# Phosphate Limitation Increases Content of Protease Inhibitors in the Cyanobacterium Microcystis aeruginosa

^{1}

^{2}

^{*}

## Abstract

**:**

**Key Contribution:**Phosphorus limitation increases the content of protease inhibitors in cyanobacterial biomass. This may enhance negative interference with their consumers and thus indirectly foster cyanobacterial blooms.

## 1. Introduction

_{4}

^{3−}) is the most common P form on earth [11], but it is often immobilized and has therefore a low bioavailability [12]. Through the extensive use of P-containing washing agents and P-containing fertilizers in agriculture, P is washed into ponds and lakes, where it promotes the development of cyanobacterial blooms. A prominent example is Lake Erie, where P-input through the Maumee River has repeatedly led to blooms of Microcystis in the western basin of the lake [13]. Reduction of P is therefore a widely used approach to reduce eutrophication and thus lower the probability of cyanobacterial blooms [9,14]. Despite attempts to reduce the input of P into freshwater systems, the effects of high P-loads are still visible in many aquatic systems, and climate change may even increase the P input in the future [13,15]. However, recent research demonstrated that P is not always the only factor that determines the formation of cyanobacterial blooms but that N and the N:P-ratio are also important, and, at least in some cases, a reduction of N and P seems necessary to suppress cyanobacterial bloom formation [16,17,18].

## 2. Results

_{4}

^{3−}) concentrations were tested for their effects on the growth performance, stoichiometry and content of two N-rich protease inhibitors, nostopeptin 920 (BN920) and cyanopeptolin 954 (CP954), in Microcystis aeruginosa strain NIVA Cya 43.

#### 2.1. Growth Performance of M. aeruginosa under Different PO_{4}^{3−} Conditions

_{4}

^{3−}-concentrations, M. aeruginosa showed logistic growth, regardless whether growth was measured as cell abundance or particular organic carbon (POC; Figure 1a,b). The carrying capacity (K; Table 1 and Table 2) was lowest at 5 µM P and reached only 20% of the biomass that was obtained with the highest P-concentration. This indicates a growth limitation at very low P-concentrations. The strength of the inhibition seems to decrease with increasing P-concentration, as both K and maximal growth rate (µ

_{max}) increased with initial P-concentration (Table 1; Table 2). Except for the 30 µM P treatment, biomass determined as POC, remained stable towards the end of the experiment.

_{part}) increased during the experiment (Figure 1c). The linear mixed model shows that especially the sampling day as well as the treatment x day interaction had an impact on P

_{part}, while the treatment alone had no significant effect on P

_{part}(Table 3). However, at the end of the experiment (on day 28), P

_{part}was highest at the highest P-concentration and lowest at 5 µM P (Figure 1c, Tukey HSD after one-way ANOVA, F = 28.78, p < 0.001).

#### 2.2. Stoichiometry of M. aeruginosa Under Different PO_{4}^{3−} Conditions

#### 2.3. Inhibitor Content of M. aeruginosa Under Different PO_{4}^{3−} Conditions

^{−1}for BN920 and from 52 to 725 pg cell

^{−1}for CP954.

#### 2.4. Inhibitor Content As a Function of Growth Rate and Stoichiometry

^{2}(Table 5). For CP954, the linear model was not significant (p = 0.83). Instead, the quadratic model was substantially better suited as indicated by a lower AIC and a higher R

^{2}. Accordingly, the CP954 content showed a reversed U–shaped (optimum) relationship with the growth rate, reaching a maximum at growth rates of 0.15. However, though significant, the models explained only a low degree of variation within our data (see R

^{2}, Table 5).

