding genes from 21 bacteria of differing species. Of these, most were of bacterial origin and most of the bacteria from which these homologous sequences were isolated have been previously associated with insects. As described in preceding chapters, the majority of sequences showed homology to members of the family Enterobacteriaceae, such as Salmonella enterica . This bacterium has been reported in midges (Moore et al., 2003), mosquitoes (Straifel et al., 1998), beetles (Davies et al., 2003, Gouge et al., 2006), termites (Shinzato et al., 2007), bees (Mohr et al., 2006) and cockroaches (Tatfeng et al., 2005). S. enterica also has reported nematode association (Melhem et al., 1984, Gouge et al., 2006). This further suggests that entomopathgoenic nematodes may be present within some of these dead canegrub specimens and may be an important pathogen(s) for further study.
143. Cassandra Trent ______Sequences associated with another Enterbacteriaceae member, Erwinia amylovora , were amplified from dead grub SSH reactions. E. amylovora is the causative agent of fire blight in many fruit crops. However, it has also been associated with many plant feeding insects. Hildebrand et al., (2000) showed that this bacterium was associated with many insects such as beetles, wasps, ants, flies, bees, plant lice and aphids. The association was also linked to the spread of the plant disease. However, specimens were not surface-sterilised so it is unknown if this bacterium was capable of colonising the insect gut (Hildebrand et al., 2000). Such a bacterium is not likely to be pathogenic to canegrubs.
Many sequences were amplified that were homologous to protein encoding genes from other non-entomopathogenic Enterobacteriaceae . Providencia stuartii has been reported in ants (Lee et al., 2008), flies (Hamilton et al., 2003, Kuzina et al., 2004, Toth et al., 2004), lice (Sasaki-Fukatsu et al., 2006) and midges (Rouf et al., 1993). Bacteria from the genera Klebsiella have been associated with many insects such as aphids (Nakabachi et al., 2003), fire ants (Lee et al., 2008), beetles (Blackburn et al., 2007), flies (Behar et al., 2005), locusts (Dillon et al., 2002) and ant lions (Dunn et al., 2005, Nishiwaki et al., 2007). Association with these insects appears to be symbiotic and there is no evidence to suggest that it may be entomopathogenic. In addition, Klebsiella is linked to nitrogen-fixation in flies (Behar et al., 2005) and aggregation-causing pheromone production in locusts (Dillon et al., 2002). No symbiotic roles in scarabs have been reported.
Citrobacter koseri is another Enterobacteriaceae member reported to have non- pathogenic associations with termites ( Odontermes formosanus ) (Shinzato et al., 2007), flies ( Anastrepha ludens ) (Kuzina et al., 2001) and moths ( Pectinophora gossypiella ) (Kuzina et al., 2002). Enterobacter cancerogenus has also been isolated from insects including the Colorado potato beetle ( Leptinotarsa decemLineata ) (Blackburn et al., 2008) and the longhorned beetle ( Oberea linearis ) (Bahar et al., 2007). Escherichia coli has been isolated from insects such as flies (Dacus tryoni , Dacus jarvisi and Wohlfahrtia magnifica ) (Fitt et al., 1985, Toth et al., 2004), deer ticks ( Ixodes scapularis ) (Benson et al., 2004) and cockroaches (Diploptera punctata ) (Tatfeng et al., 2005). None of these bacteria appear to cause pathogenesis.
144. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______
In contrast to the above mentioned Enterobacteria, an association of Yersinia frederiksenii with insects has not been reported. Interestingly, plasmids isolated from this bacterium have genes showing high homology to the virulent SepA and SepB genes from the amber disease causing Serratia entomophilia (Dodd et al., 2006). In addition, there is similarity of these genes to other endotoxin encoding genes from the entomopathogens Photorhabdus luminescens , Xenorhabdus nematophila and Yersinia pestis . When Y. frederiksenii was fed to the New Zealand grass-grub, Coleoptera zealandica , it was avirulent. However, it was suggested that a specific insect host may exist, yet to date there is no evidence of such an association (Dodd et al., 2006). The presence and possible entomopathogenicity of Y. frederiksenii needs to be explored in canegrubs. An entomopathogenic, toxin producing strain may be a promising biocontrol candidate.
Stenotrophomonas maltophilia has also been associated with a range of insects. These include deer ticks (Benson et al., 2004), moths (Indiragandhi et al., 2007, Osborn et al., 2002, Xiang et al., 2006), bees (Mohr et al., 2005), mosquitoes (Lindh et al., 2005) and beetles (Bahar et al., 2007, Blackburn et al., 2008). The remaining bacterial species identified from dead grub SSH reactions (Table 4.3) ( Solibacter usitatus , Burkholderia vietnamiensis and Gamella forsetii ) have not previously been reported in insects. Of all the homologies to protein encoding sequences, the only putatively virulence-related protein was with that of the ireA gene isolated from E. coli . This gene has been linked to the increased ability of the strain to colonise the mouse bladder and cause infection (Russo et al., 2001). Virulence in insects has not been determined.
Plasmodium chabaudi was the only eukaryotic bacterium detected in this study. It is the causative agent of malaria in humans and has a symbiotic relationship with mosquitoes (Rivero et al., 2003, Eckburg et al., 2005, Hall et al., 2005, Mackinnon et al., 2005). It is unlikely to be pathogenic to canegrubs since it’s mode of pathogenic action is on red blood cells which are absent in canegrubs.
Caution must be used when referring to any of these bacteria as being present within canegrubs. Not only are homologies low for many GenBank entries, but little is still
145. Cassandra Trent ______known about the full genomic sequences of many bacteria, especially those associated with invertebrates. Members of the family Enterobacteriaceae appear to be highly prevalent in the canegrub microflora as they have been identified in Chapters 2, 3 and 4. The genus Pseudomonas was also identified from both 16-S analysis and genomic SSH of dead grub specimens (Chapters 2 and 4). Many of the isolated sequences discussed in this chapter also appear to be associated with entomopathogenic nematodes. Canegrub specimens were collected as third instars and most have predominant fat deposits at the time of collection. At the time of death, canegrubs melanise rapidly and the visible cause of death is often masked by fat deposits and melanin. Diagnosis of nematode infections may be reduced due to the presence of these substances as visual inspection is currently the detection method.
4.4.1 General conclusions
This work reports the first application of the PCR-Select™ Bacterial Genome Subtraction Kit (Clontech) for comparing insect-associated microbial communities. Efficiency of isolating tester-unique sequences was improved when the Clontech kit was used in comparison to the 16-S PCR approach in Chapter 3. However, resulting sequences from this technique were not as homologous to GenBank database entries and hence, the usefulness of the resulting sequences in identifying bacteria was reduced. The V3-PCR-SSH technique from produced sequences that could be recognised by the GenBank database and homology of these sequences was much higher (lowest E value of 1 x 10 -38 compared to 0.001 in this study) (Chapter 3). In addition, each individual species was represented by a small number of different V3- PCR sequences when compared to the genomic sequences used in this chapter. Temperatures for hybridisation and dot blots were also important. Prior knowledge of the Tm range of the population would be advantageous in order to choose a suitable temperature that isolates tester-unique sequences without bias or false- positives. This could be achieved following 454 pyrosequencing analysis of the metagenome associated with canegrubs. From the two SSH methods described in Chapters 3 and 4, the PCR-based approach (Chapter3) shows distinct advantages over conventional genomic SSH (Chapter 4) due to the increased ability to identify bacteria based on their 16-S sequence. A larger hypervariable region may increase the efficiency and the accuracy of identification (discussed in detail in Chapter 3).
146. Chapter 4: Metagenomic SSH for pathogen detection in canegrubs ______SSH still remains a promising tool for comparing dead grub samples especially those with saprophytes as dominant community members. In addition, the ability to compare groups of microorganisms (e.g. all Archea) by choosing different primers is also advantageous as you can test for organisms that may be less common. Specific temperatures for these known groups can be used to select both in the SSH hybridisation temperature and the dot blot hybridisation temperature. However, more knowledge about the diversity of microflora both within and between canegrubs is required to evaluate the effectiveness of these techniques for pathogen detection. Microbial community diversity can be analysed using profiling methods such as terminal restriction fragment length polymorphism analysis (Chapter 5).
147. Cassandra Trent ______
148. Chapter 5: Comparing and profiling canegrub microbial communities ______
Chapter 5: Comparing and profiling microbial communities associated with canegrubs from within and between different geographical locations.
149. Cassandra Trent ______
150. Chapter 5: Comparing and profiling canegrub microbial communities ______
5.1 Introduction
As determined from Chapters 2, 3 and 4, many different bacterial species have been identified from 16-S and genomic sequences isolated via SSH. In order for the SSH techniques to successfully identify pathogen-specific sequences, both samples need to have similar background microflora in similar proportions. In the greyback canegrub, D. albohirtum , hindgut wall bacteria showed high microbial community diversity upon investigation via denaturing gradient gel electrophoresis (DGGE). Specific similarity was not quantified and only the hindgut wall was analysed (Pittman et al ., 2008). Therefore, further analysis of this observed diversity needs to be explored.
Terminal restriction fragment length polymorphism (TRFLP) is a commonly used technique that profiles PCR amplified sequences based on the first cutting site of a chosen restriction enzyme. The 5´ end of the forward PCR primer is fluorescently tagged and restriction digested PCR products are run on a DNA sequencer. The fragments produced create a pattern that can be compared to other samples. This technique has been used to profile the microflora associated with many insects such as scarabs (Egert et al ., 2003, Egert et al ., 2005), termites, (Thongaram et al ., 2005, Yang et al ., 2005, Nakajima et al ., 2006) and honeybees (Babendreier et al ., 2007). A study of three individual European cockchafer ( Melolontha melolontha ) larvae showed that TRFLP profiles of the individual hindguts were similar. However, the midguts of the same larvae showed individual diversity (Egert et al ., 2005). This was not quantified and a larger population size would be required to support these observations.
Diversity can be measured using many formulae. For microbial community diversity, the Morisita index is commonly used to determine similarity between two samples (Dollhopf et al ., 2001). The index is calculated based on both peaks present or absent in two samples and also percentage peak height. The indice ranges from 0 to 1 where 1 indicates both samples are highly similar and 0 indicates no similarity between samples. Within the Morisita index calculation, the Simpson’s dominance index is also calculated. This index refers to the distribution of peaks within each sample and also ranges from 0 to 1. An index of 0 refers to a uniform distribution of
151. Cassandra Trent ______peaks and a value of 1 indicates complete dominance by 1 peak (Dollhopf et al ., 2001).
The sensitivity of any 16-S PCR technique is limited by the following biases: cell lysis/extraction bias, PCR bias, PCR primer specificity, rrn gene copy number and heterogeneity, and PCR artefacts such as chimeras, deletion mutants and point mutants (Wintzingerode et al ., 1997). The MOBIO Powersoil kit used in this study claims to efficiently extract DNA from both Gram negative and positive bacteria but the efficiency of the kit has not been explored using canegrub samples. The extraction bias may also alter amount of template extracted from specific bacteria. For example if the bacterium is Gram-negative and easily lysed, there is a chance that a higher percentage of these bacteria will be represented in the final mix of PCR template when compared to hard-to-lyse Gram-positive bacteria, altering the composition of the community. However, these biases should be negligible in TRFLP as each sample endures the same conditions and should be under the same or similar bias.
Specificity is also limited by the specificity of the eubacterial PCR primers. Many unknown and previously uncultured bacteria may be present that were not amplified by these primers. Primer specificity is only as good as current knowledge accumulated within GenBank. However, as the database grows most sequences are being amplified using these primers and providing a bias towards bacteria that contain these primer sequences. As discussed in previous chapters, PCR bias is an issue and is greatly influenced by ribosomal copy number and gene heterogeneity. Interactions and secondary structures of the many DNA templates extracted from this diverse community may also contribute to differing PCR-bias and the high diversity seen in this study. This may cause an over-estimation of diversity both within and between grub communities. In addition, PCR artefacts and misincorporations may affect restriction enzyme cutting sites and in turn randomly affect the final profile. Detection is also limited by peak resolution and the ability to distinguish between legitimate peaks and background noise.
In this study, the aim was to (i) determine if individual canegrubs show high microbial community diversity both within and between grubs and (ii) determine if
152. Chapter 5: Comparing and profiling canegrub microbial communities ______geographical location or other treatments could alter the diversity between grub- associated microbial communities. The latter was achieved using TRFLP to profile both dead and live canegrub specimens from differing geographical locations. Treatment of a subset of specimens with perlite and DNase was conducted to determine if the community diversity could be reduced with gut clearing or exogenous DNA removing treatments respectively. Examining individual microbial community diversity is crucial in determining what methods may be suitable for pathogen identification and detection from dead specimens.
153. Cassandra Trent ______
5.2 Materials and methods
Table 5.1. Outline of origins of each canegrub specimen and treatments each specimen received. Each specimen was profiled and compared using TRFLP. Note that Experiment one compared canegrubs collected from different fields and locations, while Experiment two compared only canegrubs collected from Attard’s Farm in Mackay. Three different restriction enzymes were used in Experiment two to compare the profiles generated from each canegrub specimen.
