Identification and Classification of Marine Bacteria Associated with Poly(Ethylene Terephthalate) Degradation

Identification and classification of marine bacteria associated with poly(ethylene terephthalate) degradation

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy by Hooi Jun Ng

Department of Chemistry and Biotechnology School of Science Faculty of Science, Engineering and Technology Swinburne University of Technology

November 2014

Abstract

Poly(ethylene terephthalate) (PET) is manmade synthetic polymer that has been widely used over the past few decades due to its low manufacturing cost, together with desirable properties. The high production and usage of PET, together with the inappropriate handling of resultant wastes are becoming a major global environmental issue, especially in the marine environment due to the fact that PET is fairly stable and not easily degraded in the environment. The waste handling methods that are currently available, such as burying, incineration and recycling, have their own drawbacks and limitations. Biodegradation represents an environmentally friendly, cost effective and potentially more efficient method for the management of PET, as can be concluded historical instances where microorganisms have been shown to be capable remediating and biodegrading environmental pollutants. The potential with which microorganisms could adapt to mineralize PET has previously been reported, however, none of the studies have identified the potential of marine bacteria to biodegrade of PET.

In this project, a collection of marine bacteria belonging to two phylotypes, Alpha- and Gammaproteobacteria, which might have the potential to biodegrade PET, have been investigated to examine their ability to degrade PET. One strain, affiliated to the genus Marinobacter, and designated as A3d10T, was identified to have the ability to hydrolyze bis(benzoyloxyethyl) terephthalate, a trimer of PET. Further investigation of a short term biodegradation experiment also indicated that PET films exposed to this strain underwent significant changes with regard to their surface nanostructure, including increased crystallinity and hydrophilicity, as confirmed by surface topography, surface chemical composition analysis, and wettability, using AFM, Raman spectroscopy, and goniometry, respectively.

Representative strains of two genera, which were included in the investigation of PET degradation ability, namely Alteromonas and Marinobacter, were subjected to detail taxonomic investigation. Comparative phylogenetic and genomic analyses, together with traditional physiological and biochemical studies coupled with newly developed modern taxonomic tools, namely multi-locus sequence analysis (MLSA) and MALDI-TOF mass

I spectrometry, allowed formal description of a new species of the genus Alteromonas, Alteromonas australica (= type strain H17T = KMM 6016T = CIP 109921T), and two new species of the genus Marinobacter, Marinobacter similis (= type strain A3d10T = JCM 19398T = CIP 110589T = KMM 7501T) and Marinobacter salarius (= type strain R9SW1T = LMG 27497T = JCM 19399T = CIP 110588T = KMM 7502T). The whole genomes of the two newly proposed Marinobacter species were assembled and deposited in public databases, in which the whole genome of M. similis A3d10T provides an opportunity to investigate the genes/enzymes responsible for PET degradation.

The development, evaluation and application of modern taxonomic tools such as multi-locus sequence analysis (MLSA) and MALDI-TOF mass spectrometry in the taxonomy of marine bacteria of the genus Alteromonas, and genome taxonomic parameters in the taxonomy of the genus Marinobacter provided alternative and/or complementary path for the description of a new species, making a significant contributing to the analytical techniques that can be used in modern systematic classification of bacteria.

Acknowledgements

First and foremost, I would like to thank my principal supervisor, Professor Elena Ivanova for taking me on this project in the second year of my PhD candidature. Her dedicated supervision and guidance throughout this project, as well as her support and time sacrificed during the production of manuscripts, papers, and this thesis is invaluable.

Similarly, to my co-supervisor, Professor Russell Crawford and Dr. François Malherbe, thank you for providing feedback on my manuscripts, papers and thesis. Without your support, some of this task would not have been completed easily.

I would also like to thank Dr. John Fecondo and Associate Professor John Patterson for their supervision and guidance during the first and second year of my PhD. I am grateful to have the opportunity to work with you both.

To all the technical staff and my lab mates, your helps, suggestions, support and company have made my experience in the lab memorable. Big thanks to Ngan who provided constructive advice on microbiology related work, and Soula, Chris, Huimei, Nina and Andrea for their technical supports given during the course of this study.

My heartfelt appreciation goes to all co-workers in Prof. Ivanova group for their unreserved support and guidance in a way or another. To Dr. Hayden Webb, thank you for assisting in countless individual experiments and analyses, in particular the surface analysis experiment. To Ha and Chris, thank you for your assistance in performing SEM for the characterization of Marinobacter species.

To collaborators, my sincere acknowledgement goes to Dr. Henry Butt, Rachel Knight and all the staff at Bioscreen Medical laboratory for allowing me to use their MALDI-TOF mass spectrometer. I am really happy to have met you guys and thanks for your friendliness that have always made me feel welcome in your lab. I would also like to thank Dr. Nicholas Williamson at Bio21 Institute for his assistance in the use of MALDI- TOF mass spectrometry. Also, I would like to acknowledge the kindness of Prof Georg M. Gǖbitz from Graz University of Technology, Austria for providing 3PET powder, and

III

Professor Tomoo Sawabe from Hokkaido University, Japan for facilitating the sequencing of the whole genome sequences of strain A3d10T and R9SW1T.

To all the academic staff at Department of Chemistry and Biotechnology, thank you for your direct or indirect support, especially during the hard time when my previous supervisor departed. Special thanks go to Dr. Daniel Eldridge, Dr. Tony Barton, and Professor Linda Blackall for giving me the opportunity to involve in teaching. It has been a pleasure to work with you, and thank you for sharing your teaching experience with me.

I gratefully acknowledge Swinburne Research for providing me scholarship to pursue my PhD study. Also, thanks to all the staff in the research office for providing me support and guidance in candidature related issue.

Finally, I would like to thank my parents for their financial support and understanding throughout my study. To Jiawey, thank you for your care, constant help, support and company throughout the years.

Declaration

I hereby declare that this thesis is my original work and, to the best of my knowledge, this thesis contains no material previously published or written by another person, except where due reference is made in the text. None of this work has been submitted for the award of any other degree at any university. Wherever contributions of others were involved every effort has been made to acknowledge the contributions of the respective workers or authors.

Hooi Jun Ng November, 2014

List of Publications Book Chapters:

Webb, H.K., Ng, H.J., Ivanova, E.P. (2014) The Family Methylocystaceae. The Prokaryotes – Alphaproteobacteria and Betaproteobacteria. Springer-Verlag Berlin Heidelberg, p. 341-347.

Ivanova, E.P., Ng, H.J., Webb, H.K. (2014) The Family Pseudoalteromonadaceae. The Prokaryotes – Gammaproteobacteria. Springer-Verlag Berlin Heidelberg, p. 575-582.

Peer-reviewed journal articles:

Ng, H. J., López-Pérez, M., Webb, H. K., Gomez, D., Sawabe, T., Ryan, J., Vyssotski, M., Bizet, C., Malherbe, F., Mikhailov, V. V., Crawford, R. J., Ivanova, E. P. (2014) Marinobacter salarius sp. nov. and Marinobacter similis sp. nov., isolated from sea water. PLoS ONE 9(9): e106514.

Ng, H.J., Webb, H.K., Crawford, R.J., Malherbe, F., Butt, H., Knight, R., Mikhailov, V.V., Ivanova, E.P. (2013) Updating the taxonomic toolbox: classification of Alteromonas spp. using multilocus phylogenetic analysis and MALDI-TOF mass spectrometry. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology, 103 (2), p. 265-275.

Ivanova, E.P., Ng, H.J., Webb, H.K., Feng, G., Oshima, K., Hattori, M., Ohkuma, M., Sergeev, A.F., Mikhailov, V.V., Crawford, R.J., Sawabe, T. (2014) Draft genome sequences of Marinobacter similis A3d10T and Marinobacter salarius R9SW1T. Genome announcements, 2(3):e00442-14.

Ivanova, E.P., Ng, H.J., Webb, H.K., Kurilenko, V.V., Zhukova, N.V., Mikhailov, V.V., Ponamareva, O.N., Crawford, R.J. (2013) Alteromonas australica sp. nov., isolated from Tasman Sea. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology, 103 (4), p. 877-884.

Conferences with published abstracts:

Ng, H. J., Webb, H. K., Crawford, R. J., Malherbe, F., Patterson, J., Ivanova, E. P. (2011). Alteromonas australica sp. nov., a poly(ethylene terephthalate) degrading bacterium, isolated from the Tasman Sea, Pacific Ocean, Victoria, Australia. Australian Society for Microbiology Annual Meeting. Hobart, Tasmania.

Ng, H. J., Webb, H. K., Malherbe, F., Patterson, J., Ivanova, E. P. (2011). Development of multilocus sequence analysis (MLSA) and MALDI-TOF mass spectrometry for Alteromonas species classification. Inaugural Meeting of Bergey’s International Society for Microbial Systematics. Beijing, China.

Ng, H. J., Webb, H. K., Crawford, R. J., Malherbe, Ivanova, E. P. (2012). Streamlining the classification of Alteromonas spp. through the use of MLSA and MALDI-TOF MS. Australian Society for Microbiology Annual Meeting. Brisbane, Queensland.

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Table of Contents

Abstract ...... I Acknowledgements ...... III Declaration ...... V List of Publications ...... VI Table of Contents ...... VIII List of Figures ...... XIII List of Tables ...... XVI List of Abbreviations ...... XVIII 1. Introduction ...... 1 2. Literature Review ...... 6 2.1 Overview ...... 6 2.2 Plastics And Their Environmental Impact ...... 6 2.3 Poly(ethylene terephthalate) (PET) ...... 8 2.4 Current disposal methods ...... 9 2.4.1 Burying ...... 9 2.4.2 Incineration ...... 9 2.4.3 Recycling ...... 10 2.5 Plastic degradation ...... 10 2.5.1 Biodegradation ...... 11 2.6 Surface characterisation techniques...... 13 2.6.1 FTIR and Raman Spectroscopy ...... 13 2.6.2 X-ray photoelectron spectroscopy (XPS) ...... 15 2.6.3 Atomic Force Microscopy (AFM) ...... 16 2.6.4 Water Contact Angle (WCA) ...... 17 2.7 Bacterial taxonomy ...... 17 2.8 Phenotypic information ...... 20 2.9 Genomic Information ...... 22

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2.9.1 DNA -DNA hybridization (DDH) ...... 22 2.9.2 DNA G+C content (mol %) ...... 26 2.9.3 DNA fingerprinting ...... 26 2.9.4 The 16S ribosomal RNA (rRNA) ...... 27 2.9.5 Housekeeping genes...... 28 2.9.6 Single gene analysis ...... 29 2.9.7 Multilocus sequence analysis (MLSA) ...... 30 2.9.8 Whole genome sequence analysis ...... 33 2.10 Chemotaxonomy ...... 34 2.10.1 MALDI-TOF mass spectrometry analysis ...... 34 2.11 Summary ...... 36 3. Materials and Methods...... 38 3.1 Overview...... 38 3.2 Chemicals ...... 38 3.3 Bacterial strains and growth conditions ...... 38 3.3.1. Genus Alteromonas ...... 38 3.3.1.1 Alteromonas macleodii LMG 2843T ...... 39 3.3.1.2 Alteromonas marina SW-47T ...... 39 3.3.1.3 Alteromonas stellipolaris LMG 21861T ...... 39 3.3.1.4 Alteromonas litorea TF-22T ...... 40 3.3.1.5 Alteromonas hispanica F-32T ...... 40 3.3.1.6 Alteromonas addita R10SW13T ...... 40 3.3.1.7 Alteromonas simiduii BCRC 17572T ...... 40 3.3.1.8 Alteromonas tagae JCM 13895T ...... 41 3.3.1.9 Alteromonas genovensis LMG 24078T ...... 41 3.3.1.10 Alteromonas australica H17T ...... 41 3.3.2. Genus Marinobacter ...... 41 3.3.2.1 Marinobacter hydrocarbonoclasticus SP. 17T ...... 42 3.3.2.2 Marinobacter excellens KMM 3809T ...... 42 3.3.2.3 Marinobacter lipolyticus CIP 107627T ...... 42 3.3.2.4 Marinobacter litoralis SW-45T ...... 43

3.3.2.5 Marinobacter flavimaris CIP 108615T ...... 43 3.3.2.6 Marinobacter sediminum LMG 23833T ...... 43 3.3.2.7 Marinobacter algicola LMG 23835T ...... 43 3.3.2.8 Marinobacter koreensis KACC 11513T ...... 44 3.3.2.9 Marinobacter vinifirmus CIP 109495T ...... 44 3.3.2.10 Marinobacter gudaonensis CIP 109534T ...... 44 3.3.2.11 Marinobacter salsuginis CIP 109893T ...... 44 3.3.2.12 Marinobacter salicampi KCTC 12972T ...... 45 3.3.2.13 Marinobacter pelagius JCM 14804T ...... 45 3.3.2.14 Marinobacter psychrophilus JCM 14643T ...... 45 3.3.2.15 Marinobacter mobilis JCM 15154T ...... 45 3.3.2.16 Marinobacter zhejiangensis JCM 15156T ...... 46 3.3.2.17 Marinobacter daqiaonensis LMG 25365T ...... 46 3.3.2.18 Marinobacter adhaerens CIP 110141T...... 46 3.3.2.19 Marinobacter xestospongiae JCM 17469T ...... 47 3.3.2.20 Marinobacter sp. A3d10T ...... 47 3.3.2.21 Marinobacter sp. R9SW1T ...... 47 3.3.3 Other bacterial strains ...... 48 3.3.3.1 Salinimonas chungwhensis KCTC 12239T ...... 48 3.3.3.2 Aestuariibacter aggregatus LMG 25283T ...... 48 3.3.3.3 Hahella ganghwensis KCTC 12277T ...... 48 3.4 Maintenance and long term storage of bacterial strains ...... 49 3.5 Molecular analysis ...... 49 3.5.1 Genomic DNA extraction ...... 49 3.5.2 Primer sequences ...... 49 3.5.3 Polymerase Chain Reaction (PCR) ...... 50 3.5.4 Agarose gel electrophoresis ...... 53 3.5.5 Extraction and gel purification of DNA fragment ...... 53 3.5.6 DNA sequencing and analysis ...... 53 3.5.7 Phylogenetic analysis ...... 56 3.5.8 DNA-DNA hybridization (DDH) ...... 56

3.6 Whole genome sequence analysis ...... 57 3.7 Physiological and biochemical characterisation ...... 58

3.8 MALDI-TOF mass spectrometry analysis ...... 59 3.9 Sources and preparation of PET film and model substrate ...... 60 3.10 Screening of PET-hydrolysing bacteria ...... 61 3.11 PET biodegradation experiment ...... 61 3.12 Surface characterisation ...... 62 3.12.1 Atomic force microscopy ...... 62 3.12.2 Confocal Raman microscopy ...... 62 3.12.3 Fourier Transform-Infrared Spectroscopy (FTIR) ...... 63 3.12.4 Water Contact Angle ...... 63 4. Selection of Marine Bacteria Involve in PET Degradation ...... 65 4.1 Overview...... 65 4.2 Screening of 3PET-degrading bacteria ...... 66 4.3 Weight loss measurement of PET films ...... 67 4.4 AFM analysis ...... 69 4.5 Raman spectra analysis ...... 78 4.6 FTIR Measurements ...... 80 4.7 PET surface wettability ...... 82 4.8 Summary ...... 83 5. Development of MLSA and MALDI-TOF Mass Spectrometry for Alteromonas Species Classification ...... 86 5.1 Declaration for Chapter 5 ...... 86 5.2 Overview...... 86 5.3 House-keeping gene selection ...... 87 5.4 Individual gene analyses ...... 92 5.5 Comparative MLSA...... 96 5.6 MLSA as possible alternative to DNA-DNA hybridisation (DDH) ...... 100 5.7 MALDI-TOF mass spectrometry analysis ...... 101 5.8 Summary ...... 103

6. Description of Alteromonas australica H17T, Isolated from the Tasman Sea ...... 105 6.1 Declaration for Chapter 6 ...... 105 6.2 Overview...... 105 6.3 16S rRNA gene sequence analysis ...... 106 6.4 Multilocus Sequence Analysis (MLSA) ...... 110 6.5 DNA-DNA hybridization (DDH) ...... 115 6.6 MALDI -TOF mass spectrometry ...... 119 6.7 Physiological and biochemical analysis ...... 121 6.8 Summary ...... 123 7. Description of Marinobacter salarius R9SW1 T and Marinobacter similis A3d10T, Isolated from Sea Water ...... 125 7.1 Declaration for Chapter 7 ...... 125 7.2 Overview...... 125 7.3 16S rRNA gene sequence analysis ...... 126 7.4 gyrB and rpoD gene sequence analyses...... 130 7.5 Phenotypic analysis ...... 134 7.5.1 Morphology...... 134 7.5.2 Biochemical, physiological and metabolic characteristics ...... 136 7.6 Genotypic analysis ...... 141 7.6.1 G+C content ...... 141 7.6.2 DNA-DNA hybridization ...... 141 7.6.3 Whole genome sequence analysis ...... 142 7.7 MALDI-TOF mass spectrometry ...... 144 7.8 Summary ...... 147 8. Summary a nd Future Directions ...... 149 8.1 Overall summary ...... 149 8.2 Future directions ...... 152 8.3 Close ...... 153 References ...... 155 Appendices ...... 197

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List of Figures

Figure Page

2.1 PET logo labelled on bottles and containers (A) and the chemical structure of PET (B) ...... 8

2.2 General mechanism of plastics biodegradation ...... 11

2.3 Schematic diagram showing the steps and processes commonly used in bacterial taxonomy for novel species description ...... 19

2.4 Schematic diagram of the principle processes in MALDI-TOF mass spectrometry analysis ...... 35

4.1 (A) Clear zone (red arrow) produced by strain A3d10T on 3PET agar after 2 weeks of incubation at 25°C, and (B) a decrease in turbidity of the 3PET minimal liquid medium in the present of strain A3d10T ...... 67

4.2 Changes in mass of the PET films after one month incubation ...... 68

4.3 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media (PO, control) ...... 72

4.4 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and in the presence of strain A3d10T (PA)...... 73

4.5 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and 3PET trimer (3PO, control) ...... 74

4.6 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and 3PET trimer, and in the presence of strain A3d10T (3PA) ...... 75

4.7 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the SDS-treated PET film incubated with minimal media (SPO, control) ...... 76

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4.8 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the SDS-treated PET film incubated with minimal media and in the presence of strain A3d10T (SPA) ...... 77

4.9 Offset Raman spectra of the six PET films in the 500-2000 cm-1 spectral region ...... 79

4.10 ATR-FTIR spectra of the six PET films after one month of incubation with the bacteria ...... 81

5.1 Agarose gel electrophoresis of PCR products by using genes and primers described by Ivars-Martínez et al., 2008 ...... 89

5.2 Agarose gel electrophoresis of PCR amplification of gyrB, rpoD, gap, recA and atpD ...... 90

5.3 Agarose gel electrophoresis of PCR products of dnaK, sucC and rpoB using the primers designed from this study ...... 91

5.4 Phylogenetic analyses of the nucleotide sequences of each of the five marker genes ((A) dnaK, (B) sucC, (C) rpoD, (D) rpoB and (E) gyrB) used for MLSA of Alteromonas spp ...... 94

5.5 Genetic similarity matrix for Alteromonas type strains ...... 95

5.6 Comparative Neighbour-joining (NJ) phylogenetic analysis of Alteromonas species based on (A) 16S rRNA gene sequences obtained from NCBI GenBank and (B) Multilocus sequence analysis of concatenated sequences of dnaK, sucC, rpoB, gyrB and rpoD genes ...... 97

5.7 Phylogenetic analysis of the concatenated amino acid sequences (DnaK, SucC, RpoB, GyrB, and RpoD) ...... 98

5.8 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles from nine Alteromonas species generated by the MALDI Biotyper 3.0 software ...... 102

5.9 Three-dimensional Principal Component Analysis (PCA) plot of the nine Alteromonas species ...... 102

6.1 The taxonomic position of strain H17T and the other species of the genus Alteromonas inferred from the phylogenetic tree based on 16S rRNA gene sequence similarities ...... 107

6.2 Phylogenetic analyses of the nucleotide sequences of the five housekeeping genes ((A) dnaK, (B) sucC, (C) rpoD, (D) rpoB and (E) gyrB) used in MLSA ...... 112

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6.3 Phylogenetic analysis showing the position of strain H17T based on (A) concatenated sequences of dnaK, sucC, rpoD, rpoB, and gyrB genes, and (B) 16S rRNA gene sequences ...... 114

6.4 Thermal denaturation curves of genomic DNA from strain H17T (grey curving line) and the hybrid of strain H17T and the nine validly described Alteromonas species ...... 118

6.5 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles showing the taxonomic position of strain H17T generated by MALDI Biotyper 3.0 software ...... 120

7.1 Neighbour-joining phylogenetic tree showing the taxonomic position of strains R9SW1T and A3d10T according to their 16S rRNA gene sequences ...... 129

7.2 Gel electrophoretic analysis of PCR products for gyrB and rpoD ...... 130

7.3 Neighbour-joining phylogenetic tree showing the taxonomic position of strains A3d10T and R9SW1T according to their (A) gyrB and (B) rpoD gene sequences ...... 131

7.4 Scanning electron micrographs of strains (A) A3d10T and (B) R9SW1T ...... 135

7.5 BLAST genome ring showing the comparison between strains A3d10T, R9SW1T, M. adhaerens HP15T and M. hydrocarbonoclasticus ATCC 49840T ...... 143

7.6 MALDI-TOF mass spectra of strains A3d10T, R9SW1T, and phylogenetically related and type species of genus Marinobacter ...... 145

7.7 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles of strains A3d10T, R9SW1T and closely related Marinobacter species ...... 146

8.1 Schematic representation of surface erosion arising from the enzymatic degradation of the surface of the PET as a result of exposure to strain A3d10T ...... 150

List of Tables

Table Page

2.1 List of microorganisms and the respective enzymes reported to be involved in PET degradation ...... 12

2.2 Monitoring enzymatic surface hydrolysis of polymers ...... 14

2.3 Comparative methods and labelling techniques used for DNA-DNA hybridization experiments ...... 23

2.4 Summary of the MLSA scheme used in various genera and their respective proposed threshold value ...... 32

3.1 Primer sequences used for 16S rRNA gene amplification or sequencing ...... 51

3.2 Genes and the corresponding primer sequences used for the amplification or sequencing in MLSA study ...... 52

3.3 GenBank accession numbers for Alteromonas and Marinobacter strains derived from this study ...... 54

3.4 Experimental conditions used for PET biodegradation experiments ...... 61

4.1 Analysis of surface roughness of the PET films under various experimental conditions ...... 70

4.2 Values of the spectroscopic indexes obtained from the spectra of PET films incubated under various conditions ...... 82

4.3 Effect of WCA upon surface hydrolysis of PET film by strain A3d10T ...... 83

5.1 Interspecies similarities of the 16S rRNA gene sequences and the concatenated sequences of dnaK, sucC, rpoB, gyrB, and rpoD genes ...... 99

6.1 Interspecies similarity of the 16S rRNA gene sequences of Alteromonas spp. based on pairwise distances ...... 109

6.2 Interspecies similarity of the 16S rRNA gene sequences of Alteromonas spp. based on percent sequence similarities ...... 109

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6.3 Comparative sequence similarities of individual gene between strain H17T and the nine validly described Alteromonas species ...... 113

6.4 Comparative data showing the DNA-DNA relatedness, MLSA and the 16S rDNA sequence similarity between strain H17T and the nine validly described Alteromonas species ...... 119

6.5 Differential characteristics of strain H17T and other validly described Alteromonas species ...... 122

7.1 Percentage pairwise similarity of 16S rRNA gene sequences of strains A3d10T and R9SW1T with closely related Marinobacter species ...... 127

7.2 Sequence similarities of gyrB and rpoD genes for strains A3d10T, R9SW1T and phylogenetically related type strains and type species of the genus Marinobacter ...... 133

7.3 Susceptibility of strains A3d10T and R9SW1T to various antibiotics ...... 136

7.4 Biochemical characteristics of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using Microbact™ 24E Gram-negative identification system ...... 137

7.5 Enzymatic characteristics of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using API ZYM system ...... 138

7.6 Carbon sources utilization of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using Biolog GN2 microplates ...... 139

7.7 The average nucleotide identity (ANI) and genome-to-genome distance (GGD) between strains A3d10T, R9SW1T, M. adhaerens HP15T and M. hydrocarbonoclasticus ATCC 49840T ...... 142

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List of Abbreviations

3PET Bis(benzoyloxyethyl) terephthalate AFLP Amplified fragment-length polymorphism AFM Atomic force microscopy AGRF Australian Genome Research Facility ANI Average nucleotide identity DDH DNA-DNA hybridization dH2O Distilled water DSC Differential scanning calorimetry FTIR Fourier transform infrared G+C Guanine-cytosine content HPLC High-performance liquid chromatography IR Infrared LC-MS Liquid chromatography–mass spectrometry MALDI-TOF Matrix-assisted laser desorption/ionization-time of flight ML Maximum-likelihood MLSA Multi-locus sequence analysis MP Maximum parsimony NCBI National Center for Biotechnology Information NJ Neighbour-joining PCR Polymerase chain reaction PET Poly(ethylene terephthalate) PFGE Pulsed field gel electrophoresis RBR Relative binding ratio rRNA Ribosomal RNA SEM Scanning electron microscopy SSC Saline sodium citrate

Ta Annealing temperature

Tm Melting temperature

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UV Ultra-violet WCA Water contact angle XPS X-ray Photoelectron Spectroscopy

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Chapter 1: Introduction

Poly(ethylene terephthalate) (PET) is a semicrystalline, thermoplastic polymer that possesses exceptional chemical, physical, and mechanical properties (Awaja and Pavel 2005; Webb et al. 2013). These properties, together with its low cost of production, has led to PET being used in many applications in various sectors, such as the food packaging, automotive and biomedical device industries, home furnishing, electronics, textiles and films (Kint and Muñoz-Guerra 1999; Zheng et al. 2005). The usage of PET is especially common in the food and beverage packaging industry; it has been reported that PET comprises more than 50% of the world’s synthetic fibre production and represents more than $17 billion per year in terms of its global consumption ( Sinha et al. 2010; Webb et al. 2013).

One downside of the popularity of this material is the waste stream that results from its use. PET’s excellent physiochemical properties, intrinsic hydrophobic and inert nature has created significant environmental problems, particularly with regard to the marine ecosystems (Moore 2008; Ryan et al. 2009). Some of the common problems are entanglement in and ingestion of PET by marine creatures, distribution and invasion of non-native organisms to new locations by the plastic being used as a tool for transportation, transferring toxic chemicals to the food web, and the formation of microplastics, which can greatly adversely affect a wide range of marine organisms (Derraik 2002; Barnes et al. 2009; Andrady 2011; Engler 2012).

The current disposal methods for PET/plastic wastes include sending to landfill, incineration and recycling (Zhang et al. 2004; Webb et al. 2013). None of these methods, however, have proven satisfactory in overcoming the issue. Sending PET to landfill not only limits a large section of land for alternate uses (Tansel and Yildiz 2011), but also causes the release of harmful chemicals into the surrounding land ( Webb et al. 2013). Incineration results in the release of toxic fumes and gases into the atmosphere (Sinha et al. 2010), whilst recycling is relatively expensive and lacks suitable overall efficacy (Zhang et al. 2004; Awaja and Pavel 2005).

Biodegradation represents a suitable alternative approach for plastic waste management. It is an environmental friendly, cost effective method for plastic waste

1 handling (Bhardwaj et al. 2013). To date, however, there has been no viable biodegradation procedure established for the disposal of PET. A few studies have reported the successful identification and isolation of the microorganisms/enzymes responsible for the degradation of PET, however, none of these studies has identified the potential of marine bacteria in regard to this matter. As the majority of plastic wastes end up in the ocean (Moore 2008), the identification of marine bacteria that have the ability to degrade PET would be of great benefit to the marine environment.

Currently, the well accepted identification and classification system in bacterial taxonomy is the so-called polyphasic approach (Vandamme et al. 1996), which includes pheno-chemotaxonomic, genetic and phylogenetic analysis. The traditional ‘gold standard’ molecular methods for the delineation of bacterial species involves DNA- DNA hybridization and 16S rRNA gene sequence analysis, however, each of these methods has its own limitations. DNA-DNA hybridization experiments are time- consuming, labour-intensive and lack cross-laboratory reproducibility (Pereira et al. 2008; Schleifer 2009; Richter and Rossello-Mora 2009; Tindall et al. 2010; Ramasamy et al. 2014). Moreover, the commonly accepted 70% cut-off boundary for the identification of bacterial types is sometimes not applicable to particular bacterial groups (Fournier and Raoult 2009). The 16S rRNA gene sequence analysis, on the other hand, is relatively easy to perform and it allows the comparison of data through online databases (Schleifer 2009; Whitman 2011). The 97% cut-off criterion, however, has proven unsatisfactory as a result of the consequent increase in the availability of sequencing data that has resulted in a vast increase in the number of new species being described ( Schleifer 2009; Gevers et al. 2005).

With the decreasing costs and the availability of high throughput next- generation sequencing (Soon et al. 2013), multi-locus sequence analysis (MLSA) and whole genome sequence analysis are two techniques that have become more prominent in the systematic identification of prokaryotic species. MLSA has been reported to overcome the limitations associated with 16S rRNA gene sequence analysis, and the results have been shown to be comparable to that of DNA-DNA hybridization (Martens et al. 2008; Rong and Huang 2012). Whilst whole genome sequence analyses are relatively new, the incorporation of whole genome sequencing data into the description of new species has been suggested, with the appropriate average nucleotide identity

(ANI) threshold range for positive identification being proposed as being 95 – 96% (Kim et al. 2014; Chun and Rainey 2014).

Hence, the primary aim of this study was to identify and classify marine bacteria that have the potential to degrade PET using the traditional polyphasic approach coupled with modern classification techniques. As mentioned above, PET wastes are a global environmental issue, with a large amount of PET waste ending up in marine environments. Therefore, the microorganisms living in marine environments have the potential to be a good model organism for the study of potential PET biodegradation, as there is a possibility that these microorganisms would have already evolved to exhibit the capacity to develop PET degrading metabolic pathways. In order to achieve the primary aim of this project, four experimental chapters, with the associated intermediate aims, are included as described below.