^{2}). In the case of CP954, all applied models were significant, except for the linear model with C:N. The lower AIC values indicate that the quadratic model described the relationship of CP954 and C:N much better, while the linear model was sufficient to describe the relationship between CP954 and N:P. More specific, the content of BN920 decreased with increasing C:N-ratios (Figure 7a, Table 5); the CP954 content however increased with the C:N-ratio until a C:N-ratio of around 11 and declined at higher C:N-ratios (reversed U-shaped curve; Figure 7b, Table 5). This indicates that N-depletion of the cyanobacterium resulted in decreased PI contents. When plotted against the C:P- (Figure 7c,d; Table 5) and N:P-ratios (Figure 7e,f; Table 5) we found that the contents of BN920 and CP954 were affected differently. While the BN920 content decreased with increasing C:P- and N:P-ratios, the CP954 content increased. As CP954 makes up a larger proportion of the total PI content, a lower P-regime might promote the biomass content of PIs.

## 3. Discussion

_{part}) had already reached its maximum when biomass was still further increasing. This decoupled increase of biomass points at the well-known rapid uptake of available P and its internal storage as polyphosphate with a subsequent re-mobilization of polyphosphate for further biomass synthesis and growth in cyanobacteria [40,45].

_{part}content per carbon (0.015 mg P/mg C, which is equivalent to 1.5% per dry weight) matches values reported for P-limited M. aeruginosa [45], and the initial C:P values of 145 and 210 (Figure 2b) corroborate that internal polyphosphate pools had been depleted prior to the experiment.

_{50}values of 3.1 and 4.5 nM for the inhibition of bovine chymotrypsin have been reported [39], and IC

_{50}values for the inhibition of chymotrypsin activity in the gut of Daphnia were 5.4 and 7.4 nM respectively [37], which classifies BN920 and CP954 as the most potent inhibitors containing 3-amino-6-hydroxy-2-piperidone (Ahp).

^{2}), which most probably can be attributed to the fact, that the growth rates represent not only treatments with P-limitation but as well, other treatments with N-co-limitation. These two resource-regimes have opposite effects on the content of CP954 and BN920, and it may be hypothesized that they are as well differently related to growth rate, so that no far-reaching conclusions about growth rate effects can be drawn here.

_{i}due to several alkaline phosphatases and the capability to store P [23]. Still, not all cyanobacteria are good competitors under strong P-limitation [65], and thus cyanobacteria might not be dominating phytoplankton communities under strong P-limitation. More moderate P-limitation would be associated with N-co-limitation that would reduce PI content in cyanobacteria. However, the resulting interference with grazers like Daphnia will largely depend on tolerance traits in the grazer community. Standing populations of Daphnia harbor pronounced clonal variability with respect to tolerance of cyanobacteria [66] that allows for the reported microevolutionary adaptation of Daphnia populations both in time and space [67,68]. Additionally, phenotypic plasticity constitutes another mechanism for acquired tolerance to toxic cyanobacteria. For example, in some cladocerans, transgenerational adaptation has been shown to increase tolerance in zooplankton to toxic dietary cyanobacteria [69,70]. Although the molecular mechanisms driving these maternal effects have received some attention [71,72,73,74], the overall effects of evolution and plasticity of grazers in mitigating resource-driven changes in cyanobacterial toxin content remains to be understood.

## 4. Material and Methods

#### 4.1. Culturing Conditions

^{−2}s

^{−1}) and temperature (20 ± 1 °C) on a horizontal shaker (90 rpm). For the pre-culture we used 10 µM initial phosphate (PO

_{4}

^{3−}) to deplete the internal P-reserves of the cyanobacterium. Unlike some other Microcystis strains, NIVA Cya 43 does not produce colonies or microcystins, but it does produce cyanopetolin 954 (CP954) and nostopeptin 920 (BN920) [39], which are two nitrogen (N)-rich protease inhibitors (PIs)

**.**The cultures were not axenic, and heterotrophic bacteria made up just a small percentage compared to the cyanobacterial biomass [24].