Specimen origin Specimen name Experiment 1: Comparing TRFLP profiles from different fields 18P 6P Dead Grubs 11S 39S Milky Diseased Grub M137 A5R* Attard's Farm A10R* R5R* No Perlite Treatment Reed's Farm R7R* T2R* Tully T8R* A6P* Attard's Farm A15P* R4P* Perlite Treatment Reed's Farm R7P* T3P* Tully T14P* Controls E.coli Experiment 2: Compare grubs from Attard’s farm using three different restriction enzymes A4R A5R A7R No Perlite Treatment A8R A10R A11R A14R Attard’s Farm A1P A2P A4P Perlite Treatment A6P A9P A12P A15P E. coli Controls V. harveyi
*Specimen was chosen for heterotrophic plate count
5.1.1 Grub processing
Grub specimens from the 2007 grub season were used for analysis. Specimens used are described in Table 5.1 and treatments are described in detail in section 6.2.2.
154. Chapter 5: Comparing and profiling canegrub microbial communities ______Perlite treatment was conducted on a subset of specimens according to the flow diagram in Figure 5.1 and DNase treatment was performed on each specimen listed in Table 5.1 (excluding controls and dead grubs). Grubs were processed and DNA was extracted according to methods described in section 2.2.1.
2007 Collected Grubs
Mackay
Tully (prefix = "T") Attard's Farm Reed's Farm
Area Collected Area (prefix = "A") (prefix = "R")
Perlite 8 Perlite 8 Perlite 8 Untreated Untreated Untreated days days days (suffix = "R") (suffix = "R") (suffix = "R") (suffix = "P") (suffix = "P") (suffix = "P")
Process Treatments
Cryopreserve Treat with Dnase Heterotrophic Plate Count
DNA extraction
Figure 5.1. Flow diagram of treatments for the 2007 collected canegrubs used in the TRFLP study outlining prefixes and suffixes applied to each canegrub specimen name in Table 5.1.
5.1.2 Treatments
Grubs were placed in a tub of autoclaved perlite moistened with sterile distilled water and incubated in the dark at ambient temperature for eight days. Grubs were checked and randomly chosen representatives were photographed daily.
Representative samples (160 µL) (listed in Table 5.1) were DNase treated with 20 U of RQ1 RNase-free DNase (Promega) and incubated at 37ºC for 1 h. DNase was deactivated by heating to 65ºC for 10 min. Incubations were performed in 0.2 mL tubes in the Corbett Palm-Cycler™ PCR machine.
155. Cassandra Trent ______
Representative samples (Table 5.1) were diluted in 10 mM Phosphate buffer (1 x 10 - 5 and 1 x 10 -7) and spread plated (100 µL) onto Plate Count Agar (2 replicates per dilution). Plates were incubated aerobically at 37ºC for 48 h.
5.1.3 TRFLP PCR
Each 50 µL reaction contained 1X ExTaq Hotstart Taq master mix (Takara) and 5 µM of each primer (27 F-6 FAM- 5 ′-6-FAM-AGA-GTT-TGA-TCC-TGG-CTC-AG- 3′ and 907 R- 5´-CCG-TCA-ATT-CMT-TTR-AGT-TT-3´). Primer 27-F was labelled with a fluorescent tag at the 5´ end. Cycle conditions were as follows: 94ºC for 5 min, ten cycles of touchdown PCR (94ºC for 30s, 65ºC-55ºC for 30s, reducing by 1ºC each cycle, 72ºC for 2 min), 15 cycles of standard PCR (94ºC for 30s, 55ºC for 30s, 72ºC for 2 min), and 72ºC for 7 min. PCR products were viewed on 1.5% agarose gels containing 0.5x SYBR® Safe DNA gel stain (Invitrogen). Reactions were purified using the Wizard® SV Gel and PCR Clean-Up System (Promega) and eluted in 50 µL MBG water. PCR products (10 µL) were digested with either 10u of HhaI, MspI, or RsaI (Promega) at 37ºC for 4.5 h according to the manufacturers’ instructions. Digestion efficiency was conirmed on 1.5% TBE agarose gels (0.5x SYBR). The remainder of sample (5 µL) was ethanol precipitated and resuspend in 10 µL MBG water. Samples were submitted to the Griffith University DNA Sequencing Facility (GUDSF) for fragment analysis on an Applied Biosystems 3130xl Capillary Electrophoresis (CE) Genetic Analyser using the Applied Biosystems GeneMapper Software v3.7.
5.1.4 Chromatogram analysis
Data peaks were analysed using Peak Scanner™ Software, Version: 1.0 (© Copyright 2006 Applied Biosystems). Peak information was exported to Microsoft® Office Excel 2003 (Microsoft Corporation). To cut out background noise, peaks below 3% of the sum of all peak heights were removed from each dataset. The Morisita index (Dollhopf et al ., 2001) was calculated in Excel using the following formula:
2∑ n1in2i I M = ()l1 + l2 N1N2
156. Chapter 5: Comparing and profiling canegrub microbial communities ______where ni is the number of individuals of species i, N is the total number of individuals sampled, and l is Simpson’s dominance index for each community. Simpson’s dominance index was calculated using the following formula:
s
∑()ni ()ni −1 l = i−1 N()N −1 where s is the total number of species in the community or total number of peaks (Dollhopf et al ., 2001).
The dendogram was created using the cluster analysis tool in PAST version 1.81 (Hammer et al ., 2001) based on the paired group algorithm and the Morisita similarity index.
157. Cassandra Trent ______
5.3 Results
5.1.5 Living grubs
The Morisita index refers to the similarity of two profiles and ranges from 0 to 1 where 0 indicates no similarity and 1 indicates the samples are identical. Peak presence, height and dominance are all required to calculate this index. The Simpson’s dominance index refers to the presence of notably dominant members where 1 relates to the sample having one dominant member or peak i.e. E. coli , and 0 having no dominant peaks. The Attard’s farm grubs from all treatments clustered into seven clades exclusively. The largest of which contained nine members (n=28) (figure 5.2). Despite clustering together, the Morisita indices for these clades were low. The mean Morisita indices between samples from this field were 0.18±0.22, 0.29±0.23, and 0.16±0.23 when digested with Rsa I, Hha I and Msp I respectively. When compared to other Attard’s farm samples, those digested with RsaI had Morisita indices ranging from 0.00 to 0.85 (Table 5.3). The same samples digested with Hha I (Table 5.4) and Msp I (Table 5.5) had ranges of 0.00 to 0.89 and 0.00 to 0.96, respectively.
When the mean Morisita indices from within and between various treatments were plotted together (figure 5.8), each comparison resulted in low values with high standard errors suggesting that there is high variation between samples irrespective of treatments. This was the case when any of the three restriction enzymes were used.
When comparing profiles from Reed’s farm, samples clustered together in a group of four, three and a single sample (figure 5.2). Each group was mixed with both perlite and non-perlite samples. The average Morisita index comparing these samples was 0.27±0.14. When comparing samples from Tully, a set of four non- perlite samples clustered together, three perlite samples clustered together and a single perlite sample clustered with samples from Reed’s farm (figure 5.2). The averaged within field Morisita indices were not different between the different grub collection points (figure 5.3).
158. Chapter 5: Comparing and profiling canegrub microbial communities ______similarity between profiles. Treatments were also were Treatmentsprofiles. between similarity Rsa gruDead 5.2. Table I. Morisita indices were calculated for each speci each for calculated were indices Morisita I. b and live grub specimens from Tully and Mackay (At Mackay and Tully from specimens grub live b and compared (P = Perlite and D = DNase) (Table 5.1).(TableDNase) PerliteD = and = (P compared men pair. High community similarity incurs a Morisi a incurs similarity community High pair. men tard’s and Reed’s farms) profiled using TRFLP and d TRFLPand using farms)profiled Reed’s and tard’s
ta index of 1 whilst 0 indicates no indicates 0 whilst 1 of index ta igested with igested
159. Cassandra Trent ______Treatments were also compared (P = Perlite and D = = D and =(P Perlite alsowerecompared Treatments (Attard s community High pair. Mackay specimen each for calculated from specimens grub Live 5.3. Table DNase) (Table5.1). DNase) imilarity incurs a Morisita index of 1 whilst 0 ind 0 whilst 1 of index Morisita a incurs imilarity ’s farm) profiled using TRFLP and digested with with digested and TRFLP using profiled farm) ’s
icates no similarity between profiles. between similarity no icates Rsa . I. oiia nie were indices Morisita
160. Chapter 5: Comparing and profiling canegrub microbial communities ______Treatments were also compared (P = Perlite and D = = D and =(P Perlite alsowerecompared Treatments (Attard specim each Mackay for calculated from specimens grub Live 5.4. Table en pair. High community similarity incurs a Morisit a incurs similarity community High pair. en DNase) (Table5.1). DNase) ’s farm) profiled using TRFLP and digested with with digested and TRFLP using profiled farm) ’s
a index of 1 whilst 0 indicates no similarity betwe similarity no indicates 0 whilst 1 of index a Hha . oiia nie were indices Morisita I. en profiles. en
161. Cassandra Trent ______Treatments were also compared (P = Perlite and D =Dand =(P Perlite compared alsowere Treatments s (Attard community High pair. specimen each Mackay for calculated from specimens grub Live 5.5. Table DNa imilarity incurs a Morisita index of 1 whilst 0 ind 0 whilst 1 of index Morisita a incurs imilarity ’s farm) profiled using TRFLP and digested with with digested and TRFLP using profiled farm) ’s se) (Table 5.1).(Tablese)
icates no similarity between profiles. profiles. between similarity no icates Msp . oiia nie were indices Morisita I.
162. Chapter 5: Comparing and profiling canegrub microbial communities ______The Morisita index average of sample comparisons from the same field was not different to the comparison of samples between fields (figure 5.4). No difference was found between the mean of Morisita indices of samples undergoing different combinations of perlite or DNase treatments (figure 5.5, Figure 5.8). There was also no difference between averaged Simpson’s dominance indices of dead grubs, healthy grubs and samples under different treatments (figure 5.6, Figure 5.7). In all cases, the standard error was high indicating that there was much individual diversity between samples irrespective of treatment or field.
5.1.6 Dead grubs
From the dead grub specimens, M1-37 and 6-P were the most similar, with a Morisita index of 0.42 (where 1 shows identical samples and 0 indicates no similarity) (Table 5.1, Figure 5.2). All other Morisita indices between dead grubs were below 0.26. The average Morisita index between dead grubs was 0.14±0.13. (Table 5.1). There was no similarity (ie no common peaks and a Morisita index of 0) between specimens 11-S and 18-P. The Simpson’s dominance index in dead grubs ranged from 0.05 (M1-37) to 0.32 (18-P) with an average of 0.13±0.11.
When compared to the healthy grub samples, 18-P showed an average Morisita index of 0.01±0.03, 6-P averaged 0.11±0.11, 11-S averaged 0.12±0.07, 39-S averaged 0.13±0.12 and M1-37 averaged 0.1±0.08 (figure 5.2).
163. Cassandra Trent ______
Figure 5.2. Cluster analysis showing the relationship between all profiles digested with Rsa I from each location and with each treatment. Similarity is calculated using Morisita indices as per the top scale. Profiles clustered closer together are more similar to each other with a Morisita index closer to 1.
164. Chapter 5: Comparing and profiling canegrub microbial communities ______
0.60 0.50 0.40 0.30 0.20 0.10 AverageMorisita indices 0.00
d d ly ar e l tt e u A R T Figure 5.3. Comparison of Morisita indices within each of the three locations (Attard’s and Reed’s farms in Mackay and Tully). High community similarity incurs a Morisita index of 1 whilst 0 indicates no similarity between profiles. Error bars refer to one standard deviation.
0.50
0.40
0.30
0.20 AverageMorisita indices 0.10
0.00
ld s e ld fi e n fi i n ith e e w w et b Figure 5.4. Comparison of mean Morisita indices both within each field or location. and between fields. Note the high profile diversity within both groups, suggesting geographical location did not affect profile similarities. High community similarity incurs a morisita index of 1 whilst 0 indicates no similarity between profiles. Error bars refer to one standard deviation. 0.50
0.45
0.40
0.35
0.30 0.25
0.20
0.15 AverageMorisita indices
0.10
0.05
0.00
e e e te e e ts ts e e ts ts e s s s s li s s n n s s n s t a a a r a a e e a a e n a n N N N e N N N N e e P tm m D tm m N m -D D -D D D a t D a t D t n n n in + + a + + e a a i o h te e re re te te r e te e o th n it li lit t t li li t tr li tr N i w r r o r r o r s w n e e + e e o e o v i P p N s P p N n p n th - n b - s s - s e i in n i u s n v v n v s w h o th r v o o a it N i g e n te e n e N w y lit s li s s s w in h r v r a v a D h lt e e N N it a P te P D e D e li + s w h r a te l e te N li l P rli D r A e + e e p in P it - h rl n it e o w P n Figure 5.5. Mean Morisita indices for each treatment. High community similarity incurs a Morisita index of 1 whilst 0 indicates no similarity between profiles.
165. Cassandra Trent ______Treatments did not appear to reduce the dissimilarity or diversity seen between each set of profiles. Error bars refer to one standard deviation.
Morisita Index (within 1.00 group/category) 0.90 0.80 0.70 0.60 Morisita Index (vs all 0.50 other samples) 0.40 0.30 0.20 AverageMorisita indices 0.10 Simpson's 0.00 dominance Dead grubs Healthy Perlite Dnase-no Dnase E. coli Grubs perlite perlite
Figure 5.6. Mean Morisita and Simpson’s dominance indices for different grub groups and treatments. Simpson’s dominance index indicates the complexity of each profile where 1 shows that only one peak was present and 0 refers to an infinite number of peaks. Error bars refer to one standard deviation.