The first intermediate aim in this thesis was to select a collection of one hundred marine bacteria that might be promising as candidates for the degradation of PET. These bacteria are maintained in the Collection of Marine Microorganisms at Swinburne University of Technology. We also aimed to study the selected bacteria for their capacity to degrade PET using a plate-based screening method using bis(benzoyloxyethyl) terephthalate (3PET) as a model PET substrate over a short term (1 month) period to reconfirm and monitor any changes taking place with regard to the PET film.

The second intermediate aim was to evaluate the available advanced molecular biology and instrumental techniques that could prove useful in taxonomic studies of the bacterial strains involved in PET biodegradation. In the context of this aim, MLSA and matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry was used for the delineation of Alteromonas species, one group of microbial taxa that has been previously constantly isolated from a PET degradation experiment.

Following this, the comprehensive classification and identification of a novel species belonging to the genus Alteromonas, which was isolated from St. Kilda beach, Port Philip Bay, Melbourne, Australia, was performed using polyphasic approach, utilising a combination of traditional techniques as well as the newly developed MLSA and MALDI-TOF mass spectrometry analysis. 3

The final intermediate aim was to identify the accurate taxonomic affiliation of Marinobacter bacteria that were involved in PET degradation. Two new species belonging to this genus were studied, one strain being isolated from St. Kilda beach (as described above), and the other being previously isolated from Chazhma Bay, Gulf of Peter the Great, Sea of Japan, Pacific Ocean. This study encompassed a range of taxonomic approaches and included multigene and MALDI-TOF mass spectrometry analyses as well as whole genome sequence analysis.

Chapter 2: Literature Review

2.1 Overview

This chapter provides a detailed overview of the current knowledge on the biodegradation of poly(ethylene terephthalate) (PET), as well as the various developments in the classification of novel bacteria species. This involved a review of the applications of PET and their associated environmental impacts, the identification of organisms involved in PET degradation, the development of identification and classification techniques for the description of novel bacteria species, and a discussion on some of the techniques that could be used to measure biodegradation.

2.2 Plastics and their environmental impact

Plastics are man-made polymers that have been widely used over the past decades due to their low manufacturing cost and desirable properties. In 2011, statistics reported by the United States Environmental Protection Agency (EPA) have shown that, from a total of 250 million tons of municipal solid waste (MSW) generated in the U.S. in that year, plastics comprised about 13% of the total. From this waste stream, only a small amount of plastic waste was recovered, the majority being discarded, ending up in landfill (http://www.epa.gov/epawaste/nonhaz/municipal/msw99.htm). The high production and widespread use of plastics, together with the inappropriate handling of generated wastes are becoming a major global environmental issue, especially in the marine environment, due to the fact that plastics are fairly stable and are not easily degraded (Ng and Obbard 2006; Moore 2008).

The various threats posed by plastic debris to marine ecosystems are: entanglement in and ingestion of by wildlife, distribution of non-native organisms to new locations, absorption and transportation of toxic chemicals, and the formation of microplastics, which affect marine biota. Marine vertebrates, especially the juveniles, have been reported to be entangled in plastic debris causing injury and restricting movement, which subsequently adversely affects their normal feeding patterns and ability to avoid predators (Derraik 2002; Sazima et al. 2002). Ingestion of plastic items 6 by marine wildlife can cause blockage of digestive tracts, starvation, suffocation, and associated health problems (Derraik 2002; Gregory 2009; Barnes et al. 2009). A broad range of marine organisms have been documented to be affected by plastic debris, for example seabirds (Cadée 2002), sea turtles (Méndez et al. 2002), sharks (Sazima et al. 2002), sea lions and fur seals (Page et al. 2004; Boren et al. 2006), sea cucumbers (Graham and Thompson 2009), whales (Jacobsen et al. 2010), dolphins (Denuncio et al. 2011), deep-water fish (Anastasopoulou et al. 2013) and zooplankton (Cole et al. 2013).

Furthermore, plastic debris pose potential threats to the native marine environment by invasion and colonisation of non-native organisms that uses these debris as transportation tools (Barnes 2002; Gregory 2009). Common taxa being observed rafting were bryozoans, crustaceans, gastropods and marine hydrozoans (Martin and Lars 2005). Also, plastic debris might act as a vector for transferring toxic chemicals from marine environment to the food web (Engler 2012), as they might absorb and/or transport persistent organic pollutants (POPs) which will have toxicological effects on wildlife when ingested (Teuten et al. 2009; Elliott and Elliott 2013) or being circulated in the food web. These may lead to an increase in the risk of toxic chemical consumption throughout the marine food chain, potentially ending up in human diets (Barnes et al. 2009; Engler 2012).

Recently, plastic particles, with sizes less than 5 mm, that have accumulated in the sea for at least four decades have attracted significant research interest (Ng and Obbard 2006; Barnes et al. 2009; Andrady 2011). These litters were coined ‘microplastics’. They are not visible to the naked eye and, due to their micro sizes, they cannot be removed from the ocean by standard cleaning procedures such as net sampling (Andrady 2011). Microplastics usually find their way into the ocean via storm water runoff (Reddy et al. 2006; Fendall and Sewell 2009) or by the weathering breakdown of bigger plastic debris samples (Ryan et al. 2009; Andrady 2011). The negative impact on marine organisms has been studied on mussels, which highlighted that the transferral of microplastics into the circulatory system affected the immune response at a molecular level (Browne et al. 2008; Zarfl et al. 2011).

2.3 Poly(ethylene terephthalate) (PET)

Poly(ethylene terephthalate), commonly abbreviated as PET, is a semicrystalline, thermoplastic polymer resin belonging to the polyester family. It is commercially available under a variety of names, for instance, Arnitel®, Diolen®, Eastapac®, Hostadur®, Melinex®, Mylar®, Rynite® (Sinha et al. 2010; Webb et al. 2013). In the last few decades, PET has been extensively used in different sectors. It is primarily manufactured in the form of films, fibres and sheet extrusion, and its end uses include bottles, containers and food packaging (e.g. soft drink and water bottles), fabrics, automotive parts, sports goods, strapping tapes, photographic applications and textiles (Kint and Muñoz-Guerra 1999; Zheng et al. 2005). It has been reported that PET makes up more than 50% of the total world synthetic fibre production and represents more than $17 billion per year in terms of global consumption (Sinha et al. 2010 ; Webb et al. 2013). The high production and consumption rates of PET are due to the fact that it is strong and tensile, mechanically and thermally stable, transparent, easily processed, and has low gas permeability (Kint and Muñoz-Guerra 1999; Awaja and Pavel 2005).

Commercially available PET plastic is labelled with the #1 logo, which can be normally found at the bottom of bottles and containers (Figure 2.1(A)). It has a broad range of properties: the average molecular weight is reported to be in the range of 30,000 - 80,000 g/mol, the density is 1.41 g/cm3, it has a water absorption capacity of 0.5%, based on 24 hours analysis, and its melting and glass transition temperatures are in the range of 255-265°C and 69-115°C respectively (Awaja and Pavel 2005; Webb et al. 2013). The structure of the repeating unit of PET is shown in Figure 2.1(B).

Figure 2.1 PET logo labelled on bottles and containers (A) and the chemical structure of PET (B).

2.4 Current disposal methods

The current handling methods for PET and other plastic wastes involve burying, incineration and recycling (Zhang et al. 2004; Webb et al. 2013), however, each of these methods has its own drawbacks and limitations.

2.4.1 Burying

Burying or landfilling is the traditional method of disposal, but due to space constraints as well as the possibility of land and groundwater pollution, it is not the preferable method for disposal (Zhang et al. 2004). Plastics are very slow degrading materials; it has been shown that the ir relative persistence in landfills is more than 20 years, meaning that the occupied land cannot be used for other purposes, such as agriculture, in the meantime (Tansel and Yildiz 2011; Webb et al. 2013). Furthermore, plastic wastes buried in the ground are able to leach harmful chemicals, for example benzene, toluene, xylenes, ethyl benzenes, trimethyl benzenes and Bisphenol A (BPA) (Webb et al. 2013) into the groundwater, which in turn has the potential to harm the ecosystem and environment.

2.4.2 Incineration

Incineration, on the other hand, can overcome the need of space for landfill but there are environmental concerns associated with this method of disposal, such as the release of toxic fumes and gases into the atmosphere (Sinha et al. 2010). These gases include polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCB), heavy metals compounds and carbon dioxide, which are hazardous and may contribute significantly to the greenhouse effect (Hopewell et al. 2009; Webb et al. 2013).

2.4.3 Recycling

There are two major approaches in the recycling of PET: chemical and mechanical (Awaja and Pavel 2005). The chemical recycling of PET is carried out by depolymerisation through hydrolysis, methanolysis, glycolysis and aminolysis (Kao et al. 1998; Chen et al. 2001; Awaja and Pavel 2005; Tawfik and Eskander 2010; Sinha et al. 2010). The depolymerisation process will result in various monomers that can then be reused to create new plastics. On the other hand, mechanical recycling generally involves three processing steps: decontamination, drying and melting (Awaja and Pavel 2005). Mechanical recycling is a relatively simple approach but yields a poor quality product.

Although the recycling methods are more environmentally friendly than burying and incineration, the main drawback is that the processes are relatively expensive and inefficient (Zhang et al. 2004). Also, the presence of additives and other impurities can be problematic, and results in low quality end products (Zhang et al. 2004; Awaja and Pavel 2005).

2.5 Plastic degradation

In general, due to their strength and durability, plastics such as PET do not naturally fully degrade or break down to a large degree in the ambient environment (Yamada-Onodera et al. 2001; Zheng et al. 2005). Degradation can be traced by the chemical or physical changes in the polymer and can be categorised as photo- degradation, thermo-oxidative degradation, hydrolytic degradation and biodegradation (Shah et al. 2008; Andrady 2011). The process of degradation is usually initiated by photo-degradation where ultraviolet (UV) radiation from sunlight is absorbed by polymers, resulting in the breakage of bonds and allowing the addition of oxygen atoms, followed by thermo-oxidative degradation (Andrady 2011; Webb et al. 2013). Under humid or wet conditions, such as in a marine environment, hydrolysis also plays a significant role in the degradation of plastic materials (Hosseini et al. 2007; Andrady 2011).

2.5.1 Biodegradation

Biodegradation, generally known as microbial or enzymatic degradation, is an alternative approach for plastic waste management (Bhardwaj et al. 2013). It is defined as the ability of one or more microbial populations to utilize the synthetic materials as the sole carbon and energy source, and is generally environmentally friendly, cost effective and potentially much more efficient than other disposal methods (Shah et al. 2008 ; Sivan 2011).

Microorganisms have long been known for their metabolic versatility and their capability in the bioremediation and biodegradation of environmental pollutants. Previous studies have shown that bacteria can be used to clean up oil spills (Atlas 1995; Cohen 2002; Röling et al. 2004; Cappello et al. 2007), polychlorinated biphenyls (PCBs) (Luigi et al. 2007; Adebusoye et al. 2008), as well as heavy metals such as mercury, lead and cadmium (Gadd 1990; De et al. 2008; Bestawy et al. 2013). In terms of biodegradability of plastic materials, it is usually a heterogeneous process. The degradation of the polymer is generally initiated by the breakdown of the polymer into monomers via physical and biological (enzyme) forces (Shah et al. 2008), after which the monomers are absorbed and biodegraded within the microbial cells by respective metabolic pathways, resulting in the production of water, carbon dioxide, and in the case of anaerobic degradation, methane (Figure 2.2) (Goldberg 1995; Müller 2005; Shah et al. 2008). The adaptation potential of microorganisms with the ability to mineralise PET has previously been reported (Zhang et al. 2004) and to date, various microorganisms, as well as the associated enzymes that might be involved in the biodegradation process, have been identified (Table 2.1). None of the studies, however, has identified the potential of marine bacteria in the biodegradation of PET.

Figure 2.2 General mechanism of plastics biodegradation. 11

Table 2.1 List of microorganisms and the respective enzymes reported to be involved in PET degradation.

Enzyme Organism Isolation source Reference Cutinases Thermobifida fusca (bacteria) Manure heaps, composts, hay (Müller et al. 2005) Fusarium solani (fungus) Soil, plant debris (Alisch-Mark et al. 2006) Penicillium citrinum (fungus) Air, soil, cotton fabric (Liebminger et al. 2007) Humicola insolens (fungus) Compost (Ronkvist et al. 2009) Thermobifida alba (bacteria) Manure heaps, composts, hay (Ribitsch et al. 2012b)

Lipases Aspergillus oryzae (fungus) Fermented foods (Wang et al. 2007b) Thermomyces lanuginosus (fungus) Soil (Eberl et al. 2009)

Esterases Bacillus subtilis (bacteria) Soil, human gut (Ribitsch et al. 2011) Thermobifida halotolerans (bacteria) Saline soil (Ribitsch et al. 2012a)

2.6 Surface characterisation techniques

Microbial biodegradation of PET occurs predominantly on the outer surface of the polymer (Shah et al. 2008), and by characterizing changes in its surface chemistry and/or surface topography over time using techniques measuring material properties (Guebitz 2011), the fundamental mechanisms involved in the biodegradation of PET can be elucidated. Table 2.2 summarizes some of the techniques commonly employed for surface analyses of polymers. Some of the techniques employed in this study are discussed below.

2.6.1 FTIR and Raman Spectroscopy

The crystallinity of a material is the regular three-dimensional distribution of atoms within a given space. Understanding changes in surface crystallinity may provide information pertinent to the mechanism by which the microbial biodegradation of PET occurs. Fourier transform infrared (FTIR) and Raman spectroscopy can be used to examine the crystallinity of polymers including PET. In polymers there are crystalline regions where IR absorption occurs (IR active vibration) due to a change in the dipole of the bond as it vibrates. In the crystalline region where IR is inactive, Raman scattering occurs (Raman active vibration) due to a change in the polarizability of the symmetric bond as it vibrates. Therefore, understanding molecular vibrations that are dependent on bond strengths and configurations of a molecular unit in the polymers require information from both FTIR and Raman spectroscopies, which are complementary to each other (Pappas et al. 2004; Ivleva et al. 2009). FTIR and Raman spectroscopies can significantly benefit the characterization of polymers due to their high sensitivity, the ease of obtaining a good spectrum, with no extensive procedures involved for sample handling, and the non-destructive nature of the techniques (Khulbe and Matsuura 2000). The analysis can be used to provide information such as functional groups, structural, conformation and orientation of the chains, allowing characterizing changes in crystallinity, as well as the formation of additional functional groups following certain treatments such as enzymatic hydrolysis.

Table 2.2 Monitoring enzymatic surface hydrolysis of polymers. Method Target Polymers References FTIR Crystallinity PET, PA (Donelli et al. 2009; Parvinzadeh et al. 2009) Amino and amide groups PAN (Fischer-Colbrie et al. 2006) Raman Crystallinity PET (Paquin et al. 2007) DSC Crystallinity PET, PTT (Almansa et al. 2008; Ronkvist et al. 2009) XPS Atomic composition PET, PTT (Vertommen et al. 2005; Brueckner et al. 2008) Carboxyl and hydroxyl groups, nitrogen content PET (Brueckner et al. 2008) MALDI-TOF Changes in DP PET (Eberl et al. 2009) AFM Surface topography PET (Webb et al. 2010; Karaca and Özdoǧan 2013) SEM Morphology PET (Ronkvist et al. 2009) WCA, drop test, rising height Hydrophilicity PET (Herrero Acero et al. 2011; Ribitsch et al. 2012a) Tensiometry Surface charge PET, PA (Almansa et al. 2008) Derivatisation Quantification of carboxyl and hydroxyl groups PET (Donelli et al. 2009; Ribitsch et al. 2011; with 2-(bromomethyl)naphthaline and Ribitsch et al. 2012b) sulphobenzoic acid anhydride, respectively Titration Quantification of carboxyl groups PET (Tkavc et al. 2013) HPLC, LC-MS Quantification of oligomers and monomers released PET (Nechwatal et al. 2006; Hooker et al. 2003) Weight loss Solubilisation of oligomers PET (Müller et al. 2005)

For example, changes in the Raman scattering associated with the crystalline structure of a material were detected during a phase transition from amorphous to crystalline structure (Andrikopoulos et al. 2006; Krylov et al. 2012). In the case of PET, enzymatic hydrolysis is more likely to take place in the amorphous regions (Vertommen et al. 2005 ; Eberl et al. 2008), and these changes in the crystallinity are best monitored through FTIR (Cole et al. 1994b; Donelli et al. 2009) and Raman (Paquin et al. 2007) spectroscopies. In PET, trans and gauche rotational conformers for the ethylene glycol moiety are important characteristics of the amorphous polymer. However, only the trans conformer is present in crystalline PET. The important FTIR band associated with the trans conformation of crystalline PET domains appears at 1341 cm-1 and is related to

CH2 wagging, whereas the band associated with the gauche/amorphous conformation of PET appears at 1371 cm-1 (Walls 1991; Cole et al. 1994a). Other FTIR spectral features such as a strong carbonyl stretching band at 1719 cm-1, a doublet near 1100 cm-1, and a single intense band at 1247 cm-1 are also unique to crystalline PET. On the other hand, in Raman spectra, the trans conformation of ethylene glycol appears at 995 cm-1 band, whereas the band of the gauche conformation appears at 885 cm-1 (Paquin et al. 2007). Comparing these spectral features will allow confirmation as to whether PET biodegradation is taking place.

2.6.2 X-ray photoelectron spectroscopy (XPS)

Photoelectron spectroscopy uses the photo-ionization and energy-dispersive analyses of the emitted photoelectrons to study the composition and chemical bonding of the first 10 nm of a polymer’s surface (Liu and Webster 2007). Analysis is achieved through the use of soft X-rays at an energy ranging between 200 to 2000 eV for irradiating the samples, placed under high vacuum. In response to X-ray irradiation, elements present will give rise to photoelectrons, generating spectra of the kinetic energy of the photons which are characteristic of their binding energies (Liu and Webster 2007). The presence of peaks at particular energies indicates the presence of a specific element in the sample. The intensity of the peaks is concentration dependent; therefore the technique allows quantitative measurements of the element present within the sampled region. In our previous study, XPS was used to study the surface chemistry of PET after adhesion of marine bacteria and biodegradation (Webb et al. 2010). The 15 results revealed a decrease in surface-bound oxygen atoms, corresponding to the ester bonds in the polymer, but an increase in hydroxide ions, possibly contributed by oxygen atoms lost during hydrolysis. The exact mechanisms involved during enzyme hydrolysis are unclear, but the hydrolysis of ester is a strong indication of microbial degradation of PET. A similar study by Brueckner et al. indicated that the enzymatic hydrolysis of PET led to an increase in hydrophilicity due to the formation of surface hydroxyl (C-OH) and carboxyl groups (COOH), as determined by XPS (Brueckner et al. 2008). These studies show that XPS is a useful tool for analysing surface degradation of PET and other polymers, and the results, in combination with other techniques, can allow further understanding of the mechanisms involved. However, XPS analysis has a limited lateral spatial resolution, and the quality of analysis is prone to atmospheric contamination (Blomfield 2005; Johansson and Campbell 2004).

2.6.3 Atomic Force Microscopy (AFM)

Changes to the topographical features of PET, such as roughness, can be monitored using atomic force microscopy (AFM) (Gould et al. 1997; Dinelli et al. 2000; Webb et al. 2010; Karaca and Özdoǧan 2013). For example, in their studies of the surface of PET using AFM, Gould et al. were able to distinguish between the amorphous and crystalline regions based on the surface roughness (Gould et al. 1997). AFM applies a sharp tip mounted on a flexible cantilever probe which makes constant (contact mode) or intermittent (tapping mode) contact with the surface as controlled by a piezoelectric scanner (Giessibl 2003; Webb et al. 2011). On the back of the cantilever a laser spot is reflected to a position-sensitive photodetector. In a typical surface scanning process, the deflection of the laser occurs as the tip is raster scanned across the sample surface. The laser deflections describing forces between the tip and the sample can be measured and processed into a three-dimensional representation of the surface (Giessibl 2003; Webb et al. 2011). Compared to other topographic imaging techniques such as SEM, AFM does not require extensive sample preparation steps or the presence of a vacuum. In addition, as there is no requirement for the sample to be electron- conductive, the technique is suitable for analysing polymers and biological samples in their native state. The depth of field in AFM is limited to the micron to sub-micron

16 range, which is influenced by the travel distance of the piezoelectric scanner as well as the tip size and geometry (Liu and Webster 2007).

2.6.4 Water Contact Angle (WCA)

The enzymatic surface hydrolysis of PET can lead to an increase in the hydrophilicity of the PET substrate following the hydrolysis of backbone ester bonds and the formation of free hydroxyl and carboxyl groups (Brueckner et al. 2008). Therefore, by determining the hydrophilicity of the surface, it is possible to measure the extent of surface hydrolysis. In this context, water contact angle (WCA) provides a facile means for analysing the wettability by measuring the contact angle between the droplet and the PET surface. A decrease in the WCA indicates an increase in the hydrophilicity or wettability. In previous studies, hydrolases such as esterase (Ribitsch et al. 2011) and cutinase (Ribitsch et al. 2012b) isolated from Bacillus subtilis and Thermobifida alba, respectively, have been shown to increase the hydrophilicity of PET. Despite the technique being relatively simple and fast, the major drawback of WCA in determining the enzymatic surface hydrolysis of PET is due to the adsorption of enzyme protein which can also increase the hydrophilicity, leading to inaccuracy in the measurements (Vertommen et al. 2005). Therefore, it is often necessary to first remove completely the enzymes from the PET surface or introduce enzyme inhibitors in control experiments (Guebitz 2011).

2.7 Bacterial taxonomy

In order to study the biodegradation of PET, the classification and identification of the bacteria with potentials to degrade PET are of significant importance in this project. The main advantages of classification and identification of bacteria, or bacterial taxonomy, are that: (a) it provides databases of the validly described organisms, making the information (specific characteristics of an organism) available to future research (Federhen 2012); (b) the classification of similar organisms in groups (genera) with precise names allows the comparison of new isolates to the members of a genera and 17 facilitates scientific communications (Okafor 2011) ; and (c) it supports investigation and validation of newly developed tools in bacterial systematics (Ramasamy et al. 2014).

Studies on bacterial taxonomy have emerged since the late 19th century, resulting in present nomenclature and classification of different group of bacteria (Bergey et al. 1923). The first fundamental work in bacterial classification, Bergey’s Manual of Determinative Bacteriology, was based on metabolic, physiological and morphological features of the bacteria (Bergey et al. 1923). Recent advances in scientific research including molecular biology have prompted further development of bacterial classification. A combination of pheno-chemotaxonomic (e.g., electron microscopic examination of cell structure and morphology, lipid analysis), genetic (e.g., mol% G+C composition, chromosomal DNA-DNA hybridization (DDH)) and phylogenetic (e.g., 16S rRNA gene sequence analysis) characteristics implemented in bacterial taxonomy, the so-called polyphasic approach, is now an essential part of bacterial systematics (Brenner et al. 1982; Vandamme et al. 1996). The steps and common techniques used in bacterial taxonomy are summarized in Figure 2.3, and the techniques used in this project will be discussed in Sections 2.8 – 2.10. To date, there is no official classification of prokaryotes, and thus it has been suggested that the official classification system for prokaryotes is the system that has been widely accepted by the scientific community (Brenner et al. 2005), and is described in the Bergey’s Manual of Systematic Bacteriology.

Figure 2.3 Schematic diagram showing the steps and processes commonly used in bacterial taxonomy for novel species description. (Adapted from (Rainey 2011)). 19

2.8 Phenotypic information

Phenotypes are described as the observable or measurable biochemical and physical characteristics of cells. Description of a species based on phenotypic analysis has its advantages as the test is easy to perform and the results can be easily observed and recorded without the need for sophisticated technology (Bochner 2009). Before the introduction of molecular techniques in bacterial taxonomy, the classification of bacterial species relied solely on morphological, biochemical and physiological characteristics of the cells (Bergey et al. 1923; Rosselló-Mora and Amann 2001). The morphological features includes both colonial (shape, color, size) and cellular (shape, size, flagellation type, endospore, Gram stain) characteristics; while the biochemical and physiological tests include substrate utilization, oxidation/fermentation of carbohydrates, enzymatic activities, susceptibility to antibiotics, growth at different temperatures, media and pH, and salinity tolerance (Busse et al. 1996). As the number of methods used to describe and identify species have increased, so too has the number of validly described species; although phenotypic characteristics only are no longer regarded as being sufficient as the sole method for species delineation, they provide descriptive information when being used together with closely related taxa (Oren 2004).

For phenotypic analyses in bacterial taxonomy, the number of traits tested are usually those common in the particular taxon, while variable traits are sometimes tested for subgroupings determination (Tindall et al. 2010). An isolate can only be classified as a new species if it can be differentiated from the validly named species by at least one phenotypic property (Wayne et al. 1987; Rosselló-Mora and Amann 2001). Nevertheless, some authors sometimes compared the phenotypic data of their isolate(s) to the previously published phenotypic data of the type species/strains without repeating the tests in parallel (Tindall et al. 2010). This can lead to false positive or negative results as phenotypic data sometimes lack reproducibility due to different culturing conditions, strain responses and methodologies. Also, the large numbers of tests and the requirement of specialized skills may contribute to variations in results from different laboratories (Rosselló-Mora and Amann 2001; Tindall et al. 2010).

Commercially available identification systems, sometimes referred to as miniaturized identification methods, e.g. API (bioMérieux), Microbact (Oxoid), and Biolog microplates (Biolog), are often used by research groups to obtain phenotypic data due to their ease of use. They are available as kits, consisting of dehydrated substrates for various biochemical reactions placed in the capsules or wells of a strip or tray or microtitre plates, together with the required reagents and/or suspension media (Ling et al. 1988), and the results are recorded based on the colour changes after incubation. The upsides of using such systems are time saving, as a number of phenotypic traits can be tested in a single experiment, and space saving, as the storage (e.g. media and reagents) and incubation spaces needed are reduced (Janda and Abbott 2002). However, the interpretation of results may sometimes lead to confusions as the ambiguous colour development can be interpreted as positive/weak/negative reactions by different researcher. This phenomena can be seen in the species description paper of Marinobacter adhaerens, where urease and gelatinase activities of Marinobacter salsuginis and Marinobacter algicola were recorded as negative while the results obtained in the original studies were positive (Kaeppel et al. 2012; Antunes et al. 2007; Green et al. 2006).

Apart from the issues mentioned above, as a whole, phenotypic analysis delivers descriptive information allowing the recognition of taxa. Importantly, the tests must be performed under well-standardized conditions, and in parallel with closely related type species/strains in order to obtain reproducible data (Tindall et al. 2010). Phenotypic information serves as the basis for the formal classification system of prokaryotes, hence, it should be included with genetic information in order to have a reliable classification and differentiation of bacteria, or in other words, the reinforcement of polyphasic taxonomy.

2.9 Genomic Information

Genomic information was introduced into bacterial taxonomy in the late 1960s. Currently, genotypic methods are being routinely used in modern taxonomic studies due to advances in molecular microbiology (Vandamme et al. 1996). Commonly, genomic information of bacterial strains can be obtained from genetic relatedness (DNA-DNA hybridization (DDH)), G+C content, comparative sequence analyses (e.g. 16S rRNA gene), and DNA fingerprinting (e.g. AFLP, rep-PCR, and PFGE) (Thompson et al. 2013a). With the advances in sequencing technologies, and the decrease in sequencing costs (Soon et al. 2013), whole genome sequencing has recently been introduced into bacterial taxonomy where genomic information can be recovered from the complete genome, and applied in the classification and identification of bacterial species.

2.9.1 DNA-DNA hybridization (DDH)

DDH is the traditional method and the basic principle of genetic studies in species classification and identification. It is the ‘gold standard’ technique in bacterial systematics since 1960s where it has been used to measure the DNA-DNA relatedness of two isolates under stringent conditions (Schildkraut et al. 1961; Wayne et al. 1987; Gevers et al. 2005). There are different approaches used to perform DDH (Table 2.3), but all these methods are in common to measure the stability of hybrid double-stranded DNA under standard conditions. Generally, the techniques can be categorised into free- solution methods and bound DNA methods, the benefits/drawbacks of each method are summarized in Table 2.3.

In bacterial taxonomy, DDH is to be performed when strains share more than 97% of the 16S rRNA gene sequence similarity, and in cases where the description of a new taxon has more than one strain (Tindall et al. 2010). The degree of DNA relatedness can be expressed as relative binding ratio (RBR) and/or the difference in the melting temperature (∆Tm). The two parameters are correlated and can be used together or independently for species description (Johnson 1989; Rosselló-Mora and Amann 2001).

Table 2.3 Comparative methods and labelling techniques used for DNA-DNA hybridization experiments. Adapted from (Rosselló-Mora 2006). Free-solution methods Measurement Advantages/Disadvantages Reference Bouyant density RBR This method has been replaced by newly developed methods (Schildkraut et al. 1961) Label: heavy isotopes

Hydroxyapatite RBR; ∆Tm Advantages: various hybridization reactions can be carried out (Brenner et al. 1969b; Lind Label: radioactive isotope in parallel. The procedure for RBR measurements is short, and and Ursing 1986) there is no restriction on the temperature limit. Endonuclease RBR Disadvantages: radioactive isotopes need to be used to label (Crosa et al. 1973; Popoff Label: radioactive isotope the reference DNAs. and Coynault 1980)

Spectrophotometry RBR; ∆Tm Advantages: labelling of the DNA is not required. (De Ley et al. 1970; Huss et Label: none Disadvantages: Require sophisticated spectrophotometers, and al. 1983) high concentration and equal amount of the DNAs.

Hydroxyapatite/ RBR Advantages: same as the hydroxyapatite method but does not (Ziemke et al. 1998) microtitre plate require radioactive labelling. Label: digoxigenin-biotin Disadvantages: requires double-labelling of DNAs using nick- translation.

Fluorimetric ∆Tm Advantages: amounts of DNAs needed are lower than the (Gonzalez and Saiz-Jimenez Label: none other methods, DNAs labelling are not required, and various 2005) hybridization reactions can be performed simultaneously. Disadvantages: experiments need to be performed with a real- time PCR thermocycler, and same amount of DNAs.