_{4}

^{3−}-concentrations in a range from 5 to 75 µM with 3–4 replicates each were tested. Therefore, 400 mL of the respective medium was filled into 1 L flasks, autoclaved, and inoculated with M. aeruginosa (1.5 × 10

^{5}cells mL

^{−1}), which is similar to the inocula used by Long [62,76]. The experiment lasted for 28 days, and the flasks were randomized daily to compensate for potential heterogeneities in the light regime. Every 1–2 days, samples (0.5 mL) were taken and if necessary, diluted for cell counts using a Neubauer improved counting chamber. Per sample, at least 100–150 cells (at low densities) or 3 large squares (at high densities) were counted to ensure appropriate accuracy. In intervals of 2–4 days, 10 and 200 mL were taken to measure the particulate organic carbon (POC), nitrogen (PON), and phosphorus (P

_{part}) as well as the PIs. The volume needed for the analyses was roughly estimated based on the cell density.

#### 4.2. Determination of POC, PON, and P_{part}

_{part}, 0.5 mg C was filtered on GF/F filters, transferred into 10 mL of a potassium peroxodisulphate and sodium hydroxide, and autoclaved for 1 h at 120 °C; soluble reactive P was subsequently analyzed with the molybdate-ascorbic acid method [77] with a DR5000 UV-Vis spectrometer (Hach, Loveland, CO, USA). The obtained values were used to calculate the molar stoichiometric ratios (C:N, C:P and N:P) of the cyanobacterial biomass.

#### 4.3. Extraction and Quantification of PIs

^{−1}, Enzo Life Sciences, Farmingdale, NY, USA) were added to the cell pellet. The samples were re-suspended, sonicated, and again centrifuged (3 min, 4500× g). The supernatant was evaporated to dryness, re-dissolved in 1 mL methanol, and again dried and taken up in methanol (100 µL). Finally, the samples were centrifuged (2 min, 20,000× g), and the supernatant was transferred into HPLC vials.

_{18}-column (Nucleosil, 125/2, 100-3, Macherey and Nagel, Düren, NRW, Germany) as stationary phase with acetonitrile (A) and ultra-pure water (B), each containing 0.05% trifluoracetic acid (TFA) as mobile phase with the following gradient: 0 min: 20% A, 14 min: 100% A, 16 min: 100% A, 16.5 min: 20% A, 18 min: 20% A. The column temperature was set to 30 °C, the flow rate was 300 µL/min, and the injection volume of the sample was 10 µL.

_{2}O]

^{+}; [M+Na]

^{+}) with m/z = 903.46108 and 943.45331 (BN920 adducts) and m/z = 937.42211 and 977.41394 (CP954 adducts). For further calculations and analyses, the single adducts of each PI were summed up to ´BN920´ and ´CP954´. MC-LR ([M+H]

^{+}) was measured at m/z = 995.55604. Peak intensities were extracted from the chromatograms using the R package ´enviMass´ [78] and converted to concentrations via previous established calibration curves. Subsequently, the PI concentrations in the samples were normalized to the culture volume or to the extracted POC, as proxy for the cyanobacterial biomass.

#### 4.4. Modeling and Statistical Analyses

_{0}as the initial biomass or cell abundance, N

_{t}as the biomass or cell abundance at time (t), and the two model parameters carrying capacity (K) and maximal growth rate (µ

_{max}). Effects of the initial PO

_{4}

^{3−}-concentration on K and µ

_{max}were tested using one-way ANOVAs followed by HSD post-hoc tests (TukeyHSD). Residuals were tested for normal distribution (Shapiro–Wilk test) and variance homogeneity (Levene test). Growth rates were taken from the logistic growth model for POC, and are therefore based on changes in POC. Thus, they may be referred to as C net production rate. The growth rates were correlated to the PI content which allowed us to analyze how the inhibitor content was affected by growth rate. The effects of sampling day, initial PO

_{4}

^{3}

^{−}-concentration, and sampling day x PO

_{4}

^{3−}-concentration on P

_{part}, stoichiometry and PI content were tested applying linear mixed effects models using the R package lme4 [82].

^{2}) between consecutive models (i.e., between null model and linear model resp. between quadratic and linear model). The coefficient of determination (R

^{2}) accounts for the explanatory contribution of the fixed effect of the models only. In contrast, the Pearson correlation coefficient R

_{P}measures direct linear dependency of the transformed data. The effectivity of the transformations was checked graphically: normality of residuals with quantile–quantile plots and variance homogeneity by plotting residuals versus fitted values.

