deliverable #6: final report contract #28108, molecular ... · contract #28108, file code:...

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Deliverable #6: FINAL REPORT Contract #28108, Molecular Tool Development and Implementation to Advance

Monitoring of Pico- and Nano-Planktonic Algae in the Indian River Lagoon Lead PI: Alina Corcoran, Research Scientist, Florida Fish and Wildlife Conservation Commission/ Fish and Wildlife Research Institute (FWC), 100 8th Ave. SE, St. Petersburg FL 33701, [email protected], (727) 892-4156 Co- PI: Katherine Hubbard, FWC, [email protected], (727) 502-4961 Co-PI: Margaret Lasi, Environmental Scientist V, St. Johns River Water Management District (SJRWMD), PO Box 1429, Palatka FL 32178, [email protected], (386) 329-4825 Co-PI: Edward Phlips, Professor, Fisheries and Aquatic Sciences Program, University of Florida (UF), 7922 NW 71st Street, Gainesville, FL 32653, [email protected], (352) 273-3603 Others: Eric Muhlbach (FWC), Charles Tilney (FWC), Steven Bruzek (FWC), Susan Murasko (FWC), Lauren Hall (SJRWMD), Susan Badylak (UF) I. Introduction In the last half-century, regulatory agencies have attempted to reverse environmental degradation in the Indian River Lagoon (IRL). Management and restoration efforts, aided by periods of low rainfall contributed to improved water quality and seagrass habitat until 2011, when a “superbloom” of a small green alga destroyed over 40% of established seagrass beds in the area and affected seagrass-dependent fisheries and wildlife (Phlips et al., 2015). This green tide reached unprecedented biomass (up to 136 µg L-1 chlorophyll a) and marked a dramatic size and functional shift in the composition of bloom forming algae -- from micro-plankton (20-200 µm) to pico- (0.2-2 µm) and nano- (2-20 µm) plankton. In 2012, the first documented brown tide caused by the nanoplankton Aureoumbra lagunensis further affected the northern IRL system (Banana River excluded), with maximum recorded biomass (196 µg L-1 chlorophyll a) surpassing values from green tide recorded the year before. The presence and recurrence of blooms of pico- and nanoplankton in the last few years may reveal a shift in the IRL ecosystem that requires agencies to evaluate monitoring practices so that bloom taxa can be captured during routine and event response monitoring efforts. Currently, monitoring of phytoplankton in the IRL relies on in situ chlorophyll fluorescence via deployed or adaptive instrumentation, bulk measures of phytoplankton biomass (e.g., chlorophyll a concentrations) and microscopic identification of micro-, pico-, and nano-plankton (Fig. 1). These approaches have limitations in terms of identifying and enumerating specific pico and nanoplankton taxa of special interest, including bloom-forming species in the IRL. Neither chlorophyll fluorescence nor chlorophyll concentrations provide indicators of IRL phytoplankton community structure, and both approaches to assess biomass can underrepresent certain taxa (e.g., cyanobacteria), even during blooms. Microscopy can be inadequate for small size classes of algae because target species cannot be easily distinguished from similarly-sized, co-occurring algae. Often during routine monitoring, pico- and nano-plankton are placed in coarsely defined taxonomic groups (e.g., <15µm flagellates). Antibody and molecular probes hold promise for routine and event response monitoring in the IRL because of their sensitivity and demonstrated ability to rapidly distinguish and enumerate chlorophytes and pelagophytes in marine-influenced systems (Gobler et al., 2013; Koch et al., 2014; Not et al., 2008; Vigil et al., 2009). Because probes can hybridize to different targets (e.g.,

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Contract #28108, File Code: F4197-14-16-F Deliverable # 6, Final report

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cells, nucleic acids) and operate at specific taxonomic scales (e.g., population, species, genus, community), they can be used either to rapidly screen field samples for target taxa or to characterize community structure (Fig. 1). As such, tools can be designed and implemented to address specific monitoring and management goals. For example, DNA fingerprinting would be more appropriate to track community transitions and/or the presence/absence of target taxa during routine sampling. If monitoring agencies are instead interested in tracking a single organism during a bloom season or as part of event response efforts, species-specific probes can be used for identification and quantification. Another consideration in method selection is feasibility in implementation, as sample collection protocols, analysis time, sample throughput, and cost of each method varies (Table 1).

In this project, we aimed to assess the suitability of antibody probes and PCR-based approaches for routine detection of two target organisms: the pelagophyte Aureoumbra lagunensis and the chlorophyte originally identified as the pedinophyte Resultor sp. and later more broadly classified and described as a nanoeukaryote with flagellated and non-flagellated forms. We also explored the characterization and enumeration of other pico- and nano-plankton, including the chlorophytes. II. Approach and Methods We developed and/or validated existing nucleic acid-based and cell-based assays to identify and enumerate the target taxa, relying on established phytoplankton cultures as well as field-collected samples. Eighteen phytoplankton cultures representing target and non-target taxa were obtained from collections at UF, FWC, the National Center for Marine Algae and Microbiota (NCMA, formerly the CCMP), and the University of Texas (UTEX). Between January 2014 and September 2015, the SJRWMD and UF conducted routine sampling twice monthly at site 1.5, site 2, and site 3 (Fig. 2), resulting in the collection of 53 samples, not including replicates. These three sites were selected because they are within the SJRWMD long-term plankton monitoring network and lie within the superbloom area. Additional water samples were collected by all agencies (SJRWMD, UF, FWC) in response to events and during FWC’s flow-through sampling efforts.