Table 2.3 Continue Bound DNA methods Measurement Advantages/Disadvantages Reference Agar embedded RBR Advantages: radioactivity measurements can be done accurately. (Bolton and Mc 1962; Label: radioactive isotopes Disadvantages: samples need to be incubated at high temperature, and the Brenner et al. 1969a) hybridization reaction of the labelled DNA to the DNA embedded in agar is difficult.

Membrane filters RBR; ∆Tm Advantages: various reactions can be carried out in parallel. Thermal stability (Johnson 1981; Owen Label: radioactive isotopes calculations are independent of the quality of DNA samples. and Pitcher 1985; Disadvantages: requires radioactive labelling. Results might not be accurate as Tjernberg et al. 1989) DNA may wash off during incubation/ washing steps.

Microtitre plate bound DNA RBR Advantages: radioactive labelling is not required. The length of the experiment (Ezaki et al. 1989; Label: photobiotin might be shorter, and various measurements can be performed simultaneously. Adnan et al. 1993; Disadvantages: DNA might be washed off during incubation/washing steps, and Kaznowski 1995; require the use of denaturing agents to prevent incubation at high temperature. Christensen et al. 2000)

Membrane filters RBR Advantages: same as the above membrane filters method, but without the need of (Jahnke 1994; Label: non-radioactive labels radioactive labelling. Cardinali et al. 2000; Disadvantages: requires more incubation/ washing steps. As this is an enzymatic Gade et al. 2004) bioassay, the quantification needs to be done when the reaction is linear.

Microtitre plate bound DNA ∆Tm Advantages: high concentrations of DNAs are not required. Can be carried out (Mehlen et al. 2004) Label: digoxigenin using standard microtitre reader, and without the need of radioactive labelling. Disadvantages: failure in the amplification process will create a problem in the hybridization reaction. Requires the use of denaturing agents to prevent incubation at high temperature.

RBR is the measurement of the relative amount of the heterologous, double-stranded hybrid DNA for a given pair of genomes in comparison to the homologous DNA (reference DNA), where the reference genome is considered to hybridize 100% with itself (Rosselló-Mora and Amann 2001; Rosselló-Mora 2006). The RBR is the commonly used parameter for circumscription of species.

∆Tm, however, is a more reliable parameter as the experiment is independent of the quantity and quality of the DNAs used in the hybridization experiment (Tjernberg et al. 1989). The denaturation of the double-stranded DNA relies predominantly on the G+C content, the ionic strength of the hybridization solution, and the temperature. While the G+C content and the ionic strength of the solution are the constant parameters, hence, the temperature is the only variable parameter in the experiment.

∆Tm reflects the thermal stability of the DNA duplexes, where it is calculated based on the difference of the Tm of the homologous DNA and the Tm of the heterologous DNA (Rosselló-Mora and Amann 2001).

It is recommended that strains sharing an RBR of 70% or higher, and having a

∆Tm of 5°C or lower can be categorised into the same species (Wayne et al. 1987). However, it has been suggested that these values are not to be used as a strict species boundary (Martinez-Murcia et al. 1992; Rosselló-Mora and Amann 2001; Rosselló- Mora 2006 ; Richter and Rossello-Mora 2009; Tindall et al. 2010), as the 70% cut-off is not applicable to some bacterial groups, for example the genus Rickettsia (Fournier and Raoult 2009). Also, the limitations of DDH, such as the impossibility to establish cumulative databases, the labour-intensive and time-consuming nature of the work, the lack of reproducibility of the results, and the inapplicability to be used for unculturable bacteria (Pereira et al. 2008; Schleifer 2009; Richter and Rossello-Mora 2009; Tindall et al. 2010; Ramasamy et al. 2014), have urged the search and introduction of more reliable methods into bacterial systematics (Stackebrandt and Ebers 2006; Richter and Rossello-Mora 2009; Whitman 2011 ; Ramasamy et al. 2014).

2.9.2 DNA G+C content (mol %)

The primary structure of DNA is composed of four nucleotide bases: adenine (A), thymine (T), cytosine (C) and guanine (G). The DNA guanine cytosine content (mol % G+C) was the first genotypic method applied in bacterial taxonomy, and is still one of the classical methods for the circumscription of bacterial taxa (Vandamme et al. 1996; Whitman 2011). The G+C content is calculated as a percentage of G+C (Rosselló-Mora and Amann 2001).

[G+C] G+C (mol %) = × 100 [A+T+C+G]

The G+C content for prokaryotes varies greatly, i.e. between 16.5 and 80.0 mol% (Tamaoka 1994; Nakabachi et al. 2006). Generally, organisms may not belong to the same genus if the G+C content differs by more than 10%, while a difference of more than 5% indicates the distinct standing of the species (Goodfellow et al. 1997).

2.9.3 DNA fingerprinting

DNA fingerprinting/typing methods are usually employed to provide information at intraspecies level (Stackebrandt et al. 2002; Tindall et al. 2010). They serve as an added support to the phenotypic analyses to differentiate closely related organisms (Vandamme et al. 1996). Examples of typing methods commonly being used are: amplified fragment-length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), pulsed field gel electrophoresis (PFGE), and repetitive element primed PCR (rep-PCR) which include repetitive extragenic palindromic-PCR (REP-PCR), enterobacterial repetitive intergenic consensus sequences-PCR (ERIC-PCR), BOX repetitive element (BOX-PCR) and ribotyping (Tindall et al. 2010). Some of these methods are quick and easy to perform, but may have poor reproducibility due to the lack of standardization (Tindall et al. 2010; Whitman 2011). DNA fingerprinting/typing methods are not part of the requirements for new species descriptions, but they can provide useful information for closely related species/subspecies when used properly (Tindall et al. 2010).

2.9.4 The 16S ribosomal RNA (rRNA)

The use of ribosomal RNA (rRNA) as the component in the classification of prokaryotes was introduced by Carl Woese in the 1970s (Woese and Fox 1977; Woese et al. 1978). Since then, it has been widely accepted as one of the important tools in bacterial classification and identification. The rRNA operon comprises of three subunits: the 5S rRNA, the 16S rRNA, and the 23S rRNA. The 16S rRNA subunit is commonly used in taxonomic studies as they are larger than the 5S subunit, and thus contains more useful information for phylogenetic classification (Olsen et al. 1986; Rosselló-Mora and Amann 2001). While the 23S subunit contains a larger amount of information than the 16S, sequencing of this gene has not been as common as the 16S rRNA gene due to its length (Ludwig and Schleifer 1999; Rosselló-Mora and Amann 2001).

There are a number of reasons for the popularity of the use of 16S rRNA gene in phylogenetic analysis. Firstly, the rRNA gene is extremely well conserved and universally present, making it possible to be used for phylogenetic comparison of species and genera (Olsen et al. 1986; Malik et al. 2008). Secondly, the rRNA gene is relatively stable and has less occurrence of lateral gene transfer, which is very useful for evolutionary studies (Schleifer 2009). Also, rRNA genes are readily isolated from pure cultures and natural environments, which facilitates the identification of culturable as well as unculturable bacteria (Ward et al. 1990; Amann et al. 1995; Schleifer 2009). Furthermore, the 16S rRNA gene sequences of many taxa are readily available in online databases (e.g. GenBank, EzTaxon etc.), allowing easy identification through comparison and analysis (Whitman 2011).

The identification and classification of prokaryotes by 16S rRNA gene sequence analysis are based on the 97% cut-off criterion in which isolates sharing equal to or more than 97% 16S rRNA gene sequence similarity will be categorised into the same species (Rosselló-Mora and Amann 2001; Tindall et al. 2010). However, with the growing number of genera and species described, resulting in an exponential increase in the number of sequencing data, this threshold value appeared to be unsatisfactory as some distinct species may share more than 97% of their 16S rRNA gene sequence similarity as the result of the conserveness of the rRNA gene (Fox et al. 1992;

Stackebrandt and Goebel 1994; Rosselló-Mora and Amann 2001). Hereafter, a new threshold value of 98.7% was proposed in 2006, in which it was recommended that DNA-DNA hybridization experiment is mandatory when the 16S rRNA gene sequence similarity fall within the range of 98.7 – 99.0% (Stackebrandt and Ebers 2006).

In current bacterial taxonomy, the 16S rRNA gene sequence analysis still plays an important role in genus identification, where isolates can be easily classified to the respective genera according to the 16S rRNA gene sequences. However, in cases when the 16S rRNA gene sequence similarity fall above the 97% threshold value, the confirmation at the species level would require additional genotypic data as well as supporting information from phenotypic and chemotaxonomic analysis.

2.9.5 Housekeeping genes

Housekeeping genes are protein-encoding genes essential for the maintenance of basal cellular function. The major advantage of employing housekeeping genes in bacterial taxonomy is that they evolve fairly slowly but much faster than rRNA genes (Schleifer 2009). The inclusion of the use of housekeeping genes for species delineation has been suggested by the ad hoc committee for the re-evaluation of the species definition, whereby at least five widely distributed genes could be used for species differentiation (Stackebrandt et al. 2002).

The applicability of the housekeeping genes in comparison to the genome relatedness was reported by Zeigler (2003). The results showed that out of the 32 genes tested, 8 genes exhibited strong correlations with the whole-genome sequence identity, with recN (recombination/repair protein) showing the highest potential for predicting genome relatedness (Zeigler 2003). The 16S rRNA gene was also included in the analysis, but was shown to have the poorest potential for predicting genome similarity (Zeigler 2003). This study supported the recommendation of Stackebrandt et al. (2002) and indicated that housekeeping genes could indeed help in predicting genome relatedness when applied in bacterial taxonomy. Though, instead of using five genes, single gene phylogeny could be used to predict overall genome relatedness, although the use of two or three genes could provide a better resolution (Zeigler 2003).

2.9.6 Single gene analysis

Due to the conserveness of the 16S rRNA gene, and its poor resolving power, phylogenetic studies based on single gene analysis serve as an alternative cost effective method for species delineation. The requirements for the selected genes are: (i) they must be universally distributed among bacterial genomes, (ii) they exist in single copy and have a higher evolution rate than the 16S rRNA gene, and (iii) the length of gene sequences should be reliable to contain vital phylogenetic information and allow easy sequencing (Yamamoto and Harayama 1995; Zeigler 2003; Rajendhran and Gunasekaran 2011).

Several genes have shown to have a higher resolving power than the 16S rRNA genes, one example being gyrB. The gyrB gene encodes for DNA gyrase β-subunit, which function is to introduce negatively supercoils closed circular double-stranded DNA into bacterial chromosomes and contributes to the replication of chromosomes (Watt and Hickson 1994; Yamamoto and Harayama 1998). One of the first studies applying gyrB gene sequences in taxonomic analysis was reported by Yamamoto and Harayama (1995), where universal primer pairs able to amplify gyrB sequences of primarily Gram-negative bacteria were developed and employed on Pseudomonas strains (Yamamoto and Harayama 1995, 1998). Thereafter, the gyrB gene sequence data were compared to DDH by analysing 49 Acinetobacter strains, where it was proposed that a genetic distance of 0.041 corresponded to the 70% DDH cut-off value (Yamamoto et al. 1999). Phylogeny studies on the evolutionary relatedness of Enterobacteriaceae also proved the reliability of gyrB gene sequences for intra- and interspecies classification, in particular within the genus Serratia (Dauga 2002). The higher discriminatory power of gyrB sequences in comparison with the 16S rRNA gene sequences was also demonstrated in several other genera, for instance, the genus Aeromonas (Yáñez et al. 2003; Küpfer et al. 2006), genus Bacillus (La Duc et al. 2004; Wang et al. 2007a) and the genus Flavobacterium ( Peeters and Willems 2011).

Other genes that are commonly used as alternative marker molecules are the RNA polymerases, such as rpoB and rpoD. The rpoB encodes for the β-subunit of bacterial RNA polymerase which catalyses the transcription of DNA into RNA (Severinov et al. 1996), while the rpoD encodes for the σ70 factor which promotes the

29 attachment of RNA polymerase to initiate transcription (Paget and Helmann 2003). The practicality of the rpoB gene in phylogenetic studies have been demonstrated in numerous investigations, for example, the successful intra- and interspecies differentiation of Legionella species (Ko et al. 2002), the improvement of interspecies divergence within the genus Paenibacillus (Da Mota et al. 2004), genus Aeromonas (Küpfer et al. 2006) and genus Anoxybacillus (Inan et al. 2011), and as a complementary technique to molecular microbial ecology studies (Case et al. 2007). While the usefulness of the rpoD gene sequences in the phylogenetic studies has been established in the genus Aeromonas (Soler et al. 2004; Nagar et al. 2013), and was successfully applied for the identification of unclassified strains within two major plant pathogen, Xanthomonas and Pseudomonas syringae (Parkinson and Elphinstone 2010). Other than the genes mentioned above, additional popular housekeeping genes that have been used for phylogenetic studies include infB (Christensen et al. 2004; Nørskov- Lauritsen et al. 2004), atpD (Christensen et al. 2004), recA (Thompson et al. 2004; Zbinden et al. 2011) and( hsp60 Kwok and Chow 2003; Jiang et al. 2008).

2.9.7 Multilocus sequence analysis (MLSA)

Although various housekeeping genes have been proposed as stable marker genes that can be used in phylogenetic analysis, the application of a single locus sequence is considered to be less practical since it represents only a small section of the genome. Therefore, the sequencing of multiple housekeeping genes within the prokaryote genome has been suggested to be used for the identification and classification of bacteria groups (Stackebrandt et al. 2002; Gevers et al. 2005). The multigene approach was first named Multilocus Sequence Typing (MLST), and was used to characterize bacterial species at the intraspecies level by assessing allelic mismatches of gene sequences (Maiden et al. 1998). However, the term MLST was considered inappropriate for phylogenetic analysis in bacterial taxonomy as the latter study is based on the analysis of nucleotide sequences of a more diverse group of bacteria, and hence a more general term, Multilocus Sequence Analysis (MLSA) (Gevers et al. 2005), also sometimes referred to as Multilocus Phylogenetic Analysis (MLPA) (Whitman 2011) was proposed.

MLSA has been successfully applied in the classification of prokaryotes as it can provide reliable information at lower taxonomic levels than rRNA given that housekeeping genes evolve more rapidly than rRNA genes. Also, the involvement of multiple genes in the analysis can provide buffers against the distorting effects of recombination of single loci. Furthermore, the data derived from MLSA can be deposited in or obtained from online databases, and use together with the rRNA data for phylogenetic studies (Gevers et al. 2005; Schleifer 2009; Whitman 2011).

The first study to implement MLSA in bacterial taxonomy was carried out by Thompson et al. 2005, where it was demonstrated that the multigene approach provides a better discriminatory power among the Vibrio species than 16S rRNA gene sequence analysis (Thompson et al. 2005). Thereafter, MLSA proved to be useful in the clustering and identification of two groups of extremely closely related Vibrio species, one group being V. harveyi and V. campbellii (Thompson et al. 2007), and the other being V cholerae and V. mimicus (Thompson et al. 2008), where they both share close to 100% 16S rRNA gene sequence similarity, and about 70% and 80% DNA-DNA relatedness, respectively. MLSA was also proven useful in the other genera, for instance, the genus Marichromatium, where the application of MLSA confirmed the distinct standing of the two type strains, M. gracile and M. purpuratum, of which they share 99.55% of 16S rRNA gene sequence similarity and 75.00 ± 1.64% DDH value (Serrano et al. 2010); the genus Ensifer (Martens et al. 2008) and Streptomyces (Rong and Huang 2012) where the studies demonstrated the comparability of MLSA to DDH; and the classification and identification of novel species, e.g. Gluconacetobacter sucrofermentans (Cleenwerck et al. 2010), Rhizobium leucaenae (Ribeiro et al. 2012), Alteromonas australica ( Ivanova et al. 2013) etc.

In addition to the application of MLSA at the genus level, a few studies have focused on the applicability of this scheme at the family level, for example, the family Pasteurellaceae (Kuhnert and Korczak 2006), the family Enterobacteriaceae (Kuhnert et al. 2009), the family Halomonadaceae (de la Haba et al. 2012), and the family Vibrionaceae (Sawabe et al. 2013).

MLSA has been considered as an alternative technique to overcome the limited resolution of 16S rRNA gene sequence analysis and has been suggested to be an excellent methodology to replace DDH for species delineation (Gevers et al. 2006; 31

Konstantinidis and Tiedje 2007; Whitman 2011). However, the selection of specific genes for defining species, genera, or families has not been established, and the same question remains regarding the size of the gene sequences being used (Schleifer 2009; Kämpfer and Glaeser 2012). Currently, many MLSA studies of different groups of bacteria apply different primer pairs for amplification of the same or different set of genes. This has led to different threshold values being proposed for different groups of bacteria, depending on the genes and primer pairs employed in the study (Table 2.3).

Table 2.4 Summary of the MLSA scheme used in various genera and their respective proposed threshold value.

Genus Housekeeping genes Threshold Reference value (%) Vibrio pryH, recA, gapA, mreB, 95.0 (Thompson et al. ftsZ, gyrB, topA 2007) Marichromatium gyrB, recA, fusA, dnaK, 97.5 (Serrano et al. 2010) pufM, soxB Bacteroides dnaJ, gyrB, hsp60, recA, 97.5 (Sakamoto and rpoB Ohkuma 2011) Stenotrophomonas atpA, recA, rpoA, uvrB 95.0 (Ramos et al. 2011) Streptomyces atpD, gyrB, recA, rpoB, trpB 99.3 (Rong and Huang 2012) Alteromonas dnaK, sucC, rpoB, gyrB, 98.9 (Ng et al. 2013) rpoD

2.9.8 Whole genome sequence analysis

Sequencing of the whole bacterial genome has been previously considered to be a costly and labour intensive process, however, with the introduction of next-generation sequencing technologies, a significant reductions in cost and the time required for analysis have resulted in thousands of genomes being sequenced (Soon et al. 2013). As of 29 May 2014, there were more than 4500 prokaryotic genome sequences available in the National Center for Biotechnology Information (NCBI) database, and this number will continue to grow with the additional contribution from DSMZ (Braunschweig, Germany) and DOE Joint Genomic Institute (USA), whereby a set of 100 genomes will be sequenced from the type strains deposited in public culture collections each year (Klenk and Goker 2010) . The large number of genomes available in public databases has thus enabled the utilisation of whole genome sequences in prokaryotic taxonomy, and so it has been recommended that whole genome sequences should be incorporated into novel species description where possible (Whitman 2011; Chun and Rainey 2014; Vandamme and Peeters 2014).

A few different approaches, based on whole genome sequences, have been developed to correlate DDH data. One of these approaches, the similarity-type genome relatedness index, is based on the average nucleotide identity (ANI) between a pair of genomes being studied (Konstantinidis and Tiedje 2005), where the ANI can be calculated by BLASTN method (Altschul et al. 1990; Goris et al. 2007) or MuMmer algorithm (Kurtz et al. 2004; Richter and Rossello-Mora 2009). The other approach is the distance-type genome relatedness index, which is based on DNA maximal unique matches shared by two genomes or the distances and phylogenetic relationship between the genomes being studied, and is commonly being named as maximal unique matches index (MUMi) (Deloger et al. 2009) or genome BLAST distance phylogeny (GBDP) (Henz et al. 2005), respectively.

Currently, ANI is the most commonly used technique among the three approaches mentioned above, and is the best candidate to become the next gold standard for species circumscription (Richter and Rossello-Mora 2009; Kim et al. 2014). Early studies on the comparison of ANI values to DDH data showed that 95% ANI correspond to 70% DDH (Konstantinidis and Tiedje 2007; Goris et al. 2007). However,

33 a later study has shown that a value of 96% ANI would be more appropriate to justify the species boundary (Richter and Rossello-Mora 2009). It is now generally accepted that a value of 95 – 96% ANI can be used as a guideline for species delineation, where it has proven in a recent study that this threshold is equal to a value of 70% DDH and 98.65% 16S rRNA gene sequence similarity (Kim et al. 2014), both of which the values being consistent with previous recommendations (Wayne et al. 1987; Stackebrandt and Ebers 2006; Meier-Kolthoff et al. 2013).

2.10 Chemotaxonomy

Chemotaxonomy refers to the classification of bacteria according to the chemical components of the cell. Methodologies generally included in the classification of microorganisms are the analysis of cellular fatty acids, polar lipids, respiratory lipoquinones content (Tindall et al. 2010), Fourier-Transformed Infrared Spectroscopy (FTIR) (Helm et al. 1991; Helm and Naumann 1995) and Matrix Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (Conway et al. 2001). The following section will focus on the application of MALDI-TOF mass spectrometry in bacterial taxonomy, as it is one of the techniques being investigated in this thesis.

2.10.1 MALDI-TOF mass spectrometry analysis

MALDI-TOF mass spectrometry is a relatively new technique employed in bacterial taxonomy. This method is based on the mass/charge differentiation to generate protein fingerprinting which can then be used for classification of microorganisms by various pattern matching or clustering algorithms (Baumann et al. 2005; Freiwald and Sauer 2009). Generally, the process involves applying a minute amount of samples (e.g. cell colony, proteome extraction etc.) on a conductive target plate followed by the overlaying of a thin layer of matrix, such as α-cyano-4-hydroxycinnamic acid (CHCA), sinapinic acid (SA) or 2,5-dihydroxybenzoic acid (DHBA) (Lay 2000), on the samples. The target plate is then inserted into the sample chamber, where under vacuum, laser

34 pulses briefly irradiate a small region of the sample (analyte), causing desorption of the matrix containing the entrapped samples from the target plate. These desorbed matrix molecules are ionised and propelled by strong electric field towards an ion detector via the time of flight (TOF) tube, during which they are separated on the basis of their mass- to-charge ratio. The time required for each molecule to reach the ion detector is strictly dependent on the mass-to-charge ratio of the ionized molecules, which then quantifies and translates the signal into a characteristic mass-spectral profile (Figure 2.4) (Sauer and Kliem 2010).

Figure 2.4 Schematic diagram of the principle processes in MALDI-TOF mass spectrometry analysis. Adapted from (Marvin et al. 2003).

The benefits of MALDI-TOF mass spectrometry have been demonstrated in various studies, where it was shown to be able to detect a large spectrum of proteins from whole bacteria cells without extensive separation (Ruelle et al. 2004; Maier et al. 2006), and capable to accurately identify bacteria at the genus, species, and subspecies level (Barbuddhe et al. 2008; Dieckmann et al. 2008; Ayyadurai et al. 2010; Stephan et al. 2010). Also, this technique is reliable as it can be used to study bacteria that have been cultured under different conditions, for instance, different growth media and cell growth phases, with only minimum variability in the spectra that does not affect the reliability of identification (Lay 2001; Maier et al. 2006).

Currently, there are two commercially available systems for species identification, one being MALDI Biotyper (Bruker Daltonics, Bremen, Germany) and the other being VITEK® MS (bioMérieux, Marcy 1’Etoile, France), and both systems 35 have been proven to be comparable (Cherkaoui et al. 2010) and suitable to be used as a tool in routine tests for diagnostics of clinical bacteria, where rapid and accurate identification of unknown isolates is of critical importance (Murray 2010; Mellmann and Müthing 2013). With regard to environmental bacteria, the number of strains included in the databases of both systems is relatively low. However, with the increasing number of studies applying MALDI-TOF mass spectrometry in the investigation of environmental bacteria, so will be the expansion of databases which will provide new opportunities and reliable information for species definition in bacteriology (Welker and Moore 2011).

2.11 Summary

Biodegradation is a suitable alternative approach for plastic waste management due to the limitations of current plastic waste disposal methods. Plastic wastes in the ocean is a significant issue to the marine environment, therefore a detailed taxonomy study of marine bacteria that have the ability to degrade plastic materials would be of great benefit to the marine environment. The study of surface topography changes on the PET films can provide an insight into the interaction between bacteria and the plastic films which may improve the understanding of the biodegradation process. The incorporation of modern tools in bacterial taxonomy may assist in the current methodologies of new species delineation, and the integration of whole genome sequence analysis can also overcome the limitations of current bacteria characterization and identification approaches, which can facilitate the new taxon description in the near future.

Chapter 3: Materials and Methods

3.1 Overview

Due to the nature of this project, a number of experimental approaches involving a range of analytical techniques were used. These included microbiological, chemical, molecular biology and surface analyses. The focus of this study was on the taxonomic identification and classification of the marine bacteria involved in the degradation of PET, as well as the surface characterisation of the PET films that had been in contact with strain A3d10T for one month period.

3.2 Chemicals

All the chemicals used were of analytical grade purity. All solutions were prepared in MilliQ water (resistivity = 18.2 MΩ cm-1, Millipore, USA) unless otherwise stated.

3.3 Bacterial strains and growth conditions

Various bacterial strains were used throughout this work, including the majority of type strains of validly described Alteromonas and Marinobacter species. The strains are presented, according to their genus, as follows:

3.3.1 Genus Alteromonas

To date, eleven Alteromonas species have been validly described, of which ten were used in this study. Alteromonas halophila JSM 073008T was not included, as it was not present in the validation list at the time the experiments were initiated. The genus was first described by Baumann et al. for Gram-negative, strictly aerobic, motile, heterotrophic marine bacteria (Baumann et al. 1972), and is categorised under the family Alteromonadaceae, order Alteromonadales, class Gammaproteobacteria. All 38 strains were cultured on Difco™ marine agar/ broth 2216 (BD, USA). The specific descriptions and growing conditions for each species used are given in Sections 3.3.1.1 – 10 below.

3.3.1.1 Alteromonas macleodii LMG 2843T

A. macleodii is the type species of the genus. It was originally isolated from sea water samples taken off the coast of Oahu, Hawaii, USA. Cells are 0.7 to 1.0 µm wide and 2.0 to 3.0 µm in length, non-spore forming and motile by one polar flagellum. Growth occurs at 12 to 45°C, with the NaCl concentration range from 1 to 12% (w/v). The G+C content is 44 to 47 mol% (Baumann et al. 1972; Gauthier et al. 1995; Van Trappen et al. 2004; Yi et al. 2004; Vandecandelaere et al. 2008).

3.3.1.2 Alteromonas marina SW-47T

A. marina was originally isolated from marine water at Hwajinpo beach, East Sea, Korea. Cells are 1.0 to 1.2 µm wide and 2.5 to 4.0 µm in length, also non-spore forming and motile by means of a single polar flagellum. Growth occurs at 4 to 44°C (optimum 30 to 37°C), with the NaCl concentration range from 1 to 15% (w/v) (optimum 2 to 5%), and pH 5.5 to 9.0 (optimum pH 7.0 to 8.0). The G+C content is 44 to 45 mol% (Yoon et al. 2003a).

3.3.1.3 Alteromonas stellipolaris LMG 21861T

A. stellipolaris was isolated from the Antarctic sea during an expedition. Cells are 0.4 µm wide and 2.0 to 7.0 µm in length, also non-spore forming and motile by means of a single polar flagellum. Growth occurs at 5 to 37°C, with the salt concentration range from 1 to 10% (w/v), and at pH 6.0 to 9.0 (optimum pH 7.0 to 8.5). The G+C content is 43 to 45 mol% (Van Trappen et al. 2004).

3.3.1.4 Alteromonas litorea TF-22T

A. litorea was originally isolated from the intertidal sediment of Daepo Beach, Yellow Sea, Korea. Cells are 0.9 to 1.2 µm wide and 2.0 to 4.0 µm in length, and motile by means of a single polar flagellum. Growth occurs at 10 to 43°C (optimum 30 to 37°C), with the NaCl concentration range from 0.5 to 14% (w/v) (optimum 2 to 5%), and at pH 5.5 to 8.0 (optimum pH 7.0 to 8.0). The G+C content is 46.0 mol% (Yoon et al. 2004b).

3.3.1.5 Alteromonas hispanica F-32T

A. hispanica was originally isolated from a hypersaline wetland in Málaga, Spain. Cells are 0.75 µm wide and 1.0 to 2.0 µm in length, non-spore forming and motile by single polar flagellum. Growth occurs at 4 to 40°C (optimum 32°C), with the NaCl concentration range from 7.5 to 15% (w/v) (optimum 7.5 to 10%), and at pH 5 to 10 (optimum pH 7 to 8). The G+C content is 46.3 mol% (Martinez-Checa et al. 2005).

3.3.1.6 Alteromonas addita R10SW13T

A. addita was originally isolated from sea water samples in Chazhma Bay, Sea of Japan, Russia. Cells are approximately 0.7 to 0.9 µm wide, motile by one polar flagellum and non-endospores forming. Growth occurs at 4 to 37°C, with the salt requirement range from 1 to 10% (w/v) NaCl, and at pH 6.0 to 10.0 (optimum pH 7.5 to 8.0). The G+C content of the type strain is 43 mol% (Ivanova et al. 2005a).

3.3.1.7 Alteromonas simiduii BCRC 17572T

A. simiduii is a mercury resistant bacterium originally isolated from the Er-Jen River Bay, Tainan, Taiwan. Cells are 0.4 to 0.8 µm in diameter and 1.2 to 2.5 µm in length, and motile by single polar flagellum. Growth occurs at 10 to 40°C (optimum 30°C), with NaCl concentration range from 0.5 to 12% (w/v) (optimum 2 to 4%), and at pH 6 to 9 (optimum pH 7 to 8). The G+C content of the type strain is 45.3 mol% (Chiu et al. 2007). 40

3.3.1.8 Alteromonas tagae JCM 13895T

A. tagae is another mercury resistant Alteromonas species isolated from the Er- Jen River Bay, Tainan, Taiwan which was identified and described together with A. simiduii. The cells are 0.5 to 0.9 µm wide and 1.2 to 2.5 long, and are motile by one polar flagellum. Growth occurs at 15 to 40°C (optimum 30°C), with salt level of 0.5 to 13% (w/v) NaCl (optimum 2 to 4%), and at pH 6 to 9 (optimum pH 7 to 8). The G+C content is 43.1 mol% (Chiu et al. 2007).

3.3.1.9 Alteromonas genovensis LMG 24078T

A. genovensis was originally isolated from a marine electroactive biofilm during the study of microbial diversity analysis at Genoa, Italy. Cells are 0.9 µm wide and 1.8 µm in length, and motile by means of single polar flagellum. Growth occurs at 4 to 37°C, NaCl concentration of 2 to 15% (w/v), and at the pH range of 6.0 to 8.5 (optimum 7.0 to 8.0). The G+C content is 44.5 mol% (Vandecandelaere et al. 2008).

