Grantee Research Project Results

Final Report: Cyanobacteria and Cyanotoxins in Water Supply Reservoirs – to Develop and Validate a Microarray to Test for Cyanobacteria and Cyanotoxin Genes in Drinking Water Reservoirs as an Aid to Risk Assessment and Management of Water Supplies

EPA Grant Number: R831627
Title: Cyanobacteria and Cyanotoxins in Water Supply Reservoirs – to Develop and Validate a Microarray to Test for Cyanobacteria and Cyanotoxin Genes in Drinking Water Reservoirs as an Aid to Risk Assessment and Management of Water Supplies
Investigators: Rublee, Parke , Henrich, Vincent C. , Burkholder, Joann M. , Glasgow, Howard
Institution: University of North Carolina at Greensboro , North Carolina State University
EPA Project Officer: Aja, Hayley
Project Period: November 1, 2004 through October 31, 2007 (Extended to April 30, 2008)
Project Amount: $594,982
RFA: Microbial Risk in Drinking Water (2003) RFA Text |  Recipients Lists
Research Category: Drinking Water , Human Health , Water

Objective:

To develop and validate a gene microarray for the detection of cyanobacteria and cyanotoxin genes in drinking water reservoirs. The microarray can be used to monitor drinking water supplies as an aid to risk assessment and management.

Summary/Accomplishments (Outputs/Outcomes):

Sampling

We proposed to sample 12 water supply reservoirs in North Carolina during a three year sampling period. We accomplished by sampling during summer months from 2004 – 2006. We also took advantage of other ongoing work and were able to sample an additional 16 reservoirs at least once during this same period. Samples were collected either as surface samples taken from shore or dock, or as integrated surface to bottom water samples from docks or piers. Water samples were then split for various analyses. Metadata measures for each sample included temperature, pH, conductivity, dissolved oxygen, orthophosphorus and secchi depth. Samples for DNA extraction consisted of 100 ml water that was drawn through 25 mm GFF filters. Use of this volume of samples and these filters likely missed large filamentous or sheet-like cyanobacteria as well as cyanobacterial mat communities unless they had been fragmented and suspended. Filters were stored in CTAB buffer until extraction by an alcohol-chloroform method. Purified DNA was stored in pH 8 TE buffer and DNA concentration measured on a Thermo Scientific Nanodrop spectrophotometer. Cyanobacteria counts and microcystin concentrations were determined in selected reservoirs where blooms occurred during 2006.

Clone libraries

We selected samples from six lakes sampled in 2006 with high chlorophyll concentrations and histories of cyanobacterial blooms to generate cyanobacterial sequence libraries. Purified DNA from each sample was amplified with generic cyanobacterial primers (CYA106F, CYA781Ra/b: Nubel et al. 1997), the PCR products were cloned competent E. coli, and purified inserts from up to 200 overnight cultures from each lake were then sequenced. A total of 887 readable sequences were collected (from 123 to 187 per lake) and grouped into operational taxonomic units (OTUs) using a criterion that sequences in an OTU had ≥ 97.5% sequence similarity. We found 98 OTUs: 24 OTUs contained multiple sequences (Fig. 1).

Rank abundance curves for the sequence libraries followed expected patterns (Fig. 2). The most abundant OTUs in the combined library were also the most abundant in the individual lake libraries. In each lake there were sequences represented that were unique to each lake. Shannon-Wiener Species diversity values were similar in each lake (1.31 – 1.64), except for Badin Lake in which we recovered less than half the OTUs of any other lake. The combined sequence library had a Shannon-Wiener Species diversity value of 2.03. The rank abundance information can also be used to estimate the total number of cyanobacterial taxa that would be found in each lake with exhaustive sampling, using SACE and Chao1 estimations (Kemp and Aller 2004). These estimates ranged form 27 (SACE) and 24 (Chao1) species in Lake Wheeler, to 171 (SACE) and 177 (Chao1) species in Falls Lake. The combined sequence library estimates were 378 (SACE) and 317 (Chao1) species. This suggests that there are many additional cyanobacterial taxa in the lakes.

We compared the sequences in our library to GenBank entries. First we compared each sequence in each OTU. Not surprisingly, most sequences did not have a close match in GenBank, although some sequences within OTUs had exact matches. We then generated consensus sequences within multiple sequence OTUs both to compare them to GenBank and to design OTU-specific primers (Table 1, 2). Three of the consensus OTU sequences were identical to GenBank entries (OTU 2 = Cylindrosperopsis raciborskii; OTU 8 = Aphanizomenon issatschenkoi; OTU 90 = Nodularia spumigena). An additional 4 OTUs demonstrated ≥ 98% similarity to GenBank entries listed as “uncultured cyanobacteria.” Seven OTUs had ≥ 98% similarity to GenBank entries listed as “uncultured bacteria.” Four OTUs had ≥ 98% similarity to GenBank entries listed as eukaryotic plastids. The remaining OTUs showed ≤ 97% similarity to the closest GenBank entry. Given the difficulty of assigning genera to discrete branches of phylogenetic trees, we have provided only limited indication of taxonomic affiliation for most entries in Table 1.

