Rhizosphere Microbial Communities of Puccinellia Angustata Thriving in Pristine and Diesel-Contaminated Arctic Soils

Rhizosphere Microbial Communities of Puccinellia angustata thriving in pristine and diesel-contaminated Arctic soils

Ofelia Ferrera-Rodríguez Department of Natural Resource Sciences McGill University, Montreal May, 2011

A thesis submitted to McGill University in partial fulfillment of the requirements for the degree of Doctor of philosophy

 Ofelia Ferrera-Rodríguez, May 2011

“What we observe is not nature itself but nature exposed to our method of questioning”

— Werner Heisenberg

"Science is a way of thinking much more than it is a body of knowledge."

— Carl Sagan

"Jesús, María, Os Amo, Salvad Almas"

— Sor María Consolata Betrone

God

Dr. Ronald Ferrera Cerrato

M.Sc. Maria del Socorro Rodríguez Mendoza

Dr. Fernando José Esparza García

Dr. Roger Knowles†

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Acknowledgements

First of all, I would like to thank Dr. Lyle Whyte, my supervisor, for giving me the opportunity to become part of his research group receiving me as a student and funding my project, but more than anything for being inspirational, supportive, encouraging and for having endless patience for me. I also need to thank Dr. Charles Greer my co-supervisor for always being generous with his profound knowledge, giving the best advice at the perfect time and always encouraging me to carry on with the research and to do my best.

I would not have material to work with if Dr. Dave Juck had not collected all of my samples, I wish to sincerely thank him for this and for always provide me with the necessary additional information with a generous and amicably attitude. My work would be incomplete without the important contribution of Dr. Joann Whalen who analyzed all the soil samples, but also she is a model of hard work, precision and efficiency. I would like to thank M.Sc. Diane Labbé for her help in the THP analysis of the first samples, and Dr. Wonjae Chang for teaching me, assisting me and lending me equipment to perform the TPH analysis for the final experiments. I don’t have enough words to thank Dr. Laurie Consaul for identifying my plants and for being a wonderful next door neighbor.

Also I am in debt with my Macdonald Campus professors Dr. Knowles† and Dr. Niven who were always a source of inspiration to me and were so nice as to spend some time giving me very useful comments every time I asked them. To me, attending to Dr. Brian Driscoll classes and laboratory practices was always enlightening, his comments and questions during seminars were sharp and adirected to make me a better scientist and simultaneously he always was very encouraging and supportive.

I don’t know how to express how thankful I am to all the Macdonald campus staff, everybody works with his heart and gives the best they can at all times to all of us students, especially Marlene Parkinson, Ann Gossage, Peter Kirby and specially Marie Kubecki who besides doing the very demanding activities of a Graduate Student Coordinator, she is like a fairy godmother always helping, willing to listen and give wise advise to all who need it. I cannot imagine going through some of the technical challenges without David Meek, he always knows how to fix everything and solves any problem with amazing efficiency, but beyond that I am thankful to him for his generous

iv and sweet friendship. I am also thankful to Dr. Ian Strachan for being empathic and helping me to get an extension to finish this work.

I want to thank my laboratory mates: Ana Viquez, Béatrice Barbier, Chih Ying Lay, Elisabeth Lefrançois, Eric Bottos, Kristine Radtke, Michael Dyen, Nancy Perreault, Sara Klemm and Sara Sheibani from whom I learned a lot and had lots of fun. In my most desperate moments in Canada I had a hard core of friends which often encouraged me, gave me useful feedback regarding the lab work, endured correcting my awful writing, invited me over to have fun and even fed me. They are Blaire Steven, Roland Wilhelm and Thomas Daniel Niederberger they will always have a special place in my heart. Speaking of friendship, I am thankful to Nikita, Kevin, Dina, Keo, Julie, Dave (dude), Paschalis, Babic, Branislav, Jean Francois, Jackie and specially to Juan José Almaraz, Alida Mercado, Marianne Claire-Louise Michelle Poilly, Elisabeth Roussel, Gitanjali Arya, Kakali Mukhopadhyay, Andrew Ekins, Ruth Knowles, Louise Pilon and Emilien Pilon for being like a Canadian family to me.

Sincere thanks to all my profesors from UPIBI-IPN and CINVESTAV-IPN for they gave me the basis to get here and to Alejandro Alarcon for his spontaneous help.

With all my love and admiration I need to thank my beloved Mom Maria del Socorro Rodríguez Mendoza and my beloved Dad Ronald Ferrera Cerrato, for being a limitless source of love and inspiration and always supporting me in every possible way. I would like to thank my wonderful sisters Elena and Mariana Ronalda for their endless love, and to my niece Maria Beatríz for bringing renewed joy to my life. Sincere thanks to my extended family especially my uncles Benjamin, Ruben, Alberto and Rafael and my aunts Lolita, Lourdes, Beatríz and Nieves for their care and their prayers as well as to Alejandra, Claudia, Rodrigo, Maria de Lourdes, Miriam Evelyn, Maira, Jimena, Ruben Issac y Erick. A special thank you to my physicians Rafael de la Cruz Espinosa and Teresita del Niño Jesus, for getting me out from a health crisis, and to the Pbro. Sergio Garduño García for advising me wisely on spiritual and personal matters.

Last but not least, it is important to recognize that my studies at McGill University were only possible thanks to the scholarship granted by the Mexican Government through the Consejo Nacional de Ciencia y Tecnología (CONACyT). I also want to thank the CONACYT staff for doing an excellent job getting in touch with me regularly and for being supportive.

Contributions of Authors

All the research and writing of the present thesis, including the publishable chapters 3, 4 and 5, was done by M.Sc. Ofelia Ferrera-Rodríguez under the direct supervision of Dr. Lyle Whyte and Dr. Charles Greer. Dr. Lyle Whyte not only contributed with the finical means and infrastructure to develop the research but also contributed with very insightful opportune and useful ideas and had an important participation in the edition of the manuscripts. Dr. Charles Greer was a co-supervisor of the presented research work; his advice and opinions were tremendously important for the success of the experiments and the focus of the research. Dr. David Juck collected all of the soil samples and plant material from the Arctic used for the experiments and he also contributed with essential information from the sampling site. Dr. Laurie Consaul identified the plant species used in this investigation. Dr. Thomas Daniel Niederberger and M.Sc. Roland Wilhelm contributed in the proofreading of the present manuscript. The final list of co-authors for each paper will be decided prior to their submission to the corresponding journals.

Abstract Petroleum hydrocarbon contamination has reached Arctic soils, Puccinellia angustata thrives in such habitats. Hydrocarbon removal during phytoremediation treatments strongly depends on the catabolic activities of root associated microorganisms. This research compared the rhizospheric microorganisms from P. angustata established at Arctic soils under different degrees of contamination with petroleum hydrocarbons to determine if this plant species has phytoremediation potential. The abundance, diversity, and activity of soil bacterial communities were investigated by means of plate and microscopic counts, strains isolation, 16S rRNA gene DGGE fingerprinting, functional gene PCR detection, microcosm hydrocarbon mineralization assays, and TPH quantification. This study was divided in three experimental stages. In the first stage a comparison of samples vegetated by five different plant species as well as four bulk samples, showed that P. angustata had greater enrichment of hydrocarbon-degrading bacteria than samples vegetated by other plant species. In the second stage, a comparison between the rhizosphere of P. angustata and bulk samples collected at pristine (Pr), hydrocarbon-contaminated but non-bioremediated (NBr) and hydrocarbon-contaminated and bioremediated (Br) Arctic soils, revealed that the presence of P. angustata enhanced the abundance and the activity of hydrocarbon-degrading microorganisms in two soils (Pr and NBr) but not at Br since this soil was subjected to fertilizations as bioremediation process. In the third stage, via growth chamber experiments, in which high Arctic summer conditions were simulated, P. angustata (with and without nitrogen fertilization) was assessed for phytoremediation of a soil artificially contaminated with diesel (10,000 mg Kg-1). Puccinellia showed tolerance to fresh diesel contamination and enhanced hydrocarbon-degrading bacteria that significantly increased the TPH removal. Also, the rhizosphere of P. angusatata had high bacterial diversity, encompassing members from Actinobacteria, Bacteroidetes, Firmicutes, Gemmatimonadetes and Proteobacteria phyla. These findings indicate that P. angustata stimulates soil microflora responsible for biodegradation of hydrocarbons therefore it has potential for phytoremediating contaminated Arctic soils. Abbreviation: PCR (polymerase chain reaction), DGGE (Denaturing Gradient Gel Electrophoresis), TPH (Total Petroleum Hydrocarbons).

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Résumé La contamination par hydrocarbures pétroliers a touché les sols arctiques ; Puccinellia angustata se développe dans ces habitats. L’élimination de l’hydrocarbure pendant les traitements de phytoremédiation dépend fortement des activités cataboliques des microorganismes associés à la racine. Cette recherche a comparé les microorganismes rhizosphériques de P. angustata établis dans les sols arctiques à différents niveaux de contamination par hydrocarbures pétroliers pour déterminer si cette espèce de plante a du potentiel pour la phytoremédiation. L’abondance, la diversité, et l’activité des communautés bactériennes du sol ont été examinées au moyen de comptages sur plaque et microscopiques, isolement de souches, empreinte digitale par DGGE du gène 16S rRNA, détection fonctionnelle du gène par PCR, essais de minéralisation des hydrocarbures en microcosme et quantification de TPH. Cette étude a été divisée en trois étapes expérimentales. Dans la première étape, une comparaison d’échantillons globales et d’échantillons plantés de cinq espèces végétales différentes a montré que P. angustata avait une plus grande accumulation de bactéries de dégradation de l’hydrocarbure que les échantillons plantés avec d’autres espèces végétales. Dans la deuxième étape, une comparaison entre la rhizosphère de P. angustata et des échantillons globales pris des sols arctiques pures (Pr), des sols arctiques contaminés par hydrocarbures et non-bio-réhabilités (NBr) et des sols arctiques contaminés par hydrocarbures et bio-réhabilités (Br), a révélé que la présence de P. angustata a augmenté l’abondance et l’activité des microorganismes de dégradation d’hydrocarbures dans deux sols (Pr et NBr), mais pas dans le Br, parce que ce sol avait reçu des fertilisations comme processus de bioremédiation. Dans la troisième étape, au moyen d‘essais en chambres de croissance où les conditions d’été du Haut-Arctique étaient simulées, P. angustata (avec et sans fertilisation par hydrogène) a été évaluée pour la phytoremédiation d’un sol contaminé artificiellement avec du diesel (10,000 mg Kg-1). Puccinellia a montré tolérance à la contamination par diesel et a augmenté les bactéries de dégradation d’hydrocarbures, en améliorant ainsi beaucoup l’élimination des TPH. Aussi, la rhizosphère de P. angustata avait une haute diversité bactérienne, contenant des membres de phylums Actinobacteria, Bacteroidetes, Firmicutes, Gemmatimonadetes et Proteobacteria. Ces conclusions indiquent que P. angustata stimule la microflore du sol qui est responsable de la biodégradation des hydrocarbures et, pourtant, pourrait aider la phytoremédiation des sols arctiques contaminés. Abréviations : PCR (Réactions en chaîne par polymérase), DGGE (Électrophorèse sur gel à gradient dénaturant),TPH (hydrocarbures pétroliers totaux)

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TABLE OF CONTENTS

Acknowledgements ...... iv Contributions of Authors ...... vi Abstract ...... vii Résumé ...... viii LIST OF TABLES...... xiii LIST OF FIGURES ...... xiv CHAPTER 1. Introduction ...... 1 1.1 General Objective ...... 2 1.2 Research approach Rationale...... 2 1.3 Specific Objectives ...... 3 1.3.1 Screening Arctic plants by analyzing their microbial communities...... 3 1.3.2 Comparing microbial communities from high Arctic soils vegetated by P. angustata ...... 3 1.3.3 Assessing phytoremediation of a diesel-contaminated Arctic soil using P. angustata ...... 4 CHAPTER 2. Literature Review...... 5 2.1 Literature Review Overview ...... 5 2.1 Petroleum Hydrocarbons ...... 5 2.3 Hydrocarbon contamination in Canada ...... 5 2.4 Hydrocarbon-degrading microorganisms ...... 7 2.4.1 Archaea ...... 7 2.4.2 Eukaria ...... 7 2.4.2.1 Algae ...... 7 2.4.1.2 Fungi ...... 8 2.4.3 Bacteria ...... 9 2.4.3.1 Aerobic n-alkane degrading bacteria ...... 10 2.4.3.2 Anaerobic n-alkane degrading bacteria ...... 11 2.4.3.3 Aerobic aromatic degrading bacteria ...... 11 2.4.3.4 Anaerobic aromatic degrading bacteria ...... 12 2.5 Microbial response to abiotic factors affecting hydrocarbon biodegradation ...... 13 2.5.1 Hydrocarbon bioavailability...... 13 2.5.2 Temperature ...... 14 2.5.3 Oxygen ...... 14 2.5.4 Nutrient limitation ...... 14 2.5.5 Salinity and pH ...... 15 2.6 Bioremediation ...... 15 2.6.1 Biodegradation of hydrocarbons in cold environments ...... 15 2.6.2 Bacterial communities of petroleum hydrocarbon-contaminated cold environments ...... 17

2.7 Phytoremediation ...... 18 2.7.1 Phytoremediation in cold Canadian environments ...... 20 2.7.2 Bacterial populations from the rhizosphere of Canadian Arctic plants...... 22 CHAPTER 3. Screening Arctic plants for their hydrocarbon phytoremediation potential by analyzing their microbial communities...... 34 3.1 Abstract ...... 34 3.2 Introduction ...... 35 3.3 Materials and methods ...... 37 3.3.1 Study site description and sampling strategy ...... 37 3.3.2 Physicochemical soil characterization and plant identification ...... 38 3.3.3 Microbial enumeration and statistical analysis ...... 38 3.3.4 Culture independent microbial community analyses ...... 39 3.3.5 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 39 3.3.6 Phylogenetic analyses of gene sequences ...... 40 3.3.7 PCR detection of alkB, ndoB and xylE genes ...... 41 3.3.8 Isolation and characterization of hydrocarbon-degrading bacteria ...... 41 3.3.9 Nucleotide sequence accession numbers ...... 42 3.4 Results ...... 42 3.4.1 Plant species and physicochemical characteristics of soil samples ...... 42 3.4.2 Microbial enumeration ...... 43 3.4.3 Microbial Community Profiles ...... 43 3.4.3.1 16S rRNA gene DGGE fingerprints of soil bacterial communities ...... 43 3.4.3.2 Phylogeny of DGGE bands ...... 44 3.4.4 PCR detection of alkB, ndoB and xylE genes ...... 44 3.4.5 Isolation and characterization of hydrocarbon-degrading bacteria ...... 44 3.5 Discussion ...... 46 3.6 Conclusion ...... 51 3.7 Acknowledgements ...... 52 Connecting Text (Connecting Chapter 3 to Chapter 4) ...... 69 CHAPTER 4. Characterization of microbial communities from high Arctic soils impacted by weathered diesel contamination and vegetated by Puccinellia angustata ...... 70 4.1 Abstract ...... 70 4.2 Introduction ...... 71 4.3 Materials and methods ...... 73 4.3.1 Sampling strategy ...... 73 4.3.2 Physicochemical soil characterization ...... 73 4.3.3 Microbial enumeration ...... 73 4.3.4 Culture independent microbial community analyses ...... 74 4.3.4.1 Microbial community DNA extraction ...... 74 4.3.4.2 Polymerase chain reaction of bacterial 16S rRNA genes for Denaturing x

Gradient Gel Electrophoresis (DGGE) ...... 75 4.3.4.3 Analysis of the DGGE profiles ...... 75 4.3.4.4 Reamplification of 16S rRNA gene DGGE bands ...... 76 4.3.4.5 Phylogenetic analyses of 16S rRNA gene sequences ...... 76 4.3.5 Nucleotide sequence accession numbers...... 77 4.3.6 Detection of genes related to the catabolism of hydrocarbons by polymerase chain reaction ...... 77 4.3.7 Hydrocarbon mineralization activity analyses ...... 78 4.3.8 Statistical analyses ...... 78 4.4 Results ...... 78 4.4.1 Physicochemical soil characterization ...... 78 4.4.2 Microbial enumeration ...... 79 4.4.3 Microbial community analyses ...... 79 4.4.3.1 16S rRNA gene DGGE fingerprints of soil bacterial communities ...... 79 4.4.3.2 Phylogenetic analyses of bands excised from DGGE gels ...... 81 4.4.4 Prevalence of genes related to the metabolism of hydrocarbons ...... 82 4.4.5 Hydrocarbon mineralization activity analyses ...... 82 4.4.6 Principal component analysis ...... 83 4.5 Discussion ...... 84 4.5.1 Microbial enumeration ...... 85 4.5.2 Microbial community analyses ...... 86 4.5.3 Prevalence of genes related to the metabolism of hydrocarbons ...... 89 4.5.4 Hydrocarbon mineralization activity analyses ...... 91 4.5.5 Principal Component Analyses ...... 92 4.6 Conclusions ...... 93 4.7 Acknowledgements ...... 93 Connecting Text (Connecting Chapter 4 to Chapter 5) ...... 118 CHAPTER 5. Assessing phytoremediation of diesel-contaminated Arctic soils with the narrow alkali grass Puccinellia angustata ...... 119 5.1 Abstract ...... 119 5.2 Introduction ...... 120 5.3 Materials and methods ...... 121 5.3.1 Soil and plant materials ...... 121 5.3.2 Testing adult plant, seedling and seed tolerance to diesel ...... 122 5.3.3 Establishment of the phytoremediation experiments ...... 123 5.3.4 Total petroleum hydrocarbon (TPH) analysis ...... 124 5.3.5 Total and cultivable microbial enumerations ...... 125 5.3.6 Total community DNA analyses ...... 125 5.3.6.1 Total community DNA extraction ...... 125 5.3.6.2 Bacterial 16S rRNA gene PCR for denaturing gradient gel electrophoresis (DGGE) ...... 126 xi

5.3.6.3 Analysis of the DGGE profiles ...... 126 5.3.6.4 Sequencing of DGGE bands of interest ...... 127 5.3.6.5 Phylogenetic analyses of 16S rRNA gene sequences ...... 127 5.3.7 Nucleotide sequence accession numbers ...... 127 5.3.8 Determining the presence of alkB, ndoB and xylE genes ...... 127 5.3.9 Hydrocarbon microcosm mineralization assays...... 128 5.3.10 Statistical analyses ...... 128 5.4 Results ...... 129 5.4.1 Tolerance of P. angustata adult plants, seedlings and seeds to diesel hydrocarbons ...... 129 5.4.2 Characteristics of plants and soil samples at the end of the phytoremediation experiment...... 129 5.4.3 Total petroleum hydrocarbon degradation ...... 130 5.4.4 Abundances of total cells, heterotrophic and diesel degrading soil microorganisms ...... 130 5.4.5 Culture-independent microbial community analyses ...... 131 5.4.6 Prevalence of alkB, ndoB and xylE genes ...... 133 5.4.7 Hydrocarbon mineralization activity analyses ...... 133 5.5 Discussion ...... 133 5.5.1 Tolerance of P. angustata to Diesel Hydrocarbons ...... 134 5.5.2 Phytoremediation of a diesel contaminated Arctic soil ...... 135 5.5.3 Correlation of Microbial abundance to TPH Removal ...... 137 5.5.4 Effect of phytoremediation treatments on Microbial diversity ...... 137 5.5.5 alkB, ndoB and xylE detection ...... 139 5.5.6 Hexadecane and naphthalene mineralization ...... 140 5.6 Conclusions ...... 141 5.7 Acknowledgements ...... 142 CHAPTER 6. General Discussion and Conclusion ...... 163 6.1 General Discussion ...... 163 6.2 General Conclusions ...... 168 CHAPTER 7. Contributions to Knowledge ...... 169 CHAPTER 8. References ...... 170 Appendices ...... 189 A) GenBank accession numbers and description of Isolated Strains from Chapter 3...... 189 B) Principal component analysis of variables determined in from Chapter 4 ..... 195 C) Representative Chromatograms of the TPH extracted from soil samples from chapter 5 ...... 200

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LIST OF TABLES

Table 2.1 Enzyme classes involved in the oxidation of alkanes……………………….24 Table 2.2 Genera of cultured bacteria able to degrade petroleum hydrocarbons………25 Table 2.3 Bacterial genes related to the aerobic degradation of aromatic hydrocarbons..26 Table 3.1 Primers and reference strains for PCR analysis…………………………53 Table 3.2 Characteristics of soil samples collected in the summer of 2004 …………54 Table 3.3 Microbial Enumeration……………………………………………………55 Table 3.4 Nucleotide sequence similarity of 16S rRNA gene-DGGE bands………56 Table 3.5 PCR amplification of bacterial genes involved in the catabolism of hydrocarbons..57 Table 3.6 Closest matches for the alkB, ndoB and xylE genes from the isolated strains..58 Table 4.1 Characteristics of soil samples collected from Eureka, in the summer of 2005………94 Table 4.2 Microbial Enumeration………………………………………………………95 Table 4.3 Nucleotide sequence similarity of 16S rRNA gene-DGGE bands……………97 Table 4.4 PCR amplification of bacterial genes involved in the catabolism of hydrocarbons...99 Table 5.1 Soil physicochemical variables at the end of the phytoremediation experiment…143 Table 5.2 Microbial Enumeration………………………………………………………….144 Table 5.3 Nucleotide sequence similarity of 16S rRNA gene-DGGE bands……………..145

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LIST OF FIGURES

Figure 2.1 The three main pathways for polycyclic aromatic hydrocarbon degradation by fungi and bacteria……………………………………………………………………………………...... 28 Figure 2.2 Pathways for the degradation of alkanes by terminal, sub- and biterminal oxidation...30 Figure 2.3 Aerobic and anaerobic bacterial degradation pathways of naphthalene………………..32 Figure 3.1 Plants and soils collected from Eureka during 2004……………………………………..59 Figure 3.2 Analysis of PCR-amplified 16SrRNA gene fragments from Eureka vegetated and non-vegetated soils…………………………………………………………………………………61 Figure 3.3 Analyses of carrying capacity and functionality of microbial communities from Eureka samples calculated form bands formed on a DGGE……………………………………….63 Figure 3.4 Phylogenetic relationships of 16S rRNA gene fragments excised from DGGE gel from Eureka samples……………………………………………………………………………….65 Figure 3.5 Phylotypes composition of hydrocarbon-degrading isolates from Eureka samples….67 Figure 4.1 Sampling site……………………………………………………………………………100 Figure 4.2 DGGE of PCR-amplified 16SrRNA gene fragments from triplicate samples of bulk and vegetated soils………………………………………………………………………………….102 Figure 4.3 DGGE of PCR-amplified 16SrRNA gene fragments from composite samples of bulk and vegetated soils………………………………………………………………………………….104 Figure 4.4 Phylogenetic relationships of 16S rRNA gene fragments of Gemmatimonadetes, Actinobacteria and Proteobacteria Phyla of excised bands from DGGE gels of bulk and vegetated by P. angustata soils from Eureka………………………………………………………106 Figure 4.5 Phylogenetic relationships of 16S rRNA gene fragments of Bacteroidetes Phyla of excised bands from DGGE gels of bulk and vegetated by P. angustata soils from Eureka……..108 Figure 4.6 Proportion of the 16S rRNA gene-DGGE bands classified into the Phyla Bacteroidetes, Actinobacteria, Proteobacteria and Gematimonadetes….………..……………….110 Figure 4.7 Cumulative Mineralization of [1-14C]hexadecane at 5°C incubation in the dark………112 Figure 4.8 Cumulative Mineralization of [1-14C]naphthalene at 5°C incubation in the dark……...114 Figure 4.9 Symmetric biplot illustrating results from the Principal Components Analysis……….116 Figure 5.1 Experimental set up……………………………………………………………………151 Figure 5.2 Hydrocarbons at the end of the experiment……………………………………………153 Figure 5.3 16SrRNA gene DGGE gel (45%-65% denaturant gradient) of composited samples from each treatment………………………………………………………………………………..155 Figure 5.4 16S rRNA gene DGGE gel (45%-65% denaturant gradient) of individual samples from each treatment………………………………………………………………………………..157 Figure 5.5 Phylotypes composition of the sequenced bands from the Phytoremediation experiments………………………………………………………………………………………...159 Figure 5.6 Cumulative Mineralization at 10°C incubation in the dark……………………………161

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CHAPTER 1. Introduction Petroleum hydrocarbons are a major source of contamination on earth and have polluted remote areas such as the high Arctic (Burgherr 2007). Petroleum hydrocarbons ecotoxicity is broadly recognized (Efroymson et al. 2004); therefore the removal of such contaminants from the environment is necessary to have healthy habitats. With the human activity increasing in the circumpolar north raising the risk of petroleum hydrocarbons contamination, the importance of low cost remediation methods applicable to cold-temperature environments is growing. A cleanup treatment known as phytoremediation consisting in the application of plants and their associated microorganisms to clean up contaminated soils has been used on several environments (Cunningham and Ow 1996; Gerhardt et al. 2009) and it has been assessed for sub-arctic regions (Palmroth et al. 2002; Phillips et al. 2009), but this environmentally friendly technology is still in the verge of its application at the high Arctic (Filler et al. 2006), mainly due to environmental constraints and limited information regarding suitable plants and their rhizospheric microflora. It is well established that there are indigenous microbes in polar soils capable of degrading hydrocarbons (Greer et al. 2010), however up until now there was no information assessing hydrocarbon-degrading microorganisms associated to native plants thriving on high Arctic soils contaminated by petroleum hydrocarbons. Since different soils have endemic microbial populations and plant-microorganism interactions are dependent on plant species as well as on microbial community composition, it is necessary to study the unique plant-microbial dynamics using soil and native plants from the high Arctic. The perennial grass Puccinellia angustata (alkali grass), native to northern regions of Alaska, Canada and Greenland (González et al. 2000), was one of five plant species found growing in hydrocarbon-contaminated areas in the high Arctic. In the present research, we examined the rhizospheric microbial populations of P. angustata growing on Arctic soils which were exposed to different concentrations of aged and fresh hydrocarbon contamination, mostly to evaluate this plant species potential application for Arctic phytoremediation. The abundance, diversity, and activity of soil microbial communities were investigated by means of plate and microscopic counts, strains isolation, 16S rRNA gene DGGE

1 fingerprinting, functional gene PCR detection, microcosm hydrocarbon mineralization assays, and TPH quantification.

1.1 General Objective The main objective of the present work was to compare the rhizospheric microorganisms from a plant species found thriving in Arctic soils exposed to different degrees of contamination with petroleum hydrocarbons; in order to determine whether this plant species is potentially applicable on phytoremediation treatments for hydrocarbon contaminated Arctic soils.

1.2 Research approach Rationale A combination of culture-dependent and culture independent techniques allowed us to accomplish the research goals without having to do a complete characterization of the soil microflora from all samples. Determining the total cells in the soil samples by epifluorescence microscopy is a reliable method to determine the total number of microorganism (Kepner Jr and Pratt, 1994) although it does not differentiates between live and dead cells. Bacteria are the most diverse and numerous groups of microorganisms in soils and due to their diverse metabolism they play a central role in the regulation of ecosystems (Kirk et al., 2004; Fierer and Lennon, 2011), therefore we focused our research on bacterial communities. To complement the total cell counts, the total aerobic heterotrophic and hydrocarbon-degrading cultivable bacteria were enumerated by the spread plate technique (Rennie, 1981; Greer et al., 1993; Rivera-Cruz et al., 2004). Another approach to determine the presence of hydrocarbon-degrading bacteria in the soils was testing the total community DNA for the presence of bacterial genes encoding enzymes involved in hydrocarbon degradation. Since the prevalence of the alkB, ndoB and xylE genes was previously determined by PCR in Arctic soils (Whyte et al., 2001) this strategy was selected for the present study. The 16S rRNA gene encoding for the small subunit of ribosomal RNA, has clear advantages for diversity analyses, for instance it has highly conserved regions which are good for effective PCR primer design targeting bacteria from different Phyla. The 16S rRNA gene also has regions with sufficiently variability to allow for accurate taxonomic and phylogenetic identification of community members. The lateral transfer of this gene between taxa appears to be rare, and there is a 2 large amount of accumulated 16S rRNA sequence data in databases that permit comparisons of community composition across studies (Fierer and Lennon, 2011). In the present study we compared the bacterial community profiles of different samples by studying 16S rRNA gene DGGE fingerprints. A method successfully used to compare petroleum hydrocarbon-contaminated soils from northern Canada (Juck et al., 2000) which allows estimating the bacterial diversity in terms of the relative abundance of species and their functional organization (Marzorati et al., 2008). The comparison of the microbial hydrocarbon-degradation activities was done by using a radiorespiration method sensitive at low temperatures (Steven et al., 2007) previously used in bioremediation treatability assessments of hydrocarbon-contaminated Arctic soils (Whyte et al., 2001). The present research was divided in three experimental stages having the following specific objectives.

1.3 Specific Objectives

1.3.1 Screening Arctic plants by analyzing their microbial communities To examine the microbial communities from bulk soils and soils vegetated by different plant species indigenous to Arctic regions comparing: (1) the bacterial community profiles, (2) the abundance of total and hydrocarbon-degrading cultivable bacteria and (3) the PCR detection of bacterial genes related to hydrocarbon degradation. All of this in order to identify a plant species potentially useful for phytoremediation treatments. To isolate cultivable hydrocarbon-degrading bacteria, screen their genotypes for the presence of genes related to hydrocarbon degradation as well as detect their hydrocarbon mineralization activities, in order to confirm the presence of active hydrocarbon-degrading bacteria in the studied soils and determine the phylogenetic groups to which they belong.

1.3.2 Comparing microbial communities from high Arctic soils vegetated by P. angustata The first experimental stage revealed that three genes related to hydrocarbon degradation were detected in soils vegetated by P. angustata which also possessed high

3 abundances of hydrocarbon-degrading bacteria. In order to determine whether such results were actually due to the plants influence over the microbial populations and not due to other soil characteristics, we examined three different soils vegetated by P. angustata and their corresponding bulk soils comparing: (1) their bacterial community profiles, (2) the abundance of total and hydrocarbon-degrading cultivable bacteria, (3) the PCR detection frequencies of bacterial genes related to hydrocarbon degradation and (4) their microbial hydrocarbon mineralization activities.

1.3.3 Assessing phytoremediation of a diesel-contaminated Arctic soil using P. angustata To further determine the potential application of P. angustata in phytoremediation treatments for hydrocarbon contaminated Arctic soils, we set the following objectives: (1) to determine the tolerance of P. angustata seeds, seedlings and adult plants to fresh diesel contamination, (2) to determine if P. angustata plants and seedlings increase the removal of TPHs from an freshly diesel contaminated soil, with and without nitrogen fertilization, incubated under simulated high Arctic summer conditions and (3) to determine if P. angustata influenced the abundance, diversity and/or activity of the soil microbial populations associated with the removal of TPHs on a freshly diesel-contaminated soil as it did on pristine and aged diesel contaminated soils.

CHAPTER 2. Literature Review

2.1 Literature Review Overview This literature review summarizes basic information regarding petroleum hydrocarbons, addresses the hydrocarbon contamination problem in the Canadian Arctic and summarizes the role of the microorganisms in the hydrocarbon degradation referring to their metabolism. It also refers to some factors affecting the hydrocarbon removal from soil. It includes the concepts of bioremediation and phytoremediation as well as some examples of its application in Canadian subarctic regions.

2.1 Petroleum Hydrocarbons Petroleum hydrocarbons represent one of the Earth's most important energy resources. Petroleum is found in the Earth's subsurface; petroleum is composed by a mixture of: saturated hydrocarbons (alkanes), unsaturated hydrocarbons (alkenes, alkynes), cycloalkanes and aromatic hydrocarbons (arenes). Hydrocarbons are organic compounds mainly consisting of hydrogen and carbon. They are found in any of three phases, as gases (i.e. methane and propane), liquids (i.e. hexane and benzene), solids such as asphaltenes, waxes or low melting solids (i.e. paraffin wax and naphthalene) or polymers (i.e. polyethylene, polypropylene and polystyrene). The predominant use of hydrocarbons is as fuel source, such as gasoline (composed by a range of organic compounds such as small chain alkanes (C6-C10) with low boiling point (60-170°C) i.e. isopentane, 2,3-dimethyl butane, n-butane and n-pentane, as well as volatile aromatic compounds i.e. benzene, toluene, ethylbenzene, and xylenes) and diesel (composed mainly by fractions F2 (C10-C16) and F3(C16-C34) in a proportion of 75% saturated hydrocarbons and 25% aromatic hydrocarbons including naphthalene) (Carey and Rogers, 2008).

2.3 Hydrocarbon contamination in Canada According to the Canadian Energy Facts (December 2006), petroleum accounts for the 23% of the total energy produced and for a 31% of the Canadian energy consumption (Foreign-Affairs-and-International-Trade-Canada, 2006). There are ~23,000 km of main trunk lines transporting crude oil from the Western Canada Sedimentary Basin to Canadian refineries and international border crossing points 5

(Natural-Resources-Canada, 2007). Canada’s oil reserves are second only to those of Saudi Arabia and total an estimated 178.8 billion barrels (Foreign-Affairs-and-International-Trade-Canada, 2006); Approximately 61 oil and natural gas fields have been discovered in the Arctic regions of Russia, Alaska, Canada, and Norway. Eleven of those 61 unexploited oil fields are in Canada´s Northwest Territories (Budzik, 2009). Moreover, ~13% of the world’s undiscovered oil may be in the Arctic circle (Gautier, et al., 2009). Oil and fuel spills are among the most extensive and environmentally damaging pollution problems in cold regions and are recognized as potential threats to human and ecosystem health (Filler et al. 2008). The Canadian Environmental Science Technology Centre reported 742 tanker spills (minimum 1,000 barrels in size) in Canada from 1974 to 2001 (Environment-Canada, 2001). In the Arctic, crude oil spills from ruptured pipelines are the largest sources of terrestrial petroleum pollution, followed by shoreline spills from tankers or resupply vessels and diesel fuels are the next most common spills, especially within human settlements, for example, during the 1970s and 1980s, in Rankin Inlet, diesel fuel spills from tank farms accounted for 289 000 L, and in Iqaluit (Nunavut, Canada) reported diesel fuel spills totaled 627 000 L between 1971 and 2006 (Filler et al. 2008); Moreover, the risk of hydrocarbon contamination in northern areas has increased due to recent and projected human activities as a consequence of global warming (Carnaghan and Goody 2006), specifically increasing tourism, the opening of the northwest passage (Midkhatovich and Fenton 2007).

The increasing risk of oil contamination in the Arctic can be illustrated by two recent accidents suffered by ship tanks belonging to Woodward's Oil Limited, the company supplying oil to Canada's Arctic. The two accidents happened on August 8, 2010 and September 1, 2010 almost resulting in major oil spills. The first one occured near Pangnirtung, on southern Baffin Island, when the tanker became grounded in the local harbor, just after finishing loading a bulk shipment of gasoline. The second one, happened when the fuel tanker “Nanny” carrying 9½ million liters of diesel fuel ran aground on an uncharted sandbar at Simpson Strait (about 50 kilometers southwest of Gjoa Haven, a hamlet on King William Island) in western Nunavut (CBCNews-Canada, 2010).

Total petroleum hydrocarbons (TPH) are considered persistent hazardous pollutants, and include compounds that can bioaccumulate in food chains, are acutely toxic, and some such as benzene and benzo[a]pyrene are recognized mutagens and carcinogens (Sikkema et al. 1995). Because of these reasons it is important to clean up as best as possible the contaminated sites. 2.4 Hydrocarbon-degrading microorganisms The capacity to biodegrade hydrocarbons has been reported in species from the three Domains of life: archaea (Al-Mailem et al. 2010), eukaryote (Sample et al. 1999) and bacteria (Reddy et al. 2004), and some enzyme classes known to be involved in the oxidation of alkanes, from different organisms (Van-Beilen and Funhoff 2007) are listed in Table 2.1 Moreover hydrocarbon-degrading microorganisms are frequently found in contaminated and non contaminated environments (Atlas and Cerniglia 1995).

2.4.1 Archaea Few archaea with the ability to use hydrocarbons as carbon source, have been isolated, but some halophilic strains from the genera Haloferax (Emerson et al. 1994), Haloarcula (Tapilatu et al. 2010), Halobacterium and Halococcus (Al-Mailem et al. 2010) are able to aerobically degrade n-alkanes or aromatic hydrocarbons, although their metabolic pathways for the degradation of these compounds have not been described yet. Archaea members of Methanosarconales are reported to be anaerobic hydrocarbon degraders (Widdel and Rabus 2001).

2.4.2 Eukaria Although the cytochrome P450 monoxygenase enzyme present in eukaryotic organisms catalyzes the oxidation of hydrocarbons generally algae and fungi are the ones well recognized as hydrocarbon-degrading eukaryotes (Coon 2005). Only a few algae are known to be able to grow on hydrocarbons as carbon source but generally do this in addition to their photoautotrophic way of life (Prince 2010).

2.4.2.1 Algae

Recognized hydrocarbon-degrading algae are: Phylum Chlorophyta “green algae” (Ankistrodesmus, Chlorella, Chlamydomonas, Dunaliella, Prototheca, Scenedesmus,

Selenastrum Ulva) (Sample et al. 1999), Phylum Rhodophyta “red algae” (Porphyridium), Phylum Heterokontophyta “diatoms” (Achnanthes, Amphora, Cyclotella, Cylindrotheca, Navicula, Nitzschia, Skeletonema, Synedra) (Cerniglia et al. 1982), “brown algae” (Petalonia), “water molds” (Phytophthora, Saprolegnia), “slime nets” (Thraustochytrium) and Hyphochytrium (Prince 2010). Metabolic pathways for the degradation of hydrocarbons in algae have not been yet fully elucidated, but the aromatic hydrocarbons transformation is aerobic and involves trans-dihydrodiols typically originated from epoxidation by the action of cytochrome P450 monooxygenases and epoxide hydrolases (Sample et al. 1999).