3.3.1.10 Alteromonas australica H17T

A. australica is being described in this study. It was isolated by Hayden K. Webb from the sea water collected from St. Kilda Beach, Port Phillip Bay, Victoria, Australia. Cells are approximately 0.7 to 0.9 µm wide, non-endospore forming, motile by one polar flagellum. Growth occurs at 4 to 37°C, with NaCl concentration range from 1 to 10% (w/v), and at pH 6.0 to 10.0 (optimum 7.5 to 8.0). The DNA G+C content of the type strain is 43.0 mol% (Ivanova et al. 2013 ; Euzéby 2013).

3.3.2 Genus Marinobacter

Genus Marinobacter was first described by Gauthier et al. as a hydrocarbon degrading species, and is currently categorised under the family Alteromonadaceae, order Alteromonadales, class Gammaproteobacteria (Gauthier et al. 1992; Bowman and McMeekin 2005). To date, there are 32 validly described species in this genus. From these, 19 species were obtained from various culture collections and used in this study. 41

All strains were culture on Difco™ marine agar/ broth 2216 (BD, USA) unless otherwise stated. The specific descriptions and growing conditions for each species used are given in Sections 3.3.2.1 – 21 below.

3.3.2.1 Marinobacter hydrocarbonoclasticus SP. 17T

M. hydrocarbonoclasticus is the type species of the genus. It is a hydrocarbon degrading bacterium which was first isolated from Mediterranean seawater, French. Cells are 0.3 to 0.6 µm wide and 2.0 to 3.0 µm in length, non-spore forming, motile by one unsheathed polar flagellum in the present of 0.2 to 1 M NaCl and non-motile if the NaCl concentration is out of the above range. Growth occurs at 10 to 45°C (optimum 32°C), with wide range of salt tolerance (0.08 to 3.5 M), and at pH 6 to 9.5 (optimum pH 7 to 7.5). The DNA G+C content is 52.7 mol% (Gauthier et al. 1992).

3.3.2.2 Marinobacter excellens KMM 3809T

M. excellens was originally isolated from marine sediments in Chazhma Bay, Sea of Japan, Russia. Cells are 0.6 to 1.4 µm in diameter and 1 to 8 µm long, motile and non-endospores forming. Growth occurs at 10 to 41°C (optimum 20 to 25°C), with NaCl concentration of 1 to 15% (w/v), and at pH 6.0 to 10.0 (optimum pH 7.5). The G+C content is 55.0 to 56.0 mol% (Gorshkova et al. 2003).

3.3.2.3 Marinobacter lipolyticus CIP 107627T

M. lipolyticus was the first reported moderate halophilic lipolytic enzyme- producing strain. It was isolated from saline soil in Cádiz, Spain. Cells are 0.3 to 0.5 µm wide and 2.5 to 3.5 µm long, motile and non-spore forming. Growth occurs at 15 to 40°C (optimum 37°C), with the NaCl concentration of 1 to 15 % (w/v) (optimum 7.5%), and at the pH of 5.0 to 10.0 (optimum pH 7.5). The G+C content is 57.0 mol% (Martin et al. 2003).

3.3.2.4 Marinobacter litoralis SW-45T

M. litoralis was originally isolated from sea water at Jungdongjin beach, East Sea, Korea. Cells are 0.5 to 0.8 µm wide and 1.5 to 3.0 µm long, non-spore forming and motile by one polar flagellum. Growth occurs at 4 to 46°C (optimum 30 to 37°C), with the NaCl concentration of 0.5 to 18% (w/v) (optimum 2 to 7%), and at the optimal pH of 7.0 to 8.5. The G+C content of the type strain is 55 mol% (Yoon et al. 2003b).

3.3.2.5 Marinobacter flavimaris CIP 108615T

M. flavimaris was originally isolated from Daepo Beach sea water sample at Yellow Sea, Korea. Cells are 0.6 to 0.9 µm in diameter and 1.5 to 3.0 µm in length, non-spore forming and motile by means of single polar flagellum. Growth occurs at 4 to 45°C (optimum 37°C), with the NaCl concentration range from 1 to 20% (w/v) (optimum 2 to 6%), and at the optimal pH of 7.0 to 8.0. The G+C content is 57.0 mol% (Yoon et al. 2004a).

3.3.2.6 Marinobacter sediminum LMG 23833T

M. sediminum was originally isolated from sediment sample collected from Peter the Great Bay, Sea of Japan, Russia. Cells are 0.3 to 0.4 µm wide and 1.8 to 2.5 µm in length and motile. Growth occurs at 4 to 42°C, with the salt requirement of 0.5 to 18.0 % (w/v) NaCl. The DNA G+C content is 56.5 mol% (Romanenko et al. 2005).

3.3.2.7 Marinobacter algicola LMG 23835T

The type strain of M. algicola was originally isolated from a laboratory culture of the dinoflagellate Gymnodinium catenatum from Korea. Cells are 0.45 to 0.55 µm in diameter and 1.6 to 2.5 µm in length, non-spore forming and motile by single unsheathed polar flagellum. Growth occurs at 5 to 40°C (optimum 25 to 30°C), with the salt tolerance range of 1 to 12% (w/v) NaCl (optimum 3 to 6%), and at the pH of 5 to 10 (optimum pH 7.5). The G+C content of the type strain is 55 mol% (Green et al. 2006).

3.3.2.8 Marinobacter koreensis KACC 11513T

M. koreensis was isolated from sea-shore sand at Homo Cape, Pohang, Korea. Cells are 0.3 to 0.5 µm wide and 1.5 to 3.0 µm long, non-spore forming and motile by one polar flagellum. Growth occurs at 10 to 45°C, with the salt requirement of 0.5 to 20% (w/v) NaCl, and at the pH of 5 to 9. The G+C content was determined to be 54.1 mol% (Kim et al. 2006).

3.3.2.9 Marinobacter vinifirmus CIP 109495T

M. vinifirmus was isolated from the waste water in France. The cells are 0.5 to 0.7 µm wide and 0.5 to 2.0 µm long, non-spore forming and weakly motile. Growth occurs at 15 to 45°C (optimum 20 to 30°C), with the salinity range of 0 to 20% (w/v) NaCl (optimum 3 to 6%), and at the pH of 6.5 to 8.4. The G+C content is 58.7 mol% (Liebgott et al. 2006)

3.3.2.10 Marinobacter gudaonensis CIP 109534T

M. gudaonensis was originally isolated from an oil contaminated soil sample in China. The cells are 0.3 to 0.5 µm wide and 1.2 to 1.8 µm long, and motile by one polar flagellum. Growth occurs at 10 to 45°C, with the NaCl concentration of 0 to 15% (w/v). The DNA G+C content is 57.9 mol% (Gu et al. 2007).

3.3.2.11 Marinobacter salsuginis CIP 109893T

M. salsuginis was originally isolated from water samples from Red Sea during a Cruise. Cells are 1 µm wide and 2 to 4 µm long, non-spore forming, and motile by one polar flagellum. Growth occurs at 10 to 45°C (optimum 35 to 37°C), with salt concentration of 1 to 20% (w/v) NaCl (optimum 5%), and pH of about 6.5 to 9.5 (optimum pH 7.5 to 8.0). The DNA G+C content is 55.9 mol% (Antunes et al. 2007).

3.3.2.12 Marinobacter salicampi KCTC 12972T

M. salicampi was originally isolated from the Yellow Sea marine solar saltern, Byunsan, Korea. Cells are 0.4 to 0.8 µm wide and 1.0 to 7.0 µm long, appear in straight or curved rods, and motile by single polar flagellum. Growth occurs at 4 to 39°C (optimum 30°C), in about 8% (w/v) NaCl, and at the optimal pH of 7.0 to 8.0. The G+C content of the type strain is 58.1 mol% (Yoon et al. 2007).

3.3.2.13 Marinobacter pelagius JCM 14804T

M. pelagius was originally isolated from sea water samples at Zhoushan Archipelago, Zhejiang Province, China. The cells are 0.4 to 0.8 µm wide and 2.0 to 4.0 µm long and motile. Growth occurs at 4 to 48°C (optimum 25 to 30°C), in 0.5 to 15% (w/v) NaCl (optimum 5.0%), and at pH 6.0 to 9.0 (optimum pH 7.0 to 8.0). The G+C content is 59.0 mol% (Xu et al. 2008).

3.3.2.14 Marinobacter psychrophilus JCM 14643T

M. psychrophilus was originally isolated from sea-ice samples taken from Canadian Basin of the Arctic Ocean. The cells are 0.3 to 0.4 µm wide and 1.0 to 2.5 µm long and motile. Growth occurs at 0 to 22°C (optimum 16 to 18°C), with the presence of 2 to 8% (w/v) NaCl, and at the pH of 5.0 to 10.0 (optimum pH 6.0 to 9.0). The DNA G+C content is 55.4 mol% (Zhang et al. 2008).

3.3.2.15 Marinobacter mobilis JCM 15154T

M. mobilis was isolated from marine sediment in Zhejiang, China. The cells are 0.5 to 0.8 µm wide and 1.5 to 3.0 µm long and motile by means of a single polar flagellum. Strains were culture on halophilic medium (HM) (Ventosa et al. 1982). Growth occurs at 15 to 42°C (optimum 30 to 35°C), in 0.5 to 10.0% (w/v) NaCl (optimum 3.0 to 5.0%), and at the pH of 6.5 to 9.0 (optimum 7.0 to 7.5). The G+C content is 58.0 to 58.9 mol% (Huo et al. 2008).

3.3.2.16 Marinobacter zhejiangensis JCM 15156T

M. zhejiangensis was isolated together with M. mobilis from the marine sediment in China. The cells are 0.4 to 0.8 µm wide and 1.0 to 2.5 µm long, and motile by means of a single polar flagellum. Strains were culture and maintained on HM medium (Appendix 1A). Growth occurs at 15 to 42°C (optimum 30 to 35°C), with salt concentration of 0.5 to 10.0% (w/v) (optimum 1.0 to 3.0%), and at the pH of 6.0 to 9.5 (optimum pH 7.0 to 7.5). The G+C content of the type strain is 58.4 ± 0.1 mol% (Huo et al. 2008).

3.3.2.17 Marinobacter daqiaonensis LMG 25365T

M. daqiaonensis was obtained from sediment sample at Daqiao saltern at Jima, Qingdao, China. Cells are 0.4 to 0.7 µm in diameter and 2.5 to 4.0 µm in length, and motile by means of a single polar flagellum. Growth occurs at 10 to 45°C (optimum 30°C), in 1 to 15% (w/v) NaCl concentration (optimum 5 to 10%), and at the pH of 5 to 9 (optimum pH 7.5). The G+C content of the DNA is 60.8 mol% (Qu et al. 2011).

3.3.2.18 Marinobacter adhaerens CIP 110141T

M. adhaerens was originally isolated from marine aggregates at German Wadden Sea. The cells are 0.6 to 0.8 µm wide and 1.7 to 2.4 µm long, non-spore forming and motile by one polar flagellum. Growth occurs at 4 to 45°C (optimum 34 to 38°C), with the salt concentration of 0.5 to 20% (w/v) NaCl (optimum 2 to 6%), and at pH 5.5 to 10.0 (optimum 34 to 38°C). The G+C content of the DNA is 56.9 mol% (Kaeppel et al. 2012).

3.3.2.19 Marinobacter xestospongiae JCM 17469T

M. xestospongiae was originally isolated from the sponge Xestospongia testudinaria, taken from Red Sea coast in Saudi Arabia. Cells are 0.6 to 0.8 µm wide and 2.0 to 2.5 µm long, non-spore forming and motile by a polar flagellum. Colonies will turn brown if incubated for more than 14 days. Growth occurs at 15 to 42°C (optimum 28 to 36°C), in the presence of 0.5 to 6.0% (w/v) NaCl (optimum 2.0%), and at the pH of 5.0 to 10.0 (optimum pH 7.0 to 8.0). The DNA G+C content is 57.1 mol% (Lee et al. 2012).

3.3.2.20 Marinobacter sp. A3d10T

The strain designated A3d10T was isolated from sea water sample collected from St. Kilda beach, Victoria, Australia by Hayden K. Webb (Webb et al. 2009). Colonies grown on marine agar are semi-translucent, non-pigmented, circular to slightly irregular (0.5 – 1.0 mm) and smooth after 48 hours of incubation. Almost complete 16S rRNA gene sequence analysis shown that this strain belongs to the genus Marinobacter. The species description of this strain is discussed in Chapter 7.

3.3.2.21 Marinobacter sp. R9SW1T

The strain designated R9SW1T was isolated from sea water sample collected from Chazhma Bay, Sea of Japan, Russia by Elena P. Ivanova (Ivanova et al. 2005b). Colonies grown on marine agar were transparent, non-pigmented, smooth and circular in shape. Colonies are about 0.8 to 1.0 mm in diameter after 48 hours of cultivation on marine agar. Almost complete 16S rRNA gene sequence analysis shown that this strain belongs to the genus Marinobacter. The species description of this strain is discussed in Chapter 7.

3.3.3 Other bacterial strains

A few bacterial strains that are phylogenetically related to the genus Alteromonas and Marinobacter were used in the taxonomy studies. They are all belong to the class Gammaproteobacteria.

3.3.3.1 Salinimonas chungwhensis KCTC 12239T

S. chungwhensis belongs to the family Alteromonadaceae, and is the type species of the genus. It was originally isolated from a solar saltern located at Chungwha, Korea. The cells are about 0.8 to 1.0 µm in diameter and 1.2 to 1.5 µm in length, non- spore forming, and motile by single polar flagellum. Strains were culture and maintained on Difco™ marine agar/ broth 2216 (BD, USA). Growth occurs at 10 to 45°C (optimum 30 to 35°C), with optimum NaCl concentration of 2 to 5% (w/v), and at pH 6.5 to 9.0 (optimum pH 7.0 to 8.0). The G+C content of the DNA for the type strain is 48 mol% (Jeon et al. 2005).

3.3.3.2 Aestuariibacter aggregatus LMG 25283T

A. aggregatus also belongs to the family Alteromonadaceae. It was originally isolated from seawater of the Yellow Sea in China. Colonies are about 2 to 5 mm after 2 days of incubation on marine agar. Growth occurs at 15 to 45°C (optimum 37°C), at pH 6.0 to 10.0 (optimum pH 7.0 to 8.0), and in the presence of 0.5 to 10.0% (w/v) NaCl. The G+C content of the DNA is 49.4 mol% (Alikhan et al. 2011).

3.3.3.3 Hahella ganghwensis KCTC 12277T

H. ganghwensis is a Gammaproteobacteria belongs to the family Hahellaceae. It was originally isolated from a marine sediment sample collected at Ganghwa Island, Korea. Colonies are about 1 mm in size, circular, smooth and slightly cream in colour after 5 days of incubation on marine agar at 30°C. Growth occurs at 15 to 40°C (optimum 35°C), at pH 5.0 to 10.0 (optimum pH 7.0 to 8.0), and in the presence of 1 to

10% (w/v) sea salts (optimum 4 to 6%). The G+C content of the DNA is 44 mol% (Baik et al. 2005).

3.4 Maintenance and long term storage of bacterial strains

Strains in current used were either maintained on Difco™ marine agar/ broth 2216 (BD, USA) or on the specific culture media as stated in section 3.3 at 4°C for approximately 2 weeks. Long term storage was performed by resuspending fully grown bacterial cultures in respective culture media supplemented with 20% glycerol and stored at -80°C. 3.5 Molecular analysis

A range of molecular techniques were employed in this study for taxonomic classification and identification. The 16S rRNA gene sequence analysis was first performed to classify bacterial strains to the genus level, followed by MLSA and DDH to determine the taxonomic position of the strains.

3.5.1 Genomic DNA extraction

Genomic DNA extraction was performed under two different methods. For PCR amplification, DNA was isolated by using Wizard® Genomic DNA Purification Kit (Promega, USA) according to the manufacturer’s instruction; whereas a modified CTAB method specified by the DOE joint Genome Institute (JGI, http://my.jgi.doe.gov/general/protocols.html) was used to extract the total genomic DNA for DDH study. All the purified DNA were stored at -20°C until further use.

3.5.2 Primer sequences

The oligonucleotide primers used in this study were synthesised by Invitrogen (USA) or Sigma-Aldrich (Australia). The primers used in previously reported MLSA study of Alteromonas macleodii (Ivars-Martinez et al. 2008) were used as the basis for

49 the design of new primers in this study. The primer pairs used for both amplification and sequencing are summarised in Tables 3.1 and 3.2.

3.5.3 Polymerase Chain Reaction (PCR)

PCR amplifications were carried out by using a MyCycler™ Thermal Cycler (Bio-Rad, USA). Each 50 µL PCR reaction consisted of 25 µL MangoMix™ (Bioline, USA), 0.2 µM of each of the primers, 4 µL of genomic DNA and 19 µL sterile MilliQ water. PCR amplifications of the gyrB and rpoD genes were performed as previously described (Yamamoto and Harayama 1995, 1998) while the 16S rRNA gene, dnaK, rpoB and sucC were subjected to an initial denaturation step of heating to 94°C for 4 min, followed by 30 cycles of repetitive DNA denaturation (94°C for 2 min), primer hybridization (Ta for 1 min) and primer extension (72°C for 2 min), respectively. A last step of heating to 72°C for 10 min was included to allow final extensions to occur before the holding step at 4°C. The annealing temperature (Ta) for each pair of primers is shown in Table 3.1 and 3.2.

Table 3.1 Primer sequences used for 16S rRNA gene amplification or sequencing.

PCR amplification/ Locus sequencing Primers Sequence (5' → 3') Ta Reference 16S rDNA A/S 27F AGAGTTTGATCCTGGCTCAG 52.0 (Lane 1991) 907R CCGTCAATTCMTTTGAGTTT

16S rDNA A/S 8F AGAGTTTGATCCTGGCTCAG 54 .0 ( Turner et al. 1999) 1492R TACGGYTACCTTGTTACGACTT

16S rDNA A/S 8F AGAGTTTGATCCTGGCTCAG 52 .0 ( Turner et al. 1999) 907R CCGTCAATTCMTTTGAGTTT (Lane 1991)

16S rDNA S 530F GTGCCAGCMGCCGCGG 65 .0 ( Handley et al. 2009) 943R ACCGCTTGTGCGGGCCC

Table 3.2 Genes and the corresponding primer sequences used for the amplification or sequencing in MLSA study.

PCR amplification/ Locus sequencing Primers Sequence (5' → 3') Ta Reference dnaK A/S JdnaK-F GCGTTTTCGCTTCRATKTCWGC 61.0 This study JdnaK-R ATTCCAACGAAGAAGTCGCAAAC

sucC A/S JsucC -F GCACCGTTACCATACAACCTAC 54.3 This study JsucC-R TTGGTGACKTTYCAGACTGAC

rpoD A rpoD 70F ACGACTGACCCGGTACGCATGTAYATGMGNGARATGGGNACNGT 58.0 (Yamamoto and rpoD 70R ATAGAAATAACCAGACGTAAGTTNGCYTCNACCATYTCYTTYTT Harayama, 1998) S rpoD 70Fs ACGACTGACCCGGTACGCATGTA rpoD 70Rs ATAGAAATAACCAGACGTAAGTT

rpoB A/S JrpoB -F AAAGTGCTTTATAAYGCACG 51.0 This study JrpoB-R GRTTYTCWGCCATTTCRCC

gyrB A UP -1 GAAGTCATCATGACCGTTCTGCAYGCNGGNGGNAARTTYGA 60.0 (Yamamoto and UP-2r AGCAGGGTACGGATGTGCGAGCCRTCNACRTCNGCRTCNGTCAT Harayama, 1995) S UP-1S GAAGTCATCATGACCGTTCTGCA UP-2Sr AGCAGGGTACGGATGTGCGAGCC

3.5.4 Agarose gel electrophoresis

Agarose gel electrophoresis was carried out using a 1% agarose gel. The gel was prepared in 1X TAE buffer (Appendix I) and stained with 0.5 µg/mL ethidium bromide (Promega, USA). One µl of TrackIt™ 1 Kb Plus DNA Ladder (Invitrogen, USA) was used as the molecular size marker. Genomic DNA samples were mixed with 6X gel loading buffer (Appendix I) before loading while PCR products were loaded directly onto the gel. Electrophoresis was carried out at 100V for approximately 40 minutes. The DNA bands were visualized under UV light and image of the gel was captured using the ChemiDoc-It™ Imaging System (UVP, USA) or Quantity One® (Bio-Rad, USA).

3.5.5 Extraction and gel purification of DNA fragment

The DNA band corresponding to the correct molecular size was excised from the 1% agarose gel using a sterile surgical blade. Extraction and purification of the DNA fragments were carried out with the Wizard® SV Gel and PCR Clean-Up System (Promega, USA) according to the manufacturer’s protocol. The purified DNA fragments were stored at -20oC.

3.5.6 DNA sequencing and analysis

Purified products were sent to Australian Genome Research Facility Ltd. (AGRF) for purified DNA (PD) sequencing service using the AB 3730xl platform. DNA sequences were compiled and analysed using the BioEdit version 7.0.9.0 software (Hall 1999). The nucleotides sequences were submitted to the National Center for Biotechnology Information (NCBI) GenBank DNA database. Accession numbers for sequences derived from this study were presented as in Table 3.3.

Table 3.3 GenBank accession numbers for Alteromonas and Marinobacter strains derived from this study.

Species Name GenBank Accession Numbers dnaK sucC rpoD rpoB gyrB 16S rDNA T Alteromonas macleodii LMG 2843 JQ407010 JQ411701 JQ411711 JQ411691 JQ406999 Y18228* (= Baumann 107T, ATCC 27126T, DSM 6062T) T A. marina SW-47 JQ407011 JQ411702 JQ411712 JQ411692 JQ407000 AF529060* (= LMG 22057T, JCM 11804T, KCCM 41638T) T A. stellipolaris LMG 21861 JQ407013 JQ411704 JQ411714 JQ411694 JQ407002 AJ295715* (= ANT 69aT, R-15466T, R-9875T, DSM 15691T) T A. litorea TF-22 JQ407009 JQ411700 JQ411710 JQ411690 JQ406998 AY428573* (= JCM 12188T, CIP 108505T, LMG 23846T, KCCM 41775T) T A. hispanica F-32 JQ407008 JQ411699 JQ411709 JQ411689 JQ406997 AY926460* (= CECT 7067T, LMG 22958T) T A.addita R10SW13 JQ407006 JQ411697 JQ411707 JQ411687 JQ406995 AY682202* (= KCTC 12195T, LMG 22532T, CIP 108794T) T A. simiduii BCRC 17572 JQ407012 JQ411703 JQ411713 JQ411693 JQ407001 DQ836766* (= AS1T, JCM 13896T) T A. tagae JCM 13895 JQ407014 JQ411705 JQ411715 JQ411695 JQ407003 DQ836765* (= AT1T, BCRC 17571T) T A. genovensis LMG 24078 JQ407007 JQ411698 JQ411708 JQ411688 JQ406996 AM885866* (= R-28792T, CCUG 55340T)

Table 3.3 Continue Species Name GenBank Accession Numbers dnaK sucC rpoD rpoB gyrB 16S rDNA T A. australica H17 JQ446370 JQ446372 JQ446373 JQ446371 JQ407005 FJ595485 (= KMM 6016T, CIP 109921T) T Marinobacter hydrocarbonoclasticus SP.17 - - KF811472 - KF811470 X67022* (= ATCC 49840T, CIP 103578T , DSM 8798T) M. algicola LMG 23835T - - KF811474 - KF811463 AY258110* (= DG893T, DSM 16394T, NCIMB 14009T) T M. sediminum LMG 23833 - - KF811477 - KF811466 AJ609270* (= R65T, DSM 15400T, KMM 3657T) T M. adhaerens CIP 110141 - - KF811473 - KF811467 AY258110* (= HP15T, DSM 23420T) T M. flavimaris CIP 108615 - - KF811475 - KF811468 AY517632* (= SW-145T, DSM 16070T, JCM 12323T, KCTC 12185T) T M. salsuginis CIP 109893 - - KF811476 - KF811469 EF028328* (= SD-14BT, DSM 18347T, LMG 23697T) Strain A3d10T - - KF811471 - KF811465 KJ547704 (= JCM 19398T, CIP 110589T, KMM 7501T Strain R9SW1T - - KF811478 - KF811464 KJ547705 (= LMG 27497T, JCM 19399T, CIP 110588T, KMM 7502T) *Accession numbers from previous publications.

3.5.7 Phylogenetic analysis

The almost complete 16S rRNA gene sequences from the validly described species were retrieved from GenBank. Peptide sequences used in the MLSA study were translated using ExPASy (SIB Swiss Institute of Bioinformatics, http://web.expasy.org/translate/) (Gasteiger et al. 2003). Both nucleotide and peptide sequences were aligned using the CLUSTAL W program (Thompson et al. 1994). Phylogenetic tress were constructed using the neighbour-joining (NJ) (Saitou and Nei 1987), maximum-parsimony (MP) (Fitch 1971) and maximum-likelihood (ML) (Felsenstein 1981) algorithms. Genetic distances were calculated using Kimura’s two- parameter model (Kimura 1980) for nucleotide sequences and the P-distance model for amino acid sequences, using MEGA 5 software (Tamura et al. 2011).

3.5.8 DNA-DNA hybridization (DDH)

Genomic DNA was extracted using the modified CTAB method (Section 3.5.1), and the concentration and purity of the samples were determined by using Biowave II UV/Visible spectrophotometer (Biochrom WPA, UK) and a Hellma cell. DDH was performed by using a simple fluorimetric method that uses quantitative real-time PCR thermocyclers (Gonzalez and Saiz-Jimenez 2005; Rosselló-Móra et al. 2011). Initially, samples were diluted to 0.1 µg/µL using 2X Saline Sodium Citrate (SSC) buffer (Appendix I). DNA samples of each homologous and heterologous (5 + 5 µL) combination were prepared in triplicate with a final volume of 10 µL and pipetted into a 96 well PCR plate.

The DNA denaturation step was carried out using a MyCycler™ Thermal Cycler (Bio-Rad, USA). The samples were subjected to the following conditions: denaturation at 99.9°C for 10 min, renaturation at the calculated Tor for 8 h, followed by subsequent renaturation at Tor - 10°C for 30 min, Tor - 20°C for 30 min, Tor - 30°C for 30 min, and left at 15°C until the measurements for the determination of melting curves were carried out.

Tor = 0.51 (%GC) + 47.0 (De Ley et al. 1970)

SYBR® Green I nucleic acid gel stain (Invitrogen, USA) was diluted to 1: 10,000 and added to the reaction mixture in each well of the 96 well PCR plate. Melting curves were generated using iQ™5 real-time PCR detection system (Bio-Rad, USA) with a melting ramp of 0.2°C/6 s starting at 20°C up to 99°C for 395 cycles. Melting temperature (Tm) for each sample was estimated based on the melt curves where 50% of the DNA is still double stranded, and the ∆Tm and percentage of relative binding ratio (RBR%) were calculated using the equations below. Values lower than 70% RBR and higher than 5°C in ∆Tm were used as cut off values for the definition of a new species (Wayne et al. 1987; Rosselló-Mora and Amann 2001).

∆Tm = Tm (homologous sample) - Tm (heterologous)

RBR% = -5.0501 ∆Tm + 90.329 (Rosselló-Móra et al. 2011)

3.6 Whole genome sequence analysis

The whole genome sequences of strains A3d10T and R9SW1T have been sequenced using an Ion PGM system (Life Technologies, Carlsbad, CA) and de novo assembled using the Newbler version 2.8 software (This part of the work was carried out by Professor Tomoo Sawabe from Hokkaido University, Japan). The assembled genome sequences of strains A3d10T and R9SW1T were deposited at GenBank under the accession number of CP007151 and CP007152, respectively (Auch et al. 2010). Complete genome sequences for only two validly described species of Marinobacter, M. hydrocarbonoclasticus ATCC 49840T (Grimaud et al. 2012) and M. adhaerens HP15T (Gardes et al. 2010), which have previously been assembled, were used in this study for genomic analysis. The fully sequenced and assembled genomes of both these species were retrieved from GenBank, and compared to those of A3d10T and R9SW1T. Genome comparison between strains A3d10T, R9SW1T, M. adhaerens HP15T and M. hydrocarbonoclasticus ATCC 49840T was carried out using reciprocal BLAST analysis, according to the method previously described (Goris et al. 2007). A map of the percentage identity between each of M. adhaerens HP15T, A3d10T and R9SW1T to the type species was generated using the BLAST Ring Image Generator (BRIG) software (Alikhan et al. 2011). In silico genome-to-genome distance (GGD) between the four

57 strains were also calculated using genome-to-genome distance calculator 2.0 (GGDC) provided by DSMZ, http://ggdc.dsmz.de (Auch et al. 2010; Meier-Kolthoff et al. 2013).

3.7 Physiological and biochemical characterisation

In general, colonies grown on marine agar/broth for 24 to 48 hours and 48 to 72 hours were used in the study for Alteromonas sp. and Marinobacter sp. respectively. The Gram stain and the cell morphology were observed by light microscopy. The size and shape of the cells were observed using scanning electron microscopy (SEM) (Carl Zeiss NTS GmbH, Oberkochen, BW, Germany), in which the cells were initially fixed in 2.5% glutaraldehyde (Sigma-Aldrich, USA) followed by dehydration in 30, 50, 70, 90 and 100% (v/v) ethanol solutions. Motility tests were carried out using the hanging drop technique. Determination of the presence of the catalase enzyme was performed using 5% hydrogen peroxide (H2O2), where a positive result was evident by the immediate presence of effervescence. Oxidase tests were carried out using Bactident® oxidase strips (Merck Millipore, Germany) according to the manufacturer’s specifications. The ability of each strain to hydrolyse starch was examined by culturing strains on marine agar with 0.2% (w/v) starch, with results being recorded 48 hours following the addition of the iodine solution (Smibert and Krieg 1994). Oxidation/ fermentation of D-glucose and lactose were performed by a modified O/F medium (per litre: 9.4 g O/F medium (Oxoid, UK), 20 g sea salt (Sigma-Aldrich, USA) and 1% (w/v) carbohydrate).