Figure 1. Tools available for characterizing and quantifying phytoplankton and HAB taxa in natural systems. The traditional approach using microscopy (blue box) is contrasted with new tools using probes and flow cytometry (green box).


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Figure 2. Map of the Indian River Lagoon system showing routine monitoring stations.

Nucleic Acid-Based Assays Prior work by Vigil et al. (2009) found that DNA fingerprinting of eukaryotic phytoplankton was successful in resolving Aureococcus anophagefferens, a pelagophyte closely related to A. lagunensis, as well as other eukaryotes present during Aureococcus blooms. In silico and lab testing showed that the same genetic approach was unable to resolve IRL bloom-forming taxa from other species, even at higher taxonomic levels (see also Kim et al. 2012). To address this challenge, we obtained additional chlorophyte and pelagophyte sequences; explored, designed, and/or tested >15 additional probes and primers (Table A1); and validated specific primer sets and/or probes using a combination of PCR-based approaches. For A. lagunensis, we generated a pelagophyte sequence database from publicly available sequences, IRL bloom samples collected from the Mosquito Lagoon during 2012, and A. lagunensis cultures isolated from marine systems in Texas and acquired from the NCMA and UTEX collections. Since sequence data were not available for either A. lagunensis isolate, we used direct sequencing of PCR products to obtain partial 18S rRNA gene sequences (898 bp) for both isolates. Direct sequencing was conducted on the 2012 bloom samples using eukaryotic-specific 18S rRNA primers (Vigil et al., 2009) coupled with a newly-designed pelagophyte-specific primer Aureo933R (Table A1). The sequence database permitted in silico analysis of sequence variability in the 18S rRNA gene, which informed the design and lab-based testing of genus-specific pelagophyte probes for Aureoumbra and Aureococcus, as well as a strain-specific probe targeting the insert shared in sequences of A. lagunensis from the UTEX isolate and IRL field samples. A suite of more than 10 probes (Table A1) was tested to evaluate 18S rRNA probe specificity using cultures of Aureoumbra and/or Aureococcus, as well as bloom samples. For the chlorophytes, we obtained and aligned 18S and ITS1 rRNA sequences from a June 21, 2011 Banana River sample that was dominated by nanoeukaryotes with respect to biomass (45.8%; 258,164 cells mL-1), and from public databases including NCBI, Silva/ARB and the Ribosomal Database Project. For direct sequence analysis of the IRL sample, a chlorophyte-specific/universal eukaryote primer set was utilized. Sequences were selected to represent inter- and intraspecific diversity across an array of classes, including members of the Pedinophyceae. The resulting sequence alignments were used to identify conserved and variable regions within the small subunit rRNA operon to inform further probe development and testing. Following in silico analysis of rRNA sequence variability, we determined that universal probes (for eukaryotes and/or chlorophytes) were generally inadequate for PCR-amplification of taxa typically observed in the IRL. Taxa-specific probes and/or enzymes were necessary to discriminate these taxa within a mixed assemblage consisting of multiple phyla, classes, genera,


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species, etc. A DNA fingerprinting approach targeting chlorophyte communities was designed to incorporate newly and previously designed probes for PCR amplification, followed by digestion with the restriction enzyme(s) that allowed the most robust differentiation of community members based on in silico predictions. A qPCR approach to identify/quantify all chlorophytes was designed and tested using the same chlorophyte-specific primer, but used a different universal eukaryotic primer (to produce an appropriately sized PCR amplicon). Both the fingerprinting and qPCR approaches for investigating changes in chlorophyte communities were successfully applied to a limited subset of field samples and the fingerprinting especially highlighted diversity within the chlorophytes with well over 20 distinct operational taxonomic units (~species) observed in samples collected from Florida’s waters (see further description in Tilney et al. 2015). A newly designed taxon-specific chlorophyte qPCR assay, guided by sequence analysis from an IRL community from 2011, incorporated a probe in addition to universal primers to enhance specificity, and was used to screen field samples. In addition, Fluorescent In-Situ Hybridization (FISH) was explored as a method to quantify chlorophyte taxa. Specifically, we applied protocols similar to Simon et al. (2001) with cultures of Chlamydomonas, but were not able to detect cells by either microscopy and flow cytometry. The established protocol called for very specific timing, materials, and procedures, some of which were modified in these initial tests. We thus suspect that the lack of hybridization was due to the methodological adaptations imposed and not the CHLO02 FAM labeled probe, as it was successfully validated using PCR based assays. Further testing was not conducted because sample collection and processing was laborious and not practical (i.e., FISH samples require immediate lab-based processing steps shortly after field collection). Cell-Based Assays Cell-based assays included microscopy and flow cytometry. For the former, phytoplankton taxa from preserved field samples were identified to the lowest practical taxonomic unit via light microscopy following Sournia (1978). For each preserved culture sample, we used light microscopy with a Neubauer heamocytometer to enumerate cells in 4 sub-samples. For the latter, we used flow cytometry to test and validate existing and newly developed probes, as well as to directly quantify unstained/unprobed cells based on gating parameters. For the existing antibody-based A. lagunensis probe, sample collection and probing procedures were conducted following Koch et al. (2014). Additional method validation, including tests of cross-reactivity, followed standard protocols although the data analysis procedure was amended. To characterize picoplankton populations, methods followed Marie et al. (1999; 2005).

Contract #28108, File C

ode: F4197-14-16-F D

eliverable # 6, Final report

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able 1. Com

parison of methods including processing and analysis tim

e, sample throughput, taxonom

ic resolution and advantages/disadvantages.

