MICROBIAL COMMUNITIES AND PARASITES ASSOCIATED WITH DIPOREIA SPP. (AMPHIPODA, GAMMARIDAE) AND THEIR POTENTIAL IMPACTS ON DIPOREIA SPP. HEALTH IN THE LAURENTIAN GREAT LAKES BASIN By Andrew David Winters A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Fisheries and Wildlife – Doctor of Philosophy 2013 ABSTRACT MICROBIAL COMMUNITIES AND PARASITES ASSOCIATED WITH DIPOREIA SPP. (AMPHIPODA, GAMARIDAE) AND THEIR IMPACTS ON DIPOREIA HEALTH IN THE LAURENTIAN GREAT LAKES BASIN By Andrew David Winters Due to their unique position in the foodweb, Diporeia (Amphipoda, Gammaridae) are an important component in the Great Lakes ecosystem. Unfortunately, Diporeia abundances have been declining from the majority of their habitat throughout lakes Michigan, Huron, Erie, and Ontario. The hypothesis that pathogens are the probable disease agents behind Diporeia declines, whether due to the presence of invasive dreissenid mussels or not, was investigated. This was investigated through examination of the bacterial communities associated with Diporeia through high throughput molecular techniques and gene sequencing, and identification of pathogens and their lesions through light and electron microscopical studies as well as phylogenetic studies. Analysis of 16S rRNA genes revealed that the bacterial communities associated with sediments were dominated by Actinobacteria and Acidobacteria while those associated with Diporeia were dominated by Flavobacterium spp. (Bacteroidetes) and Pseudomonas spp. (Gammproteobacteria). The presence of a bacterium belonging to Rickettsiales, an order of Bacteria containing known pathogens for freshwater amphipods, in Diporeia was confirmed. A significant temporal shift in bacterial community diversity was observed for Diporeia samples collected from one site in Lake Superior; however, the ecological significance of the shift remains to be determined. Microscopical examination of Diporeia collected from multiple sites in the southern basin of Lake Michigan between 1980 and 2007 revealed that Diporeia were host to a total of eight different groups of uni- and multicellular pathogens including, amoeba, microsporidia, haplosporida, filamentous fungi, yeast, ciliates, acanthocephala, and cestodes. Spatio-temporal variability in parasitic infections was observed with prevalences often fluctuating by depth, sampling site, and life stage of Diporeia. Additionally, the presence of the fish-pathogenic rhabdovirus viral hemorrhagic septicemia virus (VHSV) genotype IVb was confirmed in Diporeia samples collected from lakes Ontario, Huron, and Michigan, illustrating the role macroinvertebrates may play in VHSV ecology. Pathologies associated with pathogenic parasitic infections ranged from inflammation to destruction of tissues vital for the ecological performance of the amphipod. No significant positive correlations were observed between any group of parasites and dreissenid densities. My prevailing hypothesis is that parasite species belonging to Microsporidia and Haplosporidia are associated with detrimental effects that may have impacted Diporeia populations. Microscopical and phylogenetic investigations revealed the presence of two novel parasite species infecting Diporeia. The first parasite is a Haplsporidium sp. (Haplosporidia) that is similar to H. nelsoni, the causative agent of MSX disease in the eastern oyster (Crassostrea virginica). The second parasite is a Dictyocoela sp. (Microsporidia), a group of vertically transmitted parasites that infect both ovarian tissue and adjacent muscle of their amphipod hosts and are often associated with sex-ratio distortion in amphipod populations. Both novel parasites elicited a host immune response and were associated with destruction of muscle tissue in Diporeia. The findings of these studies shed light on pathogens as potential causes of Diporeia declines in the Laurentian Great Lakes. ACKNOWLEDGEMENTS I would like to express my sincere gratitude to the people who have made this project possible. In particular, I would like to thank my major advisor, colleague, mentor, and friend, Dr. Mohamed Faisal for providing me with this opportunity to pursue my doctoral study. He has been an invaluable source of guidance, supervision, and support for me. The ever-growing energy he instills into research has been a great source of inspiration for me as an aspiring scientist. With his enthusiasm, motivation, and talent for elegantly explaining concepts with clarity and simplicity, he has helped to make me the person I am today. He has provided encouragement, sound advice, and good company. I will always be indebted to him. I would like to thank my committee members, Dr. Travis Brenden, Dr. Scott Fitzgerald, and Dr. Terence Marsh for their insights on the organization of this project and editing of this manuscript. Dr. Fitzgerald provided invaluable interpretive expertise regarding the pathological analysis of samples essential for my research. Dr. Brenden was devoted to guiding the development of my quantitative analytical skills. Dr. Marsh has inculcated the importance of understanding microbial ecology and has sparked a passion for this subject within me. iv I would like to thank Mr. Tom Nalepa of the National Oceanic and Atmospheric Administration - Great Lakes Environmental Research Laboratory for donating samples, historical data, and his personal expertise. Tom’s generosity is greatly appreciated. The financial support of the Great Lakes Fisheries Trust (Grant #: 2009.1058), the United States Department of Agriculture - Animal and Plant Health Inspection Service (Grant #: 10-9100-1293-GR), and the United States Environmental Protection Agency Great Lakes National Protection Office (Grant #: GL00E36101) is gratefully acknowledged for funding this study. I wish to thank the current and past members of Dr. Faisal’s Aquatic Animal Health Laboratory. I am especially grateful to Dan Bjorklund, Robert Ford, Michelle Gunn, Dr. Robert Kim, Dr. Tom Loch, Elena Millard, Carolyn Schulz, Isaac Standish, Elizabeth Throckmorton, Danielle Van Vliet, Dr. Chris Weeks, Dr. Wei Xu, and Dr. Qingli Zhang for providing a stimulating and friendly environment in which to learn and grow. I also wish to thank my parents for always being there for me, even miles away from home. Thank you for believing in me. And finally, this journey would not have been completed without the encouragement, patience, and understanding of my wife, Blair. Thank you, “Neugs”. I love you. v TABLE OF CONTENTS LIST OF TABLES .................................................................................................. x LIST OF FIGURES ............................................................................................. xiii INTRODUCTION ...................................................................................................1 REFERENCES. ..................................................................................................6 CHAPTER 1. Literature Review and Overall Objectives of the Study ...................9 Diporeia Taxonomy ..........................................................................................10 Distribution .......................................................................................................10 Life History .......................................................................................................11 Diet… ...............................................................................................................12 Immune System................................................................................................13 Diporeia Decline in the Great Lakes .................................................................16 Ecological Significance .....................................................................................19 Invasion of the Great Lakes by Dreissenid Mussels .........................................19 Starvation .........................................................................................................20 Ecological Stressors .........................................................................................22 Microcystin .......................................................................................................22 Botulism............................................................................................................23 Pollutants..........................................................................................................24 Oxygen Depletion .............................................................................................24 Infectious Agents ..............................................................................................26 Plausibility of the Disease Theory.....................................................................28 Gaps in Knowledge ..........................................................................................29 Overall Objectives of the Study ........................................................................29 APPENDIX 1 ....................................................................................................32 REFERENCES ................................................................................................ .34 CHAPTER 2. Bacterial Communities Associated with Sediments in the Laurentian Great Lakes .......................................................................................46 Abstract ............................................................................................................47 Introduction.......................................................................................................48 Materials and Methods .....................................................................................49 Background on T-RFLP and Pyrosequencing................................................49 Sediment Sampling and Nutrient Analysis........................................................51 DNA Isolation .................................................................................................52 T-RFLP Analysis of Sediment Community Fingerprints .................................52 454 Pyrosequencing of Sediment 16S rDNA .................................................54 Statistical Analyses ........................................................................................55 Results .............................................................................................................57 Sediment Properties ......................................................................................57 T-RFLP Analysis of Sediment Communities ..................................................58 vi Pyrosequencing of Sediment Communities ...................................................58 Similarity Profile Analysis of Bacterial Community Profiles ............................59 Phylogenetic Structure of Sediment Communities .........................................60 Differences in Relative Abundances of OTUs among Pyrosequencing Clusters..........................................................................................................63 Redundancy Analysis ....................................................................................64 Relationship between Bacterial Community Structure and Sediment Properties ......................................................................................................65 Discussion ........................................................................................................65 APPENDIX 2 ....................................................................................................71 REFERENCES .................................................................................................93 CHAPTER 3. Bacterial Communities Associated with Diporeia spp. in the Laurentian Great Lakes .......................................................................................99 Abstract ..........................................................................................................100 Introduction.....................................................................................................101 Materials and Methods ................................................................................... 104 Sample Collection ........................................................................................ 104 DNA Isolation ............................................................................................... 104 T-RFLP Analysis .......................................................................................... 105 DNA Sequence Analysis ..............................................................................108 Estimation of Bacterial Community Coverage ..............................................109 Sequence Alignment and Phylogenetic Affiliation ........................................110 Results ...........................................................................................................111 Bacterial Communities of Diporeia ............................................................... 111 Temporal Shifts in Bacterial Diversity .......................................................... 113 Virtual Digestion and Phylogenetic Analysis ................................................ 113 Discussion ......................................................................................................116 Acknowledgements ........................................................................................ 121 APPENDIX 3 ..................................................................................................122 REFERENCES ............................................................................................... 138 CHAPTER 4. Spatio-temporal Dynamics of Parasites Infecting Diporeia spp. in Southern Lake Michigan ................................................................................ 145 Abstract ..........................................................................................................146 Introduction.....................................................................................................147 Materials and Methods ................................................................................... 149 Sample Collection ........................................................................................ 149 Identification of Organisms Infecting Diporeia ..............................................150 Analysis of Diporeia Parasite and Fungus Community Assemblages ..........150 Analysis of Infection Parameters and Diporeia Density ............................... 151 Results ...........................................................................................................153 Identification of Lesions Associated with Pathogens in Stained Diporeia Sections ......................................................................................... 153 Community Structure of Diporeia Parasite Communities ............................. 155 Investigation of Infection Parameters ........................................................... 156 vii Investigation of Infection Prevalence, Depth, and Dreissenid Density in Relation to Diporeia Density ........................................................................157 Discussion ......................................................................................................157 Acknowledgements ........................................................................................ 163 APPENDIX 4 ..................................................................................................164 REFERENCES ............................................................................................... 182 CHAPTER 5. Faisal, M. and Winters, A.D. (2011) Detection of Viral Hemorrhagic Septicemia Virus (VHSV) from Diporeia spp. (Pontoporeiidae, Amphipoda) in the Laurentian Great Lakes, USA. Parasites and Vectors. 4, 2 ..................... 188 Abstract ..........................................................................................................189 Findings ..........................................................................................................189 Acknowledgements ........................................................................................ 193 APPENDIX 5 ..................................................................................................194 REFERENCES ............................................................................................... 199 CHAPTER 6. Molecular and Ultrastructural Characterization of Haplosporidium Diporeiae n. sp., a Parasite of Diporeia spp. (Amphipoda, Gammaridae) ....................................................................................................202 Abstract ..........................................................................................................203 Introduction.....................................................................................................203 Material and Methods ..................................................................................... 205 Sample Collection and Morphological Examination .....................................205 DNA Isolation, Amplification, and Sequencing .............................................207 Sequence and Phylogenetic Analyses ......................................................... 207 Results ...........................................................................................................209 Pathology and Morphological Characterization............................................209 Phylogenetic Analysis .................................................................................. 210 Discussion ......................................................................................................211 Description......................................................................................................212 Taxonomic Summary ................................................................................... 212 Type Host ....................................................................................................212 Type Locality................................................................................................ 212 Type Material ............................................................................................... 212 Genetic Sequence ....................................................................................... 212 Etymology ....................................................................................................213 Acknowledgements ........................................................................................ 213 APPENDIX 6 ..................................................................................................215 REFERENCES ............................................................................................... 227 CHAPTER 7. Molecular and Morphological Characterization of Dictyocoela Diporeiae n. sp., a Parasite of Diporeia spp. (Amphipoda, Gammaridae) and Redescription of the Genus Dictyocoela (Microsporidia) ...................................232 Abstract ..........................................................................................................233 Introduction.....................................................................................................233 Material and Methods ..................................................................................... 235 viii Sample Collection and Morphological Examination .....................................235 DNA Isolation, Amplification, and Sequencing .............................................236 Sequence and Phylogenetic Analyses ......................................................... 237 Results ...........................................................................................................238 Pathology and Morphological Characterization............................................238 Phylogenetic Analysis .................................................................................. 239 Discussion ......................................................................................................239 Description......................................................................................................241 Taxonomic Summary ................................................................................... 241 Type Host ....................................................................................................242 Type Locality................................................................................................ 242 Genetic Sequence ....................................................................................... 242 Etymology ....................................................................................................242 Acknowledgements ........................................................................................ 242 APPENDIX 7 ..................................................................................................242 REFERENCES ............................................................................................... 252 CONCLUSIONS AND FUTURE STUDIES ....................................................... 256 Conclusions ....................................................................................................257 Future Studies ................................................................................................ 259 REFERENCES ............................................................................................... 262 ix LIST OF TABLES Table 2.1 Biological replicate sample identification (ID) for each sediment sample pyrosequenced or analyzed with T-RFLP using either the HhaI or MspI restriction endonuclease ...............................................72 Table 2.2 Sediment properties of sampling sites in the Great Lakes (phosphate = P, potassium = K, calcium = Ca, magnesium = Mg, nitrate = NO3, and ammonium = NH4)......................................................73 Table 2.3 Library sequence diversity of Great Lakes sediment samples based on T-RFLP analysis of 16S rRNA genes digested with either HhaI or MspI ...................................................................................74 Table 2.4 Library coverage estimations and sequence diversity of 16S rRNA sequence libraries derived from Great Lakes sediment samples based on 97% and 95% sequence similarities. Library coverage was calculated as C = 1-n/N, where n is the number of OTUs without a replicate, and N is the number of sequences. The numbers in the parentheses are lower and upper 95% confidence intervals for the Chao 1 estimators. The Shannon index -∑pi, where pi = ni / N, ni is the number of OTUs with i individuals, and N is the total number of individuals................................................................................75 Table 2.5 Correlations between the relative abundances of abundant bacterial phyla and proteobacterial classes, richness (Chao 1 values), and diversity (Shannon index), and the soil properties in Great Lakes sediments (P=phosphorus, K=potassium, Ca=calcium, Mg=magnesium, NO3=nitrate, NH4=ammonium). Bold values are significant (α=0.05 level)...........................................................................77 Table 2.6 Correlations between the relative abundances of bacterial phylotypes related with pathogenic bacteria and human fecal pollution and the soil properties in Great Lakes sediments (P=phosphorus, K=potassium, Ca=calcium, Mg=magnesium, NO3=nitrate, NH4=ammonium). P values are in parentheses. Significant correlations are in bold. Bold values are significant (α=0.05 level)............................................................................................79 x Table 3.1 Name, location, and depth of sampling sites, year of collection, and number of consensus T-RFLP profiles generated for each sample type and restriction endonuclease combination for this study................. 123 Table 3.2 Significant differences (P < 0.05) in relative peaks heights (MANOVA) for both 16S rRNA T-RFLP datasets (HhaI and MspI) generated for Diporeia samples collected from lakes Michigan and Superior and Cayuga Lake (New York) between 2007 and 2008 (T-RF in base pairs / P value) .................................................................124 .1 Table 3.3 Ribosomal Database Project (RDP) matches (≥98%) and predicted terminal-restriction fragment lengths (numbers of base pairs determined by both virtual restriction digestion and T-RFLP analysis of each base pairs) of selected 16S rRNA clones from Great Lakes Diporeia based on clone ....................................................................................... 125 Table 4.1 Total number of adult and juvenile (in parenthesis) Diporeia collected for each sampling occasion in the southern basin of Lake Michigan from 1980-2007 .............................................................. 165 Table 4.2 Summary of model selection for predicting parasite community richness in Diporeia, including the QICu value, the difference between the QICu, and the lowest QICu (ΔQICu). The other 100 models tested had QICu values > 17929.86. Main effects included sampling site, sampling year, and age of Diporeia ........................................................ 166 Table 4.3 Parameter estimates based on the model with the lowest BIC value for parasite community richness in Diporeia in southern basin of Lake Michigan from 1980-2007 .......................................................... 167 Table 4.4 Summary of model selection for predicting the abundance of Diporeia, including the BIC value, the difference between the BIC, and the lowest BIC (ΔBIC). The other 100 models tested had AIC values > 17929.86. Main effects included arcsine-root transformed parasite prevalence (individual and combined), abundance of dreissenids (Dreissenids), and depth (Depth).........................................169 Table 4.5 Parameter estimates based on the model with the lowest BIC value for Diporeia density in southern basin of Lake Michigan from 1980-2007 .............................................................................................. 170 Table 5.1 Locations in the Laurentian Great Lakes from which Diporeia spp. were collected for this study (CPE = formation of cytopathic effect; RT-PCR = results for amplification of the viral hemorrhagic septicemia virus nucleoprotein gene) .......................................................................195 xi Table 6.1 Samples in which Haplosporidium Diporeiae infection was observed in sections of Diporeia collected from Lake Superior in 2008 .......................................................................................................216 Table 6.2 Morphological characteristics and genetic similarity of all Freshwater species of haplosporidia and Haplosporidium nelsoni compared to H. Diporeiae .......................................................................217 Table 7.1 Morphological comparison and genetic similarity of Dictyocoela spp. ..................................................................................... 244 xii LIST OF FIGURES Figure 1.1 Histopathological section of Diporeia sp. (hematoxylin and eosin) showing the caecum, intestine, and extensive musculature. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation............33 Figure 2.1 Map of the Laurentian Great Lakes showing sampling sites for this study .............................................................................................80 Figure 2.2 Occurrence of observed operational taxonomic units (OTUs) and the corresponding average relative abundance among two terminal-restriction fragment length polymorphism (T-RFLP) profiles (either HhaI or MspI restriction endonuclease) and one 454 pyrosequencing library generated from amplified 16S rRNA genes from sediment samples collected from lakes Superior, Michigan, Huron, and Ontario. Axes are not on the same scale. A total of 443 OTUs were observed for the HhaI dataset, a total of 833 OTUs were observed for the MspI dataset, and a total of 7,344 OTUs (genetic distance level of 0.03) were observed for the 454 pyrosequencing library. Corresponding sequences for each analysis are displayed in Table 2.1...................................................................................................81 Figure 2.3 Hierarchical cluster analysis of Great Lakes sediment bacterial communities based on Bray-Curtis similarity. Scale bar indicates the level of dissimilarity. Branch color corresponds to the number of significant clusters detected in the similarity profile (SIMPROF). Red = cluster 1, blue = cluster 2, and green = cluster 3 ...........................82 Figure 2.4 Hierarchical cluster analysis of Great Lakes sediment bacterial communities based on Bray-Curtis similarity. Scale bar indicates the level of dissimilarity. Branch color corresponds to the number of significant clusters detected in the similarity profile (SIMPROF). Red = cluster 1, blue = cluster 2, and green = cluster 3 ...........................83 Figure 2.5 Relative abundances of phylogenetic groups among the three significant clusters detected in the similarity profile (SIMPROF) analysis of Great Lakes sediment bacterial communities. Figure 7 displays which the sediment bacterial samples are contained within each significant cluster ......................................................................................84 xiii Figure 2.6 Bacterial community structure of Great Lakes sediments at phylum level. The relative abundance was defined as the percentage of the total bacterial sequences in a sample, classified using Ribosomal Database Project database training set 9. Phylogenetic groups accounting for less than 1% of total composition are summarized as ‘‘other’’ in the figure ........................................................................................................85 . Figure 2.7 Bacterial community structure of Great Lakes sediments at genus level. The relative abundance was defined as the percentage of the total bacterial sequences in a sample, classified using Ribosomal Database Project database training set 9. Genera accounting for less than 1% of total composition are summarized as ‘‘other’’ in the figure ...................86 Figure 2.8 Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties, based on the relative abundance of 16S rRNA terminal-restriction fragments within the HhaI dataset. Refer to Table 1 for sample identification....................................87 Figure 2.9 Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties, based on the relative abundance of 16S rRNA terminal-restriction fragments within the MspI dataset. Refer to Table 1 for sample identification....................................89 Figure 2.10 Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties, based on the relative abundance of dominant bacterial phyla and proteobacterial classes within the 16S rRNA 454-pyrosequencing library. Refer to Table 1 for sample identification. Alpha = Alphaproteobacteria, Acido = Acidobacteria, Actino = Actinobacteria, UncBac = Unclassified Bacteria, UncPro = Unclassified Proteobacteria ......................................................................91 Figure 3.1 Map of the Laurentian Great Lakes (Michigan) and The Finger Lakes (New York) showing sampling sites for this study ........................ 126 Figure 3.2 Average diversity index values (±SE) for two T-RFLP datasets (amplified 16S rDNA digested with HhaI and MspI) generated for replicate Diporeia samples collected from lakes Michigan (MI), Superior (SU) and Huron (HU) and Cayuga Lake (New York) between 2007 and 2008. Richness is the number of operational taxonomic units, diversity is the Shannon-Weiner Diversity index, and Evenness is Pielou’s Evenness .................................................................................. 127 xiv Figure 3.3 Distance tree of 353 16S rRNA gene sequences detected in Diporeia spp. collected from five waterbodies. The tree was generated with the neighbor joining algorithm and the Jukes–Cantor correction. Lineage-specific significance (*) was determined using UniFrac (blue = Lake Superior, red = Lake Michigan, light blue = Lake Huron, green = Lake Ontario and yellow = Cayuga Lake). Others = Actinobacteria, Chloroflexi, Deltaproteobacteria, Firmicutes, Planctomycetes, and Verrucomicrobia ................................................... 129 Figure 3.4 Neighbor-joining phylogeny of Bacteroidetes 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes .................................................. 131 Figure 3.5 Neighbor-joining consensus phylogeny of Alphaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes .................................................. 133 Figure 3.6 Neighbor-joining consensus phylogeny of Betaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes .................................................. 134 Figure 3.7 Neighbor-joining consensus phylogeny of Gammaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences ..................................................................136 Figure 3.8 Neighbor-joining consensus phylogeny of Actinobacteria and Firmicutes 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values >70% from 1000 resamplings are indicated at the nodes .................................137 Figure 4.1 Location of sampling stations for Diporeia in the southern basin of Lake Michigan from 1980-2007. Depth contours are 5 m ................... 171 xv Figure 4.2 Histological sections of Diporeia collected from the southern basin of Lake Michigan between 1980 and 2007 showing microsporidian infection. Notice the microsporidians filling and replacing the muscle tissue (panels A-D), the melanized hemocytes encapsulating microsporidian spores surrounding the muscle tissue (panel C), and the differentiated, basophilic, encapsulating hemocytes within the mass of microsporidans (panel D). All sections were stained with Mayer’s hematoxylin and eosin. Scale bars denote 25 µm .............172 Figure 4.3 Histological section of Diporeia collected from the southern basin of Lake Michigan showing haplosporidian infection. Notice the mature spores with a well-defined, basophilic endosporoplasm within sporocysts (small arrows), and the differentiated circulating host hemocytes surrounding the sporocysts (arrows). The section was stained with Mayer’s hematoxylin and eosin. Scale bar denotes 25 µm..................... 173 Figure 4.4 Histological sections of Diporeia collected from the southern basin of Lake Michigan between 1980 and 2007 showing parasitic infection. Ciliate with a large nucleus among the gills (panel A). Amoebae within the digestive tract (panel B). A yeast-like fungus (indicated by the arrow) within a hemal sinus (large arrows) and a melanized nodule (small arrow) (panel C). Filamentous fungi within the coelom of Diporeia (panels D and E). Notice the circulating hemocytes within the hemocoel of Diporeia (panel E). Acanthocephala within the hemocoel (panel F) displaced the amphipod intestine (panel G). Panels A, B, and D-G were stained with Mayer’s hematoxylin and eosin stain. Panel C was stained with Grocott’s methenamine silver. Scale bars denote 25 µm ....................... 