Agreement between theory and measurement in quantification of ammonia-oxidizing bacteria

Abstract

Autotrophic ammonia-oxidizing bacteria (AOB) are of vital importance to wastewater treatment plants (WWTP), as well as being an intriguing group of microorganisms in their own right. To date, corroboration of quantitative measurements of AOB by fluorescence in situ hybridization (FISH) has relied on assessment of the ammonia oxidation rate per cell, relative to published values for cultured AOB. Validation of cell counts on the basis of substrate transformation rates is problematic, however, because published cell-specific ammonia oxidation rates vary by over two orders of magnitude. We present a method that uses FISH in conjunction with confocal scanning laser microscopy to quantify AOB in WWTP, where AOB are typically observed as microcolonies. The method is comparatively simple, requiring neither detailed cell counts or image analysis, and yet it can give estimates of either cell numbers or biomass. Microcolony volume and diameter were found to have a log-normal distribution. We were able to show that virtually all (>96%) of the AOB biomass occurred as microcolonies. Counts of microcolony abundance and measurement of their diameter coupled with a calibration of microcolony dimensions against cell numbers or AOB biomass were used to determine AOB cell numbers and biomass in WWTP. Cell-specific ammonia oxidation rates varied between plants by over three orders of magnitude, suggesting that cell-specific ammonia oxidation is an important process variable. Moreover, when measured AOB biomass was compared with process-based estimates of AOB biomass, the two values were in agreement.

FIG. 1.

Fluorescence conferred by probe Nso1225 to whole fixed cells of Nitrosomonas europaea and Ralstonia eutropha at different formamide concentrations. Optimization was done under the protocol of Daims et al. (11) (squares and dashed line), under the protocol used in this paper (diamonds and solid line), and to the nontarget species Ralstonia eutropha (triangles and dotted line). Error bars indicate standard deviations among individual cells in a sample.

FIG. 2.

A. Number of microcolonies that must be counted to ensure having a 95% chance of detecting a given difference in microcolony diameter significant at the 5% level. The number of microcolonies can be decreased by accepting a marginally lower chance of detecting a given difference. B. Numbers of fields of view (FOV) that must be counted to give an 80% chance of detecting a difference of one AOB microcolony per field of view between two samples at the 5% level of significance. It was calculated that a sample size of 46 fields of view was required.

FIG. 3.

Single optical slice through a section of an activated sludge floc (from Wanlip), showing Nso1225-labeled microcolonies of a range of diameters, most of which are of above average (see Fig. 4) for this plant. Bar, 10 μm. Although the cells are very close together, they will not all appear to touch, as even perfectly packed spheroids make contact with adjacent particles at only one point on any given side.

FIG. 4.

Probability distributions of microcolony diameter for raw measurements (lower panel) and log-transformed measurements (upper panel) for 89 microcolonies from a full-scale wastewater treatment plant (Wanlip), using Nso1225. The putative unobserved fractions are shown as the shaded area in the log-transformed data and were calculated to be less than ca. 3.5% of the total possible observations.

FIG. 5.

Relationship between microcolony size and AOB cell numbers in activated sludge for the raw (A) and natural-log-transformed (B) data. For the raw data, the r² value was 0.89 and both the slope (0.66) and the intercept (101) were statistically significant (P < 0.001). However, both sets of raw data were log-normally distributed, and a Box-Cox analysis showed that a natural log transformation was appropriate. For the natural-log-transformed data, the r² value was 0.81 and both the slope (0.64) and the intercept (2.1) were statistically significant (P < 0.001). We recommend the use of the log-transformed data. CI, confidence interval; PI, prediction interval.

FIG. 6.

Variation of the cell-specific ammonia oxidation rate with the concentration of AOB. The plant with the lowest rate is Wanlip, while the plant with the highest rate is Hydburn.

FIG. 7.

A. Relationship between theoretical and measured AOB fractions in full-scale activated sludge reactors in the United Kingdom and a bench-scale reactor. The regression line has a statistically significant (P = 0.003) slope of 1.28 (standard error of 0.20) and an intercept (−2.8) that is statistically distinguishable from 0 (P = 0.022). The regression line explained 89% of the variation between the two estimates. X_aob/X_v is the proportion of the total biomass measured as MLVSS that is contributed by the AOB. Hydburn was identified as an outlier by using Dixon's test (P < 0.0.05). B. Relationship between theoretical and measured AOB fractions in full-scale activated sludge reactors in the United Kingdom and a bench-scale reactor, but with Hydburn removed. The slope is 1.23 (standard error of 0.16), r² rises to 94%, and the intercept (−2.47) is still significantly different from zero (although only marginally so) (P = 0.034). CI, confidence interval.