Bacterial growth was tested at 4, 10, 15, 20, 25, 30, 37, 45 and 50°C. Salinity tolerance was tested at 0, 0.5, 1, 3, 6, 10, 15, 20 and 25% (w/v) NaCl, where Difco™ nutrient agar/broth (BD, USA) was used for the Alteromonas sp. medium, whereas a modified salinity agar (per litre: 5 g peptone, 1 g yeast extract, 0.1 g ferric citrate, 3.24 g magnesium sulphate (MgSO4), 0.55 g dipotassium phosphate (K2HPO4), 15 g agar) was used for the Marinobacter sp. growth medium. pH tolerance was tested at pH levels of 4, 6, 7, 8, 9, and 11 by adjusting the pH of the marine agar with HCl or NaOH. All the tests were carried out for a period of 7 days, with results being recorded daily.

The susceptibility/resistance of the strains to antibiotics was tested by placing the antibiotics disks onto a modified medium (per litre: 21 g Mueller-Hinton broth 58

(Oxoid, UK), 7.5% (w/v) sea salt (Sigma-Aldrich, USA), 15 g agar No. 1 (Oxoid, UK)). The antibiotics studied are listed in Chapter 6 and chapter 7.

The ability of the strains to oxidise a range of organic substrates was investigated by exposure to these substrates in a 96-well Biolog GN2 microplates system (Biolog, USA) in triplicate. Inoculates were prepared by suspending a culture that had been grown overnight into 3% (w/v) saline solution and adjusting the density to McFarland standard no. 1, followed by pipetting 150 µL of the suspension into each well. All the plates were incubated at 30°C and results were read manually after 24 and 48 h. Enzymatic tests were performed using API ZYM test strips (bioMérieux, France) in two individual experiments. Inoculations were prepared by placing a culture that had been grown overnight into 3% (w/v) saline solution and adjusting the density to McFarland standard no. 5. Microbact™ 24E Gram-negative identification system (Oxoid, UK) was also used to test other biochemical reactions, namely: lysine and ornithine decarboxylase; H2S production; glucose, mannitol and xylose fermentation; hydrolysis of o-nitrophenyl-β-d-galactopyranoside (ONPG); indole production; urea hydrolysis; acetoin production (Voges-Proskaüer reaction); citrate utilisation; production of indolepyruvate; gelatin liquefaction; malonate inhibition; inositol, sorbitol, rhamnose, sucrose, lactose, arabinose, adonitol, raffinose and salicin fermentation; and arginine dihydrolase. All the tests were carried as recommended by the manufacturer, unless otherwise stated.

3.8 MALDI-TOF mass spectrometry analysis

Samples for analysis by MALDI-TOF mass spectrometry were prepared according to the Bruker Daltonics protocol. Firstly, 5 µL aliquots of fully grown colonies were transferred by sterile disposable loops into 1.5 mL centrifuge tubes containing 300 µL of sterile MilliQ water. Homogeneous suspensions were generated by pipetting and vortexing the samples for at least one minute. This procedure was followed by adding 900 µL of 100% ethanol, again vortexing the samples for at least one minute. The supernatant containing the ethanol was removed by centrifugation for 2 min at 13,000 rpm, and this step was repeated twice. A 60 µL aliquot of 70% formic acid was then added onto the dried pellet, which was dispersed thoroughly by pipetting,

59 vortexing, followed by water bath sonication. This procedure was followed by the addition of 60 µL of 100% acetonitrile and mixed gently. Tubes were centrifuged for 2 min at 13,000 rpm. One uL aliquots of supernatant from each tube were then added onto the MALDI target plate and air dried at room temperature. Samples were then overlaid with 2 µL (Alteromonas sp. samples) or 1 µL (Marinobacter sp. samples) of matrix solution [saturated solution of α-cyano-4-hydroxycinnamic acid (HCCA) in a mixture of 47.5% ultra-pure water, 2.5% trifluoroacetic acid, and 50% acetonitrile] and again air dried at room temperature until a yellow crystal formation was observed.

Samples prepared on the MALDI target plate were then analysed using a Microflex MALDI-TOF mass spectrometer (Bruker Daltonik GmbH, Germany) equipped with a 60 Hz nitrogen laser. Spectra were recorded in the positive linear mode for the mass range of 2000 – 20000 Da at maximum laser frequency. Raw spectra were analysed using the MALDI Biotyper 3.0 software package (Bruker Daltonik GmbH, Germany) using the default settings. Measurements were performed automatically without any user intervention.

3.9 Sources and preparation of PET film and model substrate

Mylar® PET films were obtained from the Australian office of DuPont Teijin Films, Melbourne. The films were cut into approximately 1 cm × 3 cm pieces, and sterilised by sonication in 70% ethanol for 20 minutes prior to use. The PET model substrate, bis(benzoyloxyethyl)terephthalate (3PET) was a gift from Prof Georg M. Gǖbitz, Graz University of Technology, Department of Environmental Biotechnology, Graz, Austria. The powder substrate was dissolved by heating 1.2 g of the substrate in 50 mL of acetone containing 1.0 mL of Triton X-100. A 50 mL of 50 mM phosphate buffer at pH 7 was then added. The resulting solution was suspended by sonication. The solution was left for 24 hours at room temperature to allow the acetone to evaporate. The 3PET culture medium was prepared by adding 40 g/L sea salts (Sigma-Aldrich, USA), 0.1 g/L yeast extract, 0.5 g/L peptone, 0.1 g/L ferric citrate and 50 mL/L of 3PET solution (liquid minimal medium). A 15g/L of agar was added into the above solution for the preparation of 3PET screening plates. 60

3.10 Screening of PET-hydrolysing bacteria

A total of one hundred bacterial strains were cultivated on the 3PET screening plates, and incubated at 25°C for a period of one month. An observation of the clear zone surrounding the inoculation site was made on daily basis during the one-month test period. Strain that displayed a clear zone was then selected for detailed taxonomic analysis in addition to being used as the pure culture for subsequent PET biodegradation experiments.

3.11 PET biodegradation experiment

PET biodegradation experiments were performed by adding 5 mL of liquid minimal medium with or without the inclusion of 3PET solution into each of the 2 cm × 9 cm glass vials. The vials and media were sterilised by autoclaving at 121°C for 20 minutes, and followed by the addition of a piece of pre-sterilised PET film into each T vial. The strain A3d10 bacterial culture at the OD600 = 0.3 was used for inoculation. The mass of the PET films was recorded before and after the 1 month incubation period. A detailed summary of the experimental conditions is presented in Table 3.4.

Table 3.4 Experimental conditions used for PET biodegradation experiments.

Sample PET film/3PET Bacterial culture PO Film None (control)

PA Film Marinobacter sp. A3d10

SPO SDS-treated film None (control)

SPA SDS-treated film Marinobacter sp. A3d10

3PO Film + 3PET solution None (control)

3PA Film + 3PET solution Marinobacter sp. A3d10

3.12 Surface characterisation

In order to study the biodegradation potential of strain A3d10T on the PET films, a range of surface characterisation techniques were employed to analyse the surface of the films after the 1 month incubation period.

3.12.1 Atomic force microscopy

PET films for analysis were cleaned by sonication for 30 minutes in 70% ethanol followed by rinsing with MilliQ water before surface topographical profiles were acquired. The plastic surface analysis was carried out in air at room temperature using an Innova Scanning Probe Microscope (Veeco Instruments Inc., USA) in tapping mode. Phosphorus-doped silicon probes (MPP-31220-10, Bruker, USA) with a spring constant of 0.9 Nm-1, tip radius of curvature of 10 nm, aspect ratio of 10:1, and a resonance frequency of ~20 kHz were used to acquire surface topography of the films. The lateral movement of the silicon tip was adjusted to the rate of 1 Hz to avoid sample damage.

The resulting topographical data were exported to NanoScope Analysis Version 1.40 (Bruker, USA) and Gwyddion Version 2.31 software (free scanning probe microscopy data analysis software) for data analysis. Correction on lines and horizontal scars were performed before roughness analysis. The roughness parameters that were included in the analysis were average roughness (Sa), root-mean-square roughness (Sq), maximum height difference (Smax), skewness (Ssk) and kurtosis (Sku).

3.12.2 Confocal Raman microscopy

PET films were cleaned as described in Section 3.11.1. The Raman spectra and images were collected by using an alpha300 AFM confocal Raman microscope (WiTec, Germany) equipped with a piezoelectrically driven table and the x100 microscope objective (numerical aperture 0.90; Zeiss, Germany). Samples were excited using a 532 nm laser. Raman spectra were collected in the range 300 to 3600 cm-1 with an average accumulation of 15 and an integration time of 0.5 second per spectrum. The Raman

62 image scans were acquired with 100 points per line × 100 lines per image and at spatial resolution of 100 nm.

3.12.3 Fourier Transform-Infrared Spectroscopy (FTIR)

The Attenuated Total Reflectance - Fourier Transform-Infrared Spectroscopy (ATR-FTIR) spectra of PET films were acquired using a Nicolet iS5 spectrometer (Thermo Scientific, USA) equipped with a diamond crystal. Spectra were collected in absorbance over the wavenumber range from 600 to 2000 cm-1, a resolution of 0.8 and 10 scans per spectrum.

3.12.4 Water Contact Angle

Surface wettability measurements were performed on the PET films using an FTA 1000c (First Ten Ångstroms Inc., USA) instrument equipped with a nanodispenser which is able to deliver water droplets in picolitres. An average of three independent experiment was carried out for each PET films. Each contact angle measurement was recorded with a Pelco Model PCHM 575-4 camera and the contact angle was determined from the images analysed by the FTA Windows Mode 4 software.

Chapter 4: Selection of Marine Bacteria Involved in PET Degradation

4.1 Overview

Poly(ethylene terephthalate) (PET) has been widely used over the past decades due to its desirable characteristics and inexpensive manufacturing. The booming in the manufacturing and use of PET, together with inappropriate handling of PET wastes have led to a number of environmental issues, particularly in the marine environment, due to the fact that PET is fairly stable and thus resilience to degradation (Moore 2008). Current methods of PET disposal include burying, incineration and recycling (Zhang et al. 2004; Webb et al. 2013). However, there are downsides for each of these methods, for example, adverse environmental consequences, involvement of excessive expenses, and lack in overall efficiency as a whole (Awaja and Pavel 2005; Sinha et al. 2010).

Biodegradation can be seen as an alternative way of PET waste disposal owing to its environmental friendliness and cost effective. In recent years, various organisms have been studied for their potential in degrading PET via enzymatic hydrolysis of the polyester backbone, namely Thermobifida fusca (Müller et al. 2005), Penicillium citrinum (Liebminger et al. 2007), Aspergilus oryzae (Wang et al. 2008), Bacillus subtilis (Ribitsch et al. 2011), Thermobifida halotolerans (Ribitsch et al. 2012a) and Thermobifida alba (Ribitsch et al. 2012b). However, none of the study has reported the ability of marine bacteria in hydrolysing PET.

Marine bacteria have been well known for their production of an array of active secondary metabolites which can be used in a variety of fields such as isolation of new enzyme (Sugano et al. 1993), identification of antitumor and antimicrobial compounds (Bowman 2007; Romero et al. 1997), biosorption and bioremediation of heavy metal (Dash et al. 2013; Iyer et al. 2005) as well as the degradation of hydrocarbon (Teramoto et al. 2011; Yakimov et al. 2007). Previous studies have also shown that various microorganisms that belong to the genus Marinobacter (family Alteromonadaceae, order Alteromonadales, class Gammaproteobacteria) are able to utilize aromatic and

65 aliphatic hydrocarbons as the sole carbon and energy sources (Berlendis et al. 2010; Gauthier et al. 1992; Green et al. 2006; Shivaji et al. 2005; Huu et al. 1999).

During the course of evaluating the occurrence of bacterial groups isolated from Port Philip bay in Melbourne, Australia, which have been previously described in the study of bacterial attachment and biofilm formation on PET polymer surfaces (Webb et al. 2009; Webb 2012), bacteria belonging to the taxonomic class Alpha- and Gammaproteobacteria were constantly recovered from the initial PET biodegradation experiment. Hence, in this study, strains isolated from the previous enrichment experiment together with some potential PET degraders including the type species of the represented taxa were selected to include in this study.

Bacterial degradation of PET occurs preferentially in the amorphous regions of polyesters where the chain mobility is less restricted (Müller et al. 2005; Vertommen et al. 2005). It is also one of the critical factors contributing to the biodegradability of polyesters. As a result of biodegradation, enzyme released from the bacteria facilitates the hydrolysis of PET and subsequently results in cleaving of the backbone ester bonds as well as generation of new functional groups including hydroxyl and carboxyl groups at the surface of the polymer. Consequently, this has often led to increase in hydrophilicity of the PET substrate (Brueckner et al. 2008; Donelli et al. 2010). Thus, this study aims to screen for the marine bacteria that are able to utilize PET substrate and to examine the surface structural changes of the PET films upon exposure to the selected strains. Techniques used to examine the surface changes include Atomic force microscopy (AFM), Raman and Fourier Transform Infrared spectroscopy (FTIR) and water contact angle measurement.

4.2 Screening of 3PET-degrading bacteria

Initially, a screening technique for the isolation and identification of PET- hydrolysing marine bacteria was developed using a modified procedure pioneered by Guebitz group (Ribitsch et al. 2011). A total number of 100 bacterial strains which include genus Aestuariibacter, Alteromonas, Glaciecola, Idiomarina, Pseudoalteromonas, Salinimonas, Thalassospira, Marinobacter and strains isolated from the previous enrichment study (Webb et al. 2009; Webb 2012) was selected for the

66 screening of utilization of bis(benzoyloxyethyl) terephthalate (3PET) substrate (refer to Chapter 3 for the preparation of 3PET screening plate, and Appendix II for the list of strains used in this study).

From the total of 100 strains, one of the strains designated as A3d10T (which was later identified as Marinobacter similis in Chapter 7) has shown the ability to hydrolyse 3PET substrate as can be observed through the production of a clear zone after 2 weeks of incubation at 25°C (Figure 4.1 (A)). The result of the production of a clear zone is similar to the study by Guebitz group, who indicated the breakdown of 3PET substrate upon enzymatic hydrolysis (Ribitsch et al. 2011). Thereafter, strain A3d10T was selected to be used as the pure culture for the subsequent PET biodegradation experiments, the experimental setup has been discussed in Chapter 3, Section 3.11. The selected strain was also cultured in 3PET minimal liquid medium and the decrease in medium turbidity also confirmed the utilization of 3PET substrate by strain A3d10T (Figure 4.1 (B)).

Figure 4.1 (A) Clear zone (red arrow) produced by strain A3d10T on 3PET agar after 2 weeks of incubation at 25°C, and (B) a decrease in turbidity of the 3PET minimal liquid medium in the present of strain A3d10T.

4.3 Weight loss measurement of PET films

In order to trace the mass changes of the PET films, before and after the biodegradation experiments, each piece of the PET film was weighed before and after the one month incubation period. In general, as can be seen from Figure 4.2, the results

67 showed that the PET films loses its mass significantly in the presence of strain A3d10T. The highest mass loss was recorded for the PET film incubated in the absence of 3PET substrate. 3PET is a synthetic short chain PET trimer (Heumann et al. 2006), hence it is assumed and more likely that the bacteria use the substrate as the carbon source than the long chain PET polymer. However, in the absence of 3PET substrate, the bacteria can only utilize the PET polymer as the carbon and energy source. .

SDS-treated PET films were also tested in this experiment, as it is believed that surface modification of the PET film with SDS will increase the hydrophilicity of the polymer which in turn increase the rate of biodegradation (Marten et al. 2003; Müller et al. 2005). For SDS-treated PET films (control films and films with the bacteria) used in this study, as can be seen from Figure 4.2, the results showed a decrease in mass. In the case of the SDS-treated PET films incubated with the bacteria, the decrease in mass is not as much as the other cases where the untreated PET films were used. This may be due to the presence of SDS surfactant that inhibited the bacteria from attacking the surface to a certain degree. Also, as the experiment involved biological systems, measurement of the changes in mass of the PET films is likely to involve a relatively large error, therefore, the results from this section can only be used as an indication/prediction of the biodegradation of PET films by strain A3d10T.

0 C o n tro l

T -5 with strain A3d10

-1 0

-1 5

M ass-2 Difference 0 (% )

-2 5 M ed ia M ed ia M ed ia + P E T film + P E T film + SDS-treated + 3 P E T P E T film

Figure 4.2 Changes in mass of the PET films after one month incubation.

4.4 AFM analysis

AFM analysis was employed in this study to characterize the changes in the topography of the PET films after exposure to the strain A3d10T. The changes in topography can be achieved by analysing the surface roughness using various parameters; maximum height difference (Smax), average roughness (S a), root-mean- square (RMS) roughness (Sq), skewness (Ssk) and kurtosis (Sku) (Table 4.1).

Compared to the calculated surface roughness of the control PET films, the PET films that were exposed to strain A3d10T showed statistically significant decrease in maximum height difference (Smax), average roughness (Sa) and RMS roughness (Sq), in the presence or absence of 3PET substrate. The reduction in these parameters indicated that the surface of the PET films may have been smoothened as a result of degradation by strain A3d10T.

Meanwhile, skewness (Ssk) and kurtosis (Sku) have been shown to increase for the PET films that were exposed to strain A3d10T in the presence or absence of 3PET substrate. Any increase in Ssk and Sku values indicate an increase in the peak height (shallower and broader valley) and peak sharpness in a profile or surface of PET films, while any decrease in the two parameters indicate a reduction in peak height (narrower and deeper valley) and sharpness (Thomas 1998). Statistically significant increase in T kurtosis (Sku) was also observed for the strain A3d10 exposed PET film in the absence of 3PET, which suggested the events of erosion occurred to the PET surface peaks resulting in thinner and sharper peaks.

On the contrary, SDS-treated PET films showed an overall increase in Smax, Sa, T Sq, Ssk and Sku after exposure to strain A3d10 for one month (Table 4.1). The results suggested that the overall roughness, the height and sharpness of the peaks on the SDS- treated PET surface increased (Sample SPA, Table 4.1).

Overall, changes in the surface parameters were recorded when the PET films were exposed to strain A3d10T. In general, surfaces of untreated PET films became smoother while the SDS-treated PET films showed increase in roughness. This showed that under limited nutrient conditions, the bacteria, strain A3d10T utilized the PET film as the sole carbon and energy sources, which showed a significant impact on the surface of the PET film.

Table 4.1 Analysis of surface roughness of the PET films under various experimental conditions.

Sample Smax (nm) Sa (nm) Sq (nm) Ssk Sku

PO (Control) 42.68±2.7 2.47±0.5 3.51±0.7 1.57±0.4 4.20±2.1

PA 40.05±5.3 1.87±0.4 2.67±0.5 2.10±0.2 8.59±2.4

3PO (Control) 33.73±4.2 2.26±0.5 3.37±0.6 2.03±0.3 5.67±1.3

3PA 25.89±5.1 1.71±0.01 2.54±0.02 2.32±0.1 7.26±1.4

SPO (Control) 44.98±16.6 2.28±0.8 3.04±1.0 0.46±0.1 3.24±0.4

SPA 48.68±9.7 2.31±1.1 3.16±1.2 1.58±1.8 11.79±15.0

All values were obtained from 5 × 5 µm scanned areas.

PO, Minimal medium + PET film; PA, Minimal medium + PET film + strain A3d10T; 3PO, Minimal medium + PET film + 3PET; 3PA, Minimal medium + PET film + 3PET + strain A3d10T; SPO, Minimal medium + SDS treated PET film; SPA, Minimal medium + SDS treated PET film + strain A3d10T

The 5 µm × 5 µm AFM surface scans of the PET films exhibited distinct visual differences when comparing the two dimensional and three dimensional images of the films incubated with the bacteria under different conditions (Figure 4.3 – 4.8), in particular, it can be seen from the three dimensional images that the films that were exposed to strain A3d10T had shown significant changes in surface nanoarchitecture with smaller and thinner peaks. However, this observation is not noticeable from the respective control samples.

Surface structure of the control PET film incubated in minimal media (Figure 4.3) appeared to have isolated and broader peaks, with relatively similar size of surface peaks. However, in the presence of strain A3d10T (Figure 4.4), it can be seen that larger surface features have become smaller in size (as shown in two dimensional image), as well as the larger peaks have become narrower (as shown in three dimensional image), suggesting surface erosion. Surface of the control PET film incubated in minimal media 70 with 3PET trimer (Figure 4.5) appeared to have the largest surface features among all samples, showing broadest and more isolated peaks on the surface. Significant changes in the surface nanostructure can be observed after the interaction of strain A3d10T with the PET film in the presence of 3PET trimer (Figure 4.6). The surface peaks showed an overall reduction in size and the surface became smoother as demonstrated by the surface line profile. On the other hand, SDS-treated PET film (Figure 4.8) appeared to have a different surface architecture compared to the untreated PET film, as can be observed through the missing surface features (two dimensional image) and the relatively rough surface (three dimensional image). After exposure to the strain A3d10T, the surface of SDS-treated PET film (Figure 4.9) changes significantly, showing surface peaks with more distinctive characteristics and an increase in sharpness.

In this experiment, the AFM analysis of the PET films showed that the bacteria strain A3d10T could play a role in changing the surface nanostructure of the PET films. Areas of the surface peaks have generally reduced and have become narrower as a result of utilization of the PET by the bacteria. PET film exposed to strain A3d10T and in the presence of 3PET appeared to show the most significant decrease in surface roughness, while the increased roughness of the SDS-treated PET film could possibly be due to the different surface nanoarchitecture of the untreated and SDS-treated films that resulted in different surface area being attacked by the bacteria.

Analysis of the surface topography of the PET film after exposure to the bacterial culture has been previously reported. Surfaces of PET films were reported to become smoother by a microbial consortium (Webb 2012), while increase in the roughness of the film when in contact with a mix culture of microorganisms were also reported (Webb et al. 2009; Gurevich et al. 2012). The process of biodegradation can be established to occur preferentially on the outer surface of the PET films, and the mode of degradation might be different depending on the microorganisms being used, the metabolic pathway of the degradation and the accessibility of the surface structure of the film used.

Figure 4.3 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media (PO, control). 72

Figure 4.4 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and in the presence of strain A3d10T (PA). 73

Figure 4.5 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and 3PET trimer (3PO, control). 74

Figure 4.6 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the PET film incubated with minimal media and 3PET trimer, and in the presence of strain A3d10T (3PA). 75

Figure 4.7 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the SDS-treated PET film incubated with minimal media (SPO, control). 76

Figure 4.8 Two dimensional and three dimensional 5 µm × 5 µm AFM surface scans and line profile of 1 month experiment for the SDS-treated PET film incubated with minimal media and in the presence of strain A3d10T (SPA).

4.5 Raman spectra analysis

Raman microscopy was used to characterize the chemical composition of the PET films before and after exposure to the 3PET-hydrolysing bacteria, strain A3d10T. As PET is a semi-crystalline material, the biodegradation process will likely take place in the amorphous regions, which can be proven from a study of the biodegradation of poly(hydroxybutyrate-co-valerate) (PHBV) (Mueller 2006; Webb et al. 2013). Therefore, in this study an assumption is made that crystalline region of the PET film will not be affected upon biodegradation. In this context, the band at 1096 cm-1, which is the band that is attributed to stretching vibrations of the ester, glycol, and ring units of the trans conformation of the crystalline PET chain (Fleming et al. 2005; Paquin et al. 2007), was selected for use in normalization.

As can be seen from Figure 4.9, Raman spectra of the PET films after incubated for one month under various conditions are compared accordingly over the wavenumber range of 500 – 2000 cm-1. All of the spectra show similar peak pattern and some main characteristic peaks of the PET polymer: the 860 cm-1 band is attributed to the C-C of the aromatic ring ; the 995 cm-1 band is attributed to the trans conformations of the ethylene glycol (Paquin et al. 2007); the 1096 cm-1 band is attributed to the degree of crystallinity (Fleming and Kazarian 2004); the 1125 cm-1 band represents the ester CO- O and ethylene glycol C-C stretching; the 1281 cm-1 band represents the CO-C stretching (Litchfield and Baird 2008); the 1616 cm-1 band represents the symmetric C=C stretching of the benzene ring (Paquin et al. 2007); and finally the 1725 cm-1 band is attributed to the symmetric stretching of the carbonyl groups with respect to the benzene ring (Fleming and Kazarian 2004).

From these results, it can be stated that the PET films have a higher percentage of crystalline region as can be seen from the Raman spectra where only the band of 995 cm-1 is present in all samples, while the band at 885 cm-1 that is attributed to the gauche/amorphous conformations of the ethylene glycol is absent. A strong and isolated band at 1616 cm-1 can be seen from all of the spectra. This band together with the carbonyl band at 1725 cm-1 showed a significant increase in the intensities after the PET films were exposed to strain A3d10T. The results are most noticeable for both of the PET films without SDS treatment (Figure 4.9). As this two bands represent the C=C and C=O stretching that are associated with the aromatic ring, this indicated that the 78 proportion of aromatic/crystalline phase on the PET films increases after the interaction with the bacteria. However, as the crystalline portion of the PET is unlikely to be excised by the bacteria, this phenomena can be explained by the consumption of amorphous portion of the PET by the bacteria, in which the peak corresponding to the amorphous region is relatively weak and undetected by Raman spectroscopy, but the exposed region of the crystallinity portion due to the decrease in amorphous region was detected.

3 P O 3 P A

In te n sity PO PA

SPO SPA

5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 W avenumber (cm -1 )

Figure 4.9 Offset Raman spectra of the six PET films in the 500-2000 cm-1 spectral region. Data obtained after 1 month incubation period.

4.6 FTIR Measurements

Another technique based on the conformational and structural characterization of PET was carried out by using ATR-FTIR spectroscopy. This technique has been frequently used in the characterization of PET to study for the enzymatic surface modification of the polymer (Donelli et al. 2009; Ribitsch et al. 2011). The intensities of all the spectral bands were normalized to be equivalent at 1410 cm-1, which has been used in the previous studies as an internal reference band (Walls 1991; Cole et al. 1994a). As discussed in Chapter 2, Section 2.6.1, the main characteristic of the molecular conformation of PET polymer is the gauche and trans rotational conformers of the ethylene glycol moiety. Both gauche and trans conformers are present in the amorphous phase of PET, however only the trans conformer is present in the crystalline phase of PET (Walls 1991; Cole et al. 1994a). As can be seen from Figure 4.10, the PET films used in this study were largely constituted of crystalline phase, which is evident from the presence of bands associated with the trans configuration: 1471 cm-1 -1 -1 (CH2 bending), 1341 cm (CH2 wagging), 1123 cm (O=CH2 and ring CC stretching, -1 -1 ring CH in plane bending), 972 cm (O=CH2 and C(=O)=O stretching) and 849 cm (various bending modes of the benzene ring) band. In addition, the absence of bands -1 -1 associated with the gauche configuration: 1371 cm (CH2 wagging), 1044 cm (C=O -1 stretching) and 898 cm (CH2 rocking), indicated the low amorphous properties of this PET (Donelli et al. 2009). Also, the presence of the ring CH in-place bending mode at 1021 cm-1 (Cole et al. 1994a), and the presence of the strong doublet at the wavenumbers of 1120 – 1100 cm-1 confirmed the high crystallinity of the PET film (Donelli et al. 2010).

No significant changes/differences between the control PET films and the films exposed to strain A3d10T can be found from the spectra. Hence, the ratio between the -1 area of the bands at 1341 and 1410 cm (A1341/A1410), and the ratio between the height -1 of the bands at 1120 and 1100 cm (I1120/I1100) were used to quantitatively compare the biodegradability of strain A3d10T on the PET films (Table 4.2), from which the increase in the crystallinity can be observed when there is an increase in the ratios (Donelli et al. 2010). However, in the present study, no significant changes can be found from the PET samples.

From the results of FTIR measurements, no trace of biodegradation can be detected in all of the PET samples. This findings are consistent with a similar study by Ribitsch et al. 2011 in which the hydrolysis of PET using esterase had showed no significant changes, as the degree of biodegradation was too low to be detected by FTIR (Ribitsch et al. 2011). However, the FTIR results showed that the PET films used in this study has a high crystallinity which shows the trans configuration on the surface, and so limiting chain mobility which is vital for the effective biodegradation process.

3 P A 3 P O

PA PO A b so r b a n c e

SPA SPO

1 8 0 0 1 6 0 0 1 4 0 0 1 2 0 0 1 0 0 0 8 0 0 6 0 0 W avenumber (cm -1 )

Figure 4.10 ATR-FTIR spectra of the six PET films after one month of incubation with the bacteria.

Table 4.2 Values of the spectroscopic indexes obtained from the spectra of PET films incubated under various conditions. Each value represents the average of six spectra.

Sample A1341/A1410 I1120/I1100

PO (Control) 1.40 ± 0.10 0.25 ± 0.06

PA 1.24 ± 0.09 0.30 ± 0.04

3PO (Control) 1.23 ± 0.14 0.28 ± 0.06

3PA 1.29 ± 0.13 0.27 ± 0.04

SPO (Control) 1.19 ± 0.09 0.32 ± 0.01

SPA 1.13 ± 0.06 0.33 ± 0.01

4.7 PET surface wettability

In order to determine the surface hydrophilicity of the PET films, water contact angle measurement was performed in this study. As can be seen from Table 4.3, in the presence of strain A3d10T, WCA for the untreated PET film decreased, in particular, the film incubated together with the 3PET trimer. SDS-treated PET film demonstrated a lower WCA values in general, and showed an increase in WCA after putting in contact with the bacteria.

The results of the untreated PET films are in concordant with the previous reports on enzymatic hydrolysis of PET substrate. The decrease in WCA may indicate an increase in surface hydrophilicity, due to the partial surface hydrolysis and the formation of free hydroxyl and carboxyl groups (Brueckner et al. 2008; Guebitz and Cavaco-Paulo 2008; Ribitsch et al. 2011; Ribitsch et al. 2012b). SDS-treated PET film showed the lowest WCA in the control samples, as SDS is a strong ionic detergent, which is hydrophilic, however, the increase in WCA upon exposure to the bacteria might be due to the hydrophilic interaction of the bacteria with the surfactant, and thus removing the surfactant from the PET surface and exposing the hydrophobic portion of the film.

Table 4.3 Effect of WCA upon surface hydrolysis of PET film by strain A3d10T

Experimental conditions Control with strain A3d10T

PO, PA 70.69 ± 0.55 69.02 ± 2.34

3PO, 3PA 82.66 ± 0.05 77.26 ± 0.14

SPO, SPA 64.10 ± 0.42 71.21 ± 0.45

4.8 Summary

Out of a hundred of marine Alpha- and Gammaproteobacteria screened for PET model substrate (3PET) utilization, a Marinobacter strain designated as A3d10T was found to have the potential to degrade PET. This strain was selected for the biodegradation experiments using PET films, PET films and 3PET, and SDS-treated PET films, under minimal nutrient condition, for one month period.