174 Figure 4.5 Prevalence of combined parasite infections and Ciliata (Cil.) infections in Diporeia collected from nine stations in Lake Michigan collected between 1980 and 2007 (±95% confidence interval) Sampling sites (x axes) are ordered by increasing depth. The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one infection ......................................176 Figure 4.6 Prevalence of parasites in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval). Stations (x axes) are ordered by increasing depth ......................................................................................................177 xvi Figure 4.7 Prevalence of combined parasite infections and Ciliata (Cil.) infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval). The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one Infection ..................................................................................................178 Figure 4.8 Prevalence of parasites in Diporeia (±95”% confidence interval) collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007. Acanth. = acanthocephala, Am. = Amoeba, Cest. = cestodes, Fil. = filamentous fungi, Haplo. = haplosporidia, and Micro. = microsporidia .....................................................................179 Figure 4.9 Prevalence of combined parasite infections and Ciliata (Cil) infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval) by size class of Diporeia (J < 5 mm, A > 5 mm). The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one infection ...................... 180 Figure 4.10 Prevalence of Amoeba (Am.), Microsporidia (Mi.), Cestoda (Ce.), Haplosporidia (Ha.), Acanthocephala (Ac.), Filamentous Fungi, (Fil.), and Yeast infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval) by size class of Diporeia (J < 5 mm, A > 5 mm) ........................ 181 Figure 5.1 Map of the Laurentian Great Lakes showing where Diporeia spp. were collected for this study. The solid circles denote sampling locations .................................................................................................196 Figure 5.2 Distance tree constructed for phylogenetic comparison of isolates obtained in this study. The tree generated using the Neighbor-joining algorithm and maximum likelihood method shows high phylogenetic similarity between the five viral hemorrhagic septicemia virus (VHSV) isolates obtained in this study (ON41, ON55-M, HU54-M, MI18-M, and MI27-M) and other isolates belonging to the VHSV genotype IVb. The alignment file used to produce the tree contained partial VHSV nucleoprotein (N) gene sequences (737 nucleotide positions). Snakehead rhabdovirus was used as the outgroup. The scale bar indicates the number of substitutions per nucleotide site.............................................197 Figure 6.1 Sampling sites in Lake Superior (USA) where Diporeia spp. specimens were examined for a haplosporidian infection ...................... 219 xvii Figure 6.2 Histological sections (hematoxylin and eosin) of Haplosporidium Diporeiae in Diporeia sp. collected from Lake Superior (USA). Notice plasmodia undergoing various stages of shizogeny lining the epicuticle (small arrow), sporocysts containing developing spores lining host digestive tissue (large arrow). Scale bar denotes 75 µm ........................ 220 Figure 6.3 Transmission electron micrograph of Haplosporidium Diporeiae infecting Diporeia in Lake Superior. Notice sporocysts containing spores undergoing various stages of shizogeny. Scale bar denotes 10,000 nm............................................................................................... 221 Figure 6.4 Histological sections (hematoxylin and eosin) of Haplosporidium Diporeiae in samples of Diporeia (Amphipoda) showing similar morphology of developmental stages between those collected from Lakes Superior (A) and Michigan (B) (USA). Notice dinucleated Plasmodia within sporocysts (arrows). Scale bars denote 25 µm ...........222 Figure 6.5 Histological sections (hematoxylin and eosin) of Haplosporidium Diporeiae in Diporeia sp. collected from Lake Superior (USA). Notice the mature spore that has been liberated from the sporocyst (large arrow) and thin filaments projecting from the end and multiple developing spores with spore (arrows). Scale bar denotes 25 µm............................ 223 Figure 6.6 Transmission electron micrographs of Haplosporidium Diporeiae infecting Diporeia in Lake Superior. Notice (A) spore with a hinged opercular lid (arrow), (B) spores exhibiting a thickening of the spore wall at the abopercular end (arrows), and (C) mature spore exhibiting what appears to be thin filaments projecting from a thickening of the cell wall (arrow). Scale bars: 3A = 1,000 nm, 3B = 5,000 nm, 3C = 500 nm ........224 Figure 6.7 Phylogenetic tree (50% majority-rule consensus) based on Bayesian Inference (MrBayes 3.1.2) of Haplosporidia based on the small subunit ribosomal gene. Numbers at the nodes are Bayesian posterior probabilities. Cercomonas longicauda, Perkinsus chesapeaki, and P. marinus were used as an outgroup for Haplosporidia based on the results of Reece et al. (2004) ................................................................................ 225 Figure 7.1 Sampling sites in Lake Superior where Diporeia (Amphipoda, Gammaridae) were collected ..................................................................245 Figure 7.2 Histological sections (hematoxylin and eosin) of microsporidian developmental stages in an infected Diporeia sp. (Amphipoda) collected from Lake Superior. Notice the individual spores are not enclosed in a sporophorous vesicle (small arrow) and melanized hemocytic infiltration in adjacent muscle tissue (large arrows). Scale bar denotes 25 µm .....................................................................................................246 xviii Figure 7.3 Histological sections (hematoxylin and eosin) of a microsporidian in a Diporeia sp. (Amphipoda) sample collected from Lake Superior. Notice the microsporidians filling and replacing muscle tissues (small arrows) surrounding the ovaries (large arrows). Scale bar denotes 100 µm .......247 Figure 7.4 Histological sections (hematoxylin and eosin) of Diporeia (Amphipoda) collected from Lake Superior. Notice (A) the histologically normal ovaries (Large arrows) of an amphipod not displaying a microsporidian infection in the muscle tissue (small arrow) and (B) melanized hemocytic encapsulation near the ovaries (large arrow) of an amphipod displaying a microsporidian infection in the muscle tissue (small arrow). Scale bars denote 25 µm........................................................................248 . Figure 7.5 Transmission electron micrograph of a microsporidian infecting Diporeia in Lake Superior. Notice (A) the monokaryotic meront (small arrow) and spore (large arrow), (B) eight coils of isofilar polar filaments arranged in single ranks (small arrows) and the prominent polar vacuole (large arrow), (C) spore wall composed of a thick electron-lucent endospore (large arrow) overlaid with a thinner electron-dense exospore (small arrow), and (D) lamellar polaroplast composed of ordered concentric membranes surrounding the polar filament (arrow). Scale bars: 4A = 1,000 nm, 4B-4D = 500 nm ..................................................................................... 249 Figure 7.6 Phylogenetic tree (50% majority-rule consensus) based on Bayesian Inference (MrBayes 3.1.2) of Dictyocoela spp. based on the small subunit ribosomal gene. Numbers at the nodes are Bayesian posterior probabilities. Spaguea lopii, Kabatana takedai, Nosema granulosis, Thelohania parastaci, Pleistophra mulleri, P. typicalius, Glugea anomala, and Loma acerinae were used as an outgroup for Dictyocoela spp. based on the results of Krebes et al. (2010) ...........................................250 xix INTRODUCTION 1 The amphpipod Diporeia (Amphipoda, Gammaridae) is the major consumer of the primary production associated with material deposited during the spring diatom pulse (Gardner et al. 1990; Fitzgerald & Gardner 1993) and, in turn, is an important food resource for a number of Great Lakes fish species. Diporeia therefore is an important component in the Great Lakes ecosystem and has been the dominant benthic 2 macroinvertebrate in the Great Lakes, averaging over 7,000 individuals/m (Nalepa 1987; 1989). Unfortunately, Diporeia abundances have been declining from the majority of its habitats throughout four of the five Great Lakes (Nalepa et al. 1998; 2005; 2007; Dermott 2001; Dermott & Kerec 1997; Lozano et al. 2001; Barbiero & Tuchman 2002; Barbiero et al. 2011). Although a number of hypotheses have been proposed and investigated to explain Diporeia declines, the exact causal mechanism for the declines remains unknown. The hypothesis that pathogens are the probable disease agents behind Diporeia declines, whether due to the presence of invasive dreissenid mussels or not, was investigated. This was investigated through examination of the bacterial communities associated with Diporeia through high throughput molecular techniques and gene sequencing, and identification of pathogens and their lesions through light and electron microscopical studies as well as phylogenetic studies. To achieve this, since Diporeia are benthic detritivores and are intimately associated with the sediment, I first performed both metagenomic pyrosequencing and terminalrestriction fragment polymorphism (T-RFLP) analyses of bacterial 16S rRNA genes present in sediment samples (collected from multiple locations throughout Lakes Superior, Michigan, Huron, and Ontario) to characterize the bacterial communities 2 associated with Great Lakes sediment and screen for the presence of potential Diporeia pathogens. Using multivariate analysis, these results were then compared to sediment properties. As I discuss in detail throughout the next chapter, diverse bacterial communities were detected in Great Lakes sediments. In Chapter 3, I use T-RFLP coupled with sequence analysis of bacterial 16S rRNA genes present in Diporeia samples collected from lakes Superior, Michigan, Huron, and Ontario and Cayuga Lake to characterize the bacterial communities associated with Diporeia and to determine if these communities vary spatially or temporally and provide important insights about the microbial ecology of Diporeia. A lower bacterial diversity was observed for Diporeia samples compared to sediment samples and distinct bacterial groups were commonly associated with Diporeia samples indicating they are natural members of the Diporeia microbiome and are likely important for the the ecological performance of Diporeia. Additionally, temporal shifts in bacterial community diversity were observed, however the ecological significance of this remains to be determined. In Chapter 4, I present an in-depth histopathological analysis of pathogen prevalences in Diporeia samples collected from several sites in southern Lake Michigan from as far back as 1980 to determine pathogen presence and to determine the trends of these infections over the past three decades and to shed light on pathogens as potential causes of Diporeia declines in the Laurentian Great Lakes. I report the presence of multiple parasites infecting Diporeia, one of which has yet to be reported in Diporeia. A number of parasites elicited considerable host immune response indicating they could be potentially serious pathogens for Diporeia. Models were fit in an effort to 3 determine the relationship between Diporeia density and infection prevalence as well as to examine other possibly contributing factors, such as dreissenid density. Spatiotemporal variability in parasitic infections was observed with prevalences often fluctuating by depth, sampling site, and life stage of Diporeia. Models were also fit to test for associations among infection prevalences, Diporeia densities, and dreissenid densities. The findings of this study provide valuable insights not only on the dynamics of parasites infecting Diporeia during the declines but also on the potential transmission of parasites that use Diporeia as an intermediate host. In Chapter 5, I screen Diporeia from seven locations in Lake Superior, Huron, and Ontario for viruses using a readily available fish cell line to determine if viral infections have potentially contributed to declines in their abundance. The fish pathogenic rhabdovirus, viral hemorrhagic septicemia virus (VHSV), was isolated from Diporeia collected Lakes Michigan, Huron, and Ontario, but not Lake Superior. Although VHSV is probably not pathogenic to Diporeia and has not contributed to the decline, this study reports the first incidence of a fish-pathogenic rhabdovirus being isolated from Diporeia, or other crustacean and underscores the dire need to better understand the role of macroinvertebrates in disease ecology. In Chapters 6 and 7, I describe the morphology, ultrastructure of two novel species of parasites infecting Diporeia. The first parasite is a Haplsporidium sp. (Haplosporidia) that was observed causing systemic infection Diporeia that often resulted in tissue destruction and elicited a host immune response in Diporeia collected from both Lakes Superior and Michigan. This study reports the first incident of a haplosporidian infecting Diporeia in Lake Superior. The second parasite is a Dictyocoela spp. (Microsporidia) 4 that destroyed the host muscle tissue and, again, elicited a host immune response. It is likely that the microsporidian conciderably impairs the normal movement, feeding, swimming, and overall functioning, fitness, and performance of Diporeia. While both parasites appear to be serous pathogens for Diporeia, the geographical distribution and prevalence of this parasite in Great Lakes Diporeia populations remains to be determined In the last Chapter, I end with a list of major findings based on my research involving both molecular and microscopical analyses. I also present the potential implications of my findings to the cause for the declines of Diporeia in the Great Lakes. My research should provide the valuable insights into the cause of Diporeia declines in the Laurentian Great Lakes. 5 REFERENCES 6 REFERENCES Barbiero, R.P., Schmude, K., Lesht, B.M., Riseng, C.M. Warren, G.J., & Tuchman, M.L. (2011) Trends in Diporeia populations across the Laurentian Great Lakes, 1997– 2009. Journal of Great Lakes Research. 37(1), 9-17. Barbiero, R.P., & Tuchman, M.L. (2002) Results from GLNPOS’s biological open water surveillance program of the Laurentian Great Lakes. U.S. Environmental Protection Agency, Great Lakes National Program Office, 2002. Dermott, R., & Kerec, D. (1997) Changes to the deepwater benthos of eastern Lake Erie since the invasion of Dreissena: 1979-1993. Canadian Journal of Fisheries and Aquatic Sciences. 54(4), 922-930. Dermott, R. (2001) Sudden Disappearance of the Amphipod Diporeia from Eastern Lake Ontario, 1993–1995. Journal of Great Lakes Research. 27(4), 423-433. Fitzgerald, S.A., & Gardner, W.S. (1993) An algal carbon budget for pelagic-benthic coupling in Lake Michigan. Limnology and oceanography. 38(3), 547-560. Gardner, W.S., Quigley, M.A., Fahnenstiel, G.L., Scavia, D., & Frez, W.A. (1990) Pontoporeia hoyi-a direct trophic link between spring diatoms and fish in Lake Michigan. In Large Lakes (pp. 632-644). Springer Berlin Heidelberg. Lozano, S.J., Scharold, J.V., & Nalepa, T.F. (2001) Recent declines in benthic macroinvertebrate densities in Lake Ontario. Canadian Journal of Fisheries and Aquatic Sciences. 58(3), 518-529. Nalepa, T.F. (1987) Long-term changes in the macrobenthos of southern Lake Michigan. Canadian Journal of Fisheries and Aquatic Sciences, 44(3), 515-524. Nalepa T.F. (1989) Estimates of macroinvertebrate biomass in Lake Michigan. Journal of Great Lakes Research. 15(3), 437–443. Nalepa T.F., Hartson D.J., Fanslow D.L., Lang G.A., & Lozano S.J. (1998) Declines in benthic macroinvertebrate populations in southern Lake Michigan, 1980– 1993. Canadian Journal of Fisheries and Aquatic Sciences. 55(11), 402–2413. Nalepa, T.F., Fanslow, D.L., & Messick, G. (2003) Characteristics and potential causes of declining Diporeia spp. populations in southern Lake Michigan and Saginaw Bay, Lake Huron. Great Lakes Fishery Commission Technical Report 66. 7 Nalepa, T.F., Fanslow, D.L., Pothoven, S.A., Foley, A.J., Lang, G.A. (2007) Long-term trends in benthic macroinvertebrate populations in Lake Huron over the past four decades. Journal of Great Lakes Research. 33(2), 421-436. 8 CHAPTER 1 Literature Review and Overall Objectives of the Study 9 Diporeia Taxonomy Diporeia represents a genus of amphipods that can be distinguished from other genera of pontoporeiids based on differences in morphological characteristics including the gnathopods, coaxal plate of the peraepod, uropod, telson, and sternal gills on the sternum of the peraeonal segments. The genus Diporeia contains at least two and perhaps as many as eight species (Bousfield 1989). Because taxonomic differences have not been resolved, it is extremely difficult to differentiate between the different Diporeia spp. Therefore, for the sake of this dissertation, I have grouped them under the genus Diporeia (hereafter referred to as Diporeia) with no specific designation of the species and with the realization that future studies may define taxonomic distinctions and differences in ecological and physiological characteristics. Distribution Diporeia are holarctic amphipods that inhabit cold, deep water environments of proglaciated lakes in North America including the Laurentian Great Lakes (Cook & Johnson 1974; Bousfield 1989). Although Diporeia are considered to be benthic amphipods, they are able to migrate to the pelagic zone, particularly at night (Marzolf 1965a). Diporeia are predominantly found in warm nearshore shelf regions, slope regions near the thermocline, and deep, cold profundal regions. In general, Diporeia densities are highest just below the thermocline (30-50 m), beyond which their densities gradually decrease as depth increases (Dadswell 1974; Nalepa et al. 2000). In some large inland lakes, Diporeia are most abundant in the slope region and in the deep cold regions (Evans et al. 1990; 10 http://data.gtbay.org/downloads/chain_of_lakes_report_2010_online.pdf). They are found in different types of substrates containing varying amounts of organic material (Marzolf 1965a) and are primarily found in the organic top layer (<5mm) of sand and silty sediments where they have a preference for feeding on small particles that are rich in bacteria (Marzolf 1965b). Life History Diporeia life span ranges from 1 to 3 years (Sarvala 1986). Females are continuously benthic and longer-lived while mature males are often pelagic and shortlived (Bousfield 1989). In warm waters, Diporeia have a one year life cycle where the young grow quickly and reach maturity by the first winter. In the slope region, near the thermocline, Diporeia have a two year life cycle where spawning occurs twice per year (summer and winter). In the cold, profundal region, Diporeia reach maturity between 2.5-3 years and spawning occurs twice per year (Siegfried 1985). Mature female Diporeia have a marsupium (brood pouch) which holds eggs while they are fertilized and until the young are ready to hatch (Alley 1968). It has been shown that older, larger female Diporeia produce more eggs/young. The successful hatch rate of eggs is around 50-75% (Sarvala 1986). Eggs hatch into juvenile amphipods which reach sexual maturity after approximately six molts (Sarvala 1986). Diporeia typically spawn in the winter and recruitment of newly hatched young occurs in April (Green 1965). This is evident because most Diporeia collected in April are <2mm in length (Alley 191168). Additionally, most adult Diporeia found in nearshore regions during the 11 spring are female (Bousfield 1989). For these reasons, it is thought that the nearshore regions are major spawning and recruitment sites. Diet Diatoms are the dominant phytoplankton component associated with spring blooms in the Great Lakes. As the season progresses, diatoms decrease in abundance (Vollenweider et al. 1974; Fahnenstiel & Scavia 1987). During the spring, large diatoms such as Melosira spp. sink, causing losses of phytoplankton from the epilimniom. Zooplankton grazing removes the majority of phytoplankton diatoms from the water column throughout the remainder of the season (Scavia & Fahnenstiel 1987). Therefore, nutrient-rich, large diatoms are mainly available to Diporeia during the spring but not during the rest of the year when the nutritional quality of available particles is lower (Gardner 1989a, 1989b). Diporeia have been shown to be well-adapted to seasonal inputs of high-quality food associated with the spring blooms in the Great Lakes. Analysis of ammonium and phosphate excretion rates of field collected amphipods and laboratory held amphipods that were deprived of food showed that the excretion rates of Diporeia are lower than those reported for other benthic and pelagic invertebrates (Gauvin et al. 1989). Additionally, analysis of lipid levels in the field animals showed that Diporeia accumulated increased levels of lipids following the spring diatom bloom. Lipid levels peaked in May and decreased during the rest of the season at rates similar to those of starved and control animals (Gauvin et al. 1989). Both Diporeia growth rates and lipid levels increase following the spring blooms and then decrease during late summer and 12 winter as the year progresses (Johnson & Brinkhurst 1971; Gardner et al. 1985b; Johnson 1988; Landrum 1988). The ability of Diporeia to accumulate and store lipids for periods when food is relatively scarce appears to be important for the survival of the amphipod in the Great Lakes. Unlike many other amphipods, Diporeia feed intermittently and, based on analysis of gut contents, appear to eat more frequently in the spring than in other seasons (Quigley 1988). Although some studies have stated that analysis of Diporeia gut contents is of little value due to most of the food in the gut being unrecognizable, such analyses have revealed that the major portion of the diet of Diporeia is comprised of diatoms, specifically Cyclotella and Melosira, while the remaining portion of the contents contained silt and sediment, pollen structures, fungal spores, yeasts, insect fragments, chitin fragments, dead rotifers, and amoeboid tests (Quigley et al. 1991). It is believed that Diporeia are the major consumer of the primary production associated with material deposited during the spring diatom pulse (Gardner et al. 1990; Fitzgerald & Gardner 1993). Immune System Like all crustaceans, Diporeia have an innate immune system that is comprised of both cellular and humoral components that are integrated to produce a single coordinated system. Cellular immune responses are triggered by components within the plasma while humoral components are produced in, and secreted from, both circulating and fixed hemocytes. Immune responses are triggered by pathogen associated molecular patterns (PAMPs) (Janeway & Medzhitov 2002) that include multiple bacterial 13 lipopolysaccharides, peptidoglycans and fungal beta glucan-binding proteins (YepizPlascencia et al. 1998; Lee et al. 2000; Vargas-Albores & Yepiz-Plascencia 2000). The first step of an immune response is recognition of different PAMPs to activate pathways that release a battery of active effector molecules. Next, a myriad of pattern recognition proteins (PRPs) recognizes PAMPs and, in turn, activates pathways that release a battery of active effector molecules (Yepiz-Plascencia et al. 1998; Lee et al. 2000; Vargas-Albores & Yepiz-Plascencia 2000). Downstream immune responses are then coordinated with hemocytes that are both circulating within the haemocoel, or hemal sinus, and embedded within tissues. Three types of hemocytes have been identified in most crustaceans (Lin & Söderhäll 2011). These include hyaline cells, semigranular haemocytes, and granular haemocytes. Hyaline cells are involved in phagocytosis. Semigranular haemocytes are involved with encapsulation, early non-self recognition, melanization and may be involved in coagulation and phagocytosis. Granular hemocytes are involved with melanization, cytotoxic reactions, and produce and secrete antimicrobial peptides. When in the presence of PAMPs, both semigranular haemocytes and granular hemocytes undergo rapid degranulation and may release components that trigger further degranulation resulting in a positive feedback cascade (Johansson et al. 1995). This cascade effect is believed to be a form of cell to cell communication that is a vital component of the innate immune system of all crustaceans. Degranulation of semigranular and granular hemocytes releases a battery of immune effector molecules, such as zymogen prophenoloxidase (proPO) (reviewed in Sritunyalucksana & Söderhäll 2000) into circulation to be triggered by the PAMPs. Phenoloxidase catalyzes the 14 oxidation of diphenols to quinones, and the process of melanin formation begins. Melanin and intermediates produced by this process are known to be fungitoxic and fungistatic and to aid in the melanization of carapace wounds and invading bacteria and fungi. Additionally, hemocyte degranulation has been shown to release antimicrobial peptides into circulation. In some crustaceans, degranulation has shown to release penaeidins (Tassanakajon et al. 2010), which are small peptides (~ 5-7kDa) with aminoterminal cysteine rich and carboxy-terminal proline rich domains. Penaeidins have been reported to have activity against Gram-positive and Gram-negative bacteria, fungi and viruses (Tassanakajon et al. 2010; Woramongkolcha et al. 2011). In greater numbers of crustaceans, degranulation has shown to release crustin antimicrobial peptides (Smith et al. 2008). These small peptides (~ 12 kDa) with a characteristic carboxy-terminal whey acidic protein domain have been reported to have a role in the immune response to infection with Protozoa, Gram-positive and Gramnegative bacteria, fungi and viruses (Battison et al. 2008; Garcia et al. 2009; Prapavorarat et al. 2010; Sakai et al. 2010; Söderhäll et al. 2010; Yang et al. 2010; Antony et al. 2011; Wang et al. 2011). Other antimicrobial peptides released within the Crustacea as a result of hemocyte degranulation is the Anti-Lipopolysaccharide Factors (ALFs). ALFs are small proteins which bind lipopolysaccharides and have strong antibacterial activity against both Gram-negative and Gram-positive bacteria and fungi (De la Vega et al. 2008). It has also been reported that ALFs may act by directly aiding in the inhibition of viral replication (Liu et al. 2006). 15 Diporeia Decline in the Great Lakes Historically, Diporeia have been the dominant benthic macroinvertebrate in the Great 2 Lakes, averaging over 7,000 individuals/m , reaching mean densities as high as 12,216 2 /m , and accounting for approximately 70% of the macrobenthic community of the Great Lakes (Nalepa 1987; 1989). However, Diporeia abundances have been declining from the majority of its habitats throughout the four of the five Great Lakes (Dermott & Kerec 1997; Nalepa et al. 1998; 2005; 2007; Dermott 2001; Lozano et al. 2001; Barbiero & Tuchman 2002; Barbiero et al. 2011). Although Diporeia were not historically found in the warm, shallow western basin of Lake Erie, they were once the dominant macroinvertebrate in the eastern basin of Lake Erie. Additionally, small densities of Diporeia were present in the central basin. In 1993, a study reported that Diporeia were present in five out of 13 sites sampled in the 2 eastern basin of Lake Erie and that considerably high densities (~2,000/m ) were observed off of Long Point (Dermott & Kerec 1997). Since then, no Diporeia have been observed in Lake Erie (Dermott & Dow 2008; Barbiero et al. 2011). Of all Great Lakes Diporeia populations, those in Lake Michigan have been most extensively monitored. In the early 1990s, declines were first reported in shallow (<50 m) sites in the southeastern portion of the lake (Nalepa et al. 1998). By 1999, it was revealed that declines had also occurred at varying depths in northern portions of the lake (Barbiero & Tuchman 2002). More extensive lake-wide surveys conducted in the following years revealed that the declines appeared to proceed from the southeast and north towards the western shore of the lake (Nalepa et al. 2006; 2009). By 2005, lake- 16 2 wide densities had been reduced by approximately one tenth (548/m ) at depths 2 between 51–90 m and one quarter (1244/m ) at depths >90 m, compared to densities observed in the 1980s at those depth interval in the southern portion of the lake. As of 2011, the declines have appeared to continue and Diporeia appear to be absent from the majority of habitats <90 m (Barbiero et al. 2011). Less information regarding the trend of Diporeia declines is available for Lake Huron; however, extensive lake-wide surveys conducted in 2000 and 2003 revealed similarities in the dynamics of Diporeia populations in Lakes Michigan and Huron in that the declines in Lake Huron appeared to have also begun in the shallow southeastern portion of the lake and proceed to deeper regions. Temporally detailed information provided by Barbiero et al. (2011) revealed additional similarities between the declines observed in Lakes Michigan and Huron. An apparent synchrony of interannual fluctuations in population size between the two lakes was observed, where increased rates of decline were observed between 1997 and 2000, and again between 2003 and 2004. Decreased rates of decline or increases in densities were observed between 2001 and 2002 and between 2003 and 2004. At this time, Diporeia are effectively extirpated from sites shallower than 90 m in both lakes, while at greater depths, the rates of decline have apparently either decreased or remain stable (Barbiero et al. 2011). In Lake Ontario, trends of Diporeia decline have differed from those lakes Michigan and Huron, and declines in deeper sites appear to be continuing (Barbiero et al. 2011). Declines were first reported in Lake Ontario at depths between 30 and 90 m where, 2 2 during 1990 through 1995, densities fell from 5,420/m to 1,937/m (Dermott & 17 Geminiuc 2003). In another study, Diporeia declines were reported at depths between 36 and 90 m during1994 through 1997 (Lozano et al. 2001). Neither study reported declines at deeper regions. In 2007, Watkins et al. (2007) reported that the average 2 Diporeia density at shallow sites (30-90 m) had fallen to 63/m by 2003. Additionally, Watkins et al. (2007) reported declines at deeper sites (>90 m) for the first time. As of 2001, Diporeia have not been reported to be present in sites <90 m since 2004 and further declines have been reported from deeper sites in Lake Ontario (Barbiero et al. 2011). Currently, there is no evidence of Diporeia population declines in Lake Superior. It was reported that, based on a survey of 16 sites (average depth=41 m), densities above 2 2,000/m were observed (Hiltunen 1969). In 1974, the average Diporeia density along three transects (50-80 m) along the western shore of the Keweenaw Peninsula were 2 733, 1036, and 2438/m (Kraft 1979). In the mid-1990s, similar Diporeia densities from western Lake Superior were reported by Scharold et al. (2004) and Barbiero et al. 2 (2011) where nearshore (<100 m) densities were around 2,114/m and offshore (>110 2 m) densities were around 380/m . In 2008, the average Diporeia density at sites 2 between 50-80 m in Lake Superior was 1,846/m (Barbiero et al. 2011). While no long-term Diporeia declines have been reported in Lake Superior, substantial inter-annual density fluctuations for some sites have been reported. At two shallow (<90 m) sites, one in Whitefish Bay and one near Duluth, densities varied by 2 more than 1,500 individuals/ m over a 13 year period. (Barbiero et al. 2011). 18 Additionally, a high degree of variability was also reported for deeper sites, where population increases of over an order of magnitude were observed. Although year to year differences in Diporeia densities have been observed in Lake Superior, estimates do appear to be similar to those from the 1960s and 1970s. Ecological Significance Due to their unique position in the foodweb, Diporeia are an important component in the Great Lakes ecosystem. They are important food resources for a number of Great Lakes fish species. For example, studies in southern Lake Superior and Lake Michigan showed that Diporeia dominated the diet of multiple sculpin species (Wojcik et al. 1986; Selgeby 1988). Several other fish species rely on Diporeia for their diet, including alewife (Alosa pseudoharengus ) (Hondorp et al. 2005), lake whitefish (Coregonus clupeaformis) (Pothoven 2005), rainbow smelt (Osmerus mordax) (Selgeby et al. 1994), and yellow perch (Perca flavescens) (Wells 1980). For this reason, Diporeia serve as an important pathway of energy flow from lower to upper trophic levels, thereby acting as a coupling mechanism between pelagic and benthic zones of the Great Lakes (Fitzgerald & Gardner 1993). Since Diporeia are a major energy link between pelagic production and fish (Gardner et al. 1990), these large-scale declines in Diporeia will likely lead to serious disruptions in the Great Lakes foodweb. Invasion of the Great Lakes by Dreissenid Mussels The pattern of Diporeia decline has coincided with the spread of invasive, filterfeeding zebra (Dreissena polymorpha) and quagga (Dreissena rostriformis bugensis) 19 (dreissenids) mussels in the Great Lakes (Nalepa et al. 2009). During the early 1990’s, Diporeia declines were first observed in shallow habitats in lakes Michigan and Erie, shortly after the colonization of dreissenids in these lakes (Dermott 2001; Dermott & Kerec1997; Nalepa et al. 1998; 2003). In Lake Michigan, Diporeia declines became more extensive and were associated with the spread of D. polymorpha, and by 2000, Diporeia had largely disappeared in shallow (<50 m) portions of the lake (Nalepa et al. 2006a). A similar pattern of Diporeia decline was observed with the expansion of D. bugensis into deeper habitats, and as of today, Diporeia has largely disappeared from habitats greater than 100 m in depth in lakes Michigan and Huron (Nalepa et al. 2009). However, in Lake Superior where there is no presence of dreissenids, Diporeia abundances have remained virtually stable (Barbiero et al. 2011). It therefore seems strongly plausible that dreissenids played a role in the decline of Diporeia; however, in Lake Michigan, Diporeia abundances were declining in the late 1990s despite what was considered to be sufficient flux of organic matter reaching the benthos (Nalepa et al. 2006) suggesting that declining abundances were not simply a result of competition with dreissenid mussels. Additionally, in Cayuga Lake (New York), where Diporeia and dreissenids coexists, declines in Diporeia have not been observed (Mohr & Nalepa 2005; Watkins et al. 2007) suggesting the declines in the Great Lakes are not the direct result of competition with dreissenids. Starvation A number of laboratory experiments have been conducted on physiological changes in Diporeia as a result of starvation or exposure to dreissenids or dreissenid 20 pseudofeces. Pseudofeces is comprised of fine particles (<10 μm) that may accumulate phytoplankton-derived toxins or biological pathogens that are resuspended and transported to offshore profundal areas by currents (Lick et al. 1994). To investigate the hypothesis that the decline is the result of toxicity of dreissenid pseudofeces, Dermott et al. (2005) conducted a series of laboratory tests in which Diporeia were exposed to dreissenid pseudofeces, water containing pseudofeces that had been filtered (0.45-μ filter), or dying Diporeia. Results showed a 25% decrease in survival of Diporeia exposed to pseudofeces and high survival was observed in the filtered water assay, suggesting that, due to the filter mesh size, the harmful agent was not a chemical which would pass through the filter. The authors concluded that it is possible that dreissenids may act as a secondary host in transmitting unknown pathogens to Diporeia. Additionally, increased mortalities were observed in healthy Diporeia that were kept over sediments that were exposed to dying Diporeia, suggesting the presence of an infectious agent; however, this study was not followed up to test this infectious agent theory. Another laboratory experiment investigated the response of Diporeia collected from a single site in Lake Michigan to 60 days of starvation (Maity et al. 2012). Over the course of the study, a significant down-regulation of metabolites including polyunsaturated fatty acids, phospholipids, and amino acids and their derivatives was observed. However, the lack of available metabolite databases regarding the metabolome of crustaceans hindered the interpretation of the findings. Additionally, the current information within databases cannot distinguish if the generated metabolic profile is due to starvation, seasonal effects, or presence of dreissenids. 