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Characterization of Natural and Affected Environments
Automated Sub-Daily Sampling of Cyanobacterial Toxins on a Buoy Reveals New Temporal Patterns in Toxin Dynamics Todd R. Miller, Sarah Bartlett, Chelsea A Weirich, and John Hernandez Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00257 • Publication Date (Web): 30 Apr 2019 Downloaded from http://pubs.acs.org on May 6, 2019
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Figure 3. Time series of the cyanobacterial blue pigment phycocyanin fluorescence and the major toxin classes detected. MC = microcystins, AT-A = anatoxin-a, AP = anabaenopeptins, CP = cyanopeptolins. The phycocyanin six hour aggregate series was created by averaging phycocyanin on every hour that toxins were sampled. 108x128mm (600 x 600 DPI)
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Environmental Science & Technology
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Automated Sub-Daily Sampling of Cyanobacterial Toxins on a Buoy Reveals New Temporal
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Patterns in Toxin Dynamics
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Todd R. Miller1, *, Sarah L. Bartlett1,2, Chelsea A. Weirich1, and John Hernandez1 1Joseph
J Zilber School of Public Health, University of Milwaukee-Wisconsin, Milwaukee WI,
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USA 2School
of Freshwater Sciences, University of Milwaukee-Wisconsin, Milwaukee, WI, USA
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Keywords: cyanobacteria, microcystins, auto-sampling, anabaenopeptins, cyanopeptolins,
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anatoxin-a
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Running title: Hourly cyanotoxin dynamics
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*For correspondence:
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Todd R. Miller
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Joseph J Zilber School of Public Health,
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University of Wisconsin Milwaukee
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3135 N. Maryland Ave
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Milwaukee, WI 53211
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Email:
[email protected] 23
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Environmental Science & Technology
Abstract Temporal variability of toxins produced by cyanobacteria in lakes is relatively unknown
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at timescales relevant to public health (i.e. hourly). In this study, a water quality monitoring buoy
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was outfitted with an automated water sampler taking preserved samples every six hours for
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68.75 days over a drinking water intake. A total of 251 samples were analyzed by tandem mass
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spectrometry for 21 cyanotoxin congeners in five classes producing 5,020 data points.
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Microcystins (MCs) were most abundant toxins measured (mean +/- sd = 3.9 +/- 3.3 µg/L)
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followed by cyanopeptolins (CPs) (1.1 +/- 1.5 µg/L), anabaenopeptins (APs) (1.0 +/- 0.6 µg/L),
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anatoxin-a (AT-A) (0.03 +/- 0.06 µg/L), and microginin-690 (MG-690) (0.002 +/- 0.01 µg/L).
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Advanced timeseries analyses uncovered patterns in cyanotoxin production. The velocity of
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cyanotoxin concentration varied from -0.7 to 0.9 µg/L/hour with a maximum positive velocity
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just prior to peak toxin concentration during non-bloom periods. A backwards looking moving
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window of variance analysis detected major increases in cyanotoxin concentration and predicted
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the two greatest increases in MC. A wavelet analysis identified a significant (p 95%), MCLY (> 95%), MCWR (> 95%), MCLW (>95%), MCHtyR (> 95%),
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MCHilR (> 95%) were purchased from Enzo Life Sciences (Farmington, NY, USA). AP-A (>
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95%), AP-B (> 95%) and AP-F (> 95%), CP-1007 (> 95%), CP-1020 (> 95%), and CP-1041 (>
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95%), and MG-690 (> 95%) were purchased from MARBIONC (Wilmington, NC, USA). AT-A
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(> 96%) was purchased from Tocris Bioscience (Minneapolis, MN) as a racemic mixture. hAT-
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A (> 95%) was purchased from Abraxis (Warminster, PA).
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Statistics. All statistical analyses were conducted in R. A Wilcoxon ranksum test was
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used to test differences in the mean concentration of cyanotoxins. For congener non- specific
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analyses the sum concentration of all MCs (SumMC), CPs (SumCP), or APs (SumAP) by sample
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timepoint was used. Fifteen samples were removed from the dataset due to either the buoy
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capsizing or a lack of sample volume to complete all analyses. AT-A could not be quantified in
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eleven samples as a result of interferences that prevented distinguishing AT-A from
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phenylalanine. For principle component analysis (PCA), heatmap construction, and cluster
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analysis we linearly interpolated those missing AT-A data points.
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A heat map of individual toxin congener concentration was constructed using the
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heatmap.2 function in the R package gplots using a log transformed matrix of cyanotoxin data.
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Congeners were clustered during heatmap concentration based on Euclidean distance.
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A PCA was performed with the princomp function in R and a log transformed matrix of
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cyanotoxin concentration. An ordination of the first two dimensions of sample scores was
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constructed using the factoextra package. The envfit function in the zoo package was used to
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identify toxins that were significantly (p