Primer design

Our strategy in primer design was to target them as much as possible to the V1 through V4 regions of the SSU rDNA sequence, since we had done this successfully for prokaryotic and eukaryotic microbes in previous work (Marshall, et al. 2008) and they provided relatively short amplicons for rapid amplification. We were able to design unique primers 18-32 bp in length to most, but not all, of the OTU sequences in the library (Table 2). Additionally, in order to be amenable to PCR arrays (see section below), we designed primers with similar Tm’s of around 60°C. Because of the relatively high degree of similarity of the different OTU sequences, we used a strategy that sometimes included the same forward or reverse sequence for multiple OTUs, but combined that with a unique primer sequence in the other directions. We then tested the primers against the clone standards to assure amplification of the targeted sequence. When OTUs contained multiple clones we mixed aliquots of clones to produce the standard.

We field tested the primers by running PCR assays across a range of lakes, or by using samples collected over an annual period from three water supply reservoirs: City Lake, Oak Hollow Lake, and Falls Lake, that were sampled from leveraged funding (see description below). We also tested one primer set for the Cylindrospermopsis raciborskii pks gene (Schembri, et al. 2001)

PCR Assays

PCR assays were run on an AB StepOne™ real-time PCR system using 48-well plates. Each plate included three negative controls (no template), 18 samples run in duplicate, and positive control standards which consisted of triplicate samples of three 10-fold serial dilutions of the standard. Preliminary PCR runs established appropriate dilutions of isolated genomic DNA and standards. Reaction mixtures (20 μl) included 20 μl AB Power SYBR Green PCR master mix, 7  μl dd H2O, 1 μl of a 10 μM solution of each primer, and 1 μl of the template, standard (positive control) or water (negative control). Reaction conditions were: 10m at 95ºC; 35 cycles of 95ºC for 15s, 60ºC for 30s, 72ºC for 1m, 80ºC for 15s with detection on; followed by a melt curve analysis. The added step of raising the temperature to 80ºC before fluorescence detection reduces the likelihood of false positives, Further, examination of the melt curve is a quality control step to eliminate false positives.

The results of our PCR assays (Figures 2,3) indicate widespread distribution of the targeted cyanobacteria and considerable variability in abundance across lakes and over a single season. The relative abundance of OTU 23 in 27 reservoirs over three summers spanned more than six orders of magnitude (Fig. 2). Generally, reservoirs with high amounts of the target in one year exhibited the same pattern in other years, although clear peaks (> 10-fold increases) were often evident. Relative abundance of the C. raciborskii pks gene also varied across lakes: it was absent from some lakes and common over multiple years in others (Fig. 2).

Assays of 30 OTU targets over a year in City Lake (Fig. 3) also indicated that relative abundance of OTUs was widely variable. Two patterns of distribution over the year seemed evident: relatively constant abundance throughout the year (e.g. OTUs 14, 23, 36, 87, and 89) or high abundance from June through October, with low abundance in winter and spring (e.g. OTUs 44, 59, 63, 98, and Cylindrospermopsis raciborskii and Psuedanabaena sp.). Despite these apparent patterns of distribution, there were few correlations of OTU abundance with environmental parameters.

There were a few significant correlations among 7 OTU targets and environmental parameters that were tested in three lakes over an annual season (Table 3). Three OTUs showed no correlations, but four showed significant positive correlation with suspended solids and negative correlations with TN:TP ratio. Two of these OTUs also showed a negative correlation with total organic carbon. Only one OTU had a positive correlation with total phosphorus. There were no significant correlations found across 27 reservoirs between OTU23 or the C. raciborskii pks gene and seven environmental parameters (temperature, pH, dissolved oxygen, conductivity, orthophosphate, chlorophyll, secchi depth).

PCR arrays

During the course of this study we shifted our focus from developing a classic silica slide microarray format to developing a PCR array format. This change came about for multiple reasons, including drawbacks to use of the microarray and advantages inherent the emerging PCR array format. First, in extensive consultation with our end user (a municipal water quality laboratory supervisor) and visits to his laboratory it became apparent that the silica slide microarray format was likely not translatable to the municipal water quality laboratory based on the level of technological expertise required and the cost of equipment and supplies. Our experience with microarrays suggested that a high level of expertise was needed, especially for the hybridization step to be completed with consistent quality. Many municipal water quality laboratories do not have budgets that allow employment of technicians with the level of training and experience to use microarrays effectively. Second, while the cost of purchasing pre-made microarray slides may not have been prohibitive once they are mass produced, the commitment of technician time, array reader costs, and reagents required would be marginally affordable. In contrast, by the second year of this grant PCR arrays had developed to the extent that we began to see distinct advantages to their use for cyanobacterial testing. These included: 1) the level of technical expertise was not as high as that required for DNA microarrays. Indeed, we trained both MS candidates and undergraduate students in the three phases necessary for this approach (DNA extraction, quantitative real-time PCR assay, and data review) in just a few weeks. 2) The cost of PCR arrays is lower than the cost of slide microarrays. Not only does it require a less skilled technician, but the PCR array instrument has comparable or lower cost than an Array Scanner, and fewer chemicals are required since there is no hybridization step. 3) The results of PCR arrays can be generated in near real-time. Once a water sample is taken, DNA can be extracted in as little as 1 hour (method dependent) and the PCR assay run takes from 1-3 hours. Thus, samples can be taken and results determined in less than a day, compared to slide microarrays which generally use and overnight hybridization step. 4) PCR arrays are quantitative, while slide microarrays are semi-quantitative at best. PCR arrays compare samples to standards run concurrently with each assay. While this is also possible for the slide microarray, it is technically more challenging. 5) There are multiple quality control points in the PCR array. First, both negative controls and positive control standards are included in each run. Second, a melt curve of the PCR products can be included in each run – the melt curve is a check against false positives, since the melting temperature (Tm) the product is different if an unintended target is amplified. 6) PCR arrays technology is developing rapidly. 7) Finally, PCR arrays offer the possibility of multiplexing, thereby further reducing costs and increasing the number of targets that can be evaluated in each run.