21 Ecological Stressors Another hypothesis is that, given the possibility of evolutionarily distinct lineages of Diporeia, variations in declines in response to ecological stressors may differ by genetically divergent populations of Diporeia. To investigate this theory, Pilgrim et al. (2009) conducted a phylogenetic study to show that phylogenetically distinct populations of Diporeia exist in the Great Lakes. Through analysis of the mitochondrial gene (COX1) from Great Lakes Diporeia, the authors revealed that Lake Superior Diporeia form a distinct lineage that likely diverged from populations of the other Great Lakes several hundred thousand years ago. However, it was determined that the differences could not explain declines in the other Great Lakes. Microcystin Using both quantitative polymerase chain reaction (qPCR) and culturing techniques, Rinta-Kanto et al. (2009) investigated the abundance of Microcystis spp. in Lake Erie sediment samples and suggested that potentially toxigenic strains persist spatially and temporally in the sediment and that these populations may even act as bloom sources in large lake systems. Microcystis toxins have been shown to cause mortality in crustaceans (DeMott et al. 1991; Reinikainen et al. 1994; Smith & Gilbert 1995), and reduce productivity and growth of isopods and amphipods (Swiss & Johnson 1976) and reproduction of Daphnia (Shurin & Dodson 1997). Recently, microcystis blooms have increased in Lake Erie where Diporeia have been effectively extirpated and Saginaw Bay due to selective rejection of cyanobacteria by filtering dreissenids (Lavrentyev et al. 1995; Vanderploeg et al. 2001, Barbiero et al. 2001). Furthermore, a substantial 22 increase in blue-green algae in western Lake Erie since the invasion of zebra mussels has been associated with a decrease in diatoms (Makarewicz et al. 1999). It is therefore possible that increases in potentially harmful algal blooms, like the one observed in Lakes Erie (Vanderploeg 2001), may be one of the contributing reasons for population declines in Diporeia. Botulism Pérez-Fuentetaja et al. (2011) used qPCR to quantify Clostridium botulinum type E spores in Lake Erie sediments and determined that, although the abundance of the bacterial spores were patchy, sediments may serve as sources of the bacteria that could initiate food web transfer of this pathogen. It is believed that an increase in botulism in Lake Erie is possibly due to biomagnification of the botulism toxin by dreissenids or increased spore abundance among the decomposing mussels and pseudofeces in the sediments (Getchell et al. 2002; Dermott et al. 2005). Additionally, although it was determined that benthic organisms (chironomids, oligochetes, nematodes, dreissenids and mayflies) carried higher levels of bacterial spores than sediments, dreissenid tissue contained up to 2280 copies DNA/mg, and their pseudofeces contained up to 275 copies DNA/mg. Chironomids contained some of the highest levels reaching 820,000 copies DNA/mg (Pérez-Fuentetaja et al. 2011). While it is unlikely that C. botulinum type E is toxic to Diporeia, it is clear that there are multiple pathways to transmit type E botulism to upper trophic levels in the Great Lakes. 23 Pollutants By exposing various amphipods to contaminated sediments from multiple Great Lakes harbors, it was shown that Diporeia are considerably more sensitive to contaminants than the amphipod Gammarus (Gannon & Beeton 1969). After 48-h exposures, mortality was 70% in Diporeia but only 9% in the amphipod Gammarus. For this reason, Diporeia are not found or are present only in low numbers in areas that are heavily influenced by contaminants (Nalepa & Thomas 1976; Vander Wal 1977; Kraft 1979). Additionally, due to its high lipid content and the fact that most contaminants are lipophilic, Diporeia have a high potential to accumulate contaminants. Moreover, due to the benthic nature of Diporeia, they are more likely to accumulate contaminants compared to pelagic crustaceans such as the opossum shrimp (Mysis relicta) (Helm et al. 2008). Considering all of these factors, it is possible that contaminants present in Great Lakes sediments have played a role in the declines. Oxygen Depletion While many regions in the Great Lakes that experience hypoxia are too warm and shallow to support Diporeia populations, distribution of Diporeia in the Great Lakes are said to be related to the oxidative properties of the sediment surface (Sly & Christie 1992; Nalepa et al. 2005). Deepwater amphipods in general, such as pontoporeiids, are sensitive to low dissolved oxygen concentrations. For example, the disappearance of Monoporeia affinis from European lakes has been attributed to oxygen deficiencies (Johansson 1997). Additionally, it was shown that reduced oxygen concentrations can 24 cause higher frequencies of both unfertilized females and females carrying dead broods (Ericksson-Wiklund & Sundelin 2001). It has therefore been suggested that dissolved oxygen deficiencies have contributed to the decline of Diporeia in the Great Lakes (Nalepa et al. 2005) In the Great Lakes, a number of factors can lead to reduced dissolved oxygen concentrations that may threaten Diporeia populations. Inputs of sewage are often cited as causes for decreases in dissolved oxygen (Beeton 1961, 1965). Additionally, it has been suggested that the presence of dreissenids may reduce dissolved oxygen at the sediment-water interface, thereby negatively impacting Diporeia populations (Nalepa et al. 2005). Dreissenids are capable of reducing dissolved oxygen in multiples ways. It has been shown that the respiration of large abundances of zebra mussels can considerably reduce dissolved oxygen in the aquatic environment (Caraco et al. 2001). Pseudofeces produced by dreissenids has also been shown to have a high oxygen demand when it becomes resuspended (Nalepa et al. 2005). It is possible for pseudofeces that has settled in nearshore areas to become resuspended and deposited in deeper regions causing deficiencies of dissolved oxygen where Diporeia reside. Similarly, decomposition of dreissenid deposits and dead mussels in areas with high dreissenid densities may lead to localized areas of decreased dissolved oxygen. Additionally, decomposition of macrophytes that become abundant as a result of increased water clarity due to the filter-feeding activity dreissenids may also lead to decreased dissolved oxygen. It is therefore possible that dreissenid-induced oxygen deficiencies may have contributed to the declines in Diporeia. 25 Infectious Agents Studies on diseases of amphipods are rather scarce and when found they focused on the role of amphipods as intermediate hosts to helminthes as parasites of other aquatic organisms. For example, Amin (1978) reported a higher frequency of one cestode (Cyathocephalus truncatus) and two acanthocephalans (Echinorhynchus salmonis and Acanthocephalus parkside) in Diporeia that were collected from the stomachs of slimy sculpins (Cottus cognatus) compared to amphipods that were collected from the sediment and determined that Diporeia serve as the intermediate host for these parasites in Lake Michigan. Additionally, Burbot (Lota lota) can also be infected by E. salmonis when they feed on infected Diporeia (Muzzal et al. 2003). In the field of Diporeia diseases, there are only a handful of published studies; however, it has been demonstrated that Diporeia are vulnerable to a myriad of pathogens including microsporidia, filamentous fungi, yeasts, haplosporidia, ciliates, bacteria, and viruses (Messick et al. 2004; Messick 2009; Hewson et al. 2013). Messick et al. (2004) described a number of lesions in Diporeia from lakes Michigan and Huron that were associated with several types of parasites which varied from nodules containing hypertrophy and inflammation to tissue destruction. Rickettsia-like bacteria are known to infect amphipods and causes systemic disease in multiple species of amphipods (Frederici et al. 1974; Larsson 1982; Graf 1984; Messick et al. 2004). Additionally, there is evidence that rickettsia-like bacteria can cause epizootic mortalities (Frederici et al. 1974). Within Diporeia, rickettsia-like infections appeared as basophilic masses of a small intracellular gram-negative bacterium that were confined within the cytoplasm hypertrophied cells. While infections 26 in epithelial cells of gut and hemocytes appeared to be of a focal nature, infections in adipose cells appeared to be systemic. The authors therefore suggested that rickettsialike bacterial infections have contributed to the decline of Diporeia in the Great Lakes. Microsporidians are pathogens that commonly cause serious disease in crustacean hosts (Sindermann 1971; Meyers 1990). In shrimp and crayfish species, microsporidia infect multiple tissues and organs, including heart, connective tissues, hepatopancreas, hemocyte-forming organs and other tissues (Kelly 1979; Langdon 1991; Edgerton et al. 2002) causing pathologies ranging from inflammation to tissue destruction. For this reason, microsporidiosis has been called one of the most significant diseases of freshwater crayfish globally (Alderman & Polglase 1988). In amphipod crustaceans of the family Gammaridae, the family in which Diporeia resides, microsporidia have commonly been reported to infect muscle tissue and have been shown to have a range of effects that may affect host behavior, fitness, population size, stability, and sex ratio (Dunn et al. 1995; 2001; Hatcher et al. 1999; Dunn & Smith 2001; Terry et al. 2004; Fielding et al. 2005; Krebes et al. 2010). In Diporeia, microsporidian infections have been shown to destroy host muscle tissue; however, they are not associated with any apparent host immune response (Messick et al. 2004). Within Diporeia is a vast network of muscles that are essential for normal movement, feeding, swimming, and overall functioning, performance, and fitness of the amphipods (Figure 1.1). Given the importance of proper muscle functioning for the survival of Diporeia, it is possible that microsporidian infections can have severe impacts on Diporeia populations. Aquatic viruses have also been shown to cause mortality in a number of crustaceans, including decapod crustaceans (Sprague & Beckett 1966; Mari & Bonami 27 1988; Vogt 1996; Bonami et al. 1997; Widada & Bonami 2004), isopods (Kuris et al. 1979), and cladocerans (Bergoin et al. 1984). Recently, Diporeia were shown to host at least ten groups of viruses suggesting that viruses may be abundant constituents of the Diporeia microbiome (Hewson et al. 2013). Interestingly, the prevalence of one genotype was over two orders of magnitude greater in Lake Michigan compared to Lake Superior. Although this finding could suggest that population or spatial factors affect the presence of viruses in Diporeia it could also suggest that, since Diporeia declines have been observed in Lake Michigan and not in Lake Superior, the observed viruses may have played a role in the decline Plausibility of the Disease Theory The decline of Diporeia in four of the Great Lakes has puzzled scientists and managers alike. Despite sincere efforts and high quality research, the exact etiology of Diporeia losses is far from being unraveled. Although studies have shown a negative relationship between the presence of dreissenid mussels and Diporeia abundance, efforts to define a mechanistic cause have been unsuccessful. In the same context, reports indicated that fish predation, chemical contamination, and low dissolved oxygen cannot be solely blamed for Diporeia population declines (Whittle et al. 2000; Nalepa et al. 2005). Despite the severe dearth of knowledge about Diporeia taxonomy, host defense mechanisms, and pathogens, the disease theory as the major contributor to Diporeia decline is very plausible. 28 Gaps in Knowledge While Diporeia are intimately associated with the sediment, practically nothing is known regarding the bacterial communities associated with either Diporeia or the sediment environment. To fully understand the bacterial communities associated with Diporeia, it is first necessary to understand the structure of the bacterial communities associated with the sediment. Comparison of these two communities will allow for the distinction of bacteria that plays crucial roles in the ecological performance of Diporeia and will help to identify if shifts in bacterial communities are due to the presence of dreissenids. As of today, our understanding of pathogens infecting Diporeia is limited. Despite the fact that there is a wealth of knowledge on Diporeia decline, a comprehensive evaluation of Diporeia diseases over the decades of decline has never been conducted. Analysis of a number of Diporeia samples adequate enough to allow for spatio-temporal inferences to be made will allow for better understanding of the decline of Diporeia in the Great Lakes. Overall Objectives of the Study In specific, the overarching goal of my studies has been to examine the hypothesis that pathogens, directly or indirectly, have contributed to the declines of Diporeia in the Great Lakes region. This was achieved by following two main paths; one is to determine the bacterial communities through high throughput molecular techniques and gene sequencing, and the second to identify pathogens and their lesions through light and electron microscopical studies as well as phylogenetic studies. 29 Since Diporeia are benthic detritivores and are intimately associated with the sediment, I first performed both metagenomic pyrosequencing and terminal-restriction fragment polymorphism (T-RFLP) analyses of bacterial 16S rRNA genes present in sediment samples collected from multiple locations throughout lakes Superior, Michigan, Huron, and Ontario to characterize the bacterial communities associated with Great Lakes sediment and screen for the presence of potential Diporeia pathogens. Using multivariate analysis, these results were then compared to sediment properties. The second objective was to characterize the bacterial communities associated with Diporeia and to determine if these communities vary spatially or temporally and provide important insights about the microbial ecology of Diporeia. This was performed by coupling T-RFLP with sequence analysis of bacterial 16S rRNA genes present in Diporeia samples collected from multiple sites within Lakes Superior, Michigan, Huron, and Ontario, and Cayuga Lake. The third objective was to perform histopathological analysis of pathogen prevalences in Diporeia samples collected from as far back as 1980 from several sites in southern Lake Michigan to determine pathogen presence and to determine the trends of these infections over the past three decades and to shed light on pathogens as potential causes of Diporeia declines in the Laurentian Great Lakes. Models were fit in an effort to determine the relationship between Diporeia density and infection prevalence as well as to examine other possibly contributing factors, such as dreissenid density. Models were also fit to test for associations among infection prevalences, Diporeia densities, and dreissenid densities. 30 The fourth objective was to screen Diporeia from seven locations in Lakes Superior, Huron, and Ontario for viruses using a readily available fish cell line to determine if viral infections have potentially contributed to the decline. Interestingly, the fish pathogenic rhabdovirus, Viral Hemorrhagic Septicemia Virus (VHSV), was isolated from Diporeia collected from Lakes Michigan, Huron, and Ontario, but not Lake Superior. Although VHSV is probably not pathogenic to Diporeia and has not contributed to the decline, this study reports the first incidence of a fish-pathogenic rhabdovirus being isolated from Diporeia or other crustacean, and underscores the dire need to better understand the role of macroinvertebrates in disease ecology. The fifth objective was to describe the morphology, ultrastructure, and phylogeny of two novel species of parasites infecting Diporeia. The first parasite is a Haplosporidium sp. (Haplosporidia) that was observed causing systemic infection in Diporeia and often resulted in tissue destruction and elicited a host immune response in Diporeia collected from both Lakes Superior and Michigan. This study reports the first incident of a haplosporidian infecting Diporeia in Lake Superior. The second parasite is a Dictyocoela spp. (Microsporidia) that destroyed the host muscle tissue and, again, elicited a host immune response. 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Developmental and Comparative Immunology. 34(1), 49-58. Yepiz-Plascencia, G., Vargas-Albores, F., Jimenez-Vega, F., Ruiz-Verdugo, L.M., & Romo-Figueroa, G. (1998) Shrimp plasma HDL and beta-glucan binding protein (BGBP): comparison of biochemical characteristics. Comparative Biochemistry and Physiology-Biochemistry and Molecular Biology. 121, 309-314. 45 CHAPTER 2 Bacterial Communities Associated with Sediments in the Laurentian Great Lakes 46 Abstract Disease ecology studies in the Laurentian Great Lakes require thorough knowledge of fauna and flora prevailing in its sediments. The goal of this study was to identify and assess the relative abundances of bacteria in sediment samples from 13 locations in four of the five Great Lakes (lakes Superior, Michigan, Huron, and Ontario). For bacterial characterization, we used barcoded pyrosequencing and terminal-restriction fragment length polymorphism (T-RFLP) based on the 16S rRNA sequences. Pyrosequencing analysis revealed abundances of 26 bacterial phyla and proteobacterial classes. Actinobacteria, Acidobacteria, Betaproteobacteria, and Alphaproteobacteria were the most abundant bacteria in the collected samples. Both Similarity Profile Analysis and Redundancy Analysis of T-RFLP profiles and pyrosequencing dataset indicated that the bacterial communities of sediment samples from Lake Superior and Ontario were distinctly different from each other and from those of lakes Michigan and Huron. Additionally, both Similarity Profile Analysis and Redundancy analysis of the pyrosequencing dataset showed the bacterial communities of one sample from Lake Huron were distinct different from the other samples. Phylogenetic analysis revealed that the sample contained greater abundances of bacteria associated with polluted environments. Significant differences in the relative abundance of particular bacterial taxa were observed among sediment bacterial communities. Results of Redundancy Analysis based on bacterial community structure and sediment properties suggest that calcium concentration plays a large role in regulating bacterial community structure. This study constitutes the most extensive examination of bacteria associated with 47 Laurentian Great Lakes sediments and sheds useful insight into the microbial ecology of the lakes. Introduction In aquatic systems, sediment bacteria are widely recognized as major components of benthic food webs (Kemp 1990). Additionally, sediment bacteria play an important role in organic matter decomposition and nutrient cycling (Billen 1982, Jones 1982, Jorgensen 1983). In the Great Lakes food web, for example, sediment-associated bacteria constitute a major portion of carbon and energy consumed by the benthic amphipod Diporeia (Marzolf 1965), which in turn transfers the energy to the forage fishes that prey upon it, such as alewife (Alosa pseudoharengus ) (Hondorp et al. 2005), bloaters (Coregonus hoyi) (Rand et al. 1995), rainbow smelt (Osmerus mordax) (Selgeby et al. 1994), slimy (Cottus cognatus) and deepwater sculpin (Myoxocephalus thompsoni ) (Davis et al. 1997), lake whitefish (Coregonus clupeaformis) (Pothoven, 2005), and yellow perch (Perca flavescens) (Wells, 1980), In terms of decomposition and nutrient cycling, sediment bacteria affect overall benthic metabolism by regulating pH and redox conditions in the sediment through their consumption of hypolimnetic oxygen (Jones 1982), nitrate, and sulphate (Brezonik et al. 1987; Kelly et al. 1988). Despite their importance in benthic foodwebs and organic matter decomposition, numerous studies have indicated that sediment bacteria also can serve as disease reservoirs, and thus can affect the health of aquatic organisms. Pérez-Fuentetaja et al. (2011) used quantitative polymerase chain reaction (qPCR) to quantify Clostridium botulinum type E spores in Lake Erie sediments and determined that, although the abundance of the bacterial spores were patchy, sediments may serve as sources of the 48 bacteria that could initiate food web transfer of this pathogen. Similarly, Rinta-Kanto et al. (2009) used both qPCR and culturing techniques to determine the abundance of Microcysytis spp. in Lake Erie sediment samples and suggested that potentially toxigenic strains persist spatially and temporally in the sediment and that these populations may even act as bloom sources in large lake systems. Although studies have investigated the presence of several bacterial species in Great Lakes sediments, presently, no studies have been conducted to characterize the diversity of bacterial communities associated with sediments in offshore regions of the Great Lakes. Therefore, there is a definite lack of knowledge regarding geographic distribution of most bacterial species within Great Lakes sediments. Information on the diversity of sediment bacterial communities in the Great Lakes will allow for a better understanding of how communities are influenced by environmental factors and ecosystem functions and of the role sediment bacteria play in the disease ecology of the Great Lakes. To this end, the aim of this study was to use 16S ribosomal RNA (rRNA) gene barcoded pyrosequencing and the community fingerprinting method terminal restriction fragment length polymorphism (T-RFLP) to characterize the heterotrophic bacterial communities associated with Great Lakes sediment from 13 locations in four of the Great Lakes (lakes Superior, Huron, Michigan, and Erie). Materials and Methods Background on T-RFLP and Pyrosequencing Terminal-Restriction Fragment Length Polymorphism (T-RFLP) is a molecular technique used to characterize and compare diversity of bacterial communities among 49 environmental samples (Marsh et al. 1999). T-RFLP measures the DNA fragment size (bp) and fluorescent intensity of a fluorescently labeled restriction digestion fragments based on their electrophoretic mobility in a capillary. Initially, genomic DNA is used as a template for PCR amplification using one or more fluorescently labeled eubacterial primer sets. Amplified PCR products are then digested with one or more restriction endonucleases. Often, for T-RFLP analysis of bacterial communities present in sediment and soil environments, amplified 16S ribosomal rRNA genes are digested with either the HhaI or MspI restriction endonuclease (Tipayno et al. 2012; Dang et al. 2009; Nyman et al. 2006; Konstantinidis, et al. 2003). Digested amplicons of various lengths are then separated through capillary electrophoresis and detected with an automated DNA analyzer to produce electropherograms. Microbial community profiles based on fluorescently labeled fragment lengths are generated and analyzed with various computer-based bioinformatics programs. Due to variable conservation of restriction sites in 16S rDNA, the resolution of T-RFLP analysis is often reduced from the level of species to that of higher-order groups, thereby reducing complexity of the community profile (Liu et al, 1997). For 454 pyrosequencing, template/library preparation starts with shearing of the sample DNA. Adapters are then ligated to both the 5’ and 3’ end of the ssDNA. A single molecule of ssDNA is then annealed to a styrofoam bead containing bases that are complementary to the adapters. Next, clonal amplification of ssDNA occurs in microreactors in an emulsion of oil, water, and complementary bases. The emulsions are then broken and the beads are placed into picotiter plates containing PCR reagents. Only templates with two adapters will be polymerized and sequenced based on the 50 emission of light produced by the chemical reaction of luciferase + ATP. Images are taken after every cycle. The emission of light is proportional to the number and type nucleotides detected. The images are then used construct a flow gram to allow for base calling. Regardless of the ecosystem studied or the specific ecological question asked, as of today, the vast majority of studies making use of NGS platforms and environmental samples have employed the 454 pyrosequencing platform mainly because of its longer sequence read lengths (~400 bp). Studies have suggested that 454 pyrosequencing of 16S rRNA genes allows for detailed analysis of community structure while providing fairly high taxonomic resolution (Sogin et al. 2006; Andersson et al. 2008). For example, 454 pyrosequencing analysis of ~118 000 16S sequences from the North Atlantic deep sea revealed a high microbial diversity and thousands of low-abundance populations (Sogin et al. 2006). Additionally, Claesson et al. (2010) stated that the resolution of microbial community composition with amplicon pyrosequencing is likely several orders of magnitude larger than clone library sequencing. For taxonomic differentiation, distance values of 0.03 have been used to differentiate bacteria at the species level, 0.05 at the genus level, 0.10 at the family/class level, and 0.20 at the phylum level (Hugenholtz et al. 1998; Sait et al. 2002; Stackebrandt and Goebel, 1994; Hughes et al. 2001). Sediment Sampling and Nutrient Analysis For T-RFLP analysis, between two and three replicate sediment samples were collected from 13 offshore sites at depths between 50 and 230 meters (Figure 2.1). The number of replicate samples collected at each site is displayed in Table 2. All samples 51 were collected in August 2008 by taking Ponar grabs (sampling area 25.1 x 25.1cm/ 8.2 liters). Immediately after benthic samples were brought to the surface, undisturbed sediment samples were collected from the top centimeter of the sediment sample with a 50 ml tube, placed in sterile 80% ethanol, and stored at -20 °C. Phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), nitrate-nitrogen (NO3), and ammoniumnitrogen (NH4) concentrations, as well as pH, for the sediment samples were determined according to Dahnke (1998) by the Michigan State University Soil and Plant Nutrient Laboratory. DNA Isolation Genomic bacterial community DNA was extracted from sediment samples using the PowerSoilTM DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA) following the manufacturer’s protocol. DNA was quantified with a Qubit fluorometer and the Quant-it dsDNA BR Assay kit (Invitrogen Corp., Austin, TX). The isolated bacterial DNA was then used as a template for PCR amplification for both Terminal-Restriction Fragment Length Polymorphism (T-RFLP) and pyrosequencing analyses. T-RFLP Analysis of Sediment Community Fingerprints PCR amplification of the 16S gene was performed using the universal eubacterial primer set 27f-1387r (27f: 5’-AGA GTT TGA TCM TGG CTC AG-3’) labeled with carboxyfluorescein (6-FAM) and 1387r (5’-GGG CGG WGT GTA CAAGGC-3’) (Marchesi et al. 1998). PCR amplification was conducted in duplicate 50-μl reactions. PCR reactions contained 25 µl of 2X Green GoTaq® Master Mix (Promega Corporation, 52 WI, USA), 45.0 μM of each primer, and 5.0 μl of template DNA. PCR reaction conditions for T-RFLP samples were as follows: initial 94.0 °C for 4.0 min, followed by 30 cycles of 94.0 °C for 30 s, 56.0 °C for 30 s, 72.0 °C for 1.0 min and a final extension for 7.0 min at 72 °C. PCR products were purified using a Wizard® SV Gel and PCR Clean-Up System (Promega Corporation, WI, USA) following the manufacturer’s protocol. To construct profiles of sediment bacterial communities for each restriction enzyme, three hundred nanograms of purified fluorescently labeled PCR product were cut individually with five units of HhaI and MspI (New England Biolabs, Beverly, MA) for two h at 37°C. Digested PCR products were precipitated with three volumes of absolute ethanol. After an eight-hour incubation at -20 °C, the precipitates were pelleted by centrifugation at 14,000 rpm for 15 min. Pellets were resuspended in six μl sterile water (DEPC-treated, DNASE, RNASE free). The DNA fragments were separated on an ABI 3100 Genetic Analyzer automated sequence analyzer (Applied Biosystems Instruments, Foster City, CA) in GeneScan mode at Michigan State University’s Sequencing Facility. The 5’-terminal restriction fragments (T-RFs) were detected by excitation of the 6-FAM molecule attached to the forward primer. The sizes and abundances of the fragments were calculated using GeneScan 3.7 in relation to the MM1000 internal standard (BioVentures Inc.). T-RFLP data were analyzed with T-REX (http://trex.biohpc.org). TREX software uses the methodology described by Abdo et al. (2006) and Smith et al. (2005) to identify and align true peaks in electropherograms respectively. We used one standard deviation in peak area as the limit to identify true peaks and defined T-RFs by rounding off fragment sizes to nearest integer. Additionally, only profiles with a cumulative peak height ≥ 5,000 fluorescence units were used in the analysis. 53 454 Pyrosequencing of Sediment 16S rDNA For pyrosequencing, amplification of the V3-V5 region of the 16S rRNA gene was accomplished using the Broad HMP protocol (HMP website). Briefly, barcodes that allow sample multiplexing during pyrosequencing were incorporated between the 454 adaptor and the forward primers. PCR amplification was conducted in duplicate 50-µl reactions. PCR reactions contained 5.0 µl of 10X AccuPrime PCR Buffer II (Invitrogen, Carlsbad, CA), 50 µM of each primer, 0.5 µl Accuprime Taq Hifi (Invitrogen), and 5.0 µl of template DNA. PCR reaction conditions for pyrosequencing samples were as follows: initial 95.0 °C for 2 min, followed by 30 cycles of 95°C for 20 s, 50.0°C for 30 s, and 72.0 °C for 30 s, and a final extension of 72.0°C for 5 min. To determine the repeatability of pyrosequencing one sample (HU95) was amplified and sequenced with three different barcodes in triplicate (HU951, HU952, and HU953). PCR products were purified by using AmPure Beads (Agencourt Bioscience, Beverly, MA) following the manufacturer's protocol and then mixed equally before conducting pyrosequencing. The purified PCR products of the V1-V3 region of 16S rRNA gene were sequenced using the Roche 454 Y (Roche, Nutley, NJ, USA). Mothur software version 1.29.2 (Schloss et al. 2009) was used to de-noise, trim, filter and align sequences, find chimeras (error sequence reads composed of at least two partial sequences from different real genes), and assign sequences to operational taxonomic units (OTUs) (based on both 97% and 95% sequence similarity). Briefly, after extracting sequence, flow, and quality files from the raw Standard Flowgram Format (sff) file, the data was de-noised with the “shhh.flows” command. Quality-filtered sequences (minimum length 200 bp, with no low quality or ambiguous bases, no more 54 than 1 and 2 mismatches to the barcode and primer, respectively, and homopolymers of 8 bp as a maximum) were separated by primer and barcode, and then trimmed. Unique sequences from each were aligned to the SILVA-database reference alignment v 102. Sequences outside the most represented alignment space were removed. Chimeras were identified with the “uchime.chimera” function algorithm and were removed. The remaining sequences were classified against the 16S rRNA Ribosomal Database Project (RDP) database, training set 9, using a k-nearest neighbor approach with a bootstrap cutoff of 80%. Since chloroplasts and mitochondria are generally considered to not be an active component of microbial communities, sequences affiliated with organelles (Mitochondria, Chloroplast, and Eukaryota) were removed from the dataset. The removed sequences were then classified against the RDP database to ensure that no cyanobacteria had inadvertently been removed. The average length of all bacterial sequences without the primers in the final dataset was 281 bp. Statistical Analyses For all analyses, abundance values for both T-RFLP and pyrosequencing datasets were standardized by expressing the abundance of each OTU as a percentage of total abundance for each sample. Additionally, to account for “blind sampling” and large numbers of absences in the data sets, the datasets were transformed using the Hellinger equation as suggested by Ramette (2007). Species richness and diversity for the T-RLP community profiles at each sampling site (for both the HhaI and MspI datasets) were calculated in R (R Development Core Team, 2009) with the Vegan package (Oksanen et al. 2013). For calculating species richness and diversity, we 55 assumed that the number of T-RFs present in a profile represented the operational taxonomic units (OTUS) and that the T-RF height represented the relative abundance of each bacterial species. For characterizing species diversity, we used the Shannon diversity index, which accounts for both abundance and evenness of the community at a particular site. For pyrosequencing data, a Bray-Curtis (Bray and Curtis, 1957) distance matrix was generated using the “dist.seqs” command in Mothur and sequences were assigned to OTUs (defined at sequence similarities of 97% and 95%) with the “cluster.split” command using the “nearest” option. Species richness statistics including Good’s Coverage (Good, 1953), nonparametric richness Chao (Chao, 1987), and Shannon Index (Shannon, 1948) were calculated for each sequence library. ShannonWeaver and Chao indices were calculated using the “collect.single” command with a sampling frequency of 1. To define natural grouping of sediment bacterial communities generated by both TRFLP and pyrosequencing a Similarity Profile Test (SIMPROF) was performed using R package “clustsig” (Whitaker and Christman, 2010) where clustering was considered significant if P < 0.05. To determine which bacterial groups identified through pyrosequencing had significantly different relative abundances among clusters the “metastats” method (White et al. 2009) was executed in mother based on 1000 permutations. Metastats conducts two-sample t-test comparisons of all pairwise treatment combinations and the threshold for assessing significance of the pairwise tests is chosen by trying to minimize the false discovery rate. Differences in relative abundances of bacterial groups were determined to be significant if both P and false discovery rate (q) values were less than 0.0001. 56 To explain observed variance in the bacterial community profiles and sediment properties in relation to sampling site (for both T-RFLP and pyrosequencing datasets), ordination was performed by correlating their data matrices with redundancy analysis (RDA) and by fitting sediment properties vectors onto ordination using VEGAN package (version 2.0-2) (Oksanen et al. 2013) for R software (R Development Core Team, 2009). Additionally, Spearman correlation analysis was used to assess the association between sediment properties and bacterial community richness and relative abundance of individual bacterial taxa (97% similarity) was used to identify individual taxa. Results Sediment Properties Sediment properties of samples from each sampling site are shown in Table 2.2. Sediment pH ranged from 7.0 to 7.6. Sediment concentrations of phosphorus, potassium, and calcium for the ON41 sample were higher than the other samples analyzed (55, 246, and 5414 ppm respectively). Sediment concentrations of potassium, calcium, and magnesium for the SU23 sample were lower than the other samples analyzed (4, 202, and 4 ppm, respectively). Magnesium concentrations ranged from 4 (SU23) to 585 (MI18) ppm. The sediment concentration of nitrate ranged from from 0.6 (MI27 and HU95) to 2.5 (ON41) ppm. Similarly, the sediment concentration of ammonium ranged from 1.8 (SU011) to 37.4 (ON41) ppm. 57 T-RFLP Analysis of Sediment Communities Analysis of T-RFLP data revealed the presence of diverse bacterial communities in sediment samples. For the entire HhaI and MspI datasets, a total of 443 and 833 OTUs, which are meant as an index of bacteria types, were observed respectively. The range of OTUs by sampling site was 11-144 with a mean of 95.77 ± SE 6.51 for the HhaI dataset and 51-399 with a mean of 103.13 ± SE 19.49 for the MspI dataset. Shannon Index values ranged between 1.13-2.71 and 3.30-4.96 for the HhaI and MspI datasets, respectively. In general, both the number of OTUs and Shannon-Weiner diversity values were greater for the MspI dataset compared to the HhaI dataset (Table 2.3). Very few of the OTUs were found across all the sampling sites, indicating that few bacteria were universally distributed. In general, OTUs that were observed in a higher proportion of samples had higher relative abundances within each bacterial community. For both HhaI and MspI, less than 5% of the observed T-RFs occurred in more than 90% of profiles with average relative abundances greater than between 1.9 and 6.3%. Pyrosequencing of Sediment Communities The Library coverage estimations and sequence diversity of 16S rRNA derived from Great Lakes sediment samples are displayed in Table 2.4. The pyrosequencing analysis of 16S rRNA gene amplicons from the 13 Great Lakes sediment samples produced 133,155 reads, leaving 120,264 reads after quality filtering, and removal of chimeric sequences. The total number of reads from each sediment sample varied from 1,593-24,017 reads. 