2.4.1.2 Fungi Fungi are important contributors to hydrocarbon degradation in the biosphere, particularly for the degradation of polycyclic aromatic hydrocarbons. Over 80 genera from the phylum Ascomycota have hydrocarbon-degrading representatives, such as: Alternaria, Aspergillus, Aureobasidium, Beauveria, Bipolaris, Botrytis, Cephalosporium, Cladophialophora, Cladosporium, Claviceps, Cochliobolus, Colletotrichum, Coniothyrium, Dichotomomyces, Doratomyces, Emericella, Eupenicillium, Exophiala, Fusarium, Gliocladium, Graphium, Keratinomyces, Microsporon, Morchella, Myceliophthora, Neosartorya, Neurospora, Paecilomyces, Penicillium, Pithomyces, Scedosporium, Sphaeropsis, Sporothrix, Talaromyces, Trichoderma, Verticillium, Candida, Geotrichum, Kluyveromyces, Pichia, Saccharomyces, Saccharomycopsis, Yarrowia, Amorphotheca, Botryotrichum, Gonytrichum, Humicola and Oospora (Haritash and Kaushik 2009; Prenafeta-Boldú et al. 2006; Prince 2010) and more than 60 genera from the phylum Basidiomycota are able to degrade hydrocarbons such as: Agaricus, Amanita, Bjerkandera, Boletus, Ceriporiopsis, Coniophora, Coprinus, Cryptococcus, Daedalea, Ganoderma, Gymnopilus, Gyrodon, Hebeloma, Hypholoma, Irpex, Laccaria, Lactarius, Laetiporus, Leccinum, Lentinus, Marasmiellus, Minimedusa, Oxysporus, Panaeolus, Phanerochaete, Phlebia, Pisolithus, Pleurotus, Polyporus, Psilocybe, Pycnoporus, Ramaria, Rhizoctonia, Rigidoporus, Sporotrichum, Trametes, Trichosporon, Rhodosporidium, Sporobolomyces, Basidiobolus, Choanephora, Circinella, Conidiobolus, Cunninghamella, Gilbertella, Mortierella, Mucor, Phlyctochytrium, Phycomyces, Rhizomucor, Rhizophlyctis, Rhizopus, 8

Smittium, Syncephalastrum, Thamnidium and Zygorhynchus (Haritash and Kaushik 2009; Prenafeta-Boldú et al. 2006; Prince 2010). Biodegradation of hydrocarbons is also done by as some fungi from the Zygomycota Phylum: Absidia, Circinella, Cokeromyces, Cunninghamella, Gilbertella, Phycomyces, Rhizomucor, Rhizopus, Syncephalastrum, Thamnidium, Umbelopsis, and Zygorhynchus (Prince 2010). Alkanes are usually activated by terminal oxidation to the corresponding primary alcohol, which is further oxidized by alcohol and aldehyde dehydrogenases. The resulting fatty acids enter the β-oxidation pathway. Sub terminal oxidation has been detected in Penicillium. Several cytochrome P-450 isozymes are known to be responsible for the first steps in alkane assimilation by yeasts (Van-Beilen et al. 2003). There are two main types of fungal metabolism to degrade polyaromatic hydrocarbons, which are mediated by the non-ligninolytic and ligninolytic fungi (Figure 2.1). The first step in the metabolism of PAHs by non-ligninolytic fungi is the oxidation of the aromatic ring by a cytochrome P450 monoxygenase enzyme producing an arene oxide, subsequently, via a reaction catalyzed by an epoxide-hydrolase, the arene oxide is converted to a trans-dihydrodiol; additionally, phenol derivatives may be produced from arene oxides by non-enzymatic rearrangements, which can be substrates for subsequent reactions of sulfation, methylation or conjugation with glucose, xylose, or glucuronic acid (Bamforth and Singleton 2005). The ligninolytic fungy produce ligninolytic enzymes (peroxidases and laccases) which are secreted extracellularly, to oxidise organic matter (including lignine an aromatic polymer) by cometabolism, via a non-specific radical based reactions. Extracellular peroxidases working simultaneously with a complex array of secondary enzymes, such as laccases, P450 monooxygenases, and epoxide hydrolases that can result in the complete mineralization of hydrocarbons (Prenafeta-Boldú et al. 2006).

2.4.3 Bacteria Bacteria are the most studied organisms able to use hydrocarbons as a sole carbon and energy source. Over 180 hydrocarbon-degrading bacterial genera (listed in Table 2.2), are identified in 7 of the 24 major phyla, principally belonging to the Actinobacteria, the Bacteroidetes, the Firmicutes, and the Proteobacteria phyla (Prince 9 et al. 2010). Both aerobic and anaerobic degradation of alkanes can be performed by different bacteria. 2.4.3.1 Aerobic n-alkane degrading bacteria Two major pathways are recognized for the aerobic degradation of n-alkanes in bacteria illustrated in Figure 2.2: (1) the terminal alkane oxidation and the (2) subterminal alkane oxidation. In the first one there are two possible different first reactions: (1) the transformation of the alkane into a primary alcohol occurs by the action of cytochrome-P450-related enzymes or (2) the oxidation of the n-alkane to a primary alcohol by alkane hydroxylases (Van Beilen and Funhoff 2007). Alkane hydroxylases are integral membrane non-heme diiron monooxygenases of the AlkB-type, which allows a wide range of bacteria to grow on n-alkanes (C5 to C16). AlkB type enzymes are coupled with two electron transfer proteins, a dinuclear iron rubredoxin, and a mononuclear iron rubredoxin reductase channeling electrons from NADH to the active site of the alkane hydroxylase (Van-Beilen et al. 2003).

In the terminal oxidation pathway, after the initial oxidation of the n-alkane (either by cytochrome-P450 or alkane hydroxylase enzymes), the corresponding primary alcohol is subsequently oxidized (by an alcohol dehydrogenase) and converted into the corresponding aldehyde which then is transformed (by an aldehyde dehydrogenase) into the corresponding carboxylic acid and further the carboxylic acid serves as a substrate for acyl-CoA (Wentzel et al. 2007). In the subterminal oxidation pathway a subterminal alkane monooxygenase transforms the n-alkanes into secondary alcohols which is then converted (by an alcohol dehydrogenase) to the corresponding ketone and further is oxidized by a Baeyer-Villiger monooxygenase to an ester. The ester is subsequently hydrolyzed by an esterase to an alcohol and a fatty acid (Van-Beilen et al. 2003). The genes related to hydrocarbon degradation, can be located either in plasmids (like the OCT plasmid in P. putida GPo1) (Dinamarca et al. 2003) or in the bacterial chromosome (as in Rhodococcus sp. Q15) (Whyte et al. 1998). The most extensively studied alkane degradation genes are alkB, alkF, alkG, alkH, alkJ, alkK, alkL, alkK and alkS) found in Pseudomonas oleovorans (Smits et al. 2002). More than 60 AlkB homologues are known to date; they have been found in both Gram-positive and Gram-negative microorganisms and show high sequence diversity (Rojo 2009). Some bacteria 10 possessing genes homologous to alkB from Pseudomonas oleovorans are: Acinetobacter sp. Burkholderia cepacea, Acinetobacter calcoaceticus, Pseudomonas aeruginosa, Pseudomonas fluorsecens, Alcanivorax borkumensis, Mycobacterium tuberculosis, Rhodococcus erythropolis, Rhodococcus rhodochrous, Rhodococcus sp. Q15 and Prauserella rugosa (Van-Beilen et al. 2003; Wentzel et al. 2007; Whyte et al. 2002).

2.4.3.2 Anaerobic n-alkane degrading bacteria Anaerobic oxidation of n-alkanes has been reported under strictly anoxic conditions with pure cultures of sulfate-reducing and denitrifying bacteria, in these organisms, sulfate and nitrate serve as terminal electron acceptors (Wentzel et al. 2007). Also methanogenic bacterial communities have the ability to anaerobically degrade hydrocarbons (Widdel and Rabus 2001). The anaerobic initiation reactions known are surprisingly diverse compared to aerobic reactions, yet in general, two different reaction mechanisms for the activation of n-alkanes have been proposed: (1) n-alkane carboxylation and (2) the addition of n-alkane to fumarate as well as some methabolic pathways for the anaerobic degradation of alkanes are partially elucidated (Heider et al. 1998) For example, The pathway for sulfate-reducing Beta-proteobacteria strain Hxd3 which utilizes n-alkanes from C12 to C20, has been shown to carboxylate C16 at the C-3 position, subsequently eliminating the two subterminal carbon atoms and leading to C-even and C-odd fatty acids from C-odd and C-even n-alkane substrates, respectively, but this does not seem to be the norm in other strains (Wentzel et al. 2007). Some anaerobic alkane degraders are the denitrifying bacteria: Azoarcus sp. strain HxN1, Strain OcN1 (Beta-proteobacgeria), Strain HdN1 (Gamma-proteobacteria), the sulfate-reducing strains: Strain TD3, Strain Hxd3, Strain Pnd3, Strain AK01 (Delta-proteobacteria) and members of Methanosarcinales (Widdel and Rabus 2001).

2.4.3.3 Aerobic aromatic degrading bacteria Metabolism of aromatic hydrocarbons by bacteria can be either aerobic or anaerobic. The first step in the aerobic polyaromatic hydrocarbons catabolism mainly involves the action of dioxygenase or monoxygenase enzymes, which incorporate atoms of molecular oxygen into the aromatic nucleus of the hydrocarbons, resulting in the oxidation of the aromatic ring. The two hydroxyl groups may be positioned either ortho

(as in catechol and protocatechuate) or para to each other (as in gentisate and homogentisate), depending on the substituents on the original molecule then, the cis-dihydrodiols formed in this reaction are further oxidized, first to the aromatic dihydroxy compounds (catechols), and then through the ortho- or meta cleavage pathways. Further reactions produce precursors of tricarboxylic acid cycle (Chauhan et al. 2008). A typical example of the degradation of hydrocarbons is naphthalene (Figure 2.3), and its degradation was broadly characterized in Pseudomonas putida strain G7. This bacterial strain, posses the plasmid NAH7 were the genes encoding proteins required for the degradation of naphthalene are located (Larkin et al. 1999). The operon nahAaAbAcAdBFCED encodes the upper pathway (naphthalene conversion to salicylate), and the operon nahGTHINLOMKJ encodes the lower pathway (metabolism of salicylate via meta cleavage of catechol) (Hamme et al. 2003). nahAc encodes the Alpha-subunit of the iron sulfur protein, an essential component of naphthalene dioxygenase. This enzyme adds two oxygen atoms to naphthalene converting it to cis-naphthalene dihydrodiol -first step of upper pathway-). nahH encodes catechol 2,3-dioxygenase, which converts catechol into 2-hydroxymuconic-semialdehyde (second step in lower pathway). Homologous genes to the previously mentioned (such as ndoB homologous to nahAc and xylE homologous to nahH) have been detected in diverse bacterial strains (Habe and Omori 2003). Table 2.3 summarizes some examples. Some bacteria recognized by their aromatic degradation activities are: Pseudomonas putida, Pseudomonas aeruginosa, Pseudomonas stutzeri, Comomonas testosterone, Ralstonia sp., Burkholderia sp, Acalcaligens faecalis, Nocardioides sp. Mycobacterium sp., Sphingomonas paucimobilis (Habe and Omori 2003; Hamme et al. 2003; Larkin et al. 1999).

2.4.3.4 Anaerobic aromatic degrading bacteria The anaerobic degradation of aromatic hydrocarbons by bacteria has been reported using nitrate, ferrous and sulphate ions as electron acceptors (Widdel and Rabus 2001) Some bacteria able to degrade aromatic hydrocarbons in anoxic conditions are for example, the anoxygenic photoheterotrophic bacterium Blastochloris sulfoviridis ToP1, the denitrifying bacteria Azoarcus sp. strain EB1, Azoarcus sp. strain T, Azoarcus tolulyticus Td15, Azoarcus tolulyticus To14, Dechloromonas sp. strain JJ, 12

Dechloromonas sp. strain RCB, Pseudomonas sp. strain NAP-3, Strain M3, Strain mXyN1, Strain PbN1, Strain pCyN1, Strain pCyN2, Strain T3, Strain ToN1, Thauera aromatica K172, Thauera aromatica T1 and Vibrio sp. strain NAP-4. Also the Fe(III)-reducing bacteria Geobacter grbiciae TACP-2T, Geobacter grbiciae TACP-5, Geobacter metallireducens GS15 as well as the sulfate-reducing bacteria Desulfobacula toluolica To12, Desulfobacterium cetonicum, Strain mXyS1, Strain NaphS2, Strain oXyS1, and Strain PRTOL1.(Van Hamme et al. 2003). The mechanisms of anaerobic PAH degradation are still tentative, a proposed mechanism for the anaerobic degradation of naphthalene has the carboxylation of the aromatic ring to 2-naphthoic acid as the first step, to activate the aromatic ring prior to hydrolysis. Then the reduction of 2-naphthoic acid via a series of hydrogenation reactions results in decaclin-2-carboxylic acid which is subsequently converted to decahydro-2-naphthoic acid. Another possible pathway proposes an initial step consisting of a hydroxylation reaction to form a naphthol intermediate in sulfate-reducing conditions (Bamforth and Singleton 2005).

2.5 Microbial response to abiotic factors affecting hydrocarbon biodegradation Considering that the degradation of hydrocarbons is a direct consequence of the metabolism of living organisms it is understandable that every factor which may alter such metabolism will consequently affect the biodegradation of hydrocarbons. Some of the factors strongly driving or modifying the hydrocarbon biodegradation efficiency are: physical state, concentration and availability of hydrocarbons, environmental factors such as temperature, water availability, pH and salinity as well as the availability of oxygen and nutrients (Leahy and Colwell 1990).

2.5.1 Hydrocarbon bioavailability There is a difference between the equilibrium concentration in the aqueous phace and the actual concentration of the hydrocarbon in the bulk phase (Sikkema et al. 1995), which is dictated by the hydrocarbon hydrophobicity indicated by the logarithm of the octanol-water partitioning coefficient (log Kow) (Miller et al. 1985). It is broadly accepted that only molecules of hydrocarbons that are dissolved in the aqueous phase are available for intracellular metabolism. To counter this limitation some bacteria are able to produce biosurfactants to enhance the bioavailability hydrophobic compounds

(Amézcua-Vega et al. 2004). The special distribution of the hydrocarbons in the soil matrix also affects de availability of the contaminants; such limitation is sometimes overcome by bacteria with chemotactic behavior (Bhushan et al. 2000). When plants are present in the contaminated area they can help the microorgamisms to reach the hydrocarbons by transporting bacteria in the surface or their roots (Gerhardt et al. 2009). But also high concentrations of the hydrocarbons inhibit the microbial activity by toxic effects (Leahy and Colwell 1990) such as the accumulation of exogenous lipophilic compounds into the membrane lipid bilayers (Sikkema et al. 1995).

2.5.2 Temperature Environmental temperature affects the solubility of hydrocarbons in water, at higher temperatures higher solubility (Pereda et al. 2009) therefore their availability, but more importantly temperature changes have a broad effect in the microbial diversity and metabolism (Margesin and Schinner 2001). Microorganisms have different mechanisms to adapt themselves to various temperatures, for example microorganisms adapting to low temperatures may adapt by changing the structure of their enzymes (e. g. fewer residues of prolines or arginines, a decrease in hydrophobic residues coupled with an increase in polar residues and a decrease in the number of disulfide bonds) changes in their cell membrane fluidity (i.e. by converting saturated fatty acids into unsaturated fatty acids, preferential synthesis of short-chain fatty acids) the synthesis of cold-shock proteins, antifreeze proteins and the production of small compound which work as cryoprotectants (Chattopadhyay 2006).

2.5.3 Oxygen The concentration of oxygen limits the rate at which petroleum is biodegraded in soils, because aerobic degradation generally has higher rates than the anaerobic degradation. The oxygen availability is affected by the microbial oxygen consumption, the type of soil and the moisture levels, like in waterlogged soils (Leahy and Colwell 1990).

2.5.4 Nutrient limitation Another well studied limiting factor in the biodegradation of hydrocarbons are the nutrients, especially nitrogen and phosphorous, several studies report an increase in 14 the hydrocarbon degradation after the addition of nutrients to nutrient-limited soils (Børresen and Rike 2007; Ferguson et al. 2003; Walworth et al. 2007). But nitrogen limitation can potentially be reduced by biological nitrogen fixation which is done only by prokaryotes -Bacteria and Archaea- (Raymond et al., 2004) which can fix nitrogen

(reduction of N2 to NH3) thanks to the activity of their nitrogenase enzymatic complex, conformed by two proteins: the dinitrogenase α2β2 heterotetramer (α is codified by nifD and β by nifK genes), and the dinitrogenase reductase γ2 homodimer (codified by the nifH gene) (Zehr et al. 2003).

2.5.5 Salinity and pH In general it has been reported a decrease in the mineralization of hydrocarbons at high salinity environments, but recently the biodegradation of hydrocarbons by halophilic bacteria and archaea has been reported (Le Borgne et al. 2008). Another important factor for the biodegradation of hydrocarbons is pH, generally a neutral pH seems to be the best for the process, but degradation at high pH (Betancur-Galvis et al. 2006) and low pH (Dai et al. 2005) has been reported.

2.6 Bioremediation Bioremediation restores the environmental quality of hydrocarbon polluted areas, by taking advantages of the capacities of microorganisms to degrade hydrocarbons. It does so, counteracting the limiting factors above mentioned according to the necessities of the case. Biostimulation (addition of nutrients to stimulate the microbial activity), bioaugmentation (inoculation of the system with hydrocarbon-degrading microorganisms), bioventing and land farming (increases the oxygen availability by introducing air to the system), and phytoremediation (utilization of plants to clean up polluted sites) are some of the most commonly applied bioremediation techniques (Stegmann et al. 2001).

2.6.1 Biodegradation of hydrocarbons in cold environments Biodegradation of hydrocarbons in polar environments takes more time than it does in temperate environments because some factors involved in the process are temperature dependent. Three of the most important are a reduced solubility and bioavailability of hydrocarbons, a reduction in microbial activities and nutrient 15 limitations (Aislabie et al. 2006; Margesin and Schinner 2001). Still, significant oil biodegradation happens in cold environments at 5oC (Leahy and Colwell 1990; Margesin and Schinner 2001). A study done by Børresen and Rike where they added ammonium chloride to soil in order to get nitrogen concentrations ranging from 0 to 1000 mg NH4-N/kg soil, showed that the added nitrogen (at all concentrations) increased the total hexadecane mineralization compared to unfertilized soil. An increased salinity in soil caused by addition of NH4+ and Na+ extended the acclimation time but did not have an inhibitory effect on the total hexadecane mineralization at the end of the experiment. But the total hexadecane mineralization was reduced in soils with highest moisture content (20%) compared to soils with 10, 12 and 15% moisture presumably as a consequence of oxygen shortage (Børresen and Rike 2007). Examples of biodegradation increased by mitigating nutrient limitations in Polar Regions are numerous, such as: the bioremediation treatments used to clean up the contamination produced by the accident of the Exxon Valdez in Alaska (Taba k et al. 2005). At Old Casey Station in Antarctica, Ferguson and coworkers investigated the effects of nitrogen (and phosphorus) amendments and water limitation on microbial mineralization, and found that total 14C-octadecane mineralization increased with -1 increasing nutrient concentration peaking in the range 1000-1600 mg N kg soil. low nutrient concentrations rather than water were the main limiting factor for biodegradation of hydrocarbons (Ferguson et al. 2003). But the addition of nutrients must not be excessive, as was shown by the study done by Walworth and coworkers on soil collected from a petroleum-contaminated site on Macquarie Island, Australia, located in the sub-Antarctic. This soil contained ~5250 mg kg-1 of hydrocarbons and

20.9% H2O and presented inhibition in the hydrocarbon degradation due to nitrogen addition at 1200 mg N kg-1 and over. This soil was treated by adding nitrogen at rates of 0, 125, 250, 375, 500, and 625 mg nitrogen kg-1 of dry soil. The soil was incubated at -1 6 °C. Maximum O2 uptake was observed with the 125 and 250 mg nitrogen kg of soil application rates. Respiration was maximized when N was 604 mg nitrogen kg-1. Petroleum removal following incubation was highest in soil amended with 125 mg N kg-1 and was lowest in unfertilized soils or in soils receiving 250 mg N kg-1 or more (Walworth et al. 2007).

Studies done to Canadian Arctic soils generally show an increase in the biodegradation of hydrocarbons due to the addition of nutrients, for example: hydrocarbon-polluted soils from Alert (Canadian Forces station) and the Eureka (Canadian High Arctic weather station) have indigenous hydrocarbon-degrading microorganisms, which are actively cleaning up those sites. The addition of limiting nutrients to these soils stimulates the biodegradation efficiency (Margesin and Schinner 2001). A combination of tilling and fertilizers reduced 47.8% the TPH level in Eureka High Arctic soils after two years of summer treatments (Whyte et al. 1999; Whyte et al. 2001). Finding an adequate treatment for contaminated soils depends as much on the knowledge of the potential physicochemical limitations as on the knowledge of the microbial populations present in the soils, since they are the main ingredient of the bioremediation process. Hydrocarbon impacted soils have complex microbial communities that are studied to improve our understanding of hydrocarbon transformation on these environments (Powell et al. 2006), therefore several research studies have focused on the characterization of the microbial populations from polluted and pristine cold environments.

2.6.2 Bacterial communities of petroleum hydrocarbon-contaminated cold environments It is well recognized that changes to microbial communities in soils observed through community profiles are site dependent and vary due to hydrocarbon contamination (Hamme et al. 2003). Accordingly, hydrocarbon degradation efficiencies are different among different soils (Bundy et al. 2002; Juck et al. 2000), for example: Arctic soil samples (from Alert, and from Kuujjuaq) presented different community profiles. These profiles were modified due to contamination by jet or diesel fuels. Some soils had an increased diversity while others presented lower diversity compared to the non polluted soils. The predominant bacterial populations were closely related to high G+C Gram-positive bacteria, or to Proteobacteria. A large proportion of the high G+C Gram-positive bacteria had high homology to bacteria from the genera Arthrobacter (Bundy et al. 2002). Arctic sea-ice samples suffered a shift in their community composition after one year incubation in microcosms with crude oil a 1oC. Previous to the contamination the predominant population was bacteria from 17

Cytophaga-Flavobacterium group, Alpha-proteobacteria and Gamma-proteobacteria, after the contamination the predominant population narrowed to bacteria from Gamma-proteobacteria, mostly Marinobacter spp. Shewanella spp. and Pseudomonas spp (Juck et al. 2000) Hydrocarbons not only change the community profiles, but also the prevalence of genes involved on hydrocarbon metabolism. The prevalence of seven genotypes (alkM, alkB, alkB1, alkB2, xylE, ndoB, nidA) involved on the degradation of n-alkanes and aromatic hydrocarbons was assessed on contaminated and pristine soils from Alpine sites in Tyrol Australia. Genotypes from Gram-negative bacteria (aklB, xylE, ndoB, alkM) were detected in 50-75% of the contaminated soils and in 0 to 12.5% of the pristine soils. The genotypes of Gram-positive bacteria were found in similar percentages in both types of samples. But no correlation was found between the prevalence of the studied genotypes and the numbers of cultivable hydrocarbon-degrading bacteria (Gerdes et al. 2005). These results indicate that genes involved in the metabolism of hydrocarbons are often enriched in hydrocarbon contaminated soils. Even though nitrogen seems to be a limiting nutrient in Arctic soils, some studies have found a significant diversity of nitrogen fixing bacteria in these environments, for example Izquierdo and Nüsslein found that nifH from Alpha-proteobacteria (Rhodopseudomonas) Gamma-proteobacteria were predominant in Arctic soil agreagates, but also found nifH sequences were related to Delta-proteobacteria (Izquierdo and Nüsslein 2006). In another study, five strains isolated from enrichment cultures of contaminated Antarctic soils were able to use hydrocarbons as sole carbon source and were able to fix nitrogen but not simultaneously. These strains were closely related to Pseudomonas (3 strains), Azospirillum (1 strain) and Aquaspirillum (1 strain) bacteria. The Pseudomonas related strains fixed nitrogen at 4, 10 and 22oC, but the other two strains did not have nitrogenase activity below 10oC (Eckford et al. 2002).

2.7 Phytoremediation Phytoremediation is a biological technology that utilizes natural plant processes (and the processes of their associated microflora) to enhance natural degradation and removal of contaminants in contaminated soil or groundwater (Cunningham et al. 1996). 18

There are many advantages of phytoremediation, one of them being the cost. The estimated cost of land filling or incineration of a ton of soil is between $200–$1500, significantly higher than the $10–$50/ton estimate for rhizoremediation (Gerhardt et al. 2009). With increasing fuel costs, the disparity is certain to increase. Protocols are relatively easy to implement, and after initial site preparation and planting, the maintenance costs for phytoremediation and rhizoremediation are minimal. Additional benefits are for example that organic materials, nutrients and oxygen are added to soil via plant and microbial metabolic processes, therefore it improves the overall quality and texture of soil at remediated sites. Plants also mitigate soil erosion from both wind and water because they provide groundcover, and their roots help to stabilize soil. There is no size restriction for sites utilizing phytoremediation, and this strategy can be employed in any geographical area that can support plant growth (Cunningham and Ow 1996).

Plants are known to extract (phytoextraction), sequester (phytostabilisation), degrade (phytodegradation), evapotranspirate (phytovolatilization) and stimulate the degradation of organic contaminants in soil (rhizospheric degradation, rhizodegradation or rhizoremediation) (Wenzel 2009). The extent to which each of these processes contribute to the overall degradation and removal of hydrocarbons depends on the plant species (Siciliano and Germida 1998) and has not yet been fully explored. Sequestration and degradation of contaminants by plants has been recently studied in more detail, for example: The degradation of anthracene to the partial breakdown products anthrone, anthraquinone and hydroxyanthraquinone within the cortex cells of maize and wheat, was observed and the uptake and movement of phenanthrene was determined using two-photon excitation microscopy. Initially both compounds bound to the epidermis along the zone of elongation, passing through the epidermal cells to reach the cortex within the root hair, and branching zones of the root (Wild et al. 2005). According to the same study, the polyaromatic hydrocarbons remained in the root system entering the epidermis radially; however, once within the cortex cells this movement was dominated by slow lateral movement of both compounds toward the shoot.

The literature suggests that the degradation of petroleum hydrocarbons by microorganisms in the rhizosphere of plants is the primary loss mechanism for these

19 compounds (McGuinness and Dowling 2009). There are several rhizosphere interactions leading to what is called rhizoremediation most of them driven by rhizodeposition. Rhizodeposition can account for release of up to 40 percent of the total carbon fixed during plant photosynthesis which varies between plants, the age, the health of the plants as well as depends on environmental factors (Lynch and Whipps 1990). It generally contains sugars, organic acids, amino acids and phenolics, which can cause different interactions with the soil microorganisms i.e. (1) serve as carbon sources by microorganisms generally producing a 4 -100 fold increase in the microbial abundance (Kamath et al. 2004b), (2) act as surfactants increasing the availability of hydrocarbons (Read et al. 2003), (3) induce or repress catabolic enzymes, as was found by Kamath et al. (2004a) who did an experiment to investigate the expression of nahG, one of the genes responsible for naphthalene dioxygenase transcription and found that nahG was induced by some phenolic substrates that could be released by plants (i.e., salicylate, methyl salicylate, and acetyl salicylate) but root extracts (from hybrid poplars, willow, kou, milo, Osage orange, mulberry, and switch grass) decreased nahG expression at the individual cell level during naphthalene degradation assays.

On the other hand, treatments with root extracts caused significantly higher microbial growth and overall higher level of nahG expression by the resulting larger microbial population, increasing naphthalene degradation rates (4) Root exudates can also participate in the co-metabolism of contaminants (Rentz et al. 2005). The root itself provides changes in the water distribution, and O2 in the soil microenvironment, it also reaches large extensions of soil transporting microorganisms within the root-surface or the endorhizosphere (Hodge et al. 2009). Due to its potential, phytoremediation has increasingly been studied, for its application in extreme environments such as the Arctic and Antarctica, where other bioremediation technologies although efficient could become very costly (Filler et al. 2006).

2.7.1 Phytoremediation in cold Canadian environments Besides being tolerant to hydrocarbon contamination, being able to withstand environmental conditions is one of the major challenges that plants most overcome in order to phytoremediate contaminated soils in cold environments, but few reported

20 experiments screening plants with both capacities have been done. An early study found that Poa alpina L and Medicago sativa L were able to grow at 10°C in 2000 mg/kg of hyrdocarbons and there was a reduction of 95% of the contaminants after 14 weeks (Rogers et al. 1996). Germida and coworkers screened thirty-nine cold-tolerant plants native, or exotic and naturalized, in western Canada for their ability to survive in crude oil-contaminated soil, and only found twelve with phytoremediation potential, based on survival: Four naturalized grasses (i.e., Agropyron pectiniforme, Bromus inermis, Phleum pratense, and Poa pratensis), three naturalized legumes (i.e., Medicago sativa, Melilotus officinalis, and Trifolium repens), and two native legumes (i.e., Glycyrrhiza lepidota and Psoralea esculenta), two native forbs (i.e., Artemisia frigida and Potentilla pensylvanica), one native grass (i.e., Bromus ciliatus) (Robson et al. 2003). Studies assessing phytoremediation in subarctic regions show encouraging results for the potential application of phytoremediation in these regions, for example: Plant species native to northern latitudes such as Scots Pine (Pinus sylvestris), Poplar (Populus deltoides × Wettsteinii), a grass mixture (Red fescue, Festuca rubra; Smooth meadowgrass, Poa pratensis and Perennial ryegrass, Lolium perenne) as well as a legume mixture (White clover, Trifolium repens and Pea, Pisum sativum), were tested in soils from subarctic regions, contaminated with diesel and amended with NPK fertilizer, a compost extract or microbial enrichment culture. This resulted in more rapid diesel fuel disappearance in the legume treatment than in other plant treatments and soil amendments did not enhance diesel fuel removal significantly. Also, the presence of poplar and pine enhanced removal of diesel fuel, but removal under grass was similar to that with no vegetation. Grass roots accumulated diesel compounds (Palmroth et al. 2002). A growth chamber study was done by (Hamme et al. 2003) to determine the diesel removal efficiency of different plant treatments on a contaminated subarctic soil. The treatment with legumes (Trifolium repens and Pisum sativum) was the most effective, but the grasses treatment (Festuca rubra, Poa pratensis and Lolium perenne) was not different from the control bulk soil. Phytoremediation was assessed on a flare-pit soil from Saskatchewan, Canada, testing six plant species and combinations of them, the most significant results were that the 16S rRNA genes-DGGE analysis revealed that

21 alfalfa (Medicago sativa) had a dominant effect on the structure of rhizosphere microbial communities in mixed plant treatments, stimulating relative increases in specific Bacteroidetes and Proteobacteria populations. Alfalfa and mixes containing alfalfa supported 100 times more culturable polyaromatic hydrocarbon degraders than other treatments but exhibited only 10% TPH reduction. But the most efficient single plant treatment was with the grass creeping red fescue (Festuca rubra) which reduced by 50% TPHs (after 4.5 months) (Phillips et al. 2006). A two-year field trial was conducted at a weathered hydrocarbon flare-pit site in southeastern Saskatchewan, Canada. Three plants commonly used in phytoremediation mixes (tall wheat grass, Altai wild rye (AWR) and alfalfa), a mix of all plants, and non-planted controls showed significant differences in the degradation trends after the first growing season. AWR promoted greater than 50% TPH degradation while no cumulative degradation occurred in mixed plant or control treatments. AWR selectively recruited endophytic hexadecane degraders in response to high TPH concentration and then maintained these communities during times of environmental stress. AWR supported up to 100 times more endophytic hexadecane degraders than the other plants, but as time progressed, the TPH degradation increased in all treatments (Phillips et al. 2009). Since Arctic soils contain particular microbial populations and the plant-microorganism interactions are plant-species dependent, studies of microorganisms found in the rhizosphere of Arctic plants are necessary for the proper selection of phytoremediation treatments.

2.7.2 Bacterial populations from the rhizosphere of Canadian Arctic plants Palmorth (2002) compared the prevalence of genes related to hydrocarbon degradation (alkB, ndoB, xylE), and nitrotoluene degradation (ntdAa, ntnM), on soils contaminated either by diesel or trinitrotoluene, and vegetated by different mixtures of plants (one containing grasses and leguminous plants, and another containing only grasses). Their results indicated that the enrichment of catabolic genotypes was influenced by the type of contaminant in the soil, and by the plant-species. The selective pressure of plants on the bacterial populations was stronger in the root interior. An increase of the catabolic genotypes abundance in the Festuca arundinacea’s rhizosphere and the opposite effect on the rhizosphere of Trifolium hirtum was observed by (Siciliano et al. 2001). Siciliano (2003) detected a shift in the abundance and composition of the 22 microbial populations of soils polluted by petroleum hydrocarbons and PAH that were treated with a grass (annual ryeagrass), a legume (summer vetch) or a crucifer (white mustard), for example bacteria identified as Alcaligenes piechaudii, Flavobacterium johnsoniae and Pseudomonas spp. were more abundant in the root zones of mustard and vetch. In a two year field study assessing the impact of warming and fertilization on nitrogen-fixing microbial communities in the Canadian high Arctic it was determined that fertilization increased nitrogen fixation activity approximately a month after the application but posterior fertilization effects were not observed. Nitrogen fixation rates increased with a warmer climate (Deslippe et al. 2005). Their results also reveal that there is a complex relation between the presence of mosses, vascular plants, the diazotrophs and nitrogen fixation in the field. An annual succession in the microbial community composition occurred and the most common genotypes were associated to members of Actinobacteria (Actinomicetales), Alpha-proteobacteria (Rhodospiralles, Rhodobacteriales, Rhizobiales, Rhodobacter), Beta-proteobacteria (Rhosocyclales), Cyanobacteria (Nostoc), and Firmicutes (Bacilliales). A recent study (Deslippe and Egger 2006) about the nifH diversity from high Arctic soil samples associated to the woody perennial plants (Dryas integrifolia, Salix arctica, Cassiope tetragona) showed that, the majority of the nifH sequences were homologous to type I nitrogenases, and few were homologous to type IV group. Genes attributed to members of the Firmicutes (low G+C Gram-positive bacteria) were the most abundant in both rhizosphere and bulk soils. nifH homologous to Gamma-proteobacteria (Pseudomonas sp.), Alpha-proteobacteria (Rhizobium-type), and very few to Beta-proteobacteria (uncultivated organism) were found on different proportions retrieved from the different perennial plants. Twelve nifH clones appeared to represent a unique clade within the type I nitrogenases.

Table 2.1. Enzyme classes involved in the oxidation of alkanes adapted from (Van-Beilen and Funhoff 2007)

Enzyme class Organisms Substrate Range

Soluble methane monooxygenase Methylococcus, Methylosinus, C1–C8 (halogenated)- (sMMO) Methylocystis, Methylomonas, alkanes, alkenes, Methylocella cycloalkanes

Particulate methane monooxygenase Methylococcus, Methylosinus, C1–C5 (halogenated)- (pMMO) Methylocystis, Methylobacter, alkanes, alkenes Methylomonas, Methylomicrobium

AlkB-related alkane hydroxylases Acinetobacter, Alcanivorax, C5–C16 alkanes, fatty Burkholderia, Mycobacterium, acids, alkylbenzenes, Pseudomonas, Rhodococcus, cycloalkanes, etc.

Eukaryotic P450 (CYP52, Class II) Candida maltosa, Candida C10–C16 alkanes, fatty tropicalis, Yarrowia lipolytica acids

Bacterial P450 oxygenase systems Acinetobacter, Alcanivorax, C5–C16 alkanes, (CYP153, class I) Caulobacter, Mycobacterium, cyclo)-alkanes, Rhodococcus, Sphingomonas, alkylbenzenes, etc.

Dioxygenase Acinetobacter sp. M-1 C10–C30 alkanes

Table 2.2 Genera of cultured bacteria able to degrade petroleum hydrocarbons

Phylum Actinobacteria Phylum Chlamydiae Phylum Proteobacteria Class Beta Proteobacteria Class Gamma Proteobacteria Order Pseudomonadales Order Actinomycetales Order Verrucomicrobia Class Alpha Proteobacteria Order Burkholderiales Order Aeromonadales Acinetobacter Actinomyces ‘Methylacidiphilum Order Caulobacterales Achromobacter Aeromonas Alkanindiges Aeromicrobium Phylum Cyanobacteria Brevundimonas Acidovorax Order Alteromonadales Azotobacter Arthrobacter Order Chroococcales Order Kordiimonadales Alcaligenes Alteromonas Moraxella Brevibacterium Agmenellum Kordiimonas Alicycliphilus Marinobacter Pseudomonas Cellulomonas Aphanocapsa Order Rhizobiales Brachymonas Microbulbifer Order Thiotrichales Cellulosimicrobium Aphanothece Afipia Burkholderia Shewanella Cycloclasticus Citricoccus Coccochloris Agrobacterium Castellaniella Order Enterobacteriales Leucothrix Clavibacter Order Nostocales Beijerinckia Comamonas Enterobacter Order Vibrionales Corynebacterium Anabaena Blastochloris Cupriavidus Erwinia Vibrio Dietzia Nostoc Hyphomicrobium Hydrogenophaga Klebsiella Order Xanthomonadales Georgenia Order Oscillatoriales Methylobacterium Janthinobacterium Leclercia Alkanibacter Gordonia Dactylococcopsis Methylocapsa Leptothrix Pantoea Hydrocarboniphaga Isoptericola Halothece Methylocella Massilia Proteus Nevskia Janibacter Microcoleus Methylocystis Methylibium Rahnella Pseudoxanthomonas Kocuria Oscillatoria Methylosinus Pandoraea Serratia Singularimonas Kytococcus Phormidium Ochrobactrum Polaromonas Order Methylococcales Stenotrophomonas Leifsonia Plectonema Parvibaculum Ralstonia Clonothrix Xanthomonas Microbacterium Phylum Deinococcus-Thermus Rhizobium Rhodoferax Crenothrix Xylella Micrococcus Order Thermales Sinorhizobium Sphaerotilus Methylobacter Class Delta Proteobacteria Mycobacterium Thermus Xanthobacter Order Hydrogenophilales Methylocaldum Order Desulfobacterales Nocardia Phylum Firmicutes Order Rhodobacterales Thiobacillus Methylococcus Desulfatibacillum Nocardioides Order Bacillales Stappia Order Nitrosomonadales Methylohalobius Desulfatiferula Nocardiopsis Bacillus Tranquillimonas Spirillum Methylomonas Desulfobacterium Pseudonocardia Brevibacillus Tropicibacter Order Rhodocyclales Methylothermus Desulfobacula Rhodococcus Exiguobacterium Tropicimonas ‘Aromatoleum’ Methylomicrobium Desulfosarcina Smaragdicoccus Geobacillus Order Rhodospirillales Azoarcus Methylosarcina Desulfotignum Streptomyces Oceanobacillus Acidocella Dechloromonas Methylosoma Order Desulfovibrionales Terrabacter Paenibacillus Azospirillum Georgfuchsia Methylosphaera Desulfothermus Williamsia Planococcus Magnetospirillum Thauera Order Oceanospirillales Order Desulfuromonadales Order Rubrobacterales Planomicrobium ‘Oleomonas’ Alcanivorax Geobacter Thermoleophilum Staphylococcus Thalassospira Halomonas Pelobacter Phylum Bacteroidetes Order Lactobacillales Order Sphingomonadales Neptunomonas Order Syntrophobacterales Order Flavobacteriales Lactobacillus ‘Lutibacterium’ Oleiphilus Desulfoglaeba Chryseobacterium Order Clostridiales Novosphingobium Oleispira Flavobacterium Desulfotomaculum Porphyrobacter Salinicola Myroides Desulfosporosinus Sphingobium Thalassolituus Weeksella Peptococcus Sphingomonas Order Pasteurellales eosuana Sarcina Pasteurella Order Sphingobacteriales Cytophaga Pedobacter Adapted from (Prince et al. 2010).