Next steps

In this study we developed primers and protocols that can be used for cyanobacterial PCR arrays by municipal or industrial water quality laboratories. Our approach has been to keep the “end-user” in mind, making an effort to minimize the level of training and resources necessary for routine use. Producing a commercial PCR Array will ultimately involve partnering with industry, such as SA Biosciences, which currently produces PCR arrays for mammalian gene expression, in order to optimize QA and QC. Currently the largest plate format for PCR arrays is 1536-well plate (Roche Diagnostics), although the capabilities are expanding rapidly. In the case of assaying for cyanobacteria, this may not be a limiting constraint, at least for detection of species of primary interest. There are several next steps to take in order to turn this approach into a viable commercial water testing approach:

1. Develop multiplexing capability: Multiplexing by using specific fluorescent dyes will allow up to a 4-fold increase in the number of targets that can be assayed in a single run. In many cases this is problematic because primers have different Tm’s. In our case we designed primers with similar Tm’s, so adaptation to multiplexing should be relatively easy.
2. As the number of known target sequences increases, it is likely that the number of targets may surpass the capacity of PCR plates even with multiplexing. Thus, consideration of a tiered approach is warranted. The tiered approach simply means that primary targets are built into an initial assay that can be followed by a choice of second tier of assays dependent upon the results of the initial assay.
3. Linkage with non-cyanobacterial microbial targets. The development of probes to microbial targets other than cyanobacteria is moving ahead rapidly, along with a growing recognition that metadata collection is necessary to define the environments in which microbes compete successfully. This also suggests that community profiles can be used as predictors of cyanobacterial blooms. Thus, relating microbial community profiles to cyanobacterial abundance is a promising avenue for further research.
4. Finally, a continuing “thorny” issue is strain differentiation. In the case of cyanobacteria this relates to the toxicity of strains and the potential for lateral gene transfer among them. Our understanding of these issues is not resolved, and continued attention to the specificity of primers and probes is essential for long term success of this approach. We note, however, this has not created an insurmountable obstacle since the literature has system for the presence of potentially harmful cyanobacteria and their toxins which should be of use to water supply managers in order to reduce health risks. To date we have produced prototype components that can serve as the basis for development of a more “user-friendly” real-time (or near real-time) monitoring platform. demonstrated valid data for many taxa. Thus, it is still mostly a concern, rather than a roadblock. Continuing expansion of sequence databases and continued development of analytical tools will help minimize the importance of this consideration.

The development of an array to detect cyanobacteria and cyanotoxin genes can provide multiple functions: a research tool for aquatic scientists to identify factors that promote growth of different cyanobacterial species and their toxins; a prototype early warning system for the presence of potentially harmful cyanobacteria and their toxins which should be of use to water supply managers in order to reduce health risks. To date we have produced prototype components that can serve as the basis for development of a more “user-friendly” real-time (or near real-time) monitoring platform.

Leveraging and outputs: The following accomplishments are due in part to EPA grant #RD083162701-0: Cyanobacteria and cyanotoxins in water supply reservoirs.

Leveraged Funding: Based in part on the funding from this EPA Grant, we applied for and received funds to accelerate the development of a practical application assessment of microbial bioindicators in aquatic ecosystems from the Univ. of NC Competitiveness Research Fund. This has allowed continued sampling of the water supply reservoirs and the installation of automated nutrient sampling devices in these systems.

Integration of Novel Technologies for Safeguarding Potable Water Supplies. $292,010, 12/3/07 – 9/30/08. (Principal Investigators: Parke Rublee, Vincent Henrich, JoAnn Burkholder)

Business Collaboration: The principal investigators and the University have been working with business partners to spin-off a company to further develop and utilize the technology that is being developed as part of the current and past EPA research funding. Helical Sciences, Inc. has been negotiating a licensing agreement with UNCG, and seeking additional funding from the North Carolina Biotechnology Center to advance the commercial development of the technology. Additionally, through Helical Sciences and Southeast TechInventures, a company located in the NC Research Triangle, one Phase I SBIR grant was received and two SBIR Phase II grants were submitted during summer 2008 to NSF and CDC. Although these SBIR grants were not directed at cyanobacteria or cyanotoxins, they were developed based on using the technological approaches that we have been using in the EPA funding.