58 At cutoffs of 97 and 95% sequence similarity, a total of 7,344 and 1,213 OTUs were observed respectively. Similarily, at cutoffs of 97 and 95% sequence similarity, a total of 28 bacterial phyla and proteobacterial classes were identified in the dataset reflecting the taxonomically complex environment of Great Lakes sediments. At a cutoffs of 97%, less than 1% of the observed OTUs occurred in more than 90% of profiles with an average relative abundance 47.6% (±0.08) (Figure 2.2).The Good’s coverage index for total diversity among samples at the species level (97.0%) ranged between 82.6 – 97.1 % and almost covered the bacterial diversity at the genus level (95.0%) (88.3-98.2%). Rarefaction curves, which are plots of the number of species as a function of the number of sequences, showed a progressive trend to cover estimated diversity at the species level for all samples while saturation was achieved at the at the genus level suggesting significant coverage of the phylogenetic diversity was obtained. Even though the value of Good’s coverage indicated that all samples were sequenced at high depth, rarefaction curves did not approach saturation at a cut-off value of 97% similarity indicating that some bacterial taxa still may have remained undetected. Similarity Profile Analysis of Bacterial Community Profiles For the HhaI and MspI T-RFLP datasets, four and three significant (P < 0.05) clusters occurred among sediment bacterial community samples respectively. In both dendograms generated for the two T-RFLP datasets (HhaI and MspI), Lake Superior samples formed a unique cluster while an overlap of samples from the other lakes sample was observed (Figure 2.3). For the pyrosequencing dataset, three significant clusters occured among sediment bacterial community samples. The sediment bacterial 59 community samples contained in each cluster are displayed in Figure 2.4. Differences in relative abundances major phyla and classes were observed for each cluster (Figure 2.5). Higher abundances of Betaproteobacteria Bacteroidetes, and Deltaproteobacteria were observed for Cluster 1(HU38) compared to clusters 2 and 3, higher abundances of Chlorofexi, Gammaproteobacteria, and Nitrospirae were observed for cluster 2 compared to HU38 and 3, and higher abundances of Acidobacteria, Actinobacteria and Alphaproteobacteria were observed for Cluster 3 compared to clusters HU38 and 2. Phylogenetic Structure of Sediment Communities Ten major bacterial phyla and proteobacterial classes (Actinobacteria, Acidobacteria, Bacteroidetes, Nitrospirae, Chloroflexi, Firmicutes, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria) classes were relatively abundant (>1.0%) and shared by all samples. Actinobacteria were highly abundant accounting for 15.7 % of the OTUs followed by Acidobacteria (14.5%), Betaproteobacteria (9.1), Gammaproteobacteria (8.23%), Alphaproteobacteria (7.99%), Bacteroidetes (4.3%), Nitrospirae (4.3%), Deltaproteobacteria (3.4%), Chloroflexi (3.3%), and Firmicutes (1.3%). The dominant bacterial groups (greater than 1% abundance) in each sample are displayed in Figure 2.6 and the dominant bacterial genera are displayed in Figure 2.7. The most abundant orders of Actinobacteria detected were Actinomycetales and Acidimicrobiales, which accounted for 29.1 and 13.0% of Actinobacteria detected, while bacteria belonging to the orders Solirubacterales, Coriobacterales, and Thermoleophilales accounted for 5.7, 0.5, and 0.2% of Actinobacteria detected. The 60 phylum Acidobacteria consisted of a wide range of bacteria; 61.2% of these belonged to the orders Gp1, Gp2, Gp3, Gp4, and Gp6. Other orders of Acidobacteria detected included Gp5, Gp7, Gp9, Gp10, Gp13, Gp17, Gp18, Gp21, Gp22, Gp23, Gp25, and Holophagales. The majority of Bacteroidetes detected belonged to the order Sphingobacteriales, which accounted for 44% of Bacteroidetes detected. The only other order of Bacteroidetes detected was Flavobacteriales which accounted for 13% of Bacteroidetes detected. Of the bacteria belonging to the phylum Nitrospirae, the majority (87%) belonged to the genus Nitrospira while the remaining 13% belong to the genus Thermodesulfovibrio. Of the bacteria belonging to the phylum Chlorofexi detected, the majority (50.3%) belonged to the orders Anaerolineales while bacteria belonging to the orders Ktedonobacterales and Sphaerobacterales accounted for 20.1% and 2.5% of Chloroflexi detected respectively. The most abundant Firmicutes detected, belonged to the order Clostridiales which accounted for 41.4% of Firmicutes detected while bacteria belonging to the orders Bacilliales, Selenomonadales, Lactobacciliales, and Erysipelotrichales accounted for 25.9, 5.0, 3.3, and 2.9% of Firmicutes detected respectively. The phylum Proteobacteria consisted of a wide range of bacteria. The majority of Alphaproteobacteria detected belonged to the orders Rhodospirillales, Rhizobiales, and Sphingomanadales which accounted for 29.9, 28.5, and 8.1% of Alphaproteobacteria detected respectively while Caulobacterales, Rhodobacterales, Alphaproteobacteria incertae sedis, Rickettsiales, and Parvularculales, accounted for 3.7, 2.8, 1.6, 1.0, and 0.2% of Alphaproteobacteria detected. The most abundant Betaproteobacteria detected belonged to the orders Burkholderiales and Rhodocycales which accounted for 23.0 61 and 13.4% of Betaproteobacteria detected respectively. The majority of Gammaproteobacteria detected belonged to the orders Xanthomonadales, Legionellales, and Pseudomonadales which accounted for 14.5, 13.3, and 5.8% of Gammaproteobacteria detected respectively while Aeromonadales, Altermonadales, Chromatiales, Enterobacterales, Gammaproteobacteria order incertae sedis, Methylococcales, Oceanospirillales, Pasteurellales, and Thiotrichales each accounted for <1.0% of Gammaproteobacteria detected. The most abundant order of Deltaproteobacteria detected was Myxococcales which accounted for 44.9% of Deltaproteobacteria detected while bacteria belonging to the orders Bdellovibrionales, Desulfobacterales, Desulfomonadales, and Syntrophobacteriales accounted for 7.5, 3.1, 1.7, 0.3% of Gammaproteobacteria detected. The average abundances of other phyla and protebacterial classes detected were Verrucomicrobia (0.901%), Gemmatimonadetes (0.599%), WS3 (0.575%), Planctomycetes (0.294%), TM7 (0.133%), Chlorobi (0.123%), Aratimonadetes (0.108%), Spirochaetes (0.045%), OD1 (0.040%), Chlamydiae (0.027%), Fusobacteria (0.023%), Epsilonprotebacteria (0.016%), Deinococcus (0.013%), ODP1 (0.010%), BRC1 (0.008%), and Elusimicrobia (0.003%). All samples contained OTUs that could not be classified into known bacterial phyla based on the existing databases (NCBI and RDP II). No Cyanobacteria were detected in any of the samples analyzed. There were notable differences in the relative abundance of taxa among the bacterial communities of Great Lakes sediment samples. The most common bacterial phylogenetic groups in HU38 sediment were Bacteroidetes and Betaproteobacteria, while Acidobacteria and Actinobacteria had the greatest abundances in the other Great 62 Lakes sediment samples. The most abundant classified genera in HU38 sediment was Zooglea (Betaproteobacteria) and Flavobacterium (Bacteriodetes), but were Gp4, and Gp6 (Acidobacteria), Longilinea (Chloroflexi), and Nitrospira (Nitrospira) in other Great Lakes sediments. Zooglea sp. (Betaproteobacteria) accounted for 17.8% of the bacterial community for HU38 (46.8% of the Betaproteobacteria observed for HU38). Zooglea was also detected in SU011, MI18, ON25, and ON41 where it only accounted for between 0.02 and 0.06% of the bacterial community for each sediment sample. Additionally, bacterial genera belonging to the order Burkholderiales accounted for 11.4% of the bacterial community for HU38 (29.8% of the Betaproteobacteria observed for HU38) while bacterial genera belonging to the order Burkholderiales ranged between 0.2 and 1.8% for other Great Lakes sediment samples. Additionally, on average, bacteria belonging to genus Aeromonas and the families Lachnospiraceae (Clostridiales) and Ruminococcaceae (Clostridiales) were 86, 110, and 10 times more abundant in in HU38 compared to the other samples analyzed. Differences in Relative Abundances of OTUs among Pyrosequencing Clusters A number of significant differences (P and q < 0.0001) in the relative abundance of particular OTUs were observed among the three clusters identified by the Profile Similarity Analysis. A greater number of significantly more abundant OTUs were observed for cluster 1(HU38) than for clusters 2 and 3. OTUs belonging to the same For HU38, the relative abundance of a number of OTUs belonging to the orders Fusobacteriales, Caulobacterales, Rhizobiales, Rhodospirillales, Sphingomonadales (Alphaproteobacteria), Burkholderiales, Neisseriales, Rhodocyclales, Myxococcales, 63 Bdellovibrionales, Desulfobacterales (Betaproteobacteria), Myxococcales (Deltaproteobacteria), Campylobacterales (Epsilonproteobacteria), Cryomorphaceae, Bacteroidaceae, Porphyromonadaceae, Rikenellaceae, Prevotellaceae (Bacteroidetes), Lactobacillales, Clostridiales (Firmicutes), Spirochaetales (Spirochaetes), and Verrucomicrobiales (Verrucomicrobia) were significantly higher compared to their relative abundances in clusters 2 and 3. For cluster 2, the relative abundance of a number of OTUs belonging to the orders Alphaproteobacteria order incertae sedis and Sphingomonadales (Alphaproteobacteria) were significantly higher compared to their relative abundances in HU38 and cluster 3. For cluster 3, the relative abundance of a number of OTUs belonging to the orders Actinomycetales (Actinobacteria), Rhodospirillales (Alphaproteobacteria), Rhodocyclales (Betaproteobacteria), and Nitrospirales (Nitrospirae) were significantly higher compared to their relative abundances in HU38 and cluster 2. Redundancy Analysis For both T-RFLP datasets (HhaI and MspI restriction endonucleases), when the RDA scores were plotted according to sampling site, different sampling sites often grouped close together in the ordination space, indicating similar bacterial communities for these sites (Figures 2.8 and 2.9). For the 454 pyrosequencing dataset, on the other hand, RDA analysis showed a greater level of overlapping of samples collected from different lakes (Figure 2.10). RDA analysis of the T-RFLP datasets showed that the bacterial communities of samples from lakes Michigan and Huron grouped together, while the bacterial communities of lakes Superior and Ontario formed distinct groups in 64 the ordination space. For both the T-RFLP datasets, the ordinations showed that calcium concentration explained a relatively large amount of variability. RDA analysis of the 454 pyrosequencing dataset, on the other hand, did not show a distinct grouping of samples from each lake; however, HU38 was a considerable distance away from other samples in the ordination space. Additionally, RDA analysis of the 454 pyrosequencing dataset showed that nitrate-nitrogen explained a relatively large amount of variability. Relationship between Bacterial Community Structure and Sediment Properties Abundances of bacteria belonging to the phylum Firmicutes had a significant correlation with nitrate-nitrogen concentration (Table 2.5). Abundances Aquicella sp. had significant positive correlations with calcium, nitrate-nitrogen, and ammoniumnitrate concentrations (Table 2.6). Discussion Freshwater environments have received less attention compared to marine environments in terms of characterization of bacterial communities. As of today, there have only been few studies that have investigated the diversity of bacterial communities in the Laurentian Great Lakes; most of which have described the diversity of either bacterioplankton (Hicks et al. 2004; Mueller-Spitz et al. 2009; Wilhelm et al. 2006) or picoplankton (Pascoe and Hicks, 2004). The present study represents the first report to utilize both T-RFLP and 454 pyrosequencing of 16S rRNA genes to characterize the bacterial communities associated with Great Lakes sediments in relation to sediment properties. 65 Pyrosequencing allowed for the detection of bacteria belonging to a total of 26 bacterial phyla and proteobacterial classes in Great Lakes sediment samples. Dominant bacteria belonged to the Actinobacteria, Acidobacteria, Alphaproteobacteria, and Betaproteobacteria bacterial phyla and proteobacterial classes. Comparison of Shannon diversity index values obtained for Great Lakes sediments to those obtained for other freshwater sediments based on massively parallel sequencing studies showed that the diversity of Great Lakes sediment bacterial communities are relatively similar to those of river sediments near Chicago, IL (Drury et al. 2013) but are lower than those of shallow, intertidal sediments in Bohai Bay, China, (Wang et al. 2013), the Pearl River, China (Wang et al. 2012) and shallow sediments in a drinking water reservoir in Saidenbach, Germany (Röske et al. 2012). Results of pyrosequencing show that the bacterial community of HU38 is distinctly different from the other samples analyzed. While the bacterial community of HU38 had the lowest diversity compared to the two large clusters in the dendogram, a greater number of significantly different bacterial taxa abundances were observed for HU38 likely reflecting the unique biochemical processes occurring at that site. For example, taxa belonging to the class Epsilonproteobacteria which has been implicated in chemoautotrophic production (Grote et al. 2008) and Desulfobacterales, an order of sulfate-reducing bacteria were significantly greater at HU38 compared to the two large clusters. At the Phylum/class level, the sediment bacterial community of HU38 appears to have a similar structure to those found in the water column of a number of freshwater lakes (Van der Gucht et al. 2005) in that it contained relatively higher abundances of Betaproteobacteria and Bacteroidetes. At the order to genus level, however, analysis of 66 the bacterial community composition of HU38 sediment revealed abundances of bacterial taxa that are associated with polluted environments. For example, the genus Zoogloea, which contains exopolymer-producing bacteria capable of forming finger-like zoogloeae and are found principally in organically polluted fresh waters, wastewaters, and aerobic biological wastewater treatment systems (Garrity et al. 2005) was detected at HU38. Similarily, relatively high abundances of bacteria belonging to the Lachnospiraceae and Ruminococcaceae (Clostridiales), two families of bacteria that have been shown to be associated with fecal pollution (McLellan et al. 2013) were detected at HU38. Altogether, these results suggest that HU38 is a polluted site and that this deep (137m) location in Lake Huron may be a sink for pollutants. Near this site there are water treatment plants of multiple cities in Ontario, Canada, including the Bruce Energy Centre, Bruce Nuclear Power Development, Kincardine, Port Elgin, and Southampton, which may contribute to the polluted nature of this site. Additionally, Drury et al. (2013) used pyrosequencing of 16S rRNA genes to determine the effect of effluent from wastewater treatment plants on downstream river sediment bacterial community diversity and concluded that polluted sites had lower diversity and increased relative abundances of bacteria belonging to the phylum Bacteroidetes. Therefore, the relatively lower diversity and higher abundance of Bacteroidetes observed for HU38 is consistent what has been reported for sediment environments that have been impacted by waste water treatment plant effluent. The finding that the results T-RFLP analysis and pyrosequencing differed for multiple analyses is interesting. We attribute this finding to the fact that different bacterial taxa can have identical restriction fragment lengths. It is therefore possible that 67 the unique bacterial structure identified in HU38 by pyrosequencing was not identified by T-RFLP because different bacterial taxa present in the other sample analyzed shared an identical restriction fragment length to bacterial taxa that uniquely abundant in HU38 (e.g. Zoogloea spp.). Similarly, it is possible that the unique bacterial structure identified for Lake Superior samples by T-RFLP but not pyrosequencing is the result of multiple bacterial taxa with identical restriction fragment lengths that are unique to Lake Superior but have low relative abundances. Overall, results suggest that, of the parameters analyzed, calcium concentration is an important driver that affects bacterial community structure in Great Lakes sediments. One possible reason for the importance of calcium concentration regulating bacterial community structure may be due to the buffering capability of calcium allowing for a more stable environment for bacterial metabolism. The importance of calcium regulating bacterial communities may also be similar in the water column. Considering the longterm trends and fluctuations in calcium concentrations that have been observed within the lower Great Lakes (Chapra et al. 2012), changes in calcium concentrations in the water column may have a large effect on bacterial community structure and ecosystem functioning either directly or by influencing the prevailing pH. Although no significant differences in the abundances of bacteria belonging to the genus Clostridium sensu stricto, a group of bacteria containing a number of medically important species (Wiegel et al. 2005) among sites were observed, they were present in the majority of pyrosequencing libraries illustrating the ubiquity of these bacteria in the Great Lakes. This finding suggests that Great Lakes sediments may act as sources of clostridia that could initiate food web transfer of these bacteria. Unfortunately, the 68 methodology used in the current study did not allow for the speciation of the clostridia detected. Comprehensive studies designed to investigate abundances of different clostridia would provide valuable information about the distribution of Clostridium spp. and much needed insight into the of disease ecology of the Laurentian Great Lakes. Cyanobacteria, which have been shown to be relatively abundant in both water column samples (Mueller-Spitz et al. 2009) and sinkhole sediment samples (Nold et al. 2010) from relatively shallow regions (<50m) in the Great Lakes, were not detected in this study. The absence of cyanobacteria in the pyrosequencing dataset may be the result of an inadequate amount of light to support the growth of cyanobacteria at the relatively deep (≥50m) sites sampled. Vollenweider et al. (1974) investigated interannual differences in cyanobacteria abundances in the Great Lakes and showed that relatively higher abundances of cyanobacteria are observed in the water column in offshore regions throughout the Great Lakes during August, the month in which samples were collected for the current study. The finding that no cyanobacteria were detected in any sediments samples in August suggests that cyanobacteria are rarely or never present in deeper sediments in the Great Lakes. In conclusion, the combination of T-RFLP analysis and pyrosequencing of 16S rRNA genes proved to be a powerful tool for analyzing Great Lakes sediment bacterial communities. Since a similar study has never been conducted, this study represents the most extensive list of bacteria associated with Great Lakes sediment and provides useful insights on the microbial ecology of the Laurentian Great Lakes. This knowledge is needed to establish information on the functional diversity of Great Lakes sediment bacterial communities. Furthermore, this knowledge will allow for a better understanding 69 of the relationship between environmental factors and ecosystem functions and of the role sediment bacteria play in benthic food webs and disease ecology of the Great Lakes. 70 APPENDIX 2 71 Table 2.1. Biological replicate sample identification (ID) for each sediment sample pyrosequenced or analyzed with T-RFLP using either the HhaI or MspI restriction endonuclease. HhaI ID HU-37 1,2 HU-38 3,4 HU-54M 5,6 HU-95 7,8 MI-18M 9 MI-27M 11,12 MI-40 13,14 MI-47 15,16 ON-25 17,18 ON-41 19,20 SU-01 21,22 SU-20B 23,24 SU-23B 25 Site MspI ID 1,2 3,4 5,6 7,8 9,10 11,12 13,14 15,16 17,18 19,20 21,22 23,24 25,26 Pyrosequencing ID 1 3 5 7a, 7b, 7c 9 11 13 17 19 21 25 72 Depth 50 137 91 70 161 112 160 186 133 122 203 116 62 Table 2.2. Sediment properties of sampling sites in the Great Lakes (phosphate = P, potassium = K, calcium = Ca, magnesium = Mg, nitrate = NO3, and ammonium = NH4). Ca K Mg P NH4 NO3 Site Depth pH (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) MI18 161 7.6 5188 186 585 23.2 1.1 6 MI27 112 7.6 655 54 110 10.5 0.6 28 MI40 160 7.5 1930 120 396 14.2 0.7 55 MI-47 186 HU37 50 1795 150 266 24.7 1.2 32 HU38 137 2657 240 477 12.8 0.8 27 HU54 91 7.1 936 80 144 12 0.5 24 HU95 70 1661 148 302 10.6 0.6 28 ON25 133 5116 194 414 37.4 2.5 1 ON41 122 5414 246 418 32.5 1.1 55 SU01 230 7.0 756 58 74 1.8 0.8 16 SU-20 116 SU23 62 7.0 202 4 4 2 0.8 10 73 Table 2.3. Library sequence diversity of Great Lakes sediment samples based on TRFLP analysis of 16S rRNA genes digested with either HhaI or MspI. No. OTUs No. OTUs Sample (HhaI) (MspI) SU01 SU20 SU23 MI18 MI27 MI40 MI47 HU37 HU38 HU54 HU951 ON25 ON41 106 138 107 10 75 115 96 76 101 106 85 58 114 47 104 128 97 106 109 42 43 134 66 58 131 248 135 281 68 86 51 307 58 270 78 315 79 322 129 276 126 399 127 284 54 255 259 355 231 229 74 Shannon Shannon Index Index (HhaI) (MspI) 3.92 4.27 4.16 4.66 4.01 4.25 2.26 4.94 3.81 3.82 3.95 4.07 3.42 4.53 3.94 3.56 3.79 4.35 4.07 3.79 4.18 4.66 3.69 3.80 3.48 4.39 3.84 3.99 3.40 4.55 3.73 4.13 3.80 4.96 3.81 4.07 3.88 4.56 3.68 3.30 2.92 4.43 3.16 4.31 3.95 4.72 3.50 4.47 3.38 4.49 Table 2.4. Library coverage estimations and sequence diversity of 16S rRNA sequence libraries derived from Great Lakes sediment samples based on 97% and 95% sequence similarities. Library coverage was calculated as C = 1-n/N, where n is the number of OTUs without a replicate, and N is the number of sequences. The numbers in the parentheses are lower and upper 95% confidence intervals for the Chao 1 estimators. The Shannon index -∑pi, where pi = ni / N, ni is the number of OTUs with i individuals, and N is the total number of individuals. SU01 No. of raw sequences 28419 No. of filtered sequences 24017 SU23 5338 5092 MI18 16375 14708 MI27 1730 1593 MI40 5068 4700 HU37 4713 4454 HU38 17978 15813 HU54 8844 8430 HU951 10795 10197 HU952 5689 5426 HU953 5921 5616 % identity Sample 97 95 97 95 97 95 97 95 97 95 97 95 97 95 97 95 97 95 97 95 97 95 No. of OTUs % coverage Chao 1 value 2217 1132 988 629 2085 1228 450 326 1084 739 908 621 955 678 1159 767 1437 928 1016 670 1062 707 96.0 98.3 89.1 94.0 93.0 96.4 82.6 88.3 87.3 92.3 88.8 92.9 97.1 98.2 92.9 95.8 93.0 96.0 90.0 94.5 90.0 93.9 3354(3324, 3782) 1613(1504, 1754) 2102(1863, 2407) 1097(973, 1266) 3699(3447, 3999) 2004(1843, 2208) 1047(873, 1294) 721(587, 926) 2075(1875, 2325) 1276(1143,1452) 1768(1580, 2010) 1168(1022, 1367) 1688(1522, 1901) 1095(983, 1248) 2192(1982, 2456) 1273(1148, 1438) 2617(2398, 2885) 1461(1338, 1621) 1877(1697, 2105) 1097(984, 1252) 2167(1937, 2458) 1266(1123, 1457) 75 Shannon index 6.20 5.23 5.68 4.97 5.87 5.17 5.11 4.67 5.83 5.20 5.50 4.97 4.44 3.92 5.50 4.80 5.69 4.98 5.54 4.85 5.60 4.91 Table 2.4. (cont’d). % identity 97 95 97 95 Sample No. of raw sequences ON25 6146 No. of filtered sequences 5678 ON41 16139 14540 No. of OTUs % coverage Chao 1 value Shannon index 960 561 1963 1119 91.8 96.0 93.6 96.7 1676(1517, 1881) 849(764, 968) 3357(3129, 3629) 1738(1602, 1906) 5.50 4.79 6.24 5.31 76 Table 2.5. Correlations between the relative abundances of abundant bacterial phyla and proteobacterial classes, richness (Chao 1 values), and diversity (Shannon index), and the soil properties in Great Lakes sediments (P=phosphorus, K=potassium, Ca=calcium, Mg=magnesium, NO3=nitrate, NH4=ammonium). Bold values are significant (α=0.05 level). NO3 NH4 P K Ca Mg Depth (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) Acidobacteria 0.196 -0.371 0.166 0.343 0.160 0.267 0.160 (0.357) (0.212) (0.588) (0.252) (0.601) (0.379) (0.601) Actinobacteria -0.298 -0.134 -0.133 -0.343 -0.359 -0.309 -0.348 (0.322) (0.663) (0.666) (0.252) (0.228) (0.305) (0.244) Bacteroidetes 0.342 0.315 -0.133 -0.220 -0.144 0.000 -0.061 (0.252) (0.294) (0.666) (0.491) (0.640) (1.000) (0.844) Chloroflexi -0.265 0.095 0.282 0.116 0.370 -0.457 0.061 (0.381) (0.758) (0.351) (0.706) (0.213) (0.116) (0.844) Firmicutes 0.221 -0.036 0.189 0.111 -0.055 0.561 0.083 (0.468) (0.906) (0.539) (0.719) (0.858) (0.046) (0.788) Planctomycetes 0.166 -0.243 0.044 0.122 -0.033 0.140 -0.094 (0.588) (0.424) (0.886) (0.692) (0.914) (0.648) (0.760) Nitrospirae -0.558 0.371 -0.459 -0.403 -0.381 -0.480 -0.072 (0.048) (0.212) (0.115) (0.172) (0.199) (0.097) (0.816) Alphaproteobacteria 0.392 -0.240 -0.020 0.088 -0.171 0.550 0.127 (0.185) (0.430) (0.943) (0.774) (0.576) (0.052) (0.679) Betaproteobacteria -0.055 0.420 -0.287 -0.309 -0.315 -0.121 -0.177 (0.858) (0.158) (0.341) (0.304) (0.295) (0.695) (0.563) Gammaproteobacteria -0.348 0.296 -0.282 -0.271 -0.177 -0.278 0.077 (0.244) (0.327) (0.351) (0.371) (0.563) (0.358) (0.802) Deltaproteobacteria 0.293 0.123 0.182 0.403 0.354 0.432 0.315 (0.332) (0.690) (0.551) (0.172) (0.236) (0.140) (0.295) Unclassified Proteobacteria -0.006 0.059 -0.149 0.006 -0.044 -0.126 -0.066 (0.986) (0.849) (0.6270 (0.986) (0.886) (0.681) (0.830) 77 Table 2.5 (cont’d). NO3 NH4 P K Ca Mg Depth (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) Unclassified Bacteria 0.066 -0.176 0.022 0.365 0.370 0.093 0.127 (0.830) (0.566) (0.943) (0.221) (0.213) (0.764) (0.679) Richness 0.276 -0.120 0.011 0.111 0.077 -0.033 -0.111 (0.361) (0.696) (0.971) (0.719) (0.802) (0.913) (0.719) Diversity 0.383 0.104 0.033 0.256 0.172 0.175 -0.028 (0.196) (0.736) (0.914) (0.399) (0.573) (0.568) (0.928) 78 Table 2.6. Correlations between the relative abundances of bacterial phylotypes related with pathogenic bacteria and human fecal pollution and the soil properties in Great Lakes sediments (P=phosphorus, K=potassium, Ca=calcium, Mg=magnesium, NO3=nitrate, NH4=ammonium). P values are in parentheses. Significant correlations are in bold. Bold values are significant (α=0.05 level). Taxonomic group Depth 0.422 (0.151) -0.512 Clostridium sensu stricto (0.074) 0.193 Zooglea sp. (0.527) 0.250 Lachnospiraceae (0.410) -0.028 Ruminococcaceae (0.928) 0.450 Aquicella sp. (0.122) -0.069 Coxiella sp. (0.822) 0.119 Legionella sp. (0.699) 0.400 Rickettsia sp. (0.176) Aeromonas sp. P (ppm) -0.460 (0.114) -0.071 (0.817) -0.275 (0.363) -0.404 (0.171) 0.048 (0.876) -0.152 (0.621) 0.251 (0.408) -0.299 (0.321) -0.513 (0.073) K (ppm) 0.161 (0.600) -0.166 (0.588) 0.044 (0.887) 0.250 (0.410) 0.451 (0.122) 0.426 (0.146) -0.072 (0.814) -0.064 (0.836) 0.071 (0.818) Ca (ppm) 0.131 (0.670) -0.227 (0.456) -0.079 (0.797) 0.138 (0.652) 0.322 (0.283) 0.571 (0.041) 0.003 (0.992) -0.158 (0.607) 0.086 (0.780) 79 Mg (ppm) 0.235 (0.439) -0.346 (0.247) -0.009 (0.977) 0.209 (0.493) 0.412 (0.162) 0.384 (0.195) -0.069 (0.822) -0.252 (0.407) 0.161 (0.600) NO3 (ppm) 0.252 (0.406) 0.051 (0.870) 0.116 (0.706) 0.011 (0.971) -0.092 (0.764) 0.784 (0.002) 0.230 (0.449) 0.232 (0.446) 0.271 (0.370) NH4 (ppm) -0.112 (0.715) -0.373 (0.209) -0.255 (0.401) 0.303 (0.315) -0.157 (0.609) 0.607 (0.028) 0.161 (0.600) -0.077 (0.801) -0.149 (0.626) Association pathogen pathogen pollution pollution pollution pathogen pathogen pathogen pathogen Reference Kersters et al. (2006) Wiegel et al. (2006) Garrity et al. (2005) McLellan et al. 2013) McLellan et al. 2013) Zhong et al. (2012) Kersters et al. (2006) Kersters et al. (2006) Kersters et al. (2006) Figure 2.1. Map of the Laurentian Great Lakes showing sampling sites for this study. 80 90-100 80-90 70-80 0.0 60-70 0 50-60 2.0 40-50 10 30-40 4.0 20-30 20 10-20 6.0 5-10 30 Average relative abundance ± SE 8.0 T-RFLP: HhaI 0-5 Percentage of observed OTUs 40 2.5 2.0 1.5 1.0 0.5 0.0 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 5-10 T-RFLP: MspI Average relative abundance ± SE 30 25 20 15 10 5 0 0-5 Percentage of observed OTUs Percentage of profiles in which T-RFs were observed 50 40 30 20 10 0 454 pyrosequencing 40 20 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 5-10 0 Average relative abundance ± SE 80 60 0-5 Percentage of observed OTUs Percentage of profiles in which T-RFs were observed Percentage of profiles in which T-RFs were observed Figure 2.2. Occurrence of observed operational taxonomic units (OTUs) and the corresponding average relative abundance among two terminal-restriction fragment length polymorphism (T-RFLP) profiles (either HhaI or MspI restriction endonuclease) and one 454 pyrosequencing library generated from amplified 16S rRNA genes from sediment samples collected from lakes Superior, Michigan, Huron, and Ontario. Axes are not on the same scale. A total of 443 OTUs were observed for the HhaI dataset, a total of 833 OTUs were observed for the MspI dataset, and a total of 7,344 OTUs (genetic distance level of 0.03) were observed for the 454 pyrosequencing library. Corresponding sequences for each analysis are displayed in Table 2.1. 81 HhaI 9 13 14 15 2 16 1112 1 5 6 7 3 4 19 20 8 1718 24 25 23 21 22 MspI 252621222324192011 15 9 13101412 1 2 5 6 4 3 161718 7 8 Figure 2.3. Hierarchical cluster analysis of Great Lakes sediment bacterial communities based on Bray-Curtis similarity. Scale bar indicates the level of dissimilarity. Branch color corresponds to the number of significant clusters detected in the similarity profile (SIMPROF). Red = cluster 1, blue = cluster 2, and green = cluster 3. 82 Figure 2.4. Hierarchical cluster analysis of Great Lakes sediment bacterial communities based on Bray-Curtis similarity. Scale bar indicates the level of dissimilarity. Branch color corresponds to the number of significant clusters detected in the similarity profile (SIMPROF). Red = cluster 1, blue = cluster 2, and green = cluster 3. 83 0 Acidobacteria Actinobacteria Armatimonadetes Bacteroidetes BRC1 Chlamydiae Chlorobi Chloroflexi Deinococcus-Thermus Elusimicrobia Firmicutes Fusobacteria Gemmatimonadetes Nitrospira OD1 ODP11 Planctomycetes Spirochaetes TM7 Verrucomicrobia WS3 Alphaproteobacteria Betaproteobacteria Gammaproteobacteria Deltaproteobacteria Epsilonproteobacteria Unclassified… Unclassified Bacteria Relative abundance 40 35 Cluster 1 (HU38) 30 Cluster 2 25 Cluster 3 20 15 10 5 Figure 2.5. Relative abundances of phylogenetic groups among the three significant clusters detected in the similarity profile (SIMPROF) analysis of Great Lakes sediment bacterial communities. Figure 7 displays which the sediment bacterial samples are contained within each significant cluster. 84 100% Acidobacteria 90% Actinobacteria Relative abundance 80% Bacteroidetes 70% Chloroflexi Firmicutes 60% Nitrospirae 50% Alphaproteobacteria 40% Betaproteobacteria Gammaproteobacteria 30% Deltaproteobacteria 20% Unclassified Proteobacteria 10% Unclassified Bacteria Other 0% Sampling site Figure 2.6. Bacterial community structure of Great Lakes sediments at phylum level. The relative abundance was defined as the percentage of the total bacterial sequences in a sample, classified using Ribosomal Database Project database training set 9. Phylogenetic groups accounting for less than 1% of total composition are summarized as ‘‘other’’ in the figure. 85 100% Gp1 (Acidobacteria) 90% Gp2 (Acidobacteria) Relative abundance 80% Gp3 (Acidobacteria) 70% Gp4 (Acidobacteria) Gp6 (Acidobacteria) 60% Gp22 (Acidobacteria) 50% Flavobacterium (Bacteroidetes) 40% Longilinea (Chloroflexi) 30% Bacillus (Firmicutes) 20% Bradyrhizobium (Alphaproteobacteria) Dechloromonas (Betaproteobacteria) 10% Zoogloea (Betaproteobacteria) 0% Acinetobacter (Gammaproteobacteria) Haliea (Gammaproteobacteria) Sampling site Figure 2.7. Bacterial community structure of Great Lakes sediments at genus level. The relative abundance was defined as the percentage of the total bacterial sequences in a sample, classified using Ribosomal Database Project database training set 9. Genera accounting for less than 1% of total composition are summarized as ‘‘other’’ in the figure. 86 Figure 2.8. Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties based on the relative abundance of 16S rRNA terminal-restriction fragments within the HhaI dataset. Refer to Table 1 for sample identification. 87 Figure 2.8. (cont’d) 88 Figure 2.9 Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties, based on the relative abundance of 16S rRNA terminal-restriction fragments within the MspI dataset. Refer to Table 1 for sample identification. 89 Figure 2.9 (cont’d). 90 Figure 2.10. Redundancy analysis (RDA) of Great Lakes sediment bacterial communities as affected by sediment properties, based on the relative abundance of dominant bacterial phyla and proteobacterial classes within the 16S rRNA 454pyrosequencing library. Refer to Table 1 for sample identification. 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PLoS ONE 6(9), e24570. doi:10.1371/journal.pone.0024570. 