In this experiment, measurement of total mass loss showed that the PET films exposed to strain A3d10T after one month incubation have the biggest mass loss. Surface topographical characterization using AFM indicated that the PET surfaces have become smoothened when the films were being used as the sole/secondary carbon source, as indicated by Smax, Sa and Sq values. Conformational and structural changes analysis of the PET films using Raman and FTIR spectroscopies revealed the high crystallinity of the PET films used in this experiment, however, no characteristic peaks correspond to the amorphous region can be detected. The increase in the intensities of aromatic (1616 cm-1) and carbonyl (1725 cm-1) bands of the PET films that were exposed to strain A3d10T was detected by Raman spectroscopy. Particularly the untreated PET films showed an apparent sign of biodegradation as indicated by the Raman spectra. Amorphous region was too weak to be detected, therefore any decrease in the spectra characteristics related to the amorphous region of PET was not able to be observed. However, the exposed crystalline region as a result of reduced amorphous region was used to indicate biodegradation. Taking into account the WCA

83 measurement, the results showed an increase in hydrophilicity of the PET surfaces, which also support that the hydrophobic surfaces have been degraded by the bacteria.

In the present study, the results supported that strain A3d10T have the potential to degrade PET surfaces and utilize PET as carbon and energy sources. The PET degradation appeared to take place at the amorphous portion of the outermost layer of the PET surface, and the degradation resulted in smoothening and exposing the bigger area of the crystalline region. The SDS-treated PET films appeared to also undergo biodegradation as can be seen from the surface topography analysed by AFM, however, the results obtained from other surface analysis as well as mass loss measurement showed less apparent sign of biodegradation.

Chapter 5: Development of MLSA and MALDI-TOF Mass Spectrometry for Alteromonas Species Classification

5.1 Declaration for Chapter 5

The results discussed in this chapter have been published as:

Ng HJ, Webb HK, Crawford RJ, Malherbe F, Butt H, Knight R, Mikhailov VV, Ivanova EP (2013) Updating the taxonomic toolbox: classification of Alteromonas spp. using multilocus phylogenetic analysis and MALDI-TOF mass spectrometry. Antonie van Leeuwenhoek 103:265-275.

5.2 Overview

Identification and classification of novel bacterial species is traditionally based on the polyphasic approach relying on phenotypic, chemotaxonomic, genotypic, and phylogenetic characteristics (Colwell 1970; Vandamme et al. 1996). With fast growing numbers of newly described bacteria and the corresponding sequencing data, it has become apparent that the 16S rRNA gene sequence analysis alone may not be sufficient for species discrimination; the commonly used threshold value of 97% 16S rRNA gene sequence similarity was found to be unsatisfactory as some distinct species can share up to 99.9% of their 16S rRNA gene sequence similarity (Gevers et al. 2005; Schleifer 2009). Other molecular approaches which may be adopted in bacterial systematics are currently under intensive exploration in an attempt to improve efficiency and accuracy of the identification of novel species.

Recently, multilocus sequence analysis (MLSA) has been introduced into bacterial systematics, whereby multiple housekeeping genes (usually 5 to 7) are assessed as a group to discriminate phylogenetically close bacteria (Stackebrandt et al.

2002; Schleifer 2009; Gevers et al. 2006). MLSA has been shown to have better taxonomic resolution for the classification of closely related bacteria at the species level (Beaz-Hidalgo et al. 2009; Rong and Huang 2012). Another technique, matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry, has been shown to be reliable, easy to perform, cost effective, can be used under different conditions, and may even be used for subspecies discrimination (Murray 2010).

The work presented in this chapter aimed to take advantage of MLSA and MALDI-TOF MS for the delineation of the Alteromonas species, as the majority of the species of Alteromonas, e.g. A. macleodii, A. marina, A. stellipolaris, A. litorea, A. hispanica, A. addita and A. genovensis share more than 97% of their 16S rRNA gene sequence similarities. Therefore, the evaluation and introduction of MLSA and MALDI-TOF mass spectrometry to the genus Alteromonas can provide an alternative platform to facilitate the classification and identification of novel species to this genus.

5.3 House-keeping gene selection

The use of MLSA for the genus Alteromonas was first reported by Ivars- Martinez et al. in 2008. This study reported the interspecies relationship of Alteromonas macleodii isolated from different geographical regions (Ivars-Martinez et al. 2008). The housekeeping genes (dnaK, rpoB, sucC, glyA, pmg, gyrB and metG) and the primer pairs used in the above study were initially tested for their applicability to the type strains of the nine validly described Alteromonas species (Figure 5.1). As evidenced by PCR amplifications, however, the primer sets appeared to be unsuitable for the Alteromonas group, i.e. the primer sets either generated more than one band or no PCR products were generated across the nine Alteromonas type strains tested. Hereafter, other genes and primer pairs used for the MLSA study in other genera of Proteobacteria were tested, namely gyrB (Yamamoto and Harayama 1995), rpoD (Yamamoto and Harayama 1998), recA (Thompson et al. 2004), gap (Martens et al. 2008) and atpD (Menna et al. 2009) (Figure 5.2). It can be seen from the information presented in Figure 5.2, gyrB and rpoD were successfully being amplified across the nine Alteromonas type strains tested, and, thus, the genes and the primer pairs were selected for the MLSA in this study; whilst the other three genes (recA, gap and atpD)

87 and the respective primer pairs failed to generate the correct PCR products across most of the Alteromonas type strains, and were therefore disregarded from this study.

As suggested by the ad hoc committee for the re-evaluation of the species definition in bacteriology, a minimum number of five housekeeping genes should be used in MLSA (Stackebrandt et al. 2002). Hence, new set of primers for the amplification of dnaK, rpoB and sucC were re-designed based on the conserved regions of the sequences of the Alteromonas type strains that were being successfully amplified using the primers described by Martinez et al. (2008). In total, five genes (dnaK, sucC, rpoD, rpoB and gyrB) were selected and employed in this study. The primer sequences for PCR amplification and sequencing are presented in Table 3.2 of Chapter 3. The size of the amplified fragments was approximately 500 bp for dnaK, 510 bp for sucC, 860 bp for rpoB, 1200 bp for gyrB and 850 bp for rpoD (Figure 5.2 and 5.3).

The developed primer sets were also tested on the type strains representing a number of genera closely related to Alteromonas, namely, Salinimonas, Aestuariibacter, Glaciecola, Pseudoalteromonas, Marinomonas and Shewanella (Appendix III), and they appeared to be specific to the Alteromonas species. As various species currently belonging to the genus Pseudoalteromonas were used to be classified under the genus Alteromonas (Gauthier et al. 1995), the newly designed primers were also tested on a number of Pseudoalteromonas strains available from Swinburne University’s culture collection. The results, however, showed that the primer sets did not worked to amplify the specific genes across the Pseudoalteromonas strains tested.

Figure 5.1 Agarose gel electrophoresis of PCR products by using genes and primers described by Ivars-Martínez et al., 2008. Lane M, TrackIt™ 1 Kb Plus DNA Ladder (Invitrogen, USA); Lane 1, A. addita R10SW13T; Lane 2, A. genovensis LMG 24078T; Lane 3, A. hispanica F-32T; Lane 4, A. litorea TF-22T; Lane 5, A. macleodii DSM 6062T; Lane 6, A. marina SW-47T; Lane 7; A. simiduii BCRC 17572T; Lane 8, A. stellipolaris LMG 21861T; Lane 9, A. tagae BCRC 17571T

Figure 5.2 Agarose gel electrophoresis of PCR amplification of gyrB, rpoD, gap, recA D and atp . Lane M, TrackIt™ 1 Kb Plus DNA Ladder (Invitrogen, USA); Lane 1, A. addita R10SW13T; Lane 2, A. genovensis LMG 24078T; Lane 3, A. hispanica F-32T; Lane 4, A. litorea TF-22T; Lane 5, A. macleodii DSM 6062T; Lane 6, A. marina SW-47T; Lane 7; A. simiduii BCRC 17572T; Lane 8, A. stellipolaris LMG 21861T; Lane 9, A. tagae BCRC 17571T

Figure 5.3 Agarose gel electrophoresis of PCR products of dnaK, sucC and rpoB using the primers designed from this study. Lane M, TrackIt™ 1 Kb Plus DNA Ladder (Invitrogen, USA); Lane 1, A. addita R10SW13T; Lane 2, A. genovensis LMG 24078T; Lane 3, A. hispanica F-32T; Lane 4, A. litorea TF-22T; Lane 5, A. macleodii DSM 6062T; Lane 6, A. marina SW-47T; Lane 7; A. simiduii BCRC 17572T; Lane 8, A. stellipolaris LMG 21861T; Lane 9, A. tagae BCRC 17571T

5.4 Individual gene analyses

Individual phylogenetic trees, based on the sequences of the selected five housekeeping genes (dnaK, sucC, rpoD, rpoB and gyrB), were constructed using neighbour- joining (NJ) (Saitou and Nei 1987), maximum-parsimony (MP) (Fitch 1971) and maximum-likelihood (ML) (Felsenstein 1981) algorithms, which are available as part of the MEGA 5.04 software package (Tamura et al. 2011). Genetic distances were calculated using the Kimura’s two-parameter model (Kimura 1980) for NJ and ML analysis, and the bootstrap analysis were performed with a robustness of 1,000 for all three algorithms. From each of the individual trees (Figure 5.4), it appeared that the five house-keeping genes could indeed provide a higher evolutionary rate than the 16S rRNA gene (Figure 5.6 (A)), as indicated by the evolutionary distance scale bars. It is noteworthy that the three genes rpoD, gyrB and rpoB demonstrated higher confidence bootstrap values, resolving power and topological stability when compared with the trees reconstructed from the dnaK and sucC gene sequences. These observations are in agreement with earlier reports in the literature (Zeigler 2003; Konstantinidis et al. 2006) suggesting that rpoD, gyrB and rpoB have the best potential to be practically used as marker genes for the effective identification and classification of Alteromonas species.

The pairwise distances between sequences of the individual house-keeping genes and the 16S rRNA gene were also calculated using the MEGA 5.04 software package (Tamura et al. 2011), in which the overall mean distances were found to be 0.2153 for rpoD, 0.2150 for gyrB, 0.1641 for rpoB, 0.1168 for sucC, 0.1062 for dnaK and 0.0166 for 16S rRNA gene. A matrix of gene similarity between each species of Alteromonas was also constructed, based on the distance matrices calculated as part of the generation of neighbour-joining trees (Figure 5.5). The results showed that the five house-keeping genes possess higher pairwise distances when compared with the 16S rRNA gene. As evidenced from the data presented in Figure 5.5, the sequence divergence based on the nucleotide sequences of the five house-keeping genes was less conservative, i.e. they possess higher percentage dissimilarity, when compared with the 16S rRNA gene sequences.

Figure 5.4 Phylogenetic analyses of the nucleotide sequences of each of the five marker genes ((A) dnaK, (B) sucC, (C) rpoD, (D) rpoB and (E) gyrB) used for MLSA of Alteromonas spp. The trees were calculated using the Neighbour Joining method. Numbers at branching points are percentage bootstrap values based on 1000 replications, with only values > 50% shown. Scale bar represents 0.02 substitutions per nucleotide position. The maximum-likelihood (ML) and maximum parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively.

Figure 5.5 Genetic similarity matrix for Alteromonas type strains. For each species, the corresponding species with the most similar DNA sequence for the given gene or group of genes is presented. 95

5.5 Comparative MLSA

In order to demonstrate the usefulness of the five marker genes selected in the MLSA study, a supergene containing 3,356 nucleotides from partial sequences of the five house-keeping genes was constructed. Based on the resulting supergene, a concatenated phylogenetic tree was constructed with bootstrap analysis of 1,000 replications and using the three tree-making algorithms (Figure 5.6 (B)).

As illustrated in Figure 5.6, a comparison of the MLSA phylogenetic tree and 16S rRNA phylogenetic tree revealed that both trees had congruent topologies; however, the phylogenetic tree constructed from the five concatenated partial sequences was better supported with higher bootstrap values. Also, the phylogenetic tree from the MLSA demonstrated a higher resolving power as indicated by the scale bars. This finding confirmed previous observations that, in order to improve the phylogenetic reconstruction (Cantarel et al. 2006), multiple genes could compensate the difference between each gene by increasing the phylogenetic signal and minimising the effects of horizontal gene transfer and recombination of single loci (Gevers et al. 2005; Schleifer 2009). Another tree based on the concatenated amino acid sequences (Figure 5.7) showed an identical topology to the concatenated nucleotide tree, but with a lower resolution. This finding is also in agreement with the results reported elsewhere (Simmons et al. 2002) and thus suggests that nucleotide sequences should be used for MLSA in order to obtain a higher resolution and sequence diversity.

Figure 5.6 Comparative Neighbour-joining (NJ) phylogenetic analysis of Alteromonas species based on (A) 16S rRNA gene sequences obtained from NCBI GenBank and (B) Multilocus sequence analysis of concatenated sequences of dnaK, sucC, rpoB , gyrB and rpoD genes. Numbers at branching points are percentage bootstrap values based on 1,000 replications, with only values >50% shown. Scale bars represent 0.002/0.02 substitutions per nucleotide position. The Maximum-likelihood (ML) and maximum Parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively.

Figure 5.7 Phylogenetic analysis of the concatenated amino acid sequences (DnaK, SucC, RpoB, GyrB, and RpoD). Numbers at branching points are percentage bootstrap values based on 1000 replications, with only values > 50% shown. Scale bar represents 0.005 substitutions per position.

The sequence similarities of dnaK, sucC, rpoB, gyrB and rpoD among nine validly described Alteromonas species were calculated and were found to be in the range of 77.9 – 98.9%, whilst the 16S rRNA gene sequence similarities were in the range of 95.9 – 99.8% (Table 5.1). A. stellipolaris and A. addita showed the highest degree of sequence similarities, which was also reflected in the trees based on each individual gene (Figure 5.4). Since six of the nine validly named species of the genus Alteromonas are described based on single strains, an assessment of the MLSA intraspecies variations was not possible. With the growing number of newly isolated bacteria, however, this issue may eventually be addressed. The evolutionary rate of the concatenated sequences was higher than that of the 16S rRNA sequences (Figure 5.5), with the overall mean distances calculated to be 0.1693 (concatenated sequences) and 0.0166 (16S rRNA gene sequences). The lower sequences similarity and the higher evolutionary rates determined from the MLSA studies highlighted the increased resolving power of this technique.

Table 5.1 Interspecies similarities of the 16S rRNA gene sequences and the concatenated sequences of dnaK, sucC, rpoB, gyrB, and rpoD genes.

Similarity of 16S rRNA/concatenated house-keeping genes (%) 1 2 3 4 5 6 7 8 9 1 A. macleodii LMG 2843T 100/100 2 A. marina SW-47T 98.7/87.4 100/100 3 A. stellipolaris LMG 21861T 98.1/80.5 98.1/79.7 100/100 4 A. litorea TF-22T 98.0/86.0 98.7/87.6 97.8/78.1 100/100 5 A. hispanica F-32T 97.3/81.4 97.7/80.1 97.8/77.9 98.4/79.7 100/100 6 A. addita R10SW13T 98.1/80.9 98.2/79.7 99.8/98.9 97.8/78.2 98.0/77.9 100/100 7 A. simiduii BCRC 17572T 97.5/97.9 97.7/87.0 96.7/80.5 96.8/86.1 96.1/81.2 96.7/80.9 100/100 8 A. tagae JCM 13895T 97.3/87.0 98.3/85.3 97.3/80.4 97.9/84.0 97.0/80.8 97.4/80.7 96.2/87.1 100/100 9 A. genovensis LMG 24078T 97.2/80.5 97.6/79.8 97.3/79.4 98.4/79.6 99.1/86.6 97.5/79.4 95.9/80.5 96.9/79.6 100/100

5.6 MLSA as possible alternative to DNA-DNA hybridization (DDH)

DNA-DNA hybridization (DDH), the gold standard technique introduced several decades ago into bacterial systematics (Brenner et al. 1969b), has been broadly discussed and studied for its consistency with results obtained from 16S rRNA, MLSA and complete genome sequences analysis (Konstantinidis and Tiedje 2007; Martens et al. 2008; Richter and Rossello-Mora 2009; Rong and Huang 2012). 16S rRNA gene sequence analysis is relatively easy to perform; however, due to the increasing number of 16S rRNA gene sequencing data, a significant number of distinct species have been documented to have their 16S rRNA gene sequence similarity higher than 97%. This phenomenon is frequently reported for different bacterial taxa, including Pseudoalteromonas, Acinetobacter and Pseudomonas, where the 16S rRNA gene sequence similarity within these genera have been found to be in the range of 90 – 99.9% (Nam et al. 2007), 93.5 – 99.1% (Kämpfer and Glaeser 2011), and 92.9 – 100% (Wolterink et al. 2002; Peix et al. 2009), respectively. Complete genome sequences analysis is an alternative to determine overall taxonomic relatedness; however, it is still not routinely practically available. Hence, MLSA may serve as an affordable alternative technique to DDH when 16S rRNA gene sequence similarity fails to give the satisfactory threshold value. Earlier studies on the genera Xanthomonas (Young et al. 2008 ), Ensifer (Martens et al. 2008), Bradyrhizobium (Rivas et al. 2009), Gluconacetobacter (Cleenwerck et al. 2010) and Streptomyces (Rong and Huang 2012) have shown that MLSA is a suitable alternative to DDH. In a recent study, the proposed MLSA cut-off value for Streptomyces spp. was reported to be 99.3% (evolutionary distance of 0.007) (Rong and Huang 2012). The results of the present study indicate that MLSA sequence similarities of the five gene-set for Alteromonas spp. was up to 98.9%, with the highest percentage found for two species, A. addita and A. stellipolaris; the reported DDH value for these species is 49% (Ivanova et al. 2005a). Thus, by considering the DDH value together with the MLSA percentage sequence similarity, 98.9% (evolutionary distance of 0.011) can be suggested to be the cut-off value for the differentiation of Alteromonas species.

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5.7 MALDI-TOF mass spectrometry analysis

In order to enhance the classification of Alteromonas spp., MALDI-TOF mass spectrometry was used as a complementary taxonomic technique for species differentiation within the genus Alteromonas. This technique has been broadly employed in clinical microbiology, where fast, accurate and rapid identification of potential pathogenic bacteria is crucial (Murray 2010). The potential of this technique in the taxonomic identification and classification of environmental bacteria has, however, not been extensively studied. In this study, a main spectra library (MSP) dendrogram was constructed using the MALDI Biotyper, based on the species-specific profiles generated by the MALDI-TOF mass spectrometry (Figure 5.8). The topology of the dendrogram correlated well with both the 16S rRNA and the MLSA phylogenetic trees. To confirm that the natural variability in each of the spectra did not affect the discrimination between the Alteromonas species, a Principal Component Analysis (PCA) plot, with spectra derived from four technical replicates for each species, was generated (Figure 5.9). From the cluster analysis, it can be seen that each of the Alteromonas species form a unique protein profile, resulting in the close clustering of the replicates and allowing discrimination of the Alteromonas species. The applicability of MALDI-TOF mass spectrometry for the differentiation of genus Alteromonas from six closely related taxa was also tested (Appendix IV). These results clearly demonstrated that each of the six closely related strains formed separate clusters to Alteromonas. The approach using MALDI-TOF mass spectrometry for the detection of protein mass fingerprinting is independent of the genomic approach, but generate results comparable to the 16S rRNA gene analysis and MLSA. Thus, the results from this work highlight the applicability of MALDI-TOF mass spectrometry as a rapid and effective tool for aiding with the classification and identification of bacteria.

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Figure 5.8 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles from nine Alteromonas species generated by the MALDI Biotyper 3.0 software. Distance is displayed in relative units.

Figure 5.9 Three-dimensional Principal Component Analysis (PCA) plot of the nine Alteromonas species. 1, A. stellipolaris; 2, A. addita; 3, A. hispanica; 4, A. genovensis; 5, A. litorea; 6, A. tagae; 7, A. macleodii; 8, A. simiduii; 9, A. marina. Each dot represents a single technical replicates. 102

5.8 Summary

The results obtained in this study indicated that MLSA and MALDI-TOF mass spectrometry were both useful and viable techniques for addition to the suite of tools routinely used in taxonomic studies. Five house-keeping genes, dnaK, sucC, rpoB, gyrB, and rpoD, could be used in MLSA studies for the identification and classification of Alteromonas spp., where strains showing sequences with a sequence similarity ≤ 98.9% can be considered as distinct species. Overall, the data demonstrated that MLSA provided a reliable classification and grouping of Alteromonas spp., and in some cases may eliminate the necessity of performing time-consuming and labour-intensive DDH experiments. MALDI-TOF mass spectrometry can also be used as a supporting or complementary technique for the classification of new strains. The addition of new techniques and improvement of existing procedures in prokaryotic systematics is of considerable importance to ensure that the field does not stagnate and is continually improving and progressing.

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Chapter 6: Description of Alteromonas australica H17T, Isolated from the Tasman Sea

6.1 Declaration for Chapter 6

The results discussed in this chapter have been published as:

Ivanova EP, Ng HJ, Webb HK, Kurilenko VV, Zhukova NV, Mikhailov VV, Ponamoreva ON, Crawford RJ (2013) Alteromonas australica sp. nov., isolated from the Tasman Sea. Antonie van Leeuwenhoek 103(4):877-884.

6.2 Overview

The genus Alteromonas was the only genus included in the first volume of Bergey’s Manual of Systematic Bacteriology (Baumann et al. 1984), which accommodate Gram-negative, strictly aerobic, heterotrophic marine bacteria with single polar flagellum (Baumann et al. 1972). Bacteria of this genus has undergone a number of detailed investigations and reclassifications (Van Landschoot and De Ley 1983; Gauthier et al. 1995; Ivanova et al. 2004), leaving Alteromonas macleodii as the only species belonging to the genus Alteromonas for approximately a decade (Baumann et al. 1972; Gauthier et al. 1995). Recently, the number of validly described species belonging to the genus Alteromonas has increased to eleven species, namely Alteromonas macleodii (Baumann et al. 1972), A. marina (Yoon et al. 2003a), A. stellipolaris (Van Trappen et al. 2004), A. litorea (Yoon et al. 2004b), A. hispanica (Martinez-Checa et al. 2005), A. addita (Ivanova et al. 2005a), A. simiduii (Chiu et al. 2007), A. tagae (Chiu et al. 2007), A. genovensis (Vandecandelaere et al. 2008), A. halophila (Chen et al. 2009) and A. australica ( Ivanova et al. 2013).

In the course of performing taxonomic surveys of the marine bacteria isolated from sea water collected in Port Phillip Bay, Melbourne, Australia (Webb et al. 2009), bacteria belonging to the genus Alteromonas were constantly recovered. These bacteria 105 represented one of the bacterial group that have the highest potential to degrade PET. In this chapter, a taxonomic description of the isolate designated as H17T, which was recovered from the PET degradation enrichment experiment and included in the PET degradation screening experiment (Chapter 4), is presented. The description includes reference to the newly developed Multilocus Sequence Analysis (MLSA) and Matrix Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry analyses.

6.3 16S rRNA gene sequence analysis

Initially, the strain isolated from the PET degradation enrichment experiment (Webb et al. 2009) and designated H17T, was subjected to a 16S rRNA gene sequence analysis as described in chapter 3. Phylogenetic analyses of the 16S rRNA gene were performed using neighbour-joining (NJ) (Saitou and Nei 1987), maximum-parsimony (MP) (Fitch 1971) and maximum-likelihood (ML) (Felsenstein 1981) algorithms. All three algorithms are available as part of the MEGA 5.04 software package (Tamura et al. 2011). For the NJ and ML analysis, genetic distances were calculated based on the Kimura’s two-parameter model (Kimura 1980). A bootstrap analysis was also performed with the robustness of 1,000 replications for all three different phylogenetic trees.

As shown in Figure 6.1, phylogenetic trees based on three different algorithms showed similar topologies where strain H17T consistently formed a distinct branching within the Alteromonas cluster, suggesting that the newly isolated strain belong to the genus Alteromonas. A recently validated Alteromonas species, A. halophila (Chen et al. 2009) was also included in the analysis to confirm that strain H17T and A. halophila are separate species. The 16S rRNA gene sequence similarity between A. halophila JSM 073008T and strain H17T was determined by using NCBI Basic Local Alignment Search Tool (BLAST) and EzTaxon-e (Kim et al. 2012), and results were found to be 96.0% and 95.6%, respectively.

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Figure 6.1 The taxonomic position of strain H17T and the other species of the genus Alteromonas inferred from the neighbour-joining (NJ) phylogenetic tree based on 16S rRNA gene sequence similarities. The type species of the genus Salinimonas, Bowmanella, Glaciecola and Aestuariibacter were used as outgroups in the analysis. Number at branching points are percentage bootstrap values based on 1,000 replications, with only values above 50% are shown. Scale bar represents 0.01 substitutions per nucleotide position. The maximum-likelihood (ML) and maximum parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively. 107

As the sequence similarity was significantly below the previously suggested 97.0% (Stackebrandt and Goebel 1994) and recently proposed 98.7% (Stackebrandt and Ebers 2006) threshold value for species discrimination, and its distinct branching in the 16S rRNA NJ tree (Figure 6.1), it can confirm that the exclusion of A. halophila JSM 073008T in the subsequent analysis did not interfere with the validation of strain H17T as a novel Alteromonas species.

To provide evidence to support the proposal that strain H17T represented a novel species to the genus Alteromonas, pairwise distances/sequence similarities of the 16S rRNA gene sequences of strain H17T, and the nine validly described Alteromonas species were calculated, based on the Kimura’s two-parameter model using the MEGA 5.04 software. As presented in Tables 6.1-6.2, the results for the pairwise distances and sequence similarities were in the range of 0.013 to 0.032 and 96.8% to 98.7%, respectively. It is common that the species within genus Alteromonas display a high 16S rRNA gene sequence similarity, for example, the 16S rRNA gene sequence similarity among the nine validly described species were found to be in the range of 95.9 to 99.8% (Table 6.2).

As a results of the conserveness of the 16S rRNA gene, various methods, involving analysis at the genetic level, need to be carried out to ensure the accurate identification of the new isolate. Recently, whole genome sequence analyses have been suggested for integration into bacterial systematic identifications (Chun and Rainey 2014; Kim et al. 2014), however, the limited availability of fully sequenced whole genome sequences of Alteromonas strains has restricted the use of this approach. Hence, the newly developed MLSA and the gold standard method, DNA-DNA hybridization (DDH), are employed in this study and are reported in Section 6.4-6.5.

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Table 6.1 Interspecies similarity of the 16S rRNA gene sequences of Alteromonas spp. based on pairwise distances. 1 2 3 4 5 6 7 8 9 10 1. A. addita R10SW13T 2. A. genovensis LMG 24078T 0.025 3. A. hispanica F-32T 0.020 0.009 4. A. litorea TF-22T 0.021 0.016 0.016 5. A. macleodii DSM 6062T 0.015 0.025 0.023 0.016 6. A. marina SW-47T 0.017 0.024 0.022 0.013 0.008 7. A. simiduii BCRC 17572T 0.033 0.041 0.039 0.032 0.021 0.022 8. A. stellipolaris LMG 21861T 0.002 0.027 0.022 0.022 0.016 0.018 0.033 9. A. tagae BCRC 17571T 0.025 0.031 0.029 0.021 0.022 0.017 0.037 0.026 10. Strain H 17T 0.016 0.023 0.020 0.020 0.013 0.016 0.032 0.019 0.030

Table 6.2 Interspecies similarity of the 16S rRNA gene sequences of Alteromonas spp. based on percent sequence similarities. 1 2 3 4 5 6 7 8 9 10 1. A. addita R10SW13T 100.0 2. A. genovensis LMG 24078T 97.5 100.0 3. A. hispanica F-32T 98.0 99.1 100.0 4. A. litorea TF-22T 97.9 98.4 98.4 100.0 5. A. macleodii DSM 6062T 98.5 97.5 97.7 98.4 100.0 6. A. marina SW-47T 98.3 97.6 97.8 98.7 99.2 100.0 7. A. simiduii BCRC 17572T 96.7 95.9 96.1 96.8 97.9 97.8 100.0 8. A. stellipolaris LMG 21861T 99.8 97.3 97.8 97.8 98.4 98.2 96.7 100.0 9. A. tagae BCRC 17571T 97.5 96.9 97.1 97.9 97.8 98.3 96.3 97.4 100.0 10. Strain H 17T 98.4 97.7 98.0 98.0 98.7 98.4 96.8 98.1 97.0 100.0 109

6 .4 Multilocus Sequence Analysis (MLSA)

In view of enhancing the techniques used in bacterial systematics, the ad hoc committee for the re-evaluation of the species description recommended that MLSA be included as one of the techniques for species delineation (Stackebrandt et al. 2002). In this study, the genus Alteromonas was employed for the evaluation of the discriminatory power of MLSA up to the species level, as the 16S rRNA gene sequence similarities for the majority of the species in this genus fall above 97.0% similarity (Stackebrandt and Goebel 1994), and in some cases, above 98.7% (Stackebrandt and Ebers 2006) (Table 6.2). The taxonomic position of strain H17T as a distinct Alteromonas species is unclear, based on the current 16S rRNA gene sequence similarity thresholds, where it shares 96.8 to 98.7% (Table 6.2) of 16S rRNA gene sequence similarities with the other nine validly described Alteromonas species.

In this study, five housekeeping genes dnaK (chaperone protein DnaK), sucC (succinyl-CoA synthetase), rpoB (RNA polymerase, β subunit), gyrB (DNA gyrase subunit B), and rpoD (RNA polymerase, sigma 70 factor) were selected and analysed. The selection of housekeeping genes and primers has been discussed in Chapter 4. The phylogenetic positions of strain H17T, based on the individual housekeeping genes, are presented in Figure 6.2. It can be seen that strain H17T is well separated from the other Alteromonas species, forming a distinct branching in each of the individual gene-based- phylogenetic tree. Sequence similarities of the individual housekeeping genes between strain H17T and the nine validly described Alteromonas species were also calculated and are reported in Table 6.3. The results show that the five housekeeping genes indeed provide a higher nucleotide sequence diversity if compared to the 16S rRNA gene sequence, where the sequence similarities of the individual gene ranges from 85.9 – 92.6% (dnaK), 85.0 – C90.6% (suc ), 74.0 – 78.4% (rpoD), 81.7 – 86.7% (rpoB), 75.0 – 78.5% (gyrB) and 96.8 – 98.7% (16S rDNA). Of note, nucleotide sequences of rpoD and gyrB are shown to be the least conservative among the five housekeeping genes used, which is in agreement with previously reported results, as discussed in Chapter 5 (Ng et al. 2013).