Method

Prep tim

e A

nalysis tim

e Sam

ple throughput

Taxonom

ic resolution

Advantages

Disadvantages

microscopy

<8 h <1 h

Low

variable im

portant for time series

datasets, validation

variable taxonomic resolution,

requires trained taxonomist,

low throughput

epifluorescent m

icroscopy <2 h

<1 h Low

groups based on

pigments/size

can be used for rapid screening of bacteria/picoeukaroytes

variable taxonomic resolution,

low throughput

flow cytom

etry <8.5 h

<4 h m

oderate groups based on

pigments/size

high-throughput cell-specific info gathered rapidly, affordably

coarse taxonomic resolution

fluorescent antibody w

/ flow

cytometry

<8.5 h <2 h

moderate

species high-throughput cell-specific

info gathered rapidly, affordably

cross-reactivity can occur, antibody required (can lead to problem

s in method longevity)

DN

A extraction

2.5 h 0.5 h

moderate

N/A

can be used in variety of assays

field collection difficult

DN

A

fingerprinting <5 d

~1-2 d H

igh population,

species, genus, class, universal

taxonomic flexibility and

specificity, high-throughput

requires molecular expertise,

sequencing machine, or

external facility, cross-reactivity can occur

qPCR

4 h

1.5 h H

igh population,



specificity, high-throughput requires m

olecular expertise, cross-reactivity can occur

rRN

A probe w

/ flow

cytometry

1 d <2 h

moderate


taxonomic flexibility, high-

throughput cell-specific info gathered rapidly

sample collection com

plex, cross-reactivity can occur

rRN

A probe w

/ epifluorescent

microscopy

1 d <4 h

moderate



specificity

sample collection com

plex, cross-reactivity can occur, low

throughput

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Figure 3. Mean (±SD) fluorescence assessed by flow cytometry of unstained and antibody-labeled phytoplankton isolates.

III. Results of Method Development, Validation, and Comparison Aureoumbra lagunensis Method Development and Validation In the IRL, researchers have identified and quantified A. lagunensis via microscopy and using antibody-based probes, the latter coupled to microscopy and/or flow cytometry for enumeration (Flewelling and Corcoran, 2013; Gobler et al., 2013; Koch et al., 2014; Phlips et al., 2015). In 2012, microscopy revealed a bloom that was assumed to be nearly monospecific. However, microscopy has shortfalls: it may miss cryptic diversity and its use to detect A. lagunensis cells is challenging given a lack of distinguishing morphological characteristics and considerable time needed for enumeration. Given these limitations, we explored the use of flow cytometry coupled with existing antibody probes (Koch et al., 2014; Lopez-Barreiro et al., 1998) and newly developed probes and primers resulting from nucleic acid-based methods. Flow cytometry, although quantitative, relies on subjective “logical gating” of subpopulations; that is, populations are gated based on their abundance in two-dimensional biplot space of fluorescence and/or size characteristics. In previous work, Koch et al. (2014), applied a two parameter gating strategy that relied on side scatter (a descriptor of internal complexity and a proxy for size) and green fluorescence resulting from the fluorescent antibody probe after hybridization with A. lagunensis cells. In our validation of flow cytometric methods for A. lagunensis, we tested the cross reactivity of the antibody with a suite of different taxa, including pelagophytes, cyanobacteria, and chlorophytes. We found cross hybridization with Synechococcus, as well as autofluorescence in Chlamydomonas with a signal magnitude that would be indistinguishable from hybridized A. lagunensis cells (Fig. 3). The fraction of the Synechococcus population that cross-reacted with the antibody probe had a larger mean forward scatter signal - a proxy of cell size (data not shown). We hypothesized that larger chained Synechococcus cells, possibly containing more mucus, may have trapped the unincorporated probe. For Chlamydomonas, the high green autofluorescence signal revealed that certain species


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may naturally have signals that could be misinterpreted as hybridized A. lagunensis cells if solely using green fluorescence to make identifications (noted also by Tang and Dobbs, 2007). Most of the chlorophytes (Pedimonas minimum, Tetraselmis sp., Chlorella sp., and Picochlorum oklahomensis) displayed comparably similar green fluorescent signal in stained and unstained samples (Fig. 3) To address this cross reactivity, we developed a new gating routine, invoking forward scatter instead of side scatter as a proxy of size, green fluorescence resulting from the fluorescent antibody probe, and red fluorescence as an indicator of autofluorescence. We developed and tested this three-parameter gating routine on established CCMP and UTEX laboratory cultures, as well as field cultures. Specifically, we used biplots of red fluorescence vs. forward scatter and red fluorescence vs. green fluorescence to distinguish populations (Fig. 4). Our sample analysis relied on the use of size standards (i.e., beads of different sizes, Fig. 4a) as well as culture standards (i.e., established cultures with size and fluorescence characteristics that are assumed to be similar to field populations, Fig. 4d) to gate populations (Fig. 4b,c,e,f). Our validation confirmed that the antibody probe increased the green fluorescent voltage signal in both Aureoumbra isolates, resulting in high green fluorescence following hybridization greater than the autofluorescence of natural populations (Fig. 4d, note that the unprobed cultures are not shown). However, the NCMA and UTEX cultures displayed varying degrees of green fluorescence following antibody hybridization. Specifically, the UTEX isolate exhibited greater fluorescence than the NCMA isolate (Fig. 4d, deliverable 3 Appendix Fig. 4). Analysis of field samples, using defined gates (i.e., boxes shown in 4d-f) demonstrated A. lagunensis populations with overall cell sizes falling between UTEX and CCMP isolate means in the Mosquito Lagoon between January and July, and fluorescence signals varying from CCMP-like to UTEX-like depending on the station and time of year (Fig. 8 deliverable 3). We found that this strategy provided the best discrimination of A. lagunensis cells, especially against those which cross reacted (i.e., the picocyanobacteria Synechococcus sp. was excluded via gating by size, and the chlorophyte Chlamydomonas sp. was excluded with the addition of red fluorescence as a gating parameter). As part of our probe development for A. lagunensis, we sequenced the 18S rRNA gene in both CCMP and UTEX A. lagunensis cultures. Our sequencing revealed that the CCMP and UTEX isolates were genetically distinct (<96% similar) and the UTEX isolate was more genetically similar to the Aureoumbra observed in the IRL (>99% similar). It should also be noted that the UTEX isolate was also more similar to isolate(s) used to generate the polyclonal antibody used by Koch et al. (2014). Some A. lagunensis strains (UTEX, IRL) contain a ~400 bp insertion region, however, the extent to which this observed genetic diversity represents population or species level diversity requires additional investigation. A probe targeting a unique region of the insertion region was designed and tested against Aureoumbra cultures and field samples using PCR. The probe successfully amplified Aureoumbra from the 2012 bloom sample (site 1.5; July 18, 2012) and from the Aureoumbra isolate confirmed to have the rRNA insertion (gel confirmation; see Report #3 Appendix Figure 4). Assay specificity was validated by screening culture and field gDNA samples using PCR and agarose gel electrophoresis. This specificity testing indicated minor amplification only with