Simpson's dominance index 1.00
0.90
0.80 HhaI 0.70
0.60
0.50 RsaI
0.40
0.30 MspI
0.20
0.10 Average Simpson's Average dominance indices 0.00 Perlite Dnase-no Dnase E. coli V. harveyi perlite perlite
Figure 5.7. Simpson’s dominance indices of canegrub subjected to different treatments collected from Attard’s farm. TRFLP profiles were digested with each of the three indicated restriction enzymes. Simpson’s dominance index indicates the complexity of each profile where 1 shows that only one peak was present and 0 refers to an infinite number of peaks. Error bars refer to one standard deviation.
166. Chapter 5: Comparing and profiling canegrub microbial communities ______
0.70 Morisita index (vs all other samples) 0.60
0.50
0.40 HhaI
0.30
0.20 RsaI
Average Morisita indices Average 0.10
0.00 MspI
e e e e e e ts s e e ts ts e ts s s s lit s s t s s n s n a a a r a a n n a a n a e N N N e N N e e N N e e N -D D D P D D tm m D D tm tm D tm n - n + + a t + + a a e a in n i e e a e e e e it e o h o h te it r re it it r tr l tr N it n it li rl t t l rl t r s n w r e o + r e o o e o v w i e p N e p N n -p n th P - s P - n s e i n n in b s n s vs o v s w i o h ru v o v n a th N it g e n e e e N i it s lit s vs s D w in w y rl v r a a h th e e N e N it l te P D s D w a P li + a e r e N te h e it li ll P rl D r e + e A te -p in P li n h r o it e n w P Figure 5.8. Averaged Morista indices comparing Attard’s farm TRFLP profiles of each canegrub treatment group digested with each of the three restriction enzymes. Each treatment group showed high variation suggesting that perlite treatment or DNase did not reduce the complexity of community profiles. Error bars refer to one standard deviation.
5.1.7 Peak identities
Fragment lengths from a subset of dead and live grub specimens were compared to a database of known 16-S fragments and their projected lengths when cut with Rsa I. Dead grub 18-P produced four dominant peaks that were the same length of members from Sphingomonas, Enterobacter, Klebsiella, Acetobacter, Clostridium, Ruminococcus, Eubacterium and uncultured bacteria. Dead grub 6-P produced nine dominant peaks with the same projected lengths of members from Rhizobium, Gramella, Flexibacter, Enterobacter, Klebsiella, Sphingomonas, Acetobacter, Clostridium, Ruminococcus, Eubacterium and uncultured bacteria. Additionally, dead grub samples 11-S and 39-S produced eight and six dominant peaks, respectively with the same projected lengths as digested members from Alcaligenes , Streptomyces and uncultured bacteria. Additionally, 11-S also showed a peak similar to the length of Clostridium . Of the six dominant peaks produced by dead grub M1-37, all were the same length as digested Gramella, Flexibacter, and uncultured bacteria. From the living grubs A-5-R, R-7-R and T-8-R, seven, nine and six dominant peaks were produced, respectively. Of these, fragment lengths were similar to members of Clostridia , Bacillus , Paenibacillus (R-7-R and T-8-R) , Streptomyces (R-7-R) and uncultured bacteria.
167. Cassandra Trent ______
Of all the peaks tabled from these samples, only ten were found in more than one specimen. These were fragments of lengths of 91 (6-P and A-5-R), 312 (39-S, A-5- R and R-7-R), 314 (6-P and M1-37), 315 (39-S, M1-37, A-5-R and R-7-R), 420 (11- S and 39-S), 424 (18-P and 6-P), 452 (11-S and R-7-R), 453 (A-5-R and T-8-R), 468 (18-P and 6-P) and 490 bp (R-7-R and T-8-R). Most of which were the same length as uncultured bacteria from the database. All 32 other fragments were unique to individual specimens. This coincides with the low similarity indices reported above. A.
B.
Figure 5.9 Representitive TRFLP profiles of one living grub and one dead grub sample. Each major peak is also labelled with the associated taxonomic group determined by comparing fragment length and enzyme cutting positions with the Ribosomal Database Project (RDP) database. The fragment length control was calibrated control peaks of known fragment length. (A.) Living grub sample A-5-R digested with Rsa I. (B.) Dead grub sample 18-P digested with Rsa I.
168. Chapter 5: Comparing and profiling canegrub microbial communities ______Table 5.6. Taxonomic identification of dominant peaks present in dead grubs and a subset of live grub samples. Dominant peaks were compared to a database of known fragment lengths for TRFLP samples digested with Rsa I. Note that identification is not species-specific and is an estimate only. Bacteria from many unrelated genera may share the same fragment length.
*Note- peak intensity is displayed as a percentage value of all peak heights present in the chromatogram.
169. Cassandra Trent ______5.4 Discussion
In order for SSH to be useful in identifying pathogen-associated sequences from canegrubs, associated microbial communities of living and diseased grubs need to be relatively similar. In this study, community profiles of canegrub-associated microflora varied greatly between individuals, even those collected from the same field. To support this, 16-S cloning and sequencing analysis (Chapter 2) also indicated that the dominant microbial-community members associated with both dead and living canegrub specimens varied both within and between both types of specimen. Reducing the presence of presumptively transient, soil-associated DNA did not appear to increase the profile similarities between canegrub specimens. To explore the observed inter-grub profile diversity, there are many canegrub behavioural traits and environmental factors that likely contribute. Some of these include solitary behaviours, gut clearance, diet, soil microbial community complexity and lifecycle.
TRFLP has routinely been used to profile and compare different types of microbial communities. Microflora associated with scarabs, as well as other insects such as termites and honeybees have been explored using this method. In contrast to this work where individual canegrubs were compared, most of the previously reported studies have analysed either pooled gut sections (Egert et al ., 2003, Schmitt-Wagner et al ., 2003, Thongaram et al ., 2005, Miyata et al ., 2007, Kohler et al ., 2008, Yu et al ., 2008) or pooled data from individuals (Babendreier et al ., 2007). Of those studies that compared individuals (Hayes et al ., 2003, Egert et al ., 2005), the primary aim was identification of similar peaks between groups of bacteria. Microbial community differences between individual insects were not quantified.
A study of three individual European cockchafer ( Melalontha melalontha ) larvae showed that TRFLP profiles of the individual hindguts were similar, but midguts of the same larvae showed increased inter-specimen variation (Egert et al ., 2005). However, field-collected larvae were maintained under laboratory conditions for several months. Gut-associated microflora may have become more similar after these prolonged periods under similar maintenance conditions. During this time, field-aquired microflora may be replaced by the bacteria present in the lab
170. Chapter 5: Comparing and profiling canegrub microbial communities ______environment and shared diet. By comparison, the live canegrub specimens analysed in this work were processed within three days of collection and within ten days for perlite-treated grubs. Therefore, the canegrubs analysed in this study are likely a more accurate representation of field-collected microbial-community diversity when compared to the M. melalontha study of Egert et al . (2005). Additionally, only three M. melalontha larvae were analysed and similarity indices comparing resulting profiles were not calculated. Therefore, all conclusions in Egert et al . (2005) regarding the similarity of TRFLP profiles were observational, and quantitative similarities cannot be drawn from this data.
In support of Egert’s observations and the findings in this chapter, DGGE analysis of D. albohirtum hindguts also showed high microbial-community diversity between individuals. Profiles of hindgut-wall-associated bacteria extracted from canegrubs, from varying geographic locations, were observed as being diverse both within and between collection locations (Pittman et al ., 2008). However, these conclusions were also based on visual profile comparisons and similarity was not quantified. Such dissimilar microbial-community profiles were observed by comparing only the microflora associated with the hindgut wall. Of the bacteria represented in the DGGE profile, many may be common symbionts involved in hindgut fermentative metabolism (Egert et al ., 2005). However, each individual may contain varied proportions of each putative symbiont. Hence, these proportional differences may affect the apparent similarity of hindgut-associated community profiles.
The degree of gut community diversity demonstrated in both this study and that of Pittman et al . (2008) may be in part due to the solitary lifestyle of canegrub larvae. Upon oviposition, batches of 24-36 eggs are laid and intermixed with soil within a chamber beneath the soil. First instar larvae emerge entirely white (Girault and Dodd, 1915) and free from soil. The first meal is the egg casing which is surrounded by soil (Illingworth and Dodd, 1921). This may be the first introduction of microbes to the canegrub gut. Throughout the solitary life of the canegrub, differences in microflora may increase due to the varied microbial communities being consumed by each individual larva. This may explain the microbial-community dissimilarity observed when individual third instar canegrubs are compared.
171. Cassandra Trent ______Solitary behaviour has also been linked to increased individual gut flora diversity in bees. The red mason bee is a solitary insect (compared to other bee species) and SSCP profiles of larval and adult guts showed lowered similarity between individuals when compared to the more social bee species (bumble and honey) (Mohr et al ., 2006). The canegrub hatches out of the egg with 23-35 siblings. However, each canegrub is exposed to different patches of the rhizosphere and soil on which to feed. Microbes in these different micro-environments may vary dramatically between regions of soil within the same field (Noll et al ., 2005). In addition, canegrubs eat not only the root system, but also other organic matter present in the soil (Illingworth and Dodd, 1921). Hence, variations in canegrub diet may influence differences in microbial-community composition.
Early experiments examining gut contents and clearance showed that all stages of grubs were continually passing large amounts of soil through their guts as well as ingesting living plant material. A note was also made on the high variability of gut contents between larval specimens (Girault and Dodd, 1915). This further illustrates the diversity within the canegrub diet and may contribute to the high individual diversity seen in this study.
Similar to the canegrub, it has been demonstrated that the humus-feeding cetoniid beetle, Pachnoda ephippiata , feeds on soil organic matter and can use microbial biomass (bacterial and fungal) as a source of energy (Li et al ., 2005). It is also well known that soil microbial communities are very diverse and highly plastic when environmental conditions are altered (e.g. flooding) (Noll et al ., 2005). Therefore, each individual canegrub may be ingesting a large range of different bacteria at each life stage; all of which may contribute to the canegrub-associated microbial diversity shown in this chapter. To further illustrate this point, diet variation under lab conditions in wood feeding termites caused shifts in TRFLP profiles from pooled gut samples (Miyata et al ., 2007). Therefore, in greyback canegrubs, differences in larval diet may therefore have a large effect on canegrub gut microflora. However, Miyata et al . (2007) only compared one TRFLP profile per diet, so the differences seen may be due to inter-colony diversity and not the change in diet per se.
172. Chapter 5: Comparing and profiling canegrub microbial communities ______In an attempt to reduce the observed microbial-diversity in living canegrubs, a combination of perlite and DNase treatment were trialled. Perlite treatment was intended to promote gut clearance and remove putatively-transient bacteria ingested from the soil. This treatment did not appear to reduce community complexity or increase the community similarity indices of individual specimens (figures 5.5 to 5.8). This may be due to the eight day perlite feeding period being too short for effective gut clearance. In other scarabs, gut clearance time is particularly lengthy. In Melalontha melalontha , midgut transit time is four to eight hours but the hindgut can take up to four days (Wildbolz, 1954 as reviewed in Egert et al ., 2005). Greyback larvae did not completely expel their gut contents within the eight days confined in perlite. Therefore, compared to other scarabs, canegrubs may have longer gut clearance times and require longer perlite feeding to successfully clear the gut contents.
Cessation of feeding may have also slowed clearance due to the lack of additional food to assist gut content ejection. Additionally, most grubs were in the late third instar during which feeding is reduced, as larvae are preparing to pupate. It has also been observed that gut contents are used to line the pupal chamber created by a grub after burrowing into the soil (Illingworth and Dodd, 1921). Thus, gut contents may be intentionally stored for this purpose. These factors may contribute to minimal gut clearance in this experiment and effect of perlite treatment on gut microflora composition. Longer treatments of more than eight days would be impractical and may increase the effect of lab conditions on microflora, possibly changing the gut community dynamics. For example, even though the perlite is sterile, the container is not and hence, canegrubs may be exposed to bacteria present in the laboratory, but absent within the field. Alternatively, they may become more susceptible to opportunistic pathogens within the gut or undergo a shift in microbial-community structure whilst under longer periods of stress.
DNase treatment did not appear to reduce the dissimilarity of TRFLP profiles. This treatment was chosen to reduce the amount of exogenous soil DNA present within each specimen and specifically amplify DNA from live bacterial cells that were intact at the time of DNA extraction. Failure of the DNase treatment to reduce this complexity may indicate that many different types of bacteria can survive midgut
173. Cassandra Trent ______conditions and colonise the canegrub gut. In addition to the DNase treatment, the midgut itself contains many enzymes and bacteria that degrade exogenous DNA. To support this, when Melalontha melalontha eggs were ingested by the carabid beetle, Poecilus versicolor , DNA from these eggs were PCR detectable in only 18% of P. versicolor grubs (32 hours after ingestion) (Juen et al ., 2005). The PCR product tested was a 585 bp fragment indicating that extensive DNA degradation is required to eliminate this product from being amplified. Therefore, not only were the conditions harsh enough to break down the tough egg casings, but genomic DNA from the eggs was almost completely degraded during this time as well. In this study of canegrubs, the DNase treatment showed a negligible effect on reducing microbial-community diversity possibly due to degradation of exogenous DNA having already occurred within the larval guts. In addition, DNase was added to crude samples where there may be inhibitors of DNase present. Solutions that bind enzyme inhibitors may alter solution pH and cause cell lysis to some bacteria present. This may change the microflora detected from the DNase treated sample. DNase was added to remove exogenous DNA from dead bacteria and soil. DNA from any lysed cells would theoretically be degraded and hence, absent from the TRFLP profile.
