A metagenomic survey of eukaryotes found in an Oak Mountain State Park pond, Shelby County, AL

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1 A metagenomic survey of eukaryotes found in an Oak Mountain State Park pond, Shelby County, AL Kathalina Tran 1 and David Johnson 2, Ph.D. 1 Tulane University, New Orleans, LA 70118; ktran4@tulane.edu 2 Department of Biology and Environmental Sciences, Samford University, Birmingham, AL 35229; djohnso2@samford.edu ABSTRACT Often overlooked, freshwater microbial communities have dynamic roles in the ecosystem. These include bacterial population control, nutrient cycling, and energy transfer. This study focuses on energy transfer particularly in the context of the microbial loop, a system by which microorganisms cycle dissolved organic matter through aquatic ecosystems and fuel heterotrophic organisms. A small pond in Oak Mountain State Park was selected for study. Molecular analysis of water samples and DNA sequencing make it possible to identify species present in the pond, and construct a model summarizing microbial loop interactions. Sanger sequencing data was used to begin modeling these interactions. However, next-generation sequencing data is required to complete this model. Unlike Sanger sequencing, next-generation technology is not limited to individual sequencing reactions. Instead, it runs massively parallel sequencing reactions. This would result in much more data and ultimately, more species identified. INTRODUCTION Metagenomics, the intersection of genomics and ecology, makes it possible to holistically investigate microbial communities. Historically, the investigation of microbial life was limited to that which could be studied in culture, a mere fraction of the world s microorganisms (Schmidt et al. 1991; Tyson et al. 2004). The development of next-generation sequencing technology introduced a vast number of applications, and paved the way for metagenomics with its ability to sequence numerous distinct genomes at a time with increased accuracy. Now, by way of environmental sampling, next-generation technology and metagenomics allow a greater understanding of the ecological contributions, general ecology, and immense diversity of microbial communities (Rohit et al. 2011; Tyson et al. 2004; Bai et al. 2014; Aagard et al. 2014). The limited number of studies exploring the metagenomics of freshwater systems (Rohit et al 2011; Rusch et al. 2007; Pope and Patel 2008; Debroas et al. 2009) reflects the fact that freshwater microbial communities are often overlooked. Yet these communities play dynamic roles in environmental stabilization in regards to their bacterial population control or nutrient cycling (Rohit et al. 2011; Azam et al. 1983; Pernthaler 2005). Studies of freshwater microbial predation have determined that compared to predation by other aquatic bacterivores, protistan bacterivory is most influential in limiting bacterial

2 populations (Pernthaler 2005). Even in pelagic areas of low bacterial concentration, omnivorous zooplankton and ciliates have been observed to graze extensively on bacteria (Sherr and Sherr 1987; Work and Havens 2003). The direct pathways observed from bacterioplankton zooplankton or bacterioplankton ciliates effectively keep bacterial populations in check, while also short-circuiting what is known as the microbial loop (see Figures 1-3) (Work and Havens 2003). This microbial loop, a term coined by Azam et al. (1983), is a system by which microorganisms cycle dissolved organic matter (DOM) through aquatic ecosystems, moving DOM up to higher consumers more rapidly (Azam et al. 1983; Dodds 2002). Without this short circuit, a traditional bacterioplankton protozoa zooplankton pathway, for example, would likely be observed (Dodds 2002). Through the microbial loop, microorganisms are actively involved in energy transfer as they reincorporate dissolved organic carbon (DOC) into the aquatic food web, fueling heterotrophic organisms (Azam et al. 1983; Hart et al. 2000; Dodds 2002). It is an integral component of the food web (Sherr and Sherr 1988). Bacterioplankton small enough to feed on DOM graze on the DOM released by phytoplankton and animals (Azam et al. 1983; Dodds 2002). DOM may also be released in the process of virus-induced cell lysis or sloppy feeding predation by protists on bacteria or phytoplankton (Azam et al. 1983; Dodds 2002; Moler 2007). The amount of DOM delivered to the microbial loop by means of sloppy feeding is affected by the physical dimensions of the predator s mouth and the predator-to-prey size ratio (Moler 2007). The microbial loop describes the movement of DOC through the food web as it travels from DOM bacteria flagellates zooplankton, and then on to organisms that prey on the zooplankton (Azam et al. 1983). Without the microbial loop, energy in the form of DOC would be inaccessible to higher order organisms, left unused in DOM or bacteria (Dodds 2002). In this study, we approach ecological concepts with molecular genetics. Utilizing next-generation sequencing and environmental sampling, we aim to survey species present in a freshwater body, and gain an understanding of the key microorganisms involved in the stabilization and maintenance of the pond ecosystem. Next-generation sequencing data will be used in conjunction with large genomic databases to identify species present in the pond. Confirming predator-prey relationships existing in the pond ecosystem would require further study beyond this investigation (see methods of Work and Havens 2003; Lovejoy and Johnson 2014). However, general food webs, with which microbial loops are inherently connected, are well established and widely accepted for both marine and aquatic ecosystems (Sherr and Sherr 1988; Azam et al. 1983; Dodds 2002; Pomeroy 1974). Figures 1-3 summarize widely accepted microbial loop models. The use of these models in conjunction with next-generation sequencing data will be used to organize species into a summative model, specific to the study site, which details microbial loop interactions. MATERIALS AND METHODS Motorcycle Pond is a small, relatively shallow pond in Oak Mountain State Park, Shelby County, AL (see Figure 4). It is connected by a culvert to one of the park s main attraction lakes, Double Oak Lake, which was formed by the damming of Dry Brook. The pond is ideal because it is too small to attract visitors, thereby remaining largely undisturbed, but an appropriate size for the scope of this investigation.