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Figure 4. Biplots of red fluorescence versus forward scatter (left) and green fluorescence (right) with size calibration beads (a) used to gate field samples (b, c, e, f) and probed A. lagunensis cultures (d) used to devise gating of unprobed (e) and probed (f) field samples. Points represent single events (i.e., cells) and colors represent the concentrations of those events in the two dimensional space, with the greatest concentrations represented by the cooler colors. In d-f, the colored polygons show gating parameters as informed by probed A.lagunensis cultures, according to the legend in d.

closely related non-target pelagophytes, Aureococcus anophagefferens and A. lagunensis CCMP1503 (Table A1). Double-stranded DNA standards for qPCR were synthesized to target a 235 bp sequence located downstream from the insert region. Testing a standard series with the A. lagunensis primer pair showed linearity across 8 orders of magnitude (101 – 108 rRNA copies) with a high efficiency (99.8%) and R2 (0.99). To convert qPCR measurements of target copy number into cellular abundance, it is necessary to estimate the number of target gene copies per A. lagunensis cell. To accomplish this, we used culture gDNA extracts from microscopy-


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validated samples to estimate DNA per cell, and to determine target copy numbers per gDNA sample with qPCR. Using this approach, we estimate that A. lagunensis contains ~15.5 (±2.6 1S.D.) copies of the 18S rRNA gene per cell. The amplification efficiency of genomic DNA from A. lagunensis was found to be lower than that of the synthetic standards, which will reduce the assay’s limit of detection and may result in underestimation of A. lagunensis cell abundance in field samples with qPCR. Method Comparison We applied the validated methods for A. lagunensis identification and enumeration to samples collected between January and September 2015 from sites 1.5, 2, and 3 in the IRL (Fig. 5). Microscopy analysis by the Phlips lab detected A. lagunensis in all samples collected with the exception of two time points at site 3. The A. lagunensis abundance data resulting from different approaches (microscopy, flow cytometry, and qPCR) were similar at certain locations and times. For example, between January and July at station 1.5, flow cytometry and microscopy revealed similar patterns in A. lagunensis abundance. In addition, flow cytometry and qPCR data tracked with one another at station 1.5 during certain periods (e.g., August and September 2015).

Figure 5. Time series of A. lagunensis as detected by flow cytometry, microscopy and qPCR (left) and relationships between the metrics (right) at stations 1.5 (top), 2 (middle) and 3 (bottom). Fits between the metrics are shown in the top right panel for microscopy vs. flow in purple; for microscopy vs. qPCR in red, flow cytometry vs. qPCR in green. Note that microscopy data after 7/29/15 are not included in this report.