Table 2.3 Bacterial genes related to the aerobic degradation of aromatic hydrocarbons Hydrocarbon (gene) encoded protein Location Strain

Naphthalene (upper (nahAa) Reductase, (nahAb) Ferredoxin, (nahAc) Iron sulfur protein large Plasmid Pseudomonas putida pathway), subunit, (nahAd) Iron sulfur protein small subunit, (nahB) cis-Naphthalene strains dihydrodiol dehydrogenase, (nahF), Salicyaldehyde dehydrogenase, (nahC) 1,2-Dihydroxynaphthalene oxygenase, (nahE) 2-Hydroxybenzalpyruvate aldolase, (nahD) 2-Hydroxychromene-2-carboxylate isomerase (nahG) Salicylate hydroxylase, (nahT) Chloroplast-type ferredoxin, (nahH) Catechol oxygenase, (nahI) 2-Hydroxymuconic semialdehyde Salicylate (lower dehydrogenase, (nahN) 2-Hydroxymuconic semialdehyde dehydrogenase, pathway) (nahL) 2-Oxo-4-pentenoate hydratase, (nahO) 4-Hydroxy-2-oxovalerate aldolase, (nahM)Acetaldehyde dehydrogenase, (nahK) 4-Oxalocrotonate decarboxylase, (nahJ) 2-Hydroxymuconate tautomerase, (nahR) Induced by salicylate

Naphthalene (ndoA, ndoB, ndoC) Naphthalene-dioxygenase Plasmid Pseudomonas putida homologous to NahAb, Nahc and Nahd listed above NCIB9816

Dibenzothiophene, (doxA, doxB, doxD) Naphthalene dioxygenase homologous to NahAb, Nahc Plasmid Pseudomonas sp. strain naphthalene and Nahd listed above C18 phenanthrene (doxE) cis-Naphthalene dihydrodiol dioxygenase, (doxF) Salicylaldehyde dehydrogenase, (doxG) 1,2-Dihydroxynaphthalene dioxygenase, (doxH) Isomerase, (doxI) Hydratase-aldolase, (doxJ) Isomerase

Naphthalene (nagAa) Ferredoxin reductase, (nagG) Subunit of salicylate 5-hydroxylase Plasmid Pseudomonas sp. strain with Rieske-type iron-sulfur centre, (nagH) Subunit of salicylate U2 5-hydroxylase, (nagAb) Ferredoxin (nagAc) Large dioxygenase subunit, (nagAd) Small dioxygenase subunit, (nagB) Naphthalene cis-dihydrodiol dehydrogenase, (nagF) Salicylaldehyde dehydrogenase

Naphthalene, (phnR) Regulatory, (phnS) Regulatory, (phnF) Aldehyde dehydrogenase, Plasmid Burkholderia sp. strain phenanthrene, (phnE) Hydratase-aldolase, (phnC) Extradiol dioxygenase, (phnD) RP007

Isomerase, (phnAc) Large dioxygenase subunit (Rieske-type [2Fe-2S], (phnAd) Small dioxygenase subunit, (phnB) Dihydrodiol dehydrogenase

Naphthalene, (pahAa) Ferredoxin reductase, (pahAb) Ferridoxin, (pahAc) Large subunit of Chromoso Pseudomonas putida phenanthrene, A variety iron-sulfur protein, (pahAd) Small subunit of iron-sulfur protein, (pahB) me OUS82 of homo-hetero-and cis-Dihydrodiol dehydrogenase, (pahC) Dioxygenase, (pahD) Isomerase, monocyclics converted (pahE) Hydratase-aldolase, (pahF) Dehydrogenase to phenols

Naphthalene (nahG) Salicylate 1-hydroxylase, (nahW) Salicylate 1-hydroxylase Chromoso Pseudomonas stutzeri 2-Methylnaphthalene me AN10

Phenanthrene (phdA) Alpha subunit of dioxygenase, (phdB) Beta subunit of dioxygenase, Chromoso Nocardiodes sp. strain (phdC) Ferredoxin, (phdD) Ferredoxin reductase, (phdK) me KP7 2-Carboxybenzaldehyde dehydrogenase

Naphthalene, toluene, (nidA) Naphthalene-inducible dioxygenase system, (nidB) Dioxygenase Chromoso Rhodococcus sp. strain indene small subunit, (nidC) cis-Dihydrodiol dehydrogenase, (nidD) Putative me 124 aldolase Anthracene, (nidD) Aldehyde dehydrogenase, (nidB) Small subunit of dioxygenase, (nidA) Chromoso Mycobacterium sp. strain Phenanthrene, Large subunit of dioxygenase me PYR-1 Fluoranthene, Pyrene, benzo [a]pyrene, 1-nitropyrene

Phenanthrene, (pbhA) Ring fission dioxygenase, (pbhB) Rieske-type ferridoxin subunit of Chromoso Sphingomonas anthracene, multicomponent dioxygenase, (pbhC) Hydratase-aldolase, (pbhD) Pyruvate me paucimobilis var. benzo[b]fluoranthene, phosphate dikinase EPA505 naphthalene, fluroanthene, pyrene, Intermediate catabolites Adapted from (Van Hamme et al. 2003).

Figure 2.1 The three main pathways for polycyclic aromatic hydrocarbon degradation by fungi and bacteria. Taken from (Bamforth and Singleton, 2005)

Figure 2.2. Pathways for the degradation of alkanes by terminal, sub- and biterminal oxidation. Terminal oxidation leads to the formation of fatty acids, which enter the -oxidation pathway. Alternatively, -hydroxylation by a fatty acid monooxygenase or alkane hydroxylase may take place, leading to dicarboxylic acids. Subterminal oxidation gives rise to secondary alcohols, which are oxidized to ketones. A Baeyer-Villiger, monooxygenase converts ketones to esters, which are subsequently cleaved by an esterase. Taken from (Van-Beilen et al., 2003)

Figure 2.3 Aerobic and anaerobic bacterial degradation pathways of naphthalene. Taken from (Chauhan et al., 2008)

CHAPTER 3. Screening Arctic plants for their hydrocarbon phytoremediation potential by analyzing their microbial communities.

Ofelia Ferrera-Rodriguez1, Charles W Greer2 and Lyle G. Whyte1 1Dept. of Natural Resource Sciences, McGill University, Montreal, Canada 2NRC-Biotechnology Research Institute, Montreal, Canada.

3.1 Abstract Human communities in Arctic regions rely on hydrocarbon fuels to perform essential activities, and as a consequence, hydrocarbon spills have polluted Arctic soils. Phytoremediation is a low cost bioremediation strategy, based on the use of plants and their rhizospheric microorganisms to treat polluted areas producing a favorable environmental impact. However, this approach has not yet been implemented on hydrocarbon-contaminated soils from high Arctic regions. Therefore the main goal of this study was to find phytoremediator plants suitable for the high Arctic regions. As a primary screening criterion the microbial populations influenced by Eriophorum scheuchzeri, Potentilla cf. rubricaulis, Oxyria digyna, Salix arctica and Puccinellia angustata, collected from diesel-impacted areas at Eureka, (Canadian high Arctic) were investigated. Bacterial community 16S rRNA-based DGGE fingerprints differed between samples, (bulk or vegetated) encompassing members from the following phyla: Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. Higher cultivable bacterial abundances (107 CFU g-1soil) were found in vegetated soils than non-vegetated soils (106 CFU g-1soil). Diesel-degrading bacteria were also isolated from all ten samples and belonged to the phyla Actinobacteria and Proteobacteria. Two Rhodococcus sp. isolates (1.3 and 3.3) from vegetated samples possessed alkB genes and mineralized hexadecane at both 5 and -5°C. A pristine (non-contaminated) soil vegetated by P. angustata contained the highest abundances of hydrocarbon-degrading bacteria (7 X 107 CFU g-1soil at 24°C and 1.3 X 107 CFU g-1soil at 5°C) with the associated diesel-impacted P. angustata vegetated sample representing the only sample where all three alkB, ndoB and xylE genes were detected. Also, the Pseudomonas sp. strain 34

5K-VPa (JF339990) was isolated from this sample and possessed both ndoB and xylE genes. These results indicate that P. angustata is a promising candidate for phytoremediation strategies for high Arctic soils polluted by hydrocarbons.

3.2 Introduction Research stations, military bases and almost all communities in Arctic regions rely on hydrocarbon fuels to perform essential activities such as heating, power generation and the operations of aircrafts and vehicles. Consequently, oil spills caused by shipping accidents, fuel tank or pipeline leaks, or inappropriate disposal practices have polluted soils and waters (Atlas and Cerniglia 1995; Burgherr 2007). Moreover, the risk of hydrocarbon contamination in northern areas has increased due to recent and projected human activities as a consequence of global warming (Carnaghan and Goody 2006), specifically increased tourism, the opening of the northwest passage (Midkhatovich and Fenton 2007; Weeks and Weller 1984) and exploitation of subsurface energy sources (Atlas 1975). Polar oil contaminated soils typically have indigenous hydrocarbon-degrading microbial populations with cultivable densities ranging from 105 to 106 CFU g-1 of soil (Greer et al. 2010; Saul et al. 2005; Whyte et al. 2001) that are typically related to bacteria from the genera Rhodococcus, Sphingomonas or Pseudomonas (Aislabie et al. 2006). Rates of hydrocarbon removal can vary greatly among Arctic soils (Mohn and Stewart 2000), most likely due to low nitrogen (~200µg g-1) and phosphorous (~0.3 µg g-1) content that limit hydrocarbon-mineralization and high sand content (>82 %) that increases the time required for hydrocarbon-mineralization (Mohn and Stewart 2000). In contrast high total carbon content (7.4% -15.3%) has been associated with higher mineralization rates (Mohn and Stewart 2000). Bioremediation strategies such as land farming, permeable reactive barriers, bioventing and biostimulation have been implemented to remediate waters and soils from Antarctica, Alaska and the Canadian Arctic. However, the more sophisticated the treatment, the most expensive it becomes, e.g. $8.7 USD per m3 for Land farming practices (1993), while modular biopile treatment being ten times more expensive (Filler et al. 2006). As a general estimate, up to 40% of a plant dry mater is released into rhizosphere, as exudates, lysates, mucilage, dead cell material secretions, and gases

35 including respiratory CO2. The amount and composition of the released material is plant species dependent (Atlas and Cerniglia 1995) consequently, some microbial populations are selected and enhanced in the rhizosphere (Read et al. 2003). Therefore, phytoremediation (bioremediation treatment based on the use of plants and microorganisms to remediate heavy metals and organic pollutants (Wenzel 2009)) is a promising treatment strategy due to its comparatively low cost ($10-$50 USD per ton of soil in 2006) (Gerhardt et al. 2009) and its favorable environmental impact (Badri et al. 2009). Phytoremediation was previously assessed in a subarctic climate on a flare pit soil in Saskatchewan Canada (Phillips et al. 2009). Over 50% of the total petroleum hydrocarbons (TPH) were removed by treating with Altai wild rye (Elymus angustus Trin) following one growing season while plant-based treatments including alfalfa (Medicago sativa L.) and tall wheat grass (Agropyron elongatum) removed over 40% of TPH following two growing seasons. Likewise, (Palmroth et al. 2002) utilized a phytoremediation-approach in subarctic soil from Tampere Finland. In a growth chamber a legume mixture of white clover (Trifolium repens) and pea (Pisum sativum) treatment reduced 68% of the diesel from the soil after a 30 day period. However, these successful bioremediation approaches have not yet been applied to hydrocarbon contaminated Arctic soils. Phytoremediation of soils contaminated by hydrocarbons relies heavily on rhizospheric degradation, also known as rhizodegradation, since a significant contribution to the removal of organic pollutants from soils (Lynch and Whipps 1990; Gerhardt et al. 2009) is due to microbial activity occurring in the proximity of the plant roots. Enhancement of the biodegrading microbial biomass, the hydrocarbon-degrading-genotypes and the hydrocarbon-mineralization activities are desired effects of phytoremediation plants since it commonly increases the removal efficiency of contaminants from soils (Killham and Yeomans 2001). As in any bioremediation treatment, phytoremediation efficiency is strongly influenced not only by plant species but also by the inherent abiotic factors such as environmental conditions (Atlas 1975; Palmroth et al. 2006; Williams 2002) and nutrient availability (Mohn and Stewart 2000). The tolerance to hydrocarbons has been determined for some cold-tolerant plant species (Robson et al. 2003; Rogers et al. 1996), nevertheless, there is

36 no information regarding phytoremediator plants directly inhabiting Arctic hydrocarbon polluted areas. Evidence suggests that plant species present on or near a target polluted area are the most appropriate for in situ phytoremediation (Lee et al. 2008; Robson et al. 2003; Sangabriel et al. 2006); therefore, it is essential to screen plant species already colonizing the high Arctic in order to find plant species suitable for phytoremediation treatments in these extreme environments. In the present research the bacterial communities of Arctic soils inhabited by five different plant species were analyzed with the aim of evaluating the effect of different plant species on the in situ microbial communities and to utilize this information as screening criteria to ascertain the plant species phytoremediation potential. To achieve our goal we determined: (1) microbial abundance, (2) bacterial community profiles, (3) detected hydrocarbon-degrading genes in the native soils, (4) isolated hydrocarbon-degrading bacteria and assessed their mineralization activities. In order to successfully implement phytoremediation treatments in Arctic soils contaminated by hydrocarbons it is necessary not only to identify plants with the capacity to increase hydrocarbon removal from soils but plants that are also adapted to the extreme Arctic conditions, including low temperatures (e.g. >0°C only occurs for 8 – 12 weeks per year) and high light periods (e.g. 24 hour day light during the summer months). To the best of our knowledge, microbial-based investigation into the potential of plants for phytoremediation based strategies in the extreme climates of the high Arctic has not yet been performed.

3.3 Materials and methods 3.3.1 Study site description and sampling strategy Samples were collect near the Eureka High Arctic weather station on Ellesmere Island, Nunavut, Canada (79° 58.800' N and 85° 55.800' W) which suffered a diesel fuel spill (37,000L) contaminating 3200 m3 of soil in 1990 (Whyte et al. 2001). The Eureka area is classified as a polar desert, with daily average temperatures ranging between -7.7°C and -38.4°C for more than nine months of the year (Canadian Climate Normal’s 1971-2000). During the summer months (June to August), there is little rainfall and the daily average temperatures are typically between +2.3°C and +5.7°C, rarely reaching a maximum of 20°C (Canadian Climate Normal’s 1971-2000). From the beginning of April to the end of August, there is almost continuous sunlight during the 37 day, and virtually no sunlight occurs between mid-October and late February. During the summer of 2004, five different plant species and the soil surrounding their roots (vegetated samples) plus three non-vegetated (Bulk) soil samples (~400 g of each, collected 1.5 m from the plant) were aseptically collected from a diesel-impacted area. An additional vegetated soil sample and bulk sample were collected from a non-contaminated (pristine) area located approximately ~500 m away from the spill area. In total, 6 vegetated soil samples as well as the 6 plants, plus 4 non-vegetated soil samples were collected and stored individually in sterile plastic bags, and transported frozen to the laboratory (McGill University, Montreal, Canada) and immediately stored at -20°C until further analysis.

3.3.2 Physicochemical soil characterization and plant identification Soil subsamples were oven-dried (60oC for 48 h) and finely-ground (< 1 mm mesh) prior to soil physicochemical analyses. Soil pH was measured in the solution of a 1:2 soil/water mixture after a 30 min settling period (Hendershot et al. 1993). Soil texture was determined with the hydrometer method described by Sheldrick and Wang (1993). Soil organic carbon and total nitrogen were determined by combustion at 900oC with a Carlo-Erba NC Soils Analyzer (Milan, Italy). Total petroleum hydrocarbons (C10–C50) were extracted from 5 g of soil and quantified by GC-MS essentially as described in Centre-d’expertise-en-analyse-environnementale-du-Québec (1997). The 6 plants were identified based on their morphological characteristics at the Department of Plant Science, McGill University (Montreal, Canada) by Dr. Laurie Consaul.

3.3.3 Microbial enumeration and statistical analysis Microscopic counts of microbial cells were undertaken using 5-(4,6-dichlorotriazinyl) aminofluorescein (DTAF) as described previously (Kepner Jr and Pratt 1994) adapted to a gram of soil subsample, counting cells from 102 and 103 dilutions (in tetrasodiun pyrophosphate buffer 0.1% w/v, pH 7) filtered onto 25-mm diameter black polycarbonate 0.22 μm pore filters (Osmonics Inc.) with an epifluorescence microscope (Eclipse E600W, Nikon). Average cell counts from twenty counted fields per filter are reported. Cultivable aerobic heterotrophic bacteria and cultivable aerobic hydrocarbon-degrading bacteria were enumerated by the spread plate

38 technique as described previously (Greer et al. 1993; Whyte et al. 2001). Five grams of soil from each sample were used for these analyses and Petri plates incubated at 24°C for 14 days or at 5°C for 28 days. R2A agar (Becton, Dickson and Co.) media was used to culture aerobic heterotrophic bacteria and a Rennie media (Rennie 1981) modified by (Rivera-Cruz et al. 2004) (with diesel as the only carbon source) used to culture aerobic diesel-degrading bacteria. Hydrocarbons were supplemented via the addition of a Whatman filter paper square (1.5 cm2) impregnated with diesel (~200 μL) onto the inner lid of the Petri plate. Averages of the colony forming units per gram of dry soil (CFU g-1 soil) are reported. The analysis of variance (ANOVA) of microscopic and spread plate counts was determined using SAS/STAT software, Version 9.2 of the SAS System for Microsoft Windows Copyright ©2008 SAS Institute Inc. Cary, NC, USA.

3.3.4 Culture independent microbial community analyses Total community DNA was extracted in duplicate from one gram of soil sample using a commercial kit (Mo Bio Laboratories Ultraclean Soil DNA Kit) following the manufacturer´s instructions with a reduction in the bead-beating time (2 min.). To overcome PCR inhibitors (such as hydrocarbons, humic and fulvic acids) from the DNA extracts, a polyvinylpolypyrrolidone (PVPP) spin column filtration step was used (Berthelet et al. 1996). Typical DNA concentrations ranged from 12-22 ng μL-1 in a total volume of 50 μL. To amplify fragments of the 16S rRNA gene by PCR the general bacterial primers 341F and 758R (MWG Operon, Huntsville Alabama, USA) (Table 3.1) were added to 25 μL mix PCR containing: 0.5 mM of primers 341F and 758R (MWG

Operon, Huntsville Alabama, USA), 1X PCR buffer and 1.5mM of MgCl2 (both supplied with the taq DNA polymerase), 0.2 mM of each deoxynucleoside triphosphate, 0.6 μL of 10 mg mL-1 bovine serum albumin, 1U of taq polymerase (Invitrogen Canada) and 2-10 ng of DNA. Thermocycling conditions consisted of an initial denaturing 96°C for 5 min, 10 cycles of 96°C for 1 min, 60°C for 45s (decreasing 1°C each cycle to 55°C), extension 72°C for 1.5 min; then 15 cycles of 96 °C for 1 min, 55°C for 45s, 72°C for 1.5 min and a final extension of 72°C for 5 min.

3.3.5 Denaturing Gradient Gel Electrophoresis (DGGE) 16S rRNA gene DGGE analyses were undertaken using universal bacterial 16S

39 rRNA gene primers, 341F with GC clamp 5’-GCGGGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGGCCT ACGGGAGGCAGCAG-3´ and 758R 5´-CTACCAGGGTATCTAATCC-3’ obtained from MWG, Canada (Table 3.1). PCR amplicons from five reactions with DNA extracts of each sample were combined and concentrated by ethanol precipitation (Sambrook and Russell 2001) and re-suspended in 20μL of water resulting in 50-100 ng μL-1 of DNA; approximately 1000 ng were loaded per lane into the polyacrylamide:bis-acrylamide gels prepared as described below. Gel casting and electrophoresis were performed using a DCode Universal Mutation Detection System (Bio-Rad, Mississauga, Ont., Canada) following the manufacturer’s instructions. An 8% polyacrylamide:bis-acrylamide (37.5:1) gel with 45-65% denaturant gradient was prepared (100% consisting of 7 M urea and 40% [vol/vol] deionized formamide) as described by (Whyte and Greer 2005). Electrophoresis conditions consisted of 60oC, 80V for 16h. Community profiles were visualized by staining with 1:10000 (v/v) Vistra Green (Amersham Pharmacia Biotech) for 35 min and destained with 1 X TAE buffer for 20 min, and observed on a Bio Imaging System (Syngene, Canada). DGGE gel images were analyzed with GelCompar II software using the neighbor joining algorithm to construct dendrograms. Bands of interest were excised from the DGGE gels and DNA eluted in 20-40 μL of water, incubated over-night at 5°C and then incubated at 64°C for 30 min and 4 μL of the resulting solution used as template for reamplification by PCR using the conditions previously described. PCR amplicons of re-amplified bands were sequenced at the Genome Québec Innovation Centre (McGill University) using the 3730XL DNA analyzer system (Applied Biosystems). DGGE was used to confirm the purity and position of the re-amplified bands. Calculation of the range-weighted richness (Rr) with the total number of bands and their distribution on each profile, as well as calculation of the functional organization (Fo) using the cumulative normalized number of bands (interpreted as the cumulative proportion of operational taxonomic units (OTUs) and the cumulative normalized intensities of bands (interpreted as the cumulative proportion of OTU abundances) was undertaken according to (Marzorati et al. 2008).

3.3.6 Phylogenetic analyses of gene sequences The Nucleotide Basic Local Alignment Search Tool (BLASTN) (Altschul et al. 40

1990) and the Sequence Match software (Cole et al. 2005) was used to identify the closest homologous sequences and compare the 16S rRNA gene sequences against the GenBank database and the Ribosomal Database Project (RDP), respectively. Sequences were aligned, phylogenetic analyses were undertaken and phylogenetic dendrograms were constructed using the Genious software package (Drummond et al. 2009).

3.3.7 PCR detection of alkB, ndoB and xylE genes Three catabolic hydrocarbon degradation genes were targeted during this study, including alkB (encoding the alkane hydroxylase), ndoB (encoding the -subunit of the iron sulfur protein of naphthalene dioxygenase), xylE (encoding the 2-3-catechol dioxygenase) (Luz et al. 2004; Whyte et al. 2001). The general bacterial primer pairs used to amplify fragments of these 3 genes are listed in Table 3.1. PCR reactions were prepared as described above and run using the following thermocycling conditions: initial denaturing 96°C for 5 min, 10 cycles of: 96°C for 1 min, annealing temperature: first 58°C for 1min, last 53°C for 1min, extension 72°C for 1.5 min; then 20 cycles of 94°C for 1 min, 55°C for 1min, extension 72°C for 1.5 min, and a final extension of 72°C for 10 min. PCR amplified DNA fragments were visualized by gel electrophoresis, in 0.8% agarose gels prepared with TAE buffer and ran at 85V for 45 min and visualized by ethidium bromide staining (Sambrook and Russell 2001).

3.3.8 Isolation and characterization of hydrocarbon-degrading bacteria Representative colonies isolated on Rennie modified Petri plates (from the 103 dilution used for the enumeration of hydrocarbon-degrading bacteria, as described above) were differentiated based on colony morphology, pigmentation and time of colony appearance. These colonies were then restreaked onto fresh Rennie modified and R2A supplemented with diesel and incubated at 24°C and 5°C to obtain pure colonies. Genomic DNA of each isolated strain was extracted via boiling lysis (Sambrook and Russell 2001), and a 400 bp fragment of the 16S rRNA gene was PCR amplified and sequenced as described previously. Genes related to the catabolism of hydrocarbons were subsequently screened by PCR for alkB, ndoB and xylE genes as described above. Resulting amplicons were sequenced at Genome Québec Innovation Centre (McGill

University) as described above and sequences searched against the GenBank databases via BLASTN (Altschul et al. 1990; Zhang et al. 2000) and TBLASTX (Altschul et al. 1997). The ability of selected strains to mineralize 14C-labelled hexadecane at 5 and -5°C was determined as described by (Whyte et al. 1996). Briefly, for each tested strain, three 30mL serum bottles (Supelco) containing 10mL of solid MSM supplemented with 50ppm of yeast extract and 5% NaCl were spiked with 100,000 dpm [1-14C] hexadecane (specific activity, 59mCi/mmol) (Sigma, St. Louis, MO), in addition to non-radioactive -1 hexadecane [100 μLg ]; and a CO2 trap consisting of a 1mL glass tube filled with 0.5mL 14 of 1M KOH. The amount of trapped CO2 was determined by liquid scintillation spectrometry (LS6500 Multipurpose Scintillation Counter, Beckman) by retrieving the KOH solution and mixing it with 20mL Scintiverse BD Cocktail (Fisher). Microcosms 14 were incubated in the dark at 5 and -5°C and CO2 evolution was monitored for a total of 93 days.

3.3.9 Nucleotide sequence accession numbers The 16S rRNA gene sequences obtained in this study were deposited in the GenBank database under the following accession numbers: HQ654251 to HQ654260 corresponding to DGGE bands, and JF339990 to JF340035 corresponding to Isolated strains. The alkB, ndoB and xylE gene sequences deposited in the GenBank database have the following accession numbers: JF520628 to JF520637.

3.4 Results 3.4.1 Plant species and physicochemical characteristics of soil samples Collected plants were identified as: Eriophorum scheuchzeri Hoppe (Arctic cotton) [V-Es], Potentilla cf. rubricaulis Lehm (Cinquefoil) [V-Pr], Oxyria digyna L. Hill (Mountain sorrel) [V-Od], Salix arctica Pall (Arctic willow) [V-Sa], Puccinellia angustata (R. Br.) Rand and Redfield (Narrow alkali grass) [V-Pa]. The specimen collected from the non-contaminated area was identified as Puccinellia angustata [PV-Pa]. The five species had very different morphologies as illustrated in Figure 3.1. Results of physicochemical analysis undertaken on each sample are summarized

42 in Table 3.2. In general, vegetated samples had higher percentages of total nitrogen and total carbon, as compared to non-vegetated bulk samples. C/N ratios were between 10 and 20 with the exception of V-Pr (1.36). Generally, vegetated samples tended to have more moisture (4% - 41.33%) than bulk samples (2% - 10%). Total petroleum hydrocarbons were below 100 mg Kg-1 in all tested samples.

3.4.2 Microbial enumeration Microscopic counts and cultivable aerobic bacteria counts are summarized in Table 3.3. Microscopic counts of vegetated samples were numerically higher and significantly different (p ≤0.05) from microscopic counts of bulk samples. The highest microscopic bacterial count (5.9 X 109 cell g-1) was detected in vegetated sample V-Es and the lowest (1.5 X 109 cell g-1) in bulk sample Bb. Cultivable heterotrophic and hydrocarbon-degrading bacteria incubated at 24°C were not significantly different (p ≤0.05) from bacteria incubated at 5°C. Cultivable heterotrophic and diesel-degrading bacteria were one or two orders of magnitude higher in vegetated samples than in bulk ones at both incubation temperatures, i.e. 24°C and 5°C. This difference was statistically significant (p ≤0.05) (Table 3.3). The pristine soil sample vegetated by Puccinellia angustata (VP-Pa) had statistically significant (p ≤0.05) higher populations of heterotrophic (1.8 X 108 CFU g-1 at 24°C and 6.9 X 107 CFU g-1 at 5°C) and diesel-degrading bacteria (6.9 X 107 CFU g-1 at 24°C and 1.3 X 107 CFU g-1 at 5°C).

3.4.3 Microbial Community Profiles

3.4.3.1 16S rRNA gene DGGE fingerprints of soil bacterial communities Figure 3.2A illustrates bacterial16S rRNA gene DGGE fingerprints of all samples. Figure 3.3 illustrates Rr and Fo for all samples. Rr values were above 10 and below 30, i.e. within the medium range-weighted richness. Fo values were obtained from interpolating in Pareto Lawrence curves at 20% of the cumulative proportion of species and the Fo values of all samples ranged from 37% and 46%. A dendrogram based on the bacterial DGGE profiles is presented in Figure 3.2B. A high significance (>98% boot strap values) of clustering occurred that formed three major branches; two branches grouped vegetated samples together and another branch clustered bulk samples. According to these analyses, bacterial communities from samples vegetated by P. 43 rubricaulis [V-Pr] were more similar to communities from E. scheuchzeri [V-Es] and O. digyna than to the E. scheuchzeri non-vegetated sample [Bb] bacterial communities., Communities from the pristine sample vegetated by P. angustata [VP-Pa] clustered with diesel contaminated sample vegetated by P. angustata [V-Pa] rather than with the pristine sample [BP], therefore the changes in the microbial community profiles caused by the plant were more evident than the changes caused by the diesel contamination .

3.4.3.2 Phylogeny of DGGE bands Phylogeny of excised DGGE bands, as indicated by arrows on Figure 3.2A, was determined and summarized in Figure 3.4. Sequences were related to the following phyla: Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria; specifically to genera: Belliella, Flavobacterium, Fusibacter, Arthrobacter, Rhodococcus, Sphingomonas and Pseudomonas. Closest matches to these genera were retrieved from: soil, rhizosphere, sediments or water substrates collected from cold or frozen environments and/or from environments polluted with organic compounds summarized in Table 3.4.

3.4.4 PCR detection of alkB, ndoB and xylE genes The prevalence of catabolic genes involved in bacterial biodegradative pathways, i.e. aliphatic (alkB), and aromatic hydrocarbons (ndoB and xylE) are summarized in Table 3.5. PCR-based amplification attempts were undertaken with direct DNA extracts as well as with dilutions (1:5, 1:10), and in triplicate. Generally, alkB (detected in 5 out of 10 samples) was more broadly distributed among samples than ndoB (detected in 2 out of 10 samples) and xylE (detected in 1 out of 10 samples). The contaminated sample vegetated by Puccinellia angustata (V-Pa) was the only sample where alkB, ndoB and xylE was detected in all replicated PCR amplifications; however, both alkB and ndoB were also detected in the pristine sample vegetated by P. angustata.

3.4.5 Isolation and characterization of hydrocarbon-degrading bacteria Forty-six putative diesel-degrading bacteria were isolated from modified-Rennie medium Petri plates (Figure 3.5). The modified-Renie media was used to isolate hydrocarbon-degrading bacteria able to fix dinitrogen, but no test was made to determine nitrogenase activity and the PCR detection of nifH was not accomplished. Genomic 44

DNA of 15 representative strains was screened for the presence of alkB, ndoB, and xylE genes. alkB was detected in eight isolates, including the genera Mycobacterium strains 1.1-VEs (JF340027) and 1.12-VEs (JF340026), Nocardia strain 3.2-VPr (JF340009), Rhodococcus strains 1.3-VEs (JF340004) and 3.3-VPr (JF340019), unclassified Intrasporangiaceae strain 7.31-VPa (JF340005), Leifsonia strain 1.5-VEs (JF340025), Arthrobacter strain 7.19-VPa (JF339999). Both ndoB and xylE genes were identified in strain 5K-VPa (JF339990), tentatively identified as Pseudomonas sp. The Rhodococcus sp. strains 1.3-VEs (JF340004) and 3.3-VPr (JF340019) were isolated from samples vegetated by E. scheuchzeri (V-Es) and Potentilla rubricaulis (V-Pr) respectively. The genomic DNA of theses samples was positive for the PCR amplification of alkB. Nucleotide sequences of the 16SRNA gene fragments of strains 1.3-VEs (JF340004) and 3.3-VPr (JF340019) were 99% identical to each other and both sequences were 95% identical to the DGGE band Bb14 retrieved from the bulk sample Bb (genomic DNA was negative for the PCR amplification of alkB). Arthrobacter strain 7.19-VPa (JF339999) was isolated from the sample vegetated by P. angustata (V-Pa) and the 16SRNA gene fragment of this strain was 97% identical to the sequence of the DGGE band VPa31 retrieved from the same sample.

The Pseudomonas sp. strain 5K-VPa (JF339990) (positive for ndoB and xylE ) was also isolated from sample (V-Pa) and had a16SRNA gene fragment 99% identical to the sequence of DGGE band VPr13 recovered from the sample vegetated by P. rubricaulis V-Pr sample (negative for the PCR amplification of ndoB and xylE). Table 3.6 shows the closest matches from the GeneBank to the alkB, ndoB and xylE sequences from the isolated strains. The alkB genes from strains 1.1-VEs (JF340027), 1.12-VEs (JF340026) and 7.31-VPa (JF340005) did not have a high similarity with previously sequenced alkB genes, which suggests that might encode novel alkane monooxygenases. The mineralization assay showed that Rhodococcus sp. 14 14 strain 1.3-VEs (JF340004) transformed 18.6% of [1- C] hexadecane into CO2 at 5°C and 1.8% at -5°C after 93 days of incubation; similarly, the Rhodococcus sp. strain 3.3-VPr (JF340019) mineralized 19.3% of [1-14C] hexadecane incubated at 5°C and 1.2% when incubated at -5°C for the same period of time.

3.5 Discussion In this study we examined the phytoremediation potential of five plant species inhabiting arctic soils by analyzing the soil microbial communities associated with them. The physicochemical characteristics of the soils were congruent with findings from previous studies of high Arctic soils (González et al. 2000), e.g. low carbon (<5%) and nitrogen (<1%) contents. Extreme environments such as the Arctic represent a challenging habitat for in situ organisms due to the prevailing low temperatures reducing biochemical reaction rates, affecting cell membrane fluidity and restricting nutrient availability yet both microorganisms and higher organisms (i.e. plants involved in this study) have adapted to survive in these conditions (Margesin et al. 2007a). Plants such as E. scheuchzeri, P. rubricaulis, O. digyna, S. arctica and P. angustata belong to the Cyperaceae, Rosaceae, Polygonaceae, Salicaceae and Poaceae families, respectively and are able to inhabit sandy or silty soils with low organic content in imperfectly drained areas, slopes and margins of ponds (Aiken et al. 2003). E. scheuchzeri, P. rubricaulis, O. digyna, S. arctica and P. angustata are widely distributed in the Canadian Arctic Archipelago including Baffin, Devon, Ellesmere, Axel Heiberg, Parry, Cornwallis, Banks, and Victoria Islands (Aiken et al. 2003).

Plants from these five species inhabited diesel-impacted soils in the vicinity of the Eureka weather station on Ellesmere Island. The capacity of these plants to grow in soils with low oxygen exchange may be one of the characteristics allowing them to grow on hydrocarbon contaminated oily soils. In this initial study, plants and their associated soils were chosen for a primary screening process, with the aim of identifying plants suitable for phytoremediation treatments in Arctic contaminated soils. Previous studies support the fact that the presence of plants modifies soil microbial populations in relation to: (1) relative abundances (Rivera-Cruz et al. 2004), community structure (Siciliano et al. 2003), (3) relative abundance of catabolic genes (Da Silva et al. 2006), (4) hydrocarbon degradation activities (Kim et al. 2006; Lee et al. 2008).

In this study, samples were collected 14 years after a diesel contamination event and were preserved frozen for months prior to laboratory analyses therefore, results obtained during this study reveal a temporal rhizospheric effect over soil microbial

46 community and/or a residual effect of a diesel contamination event. In the present research, the numbers of cultivable heterotrophic bacteria (107) from all samples were two orders of magnitude lower than the numbers of direct microscopic counts (109), but quantification by both methods revealed greater numbers of microorganisms present in vegetated samples compared to non-vegetated ones indicating that the plants stimulated microbial communities strongly and permanently enough to produce a significant increase in microbial abundance detectable even after the plants were not present anymore (at the time of the analyses). There was no significant difference between viable plate counts at 24°C or 5°C indicating that the cultivable heterotrophic bacteria were primarily psychrotolerant rather than psychrophilic, similar to previous studies on Arctic soil communities (Mannisto and Haggblom 2006; Whyte et al. 2001; Whyte et al. 1996). Plant presence not only modified bacterial numbers but also modified the community structure as shown by dendrogram analyses of DGGE banding patterns. Comparing the number, position, and intensity of DGGE bands, soils were more similar when vegetated by different species rather than between a vegetated soil and its respective non-vegetated soil. A higher or lower complexity between the bacterial communities of vegetated samples compared to non-vegetated ones was not detected.