End user collaboration: We have worked closely with Mr. William Frazier, Water Quality Laboratory Supervisor for the city of High Point, NC. During this study we met frequently with Mr. Frazier to coordinate sampling, discuss his needs in relation to water quality assessment and management, and to provide continued input on our approach. This is an ongoing collaboration.

Table 1. List of operational taxonomic units (OTUs) found in this study with best GenBank match (accession #, description, source location, and % similarity) to the consensus OTU sequence. Taxonomic identification given where possible. Similarity values ≥ 98% are noted in bold. Note that OTUs 16, 71, and 99 are likely eukaryotic chloroplast 16S ribosomal genes.

OTU	Accession	Description		% similarity	ID
1	AJ007864.1	unidentified cyanobacterium clone LD7	Lake Loosdrecht, Netherlands	96%	Prochlorothrix hollandica
2	AF516745.1	Cylindrospermopsis raciborskii strain Florida F	Florida, USA	100%	Cylindrospermopsis raciborskii
3	AF330249.1	Synechococcus sp. LBG2	Lake Biwa, Japan	99%	Synechococcus sp.
4a	EU273090.1	Uncultured bacterium clone TH_d324	Lake Taihu, China	99%
4b	GU131246.1	Uncultured bacterium clone LK15m-37-16S	Lake Kinneret, Israel	99%
5	FJ204874.1	Uncultured cyanobacterium clone NK2_CYA_2_1	Lake Kastoria, Greece	98%	chloroplast - likely cryptomonas
6	EF633015.1	Uncultured cyanobacterium clone H1w-5	Salar de Huasco, Chile	92%
7	DQ158169.1	Uncultured cyanobacterium clone LK1mC-7	Lake Kinneret, Israel	97%	Synechococcus sp.?
8	EU078536.1	Aphanizomenon issatschenkoi LMECYA 190	Maranhão Reservoir, Portugal	100%	Aphanizomenon issatschenkoi
9	EU592776.1	Uncultured bacterium clone MFBC7A05	Tucurui Reservoir, Brazil	95%	Synechococcus sp.?
10	FJ745161.1	Uncultured cyanobacterium clone SHWN_night2_16S_697	estuary, Gerogia, USA	93%
11	EU168191.1	Heterosigma akashiwo strain CCMP 452 chloroplast	Long Island Sound, USA	92%	Heterosigma akashiwo chloroplast
12	EF568905.1	Anabaena sp. XPORK15F	Gulf of Finland	96%	Anabaena sp.
13	DQ166477.1	Uncultured eukaryote clone ML-9-3 plastid	Lake Taihu, China	99%
14	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	97%
15	EU283208.1	Uncultured bacterium clone EDP-26	pond, Dongshan Island, China	91%
16	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	94%
17	FJ204854.1	Uncultured cyanobacterium clone ND2_CYA_1_8	Lake Doirani, Greece	97%	Psuedanabaena sp.?
18	DQ393278.1	Spirulina laxissima strain SAG 256.80	Laka Nakuru, Kenya	95%	Spirulina sp.?
19	AJ007864.1	unidentified cyanobacterium clone LD7	Lake Loosdrecht, Netherlands	92%
20	EU592812.1	Uncultured bacterium clone MFBC10C06	Tucurui Reservoir, Brazil	95%
21	FJ490330.1	Uncultured bacterium clone H_10	Dry Valley, Antarctica	92%
22	EU552070.1	Cylindrospermopsis raciborskii (Raphidiopsis sp. D9)	Billing reservoir, Brazil	95%	Cylindrospermopsis sp.?
23	FJ204854.1	Uncultured cyanobacterium clone ND2_CYA_1_8	Lake Doirani, Greece	98%
24	FJ490330.1	Uncultured bacterium clone H_10	Dry Valley, Antarctica	93%
25	FJ204843.1	Uncultured cyanobacterium clone ND2_CYA_4_32	Lake Doirani, Greece	91%
26	EU078524.1	Anabaena spiroides LMECYA 161 C20	Agolada de Baixo Reservoir, Portugal	97%	Anabaena sp.
27	EU168191.1	Heterosigma akashiwo strain CCMP 452 plastid	Long Island Sound, USA	89%
28	FM242084.1	Anabaena mendotae 04-45	Svet fishpond, Czech Republic	94%
29	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	95%
30	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	96%
31	EU552068.1	Cylindrospermopsis raciborskii CS-511	McKinlay farm dam, QLD, Australia	94%
32	EU409863.1	Uncultured cyanobacterium clone 16L6	Georgia, USA	92%
33	EU273090.1	Uncultured bacterium clone TH_d324	Lake Taihu, China	99%
34	AY742448.1	Nostoc sp. 8941	New Zealand	95%
35	AY328614.1	Uncultured bacterium HOClCi65	drinking water simulator, USA	98%
36	FJ204841.1	Uncultured phototrophic eukaryote clone ND2_CYA_1_22 plastid	Lake Doirani, Greece	95%
37	FJ745161.1	Uncultured cyanobacterium clone SHWN_night2_16S_697	estuary, Gerogia, USA	93%
38	AJ007864.1	unidentified cyanobacterium clone LD7	Lake Loosdrecht, Netherlands	94%
39	EU552070.1	Cylindrospermopsis raciborskii (Raphidiopsis sp. D9)	Billing reservoir, Brazil	95%
40	EF395686.1	Uncultured bacterium clone CBM01H09	Chesapeake Bay, MD, USA	95%
41	EU552070.1	Cylindrospermopsis raciborskii (Raphidiopsis sp. D9)	Billing reservoir, Brazil	96%
42	EU592798.1	Uncultured bacterium clone MFBC5F11	Tucurui reservoir, Brazil	98%