## Author Contributions

## Funding

## Acknowledgments

_{part}.

## Conflicts of Interest

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**Figure 1.**(

**a**) Cell abundance, (

**b**) particulate organic carbon (POC), and (

**c**) particulate phosphorus (P

_{part}), in batch culture growth experiments of M. aeruginosa with 8 different initial PO

_{4}

^{3−}-concentrations. Mean ± SD, n = 3–4. Displayed curves were fitted using a logistic growth model. Model parameters for (

**a**) and (

**b**) are given in Table 1; Table 2.

**Figure 2.**Elemental molar ratios (

**a**) C:N, (

**b**) C:P, and (

**c**) N:P of the biomass of M. aeruginosa grown on 8 different initial of PO

_{4}

^{3−}-concentrations. Mean ± SD, n = 3–4. The dashed horizontal lines indicate the respective Redfield ratio.

**Figure 3.**Cellular quota of (

**a**) carbon, (

**b**) nitrogen, and (

**c**) phosphorus of M. aeruginosa during early exponential (phase 1), mid-exponential (phase 2), and late growth (phase 3) in treatments with low (<15 µM), medium (15–30 µM) and high (>30 µM) initial P-concentrations. Bars denoted by the same letter are not significantly different (p ≥ 0.05), whiskers indicate standard errors.

**Figure 4.**Protease inhibitor content of (

**a**) nostopeptin 920 (BN920) and (

**b**) cyanopeptolin 954 (CP954) in the biomass of M. aeruginosa. The inhibitor content was normalized to the particulate organic carbon (mg C), which served as a proxy for cyanobacterial biomass. Mean ± SD, n = 3–4.

**Figure 5.**Protease inhibitor content of (

**a**) nostopeptin 920 (BN920) and (

**b**) cyanopeptolin 954 (CP954) of M. aeruginosa during early exponential (phase 1), mid-exponential (phase 2), and late growth (phase 3) in treatments with low (<15 µM), medium (15–30 µM) and high (>30 µM) initial P-concentrations. Bars denoted by the same letter are not significantly different (p ≥ 0.05), whiskers indicate standard errors.

**Figure 6.**Protease inhibitor content of (

**a**) nostopeptin 920 (BN920) and (

**b**) cyanopeptolin 954 (CP954) as a function of the growth rate of M. aeruginosa. Growth rates were taken from the logistic growth model for POC (see Figure 1b). Each point represents the measured inhibitor content in a single replicate at a single sampling day and the corresponding specific growth rate. Different symbols represent the different treatments. The x-axis was square root and the y-axis natural logarithmic transformed. Linear (solid lines) and squared (dashed lines) regression models were applied. Equations, R

^{2}- and p-values are shown in Table 5.

**Figure 7.**Protease inhibitor content of (

**a**,

**c**,

**e**) nostopeptin 920 (BN920) and (

**b**,

**d**,

**f**) cyanopeptolin 954 (CP954) as a function of the molar ratios between carbon, nitrogen, and phosphorus of M. aeruginosa. Each point represents the measured inhibitor content in a single replicate at a single sampling day and the corresponding stoichiometric ratio. Different symbols represent the different treatments. Both axes were natural logarithmic transformed. Linear (solid lines) and squared (dashed lines) regression models were applied. Equations, R

^{2}-, and p-values are shown in Table 5.

**Table 1.**Modeled growth parameters of M. aeruginosa grown with 8 different initial PO

_{4}

^{3−}-concentrations. The model was based on cell abundance (Figure 1a). Mean values (±SD, n = 3–4) of the initial cell abundance (N

_{0}), carrying capacity (K) and maximal growth rate (µ

_{max}) were determined and tested for differences between treatments (one-way ANOVA followed by a Tukey post-hoc test, p = 0.05). Capital letters indicate significant differences between treatments.