Community profiles of samples from environments with similar characteristics to our samples had Rr values (Marzorati et al. 2008), by which an oil-contaminated soil had an Rr value of 8.1 while a garden soil had an Rr of 220, legume rhizosphere had an Rr of 78 and an arctic sea ice sample had an Rr of 26. In this study Arctic soils whether pristine, diesel contaminated, vegetated or not-vegetated, had Rr values ranging between 10 and 30, therefore neither the presence of plants nor hydrocarbons appeared to modify the Rr of the Arctic soils studied. The phylogeny of 16S rRNA gene fragments further supports Rr and Fo estimations since four different phyla (Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria,) were detected by sequencing only ten DGGE bands that were randomly selected from all ten samples. Furthermore the Fo values (37-48) of vegetated and non-vegetated samples indicate that microbial communities have an adequate distribution of dominant and resilient microorganisms where 20% of the total diversity corresponds to a range of 37% to 48% of the total bacteria in the soil. This kind of functional organization allows the microbial populations to overcome adverse 47 conditions like drastic environmental changes or contamination (Fernandez et al. 2000).

The community composition detected in this study was congruent with results from studies of cold or polluted environments. Closest matches to band BP18 (HQ654259) belonged to the genera Belliella, including the type strain Belliella baltica isolated from surface water in the central Baltic sea (Brettar et al. 2004) and a bacterium from an Hungarian soda lake (FM179649). Closest matches to band VOd15 (HQ654254) were from the Flavobacterium genus, including the psychrophilic type strain Flavobacterium limicola which is able to degrade organic polymers and was isolated from freshwater sediments (Tamaki et al. 2003), and Flavobacterium strains isolated from agricultural soil in China (FJ614250) and Arctic sea ice (Brinkmeyer et al. 2003). Band VEs2 (HQ654251) was related to bacteria from the genus Fusibacter, including an anaerobic thiosulfate-reducing strain from an oil-producing well (Ravot et al. 1999) and oil sands tailings (EU517558).

Closest matches to band VPa31 were from the genus Arthrobacter, including Arthrobacter mysorens (Arthrobacter psychrophenolicus) isolated from an alpine ice cave (AJ617482), and a strain of Arthrobacter isolated from soil associated with ectomycorrhizal hyphae (GU300617). Band Bb14 (HQ654260) was closely related to bacteria from the genus Rhodococcus, e.g. the carbendazim-degrading type strain Rhodococcus quingshengii (Jing-Liang et al. 2006) and the psychrotrophic plant-growth-promoting rhizobacterum: Rhodococcus erythropolis (EF690428) (Trivedi et al. 2007) and a sequence detected in a permafrost and ground ice core near Eureka in the Canadian high Arctic (Steven et al. 2008). Band VSa37 (HQ654258) clustered well with two environmental unclassified sequences, one from Antarctic terrestrial habitats (Yergeau et al. 2007) and another from a waste site (GQ263141).

Closest matches for band Ba11 (HQ654252) were from the Sphingomonas genus, including the type strain, Sphingomonas haloaromaticamans, a subarctic chlorophenol-degrading bacterium (Nohynek et al. 1996), a sequence from a hydrocarbon contaminated soil (AM935082), and a sequence obtained from glacier ice of the Northern Schneeferner, Germany (Simon et al. 2009). Closely related sequences of band VOd16 (HQ654255) belonged to the Sphingomonas, including Sphingomonas 48 aquatilis (Lee et al. 2001), and sequences from a grass prairie (Elshahed et al. 2008) and agricultural soil (EU202868). The closest type strain related to band VPa32 (HQ654257) was Thaurea terpenica a mesophilic denitrifying bacterium able to use menthol, linalool, and eucalyptol as carbon sources (Foss and Harder 1998) but it was also related to unclassified environmental sequences, one from biofilms of polluted rivers (Brummer et al. 2003) and another from an Alpine lake (AJ005817). Closest matches to band VPr13 (HQ654253) belonged to the Pseudomonas, including the psychrophilic type strain Pseudomonas antarctica (Reddy et al. 2004), and the phenol and cresol degrading bacterium Pseudomonas fluorescens isolated from a river polluted with phenolic compounds (Heinaru et al. 2000). The results of the present work are not an exhaustive description of the bacterial diversity or community structure from this environment but are an insight to the bacterial community structure and composition of Eureka high Arctic soils influenced by different plant species affected by diesel contamination.

The potential to biodegrade hydrocarbons in these soil samples was determined by both culture-independent and culture-dependent methods. The gene encoding an alkane hydroxylase (alkB) was detected in four of six vegetated samples but only in one of four non-vegetated samples. Moreover, genes associated with the synthesis of naphthalene dioxygenase (ndoB) and 2-3-catechol dioxygenase (xylE) were only amplified in samples vegetated by P. angustata [V6-Pa, V7-Pa]. The fact that genes associated with alkane oxidation were detected more frequently than genes related to aromatic oxidation could be due to the diesel composition (~ 75% alkanes and 25% aromatic hydrocarbons) or simply the failure to amplify a gene is not conclusive evidence of its absence but may be present below the detection limit. Previous studies have also found that the prevalence of various genes involved in the catabolism of hydrocarbons is modified by the presence of plants, and it can either increase or decrease depending on the plant species (Siciliano et al. 2003). Also, the prevalence of alkB and ndoB was found to increase in contaminated soils as compared to pristine Eureka soils (Whyte et al. 2002b) and the proportion of alkB containing microorganisms was correlated to the concentration of n-alkanes in a contaminated Antarctic soil (Powell et al. 2006).

The number of bacteria forming colonies on culturing media is a parameter with ecological relevance even though it does not represent the real number of active bacteria in the original environment (Nichols 2007). Based on the plate count method, cultivable diesel-degrading bacteria were ~ 10 fold greater in vegetated (107) than in non-vegetated (106) soils. Pristine non-vegetated samples had the lowest values, being one order of magnitude lower (105) than the rest of the bulk samples (106). Among vegetated samples the sample vegetated by P. angustata had the highest values of diesel-degrading bacteria (7.0 ± 0.13 X 107 at 24°C and 1.3 ± 0.11 X 107 at 5°C). Other studies show that hydrocarbon contamination (Aislabie, et al., 2001), plant species presence (Liste and Prutz 2006; Rivera-Cruz et al. 2004) and nutrient addition (Margesin et al. 2007b), increase the abundance of hydrocarbon-degrading bacteria in soils. Isolated characterized hydrocarbon-degrading bacteria were mainly classified into the Actinobacteria and Proteobacteria phyla. It seems that high G+C content bacteria are principally responsible for the biodegradation of alkanes since alkB was only detected in Actinobacteria strains and their alkB nucleotide and amino acid sequences were closely related to alkB sequences from Rhodococcus sp. and Nocardioides sp. strains. These results may indicate that Actinobacteria are the predominant n-alkane degraders in the studied soils which is consistent with a previous study (Whyte et al. 2002b) which showed a higher prevalence of alkB genes from Rhodococcus spp. than Pseudomonas putida in high Arctic diesel contaminated soils.

The capacity of Rhodococcus sp. strains 1.3-VEs (JF340004) and 3.3-VPr (JF340019) to mineralize hexadecane at low (5°C) and subzero (-5°C) temperatures is evidence that there may be biodegradation activity in the Arctic during periods when the soil temperature is below zero, although the biodegradation may be happening at a ten times lower rate. According to the plate counts the hydrocarbon-degrading bacteria were more abundant in vegetated soils than in bulk soils therefore the biodegradation of hydrocarbons in vegetated soils may continue at higher rates than in bulk soils. Proteobacteria seem to be mainly responsible for the biodegradation of aromatic hydrocarbons since xylE and ndoB were only detected in a Pseudomonas strain and the nucleotide and amino acid sequences of these genes were closely related to sequences of the pNAH20 plasmid, from Pseudomonas fluorescens strain PC20 (Merimaa et al. 2006). 50

These results may be biased not only by the random selection of strains tested for the presence of alkB, ndoB and xylE but also by both the culturing media which could be selective for the growth of Actinobacteria and Proteobacteria and also by the “general” primers used, which could be more efficient amplifying sequences from bacteria closely related to the reference strains mainly Proteobacteria and Actinobacteria. A nucleotide comparison of the alkB gene (AF388181) from the positive strain Rhodoccocus Q15 (Whyte et al. 2002a) against the alkB sequences from the isolated strains, showed between 71 to 99% similarity indicating that the detected strains were certainly from the environment and not the result of cross-contamination from the positive control. A nucleotide comparison of the ndoB gene (M23914) from the positive control strain Pseudomonas putida 17484 (Kurkela et al. 1988) against the ndoB fragment from the 5K-VPa (JF339990) strain showed a 100% but the xylE gene (M65205) of the positive control strain Pseudomonas putida 33015 (Benjamin et al. 1991) showed 78% similarity with the xylE fragment from strain 5K-VPa (JF339990). According to these differences, the sequenced genes were from the isolated strains and not from cross-contamination with the type strains. The bulk sample Bb, was negative for the PCR amplification of alkB, but the 16S rRNA-DGGE band isolated from it (Bb14) was 95% similar to the 16S rRNA gene fragment from the (alkB positive) hexadecane degraders 1.3-VEs (JF340004) and 3.3-VPr (JF340019). Even though Bb14 (HQ654260) was abundant and similar to 1.3-VEs (JF340004) and 3.3-VPr (JF340019), it may not share their alkB genes furthermore the alkB gene in this strains may be in a plasmid and its copy number may have increased in the vegetated samples (but this remains untested). A similar case is the one of the 16S rRNA-DGGE band VPr13 (HQ654253) isolated from vegetated sample V-Pr (negative for PCR amplification of ndoB and xylE), which was 99% similar to strain 5K-VPa (JF339990) (positive for the PCR amplification of ndoB and xylE genes).

3.6 Conclusion All plant species included in the study, i.e. E. scheuchzeri, P. rubricaulis, O. digyna, S. arctica and P. angustata not only modified bacterial abundance but also the bacterial community structure of high Arctic soils. However, samples vegetated by P. angustata had the highest abundance of hydrocarbon-degrading bacteria and the highest detection of genes encoding hydrocarbon oxidizing enzymes from all samples. Therefore, 51 results from the present study, indicated that P. angustata has the greatest potential for phytoremediation treatments in high Arctic hydrocarbon contaminated areas. Our research group is further investigating the effect of P. angustata on the microbial communities of soils from disparate high Arctic soil biotopes and the rates of hydrocarbon removal from high Arctic soils.

3.7 Acknowledgements Authors of this paper want to thank Dr. Laurie Consaul (Department of Plant Science, McGill University, Macdonald Campus) for the identification of the plant species, Dr. Joann Whalen (Department of Soil Science, McGill University, Macdonald Campus) for the physicochemical analyses of soil samples, M. Sc. Diane Labbe (NRC-Biotechnology Research Institute, Montreal, Canada) for the determination of TPH and Dr. Blaire Stevens, Dr. Thomas Daniel Niederberger, Dr. David Meek and Marianne Claire-Louise Michelle Poilly, for their important feedback to the present research work. The present work would not be possible without the funding provided by el Consejo Nacional de Ciencia y Tecnología de México (CONACYT, México) for the first author’s scientific education.

Table 3.1 Primers and reference strains for PCR analysis Fragment size Reference Gene Reference strain Primer sequence (5’ 3’) (bp) 341(F) CCTACGGGAGGCAGCAG 16SrRNA Escherichia coli PTC 303 417 Juck et al. 2000 758(R) CTACCAGGGTATCTAATCC 341(FGC) GCGGGCGGGGCGGGGGCACGGGGG 16SrRNA GCGCGGCGGGCGGGGCGGGGGCCT with GC Escherichia coli PTC 303 - Juck et al. 2000 ACGGGAGGCAGCAG clamp

758(R) CTACCAGGGTATCTAATCC alkB(F) CIGIICACGAIITIGGICACAAGAAGG alkB Rhodococcus sp. Q15 550 Whyte et al. 2002b alkB(R) IICGITGITGATCIIIGTGICGCTGIAG ndoB(F) CACTCATGATAGCCTGATTCCTGCCCC CGGCG ndoB Pseudomonas putida 17484 642 Whyte et al. 1996 ndoB(R) CCGTCCCACAACACACCCATGCCGCT GCCG xylE(F) GTGCAGCTGCGTGTACTGGACATGAG CAAG Whyte et al. 1996 xylE Pseudomonas putida 33015 834

xylE(R)GCCCAGCTGGTCGGTGGTCCA GGTCACCGG A=adenine; guanine; cytosine; thymine; I=inosine.

Table 3.2 Characteristics of soil samples collected in the summer of 2004 Sample ID Sample characteristics Total N % Total C % C/N Moisture % V-Es Soil contaminated with diesel, vegetated by Eriophorum scheuchzeri 0.36 3.91 10.86 41.33 drainage pond V-Pr Soil contaminated with diesel, vegetated by Potentilla cf. rubricaulis Lehm 0.74 1.01 1.36 6.67 V-Od Soil contaminated with diesel, vegetated by Oxyria digyna L. Hill 0.18 2.12 12.09 23.33 VP-Pa Soil vegetated by Puccinellia angustata “pristine” 0.18 2.67 15.22 4.00 V-Pa Soil contaminated with diesel, vegetated by P. angustata 0.10 1.95 19.92 8.00 V-Sa Soil contaminated with diesel, vegetated by Salix arctica 0.13 1.73 13.19 8.67 Ba Bulk soil from slope, contaminated with diesel 0.06 0.66 11.36 3.33 Bb Bulk soil, contaminated with diesel close to Potentilla rubricaulis 0.03 0.66 20.70 2.00 Bc Bulk soil, contaminated with diesel close to Salix arctica 0.05 0.57 11.39 2.67 BP Bulk soil “pristine” 0.14 2.31 16.73 10.00 Total Petroleum Hydrocarbons were below 100 mg Kg-1 in all samples.

Table 3.3 Microbial Enumeration Microscopic 24°C 5°C Sample ID DTAF Heterotrophic Diesel- degraders Heterotrophic Diesel- degraders (107 cell g-1) (107 CFU g-1soil) (107 CFU g-1soil) (107 CFU g-1soil) (107 CFU g-1soil) V-Es 590 ± 90a 6.6 ± 0.39bb 1.8 ±0.14c 5.7 ±0.77b 0.96 ±0.01ab V-Pr 280 ± 30e 4.6 ± 0.75c 1.8 ±0.05c 2.4 ±0.27d 1.1 ±0.09a V-Od 320 ± 64d 7.2 ± 0.46b 3.6 ±0.2b 2.7 ±0.47c 0.74 ±0.02b VP-Pa 430 ± 96b 18 ± 0.19a 7.0 ±0.13a 6.9 ±0.01a 1.3 ±0.11a V-Pa 380 ± 30c 2.6 ± 0.13d 1.5 ±0.11c 2.6 ±0.13c 1.1 ±0.14a V-Sa 310 ± 79d 1.1 ± 0.09e 0.27 ±0.02d 0.15 ±0.04d 0.06 ±0.001d Ba 190 ± 63f 0.27 ± 0.01fg 0.14 ±0.03de 1.5 ±0.11d 0.04 ±0.006de Bb 150 ± 63g 0.25 ± 0.01fg 0.09 ±0.01e 0.23 ±0.10d 0.02 ± 0.01de Bc 160 ± 71g 0.8 ± 0.03ef 0.20 ±0.01d 0.5 ±0.02d 0.2 ±0.02c BP 180 ± 77fg 0.09 ± 0.01g 0.06 ±0.01e 0.05 ±0.007d 0.02 ±0.008e Data are presented as means (n = 6) plus/minus S.D. For each column, means with super indices of different letters are significantly different at p ≤0.05; and means shearing super indices of the same letters are not significantly different according to Tukey test results. CFU g-1 = Colony Forming Units per gram of dry soil.

Table 3.4 Nucleotide sequence similarity of 16S rRNA gene-DGGE bands Band Sample Closest sequence match Similarity No (accession Environment of match sequence (accession number) %(nt/nt) number) VEs2 T Fusibacter paucivorans strain SEBR 4211 86 (353/410) Anaerobic, thiosulfate-reducing bacterium 1 V-Es (HQ654251) (AF050099); Fusibacter (genus) from an oil-producing well Ba11 uncultured Kaistobacter sp. strain AMPA9 Pilot-scale bioremediation process of a 2 Ba 100 (313/313) (HQ654252) (AM935082); Sphingomonas (genus) hydrocarbon-contaminated soil p-cresol-degrading bacteria isolated from VPr13 Pseudomonas fluorescens strain PC17 99 (337/338) 3 V-Pr river water continuously polluted with (HQ654253) (AY538263); Pseudomonas (genus) phenolic compounds VOd15 TFlavobacterium limicola strain ST-82 *82 (248/302) Organic-polymer-degrading bacterium 4 V-Od (HQ654254) (AB075230); Flavobacterium (genus) isolated from freshwater sediments VOd16 Uncultured bacterium clone FFCH14994 99 (311/313) Tallgrass prairie 5 V-Od (HQ654255) (EU133435); Sphingomonas (genus) VPa31 Arthrobacter sp. strain KFC-94 (EF459530); 99 (307/309) Soil sample from Kafni Glacier in the 6 V-Pa (HQ654256) Arthrobacter (genus) Himalayas VPa32 Uncultured bacterium clone A50Sp-15 99 (336/338) 7 V-Pa Alpine lakes (HQ654257) (AJ965911); Beta-proteobacteria (class) VSa37 Unidentified bacterium clone CN-1_SL2_G08 8 V-Sa 88 (482/543) Antarctic terrestrial habitats (HQ654258) (EF219906); Actinobacteria (class) BP18 Uncultured bacterium clone LL141-1G11 9 PB 98 (451/459) Feedlot (HQ654259) (FJ674389); Cyclobacteriaceae (family) Bb14 Uncultured bacterium clone Eur3BacAL.33 97 (450/460) Permafrost/ground ice core profile from the 10 Bb (HQ654260) (EU218663);Rhodococcus (genus) Canadian high Arctic *Due to the low similarity to the best match from the GeneBank, the band VOd15 could be a unique sequence.

Table 3.5 PCR amplification of bacterial genes involved in the catabolism of hydrocarbons Sample ID alkB ndoB xylE V-Es + + + ------V-Pr + + + ------V-Od ------VP-Pa + + + + + + - - - V-Pa + + + + + + + + + V-Sa ------Ba ------Bb ------Bc + + + ------BP ------(+) Indicates positive amplification, (-) indicates no amplification (three reactions per sample)

Table 3.6 Closest matches for the alkB, ndoB and xylE genes from the isolated strains. Closest match from GeneBank Environment of origin Positive for alkB, ndoB or Hydrocarbon degradation related Similarity from GeneBank gene Strain xylE Strain clasification gene sequence (accession Gene base d on

number) accession 16S rDNA gene

% %

number (nt/nt) (a/a)

alkB ndoB xylE 1.1-VEs JF520628 Mycobacterium + alkane rubredoxin-dependent 84 86 Hydrocarbon (JF340027) monooxygenase of clone (425/502) (142/164) contaminated Gerrman alkB21mpn_ingol (GU184266) soils 1.3-VEs JF520629 Rhodococcus + alkB gene fragment of Rhodococcus 93 92 Antarctic (JF340004) sp. strain 11/8p (DQ376002) (434/462) (142/153) 1.5-VEs JF520630 Leifstonia + alkB gene fragment of Rhodococcus 95 94 Antarctic (JF340017) sp. strain 11/8p (DQ376002) (440/461) (144/153) 1.12-VEs JF520631 Mycobacterium + alkane rubredoxin-dependent 85 87 Hydrocarbon (JF340026) monooxygenase of clone (422/496) (142/163) contaminated Gerrman alkB21mpn_ingol (GU184266) soils 3.2-VPr JF520632 Nocardia + putative alkane hydroxylase from 90 95 German grassland soil (JF340009) strain alkW34 (DQ287995) (411/454) (148/155) 3.3-VPr JF520633 Rhodococcus + alkB gene from Rhodococcus sp. 93 98 Shorelines after the (JF340019) strain H1 (FJ435353) (432/461) (150/153) Prestige oil-spill in Spain 7.19-VPa JF520634 Arthrobacter + hydroxylase-rubredoxin of 93 97 Alkane-utilizing (JF339999) Nocardioides sp. strain CF8 (451/480) (156/153) bacterium (AF350429) 7.31-VPa JF520635 *Intrasporangiaceae + putative alkane monooxygenase from 86 89 (115/128) German grassland soil (JF340005) clone alkW2-3 (DQ288027) (343/398) 5K-VPa JF520636 Pseudomonas + fragment from the plasmid pNAH20 100 100 River polluted with (JF339990) (AY887963.3) (578/578) (192/192) phenolic compounds JF520637 + fragment from the plasmid pNAH20 100 100 River polluted with (AY887963.3) (743/743) (247/247) phenolic compounds *unclassified Intrasporangiaceae

Figure 3.1 Plants and soils collected from Eureka during 2004: A) Eriophorum scheuchzeri, B) Salix arctica, C) Oxyria digyna, D) Puccinellia angustata, E) Sampling of vegetated soil (a), and non-vegetated (Bulk) soil (b), F) Potentilla rubricaulis.

Figure 3.2 Analysis of PCR-amplified 16SrRNA gene fragments from Eureka vegetated and non-vegetated soils. A) Image of DGGE gel (45%-65% denaturant gradient); labels at the top refer to samples: [V-Es] Eriophorum scheuchzeri,[V-Pr]

Potentilla rubricaulis, [V-Od] Oxyria digyna, [VP-Pa] Puccinellia angustata (pristine),

[V-Pa] Puccinellia angustata (polluted) and [V-Sa] Salix arctica, [Ba] bulk from slope,

[Bb] bulk close to [V-Pr], [Bc] bulk close to [V-Sa], [BP] bulk pristine; Excised bands:

1)VEs2 (HQ654251), 2) Ba11 (HQ654252), 3) VPr13 (HQ654253), 4) VOd15

(HQ654254), 5) VOd16 (HQ654255), 6) VPa31 (HQ654256), 7) VPa32 (HQ654257),

8) VSa37 (HQ654258), 9) BP18 (HQ654259), 10) Bb14 (HQ654260).

B) Dendrogram analysis of DGGE image (constructed with Gelcompare II using the

Neighbor-Joining method, the scale shows distances among profiles based on differences).

Es Pr Od Pa Sa

- - - - -

V Ba V V Bc VP V V BP Bb

1 4

6 3 7 8 2 9

B Bc BP Bb V-Od V-Es V-Pr Ba V-Sa VP-Pa V-Pa

Figure 3.3 Analyses of carrying capacity and functionality of microbial communities from Eureka samples calculated form bands formed on a DGGE gel by PCR-amplified 16SrRNA gene fragments. Samples: [V-Es] Eriophorum scheuchzeri,[V-Pr] Potentilla rubricaulis, [V-Od] Oxyria digyna, [VP-Pa] Puccinellia angustata (pristine), [V-Pa] Puccinellia angustata (polluted) and [V-Sa] Salix arctica,

[Ba] bulk from slope, [Bb] bulk adjacent to [V-Pr], [Bc] bulk adjacent to [V-Sa], [BP] bulk pristine.

Figure 3.4 Phylogenetic relationships of 16S rRNA gene fragments excised from

DGGE gel from Eureka samples. Bootstrap values ≥50% (1000 replicates) are indicated at the nodes. Band sequences from this study are indicated in bold; their names refer to the sample of origin. Sequences from GenBank database are identified with the accession numbers. The scale bar represents the expected number of changes per nucleotide position.

Figure 3.5 Phylotype composition of hydrocarbon-degrading isolates from Eureka samples. Determined by sequencing a fragment of the 16S rRNA gene (100% corresponds to 46 strains isolated from modified-Rennie media Petri plates).

unclassified Mycobacterium Micrococcineae 7% Nocardia 4% 3%

Rhodococcus 14% Clavibacter Arthrobacter 3% 36% Leifsonia 4% Microbacterium 4% unclassified unclassified Promicromonospora 7% Alphaproteobacteria Intrasporangiaceae Sanguibacter 9% 7% 11% Polaromonas 9%

80.0 Variovorax 70.0 9% Pseudomonas 60.0 73% 50.0 40.0 76.1 % 30.0 20.0 10.0 23.9 % 0.0 Actinobacteria Proteobacteria

Connecting Text (Connecting Chapter 3 to Chapter 4)

The previous chapter summarized the findings of comparing the microbial communities from soils vegetated by Eriophorum scheuchzeri, Potentilla cf. rubricaulis, Oxyria digyna, Salix arctica and Puccinellia angustata. According to those findings, it seems that P. angustata stimulates hydrocarbon-degrading bacterial populations. The experiments summarized in the following chapter compared different soils vegetated by P. angustata aiming to confirm or refute the findings from chapter 3.

CHAPTER 4. Characterization of microbial communities from high Arctic soils impacted by weathered diesel contamination and vegetated by Puccinellia angustata

Ofelia Ferrera-Rodriguez1, Charles W Greer2 and Lyle G. Whyte1 1Dept. of Natural Resource Sciences, McGill University, Montreal, Canada 2NRC-Biotechnology Research Institute, Montreal, Canada.

4.1 Abstract The success of phytoremediation at high Arctic latitudes has not yet been assessed. The efficacy of phytoremediation is influenced by environmental factors and plant-microorganism interactions; therefore, knowledge pertaining to the microbial populations inhabiting polluted Arctic soils, is necessary for assessing phytoremediation potential. The microbial populations of soils vegetated by Puccinellia angustata, commonly inhabiting the Arctic, known as alkali grass, were studied; Samples were collected from three sites in Eureka, NU (Canada): two of the sites were contaminated by a diesel spill in 1990; one was previously bioremediated (Br) but not the other (NBr) and the third site was pristine (Pr). At the time of analysis (15 years after the spill), diesel concentrations were < 100 mg·Kg-1, yet, in both vegetated and bulk Br soils diesel-degrading bacterial populations were high (~ 106 CFU g-1dry soil) and there was a common occurrence of alkane hydroxylase (alkB), naphthalene dioxygenase (ndoB) and catechol dioxygenase (xylE) genes; moreover the cumulative mineralization of hexadecane (~20% in 80 days) and naphthalene (~60% in 60 days) was the highest from all three sampling sites. Due to P. angustata, the hydrocarbon-degrading bacterial abundances significantly increased, one order of magnitude, over the abundances in bulk soil samples and hexadecane mineralization increased more than 10% in 80 days, at NBr and Pr vegetated samples; however, only the vegetated samples from NBr had an increase in the naphthalene mineralization (~28% in 60 days) over bulk soil samples and the ndoB gene was more frequently detected. The differences in 16S rRNA gene DGGE bacterial community profiles were apparent between sampling sites, 16S rRNA gene DGGE bands from the bacterial genera: Gemmatimonas, Yeosuana, Hydrogenophaga, Subsaxibacter, Sphingomonas, Lysobacter, Williamsia, Winogradskyella, Arthrobacter 70 and Rhodococcus retrieved from the diesel contaminated samples; Arthrobacter sp. was more frequently detected in vegetated than in bulk samples. These results describe, the microbial communities of different high Arctic soils vegetated by Puccinellia angustata for the first time comparing different soils; the findings do not wholly describe the microbial communities from vegetated soils, nor provide all the necessary evidence to apply P. angustata in phytoremediation treatments, but suggest a positive influence from P. angustata on hydrocarbon-degrading microbial communities from polluted high Arctic soils.

4.2 Introduction Petroleum hydrocarbon contamination is a major hazard to ecosystems throughout remote as well as populated regions in the world. The activities related to oil extraction, transportation, fractionation, refinement, distribution and storage all possess risks to life in the surrounding environment. In Canada, the Canadian Environmental Science Technology Centre reported 742 tanker spills (minimum 1,000 barrels in size) in its database from 1974 to 2001 (http://www.etc-cte.ec.gc.ca). Approximately 61 oil and natural gas fields have been discovered in the Arctic regions of Russia, Alaska, Canada, and Norway and including 11 unexploited oil fields in Canada´s Northwest Territories (http://www.eia.doe.gov/oiaf/analysispaper/arctic/index.html). Moreover, ~13% of the world’s undiscovered oil may be in the Arctic Circle (Gautier, et al., 2009). The extremely cold regions have a reduced variety of applicable and efficient clean up technologies (Mohn & Stewart, 2000) due to the high cost, geographic isolation, and environment limitations (ie very cold temperatures). Therefore, researchers are investigating suitable cleanup treatments for challenging environments such as the high Arctic. Bioremediation and, in particular, biostimulation, has been successfully applied in cold regions (Margesin & Schinner, 2001, Filler, et al., 2006, Yang, et al., 2009) with nutrient additions (N, P) generally enhancing the intrinsic microbial capacity to transform and remove contaminants. Phytoremediation is a desirable strategy since it can be ten times less expensive (approximately $10-50/ton) than other clean up strategies (Gerhardt, et al., 2009). Its application protocols are relatively easy to implement, require relatively low maintenance after the initial set up, are 71 environmentally friendly because they reduce the soil erosion providing ground cover and also increase soil microbial biomass. Phytoremediation relies heavily on the dynamic rhizospheric interactions between plants and soil microorganisms (Cunningham, et al., 1996). These interactions are mediated by molecular communication and can be both plant-microorganism specific as well as non-specific (Badri, et al., 2009), on which the most recognized chemical signaling (during phytoremediation processes) is the plant production of allelopathic compounds inducing the microbial degradation of structurally similar pollutants (Wenzel, 2009). Phytoremediation in subarctic soils contaminated with petroleum hydrocarbons has been successfully implemented to some extent (Palmroth, et al., 2002; Phillips, et al., 2009) but phytoremediation has never been attempted to reduce hydrocarbon contamination from high Arctic soils. Puccinellia angustata is a hexaploid (Consaul, et al., 2010) monocot known as “alkali grass” and is the most commonly collected grass in the North American Arctic; it is also found in Greenland, Russia and Scandinavia. Puccinellia species are known to be early colonizers of alkaline and anthropogenic disturbed areas and are salt tolerant (Aiken, et al., 1996, Aiken, et al., 2003). In an initial study (Chapter 3), Puccinellia was found growing on a weathered diesel impacted area at Eureka, Ellesmere Island in the Canadian high Arctic; Puccinellia-vegetated soils possessed the highest abundance of hydrocarbon-degrading microbial populations and highest occurrence of hydrocarbon-degrading genotypes (alkB, ndoB and xylE) from the 5 different Arctic plant species studied, indicating that P. angustata had the greatest phytoremediation potential of all 5 plants species. Therefore, in the present study, we further characterized microbial populations from high Arctic bulk and Puccinellia-vegetated soils from three sampling points (one pristine and two impacted by a diesel spill during 1990). We determined the microbial abundance, the bacterial community fingerprints and composition, the prevalence of hydrocarbon-degrading catabolic genes, and hydrocarbon mineralization activities, aiming to detect variations in the microbial communities due to P. angustata’s effect considering the soil differences related to the sampling sites. Consequently to determine whether P. angustata would have potential to be used in phytoremediation treatments on hydrocarbon contaminated high Arctic soils.

4.3 Materials and methods

4.3.1 Sampling strategy Samples were collected near by the Eureka High Arctic weather station in Ellesmere Island, Nunavut, Canada (79° 58.800' N and 85° 55.800' W) from a site which suffered a diesel fuel spill (37,000L) in 1990 that contaminated 3200 m3 of soil (Whyte, et al., 2001). The three sampling points described in this study are indicated in Figure 4.1. Sampling site Br was contaminated by the diesel spill and was part of a bioremediation treatment (fertilizer, water amendments) from 2002 to 2005. Sampling site NBr was located on a slope downhill from the diesel spill and was contaminated by the migration of hydrocarbons. Sampling site Pr was located from a non-impacted area ~ 500 m upstream from the diesel spill. Triplicate samples of vegetated and non-vegetated soils were taken from each sampling site (Figure 4.1C). Vegetated samples (400 g) consisted of plants of P. angustata as well as the soil surrounding their roots and were stored in sterile Fisherbrand bags; bulk samples (400 g) were collected from plant-free spots in the ground from the same site area. Samples were transported on ice during transportation to the laboratory (McGill University, Montreal, Canada) and immediately stored at -20°C until further analyses.

4.3.2 Physicochemical soil characterization Soil subsamples were oven-dried (60°C for 48 h) and finely-ground (< 1 mm mesh) prior to soil physicochemical analyses. Soil pH was measured in the solution of a 1:2 soil/water mixture after a 30 min settling period (Hendershot, et al., 1993). Soil texture was determined with the hydrometer method described by Sheldrick and Wang (1993). Soil organic carbon and total nitrogen were determined by combustion at 900°C with a Carlo-Erba NC Soils Analyzer (Milan, Italy). Total Petroleum hydrocarbons (TPH) C10–C50 were extracted from 5 g of soil and quantified by GC-MS as described in Centre-d’expertise-en-analyse-environnementale-du-Québec (1997).

4.3.3 Microbial enumeration In order to estimate the abundance of microorganisms present in the soil samples, microscopic counts of microbial cells were done using 5-(4,6-dichlorotriazinyl)

73 aminofluorescein (DTAF) essentially as described by Kepner Jr and Pratt (1994) but adapted to 1 gram of soil, counting cells from 102 and 103 dilutions filtered onto 25-mm diameter black polycarbonate 0.22 μm pore filters (Osmonics Inc.) with an epifluorescence microscope (Eclipse E600W, Nikon). An average cell count from twenty counted fields per filter is reported. Estimates of culturable aerobic heterotrophic bacteria and culturable aerobic hydrocarbon-degrading bacteria are broadly accepted as indicators of the viability and the hydrocarbon-degradation potential of microbial communities therefore, these parameters were determined by the spread plate method. Serial dilutions were prepared by mixing 5 g of each soil sample with 15 mL of 0.1% w/v cold sodium pyrophosphate (Na4P2O7 10·H2O, pH 7.0) in sterile glass tubes (25 X 150 mm) containing 2.5 g of 3-mm diameter glass beads (Fisher Scientific) and vortexed for 2 min; then serial 1:10 dilutions were prepared in cold 0.1% sodium pyrophosphate and 0.1 mL of selected dilutions were spread onto triplicate Petri plates containing the appropriate media. R2A agar (Becton, Dickson and Co.) was used to culture aerobic heterotrophic bacteria. A minimal salts medium (MSM-50YE) (Greer, et al., 1993) was used to culture aerobic diesel-degrading bacteria of the samples collected during 2005. The MSM solid media was supplemented with hydrocarbons by adding ~200 μL of diesel onto a filter paper square (1.5 cm2) and placing it on the inner lid of the Petri plate. Petri plates were incubated inverted either at 24°C for 14 days or at 5°C for 28 days and an average of the colony forming units per gram or dry soil (CFU g-1 soil) were calculated.

4.3.4 Culture independent microbial community analyses 4.3.4.1 Microbial community DNA extraction Total DNA was extracted by duplicate from one gram of soil per sample via the Mo Bio Laboratories Ultraclean Soil DNA Kit, following manufacturer´s instructions only reducing the bead-beating time to two minutes in addition to the alternative lysis protocol steps. To eliminate PCR inhibitors, such as hydrocarbons, humic and fulvic acids, from the DNA extracts, a polyvinylpolypyrrolidone (PVPP) spin column filtration step was included (Berthelet, et al., 1996) yielding 50 μL of polymerase chain reaction (PCR) amplifiable DNA; DNA extracts typically ranged from 12-22 ng μL-1 as quantified with the Nano Drop Spectrophotometer ND-1000. 74

4.3.4.2 Polymerase chain reaction of bacterial 16S rRNA genes for Denaturing Gradient Gel Electrophoresis (DGGE) The PCR amplification of partial 16S rRNA genes was undertaken using the general primers for Bacteria 341F with GC clamp 5’-GCGGGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGGCCT ACGGGAGGCAGCAG-3´ and 758R 5´-CTACCAGGGTATCTAATCC-3’ (MWG Operon, Huntsville Alabama, USA) added to 25 μL mix PCR containing 0.5 mM of primers 341F and 758R (MWG Operon, Huntsville Alabama, USA), 1X PCR buffer and 1.5mM of MgCl2 (both supplied with the Taq DNA polymerase), 0.2 mM of each deoxynucleoside triphosphate, 0.6 μL of 10 mg mL-1 bovine serum albumin, 1U of Taq polymerase (Invitrogen Canada) and 2-10 ng of soil DNA extract. PCR’s were performed either in a Techne TC-312 or a Techne Touchgene gradient thermocycler using the pfollowing conditions: initial denaturing 96°C for 5 min, 10 cycles of 96°C for 1 min, 60°C for 45s (and touchdown of 1°C each cycle until reaching 55°C), extension 72°C for 1.5 min; then 15 cycles of 96 °C for 1 min, 55°C for 45s, 72°C for 1.5 min and a final extension of 72°C for 5 min.

4.3.4.3 Analysis of the DGGE profiles

PCR products from a total of five PCRs from community DNA extracts of each sample were concentrated by ethanol precipitation (Sambrook & Russell, 2001) and re-suspended in 20μL of water resulting in 50-100 ng μL-1 of DNA. Approximately 1000 ng of sample DNA was loaded onto each lane of the DGGE gel. the polyacrylamide:bis-acrylamide DGGE gels were prepared as described below. DGGE gel casting and electrophoresis were performed using a DCode Universal Mutation Detection System (Bio-Rad, Mississauga, Ont., Canada) following the manufacturer’s instructions. An 8% polyacrylamide:bis-acrylamide (37.5:1) gel with 45-65% denaturant gradient was prepared (100% consisted of 7 M urea and 40% [vol/vol] deionized formamide). The electrophoresis conditions were: 60oC, 80V for 16h. Community profiles in the gel were visualized by staining with 1:10000 (v/v) Vistra Green (Amersham Pharmacia Biotech) for 35 min and destained with 1 X TAE buffer for 20 min, then observed on a Bio Imaging System (Syngene, Canada). DGGE gel images

75 were analysed with GelCompar II software. Neighbor-Joining algorithm was used to construct dendrograms. Band numbers (N), intensity and distribution in the DGGE gel were used to estimate the range-weighted richness (Rr) as well as the Functional organization (Fo). Fo was estimated by calculating the cumulative proportion of operational taxonomic units (OTUs) and the cumulative proportion of OTU abundances and constructing Pareto-Lawrence curves following Marzorati´s procedure (Marzorati, et al., 2008).