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Spearman Rank Correlation Analysis revealed positive relationships between the metrics at this site as follows: microscopy vs. flow – p =0.84, rs <0.01, n = 13; microscopy vs. qPCR – rs =0.90, p<0.01, n=14; flow cytometry vs. qPCR – rs =0.65, p<0.01, n = 17. However, these correlations deviated considerably from a 1:1 fit (Fig. 5). In contrast to the patterns at station 1.5, there were instances in which flow cytometry data revealed peaks in A. lagunensis not captured by either microscopy or qPCR. For example, mismatches occurred on 5/19/15 at sites 2 and 3. On this date, flow cytometry (using the 3-parameter gating routine) revealed A. lagunensis abundance much greater than that revealed by either microscopy or qPCR. Specifically, A. lagunensis abundance was estimated at abundances ranging from 10,000 and 15,000 cells mL-1 at both stations, whereas microscopy found only up to 150 cells mL-1. The probed sample material was subsequently analyzed via microscopy at FWC, and 9,929 and 15,155 labeled cells mL-1 were observed at sites 2 and 3 respectively. This microscopic examination of preserved material revealed multiple labeled particles in size classes between 1 and 6 µm; most of these were round cells. In contrast, microscopy data from the Phlips lab indicated the presence of background levels of A. lagunensis (~100 cells µL-1) as well as a bloom of Chaetoceros spp. (>14 million cells L-1, roughly a third of which fell within the <5µm size class). However, the Chaetoceros spp. found at both sites did not hybridize well with the antibody probe (Fig. A1). It should be noted that for the 5/19/15 site 3 sample, flow cytometry and microscopy estimated similar picocyanobacteria abundance (i.e., 159,341 cells mL-1 for microscopy and 163,097 cells mL-1 for flow cytometry); however, the 5/19/15 sample for site 2 showed a discrepancy (i.e., 10,649 cells mL-1 for microscopy and 253 cells mL-1 for flow cytometry). Chlorophyte Method Development and Validation The detection and/or enumeration methods for chlorophytes in the IRL included microscopy, flow cytometry, and PCR-based analyses (sequencing, PCR, fingerprinting, qPCR), although only microscopy has been used for routine monitoring. Since the 2011 bloom, analysis of IRL field samples via microscopy has differentiated flagellated and non-flagellated coccoid nanoeukaryotic cells (3-4 µm in diameter) observed via microscopy. Both forms have been observed in every sample collected at the sampling stations between January and May 2015 (Fig. 6). At site 1.5, these groups, especially the non-flagellated form, often dominated chlorophyte communities with respect to cellular abundance and biomass. Notably, this represents a distinct difference in composition compared to 2011, where the flagellated form dominated abundances estimated by microscopy. At sites 2 and 3, prasinophytes instead dominated chlorophyte biomass during this period. At all sites, other chlorophyte genera, as well as non-specific groups that potentially contained chlorophytes were also observed, highlighting the complexity involved with monitoring pico- and nanoplankton communities in the IRL.

To address these challenges, we developed and implemented qPCR approaches to permit chlorophyte detection at varied taxonomic scales. These assays employed existing universal primer sets (i.e., targeting eukaryotes and chlorophytes), with and without newly designed taxa-specific probes. These efforts were guided by sequence analysis of a 2011 IRL sample dominated by the nanoeukaryotes, which indicated that the non-flagellated chlorophyte


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Picochlorum oklahomensis was a dominant member of the chlorophyte community, as well as by microscopy observations during the study period, which routinely detected a non-flagellated picoeukaryote. We developed a qPCR assay for P. oklahomensis that incorporated three newly designed species-specific probes and 5’ nuclease chemistry for detection, which facilitates greater level taxonomic specificity compared to traditional intercalating dye detection chemistries. We developed synthetic DNA standards for P. oklahomensis, and found the range of linearity to span 6 orders of magnitude with high amplification efficiency (99.2%) and r2 (0.998). We used PCR to validate assay specificity using gDNA from the P. oklahomensis culture and the 2011 archived sample as positive controls, and from 7 chlorophyte isolates and other non-chlorophyte isolates as negative controls (Table A1). The assay was highly specific to Picochlorum. For example, although minor PCR amplification was observed for Chlamydomonas sp. (Table A1), this genus contains mismatches in the central fluorophore-containing probe used for the qPCR assay and as a result, the probe cannot hybridize (or hybridizes less efficiently), minimizing non-specific amplification during qPCR. Following the approach described for A. lagunensis, we assessed 18S rRNA copy number in Picochlorum oklahomensis, and evaluated DNA content per cell using cultures as well as published genome size estimates. We estimated that this picoeukaryote contains ~0.6 (±0.46 1S.D.) rRNA copies per cell.

Figure 6. Cellular abundance of common eukaryotic phytoplankton taxa observed by microscopy in samples collected from the Mosquito Lagoon, FL (station 1.5) in 2015. The flagellated and non-flagellated forms of the Pedinophyceae sp. are both included within the chlorophyta group. A. lagunensis was the only species in the pelagophyte group.

Bio

volu

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(µm

3 m

L-1)


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Method Comparison We applied the validated methods for identification and enumeration of the chlorophyte community in samples collected between January and September 2015 from sites 1.5, 2, and 3 in the IRL, in part to determine the extent to which the the nanoeukaryote communities observed via microscopy (i.e., Pedinophyceae sp. with or without flagella) in field samples were composed of targeted chlorophyte taxa. Although the relationship between the abundance of nanoeukaryote cells based on microscopy counts (with and without observable flagella) and Picochlorum sp. based on qPCR appears weak (Fig. 7), correlation analysis of data from all three sites revealed that Picochlorum qPCR-based estimates were significantly and positively correlated with microscopy counts of the non-flagellated nanoeukaryote (rs = 0.61, p <0.0001, n = 41; Figure A2). The relationship was significant and clustered around the 1:1 line, however many qPCR samples were negative for Picochlorum sp. and the linear regression was weaker (r2 = 0.46). Our comparison suggests that at certain times/sites, Picochlorum sp. occurs in the IRL and it can represent a substantial proportion of the non-flagellated nanoeukaryote enumerated by microscopy. These data also suggest that the non-flagellated and flagellated forms of the

Figure 7. Time series of microscopy-based abundance of nanoeukaryotes (flagellated and non-flagellated forms are combined) and qPCR-based abundance of Picochlorum sp. as detected by qPCR (left) and relationships between the metrics (right) at stations 1.5 (top), 2 (middle) and 3 (bottom).