PO_{4}^{3−} [µM] | N_{0} [10^{4} Cells mL^{−1}] | K [10^{4} Cells mL^{−1}] | µ_{max} [d^{−1}] | R_{2} Fits ^{1} |
---|---|---|---|---|

5 | 24.7 ± 3.3 | 1022 ± 63 ^{A} | 0.34 ± 0.03 ^{AB} | 0.82 |

10 | 14.8 ± 4.5 | 1272 ± 172 ^{A} | 0.33 ± 0.02 ^{AB} | 0.87 |

15 | 18.2 ± 18.7 | 4718 ± 362 ^{C} | 0.45 ± 0.09 ^{A} | 0.96 |

20 | 7.28 ± 7.24 | 4284 ± 247 ^{BC} | 0.44 ± 0.06 ^{A} | 0.97 |

30 | 51.7 ± 14.8 | 4328 ± 381 ^{BC} | 0.27 ± 0.04 ^{B} | 0.94 |

40 | 62.9 ± 32.6 | 3729 ± 516 ^{B} | 0.25 ± 0.04 ^{B} | 0.92 |

50 | 7.9 ± 6.5 | 4129 ± 484 ^{BC} | 0.43 ± 0.1 ^{A} | 0.93 |

75 | 23.9 ± 19.9 | 4312 ± 239 ^{BC} | 0.33 ± 0.06 ^{AB} | 0.94 |

^{1}General fit equation: ${\mathrm{N}}_{\mathrm{t}}=\left(\mathrm{K}\xb7{\mathrm{N}}_{0}\right)/({\mathrm{N}}_{0}+({\mathrm{K}-\mathrm{N}}_{0})\text{}{\mathrm{e}}^{{-\mathsf{\mu}}_{\mathrm{max}}\mathrm{t}})$ with t = time [d], N

_{t}= cell abundance at time t [cells mL

^{−1}].

**Table 2.**Modeled growth parameters of M. aeruginosa grown with 8 different initial PO

_{4}

^{3−}-concentrations. The model was based on POC (Figure 1b). Mean values (± SD, n = 3–4) of the initial POC concentration (N

_{0}), carrying capacity (K), and maximal growth rate (µ

_{max}) were determined and tested for differences between treatments (one-way ANOVA followed by a Tukey post-hoc test, p = 0.05). Capital letters indicate significant differences between treatments.

PO_{4}^{3} [µM] | N_{0} [µg C mL^{−1}] | K [µg C mL^{−1}] | µ_{max} [d^{−1}] | R^{2} Fits ^{1} |
---|---|---|---|---|

5 | 5.02 ± 1.17 | 93.1 ± 3.6 ^{A} | 0.24 ± 0.03 ^{A} | 0.98 |

10 | 1.17 ± 0.13 | 106.2 ± 4.6 ^{AB} | 0.32 ± 0.004 ^{AB} | 0.98 |

15 | 2.92 ± 1.62 | 272.9 ± 9.2 ^{CD} | 0.41 ± 0.04 ^{BCD} | 0.99 |

20 | 0.32 ± 0.18 | 230.8 ± 5.5 ^{BC} | 0.5 ± 0.03 ^{D} | 0.99 |

30 | 1.07 ± 0.84 | 345.1 ± 20.4 ^{E} | 0.49 ± 0.05 ^{D} | 0.92 |

40 | 3.82 ± 2.33 | 415 ± 67.3 ^{F} | 0.37 ± 0.07 ^{BC} | 0.97 |

50 | 0.03 ± 0.008 | 327.5 ± 6.8 ^{DE} | 0.67 ± 0.02 ^{E} | 0.99 |

75 | 0.5 ± 0.28 | 473.4 ± 9.6 ^{F} | 0.45 ± 0.04 ^{CD} | 0.99 |

^{1}General fit equation: ${\mathrm{N}}_{\mathrm{t}}=\left(\mathrm{K}\xb7{\mathrm{N}}_{0}\right)/({\mathrm{N}}_{0}+({\mathrm{K}-\mathrm{N}}_{0})\text{}{\mathrm{e}}^{{-\mathsf{\mu}}_{\mathrm{max}}\mathrm{t}})$ with t = time [d], N

_{t}= POC at time t [µg C mL

^{−1}].