4.3.4.4 Reamplification of 16S rRNA gene DGGE bands Bands of interest were excised from DGGE gels and DNA was eluted in 20-40 μL of water, incubated over-night at 5°C then incubated at 64°C for 30 min and 4 μL of the resulting solution was used to provide template DNA for reamplification by PCR using the conditions previously described. PCR products of re-amplified bands were ligated into the pGEM-T Easy vector (Promega) following manufacturer’s instructions (Labbe, et al., 2007). Plasmid DNA was used to transform Escherichia coli strain DH5α (Invitrogen) with the providers transformation protocol, transformants were then identified using blue/white screening on Luria–Bertani plates containing 100 mgmL-1 ampicillin (Fisher Biotech) and 80 μgmL-1 of 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (X-gal; Fisher Biotech). Plasmid DNA from candidate colonies was purified by boiling lysis (Sambrook & Russell, 2001) and used as template DNA for re-amplification of cloned 16S rRNA genes. Amplification of clones was performed using primers T7 and Sp6 targeting the pGEM-T plasmid (Promega, 1996). PCR reaction mixtures were performed as previously described and the thermocycling conditions used were: initial denaturing of 5 min at 96°C for min; 25 cycles of denaturing at 96°C for 1min, annealing at 55°C for 1.5 min, followed by 72°C for 1 min, and a final extension of 72°C for 5 min (Steven, et al., 2007). PCR products were sent for sequencing to the Genome Québec Innovation Centre (McGill University) using the 3730XL DNA analyser system (Applied Biosystems). 4.3.4.5 Phylogenetic analyses of 16S rRNA gene sequences The Nucleotide Basic Local Alignment Search Tool (BLASTN) (Altschul, et al., 1990) was used to compare the 16S rRNA gene sequences against the GenBank database. The Sequence Match software (Cole, et al., 2005) was used to compare sequences 76 against the sequences of the Ribosomal Database Project (RDP). Both methods were used to identify the closest homologous sequences stored in the respective databases. Subsequently alignments and a phylogenetic tree was constructed with the Genious software (Drummond AJ, et al., 2009).

4.3.5 Nucleotide sequence accession numbers. The 16S rRNA gene sequences obtained in this study were deposited in the GenBank database under the accession numbers JF729929 to JF729969.

4.3.6 Detection of genes related to the catabolism of hydrocarbons by polymerase chain reaction To determine the presence of bacteria with the capacity to metabolize linear or aromatic hydrocarbons, three catabolic genes linked to hydrocarbon degradation were targeted during this study: alkB encoding the alkane hydroxylase, amplified using primers alkB(F) 5’-CIGIICACGAIITIGGICACAAGAAGG-3’ and alkB(R) 5’–IICGITGITGATCIIIGTGICGCTGIAG-3’; ndoB encoding the -subunit of the iron sulphur protein of naphthalene dioxygenase, amplified with primers ndoB (F) 5’-CACT CATGATAGCCTGATTCCTGCCCCCGGCG-3’and ndoB(R) 5´-CCGTCCCACAAC ACACCCATGCCGCTGCCG-3’; and xylE encoding the 2-3-catechol dioxygenase amplified with primers xylE(F) 5´-GTGCAGCTGCGTGTACTGGACATGAGCAAG-3’ xylE(R) 5´-GCCCAGCTGGTCGGTGGTCCAGGTCACCGG-3´ (Whyte, et al., 2002). Individual 25μL PCRs of DNA soil extracts for each genotype were prepared as described in the previous section but were incubated under the following thermocycling conditions: initial denaturing 96°C for 5 min followed by 10 cycles of deanaturing at 96°C for 1 min, followed by touchdown annealing conditions (starting at 58°C for 1min and ending at 53°C for 1min), extension 72°C for 1.5 min followed by 20 cycles of 94°C for 1 min, 55°C for 1min, extension 72°C for 1.5 min, and a final extension of 72°C for 10 min. The PCR-amplified DNA fragments were visualized by gel electrophoresis in 0.8% agarose gels prepared with TAE buffer and run at 85V for 45 min, stained with ethidium bromide (Sambrook & Russell, 2001).

4.3.7 Hydrocarbon mineralization activity analyses Triplicate microcosms per soil sample were set up to determine the hydrocarbon mineralization activity of microbial communities from vegetated and bulk soil. Each microcosm consisted of 1 g of soil in a 30 mL serum bottle (Supelco) and was spiked with 100,000 dpm of either [1-14C] hexadecane (specific activity, 59mCi/mmol) (Sigma, St. Louis, MO) or [1-14C] naphthalene (specific activity, 6.2mCi/mmol) (Sigma, St. Louis, MO) in addition to non-radioactive hexadecane [100 μLg-1] or naphthalene [10 -1 μg g ], respectively, and a CO2 trap consisting of a 1 mL glass tube filled with 0.5 mL of 14 1M KOH. The amount of trapped CO2 was determined by retrieving the KOH solution and mixing it with 20 mL Scintiverse BD Cocktail (Fisher) in a glass vial and subsequently subjected to liquid scintillation spectrometry (LS6500 Multipurpose Scintillation Counter, Beckman). Microcosms were incubated in the dark at 5°C and monitored for 80 days for hexadecane and ~60 days for naphthalene mineralization.

4.3.8 Statistical analyses Analysis of variance (ANOVA) of bacterial enumeration as well as hexadecane and naphthalene mineralization results were generated using Addinsoft (2010), XLSTAT 2010, data analysis and statistical software for Microsoft Excel, Paris, France. A Principal Component analysis was also used to analyze the results of the bacterial enumeration, the hexadecane and naphthalene mineralization and the amplification of alkB, ndoB and xylE using the previously cited statistical software.

4.4 Results 4.4.1 Physicochemical soil characterization The results for the pH, texture, total carbon (C) and total nitrogen (N) from all samples are summarized in (Table 4.1). Vegetated (Br-V) and bulk (Br-V) samples taken from the bioremediated site were treated by adding nitrogen and phosphorous into the soil, but the nutrient addition did not increase the total nitrogen content in comparison to the un-fertilized samples (NBr-B, NBr-V, Pr-V and Pr-B). The carbon content in the soil did not increased either due to the presence of plants since both vegetated and bulk samples from a common sampling site had very similar total carbon values. The pristine (Pr) site had a higher level of carbon content and clay loam texture, different from the sandy 78 loam texture of the Bioremediated (Br) and nonbioremediated (NBr) sites. Neither the total carbon, nor the TPH were higher in the samples from the diesel-contaminated areas (either Br or NBr); furthermore, according to the analyses, all samples had less than 100 mg Kg-1 TPH.

4.4.2 Microbial enumeration Microscopic cell counts were in the order of 108-109 cells per gram of dry soil, and there were statistical differences (P≤0.05) between the vegetated and the bulk samples from the non bioremediated and the pristine sampling sites as shown in Table 4.2. Moreover, the NBr-V and Pr-V samples, had heterotrophic and diesel-degrading bacterial counts (from both 24°C and 5°C incubation temperatures) significantly (P≤0.05) greater than the heterotrophic and diesel-degrading bacterial counts from their respective NBr-B and Pr-B samples. In general, for all samples, the viable heterotrophic bacteria incubated at 24°C (ranged from 106 to 108 CFU g-1 of dry soil) were significantly higher (P≤0.05) than heterotrophic bacteria incubated at 5°C (ranged from 104 to 106 CFU g-1 of dry soil); but there was no significant difference on the abundance of the diesel-degrading bacteria due to the different incubation temperature (24°C or 5°C)

4.4.3 Microbial community analyses 4.4.3.1 16S rRNA gene DGGE fingerprints of soil bacterial communities The bacterial diversity in the soil samples was estimated by analysing the number, intensity and position of bands in 16S rRNA gene DGGE gels. Profiles from all triplicates from Br-V, Br-B, NBr-V, Br-B, Pr-V and Pr-B samples, were reproducible and distinctive, each profile was composed ~26 to 50 detectable bands where each band was assumed to represent a single operational taxonomic unit (OTU) (Figure 4.2A). The dendrogram in Figure 4.2B, constructed with the information from Figure 4.2A, shows that the profiles from samples sharing common characteristics (i.e. sampling site, either vegetated or bulk) had more similarities among them than profiles from other samples. Only the fingerprints of samples from the non-bioremediated site appeared to have more differences among each other, as can be observed in the dendrogram (Figure 4.2B). Figure 4.3 shows the 16S rRNA-DGGE profiles from Br-V, Br-B, NBr-V, NBr-B, Pr-V and Pr-B composite samples, showing that profiles from samples of a

79 common sampling site (vegetated and bulk) were more alike than profiles from the other two sampling sites, however the intensity and position of bands revealed differences between vegetated and bulk samples from the same sampling site, but we could not clearly establish if those changes were solely due to P. angustata. Bioremediated soils had the lowest number of bands (between 26 and 30), followed by the non remediated samples (between 40 and 46) while the pristine soils had the largest number of bands (between 43 and 50). To further asses the diversity of the bacterial communities we estimated the range of weighted-richness (Rr) which is an index used to estimate the relative abundances of species detected in a community fingerprint (i.e. 16S rRNA gene DGGE) to infer the carrying capacity of the system; complemented by mathematically analyzing the position, intensity and abundance of bands in the 16S rRNA gene DGGE profile and then constructing Pareto-Lorenz evenness curves used for the estimation of the functional organization (Fo) of the communities, the value of which is determined by interpolating the 20% x-axis with the curve and determining the y-axis projection. In ecological terms, the Fo expresses abundance (relative to the total community) which corresponds to the 20% most abundant OTUs detected in the fingerprint, therefore expressing, in simplified terms, the structure of the bacterial community. The estimation and interpretation of Rr and Fo was performed following the methodology described by (Marzorati et al. 2008) on which fingerprints of microbial communities (determined by several authors) from very different environmental samples were compared. The application of such analysis revealed that the Pr-V and Pr-B samples had Rr values of 36 and 48, are on the verge of a high carrying capacity as well as the NBr-V and NBr-B samples with 36 and 42 Rr values respectively. While Br-V and Br-B samples had Rr values of 9 and 12 which are on the verge of a low carrying capacity. All samples had medium values of functional organization (36% ≤ Fo ≤ 48%) meaning that there was a balance among the few most fitting bacterial species and the majority less dominant but more diverse bacterial species. Fo values from vegetated samples were at least 5% higher than the Fo values of their correspondent bulk samples, meaning that there was a selective pressure exerted by the plant upon the bacterial populations.

4.4.3.2 Phylogenetic analyses of bands excised from DGGE gels

Eighteen 16S rRNA-DGGE bands were retrieved from the bioremediated soils, plus eight bands isolated from the non-bioremediated soils, as well as fifteen more bands retrieved from the pristine soils, yielding a total of forty-one sequenced 16S rRNA-DGGE bands (numerically identified in Figure 4.3A). The 16S rRNA-DGGE bands were classified into four bacterial phyla. Twenty-six of the forty-one bands (1-4, 9-13, 23-31, 33-38) were related to Bacteroidetes and corresponded to a 63% of the sequenced bands. Eight bands (6-8, 17, 18, 22, 32, and 40) related to Actinobacteria represented 19.5% of the total. Four bands (5, 14, 15, and 21) were related to Proteobacteria accounting for only 9.76% and three bands (20, 39, and 41) related to Gemmatimonadetes were the remaining 7.32%. The phylogeny of the forty-one bands is illustrated in the phylogenetic trees from Figures 4.4 and 4.5. Figure 4.6, illustrates the proportion of the 16S rRNA gene-DGGE bands from samples sharing characteristics, classified into the phyla Bacteroidetes, Actinobacteria, Proteobacteria and Gematimonadetes. Table 4.3 summarizes the closest matches from the GenBank and RDP databases. The 16S rRNA-DGGE bands 19, 20, 21, 22, 28 and 39 had closest matching sequences retrieved from diverse rhizospheric soils. The matching sequences of bands 20 and 22 corresponded to the Gemmatimonas, and Arthrobacter genera respectively and were recovered from a phytoremediation treatment of a soil polluted by semi-coke (the solid waste from oil-shale chemical industry). The bands 2, 5, 7, 8, 12, 14, 15, 17, 18, 23, 32 and 40 (in addition to 20 and 22) matched best with sequences isolated from hydrocarbon contaminated environments. Bands: 1, 3, 6, 13, 41 and 31 (as well as band 14) had closest matches retrieved from Arctic and Antarctic environments. The rest of the isolated bands, had closest matches retrieved from samples as diverse as: feedlot habitats (24, 27), soil (9, 11, and 36), sea water (4, 25, 26, 29, 30, 35, 37 and 38) as well as from a biodeteriorated wall painting (16) and a saline environment (10). Nineteen 16S RNA gene- DGGE band sequences had below 97% nucleotide similarity percentage to their closest match from the GeneBank and RDP databases therefore they belonged to potentially new bacteria. Moreover, among these 19 sequences, 12 belong to bands retrieved from the samples vegetated by P. angustata.

4.4.4 Prevalence of genes related to the metabolism of hydrocarbons Table 4.4, summarizes the frequency of PCR detection of alkB, ndoB and xylE genes in the 6 different sites. alkB was amplified in all triplicate Br-V, Br-B, NBr-V and NBr-B, but only in one Pr-B (1269B) and one Pr-V (1269V) samples; revealing that although the diesel spill occurred 20 years ago, its resilient effect increased the prevalence of alkB genes in the contaminated Eureka soils; on previous experiments (discussed in Chapter 3) P. angustata`s presence increased the prevalence of alkB in soils, but according to the results summarized in table 4.4, the sole presence of P. angustata did not increase the prevalence of alkB in these soils. ndoB was detected in all triplicate Br-V and Br-B and NBr-V samples, as well as in two NBr-B (1263B and 1264B) samples but was not detected in any of the Pr-V or Pr-B samples, indicating that P. angustata can increase the ndoB prevalence only if the soil was/is contaminated by hydrocarbons. xylE was only detected in one sample of Br-B (1259B), Br-V (1259V), NBr-B (1265B) and NBr-V (1265V) but was not detected in Pr-V nor in Pr-B samples. These results show that the weathered diesel spill was a driving factor for the increase of the ndoB and xylE genes prevalence in the bacterial populations of Eureka soils.

4.4.5 Hydrocarbon mineralization activity analyses

Hexadecane and naphthalene mineralization at 5°C was significantly different (p≤0.05) among the soils from different sampling sites (Figures 4.7A and 4.8A). Mineralization of hexadecane increased over 80 days at relatively stable rates (0.258±0.035% day-1 in Br-B samples, 0.041±0.004% day-1 in NBr-B samples and 0.006±0.0005% day-1 in Pr-B samples) and no plateau was detected. In contrast, naphthalene had its highest mineralization rates (2.36±0.06% day-1in Br-B samples, 0.21±0.1% day-1 in NBr-B samples, and 0.06±0.01% day-1 in Pr-B samples) for the first 10 to 22 days of incubation after which mineralization rates dropped producing a plateau. The total hexadecane mineralization detected after 80 days of incubation in the dark at 5°C was 20.0±1.68% in Br-B samples, 3.09±1.28% in NBr-B samples, and 0.48±0.2% in Pr-B samples; mineralization in sterile control microcosms was very low (≤ 0.09% total mineralization) indicating that the differences in the mineralization

82 percentages were due to biotic hexadecane transformation and not due to abiotic degradation. The highest hexadecane mineralization levels among bulk samples were observed in the Br-B samples, indicating that hexadecane mineralization was more efficient in the bioremediated soils than in the non-bioremediated NBr-B or the pristine Pr-B soils. Hexadecane mineralization was not significantly different between vegetated Br-V and non vegetated Br-B samples from the bioremediated site (Figure 4.7), but at the non-bioremediated site P. angustata significantly (p≤0.05) increased the hexadecane mineralization since in the NBr-V samples, the hexadecane mineralization was 11.56±1.47% greater than in NBr-B samples. Similarly, the presence of P. angustata in pristine soils (samples Br-V) increased the hexadecane mineralization 18.17±2.34% (p≤0.05) over the mineralization detected in the pristine bulk soils Pr-B. After 60 days of incubation at 5°C in the dark. The cumulative average naphthalene mineralization reached the following values: 58.04±3.96% in Br-B samples, 10.68±4.13% in NBr-B samples and 3.47±0.37% in Pr-B samples (Figure 4.8A). Samples from the bioremediated and pristine sites did not show a significant increase in naphthalene mineralization due to P. angustata’s presence; but naphthalene mineralization did significantly (p≤0.05) increase 28±3.06% in NBr-V samples over NBr-B samples from the non-bioremediated site. Non-biological naphthalene mineralization was very low (Figure 4.8) indicating that the differences in the mineralization percentages were due to biotic hexadecane transformation and not due to abiotic degradation.

4.4.6 Principal component analysis The biplot resulting from the Principal Component Analysis of the data from the microbial enumeration, the mineralization of hexadecane and naphthalene results, as well as to the frequencies of alkB, ndoB and xylE PCR amplification from the 18 studied samples (Figure 4.9) shows that samples from different sampling sites were far from each other and samples from the same sampling site clustered together. In addition, bulk samples clustered with bulk samples as well as vegetated grouped with vegetated samples. The biplot from the F1 and F2 factors represented 87.44% of the initial variability of the data, the first factor F1 accounted for a 65.23% of the variability and it was linked to the variability from: the microscopic cell counts (DTAF), the heterotrophic 83 bacterial counts (at 24°C and 5°C incubation temperatures), the diesel degrading bacterial counts (at 24°C and 5°C incubation temperatures), the Hexadecane and Naphthalene mineralization as well as the frequencies of ndoB detection; while the factor F2, accounts for the 22.21% of the variability and it is mainly linked to the detection frequencies of alkB and xylE; Moreover, the F1 factor is mainly linked to the variability of Br and Pr samples and the F2 factor is mainly linked to the variability of NBr samples.

4.5 Discussion Ellesmere Island is the third largest and the most northerly Arctic island. It is a true polar desert with only 70 mm of annual precipitation and daily average temperatures remaining between - 7.7°C and - 38.4°C for more than nine months of the year (Canadian-Climate-Normals, 1971-2000). During the summer months (June, July and August), there is little rainfall (26.2 mm annually) and daily average temperatures typically range between 2.3°C and 5.7°C, with a daily maximum of 8.8 in July and rarely reaching a maximum of 20°C (Canadian-Climate-Normals, 1971-2000). From the beginning of April to the end of August, there is almost continuous sunlight, while virtually no sunlight occurs between mid-October and late February. P. angustata also known as alkali grass, is well adapted to these environmental conditions and it is an early colonizer indicative of environments disturbed either by natural or anthropogenic factors (Aiken, et al., 1996). An initial screening of five different indigenous Arctic plant species in search for a plant with potential for phytoremediation treatments, revealed that, soils vegetated by P. angustata had the highest abundance of hydrocarbon-degrading bacteria as well as the highest prevalence of alkB, ndoB and xylE genotypes (Chapter 3). To determine the reproducibility of the previous observation and further assess the phytoremediation potential of P. angustata in the present study, we examined the microbial populations of vegetated and bulk Arctic soils, from a pristine (Pr) site and from two sites contaminated by a diesel spill in 1990 (Whyte et al. 2001) one of them bioremediated (Br), and the other one non-bioremediated (NBr). According to the results from the TPH analyses, none of the samples had more than 100 mg Kg-1 of soil which was expected in the pristine samples but higher concentrations were expected in the diesel-contaminated 84 samples Br and NBr. The low concentration of hydrocarbons at these samples may be partially attributed to natural attenuation for over ten years following the contamination event which seems plausible considering that weathering has been the main reason for the removal of hydrocarbons at untreated contaminated sites, and it has been documented even in sub arctic sites (Braddock, et al., 2003). The effect of natural attenuation enhanced by biostimulation of soil microorganisms by the addition of nutrients (N and P) could also explain why there were so few hydrocarbons in the Br site where diesel was directly spilled. Although N and P were added to the Br-B and Br-V soils, two weeks prior to sampling, we did not detect an increase in the total nitrogen content of these samples compared to the non-fertilized samples. In the present research the pH (7.5-8.2), total carbon (0.6-1.4), total nitrogen (0.04-0.1) and texture (sandy loam, clay loam) of the samples were congruent with the physicochemical characteristics determined in previous studies of Eureka soils (Chapter 3), (Whyte et al. 2001) as well as similar to the characteristics described in surveys of other Arctic soils (González et al. 2000).

4.5.1 Microbial enumeration Microscopic cell counts from the NBr, and Pr sites, were in the order of 108 cells per gram of dry soil. In the samples from the bioremediated site, these numbers were in the order of 109 cells per gram of dry soil, which were one order of magnitude higher than previously observed in Arctic (108 cells g-1 of dry soil) (Whyte, et al., 1999) and Antarctic (106-108cells per g-1 of dry soil) soils (Aislabie, et al., 2001), but similar abundances have been detected in soils from pastures (Janssen, et al., 2002) and alpine soils (Lipson, et al., 2002). In the present research, the cultivable heterotrophic bacterial counts (105 -107 CFU g-1 of dry soil) determined at 5°C, were similar to the previously quantified bacteria from Eureka soils. For instance the soils studied in Chapter 3 where values ranged from 105 to107 CFU g-1 of dry soil and the soils studied by Whyte (2001) which counts were from 106 to 107 CFU g-1 of dry soil. There was no statistical difference among the diesel degrading bacterial counts determined at 24°C or 5°C, indicating that the cultured diesel degrading bacteria were psychrotolerant rather than psychrophilic since psychrophiles do not grow above 20°C (Morita, 1975). The microbial counts from the bioremediated soils, estimated by both culture-dependent and 85 culture-independent methods, were significantly greater (p≤0.05) than the bacterial counts from the other two soils studied in the present research. The microbial increase at Br soils reflects the success of the treatment with N and P nutrients which were consumed by the microorganisms during the bioremediation treatment. Different studies report that the application of nutrients as part of bioremediation treatments at Arctic soils (Børresen and Rike 2007; Horel and Schiewer 2009; Whyte et al. 1999a; Yergeau et al. 2009) and Antarctic soils (Aislabie et al. 2006; Powell et al. 2006; Walworth et al. 2007) frequently increases the abundance of microorganisms. In the present study, the samples vegetated by P. angustata from both, the non-bioremediated site and from the pristine site, had significantly (p≤0.05) greater microbial abundances than their correspondent bulk samples according to the microscopic counts, as well as the cultivated heterotrophic and diesel-degrading bacteria (incubated at 24°C or 5°C) indicating that the plant increased the abundance of microorganisms in the studied soils. An increase on the abundance of heterotrophic as well as hydrocarbon-degrading bacteria due to a plant’s presence, has been commonly documented in previous phytoremediation studies (Euliss et al. 2008; Kaimi et al. 2006; Kirk et al. 2005).

4.5.2 Microbial community analyses 16S RNA-DGGE gene fingerprints have inherent limits for the study of microbial communities, therefore in the present research it was not intended to fully describe the bacterial diversity but only to determine general similarities and differences among samples, and more specifically to distinguish differences in the microbial populations attributable to the diesel contamination and the presence of P. angustata in the soils. Results from the 16S rRNA gene-DGGE bacterial community fingerprints and complementary analyses, showed that the soils from the three studied high Arctic sites, had a healthy functional organization (inferred with the Fo values which were between 36% to 48%) where 20% of the total diversity is integrated by the most fitting therefore the most abundant bacteria (also known as r strategist) and corresponds to about 36-48% of the total bacterial abundance in these soils; consequently, 80% of the total diversity (comprised by the K strategist) accounts for more than a 50% of the total bacterial abundance; this type of bacterial organization is a characteristic of communities with a good functionality under the prevailing conditions, with a great 86 flexibility for coping with environmental changes. Moreover, the results from sequencing the 16S rRNA gene-DGGE bands are consistent with the estimated Fo in the sense that by sequencing 41 bands it was possible to detect bacteria belonging to four different phyla (Bacteroidetes, Actinobacteria, Gemmatimonadetes and Proteobacteria). These results are consistent with findings from the Chapter 3, where 10 bacterial 16S rRNA gene-DGGE band sequences were sufficient to detect bacteria from Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria phyla in 10 vegetated and bulk soils from Eureka, Nunavut. The range-weighted richness of Pr-V (36) and Pr-B (48) were on the verge of a high carrying capacity which according to (Marzorati et al. 2008) are typical of habitable environments such as garden soil and rhizosphere. The Rr values were similar for the NBr-V (36) and NBr-B (42) but for the bioremediated site, Br-V (9) and Br-B (12) the Rr values were on the verge of a low carrying capacity common to adverse environments such as contaminated soils (Marzorati et al. 2008). According to the number of 16 rRNA gene-DGGE bands detected in the bacterial fingerprints and the calculated Rr indexes, the pristine soils had the most diverse bacterial communities from the three different sampling sites. The fifteen bands retrieved from pristine samples (Pr-V and Pr-B) were classified into the following genera: Arthrobacter, Nitriliruptor, Gemmatimonas, Eudora and Gillisia, as well as bacteria from unclassified Sphingobacteria and Flavobacteria (but we did not amplify any Proteobacteria from Pr samples); the closest matches of these bands were retrieved from samples as diverse as: pristine soils (Elshahed et al. 2008), hydrocarbons polluted soils (Alonso-Gutierrez, et al., 2009), sea water (Nedashkovskaya, et al., 2005a, Alain, et al., 2008), including soils and waters from Arctic (Larose et al. 2010; Perreault et al. 2008) and Antarctic (Niederberger, et al., 2008) environments; also two of the sequenced bands were closely related to bacteria retrieved from samples contaminated by organic compounds such as mesotrione (Batisson et al. 2009) and aliphatic nitriles (Sorokin et al. 2009). Ten of the fourteen bands retrieved from the bulk soils contaminated with diesel (bioremediated and non-bioremediated) (Br-B and NBr-B) were classified as Bacteroidetes (Gillisia, Subsaxibacter, Aequorivita, Winogradskella, Roseivirga, Eudora, Echinicola, unclassified Bacteroidetes, an unclassified Flavobacteria and unclassified

Sphingobacteria) which closest matches were retrieved from different samples such as feedlot habitats, non agricultural soils from USA, soil from a low precipitation zone in Israel, high altitude saline basins in northern Chile, sea water (Nedashkovskaya et al. 2005b; Nedashkovskaya et al. 2005c) from Antarctica (Bowman and Nichols 2002), Antarctic sea-ice contaminated by crude oil and a biodeteriorated wall painting in Vienna. Two more bands were Actinobacteria (Williamsia and Rhodococcus) for which closest matches were retrieved from the shoreline in northwest Spain affected by the Prestige oil spill (Alonso-Gutierrez et al. 2009; Jimenez et al. 2007) plus one Alpha-proteobacteria (Sphingomonas) whose closest match is a psychrotolerant phenanthrene-mineralizing bacteria isolated from Greenland high Arctic soils (Sorensen et al. 2010) and one Gamma-proteobacteria (Lysobacter) with a closest match isolated from the shoreline in northwest Spain affected by the Prestige oil spill (Alonso-Gutierrez et al. 2009; Jimenez et al. 2007). Moreover, the proportion of Proteobacteria (more specifically the Alpha-proteobacteria from the Sphingomonas genus) as well as Actinobacteria (specifically from the Williamsia and Rhodococcus genera), was 10% higher in the diesel-contaminated soils than in the pristine soils. These results are congruent with previous findings of psychrophilic hydrocarbon-degrading Rhodococcus sp. previously isolated from hydrocarbon contaminated Arctic soils (Whyte et al. 1999b) and the observation that shifts in the microbial populations of polar soils contaminated by petroleum hydrocarbons usually increase the proportion of Proteobacteria mainly from the Pseudomonas, Xantomonas, Halomonas, Marinobacter, Methylobacter, Psychrobacter, Varivorax and Sphingomonas genera (Greer et al. 2010). From the nineteen 16S rRNA gene-DGGE bands isolated form samples vegetated by P. angustata (Br-V, NBrV and Pr-V), eleven were classified into the Bacteroidetes phylum (Flavobacterium, Yeosuana, Algoriphagus, Eudora genera, as well as unclassified Sphingobacteria) and the closest matches to these bands were retrieved from environmental samples, such as agricultural soils, tall grass prairie soils, Arctic snow (Larose et al. 2010), Arctic sea water, Antarctic lakes (Van Trappen et al. 2002) and Benzo[a]pyrene enrichment culture (Kwon et al. 2006), but also some of the closest matches were retrieved from less analogous samples such as feedlot habitats, bacteroplankton (Sala et al. 2005), waters of the Adriatic sea and from a high altitude

88 wetland of north-western Argentina. Five more of these nineteen bands, belonged to the Actinobacteria phylum and all of them were classified into the genus Arthrobacter, the closest matches of these bands were retrieved from samples such as: a high Arctic permafrost sample (Steven et al. 2008), a creosote contaminated soil from Greece, a mesotrione enrichment culture and a soil contaminated with solid waste from an oil-shale chemical industry undergoing phytoremediation treatment. Two more of these nineteen bands were a Beta-proteobacteria (Hydrogenophaga) which closest match was isolated from a waste-activated sludge and an unclassified Gamma-proteobacteria (which closest match came from Populus tremuloides rhizospheric soil (Lesaulnier et al. 2008). Gemmatimonadetes bacteria (Gematimonas) was also retrieved from P. angustata vegetated samples, which closest match was retrieved from a soil contaminated with solid waste from an oil-shale chemical industry undergoing phytoremediation treatment.

Over all, the proportion of bands from the Actinobacteria phyla, increased ~13% in the P. angustata vegetated soils in comparison to the bulk soils. Changes in the proportion of specific groups of bacteria due to vegetation have been determined such as in a recent study (Thomson et al. 2010) done to a Scottish grassland, where in soils dominated by Alpha-proteobacteria and Acidobacteria the proportion of Alpha-proteobacteria increased in the vegetated soils compared to bulk soils in that study, the carbon and nitrogen content also increased and the researchers associated the changes in the microbial populations to the availability of compounds provided by the plants. Therefore it seems plausible that P. angustata`s exudates were a driving factor for the changes in the soil microbial communities, even though we did not detect difference in the C and N in our samples.

4.5.3 Prevalence of genes related to the metabolism of hydrocarbons alkB was successfully amplified in all the triplicate vegetated and bulk samples from both diesel-contaminated sites, bioremediated and non-bioremediated; but only a third of the samples from the pristine site were positive for alkB by PCR amplification, revealing that although the diesel spill occurred 20 years ago, its resilient effect increased the prevalence of alkB genes in the studied Eureka soils. Similarly to what was found in a Real-Time PCR assessment of the microbial community of Antarctic soils

89 during a bioremediation treatment, where the proportion of microorganisms possessing alkB was positively correlated to the concentration of n-alkanes in the soil (Powell, et al., 2006). Since we used general primers to amplify alkB, it is hard to determine the phylogeny of the bacteria possessing this gene but according to the 16S RNA gene-DGGE analyses there was a 10% increase in the detection of Sphingomonas Williamsia and Rhodococcus in the diesel contaminated soils, therefore we suspect that the detected alkB genes may belong to these bacteria. Moreover, according to a previous study of high Arctic soils (Whyte, et al., 2002), alkB genes from Rhodococcus spp. were commonly (90%) detected in both pristine and hydrocarbon-contaminated Eureka soils, while alkB genes from Pseudomonas putida were more commonly detected in hydrocarbon-contaminated soils (75%) than in pristine soils (50%).

In the present research we did not detect an increase in the prevalence of alkB genes due to P. angustata presence which we expected considering the results from the previous chapter where alkB was detected in soil vegetated by P. angustata but not in the corresponding bulk sample. It has not been clearly established whether the presence of a plant really increases the prevalence of alkB genes as there are mixed results, for example, a phytoremediation study of Canadian soils (Phillips, et al., 2006) revealed that alkB genes were present at both planted and unplanted soils. An Austrian laboratory study of the expression of alkB genes of rhizospheric bacteria from Lolium multiforum L. (Italian ryegrass) revealed that the highest abundance and expression of alkB genes happened in the rhizosphere (Andria, et al., 2009).

In the present study ndoB and xylE were only detected in the soils from the diesel-contaminated sites, these results confirm previous findings showing that the diesel increased the prevalence of ndoB and xylE genes in the bacterial populations of Eureka soils. According to Whyte, et al. (2001) xylE was not detectable by PCR either in contaminated or pristine Eureka soils, while ndoB was frequently detected in contaminated soil samples but not detected in pristine ones. Studies of alpine soils (Margesin, et al., 2003) show a statistical correlation between contamination levels and the prevalence of hydrocarbon-degrading genotypes specifically finding a very low detection of xylE and ndoB in pristine soils. In the present research, there was an

90 increase in the ndoB detection in the P. angustata vegetated samples NBr-V compared to the bulk samples NBr-B, but only in the non-bioremediates site. Siciliano, et al., (2003) found that prevalence of ndoB and xylE in the microbial community was greater in planted treatments versus non-planted soils from California. Similar results were obtained on hydrocarbon-contaminated flare-pit Canadian soils where the detection of ndoB was higher in planted treatments than in bulk soils (Phillips, et al., 2006). Since results are highly dependent of the plant species and the soil characteristics, it would be necessary to implement a different approach (i.e real time PCR) to accurately determine if there are changes in the prevalence of alkB, ndoB and xylE due to P. angustata presence in Arctic soils.

4.5.4 Hydrocarbon mineralization activity analyses Microbial populations of the three different sampling sites had different hexadecane and naphthalene mineralization rates and efficiencies, the highest mineralization occurred in the samples from the bioremediated site, for both vegetated and non vegetated samples alike. Even though total hydrocarbons were below 100 mg Kg-1, microbial populations preserved the capacity to mineralize hexadecane freshly spiked for the mineralization assays, which is congruent with the findings of a natural attenuation assessment (Braddock, et al., 2003) done on a subarctic taiga contaminated by crude oil in which the microbial population preserved high hexadecane and phenanthrene mineralization activities after twenty five years of the spill. In the present research, the lowest mineralization activities for both naphthalene and hexadecane hydrocarbons were detected in the pristine soil but mineralization of hexadecane was 18% higher in the vegetated pristine samples (Pr-V). In the diesel contaminated not-bioremediated samples, both the hexadecane and naphthalene mineralization were low in the bulk samples but increased 18% and 28% respectively in the vegetated samples. The absence of fertilization at the non-bioremediated and pristine sites could have restricted the mineralization activities of microbial communities since nutrient limitations interfere with biodegradation activities (Ferguson, et al., 2003) especially at low nutrient environments such as polar soils (Mohn & Stewart, 2000, Aislabie, et al., 2006). Moreover, on a field-scale assessment of phytotreatment on soil contaminated with weathered hydrocarbons and heavy metals 91

(Palmroth, et al., 2006) the removal of hydrocarbons was only statistically significant if along with the phytotreatment NPK fertilizer or biowaste compost was added. The sole presence of plants has been sufficient to increase the degradation of hydrocarbons even in the absence of fertilization as it was found on a field-scale assessment (Phillips, et al., 2009) where weathered hydrocarbons were efficiently degraded by mixed and single plant treatments set in Saskatchewan, Canada. The authors of that study attributed the increased degradation of total petroleum hydrocarbons in soils planted with Altai wild rye due to the plants ability to selectively recruit entophytic hexadecane degraders (in response to total petroleum hydrocarbons) and to the plants ability to retain these microorganisms during environmental stress. Results from mineralization assays are broadly (Mohn & Stewart, 2000, Whyte, et al., 2001, Phillips, et al., 2009) accepted as reliable to predict the hydrocarbon degradation potential of microbial communities but should not be taken as quantitative to predict the real microbial communities capacity to transform total hydrocarbons in soil, since removal of a complex mix of hydrocarbons requires the presence and activity of microorganisms with diverse metabolic capacities and the removal of each compound occurs at different rates and efficiencies. Therefore studies to assess the removal of fresh hydrocarbons in the presence of P. angustata are required to determine if the plant is able to promote hydrocarbon removal from contaminated soils, so that it could be used in phytoremediation treatments.

4.5.5 Principal Component Analyses From the PCA analysis we deducted that the presence of P. angustata did affect the abundance and activity of the studied microbial communities, as well as the prevalence of hydrocarbon degradation-related genes; but the diesel-contamination level and the application or lack of application of N and P nutrients (bioremediation treatment) were for the most part, responsible for the differences in the studied soil microbial communities. The reason for a residual effect of the plants presence over the microbial populations, may be that after the plant has died it releases the bacterial endophytic microorganisms (including hydrocarbon-degrading bacteria) into soil as well continues to provide organic matter, thereby, stimulating microbial populations capable of using organic compounds from plant residues as carbon source. Comparing the influence of P. angustata in microbial populations from different sites was important as 92 part of a bioremediation treatability assessment, since studies reveal that the same cleanup strategy applied to different sites may result in different hydrocarbon removal efficiencies (Yergeau, et al., 2009).

4.6 Conclusions In the present research we have shown that P. angustata increases the abundance of heterotrphic bacteria, including hydrocarbon-degrading bacteria (ie. Arthrobacter) and also increases the prevalence of ndoB genes and the mineralization of hexadecane and naphthalene, these variables increase when the microbial communities are exposed to hydrocarbons. But also we found that the addition of nutrients to the soils as a bioremediation treatment produces a stronger positive effect in the studied variables than the effect produced by P. angustata. Never the less, it is important to emphasize that the present study was performed on samples which had been frozen for over a year therefore the plant-microorganism interactions were not happening anymore at the time of the analyses and the TPH concentration was below 100 mg Kg-1; therefore, to learn the extent to which P. angustata can stimulate bioremediation, while still alive and in the presence of different concentrations of hydrocarbons, has yet to be determined.

4.7 Acknowledgements Authors of this paper want to thank Dr. Laurie Consaul (Department of Plant Science, McGill University, Macdonald Campus) for the identification of the plant species, Dr. Joann Whalen (Department of Soil Science, McGill University, Macdonald Campus) for the Physicochemical analyses of soil samples, M. Sc. Diane Labe (NRC-Biotechnology Research Institute, Montreal, Canada) for the determination of TPH and Dr. Blaire Steven, Dr. Thomas Daniel Niederberger, Dr. David Meek, M. Sc. Roland Wilhelm, M.C. Elisabeth Lefrançois and Marianne Claire-Louise Michelle Poilly, for their important feedback to the present research work. The present work would not be possible without the funding provided by el Consejo Nacional de Ciencia y Tecnología de México (CONACYT, México) supporting the first author’s scientific education.