3 Water sample collection Five collection sites at Motorcycle Pond were established. From each site, two samples were collected: one at the surface of the pond and the other 2 inches from the bottom of the pond. The samples were collected using sterile 500mL Nalgene bottles. Two additional sites were established for the collection of two samples specifically by 10µm plankton tow net dragged back and forth through the water (sites marked PN, see Figure 4). Plankton net samples were expected to yield a greater concentration of DNA, as the net filtered through a greater volume of water than the volume of samples collected. Overall, twelve 300mL water samples were collected on June 11, DNA extraction and amplification Genomic DNA was extracted from the water samples utilizing a modified PowerSoil DNA Isolation kit protocol (Mo Bio Laboratories, Inc.). The procedure was modified to initially include a filtration step to isolate sediment from the water sample. Samples were poured through a 0.22µm vacuum filter system (Santa Cruz Biotechnology, Inc.). The filter paper was removed, cut into small pieces, placed into a PowerBead Tube, and vortexed to evenly disperse the paper among the beads. The samples completed preparation according to the PowerSoil DNA Isolation protocol. DNA samples underwent polymerase chain reaction (PCR) amplification on the MJ Mini Personal Thermal Cycler (Bio Rad) in 25µL reactions, at an annealing temperature of 57 C, in accordance with standard 18S amplification protocol (Earth Microbiome Project). Each reaction was composed of 12.5µL GoTaq Colorless Master Mix (Promega), 6.5µL UltraPure Distilled Water (Life Technologies), 1µL prepared DNA sample, 4µL mammalian DNA blocker (Integrated DNA Technologies), and 0.5µL each of the forward and reverse primers (Integrated DNA Technologies). Sequences of the primers used are detailed in Table 1. The primers Euk1391f and EukBr target the 18S rrna gene. Each sample was amplified with a unique barcoded reverse primer, used to identify samples later in Illumina sequencing (Mir et al. 2013). Results of PCR were examined through gel electrophoresis, using 2% agarose gels to confirm the presence of eukaryotic DNA and E-Gel SizeSelect Gels to purify DNA samples (Life Technologies). DNA sequencing and analysis Prepared DNA samples were sent to Virginia Bioinformatics Institute (Virginia Tech), an off-site lab, for next-generation sequencing (Illumina: MiSeq). Geneious Pro software and the National Center for Biotechnology Information (NCBI) online database ( and Basic Local Alignment Search Tool (BLAST) were used to analyze sequencing reads. Alternate cloning analysis DNA samples destined for cloning and Sanger sequencing were PCR amplified on the MJ Mini Personal Thermal Cycler (Bio Rad) in 50µL reactions, at an annealing temperature of 50 C. Each reaction was composed of 25µL GoTaq Colorless Master Mix (Promega), 21µL UltraPure Distilled Water (Life