**Table 3.**Linear mixed-effects model for the particulate phosphorus (P

_{part}; Figure 1c) and biomass stoichiometry (C:N, C:P, N:P; Figure 2) of M. aeruginosa during a 28 days growth experiment. Mean Sq: mean square, NumDF: numerator degrees of freedom, DenDF: denominator DF, F value: test statistic.

Mean Sq | NumDF | DenDF | F Value | p-Value ^{1} | ||
---|---|---|---|---|---|---|

P_{part} | ||||||

Treatment | 1.18 × 10^{−7} | 7 | 261.25 | 0.6286 | 0.7321 | |

Day | 5.27 × 10^{−5} | 1 | 260.22 | 280.27 | <2 × 10^{−16} | *** |

Treatment x Day | 3.53 × 10^{−6} | 7 | 260.22 | 18.74 | <2 × 10^{−16} | *** |

C:N-ratio | ||||||

Treatment | 138.742 | 7 | 197.87 | 67.057 | <2.2 × 10^{−16} | *** |

Day | 250.839 | 9 | 197.07 | 121.265 | <2.2 × 10^{−16} | *** |

Treatment x Day | 26.879 | 63 | 197.07 | 12.994 | <2.2 × 10^{−16} | *** |

C:P-ratio | ||||||

Treatment | 614,724 | 7 | 197.02 | 54.6476 | <2.2 × 10^{−16} | *** |

Day | 1,364,784 | 9 | 197.22 | 121.3772 | <2.2 × 10^{−16} | *** |

Treatment x Day | 32,240 | 63 | 197.22 | 2.8673 | 1.323 × 10^{−08} | *** |

N:P-ratio | ||||||

Treatment | 12,892.4 | 7 | 197.97 | 110.690 | <2.2 × 10^{−16} | *** |

Day | 8209.8 | 9 | 197.06 | 70.502 | <2.2 × 10^{−16} | *** |

Treatment x Day | 724.3 | 63 | 197.06 | 6.220 | <2.2 × 10^{−16} | *** |

^{1}Significance levels: *** <0.001.

**Table 4.**Linear mixed-effects model for effects of treatment and sampling day on the protease inhibitor content of BN920 and CP954 in M. aeruginosa. Mean Sq: mean square, NumDF: numerator degrees of freedom, DenDF: denominator DF, F value: test statistic.

Mean Sq | NumDF | DenDF | F Value | p-Value ^{1} | ||
---|---|---|---|---|---|---|

BN920 | ||||||

Treatment | 71.715 | 7 | 197.36 | 18.2154 | <2.2 × 10^{−16} | *** |

Day | 172.494 | 9 | 197.14 | 43.8287 | <2.2 × 10^{−16} | *** |

Treatment x Day | 14.333 | 63 | 197.14 | 3.6419 | 2.744 × 10^{−12} | *** |

CP954 | ||||||

Treatment | 4408.5 | 7 | 197.47 | 53.8364 | <2.2 × 10^{−16} | *** |

Day | 2508.0 | 9 | 197.12 | 30.6383 | <2.2 × 10^{−16} | *** |

Treatment x Day | 342.5 | 63 | 197.12 | 4.1837 | 8.755 × 10^{−15} | *** |

^{1}Significance levels: *** <0.001.

**Table 5.**Equations, parameters, and significance tests for the regression models applied to the data in Figure 4 and Figure 5. Data were natural logarithmic (ln) resp. square root transformed and then analyzed using linear mixed-effects models with a single explanation variable (x) as fixed effect (intercept, slope and quadratic term) and individual slopes for the 28 treatment x replicate combinations. The null model (-) contains the same random effects but omitted the explanation variable. p-values indicate significant likelihood ratio (Chi

^{2}) of consecutive models (i.e., between linear and null model resp. between quadratic and linear model). R

_{p}: Pearson correlation coefficient, calculated from the transformed x and y data: R

^{2}: coefficient of determination of the mixed model (fixed effects only). AIC: Akaike information criterion.