Table 4.1 Characteristics of soil samples collected from Eureka, Nunavut, in the summer of 2005 Sample Total N Total C C/N Moisture pH Sand Clay Silt Texture Sample characteristics ID (%) (%) (%) (%) (%) (%)

Br-V Soil vegetated by P. angustata 0.04 0.61 15.06 41.33 8.09 70.2 17.3 12.5 Sandy loam from the diesel-spill area, ±0.01 ±0.02 ±2.87 ±4.87 bioremediated (fertilized 3 weeks prior to sampling), triplicate samples: 1259V, 1260V, 1261V Br-B Bulk soil from the diesel-spill area , 0.05 0.70 13.08 6.67 7.51 70.2 17.3 12.5 Sandy loam bioremediated (fertilized 3 weeks ±0.01 ±0.01 ±1.45 ±1.40 prior to sampling), triplicate samples: 1259B, 1260B, 1261B NBr-V Soil vegetated by P. angustata 0.05 0.68 14.09 23.33 7.74 67.7 14.8 17.5 Sandy loam from slope downhill from the ±0.01 ±0.12 ±1.40 ±2.87 diesel-spill area, non-bioremediated, triplicate samples: 1263V, 1264V, 1265V NBr-B Bulk soil from slope downhill from 0.04 0.58 13.34 4.00 7.80 67.7 17.3 15.0 Sandy loam the diesel-spill area, ±0.01 ±0.09 ±0.97 ±0.21 non-bioremediated, triplicate samples: 1263B, 1264B, 1265B Pr-V Soil vegetated by P. angustata 0.10 1.38 14.27 8.00 8.15 37.7 29.8 32.5 Clay loam from a pristine area, triplicate ±0.03 ±0.21 ±1.80 ±0.20 samples: 1269V, 1270V, 1271V Pr-B Bulk soil from a pristine area, 0.09 1.42 15.74 8.67 8.15 37.7 29.8 32.5 Clay loam triplicate samples: 1269B, 1270B, ±0.01 ±0.20 ±1.42 ±1.80 1271B Total Petroleum Hydrocarbons below 100mg Kg-1 in all samples.

Table 4.2 Microbial Enumeration 24°C 5°C Microscopic DTAF Heterotrophic Diesel- degraders Heterotrophic Diesel- degraders Sample ID (107 cell g-1) (107 CFU g-1soil) (107 CFU g-1soil) (107 CFU g-1soil) (107 CFU g-1soil) Br-V 280 ± 85A 21 ± 4.6A 0.83 ±0.05B 2.1 ±0.08B 0.38 ±0.006A

Br-B 230 ± 71AA 14 ± 1.2A 2.2 ±0.22A 4.4 ±0.29A 0.37 ±0.01A

NBr-V 21 ± 58B 2.2 ± 2.7B 0.14 ±0.11C 0.76 ±0.08C 0.14 ±0.02B

NBr-B 10 ± 37C 0.4 ± 0.21C 0.014 ±0.02D 0.28 ±0.034D 0.002 ±0.001C

Pr-V 34 ± 81B 2.1 ± 0.058B 0.93 ±0.07B 0.82 ±0.14C 0.29 ±0.04A

Pr-B 10 ± 39C 0.21 ± 0.04D 0.009E 0.04 ±0.01E 0.004±0.001C Data are presented as means (n = 9) plus/minus standard deviation. For each column, means super indices of different letters are significantly different at p ≤0.05; and means shearing super indices of the same letters are not significantly different according to Tukey test results. CFU g-1 = Colony Forming Units per gram of dry soil.

Table 4.3 Nucleotide sequence similarity of 16S rRNA gene-DGGE bands Similarity Accession Closest sequence match No. Band Sample (%) Environment of match sequence number (accession number)

Flavobacterium gelidilacus, (T) strain LMG21477 1 1Pa697 JF729937 Br-V 93 Microbial mats from Antarctic lakes (AJ440996) Enrichment culture with benzo[a]pyrene and 2 1Pa176 JF729953 Br-V Yeosuana aromativorans (T) strain GW1-1 ( AY682382) 96 pyrene from estuarine sediments of the South Sea 3 1Pa600 JF729949 Br-V Algoriphagus sp. clone BCw053 (FJ889580) 93 Arctic seawater 4 1Pa675 JF729930 Br-V Eudora adriatica (T) strain AS06/20a ( AM745437) 89 Coastal waters of the Adriatic Sea

5 1Pa401 JF729967 Br-V Hydrogenophagasp. clone 61 (FJ623322) 98 Waste-activated sludge Permafrost/ground ice core profile from the 6 1Pa402 JF729963 Br-V Arthrobacter sp. clone Eur3BacAL.15 (EU218648) 95 Canadian high Arctic Arthrobacter phenanthrenivorans (T) strain Sphe3 7 1Pa748 JF729958 Br-V 94 Creosote-contaminated soil in Greece (AM176541) Enrichment culture from soil in France, using 8 1Pa122 JF729964 Br-V Actinobacterium strain MES16 (EU864321) 98 mesotrione as sole carbon source Agricultural and non-agricultural soils from 9 1B137 JF729952 Br-B Bacteroidetes bacterium clone MA00186B09 (FJ532504) 98 USA High altitude saline evaporitic basins in 10 1B687 JF729934 Br-B Bacteroidetes bacterium clone J1-w-21 (FJ213809) 96 northern Chile Soil from low precipitation gradient zone in 11 1B713 JF729939 Br-B Gillisia sp. clone Ovdat-14 (GQ425245) 99 Israel Shoreline (northwestern Spain) affected by 12 1B699 JF729938 Br-B Subsaxibacter sp. clone Sc8 (EU375196) 98 the Prestige oil spill 13 1B672 JF729929 Br-B Aequorivita lipolytica (T) strain Y10-2T (AY027805) 94 Sea water of the Mertz Polynya, Antarctica Enrichment culture from soils of the 14 1B382 JF729969 Br-B Sphingomonas sp. clone 6.4 (FJ828929) 99 Greenland High Arctic, psychrotolerant phenanthrene-mineralizing bacteria

Similarity Accession Closest sequence match No. Band Sample (%) Environment of match sequence number (accession number)

Shoreline (northwestern Spain) affected by 15 1B174 JF729968 Br-B Lysobacter sp. clone AP24 (EU374887) 96 the Prestige oil spill 16 1B735 JF729941 Br-B Echinicola sp. clone G4-K5 (AJ400574) 97 Biodeteriorated wall paintings, Viena Shoreline (northwestern Spain) affected by 17 1B392 JF729962 Br-B Williamsia sp. clone Sc30 (EU375219) 100 the Prestige oil spill Shoreline (northwestern Spain) affected by 18 1B143 JF729965 Br-B Rhodoccocus sp. DGGE gel band B33 (DQ870543) 98 the Prestige oil spill Agricultural and non-agricultural soils from 19 2Pa746 JF729945 NBr-V Bacteroidetes bacterium clone MA00186B09 (FJ532504) 98 USA Phytoremediation treatment of soil of 20 2Pa377 JF729957 NBr-V Gemmatimonas sp. clone MK66a (EF540421) *93 semi-coke (solid waste from oil-shale chemical industry) Soil from (Populus tremuloides) trembling 21 2Pa388 JF729966 NBr-V Proteobacterium clone Elev_16S_890 (EF019722) 98 aspen Phytoremediation treatment of soil of 22 2Pa757 JF729960 NBr-V Micrococcaceae bacterium clone J31 (EF540547) 96 semi-coke (solid waste from oil-shale chemical industry) 23 2B736 JF729942 NBr-B Winogradskyella sp. gap-f-41 (DQ530465) 99 Crude oil contaminated Antarctic sea-ice

24 2B690 JF729936 NBr-B Bacteroidetes bacterium clone LL141-7G13 (FJ675260) 95 Feedlot habitats 25 2B728 JF729940 NBr-B Roseivirga echinicomitans (T) strain KMM 6058 (AY753206) 86 Sea urchin Strongylocentrotus intermedius Flavobacteriaceae, bacterium DGGE band BH23 Bacterioplankton during Alexandrium spp. 26 2B681 JF729933 NBr-B 98 (DQ008465) blooms 27 3Pa579 JF729947 Pr-V Bacteroidetes bacterium clone LL141-7G13 (FJ675260) 91 Feedlot habitats

Similarity Accession Closest sequence match No. Band Sample (%) Environment of match sequence number (accession number)

28 3Pa575 JF729946 Pr-V Bacteroidetes bacterium clone FFCH17211 (EU133732) 92 Tallgrass prairie soil

Flavobacteriaceae, bacterium DGGE band BH23 Bacterioplankton during Alexandrium spp. 29 3Pa604 JF729950 Pr-V 98 (DQ008465) blooms Coastal waters of the Adriatic Sea 30 3Pa188 JF729954 Pr-V Eudora adriatica (T) strain AS06/20a ( AM745437) 94

Arctic snow and meltwater from Svalbard, 31 3Pa737 JF729943 Pr-V Sphingobacteriales bacterium clone SSIM-B4 (FJ946545) 94 Norway Enrichment culture from soil in France, using 32 3Pa585 JF729961 Pr-V Actinobacterium strain MES16 (EU864321) 97 mesotrione as sole carbon source Cold perennial springs of the Canadian high 33 3B738 JF729944 Pr-B Gillisia sp. Strain NP18 (EU196339) 98 Arctic Soil from low precipitation gradient zone in 34 3B679 JF729932 Pr-B Gillisia sp. clone Ovdat-14 (GQ425245) 95 Israel 35 3B616 JF729951 Pr-B Gillisia mitskevichiae (T) strain KMM 6034 (AY576655) 96 Sea water (Sea of Japan) High altitude wetland of northwestern 36 3Pa678 JF729931 Pr-V Flavobacteriaceae bacterium clone SII-1 (AJ853873) 98 Argentina Coastal bacterioplankton polluted with 37 3B596 JF729948 Pr-B Flavobacteriaceae bacterium ACEMC 1F-6 (FM162953) 93 Toxic-metal Flavobacteriaceae, bacterium DGGE band BH23 Bacterioplankton during Alexandrium spp. 38 3B689 JF729935 Pr-B 99 (DQ008465) blooms Rhizosphere of faba bean (Vicia faba L.) in 39 3B723 JF729955 Pr-B Gemmatimonadetes bacterium clone g55 (EU979064) 80 Beijing 40 3B755 JF729959 Pr-B Actinobacteria bacterium clone F2_94X (GQ263141) 98 Simulated low level waste site Montana, USA 41 3B364 JF729956 Pr-B Gemmatimonas sp. clone LVH4-D4B (EF465019) 98 Soils of Northern Victoria Land, Antarctica

Table 4.4 PCR amplification of bacterial genes involved in the catabolism of hydrocarbons

Sample ID alkB ndoB xylE

Br-V 6/6 6/6 2/6

Br-B 6/6 6/6 2/6

NBr-V 6/6 6/6 2/6

NBr-B 6/6 4/6 2/6

Pr-V 2/6 0/6 0/6

Pr-B 2/6 0/6 0/6

Each of the 18 samples was analyzed individually in duplicate.

Figure 4.1 Sampling site. A) Eureka, Nunavut, Ellesmere Island, Canada; B) Sampling strategy for soils vegetated by P. angustata and bulk soil; C) Sampling points: (1) diesel-spill area, bioremediated by fertilization (Br) samples: vegetated (Br-V) triplicates (1259V, 1260V, 1261V) and bulk (Br-B) triplicates (1259B, 1260B, 1261B); (2) Downhill from diesel-spill area, non-bioremediated (NBr) samples: vegetated (NBr-V) triplicates (1263V, 1264V, 1265V) and bulk (NBr-B) triplicates (1263B, 1264B, 1265B); (3) Pristine (Pr) samples: vegetated (Pr-V) triplicates (1269V, 1270V, 1271V) and bulk (Pr-B) triplicates (1269B, 1270B, 1271B).

100

101

Figure 4.2 DGGE of PCR-amplified 16S rRNA gene fragments from triplicate samples of bulk and vegetated soils A) Image of DGGE gel (45%-65% denaturant gradient) Samples: from Diesel-spill area, bioremediated vegetated (Br-V) (1) 1259V, (2) 1260V, (3) 1261V and bulk (Br-B) (4) 1259B, (5) 1260B, (6) 1261B; samples from Downhill from diesel-spill area, non-bioremediated vegetated (NBr-V) (7) 1263V, (8) 1264V, (9) 1265V and bulk (NBr-B) (10) 1263B, (11) 1264B, (12) 1265B); samples from a Pristine area vegetated (Pr-V) (13) 1269V, (14) 1270V, (15) 1271V) and bulk (Pr-B) (16) 1269B, (17) 1270B, (18) 1271B. B) Dendrogram analysis of DGGE image (constructed with Gelcompare II using the Neighbor-Joining method, the scale shows distances among profiles based on differences).

102

A Br-V Br-B NBr-V NBr-B Pr-V Pr-B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

7 NBr-V B 8 NBr-V 1 Br-V 1 0 NBr-B 13 Pr-V 11 NBr-B 12 NBr-B 9 NBr-V 18 Pr-B 16 Pr-B 17 Pr-B 15 Pr-V 14 Pr-V 2 Br-V 3 Br-V 6 Br-B 4 Br-B 5 Br-B

103

Figure 4.3 DGGE of PCR-amplified 16S rRNA gene fragments from composite samples of bulk and vegetated soils A) Image of DGGE gel (45%-65% denaturant gradient); labels at the top refer to composite samples of: vegetated (Br-V) and bulk (Br-B) samples from the diesel-spill area, bioremediated; vegetated (NBr-V) and bulk (NBr-B) samples from downhill the diesel-spill area, non bioremediated; vegetated (Pr-V) and bulk (Pr-B) samples from a pristine area. Numbers in the gel point at sequenced bands: 1)1Pa697 (JF729937), 2)1Pa176 (JF729953), 3)1Pa600 (JF729949), 4)1Pa675 (JF729930), 5)1Pa401 (JF729967), 6)1Pa402 (JF729963), 7)1Pa748 (JF729958), 8)1Pa122 (JF729964), 9)1B137 (JF729952), 10)1B687 (JF729934), 11)1B713 (JF729939), 12)1B699 (JF729938), 13)1B672 (JF729929), 14)1B382 (JF729969), 15)1B174 (JF729968), 16)1B735 (JF729941), 17)1B392 (JF729962), 18)1B143 (JF729965), 19)2Pa746 (JF729945), 20)2Pa377 (JF729957), 21) 2Pa388 (JF729966), 22)2Pa757 (JF729960), 23)2B736 (JF729942), 24)2B690 (JF729936), 25)2B728 (JF729940), 26)2B681 (JF729933), 27)3Pa579 (JF729947), 28)3Pa575 (JF729946), 29) 3Pa604 JF729950), 30)3Pa188 (JF729954), 31)3Pa737 (JF729943), 32)3Pa585 (JF729961), 33)3B738 (JF729944), 34)3B679 (JF729932), 35)3B616 (JF729951), 36)3Pa678 (JF729931), 37)3B596 (JF729948), 38)3B689 (JF729935), 39)3B723 (JF729955), 40)3B755 (JF729959), 41)3B364 (JF729956). B) Dendrogram analysis of DGGE image (constructed with Gelcompare II using the Neighbor-Joining method, the scale shows distances among profiles based on differences).

104

V B

- -

V B B

- - -

Br Br NBr Pr NBr Pr 9 33 1 19 34 10 23 11 36 35 2 12 20 21

24 27

25 28 3 13 26 29 37 14 30 4 31 15 38 16 39 5 6 17 18 7 40 8 22 32

41 B

NBr-B

NBr-V

Pr-V

Pr-B

Br-B

Br-V

105

Figure 4.4 Phylogenetic relationships of 16S rRNA gene fragments of Gemmatimonadetes, Actinobacteria and Proteobacteria Phyla of excised bands from DGGE gels of bulk and vegetated by P. angustata soils from Eureka, Nunavut. Bootstrap values ≥50% (1000 replicates) are indicated at the nodes. Bands sequences from this study are indicated in bold; their names refer to the sample of origin. Sequences from GenBank database are identified with the accession numbers. The scale bar represents the expected number of changes per nucleotide position.

106

s Gemmatimonadetes

100 Uncultured Gemmatimonadetes clone (EU979064) DGGE band 3B723 (JF729955) Gemmatimonas aurantiaca (AB072735) T DGGE band 3B364 (JF729956) 100 99 Uncultured bacterium clone (EF465019) 65 Uncultured bacterium clone (AM934918) DGGE band 2Pa377 (JF729957)

100 Uncultured bacterium clone (EF540421) Thermotoga maritima DGGE band 2Pa757 (JF729960) (M21774) T Uncultured bacterium (EF540547) DGGE band 1Pa748 (JF729958) Arthrobacter phenanthrenivorans (AM176541) T 100 DGGE band 1Pa122 (JF729964) DGGE band 3Pa585 (JF729961) Actinobacterium MES16 (EU864321) DGGE band 1Pa402 (JF729963)

69 Uncultured bacterium clone (EU218648) a Actinobacteria 82 Actinobacterium TSBY-90 (DQ173036)

53 Arthrobacter methylotrophus (AF235090) T 100 Naphthalene-utilizing bacterium (AF531476) 100 DGGE band 1B143 (JF729965)

83 Williamsia marianensis (AY894336) T 81 97 Uncultured Williamsia (EU375219) 58 DGGE band 1B392 (JF729962) 54 Rhodococcus kyotonensis (AB269261) T Uncultured Nocardiaceae (DQ870543) Nitriliruptor alkaliphilus (EF422408) T 81 100 Uncultured bacterium clone (GQ263141) DGGE band 3B755 (JF729959) Sphingobium aromaticiconvertens (AM181012) T 100 DGGE band 1B382 (JF729969) α 76 Uncultured bacterium clone (EU218656) Sphingomonas enrichment culture clone (FJ828929) Proteobacteria 50 95 DGGE band 1Pa401 (JF729967) 100 Uncultured bacterium clone (FJ623322) β Hydrogenophaga pseudoflava (AF078770) T

62 76 Lysobacter spongiicola (AB299978) T

94 Uncultured Lysobacter (EU374887)

DGGE band 1B174 (JF729968) γ

65 Uncultured Proteobacterium clone (EF019722) 86 DGGE band 2Pa388 (JF729966) 0.06

107

Figure 4.5 Phylogenetic relationships of 16S rRNA gene fragments of Bacteroidetes Phyla of excised bands from DGGE gels of bulk and vegetated by P. angustata soils from Eureka, Nunavut. Bootstrap values ≥50% (1000 replicates) are indicated at the nodes. Bands sequences from this study are indicated in bold; their names refer to the sample of origin. Sequences from GenBank database are identified with the accession numbers. The scale bar represents the expected number of changes per nucleotide position.

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Bacteroidetes

Eudora adriatica (AM745437) T 99 DGGE band 3Pa188 (JF729954)

100 DGGE band 3Pa604 (JF729950) 90 Uncultured bacterium DGGE band (DQ008465) 67 DGGE band 3B689 (JF729935) 99 DGGE band 1Pa675 (JF729930) 76 DGGE band 2B681 (JF729933)

100 DGGE band 1Pa697 (JF729937) Flavobacterium gelidilacus (AJ440996) T 58 Flavobacterium frigidimaris (AB183888) T 88 Uncultured Bacteroidetes (FJ532504) 71 DGGE band 2Pa746 (JF729945)

100 100 Uncultured Flavobacteria clone (AY907287) DGGE band 1B672 (JF729929) 50 Aequorivita lipolytica (AY027805) T DGGE band 3B679 (JF729932) DGGE band 1B713 (JF729939)

98 Uncultured bacterium clone (GQ425245) a Flavobacteria DGGE band 1B687 (JF729934) 82 Uncultured Bacteroidetes (FJ213809)

100 DGGE band 1B137 (JF729952) Gillisia sp. NP8 (EU196300) Gillisia mitskevichiae (AY576655) T DGGE band 3B616 (JF729951)

Gillisia limnaea (AJ440991) T 98 89 Gillisia sp. NP18 (EU196339) 65 Uncultured bacterial clone (EF127607) 61 DGGE band 3B738 (JF729944) Uncultured Subsaxibacter sp. (EU375196) 81 Uncultured bacterium DGGE gel band (GQ336904) Yeosuana aromativorans (AY682382) T DGGE band 1Pa176 (JF729953) 92 DGGE band 1B699 (JF729938) 75 DGGE band 3B596 (JF729948) 100 Flavobacteriaceae bacterium (FM162953) Thermotoga marítima 83 58 Uncultured bacterium clone (AJ853873) (M21774) DGGE band 3Pa678 (JF729931) 55 DGGE band 2B736 (JF729942) Winogradskyella sp. (DQ530465) 97 Winogradskyella eximia (AY521225) T

100 DGGE band 1Pa600 (JF729949) Algoriphagus sp. (FJ889580) 76 80 Rhodovirga aquimarina (AJ575264) T

Echinicola pacifica (DQ185611) T a Sphingobacteria

89 Uncultured bacterium DGGE band (AJ400574) 70 DGGE band 1B735 (JF729941) Roseivirga echinicomitans (AY753206) T 52 Uncultured bacterium clone (FJ675260) 68 DGGE band 3Pa579 (JF729947) 74 DGGE band 2B690 (JF729936) 90 DGGE band 2B728 (JF729940)

92 DGGE band 3Pa575 (JF729946) Uncultured bacterium clone (EU133732) 86 DGGE band 3Pa737 (JF729943) 97 Uncultured Flexibacteraceae (FJ946545) 0.2

109

Figure 4.6 Proportion of the 16S rRNA gene-DGGE bands classified into the Phyla Bacteroidetes, Actinobacteria, Proteobacteria and Gematimonadetes

110

Bacteroidetes Actinobacteria Proteobacteria Gemmatimonadetes

0.0 7 8.3 5.3 9.1 13.3 10.5 10 0.0 9.1 13.3 25.0 20 13.6 26.3

73.3 68.2 63 66.7 57.9

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Figure 4.7 Cumulative Mineralization of [1-14C]hexadecane at 5°C incubation in the dark: A) Average of triplicate samples of vegetated by P. angustata and bulk samples from each sampling site; B) vegetated (Br-V) triplicate samples (1259V, 1260V, 1261V) and bulk (Br-B) triplicate samples (1259B, 1260B, 1261B) from diesel-spill area, bioremediated; C) vegetated (NBr-V) triplicate samples (1263V, 1264V, 1265V) and bulk (NBr-B) triplicate samples (1263B, 1264B, 1265B) from downhill of the diesel-spill area, non-bioremediated; D) vegetated (Pr-V) triplicate samples (1269V, 1270V, 1271V) and bulk (Pr-B) triplicate samples (1269B, 1270B, 1271B) from a pristine area. Error bars represent the standard error of the mean.

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40 40 Br-V (a) Br-V (1259V) Br-B (a) A Br-V (1260V) B 35 NBr-V (ab) Br-V (1261V) 35 NBr-B (bc) Br-B (1259B ) Pr-V (a) 30 Br-B (1260B ) 30 Pr-B (c) Br-B (1261B) Sterile (c) Sterile 25 25

20 20

15 15

10 10

5 5

0 0 40 NBr-V (1263V) Pr-V (1269V) 40 NBr-V (1264V) C Pr-V (1270V) D 35 NBr-V (1265V) Pr-V (1271V) 35 NBr-B (1263B) Pr-B (1269B) Pr-B (1270B) 30 NBr-B (1264B) 30

Mineralization (%) Mineralization Pr-B (1271B) NBr-B (1265B) Sterile Sterile 25 25

20 20

15 15

10 10

5 5

0 0 0 20 40 60 80 100 0 20 40 60 80 100

Time (Days)

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Figure 4.8 Cumulative Mineralization of [1-14C]naphthalene at 5°C incubation in the dark: A) Average of triplicate samples of vegetated by P. angustata and bulk samples from each sampling site; B) vegetated (Br-V) triplicate samples (1259V, 1260V, 1261V) and bulk (Br-B) triplicate samples (1259B, 1260B, 1261B) from diesel-spill area, bioremediated; C) vegetated (NBr-V) triplicate samples (1263V, 1264V, 1265V) and bulk (NBr-B) triplicate samples (1263B, 1264B, 1265B) from downhill of the diesel-spill area, non-bioremediated; D) vegetated (Pr-V) triplicate samples (1269V, 1270V, 1271V) and bulk (Pr-B) triplicate samples (1269B, 1270B, 1271B) from a pristine area. Error bars represent the standard error of the mean.

114

100 100 Br-V (a) Br-V (1259V) Br-B (a) Br-V (1260V) NBr-V (b) A Br-V (1261V) B NBr-B (c) 80 Br-B (1259B) 80 Pr-V (c) Br-B (1260B) Pr-B (c) Br-B (1261B) Sterile (c) Sterile

60 60

40 40

20 20

0 0 100 10 NBr-V (1263V) Pr-V (1269V) NBr-V (1264V) Pr-V (1270V) NBr-V (1265V) C Pr-V (1271V) D 80 NBr-B (1263B) Pr-B (1269B) 8

NBr-B (1264B) Pr-B (1270B) Mineralization Mineralization (%) NBr-B (1265B) Pr-B (1271B) Sterile Sterile 60 6

40 4

20 2

0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

Time (Days)

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Figure 4.9 Symmetric biplot illustrating results from the Principal Components Analysis. Generated with normailized data of bacterial enumeration, the hexadecane and naphthalene mineralization and the amplification of alkB, ndoB and xylE using Addinsoft (2010), XLSTAT 2010, data analysis and statistical software for Microsoft Excel, Paris, France.

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117

Connecting Text (Connecting Chapter 4 to Chapter 5)

In the previous chapter, the microbial communities from bulk soils and soils vegetated by Puccinellia angustata were compared using samples collected from areas with different history of aged diesel-contamination and fertilization. According to these findings, it seems that P. angustata stimulated hydrocarbon-degrading bacterial populations in the pristine soil and in the diesel-contaminated soil, but not in the bioremediated diesel-contaminated soil where microbial populations had been already stimulated by the treatment. According to the previous two chapters P. angustata had positive results in aged diesel-contaminated soils but nothing has been mentioned regarding fresh diesel contamination. In the following chapter, we explore the response of P. angustata and its rhizospheric microorganisms to fresh diesel contamination.

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CHAPTER 5. Assessing phytoremediation of diesel-contaminated Arctic soils with the narrow alkali grass Puccinellia angustata

Ofelia Ferrera-Rodriguez1, Charles W Greer2 and Lyle G. Whyte1 1Dept. of Natural Resource Sciences, McGill University, Montreal, Canada 2NRC-Biotechnology Research Institute, Montreal, Canada.

5.1 Abstract

This work represents the first study investigating the potential of phytoremediation as approach in diesel contaminated Canadian high Arctic soils. Puccinellia angustata thrives in a variety of Arctic habitats, including diesel contaminated soils. In simulated high Arctic summer conditions, the capacity of P. angustata to improve diesel degradation by stimulating soil microflora was assessed. P. angustata demonstrated tolerance to diesel contamination as 86% of its seeds germinated at 10,000 mg Kg-1 (diesel/soil), 70% of the seedlings survived at 40,000 mg Kg-1 (diesel/soil) and 60% of the adult plants survived at 20,000 mg Kg-1 (diesel/hydroponics). P. angustata treatments significantly increased the removal of total petroleum hydrocarbons from diesel contaminated soils, removing ~30% of the extractable hydrocarbons ~8000 mg Kg-1 diesel/soil in 14 weeks treatment and increased (~10-fold) the abundance of total soil microorganisms (2.460.33 ×108 cells g-1soil) and 1diesel-degrading bacteria (5.91.59 ×106 CFU g-1). P. angustata treatments caused shifts in the soil microbial populations stimulating bacteria putatively participating in hydrocarbon biodegradation mainly related to Bacteroidetes and Proteobacteria. Nitrogen addition to P. angustata treatments with adult plants increased the plant biomass and further increased the prevalence of hydrocarbon-degrading microbes yet it had no measurable impact the hydrocarbon removal. The augmentation of soil nitrogen in the absence of P. angustata enhanced the removal of diesel (~24%) and increased microbial abundances similar to the P. angustata seedling treatments. Collectively, these results indicate that P. angustata plants improve hydrocarbon biodegradation and therefore they are suitable for phytoremediation of hydrocarbon contaminated Arctic soils. 119

5.2 Introduction

Hydrocarbon based fuels are key energy sources for supporting human activities in Arctic regions, and pollution resulting from its widespread use has occurred in soils and waters (Yang et al. 2009). Hydrocarbons are acutely toxic (Efroymson et al. 2004) pollutants and some (i.e. benzene and benzo[a]pyrene) are recognized mutagens and carcinogens (Sikkema et al. 1995) which can bioconcentrate and bioaccumulate in food chains (Saterbak et al. 1999) and therefore are a hazard to our environment. The rate of biodegradation of hydrocarbons in cold environments is slower than in temperate environments because of temperature dependent factors such as reduced solubility of hydrocarbons, reduced biochemistry and nutrient limitations (Aislabie et al. 2006). Still, significant oil biodegradation is known to occur at low temperatures in harsh environments (Leahy and Colwell, 1990). The difficulty to clean up these remote and environmentally stringent regions compels the search for affordable and low maintenance technologies (Margesin and Schinner 2001). Phytoremediation is a low cost, self sustaining approach which may suit Arctic conditions. Phytoremediation utilizes natural plant processes and the processes that allow their associated microflora to enhance natural degradation and removal of contaminants from soil or groundwater (Cunningham et al. 1996). These proceses involve: sequestration (phytostabilisation), degradation (phytodegradation), evapotranspiration (phytovolatilization) and stimulation of the microbial biodegradation of organic contaminants in soil (rhizospheric degradation) (Wenzel 2009). The degradation of petroleum hydroarbons by microorganisms in the rhizosphere of plants is believed to be the primary mechanims of the remediative process (McGuinness and Dowling, 2009).

Finding a plant species tolerant to the extreme conditions of Arctic soils as well as to the hydrocarbon contaminants is one of the major challenges for the phytoremediation of Arctic soils. Robson et al. (2003) screened 39 cold-tolerant plants (native or naturalized to western Canada) for their ability to survive in crude oil-contaminated soil, finding only twelve with phytoremediation potential. These included four naturalized grasses, three naturalized legumes, two native legumes, two native forbs and one native grass. Cold-tolerant species native to subarctic regions such

120 as White clover (Trifolium repens), Pea (Pisum sativum), Poplar (Populus deltoides × Wettsteinii) and Scots Pine (Pinus sylvestris) have been tested on diesel contaminated soils and found to increase diesel removal (Palmroth et al. 2002). Greenhouse (Phillips et al. 2006) and field trial (Phillips et al. 2009) phytoremediation experiments in Canadian subarctic regions have shown increased hydrocarbon-degrading microorganisms and increased TPH removal in the rhizosphere. These studies indicate the potential for developing phitoremediative procesess for Arctic environments.

With human activity increasing in the circumpolar north, the importance of cold-temperature remediative methods is growing. Research has yet to be published assessing the feasibility of employing phytoremediation in high Arctic Hydrocarbon contaminated soils. Since different soils have endemic microbial populations and plant-microorganism interactions are dependent on plant species and microbial community composition, it is essential to assess the unique plant-microbial dynamic using soil and native plants from the high Arctic. Preliminary plant screening results (Chapter 3) and the study of the microbial communities from diesel-contaminated soils vegetated by Puccinellia angustata (Chapter 4) indicated that P. angustata may enhance the biodegradation of hydrocarbons in arctic soils. Therefore, the main objectives of the present research were to (1) determine if P. angustata seeds, seedlings and plants are tolerant to diesel contamination, (2) to determine if P. angustata plants and seedlings increase the removal of TPHs from diesel contaminated soils in simulated high Arctic summer conditions and (3) to determine if P. angustata influenced the abundance, diversity and/or activity of the soil microbial populations associated with the removal of TPHs.

5.3 Materials and methods

5.3.1 Soil and plant materials Soil samples (~10.8 kg) were collected at the Eureka High Arctic weather station at Ellesmere Island, Nunavut, Canada (79° 58.800' N and 85° 55.800' W) during the summer of 2005. Soil samples were stored in sterile Fisherbrand bags, kept at ~0°Cduring transport to the laboratory (McGill University, Montreal, Canada) then stored at -20°C until further analysis. The soil had loam texture (48% sand, 17% clay 121 and 36% silt) and the following physicochemical characterisctics: pH of 7.48, electrical conductivity of 993 S cm-1, cation exchange capacity of 10.4 cmol Kg-1 (Ca 5.5 cmol Kg-1, Mg 3.3 cmol Kg-1,Na 1.3 cmol Kg-1, K 0.4 cmol Kg-1), available cations (P 4.8 g g-1, K 168 g g-1, Ca 1294 g g-1, Mg 563 g g-1, Al 202 g g-1), organic matter 1.6 %, total carbon 1.4 % and total nitrogen 0.11%. The physicochemical characterization of the soil was performed by Dr. Joann Whalen at McGill University (Sainte-Anne-de-Bellevue, QC, Canada). Soil subsamples were oven-dried (60oC for 48 h) and finely-ground (< 1 mm mesh) prior to soil physicochemical analyses. Soil pH was measured in a 1:2 soil/water mixture following a 30 min settling period (Hendershot, et al., 1993). Soil texture was determined by the hydrometer method described by Sheldrick and Wang, (1993). Soil organic carbon and total nitrogen were determined by combustion at 900oC with a Carlo-Erba NC Soils Analyzer (Milan, Italy). Puccinellia angustata (alkali grass) is a perennial hexaploid (Consaul et al. 2010) grass from the Poaceae family. As described by (Aiken., et al. 1996; González et al. 2000) which is native to northern regions of Alaska, Canada and Greenland. In the summer of 2007 five adult plants (producing seeds) of the species P. angustata were collected from the previously described sampling site. The plants were kept alive inside sterile bags, on which the gas exchange occurred through an aperture covered with a millipore filter (pore size 0.22 m), and water was provided through a sterile tub capped with thick cotton and gasses plugs to prevent the introduction of exogenous microorganisms (Figure 5.1) and were kept inside a growth chamber (CONVIRON model E15) under 10°C and simulated day and night light cycles. The plants were reproduced by vegetative propagation in sterile Hoagland-Arnon hydroponic solution (Erusha et al. 2002) and seeds used in experiments were retrieved from propagated plants as well as from the collected plants.

5.3.2 Testing adult plant, seedling and seed tolerance to diesel

Three tests were established to determine the tolerance of P. angustata to diesel hydrocarbons: (1) with adult (seed producing) plants, (2) with seedlings and (3) with seeds. All experiments were incubated under 10°C and 24hr light. Four different diesel

122 concentrations of 0 mg Kg-1, 20,000 mg Kg-1, 40,000 mg Kg-1, and 60,000 mg Kg-1 (w/w) in perlite (density 1.1g cm3) as support material with five replicates each, were used to test adult P. angustata plants. Hoagland-Arnon hydroponic solution (Erusha et al. 2002) was used to provide the necessary nutrients during the 120 days of the experiment. The average weight of adult plants at the beginning of the experiment was 2.3 g having an average foliage length of 13.5 cm. Ten replicates for each diesel concentration of 0 mg Kg-1, 1 g kg-1, 10,000 mg Kg-1, 20,000 mg Kg-1 and 40,000 mg Kg-1 (w/w) in soil were used to test P. angustata seedlings with an average foliage length of 2.2 cm. At the end of the experiments, dry weight of the plants was recorded. Germination of 150 seeds in diesel contaminated soil (10,000 mg Kg-1) diesel was tested. The seeds used in this experiment were sanitized in the following manner: three rinses of 95% ethanol (for 45 s) alternating rinses in sterile water, followed by two rinses of 3% Bleach (for 45s) alternately in sterile water and a final rinse in antibiotic solution containing ampicillin (100μg mL-1), streptomycin (100μg mL-1) and erythromycin (200μg mL-1). Seeds were then incubated for 24hr at room temperature on R2A plates to confirm the absence of bacteria prior to germination in diesel contaminated soil. Filter sterilized diesel was added to the soil in order to get a concentration of 10,000 mg Kg-1. Fifty more seeds were sanitized as previously described and were germinated in sterile filter paper as a control. The test involving the germination of seeds in diesel contaminated soil was kept in conditions which prevented the introduction of exogenous microorganisms as previously described, since surviving seedlings were used in the subsequent experiment.

5.3.3 Establishment of the phytoremediation experiments

Diesel Filtered through millipore pore size 0.22µm was added to soil at final TPH concentration of 10,000 mg kg-1. Two experiments one with adult plants (A) and the other with seedlings (B) were established in a randomized complete block design with four treatments with five replicates. The treatments consisted of: (1) planted and fertilized treatment (RF and SRF), (2) a planted non-fertilized treatment (R and SR), (3) an unplanted fertilized treatment (F) and (4) an unplanted non-fertilized control (D). Experiment A encompassed a single adult plant per pot. The P. angustata plants had an

123 average root volume of 5.8 1.5 mL, root length of 13.4 2.6 cm, foliage length 20.6 3.6 cm and a wet weight of 8 1.2 g. No statistical difference was found in the plant variables at the beginning of the experiment. The plants were grown in 4” round plastic pots with 300 g of soil contaminated with diesel and seedlings (five seedlings per well), in plastic germination trays (3X3 wells of 40 mL capacity) with 60 g of soil contaminated with diesel. Fertilization was performed prior to the begining of the experiment with a solution of KNO3 and Ca(NO3)2 to get a final concentration of 250 mg [N] per Kg of soil. Each pot from experiment A and each tray from experiment B was incubated in confined conditions to prevent the introduction of exogenous microorganisms as previously described (Figure 5.1) inside a growth chamber (CONVIRON model E15) under 10°C and simulated 24h day light (intensity 3) during 14 weeks. When required, watering of the plants was performed with sterile water (approximately once a week).