Another factor affecting sample similarity is the limited sensitivity of the TRFLP method. Resolution is affected by many bacterial species with different sequences sharing the same fragment lengths. To minimise this, three enzymes were used separately to compare Attard’s farm grubs. Each enzyme produced similar Morisita indices and showed similar individual diversity between grubs. In addition to multiple species per peak, false peaks or “pseudo- T-RF’s” have been described by Egert et al ., (2003) as being a major problem. They can be formed by incomplete restriction enzyme digestion or by single stranded PCR products forming restriction nuclease-cleavable secondary structures and creating false peaks. This phenomenon was detected in some singly amplified clones created multiple peaks (Egert et al ., 2003). The multiple peaks seen when digesting controls ( E. coli and V. harveyi ) with Hha I may be due to this. However, the patterns produced were also indicative of inefficient digestion as they corresponded to the theoretical sizes produced when not all sites are cleaved.
174. Chapter 5: Comparing and profiling canegrub microbial communities ______When compared to the 16-S results (Chapter 2), the TRFLP dominant peak identities corresponded with the dominant microbial community members amplified in Chapter 2 (18-P, 6-P, 11-S, 39-S, M1-37, A-5-R, R-7-R and T-8-R). Dead grub 18- P produced 16-S sequences homologous to uncultured bacteria, Sphingomonas , Klebsiella and Pseudomonas of which the former three were possibly present in the dominant TRFLP peaks. The Pseudomonas -homologous sequence from the 16-S data would theoretically create an 842 bp peak when digested with Rsa I. Similar to this, dead grub 6P also had a Pseudomonas homologous 16-S sequence that would theoretically produce an off-scale peak (879 bp). This would be outside of the detection limits of the fragment analysis and would not be present in the TRFLP profile. Therefore, many bacteria remain unanalysed when using TRFLP if their digested sequence is beyond the detection limits of the fragment analyser. This can be rectified by use of multiple restriction enzymes and multiple profiles per sample.
TRFLP profiles of all the dead grub and live grub samples contained peaks of fragment length similar to many uncultured bacteria (Table 5.6) as per the 16-S data in Chapter 2. These bacteria may be classified as any number of already identified taxa. However, as most community analysis is performed using molecular techniques, the exact identity of these bacteria remains unknown. Isolation of these bacteria is hindered by the lack of taxonomic knowledge and subsequently a suitable isolation media (if available) cannot be chosen.
The three living grub samples, A-5-R, R-7-R and T-8-R, all contained both 16-S data and TRFLP peak identity results confirming the presence of uncultured Clostridium and other uncultured bacteria. As discussed in Chapter 2, the Clostridium may play a role in the metabolism of cellulose (Sharp et al ., 2000, Chinda et al ., 2004), in fat storage (Bakhed et al ., 2004, Ley et al ., 2006, Turnbaugh et al ., 2006) and are associated with the hindgut wall and lumen of other scarabs (Egert et al ., 2005). Therefore, the presence of Clostridium within the guts of canegrubs is highly likely and these bacteria may be symbiotic and play a role in the metabolism of cellulose.
TRFLP profiles indicated that there was high inter-grub microbial-community variation and that treatments chosen could not increase profile similarity. This
175. Cassandra Trent ______natural microbial-community variation between individuals may pose as a problem for the application of SSH for pathogen detection. This could be overcome by using pooled grub samples for SSH analysis to reduce the effect of individual differences and high through-put sequencing techniques to increase the number of tester-specific clones analysed. Therefore, SSH may still be a viable technique for the detection of pathogens from living grubs showing disease symptoms. Alternatively, dead grubs may also be screened using the above mentioned criteria.
5.5 Conclusions
There is little knowledge regarding individual insect community diversity. This is one of the first studies to compare a sample population of individual larvae from differing locations. Canegrub larvae were shown to have differing profiles of associated-microflora, independent of treatments (perlite and DNase) or geographical locations. This finding is supported by 16-S cloning and SSH results from Chapters 2 and 4 respectively. Diversity may be attributed to the canegrub diet, habitat, and behaviour (solitary nature, consuming soil while feeding and burrowing). SSH relies on consistent microflora between individuals in order to locate sequences unique to one sample. The low similarity of individual canegrub microfloras renders SSH unsuitable for pathogen detection from two individual larvae. Alternatively, a number of specimens pooled together to make a library of healthy canegrub flora, could be used as driver instead of a single specimen. SSH techniques may also be suited to comparing social insects where individuals from different colonies can be pooled together and show high microbial-community similarity. As with 16-S analysis (Chapter 2), dead grub samples were confirmed to be dominated by saprophytic bacteria that hinder pathogen detection. Another method of molecular-based pathogen discovery may be the use of entomopathogen- specific PCR to determine if bacteria similar to known insect pathogens are present within these canegrub samples. PCR is highly sensitive and the presence or absence of known entomopathogens could be determined using entomopathogen-specific primer sets (Chapter 6).
176. Chapter 6: PCR-screening assay for canegrub pathogen identification ______
Chapter 6: Pathogen identification in dead canegrub specimens via an entomopathogen-specific PCR- screening assay
177. Cassandra Trent ______
178. Chapter 6: PCR-screening assay for canegrub pathogen identification ______
6.1 Introduction
From 16-S PCR cloning and sequencing in Chapters 2 and 3 (full length and the 200bp V3 region, respectively), it was shown that dead and live grubs have differing dominant gut microflora and that there are also differences within each individual specimen. Saprophytic and psychrotrophic bacteria were identified from dead grub samples, suggesting these organisms may have become more prominent in the community after refrigerated storage. Chapter 5 confirmed these differences by comparing the TRFLP profiles of canegrub bacterial communities and showed that there was high inter- and intra-grub microbial diversity. Such diversity suggests that pooled canegrub samples are required for driver in SSH and diseased grubs pre- death are required for such an analysis to be effective.
There are many entomopathogenic bacteria that may be causing unidentified mortality in field collected canegrubs. Of these bacteria, entomopathogen-related primer sets were chosen to screen for putative disease causing agents: Rickettsia , Iridovirus, Poxvirus , Microsporidia , and the chitinase gene.
Rickettsias have been found to be pathogenic to a range of scarabs. Jani et al. (1993) reported a Rickettsia -like bacterium to cause disease in the scarab Holotrichia consanguinea. A Rickettisal infection was also shown to cause mortality in the New Zealand grass grub, Costelytra zealandica (Moore et al., 1973). Rickettsias have been specifically PCR-amplified from fleas using primers designed for Rickettsia typhi (Webb et al., 1990). They amplify a 17 kd antigen sequence and produce a 434 bp product. Reeves et al., (2005) have also used these primers to screen fleas and lice for presence of Rickettsia .
Iridescent iridoviruses have also been reported in scarabs. New Zealand grass grub larvae, C. zealandica , can be infected by two known iridoviruses (Moore et al., 1974, Webby et al., 1998). Sequences from other scarab-infecting iridoviruses have also been analysed. Hosts include scarabs such as the Japanese beetle ( Popillia japonica ), African black beetle ( Heteronychus arator ), and the pruinose scarab (Sericesthis pruinosa ) (Webby et al., 1998). Tang et al. (2007) developed a PCR-
179. Cassandra Trent ______assay to detect iridoviruses in shrimp by amplifying a 1 kb fragment. This primer set will be used in canegrubs to screen for the presence of this virus. Entomopox viruses have been isolated from many scarabs. Such scarabs include the European cockchafer ( Melolontha melolontha ) (Sezen et al., 2006), black soil scarab (Othnonius batesi ), Demodena boranensis, dung beetle (Geotrupes silvaticus ), pasture cockchafer (Aphodius tasmaniae ), cupreous chafer ( Anomala cuprea ), and the Greyback canegrub, Dermolepida albohirtum (Arif et al., 1991). A 1 kb fragment can be amplified from Poxviridae using the primers reported in Barrett et al. (2006) to screen all dead grub samples for the possible presence of these viruses.
In addition to viruses and bacteria, microsporidian are also important entomopathogens. A previously described microsporidian similar to Nosema has been isolated from greyback canegrubs (Dall et al., 1995, Robertson et al., 1997). The microsporidial SSUrRNA coding region has previously been amplified from other microsporidia using microsporidium-specific primer sets (Vossbrinck et al., 1987, Tsai et al., 2003). These primers may amplify previously described microsporidians from canegrubs and potentially, novel bacteria from this phylum may be detected.
Many bacteria showing pathogenesis to insects produce chitinase that breaks down the outer cuticle. The chitinase gene is present in many mycopathogens ( Nomuraea rileyi ) (Wattanalai et al., 2004), entomopathogens ( Xenorhabdus nematophilus , X. bovenii , and Photorhabdus luminescens ) (Chen et al., 1996) and nematopathogens (Verticillium suchlasporium and V. chlamydosporium ) (Tikhonov et al., 2002). Chitinase is also present in entomopathogenic fungi such as Metarhizium anisopliae (St. Leger et al., 1996, Bogo et al., 1998), Beauveria bassiana (St. Leger et al., 1996) and M. flavoviride (St. Leger et al., 1996). Chitinase gene primers (Dong et al., 2007) were chosen to survey dead grubs for entomopathogenic bacteria capable of penetrating canegrub cuticle.
Approximately 44% of field-collected canegrub mortality cannot be attributed to currently known entomopathogens (Dall et al., 1995) and many dominant members present in dead specimens were putatively saprophytic or psychrotrophic. Therefore, screening these samples for bacteria known to be entomopathogenic in
180. Chapter 6: PCR-screening assay for canegrub pathogen identification ______other scarabs, may help to identify new putative pathogens. Using the above mentioned primer sets, dead and live grub samples were assessed for the presence of these entomopathogens and positive samples were confirmed via sequencing. The main goal was to putatively determine if these known entomopathogens or similar organisms were present within the dead grub samples.
181. Cassandra Trent ______
6.2 Materials and methods
6.2.1 Canegrub specimens
Larvae were prepared and DNA was isolated from 2007 grub season specimens as described in section 2.2.1. A total of 90 dead grub samples and 21 live grubs were randomly chosen for analysis.
6.2.1.1 Dead grubs
A total of 90 dead grubs were chosen for analysis as listed in Table 6.1 and arranged in a 96-well plate format. In the plate setup (Table 6.2), six blank wells containing MBG water and no template were distributed throughout the plate to detect possible cross-well contamination.
6.2.1.2 Live grubs
Seven random living grub specimens from each geographic location (n=21) were used in analysis as listed in Table 6.3. Table 6.4 shows the layout of the 96-well PCR plate used in screening samples. Blank wells containing MBG water and no template (n=2) were used to ensure cross-well contamination was not occurring and positive controls from dead grub samples were used to determine if the PCR was successful.
6.2.2 Genera-specific PCR
Each 20 µL reaction contained 1x Premix Ex Taq™ Hot Start Version (Takara, Japan) and 0.5 µM of each primer. Cycle conditions were as follows: initial denaturation at 94°C for 5 min, 10 cycles of 94°C for 30 s, 65-55°C for 30 s (- 1°C per cycle), and 72°C for 2 min followed by 15 cycles of 94°C for 30 s, 55°C for 30 s, 72°C for 2 min and a final extension at 72°C for 7 min. Primers used are listed in Table 6.5. All samples producing detectable bands within the agarose gel were re- amplified as above to confirm that the PCR products were reproducible.
182. Chapter 6: PCR-screening assay for canegrub pathogen identification ______Table 6.1. Dead grub specimens used in 96-well plate PCR-screening assays.
Well position Specimen name Well position Specimen name 1 FLO 60779 49 FLO 61382 2 FLO 80579 50 FLO 70013 3 Blank 51 FLO 61286 4 FLO 60760 52 FLO 61153 5 FLO 70452 53 Blank 6 FLO 60903 54 FLO 70656 7 FLO 70052 55 FLO 61056 8 FLO 60690 56 FLO 61059 9 FLO 60558 57 FLO 70635 10 FLO 60551 58 FLO 70596 11 FLO 60702 59 FLO 61270 12 FLO 70389 60 FLO 70590 13 FLO 60684 61 FLO 61178 14 FLO 60657 62 FLO 61250 15 FLO 70117 63 FLO 61257 16 FLO 70374 64 FLO 61031 17 FLO 60477 65 FLO 70347 18 FLO 60465 66 FLO 60774 19 FLO 70028 67 FLO 70509 20 FLO 60984 68 FLO 70390 21 FLO 70018 69 FLO 60969 22 FLO 70057 70 FLO 70437 23 Blank 71 MKY1 6 24 FLO 70040 72 MKY1 18 25 FLO 70030 73 Blank 26 FLO 61408 74 MKY1 29 27 FLO 60691 75 MKY1 32 28 FLO 61044 76 MKY1 43 29 FLO 60673 77 MKY2 46 30 FLO 70019 78 MKY1 55 31 FLO 70023 79 MKY1 57 32 FLO 70033 80 MKY1 59 33 FLO 70038 81 MKY1 60 34 FLO 70049 82 MKY1 4 35 FLO 70055 83 MKY2 10 36 FLO 70031 84 MKY1 11 37 FLO 70053 85 MKY1 23 38 FLO 70306 86 MKY1 34 39 FLO 60723 87 MKY1 39 40 FLO 60562 88 MKY1 55 41 FLO 61104 89 MKY1 56 42 FLO 70431 90 MKY1 59 43 Blank 91 FLO 70456 44 FLO 70034 92 FLO 70047 45 FLO 70054 93 Blank 46 FLO 70626 94 FLO 61309 47 FLO 61288 95 MKY1 21 48 FLO 60957 96 FLO 70032 Note: MKY grubs were collected from Mackay and FLO grubs were collected from the Mulgrave region. Blank refers to wells containing no template.