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Figure 6.2 Phylogenetic analyses of the nucleotide sequences of the five housekeeping genes ((A) dnaK, (B) sucC, (C) rpoD, (D) rpoB and (E) gyrB) used in MLSA. The trees were reconstructed using Neighbour Joining method. Numbers at branching points are percentage bootstrap values based on 1,000 replications, with only values > 50% shown. The scale bar represents 0.01/0.02 substitutions per nucleotide position. The maximum- likelihood (ML) and maximum parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively.

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Table 6.3 Comparative sequence similarities of individual gene between strain H17T and the nine validly described Alteromonas species.

Strain H17T 16S rDNA dnaK sucC rpoD rpoB gyrB A. macleodii LMG 2843T 98.7 88.5 86.4 74.0 86.7 78.3 A. marina SW-47T 98.4 90.1 85.0 77.0 85.1 76.3 A. addita R10SW13T 98.4 86.5 90.6 77.6 82.6 75.9 A. stellipolaris LMG 21861T 98.1 86.7 90.6 78.4 82.3 76.1 A. litorea TF-22T 98.0 87.5 85.8 74.6 85.3 75.5 A. hispanica F-32T 98.0 89.8 85.5 76.6 81.7 76.0 A. genovensis LMG 24078T 97.7 85.9 90.0 77.6 82.1 75.0 A. tagae JCM 13895T 97.0 92.6 85.8 76.3 85.7 78.4 A. simiduii BCRC 17572T 96.8 88.0 87.5 74.0 86.4 78.5

The individual housekeeping gene sequence analyses showed a greater discriminatory power of strain H17T to the other Alteromonas species if compared to the 16S rRNA gene sequences. Single gene analysis may not be ideal, however, as it is susceptible to horizontal gene transfer (Martens et al. 2008; Rong and Huang 2012), and thus concatenation of the five housekeeping genes was performed. A phylogenetic tree, based on the concatenated sequences of the five genes, was constructed as illustrated in Figure 6.3(A). It can be seen that strain H17T consistently formed a separated branch within the other Alteromonas species, which is in agreement with the individual gene- based-phylogenetic trees (Figure 6.2) and the 16S rDNA phylogenetic tree (Figure 6.1 and 6.3 (B)). The phylogenetic tree based on the concatenated sequences also demonstrated a higher topological stability, resolving power and evolutionary rate, as indicated by the evolutionary distance scale bars (Figure 6.3). The sequence similarities, based on the concatenated sequences between strain H17T and the nine validly described Alteromonas species, were also calculated and determined to be in the range of 80.6 – 82.6% (Table 6.4), which is lower than the recently proposed MLSA cut-off value for genus Streptococcus (95%) (Thompson et al. 2013b), genus Streptomyces (99.3%) (Rong and Huang 2012), and the value of 98.9% proposed for the genus Alteromonas (Chapter 5) (Ng et al. 2013). This is an indication of the distinct standing of strain H17T within the Alteromonas genus.

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Figure 6.3 Phylogenetic analysis showing the position of strain H17T based on (A) concatenated sequences of dnaK, sucC, rpoD, rpoB, and gyrB genes, and (B) 16S rRNA gene sequences. Numbers at branching points are percentage bootstrap values based on 1,000 replications, with only values > 50% shown. The scale bar represents 0.02/0.002 substitutions per nucleotide position. The maximum-likelihood (ML) and maximum parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively.

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6.5 DNA-DNA hybridization (DDH) MLSA was successfully applied to solve the taxonomic position of strain H17T, however, it is yet to be considered a standard method for use in bacterial systematics, and therefore, DNA-DNA hybridization (DDH) still needs to be carried out in order to confirm the taxonomic status of strain H17T in addition to providing a means by which the MLSA scheme can be validated and correlated with the other data (Tindall et al. 2010). In this study, DDH was performed by the fluorimetric method that uses a quantitative real-time PCR thermocycler (Gonzalez and Saiz-Jimenez 2005; Loveland-

Curtze et al. 2011). High purity Genomic DNA, with the A260/A280 ratio being in the range of 1.8 to 2.0, was used. The detailed protocol for this experiment can be found in Chapter 3, Section 3.5.8. Representative individual denaturation curves showing the denaturation rate of strain H17T with the nine validly described Alteromonas species are shown in Figure 6.4, and the calculated DNA-DNA relatedness are summarized in Table 6.4.

As shown in Figure 6.4, the values of difference in melting temperature (∆Tm) calculated from the denaturation curves between homologous DNA (strain H17T) and the hybrid DNA (strain H17T and other Alteromonas species) were found to be more than 5°C. Also, the calculated DNA-DNA relatedness values between strain H17T and the nine validly described Alteromonas species were in the range of 30.7 ± 0.5 to 46.4 ± 3 mol% (Table 6.4). Both of these calculated values did fulfill the species differentiation threshold value, where distinct species should share less than 70% DNA-DNA relatedness, and with more than 5°C difference in ∆Tm (Wayne et al. 1987; Vandamme et al. 1996; Rosselló-Mora and Amann 2001). This suggests that the strain H17T is a novel species belonging to the genus Alteromonas.

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Figure 6.4 Thermal denaturation curves of genomic DNA from strain H17T (grey curving line), the hybrid of strain H17T and the nine validly described Alteromonas species. 118

Table 6.4 Comparative data showing the DNA-DNA relatedness, MLSA and the 16S rDNA sequence similarity between strain H17T and the nine validly described Alteromonas species.

Strain H17T DDH (mol%) MLSA (%) 16S rDNA (%) A. macleodii LMG 2843T 34.3 ± 3 82.1 98.7 A. marina SW-47T 46.4 ± 3 81.8 98.4 A. addita R10SW13T 30.7 ± 0.5 81.5 98.4 A. stellipolaris LMG 21861T 36.3 ± 0.5 81.6 98.1 A. litorea TF-22T 37.8 ± 4 80.9 98.0 A. hispanica F-32T 36.8 ± 5 80.6 98.0 A. genovensis LMG 24078T 32.3 ± 2 80.8 97.7 A. tagae JCM 13895T 42.4 ± 2 82.6 97.0 A. simiduii BCRC 17572T 33.8 ± 1 82.1 96.8

It is worthwhile mentioning that the DDH values are in agreement with the MLSA based on the concatenated five gene sequences, while the species clustering based on 16S rRNA gene was insufficiently resolved (Table 6.4). Although DDH has been a gold standard technique employed in bacterial systematics for decades, the lengthy experimental procedure and the inapplicable to develop an incremental databases (Schleifer 2009) has prompted the need to develop alternative techniques. MLSA is one of the techniques being proposed to substitute DDH (Gevers et al. 2006), and the results from this study confirmed that MLSA is comparable to DDH, and can be considered to be used as reliable alternative to DDH in Alteromonas species delineation.

6.6 MALDI-TOF mass spectrometry

The applicability of MALDI-TOF mass spectrometry in the classification of genus Alteromonas was investigated in the previous study (Chapter 5) (Ng et al. 2013). Here, the taxonomic position of strain H17T was analysed along with the nine validly described Alteromonas species as well as the neighbours of the genus Alteromonas (Figure 6.5).

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Figure 6.5 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles showing the taxonomic position of strain H17T generated by MALDI Biotyper 3.0 software. The type strains of Salinimonas chungwhensis and Aestuariibacter aggregatus were used as outgroup in the analysis. Distance is displayed in relative units.

It can be seen from the MSP dendrogram that strain H17T form a separate branch within the Alteromonas cluster. This finding supports the results of MLSA and DDH that strain H17T represent a separate species belonging to the genus Alteromonas. Although the majority of the proteins identified by MALDI-TOF mass spectrometry were shown to be ribosomal proteins, other proteins expressed by the cell also appear in the protein profile (Uhlik et al. 2011). Hence, one must bear in mind that the dendrogram based on the total protein profile carries no information regarding phylogenetic relationship or distances within the phylogenetic tree, as the distance level are subjected to change specific to the species or strains being included in the analysis.

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6.7 Physiological and biochemical analysis

According to current descriptive taxonomic practice, phenotypic analyses (e.g. biochemical tests) need to be carried out in order to show the discriminatory phenotypic properties of the proposed and validly named species, as the genotypic data cannot be solely used for the description of novel species (Stackebrandt et al. 2002; Gevers et al. 2006). In this study, selected biochemical tests were carried out, including tests for oxidase and catalase activities, the oxidation/fermentation of glucose, denitrification, indole and H2S production, arginine dihydrolase, lysine and ornithine decarboxylase, hydrolysis of starch, gelatin, Tween-80 and casein, and sodium requirement (Smibert and Krieg 1994). The extent of growth of strain H17T at various temperatures and in the presence of antibiotics was also tested. Other biochemical analyses were performed using API 50 strips (bioMérieux).

The characteristics of strain H17T that differentiates this strain from the other validly described Alteromonas species are summarised in Table 6.5. Based on these results, it can be seen that strain H17T can be differentiated from the other Alteromonas species in terms of the range of temperature and the amount of NaCl required for growth, H2S production, nitrate reduction, the ability to hydrolyse gelatin, agar and starch, and the utilization of D-mannitol, L-lactate, L-xylose, L-arabinose, sucrose, acetate, maltose and glycerol. Strain H17T was determined to be susceptible to ampicillin (10 µg), carbenicillin (100 µg), cefazolin (30 µg), erythromycin (15 µg), gentamicin (10 µg), kanamycin (30 µg), levomycetin (30 µg), nalidixic acid (30 µg), neomycin (30 µg), ofloxacin (5 µg), oleandomycin (15 µg), polymyxin (300 U), rifampicin (5 µg), streptomycin (10 µg); and resistant to benzylpenicillin (10 U), cephalexin (30 µg), lincomycin (15 µg), oxacillin (10 µg), tetracycline (30 µg), vancomycin (30 µg).

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Table 6.5 Differential characteristics of strain H17T and other validly described Alteromonas species. Characteristics 1 2 3 4 5 6 7 8 9 10 11 Colony colour Cream Cream Off-white Cream Cream Cream Cream Cream Cream Beige Pale Yellow Growth at:

4 °C + - - - + + + + - + - 10 °C + + + - + + + + + + + 40 °C + + + + + + - - + - + H2S production - ND - - - + - - - ND - Nitrate reduction - - + ------Growth in NaCl at:

10% + + + + + + + + + + + 15% - w - - + + - - - + + Hydrolysis of:

Gelatin - + + + + + + + + w + Agar ------+ - - - Starch + + - + + + + + + w + Utilization of:

D-Mannitol - - - - - + - + - - - L-Lactate - + + + - - + - - + ND L-Xylose - + - ND + + - ND - + ND L-Arabinose ------+ - - + - Sucrose, acetate, - + + + + + + V + + - Maltose, glycerol - + + + + + + V + + + G+C content (mol%) 43 45-46 45 43 44-45 46 43 45 46 44.5 47.5 Strains: 1, strain H17T; 2, A. macleodii DSM 6062T(Baumann et al. 1972; Yoon et al. 2003a; Vandecandelaere et al. 2008); 3, A. simiduii AS1T (Chiu et al. 2007); 4, A. tagae AT1T (Chiu et al. 2007); 5, A. marina SW-47T (Yoon et al. 2003a); 6, A. hispanica (F-32T Martinez-Checa et al. 2005); 7, A. addita R10SW13T (Ivanova et al. 2005a); 8, A. stellipolaris LMG 21861T (Van Trappen et al. 2004); 9, A. litorea TF-22T (Yoon et al. 2004b); 10, A. genovensis LMG 24078T (Vandecandelaere et al. 2008); 11, A. halophila JSM 073008T (Chen et al. 2009). All strains are Gram-negative, catalase- and oxidase-positive, negative for indole production, require NaCl for growth and are able to hydrolyse Tween 80. +, Positive; -, negative; ND, not determined; w, weak reaction 122

6.8 Summary

Strain H17T has been assigned to the genus Alteromonas based on the results of the phylogenetic and phenotypic analysis presented in this study. It can be differentiated from the other validly described Alteromonas species by its physiological and biochemical properties, based on the phylogenetic analysis using MLSA, and DNA- DNA relatedness; where strain H17T can be easily differentiated from the other Alteromonas species by its inability to hydrolyse gelatin, and the utilization of maltose and glycerol, sharing sequence similarities of less than 98.9% and DNA-DNA relatedness that was significantly lower than the 70% cut-off value with the other Alteromonas species based on the MLSA and DDH, respectively. Together with its distinct position in the dendrogram generated by MALDI-TOF mass spectrometry, the results obtained in this study clearly suggested that strain H17T is a new member of the genus Alteromonas, for which the name Alteromonas australica, has been proposed. The etymology for Alteromonas australica is aus.tra'li.ca N.L. fem. adj., australica, pertaining to the geographic region, Australia, where the type strain was isolated, and the type species is H17T (= KMM 6016T = CIP 109921T).

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Chapter 7: Description of Marinobacter similis A3d10T and Marinobacter salarius R9SW1T, Isolated from Sea Water

7.1 Declaration for Chapter 7

The results discussed in this chapter have been published as:

Ivanova EP, Ng HJ, Webb HK, Feng G, Oshima K, Hattori M, Ohkuma M, Sergeev AF, Mikhailov VV, Crawford RJ, Sawabe T (2014) Draft genome sequences of Marinobacter similis A3d10T and Marinobacter salarius R9SW1T. Genome Announcements 2(3):e00442-14. doi: 10.1128/genomeA.00442-14

Ng HJ, López-Pérez M, Webb HK, Gomez D, Sawabe T, Ryan J, Vyssotski M, Bizet C, Malherbe F, Mikhailov VV, Crawford RJ, Ivanova EP (2014) Marinobacter salarius sp. nov. and Marinobacter similis sp. nov., isolated from sea water. PLoS One (Accepted).

7.2 Overview

The genus Marinobacter (family Alteromonadaceae, order Alteromonadales, class Gammaproteobacteria) was created by Gauthier et al. for a hydrocarbon degrading bacterium. At the time of writing, the genus comprises 33 validly described species, http://www.bacterio.net/marinobacter.html (Euzeby 1997), which accommodates Gram-negative, chemoheterotrophic and halophilic, rod-shaped bacteria (Gauthier et al. 1992; Bowman and McMeekin 2005). The important role played by Marinobacter spp. in metabolizing hydrocarbons has long been noted, with M. hydrocarbonoclasticus (Gauthier et al. 1992), M. aquaeolei (Huu et al. 1999; Marquez and Ventosa 2005), M. maritimus (Shivaji et al. 2005), and M. algicola (Green et al. 2006) having been characterized as being able to utilise aromatic and aliphatic hydrocarbons as their sole carbon and energy sources. It was also shown that bacteria of

125 the genus Marinobacter are one of the dominant bacterial community groups constantly recovered from hydrocarbon polluted sites (Cui et al. 2008; Dastgheib et al. 2012; Lal et al. 2013). For example, it was recently demonstrated that M. vinifirmus was able to effectively degrade toluene, benzene, ethylbenzene, and p -xylene (Berlendis et al. 2010).

Two strains that have been used in the 3PET screening experiment (Chapter 4) and previously isolated from sea water samples were taxonomically identified as two novel Marinobacter species in this study. The first strain designated as A3d10T was isolated from Port Philip Bay (the Tasman Sea, Pacific Ocean) during the course of polymer biodegradation studies (Webb et al. 2009), while the second strain designated as R9SW1T was isolated from Chazhma Bay (Gulf of Peter the Great, Sea of Japan, Pacific Ocean) during taxonomic studies of microbial communities developed in sea water contaminated by radionuclides (Ivanova et al. 2005b). Detailed taxonomic study based on polyphasic approach was employed and presented in this chapter.

7.3 16S rRNA gene sequence analysis

Sequencing of the almost complete 16S rRNA gene sequences of strains A3d10T and R9SW1T were initially carried out as described in Chapter 3.5.3. With the recent success of whole genome sequencing of this two strains in collaboration with Professor Tomoo Sawabe from Hokkaido University, the complete 16S rRNA gene sequences of the two strains were extracted from the whole genome sequences and being used in this study. By comparing the 16S rRNA gene sequences of strains A3d10T and R9SW1T via GenBank and EzTaxon-e (Kim et al. 2012), it can be confirmed that both strains belong to the genus Marinobacter. The sequence similarity between strains A3d10T and R9SW1T with all validly described Marinobacter species are found to be in the range of 93.91 – 99.53% and 93.84 – 99.40%, respectively. Both strains appeared to have shared more than 97% 16S rRNA gene sequence similarities with the other nine validly described Marinobacter species, the results of which are presented in Table 7.1.

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Table 7.1 Percentage pairwise similarity of 16S rRNA gene sequences of strains A3d10T and R9SW1T with closely related Marinobacter species.

16S rRNA gene sequence similarity (%) Strain A3d10T R9SW1T M. sediminum R65T 99.53 97.30 M. lipolyticus SM19T 98.06 98.06 M. adhaerens HP15T 98.00 97.86 M. salsuginis SD-14BT 97.98 97.85 M. flavimaris SW-145T 97.83 97.43 M. algicola DG893T 97.53 99.40 M. gudaonensis SL014B61AT 97.46 97.46 M. xestospongiae UST090418-1611T 97.38 96.55 M. goseongensis En6T 97.35 97.28 M. guineae M3BT 96.81 97.15

Phylogenetic analysis of the 16S rRNA gene were also performed using neighbour-joining (NJ) (Saitou and Nei 1987), maximum-likelihood (ML) (Felsenstein 1981) and maximum-parsimony (MP) (Fitch 1971) algorithms. As shown in Figure 7.1, strains A3d10T and R9SW1T formed a distinct cluster with M. sediminum R65T, M. salsuginis SD-14BT, M. algicola DG893T, M. adhaerens HP15T and M. flavimaris SW- 145T with the bootstrap value of 72%, and supported by NJ and ML methods.

Although the 16S rRNA gene sequence analyses revealed that strains A3d10T and R9SW1T exhibited high sequence similarities to M. sediminum R65T and M. algicola DG893T, it could not be concluded that these two strains belonged to the previously described species until further analyses were carried out. The limitations associated with the use of 16S rRNA gene sequence analysis for species differentiation have long been discussed and the suggested threshold value has been updated in recent years. The original suggested threshold value of 97% (Stackebrandt and Goebel 1994) was later revisited and recommended to be 98.7 – 99% (Stackebrandt and Ebers 2006). More recently a threshold value of 98.65% has been proposed, based on the comparison with average nucleotide identity (ANI) value (Kim et al. 2014). For the genus

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Marinobacter, there have been cases identified where two distinct species shared >99% of their 16S rRNA gene sequences, namely M. lacisalsi FP2.5T and M zhanjiangensis JSM 078120T (99.12%), M. koreensis DD-M3T and M. santoriniensis NKSG1T (99.32%), and M. adhaerens HP15T and M. flavimaris SW-145T (99.32%). Hence, further analysis based on phenotypic, phylogenetic, genomic and chemotaxonomic approaches will be discussed in this chapter with a view to determining the taxonomic positions of strains A3d10T and R9SW1T.

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Figure 7.1 Neighbour-joining phylogenetic tree showing the taxonomic position of strains R9SW1T and A3d10T according to their 16S rRNA gene sequences. The sequence of Hahella chejuensis KCTC 2396T (AF195410) was used as outgroup. Numbers at branching points are percentage bootstrap values based on 1000 replications, with only values above 50% are shown. Scale bar represents 0.01 substitutions per nucleotide position. The Maximum-likelihood (ML) and maximum Parsimony (MP) algorithms were also used for tree construction, where branches in agreement with ML and MP methods were marked with + and X respectively.

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7.4 gyrB and rpoD gene sequence analyses

Due to the high 16S rRNA gene sequence similarity that exists between strains A3d10T and M. sediminum R65T, and between R9SW1T and M. algicola DG893T, an extended phylogenetic analysis, based on gyrB and rpoD genes, was carried out. The use of housekeeping genes in phylogenetic analyses can be beneficial, in that it overcomes the possibility of the presence of nucleotide polymorphisms in the 16S rRNA gene (Cilia et al. 1996; Alperi et al. 2008). Genes gyrB and rpoD were selected, since they have been previously reported to be excellent marker genes for the identification and classification of various groups of microorganism (Yamamoto et al. 2000 ; Puthucheary et al. 2012; Ng et al. 2013; Táncsics et al. 2014). The primer sequences for PCR amplification and sequencing were presented in Table 3.2 in Chapter 3. Type strains of M. sediminum, M. salsuginis, M. algicola, M. adhaerens, and M. flavimaris were selected in this study as they are phylogenetically related to strains A3d10T and R9SW1T based on the 16S rRNA gene sequences. The type species of the genus M. hydrocarbonoclasticus was also included for comparison. The resulting amplified PCR products were detected in a 1% agarose gel as shown in Figure 7.2.

Figure 7.2 Gel electrophoretic analysis of PCR products for gyrB and rpoD. Lane M, TrackIt™ 1Kb Plus DNA Ladder (Invitrogen, USA); Lane 1, Strain A3d10T; Lane 2, M. hydrocarbonoclasticus SP.17T; Lane 3, M. adhaerens CIP 110141T; Lane 4, M. algicola LMG 23835T; Lane 5, M. flavimaris CIP 108615T; Lane 6, M. salsuginis CIP 109893T; Lane 7, M. sediminum LMG 23833T; Lane 8, Strain R9SW1T

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PCR products were purified and sent to the Australian Genome Research Facility (AGRF) for sequencing. The resulting sequences were used to construct the phylogenetic tree presented in Figure 7.3. Both trees showed similar topologies with the phylogenetic tree that was constructed based on the 16S rRNA gene sequences, and reconfirm the clustering of strain A3d10T with M. sediminum LMG 23833T and strain R9SW1T with M. algicola LMG 23835T, which was supported by the high bootstrap value of 100%.

Figure 7.3 Neighbour-joining phylogenetic tree showing the taxonomic position of strains A3d10T and R9SW1T according to their (A) gyrB and (B) rpoD gene sequences. Numbers at branching points are percentage bootstrap values based on 1000 replications, with only values above 50% are shown. Scale bar represents 0.02 substitutions per nucleotide position.

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The percentage sequence similarities of gyrB and rpoD for strains A3d10T, R9SW1T and their phylogenetically related species were also determined, the results of which are presented in Table 7.2. In general, the sequence similarities for strain A3d10T with the strains tested were found to be in the range of 80.0 – 93.5% for gyrB and 78.6 – 96.2% for rpoD; while the sequence similarities for strain R9SW1T with the strains tested were found to be in the range of 77.8 – 94.3% for gyrB and 78.6 – 93.8% for rpoD. These results were consistent with those obtained using the 16S rRNA gene sequence analysis, but with a lower percentage sequence similarity value. It is noteworthy that two previously described Marinobacter species, M. adhaerens CIP 110141T (Kaeppel et al. 2012) and M. flavimaris CIP 108615T (Yoon et al. 2004a), which displayed 99.32% pairwise similarity in their 16S rRNA gene sequences (also included in this study), and the comparison of these two gene sequences showed that they possess the gene sequence similarities of 99.0% for gyrB and 98.4% for rpoD (Table 7.2), which is higher than that found for strains A3d10T, R9SW1T and their respective closest phylogenetic relatives.

The gyrB gene sequence similarities of 93.5% and 94.3% for strains A3d10T, R9SW1T with their closest relatives were also found to be lower than that of the previously proposed gyrB sequence similarity cut-off value of 98.95% for the genus Amycolatopsis (Everest and Meyers 2009) and 98.22% for genus Kribbella (Kirby et al. 2010). The data reported for the two validly described Vibrio species, V. gigantis LGP 13T and V. crassostreae LGP 7T, also indicated that the sequence similarities of the two genes for strains A3d10T, R9SW1T and their closest relatives with these Vibrio species were 93.5% and 94.3% versus 98% for gyrB; and 96.2% and 93.8% versus 97% for rpoD (Le Roux et al. 2005). The sequence similarities for gyrB and rpoD between strains A3d10T and R9SW1T were significantly lower than the values mentioned above, i.e., 81.6% for gyrB and 78.2% for rpoD, suggesting the existence of a distinct standing of these new strains on the species level.

Therefore, it is evident that the sequence similarities of 94.3/93.8% and 93.5/96.2% (gyrB/rpoD) between strain R9SW1T and M. algicola LMG 23835T, and strain A3d10T and M. sediminum LMG 23833T fall below the possible threshold value of 99.0% (gyrB) and 98.4% (rpoD) for the genus Marinobacter, suggesting that they could be novel species within the genus Marinobacter.

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Table 7.2 Sequence similarities of gyrB and rpoD genes for strains A3d10T, R9SW1T and phylogenetically related type strains and type species of the genus Marinobacter. Similarity of gyrB/rpoD genes (%) 1 2 3 4 5 6 7 8 1. M. adhaerens CIP 110141T 100/100

2. M. algicola LMG 23835T 78.0/81.2 100/100

3. M. flavimaris CIP 108615T 99.0/98.4 77.8/81.0 100/100

4. M. hydrocarbonoclasticus SP. 17T 80.7/81.7 78.2/77.0 80.0/81.5 100/100

5. M. salsuginis CIP 109893T 86.5/93.4 76.2/80.3 86.1/93.5 80.8/80.0 100/100

6. M. sediminum LMG 23833T 84.1/83.5 80.3/77.8 83.8/84.2 83.6/78.7 85.8/84.0 100/100 7. Marinobacter sp. A3d10T 83.7/83.8 80.0/78.8 83.6/84.4 82.2/78.6 84.7/84.1 93.5/96.2 100/100 8. Marinobacter sp. R9SW1T 78.2/80.5 94.3/93.8 78.2/80.3 77.8/79.5 78.0/80.6 81.9/78.6 81.6/78.2 100/100

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7.5 Phenotypic analysis

Other than phylogenetic analysis, phenotypic analysis is one of the classification techniques used in bacterial taxonomy that includes a study of morphological, physiological and biochemical features of the bacterial strains (Vandamme et al. 1996). In this study, phenotypic analyses were carried out for strains A3d10T, R9SW1T and the phylogenetic related species of the genus Marinobacter. The type species of the genus, M. hydrocarbonoclasticus was also included in the study as a reference strain, as suggested in one of the taxonomic notes, “Notes on the characterization of prokaryote strains for taxonomic purposes” (Tindall et al. 2010).

7.5.1 Morphology

The morphology of strains A3d10T and R9SW1T was determined using light and scanning electron microscopy. As can be seen in Figure 7.4, the cell size of strain A3d10T was between 1.2 to 3.6 µm in length and 0.4 to 0.8 µm in width, while the cell size of strain R9SW1T was between 1.9 to 3.2 µm in length and 0.4 to 0.72 µm in width, after 2 days of incubation in Marine broth 2216 at 25°C. Both strains appeared to be rod shaped, and Gram-negative. When grown on solid Marine agar 2216, colonies of strains A3d10T and R9SW1T appear semi-translucent, non-pigmented, circular to slightly irregular and smooth with edges. The size of the colonies ranged between 0.5 to 1.0 mm for strain A3d10T, and 0.8 to 1.0 mm for strain R9SW1T after 2 days of incubation on Marine agar 2216 at 25°C. A motility test was carried out using the hanging drop technique, the results of which demonstrated that both strains were motile by means of a single polar flagellum.

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Figure 7.4 Scanning electron micrographs of strains (A) A3d10T and (B) R9SW1T

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7.5.2 Biochemical, physiological and metabolic characteristics

The temperature tolerance of growth for strains A3d10T and R9SW1T was found to be between 4 and 40°C, and their salinity tolerance was found to be in the range 0.5 to 20% (w/v) NaCl. The pH range of growth for these two strains was found to be between pH 6 to 9. Both strains were tested to be catalase and oxidase positive. Oxidation/fermentation of lactose was found to be negative for both strains, while strain T T A3d10 (but not strain R9SW1 ) exhibited weak fermentation ability towards D-glucose. Strain R9SW1T was able to degrade starch; however strain A3d10T was not able to do so. The susceptibility of strains A3d10T and R9SW1T to variety of antibiotics were also tested, the results of which are presented in Table 7.3.

Table 7.3 Susceptibility of strains A3d10T and R9SW1T to various antibiotics

Antibiotics Concentration Strain A3d10T Strain R9SW1T Ampicillin 10 µg S S Chloramphenicol 30 µg S S Penicillin G 10 µg S S Streptomycin 10 µg R R Tetracycline 30 µg R R S, Susceptible; R, Resistant

Commercially available testing kits were also being used to obtain the physiological and biochemical characteristics of strains A3d10T and R9SW1T, with all tests being performed in duplicate. The kits being used were Microbact™ 24E Gram- negative identification system (Oxoid, UK), API ZYM test strips (bioMétieux, France) and Biolog GN2 microplates (Biolog, USA). These tests were carried on strains A3d10T and R9SW1T in addition to the 5 phylogenetically related species and the type species of genus Marinobacter. The results were summarised and presented in Tables 7.4 – 7.6.