98 CHAPTER 3 Bacterial Communities Associated with Diporeia spp. in the Laurentian Great Lakes 99 Abstract Little is known about the microbial ecology of Diporeia, which is an ecologically important benthic amphipod in the Laurentian Great Lakes that has declined considerably in abundance over the last 20 years. The prevailing hypothesis is that the decline in Diporeia has been caused by competition for food resources with invasive dreissenid mussels (Dreissena polymorpha and D. bugensis); although it also has been hypothesized that virulent pathogens have also been contributing factors. In this study, we used 16S rRNA gene sequencing and terminal-restriction fragment length polymorphism (T-RFLP) to better understand the microbial ecology of Diporeia in the Great Lakes. T-RFLP analysis revealed a total of 175 and 138 terminal restriction fragments (T-RFs) in Diporeia samples for the HhaI and MspI datasets, respectively. Relatively abundant and prevalent T-RFs were affiliated with the genera Flavobacterium and Pseudomonas, and the class β- proteobacteria. T-RFs affiliated with the order Rickettsiales were also detected. A significant difference in T-RF presence and abundance (P = 0.035) was detected among profiles generated for Diporeia collected from four sites in Lake Michigan. Comparison of profiles generated for Diporeia samples collected annually from lakes Superior and Michigan showed a significant change in diversity for Lake Superior Diporeia but not Lake Michigan Diporeia; however, significant shifts in the relative abundance of particular T-RFs were observed for Diporeia samples from both lakes. Profiles from one Lake Michigan site contained multiple unique T-RFs compared to other Lake Michigan Diporeia profiles, most notably one that represents the genus Methylotenera. This study represents the most extensive 100 list of bacteria associated with Diporeia and sheds useful insights on the microbial ecology of Diporeia in the Laurentian Great Lakes. Introduction Amphipods belonging to the genus Diporeia (hereafter referred to as Diporeia), are found in abundance in proglacial lakes of the Holarctic (Dermott and Corning, 1988). Diporeia have historically been the dominant benthic macroinvertebrate in deep (> 30m) waters of the Laurentian Great Lakes in North Amercica, historically comprising over 70% of benthic biomass in offshore regions (Cook and Johnson, 1974). It is believed that the infaunal amphipod, which feeds on organic material settled from the water column, is the major consumer of the primary production associated with material deposited during the spring diatom pulse (Gardner et al. 1990; Fitzgerald and Gardner, 1993). In turn, Diporeia are an important food resource for a number of Great Lakes forage fish, including alewife (Alosa pseudoharengus ) (Hondorp et al. 2005), bloaters (Coregonus hoyi) (Rand et al. 1995), rainbow smelt (Osmerus mordax) (Selgeby et al. 1994), slimy (Cottus cognatus) and deepwater sculpin (Myoxocephalus thompsoni ) (Davis et al. 1997), lake whitefish (Coregonus clupeaformis) (Pothoven, 2005), and yellow perch (Perca flavescens) (Wells, 1980), and thus serve as important pathway of energy flow from lower to upper trophic Levels (Fitzgerald and Gardner, 1993). Over the past two decades, Diporeia abundance has declined considerably in most areas of the Great Lakes. Declines were first reported in shallow regions in southeastern Lake Michigan (Nalepa et al. 1998) and deeper regions in eastern Lake Erie (Dermott and Kerec, 1997). Presently, Diporeia are believed to have been 101 extirpated from Lake Erie (Barbiero et al. 2011). Between 1997 and 2009, significant declines in Diporeia abundances were observed across lakes Michigan, Huron, and Ontario (Barbiero et al. 2011). Although the exact cause of the decline has not been established, the fact that the declines coincided with increases in abundances of invasive dreissenid museels (Dreissena polymorpha and D. bugensis; Dermott, 2001; Dermott and Kerec, 1997; Nalepa et al. 1998, 2007) has led to the prevailing belief that the decline is a result of competition for food resources (Nalepa et al. 1998, 2000). Although it is widely hypothesized that the decline in Diporeia in the Great Lakes has been a result of expansion of dreissenid mussel abundance, alternative theories as to factors that may have contributed to the decline do exist. In particular, Messick et al. (2004) hypothesized that a synergism between virulent pathogens and other physical or biological factors or disruption in the normal bacterial flora of Diporeia may have contributed to declining abundances. Other studies have shown that prevalence of pathogenic microorganisms can result in reduced abundances of other amphipod populations (Pixell Goodrich, 1929; Johnson, 1986). The research of Messick et al. (2004) provided evidence that Diporeia are vulnerable to diseases caused by a number of microorganisms. The authors identified serious bacterial pathogens such as rickettsia-like organisms and speculated that due to unknown stressors, Diporeia may have become more susceptible to opportunistic pathogens. Although much is known regarding Diporeia life history, diet, and rates of decline of Great Lakes, little is known about the microbial ecology of Diporeia in the region. Bacterial communities have been studied in depth in other important species of the foodweb. By constructing 16S rRNA gene clone libraries, Winters et al. (2011) showed 102 that the bacterial communities associated with the Dreissena polymorpha were diverse and contained abundances of Alpha-, Delta, and Gamma- proteobacteria, Actinobacteria, Cyanobacteria, and Planctomycetes and that bacterial communities varied among different organs. In an investigation of bacterial community diversity of freshwater zooplankton, Hannes and Sommaruga (2008) used fluorescence in situ hybridization (FISH), catalyzed reporter deposition (CARD)-FISH, cultivation, and transmission electron microscopy (TEM) on homogenates and whole-specimens of five copepod and cladoceran species and found that the Cytophaga-Flavobacteria group and different Proteobacteria dominated the gut of zooplankton, suggesting that flavobacteria were more widespread in these organisms than originally thought. Pangastuti et al. (2010) used T-RFLP analysis and 16S rRNA gene sequencing to investigate the dynamics of bacterial communities associated with eggs and larvae of the white shrimp (Litopenaeusvannamei) and determined that α- and γ-proteobacteria were the dominant bacteria associated with shrimp developmental stages and suggested that these bacteria play key roles in determining the survival of shrimp larvae, with high diversity levels possibly preventing the growth of opportunistic pathogenic bacteria. The goal of this study was to use the high-throughput terminal-restriction fragment length polymorphism assay (T-RFLP) coupled with sequence analysis of bacterial 16S rRNA genes present in Diporeia samples collected from multiple lakes Superior, Michigan, Huron, and Ontario and the inland Cayuga Lake (New York) to characterize the bacterial communities associated with Diporeia and to determine if these communities vary spatially or temporally. The result of this study should provide 103 important insights about the microbial ecology of Diporeia and will help to shed light on the cause for decline of the amphipod in the Laurentian Great Lakes basin. Materials and Methods Sample Collection Diporeia samples were collected from 7 locations in the Great Lakes basin and a single location in Cayuga Lake, an inland lake in New York, (Figure 3.1) at depths ranging from 40 to186 meters. Samples were collected during the month of August in 2007 and 2008, for a total of 10 sampling occasions. One site in Lake Superior and one site in Lake Michigan were sampled in two consecutive years. The number of replicate samples collected at each site is displayed in Table 3.1. Sediment samples were collected using a Ponar grab (sampling area 22.86 x 22.86 mm/ 8.2 liters). Once a sample was returned to the surface, it was sieved (mesh = 0.25 mm) and Diporeia were identified according to Edmonson (1959). Diporeia were then pooled (five amphipods/pool), rinsed several times in sterile water, placed in sterile 80% ethanol in a 1.5 ml tube, and immediately stored at -20 °C until further processing. DNA Isolation Genomic bacterial community DNA was extracted from Diporeia samples using the PowerSoil TM DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA) following the manufacturer’s protocol. DNA was quantified with a Qubit fluorometer and the Quant-it dsDNA BR Assay kit (Invitrogen Corp., Austin, TX). The isolated bacterial DNA was then used as a template for PCR amplification. The bacterial 16S gene was amplified 104 using the universal eubacterial primer set 27f-1387r (27f: 5’-AGA GTT TGA TCM TGG CTC AG-3’) labeled with carboxyfluorescein (6-FAM) and 1387r (5’-GGG CGG WGT GTA CAAGGC-3’) (Marchesi et al. 1998). Each 50 µl PCR reaction contained 25µl of 2X Green GoTaq® Master Mix (Promega Corporation, WI, USA), 45 μM of each primer, and ~100 ng of template DNA. PCR reaction conditions for amplification of partial 16S rRNA genes were as follows: initial 94 °C for 4 min, followed by 29 cycles of 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 1 min and a final extension for 7 min at 72 °C. A negative control containing no DNA was included in each set of PCR reactions. Resulting PCR products were visualized by agarose gel electrophoresis. Amplification of the proper gene fragment (~1.36 Kb) was confirmed by comparison with a DNA size ladder. The PCR reaction was carried out in triplicate for each sample and resulting products were pooled and purified using a Wizard® SV Gel and PCR Clean-Up System (Promega Corporation, WI, USA) following the manufacturer’s protocol. T-RFLP Analysis To construct profiles of sediment bacterial communities for each restriction enzyme, three hundred nanograms of purified fluorescently labeled PCR product were cut individually with five units of HhaI and MspI (New England Biolabs, Beverly, MA) for two h at 37°C. Digested PCR products were precipitated with three volumes of absolute ethanol. After an eight-hour incubation at -20 °C, the precipitates were pelleted by centrifugation at 14,000 rpm for 15 min. Pellets were resuspended in six μl sterile water (DEPC-Treated, DNASE, RNASE free). The DNA fragments were separated on an ABI 3100 Genetic Analyzer automated sequence analyzer (Applied Biosystems Instruments, 105 Foster City, CA) in GeneScan mode at Michigan State University’s sequencing facility. The 5’-terminal restriction fragments (T-RFs) were detected by excitation of the 6-FAM molecule attached to the forward primer. The sizes and abundance of the fragments were calculated using GeneScan 3.7 in relation to the MM1000 internal standard (BioVentures Inc.). T-RFLP data was analyzed with T-REX (http://trex.biohpc.org). TREX software uses the methodology described by Abdo et al. (2006) and Smith et al. (2005) to identify and align true peaks respectively. We used one standard deviation in peak area as the limit to identify true peaks and defined T-RFs by rounding off fragment sizes to nearest integer. Additionally, only profiles with a cumulative peak height ≥ 5,000 fluorescence units were used in the analysis. T-RFLP analysis was also performed on 10 representative clones which showed high similarity (≥98%) to bacterial sequences contained in GenBank. The purified cloned DNA was also digested and analyzed using T-RFLP as described above to both ensure that complete digestion of PCR products was achieved and to help determine the placement of each represented bacterial group in the Diporeia community profiles. Additionally, to obtain corresponding T-RFLP fragments, an in silico digest of the representative clones was carried out in MEGA 4.0 (Tamura et al. 2007). For all analyses, abundance values for both T-RFLP datasets were standardized by expressing the abundance of each OTU as a percentage of total abundance for each sample. Additionally, to account for “blind sampling” and large numbers of absences in the data sets, the datasets were transformed using the Hellinger equation as suggested by Ramette (2007). Species richness and diversity for the T-RLP community profiles at each sampling site (for both the HhaI and MspI datasets) were calculated in R (R 106 Development Core Team, 2009) with the Vegan package (Oksanen et al. 2013). For calculating species richness and diversity, we assumed that the number of T-RFs present in a profile represented the operational taxonomic units (OTUs) and that the TRF height represented the relative abundance of each bacterial species. For characterizing species diversity, we used the Shannon diversity index, which accounts for both abundance and evenness of the community at a particular site. For characterizing species evenness, we used Pielou’s Evenness, which is based on the Shannon index and is constrained between 0.0 and 1.0 where less variation in communities between the species abundance approaches 1.0. We tested for significant differences in OTU composition and abundance among sites and years using permutational multivariate analysis (PERMANOVA). For the PERMANOVA, the Bray-Curtis distance matrix of hellinger transformed OTU composition was the response variable and site or year was the independent variable. The total number of permutations performed was 999. The PERMANOVA analysis was conducted in R using ADONIS function in the VEGAN library. Additionally, permutation tests (999 permutations) were used to test for significant differences in multivariate dispersion among sites and years using the BETADISPER function in VEGAN which is a multivariate analogue of Levene's test for homogeneity of variances. Since, the Chao method takes the fraction of rarely present/low abundant T-RFs into account (Chao et al. 2005) it was used to define β diversity. To determine whether community structure was significantly correlated with depth we used Mantel-tests (Legendre & Legendre, 1998) based on Pearson's product-moment correlation. For Mantel tests, depth values were Z-score standardized and community dissimilarities were standardized using 107 Wisconsin standardization (dividing all species by their maxima, and then standardizing sites to unit totals). To test for differences in relative abundance of individual T-RFs among samples, MANOVA test were conducted using the PROC GLIMMIX procedure (SAS Institute Inc., 2010). DNA Sequence Analysis Comparison of Diporeia T-RFLP profiles with profiles generated for individual 16S rDNA clones was conducted in an attempt to identify and quantify the presence of particular bacterial groups, including potential pathogens, in Diporeia populations. PCR products from five samples (representing Lakes Superior, Michigan, Huron, and Ontario and Cayuga Lake) were amplified with the unlabeled primer set (27F-1387R) and cloned using a TOPO TA Cloning Kit® (Invitrogen, CA, USA) following the manufacturer’s protocol. A total of 399 clones (between 93 and 95 for the Great Lakes and 23 for Cayuga Lake) were cultured on Luria-Bertani agar plates (Fisher Scientific Inc., PA, USA) containing 50 μg/ml Kanamycin as directed by the manufacturer’s protocol and screened for positive transformation with PCR using the primer set M13f (5’-GTT TTC CCA GTC ACG AC-3’) and M13r (5’-CAG GAAACA GCT ATG ACC-3’). The selected clones were then purified using QIAprep Spin Miniprep Kit © (Qiagen Inc., CA, USA) and the resulting purified plasmid DNA was partially sequenced from the 5’ end using either the M13F or M13R primer. All sequences generated in this study were screened for chimeras with Pintail (Ashelford et al. 2005), and deposited in GenBank (JQ772645-JQ773027 and KC436139-KC436264). Phylogenetic assignments were 108 determined using the Ribosomal Database Project (RDP) (Cole et al. 2003) “Classifier” using a 95% confidence threshold and “Seqmatch” (Wang et al. 2007). Estimation of Bacterial Community Coverage Analyzing the community structure of bacteria in Diporeia samples included estimating coverage, which is defined as an estimation of the percentage of bacterial groups or operational taxonomic units (OTU’s) identified in a sample out of the total number of groups or OTUs. Calculating distances between known and unknown 16S rRNA sequences is important in comparing such sequences. In previous studies, distance values of 0.03 have been used to differentiate bacteria at the species level, 0.05 at the genus level, 0.10 at the family/class level, and 0.20 at the phylum level (Hugenholtz et al. 1998; Sait et al. 2002; Stackebrandt and Goebel, 1994; Hughes et al. 2001). With this in mind, bacterial community coverage was calculated based on a distance value of 0.03 using the formula C = [1 – (n / N)] X 100 where n is the number of unique clones and N is the total number of clones analyzed (Good, 1953; Mesbah et al. 2007). To gain a better understanding of the bacterial diversity associated with Great Lakes Diporeia, an overall estimate of coverage was obtained by analyzing a single composite library constructed from sequences from four representative Great Lakes Diporeia clone libraries from lakes Superior, Michigan, Huron, and Ontario (SU-20B, MI-27M, HU-54M, and ON-41, respectively). 109 Sequence Alignment and Phylogenetic Affiliation For phylogenetic analyses of cloned Diporeia sequences, Methanocaldococcus jannaschii (Accession: L77117), was used as the outgroup taxon. A total of 353 selected 16S rRNA gene sequences and the single outgroup sequence were aligned with the Jukes–Cantor correction (Jukes and Cantor, 1969) using RDP II. The average length of sequences in the final alignment file was 719 bp. The alignment file was visually checked for alignment gaps and missing data in nucleotide positions using MEGA 5.0 (Tamura et al. 2011). The phylogenetic tree containing all 16S rRNA sequences from this study was generated using the neighbor-joining (Saitou and Nei, 1987) method included in MEGA 5.0 using Maximum Composite Likelihood as a measure of genetic distance. To test for significant clustering of taxa among the five clone libraries based on phylogenetic relationships, the UniFrac Lineage-specific analysis was used to break the tree up into the lineages at a specified distance from the root (Lozupone et al. 2006). To determine taxonomic assignments and phylogenetic relationships additional trees were generated for cloned sequences and sequences identified as closely related reference sequences contained in RDP. Sequences were aligned and trees were generated as described above. Trees were then annotated to indicate the Lake of origin for each sequence. 110 Results Bacterial Communities of Diporeia Analysis of T-RFLP data revealed the presence of diverse bacterial communities in Diporeia samples. For the HhaI dataset, a total of 175 terminal restriction fragments (TRFs) ranging from 50 to 983 base pairs in size were identified across all samples while for the MspI dataset, a total of 138 terminal T-RFs ranging from 55 to 983 base pairs in size were identified across all samples. For the HhaI dataset, the mean ( SE) number of T-RFs identified at the sampling sites was 23.91 ± SE 2.00 and ranged from 5 to 53 across all sites. For the MspI dataset, the mean ( SE) number of T-RFs identified at the sampling sites was 21.46 ± 1.25 and ranged from 7 to 35 across all sites. Species richness values and diversity indices (Shannon-Weiner diversity Index and Pielou's Evenness Index) showed that diversity of profiles for Diporeia communities varied among each sampling event (Figure 3.2). For both the HhaI and MspI datasets, 08MI18M had the highest diversity and relatively high species evenness compared to the other samples analyzed. Additionally, for both the HhaI and MspI datasets 08SU-23B had the highest number of OTUs and the lowest species evenness. For both HhaI and MspI, less than 5% of the observed T-RFs occurred in more than 90% of profiles with average relative abundances greater than 20%. For both the HhaI and MspI dataset, a significant difference in β diversity was observed among all Diporeia sampling events for all indices of similarity (HhaI: df = 9, F = 3.583, P = 0.001; MspI: df = 9, F = 5.6686, P = 0.001. For the HhaI dataset, a significant difference in multivariate dispersions among all Diporeia sampling events was observed (df = 4.406, 9, F = P = 0.015). T-RF’s 489 H, 496 H, and 484 M occurred 111 in 23.53, 20.59%, and 60.00% of samples respectively with average relative abundances of 7.77, 1.67%, and 1.96%, respectively. Although T-RF 846 H accounted for 3.20% of the average relative abundance for the T-RFLP profiles, it only occurred in the three Cayuga Lake samples with average relative abundances ranging from 24.4429.95%. For both the entire HhaI and MspI datasets, a significant positive correlation (Mantel test) between diversity and depth was observed for Diporeia (HhaI: P = 0.002, MspI: P = 0.001). A number of interesting findings were observed among profiles generated for Diporeia samples collected from multiple locations in Lake Michigan (07MI-18M, 07MI27M, 07MI-40, and 07MI-47). First, for the MspI dataset, a significant difference in β diversity was observed only among Lake Michigan Diporeia profiles (df = 3, F = 2.50, P = 0.035). Second, results of multivariate analysis of variance showed that, site 07MI18M was unique in that it had higher abundances of certain T-RFs compared to the other sites. Third, For the HhaI and MspI datasets, 20 and 27 T-RFs were shown to be unique to MI-18M profiles respectively. For the T-RFs that were unique to 07MI-18M in the HhaI dataset, with the exception of T-RF 366 H which had an average relative abundance of 21.15% ± 4.68%, the overall average relative abundance was 2.03% ± 1.09%. Similarly, for the T-RFs that were unique to 07MI-18M in the MspI dataset, the overall average relative abundance was 1.18% ± 1.09%. No significant association was found between depth and the diversity of Lake Michigan Diporeia profiles (HhaI: P = 0.898; MspI: P = 0.892). Additionally, for both the HhaI and MspI datasets no significant difference in variance was detected among Lake Michigan Diporeia samples. 112 Temporal Shifts in Bacterial Diversity For the Lake Superior site that was sampled in both years, the β diversity in bacterial community structure in 2008 was found to be significantly greater than in 2007 for the HhaI dataset but not the MspI dataset (HhaI: df = 1, F = 3.682, P = 0.021; MspI: df= 1, F = 1.859, P = 0.101). However, for Lake Michigan no significant differences in beta diversity were found for the revisited site. (HhaI: df = 1, F = 0.171, P = 0.456; MspI: df= 1, F = 1.766, P = 0.146). Significant differences in the abundance of particular T-RFs (MANOVA) were observed between 2007 and 2008 profiles for both SU-23B and MI18M (Table 3.2). For the HhaI dataset, of the 45 total T-RFs present for MI-18M, 27 were detected in samples from both 07MI-18M and 08MI-18M, 12 were unique to 07MI18M, and six were unique to 08MI-18M. For the MspI dataset, of the 57 total T-RFs present for MI-18M, 37 were detected in samples from both 07MI-18M and 08MI-18M, 11 were unique to 07MI-18M, and nine were unique to 08MI-18M. For the HhaI dataset, of the 141 total T-RFs present in SU-23B, 41 were detected in samples from both 07SU-23B and 08SU-23B, 20 were unique to 07SU-23B and 80 were unique to 08SU23B. For the MspI dataset, of the 75 total T-RFs present SU-23B, 34 were detected in samples from both 07SU-23B and 08SU-23B, 18 were unique to 07SU-23B and 23 were unique to 08SU-23B. Virtual Digestion and Phylogenetic Analysis RDP’s “classifier” revealed the presence of Alpha-, Beta-, Gamma-, and Deltaproteobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes Planctomycetes and Verrucomicrobia in Diporeia 16S rRNA clone libraries. The in silico digestion of the 113 representative clones provided for the putative identification of several of T-RFs. Despite evidence of “T-RF drift”, (T-RF length differing from true length; Kaplan and Kitts, 2003), patterns were observed in that a number of T-RFs can be matched to cloned sequences (Table 3.3). For example, T-RFs at 86 H / 80 M are likely to match Flavobacterium; T-RFs at 205 H / 489 M are likely to match Pseudomonas; and T-RFs at 366 H/ 493 M are likely to match Methylotenera. As displayed in Figure 3.3, phylogenetic analysis of 16S rRNA sequences using UniFrac revealed significant clustering among Diporeia clone libraries indicating a unique bacterial structure for Diporeia from each lake. Further analysis of the sequences in comparison to similar reference sequences derived from RDP revealed the presence of diverse bacterial groups present in Diporeia clone libraries. The phylum Actinobacteria, the class β-proteobacteria, the order Pseudomonadales (γproteobacteria), the genus Flavobacterium (Bacteroidetes), were present in all five libraries. For the composite Diporeia clone library (353 16S rRNA sequences), a coverage levels of 81.1, 89.1, 96.1, and 100% was obtained for a genetic distances of 0.02, 0.05, 0.10, and 0.20, respectively. At a genetic distance of 0.02, a total of 126 OTUs were observed. For the phylum Bacteroidetes (Figure 3.4), two significant clusters were observed. In one of these clusters the higher proportion of sequences from the Lake Ontario Diporeia library had the highest relative contribution to the overall differences between environments. In the second Bacteroidetes cluster, sequences from the Lake Michigan Diporeia library had the highest relative contribution to the overall differences between environments. Phylogenetic analysis of these sequences showed that, while a greater 114 genetic diversity was observed for the Lake Ontario Diporeia sequences, both clusters were represented by Flavobacterium spp. Additionally, although not significant (P > 0.05) based on the specified distance from the root (11.3333) a third Flavobacterium cluster characterized by and abundance of sequences from the Lake Superior library was observed. For Alphaproteobacteria (Figure 3.5), sequences belonging to the order Rhodobacteriales were from both the Lake Michigan and Cayuga Lake libraries. Sequences belonging to the order Sphingomonadales were only detected in the Lake Ontario library. Interestingly, a bacterium belonging to the order Rickettsiales was only detected in the Lake Ontario library. However, the corresponding T-RF (450 M) was only detected in Diporeia samples collected from lakes Michigan and Superior with a prevalence of 0.03% and an average relative abundance of 1.73% ±0.56. For the class Betaproteobacteria (Figure 3.6), a significant clustering of sequences similar to Rhodoferax spp. and Albiferax spp. was observed with the higher proportion of sequences from the Lake Michigan Diporeia library having the highest relative contribution to the overall differences between environments. For the class Gammaproteobacteria (Figure 3.7), a significant clustering of sequences similar to Perlucidibaca piscinae was observed with the higher proportion of sequences from the Lake Ontario Diporeia library having the highest relative contribution to the overall differences between environments. For Gram-positive bacteria (Figure 3.8), with the exception of the Lakes Huron library, bacteria belonging to the orders Actinomycetales and Acidimicrobiales (Actinobacteria) were detected all libraries. A bacterium belonging to the order Bacilliales (Firmicutes) was only detected in the Lake Michigan library. 115 Discussion This study investigated the bacterial communities associated with Diporeia, an ecologically important organism in the Great Lakes, which has declined considerably in abundance across much of the lakes (Barbiero et al. 2011). Results of this study show that the diversity of the bacterial communities associated with Diporeia is relatively high compared to what has been reported for both freshwater and deep-sea amphipods (Atlas et al. 1982; Oliver and Smith, 1982) and other zooplankton (Musko, 1988; Gunzl, 1991; Gowing and Wishner, 1992; Wishner et al. 2000; Hannes and Sommaruga, 2008). Results of 16S rRNA sequence and T-RFLP analysis demonstrate that the bacterial communities of Diporeia contain abundances of bacteria belonging to the phylum Actinobacteria, class β-proteobacteria, the order Pseudomonadales (γproteobacteria), and the genus Flavobacterium (Bacteroidetes), where Flavobacterium (86 H and 80 M) and to a lesser extent, pseudomonads (205 H and 489 M). Analysis of the phylogenetic tree using UniFrac revealed a number of significant clusters within the composite clone library suggesting distinct bacterial strains are associated with different Diporeia populations. Although the exact biochemical potential of these bacteria is unknown, they likely represent groups of bacteria that play an important role in the ecological performance of the amphipod at each site. In regard to the relationship between depth and the diversity of bacteria associated with Diporeia, significant positive correlations were observed demonstrating the effect environmental parameters have on the diversity of these bacterial communities. The finding that the T-RFs representing the genus Methylotenera (366 H / 488 M) were enriched in Diporeia samples collected from site MI-18M, suggests this bacterium 116 is intimately associated with Diporeia at this location. While the exact function of this bacterial group is unknown, 16S rDNA sequence analysis shows that it is closely related (98% sequence similarity) to the genus Methylotenera. Given the apparent ubiquity and metabolic diversity of Methylotenera (Bosch et al. 2009; Kalyuzhnaya et al. 2012), sequence information alone is not enough to determine the exact metabolic capabilities of this bacterium. However, according to the entries in the non-redundant database, http://www.ncbi.nlm.nih.gov, Methylotenera species have been detected in acid mine drainage and polluted soil (Bosch et al. 2009) suggesting the possibility that the Methylotenera associated with Diporeia may play an important role in the amphipod’s ability to cope with contaminants in the surrounding environment. However, the ecological significance of this bacterium to the performance of Great Lakes Diporeia requires additional studies. A number of differences were observed among the profiles for Diporeia samples collected from multiple sites in Lake Michigan in 2007 (07MI-18M, 07-MI27M, 07-MI40, and MI47). The finding that the Chao abundance-based similarity index revealed a significant difference in β diversity among Lake Michigan profiles (MspI dataset) suggests that, since the Chao method takes the fraction of rarely present/low abundant T-RFs into account (Chao et al. 2005), the observed difference in diversity is due the presence of bacterial multiple species with low relative abundances in the Lake Michigan Diporeia. This is further supported by both the finding that higher species richness was observed for 07MI-18M compared to other Lake Michigan profiles and the finding that a high number of T-RF with low average relative abundances that were unique to the 07MI-18M profiles. Altogether, these findings suggest the bacterial 117 communities associated with Diporeia at site MI-18M are distinctly different from others in Lake Michigan. The biological significance of the temporal shifts in diversity and variability of bacterial community structure observed for Diporeia samples collected from Lake Superior (07SU-23B and 08SU-23B) remains unknown. Whether the observed temporal shifts are due to, the relatively shallow depth of this site, it’s unique location in Whitefish Bay near the Saint Mary’s River through which Lake Superior drains into Lake Huron, or some unknown limnological feature remains to be determined. These findings warrant further investigation of the bacterial communities associated with Lake Superior Diporeia. In the same context, although a significant correlation between depth and the diversity of Diporeia bacterial communities was not observed for Diporeia samples collected from the four Lake Michigan sites (P > 0.05), a highly significant correlation was also observed for the composite dataset suggesting depth may play a role in regulating the bacterial communities associated with Diporeia. Further research is required to determine the relationship between depth and the structure of bacterial communities associated with Diporeia. In terms of obligate pathogens, a bacterium belonging to Rickettsiales, an order of Bacteria containing known pathogens for freshwater amphipods (Federici et al. 1974; Larsson 1982; Graf 1984) was detected in the Lake Ontario Diporeia clone library. Although, based on 16S rRNA sequence data alone, we cannot be certain that the species of Rickettsiales detected is in fact a Diporeia-pathogenic bacterium; however, Its presence in the clone library proves the presence of a Rickettsia-like bacterium in this declining amphipod. Messick et al. (2004) reported that, in stained tissue sections, 118 Rickettsia-like infections, which were sometimes systemic, elicited a host response and caused Diporeia adipose cells to become greatly hypertrophied, and suggested that Rickettsia-like could contribute to Diporeia declines Messick et al. (2004). One of the TRF representing the order Rickettsiales (449 M) had a prevalence of 0.03% and an average relative abundance of 1.73% ±0.56. This is considered a relatively high abundance for free-ranging organisms like Diporeia and can contribute, alone or combined with other opportunistic bacteria, to Diporeia declines (Anderson & May 1981). Despite the constraints that can be associated with T-RFLP analysis of complex bacterial communities, such as multiple taxa sharing similar T-RFs and the possibility of pseudo T-RF formation (Egert and Friedrich, 2003), the combination of analysis of multiple restriction enzyme datasets (HhaI and MspI) with T-RF pattern confirmation by in silico digestion of cloned 16S rRNA gene sequences allowed for considerable elucidation of the microbial ecology of Diporeia. Furthermore, the reasonably high estimate of coverage obtained for the composite Great Lakes Diporeia 16S rRNA library suggests a substantial understanding of the composition of microbial communities associated with Diporeia in the Great Lakes was obtained. Even though we did not sample to saturation, after 16S rRNA gene clones were analyzed with T-RFLP a few corresponding TRFs were not found in the community profiles. Similar findings were observed in an interlaboratory comparison for the microbial communities of seafloor basalts (Orcutt et al. 2009). With this in mind, it is likely these detected bacteria represent a rather small fraction of bacterial community associated with the Diporeia. 119 Results of 16S rRNA sequence and T-RFLP analysis demonstrate that Flavobacterium spp., and to a lesser extent, Pseudomonas spp. are the dominant bacterial groups associated with Diporeia. Members of the Flavobacterium and Pseudomonas genera are known to be pathogens of aquatic animals. Since flavobacteria have the ability to produce a range of cold-active enzymes capable of breaking down macromolecules (Bernardet and Bowman, 2006), it is possible they aid in digestion for a number of aquatic organisms including Diporeia. Additionally, these enzymes may be implicated in pathogenicity of flavobacteria for fish (Bernardet and Bowman, 2006). It is therefore possible that some species flavobacteria may also be pathogenic to Diporeia. In another study performed in our laboratory, flavobacteria, several of which were previously undescribed Flavobacterium and Chryseobacterium spp., were associated with fish diseases (Loch et al. 2013) In conclusion, the combination of T-RFLP analysis and 16S rRNA gene sequencing proved to be a powerful tool for investigating the bacterial diversity of Diporeia. Since a similar study has never been conducted on Diporeia, this study represents the most extensive list of bacteria associated with this amphipod in relation to its surrounding environment and provides useful insights on the microbial ecology of Great Lakes Diporeia. Our hope is that the knowledge gained in this study will shed light on the potential causes of the decline of Diporeia populations and foster the development of efficacious management strategies for the restoration and conservation of both Diporeia and other Great Lakes organisms that rely on Diporeia. 120 Acknowledgements We are very thankful to the crew and staff of the R/V Lake Guardian for helping in sample collection. We appreciate the assistance of Dr. Jim Watkins (Cornell University) for providing the Cayuga Lake samples. This study was partially funded by the United States Environmental Protection Agency - Great Lakes National Protection Office (Grant #: GL00E36101) and the Great Lakes Fisheries Trust (Grant #: 2009.1058). 