183. Cassandra Trent ______Table 6.2. Diagram of the 96-well plate setup as per samples in Table 6.1. Numbers in bold are blank wells (no template added). Sample numbers were staggered to account for use of an eight-channel multiple pipette and compatible agarose gel.
1 2 3 4 5 6 7 8 9 10 11 12 A 15 16 31 32 47 48 63 64 79 80 95 96 B 13 14 29 30 45 46 61 62 77 78 93 94 C 11 12 27 28 43 44 59 60 75 76 91 92 D 9 10 25 26 41 42 57 58 73 74 89 90 E 7 8 23 24 39 40 55 56 71 72 87 88 F 5 6 21 22 37 38 53 54 69 70 85 86 G 3 4 19 20 35 36 51 52 67 68 83 84 H 1 2 17 18 33 34 49 50 65 66 81 82
Table 6.3. Live grubs used as “healthy” controls for comparison to dead grubs by PCR-screening. Live grub samples were used to determine if any of these putatively- pathogenic organisms were present in the living canegrubs or if they were only detected in dead specimens.
Well position Specimen name* Well position Specimen name 1 A-4-R 13 R-7-R 2 A-5-R 14 R-8-R 3 A-7-R 15 R-9-R 4 A-8-R 16 R-10-R 5 A-10-R 17 T-1-R 6 A-11-R 18 Blank 7 Blank 19 T-2-R 8 +ve 20 T-3-R 9 A-14-R 21 T-8-R 10 R-3-R 22 T-11-R 11 R-5-R 23 T-12-R 12 R-6-R 24 T-13-R
* The prefix “A” refers to grubs collected from Attard’s farm in Mackay, “R” refers to grubs collected from Reed’s farm in Mackay and “T” refers to grubs collected from Tully. The “+ve” sample refers to a positive control of a dead grub known to amplify products with the chosen primer set. The “blank” wells contain no template and are present to ensure no cross-well contamination.
184. Chapter 6: PCR-screening assay for canegrub pathogen identification ______Table 6.4. Diagram of the 96-well plate setup of the living grubs as per samples in Table 6.3. Numbers in bold are blank wells (no template added). Numbers in italics were positive dead grub controls (see Table 6.3 for more details).
1 2 3 4 5 6 7 8 9 10 11 12 A 1 3 5 7 9 11 13 15 17 19 21 23 B 2 4 6 8 10 12 14 16 18 20 22 24
6.2.3 Cloning
PCR products were analysed on 1.5% agarose gels in 0.5 x TBE buffer at 80 v for 2.5 h. Bands of interest were excised and DNA purified using the Wizard® SV Gel and PCR Clean-Up System (Promega, USA). PCR products were cloned into pGem-T Easy (Promega, USA) as per section 2.2.3 and three individual clones sequenced from each band excised.
6.2.4 Sequencing analysis
Plasmid and primer sequences were removed using the ContigExpress application in the Vector NTI Advance 10.3.0 Suite (Invitrogen, USA). Sequences were cross- referenced to the NCBI nucleotide database via a tblastx search after blastn was unsuccessful at finding homologies (Altschul et al., 1990).
185. Cassandra Trent ______
6.3 Results
Table 6.5. Iridovirus-amplified grub samples loaded in each lane of figure 6.1 and bands chosen for sequencing. MKY grubs were collected from Mackay and FLO grubs were collected from the Mulgrave region. The prefix “A” refers to grubs collected from Attard’s farm in Mackay, “R” refers to grubs collected from Reed’s farm in Mackay and “T” refers to grubs collected from Tully.
Lane Sample* Clone ID** Lane Sample* Clone ID** 1 1 kb DNA marker 13 FLO 70038 2 FLO 70452 a 14 FLO 70049 3 FLO 60903 b 15 FLO 61104 F 4 FLO 60558 16 FLO 70390 5 FLO 60551 c 17 MKY1 60 6 FLO 60702 18 FLO 70456 7 FLO 70389 d 19 MKY1 21 8 FLO 60657 20 R-7-R G 9 FLO 70374 21 T-2-R 10 FLO 60984 22 T-11-R 11 FLO 70030 23 Blank 12 FLO 61044 e 24 1 kb DNA marker
*Lanes 2-19 contain products amplified from dead grub specimens. Lanes 20-22 contain products amplified from live “control” specimens. **Letters correspond to bands that were labelled and sequenced from figure 6.1.
Figure 6.1. PCR-amplified sequences using the Iridovirus-specific primer set from dead and living grub samples as listed in Table 6.5.
186. Chapter 6: PCR-screening assay for canegrub pathogen identification ______6.3.1 Iridovirus
The expected 1 kb fragment was amplified in six dead grubs and one living control grub (refer to figure 6.1 and Table 6.5). Bands “a” through “f” were amplified from dead grubs and were slightly larger than 1 kb while band “g” was amplified from the living grub, R7R (Table 6.5) and was smaller than 1kb. All dead grub cloned fragments were homologous. When this sequence was compared to the GenBank database, it was identified as being homologous to a hypothetical protein associated with Citrobacter koseri (Table 6.6). Band “g” was homologous to an ABC-type phosphate transporter associated with Heliobacillus mobilis (Table 6.6).
Table 6.6. BLAST matches of sequenced bands from Iridovirus PCR-amplified grub samples (Table 6.5 and figure 6.1). Note-all but one band produced the same amplified sequence. All of these bands were amplified from dead grub samples.
Clone ID Accession Protein Bacterium E value a,b,c,d,e,f Gb|CP000822.1| hypothetical protein Citrobacter koseri 0 ABC-type phosphate transporter g Gb|DQ831234.1| Heliobacillus mobilis 2.00E-78 permease component pstC
6.3.2 Rickettsia
All PCR-positive samples (n=11) were amplified from dead grubs (figure 6.2). From these, 22 bands were cloned and sequenced as per figure 6.2. Band “a” was homologous to an elongation factor TU associated with Agrobacterium tumefaciens and was between 2.5 and 3 kb. Bands “k” and “p” were homologous to a secretion factor from the same genbank entry of Agrobacterium tumefaciens and were about 2.5 and >1 kb, respectively. Bands “b, c, g, i, j, m, n, and q” were all homologous to a NAD-glutamate dehydrogenase protein associated with Ochrobactrum anthropi . While bands “b, i, and m” were all sized between 1.5 and 2 kb, bands “c, g, j, n, and q” were sized at <1 kb.
Band “d” was between 2 and 2.5 kb and was homologous to a nucleotidase domain protein associated with Rhizobium leguminosarum . Band “o” appeared slightly larger and was also homologous to a Rhizobium spp. protein. This band showed homology to a Rhizobium etli elongation factor (EF-Tu protein). Bands “e, f, l and t” showed homology to the genus Pseudomonas . Band “e” was <1.5 kb and showed
187. Cassandra Trent ______homology to a Pseudomonas fluorescens endonuclease/exonuclease/phosphatase family protein. In addition, bands “f, l and t” showed homology to a putative carboxyphosphonoenolpyruvate phosphonomutase associated with Pseudomonas putida .
Band “h” was ~3 kb and showed homology to an aceyl-CoA carboxylase associated with an Acidobacteria bacterium Ellin345. Bands “r and u” were >3 kb while bands “s and v” were between 1.5 and 2 kb. These four bands showed homology to a putative purine/xanthine transport protein of Klebsiella pneumoniae .
Table 6.7. PCR-amplified grub samples using the Rickettsial primer set were loaded in each lane of figure 6.2 and bands chosen for sequencing. MKY grubs were collected from Mackay and FLO grubs were collected from the Mulgrave region. The prefix “A” refers to grubs collected from Attard’s farm in Mackay, “R” refers to grubs collected from Reed’s farm in Mackay and “T” refers to grubs collected from Tully.
Lane Sample* Clone ID** Lane Sample* Clone ID** 1 1 kb ladder 16 FLO 70509 m n 2 FLO 80579 a b c 17 MKY1 6 o p q 3 FLO 70452 d e f 18 MKY1 18 r s t 4 FLO 70028 g 19 MKY1 32 5 FLO 70018 h i 20 MKY1 43 6 1 kb ladder 21 1 kb ladder 7 FLO 70054 j 22 1 kb ladder 8 FLO 61382 23 MKY1 55 9 FLO 61153 24 MKY1 39 10 FLO 61056 25 MKY1 56 u v 11 FLO 61059 26 A-4-R 12 FLO 70596 27 A-10-R 13 FLO 61270 28 R-7-R 14 FLO 70590 29 T-2-R 15 FLO 61178 k l 30 1 kb ladder
*Lanes 2-5, 7-20, and 23-25 contain products amplified from dead grub specimens. Lanes 26-29 contain products amplified from live “control” specimens. **Letters correspond to bands that were labelled and sequenced from figure 6.2.
188. Chapter 6: PCR-screening assay for canegrub pathogen identification ______
Figure 6.2. PCR-amplified sequences using the Rickettsia -specific primer set from dead and living grub samples as listed in Table 6.7.
Table 6.8. BLAST matches of sequenced bands from Rickettsia -amplified grub samples (Table 6.7 and figure 6.2). Note that bands “b” and “m: were a series of repeated bands “c” and “n”.
Clone ID Accession Protein Bacterium E value a gb|AE007869.2| Elongation factor TU Agrobacterium tumefaciens 1.00E-143 b, c, g, i, j, m, gb|CP000758.1| NAD-glutamate dehydrogenase Ochrobactrum anthropi 0.00E+00 n, q d gb|CP001191.1| 5'-Nucleotidase domain protein Rhizobium leguminosarum 0.00E+00 endonuclease/exonuclease/phosphatase e gb|CP000076.1| Pseudomonas fluorescens 0.00E+00 family protein Carboxyphosphonoenolpyruvate f, l, t gb|AE015451.1| Pseudomonas putida 7.00E-107 phosphonomutase, putative h gb|CP000360.1| aceyl-CoA carboxylase Acidobacteria bacterium Ellin345 4.00E-123 k, p gb|AE007869.2| secretion protein Agrobacterium tumefaciens 1.00E-97 o gb|CP000133.1| elongation factor EF-Tu protein Rhizobium etli 3.00E-147 putative purine/xanthine transport protein r, s, u, v gb|CP000647.1| Klebsiella pneumoniae 0.00E+00 (NCS2 family)
6.3.3 All other genera/genes
All other primer pairs did not amplify sufficient products from dead or living grub specimens to be detectable on an agarose gel.
189. Cassandra Trent ______
6.4 Discussion
Dead grub specimens containing unknown microflora were screened for the presence of known entomopathogens. In previous chapters, these dead specimens showed that their associated microflora was dominated by saprophytic and psychrotrophic bacteria. Due to the decomposed state of these specimens, SSH was deemed as not the most suitable method for pathogen detection and hence, a more sensitive PCR approach was chosen. While the specific entomopathogens targeted were not identified in these specimens, sequences homologous to other interesting bacteria were identified and are discussed below.
The aim of this work was not to definitively determine presence or absence of target organisms. The main aim was to amplify any known entomopathogen-associated sequences and identify putative organisms for future investigation. However, omission of control target organisms did not allow verification that these primers amplified the desired target regions. Therefore, presence or absence of these entomopathogenic organisms in dead and living canegrub specimens could not be definitively verified. However, amplified products still produced promising results (Pseudomonas fluorescens such as Pseudomonas putida ) and indicated areas for further investigation.
This PCR-screening method relies solely on prior knowledge of previously described entomopathogens. Many other known entomopathogens exist that were not represented by these primer sets. Some of these include the known canegrub entomopathogens Paenibacillus popilliae , Adelina sp. and Metarhizium anisopliae (Robertson et al., 1995). Specific primer sets may need to be developed for future screening work. However, these canegrub pathogens were not detected from initial microscopy of hemolymph. Dead specimens were monitored after field collection for known diseases and hence, these known entomopathogens were not the likely cause of death.
The primer sets chosen did not amplify sequences belonging to bacteria of the chosen genera. However, interesting sequences that may be associated with a putative pathogen were still amplified using two of the primer sets. Either the
190. Chapter 6: PCR-screening assay for canegrub pathogen identification ______specific target bacteria were not present or were not detectable using the chosen primers. Positive controls were not used to check the specificity of the primer sets. Therefore, the validity of each primer set was not verified. However, as each positive result was sequenced, the likelihood of any of these bacteria being present is slim. In addition, detection is also reliant on the bacterium surviving decomposition and refrigeration post-death. These conditions may not have been favourable for the longevity of such entomopathogens.