4 Technologies), 2µL prepared DNA sample, and 1µL each of the forward and reverse primers (Eurofins Genomics). The primers Euk1391f (forward) and EukBr (reverse), without adapters or barcodes, were used. Sequences of the primers used are detailed in Table 1. Results of PCR were examined by gel electrophoresis, using 2% agarose gels to confirm the presence of eukaryotic DNA and E-Gel SizeSelect Gels to purify DNA samples (Life Technologies). Cloning analysis was performed utilizing the TOPO-TA Cloning kit protocol (Life Technologies). This was intended to insert amplified DNA fragments into the pcr4-topo vectors of competent Escherichia coli (Life Technologies). Transformed cells were inoculated in LB-ampicillin broth and left in a shaking incubator overnight at 200rpm, 35 C (Fisher Scientific). DNA from transformed E. coli underwent PCR and gel analysis to confirm the transformation of the E. coli bacterial vectors. The DNA was amplified in in 25µL reactions, at an annealing temperature of 50 C. Each reaction was composed of 12.5µL GoTaq Colorless Master Mix (Promega), 11.5µL UltraPure Distilled Water (Life Technologies), and 0.5µL each of the forward and reverse primers, M13F and M13R, respectively (Eurofins Genomics) (see Table 1). Trace amounts of cloned DNA were deposited in each reaction by dipping a sterilized inoculation loop into a test tube of transformed E. coli and then into the reaction solution. Gel electrophoreses were conducted on E-Gel 2% agarose gels (Life Technologies) to aid determination of successfully cloned samples. Selected samples were sent to Genewiz, Inc. (South Plainfield, NJ), an offsite lab, for Sanger sequencing. Geneious Pro software and NCBI s BLAST were used to analyze sequencing reads ( Modeling the microbial loop Organisms participating in the freshwater microbial loop can be sorted into the following categories: bacteria, protists, rotifers, zooplankton, carnivorous zooplankton, net phytoplankton, picophytoplankton, and planktivorous fish (Dodds 2002). Upon identifying species present in the pond, further investigation as to which category a given organism belongs was required. Large online databases detail taxonomic classifications of organisms, making it possible to do so. Databases consulted in construction of the model are summarized in Table 3. Online flow chart software was used to create this model ( RESULTS Sixteen samples of clonal DNA were sequenced via Sanger method, and of the 16, eight were matched to eukaryotic organisms through NCBI BLAST. The remaining eight samples were matched to bacterial organisms. Only eukaryotic primers were used during DNA amplification, thus bacterial results were disregarded. BLAST search results are summarized in Table 2. Search results were applied to a freshwater microbial loop model (Dodds 2002) to predict microbial loop interactions in Motorcycle Pond (see Figure 5). DISCUSSION The following classifications were represented by data returned from Sanger sequencing:

5 protists, rotifers, zooplankton, carnivorous zooplankton, and picophytoplankton. These results were applied to a freshwater microbial loop model to predict microbial loop interactions in Motorcycle Pond (see Figure 5). As Figure 5 illustrates, bacteria, picophytoplankton, and net phytoplankton are the primary consumers of dissolved organic matter and carbon, thus forming the foundation of the microbial loop. The group of secondary consumers consists of rotifers, zooplankton, and protists. Finally, carnivorous zooplankton and planktivorous fish are positioned as the highest consumers. Figure 5 illustrates the flow of energy both as it moves upwards through consumption and downwards in the return of organisms to DOM or DOC. Clearly, the interconnectivity of the microbial loop lends itself to the circulation of DOM and DOC throughout the community. DOM and DOC initially consumed by bacteria, picophytoplankton, and net phytoplankton move through the microbial loop and supply energy to heterotrophic organisms. The study intended to use microbial loop models in conjunction with next-generation sequencing data to predict microbial loop interactions in Motorcycle Pond. Time constraints did not allow for the completion of next-generation sequencing, thus the model was made with Sanger sequencing data from cloning analysis. Though methods of sample collection and preparation were similar between next-generation and traditional sequencing, traditional sequencing data via E. coli cloning did not yield enough data to complete the model (see Figure 5). This is largely due to Sanger sequencing s limitation to individual sequencing reactions (Voelkerding et al. 2009). In contrast, next-generation sequencing technology runs massively parallel sequencing reactions (Voelkerding et al. 2009). Thus, while Sanger sequencing returned only one set of sequences per DNA sample, and therefore one species identified, nextgeneration sequencing data would have ultimately resulted in the identification of all DNA present in a given sample. Next-generation data must be secured in order to better represent the ecology of the microbial loop in the pond. Finally, it would be worthwhile of further study to expand this investigation to bacterial members of the ecosystem. With DOM as their primary energy source, bacteria have critical roles in nutrient cycling and energy transfer (Azam et al. 1983). ACKNOWLEDGEMENTS Special thanks to the National Science Foundation for providing funding and the Samford University REU program and its directors, Dr. Betsy Dobbins and Dr. Malia Fincher, for the opportunity to have this wonderful experience. I also thank Dr. Teri O Meara for her guidance throughout the program. Finally, I thank my mentor, Dr. Dave Johnson, for his lab, teaching, and invaluable support. LITERATURE CITED "18S rrna Amplification Protocol." Earth Microbiome Project. Web. Accessed 25 July < Aagaard, K., J. Ma, K. M. Antony, R. Ganu, J. Petrosino, et al., The Placenta Harbors a