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nanoeukaryote may consist of multiple morphologically indistinguishable taxa. For example, at site 1.5, qPCR- and microscopy- based abundance estimates tracked well until July 2015 (Fig. 7), when qPCR data indicated that only a small fraction of the observed cells consisted of Picochlorum. Similarly, at station 2 and 3, for periods in which both microscopy and qPCR data were available, qPCR did not detect Picochlorum, suggesting that other picoeukaryotes may have instead dominated the flagellated and non-flagellated nanoeukaryote groups identified via microscopy at these times. It is also possible that the qPCR assay may be underestimating the abundance of Picochlorum sp. in these samples. For example, some of the abundances in these samples were below the qPCR assay’s limit of detection; that is, abundance based on microscopy averaged 826 ± 1227 cells mL-1, and the theoretical limit of detection for the qPCR assay based on copy number is 32 Picochlorum cells mL-1. Lower amplification efficiency in field-sample extracted DNA will increase this limit. Alternative approaches to increasing the sensitivity of this assay (and others in this report) include increasing and/or varying the concentration of DNA added to reactions, as well as increasing reaction volume, although both increase the assay cost per sample. Picoplankton Method Development and Validation In the 2011 blooms, picocyanobacterial populations co-occurred in high abundance with the more carbon-rich nanoeukaryote. Although we did not propose to explore IRL nano- and picoplankton identification or enumeration beyond the target taxa, we conducted a method comparison using microscopy and flow cytometry data to evaluate differences in eukaryotic and prokaryotic picoplankton communities over time at the three sites. Microscopy, which has been used to enumerate multiple

taxonomic groups in the nano- and picoplankton size class since 1997, has indeed revealed monospecific and co-occurring blooms of picocyanobacteria in the IRL. However, the same previously discussed limitations that apply for microscopy-based enumeration of pico- and nano-plankton apply for

Figure 8. Biplots of red fluorescence versus forward scatter (left) and orange fluorescence (right) with size calibration beads (a) and Synechococcus culture (c) used to inform gating of field samples (d). Points represent single events (i.e., cells) and colors represent the concentrations of those events in the two dimensional space, with the highest concentrations represented by the cooler colors. In c-d, the colored polygons show gating parameters for cultures according to the legend in c.


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picocyanobacteria, which are even smaller and can be more difficult to specifically identify/enumerate. In addition, fixation can result in changes to nano- and picoplankton morphology, including loss of flagella in some taxa; in general the fixation for flow cytometry (preservation with glutaraldehyde followed by flash freezing) works well but is not practical for larger volumes. To address these limitations, we assessed, implemented, and refined flow cytometry based methods for pico/nanoplankton community analysis following Marie et al. (1997, 1999). An initial hold-over experiment was conducted to determine if community differences occurred between samples preserved in the field or after shipment. To quantify cell loss, field samples were collected in quadruplicate and were preserved immediately upon collection, and again 12 to 24 hours post collection. Flow cytometry analysis indicated that picocyanobacterial abundance decreased during the holding period, with ~28% and 58% cell loss observed for all picoplankton events and picoplankton events within Synechococcus gates respectively, within 24 hours. As such, we decided to retain the established protocol of flash freezing and preserving samples in the field. Culture testing and validation was conducted to refine existing gating routines for analysis of data. This approach was similar to the approach described for A. lagunensis with respect to gating on fluorescence and size characteristics (Fig. 8), but no probes were used. We employed gating based on red and orange fluorescence, as well as cell size, to quantify picoplankton with characteristics similar to Synechococcus, and picoeukaryotes (Fig. 8).

Figure 9. Time series of picoplankton as detected by flow cytometry and microscopy (left) and relationships between these metrics (right, with microscopy data on the x-axis and flow data on the y-axis) at stations 1.5 (top), 2 (middle) and 3 (bottom). Microscopy data collected after 7/29/15 are not included.


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Method Comparison Applying flow cytometric methods to picoplankton identification and enumeration for routine monitoring samples generally resulted in agreement between the methods (Fig. 9). Spearman Rank Correlation Analysis revealed positive relationships between the metrics at sites 1.5 (rs=0.80, p <0.001, n = 13) and 2 (rs =0.50, p=0.0776, n=13). However, these correlations deviated from a 1:1 fit (Fig. 9). Site 3, which contained the most mismatched data revealed a very weak correlation influenced by the low flow count outliers. This work demonstrates the utility of using flow cytometry to measure picoplankton routinely in the IRL. IV. Summary and Recommendations In this project, we aimed to assess the suitability of probes and PCR-based approaches for routine detection of Aureoumbra lagunensis and pico- and nano-plankton taxa within the chlorophytes. For the former, we found utility in the existing antibody probe with modifications made to gating parameters to reduce cross-reactivity. For the latter, we found that qPCR could rapidly detect changes in target organisms based on quantifying rRNA copy numbers in IRL samples, which were dynamic in space and time. The results from our method comparisons suggest that additional taxonomic diversity likely occurs within, and at times dominates, pico- and nano-plankton size classes. Current monitoring practices can be amended with these flexible methods to produce more robust and informative monitoring data, and methods can be adapted in the future to include additional and/or novel targets. We recommend the incorporation of flow cytometry into routine monitoring given its ease of use both in detection of targeted taxa (i.e., A. lagunensis) and size classes (i.e., picoplankton). In contrast, qPCR is likely better integrated when seeking targeted taxa. Once an assay is available, it can be used to rapidly confirm taxonomic identification, or investigate bloom events in archived material. In addition, next-generation amplicon sequencing approaches show promise for enhancing detection of pico- and nano-plankton diversity, although these methods are not yet adapted for rapid analyses. For this purpose, in addition to flow cytometry collection, we recommend the continued and/or enhanced archival of filters for nucleic acid analyses, especially during bloom and event response, which can be subsequently analyzed using methods that are appropriate to target and/or identify the taxa of interest.