121 APPENDIX 3 122 Table 3.1. Name, location, and depth of sampling sites, year of collection, and number of consensus T-RFLP profiles generated for each sample type and restriction endonuclease combination for this study. Sample type Diporeia Diporeia Restriction endonuclease HhaI MspI Site Latitude Longitude Depth 2007 2008 2007 2008 MI-18M 42.733 -87.000 161 3 4 4 4 MI-27M 43.600 -86.917 112 3 3 MI-40 44.760 -86.967 160 3 3 MI-47 45.178 -86.375 186 3 2 HU-54M 45.517 -83.417 91 5 5 SU-20B 46.883 -90.283 116 3 3 SU-23B 46.598 -84.807 62 3 4 4 4 Cayuga 42.538 -76.553 40 3 3 - 123 Table 3.2. Significant differences (P < 0.05) in relative peaks heights (MANOVA) for both 16S rRNA T-RFLP datasets (HhaI and MspI) generated for Diporeia samples collected from lakes Michigan and Superior and Cayuga Lake (New York) between 2007 and 2008 (T-RF in base pairs / P value). Lake Michigan sites (2007) HhaI 60 / <0.0001 83 / 0.004 330 / 0.001 366 / 0.002 MspI 127 / 0.012 146 / 0.001 167 / 0.049 428 / 0.039 534 / 0.015 563 / 0.009 MI-18M SU-23B (2007-2008) (2007-2008) 67 / 0.039 61 / 0.034 231 / 0.045 205 / 0.004 367 / 0.034 375 / 0.004 312 / 0.038 127 / 0.045 128 / 0.026 146 / 0.024 496 / 0.034 124 Table 3.3. Ribosomal Database Project (RDP) matches (≥98%) and predicted terminal - restriction fragment lengths (numbers of base pairs determined by both virtual restriction digestion and T-RFLP analysis of each base pairs) of selected 16S rRNA clones from Great Lakes Diporeia based on clone. Clone 1 2 3 4 5 6 7 8 9 10 Accession # (clone) JQ772925 JQ772936 JQ772844 JQ772993 JQ772911 JQ772796 JQ772985 JQ772737 JQ772726 JQ772740 Phylogenetic group Sequence T-RFLP Sequence T-RFLP (RDP match) HhaI HhaI MspI MspI Bacillus sp. (Firmicutes) 574 576 162 157 Comamonadaceae (β-proteobacteria) 188 188 491 494 Flavobacterium sp. (Bacteroidetes) 87 86 82 80 Methylotenera sp. (β-proteobacteria) 366 365 488 491 Microbacteriaceae (Actinobacteria) 369 372 279 277 Pseudomonas sp. (γ-proteobacteria) 205 205 488 489 Rhodobacteraceae (α-proteobacteria) 569 568 440 440 Rickettsiaceae (α-proteobacteria) 526 528 450 449 Sphingobacteriaceae (Bacteroidetes) 93 90 539 542 Oxalobacteraceae (β-proteobacteria) 565 566 487 494 125 Figure 3.1. Map of the Laurentian Great Lakes (Michigan) and The Finger Lakes (New York) showing sampling sites for this study. 126 Figure 3.2. Average diversity index values (±SE) for two T-RFLP datasets (amplified 16S rDNA digested with HhaI and MspI) generated for replicate Diporeia samples collected from lakes Michigan (MI), Superior (SU) and Huron (HU) and Cayuga Lake (New York) between 2007 and 2008. Richness is the number of operational taxonomic units, diversity is the Shannon-Weiner Diversity index, and Evenness is Pielou’s Evenness. 127 3.00 1.00 128 Total 0.50 Richness 55.0 45.0 35.0 25.0 15.0 5.0 Cayuga 0.60 07-HU-54M 0.70 08-SU-23B 0.80 07-SU-23B HhaI 07-SU-20B 0.00 07-MI-47 1.00 07-MI-40 HhaI 07-MI-27M 2.00 Diversity Richness HhaI 08-MI-18M 1.00 07-MI-18M 0.90 Evenness 3.00 Total Diversity 55.0 45.0 35.0 25.0 15.0 5.0 Cayuga 07-HU-54M 08-SU-23B 07-SU-23B 07-SU-20B 07-MI-47 07-MI-40 07-MI-27M 08-MI-18M 07-MI-18M Evenness Figure 3.2 (cont’d). MspI MspI 2.00 1.00 0.00 0.90 MspI 0.80 0.70 0.60 0.50 Figure 3.3. Distance tree of 353 16S rRNA gene sequences detected in Diporeia spp. collected from five waterbodies. The tree was generated with the neighbor joining algorithm and the Jukes–Cantor correction. Lineage-specific significance (*) was determined using UniFrac (blue = Lake Superior, red = Lake Michigan, light blue = Lake Huron, green = Lake Ontario and yellow = Cayuga Lake). Others = Actinobacteria, Chloroflexi, Deltaproteobacteria, Firmicutes, Planctomycetes, and Figure Verrucomicrobia. 129 3.3 (cont’d). *P < 0.0001 *P = 0.0001 Bacteroidetes Others Alphaproteobacteria *P = 0.0003 Gammaproteobacteria Betaproteobacteria *P = 0.0003 130 Figure 3.4. Neighbor-joining phylogeny of Bacteroidetes 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes. 131 Figure 3.4 (cont’d) 99 72 93 99 100 100 96 80 78 100 99 100 100 72 81 95 80 70 96 88 84 71 100 88 99 100 100 100 91 85 100 100 100 121 Superior, Michigan, Huron, Ontaio, and Cayuga EU109724 Flavobacterium fluvii EF575563 Flavobacterium resistens JQ772789 Ontario KC436245 Ontario JQ772734 Ontario KC436232 Huron JQ772808 Ontario AM398681 Flavobacterium psychrophilum KC436159 Superior JQ772829 Superior KC436220 Huron 15 Superior, Huron, Ontario, and Cayuga AB015480 Flavobacterium columnare AM230485 Flavobacterium aquatile 11 Superior, Michigan, and Huron AB682419 Flavobacterium cucumis KC436215 Huron JQ772826 Ontario DQ222427 Flavobacterium daejeonense JQ772813 Ontario JQ772814 Ontario KC436248 Ontario 8 Superior, Michigan, and Huron JQ692099 Flavobacterium terrigena JQ772768 Ontario DQ889724 Flavobacterium terrigena KC436250 Ontario AB176674 Lishizhenia caseinilytica KC436207 Huron KC436231 Huron AF493694 Fluviicola taffensis JQ772809 Ontario JQ772864 Superior 6 Huron and Ontario M59052 Empedobacter brevis AM084341 Wautersiella falsenii JQ772698 Huron JQ772706 Huron JQ772726 Huron AB267720 Pedobacter composti EU109726 Pedobacter oryzae JQ773018 Cayuga JQ772763 Ontario JQ772975 Michigan JQ772892 Superior L77117 Methanocaldococcus jannaschii 132 100 100 75 98 72 91 85 100 96 72 99 96 JQ772959 Michigan JQ772985 Michigan AB680963 Pseudorhodobacter ferrugineus EU342372 Thalassobacter arenae JQ773013 Cayuga AJ748748 Nereida ignava U02521 Anaplasma phagocytophilum JQ772737 Ontario FM201293 Candidatus Cryptoprodotis polytropus D38623 Orientia tsutsugamushi AE006914 Rickettsia conorii CP000683 Rickettsia massiliae AB248285 Sphingomonas molluscorum JQ772807 Ontario Y13774 Blastomonas natatoria U20773 Novosphingobium subterraneum JQ772773 Ontario FM164634 Novosphingobium mathurense L77117 Methanocaldococcus jannaschii Figure 3.5. Neighbor-joining consensus phylogeny of Alphaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes. 133 Figure 3.6. Neighbor-joining consensus phylogeny of Betaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values > 70% from 1000 resamplings are indicated at the nodes. 134 Figure 3.6 (cont’d). 94 92 75 JQ772769 Ontario AF435948 Albidiferax ferrireducens CP000267 Albidiferax ferrireducens 99 JQ772996 MI JQ772962 MI 77 100 JQ772696 HU 96 GU233447 Rhodoferax antarcticus AY609198 Rhodoferax antarcticus 100 12 Michigan 99 AB120966 Aquamonas fontana Aquamonas fontana 100 KC436264 Ontario DQ349098 Caenimonas koreensis AB245358 Variovorax ginsengisoli HQ845986 Variovorax paradoxus 98 80 6 Superior, Michigan, Huron, and Ontario AF078754 Giesbergeria sinuosa 80 AB074522 Giesbergeria giesbergeri 99 99 8 Michigan and Cayuga X97071 Leptothrix mobilis 94 AM397629 Undibacterium parvum 83 KC436241 Ontario 96 JQ772823 Ontario JQ772740 Ontario GQ379228 Undibacterium oligocarboniphilum AM397630 Undibacterium pigrum AY061962 Oxalicibacterium flavum 11 Superior, Michigan, Huron, and Cayuga 96 CP002056 Methylotenera versatilis AY486132 Methylovorus mays 91 DQ287786 Methylotenera mobilis 88 AB193724 Methylophilus methylotrophus 84 JQ772649 Huron 99 99 KC436141 Superior JQ795771 Deefgea chitinilytica 91 FJ669217 Jeongeupia naejangsanensis L77117 Methanocaldococcus jannaschii 135 AM921638 Acinetobacter rhizosphaerae DQ129724 Acinetobacter calcoaceticus 76 AF513979 Alkanindiges illinoisensis 15 Superior, Huron, and Michigan 99 6 Ontario DQ664237 Perlucidibaca piscinae 77 JQ772790 Ontario JQ772757 Ontario 74 99 JQ772784 Ontario JQ772760 Ontario 90 JQ773021 Cayuga 95 JQ773008 Cayuga AF064461 Pseudomonas cedrina 99 JQ867398 Pseudomonas viridiflava JQ772820 Ontario AY047218 Pseudomonas migulae JQ772796 Ontario JQ772694 Huron 82 AB302402 Pseudomonas multiaromavorans AY259924 Pseudomonas filiscindens JQ773015 Cayuga JQ773017 Cayuga KC436243 Ontario KC436258 Ontario KC436256 Ontario JQ772799 Ontario JQ772788 Ontario JQ772783 Ontario AM921634 Pseudomonas putida 99 3 Cayuga 100 DQ295891 Crenothrix polyspora 99 AJ414655 Methylobacter tundripaludum AF152597 Methylobacter psychrophilus 99 10 Superior, Michigan, and Huron 100 DQ011528 Marinomonas communis X67025 Marinomonas vaga 100 CP000514 Marinobacter hydrocarbonoclasticus JQ772658 Huron 74 L77117 Methanocaldococcus jannaschii 100 91 72 99 96 Figure 3.7. Neighbor-joining consensus phylogeny of Gammaproteobacteria 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Figure 3.7 (cont’d). Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. Bootstrap values >70% from 1000 resamplings are indicated at the nodes. 136 81 99 97 100 96 99 96 100 92 99 100 89 93 100 96 76 80 JQ773027 Cayuga AB282862 Microterricola viridarii AJ310412 Agreia pratensis JQ772911 Superior KC436165 Superior AM040493 Leucobacter iarius JN257093 Oerskovia jenensis D86182 Bifidobacterium angulatum U25952 Bifidobacterium bifidum JQ772890 Superior KC436162 Superior JQ772850 Superior JQ772889 Superior KC436244 Ontario JQ772978 Michigan DQ147280 Candidatus Microthrix parvicella AB517669 Aciditerrimonas ferrireducens GU562000 Bacillus thuringiensis GQ149481 Bacillus cereus JQ772925 Michigan JQ799058 Bacillus aquimaris X98529 Listeria ivanovii L37585 Clostridium botulinum L77117 Methanocaldococcus jannaschii Figure 3.8. Neighbor-joining consensus phylogeny of Actinobacteria and Firmicutes 16S rRNA gene sequences of clones from Diporeia and closely related reference sequences. Bootstrap values (1000 replicates) higher than 70 are indicated at the nodes. 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(2000) Living in suboxia: ecology of an Arabian Sea oxygen minimum zone copepod. Limnology and Oceanography. 45(7), 1576-1593. 144 CHAPTER 4 Spatio-temporal Dynamics of Parasites Infecting Diporeia spp. in Southern Lake Michigan 145 Abstract Since the 1990s, populations of the benthic amphipod Diporeia spp. have sharply declined across much of the Laurentian Great Lakes. This study was undertaken to identify the community composition, structure, and dynamics of parasites and fungi infecting Diporeia collected from nine sites in the southern basin of Lake Michigan, a portion of the Great Lakes where the decline of the amphipod has been well documented over 27 years. An additional aim of this study was to assess the effect of infection dynamics and dreissenid densities on Diporeia densities over the course of the study period. The study demonstrated that Diporeia were host to a total of eight different groups of uni- and multicellular pathogens. Of the 3,082 amphipods analyzed, 1,624 amphipods (52.7%) exhibited at least one infection. Ciliophora was most prevalent (50.08%), followed by Amoeba (2.79%), Microsporidia (0.68%), Cestoda (0.45%), Haplosporidia (0.23%), Acanthocephala (0.36%), filamentous Fungi (0.10%), and Yeast (0.32%). Spatio-temporal variability in parasitic infections was observed with prevalences often fluctuating by depth, sampling site, and life stage of Diporeia. No significant positive correlations were observed between any group of parasites and dreissenid densities. Parasite species belonging to Microsporidia and Haplosporidia are likely associated with detrimental effects on Diporeia that may have impacted Diporeia populations. The findings of this study shed light on pathogens as potential causes of Diporeia declines in the Laurentian Great Lakes. 146 Introduction Amphipods of the genus Diporeia (hereafter referred to as Diporeia) occupy a central position in the foodweb of the Laurentian Great Lakes in North America. As an infaunal detritivore that feeds on pelagic material that settles to the benthos, Diporeia are the foremost consumer of the primary production in the Great Lakes (Gardner et al. 1990). Since Diporeia are an important food resources for a number of Great Lakes fish species (Wells, 1980; Selgeby et al. 1994; Rand et al. 1995; Davis et al. 1997; Hondorp et al. 2005; Pothoven, 2005), they function as important conduits of nutrients and energy to higher trophic levels and serve as coupling mechanisms between pelagic and benthic zones of the Great Lakes (Fitzgerald and Gardner, 1993). Historically, Diporeia have been the most widespread and dominant benthic 2 macroinvertebrate in the Laurentian Great Lakes, reaching abundances of 14,000 m (Henson 1966, 1970; Cook and Johnson, 1974). Recently, however, Diporeia abundances have declined across much of the Great Lakes (Nalepa et al. 1998, 2007; Dermott and Kerec, 1997; Lozano et al. 2001 Barbiero et al. 2011). Due to the unique position of Diporeia in the foodweb and the fact that it once accounted for a major portion of the benthic biomass in the lakes (Nalepa, 1989), it is believed that these large-scale declines in Diporeia have resulted in major restructuring of Great Lakes foodweb (Nalepa et al. 1998). Several hypotheses have been proposed to explain Diporeia declines in the Great Lakes, including 1) greater predation mortality stemming from increases in certain fish populations, such as lake whitefish Coregonus clupeaformis (Ebener et al. 2008), 2) decreased dissolved oxygen concentration (Nalepa et al. 2005), 3) decreased food 147 availability due to increased abundance of invasive, filter-feeding dreissenid mussels) in the Great Lakes (Nalepa et al. 2006), 4) the release of toxins by dreissenid mussels (Dermott et al. 2005), and 5) increased pollutants in sediments (Landrum et al. 2000). Although research studies have been conducted exploring each of these hypotheses, the exact cause of the Diporeia declines in the Great Lakes remains uncertain (Nalepa et al. 2009). In Lake Michigan, Diporeia abundances were declining in the late 1990s despite what was considered to be sufficient flux of organic matter reaching the benthos (Nalepa et al. 2006) suggesting that declining abundances were not simply a result of competition with dreissenid mussels. Similar questions have been raised as to whether predation, chemical contamination, and low dissolved oxygen were of a sufficient degree to be the sole cause of declining Diporeia abundances in Lake Michigan (Nalepa et al. 2005). Past studies have found that Diporeia can be the host of a wide array of aquatic pathogens, including a rickettsia-like bacterium, helminthes, haplosporidia, and microsporidia (Messick et al. 2004; Messick 2009; Muzzall and Whelan, 2011). Messick et al. (2004) documented that lesions found on individual Diporeia from lake Michigan and Huron were associated with several types of parasites and fungi. Based on this, it seems at least plausible that pathogenic outbreaks may have contributed to Diporeia declines in at least some area of the Great Lakes (Messick et al. 2004) The aim of this study was to conduct an in-depth analysis of pathogen prevalences in Diporeia samples from the southern basin of Lake Michigan, a portion of the Great Lakes where the decline of the amphipod has been well documented (Nalepa et al. 1998). Archived Diporeia samples collected from several sites in southern Lake 148 Michigan from as far back as 1980 were examined to determine pathogen presence and to determine the trends of these infections over the past three decades. Models were then fit in an effort to determine the relationship between Diporeia density and infection prevalence as well as to examine other possibly contributing factors, such as dreissenid density. Materials and Methods Sample Collection For this study, a total of 3,082 Diporeia (Diporeia) were randomly subsampled from archived specimens collected between 1980 and 2007 at nine stations in southern Lake Michigan (Table 4.1). Diporeia that had been preserved in 5% buffered formalin were sorted by stage (juvenile < 5.0 mm, adult > 5.0 mm) (Nalepa et al. 2000) and transferred to 70% ethanol until further processing. For determining infection status, Diporeia samples were embedded in paraffin, sectioned (3 to 4 µm), mounted on glass slides, and stained with Mayer’s hematoxylin and eosin (Luna, 1968). Additionally, selected sections were stained with Grocott’s methenamine silver (GMS, Luna 1968). The location of each sampling station in southern Lake Michigan is displayed in Figure 4.1. Sampling stations were chosen such that there was contrast in location, depth, and the rate in which Diporeia densities declined over time. Since, Diporeia declined more rapidly on the east side of the lake (A-1, H-22, H-21, EG-14) compared to the west side (H-8, B-7, B-6, B-5) (Nalepa et al. 1998), samples were collected along an east-to-west gradient. Additionally, since populations generally declined progressively from shallow to deep regions (Nalepa et al. 2005), sites on the two sides of the lake 149 were matched by depth with depths ranging from 18 m to 108 m. The 93-m site station (X-2) located on the far northeast side of the southern basin was also chosen for 2 analysis because Diporeia density at this site was 10 per m in 2005, while densities at 2 B-6 and EG-14 were > 1,000 per m , and by 2010, populations were extirpated from X2. Since Diporeia declined at different rates at the chosen sites after dreissenids became established (Nalepa et al. 1998), we analyzed several samples collected at or after 1992. Together these samples representing a span of 27 were examined to provide an understanding of the range of parasites infecting Diporeia, to determine the parasites associated with Diporeia in the period before the declines, and establish if there were any temporal trends in the parasites infecting Diporeia during this pre-event period. Identification of Organisms Infecting Diporeia Taxonomic systems for groups of organisms infecting Diporeia were based on the following sources: Ciliophora (Lom and Dykova, 1992; Corliss, 1979), Haplosporidia (Sprague, 1979), microsporidia (Wittner and Weiss 1999), yeast (de Becze, 1956), filamentous fungi (Dick, 1990), Amoeba (Page, 1983), Cestoda (Wardle and Mcleod 1952; Yamaguti 1959), and Acanthocephala (Yamaguti 1963b; Amin 2002). Analysis of Diporeia Parasite and Fungus Community Assemblages The Shannon-Wiener diversity index and parasite group richness were used to determine parasite diversity for all replicates. The Shannon-Wiener diversity index was calculated for replicate Diporeia samples as described in Shannon (1948). We used a 150 generalized estimating equation (GEE) model with an exchangeable correlation structure to test whether parasite community richness differed by site, years, or age of Diporeia. We included first-order interactions between different site, years, or age of Diporeia in the GEE model to determine if there were significant interactions among any of these variables. These models ranged in complexity from intercept-only models to models that contained all possible combinations of depth, year, and stage as main effects. The GEE model with the lowest QICu value was identified as the best models in terms of fit and parsimony. Because richness is a count, the GEE model assumed a Poisson distribution with a log link. First-order interactions between sampling site and year factors were assessed using pairwise comparisons of least-squares means. Because of the very low parasite community diversity of Lake Michigan Diporeia (see Results below), we did not attempt to fit a GEE model to the diversity data. Analysis of Infection Parameters and Diporeia Density Preliminary analyses were conducted to test for associations among infection prevalences, Diporeia densities, and dreissenid densities. Diporeia and dreissenid mussels densities for each sampling event were obtained from National Oceanic and Atmospheric Administration Great Lakes Environmental Research Laboratory, Ann Arbor, MI (unpublished data). Since all observed Ciliata infections were external and all other observed infections were internal three combinations of parasite groups were also analyzed: combined infections (CI), combined infections excluding Ciliophora (CIC), and 151 more than one infection (MOI). MOI was defined as an amphipod being infected by more than one group of organisms. Kendall rank correlation analyses were performed for all possible combinations of infections, Diporeia density, and dreissenid density. Correlations were conducted in SAS using the PROC CORR procedure (SAS Institute Inc., 2010). Generalized linear modeling (GLM) was used to assess the effect of infection prevalence, dreissenid density, and depth on Diporeia density. Models were fit assuming a negative binomial distribution and a log link. To evaluate the relative importance of infection prevalence, depth, stage, and dreissenid density to Diporeia density, a total of 69 models were fit to Diporeia density. These models ranged in complexity from intercept-only models to models that contained infection prevalence of a single parasite group, depth, and dreissenid density as main factor levels and models that contained all possible combinations of these parameters as main factor levels. Infection prevalence values were arcsine-root transformed when used as independent variables in the Diporeia density models. Diporeia and dreissenids density values were log transformed when used as dependent and independent variables respectively (log y +1 or log x +1). The model with the lowest BIC values was identified as the best models in terms of fit and parsimony (the best performing model based on BIC comparison was also the best performing model based on AIC comparison). Each of the models was fit in SAS using the GLIMMIX procedure (SAS Institute Inc., 2010). 152 Results Identification of Lesions Associated with Pathogens in Stained Diporeia Sections Several parasites and fungi were identified in the examined Diporeia, including Microsporidia, Haplosporidia, Ciliophora, Amoeba, filamentous fungi, yeast, Acanthocephala, and Cestoda. Altogether, 1,624 amphipods (52.2%) exhibited at least one parasitic infection. The reaction to the pathogens varied greatly from differentiated and melanized hemocytic encapsulations in tissues adjacent to parasites to no obvious or negligible responses. Microsporidian infections were observed in 0.68% of amphipods. These infections were always associated with muscle tissues where infected tissues appeared to be replaced by the parasite (Figure 4.2 panels A-D). Melanized hemocytic encapsulations were often observed in Diporeia exhibiting a microsporidian infection (Figure 4.2 panel C). In a few amphipods, the microsporidian had apparently “used up” the host tissue, and appeared like groups of extracellular, closely knit, vegetative and sporulating stages (Johnson, 1985). In these individuals, differentiated, basophilic, encapsulating hemocytes were observed within the mass of microsporidans (Figure 4.2 panel D). Haplosporidian infections were observed in 0.23% of amphipods. Plasmodia in various stages of schizogony were observed in high densities throughout hemal sinuses, muscle tissue, and connective tissue where infections commonly advanced to sporogenesis (Figure 4.3). Sporocyst often contained mature spores with a welldefined, basophilic endosporoplasm. Differentiated, melanized circulating host hemocytes were observed surrounding sporocyst containing mature spores within multiple Diporeia exhibiting haplosporidian infection (Figure 4.3). 153 Ciliate infections were by far the most prevalent (50.08%) compared to the other parasitic infections observed in Diporeia. Oval ciliates with a characteristic large macronucleus were found throughout the ventrum of amphipods and were closely associated with the gills and periopods (Figure 4.4 panel A). All ciliate infections appeared to be external. No obvious host immune response or tissue damage accompanying ciliate infections was observed. Amoeba infections were observed in (2.79%) of amphipods. In sections of Diporeia, trophozoites with moderately basophilc cytoplasm containing a single nucleus were present in the digestive tract (intestine and the anterior caecum) and were in close proximity to the outer epithelium of the mucosa (Figure 4.4 panel B). There was no evidence of host immune response or tissue damage accompanying Amoeba infections. Two types of mycoses (yeast and filamentous fungi) were observed in sections of Diporeia. Yeast were present in a single amphipod collected from sampling station A-1 in 1980. Yeast cells appeared as oval and hyaline and ranged between 2.5 to 5.5 µm in diameter and were often contained within melanized nodules (Figure 4.4 panel C). Filamentous Fungi were present in 0.10% of amphipods. Filamentous Fungi appeared as coelozoic, branching, saprophytic-like Fungi (Figure 4.4 panel D). One amphipod heavily infected with a filamentous, branching fungus had degenerated tissues with many circulating hemocytes within the hemocoel (Figure 4.4 panel E). Two groups of helminthes were present in the hemocoels of Diporeia. Acanthocephala (Figure 4.4 panel F) was present in 0.36% of amphipods and Cestoda was present in 0.45% of amphipods. While no host immune response accompanying 154 helminth infection, each helminth filled the majority of the hemocoel and often displaced the intestine (Figure 4.4 panel g). Community Structure of Diporeia Parasite Communities Shannon-Wiener diversity index values ranged between 0.0 and 1.099, indicating that the diversity of parasite communities infecting Diporeia was low. In terms of richness of parasite communities infecting individual Diporeia, 95.2% of infected amphipods exhibited a single infection, 4.0% exhibited two infections, and a single amphipod exhibited three infections. Overall richness of parasites observed for each sampling occasion ranged between one and five. Furthermore, the overall richness was one on 14 occasions. In terms of richness for an entire year, the lowest richness (1) was observed in 2007 and the highest richness (6) was observed in 1980. In terms of overall richness for each sampling station over the duration of the study period, with the exception of station H-22 which was only sampled in two consecutive years and had a richness of 2, A-1 had the highest richness (6), followed by X-2 (5), and B-5, B-6, B-7, H-8, and H-21 (4). For richness, the GEE model with the lowest QICu value was the model that contained site and year as the main effects and site x year as the first-order interaction, suggesting that differences among sites was not consistent between years (and vice versa) (Table 4.2). Estimates of the QICu-selected model (Table 4.3) showed a number of differences in the richness of parasite community of Diporeia among sites and years. Pairwise comparisons of least-squares means for the site-by-year interaction indicated that the greatest difference in richness between different stations within a single year 155 was for EG-14 and H-8 in 2006 where higher richness was observed for EG-14. The least difference in richness between different stations within a single year was for B7 and H21 in 1986 where B7 had slightly higher richness. Additionally, the greatest difference in richness between sampling events at a single site over different years was for H-8 in 1986 and 2006 where higher richness was observed in 1986. The least difference in richness between sampling events at a single site over different years was for EG14 in 1993 and 2007 where no difference in richness was observed. Investigation of Infection Parameters Fluctuations in infection prevalences across sampling sites, years, and stage were observed for both combined and individual infections (Figures 4.5-4.10). Adult Diporeia, on average, had higher infection prevalences than did juvenile Diporeia. The one exception was for Amoeba infections. The same trend was observed for the combined datasets (COI, CIC, and MOI prevalences). The largest difference in infection prevalence between adult and juvenile Diporeia was observed for Microsporidia infections. As for differences in infection prevalences among sampling stations, Diporeia from EG14 had the highest prevalences of Amoeba and Acanthocephala infections compared to the other stations sampled. Similarly, Diporeia from this station had the highest CIC and MOI prevalences. In general, Acanthocephala infections prevalences increased with depth. As for differences in infection prevalences during the course of the study, on average, increases in prevalence of Acanthocephala, Amoeba, and Microsporidia infections were observed for 1992 and 1993. The same trend was also observed for MOI prevalences. 156 Six statistically significant correlations were detected among infection prevalences. MOI, Amoeba infection prevalence, and Diporeia density were all negatively correlated with dreissenid density (τ = -0.20, -0.164, -0.396, P =, 0.014, 0.041, <0.0001 respectively). Haplosporidia infection prevalence, on the other hand, was positively correlated with Diporeia density (τ = 0.184, P = 0.016). Microsporidia infection prevalence was positively correlated with Acanthocephala infection (τ = 0.31, P = 0.0006). Acanthocephala infection prevalence was positively correlated with depth (τ = 0.16, P = 0.048). No infection prevalences were significantly correlated (τ > 0.05) with Diporeia abundance. Investigation of Infection Prevalence, Depth, and Dreissenid Density in Relation to Diporeia Density For Diporeia density data, the model with the lowest BIC value contained Microsporidia infection prevalence, dreissenids density, and depth as main effects. Eight of the 20 models with the lowest BIC values also contained the same parameters as main effects (Table 4.4) further demonstrating their high relative importance for Diporeia density. Estimates of the BIC-selected model (Table 4.5) showed that Diporeia density was positively correlated with Microsporidia infection prevalence and negatively correlated with dreissenid density and depth. Discussion In this study, microscopical examination revealed the presence, of eight different pathogens infecting Diporeia specimens collected in the southern basin of Lake 157 Michigan. Results of this study show that the relative diversity of parasite communities infecting Diporeia in southern Lake Michigan was low, with a maximum of six different parasite groups being present at a single sampling site over the study period and only two dominant parasite infections. While, most of the observed groups of pathogens, such as Microsporidia, Haplosporidia, Ciliophora, yeast-like and filamentous fungi, Acanthocephala, and Cestoda have been previously reported to infect Diporeia in this region (Messick et al. 2004), an unidentified amoeba not previously reported in Diporeia was observed. On the contrary, rickettsia-like bacteria and gregarines (Apicomplexa) which have been reported by Messick et al. (2004) could not be observed in the present study. The observed variation in parasites and fungi infecting Diporeia may be due to the distributions of these pathogens in different geographical areas and seasons. However, in comparison to Messick et al. (2004), although differences in infection prevalence were observed for multiple groups of parasites, similar relative infection prevalences were observed in that much higher ciliate infection prevalences were observed compared to those of the other parasites detected (e.g. Haplosporidia and Microsporidia). Nonetheless, the observed variation in parasite infection prevalences underscores the role spatio-temporal ecological components play in determining the prevalence of different parasitic infections in Diporeia. Results of this study show that the relative diversity of parasite communities infecting Diporeia in southern Lake Michigan is low, with a maximum of six different parasite groups being present at a single sampling site over the study period and only two dominant parasite infections. The occurrence of site and year as first-order interaction 158 terms in the QICu-selected model for both parasite richness measures revealed the importance of spatio-temporal factors on overall parasite community diversity. These results clearly demonstrate the effect abiotic components play in determining parasite community composition in Diporeia. Acanthocephala infections were positively correlated with depth while other infection prevalences differed mainly by sampling site suggesting that variations in limnological features at each sampling site have a stronger influence on particular infection prevalences in Diporeia. Unfortunately, limnological information available on each of the sampling sites of this study is not detailed enough to allow for drawing correlations between the site characteristics and infection parameters of each of the sites. Given the range in host specificity and the effect of depth on different acanthocephalan infection prevalences in amphipods (Zdzitowiecki and Presler, 2001), it is possible that multiple acanthocephalan species having varying infection characteristics are present in the specimens examined. Although we were not able to determine the number of different acanthocephalan infections in the sections of Diporeia examined, it is likely that the observed species were Acanthocephalus dirus and Echinorhynchus salmonis (Amin 1978; Messick et al. 2004; Muzzall and Whelan, 2011). Nonetheless, this study represents the first report of the spatial factors associated with infection prevalences of different helminthes in both adult and juvenile Diporeia. Since, Messick et al. (2004), reported infection prevalences for combined helminth species in Diporeia for the total number of amphipods examined for both Lake Michigan and Huron, a direct comparison of the authors’ results to those of the current study cannot be made. 159 Acanthocephalans are known to both oophorectomize their intermediate hosts and modify the behavior of the hosts to increase the probability of predation of the intermediate host by the definitive host (Haine et al. 2005; Bethel & Holmes 1977). Additionally, microsporidians are known to burden their hosts and are associated with a range of pathogenicity (Dunn and Smith 2001). Interestingly, in the current study, Microsporidia infection prevalence showed a significant positive correlation with Acanthocephala infection prevalence in Diporeia. While the reproductive and behavioral responses of Diporeia to acanthocephalan infection are currently unknown, the negative impact to Diporeia fitness as a result of muscle replacement by the observed microsporidian is obvious. It is therefore possible that these two parasites synergistically affect the fecundity, behavior, and fitness of Diporeia in the Great Lakes. It has been shown that, in addition to competition and predation, parasitism is an important biological force that controls zooplankton community structure (Yan and Larsson 1988). Some parasites of invertebrates are predicted to greatly reduce host survival and host fecundity despite having low infection prevalences in invertebrate hosts (Anderson & May 1991, McCallum 1994). In the current study, lower prevalences were observed for both Haplosporidia and Microsporidia infections compared to infections for other groups of parasites observed (e.g. Ciliophora and Amoeba). It is known, however, that haplosporidians and microsporidians may have more harmful effects on Diporeia despite their relatively low prevalence (Messick et al. 2004). It is therefore possible that even seemingly subtle increases in infection prevalences of these parasites (e.g. 0.01 to 0.1%) can significantly impact Diporeia populations. The fact that tissue alteration and host inflammatory immune response was associated with 160 these infections further highlights the negative impacts these infections have on Diporeia. Several reports have documented haplosporidian infections in freshwater invertebrates, including oligochaetes (Granata, 1913; Jírovic, 1936), snails (Barrow, 1961, 1965; Burreson, 2001), amphipods (Ryckeghem 1930; Messick et al. 2004; 2009), and mussels (Molloy et al. 2012). In this context, it is possible the decline in Diporeia could have been influenced by dreissenids introducing a haplosporidian in to the Great Lakes. Interestingly, both haplosporidians observed in Diporeia in the Great Lakes and zebra mussels in European waterbodies were reported to have similar spore morphologies (i.e. dimension ~8.0 x 6.0 µm and operculate ornamentation) (Molloy et al. 2012; Messick et al. 2009). Thus it is possible that, since zebra mussels and Diporeia coexist in the Great Lakes benthos, zebra mussels may serve as a reservoir for a potentially amphipod-pathogenic haplosporidian. Although the exact balance between virulence and transmission rates of the parasites infecting Diporeia is unknown, based on the host-parasite model of Anderson and May (1981), a number of biological conclusions about the persistence of parasitic infections of Diporeia in the southern basin of Lake Michigan can be drawn. For example, the finding that both microsporidian infection prevalence was a main effect in the selected Diporeia density model and microsporidian infection prevalence declined as Diporeia density declined and suggest that the transmission efficiencies of microsporidians is strongly dependent on Diporeia density. Furthermore, it is plausible that the interrelationship and dynamics of different parasitic infections may synergistically contribute to the decline in Diporeia populations in the southern basin of 161 Lake Michigan. Additional research is required to elucidate the characteristics of parasitic coinfections in Diporeia. Unfortunately, we were not able to identify a clear mechanistic link between parasitic infection and the decline of Diporeia. However, the observed increase in prevalence for a number of infections (both individual and MOI) in 1992 and 1993 coincides with the establishment of the zebra mussel (Dreissena polymorpha) in the southern basin of Lake Michigan (Nalepa et al. 1998). This finding provides evidence to suggest a mechanistic link exist between the presence of zebra mussels and increased infection prevalence in Diporeia. One explanation for this result may be that zebra mussels are capable of harboring and spreading Diporeia-pathogenic organisms; however the finding that infection prevalences did not continue to increase as dreissenid densities increased suggests the observed increase in infection prevalences may have been caused by a different mechanism. Another explanation may be that the ecological impact of the initial establishment of filter feeding zebra mussels stressed Diporeia populations to the point of being susceptible to infection. Predictions for Diporeia density based on the BIC-selected model appear to be inconsistent with the observed trends in Diporeia densities in that estimates show a negative correlation between depth and Diporeia density while research has shown that Diporeia populations in shallow regions in southern Lake Michigan are declining at a faster rate than those in deeper regions (Nalepa et al. 1998). We attribute this finding to the historically high relative Diporeia density at the shallow sites sampled, particularly A1, B-7, and H-8. Additionally, since Diporeia have been extirpated from a number of regions in the study area, data for those sites were not included in the model further 162 contributing to the observed inconsistency. Keeping in mind that the selected model for Diporeia density is based on data collected over 27 years, it is likely that similar models fit to data collected within the last decade would show a positive correlation between depth and Diporeia density. In conclusion, through this work, we were able to determine the spatio-temporal patterns of a number of parasites infecting Diporeia. Additionally, we were able to select a model that describes trends in Diporeia density in the southern basin of Lake Michigan over the past three decades in relation to physical and biological factors. These findings provide valuable insights not only on the dynamics of parasites infecting Diporeia during the declines but also on the potential transmission of parasites that use Diporeia as an intermediate host. This new knowledge is needed to understand the potential causes of the decline in Diporeia and foster the development of efficacious management strategies for the restoration and conservation of Diporeia and other ecologically important organisms in the Great Lakes foodweb. Acknowledgements Funding was provided by the Great Lakes Fisheries Trust, Grant # 2009.1058 for the project entitled “Mechanistic Approach to Identify the Role of Pathogens in Causing Diporeia spp. Decline in the Laurentian Great Lakes”. 