6 Unique Microbiome. Science Translational Medicine 6(237): Azam, F., T. Fenchel, J. G. Field, J. S. Gray, L. A. Meyer-Reil, et al., The ecological role of water-column microbes in the sea. Marine Ecology Progress Series 10: Bai, Y., J. Liang, R. Liu, C. Hu, and J. Qu, Metagenomic analysis reveals microbial diversity and function in the rhizosphere soil of a constructed wetland. Environmental Technology 35(20): Baker, A. Al. et al., Phycokey an image-based key to Algae (PS Protista), Cyanobacteria, and other aquatic objects. University of New Hampshire Center for Freshwater Biology. Accessed 20 August Debroas, D., J. F. Humbert, F. Enault, G. Bronner, M. Faubladier, et al., Metagenomic approach studying the taxonomic and functional diversity of the bacterial community in a mesotrophic lake (Lac du Bourget-France). Environmental Microbiology 11: Dodds, W. K., Freshwater Ecology: Concepts and Environmental Applications. Encyclopedia of Life. Accessed 20 August Guiry, M.D. & Guiry, G.M., AlgaeBase. World-wide electronic publication, National University of Ireland, Galway. Accessed 20 August Hart, D. R., L. Stone, and T. Berman, Seasonal dynamics of the Lake Kinneret food web: the importance of the microbial loop. Limnology and Oceanography 45(2): Kleppel, G. S., On the diets of calanoid copepods. Marine Ecology Progress Series 99: Lovejoy, R. T. and D. A. Johnson, A Molecular Analysis of Diet and Symbiotic Bacteria of Herbivory in adults of the Invasive Bean Plataspid, Megacopta cribraria. Manuscript accepted June 2014: The Southeastern Naturalist. Mir, K., K. Neuhaus, M. Bossert, and S. Schober, Short Barcodes for Next Generation Sequencing. PLoS One 8(12): e Moller, E. F., Production of dissolved organic carbon by sloppy feeding in the copepods Acartia tonsa, Centropages typicus, and Temora longicornis. Limnology and Oceanography 52(1): Pernthaler, J., Predation on prokaryotes in the water column and its ecological implications. Nature Review Microbiology 3:

7 Pomeroy, L. R., The Ocean s Food Web, A Changing Paradigm. BioScience 24(9): Pope P. B. and B. K. C. Patel, Metagenomic analysis of a freshwater toxic cyanobacteria bloom. FEMS Microbiology Ecology 64: Rohit, G., F. Rodriguez-Valera, K. D. McMahon, D. Toyama, R. Rinke, et al., Metagenomics of the Water Column in the Pristine Upper Course of the Amazon River. PLoS One 6(8): e Schmidt, T. M., E. F. DeLong, and N. Pace, Analysis of a Marine Picoplankton Community by 16S rrna Gene Cloning and Sequencing. Journal of Bacteriology 173: Sherr, E. B. and B. F. Sherr, High rates of consumption of bacteria by pelagic ciliates. Nature 328: Sherr, E. B. and B. F. Sherr, Role of microbes in pelagic food webs: A revised concept. Limnology and Oceanography 33(5): Tseng, C., P. Chiang, F. Shiah, Y. Chen, J. Liou, et al., Microbial and viral metagenomes of a subtropical freshwater reservoir subject to climatic disturbances. Multidisciplinary Journal of Microbial Ecology 7(12): Tyson, G. W., J. Chapman, P. Hugenholtz, E. E. Allen, R. J. Ram, et al., Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428: Voelkerding, K. V., S. A. Dames, and J. D. Durtschi, Next-generation sequencing: from basic research to diagnostics. Clinical Chemistry 55(4): Work, K. A. and K. E. Havens, Zooplankton grazing on bacteria and cyanobacteria in a eutrophic lake. Journal of Plankton Research 25(10):