The main limitation was primer specificity as the genetic material present in each sample was highly diverse as determined by comparing TRFLP profiles in Chapter 5. Putatively bacterium-specific primers may also amplify random genetic material from this diverse population of unknown bacteria. Primer-specificity is limited to current knowledge of known genetic material. Environmental genetic diversity is ever evolving and vast. Therefore, full coverage of this material in the genbank database is unlikely. Hence, there is the risk of amplifying non-specific bacteria.
The Iridovirus-PCR reactions amplified many template sequences including those of the expected size (~1 kb). However, none of these PCR-amplified sequences were homologous to the Iridoviritidae. These viruses are rare and have not previously been reported in greyback canegrubs. However, they have been reported to cause death in other scarabs such as the New Zealand grass grub, Costelytra zealandica (Webby et al., 1998). The Iridovirus primer set used in this work amplified two different sequences. These sequences were homologous bacteria of which no pathogenesis has been previously reported in insects.
Of the homologous sequences amplified using Iridovirus primers, both were known to be isolated from bacteria associated with insects. Citrobacter koseri was only amplified from dead grub specimens and has previously been reported in the pink bollworm ( Pectinophora gossypiella ) (Kuzina et al., 2002) and the Mexican fruit fly (Anastrepha ludens ) (Kuzina et al., 2001). However, both associations were thought to be symbiotic. Therefore, C. koseri is unlikely to be entomopathogenic in canegrubs. The other PCR-product sequenced demonstrated homology to Heliobacillus mobilus (also known as H. mobilis ) and was only amplified from one living grub specimen. This bacterium is a photosynthetic member of the
191. Cassandra Trent ______Clostridiales that has been suggested as one of the earliest photosynthetic lincages (associated with the evolution of photosynthetic organelles present in higher bacteria) (Xiong et al., 1998). No insect associations have been reported for this bacterium and it may have been present in the soil ingested by the grub and may have been transiently present in the canegrub gut.
The additional bands to the ~1 kb expected product seen on the Iridovirus-PCR gels were thought to be PCR-artifacts, specifically concatemers. Concatemers are multiple DNA copies linked in a series and are commonly present in bacterial genomic DNA (Osborn et al., 2005). Therefore, these primers may have bound to regions within these concatemers and hence, PCR products of many different lengths were amplified. In addition, these bands appeared when the clones containing the 1 kb inserts were re-amplified using the original primer set. This confirms that multiple priming sites existed within these 1 kb fragments and the presence of a concatemer was the most likely explanation. Concatemers have previously been reported during 16S-PCR of bacterial communities (Osborn et al., 2005). However, source bacterium containing these regions was unknown. As seen with the Iridovirus results, Rickettsia primers also produced concatemeric PCR-artefacts.
From sequences amplified using the Rickettsial primers, no sequenced clones were homologous to primer-specific Rickettsia . In addition, no living grub specimens amplified any sequences using this primer set. Of the amplified sequences, those homologous to Agrobacterium tumefaciens were not likely amplified from an entomopathogen, as A. tumefaciens is the causative agent of crown gall disease in plants (Smith et al., 1907). A. tumefaciens is currently used as a tool to incorporate foreign DNA into plant cells and is an important tool in the biotechnology field (Grimsley et al., 1986). Such a bacterium is commonly isolated from soil and was most likely introduced to the canegrub during feeding prior to death or from within the soil that the grub decomposed.
Of the 22 Rickettsial -PCR bands sequenced, eight (amplified from seven different canegrub specimens) were homologous to Ochrobactrum anthropi proteins. O. anthropi has been associated with the entomopathogenic nematode, Heterorhabditis indica (Babic et al., 2000). As per the other nematode-associated
192. Chapter 6: PCR-screening assay for canegrub pathogen identification ______pathogens identified during genomic SSH in Chapter 5, this raises the possibility that some of these dead grub specimens may have succumbed to nematode-mediated disease. As reported in other insects, nematode-independent pathogenicity has been conducted using O. anthropi to determine if the nematode association was required for pathogenesis. In-vitro feeding trials were conducted in the greater wax moth (Galleria mellonella ) and the African cotton leafworm ( Spodoptera littoralis) to determine if this bacterium can cause disease without the presence of the nematode. However, there was no evidence of pathogenicity in either of these insects (Babic et al., 2000) suggesting an association with the entomopathogenic nematode was required for pathogenesis. O. anthropi has also been associated with ants (Cephalotes atratus ) (Jaffe et al., 2001), flea beetles ( Aphthoria flava ) (Ceasar et al., 2004), moths ( Hylesia metabus ) (Osborn et al., 2002) and flies ( Musca domestica ) (Zurek et al., 2000). Therefore, it may also be symbiotic to insects and is unlikely to be pathogenic to canegrubs even though it was only amplified from dead grub specimens. The presence of entomopathogenic nematodes in canegrubs needs to be explored in more detail to determine if suitable nematodes exist that can be developed for biocontrol purposes.
Rhizobium -associated sequences were also amplified from dead grub samples. This genus is commonly associated with root nodulation and nitrogen fixation in plants (Herrera-Cervera et al., 1999, Mhamdi et al., 1999, Chow et al., 2002, Mhamdi et al., 2002). Therefore, it may have been consumed by the canegrub during root feeding. A non-pathogenic association has also been established with the ant, Tetraponera binghami (van Borm et al., 2002) and hence, this bacterium is not- likely a pathogen of canegrubs. However, a symbiotic relationship may be likely. In the ant, van Borm et al. (2002) reported that Rhizobium legumenosarum was found associated with a pouch between the midgut and the intestines in the ant (T. binghami ). The predicted role of this pouch was nitrogen-cycling (van Borm et al., 2002). Unidentified Rhizobia have also been reported to be associated with ectoparasitic lice from the genera Geomydoecus and Thomomydoecus (Reed et al., 2002). No other such insect associations have been published. However, it is likely that the Rhizobium sp. was ingested by the canegrubs during feeding and may be symbiotically-associated with root-feeding canegrubs.
193. Cassandra Trent ______Sequences homologous to Pseudomonas fluorescens were amplified from dead grub specimens. This bacterium has previously been identified from dead specimens in Chapters 2 and 4. This bacterium has been reported in flies (Fitt et al., 1985, Corby- Harris et al., 2007), mosquitoes (Prabakaran et al., 2003), ants (Lee et al., 2008), moths (Pechy-Tarr et al., 2008, Dangar et al., 2008) and grasshoppers (Mead et al., 1988). As discussed in previous chapters, P. fluorescens also produces an insecticidal toxin known to cause mortality in the tobacco hornworm ( Manduca sexta ) (Pechy-Tarr et al., 2008), greater wax moth ( Galleria mellonella ) (Pechy-Tarr et al., 2008), mosquitoes ( Anopheles stephensi , Culex quinquefasciatus and Aedes aegypti) ( Prabakaran et al., 2003) and the rice leaf folder ( Cnaphalocrocis medinalis) (Dangar et al., 2008).
P. fluorescens has also been reported to produce antimicrobial compounds that protect plant roots from plant pathogens (Keel et al., 1996, Haas and Keel, 2003, Loper and Gross, 2007). Such antimicrobial compounds may assist in reducing competing bacteria and dominating dead canegrub specimens. Further examination of P. fluorescens and possible pathogenesis needs to be explored as this bacterium was isolated exclusively from dead grub samples using 16-S cloning (Chapter 2), SSH (Chapter 4) and a non-Pseudomonas specific primer pair (Chapter 6). Isolation of this bacterium from living-diseased or recently-dead specimens is required to further determine the putative pathogenicity in canegrubs. Upon isolation, feeding trials of both small tub and full cane pot trials would be required to determine pathogenesis. Injection studies in larvae would also be helpful to determine lethal doses. Insecticidal toxins may also be a viable option for biocontrol. Isolating putative toxins and determining their dosage may be an effective way of causing death to larvae without applying a live bacterium to the crop. In addition to pathogenesis, cost of biocontrol production, shelf-life, ease of application to the crop and persistence in the soil are all factors that influence biocontrol commercialisation success. All of these factors must be considered when determining if a bacterium or insecticidal toxin is suitable for further development.
Other sequences amplified from the dead grubs were identified as being homologous to Acidobacteria. Acidobacteria are commonly detected in soil (Sait et al., 2002, Joseph et al., 2003, Noll et al., 2005, Chan et al., 2006, DeSantis et al., 2007) and
194. Chapter 6: PCR-screening assay for canegrub pathogen identification ______have also been reported in insects such as termites ( Reticulitermes speratus and Macrotermes gilvus ) (Hongoh et al., 2003, Hongoh et al., 2006), bumble bees (Bombus terrestris ) (Mohr et al., 2006), longicorn beetles ( Moechotypa diphysis and Mesosa hirsute ) (Park et al., 2007) and the emerald ash borer ( Agrilus planipennis ) (Vasanthakumar et al., 2008). In longicorn beetles, Acidobacteria were associated with xylanase production and wood digestion in the gut (Park et al., 2007). With the exception of the bee, all the above mentioned insects have a diet rich in woody materials. Greyback canegrubs feed on sugarcane roots which are also high in xylan and hence the Acidobacteria may be symbiotic and aid in digestion of these woody materials.
The Rickettsial -primers also amplified Klebsiella spp . from dead grub specimens as seen in previous chapters. Klebsiella spp. have been reported in many insects such as aphids (Nakabachi et al., 2003), fire ants (Lee et al., 2008), beetles (Blackburn et al., 2007), flies (Behar et al., 2005), locusts (Dillon et al., 2002) and ant lions (Dunn et al., 2005, Nishiwaki et al., 2007). Their exact role is unknown. However, Klebsiella is linked to nitrogen-fixation in flies (Behar et al., 2005) and aggregation- causing pheromone production in locusts (Dillon et al., 2002). The 16-S sequences from K. pneumoniae were also amplified from two of the five dead grub samples reported in the 16-S cloning chapter (Chapter 2) and were also detected in the dead grub TRFLP profiles (Chapter 5). Similarly, K. pneumoniae was not amplified from living grub samples suggesting that it may only be present in dead grub specimens. As this bacterium has not previously been reported as an insect pathogen, it may have started to dominate the grub gut flora post-death or may be a secondary/opportunistic pathogen. However, as K. pneumoniae is present in a number of dead specimens, further analysis of pathogenicity (feeding and injection trials) may be required to dismiss this bacterium as a pathogen.
There were no PCR-products amplified using the chitinase, Poxvirus and Microsporidia primer sets. These bacteria were either not present or not amplified by the primers selected. Genera-specific primers are limited to the current sequence data of bacteria from those genera. Hence, these previously reported “specific” primers amplified sequences from other bacteria. Genomic genetic diversity affects the apparent “specificity” of a known primer set. For example, bacterial genomic
195. Cassandra Trent ______DNA is known to be highly variable within species (Lan and Reeves, 2000, Zhang et al., 2000, Mavrodi et al., 2002, Parsons et al., 2002, Bae et al., 2005). The genomic DNA size from strains of E. coli or Salmonella enterica is known to differ up to 20%. In addition, there are more than 2000 reported serovars of S. enterica with differing genomic sequence composition (Lan and Reeves, 2000). Such a diverse range of sequences likely exist that are not represented on the Genbank database. Hence, specificity cannot be guaranteed as not all sequences present in a complex microbial community are able to be cross-referenced in the database. Novel pathogenic bacteria may exist that may cause unknown canegrub mortality. Such bacteria may belong to known genera but may be one of the first strains shown to be an insect pathogen. Pathogenicity can only be proven once an bacterium has been cultured and feeding trials conducted.
Dead grub specimen analysis is required at the time of death before other secondary bacteria can mask the original cause of death. Timely analysis of these larvae is essential for determining if these bacteria were present prior to larval death, if they putatively cause disease, if infected larvae show visible symptoms of disease or if these bacteria invaded the canegrub larvae post-death. All of these factors are important in determining if an identified bacterium is putatively pathogenic. In addition, fresh samples are required for culturing putative pathogens. Cultured bacteria are required for use in feeding and injection trials to fulfil Koch’s postulates and determine pathogenicity.
As discussed above, many bacteria detected from the work in this chapter and also throughout previous chapters were associated with entomopathogenic nematodes. Thorough screening of all living larvae for signs of nematode infection may be required to determine if entomopathogenic nematodes are present and if they are possibly suitable for biocontrol application. Culturing putatively-entomopathogenic nematodes and associated bacteria would be required for determining pathogenicity and fulfilling Koch’s postulates as discussed above.
6.4.1 Conclusions
Genera-specific PCR did not identify the target entomopathogens listed in the goals. However, putative pathogens were still amplified using these primers. These
196. Chapter 6: PCR-screening assay for canegrub pathogen identification ______putative pathogens, such as P. fluorescens and P. luminescens , were also identified using 16-S cloning (Chapter 2) and genomic SSH (Chapter 4). Identifying these bacteria from dead grub specimens using three different approaches, suggests that they may be present and may require further analysis to determine pathogenesis in canegrub larvae. Primary limitations with these analyses were the lack of knowledge of canegrub microflora and the state of the dead grub specimens prior to analysis. Isolation of these bacteria is required to demonstrate pathogenesis. Hence, analysing dead specimens prior to decomposition may increase the viability of putative pathogens and increase the chance of culturing these bacteria. Isolating and culturing such bacteria is crucial in determining pathogenesis and calculating the effectiveness of such bacteria as biocontrol agents.