43	FJ204841.1	Uncultured phototrophic eukaryote clone ND2_CYA_1_22	Lake Doirani, Greece	92%
44	EU168191.1	Heterosigma akashiwo strain CCMP 452 plastid	Long Island Sound, USA	87%
45	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	97%
46	FJ204874.1	Uncultured cyanobacterium clone NK2_CYA_2_1	Lake Kastoria, Greece	92%
47	AJ133160.1	Anabaena sp. strain A277	River Perniönjoki, Finland	94%
48	EU980182.1	Uncultured cyanobacterium clone TH_f27	Lake Taihu, China	92%
49	EU255706.1	Uncultured cyanobacterium clone Mat-CYANO-S8	freshwater mat, CA, USA	92%
50	AY328614.1	Uncultured bacterium HOClCi65	drinking water simulator, USA	95%
52	EU283259.1	Uncultured bacterium clone PVP-121	pond, Dongshan Island, China	92%
53	FJ204890.1	Uncultured cyanobacterium clone NV1_CYA_1_12	Lake Volvi, Greece	95%
54	EU592777.1	Uncultured bacterium clone MFBC9A02	92%
55	FJ638592.1	Uncultured bacterium clone KCLunmb_25_16	spring sediment, Taiwan	92%
56	AY742448.1	Nostoc sp. 8941	New Zealand	96%
58	EU078535.1	Aphanizomenon issatschenkoi LMECYA 166	Vale Michões Reservoir, Portugal	96%	Aphanizomenon sp.
59	EU552070.1	Cylindrospermopsis raciborskii (Raphidiopsis sp. D9)	Billing reservoir, Brazil	97%
60	AJ006286.1	Unidentified cyanobacterium clone LD25	Lake Loosdrecht, Netherlands	95%
61	EU641407.1	Uncultured cyanobacterium clone LW9m-1-3	Lake Michigan, WI, USA	93%
62	EF632984.1	Uncultured cyanobacterium clone H6w-73	Salar de Huasco, Chile	94%
63	FJ999606.1	Uncultured cyanobacterium clone B97	South China Sea, China	94%
64	EU592816.1	Uncultured bacterium clone MFBC10H09	Tucurui reservoir, Brazil	95%
65	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	97%
66	EU592789.1	Uncultured bacterium clone MFBC2C08	Tucurui reservoir, Brazil	99%
67	DQ158169.1	Uncultured cyanobacterium clone LK1mC-7	Lake Kinneret, Israel	94%
68	EU273090.1	Uncultured bacterium clone TH_d324	Lake Taihu, China	93%
69	EF632962.1	Uncultured cyanobacterium clone H4s-61	Salar de Huasco, Chile	93%
70	AY948063.1	Uncultured phototrophic eukaryote clone PRD18G11 plastid	Parker River, MA, USA	99%
71	DQ158169.1	Uncultured cyanobacterium clone LK1mC-7	Lake Kinneret, Israel	93%
72	EU980295.1	Uncultured cyanobacterium clone TH_h52	Lake Taihu, China	95%
73	U70724.1	Unidentified cryptomonad OM283	Cape Hatteras, NC, USA	94%
74	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	98%
75	EU375434.1	Uncultured bacterium clone P4O-58	Puma Yumco Lake, Tibet, China	94%
76	GQ091396.1	Uncultured bacterium clone nbw345e08c1	human skin bacterium	97%	Synechococcus sp.?
77	EF520521.1	Uncultured cyanobacterium clone ADK-SGh02-76	Adirondack Lake, NY, USA	97%
78	AJ007864.1	unidentified cyanobacterium clone LD7	Lake Loosdrecht, Netherlands	94%
79	AJ133160.1	Anabaena sp. strain A277	River Perniönjoki, Finland	95%
80	EF395634.1	Uncultured bacterium clone CBM01C08	Chesapeake Bay, MD, USA	93%
81	EF395634.1	Uncultured bacterium clone CBM01C08	Chesapeake Bay, MD, USA	93%
82	EU642323.1	Uncultured Verrucomicrobiales clone LW18m-2-26	Lake Michigan, WI, USA	98%
83	FJ933264.1	Uncultured proteobacterium clone REG_R2P1_F7	wastewater tretment plant, Brazil	95%
84	AY858017.1	Uncultured cyanobacterium clone Erie 24	Lake Erie, USA	94%
85	AY541558.1	Uncultured Antarctic cyanobacterium clone FreP30	Pond, McMurdo Shelf, Antarctica	95%
86	FJ204874.1	Uncultured cyanobacterium clone NK2_CYA_2_1	Lake Kastoria, Greece	93%
87	DQ158166.1	Uncultured cyanobacterium clone LK1mC-3	Lake Kinneret, Israel	97%	Synechococcus sp.?
88	EF471533.1	Uncultured Synechococcus sp. clone CB22A07	Chesapeake Bay, MD, USA	95%
89	AM709625.1	Prochlorothrix hollandica SAG 10.89	Lake Loosdrecht, Netherlands	93%
90	FJ546713.1	Nodularia spumigena GSL023 e	Great Salt Lake, UT, USA	98%	Nodularia spumigena
81	AJ007864.1	unidentified cyanobacterium clone LD7	Lake Loosdrecht, Netherlands	91%
92	FJ916323.1	Uncultured cyanobacterium clone GR1G3	Green Lake, WI, USA	94%
93	AY948073.1	Uncultured phototrophic eukaryote clone PRD18H11	Parker River, MA, USA	94%
94	FJ204874.1	Uncultured cyanobacterium clone NK2_CYA_2_1	Lake Kastoria, Greece	91%
95	FJ204890.1	Uncultured cyanobacterium clone NV1_CYA_1_12	Lake Volvi, Greece	99%
96	FJ916557.1	Uncultured cyanobacterium clone MI1F2	Mirror Lake, WI, USA	95%
97	DQ158169.1	Uncultured cyanobacterium clone LK1mC-7	Lake Kinneret, Israel	98%	Plectonema (Leptolyngbya) sp.
98	EF114678.1	Chlorella sp. SUN-2 plastid	China	98%
99	FN297771.1	Uncultured bacterium clone Gerber_L7-E3-T7	Lake Gerber, Spain	97%
100	EU592544.1	Uncultured bacterium clone IFBC1C11	Tucurui reservoir, Brazil	96%