8 Table 1: Primers used in polymerase chain reactions Primer Sequence Manufacturer Euk1391f 5 -GTACACACCGCCCGTC-3 Integrated DNA Technologies EukBr 5 -TGATCCTTCTGCAGGTTCACCTAC-3 Integrated DNA Technologies M13F 5 -TGTAAAACGACGGCCAGT-3 Eurofins Genomics M13R 5 -CAGGAAACAGCTATGAC-3 Eurofins Genomics

9 Table 2: Results of BLAST search for DNA amplified with Euk1391f and EukBr primers and inserted in E. coli vectors. Sample is lab-issued name of sample of cloned DNA. Identities is ratio of positive base pair matches between cloned DNA and best match search result to the total number of bases in aligned segment. Gaps is number of gaps between cloned DNA and aligned segment. Sample Best match Identities Percent Match Gaps EA101 Cryptomonas marssonii 126/ % 0 EA109 Cryptomonas marssonii 134/137 98% 1 EB101 Trachelius ovum 100/ % 0 EB103 Stentor amethystinus 108/112 96% 0 EC101 Sinantherina ariprepes 130/131 99% 0 EC102 Strombidium basimorphum 115/121 95% 0 EC104 Sinocalanus sinensis 124/127 98% 0 EC108 Mallomonas annulata 117/127 92% 0

10 Table 3: Classification of Organisms Identified in Motorcycle Pond. Classification is category assigned for purposes of microbial loop model (see Figure 5). Family is taxonomic rank above genus. Source is database from which information was used to classify organism. Species Classification Family Source Cryptomonas marssonii Picophytoplankton Cryptomonadaceae AlgaeBase Phycokey Trachelius ovum Protista Tracheliidae Encyclopedia of Life Stentor amethystinus Protista Stentoridae Encyclopedia of Life Sinantherina ariprepes Rotifer Flosculariacea Encyclopedia of Life Strombidium basimorphum Protista Strombidiidae Encyclopedia of Life Sinocalanus sinensis Zooplankton Centropagidae Encyclopedia of Life Carnivorous zooplankton Kleppel 1993 Mallomonas annulata Picophytoplankton Mallomonadaceae AlgaeBase Encyclopedia of Life

11 Figure 1: Microbial loop model presented by Azam et al. (1983), modified from Sheldon (1972) particlesize model. The model is based on the principle that organisms consume particles or prey one order of magnitude smaller than themselves (Azam et al. 1983). Solid arrows illustrate the flow of both energy, in the form of DOC, and inorganic compounds (Azam et al. 1983). Outlined arrows illustrate the flow of only inorganic compounds (Azam et al. 1983). The thickness of the arrows is related to the amount of inorganic compounds or DOC released; as the arrows grow thinner, less energy and materials are released (Azam et al. 1983).

12 Figure 2: Traditional food web vs. microbial loop (Dodds 2002). The model is based on the principle that organisms consume particles or prey one order of magnitude smaller than themselves (Dodds 2002). Dodds compares the traditional aquatic food web and microbial loop to demonstrate the microbial loop is an integral component of the aquatic food web.

13 Figure 3: The microbial loop observed in marine systems (Pomeroy 1974). In his 1974 The Ocean s Food Web, A Changing Paradigm paper, Pomeroy presents the changing perception of marine food webs from zooplankton as primary consumers to microorganisms as the primary consumers largely responsible for energy transfer and nutrient cycling. The traditional paradigm is enclosed in the circle, while more newly formed ideas regarding the significance of microorganisms in the food web lay outside the circle.

14 Figure 4: Googlemap-retrieved image of Motorcycle Pond, marked with sample sites

15 Figure 5: Microbial loop model of Motorcycle Pond. Species listed are BLAST search results (see Table 2). Arrows represent the flow of energy through the pond. Solid arrows represent consumer relationships, while dashed arrows represent the return of organisms to dissolved organic matter or dissolved organic carbon.