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Table 7.4 Biochemical characteristics of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using Microbact™ 24E Gram-negative identification system

1 2 3 4 5 6 7 8 Lysine decarboxylase - - - - + - - + Ornithine decarboxylase ------

H2S production ------Acid from glucose ------Acid from mannitol ------Acid from xylose ------β-Galactosidase ------Indole production ------Urea hydrolysis ------Voges-Proskauer reaction ------Citrate utilisation - - - - (+) - - - - (+) Tryptophan deaminase ------+ (-) Gelatin liquefaction ------Malonate utilisation ------Acid from: Inositol ------Sorbitol ------Rhamnose ------Sucrose ------Lactose ------Arabinose ------Adonitol ------Raffinose ------Salicin ------Arginine dihydrolase - - - - (+) - - - - Reduction of Nitrate + + - + (-) - + + + Reduction of Nitrite - + - + - - - (+) - (+) Strains: 1, Strain A3d10T; 2, M. sediminum LMG 23833T (Romanenko et al. 2005); 3, Strain R9SW1T; 4, M. algicola LMG 23835T (Green et al. 2006); 5, M. adhaerens CIP 110141T (Kaeppel et al. 2012); 6, M. flavimaris CIP 108615T (Yoon et al. 2004a); 7, M. salsuginis CIP 109893T (Antunes et al. 2007); 8, M. hydrocarbonoclasticus SP.17T (Gauthier et al. 1992). Individual data in parentheses are previously reported data; +, Positive; -, Negative

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Table 7.5 Enzymatic characteristics of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using API ZYM system

1 2 3 4 5 6 7 8 Alkaline phosphatase + + + + + + + + Esterase (C4) + + (-) + + + + + + Esterase lipase (C8) + + (w) + + + + + + Lipase (C14) w w (-) - w w w (+) - (+) w (-) Leucine arylamidase + + (w) + + + + + + Valine arylamidase w - + w - - - (w) - (w) Cystine arylamidase w - + w w - w - Trypsin ------α-chymotrypsin ------Acid phosphatase w w (-) w w + w (+) w w (+) Naphthol-AS-BI- + + w + + w (+) w (+) + phosphohydrolase α-Galactosidase ------β-Galactosidase ------β-Glucuronidase ------α-Glucosidase - - w w w (-) - - - β-Glucosidase ------N-Acetyl-β-glucosaminidase + w (+) + w w + w (+) + α-Mannosidase ------α-Fucosidase ------Strains: 1, Strain A3d10T; 2, M. sediminum LMG 23833T (Romanenko et al. 2005); 3, Strain R9SW1T; 4, M. algicola LMG 23835T (Green et al. 2006); 5, M. adhaerens CIP 110141T (Kaeppel et al. 2012); 6, M. flavimaris CIP 108615T (Yoon et al. 2004a); 7, M. salsuginis CIP 109893T (Antunes et al. 2007); 8, M. hydrocarbonoclasticus SP.17T (Gauthier et al. 1992). Individual data in parentheses are previously reported data; +, Positive; -, Negative; w, Weakly positive reaction

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Table 7.6 Carbon sources utilization of strains A3d10T, R9SW1T and phylogenetically related species and type species of genus Marinobacter determined by using Biolog GN2 microplates

1 2 3 4 5 6 7 8 Dextrin - - + + w (+) w - - Glycogen + - + + - - - -

D-Cellobiose - - (+) ------

D-Fructose - - + w (+) - - (+) - - Maltose - - + w (+) - - - - Methyl-Pyruvate + + (-) + + + + + + Mono-Methyl-Succinate + + (-) + - + w + + Acetic acid - - + + w - - (+) + Cis-Aconitic acid - - - - (+) - (+) - - - Citric acid - - - - (+) - - - - (+)

D-Gluconic acid - - - - (+) - - (+) - - α-Hydroxy butyric acid - - - - (+) - - - - β-Hydroxy butyric acid + + (-) + + + + + + (-) γ-Hydroxy butyric acid + - + + w (+) w - - α-Keto glutaric acid - - - - + - - - α -Keto butyric acid - - (+) ------α-Keto valeric acid - - (+) - - + - - +

D, L-Lactic acid + + (-) + + + + + + Propionic acid - - + + - - - - Succinic acid - - + - (+) w - (+) + + Bromo succinic acid - - - - (+) - (+) - + + Succinamic acid - - - - w - - -

L-Alaninamide - - w - + - w -

D-Alanine + + (-) w + + + (-) w -

L-alanine + + (-) w + + + (-) + w (-)

L-Asparagine - - - - w - - -

L-glutamic acid + - + + + + (-) + +

L-Leucine - - w + + - - -

L-Phenylalanine - - - + - - - (+) -

L-Proline + + (-) + + + + + +

L-Pyroglutamic acid - - - - (+) - - - -

L-Serine - - (+) + - - - - - Glycerol - - + - (+) + (-) - w (+) - Strains: 1, Strain A3d10T; 2, M. sediminum LMG 23833T (Romanenko et al. 2005); 3, Strain R9SW1T; 4, M. algicola LMG 23835T (Green et al. 2006); 5, M. adhaerens CIP 110141T (Kaeppel et al. 2012); 6, M. flavimaris CIP 108615T (Yoon et al. 2004a); 7, M. salsuginis CIP 109893T (Antunes et al. 2007); 8, M. hydrocarbonoclasticus SP.17T (Gauthier et al. 1992). All strains were tested positive for Tween 40 and Tween 80, and negative for α-cyclodextrin, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, adonitol, L-arabinose, D-arabitol, i-erythritol, L-fucose, D-galactose, gentiobiose, α-D-glucose, myo-inositol, α-D-lactose, lactulose, D-mannitol, D-mannose, D-melibiose, β-methyl-D- 139 glucoside, D-psicose, D-raffinose, L-rhamnose, D-sorbitol, sucrose, D-trehalose, turanose, xylitol, formic acid, D-galactonic acid lactone, D-galacturonic acid, D-glucosaminic acid, D-glucuronic acid, p-hydroxyphenylacetic acid, itaconic acid, malonic acid, quinic acid, D-saccharic acid, sebacic acid, glucuronamide, L-alanyl-glycine, L-aspartic acid, glycyl- L-aspartic acid, glycyl-L-glutamic acid, L-histidine, hydroxyl-L-proline, L-ornithine, D- serine, L-threonine, D,L-carnitine, γ-amino butyric acid, urocanic acid, inosine, uridine, thymidine, phenyethylamine, putrescine, 2-aminoethanol, 2,3-butanediol, D,L-α- glycerol, glucose-1-phosphate and glucose-6-phosphate. Individual data in parentheses are previously reported data; +, Positive; -, Negative; w, Weakly positive reaction.

As can be seen from the results presented in Tables 7.4 – 7.6, strain A3d10T can be clearly differentiated from M. sediminum LMG 23833T by its inability to reduce nitrite, its ability to utilise glycogen, γ-hydroxy-butyric acid and L-glutamic acid, and its weak activities for valine arylamidase and cystine arylamidase; while strain R9SW1T can be clearly differentiated from M. algicola LMG 23835T by its inability to reduce nitrate and nitrite, its ability to utilise mono-methyl succinate and L-serine, its inability to utilise L-phenylalanine and the absence of lipase (C14). The major phenotypic difference between strains R9SW1T and A3d10T are nitrate reduction, hydrolysis of starch, fermentation of D-glucose, and their utilisation of dextrin, D-fructose, maltose, acetic acid, propionic acid, succinic acid, L-serine and glycerol. Other phenotypic characteristics which differentiate the two novel strains from each other and their closest phylogenetic neighbours are shown in Tables 7.4 – 7.6. Interestingly, some results obtained from this study were inconsistent with the previously reported data, perhaps due to differences in the cultivation and incubation conditions being used.

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7 .6 Genotypic analysis

Genotypic analysis, which includes determination of the G+C content and DNA- DNA hybridisation (DDH), are crucial in bacterial taxonomic studies. The G+C content acts as a taxonomic marker and is essential for inclusion in the bacterial taxa description. DDH is the gold standard in bacterial taxonomy in which strains that share less than 70% in their DDH value can be considered as distinct species.

7.6.1 G+C content

In general, strains within a validly described species share ≤ 3% of their G+C content while for species, they share ≤ 10% of their content within a well-described genus (Vandamme et al. 1996). The G+C content for strains A3d10T and R9SW1T were calculated based on the whole genome sequences, and found to be 57.6 and 57.1 mol%, respectively, which is similar to the G+C content of validly Marinobacter species. The G+C content for all validly described Marinobacter species are summarised in Appendix V.

7.6.2 DNA-DNA hybridization

As strain A3d10T and M. sediminum LMG 23833T, and strain R9SW1T and M. algicola LMG 23835T were being found to have the highest sequence similarities in term of their gyrB, rpoD and 16S rRNA gene sequences, 3 g of cells from each of these strains were sent to the German Collection of Microorganisms and Cell Cultures GmbH (DSMZ) for DNA-DNA hybridization identification. The tests were carried out in duplicate and the results showed that strain A3d10T and M. sediminum LMG 23833T possess a 68.9/ 66.3% DNA similarity, while strain R9SW1T and M. algicola LMG 23835T have a 64.9/ 61.2%the DNA similarity. These values are lower than the 70% cut-off value recommended for the differentiation of species (Wayne et al. 1987), and hence it is suggested that strains A3d10T and R9SW1T can be considered as two novel species of the genus Marinobacter.

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7.6.3 Whole genome sequence analysis

Recently, whole genome sequences have been recommended to be integrated into bacterial systematics (Chun and Rainey 2014; Kim et al. 2014; Ramasamy et al. 2014). In this study, the whole genome sequences of strains A3d10T, R9SW1T, M. adhaerens HP15 T and M. hydrocarbonoclasticus ATCC 49840T were visually compared using BLAST (Figure 7 .5) and the average nucleotide identity (ANI) and genome-to- genome distance (GGD) between the four strains were calculated as shown in Table 7.7. Due to the lack of the availability of the assembled, whole genome sequences for validly described Marinobacter species, genome relatedness in terms of ANI and GGD between strains A3d10T, R9SW1T and validly described Marinobacter species can only be performed with M. adhaerens HP15T (Gardes et al. 2010) and M. hydrocarbonoclasticus ATCC 49840T (Grimaud et al. 2012). The ANI and GGD between the four strains were in the range of 82.3 – 83.3% and 19.8 – 20.7% (Table 7.7), which is significantly lower than the suggested threshold range of 95 – 96% (Richter and Rossello-Mora 2009; Kim et al. 2014), and 70% (Thompson et al. 2013a), respectively, again indicating that strains R9SW1T and A3d10T can be considered as two novel species of the genus Marinobacter.

Table 7.7 The average nucleotide identity (ANI) and genome-to-genome distance (GGD) between strains A3d10T, R9SW1T, M. adhaerens HP15T and M. hydrocarbonoclasticus ATCC 49840T. ANI/ GGD (%) 1 2 3 4 1. M. hydrocarbonoclasticus ATCC 49840T 100 2. M. adhaerens HP15T 83.1/20.1 100 3. Strain R9SW1T 82.3/20.0 82.7/20.2 100 4. Strain A3d10T 82.5/19.8 83.3/20.7 82.3/19.8 100

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Figure 7.5 BLAST genome ring showing the comparison between strains A3d10T, R9SW1T, M. adhaerens HP15T and M. hydrocarbonoclasticus ATCC 49840T.

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7 .7 MALDI-TOF mass spectrometry

The use of MALDI-TOF mass spectrometry in bacterial taxonomy has been of interest since it was recommended as an analytical technique in the report of the ad hoc committee for the re-evaluation of the species definition in bacteriology (Stackebrandt et al. 2002). In this study, an analysis based on the protein profiles of strains A3d10T and R9SW1T with its closely related species was performed by using MALDI-TOF mass spectrometry in order to further assess the taxonomic affiliation of these two new bacteria. Comparison of the mass spectra for strains A3d10T, R9SW1T and the phylogenetically related strains and type species of genus Marinobacter were performed, the results of which are presented in Figure 7.6. A comparison based upon the protein profiles in term of dendrogram for strains A3d10T, R9SW1T and closely related strains and type species of genus Marinobacter is shown in Figure 7.7.

From the MALDI-TOF mass spectra (Figure 7.6), it can be seen that all the strains tested share some common peaks, e.g. the peaks at approximately m/z 6000, 7200, and 9100, which can be identified as the representative peaks that belong to genus Marinobacter. The different strains also showed some distinctive differences where shifts of m/z value or present/absent of peaks were observed. The noticeable difference between strain A3d10T and M. sediminum LMG 23833T can be seen from the present of peaks for strain A3d10T at m/z 7500 and 10000 but absent from M. sediminum LMG 23833T; while for strain R9SW1T, shifting of high intensities peaks between m/z 2000 to 6000 can be observed if compared to its closest relative, M. algicola LMG 23835T. As can be seen from the data presented in Figure 7.7, the position of strains A3d10T and R9SW1T from the resulting dendrogram were in agreement with the phylogenetic analyses, clearly indicating that strain A3d10T is clustering with M. sediminum LMG 23833T , and strain R9SW1T is clustering with M. algicola LMG 23835T with a critical distance level below 500. As suggested in the previously reported studies, clustering below the distance level of 500 can be considered as reliable clustering (Sauer et al. 2008 ; Dubois et al. 2010), which was also in agreement with the recent studies on Alteromonas spp., where the clustering within the distance level of 500 was shown to be able to differentiate the closely related Alteromonas species (Ng et al. 2013; Ivanova et al. 2013). Hence, the results of this study confirmed the confident clustering of the two new isolates within other species of the genus Marinobacter. Also, the clusters of both

144 strains A3d10T and R9SW1T with their nearest neighbour were found to be stable, but exceeded the minimum differences between existing species, e.g., the distance level between species in both clusters were greater than those within a cluster that contained M. gudaonensis CIP 109534T, M. adhaerens CIP 110141T, M. salsuginis CIP 109893T, and M. flavimaris CIP 108615T; so does the position of strains R9SW1T and A3d10T resulting in different clusters in the MALDI dendrogram, provide evidence of the distinctive standing of two new bacteria.

Figure 7.6 MALDI-TOF mass spectra of strains A3d10T, R9SW1T, and phylogenetically related and type species of genus Marinobacter. Strains: (A) A3d10T, (B) M. sediminum LMG 23833T, (C) R9SW1T, (D) M. algicola LMG 23835T, (E) M. adhaerens CIP 110141T, (F) M. flavimaris CIP 108615T, (G) M. salsuginis CIP 109893T, (H) M. hydrocarbonoclasticus SP.17T. Y-axis are the relative intensities of the ions and X-axis are the mass-to-charge ratios (m/z). 145

Figure 7.7 Main spectra library (MSP) dendrogram of MALDI-TOF mass spectral profiles of strains A3d10T, R9SW1T and closely related Marinobacter species. Hahella ganghwensis KCTC 12277T was used as outgroup. The dendrogram was generated by MALDI Biotyper 3.0 software with distance is displayed in relative units. 146

7.8 Summary

In summary, strain A3d10T and R9SW1T were found to grow between 4 C and 40 C, between pH 6 to 9, and were moderately halophilic, tolerating up to 20% (w/v) NaCl. They are Gram-negative, motile and non-pigmented. Both strains were found to be able to degrade Tween 40 and 80, but only strain R9SW1T was found to be able to degrade starch. The G+C content of the DNA for strains A3d10T and R9SW1T were determined to be 57.6 mol% and 57.1 mol%, respectively. The two new strains share 97.6% of their 16S rRNA gene sequences; but the average nucleotide identity (ANI) between the two new isolates and the genome-to-genome distance (GGD) were found to be 82.3% and 19.8%, respectively. A phylogenetic analysis showed that strain A3d10T clusters with M. sediminum R65T sharing 99.53%, and strain R9SW1T clusters with M. algicola DG893T sharing 99.40% of 16S rRNA gene sequence similarities. However, the other results from the polyphasic taxonomic study, including the phenotypic, phylogenetic analyses based on the gyrB and rpoD genes, analysis of the protein profiles generated using MALDI-TOF mass spectrometry and DNA-DNA relatedness, clearly indicate that strains A3d10T and R9SW1T represent two novel species of the genus Marinobacter.

The names Marinobacter similis sp. nov. and Marinobacter salarius sp. nov. are proposed for these new species. The etymology for Marinobacter similis is si'mi.lis, L. masc. adj., similis, like, resembling, similar, pertaining to close similarity with other species, and the type strain is A3d10T (= JCM 19398T = CIP 110589T = KMM 7501T), isolated from sea water from Port Philip Bay of the Tasman Sea, the Pacific Ocean; while the etymology for Marinobacter salarius is sa.la'ri.us, L. masc. adj., salarius, of or belonging to salt, pertaining to salt tolerance, and the type strain is R9SW1T (= LMG 27497T = JCM 19399T = CIP 110588T = KMM 7502T), isolated from sea water from Chazhma Bay in the Sea of Japan, Pacific Ocean.

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Chapter 8: Summary and Future Directions

8.1 Overall summary

In the attempt to identify marine bacteria that have the potential to biodegrade PET, a number of marine bacteria have been constantly recovered from the enrichment experiments conducted in the Nano-Biotechnology laboratory at Swinburne University of Technology. The isolated bacteria were tentatively identified through 16S rRNA gene sequence analyses and assigned to the genera Alteromonas, Thalassospira, Roseovarius, Limnobacter, Marinomonas and Pseudoalteromonas (Webb 2012). In this project, the ability of these bacteria to degrade PET, together with the type species of the respective genus, maintained in the Culture Collection of Marine Microorganisms at Swinburne University of Technology, were further tested using a PET trimer, bis(benzoyloxyethyl) terephthalate (3PET), where 3PET was employed as a model substrate that has been previously successfully used to isolate soil derived PET hydrolysing bacterium, Bacillus subtilis ( Heumann et al. 2006; Ribitsch et al. 2011).

3PET agar plate-based screening was carried out on eighty marine Alpha- and Gammaproteobacteria that have been isolated from previous enrichment experiment, as well as respective type strains of the selected genera. From the tested strains, one strain recovered from the enrichment experiments, designated as A3d10T was found to have the ability to hydrolyse 3PET substrate by producing a clear zone on the screening agar plate. Based on the 16S rRNA gene sequence analysis, the strain was identified to belong to the genus Marinobacter, and therefore, an additional twenty type strains of Marinobacter species were screened for their potential to degrade PET. None of these strains, however, showed a similar PET biodegradation ability as strain A3d10T.

Therefore, strain A3d10T was selected for the short term PET degradation study undertaken in this project. The investigation of the interactions of strain A3d10T with untreated and SDS-treated PET films in the mineral medium for a one month period showed that notable changes took place in the surface nanostructure of the films as a result of bacterial action. A statistical analysis of the surface roughness, based on an

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AFM study, demonstrated that a smoothening of the surface of the untreated films occurred, while the surface of the SDS-treated film became more nano-rough (Figure 8.1). Although the surface roughness parameters obtained showed differences between the untreated and SDS-treated films, the changes in the surface features were observed to be similar after one month exposure to strain A3d10T, where the peaks became narrower (Figure 8.1).

Figure 8.1 Schematic representation of surface erosion arising from the enzymatic degradation of the surface of the PET as a result of exposure to strain A3d10T.

It is believed that strain A3d10T utilizes a certain metabolic pathway that results in the secretion of specific enzymes that preferentially degrade the sides of surface peaks, causing the formation of PET surface protrusions, in which the hydrolytic enzymes are most likely be cutinase, lipase, or esterase, enzymes previously shown to be capable of hydrolysing PET (Alisch-Mark et al. 2006; Liebminger et al. 2007; Wang et al. 2007b; Eberl et al. 2009; Ronkvist et al. 2009; Ribitsch et al. 2011; Ribitsch et al. 2012a; Ribitsch et al. 2012b).

A Raman spectral analysis further confirmed that biodegradation of the PET had indeed taken place. Changes in the chemical composition of the untreated PET film 150 were detected, where an increase in the amount of crystalline phase was detected after one month of incubation with strain A3d10T. The increase in crystallinity arose from the degradation of the amorphous region of the surface, as can be seen through the presence of more intense Raman peaks that correspond to the aromatic (crystalline) portion of the PET. The surface wettability was also found to increase as the PET films were found to be more hydrophilic after exposure to strain A3d10T, confirming that biodegradation had taken place. The taxonomic identity of this strain has been established, and described in this thesis.

A preliminary taxonomic survey of marine bacteria recovered from enrichment experiments revealed the presence of a few dominated taxa that might play a role in PET biodegradation. These taxa comprised bacteria of the genera Alteromonas and Marinobacter. In the context of this finding, this project focussed on the accurate classification of respective representatives of these genera. One strain, designated H17T, was assigned to the genus Alteromonas based on the 16S rRNA gene sequence similarities. It also appeared that this strain shared more than 97% sequence similarity with the other validly named Alteromonas species and therefore its standing on the species level remained unclear. Therefore, modern taxonomic tools including MLSA and MALDI-TOF mass spectrometry analysis were developed and employed, together with traditional physiological and biochemical methods in the formal description of this strain, for which the name Alteromonas australica was proposed.

As part of the taxonomic investigation of strain H17T, MLSA was used to analyse all currently validly described Alteromonas species. As the majority of Alteromonas species share more than 97% 16S rRNA gene sequence similarities, the study of MLSA in this project served to be an additional tool in the taxonomic identification of the genus Alteromonas, and has the potential be used as an alternative to DDH, which is a time-consuming and labour-intensive method. Another technique, MALDI-TOF mass spectrometric analysis was also applied for the first time in the classification of Alteromonas species, where the results proved to be useful as a supporting technique in bacterial taxonomy.

In the 16S rRNA gene sequence analysis of the 3PET degrading bacteria, strain A3d10T was found to be most closely related to Marinobacter sediminum, although this close phylogenetic neighbour did not exhibit any ability to utilise 3PET. A detailed

151 taxonomic study of the classification of strain A3d10T was further undertaken. Another Marinobacter species, strain R9SW1T, which was previously isolated from a radionuclide contaminated site, and included in the 3PET screening experiment was taxonomically investigated together with strain A3d10T as it was closely related to strain A3d10T. Through the detailed taxonomic investigation, including multigene and whole genome sequence analysis and a MALDI-TOF mass spectrometric analysis, both strains were found to be two new distinct species belonging to the genus Marinobacter, for which the names M. similis, and M. salarius were proposed for strains A3d10T and R9SW1T, respectively.

8.2 Future directions

While the current project has provided a unique marine bacterium candidate for PET degradation, additional work is anticipated in order to examine the applicability of M. similis A3d10T to large scale biodegradation processes. To elucidate the degradation process in detail, identification of the gene/s and enzyme/s responsible for the biodegradation will be necessary which may also allow genetic engineering to be carried out for enhancing the rate and overall efficiency of degradation. This direction will be greatly facilitated by the available genomic information. Meanwhile, studies involving the chemical analysis of the solution in which the biodegradation is taking place could provide a better understanding for the chemical mechanisms of the biodegradation process, which can assist in the future development of an environmental friendly plastic production process.

In terms of bacterial taxonomy, the development and applicability of MLSA and MALDI-TOF mass spectrometry in classifying bacteria in the genus Alteromonas and Marinobacter proved to be eminently suitable complementary methods for the current classification system. With decreasing costs associated with genome sequencing, whole genome sequence analyses can also be used to provide reliable and reproducible data for the accurate classification of bacteria. The genome of the two newly proposed species, M. similis A3d10T and M salarius R9SW1T has been recently sequenced. In the future, with the increasing number of whole genome being sequenced, genome to genome comparison could be used to replace the current wet laboratory classification techniques

152 such as DDH, and the comparison of genes such as MLSA or 16S rRNA gene sequence analysis could be easily extracted from the whole genome. The bacterial phenotypic characteristics could also be predicted from the whole genome sequences by analysing the presence or absence of a specific gene.

8.3 Close

The increasing production of global plastic wastes, together with the current inefficient methods for waste handling, have driven the desire to search for an alternative, environmentally friendly way degrade plastics, especially in the marine environment. The results from this project have significantly contributed to the current knowledge regarding the potential of marine bacteria to be used for the biodegradation of PET. For the first time, a novel species belonging to the genus Marinobacter, M. similis A3d10T has shown the ability to degrade PET. Interaction of this bacterium with the PET films resulted in changes to the surface topography and chemical composition of the films within only one month of exposure. Taxonomic investigation of the strains that have the potential to degrade PET also revealed another two novel species, Alteromonas australica H17T and Marinobacter salarius R9SW1T, which have been validly described in this project.

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Appendices

Appendix I

50X TAE stock solution (per litre)

Tris Base (MW=121.1) 242 g Acetic acid 57.2 mL 0.5M EDTA, pH8.0 100 mL

Add dH2O up to 1000 mL

6X gel loading buffer

0.25% Bromophenol blue 40% Sucrose

20X Saline Sodium Citrate (SSC) buffer (per litre)

Sodium chloride 175 g Trisodium citrate 88 g

Add dH2O up to 1000 mL

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Appendix II

List of validly described species used for screening of 3PET-degrading bacteria.

Strain Working number Aestuariibacter aggregatus LMG 25283T Alteromonas addita R10SW13T Alteromonas australica H17T Alteromonas genovensis LMG 24078T Alteromonas hispanica F-32T Alteromonas litorea TF-22T Alteromonas macleodii LMG 2843T Alteromonas marina SW-47T Alteromonas simiduii BCRC 17572T Alteromonas stellipolaris LMG 21861T Alteromonas tagae JCM 13895T Glaciecola agarilytica LMG 23762T Glaciecola mesophila LMG 22017T Glaciecola polaris LMG 21857T Idiomarina abyssalis KMM 227T Idiomarina baltica LMG 21691T Idiomarina fontislapidosi LMG 22170T Idiomarina ramblicola LMG 22169T Idiomarina seosinensis CIP 108665T Idiomarina zobellii KMM 231T Marinobacter adhaerens CIP 110141T Marinobacter algicola LMG 23835T Marinobacter daqiaonensis LMG 25365T Marinobacter excellens Fg86T Marinobacter flavimaris CIP 108615T Marinobacter gudaonensis CIP 109534T Marinobacter hydrocarbonoclasticus SP.17T

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Strain Working number Marinobacter koreensis KACC 11513T Marinobacter lipolyticus CIP 107627T Marinobacter litoralis SW-45T Marinobacter mobilis JCM 15154T Marinobacter pelagius JCM 14804T Marinobacter psychrophilus JCM 14643T Marinobacter salicampi KCTC 12972T Marinobacter salsuginis CIP 109893T Marinobacter sediminum LMG 23833T Marinobacter vinifirmus CIP 109495T Marinobacter xestospongiae JCM 17469T Marinobacter zhejiangensis JCM 15156T Pseudoalteromonas aliena SW19T Pseudoalteromonas antarctica CECT 4664T Pseudoalteromonas atlantica IAM 12927T Pseudoalteromonas bacteriolytica IAM 14595T Pseudoalteromonas carrageenovora IAM 12662T Pseudoalteromonas citrea ATCC 29719T Pseudoalteromonas denitrificans ATCC 43337T Pseudoalteromonas distincta KMM 638T Pseudoalteromonas elyakovii KMM 162T Pseudoalteromonas espejiana CIP 104112T Pseudoalteromonas flavipulchra LMG 20361T Pseudoalteromonas haloplanktis IAM 12915T Pseudoalteromonas issachenkonii KMM 3549T Pseudoalteromonas maricaloris KMM 636T Pseudoalteromonas mariniglutinosa KMM 3635T Pseudoalteromonas nigrifaciens IAM 13010T Pseudoalteromonas paragorgicola KMM 3548T Pseudoalteromonas peptidolytica MBICC F1250A1T

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Strain Working number Pseudoalteromonas piscicida CIP 103300T Pseudoalteromonas ruthenica 115T Pseudoalteromonas tetraodonis IAM 14160T Pseudoalteromonas translucida KMM 520T Pseudoalteromonas tunicata D2T Pseudoalteromonas undina LMG 2880T Salinimonas chungwhensis KCTC 12239T Thalassospira tepidiphila 1-1BT

List of strains isolated from St. Kilda Beach, Melbourne (Webb et al. 2009; Webb 2012) and used for screening of 3PET-degrading bacteria in this study.

A1h1 A3d10 D2d1 T1d1

A3d1 A3e1 D2d2 T1d2

A3d2 A3e2 D2d3 T2d1

A3d3 A3e3 D2m1 T2d2

A3d4 A3e4 D2o1 T3d1

A3d5 A3e5 D3b1

A3d6 A3e6 D3f1

A3d7 A3e7 D3f2

A3d8 A3f3 D3p1

A3d9 A3f4 R9SW1

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Appendix III

Agarose gel electrophoresis of PCR products from five house-keeping genes (dnak, sucC, rpoB , gyrB, and rpoD).Lane M, TrackIt™ 1 Kb Plus DNA Ladder (Invitrogen, USA); Lane 1, A. addita; Lane 2, A. genovensis; Lane 3, A. hispanica; Lane 4, A. litorea; Lane 5, A. macleodii; Lane 6, A. marina; Lane 7; A. simiduii; Lane 8, A. stellipolaris; Lane 9, A. tagae; Lane 10, Pseudoalteromonas translucida; Lane 11, Shewanella colwelliana; Lane 12, Marinomonas communis; Lane 13, Salinimonas chungwhensis; Lane 14, Aestuariibacter aggregatus; Lane 15, Glaciecola mesophila.

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Appendix IV

Main Spectra Library (MSP) dendrogram of MALDI-TOF mass spectral profiles from nine Alteromonas species and type strains of closely related species generated by the MALDI Biotyper 3.0 software. Distance is displayed in relative units. 202

Appendix V

G+C content of strains A3d10T, R9SW1T and all validly described Marinobacter species.

Strain DNA G+C content (mol %) M. adhaerens HP15T 56.9 M. algicola DG893T 55.0 M. antarcticus ZS2-30T 55.8 M. bryozoorum KMM 3840T 59.6 M. daepoensis SW-156T 57.0 M. daqiaonensis YCSA40T 60.8 M. excellens KMM 3809T 56.0 M. flavimaris SW-145T 58.0 M. goseongensis En6T ND M. gudaonensis SL014B61AT 57.9 M. guineae M3BT 57.1 M. hydrocarbonoclasticus SP.17T 52.7 M. koreensis DD-M3T 54.1 M. lacisalsi FP2.5T 58.6 M. lipolyticus SM19T 57.0 M. litoralis SW-45T 55.0 M. lutaoensis T5054T 63.5 M. maritimus CK 47T 58.0 M. mobilis CN46T 58.0 - 58.9 M. oulmenensis Set74T 57.4 M. pelagius HS225T 59.0 M. persicus M9BT 58.6 M. psychrophilus 20041T 55.4 M. salicampi ISL-40T 58.1 M. salsuginis SD-14BT 55.9 M. santoriniensis NKSG1T 58.1 M. sediminum KMM 3657T 56.5 M. segnicrescens SS011B1-4T 62.2 M. szutsaonensis NTU-104T 56.5 M. vinifirmus FB1T 58.7 M. xestospongiae UST090418-1611T 57.1 M. zhanjiangensis JSM 078120T 60.6 M. zhejiangensis CN74T 58.4 Strain A3d10T 57.6 Strain R9SW1T 57.1

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