163 APPENDIX 4 164 Table 4.1. Total number of adult and juvenile (in parenthesis) Diporeia collected for each sampling occasion in the southern basin of Lake Michigan from 1980-2007. Station Depth (m) A-1 18 B-5 108 B-6 83 B-7 45 EG-14 95 H-8 19 H-21 73 H-22 46 X-2 93 1980 60(30) 53(29) 51(23) 56(27) - 1986 56(30) 53(24) 58(29) 58(29) 48(25) 60(30) 61(30) 45(15) 1987 43(14) 56(29) 59(30) 57(30) 24(14) 59(31) 55(25) - 1992 13(2) 60(30) 57(29) 61(31) 60(30) 61(30) 60(30) 60(30) 1993 53(28) 59(30) 59(30) 60(31) 62(30) 59(29) 60(30) 165 1998 54(29) 57(29) 61(30) 58(29) 58(29) 30(0) 55(30) 1999 2001 2004 52(30) 54(30) 60(30) 13(3) 53(29) 59(30) 47(30) 59(30) 55(29) 44(27) - 2006 2007 49(21) 60(29) 59(30) 49(29) 58(30) 4(1) 60(30) 20(15) - Table 4.2. Summary of model selection for predicting parasite community richness in Diporeia, including the QICu value, the difference between the QICu, and the lowest QICu (ΔQICu). The other 100 models tested had QICu values > 17929.86. Main effects included sampling site, sampling year, and age of Diporeia. Main effect Site, Year Age, Site, Year Site Age, Site Age, Site Age, Site, Year Age, Site, Year Site, Year Age, Year Age, Depth, Year Age, Site, Year Age, Depth, Year Depth, Year Intercept only Year Age, Year Age Depth Depth, Year Age, Depth Interaction Site X Year Site X Year (none) (none) Age X Site Age X Site (none) (none) Age X Year Age X Year Age X Year Age X Depth X Year Depth X Year (none) (none) (none) (none) (none) (none) Age X Depth 166 QICu 9656.661 9681.178 9817.299 9821.938 9823.697 9827.945 9831.684 9833.417 9834.083 9839.975 9840.141 9841.884 9857.453 9860.378 9864.464 9864.979 9868.476 9868.632 9872.104 9877.887 ΔQICu 24.51670 160.6384 165.2775 167.0362 171.2842 175.0235 176.7557 177.4223 183.3140 183.4805 185.2235 200.7919 203.7169 207.8036 208.3178 211.8153 211.9713 215.4436 221.2263 Table 4.3. Parameter estimates based on the model with the lowest QICu value for parasite community richness in Diporeia in southern basin of Lake Michigan from 19802007. Parameter Estimate Intercept -0.3193 A1 -0.2624 B5 -0.1916 B6 -0.5768 B7 -0.2819 EG14 0.0316 H21 -0.0309 H22 1.0268 H8 -0.1916 X2 0 1980 -0.023 1986 -0.6542 1987 -1.2809 1992 -0.1916 1993 -0.2754 1998 -0.0772 1999 -0.1327 2001 -0.5746 2004 -0.1721 2006 -1.3863 2007 0 A1*1980 0.1992 A1*1986 0.3015 A1*1987 1.3202 A1*1992 0 B5*1980 0.3012 B5*1986 -0.2403 B5*1987 1.0986 B5*1992 0.219 B5*1993 0.0741 B5*1998 0.0647 B5*1999 -0.4173 B5*2006 1.2634 B5*2007 0 B6*1980 -0.7101 B6*1999 0.5952 B6*2006 1.6388 B6*2007 0 167 Table 4.3 (cont’d). Parameter Estimate B7*1980 0 B7*1986 0.6289 B7*1987 1.5026 B7*1992 0.2653 B7*1993 0.0179 B7*1998 0.205 B7*1999 0.1052 B7*2004 0 EG14*1992 0.0985 EG14*1993 0.2754 EG14*1998 -0.1991 EG14*1999 0.061 EG14*2006 1.4202 EG14*2007 0 H21*1986 0.3757 H21*1987 1.4037 H21*1992 -0.0561 H21*1993 0.4601 H21*1998 0.0218 H21*1999 0 H21*2004 0 H22*1986 -0.2324 H22*1987 0 H8*1986 0.7278 H8*1987 0 H8*1992 0.0573 H8*1993 -0.1628 H8*1998 0.024 H8*1999 0 H8*2006 0 H8*2007 0 X2*1986 0 X2*1992 0 X2*1993 0 X2*1998 0 X2*1999 0 X2*2001 0 168 Table 4.4. Summary of model selection for predicting the abundance of Diporeia, including the BIC value, the difference between the BIC, and the lowest BIC (ΔBIC). The other 100 models tested had AIC values > 17929.86. Main effects included arcsineroot transformed parasite prevalence (individual and combined), abundance of dreissenids (Dreissenids), and depth (Depth). Main effects Microsporidia, dreissenids, Depth dreissenids, Depth Cestoda, Microsporidia, dreissenids, Depth Haplosporidia, Microsporidia, dreissenids, Depth Acanthocephala, Microsporidia, dreissenids, Depth Cestoda, dreissenids, Depth Acanthocephala, dreissenids, Depth Haplosporidia, dreissenids, Depth Amoeba, Microsporidia, dreissenids, Depth Cestoda, Haplosporidia, Microsporidia, dreissenids, Depth Amoeba, dreissenids, Depth Dual infection, dreissenids, Depth Combined infection without Ciliata, dreissenids, Depth Combined infection, dreissenids, Depth Acanthocephala, Haplosporidia, Microsporidia, dreiisenids, Depth Ciliata, dreissenids, Depth Cestoda, Haplosporidia, dreissenids, Depth Acanthocephala, Haplosporidia, dreissenids, Depth Amoeba, Haplosporidia, Microsporidia, dreissenids, Depth Amoeba, Haplosporidia, dreissenids, Depth 169 BIC 587.71 589.10 591.84 592.10 593.11 593.40 593.67 593.77 595.08 596.07 596.66 596.68 597.03 597.40 597.41 597.54 597.95 598.14 599.54 601.36 ∆BIC 1.39 4.13 4.39 5.40 5.69 5.96 6.06 7.37 8.36 8.95 8.97 9.32 9.69 9.70 9.83 10.24 10.43 11.83 13.65 Table 4.5. Parameter estimates based on the model with the lowest BIC value for Diporeia density in southern basin of Lake Michigan from 1980-2007. Main effect Intercept Microsporidia Dreissenid Depth Estimate 4.0370 1.0677 -0.3895 -0.0054 170 Standard error 0.0526 0.3863 0.0238 0.0007 DF 467 467 467 467 Figure 4.1. Location of sampling stations for Diporeia in the southern basin of Lake Michigan from 1980-2007. Depth contours are 5 m. 171 A B C D Figure 4.2. Histological sections of Diporeia collected from the southern basin of Lake Michigan between 1980 and 2007 showing microsporidian infection. Notice the microsporidians filling and replacing the muscle tissue (panels A-D), the melanized hemocytes encapsulating microsporidian spores surrounding the muscle tissue (panel C), and the differentiated, basophilic, encapsulating hemocytes within the mass of microsporidans (panel D). All sections were stained with Mayer’s hematoxylin and eosin. Scale bars denote 25 µm. 172 Figure 4.3. Histological section of Diporeia collected from the southern basin of Lake Michigan showing haplosporidian infection. Notice the mature spores with a welldefined, basophilic endosporoplasm within sporocysts (small arrows), and the differentiated circulating host hemocytes surrounding the sporocysts (arrows). The section was stained with Mayer’s hematoxylin and eosin. Scale bar denotes 25 µm. 173 Figure 4.4. Histological sections of Diporeia collected from the southern basin of Lake Michigan between 1980 and 2007 showing parasitic infection. Ciliate with a large nucleus among the gills (panel A). Amoebae within the digestive tract (panel B). A yeast-like fungus (indicated by the arrow) within a hemal sinus (large arrows) and a melanized nodule (small arrow) (panel C). Filamentous fungi within the coelom of Diporeia (panels D and E). Notice the circulating hemocytes within the hemocoel of Diporeia (panel E). Acanthocephala within the hemocoel (panel F) displaced the amphipod intestine (panel G). Panels A, B, and D-G were stained with Mayer’s hematoxylin and eosin stain. Panel C was stained with Grocott’s methenamine silver. Scale bars denote 25 µm. 174 Figure 4.4 (cont’d) A B C D F E G 175 Figure 4.5. Prevalence of combined parasite infections and Ciliata (Cil.) infections in Diporeia collected from nine stations in Lake Michigan collected between 1980 and 2007 (±95% confidence interval). Sampling sites (x axes) are ordered by increasing depth. The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one infection. 176 Figure 4.6. Prevalence of parasites in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95% confidence interval). Stations (x axes) are ordered by increasing depth. 177 Figure 4.7. Prevalence of combined parasite infections and Ciliata (Cil.) infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval). The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one infection. 178 Figure 4.8. Prevalence of parasites in Diporeia (±95”% confidence interval) collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007. Acanth. = acanthocephala, Am. = Amoeba, Cest. = cestodes, Fil. = filamentous fungi, Haplo. = haplosporidia, and Micro. = microsporidia. 179 Figure 4.9. Prevalence of combined parasite infections and Ciliata (Cil) infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval) by size class of Diporeia (J < 5 mm, A > 5 mm). The y axes (prevalence) are not on the same scale. CI = combined infections, CIC = combined infections excluding Ciliophora, and MOI = more than one infection. 180 Figure 4.10. Prevalence of amoeba (Am.), microsporidia (Mi.), cestodes (Ce.), haplosporidia (Ha.), acanthocephala (Ac.), filamentous fungi, (Fil.), and Yeast infections in Diporeia collected from nine sites in the southern basin of Lake Michigan between 1980 and 2007 (±95”% confidence interval) by size class of Diporeia (J < 5 mm, A > 5 mm). 181 REFERENCES 182 REFERENCES Amin, O.M. (2002) Revision of Neoechinorhynchus Stiles & Hassall, 1905. (Acanthocephala: Neoechinorhynchidae) with keys to 88 species in two subgenera. Systematic Parasitology. 53(1), 1-18. Anderson, R.M. & May, R.M. (1981) The population dynamics of microparasites and their invertebrate hosts. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 291, 451-424. Barbiero, R.P., Schmude, K., Lesht, B.M., Riseng, C.M., Warren, G.J., & Tuchman, M.L. 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Parasites and Vectors. 4, 2. Reproduction and reprint has been permitted and granted by BioMed Central. 188 Abstract The mode of viral hemorrhagic septicemia virus (VHSV) transmission in the Great Lakes basin is largely unknown. In order to assess the potential role of macroinvertebrates in VHSV transmission, Diporeia, a group of amphipods that are preyed upon by a number of susceptible Great Lakes fishes, were collected from seven locations in four of the Great Lakes and analyzed for the presence of VHSV. It was demonstrated that VHSV is present in some Diporeia samples collected from lakes Ontario, Huron, and Michigan, but not from Lake Superior. Phylogenetic comparison of partial nucleoprotein (N) gene sequences (737 base pairs) of the five isolates to sequences of 13 other VHSV strains showed the clustering of Diporeia isolates with the VHSV genotype IVb. This study reports the first incidence of a fish-pathogenic rhabdovirus being isolated from Diporeia, or any other crustacean and underscores the role macroinvertebrates may play in VHSV ecology. Findings The viral hemorrhagic septicemia virus (VHSV), genotype IVb, is a recent invader to the Laurentian Great Lakes basin and has been associated with mortalities in a number of resident freshwater fish species (Elsayed et al. 2006; Gagné et al. 2007; Groocock et al. 2007; Lemsden et al. 2007). While laboratory studies demonstrated that the virus can be transmitted to naïve fish by both immersion and injection (Kim and Faisal 2010), the mode of VHSV transmission in the Great Lakes basin is largely unknown. In a previous study, it was concluded the pisocolid intermittent leech Myzobdella lugubris harbors VHSV Faisal and Schulzs (2009). Whether other macroinvertebrates can act as 189 a vector or reservoir for VHSV remains to be elucidated. In the Great Lakes foodweb, amphipods of the genera Diporeia, Gammarus, and Hyalella occupy a central position as they transform energy from lower to higher trophic levels Quigley and Vanderploeg (1991). Unfortunately, Diporeia spp. have experienced a sharp decline in abundance over the last two decades Nalepa et al. (2009); the cause(s) of which puzzles scientists. To tackle this enigma, a study was designed that involved comprehensive parasitological and microbiological analysis of Diporeia spp. collected from lakes Ontario, Huron, Michigan and Superior [Faisal and Winters: Pathogens impacting Diporeia spp. in the Great Lakes, submitted]. Diporeia spp. were collected between August 2007 and April 2008 by taking Ponar grabs from seven locations in the Great Lakes basin at depths between 74-190 meters. The approximate locations of collections of Diporeia spp. are shown in Figure 5.1. Collected Diporeia spp. were pooled (five amphipods/pool), immersed briefly in absolute ethanol for surface disinfection, and then rinsed several times in sterile water. Samples (~100 μg) were homogenized with a sterile mortar and pestle and then diluted with 1 ml Earle’s salt-based minimal essential medium (MEM, INVITROGEN). Homogenized Diporeia contents were removed with a sterile transfer pipette, dispensed into a sterile 1.5 ml centrifuge tube, and centrifuged at 5500 rcf for 20 min and supernatants were immediately used for virus isolation. Since there is currently no amphipod cell line that could be used to aid in the isolation of amphipod-pathogenic viruses, virus isolation was performed according to the standard protocols detailed in the American Fisheries Society Blue Book (American Fisheries Society 2007) and the Office International des Epizooties (OIE 2006), using the Epithelioma papulosum cyprinii (EPC) cell line (Fijan et al. 1983). Inoculated 96-well 190 plates containing EPC cells grown with MEM (5% fetal bovine serum) were incubated at 15°C for 21 days, and were observed for the formation of cytopathic effects (CPE). Second and third blind passages were performed and assessed for the presence of CPE. All cell culture positive samples of Diporeia homogenates (ON41, ON55-M, HU54M, MI18-M, and MI27-M) caused CPE on EPC in the form of focal areas of rounded, refractile cells which progressed to full lysis of the cell monolayer. Reverse transcriptase polymerase chain reaction (RTPCR) was then performed on all samples (Table 5.1) Total RNA was extracted from inoculated cell culture supernatant using a QIAamp® Viral RNA Mini Kit (QIAGEN). Reverse transcription was accomplished by a two-step protocol using the Affinity Script Multiple Temperature Reverse Transcriptase RT-PCR™ (AGILENT TECHNOLOGIES). The primer set used in this assay was recommended by the Office International des Epizootics for the detection of a 811 base pair sequence of the VHSV nucleocapsid (N) gene: 5’-GGG GAC CCC AGA CTG T- 3’ (forward primer) and 5’-TCT CTG TCA CCT TGA TCC-3’ (reverse primer). Amplicons of 811 base pairs were amplified in all cell culture positive samples. The RT-PCR yielded five samples (ON41, ON55-M, HU54-M, MI18-M, and MI27-M) with the characteristic VHSV 811 bp band. The amplicons were further purified with the Wizard® SV Gel and PCR Clean-up System (PROMEGA) and then sequenced from both directions. Overlapping sequences for each isolate were aligned using the BioEdit contig assembly program version 7.0.9.0 (Hall 1999) and the aligned contigs were used for multiple alignments performed by ClustalW (Thompson et al. 1994). Phylogenetic analysis of the VHSV Diporeia strain with 14 nucleoprotein encoding genes from other 191 species of rhabdovirus was done by generating the phylogenetic dendrogram (Figure 5.2) using MEGA 4 (Tamura et al. 2007) and the Neighbor-Joining algorithm Saitou and Nei 1987). Phylogenetic analysis of the five Diporeia isolate sequences (737 bp) base pairs [GenBank: HQ214133-HQ214135, HQ415762- HQ415763] showed that the sequences clustered with the VHSV IVb-MI03 strain, the index strain of the Great Lakes VHSV [GenBank: DQ427105]. In comparison to the Great Lakes VHSV strain, two nucleotide substitutions were observed in Diporeia isolates from both lakes Huron and Michigan: a transition from cytosine to thymine at nucleotide position 408 which caused a silent mutation and a transversion from guanine to thymine at nucleotide position 907 which caused a mutation from glycine to cysteine. Our findings provided evidence that VHSV can exist within Diporeia spp. Whether VHSV propagates in the cells of these amphipods or just existed in their viscera or gills is currently unknown and deserves further investigation. Indeed, the presence of VHSV in Diporeia spp. in three of the four Great Lakes sampled is surprising since these amphipods were collected from depths that ranged from 74-190 meters where none of the susceptible fish are known to reside. Diporeia spp. feed on detritus and planktonic organisms and is known to scavenge for food items in lower depths. Such a feeding habit has the potential to transfer VHSV from the benthos to the pelagic zone through their excreta or by being preyed upon by susceptible fish; lake whitefish for example. Moreover, based exclusively on data generated in this study, one cannot rule out that VHSV is a pathogen of Diporeia spp. or that Diporeia spp. can be a reservoir for VHSV in the Great Lakes. Regardless of these currently unanswered questions, this study reports the first incidence of a fish-pathogenic rhabdovirus being isolated from Diporeia, 192 or other crustacean. This finding underscores the dire need to better understand the role of macroinvertebrates in disease ecology. Acknowledgements The authors thank the crew of the R/V Lake Guardian for their assistance with sample collection. In order to conduct the study, we are indebted to the generous funding provided by the United States Environmental Protection Agency - Great Lakes National Protection Office (Grant #: GL00E36101) and the United States Department of Agriculture - Animal and Plant Health Inspection Service (Grant#: 10-9100-1293-GR). 193 APPENDIX 5 194 Table 5.1. Locations in the Laurentian Great Lakes from which Diporeia spp. were collected for this study (CPE = formation of cytopathic effect; RT-PCR = results for amplification of the viral hemorrhagic septicemia virus nucleoprotein gene). Lake Station Depth (m) Latitude CPE RT-PCR Ontario ON41 129 4/25/08 + + ON55-M 077°26.29 W 4/25/08 + + 44°45.70 N 082°47.01 W 8/7/07 - - Date 43°43.00 N 078°01.62 W 190 43°26.60 N HU37 74 HU54-M 124 45°31.00 N 083°25.03 W 8/7/07 + + Michigan MI18-M 160 42°44.06 N 086°59.98 W 4/16/08 + + MI27-M 103 43°36.01 N 086°55.00 W 4/18/08 + + Superior SU01-M 95 46°59.56 N 085°09.63 W 8/18/07 - - Huron Longitude 195 Figure 5.1. Map of the Laurentian Great Lakes showing where Diporeia spp. were collected for this study. The solid circles denote sampling locations. 196 Figure 5.2. Distance tree constructed for phylogenetic comparison of isolates obtained in this study. The tree generated using the Neighbor-joining algorithm and maximum likelihood method shows high phylogenetic similarity between the five viral hemorrhagic septicemia virus (VHSV) isolates obtained in this study (ON41, ON55-M, HU54-M, MI18-M, and MI27-M) and other isolates belonging to the VHSV genotype IVb. The alignment file used to produce the tree contained partial VHSV nucleoprotein (N) gene sequences (737 nucleotide positions). Snakehead rhabdovirus was used as the outgroup. The scale bar indicates the number of substitutions per nucleotide site. 197 Figure 5.2 (cont’d). 198 REFERENCES 199 REFERENCES American Fisheries Society-Fish Health Section. (2007) Suggested Procedures for the Detection and Identification of Certain Finfish and Shellfish Pathogens American Fisheries Society: Bethesda, MD. Elsayed, E., Faisal, M., Thomas, M., Whelan, G., Batts, W., & Winton, J. (2006) Isolation of Viral Haemorrhagic Septicaemia Virus from Muskellunge, Esox masquinongy (Mitchill), in Lake St. Clair, Michigan, USA reveals a new sublineage of the North American genotype. Journal of Fish Diseases. 29, 611-619. Faisal, M. & Schulz, C. (2009) Detection of Viral Hemorrhagic Septicemia Virus (VHSV) from the Leech Myzobdella lugubris Leidy, 1851. Parasites & Vectors. 2, 45. 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Molecular Biology and Evolution. 24, 1596-1599. Thompson, J.D., Higgins, D.G., & Gibson, T.J. (1994) CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignments through Sequence Weighting, Position Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Research. 22, 4673-4680. 201 CHAPTER 6 Molecular and Ultrastructural Characterization of Haplosporidium Diporeiae n. sp., a Parasite of Diporeia spp. (Amphipoda, Gammaridae) 202 Abstract In this study, we describe the morphology and taxonomic affiliation of a novel coelozoic and histozoic haplosporidian that was found in Diporeia spp. (Amphipoda) collected from the Great Lakes basin. In stained sections, the haplosporidian was observed to cause systemic infections in Diporeia that were often accompanied with host tissue degeneration. The haplosporidian appeared as dinucleated plasmodia and sporocysts containing different spore maturation stages in the coelom, connective tissue, digestive tissue, and muscle. All of the observed systemic infections progressed to sporogenesis. Transmission electron microscopy revealed that mature spores were slightly ellipsoidal and had a mean spore length X width of 5.34±0.17 X 4.09±0.15 nm. A hinged opercular lid with a length of 3.1±0.17 nm was observed for a number of developing spores. The average thickness of the cell wall was 90.0±8.33 nm. Thin filaments (70 nm) composed of spore wall material were observed projecting from an abopercular thickening of the spore wall. Phylogenetic analysis showed that the haplosporidian is novel bearing some similarities with the oyster pathogen Haplosporidium nelsoni, yet distinctly different. Based on its morphology, genetic sequence, and host, it became evident that the Diporeia haplosporidian is taxonomically novel and we propose its nomenclature as Haplosporidium diporeiae. Introduction Amphipods of the genus Diporeia constitute an important component of the foodweb in the Laurentian Great Lakes of North America. Over the last three decades, Diporeia abundances have declined condiserably across much of the Great Lakes (Nalepa et al. 203 1998, 2007; Dermott and Kerec, 1997; Lozano et al. 2001; Barbiero et al. 2011). In a previous study (Winters et al. in prep), the authors reported on the presence of multiple parasites and fungi infecting Diporeia collected from Lake Michigan (USA). Among these, a Haplosporium sp. was found in seven out of 3,082 (0.23%) Diporeia collected from nine sites in Lake Michigan between 1980 and 2007. Actively dividing developmental stages of the haplosporidian included plasmodia in various stages of schizogony in high densities throughout the hemal sinuses, muscle tissue, and connective tissue where infections commonly advanced to sporogenesis. Differentiated, melanized circulating host hemocytes were observed surrounding sporocysts containing mature spores within multiple amphipods exhibiting haplosporidian infection, suggesting that the parasite is pathogenic to Diporeia (Winters et al. in prep). The phylum Haplosporidia contains coelozoic and histozoic, spore-forming, obligate protozoan endoparasites that infect a number of freshwater and marine invertebrates including bivalves, crustaceans, and polychaetes. In mollusks, haplosporidians have been reported to cause a range of lesions, from destruction of gills, gonads, or digestive gland to general destruction of all tissues associated (Ford and Haskin, 1982; Ford, 1986; Ford & Figueras, 1988; Bowmer and van der Meer, 1991; Molloy et al. 2012). In the brackish environment, Haplosporidium nelsoni, the causative agent of MSX disease, has contributed to major mortalities in the eastern oyster (Crassostrea virginica) populations along the eastern coast of the United States for decades (Burreson and Ford, 2004). In the freshwater environment, Haplosporidium pickfordi was found infecting the digestive gland of snails in multiple lakes in northern Michigan, USA (Barrow, 1961, 1965) with limited evidence that the parasite is pathogenic to its hosts. 204 Additionally, one species (H. raabei) was reported in the connective tissue of the gills, gonads, and digestive gland of zebra mussels (Dreissena polymorpha) from the Rhine and Meuse river basins in France, Germany, and the Netherlands (Molloy et al. 2012). In amphipods, haplosporidians have been shown to cause systemic infection where a range of pathologies have been reported. For example, Haplosporidium gammari has been shown to develop in the adipose tissue throughout the body of Rivulogammarus pulex where it destroys fat cells (Larsson, 1986). In another amphipod, (Parhyale hawaiensis) infections by two different unidentified haplosporidians were associated with tissue damage that ranged from stretching, swelling, and fusion to vacuolation, fragmentation, and liquefaction necrosis of digestive epithelial cells and necrosis and rupture of skeletal muscle fibers surrounding the digestive canal and hepatopancreas (Ismail, 2011). While Diporeia in Lakes Michigan and Huron have been shown to host haplosporidians (Messick et al. 2004; Messick, 2009; Winters et al. in prep), the taxonomic relationship of the Diporeia haplosporidian(s) is largely unknown due to the lack of phylogenetic and detailed ultrastructural studies. The current study determines the phylogenetic relationship of a haplosporidian infecting Diporeia to other haplosporidians. We also shed light on major morphological criteria of importance in classifying the novel haplosporidian. Material and Methods Sample Collection and Morphological Examination A total of 332 Diporeia were collected from four sites in Lake Superior for determining the presence of haplosporidian infection. Samples were collected by taking 205 Ponar grabs (sampling area 22.86 x 22.86 mm / 8.2 liters) at depths between 18-136 meters. Benthic samples were sieved (mesh = 0.25 mm) and Diporeia were identified according to Bousfield (1989) and placed in either 10% neutral buffered formalin for histopathological analysis or filter-sterile (0.2 µm) 80% ethanol for molecular analysis. For histopathological analysis, amphipods preserved in formalin were dehydrated in a graded series of alcohols, embedded in paraffin, cut into 3-4 μm thick serial sections, and stained with Mayer’s hematoxylin and eosin (H&E) (Luna, 1968). An average of 83 amphipods was sampled from each site. The taxonomic system for haplosporidia infecting Diporeia was based on the morphological criteria used for taxonomy detailed in Sprague (1979). To ascertain morphological similarities, we compared the haplosporidian development stages observed in Lake Superior Diporeia with those observed in samples of Diporeia collected from nine sites in southern Lake Michigan between 1980 and 2007 (Winters et al. in prep). Ultrastructural studies were performed on a representative, infected Diporeia sample collected from site SU-23B in Lake Superior (Figure 6.1) that was embedded in a paraffin block. The sample was deparaffinized, post-fixed, and processed for transmission electron microscopy (TEM). For TEM, ultra-thin sections (60–100 nm) were stained with 2% (w/v) uranyl acetate in 50% ethanol followed by Reynold’s lead citrate and examined in a JEM-100 CX II electron microscope at an accelerating voltage of 100 kV. 206 DNA Isolation, Amplification, and Sequencing Genomic DNA from an infected Diporeia collected from the same sampling station in Lake Superior was extracted using the DNeasy DNA extraction kit (QIAGEN) according to the manufacturer’s instructions. PCR amplification of 16S rDNA was amplified according to the protocol of Molloy et al. (2012). Specifically, the HAP-F1+16S-B primer set was initially used to screen Lake Superior Diporeia populations for haplosporidian 16S rRNA genes. A negative control containing no DNA was included in the PCR reaction. The resulting PCR product was visualized by agarose gel electrophoresis to confirm only a single fragment was amplified, cloned using a TOPO TA Cloning Kit® (Invitrogen, CA, USA) following the manufacturer’s protocol, cultured on Luria-Bertani agar plates (Fisher Scientific Inc., PA, USA) containing 50 μg/ml Kanamycin as directed by the manufacturer’s protocol, and sequenced using the M13f (5’-GTT TTC CCA GTC ACG AC-3’), M13r (5’-CAG GAA ACA GCT ATG ACC-3’) and amplification primers. The resulting sequence (1,522 bp) was deposited in GenBank (Accession #: KF378734). Sequence and Phylogenetic Analyses The 16S rRNA gene sequence was submitted for a BLAST (National Center for Biotechnology Information) search and highly similar matches were included in the dataset for phylogenetic analysis. Selection of sequences to include in phylogenetic analyses was based on the findings of Reece et al. (2004), Burreson and Reece (2006), and Molloy et al. (2012). A total of 19 haplosporidian and three non-haplosporidian outgroup 16S rRNA gene sequences were aligned with ClustalW as implemented in MEGA 5.0 (Tamura et al. 2011) using default settings. The average length of 207 sequences in the final alignment file was 1,754 bp. The alignment file was visually checked for alignment gaps and missing data in nucleotide positions. Prior to phylogenetic analysis, the program jModelTest (Darriba et al. 2012) was used to select the best fitting substitution model according to the corrected Akaike information criterion (AICc) (Hurvich & Tsai, 1993); the model with the lowest AICc value was identified as the best model in terms of fit and parsimony (Burnham & Anderson, 2002). A total of 1,624 candidate models, including models with equal/unequal base frequencies, with/without a proportion of invariable sites (+I), and with/without rate variation among sites (+G) were tested. The best-fit model of nucleotide substitution was the transitional model (Rodríguez et al. 1990) with γ distributed rates (TIM + I + G) with unequal base frequencies. Tree topologies were inferred using a Bayesian approach using MRBAYES v 3.1.2 (Huelsenbeck and Ronquist, 2001). For Bayesian analysis, we used the GTR + I + G model of nucleotide substitution, given that it is the model available in MRBAYES that best matches the TIM + I + G model. Bayesian analysis included four Monte Carlo Markov chains (MCMC) for 2,000,000 generations, and trees sampled every 1,000th generation. The first 25% of samples were discarded as burn-in. After discarding the burn-in samples, the remaining data were used to generate a 50% majority-consensus tree. 208 Results Pathology and Morphological Characterization Haplosporidian infections were observed in Diporeia collected from all four sites in Lake Superior (Table 6.1). Prevalence ranged from 1.08% in site SU-22B, to 2.97% in site SU-20B for an overall prevalence of 2.11%. In all infected Diporeia (7/7), haplosporidian developmental stages were widespread and distributed in a systemic way and all had progressed to sporogenesis. Plasmodial development and sporogenesis was relatively synchronous. For all observed infections, plasmodia and developing spores were observed in high densities throughout the coelom. Additionally, developing spores were often associated with connective, digestive, and muscle tissues. Plasmodia and sporocysts were commonly observed lining the epicuticle and digestive tissue (Figure 6.2). Infections were often accompanied by degeneration of host tissues. An apparent host immune response to infection was observed as differentiated, melanized circulating host hemocytes surrounding sporocysts. Stained, paraffin-embedded sections revealed that the haplosporidian spores within Lake Superior Diporeia were contained in round to amorphous sporocysts averaging 29.09±3.20 μm (n=8) in length ranging from 19.94 to 42.47 μm. TEM of the sample collected from SU-23B revealed the presence of both immature spores that were amorphous and electron-dense and spherical to slightly ellipsoidal mature spores that exhibited a lower electron density within sporocysts (Figure 6.3). In stained histological sections, dinucleated plasmodia were observed (Figure 6.4). Sporocysts often contained mature spores with a well-defined, basophilic endosporoplasm. Mature 209 spores were observed outside of sporocysts and multiple spores showed what appeared to be thin filaments projecting from the end of the spore (Figure 6.5). By TEM, individual spores measured 5.34±0.17 μm long by X 4.09±0.15, μm wide (n=14). The average thickness of the cell wall was 90.0 nm (SE = 8.33, n=6). A hinged opercular lid with a length of 3.1±0.17 nm (n=6) composed of spore wall material was observed for a number of developing spores (Figure 6.6A). A thickening of the spore wall at the abopercular end was observed for several mature spores (Figure 6.6B). Thin filaments (70 nm) that were determined to be composed of spore wall material appeared to project from the abopercular thickening (Figure 6.6C). All morphological criteria and dimensions of the Diporeia haplosporidian were identical in both Lake Superior and Lake Michigan H&E stained preparations. Phylogenetic Analysis A BLAST search of the 16S rDNA sequence obtained from Diporeia showed that the closest matches (86% similarity) were for two Haplosporidium nelsoni sequences (GenBank Accessions U19538 and AB080597). The resulting tree of phylogenetic inference had the genus Haplosporidium as paraphyletic as previously reported (Reece et al. 2004; Burreson and Reece, 2006; Molloy et al. 2012), and showed that the sequence obtained from Diporeia aligned with H. nelsoni but was distinctly different (Figure 6.7). Posterior probabilities of branching points based on Bayesian inference indicated that the node support of the Lake Superior Diporeia taxon was 96%. This result strongly suggested that the Lake Superior Diporeia is a novel species of Haplosporidium. 