V. Outreach Efforts Outreach efforts engaged a diverse audience including the public, management and phytoplankton monitoring agencies, and academic institutions. Transfer of knowledge and skillsets was prioritized throughout the project, especially field collection protocols and flow cytometry analyses. These efforts were facilitated by the development, dissemination, and implementation of SOPs across project partners (Deliverable 1, Deliverable IV), as well as hands-on field and lab training. Field efforts focused on incorporating genetic and flow-cytometric sampling into routine sampling by UF and SJRWMD. Training in flow cytometric enumeration of pico- and nano-plankton and A. lagunensis with the Accuri C6 (BD Biosciences) cytometer was provided at a workshop for a UF graduate student from the Phlips lab, held in September 2015 at FWRI. Additional SOPs for flow cytometry samples (probed and preserved) and analysis using FCS Express 4 (De Novo Software) were provided by FWRI at, and following, the workshop. A seminar describing the project background and methodology was presented at the SJRWMD in Palatka, FL in December 2014 (Hubbard 2014). In addition, five national and one


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international presentations targeting management and academic audiences described project tool development, implementation in the IRL, and utility for analyzing new and archived field samples within a monitoring/management context. Specifically, the project approach was highlighted during a speed-talk at the 2015 IRL Symposium, Florida Atlantic University Harbor Branch Oceanographic Institute (FAU-HBOI), Ft Pierce, FL (Muhlbach et al. 2015), At the FWC-MOTE HAB Mini-Symposium at FWRI, St Petersburg, FL, an oral presentation reviewed new project tools and preliminary results of genetic sequencing assays (Muhlbach and Tilney, 2015). An overview of project methods and results was presented at the poster session of the 11th Advanced Phytoplankton Taxonomy Course in Naples, Italy (Hubbard 2015). A speed-talk and a poster highlighting the new qPCR assays developed for A. lagunensis and Picochlorum sp. were presented at the 8th US HAB Symposium in Long Beach, California (Tilney et al. 2015). At the end of the project, speed-talk and poster presentations at the 2016 IRL Symposium described the application and utility of project tools in characterization of specific pico- and nano-plankton taxa or algal groups in the IRL (Tilney et al. 2016, Muhlbach et al. 2016). Collectively, these efforts greatly facilitated the exposure of scientific research conducted as part of the project, and enabled constructive discussions among project partners (e.g., at the IRL Symposia) and other experts on nanoplankton and/or the IRL to improve project sampling strategies, tools and products. The resulting project SOPs and the strengthened relationships across project partners through numerous project meetings greatly facilitates a more informed and integrated sampling effort for pico- and nano-plankton in the IRL moving forward. Public outreach purposefully targeted a general audience, and included various forms of social media focused on the project and IRL sampling during the project period. These included a web article describing the IRL NEP project, written and reviewed by project partners, and published on the FWRI HAB website in June 2015 (http://myfwc.com/research/redtide/research/current/irl_nep/). The article is approximately 600 words long and has received 192 hits thus far. Eight shorter multimedia project updates describing sampling efforts, personnel, methodology, field data, and outreach presentations were posted on the “Florida Red Tide and other Harmful Algal Blooms” Facebook page (https://www.facebook.com/FLHABs/). These posts were collectively “liked” >130 times. We will continue to provide links to this material to enhance project exposure as well as public knowledge about specific nanoplankton that form blooms in the IRL and the tools necessary to discriminate them. Outreach presentations in chronological order: Hubbard, KA. 2014. Development of sensitive tools for rapid identification of pico- and

nanoplankton in the Indian River Lagoon. Saint John’s River Water Management District, Palatka, FL.

Muhlbach, E, Hubbard, KA, and Corcoran, AA. 2015. Application of molecular and flow-cytometric tools for characterizing pico- and nano-plankton communities in the Indian River Lagoon. Indian River Lagoon Symposium 2015. Harbor Branch Oceanographic Institute at Florida Atlantic University, Fort Pierce, FL (speed-talk).

Tilney CL, Muhlbach EG. 2015. Evaluating PCR-based Methods and Flow Cytometry for Pico/Nanoplankton. FWC-MOTE HAB Symposium at FWRI, St. Petersburg, FL (oral).

Hubbard, KA. 2015. When specificity matters: Integrating diverse genetic detection


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approaches into phytoplankton and Harmful Algal Bloom (HAB) monitoring in the United States. 11th Advanced Phytoplankton Taxonomy Course Naples, Italy (poster).

Tilney CL, Muhlbach EG, Murasko S, Bruzek S, Phlips EJ, Badylak S, Lasi MA, Hall LM, Parks A, Corcoran AA, Hubbard KA. 2015. Diversity, Dynamics & Distributions of Pico- and Nano-Planktonic Pelagophytes and Chlorophytes in the Indian River Lagoon, FL. 8th Symposium on Harmful Algae in the US, Long Beach, CA (speed-talk and poster).

Tilney CL, Muhlbach EG, Murasko S, Bruzek S, Badylak S, Hall LM, Phlips EJ, Lasi MA, Hubbard KA, and Corcoran AA. 2016. In the Wake of the Superbloom: Development and Validation of Integrated Technologies for Monitoring Green and Brown Tides in the Indian River Lagoon. Indian River Lagoon Symposium 2016. Harbor Branch Oceanographic Institute at Florida Atlantic University, Fort Pierce, FL (poster).

Tilney CL, Muhlbach EG, Murasko S, Bruzek S, Badylak S, Hall LM, Phlips EJ, Lasi MA, Hubbard KA, Corcoran AA. 2016. In the Wake of the Superbloom: Tracking Green and Brown Tides in the Indian River Lagoon with Integrated Technologies. Indian River Lagoon Symposium 2016. Harbor Branch Oceanographic Institute at Florida Atlantic University, Fort Pierce, FL (speed-talk).