Table 2. PCR Primers generated during this study. Upper: Primer sets with a forward primer to a single OTU paired with equimolar amounts of reverse primers CYA781Ra and CYA781Rb. Lower: Forward primers designed to multiple OTUs followed by the unique OTU reverse primers.

A.

OTU	Forward Primers	Length	% GC	Tm	Amplicon
OTU02	GGTGAAAGATTTATCGCCTGGAGATGA	27	44.4	63.45	614
OTU04(b)-33	CCTTAGGAGGAGGATACAGCT	21	52.4	59.82	668
OTU05a	GGATGTATCCACCTTAGGAAGAGCT	26	46.2	63.22	607
OTU11F (a)	CGGAAACGACTGCTAATACCTTATATG	27	40.7	61.93	647
OTU12	GGTAGTGTAAGAGACAACCAAGG	23	47.8	60.65	565
OTU13-70F	GGATTTATCTACCTTAGGAAGAGCTC	26	42.3	61.65	606
OTU15F (a)	CCTCTGCCTGAAGAGAAGCT	20	55.0	59.35	584
OTU18F (a)	GGTTGGGACAACCATTGGAAAC	22	50.0	60.25	663
OTU23 (b)	CCTACAGACTCGGGACACAG	20	60.0	61.40	669
OTU25F (a)	CCTCTAGGAAAGGGATAACAATCG	24	45.8	61.01	669
OTU26	CCCTCAGGTCGGGGACAACCA	21	66.7	65.68	669
OTU27F (b)	CCTTTAGGAAAGGGACACAATTGGAAAC	28	35.7	60.73	669
OTU32F	CCCTAGGGTGAAAGATTAATTGCCA	25	44.0	61.34	622
OTU36	GGATGACAGCCCTTGGGTTGTAAA	24	50.0	62.72	405
OTU38	CCTAGTCGGGGACAACAGTTGGAA	24	54.2	64.43	668
OTU39F	GCTCAGTCGGGATAACAGTTGAA	23	47.8	60.65	670
OTU40F	CGGGATACCGCCTGAGAATGA	21	57.1	61.78	607
OTU41F (b)	GAGATCTGCTCCAAGGTCGG	20	60.0	61.40	678
OTU42F (b)	CCTGAAAACGGCCCGCCT	18	66.7	60.52	648
OTU43F	GAAACGATGCTAATACCCCATATGCT	26	42.3	61.65	650
OTU44F	CCTTTAGGAAAGGGATACAATCGGAA	26	42.3	61.65	669
OTU52F	GCTCAGGAGGGAATAACGCTGA	22	54.5	62.12	670
OTU53F	CGCGGGAGGAAGGTTTTAGGA	21	57.1	61.78	412
OTU58F (a)	CCTTCAGGTTTGGGACAACCAC	22	54.5	62.12	669
OTU59F	GCTTCCAGTCGGGGATACAGTT	22	54.5	62.12	668
OTU63F	CTGCTTCCAGGTCGGGGATAA	21	57.1	61.78	672
OTU64	GGATGTAGGCCTCTGGGCT	19	63.2	60.98	410
OTU68	CCTTAGGAGGAGGATAACAGCT	22	50.0	60.25	668
OTU71F	GGTTAATTCTGCCTAGGATGAGCT	24	45.8	61.01	608
OTU79F	CCGTAGGTCGGGGACAACA	19	63.2	60.98	668
OTU82	CGCTTTGAGCTAATAGTTCAAAGCCT	26	42.3	61.65	365
OTU83	CAGCTAGTTGGCGAGGTAAC	20	55.0	59.35	573
OTU84F (a)	GGATGTAAACTTCGCAAGTATGGGAA	26	42.3	61.65	399
OTU88	GGACGAAGGCTTACTGAGTTGTA	23	47.8	60.65	408
OTU89F	GGTTTATCGCCTGAAGATGAGCT	23	47.8	60.65	608
OTU90F	GTGAAAGGTTAATCGCCTGAAGGT	24	45.8	61.01	614
OTU91	GCTTCAGGTCGGGGACACACT	21	61.9	63.73	669
OTU92F	GGGATACAGAAGGAAACTACTGCT	24	45.8	61.01	658
OTU93	GTGAGGGAGGAAGGTTTTAGGACTGTAAACCA	32	46.9	68.21	699
OTU97F (d)	GAGATGGGCTTGCGGCTGAT	20	60.0	61.40
OTU98F	CCTCTAGGAAAGGGATACAATCGGAA	26	46.2	63.22	669
CYA781R(a)	GACTACTGGGGTATCTAATCCCATT	25	44.0	61.34
CYA781R(b)	GACTACAGGGGTATCTAATCCCTTT	25	44.0	61.34