210 Discussion Phylogenetic analysis showed that the Diporeia haplosporidian was distinct from all other Haplosporidium spp. with sequences available in GenBank. The most similar sequence was the oyster pathogen H. nelsoni; however, the Diporeia haplosporidian formed a separate clade in the tree with high node support (96% posterior probability). The phylogenetic relationship between these two haplosporidians is also reflected in their spore morphologies as well since both have similar spore ornamentation, i.e., parallel rows of filaments composed of spore wall material projecting away from the spore wall; however, the spores of the haplosporidian observed infecting Diporeia are smaller than those of H. nelsoni. As with H. nelsoni, the Diporeia haplosporidian can be distinguished from the other five recognized freshwater species by its smaller spore size (Table 6.2). In comparison to H. pickforii, both haplosporidians are reported to infect invertebrates in Michigan (USA) and have an abopercular thickening of the spore wall (Burreson, 2001). However, in addition to having somewhat smaller spores, the Diporeia haplosporidian has thinner filaments than that reported for H. pickfordi (Burreson, 2001). Additionally, phylogenetic analysis shows that the two species are considerably different (81% sequence similarity) (Table 6.2). The Diporeia haplosporidian described in this study is morphologically similar to those previously described in Lakes Michigan and Huron Diporeia (Messick et al. 2004; Messick, 2009). However, the spore measurements reported in Messick et al. (2004) (5.0±0.1 μm long by 4.3±0.1 μm wide) are smaller than those reported in Messick (2009) (5.0±0.1 μm long by 4.3±0.1 μm wide). Therefore, in the absence of gene 211 sequence data, it is almost impossible to ascertain if they are identical to the Diporeia Haplosporidium sp. of this study. In the current study, average spore measurements (5.34±0.17 μm long by X 4.09±0.15) were more similar to those reported in Messick et al. (2004). Given the extent of systemic infection and the number of infections progressing to sporogenesis, tissue alteration, and host immune response, it is likely that the observed haplosporidian is a serious pathogen of Diporeia. This is alarming since Diporeia function as important conduits of nutrients and energy to higher trophic levels and serve as coupling mechanisms between pelagic and benthic zones of the Great Lakes (Fitzgerald and Gardner, 1993), and therefore, occupy a central position in the foodweb of the Laurentian Great Lakes ecosystem. Historically, Diporeia has been the most widespread and dominant benthic macroinvertebrate in the Laurentian Great Lakes. Recently, however, Diporeia abundances have declined across much of the Great Lakes (reviewed in Nalepa et al. 2007). Presently, the cause of the decline of Diporeia in the Great Lakes is unknown. Further research is needed to better understand both the geographic distribution of haplosporidian infections in Diporeia and the potential impact these infections have on Diporeia populations in the Great Lakes. This is the first report of a haplosporidian infecting Diporeia in Lake Superior. Based on its morphology, genetic sequence, and host, we concluded that this Haplosporidium sp. is novel and we propose naming it Haplosporidium diporeiae n. sp. 212 Description Taxonomic Summary Phylum Haplosporidia (Caullery & Mesnil, 1899) Class Haplosporea Order Haplosporida Family Haplosporidiidae Haplosporidium diporeiae n. sp. Type Host Diporeia spp. Amphipoda, Gammaridae, Phoxocephaloidae, Haustoriidae Type Locality Lake Superior (USA) Sampling site SU-23B (46.60°N & 84.81°W) Depth = 60 m Type Material Reference materials are deposited at the U.S. National Parasite Collection, U.S. Department of Agriculture, Beltsville, MD (USNPC # 107252.00). Genetic Sequence Ribosomal DNA sequence: Deposited to GenBank (Accession # KF378734). 213 Etymology The specific epithet refers the genus of the host Diporeia. Acknowledgements The authors would like to thank the Great Lakes Fisheries Trust (Grant #: GL00E361) and the United States Environmental Protection Agency - Great Lakes National Protection Office (Grant #: GL00E36101) for their generous support of this study. We also would like to thank Mr. Tom Nalepa of the National Oceanic and Atmospheric Administration - Great Lakes Environmental Research Laboratory for donating Diporeia samples collected prior to 2008. 214 APPENDIX 6 215 Table 6.1. Samples in which Haplosporidium Diporeiae infection was observed in sections of Diporeia collected from Lake Superior in 2008. Site Coordinates SU-01M SU-20B SU-22B SU-23B 46.99°N & 85.16°W 46.88°N & 90.28°W 46.80°N & 91.75°W 46.60°N & 84.81°W Depth (m) Month/year sampled 95 8/2008 113 8/2008 53 8/2008 60 8/2008 216 Prevalence 2.94% (2/68) 2.97% (3/101) 1.08% (1/93) 1.43% (1/70) Table 6.2. Morphological characteristics and genetic similarity of all freshwater species of haplosporidia and Haplosporidium nelsoni compared to H. Diporeiae. Haplosporidium sp. Host H. Diporeiae Diporeia spp. Haplosporidium sp. Diporeia spp. Haplosporidium sp. Diporeia spp. H. nelsoni H. cernosvitovi H. limnodrili H. pickfordi oysters Crassostrea virginica, C. gigas oligochaetes Opistocysta flagellum oligochaetes Limnodrilus udekemianu snails Physella parkeri, Lymnaea stagnalis, and Heliosoma companulatum 5.3 4.4-6.7 5.0 range not stated 8.1 range not stated Mean spore width and range (μm) 4.1 3.0-4.9 4.3 range not stated 6.1 range not stated 8.1 5.3-10.7 5.5 4.8-7.5 mean not stated 10–11 mean not stated 10–12 mean not stated 6–7 mean not stated 8–10 8·9 8·5–10·4 4·5 3·8–4·6 Mean spore length and range (μm) 217 GenBank accession numbers Reference Sequence similarity This study KF378734 Messick et al. 2004 - Data not available Messick, 2009 - Data not available Perkins, 1968 U19538 AB080597 X74131 86% 86% 85% Jírovic, 1936 - Data not available Granata, 1913 - Data not available Barrow, 1961 AY452724 81% Table 6.2 (cont’d). Haplosporidium sp. H. raabei H. vejdovskii Host zebra mussels Dreissena polymorpha oligochaetes Mesenchytraeus flaviduus Mean spore length and range (μm) Mean spore width and range (μm) GenBank accession numbers Reference Sequence similarity 7·5 6·5–9·4 5·2 4·5–5·9 Molloy et al. 2012 HQ176468 HQ176469 83% 83% mean not stated 10–12 mean not stated 8–10 Caullery and Mesnil, 1905 - Data not available 218 Figure 6.1 Sampling sites in Lake Superior (USA) where Diporeia spp. specimens were examined for a haplosporidian infection. 219 Figure 6.2. Histological sections (hematoxylin and eosin) of a haplosporidian in Diporeia sp. collected from Lake Superior (USA). Notice plasmodia undergoing various stages of shizogeny lining the epicuticle (small arrow), sporocysts containing developing spores lining host digestive tissue (large arrow). Scale bar denotes 75 µm. 220 Figure 6.3. Transmission electron micrograph of a haplosporidian infecting Diporeia sp. in Lake Superior. Notice sporocysts containing spores undergoing various stages of shizogeny. Scale bar denotes 10,000 nm. 221 A B Figure 6.4. Histological sections (hematoxylin and eosin) of a haplosporidian in samples of Diporeia (Amphipoda) showing similar morphology of developmental stages between those collected from Lakes Superior (A) and Michigan (B) (USA). Notice dinucleated plasmodia within sporocysts (arrows). Scale bars denote 25 µm. 222 Figure 6.5. Histological sections (hematoxylin and eosin) of a haplosporidian in Diporeia sp. collected from Lake Superior (USA). Notice the mature spore that has been liberated from the sporocyst (large arrow) and thin filaments projecting from the end and multiple developing spores with spore (arrows). Scale bar denotes 25 µm. 223 A B C Figure 6.6. Transmission electron micrographs of Haplosporidium diporeiae infecting Diporeia in Lake Superior. Notice (A) spore with a hinged opercular lid (arrow), (B) spores exhibiting a thickening of the spore wall at the abopercular end (arrows), and (C) mature spore exhibiting what appears to be thin filaments projecting from a thickening of the cell wall (arrow). Scale bars: 3A = 1,000 nm, 3B = 5,000 nm, 3C = 500 nm. 224 Figure 6.7. Phylogenetic tree (50% majority-rule consensus) based on Bayesian Inference (MrBayes 3.1.2) of Haplosporidia based on the small subunit ribosomal gene. Numbers at the nodes are Bayesian posterior probabilities. Cercomonas longicauda, Perkinsus chesapeaki, and P. marinus were used as an outgroup for Haplosporidia based on the results of Reece et al. (2004). 225 Figure 6.7 (cont’d). 100 Urosporidium sp. 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Molecular Biology and Evolution. 28, 2731-2739. doi: 10.1093/molbev/msr121. 231 CHAPTER 7 Molecular and Morphological Characterization of Dictyocoela Diporeiae n. sp., a Parasite of Diporeia spp. (Amphipoda, Gammaridae) and Redescription of the Genus Dictyocoela (Microsporidia) 232 Abstract In this study, we describe the morphology and taxonomic affiliation of a novel microsporidian that was found in Diporeia spp. (Amphipoda) collected from Lake Superior. In hematoxylin and eosin stained sections of infected amphipods, the microsporidian was observed to infect muscle tissue surrounding the ovaries. Differentiated, basophilic, or melanized encapsulating host hemocytes were often observed in or near masses of microsporidans. The microsporidian appeared as monokaryotic spores measuring 1.99±0.09 μm long by 1.19±0.05 μm wide. Spores were not contained within a sporophorous vesicle. Each spore contained eight coils of isofilar polar filaments that were arranged in single ranks. Polar filaments measured 71.27±3.33 nm in diameter. A prominent lamellar polaroplast composed of ordered concentric membranes was found at the apical end of the spore surrounding the polar filament. A distinct posterior vacuole was observed at the distal end of the spore. Phylogenetic analysis showed that the microsporidian belongs to the genus Dictyocoela, and is most similar to D. berillonum, yet distinctly different. Based on its morphology, genetic sequence, host, and location within the host, it became evident that the Diporeia microsporidian is taxonomically novel and we propose its nomenclature as Dictyocoela diporeiae. Introduction Over the past three decades, a steady decline of amphipods of the genus Diporeia has been observed in four of the Laurentian Great Lakes in North America. This is concerning since Diporeia constitute an important component of the foodweb and 233 traviditionally have been a major prey item for a number of commercial fisheries (e.g., lake whitefish, Coregonus clupeaformis) (Nalepa et al. 1998, 2007; Dermott and Kerec, 1997; Lozano et al. 2001; Barbiero et al. 2011). In a previous study (Messick et al. 2004), the authors reported on the presence of multiple parasites and fungi infecting Diporeia collected from Lake Michigan (USA). Among these, microsporidia were found in 0.68% (21/3,082) of Diporeia collected from nine sites in Lake Michigan between 1980 and 2007. Microsporidian spores were observed in high densities where they filled and replaced muscle tissue. Melanized encapsulating host hemocytes were often observed in or near masses of microsporidians, suggesting that the parasite is pathogenic to Diporeia. Microsporidia are a diverse and ubiquitous group of obligate intracellular single-cell fungi with an extraordinary host range; from protists to humans. In shrimp and crayfish species, microsporidia infect multiple tissues and organs, including heart, connective tissues, hepatopancreas, hemocyte-forming organs and other tissues (Kelly, 1979; Langdon, 1991; Edgerton et al. 2002) causing pathologies ranging from inflammation to tissue destruction. For this reason, microsporidiosis has been called one of the most globally significant diseases of freshwater crayfish globally (Alderman & Polglase, 1988). In amphipod crustaceans of the family Gammaridae, vertically transmitted microsporidia have commonly been reported to occur at high prevalences and have been shown to have a range of effects on host behavior, fitness, population size, stability, and sex ratio (Dunn et al. 1995; 2001; Hatcher et al. 1999; Dunn & Smith, 2001; Terry et al. 2004; Fielding et al. 2005; Krebes et al. 2010). 234 While a wide genetic diversity of microsporidia has been reported to infect gammarids in France, Scotland, (Terry et al. 2004) and Iceland (Hogg et al. 2002), little is known about microsporidia infecting gammarids in the Great Lakes basin. In one study, Ryan and Kohler et al. (2010) used PCR and DNA sequence analyses to reveal the presence of two microsporidia (Dictyocoela sp. and Microsporidium sp.) infecting Gammarus pseudolimnaeus populations from four cool water streams in southwestern Michigan, USA, providing evidence that a range of genetically diverse microsporidia are impacting amphipod populations in the Great Lakes. While multiple studies have employed light microscopy techniques to investigate microsporidia infections in Diporeia, due to the lack of phylogenetic and detailed ultrastructural studies, the taxonomic affiliation of microsporidia infecting Diporeia is currently unknown. Herein, we report the phylogenetic relationship of a microsporidian infecting Lake Superior Diporeia to other microsporidia reported to infect amphipods. We also shed light on major morphological criteria of importance in classifying the novel microsporidian. Material and Methods Sample Collection and Morphological Examination A total of 338 Diporeia were collected from four sites in Lake Superior for determining the presence of microsporidian infection (Figure 7.1). Samples were collected by taking Ponar grabs (sampling area 0.251 x 0.251 meters / 8.2 liters) at depths between 18-136 meters. Benthic samples were sieved (mesh = 0.25 mm) and Diporeia were identified according to Bousfield, (1989) and placed in either 10% neutral buffered formalin for histopathological analysis or filter-sterile (0.2 µm) 80% ethanol for 235 molecular analysis. An average of 80 amphipods was sampled from each site. The taxonomic system for microsporidia infecting Diporeia was based on the morphological criteria used for taxonomy detailed in (Wittner and Weiss 1999). For histopathological analysis, amphipods preserved in formalin were dehydrated in a graded series of alcohols, embedded in paraffin, cut into 3-4 μm thick serial sections, and stained with Mayer’s hematoxylin and eosin (Luna, 1968). Ultrastructural studies were performed on a representative, heavily infected Diporeia sample collected from site SU-01M in Lake Superior that was embedded in a paraffin block. The sample was deparaffinized, post-fixed, and processed for transmission electron microscopy (TEM). For TEM, ultra-thin sections (60–100 nm) were stained with 2% (w/v) uranyl acetate in 50% ethanol followed by Reynold’s lead citrate and examined in a JEM-100 CX II electron microscope at an accelerating voltage of 100 kV. DNA Isolation, Amplification, and Sequencing Genomic DNA from an infected Diporeia collected from a site near SU-01M (SU23B) was extracted using the DNeasy DNA extraction kit (QIAGEN) according to the manufacturer’s instructions. PCR amplification of microsporidian 16S rDNA was amplified using microsporidian 16S primers V1 (forward) 5’CACCAGGTTGATTCTGCCTGAC-30 (Vossbrinck and Woese, 1986) and 530R (reverse) 5’-CCGCGGCTGCTGGCAC-3’ (Baker et al. 1995). A negative control containing no DNA was included in the PCR reaction. The resulting PCR product was visualized by agarose gel electrophoresis to confirm only a single fragment was amplified, cloned using a TOPO TA Cloning Kit® 236 (Invitrogen, CA, USA) following the manufacturer’s protocol, cultured on Luria-Bertani agar plates (Fisher Scientific Inc., PA, USA) containing 50 μg/ml Kanamycin as directed by the manufacturer’s protocol, and sequenced using the M13f (5’-GTT TTC CCA GTC ACG AC-3’), M13r (5’-CAG GAAACA GCT ATG ACC-3’) and amplification primers. The resulting sequence (1,522 bp) was deposited in GenBank (Accession #: KF537632). Sequence and Phylogenetic Analyses The 16S rRNA gene sequence was submitted for a BLAST (National Center for Biotechnology Information) search and highly similar matches were included in the dataset for phylogenetic analysis. Selection of sequences included in phylogenetic analyses was based on the findings of Krebes et al. (2010). A total of 14 Dictyocoela spp. and three non-Dictyocoela spp. outgroup 16S rRNA gene sequences were aligned with ClustalW as implemented in MEGA 5.0 (Tamura et al. 2011) using default settings. The average length of sequences in the final alignment file was 1,275 bp. The alignment file was visually checked for alignment gaps and missing data in nucleotide positions. Bayesian inference phylogenetic construction was performed with MRBAYES v 3.1.2 (Huelsenbeck and Ronquist, 2001) using the transitional model (Rodríguez et al. 1990) with γ distributed rates (GTR + G) as selected by the program jModelTest (Darriba et al. 2012). Bayesian analysis included four Monte Carlo Markov chains (MCMC) for 2,000,000 generations with one tree retained every 1,000th generation. After discarding the burn-in samples (first 25% of samples), the remaining data were used to generate a 50% majority-consensus tree. 237 Results Pathology and Morphological Characterization In stained histological sections, microsporidian infections were observed in Diporeia collected from three of the four sites sampled. Prevalences for SU-01, SU-20B, SU-22B, and SU-23B were 2.94 (2/68), 1.98 (2/101), 3.23 (3/93), and 0.00% (0/70), respectively, making the overall prevalence for Lake Superior 2.11% (7/332). These infections were always associated with muscle tissues where infected tissues appeared to be replaced with spores that were not enclosed in a sporophorous vesicle. Differentiated, basophilic, or melanized encapsulating host hemocytes were often observed in or near masses of microsporidans (Figure 7.2). In one amphipod, microsporidians were observed filling and replacing the muscle tissue surrounding the ovaries (Figure 7.3) where a melanized hemocytic encapsulation was present near the ovaries (Figure 7.4). By TEM, meronts were roundish monokaryotic cells surrounded by a plasma membrane. Meronts measured 1.49±0.11 μm in diameter. No developing sporoblasts were observed. Mature monokaryotic spores measured 1.99±0.09 μm long by 1.19±0.05 μm wide (n=14). Eight coils of isofilar polar filaments were arranged in single ranks. Polar filaments measured 71.27±3.33 nm in diameter. The spore wall was composed of a thick electron-lucent endospore overlaid with a thinner electron-dense exospore. The average thickness of the spore wall was 97.0±8.33 nm. A lamellar polaroplast composed of ordered concentric membranes was found at the apical end of the spore surrounding the polar filament. A distinct posterior vacuole was observed at the distal end of the spore (Figure 7.5). 238 Phylogenetic Analysis A BLAST search of the 16S rDNA sequence obtained from Diporeia showed that the closest matches (95% similarity) were for seven Dictyocoela spp. sequences (GenBank Accessions AJ438957, JQ673481, AJ438955, FN434091, AJ438956, FN434090, and AF397404) (Table 7.1). The resulting phylogeny showed that the sequence obtained from Diporeia was positioned deep within a large clade containing Dictyocoela spp. but formed a unique clade containing no sister taxa (Figure 7.6). Posterior probabilities of branching points based on Bayesian inference indicated that the node support of the Lake Superior Diporeia microsporidian taxon was 92%. This result strongly suggested that the Lake Superior Diporeia microsporidian is a novel species within the genus Dictyocoela. Discussion Phylogenetic analysis demonstrated that the Diporeia microsporidian fell deep within the large clade containing the genus Dictyocoela. However, electron microscopy revealed that the spores observed in Diporeia were not contained in sporophorus vesicles filled with tubules, a defining characteristic for the genus (Terry et al. 2004). We therefore propose a redescription of the genus to include species that are not contained in vesicles. The genus Dictyocoela was proposed based on group of eight novel sequences that clustered into a discrete clade basal to the major lineage of microsporidia infecting fishes. From these sequences, six species were designated, placing isolates within the same species where sequence homology was within 1% (Terry et al. 2004). Additionally, the study of Wilkinson et al. (2011), which investigated 239 the diversity of Dictyocoela spp. across Europe and from Lake Baikal in Siberia, supported the designation of D. berillonum as a species separate from D. duebenum and D. muelleri and stated that host species distribution appears to influence structuring of Dictyocoela populations. In comparison to the Diporeia microsporidian, the results of the current study show that the most similar Dictyocoela sequences had a sequence homology of 5% or greater (Table 7.1) indicating that the observed microsporidian is novel. All Dictyocoela spp. are vertically transmitted parasites that infect both ovarian tissue and adjacent muscle of their amphipod hosts (Terry et al. 2004). Observation of microsporidia infecting the muscle surrounding the ovaries of Diporeia further suggests its placement in the genus Dictyocoela. The impact of this fungus on reproduction in Diporeia remains to be determined. However, given the extent of infection and involvement of the muscles surrounding ovaries, it is possible that the observed microsporidian can have severe impacts on Diporeia populations. Moreover, it is likely that the observed destruction of muscle tissue caused by microsporidian infection impairs the normal movement, feeding, swimming, and overall functioning and fitness of Diporeia. The fact that tissue alteration and host inflammatory immune response were associated with these infections further highlights the negative impacts these infections have on Diporeia. Given the fact that Diporeia serve as conduits of nutrients and energy to higher trophic levels and coupling mechanisms between pelagic and benthic zones of the Great Lakes (Fitzgerald and Gardner, 1993), the observed infections could have considerable impacts to the normal functioning of the Great Lakes ecosystem. Diporeia was once the most dominant benthic 240 macroinvertebrate throughout the Laurentian Great Lakes. Recently, however, Diporeia abundances have effectively been extirpated from many of its habitats in the Great Lakes, as reviewed in Nalepa et al. 2007). Currently, the cause of these declines is unknown. Additional morphological, phylogenetic, and pathological analyses are needed to better understand both the genetic diversity of microsporidia infecting Diporeia and the potential impact these infections have on Diporeia populations in the Great Lakes. This is the first report of a microsporidian infecting Diporeia in Lake Superior. Based on its morphology, genetic sequence, host, and location in the host, we conclude that this Dictyocoela sp. is novel and we propose naming it Dictyocoela diporeiae n. sp. Description Taxonomic Summary Kingdom Fungi Phylum Microsporidia Order incertae sedis Family incertae sedis Dictyocoela diporeiae n. sp. 241 Type Host Diporeia spp. Amphipoda, Gammaridae, Phoxocephaloidae, Haustoriidae Type Locality Lake Superior (USA) Sampling site SU-23B (46.60°N & 84.81°W) Depth = 60 m Genetic Sequence Ribosomal DNA sequence: Deposited to GenBank (Accession #: KF537632) Etymology The specific epithet refers the genus of the host Diporeia. Ackowledgements The authors would like to thank the Great Lakes Fisheries Trust (Grant #: GL00E361) and the United States Environmental Protection Agency - Great Lakes National Protection Office (Grant #: GL00E36101) for their generous support of this study. We are very thankful to the crew and staff of the R/V Lake Guardian for helping in sample collection. 242 APPENDIX 7 243 Table 7.1. Morphological comparison and genetic similarity of Dictyocoela spp. Dictyocoela sp. D. diporeiae D. berillonum D. berillonum D. muelleri D. duebenum D. muelleri D. muelleri D. duebenum Dictyocoela sp. D. duebenum D. cavimanum D. deshayesum D. cavimanum D. gammarellum Spores contained in vesicle filled with a tubular network no yes yes yes yes yes yes yes yes yes yes yes yes yes Amphipod host Diporeia sp. Echinogammarus berilloni Echinogammarus marinus Gammarus duebeni celticus Gammarus duebeni duebeni Gammarus roeseli Gammarus duebeni duebeni Gammarus duebeni duebeni Gammarus pseudolimnaeus Echinogammarus marinus Talitrus sp. Talorchestia deshayesei Orchestia cavimana Orchestia gammarellus 244 GenBank accession number KF537632 AJ438957 JQ673481 AJ438955 FN434091 AJ438956 FN434090 AF397404 HM991451 JQ673482 AJ438959 AJ438961 AJ438960 AJ438958 Sequence similarity 95% 95% 95% 95% 95% 95% 95% 94% 93% 92% 92% 92% 90% Figure 7.1. Sampling sites in Lake Superior where Diporeia (Amphipoda, Gammaridae) were collected. 245 Figure 7.2. Histological sections (hematoxylin and eosin) of microsporidian developmental stages in an infected Diporeia sp. (Amphipoda) collected from Lake Superior. Notice the individual spores are not enclosed in a sporophorous vesicle (small arrow) and melanized hemocytic infiltration in adjacent muscle tissue (large arrows). Scale bar denotes 25 µm. 246 Figure 7.3. Histological sections (hematoxylin and eosin) of a microsporidian in a Diporeia sp. (Amphipoda) sample collected from Lake Superior. Notice the microsporidians filling and replacing muscle tissues (small arrows) surrounding the ovaries (large arrows). Scale bar denotes 100 µm. 247 A B Figure 7.4. Histological sections (hematoxylin and eosin) of Diporeia (Amphipoda) collected from Lake Superior. Notice (A) the histologically normal ovaries (Large arrows) of an amphipod not displaying a microsporidian infection in the muscle tissue (small arrow) and (B) melanized hemocytic encapsulation near the ovaries (large arrow) of an amphipod displaying a microsporidian infection in the muscle tissue (small arrow). Scale bars denote 25 µm. 248 A B C D Figure 7.5. Transmission electron micrograph of a microsporidian infecting Diporeia in Lake Superior. Notice (A) the monokaryotic meront (small arrow) and spore (large arrow), (B) eight coils of isofilar polar filaments arranged in single ranks (small arrows) and the prominent polar vacuole (large arrow), (C) spore wall composed of a thick electron-lucent endospore (large arrow) overlaid with a thinner electron-dense exospore (small arrow), and (D) lamellar polaroplast composed of ordered concentric membranes surrounding the polar filament (arrow). Scale bars: 4A = 1,000 nm, 4B-4D = 500 nm. 249 Figure 7.6. Phylogenetic tree (50% majority-rule consensus) based on Bayesian Inference (MrBayes 3.1.2) of Dictyocoela spp. based on the small subunit ribosomal gene. Numbers at the nodes are Bayesian posterior probabilities. Spaguea lopii, Kabatana takedai, Nosema granulosis, Thelohania parastaci, Pleistophra mulleri, P. typicalius, Glugea anomala, and Loma acerinae were used as an outgroup for Dictyocoela spp. based on the results of Krebes et al. (2010). 250 Figure 7.6 (cont’d). 66 100 64 100 100 69 90 100 92 92 100 78 100 97 77 100 100 100 100 100 100 251 Dictyocoela muelleri AJ438956 Dictyocoela muelleri AJ438955 Dictyocoela muelleri FN434090 Dictyocoela sp. HM991451 Dictyocoela duebenum AF397404 Dictyocoela duebenum FN434091 Dictyocoela duebenum JQ673482 Diporeia microsporidian Dictyocoela berillonum AJ438957 Dictyocoela berillonum JQ673481 Dictyocoela cavimanum AJ438959 Dictyocoela cavimanum AJ438960 Dictyocoela deshayesum AJ438961 Dictyocoela gammarellum AJ438958 Spraguea lophii AF104086 Kabatana takedai AF356222 Nosema granulosis AJ011833 Nosema granulosis FN434088 Thelohania parastaci AF294779 Pleistophora mulleri FN434084 Pleistophora typicalis AJ252956 Glugea anomala AF044391 Loma acerinae AJ252951 REFERENCES 252 REFERENCES Alderman, D.J. & Polglase, J.L. (1988) Pathogens, parasites and commensals. pp. 167212 in (ed. Holdich, D. M. and Lowery, R. S. Freshwater crayfish: biology, management and exploitation. Croom Helm (Chapman and Hall), London. Baker, M.D., Vossbrinck, C.R., Didier, E., Maddox, J.V. & Shaduck ,J.A. (1995) Small subunit ribosomal DNA phylogeny of various microsporidia with emphasis on AIDS related forms. 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ASM Press, Washington DC, USA. 255 CONCLUSIONS AND FUTURE STUDIES 256 Conclusions In the first two studies, terminal-restriction fragment length polymorphism (T-RFLP) analysis coupled with sequence analysis of bacterial 16S rRNA present in both sediment and Diporeia samples revealed that 1) distinct bacterial groups were associated with Diporeia samples indicating they are natural members of the Diporeia microbiome and likely have ecological significance to the performance of Diporeia in the Great Lakes, 2) rickettsia-like bacteria, which are known to be serious pathogens for freshwater amphipods (Federici et al. 1974; Larsson 1982; Graf 1984), are present in Diporeia, 3) the structure of bacterial communities associated with Diporeia can undergo temporal shifts; however, the ecological significance of this remains to be determined and 4) with the exception of one sample, in general, the bacterial communities of sediments from Lakes Michigan and Huron were similar while those from Lakes Superior and Ontario were unique which is consistent with observed trends in Diporeia declines from the four lakes (Barbiero et al. 2011). The third study revealed that 1) multiple parasites, one of which has yet to be reported, infect Diporeia in Lake Michigan, 2) a number of parasites, particularly a microsporidian and a haplosporidian, elicited considerable tissue destruction and host immune response indicating they could be potentially serious pathogens for Diporeia, 3) spatio-temporal variability in parasitic infections was observed with prevalences often fluctuating by depth, sampling site, and life stage of Diporeia, 4) an increase in infection prevalences was associated with the establishment of dreissenids in Lake Michigan suggesting there is evidence to suggest a mechanistic link exist between dreissenids and the decline of Diporeia, however no infections were significantly correlated with 257 dreissenid density. Overall, the findings of this study provide valuable insights not only on the dynamics of parasites infecting Diporeia during the declines but also on the potential transmission of parasites that use Diporeia as an intermediate host. The fourth study revealed the presence of the fish-pathogenic rhabdovirus Viral Hemorrhagic Septicemia Virus (VHSV) in Diporeia collected from Lakes Michigan, Huron, and Ontario, but not Lake Superior. Since most viruses are host specific or strain specific, or as carriers of viruses hosts do not experience disease, VHSV is probably not pathogenic to Diporeia. Additionally, since VHSV was first reported in the Great Lakes in 2003, (Elsayed et al. 2006), well after Diporeia declines were first noted (Nalepa et al. 1998) it has not likely contributed to the declines. This study reports the first incidence of a fish-pathogenic rhabdovirus being isolated from Diporeia, or other crustacean and underscores the dire need to better understand the role of macroinvertebrates in disease ecology. The remaining studies revealed the presence of two novel parasites infecting Diporeia. The first parasite is a Haplsporidium sp. (Haplosporidia) that was observed causing systemic infection Diporeia that often resulted in tissue destruction and elicited a host immune response in Diporeia collected from both Lakes Superior and Michigan. This study reports the first incident of a haplosporidian infecting Diporeia in Lake Superior. The second parasite is a Dictyocoela spp. (Microsporidia) that destroyed the host muscle tissue and, again, elicited a host immune response. It is likely that the microsporidian considerably impairs the normal movement, feeding, swimming, and overall functioning, fitness, and performance of Diporeia. While both parasites appear to be serious pathogens for Diporeia, the geographical distribution and prevalence of 258 these parasites in Great Lakes Diporeia populations remains to be determined. The information gained in these studies will help future researchers to determine the role these parasites have played in the decline of Diporeia in the Great Lakes. Since, a similar study has never been conducted on Diporeia; these studies represent the most extensive epidemiological investigation into the decline of Diporeia in the Great Lakes. The knowledge gained sheds light on the potential causes of the decline of Diporeia populations and fosters the development of efficacious management strategies for the restoration and conservation of both Diporeia and other Great Lakes organisms that rely on Diporeia. Future Studies Presently, our understanding of pathogens infecting Diporeia is limited. Despite the fact there is a wealth of knowledge on Diporeia decline; prior to these studies, a comprehensive evaluation of Diporeia diseases over the decades of decline has never been conducted. Further analysis of a number of Diporeia samples adequate enough to make to allow for spatio-temporal inferences to be made will allow for better understanding of the decline of Diporeia in the Great Lakes. These studies revealed that, with the exception of one sample, in general, the bacterial communities associated with sediments from Lakes Michigan and Huron are similar while those from Lakes Superior and Ontario each contained their own unique communities. Additionally, a significant temporal shift in bacterial community structure was observed for Diporeia collected from a single station in Lake Superior. Interestingly, these findings are consistent with the observed regional pattern of Diporeia declines in 259 that patterns in declines have shown considerable synchrony between Lakes Michigan and Huron (Barbiero et al. 2011) while considerable year to year fluctuations in Diporeia densities have been observed in Lake Superior. Further research is required to determine the relationship between the structure of bacterial communities of the sediment and Diporeia density. These studies revealed that a number of organisms, some of which have never before been describe, are associated with Diporeia in the Great Lakes. For example, the amoeba observed infecting the caecum of Diporeia is interesting. It is currently unknown if this amoeba is commensal or parasite of Diporeia. Unfortunately, since all samples analyzed in these studies where preserved in fixative, culturing of the amoeba was not possible. Additionally, post-fixation of paraffin embedded sections for transmission electron microscopy will not allow for detailed ultrastructural descriptions of amoebae. Culturing of the observed amoeba will allow not only for better systematic descriptions of the organism but also allow for experimental infections of Diporeia to be conducted for determining both the pathogenicity and potential impact of the amoeba infection on Diporeia populations. Similarily, preservation of samples does not allow for the culturing and experimental infections of Diporeia with either the seemingly novel haplosporidian or microsporidian observed in samples. Since host tissue alteration and immune responses were associated with both infections, they are likely serious pathogens of Diporeia. Phylogenetic analysis demonstrated that the haplosporidian was similar to Haplosporidian nelsoni, the causative agent of MSX disease, which has contributed to major mortalities in the eastern oyster (Crassostrea virginica) populations along the 260 eastern coast of the United States for decades (Burreson and Ford, 2004). Additionally, phylogenetic analysis demonstrated that the Diporeia microsporidian was a novel species within the genus Dictyocoela, a group of vertically transmitted between parasites that often distort the sex-ratio of amphipod populations. Experimental infections of these pathogens will shed light on their ability to cause disease and alter the sex-ratio in Diporeia populations. 261 REFERENCES 262 REFERENCES Barbiero, R.P., Schmude, K., Lesht, B.M., Riseng, C.M., Warren, G.J. & Tuchman, M.L. (2011) Trends in Diporeia populations across the Laurentian Great Lakes, 19972009. Journal of Great Lakes Research. 37, 9-17. Burreson, E.M. & Ford, S.E. 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