Euation | R_{p} | R^{2} | AIC | p-Value ^{1} | ||
---|---|---|---|---|---|---|

BN920 | ||||||

- | ln y = 1.576 | 497.1 | - | |||

µmax | ln y = 1.072 + 0.9555 $\sqrt{\mathrm{x}}$ | 0.37 | 0.13 | 493.1 | 1.00 × 10^{−6} | *** |

µmax | ln y = 0.9178 + 2.578 $\sqrt{\mathrm{x}}$ − 2.28 x | 0.14 | 485.3 | 3.6 × 10^{−4} | ** | |

C:N ratio | ln y = 3.098 − 0.7396 ln x | −0.43 | 0.18 | 442.3 | 1.20 × 10^{−14} | *** |

C:N ratio | ln y = 6.426 − 3.612 ln x + 0.6036 (ln x)^{2} | 0.21 | 439.7 | 0.018 | * | |

C:P ratio | ln y = 2.916 − 0.2622 ln x | −0.38 | 0.14 | 449.0 | 2.00 × 10^{−13} | *** |

C:P ratio | ln y = 3.179 − 0.3684 ln x + 0.01027 (ln x)^{2} | 0.14 | 456.3 | 0.7 | ||

N:P ratio | ln y = 2.301 − 0.2586 ln x | −0.24 | 0.05 | 473.6 | 4.60 × 10^{−8} | *** |

N:P ratio | ln y = 2.838 − 0.6473 ln x + 0.06414 (ln x)^{2} | 0.09 | 477.5 | 0.081 | ||

CP954 | ||||||

- | ln y = 2.768 | 549.1 | - | |||

µmax | ln y = 2.989 − 0.03925 $\sqrt{\mathrm{x}}$ | −0.07 | 0.002 | 597.5 | 0.83 | |

µmax | ln y = 2.591 + 4.081 $\sqrt{\mathrm{x}}$ − 5.75 x | 0.11 | 557.9 | 2.40 × 10^{−10} | *** | |

C:N ratio | ln y = 2.602 + 0.1626 ln x | −0.009 | −0.01 | 565.0 | 0.16 | |

C:N ratio | ln y = −7.357 + 8.758 ln x − 1.807 (ln x)^{2} | 0.10 | 533.8 | 5.60 × 10^{−9} | *** | |

C:P ratio | ln y = 1.736 + 0.2134 ln x | 0.37 | 0.13 | 533.5 | 1.50 × 10^{−7} | *** |

C:P ratio | ln y = −1.478 + 1.515 ln x − 0.126 (ln x)^{2} | 0.14 | 524.6 | 6.10 × 10^{−5} | *** | |

N:P ratio | ln y = 1.896 + 0.3076 ln x | 0.42 | 0.18 | 519.5 | 1.70 × 10^{−9} | *** |

N:P ratio | ln y = 1.135 + 0.8565 ln x − 0.09022 (ln x)^{2} | 0.17 | 521.1 | 0.026 | * |

^{1}Significance levels: *** <0.001, ** <0.01, * <0.05.

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**MDPI and ACS Style**

Burberg, C.; Petzoldt, T.; von Elert, E.
Phosphate Limitation Increases Content of Protease Inhibitors in the Cyanobacterium *Microcystis aeruginosa*. *Toxins* **2020**, *12*, 33.
https://doi.org/10.3390/toxins12010033

**AMA Style**

Burberg C, Petzoldt T, von Elert E.
Phosphate Limitation Increases Content of Protease Inhibitors in the Cyanobacterium *Microcystis aeruginosa*. *Toxins*. 2020; 12(1):33.
https://doi.org/10.3390/toxins12010033

**Chicago/Turabian Style**

Burberg, Christian, Thomas Petzoldt, and Eric von Elert.
2020. "Phosphate Limitation Increases Content of Protease Inhibitors in the Cyanobacterium *Microcystis aeruginosa*" *Toxins* 12, no. 1: 33.
https://doi.org/10.3390/toxins12010033