 

B.

OTU	Forward Primers	Length	G/C %	Tm	Amplicon
F53-01/09/10/24/31/34/56/60/69/94	GGAAACGACTGCTAATACCCGATGT	25	48.0	62.98
R431-01	GCCTACGAACGCTTTACGCCCAA	23	56.5	64.21	390
R293-09	GGYTTACAGCCCAGAGGCCTT	21	57.1	61.78	260
R388-10	GTGATTCCGGATAACGCTTGCATCCTCTGTA	31	48.4	68.17	367
R422-24	CACYTACAGACGCTTTACGCCCA	23	52.2	62.43	389
R294-31	GGTTTACGACCCAAGAGCCTT	21	52.4	59.82	261
R421-34	CCTGCGGACTCTTTACGCCCAA	22	59.1	63.98	389
R421-56	CCACCTACAGACCCTTTACGCCCAA	25	56.0	66.26	392
R419-60	GTTCCACCTGCAGACCCTTTACGCCCAA	28	57.1	69.51	393
R416-69	CCTACGGACGCTTTACGCCCAATGATT	27	51.9	66.49	389
R131-94	CCTTGGTAAGCCATTACCCTACCAA	25	48.0	62.98	102
F62-04a_74/14/16/29/30/45/65/78/81	GCTAATACTCTATATGCCGAGAGGT	25	44.0	61.34
R292-04(a)-74	GGTTTACAACCCACAGGCTTTCAT	24	45.8	61.01	253
R292-14	GAGGTTTACAACCCACAGGCTGTCAT	26	50.0	64.80	254
R169-16	GCTGATCATCCTCTTAGACCAGCTA	25	48.0	62.98	130
R294-29	GAGGTTTACGACCCAAGAGCCTT	23	52.2	62.43	253
R402-45	CCAATAATTCCGGATAACGCTTGCAT	26	42.3	61.65	364
R412-30	CCTTTACGCCCAATCATTCCGGAT	24	50.0	62.72	372
R295-65	GAGGTTGACAACCCACAGGCCTT	23	56.5	64.21	254
R295-78	GAGGTTTACAACCCACAGGCCTT	23	52.2	62.43	254
R400-81	CCAATAATTCCGGATAACGCTCGCCT	26	50.0	64.80	362
F36-05a/05b/46/87/95	GGAAGAGAATACAATTGGAAACGGTTGCTA	30	40.0	64.03
R289-05a	GGTTTACAACCCACAGGCCTTCATCCCT	28	53.6	68.05	280
R288-05b	GGTTTACAACCCACAGGCCGTCATCCCT	28	57.1	69.51	279
R288-46	GGTTTACAACCCATAAGGCAGTCATCCCT	29	48.3	66.68	280
R313-87	GTCAGTGCTTCTTCCCTGAGAAAAGTGGT	29	48.3	66.68	305
R313-95	GTCAGAGCTTCTTCCCTGAGAAAAGCGGT	29	51.7	68.09	305
F59-08_57/47/80	GTGGCTAATACCGAATGTGCCGA	23	52.2	62.43
R409-57-08	CCCTTWACGCCCAATCATTCCGGATAA	27	48.1	64.97	373
R406-47	GCTTTACGCCCAATAATTCCGGATAA	26	42.3	61.65	371
R406-80	GCTTTACGCCCAGTGATTCCGGATAA	26	50.0	64.80	371
F30-07_96/67/76/100	GCCCTAGGAGGGGGACAA	18	66.7	60.52
R297-07_96	GAGGTTTACAACCCTAAGGCCTT	23	47.8	60.65	289
R295-67	GAAGTTTACAATCCACAGACCGTCT	25	44.0	61.34	289
R297-76	GAGGTTTACAACCCACAGGCCTT	23	52.2	62.43	289
R288-100	CCAAGAGCCTTCCTCCCTCA	20	60.0	61.40	277
F30-17	CCTACAGACTCGGGGACAAAC	21	57.1	61.78
R411-17	CCTTTACGCCCAATCATTCCGGA	23	52.2	62.43	424
F84-20/22_51/35/50	GGTGAAAGATTTATCGCCTGGAGAT	25	44.0	61.34
R335-20	GGCTTATTCATCAAGTACCGTCAGA	25	44.0	61.34	274
R405-22_51	GCCCAGTGATTCCGGATAACG	21	57.1	61.78	340
R304-35	CCCTGAGAAAAGAGGTTTACAACC	24	45.8	61.01	242
R292-50	GCGGTTTACAGTCCTAAAACCTTC	24	45.8	61.01	230
F30-23	CCTACAGACTCGGGACACAG	20	60.0	61.40
R408-23	GCTTTACGCCCAATAATTCCGGA	23	47.8	60.65	400
F30-61	CCTACAGACTCGGGACAACAGT	22	54.5	62.12
R413-61	GCTTTATGCCCAGTGATTCCGGA	23	52.2	62.43	403
F81-19/49	GGAGGTGAAAAGAGTTTTGCCTA	23	43.5	58.87
R293-19	GCGGTTTACAGTCCTAAAACCTT	23	43.5	58.87	234
R323-21/06/49	GCTACCGTCATTATCTTCACAGA	23	43.5	58.87	263
F30-53/86	GCCTTAGGAGGGGGACACA	19	63.2	60.98
R349-53	GGAATTAGCCGATGCTTATTCCTCA	25	44.0	61.34	331
R303-86	CCAGAGAAAAGAGGTTTACAACCCTA	26	42.3	61.65	298
F92-21	GGTTAACTGCCTGAGGGTGA	20	55.0	59.35
R323-21/06/49	GCTACCGTCATTATCTTCACAGA	23	43.5	58.87	252