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VI. References

Flewelling, L.J., Corcoran, A.A., 2013. Identity of the brown tide organism Aureoumbra lagunensis verified with a polyclonal antibody, Indian River Lagoon Symposium 2013 - Health of the Lagoon, Ft. Pierce, FL.

Gobler, C.J., Koch, F., Karig, Y., Berry, D.L., Tang, Y.Z., Lasi, M., Walters, L., Hall, L., Miller, J.D., 2013. Expansion of harmful brown tides caused by the pelagophyte, Aureoumbra lagunensis DeYoe et Stockwell, to the US east coast. Harmful Algae 27, 29-41.

Koch, F., Kang, Y., Villareal, T.A., Anderson, D.M., Gobler, C.J., 2014. A Novel Immunofluorescence Flow Cytometry Technique Detects the Expansion of Brown Tides Caused by Aureoumbra lagunensis to the Caribbean Sea. Applied and Environmental Microbiology 80, 4947-4957.

Lopez-Barreiro, T., Villareal, T.A., Morton, S.L., 1998. Development of an antibody against the Texas Brown Tide (Aureoumbra lagunensis), in: Reguera, B., Blanco, J., Fernandez, M.L., Wyatt, T. (Eds.), Harmful Algae, . Xunta de Galicia and Intergovernmental Oceanographic Commission of UNESCO, Santiago de Compostela, pp. 263-265.

Marie, D., Peartensky, F., Vaulot, D., Brussaard, C., 1999. Enumeration of phytoplankton, bacteria and viruses in marine samples. Current Protocols in Cytometry 11.11, 1-15.

Marie, D., Simon, N., Vaulot, D., 2005. Phytoplankton cell counting by Flow Cytometry, in: Andersen, R.A. (Ed.), Algal Culturing Techniques. Elsevier, London.

Not, F., Latasa, M., Scharek, R., Viprey, M., Karleskind, P., Balagué, V., Ontoria-Oviedo, I., Cumino, A., Goetze, E., Vaulot, D., Massana, R., 2008. Protistan assemblages across the Indian Ocean, with a specific emphasis on the picoeukaryotes. Deep Sea Research Part I: Oceanographic Research Papers 55, 1456-1473.

Phlips, E., Badylak, S., Lasi, M., Chamberlain, R., Green, W., Hall, L., Hart, J., Lockwood, J., Miller, J., Morris, L., Steward, J., 2015. From Red Tides to Green and Brown Tides: Bloom Dynamics in a Restricted Subtropical Lagoon Under Shifting Climatic Conditions. Estuaries and Coasts 38, 886-904.

Sournia, A., 1978. Phytoplankton Manual. United Nations Educational, Scientific and Cultural Organization, Paris.

Tilney C.L., Muhlbach E.G., Murasko S., Bruzek S., Phlips E.J., Badylak S., Lasi M.A., Hall L.M., Parks A., Corcoran A.A., Hubbard K.A. (2015) Diversity, Dynamics & Distributions of Pico- and Nano-Planktonic Pelagophytes and Chlorophytes in the Indian River Lagoon, FL. 8th Symposium on Harmful Algae in the US, Long Beach, CA.

Vigil, P., Countway, P.D., Rose, J., Lonsdale, D.J., Gobler, C.J., Caron, D.A., 2009. Rapid shifts in dominant taxa among microbial eukaryotes in estuarine ecosystems. Aquatic Microbial Ecology 54, 83-100.


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Appendix

Appendix Table 1. Specificity of 5 primer-pairs targeting A. lagunensis (Aureoumbra720F), A. lagunensis CCMP1503 (AureoF_Charm) Picochlorum sp. (PicochlorumF), chlorophytes (Euk570F), and Aureococcus anophagefferens (Aureococcus761F). Specificity detected by end-point PCR and agarose gel electrophoresis.


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Appendix Figure 1. Flow cytometry biplots (left) and associated images (right) of samples collected from station 2 and 3 following hybridization with the antibody probe and analysis. Images in (a) and (c) show A. lagunensis and the image in (b) shows non-hybridized Chaetoceros.


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Appendix Figure 2. Correlation of Picochlorum sp. abundance estimated by qPCR, with the non-flagellated nanoeukaryote estimated by microscopy. Points on the x-axis represent samples that did not detect Picochlorum sp. by qPCR. Non-parametric Spearman Rank Correlation Analysis indicated a significant positive correlation (Spearman ρ = 0.61, P <0.0001, n=40), but the strength of the relationship was low (linear regression R2 = 0.46), and was driven entirely by the samples on the x-axis that resulted in a bi-modal distribution (Linear regression excluding the 21 samples that were undetectable for Picochlorum sp. R2 = 0.005).


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Appendix Figure 3. Cellular abundance of common eukaryotic phytoplankton taxa observed by microscopy in samples collected from Titusville, FL (station 2) in 2015. The flagellated and non-flagellated forms of the Pedinophyceae sp. are both included within the chlorophyta group. A. lagunensis was the only species in the pelagophyte group.

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Appendix Figure 4. Cellular abundance of common eukaryotic phytoplankton taxa observed by microscopy in samples collected from the Banana River, FL (station 3) in 2015. The flagellated and non-flagellated forms of the Pedinophyceae sp. are both included within the chlorophyta group. A. lagunensis was the only species in the pelagophyte group.

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