5.3.4 Total petroleum hydrocarbon (TPH) analysis

To determine the concentration of diesel hydrocarbons remaining in the soil after the treatments, TPH analyses were performed as described in the Canada-Wide Standard (CWS) for Petroleum Hydrocarbons in Soil (Canadian Council of Ministers of the Environment (CCME) -Tier 1 Method-(CCME., 2001). Each soil sample (10 g) was mixed with anhydrous sodium sulphate (1:1) and placed in a cellulose extraction thimble and subjected to Soxhlet extraction during 8 hr with 170 mL of the extraction solvent composed of a mix of hexane and acetone 50:50 (v/v). The soxhlet extraction system was coupled with an Allihn condenser with cooling water maintained at 4 °C for consistent extraction recovery efficiency (~90%) of o-terphenyl spiked into the soil prior to extraction as a surrogate standard. To eliminate impurities the TPH-extracts were passed through a silica gel bed equilibrated with a mix of hexane and dichloromethane 50:50 (v/v). Extracts were then concentrated with a Kontes concentrator. The volume of final TPH-extract was measured and filtered with a 1-μm Teflon filter. The TPH from the extracts were analyzed by gas chromatography with a flame ionization detector (GC-FID, Agilent 6890, DB-11 capillary column). Calibration stocks were prepared to determine

124 the average response factors for F2, F3 and F4 fractions. TPH, F2 and F3 concentrations were quantified by a horizontal baseline method integrating resolved and unresolved peak areas after subtraction of a blank run as described by De Jonge et al. 1997.

5.3.5 Total and cultivable microbial enumerations

The abundance of microorganisms present in the soil samples was estimated by microscopic counts of microbial cells using 5-(4,6-dichlorotriazinyl) aminofluorescein (DTAF) as described by (Kepner Jr & Pratt, 1994). From a total of 1 gram of soil, cells were counted from 102 and 103 dilutions filtered onto 25-mm diameter black polycarbonate 0.22 μm pore filters (Osmonics Inc.) with an epifluorescence microscope (Eclipse E600W, Nikon). The total cell count was an average from twenty fields per filter. Viable cell enumerations were made in triplicate using serial dilutions by the spread plate method as performed by (Steven et al. 2007). R2A agar (Becton, Dickson and Co.) was used to culture aerobic heterotrophic bacteria and minimal salts medium (MSM-50YE) (Greer, et al., 1993) was used to culture aerobic diesel-degrading bacteria. The MSM solid media was supplemented with hydrocarbons by adding ~200 μL of diesel onto a filter paper square (1.5 cm2) and placing it on the upper lid of the Petri plate. Petri plates were incubated inverted at 10°C for 20 days and the average of the colony forming units per gram or dry soil (CFU g-1 soil) calculated.

5.3.6 Total community DNA analyses 5.3.6.1 Total community DNA extraction

Total DNA was extracted in duplicate from one gram of soil per sample via the MoBio Laboratories Ultraclean Soil DNA Kit (Carlsbad, California, USA), following manufacturer´s instructions with a reduction in the bead-beating time to two minutes and the use of the alternative lysis protocol. Samples were extracted in duplicate and pooled to minimize extraction bias. To eliminate PCR inhibitors from the DNA extracts, such as hydrocarbons, humic and fulvic acids, a polyvinylpolypyrrolidone (PVPP) spin column filtration step was included (Berthelet, et al., 1996) yielding 50 μL of polymerase chain reaction (PCR) amplifiable DNA. DNA extracts typically ranged from 12-22 ng μL-1 as

125 quantified with the Nano Drop Spectrophotometer ND-1000 (Wilmington, USA).

5.3.6.2 Bacterial 16S rRNA gene PCR for denaturing gradient gel electrophoresis (DGGE) The PCR amplification of partial 16S rRNA genes was undertaken using the general bacterial-specific PCR primers 341F(GC) 5’-GCGGGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGGCCT ACGGGAGGCAGCAG-3´ and 758R 5´-CTACCAGGGTATCTAATCC-3’ (MWG Operon, Huntsville Alabama, USA). PCRs consisted of a final volume of 25 μL and contained 0.5 mM of each primer, 1X PCR buffer and 1.5mM of MgCl2 (as supplied with the Taq DNA polymerase (Invitrogen Canada), 0.2 mM of each deoxynucleoside triphosphate, 0.6 μL of 10 mg mL-1 bovine serum albumin, 1U of Taq DNA polymerase and 2-10 ng template DNA. PCR’s were performed either in a Techne TC-312 or a Techne Touchgene gradient thermocycler (Staffordshire, UK ) using the following thermocycling conditions: 96°C for 5 min, 10 cycles of 96°C for 1 min, 60°C for 45s (and touchdown of 1°C each cycle until reaching 55°C), 72°C for 1.5 min; then 15 cycles of 96 °C for 1 min, 55°C for 45s, 72°C for 1.5 min and a final extension of 72°C for 5 min. 5.3.6.3 Analysis of the DGGE profiles DGGE was performed using 16 rRNA gene PCR products pooled from five reactions per sample concentrated by ethanol precipitation (Sambrook & Russell, 2001) and re-suspended in 20μL of water resulting in 50-100 ng μL-1 of DNA. Approximately 1000 ng of DNA was loaded per lane. DGGE was performed according to (Steven et al. 2007) on a 8% polyacrylamide:bis-acrylamide gel with 45-65% denaturant gradient. Electrophoresis was performed at 60oCand 80V for 16h. Gel banding patterns were visualized by staining for 35 min with 1:10000 (v/v) Vistra Green (Amersham Pharmacia Biotech) and destained with 1 X TAE buffer for 20 min, then observed on a Bio Imaging System (Syngene, Canada). DGGE gel images were analyzed with GelCompar II software and the Neighbor-Joining algorithm was used to construct dendrograms.

126

5.3.6.4 Sequencing of DGGE bands of interest

Bands of interest were excised from the polyacrylamide gels (with GeneCatcher disposable gel excision tips) and DNA was eluted in 20-40 μL of double distilled water, incubated over-night at 5°C then incubated at 64°C for 30 min. The resulting solution was used as template DNA for PCR using the previously described conditions. PCR products were then cloned according to (Labbe, et al., 2007). Plasmid DNA from the clones was purified by boiling lysis (Sambrook & Russell, 2001) and used as template DNA for PCR using primers T7 and Sp6 (Promega, 1996). PCR products were sequenced at the Genome Québec Innovation Centre (McGill University) using the 3730XL DNA system (Applied Biosystems).

5.3.6.5 Phylogenetic analyses of 16S rRNA gene sequences

The Nucleotide Basic Local Alignment Search Tool (BLASTN) (Altschul, et al., 1990) was used to compare the 16S rRNA gene sequences against the GenBank database and the Sequence Match software (Cole et al., 2005) used to compare sequences against sequences of the Ribosomal Database Project (RDP). Both methods were used to identify the closest homologous sequences stored in the respective databases. Subsequent alignments and phylogenetic trees were performed with Geneious software (Drummond, et al., 2009).

5.3.7 Nucleotide sequence accession numbers The 16S rRNA gene sequences obtained in this study have been deposited in the GenBank database under the accession numbers JF729970 to JF730039.

5.3.8 Determining the presence of alkB, ndoB and xylE genes To determine the presence of bacteria with the capacity to metabolize linear or aromatic hydrocarbons, three catabolic genes linked to hydrocarbon degradation were targeted: (1) alkB encoding the alkane hydroxylase, amplified using primers alkB(F) 5’-CIGIICACGAIITIGGICACAAGAAGG-3’ and alkB(R) 5’–IICGITGITGATCIIIGTGICGCTGIAG-3’; (2) ndoB encoding the -subunit of the iron sulphur protein of naphthalene dioxygenase, amplified with primers ndoB (F) 5’-CACT 127

CATGATAGCCTGATTCCTGCCCCCGGCG-3’and ndoB(R) 5´-CCGTCCCACAAC ACACCCATGCCGCTGCCG-3’; and (3) xylE encoding the 2-3-catechol dioxygenase amplified with primers xylE(F) 5´-GTGCAGCTGCGTGTACTGGACATGAGCAAG-3’ xylE(R) 5´-GCCCAGCTGGTCGGTGGTCCAGGTCACCGG-3´ (Whyte, et al., 2002). 25μL volume PCRs were prepared as previously described but were subjected to the following thermocycling conditions: 96°C for 5 min followed by 10 cycles of 96°C for 1 min, touchdown annealing conditions starting at 58°C and ending at 53°C for 1min), 72°C for 1.5 min followed by 20 cycles of 94°C for 1 min, 55°C for 1min, extension 72°C for 1.5 min, and a final extension of 72°C for 10 min. The PCR-amplified DNA fragments were separated by electrophoresis (0.8% agarose, TAE gels at 85V for 45 min) and visualized by ethidium bromide staining (Sambrook & Russell, 2001).

5.3.9 Hydrocarbon microcosm mineralization assays Microcosms from composited soil samples from each treatment were established in triplicate to determine the hydrocarbon mineralization activity of microbial communities. Each microcosm consisted of 1 g of soil in a 30 mL serum bottle (Supelco) spiked with 100,000 dpm of either [1-14C] hexadecane (specific activity, 59mCi/mmol) (Sigma, St. Louis, MO) or [1-14C] naphthalene (specific activity, 6.2mCi/mmol) (Sigma, St. Louis, MO) in addition to non-radioactive hexadecane [100 μLg-1] or naphthalene -1 [10 μg g ], and a CO2 trap consisting of a 1 mL glass tube filled with 0.5 mL of 1M KOH. Control microcosms were prepared with sterile soil. Microcosms were incubated 14 in the dark at 10°C and monitored during 42 days. The amount of trapped CO2 was measured from the KOH solution by liquid scintillation spectrometry (LS6500 Multipurpose Scintillation Counter, Beckman).

5.3.10 Statistical analyses Analysis of variance (ANOVA) and Tukey test of plant biomas, bacterial enumeration, hexadecane and naphthalene mineralization data were generated using Addinsoft (2010), XLSTAT 2010, data analysis and statistical software for Microsoft Excel.

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5.4 Results

5.4.1 Tolerance of P. angustata adult plants, seedlings and seeds to diesel hydrocarbons After 120 days of exposure, the survival percentages of the P. angustata adult plants were as follows: 100% at 0 mg Kg-1, 60% at 20,000 mg Kg-1, 40% at 40,000 mg Kg-1, and 0% at 60,000 mg Kg-1 (diesel/perlite). No significant differences (p≤0.05) occurred in the dry weight of the survivors (average ~1.7 g). Following 120 days of exposure to diesel contaminated soil, P. angustata seedlings had the following survival percentages: 100% at 0 mg Kg-1, 1000 mg Kg-1 and 10,000 mg Kg-1, 90% at 20,000 mg Kg-1 and 70% at 40,000 mg Kg-1. The seedling biomass at the end of the experiment as measured as dry weight had significant (p≤0.05) differences: 0.05 g (A) at 0 mg Kg-1 0.03 g (B) at 1000 mg Kg-1 0.015 g (C) at 10,000 mg Kg-1, 0.011 g (C) at 20,000 mg Kg-1 and 0.0098 g (D) at 40,000 mg Kg-1 (The letter beside the dry weight values refers to the categories formed by the media comparisons with Tukey`s analysis; different letters represent significant differences, the same letter represents no significant difference). Eighty six percent of the seeds germinated in soil contaminated with 10,000 mg Kg-1 of diesel compared to 93% in the control.

5.4.2 Characteristics of plants and soil samples at the end of the phytoremediation experiment All P. angustata adult plants and seedlings were alive at the end of the phytoremediation experiments (14 weeks). There was a significant difference (p≤0.05) between the fertilized and the unfertilized treatments in the biomass of adult plants. Plants grown in fertilized treatment had an average total dry weight of 3.568  0.99 g compared to plants grown in the unfertilized soil which had an average dry weight of 1.424  0.22 g. No statistically significant differences were detected in the biomass of the P. angustata seedlings, though the unfertilized treatment did result in a lesser average dry weight (43.1  14.6 mg) than the fertilized treatment (49.2  11.1 mg). In both the plants and the seedlings approximately one third of the total biomass (dry weight) corresponded to the roots. Table 5.1 summarizes the total carbon and total nitrogen content detected in the soil samples at the beginning and at the end of the experiments.

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5.4.3 Total petroleum hydrocarbon degradation The residual concentrations of extractable TPHs in the soils at the end of the 14 weeks phytoremediation experiment were significantly lower that at the beginning (p≤0.05). The lowest residual THP concentration was detected in the rhizospheric samples from adult P. angustata plants fertilized (5497  104 mg Kg-1) and non-fertilized (5446  118 mg Kg-1). The highest residual THP concentration was detected in the untreated samples (6813  311 mg Kg-1) (Figure 4.2A). Therefore, P. angustata plants increased the removal of hydrocarbons in soil. The fertilized and non-fertilized phytoremediation treatment with P. angustata adult plants removed over 30% of the extractable THP from the soil samples while the fertilized and non-fertilized phytoremediation treatment with P. angustata seedlings ~24% of the extractable TPHs from the soil. Also the unplanted fertilized treatment removed ~24% of the extractable TPHs from the soil. In contrast, in the untreated control only 14% of the TPHs were removed (Figure 4.2B). The composition of diesel used as contaminate had a proportion of F2(C10-C16) /F3(C16-C34) equal to 1.4 (59%/41%). The extractable diesel from the sample at time zero had an F2/F3 proportion of 1.2 (55%/45%), which may indicate that some of the hydrocarbons from the fraction F2 were either strongly adsorbed by the soil particles or lost by evaporation. After the treatments, the F2/F3 proportion ranged from 0.9 to 1.3. Althoug the quantity of the removed TPH varied according to the treatment, the chromatogram profiles of the extractable TPHs did not show considerable changes in composition between prior to or after the treatments, indicating that hydrocarbons from both fractions (F2 and F3) were removed from the soils at similar proportions.

5.4.4 Abundances of total cells, heterotrophic and diesel degrading soil microorganisms Microbial abundance at the end of the experiment, as determined by microscopic counts, significantly (p≤0.05) increased in all treatments with the exception of the unplanted non-fertilized control (Table 5.2). The cultivable heterotrophic and diesel-degrading bacteria also significantly increased (p≤0.05) by the end of the experiment, including the unplanted non-fertilized (Table 5.2). Furthermore, all samples

130 treated with P. angustata adult plants (both fertilized and non-fertilized) had the greatest microbial abundances as determined by microbial microscopic counts (2.460.33 ×108 cell g-1), cultivable heterotrophic (2.720.54 ×107 CFU g-1) and diesel-degrading bacteria (5.91.59 ×106 CFU g-1). The treatments with P. angustata seedlings stimulated lesser the microbial abundance according to the microscopic (1.210.03 ×108 cell g-1), cultivable heterotrophic (1.50.77 ×107 CFU g-1) and diesel-degrading bacterial (1.180.27×106 CFU g-1) quantities. The fertilized unplanted treatment also had significantly higher (p≤0.05) microbial abundances according to microscopic counts 1.410.24 ×108 cell g-1 and cultivable heterotrophic bacteria 6.142.25×107 CFU g-1 compared to the unplanted non-fertilized (control) treatment but no significant differences occurred in the abundance of the cultivable diesel-degrading bacterial (3.812.1×105 CFU g-1).

5.4.5 Culture-independent microbial community analyses The 16S rRNA gene DGGE fingerprints of the soil bacterial communities seen in Figure 5.3 (composite samples). A total of 70 bands were isolated from the DGGE gels and the majority of the bands (19/20) retrieved from the top part of the gel were classified into the Phylum Bacteroidetes, while bands retrieved from the middle were more diverse and were classified as Proteobacteria (32/50), Firmicutes (6/50) and Actinobacteria (4/50). A single band retrieved from the bottom of the gel, was classified as the Gemmatimonadetes. Bacterial community profiles observed after the application of treatments were different from the community profiles observed at time zero (Figure 5.3A). The dendrogram from Figure 5.3B shows that the fingerprints of samples from experiment A clustered with samples from the same experiment and the same holds true for experiment B. Furthermore profiles from P. angustata treatments had higher resemblance to each other than to profiles of unplanted treatments (Figure 5.4). The profiles from the unplanted non-fertilized control treatment and the profiles from the time zero clustered separately from the rest of the profiles indicating that microbial communities shifted as a result of the fertilization and rhizospheric influence. The presence or intensity of individual bands was associated to certain treatments

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Figure 5.4), for example bands 1 and 27 classified as Bacteroidetes (Chitinophaga and Salinimicrobium) had a higher intensity at the fertilized treatments ether planted or unplanted. Bands 12, 14, and 18 classified into the Bacteroidetes phylum (Bacteroidetes_incertae_sedis) were more evident at the P. angustata treatments especially in the absence of fertilizer. At treatments with P. angustata seedlings bands 55 and 61 classified into the Gamma-proteobacteria class, had increased intensity compared to other bands. Bands detected in the soil prior to the treatments were also present at the end regardless of the treatment two examples of such endemic abundant bacteria are band 46 (Actinobacteria) and band 70 (Beta-proteobacteria). The abundance of some bacterial taxa increased to a detectable level during the experiment. Band 8 (Firmicutes) appeared in the profiles of all treatments from the experiment with adult plants. Likewise, some taxa were no longer detected at the end of the experiments, such as 47 (Gemmatimonadetes). The proportion of phylotypes was different in each treatment: bands classified as Proteobacteria accounted for 63% of the sequenced bands in the unplanted fertilized treatment, compared to 47% in the P. angustata fertilized treatment and only 37% in the P. angustata non-fertilized treatment. Bands classified as Bacteroidetes were more frequently detected in P. angustata treatments (fertilized and non-fertilized) accounting for 47% of their sequenced bands. Firmicutes and Actinobacteria were detected at low frequencies across all treatments (Figure 5.5). The sample characteristics of the closest related sequences retrieved in GeneBank to the 70 isolated 16S rRNA-DGGE bands had similarities with the samples from the present study. The closest BLASTn sequences came from: Arctic, Antarctic or glacier environments (41%), hydrocarbon-contaminated environments (37%), rhizospheric samples (14%) and other environments (7%) such as eutrophic lakes, haloalkaline soils or marine environments. The closest matches to the isolated bands are summarized in table 5.3. Closest matches to the 16S rRNA gene DGGE bands isolated from P. angustata phytoremediation treatments were often related to bacteria retrieved from hydrocarbon contaminated sites putatively classified into the genera: Sphingomonas, Denitratisoma, Nitrosospira, Sterolibacterium, Oleiphilus,

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Rhabdochromatium, Chitinophaga, Salinimicrobium, Fusibacter, and Zhihengliuella Several isolated bands also from P. angustata treatments were classified into the Bacteroidetes phylum and had closest matches retrieved from the rhizosphere. These could not be properly classified and were labeled Bacteroidetes_incertae_sedis by the RDP classifier.

5.4.6 Prevalence of alkB, ndoB and xylE genes At the commencement of the experiment only alkB was detected by PCR, however, at the conclusion of the experiment (14 weeks) alkB, ndoB and xylE genes were detectable by PCR from the DNA extracts from all treatments, including the non-fertilized unplanted control.

5.4.7 Hydrocarbon mineralization activity analyses The mineralization of hexadecane at 10°C was relatively low compared to the mineralization of naphthalene (Figure 5.6A). After 42 days of incubation 11.41.3 % of the hexadecane was mineralized in the fertilized sample from time zero and 6.4  0.68 % in the fertilized rhizospheric samples from P. angustata seedlings (BPaF). All other treatments demonstrated less than 4% mineralization of hexadecane. Naphthalene mineralization was higher, where 78.7  2.1% of the naphthalene was mineralized in the rhizospheric samples from the non-fertilized P. angustata adult plant treatment, and 74.8 0.96% was mineralized in the rhizospheric samples from the fertilized adult plant treatment. Naphthalene mineralization in the fertilized unplanted treatment was 64.9  9.72% and in the fertilized sample from time zero it was 57.0  2.6%. In the fertilized and non-fertilized rhizospheric samples from the seedling treatments, the naphthalene mineralization was ~22% (Figure 5.6B).

5.5 Discussion The present study assessed the phytoremediative capacity of P. angustata (seedlings and adult plants) under temperature and light conditions simulating Arctic summer. The feasibility of phytoremediation using P. angustata was based on five measures: (1) tolerance to diesel contamination at different life stages; (2) effect on hydrocarbon removal from contaminated soil; (3) effect on hydrocarbon degrading

133 bacteria (4) overall rhizospheric microbial community; and (5) degradation rates in hydrocarbon mineralization assays. The scenario of coupling phytoremediation with biostimulation, by adding nitrogen fertilizer (NO3) to the soil, was considered given the low nitrogen content of Arctic soils. Overall the results indicate a positive scenario in the potential application of P. angustata to remediate hydrocarbon contamination in Arctic soils and corroborate that microbial hydrocarbon biodegradation, via rhizospheric stimulation, is influential in phytoremediation.

5.5.1 Tolerance of P. angustata to Diesel Hydrocarbons Puccinellia angustata known as narrow alkali grass is native from the northern regions of Alaska, Canada and Greenland (United-States-Department-of-Agriculture, 2010). Its natural distribution across the Canadian Arctic Archipelago and along the Northwest Passage (González et al. 2000) reveals its natural adaptation to a range of Arctic environments. P. angustata inhabits a range of ecosystems such as river terraces, meadows, slopes, landslides, ridges, cliffs (margins of glacier moraines), seashore (occasionally, on fiords). It can grow on poorly drained soil, moderately wet soil and dry areas on gravel, sand, silt, clay and till (Aiken., et al. 1996). It also grows in calcareous, nitrophilous and saline habitats, having been found growing in abundance nearby cold saline springs on, Axel Heiberg Island (Aiken., et al. 1996). The capacity of P. angustata to survive in diverse habitats allows it to be a successful and often early colonizer of disturbed sites such as diesel contaminated soils near the Eureka high Arctic weather station on Ellesmere Island, Nunavut, Canada (as previously discussed in Chapters 3 and 4). The tolerance of P. angustata to different conditions is indicative of its hardiness and adaptability, which includes tolerance to diesel as shown in the present work. P. angustata seedlings demonstrated similar tolerance to hydrocarbons as that reported for, Mirabilis Jalapa L. (Peng et al. 2009) and Impatiens balsamina L. (Cai et al. 2010) having 100% survival at 10,000 mg Kg-1 with a plant biomas production between 30-70% compared to non contaminated controls and a 90% survival at 20,000 mg Kg-1 in a hydrocarbon-contaminated soil (with a plant biomas production between 20-50%). Although P. angustata showed less tolerance with only 70% survival in 40,000 mg Kg-1 in a hydrocarbon-contaminated soil compared to Brachiaria híbrido, Brachiaria 134 brizantha and Panicum maximum (Sangabriel et al. 2006) which had a 100% survival at 50,000 mg Kg-1. In contrast P. angustata adult plants had a 60% and 40% survival at 20,000 mg Kg-1 and 40,000 mg Kg-1, respectively, in hydroponics showing no biomas reduction compared to the non-contaminated controls, a similar response to that observed in Phaseolus coccineus with 50% survival and no biomas reduction in an hydrocarbon concentration of 50,000 mg Kg-1 (Sangabriel et al. 2006). P. angustata seeds produced only 7% fewer seedlings when germinated in 10 000 mg Kg-1 diesel-contaminated soil compared to germination in pristine soil. This result is promising for field applications, since sowing seeds is more practical than transplanting seedlings. Germination under arctic soil conditions is crucial in reflecting true germination rates, known to vary with differing soil physicochemical characteristics (Robson et al. 2003). In a study of 11 native Canadian grasses, crude-oil contaminated soil (10,000 mg Kg-1) decreased germination between 10 to 60% depending on the soil type, with Bromus ciliates showing a good percentage germination (40-80%). In comparison, P. angustata seeds show considerable germination capacity in diesel contaminated soils. This attribute, combined with the adult and seedling diesel tolerance and widespread distribution, proves that P. angustata has a potential application for phytoremediation of hydrocarbon contamination in Arctic soils.

5.5.2 Phytoremediation of a diesel contaminated Arctic soil Approximately 30% of the extractable TPHs present in the soil at the beginning of the experiment (~8,000 mg Kg-1) were removed via treatments with P. angustata adult plants during a 14 week period under 24 h light at 10°C, which represented an increase of 16-17% greater than the removal resulting from natural attenuation in the controls. This removal efficiency is similar to that achieved by plants in phytoremediation studies in temperate climates, for example Mirabilis jalapa L. achieved 21-25% hydrocarbon removal over the controls after 127 days of treatment (Peng et al. 2009) and Impatiens balsamina L. increased the removal of hydrocarbons 8-29% over the controls after 140 days of treatment (Cai et al. 2010). The addition of nitrogen to P angustata adult plant treatments did not increase the TPH removal even though there was a significant (p≤0.002) increase in the biomass of the adult plants due to the fertilized treatment (3.568  0.99 g) in comparison to the 135 non-fertilized (1.424  0.22 g) treatments. Thus it seems that the additional nitrogen in the fertilized treatments was used by the plants to increase biomass but this did not result in greater TPH removal. During the course of the experiment total nitrogen content of the fertilized unplanted treatment shifted from 0.121% to 0.094% as it did in the P. angustata treatments suggesting that in the absence of plants the nitrogen was used by the microbial populations resulting in greater removal of hydrocarbons (24.4%) than in the absence of fertilizer (14%). This is observation is supported by several studies (Børresen and Rike 2007; Ferguson et al. 2003; Mohn and Stewart 2000) that report nitrogen as a limiting nutrient in both Arctic and subarctic soils and the addition of nitrogen to hydrocarbon polluted Arctic soils is a successful bioremediation treatment (Walworth et al. 2007; Whyte et al. 1999). Three of the treatments using seedlings, fertilized and unfertilized planted and fertilized unplanted, had comparable remediation effects, removing ~24% of TPH, compared to the control which exhibited a significantly lower (p≤0.05) removal. Akin to the adult plant experiments, no significant differences were detected in seedling biomass between fertilized and non-fertilized treatments, despite a decline in total soil nitrogen content. Both cases implicate the utilization of nitrogen by the microbial population preferentially to the seedlings themselves. The lower level of TPH removal seen in seedlings compared to adults correlates to differences in the root biomass, where adults had ~100 times larger root biomass. The efficiency of phytoremediation has been positively correlated to root growth (Kaimi et al. 2006), the larger the root biomass (i.e. adult plant), the better the hydrocarbon removal (Zhuang et al. 2007). Nitrogen addition did increase mineralization of diesel, but this effect was independent of the presence of P. angustata. A different limiting factor was likely in effect, excess moisture is a likely candidate since after watering once a weak, soils were saturated by water for approximately a day possibly because the hydrophobicity of diesel limited the water adsorption to the soil particles as well as the absortion by the roots. According to Børresen and Rike (2007), who assessed the effects of nutrient content and moisture on the mineralization of hexadecane in an Arctic soil, an excess of moisture hindered the mineralization of hexadecane independent of nitrogen concentration. Approximately 50% of the isolated bands were closely related to bacteria

136 found in waterlogged environments (such as rice paddy soil, springs, sludge, sediments, lakes and marine environments), the soil moisture (0.09-0.21) which was close to field capacity at the end of the experiment appeared to have had significant effects on plant and microbial functions.

5.5.3 Correlation of Microbial abundance to TPH Removal Although it has been documented that several mechanisms, such as uptake, transformation, volatilization, and rhizodegradation occur during phytoremediation treatments, it is often concluded that most of the removal is due to the microbial activity (Kamath et al. 2004). Further, Siciliano and Germida (1998) demonstrated that the degradation potential of the rhizosphere is related to the abundance of hydrocarbon degrading microorganisms. An increase in hydrocarbon degraders could result from selective pressure through the root exudation of contaminant analogous (i.e. polyphenolic compounds), or indirectly from a widespread increase in microbial abundance due to rhizospheric conditions regulated by plant roots. The observed rise in microbial populations in P. angustata (plants or seedlings) and unplanted fertilized treatments shows that both root exudates and nitrate will stimulate growth. However, the largest microbial abundances including that of diesel-degrading bacteria occurred in P. angustata treatments, indicative that the rhizosphere environment stimulates heterotrophy and contributes to the growth of bacteria capable of metabolizing complex organic molecules.

5.5.4 Effect of phytoremediation treatments on Microbial diversity The bacterial community profiles revealed varying abundances of 16S rRNA genes from different phylotypes consistently associated with specific treatments. These differences may highlight community characteristics of the Arctic rhizosphere, and taxa tolerant to diesel contamination. However, abundances of PCR amplified 16S rRNA genes are subject to experimental biases. Differences in the detection frequency of diverse phylotypes showed for example that bands classified into the Proteobacteria phylum accounted for as much as 63% of the bands excised from the unplanted fertilized treatments, 47% for the planted fertilized treatments and 37% for the planted non-fertilized treatments. A shift in Proteobaceria can have a broad impact on the

137 hydrocarbon degradation since a dominance of Proteobacteria has been previously observed especially in the early stages of bioremediation (Greer et al., 2010) as well as in phytoremediation (Palmroth et al., 2007). Bands classified as Bacteroidetes were more abundant in the planted treatments (fertilized and non-fertilized), accounting for 47% of the total excised bands. Bacteroidetes are a metabolically diverse group, involved in the degradation of sugars, e.g. glucose and N-acetylglucosamine, and may participate in the conversion of lipopolysaccharides and peptidoglycan liberated by decaying cells (Kragelund et al., 2008) and have been previously detected in pytoremediation treatments (Palmroth et al., 2007) while bands classified as Firmicutes and Actinobacteria were detected at similarly low frequencies for all treatments. Hydrocarbon contamination (Gerdes et al. 2005; Labbe et al. 2007), fertilization (Deslippe et al. 2005), and plant presence (Costa et al. 2006; Kim et al. 2006; Kirk et al. 2005) have been recognized as the driving factors for shifts in microbial community composition and abundance although it is also recognized that these shifts are different according to the soil source (Juck et al. 2000; Yergeau et al. 2009). Changes in the microbial community due to P. angustata could tentatively be associated with root exudates. According to Eilers et al. (2010) common low molecular weight root exudates such as glucose, glycine and citric acid were able to increase the proportion of Beta-proteobacteria, Alpha-proteobacteria and Actinobacteria respectively, when added to a pristine grassland soil from Minnesota USA. In P. angustata treatments (with adult plants or seedlings), in the absence of fertilizer, greater intensities were apparent in bands related to the Chitinophagaceae family (96% similarity), from the rhizoplane of aquatic plants exposed to cadmium (Stout and Nusslein 2005), Bacteroidetes_incertae_sedis (98%), from the root of rice and another phyletically different Bacteroidetes_incertae_sedis (99% similarity), from rice paddy soil (Ishii et al. 2009). The greater intensity of these bands specific to P. angustata treatments which had closest matches to sequences from rhizosphere environments suggested that these bacteria may have a specific niche in the rhizosphere. The relative abundance of bands closely related to bacteria found in hydrocarbon contaminated environments was greater (47%) in the rhizosphere of fertilized P. angustata, followed by 35% in the non-fertilized rhizosphere of P. angustata these bands

138 were closely related to bacteria from the genera: Sphingomonas, Denitratisoma, Nitrosospira, Sterolibacterium, Oleiphilus, Rhabdochromatium, Chitinophaga, Salinimicrobium, Fusibacter, and Zhihengliuella. All of these genera have strains able to degrade hydrocarbons (Liu et al. 2009; Nohynek et al., 1996; Penner and Foght 2010; Rotaru et al., 2010). These results show that the treatments with P. angustata (with or without fertilization) stimulate the “prevalence” of bacteria potentially involved in hydrocarbon degradation. At the end of the experiment bands (8, 21, 34 and 42) related to Fusibacter sp. clone 22-7A (EU517558) detected in tailings from oil sands (Penner and Foght 2010) were evident in all treatments, but these bands were not present at time zero (prior to diesel contamination, indicating that the sole presence of hydrocarbons had enriched this type of bacteria. Never-the-less, some bands detected in the soil prior to the treatments were also present at the end regardless of the treatment applied, such as Arthrobacter bands 24, 46 and 51 and Rhodoferax bands 6, 17, 30, 40 and 49. Closely related to the bacterium clones (EU218648 and EU218810), previously detected in permafrost samples at Eureka, NU (Steven et al. 2008). These represent taxa unaffected by rhizospheric, or diesel treatments, and may be naturally abundant at the Eureka soil sampling site.

Therefore all treatments, had phylotypes closely related to bacteria previously found in polar environments as well as in hydrocarbon contaminated environments indicating that the diversity of microbial communities in Arctic soil includes endemic taxa commonly found in hydrocarbon rich environments, enriched by the diesel contamination employed in this study, but the combination of P. angustata and fertilization further increased the frequency of phylotypes closely related to bacteria from hydrocarbon contaminated environments. Also the presence of P. angustata (non-fertilized) increased the frequency of detection of phylotypes associated to bacteria from rhizosphere environments.

5.5.5 alkB, ndoB and xylE detection In the event that the P. angustata rhizosphere selectively enriches hydrocarbon degrading microorganisms, an increase in the prevalence of genes encoding

139 hydrocarbon-degrading enzymes such as alkB, ndoB and xylE genes would be apparent. Prior to treatment, alkB was the only gene detected in the soil. This finding was consistent with previous research which found alkB present in both hydrocarbon contaminated and pristine Arctic soils (Whyte et al. 2002). Following experimental period, it was found that alkB, ndoB and xylE were detectable in all treatments, suggesting that ndoB and xylE genes were enriched by the hydrocarbon contamination. This pattern has also been observed in Eureka, NU soil remediation experiments (Whyte et al. 2001). The improved detection of all three genes in all treatments does not establish a physiological correlation between P. angustata and hydrocarbon degrading microorganisms. However, a comparison using quantitative PCR would provide a more detailed account of the influence of the rhizosphere on alkB, ndoB and xylE levels.

5.5.6 Hexadecane and naphthalene mineralization The mineralization activities of hexadecane did not reflected the total petroleum hydrocarbon removal at the end of the experiments, since mineralization of hexadecane seemed to be stimulated by the fertilization but not by P. angustata whose presence significantly increased TPH removal. The conditions used for the mineralization experiments had some limitations inherent to the experimental set up: (1) competition between the non-radio labeled hydrocarbons and the radio labeled hexadecane to be mineralized by the microorganisms, (2) the experiments were done in the absence of P. angustata after the phytoremediation experiment was finished. A microcosms study (Mueller and Shann 2007) where the effects of Red maple root-derived substrates and inorganic nutrients showed that pyrene mineralization was enhanced by the addition of root extracts (mimicking the presence of a plant), but it was inhibited by the decay of excised roots in the microcosms, if this tendency was general regardless of the plant species could explain why P. angustata treatments increased hydrocarbon-degrading bacterial abundances and TPH removal but apparently not hexadecane mineralization. Larger activities of naphthalene mineralization than hexadecane mineralization were detected in the present study consotent with previous observations in hydrocarbon contaminated Eureka soils bioremediated (Whyte et al. 2001) or vegetated by P. angustata (data from Chapter 4). In arid, alpine and polar hydrocarbon-contaminated soils, K-strategists such as Actinobacteria tend to be dominant and tend to remain stable 140

(Greer et al. 2010). Based on 16S rRNA gene-DGGE band intensities of bands form the genera Arthrobacter (24, 46, 50, 51) the same phenomenon was observed in the present research. Moreover Whyte et al. (2002) found that rhodococcal alkB genotypes have a greater prevalence than other alkB genotypes at Arctic soils. The convergences of these observations suggest that low rates of hexadecane mineralization could be a consequence of the slow growth characteristic of k-strategists arguably the predominant n-alkane degraders in the soil. In contrast, r-strategist such as Proteobacteria, tend to become dominant after hydrocarbon contamination as well as after treatments such as nutrient addition (Greer et al. 2010) also observed after phytoremediation treatments with P. angustata as shown by the prevalence of alpha, beta and gamma Proteobacteria also consistent with results from chapter 3 showing that ndoB and xylE were only detected in samples vegetated by P. angustata, from which the Pseudomonas sp. strain 5K-VPa (JF339990) having both genes was isolated. In contrast, even if the biodegradation of n-alkanes and aromatic compounds occurred at different rates, it seems that at the end of the experiment a good proportion of both types of hydrocarbons were degraded, since the GC chromatograms of the remaining TPHs showed that hydrocarbons from the entire spectrum were reduced by the treatments.

5.6 Conclusions In the present research, it was shown that P. angustata seeds germinate in soil contaminated with diesel and P. angustata seedlings and adult (reproductive) plants are able to survive in the contaminated soil under summer arctic conditions. P. angustata increased the removal of THPs from diesel contaminated soils not only at its early life stage (seedling) but most efficiently at adult (reproductive) stage. P. angustata also increased the abundance of soil microorganisms and produced shifts in the soil microbial populations which stimulated the prevalence of bacteria, in particular bacteria in the Bacteroidetes and Proteobacteria phyla, putatively associated with hydrocarbon biodegradation. Nitrogen addition stimulated plant growth and mineralization of both hexadecane and naphthalene, as well as the prevalence of putative hydrocarbon degrading bacteria, therefore, the amendment of soils with nitrogen has potential to increase the efficiency of hydrocarbon removal over time. The collective data of the

141 present research supports that P. angustata represents a candidate for the phytoremediation high Arctic soils contaminated with hydrocarbons, but phytoremediation with P. angustata has yet to be tested in situ at the high Arctic, to determine its real phytoremediation capacity.

5.7 Acknowledgements Authors of this paper want to thank Dr. Joann Whalen (Department of Soil Science, McGill University, Macdonald Campus) for the physicochemical analyses of soil samples, and Dr. Thomas Daniel Niederberger, M. Sc. Roland Wilhelm, Dr. Wonjae Chang for their important feedback to the present research work. El Consejo Nacional de Ciencia y Tecnología de México (CONACYT, México) financed the first author’s current scientific education.

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Table 5.1 Soil physicochemical variables at the end of the phytoremediation experiment Sample ID %C %N C/N Water Content (w/w) Pristine soil 1.45  0.08 0.109  0.003 13.3 0.090 Time Zero 1.65  0.02 0.087  0.00 18.9 0.077  0.001 Time Zero 1.68  0.08 0.121  0.006 13.9 0.082  0.002 Fertilized RF 1.73  0.04 0.100  0.002 17.4 0.172  0.018 R 1.77  0.01 0.096  0.00 18.5 0.185  0.019 SRF 1.73  0.02 0.101  0.008 17.1 0.221  0.011 SR 1.72  0.18 0.092  0.010 18.7 0.178  0.012 F 1.72  0.05 0.094  0.002 18.3 0.089  0.021 D 1.76  0.02 0.091  0.00 19.3 0.210  0.017 RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized). C and N analyses were performed in composite samples of each treatment in duplicate. Water content was determined on each of the five replicates per treatment.