197. Cassandra Trent ______
198. Chapter 7: General discussion ______
Chapter 7: General discussion
199. Cassandra Trent ______
200. Chapter 7: General discussion ______Throughout this project, the primary goal has been to identify putative-pathogens from greyback canegrub microbial communities for potential development as biocontrol agents. To achieve this, microflora associated with both live and deceased specimens was explored in detail and compared. To date, such a study has not been reported in greyback canegrubs. The only previously reported molecular microbiological analysis of canegrub microflora was the partial identification of hindgut-wall associated bacteria by partial 16-S sequence PCR and denaturing gradient gel electrophoresis (DGGE). Only a subset of DGGE bands were sequenced and identified via database homology (Pittman et al. , 2008). Therefore, the findings from all chapters in this work are novel, irrespective of the techniques chosen and contribute to increasing the current knowledge about canegrub- associated microflora. In addition, development and verification of a novel SSH application has proved to be a promising tool for comparing microbial communities. In particular, for pathogen detection from complex microbial communities as previous molecular techniques could not resolve single species differences between communities.
The first objective was the examination of dominant members of both live and dead whole grubs via 16-S shotgun cloning to identify putative pathogens from dead canegrub samples (Chapter 2). From this shotgun cloning analysis, no common entomopathogens were identified. However, it was clear that live and dead specimens differed substantially in dominant bacterial flora. In addition, the sequences amplified from each sample corresponded with the respective TRFLP profiles in Chapter 5 as observed in other combined 16-S and TRFLP studies (Egert et al. , 2003, Egert et al. , 2005).
From the initial 16-S study in Chapter 2, the only entomopathogen-related bacteria identified were from the genera Pseudomonas . Pseudomonads are known to be associated with soil and also as opportunistic pathogens. Therefore, a more thorough analysis of the microflora was required before isolating and testing the pathogenicity of these bacteria. Many of the clones analysed suggested that dead specimens harboured bacteria that had flourished either post-death during refrigerated storage, or during events of stress, such as collection or infection with another agent. Due to the dominance of these bacteria, alternate methods were
201. Cassandra Trent ______chosen to enhance the discovery of putative pathogens. Therefore, the SSH techniques (Chapter 3 and 4) and PCR-screening (Chapter 6) methods were chosen based on the assumption that pathogen-associated sequences were no longer dominant members of the community. In addition, the presence of these bacteria may affect attempts to classically culture putative pathogens from these specimens. For future experiments regarding dead/diseased grub samples, specimens should be processed either before or immediately after death. This may reduce the observed dominance of these saprophytic and psychrotrophic bacteria.
Of the sequences amplified from live whole-grub specimens (Chapter 2), most bacteria identified appeared to have specific roles in other insect gut systems. Many of the bacteria appeared to be involved in insects and mammal metabolism and fat storage. Fat storage in larvae is particularly important in the late third instar where larvae prepare for pupation. Investigation of microflora during other larval stages may help to indicate roles of such bacteria and provide more specific information on canegrub diet during the lifecycle. For example, the role and composition of the gut microflora may change based on developmental cues from the larvae as per in the obliate parasitic fly ( Wohlfahrtia magnifica ) (Toth et al. , 2006). From a culturing study of pooled individuals (three larvae, two pupae and one adult), bacterial population dynamics were shown to change between each of the three larval stages, pupation and adulthood (Toth et al. , 2006). The low number of specimens analysed may influence the differences seen between individudals. However, as a preliminary study, the microbial community associed with the fly appears to differ between different developmental stages. Canegrubs may also show developmental differences in gut floral dynamics, particularly before each molt and during pupation preparation in the third instar. Use of 454 pyrosequencing of pooled specimens from each developmental stage may provide insight into the differences between these bacteria. In order to reduce the observed individual diversity as observed in Chapter 6 TRFLP profiles, specimens may need to be lab reared under identical conditions.
Another potential approach is functional analysis where specific genes responsible for the metabolism of different dietary constituents, such as cellulose, could be analysed via gene-specific PCR and sequenced to determine the origins of such genes. By analysing the microorganisms present and genes involved in different
202. Chapter 7: General discussion ______metabolic pathways, the specific metabolic products being utilised by the larvae could be monitored and a better understanding of feeding and grub metabolism could be obtained. Determining the specific roles of the gut microflora at different life stages may help to understand community dynamics and bacteria crucial in canegrub nutrition. In turn, by understanding the associated microflora of healthy individuals, pathogen detection in stressed/diseased specimens may be more obvious.
Of the sequences amplified from a live grub mid-region sample in Chapter 3, sequences homologous to the Enterobacteriaceae appeared dominant in comparison to Clostridiales present in the whole grub sample (Chapter 2). The Enterobacteriaceae are known to be associated with insects and have been reported to have symbiotic and metabolic roles. They have also been associated with entomopathogenic nematodes. As mentioned above, a functional analysis of grub microflora would be an ideal way to better understand the roles of such bacteria. By understanding canegrub feeding and metabolism, alternate control strategies targeting feeding behaviour and metabolic rate may be devised to reduce crop damage. In addition, specific metabolic pathways determined from this analysis may be targeted with toxins, proteins or enzymes that block them. Blocking these pathways may reduce feeding or hinder development. Therefore, this knowledge may unveil new opportunities for canegrub biocontrol research.
Another alternative to 16-S techniques are metagenomic analyses. Metagenomic analysis of communities has been performed using SSH (Galbraith et al. , 2004) and 454 pyrosequencing. The latter uses restriction-digested genomic DNA and pyrosequencing. Individual genomic sequences are independently pyrosequenced to provide information about both the bacteria and genes present within the population (reviewed in Cardenas and Tiedje et el., 2008). However, as discussed in Chapter 4, the genomic approach in the context of SSH provides sequences with limited homology to known Genbank entries as discovered by this study and by the findings of Galbraith et al. (2004). An increased knowledge of genomic biodiversity is required before these approaches can provide high community resolution. Alternatively the affect of bias can be reduced by analysing large datasets (~5000 sequences per run) of amplified 16-S hypervariable region sequences (100-350 bp)
203. Cassandra Trent ______using pyrosequencing. The main drawback of this approach is resolution, as most sequences are only resolved to the family or genera level due to shared hypervariable region sequences at higher taxonomical levels (Liu et al. , 2007, Roesch et al. , 2007, Andersson et al 2008, reviewed in Cardenas and Tiedje et el., 2008).
As discussed above, the basic 16-S cloning technique was not suitable for specifically resolving pathogens from the dead grub specimens. Only 20 clones were sequenced per sample and likely thousands would be required to provide a broader analysis of such a complex community. Such a large-scale approach was beyond the budget constraints of this project. Therefore, other methods were chosen to further explore microbial community differences with the goal of enhancing less dominant sequences unique to dead grubs. However, 16-S PCR was still the basis of the techniques used in Chapters 2, 3 and 5. In addition to statistical limitations, all 16-S PCR approaches are limited by the following:
• DNA extraction bias • Universal primer limitations • BLAST homology limitations • General PCR bias • 16-S gene copy number and heterogeneity • 16-S sequence divergence between closely related species • Possible BLAST entries that are incorrectly taxonomically classified • Commercial Taq contamination
Contamination of commercial Taq preparations was discovered (Chapter 3) during the use of universal V3-primers. Such a finding raises questions regarding commercial quality control, and the need for additional controls to test the quality of commercially prepared kits and reaction mixtures. More information regarding batch quality control should be available for the consumer as commercial preparation bacterial contamination is not a new issue, is widespread and is an important experimental consideration when using universal primers (Hanaki et al. ,
204. Chapter 7: General discussion ______2000). Therefore, care must be taken when choosing Taq for all PCR reactions containing universal primers.
Due to the above mentioned findings in Chapter 2, an alternate approach was chosen to specifically identify sequences unique to one sample and ideally identify putative pathogens. The technique chosen was suppressive subtractive hybridisation (SSH). When 16-S V3-region amplified sequences were applied as tester and driver in SSH (Chapter 3), the above-listed biases were noticed not only between replicate PCR reactions, but particularly when adaptor sequences were added to the V3-primer sequences and the same community re-amplified. The effect of this contributes to a lowered efficiency of this method and demonstrates the pitfalls of the 16-S PCR approach in quantitatively representing a community. However, the clones that are generated from this technique are taxonomically identifiable to at least the genus level. Therefore, the high quality of the data produced outweighs a drop in efficiency.
In the cloning analysis within Chapter 3, it was evident that there was less sequence divergence within the shorter (~200bp) V3 hypervariable region when compared to the full-length 16-S sequences (~1500bp) analysed in Chapter 2. Many similar species shared the same V3-region sequence. Therefore, the chosen DNA separation techniques, temperature gradient gel electrophoresis and denaturing high performance liquid chromatography, were not sensitive enough to separate closely related sequences. A larger PCR-product may be required for more successful separation, or a different screening approach may prove optimal such as the dot blots performed in Chapter 4 that resolve tester-specific clones. However, stringent washes would be required to reduce the amount of non-specific hybridisation that could occur between sequences with such similarity.
An advantage of the highly similar V3-sequences is that they share similar Tm values due to being of similar length and composition. During the SSH hybridisation steps, a single temperature is chosen. Due to the similar Tm values, the reassociation bias caused by differing rates of sequence hybridisation is reduced. Therefore, Tm value does not affect the chance of reassociation during the normalisation process. However, in the genomic approach, sequences are highly
205. Cassandra Trent ______varied in length, composition and Tm value. A high hybridisation temperature is required to reduce the amount of non-specific hybridisation at the cost of failing to detect smaller sequences with lower GC content and low Tm values. Therefore, the V3-region is more suited to comparing individuals present in two communities as each individual should be similarly represented within the sequence pool.
The novel V3-PCR based SSH technique was shown to enhance a tester-specific control sequence from the grub microbial community V3-PCR pool (section 3.3.3). SSH uses the hybridisation-dependent, normalisation process to enrich rare and tester-unique sequences in the tester sample (see section 1.4.3 for more details). Based on cloning and sequencing, the efficiency of isolating tester-specific sequences was low (21.7% compared to 92 and 40% in initial reports by Diatchenko et al. , 1996 and Gurskaya et al. , 1996, respectively). However, the measured tester- specific control sequence (Chapter 3) may have been present at low concentration within the driver sample, reducing the apparent efficiency. The technique was also shown to have low efficiency when a small subset of control bacteria were used which may be due to the differing PCR biases during tester and driver preparations. However, when sequence pools of low complexity are used, the “normalisation” process is accelerated, as there is more chance of each sequence hybridising due to the relative abundance of each species in the sequence population (Diatchenko et al. , 1996). Therefore, by using the above controls and reducing the sequence pool complexity, tester-specific heterohybrids formed at the second SSH hybridisation stage are reduced. As a result, there is a reduced efficiency of tester-specific clones from the final SSH PCR.
When comparing the complexity of sequences from both the V3-PCR and genomic approaches, the genomic approach shows much more sequence diversity. The Clontech SSH kit was originally designed to compare genetic difference between strains of bacterial species. Many genetic differences between strains of the same bacterial species have been identified using the Clontech SSH kit (Zhang et al. , 2000, Mavrodi et al. , 2002, Parsons et al. , 2002, Bae et al. , 2005). For example, the size of the genomic DNA present in different strains of Salmonella enterica is known to differ by up o 20% across more than 2000 known serovars with differing genomic composition (Lan and Reeves, 2000). Of these differences most can be
206. Chapter 7: General discussion ______attributable to genes enabling pathogenicity or metabolic genes allowing the metabolism of alternate sugars or other substrates (Lan and Reeves, 2000). Such diversity seen within strains of the same species produces a complex pool of sequences for SSH. A complex microbial community, such as soil, has been estimated to contain at least 52,000 different bacterial species per gram (Roesch et al. , 2007). Therefore, in combination with the known community diversity as shown by Pittman et al. (2008) and the TRFLP profiles in Chapter 6, each species present contains its own level of genetic complexity. Hence, the sequence pool used in SSH would be much more complex when compared to the V3-PCR technique that targets only bacterial DNA with minimal sequence divergence per species. This staggering level of sequence complexity in the genomic approach may increase the efficiency of the technique. However, many rare sequences may remain single stranded due to the reduced chance of hybridisation in such a complex sequence pool. Therefore, although the efficiency is higher, the sequence information obtained may not represent such a broad range of individual differences between populations.
When comparing such diverse complex communities, the likelihood of isolating all genetic differences between them is unlikely. Harakava and Gabriel (2003) estimated that around 4,520 SSH clones would need to be analysed to successfully determine the 152 gene differences in a strain of Xylella fastidiosa pathogenic to citrus. This is taking into account that 50% of clones are not tester-specific and that all tester-specific clones would contain different fragments (Harakava and Gabriel, 2003). In addition to these calculations, the preferential amplification of certain tester-specific sequences may be attributed to hybridisation temperature and PCR bias (as mentioned above). Therefore, even more clones would need to be analysed to obtain a full dataset of sequences unique to this strain. In relation to pathogen detection in canegrubs, there are so many genetic differences between two populations that the likelihood of amplifying a piece of genomic DNA specific to a pathogen is not likely unless a large number of clones (20,000 +) are analysed. This would be too expensive and laborious using traditional sequencing methods and may only be achieved with the use of high throughput methods such as 454 pyrosequencing (as discussed previously).