 

Table 3. Significant correlation coefficients between target OTUs and environmental parameters in City, Oak Hollow, and Falls lakes, NC over the period Dec 2007 – Dec 2008. *= p<0.95, ** = p<0.99

	OTU11	OTU12	OTU15	OTU17	0TU18	OTU19	OTU61
Suspended solids	0.415**			0.519**		0.641**	0.302*
Total Kjeldahl Nitrogen
Nitrate + Nitrite
Total Nitrogen
Total phosphorus						0.465**
TN:TP ratio	-0.390*			-0.393*		-0.605**	-0.284*
Ammonia
Chlorophyll a
Total organic carbon				-0.369*		-0.643**

 

Figure 1. Rank abundance curves of OTUs found in this study. Top figure is combined for all six source lakes. Individual lakes shown below.

Figure 2. Relative abundance of OTU 23 (uncultured cyanobacterial clone) and Cylindrospermopsis raciborskii pks genes in 27 NC water supply reservoirs. Most points are average of samples from 3 summer months (June, July, August) for each year. ND = not determined.

Figure 3. Abundance of selected OTUs in City Lake over an annual cycle.

References:

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Publications Views

Other project views:	All 33 publications	6 publications in selected types	All 2 journal articles

Publications

Type	Citation	Project	Document Sources
Journal Article	Marshall MM, Amos RN, Henrich VC, Rublee PA. Developing SSU rDNA metagenomic profiles of aquatic microbial communities for environmental assessments. Ecological Indicators 2008;8(5):442-453.	R831627 (2007) R831627 (Final)	Abstract: Science Direct abstract Exit Other: UNC PDF Exit
Journal Article	Touchette BW, Burkholder JM, Allen EH, Alexander JL, Kinder CA, Brownie C, James J, Britton CH. Eutrophication and cyanobacteria blooms in run-of-river impoundments in North Carolina, U.S.A. Lake and Reservoir Management 2007;23(2):179-192.	R831627 (Final)	Abstract: Informaworld abstract Exit Other: Mendeley abstract Exit

Supplemental Keywords:

RFA, Scientific Discipline, INTERNATIONAL COOPERATION, Water, Environmental Chemistry, Health Risk Assessment, Environmental Monitoring, Environmental Engineering, Drinking Water, microbial contamination, microbial risk assessment, monitoring, real time analysis, gene microarray assay, aquatic organisms, other - risk assessment, early warning, drinking water contaminants, drinking water system

Relevant Websites:

Real time physical and chemical data from some of our study sites can be found at the Center for Applied Aquatic Ecology website at NCSU: http://www.ncsu.edu/wq/

Progress and Final Reports:

Original Abstract

2005 Progress Report

2006 Progress Report

2007 Progress Report