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Table 5.2 Microbial Enumeration Microscopic Heterotrophic Diesel- degraders Sample DTAF (106 CFU g-1soil) (106 CFU g-1soil) (108 cell g-1) Time Zero 0.93  0.001C 0.535  0.007D 0.037  0.001D Time Zero 0.93  0.001C 0.420  0.060D 0.040  0.001D Fertilized RF 2.46  0.327A 27.152  5.405A 5.898  1.596A

R 2.37  0.137A 13.188  7.728A 3.441  0.620A

SRF 1.21  0.026B 15.048  7.728A 1.175  0.273B

SR 1.25  0.026B 2.869  3.336BC 1.416  0.804B

F 1.41  0.024B 6.141  2.205B 0.381  0.208C

D 0.92  0.028BC 3.043  0.736C 0.141  0.047C RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized). Data are presented as means (n = 15) plus/minus standard deviation. For each column, means super index of different letters are significantly different at p ≤0.05; and means shearing super indices of the same letters are not significantly different according to Tukey test results. CFU g-1 = Colony Forming Units per gram of dry soil.

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Table 5.3 Nucleotide sequence similarity of the 16S rRNA gene-DGGE bands. Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Bacterium isolate DGGE gel 368/368 1 1PaRF1 JF729970 Sphingobacteria Cyanobacterial cultures band SG2 (GQ920626) (100%) Flavobacterium sp. 383/400 Supraglacial spring in the 2 2PaRF2 JF729971 Flavobacteria enrichment culture clone (95%) Canadian High Arctic BF64A_C64 (HM141520) Bacterium clone 371/374 Brine of an Ice-Sealed Antarctic 3 2PaRF2 JF729972 Flavobacteria LVBR10cD02 (GQ167311) (99%) Lake Bacterium isolate DGGE gel 391/394 4 4PaRF4 JF729973 BIS Antarctic soil band P1_24 (FJ770009) (99%) Enrichment culture from Flavobacterium sp. clone 390/392 5 5PaRF5 JF729974 Flavobacteria Supraglacial Spring in the BF64A_C64 (HM141520) (99%) Canadian High Arctic Bacterium clone 374/378 Permafrost/ground ice core from 6 6PaRF6 JF729975 -proteobacteria Eur3Bac2.68 (EU218810) (98%) the Canadian high Arctic Bacterium DGGE gel band 375/387 7 7PaRF7 JF729976 -proteobacteria Soil from the Antarctic Peninsula P9_8 1 (FJ770016) (96%) Fusibacter sp. enrichment 391/396 8 8PaRF8 JF729977 Clostridia culture clone 22-7A Tailings from oil sands (99%) (EU517558) Rhodocyclaceae bacterium clone 366/373 Activated sludge processes from 9 9PaRF9 JF729978 -proteobacteria REG_R2P2_G1 (98%) petroleum refineries (FJ933281) Bacterium clone 387/393 10 10PaRF10 JF729979 -proteobacteria Phenanthrene Contaminated Soil SBPostOx65 (HM622199) (98%) Bacterium clone anNSA11 385/395 High Arctic permafrost soil from 11 11PaR1 JF729980 BIS (EF034949) (97%) Spitsbergen, Northern Norway

145

Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Rhizoplane communities of aquatic Bacterium clone B98 369/383 12 12PaR2 JF729981 Sphingobacteria plants after cadmium (AY707561) (96%) exposure Bacterium isolate DGGE gel 375/378 13 13PaR3 JF729982 BIS Antarctic soil band P1_24 (FJ770009) (99%) Rice root associated nitrate, Bacterium clone 346/350 14 14PaR4 JF729983 BIS sulfate and ferric iron reducing IRR-DS5-11 (AJ621961) (98%) bacteria Bacterium clone P1mT_028 379/390 Sulfate-reducing bioreactors 15 15PaR5 JF729984 BIS (EF551967) (97%) treating mine drainage Bacterium clone 375/384 Brine of an Ice-Sealed Antarctic 16 16PaR6 JF729985 Clostridia LVBR10aG04 (GQ167327) (97%) Lake Bacterium clone 376/383 Permafrost/ground ice core from 17 17PaR7 JF729986 -proteobacteria Eur3Bac2.68 (EU218810) (98%) the Canadian high Arctic Bacterium clone: 364/365 18 18PaR8 JF729987 BIS Rice paddy soil TSNIR002_I05 (AB487276) (99%) Bacterium clone 356/374 Quinoline-degrading microbial 19 19PaR9 JF729988 -proteobacteria DROTU-29 (FJ546399) (95%) consortium Beta proteobacterium 330/351 20 20PaR10 JF729989 -proteobacteria clone MEf05cnp11G9 Eutrophic lake (94%) (FJ828069) Fusibacter sp. enrichment 405/407 21 21PaR11 JF729990 Clostridia culture clone 22-7A Tailings from oil sands (99%) (EU517558) Bacterium clone 394/404 Permafrost/ground ice core from 22 22PaR12 JF729991 BIS Eur3Bac2.16 (EU218774) (97%) the Canadian high Arctic Bacterium DGGE gel band 387/390 23 23PaR13 JF729992 -proteobacteria Soil from the Antarctic Peninsula BLMA_25 (GQ336963) (99%)

146

Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Bacterium clone 390/390 Permafrost/ground ice core from 24 24PaR14 JF729993 Actinobacteria Eur3BacAL.15 (EU218648) (100%) the Canadian high Arctic Bacterium clone 1_2-D1 411/416 Benzene degradation in liquid 25 25PaR15 JF729994 -proteobacteria (FN824929) (98%) culture Bacterium clone 384/387 26 26PaR16 JF729995 -proteobacteria Phenanthrene Contaminated Soil SBPostOx65 (HM622199) (99%) Bacterium clone 91-103 349/353 Asphalts of the Rancho La Brea 27 27FF1 JF729996 Flavobacteria (EF157129) (98%) Tar Bacterium clone 368/373 Sediment from East Mediterranean 28 28FF2 JF729997 Flavobacteria AMSMV-S1-B6 (FJ649501) (98%) Sea Beta proteobacterium 381/405 29 29FF3 JF729998 -proteobacteria clone MEsu06b11A8 Eutrophic lake (94%) (FJ828435) Bacterium clone 387/393 Permafrost/ground ice core from 30 30FF4 JF729999 -proteobacteria Eur3Bac2.68 (EU218810) (98%) the Canadian high Arctic Bacterium clone B2R 411/412 31 31FF5 JF730000 -proteobacteria Artesian water supply (AY962272) (99%) Bacterium clone 362/373 Sediments treated with nitrate and 32 32FF6 JF730001 -proteobacteria TCCC-N30-12 (97%) air treatments (GU233632) Bacterium clone 410/419 Permafrost/ground ice core from 33 33FF7 JF730002 -proteobacteria Eur3BacAL.88 (97%) the Canadian high Arctic (EU218697) Fusibacter sp. enrichment 392/394 34 34FF8 JF730003 Clostridia culture clone 22-7A Tailings from oil sands (99%) (EU517558)

147

Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Herbaspirillum sp. IHB B 400/412 35 35FF9 JF730004 -proteobacteria Roots of Tea 2274 (HM233968) (97%) Lysobacter sp. YIM C734 379/383 36 36FF10 JF730005 -proteobacteria Haloalkaline soil (EU135665) (98%) Rhodocyclaceae Non-sulfonated 368/378 37 37FF11 JF730006 -proteobacteria bacterium DGGE gel band alkylbenzene-contaminated (97%) B7 (GU734025) groundwater Bacterium isolate DGGE gel 408/410 38 38DD1 JF730007 BIS Antarctic soil band P1_1 (FJ770007) (99%) Bacterium clone KS-246 341/350 Soil Sample from Kafni Glacier in 39 39DD2 JF730008 BIS (EU809779) (97%) the Himalayas Bacterium clone 407/412 Permafrost/ground ice core from 40 40DD3 JF730009 -proteobacteria Eur3Bac2.68 (EU218810) (98%) the Canadian high Arctic Bacterium clone M29 408/415 41 41DD5 JF730010 Clostridia Oil polluted soil from Romania (DQ378249) (98%) Fusibacter sp. enrichment 389/395 42 42DD6 JF730011 Clostridia culture clone 22-7A Tailings from oil sands (98%) (EU517558) Bacterium clone 1_2-D1 376/377 Groundwater contaminated with 43 43DD7 JF730012 -proteobacteria (FN824929) (99%) BTEX, MTBE and ammonium 393/404 Sulfidic spring, Canadian High 44 44T01 JF730013 Flavobacteria Gillisia sp. NP8 (EU196300) (97%) Arctic Gamma proteobacterium 392/403 Bioremediation process of a 45 45T02 JF730014 -proteobacteria clone CM38F12 (97%) hydrocarbon-contaminated soil (AM936486) Bacterium clone 344/345 Permafrost/ground ice core from 46 46T03 JF730015 Actinobacteria Eur3BacAL.15 (EU218648) (99%) the Canadian high Arctic

148

Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Gemmatimonade Bacterium clone UOXC-f06 398/403 Sediment from Onyx River, Wright 47 47T04 JF730016 tes (EU869755) (98%) Valley, Victoria Land, Antarctica 380/388 Sulfidic spring, Canadian High 48 48T0F1 JF730017 Flavobacteria Gillisia sp. NP8 (EU196300) (97%) Arctic Bacterium clone 403/408 Permafrost/ground ice core from 49 49T0F2 JF730018 -proteobacteria Eur3Bac2.68 (EU218810) (98%) the Canadian high Arctic Bacterium clone CZ52E07 343/350 Pine root zone contaminated with 50 50T0F3 JF730019 Actinobacteria (EF507147) (98%) polychlorinated biphenyls (PCBs) Bacterium clone 355/355 Permafrost/ground ice core from 51 51T0F4 JF730020 Actinobacteria Eur3BacAL.15 (EU218648) (100%) the Canadian high Arctic Bacterium isolate DGGE gel 360/362 Oil-degrading microalgal-bacterial 52 52PaSRF5 JF730021 Sphingobacteria band SG2 (GQ920626) (99%) consortium 356/366 Sulfidic spring, Canadian High 53 53PaSRF1 JF730022 Flavobacteria Gillisia sp. NP8 (EU196300) (97%) Arctic Rice root associated nitrate, Bacterium clone 345/350 54 54PaSRF2 JF730023 BIS sulfate and ferric iron reducing IRR-DS5-11 (AJ621961) (98%) bacteria Gamma proteobacterium 393/401 Bioremediation process of a 55 55PaSRF6 JF730024 -proteobacteria clone CM38F12 (98%) hydrocarbon-contaminated soil (AM936486) Sphingomonas clone 345/362 Paddy soils irrigated with 56 56PaSRF4 JF730025 -proteobacteria SF640-62 (AB549869) (95%) petroleum wastewater Alcanivorax sp. clone XJ40 366/390 57 57PaSRF3 JF730026 -proteobacteria Aerobic activated sludge (EF648121) (93%) 2-Methyl-4-Chlorophenoxyacetic Bacterium clone MbH51 333/364 58 58PaSRF7 JF730027 -proteobacteria Acid Herbicide Degraders in (FN567699) (92%) Agricultural Soil

149

Similarity No Band Accession Class Closest match nt/nt Environment Number (%) Rice root associated nitrate, Bacterium clone 411/413 59 59PaSR1 JF730028 BIS sulfate and ferric iron reducing IRR-DS5-11 (AJ621961) (99%) bacteria Bacterium clone: 365/370 60 60PaSR3 JF730029 BIS Rice paddy soil TSNIR002_I05 (AB487276) (98%) Bacterium clone SC46 325/327 Heavily oil-contaminated and 61 61PaSR2 JF730030 -proteobacteria (EU735610) (99%) pristine soils Bacterium clone 91-103 357/359 Asphalts of the Rancho La Brea 62 62SF1 JF730031 Flavobacteria (EF157129) (99%) Tar Burkholderia sp. LM0705 352/393 Deep water octocoral Leptogorgia 63 63SF2 JF730032 -proteobacteria (DQ517221) (90%) minimata Beta proteobacterium clone 377/388 Marine bacterioplankton after 64 64SF3 JF730033 -proteobacteria 4234-27F (FR647805) (97%) environmental disturbance Hydrogenophaga sp. 348/354 Solid waste from oil-shale 65 65SF4 JF730034 -proteobacteria 1/4_C16_62 (EF540470) (98%) chemical industry (semi-coke) Bacterium clone KS-246 363/367 Soil Sample from Kafni Glacier in 66 66SD1 JF730035 BIS (EU809779) (98%) the Himalayas Bacterium clone 407/408 Permafrost/ground ice core from 67 67SD2 JF730036 -proteobacteria Eur3Bac2.68 (EU218810) (99%) the Canadian high Arctic Rhodocyclaceae 351/361 Oxic-anoxic interface of Lac Pavin, 68 68SD3 JF730037 -proteobacteria bacterium clone Eub62D3 (97%) a meromictic crater lake (GQ390174) Bacterium clone 1_2-D1 362/364 Benzene degradation in liquid 69 69SD4 JF730038 -proteobacteria (FN824929) (99%) culture Bacterium clone 380/383 Permafrost/ground ice core from 70 70OO13 JF730039 -proteobacteria Eur3Bac2.68 (EU218810) (99%) the Canadian high Arctic BIS= Bacteroidetes_incertae_sedis

150

Figure 5.1 Experimental set up: A) P. angustata plant collected from Eureka, Nunavut, Ellesmere Island, Canada, B) Vegetative propagation, C) Transplant to diesel contaminated soil, D) Conditions to keep exogenous microorganisms out of the plant-soil system, E) sterilized seeds, F) Seeds germinated in diesel contaminated soil (10,000 mg kg-1), G) Seedlings transplant, E) Conditions to keep exogenous microorganisms out of the plant-soil system.

151

152

Figure 5.2 Hydrocarbons at the end of the experiment, 14 weeks of incubation at 10°C, 24hr light. A) Residual Total Petroleum Hydrocarbons, Fraction F2 contains C10-C16 hydrocarbons, fraction F3 contains C16-C34 hydrocarbons. Letters at the top of the bars correspond to the groups formed by the means Tukey`s test, different letters indicate significant differences among treatments. B) Total Petroleum Hydrocarbons removal. Error bars indicate standard deviation of the data. The horizontal axis labels refer to the samples: T0 = time zero, RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized).

153

9000 A 8000 A 3547 B 7000 C 2974 C C 6000 D D 2632 3090 2936 5000 2488 2479

4000 4377 3839 3000 3356 3107 3009 2967 2883 Residual TPH (mg/Kg) TPH Residual 2000

1000

0 To D F RF R SRF SR

F2 (mg/Kg) F3 (mg/Kg)

35 B 30

TPH RemovalTPH(%) 10

5 14.0 24.4 30.6 31.3 24.6 23.7 0 D F RF R SRF SR

154

Figure 5.3 16S rRNA gene DGGE gel (45%-65% denaturant gradient) of composited samples from each treatment. The letters at the top of the gel refer to the treatments: T0 = time zero, RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized).

155

RF R F D T0 T0F SRF SR SF SD Pr 1 11 27 52 2 44 48 62 3 12 28 53 4 13 38 5 29 54 59 14

15 16 39 66 6 17 30 40 49 55 67 70 7 45 60 18 31 41 19 32 56 61 63 68 20 33 8 21 34 42 50 9 22 35 57 64 23 36 24 46 51 25 37 43 58 10 69 26 65 1 3 2 4 5 6 7 8 9 10

DDGGE 0 RF R F D T0 T0F SRF SR SF SD Pr 2

8 100

12 57 100

14 100 80

16 78 18

22 53

24 81 26 63 Pearson correlation [0.0%-100.0%] correlation Pearson DDGGE

156

Figure 5.4 16S rRNA gene DGGE gel (45%-65% denaturant gradient) of individual samples from each treatment.

A) Experiment with adult plants, B) Experiment with seedlings. T0 = time zero, RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized).

157

T0 T0F T0 A T0F 70 47 46 RF

12 14 18 F

27 1 D

38 39 8 B SD

SRF

55 SR

61 SF

158

Figure 5.5 Phylotypes composition of the sequenced bands from the Phytoremediation experiments. R = Rhizospheric from P. angustata, F = Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized).

159

Proteobacteria Bacteroidetes Firmicutes Actinobacteria Gemmatimonadetes

100.0 2.0 0.0 1.4 5.3 6.7 5.9 10.5 5.7 6.7 90.0 10.5 8.6 5.3

80.0 13.3

21.1 70.0 46.1 35.7

60.0 47.4 26.7

50.0

Proportion(%) 40.0

63.2 30.0

46.1 46.7 48.6 20.0 36.8

10.0

0.0 RF+SRF R+SR F D Total

Treatments

160

Figure 5.6 Cumulative Mineralization at 10°C incubation in the dark. A) [1-14C]hexadecane, B) [1-14C]naphthalene. Error bars indicate standard deviation of the data. Super index of the legends correspond to the groups formed by the means Tukey test, different letters indicate significant differences among treatments.

Treatments: T0 = time zero, RF = Rhizospheric from P. angustata adult plants fertilized, R = Rhizospheric from P. angustata adult plants, SRF = Rhizospheric from P. angustata Seedlings fertilized, SR = Rhizospheric from P. angustata Seedlings, F =Fertilized (250 mg Kg-1 [N]), D = Diesel contaminated (control unplanted, non-fertilized).

161

14 ToF A C A 12 To

RF BC 10 BC F

RBC 8 C D B 6 SRF BC Mineralization (%) Mineralization SR 4 sterile C

0 0 5 10 15 20 25 30 35 40 45 Time (days)

90 B ToF C 80 ToE B A RF 70 B C F A 60 R E D 50 D SRF D 40 SR E Mineralization (%) Mineralization sterile 30

0 0 5 10 15 20 25 30 35 40 45 Time (days)

162

CHAPTER 6. General Discussion and Conclusion

6.1 General Discussion

P. angustata increases significantly by at least an order of magnitude, the abundance of microorganisms including hydrocarbon-degrading bacteria in pristine, aged-diesel-contaminated and freshly-diesel-contaminated soils (as discussed in chapters 3, 4 and 5). An increase in the abundance of heterotrophic as well as hydrocarbon-degrading bacteria due to a plant’s influence has been documented in previous phytoremediation studies (Euliss et al. 2008; Kaimi et al. 2006; Kirk et al. 2005). P. angustata in combination with nitrogen addition has a stronger effect in freshly diesel contaminated soils than in aged diesel-contaminated soils. Nitrogen addition has a stronger effect over the abundance of microbial populations in aged diesel contaminated soils than in freshly diesel contaminated soils. The potential application of P. angustata for phytoremediation is apparent in terms of abundance mostly in fresh contaminated soils where microbial abundances were more efficiently increased by the plant stimulus than by nitrogen addition. Contrastingly in aged hydrocarbon-contaminated soils the microorganisms were more responsive to nutrient application than to the plant’s presence. Generally hydrocarbon removal efficiency is correlated with the abundance of hydrocarbon degrading microorganisms, therefore the previous observation could partially explain why contaminated soils that have undergone prolonged periods of ageing generally appear to be less responsive to phytoremediation than freshly contaminated soils (Wenzel, 2009). Fierer and Lennon (2011) observed a significant positive correlation between the number of individuals in a sample and the species richness. The Arctic soils analyzed in our study had Rr (reflecting the carrying capacity) and Fo values (reflecting the functional organization) similar to healthy ecosystems, with an adequate quantity and distribution of fast growing, (r-strategist) and resilient (k-strategist) microorganisms, indicating that these soils have microbial communities able to overcome cyclic changes of environmental conditions (summer, winter) as well as drastic changes caused by contamination events. More specifically, we observed that the microbial abundance (in terms of cells g-1 and CFU g-1) was greater in diesel-contaminated rhizospheric and/or fertilized soils where the microbial communities were predominantly r-strategist or “dominant phylotypes”. In

163 contrast, the pristine none-fertilized bulk soils had less microbial abundance but their communities were more diverse, with a larger proportion of k-strategists. The number of DGGE bands and their relative intensity, as well as the prevalence of different phylotypes, showed that P. angustata modified the structure of the bacterial communities, seemingly increasing the prevalence of phylotypes putatively capable of hydrocarbon degradation; but the changes in the community structure and composition were relative to the aging of the contaminant, for instance, in aged-diesel-contaminated soil, the proportion of DGGE bands from the Actinobacteria phylum, increased ~13% in the rhizosphere of P. angustata compared to bulk soil. Contrastingly bands isolated from freshly diesel contaminated rhizosphere soils were mainly classified as Bacteroidetes. Moreover, sequenced bands from aged-diesel-contaminated soil belonged to genera Arthrobacter, Algoriphagus, Eudora, Flavobacterium, Gematimonas, Hydrogenophaga, Yeosuana, as well as unclassified gamma-Proteobacteria and unclassified Sphingobacteria. while bands isolated from freshly diesel contaminated rhizosphere soils were mainly close relatives to Bacteroidetes from rhizosphere environments and hydrocarbon-degrading bacteria from the genera Sphingomonas, Denitratisoma, Nitrosospira, Sterolibacterium, Oleiphilus, Rhabdochromatium, Chitinophaga, Salinimicrobium, Fusibacter, and Zhihengliuella. These results are biased by intrinsic limitations of the applied methodology such as biases introduced during the total community DNA extraction, the PCR amplification, the co migration of DNA fragments as well as the intrinsic DNA detection limit of the DGGE methodology of ~of 5 X 106 genome numbers per g of dry soil corresponding to about 1–3 X104 genomes per PCR reaction (Gelsomino et al., 1999) which meant that we are only able to detect the more abundant “dominant” species in the soil samples among which were putative hydrocarbon degrading bacteria. But in my opinion, the most significant biases are caused by the low number of successfully sequenced bands which restricted the number of phylotypes known to be part of the studied bacterial communities, therefore probably overlooking other members of the community which might also be important. To determine more precisely which phylotypes were selectively enhanced by P. angustata would require a more extensive characterization of the microbial populations, complemented by assessments of the relative abundance of hydrocarbon catabolic genes for

164 example using real time PCR for 16S rRNA genes along with alkB, ndoB and xylE genes or to be more efficient in the determination of a broad variety of hydrocarbon degradation related genes we could use functional microarrays. alkB seemed to be prevalent in pristine, aged-diesel-contaminated soils as well as freshly diesel contaminated soils (as it was shown in chapters 3, 4 and 5) similarly to a previous study (Whyte, et al., 2002) in which alkB genes from Rhodococcus sp. were commonly (90%) detected in both pristine and hydrocarbon-contaminated soils from Eureka. Although alkB genes seem to be prevalent in different Arctic soils, the hexadecane mineralization activity appears to be affected by the aging of the hydrocarbons, the addition of nutrients (mainly nitrogen), and the presence of vegetation, as shown in the present study where P. angustata rhizospheric soils with diesel-contamination (aged or fresh), as well as rhizospheric pristine soils, had greater hexadecane mineralization than bulk soils. Here the addition of nitrogen further increased the hexadecane mineralization even though the final cumulative hexadecane mineralization was greater in the diesel-contaminated rhizospheric soils supplemented with nitrogen. In fresh diesel-contamination we found that the sole presence of the hydrocarbons was sufficient to increase the prevalence of ndoB and xylE. According to (Whyte, et al., 2001) xylE was not detectable by PCR either in contaminated or pristine Eureka soils, while ndoB was frequently detected in contaminated soil samples but not in pristine ones. In aged diesel contaminated soil P. angustata seems to increase the prevalence of ndoB, as it happened in Canadian flare-pit soils (Phillips, et al., 2006) and in soils from California (Siciliano, et al., 2003). An increase in the ndoB prevalence was mirrored by the naphthalene mineralization which increased 28% in P. angustata vegetated samples in aged diesel-contaminated soils. The previously discussed evidence led us to hypothesize that the rates of hexadecane mineralization may result from the slow growth characteristic of k-strategists such as Actinobacteria, considering that arguably they may be the predominant n-alkane degraders in the soil, and that r-strategist such as Proteobacteria, might be responsible for the naphthalene mineralization, but this hypothesis remains to be properly tested. In agreement with previous studies of Arctic soil communities (Mannisto and Haggblom 2006; Whyte et al. 2001; Whyte et al. 1996) in this study we observed that the

165 cultured hydrocarbon-degrading bacteria might be psychrotolerant rather than psychrophilic, but two Rhodococcus sp. strains showed hydrocarbon mineralization activity at -5°C (although it was 10 times lower than at 5°C). Consequently, in the Arctic most of the hydrocarbon degradation probably occurs during the summer months, period in which the daily average temperatures typically range between 2.3°C and 5.7°C, with a daily maximum of 8.8 rarely reaching a maximum of 20°C (Canadian-Climate-Normals, 1971-2000). None the less it is reasonable to expect some hydrocarbon degradation to be happening the rest of the year when daily average temperatures remain between -7.7°C and -38.4°C (Canadian-Climate-Normals, 1971-2000). Although the plants may be dormant or dead at subzero temperatures, their rhizospheric bacteria could mineralize hydrocarbons at subzero temperatures, therfore increasing the cumulative mineralization after this long period of time even though hydrocarbon mineralization is likely to happen at a lower rate than in the summer, but this reminds to be tested in situ. Although it has been documented that several mechanisms, such as uptake, transformation, volatilization, and rhizodegradation occur during phytoremediation treatments, it is often concluded that most of the removal is due to microbial activity (Kamath et al. 2004). Further, Siciliano and Germida (1998) demonstrated that the degradation potential of the rhizosphere is related to the abundance of hydrocarbon degrading microorganisms. An increase in hydrocarbon degraders could result from selective pressure through the root exudation of contaminant analogous (i.e. polyphenolic compounds), or indirectly from a widespread increase in microbial abundance due to rhizospheric conditions regulated by plant roots. The observed increment in microbial populations in P. angustata (plants or seedlings) and unplanted fertilized treatments shows that both root exudates and nitrate will stimulate growth. However, the largest microbial abundances including that of diesel-degrading bacteria occurred in P. angustata treatments, indicative that the rhizosphere environment stimulates heterotrophy and contributes to the growth of bacteria capable of metabolizing complex organic molecules. Besides its effect on the soil microbial populations, P. angustata seedlings and adult plants tolerated over 10,000 mg Kg-1 of diesel and the same concentration did not significantly decrease seed germination. In growth chamber simulated summer Arctic conditions treatments with seedlings and adult plants during 14 weeks, removed over 25%

166 of the diesel from freshly contaminated soil. The previously mentioned characteristics and this plants species widespread distribution in Arctic regions along with its capacity to thrive in diverse habitats (Aiken et al., 2003), indicate that P. angustata has potential for the phytoremediation of hydrocarbon-contaminated Arctic soils, although to determine its real phytoremediation capacity, it has yet to be tested in situ at the high Arctic. Phytoremediation holds promise for in situ treatment of polluted soils (Wenzel, 2009). Understanding plant-microbe interactions involved in phytoremediation as well as how they are affected by other parameters such as nutrient and contaminant availability is essential to better predict and control the outcome of this environmentally friendly treatment. The present research represents a first approach towards understanding the dynamic plant-bacteria interactions happening in Arctic soils contaminated by hydrocarbons. The preliminary analyses led us to focus on P. angustata but other plants such remain to be investigated. Results from the present study revealed that in Arctic soils, P. angustata increased the bacterial abundance (including the abundance of hydrocarbon-degrading bacteria) and also increased the relative detection of bacteria closely related to known hydrocarbon-degraders or to bacteria discovered in oil-contaminated soils and waters, mainly from the phyla Actinobacteria (in aged diesel-contaminated soil), Bacteroidetes, and Proteobacteria (in freshly diesel-contaminated soil). In some of the studied soils, this plant species also increased the PCR detection of genes related to hydrocarbon degradation (alkB and ndoB). Moreover, P. angustata stimulated an increment in the mineralization of hydrocarbons (in aged diesel contaminated Arctic soils) and enhanced the removal of TPH (from freshly contaminated Arctic soils). The concordance of these observations as well as P. angustata’s intrinsic hardiness, adaptability, and tolerance to hydrocarbon contamination strongly indicates that this plant species has potential for hydrocarbon phytoremediation in contaminated polar soils and should be considered for further studies in Arctic regions.

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6.2 General Conclusions This study shows that soils vegetated by Puccinellia angustata had great abundances of hydrocarbon-degrading bacteria and an increased prevalence of genes encoding hydrocarbon oxidizing enzymes when compared to samples vegetated by Eriophorum scheuchzeri, Potentilla cf. rubricaulis, Oxyria digyna and Salix arctica. Relatively greater abundances of heterotrophic bacteria and hydrocarbon degrading bacteria were observed in pristine Arctic soils and soils with aged or fresh diesel contamination where P. angustata grew. Moreover, this plant species modified the structure of the bacterial communities increasing the prevalence of phylotypes putatively capable of hydrocarbon degradation, mostly belonging to Actinobacteria, Bacteroidetes, Gemmatimonadetes and Proteobacteria phyla in soils with aged diesel contamination, as well as phylotypes from Bacteroidetes, Firmicutes and Proteobacteria phyla in freshly diesel contaminated soils. Nitrogen addition to bulk soils with aged or fresh diesel contamination also had a positive effect on hydrocarbon degrading bacterial populations and the consequent hydrocarbon removal from soil, but it did not have a synergetic effect when combined with P. angustata treatments. These findings may be biased by intrinsic limitations of the applied methodology but they are evidence of positive microbial community shifts enhancing hydrocarbon removal from soils caused by this plant species. Besides its effect on the soil microbial populations, P. angustata seedlings and adult plants tolerated over 10,000 mg Kg-1 of diesel and the same concentration did not significantly decrease its seed germination. In growth chamber simulated summer Arctic conditions, the treatments with seedlings and adult plants removed more diesel (10-16%) from freshly contaminated soil than the unplanted controls. The previously mentioned characteristics and this plant species, widespread distribution in Arctic regions along with its capacity to thrive in diverse habitats, indicate that P. angustata has potential for the phytoremediation of hydrocarbon-contaminated Arctic soils, although to determine its real phytoremediation capacity, it has yet to be tested in situ at the Arctic.

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CHAPTER 7. Contributions to Knowledge

 This study was the first to compare the bacterial community structures of Arctic soils vegetated by the native plants Eriophorum scheuchzeri, Potentilla cf. rubricaulis, Oxyria digyna, Salix arctica and Puccinellia angustata.

 This research demonstrated for the first time that bacterial communities from the rhizosphere of P. angustata are different in abundance (more abundant), structure (increased the prevalence of specific phylotypes) and diversity (perceptively larger Fo and lower Rr) than bacterial communities from bulk soils.

 This study revealed that, P. angustata increases the prevalence of bacterial phylotypes putatively capable of hydrocarbon degradation, causing this increment during the seedling stage as well as in the adult reproductive stage.

 This was the first study determining that P. angustata has a great germination and survival percentage growing in freshly diesel-contaminated soils, proving its tolerance to hydrocarbons.

 This was the first study to assess (at growth chamber scale) phytoremediation treatments using P. angustata seedlings or adult plants, showing it increased the removal of TPH from an Arctic diesel-contaminated soil.

 This study showed for the first time that the Arctic plant P. angustata, is easily propagated, tolerates hydrocarbons, stimulates the native hydrocarbon degrading bacteria, characteristics which complemented by its hardiness and adaptability make it good candidate for phytoremediation of Arctic soils contaminated by petroleum hydrocarbons

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Appendices

A) GenBank accession numbers and description of Isolated Strains from Chapter 3 (JF339990-JF340035)

JF339990>1144952 [organism=Pseudomonas sp.][strain=5K-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339991>1144976 [organism=Pseudomonas sp.][strain=5T-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339992>1144982 [organism=Pseudomonas sp.][strain=6S-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339993>1144984 [organism=Pseudomonas sp.][strain=5B-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004] JF339994>1144988 [organism=Pseudomonas sp.][strain=5KS-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339995>1145004 [organism=Comamonadaceae bacterium][strain=12S-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339996>1145017 [organism=Pseudomonas sp.][strain=10S-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339997>1145028 [organism=Pseudomonas sp.][strain=5S-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339998>1145031 [organism=Pseudomonas sp.][strain=5KW-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF339999>729910 [organism=Arthrobacter sp.][strain=7.19-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

189

JF340000>729939 [organism=Intrasporangiaceae bacterium][strain=7.4-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340001>729954 [organism=Arthrobacter sp.][strain=7.11-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340002>729962 [organism=Sanguibacter sp.][strain=1.23-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340003>729976 [organism=Arthrobacter sp.][strain=7.3-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340004>729979 [organism=Rhodococcus sp.][strain=1.3-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340005>729981 [organism=Intrasporangiaceae bacterium][strain=7.31-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340006>729990 [organism=Polaromonas sp.][strain=7.23-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340007>864083 [organism=Promicromonospora sp.] [strain=3.1- VPr][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Potentilla rubricaulis][collection-date=2004]

JF340008>864085 [organism=Arthrobacter sp.][strain=5.6-Bc][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340009>864089 [organism=Nocardia sp.][strain=3.2-VPr][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Potentilla rubricaulis][collection-date=2004]

JF340010>864103 [organism=Sanguibacter sp.][strain=1.23S-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon 190 source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340011>864104 [organism=Arthrobacter sp.][strain=8.16-VSa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Salix arctica][collection-date=2004]

JF340012>864109 [organism=Arthrobacter sp.][strain=2.13-Ba][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340013>864115 [organism=Micrococcineae bacterium][strain=9.2-BP][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340014>864119 [organism=Arthrobacter sp.][strain=2.4-Ba][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340015>864122 [organism=Rhodococcus sp.][strain=4.20-VOd][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Oxyria digyna][collection-date=2004]

JF340016>864128 [organism=Promicromonospora sp.][strain=10.25-Bb][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340017>864134 [organism=Rhodococcus sp.][strain=1.15-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340018>864138 [organism=Microbacterium sp.][strain=6.11-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340019>864142 [organism=Rhodococcus sp.][strain=3.3-VPr][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Potentilla rubricaulis][collection-date=2004]

JF340020>864143 [organism=Alpha-proteobacteria] [strain=6.6-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340021>864144 [organism=Arthrobacter sp.][strain=7.11S-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S 191 small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340022>864162 [organism=Sanguibacter sp.][strain=1.16-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][note=soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340023>864163 [organism=Arthrobacter sp.][strain=9.22-BP][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340024>864167 [organism=Arthrobacter sp.][strain=10.1-Bb][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340025>739688 [organism=Leifsonia sp.][strain=1.5-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340026>739728 [organism=Mycobacterium sp.][strain=1.12-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340027>739748 [organism=Mycobacterium sp.][strain=1.1-VEs][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF340028>4375799 [organism=Arthrobacter sp.][strain=2.13ST-Ba][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004] Arthrobacter sp. strain 2.13ST-Ba, 16S ribosomal RNA gene partial sequence, isolated from high Arctic soil

JF340029>4375847 [organism=Rhodococcus sp.][strain=4.20ST-VOd][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Oxyria digyna][collection-date=2004]

JF340030>4375836 [organism=Arthrobacter sp.][strain=7.30ST-VPa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF340031>4375814 [organism=Arthrobacter sp.][strain=9.29ST-BP][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon 192 source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340032>4375822 [organism=Clavibacter sp.][strain=10.27ST-Bb][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340033>4375816 [organism=Arthrobacter sp.][strain=2.5ST-Ba][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340034>4375873 [organism=Arthrobacter sp.][strain=10.26ST-Bb][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil][collection-date=2004]

JF340035>4375824 [organism=Arthrobacter sp.][strain=8.25ST-VSa][molecule=DNA][location=genomic][moltype=Ribosomal RNA][note=16S small subunit ribosomal RNA][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Salix arctica][collection-date=2004] alkB,ndoB, xylE from Isolated Strains 2004

JF520628>739699 [organism=Mycobacterium sp.] [strain=1.1-VEs][isolate=true] [molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase] [note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF520629>729914 [organism=Rhodococcus sp.] [strain=1.3-VEs][isolate=true] [molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF520630>739726 [organism=Leifsonia sp.] [strain=1.5-VEs][isolate=true][molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF520631>4375874 [organism=Mycobacterium sp.] [strain=1.12-VEs][isolate=true] [molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Eriophorum scheuchzeri][collection-date=2004]

JF520632>4375826 [organism=Nocardia sp.] [strain=3.2-VPr][isolate=true][molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][isolation-source=high-Arctic-soil vegetated by Potentilla rubricaulis][collection-date=2004]

JF520633>4375844 [organism=Rhodococcus sp.] [strain=3.3-VPr] [isolate=true][molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon 193 source][isolation-source=high-Arctic-soil vegetated by Potentilla rubricaulis][collection-date=2004]

JF520634>729952 [organism=Arthrobacter sp.] [strain=7.19-VPa] [isolate=true][molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF520635>729907 [organism=Intrasporangiaceae bacterium] [strain=7.31-VPa] [isolate=true][molecule=DNA][location=genomic][gene=alkB][note=putative alkane monooxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF520636>4375871 [organism=Pseudomonas sp.] [strain=5K-VPa] [isolate=true][molecule=DNA][location=genomic][gene=ndoB][note=putative naphthalene dioxygenase ndoB][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

JF520637>4375797 [organism=Pseudomonas sp.] [strain=5K-VPa] [isolate=true][molecule=DNA][location=genomic][gene=xylE][note=putative catechol 2,3-dioxygenase][note=bacteria cultivated on media with diesel fuel as carbon source][isolation-source=high-Arctic-soil vegetated by Puccinellia angustata][collection-date=2004]

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B) Principal component analysis of variables determined in from Chapter 4 Normalized data

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C) Representative Chromatograms of the TPH extracted from soil samples from chapter 5 Note: in this graphics the Area underneath the curve is not directly related to the concentrations of TPH in samples since there were some differences in the dilutions injected in the chromatographer which were considered for the calculations.

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Typical profile after treatments with P. angustata adult plants plus nitrogen fertilization

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