207. Cassandra Trent ______In both genomic and V3-PCR based SSH, the canegrub community and bacterial controls containing identical tester and driver preparations were not entirely subtracted as evidenced by the presence of final PCR products that were not tester- specific. When using the V3-PCR-based technique (Chapter 4), this failure to fully subtract sequences may be attributed to the differing PCR biases in preparation as shown by cloning the dominant members at each SSH preparation stage. The inability to fully subtract sequences from the genomic approach may be due to an adaptor ligation bias based on the secondary structures of the restriction digested template. Hairpin structures may reduce the ligation efficiency of some tester sequences affecting the sequences present in the final tester pool. As mentioned above, the normalisation process in SSH is based on random hybridisations of sequences within a complex pool and is designed to “enrich” tester-specific sequences and not completely subtract all sequences common to both tester and driver (Diatchenko et al. , 1996). However, the genomic approach (Chapter 4) showed that there was an observed reduction in final PCR product when tester and driver were the same, compared to other control samples containing tester-unique sequences. This reinforces the theory of Diatchenko et al. (1996), that the majority of final PCR amplification is of tester-unique or tester-enriched sequences. This could be quantified using real-time PCR.
When genomic DNA was used in SSH (Chapter 4), the homology of resulting clones to Genbank database entries was reduced. Lowered Genbank homology was evident in Chapter 4 data as well as previously reported in metagenomic studies (Galbraith et al. , 2004) and strain-based analyses (Zhang et al. , 2000, Mavrodi et al. , 2002, Parsons et al. , 2002, Bae et al. , 2005) using the SSH kit. Using a genomic-DNA SSH approach, random genomic fragments are amplified, cloned and sequenced. Due to the Genbank database containing fewer bacteria with known full-length genomic data than 16-S data, fewer sequences can be accurately identified and there is less homology of the genomic sequences to entries in the database. In addition, many sub-species contain unique regions of genomic sequence comparatively to other sub-species, reducing the chance of high database sequence homology. This is due to the high diversity of bacteria within an environmental community (as mentioned above) and hence, each individual sub-species will likely not have known genomic sequence data within the Genbank database. Hence, the specific bacterium
208. Chapter 7: General discussion ______cannot be identified by these sub-species specific sequences. Uncultured bacteria are also not represented within genomic databases, limiting the detection of these bacteria to known amplified nucleotide sequences.
The advantage of the 16-S gene is that it is highly evolutionarily conserved and therefore closely-related species are highly-homologous. This region has also been well studied and most known bacteria are highly represented within the Genbank database. In general, some regions of random genomic DNA evolve rapidly and are highly divergent between species and sub-species. Therefore, the genomic approach may provide a broader range of analysis with respect to different taxonomic groups (eukaryotes and prokaryotes); however the resulting clones are less likely to be homologous to known deposited sequences. In addition, each individual species may be represented by many unique genomic sequences, and DNA from non-viable cells present within the consumed soil may also be detected. However, this may be negligible based on the failure of DNase treatment to reduce TRFLP profile complexity (Chapter 5). These combined factors reduce the usefulness of many resulting sequences in determining community differences between two samples. Therefore, a gene-specific PCR-based approach is favourable in this respect.
When comparing two communities using SSH, hybridisation temperature plays a crucial role in determining detected sequences. Hybridisation within the normalisation process is important for reducing the amount of sequences common to both tester and driver. When a community is so diverse that sequences have highly- varied Tm values, choosing a suitable hybridisation temperature may determine the efficiency of the technique and may create a bias towards sequences with a specific Tm value. A reduction of hybridisation temperature reduces the overall specificity of the technique and may cause the formation of non-specific heterohybrids. In contrast, an increase in temperature reduces the chance of lower Tm sequences hybridising and reduces the chance of these sequences being PCR amplified in the final stages. Therefore, by increasing the temperature, the reaction is biased towards high GC-content sequences such as those isolated from bacteria but a reduction in temperature would compromise the accuracy of hybridisation and non-specifically hybridised products may cause amplification of heterohybrid artefacts. Such artefacts reduce the amount of resulting clones showing homology to known
209. Cassandra Trent ______database sequences. Addition of PCR in the tester and driver preparation was used to reduce this phenomenon however, at the cost of the above-mentioned bias.
Within the genomic approach, dot blot efficiencies are also reliant on hybridisation temperature and the same dilemma exists between high-temperature specificity and lower-temperature reduced bias. Performing SSH and dot blots on eukaryotic and prokaryotic bacteria separately may reduce this temperature difference and hence reduce a possible temperature created bias. In contrast to the V3-region tested, a larger (~1000bp), evolutionarily conserved, taxonomically diverse PCR product from 16/18S ribosomal DNA or another well studied gene, such as rpoB, may be more suitable for use in community-based SSH. By using this alternate gene in the tester and driver PCR preparation, more taxonomically useful tester-specific clones may result. The larger sequence length allows the use of a higher hybridisation temperature as these sequences will have higher, less divergent Tm values.
With the use of modern sequencing technologies such as the various applications of pyrosequencing, thousands of community sequences can be created from a single run (as reviewed by Hall et al. , 2007). By profiling individual communities and subtracting sequences common to another compared sample, a subtractive analysis could be performed on resulting data. Via this computational analysis of genetic data, taxonomic information from both dead and live specimens could be compared. This would bypass the afore-mentioned problems associated with the SSH technique. However, the SSH technique is still useful in removing common sequences and an in-depth analysis of SSH products would provide even more information regarding genetic differences between the two populations. In addition, the pyrosequencing technique could be used to further analyse the SSH technique at each stage and develop strategies to improve efficiency.
Functional analysis focussing on for example, metabolism, could also be performed using this technology and large amounts of data (~4000 sequences per reaction, per run) could be created quickly and with increasing affordability. By using metabolic gene-specific PCR to target an enzyme such as cellulase, 454 pyrosequencing would provide an indication of what bacteria are present with the ability to degrade cellulose. This could be performed for a number of metabolic or pathogen-specific
210. Chapter 7: General discussion ______genes. At the commencement of this project (2005), these technologies were still emerging and were beyond the financial constraints of this work, hence alternate methods were chosen. However, commercial laboratories such as at the University of Florida Center for Pharmacogenomics, can now analyse samples for as little as AU $3.65 per sample, making this method more economically viable.
Any subtractive technique comparing two samples depends on both samples having highly similar sequence composition. Microflora diversity associated with canegrubs was shown to be high between individuals (Chapter 5). This diversity may be attributed to canegrub behaviour, soil ingestion, life stages, etc. and should be explored in more detail using a larger group of specimens in combination with high-throughput sequencing techniques. In this study, attempts to reduce this individual diversity by means of perlite gut clearance and DNase treatment of exogenous DNA were unsuccessful and did not affect overall diversity. The simplest way to reduce the influence of individual diversity is to pool large groups of grubs and compare the microflora from the pool of specimens. Many TRFLP studies comparing insects use pooled gut sections from different locations (Egert et al. , 2003, Schmitt-Wagner et al. , 2003, Thongaram et al. , 2005, Miyata et al. , 2007, Kohler et al. , 2008, Yu et al. , 2008). By comparing a broad range of specimens from different geographical locations, the pooled specimens could provide a better indication of regional diversity and not highlight the individual diversity shown in Chapter 5.
In addition to individual grub diversity, the microflora may likely change once a grub becomes stressed during collection or infection with a pathogen. Little is known of how entomopathogens affect other insect-associated microflora within a host. The immune response triggered by an entomopathogen attack may also play a role in changing community dynamics. Some entomopathogens such as Serratia entomophilia in the New Zealand grass grub, cause cessation of feeding and gut clearance. Both of which are likely to change the community dynamics and composition. By comparing the gut flora of grubs reared under lab conditions, with non-infected controls and infected individuals, communities could be compared and an indication of gut community differenes could be determined. By rearing these
211. Cassandra Trent ______insects in the lab, the individual gut community diversity should decrease and the specific effect of the entomopathogen should be evident.
The assessment of diversity using terminal restriction fragment length polymorphisms (TRFLP) was subject to all of the aforementioned biases associated with 16-S PCR techniques. TRFLP provides only a fingerprint of the community and doesn’t take into consideration fragments from different species that share the same restriction digested sequence length or fragments that are too large for detection (>600 bp). Moreover, only the most dominant sequences are detected and analysed using this technique. To identify the effect of geographical location or life stage, pooling large batches (>10) of specimens may be required to reduce the individual diversity and compare the canegrub-associated communities as a whole. For example, comparing large groups of specimens (>50) from different locations (example Tully and Mackay), to determine the effect of the geographical environment on the gut microflora. This may also help to determine which bacteria may be crucial symbionts required in canegrub metabolism. In SSH, due to the observed sample heterogeneity, creating a common driver pool of >30 specimens may be useful in subtracting commonly found microflora from a tester specimen of interest. This would create a large pool of bacteria commonly associated with healthy specimens and may be useful for subtracting against a specimen showing symptoms of unknown disease. Early detection of disease is important to reduce the influence of saprophytic bacteria or secondary infections.
Individual live grubs showed substantial individual microbial community diversity. In addition to high individual diversity, the dead grub samples showed little-to-no similarity (Morisita indices between 0.00 and 0.29) to their live untreated counterparts (Chapter 5). Dead specimens were highly-physically-decomposed and had been refrigerated for extended periods (months) before analysis. Moreover, 16- S PCR analysis showed that dominant members present in these communities were homologous to many saprophytic bacteria, or psychrotrophic bacteria capable of growing during refrigeration. In order to overcome these issues, new methods of pathogen detection and disease confirmation need to be explored if novel pathogens are to be identified from these specimens. Storage conditions post-death need to be reviewed and amended in order to preserve the microbial community at the time of
212. Chapter 7: General discussion ______death and prevent growth of saprophytic or psychrotrophic bacteria. Collection of live grubs is also stressful for the insect. Stress of collection and subsequent handling may also contribute to the high unexplained mortality found within these field collected samples. Development of an immune response detection method may be useful in determining if a larva is in the early stages of an unknown infection. By monitoring the response of the immune system, the microflora may be analysed before a large change in the healthy microflora can occur. This would render the SSH technique more suitable for pathogen detection in canegrubs.
When dead grub specimens were PCR amplified for known entomopathogens, no specific matches were found (Chapter 6). PCR-reactions produced bands that were random primed regions from a range of different soil-borne bacteria present within the canegrub specimens. Target entomopathogenic bacteria were either not present, showed sequence divergence from the published primer sequences or due to the above-mentioned collection, storage and/or bias problems were not detected during PCR. If novel canegrub pathogens are to be detected and identified, a more specific method needs to be applied so as to overcome these PCR issues. In addition, novel pathogens may not be discovered by using primers from known entomopathogens. Novel bacteria may not contain the primer sites specific for these known genera.
Many of the dead grub amplified sequences showed some association to nematodes. Further investigation into the presence of entomopathogenic or root-associated nematodes should be conducted to determine if some of these may be causing disease in these larvae. Many nematodes are naturally present within the soil and hence, the presence of which may not be significant in these decomposed specimens. However, there are a number of entomopathogenic nematodes known to infect scarabs. Discovering and isolating canegrub entomopathogenic nematodes may be another strategy for finding possible biocontrol bacteria. Many known entomopathogenic nematodes have been developed for biocontrol use and have been shown to be effective against insects within crops.
New collection techniques need to be implemented to be able to collect first and second instar larvae from further within the soil as eggs are laid below the root structure beneath the soil. Current collection methods require the detection of
213. Cassandra Trent ______extensive canegrub damage during the late third instar. Detection and collection of younger larvae from deeper within the soil would aid in isolation of pathogens that target grubs before they enter the main feeding stages and cause extensive root damage. Little is known about these younger larval stages and understanding the entire life cycle is important in implementing successful control strategies and finding suitable biocontrol bacteria.
7.1 Conclusions
This is the first study to analyse the bacterial microbial communities associated with live and dead greyback canegrubs. The gut-bacteria associated with live grubs (predominantly Enterobacteriaceae and Clostridiales) were shown to have metabolic roles in similar insects and may have similar symbiotic relationships with canegrubs. Most dominant bacteria associated with dead grubs appeared to be either saprophytic or psychrotrophic highlighting the need for freshly deceased canegrub specimes. However, some known insect pathogens (such as Pseudomonas fluorescens and Photorhabdus luminescens ) were putatively identified. Freshly deceased canegrub samples need to be obtained to isolate these organisms and determine pathogenicity in canegrubs. Such bacteria may be suitable for biocontrol development. The 16-S based SSH technique developed throughout the course of this project is an exciting new tool for microbial ecology. It is the first technique useful for determining species differences between communities and with some further development, can be applied to a range of other fields. Combining this technology with pyrosequencing also opens up a new avenue for thorough differential analyses of complex communities. The suite of molecular techniques used throughout this project has yielded some insights into the microbial communities associated with both live and dead canegrubs. Such a thorough microbial analysis has never been undertaken on greyback canegrubs before. Future directions for work on canegrubs may involve the use of high throughput community sequencing techniques such as 454 sequencing to further investigate both the functional and taxonomic differences between both individual and pooled canegrub samples. Knowledge of the canegrub- associated community dynamics throughout the lifecycle from 454 sequencing would ensure a more thorough search for putative pathogens of all larval stages and enable more strategic integrated pest management plans could be implemented. For example if a pathogen targeting canegrub eggs was discovered, the use of such an
214. Chapter 7: General discussion ______agent may control the larvae prior to root damage. Controlling the earlier life stages of the canegrub is crucial in reducing the amount of damage caused by this pest and identifying novel, cost-effective biocontrol agents.
215. Cassandra Trent ______
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