Assessing microbial growth monitoring methods for challenging strains and cultures

This is a paper focusing on the comparison of growth curves using field relevant testing methods and moving away from colony counts. Challenges exist to explore antimicrobial growth of fastidious strains, poorly culturable bacterial and bacterial communities of environmental interest. Thus, various approaches have been explored to follow bacteria growth that can be an efficient surrogate for classical optical density or colony forming unit measurements. Here we tested optical density, ATP assays, DNA concentrations and 16S rRNA qPCR as means to monitor pure culture growth of six different species including Acetobacterium woodii, Bacillus subtilis, Desulfovibrio vulgaris, Geoalkalibacter subterraneus, Pseudomonas putida and Thauera aromatica. Optical density is and excellent, rapid monitoring method of pure culture planktonic cells but cannot be applied to environmental or complex samples. ATP assays provide rapid results but conversions to cell counts may be misleading for different species. DNA concentration is a very reliable technique which can be used for any sample type and provides genetic materials for downstream applications. qPCR of the 16S rRNA gene is a widely applicable technique for monitoring microbial cell concentrations but is susceptible to variation between replicates. DNA concentrations were found to correlate the best with the other three assays and provides the advantages of rapid extraction, consistency between replicates and potential for downstream analysis, DNA concentrations is determined to be the best universal monitoring method for complex environmental samples.


34
Assessing growth is fundamental to nearly all microbial studies. On the surface this is an easy 35 procedure done in introductory courses worldwide [1]. However, it turns out this routine experiment is not as trivial as one thinks. For easily culturable aerobic species, the process is relatively simple as the 37 growth medium need only contain the appropriate carbon sources and essential nutrients to culture the 38 typical well studied model microbes. After the appropriate growth medium has been selected, direct cell 39 counting on agar plates can be performed for accurate quantification providing colony forming units (CFU) 40 or viable cell count (VCC) values. However, it has become apparent that this method restricts the scope 41 of species possible for study and cannot but use to study complex environments [2][3][4]. For more rapid 42 analysis, optical density (OD) is evaluating the scatting of light by cells, either using the classical Klett meter 43 or an absorption spectrometer set to 550 or 600 nm. This however is also limiting due to suspended 44 material in various growth media and the inability to decipher living vs dead vs flocculation from 45 extracellular polysaccharides. Additionally, size and shape of cells effect scattering and can lead to 46 misinterpretation of cell numbers. Related rapid assays includes color dependant activity assays (colloquially known as bug bottles or biological activity reaction test (BART) bottles [5]), microscopy (grid 48 cell counting and live dead staining ) [6][7][8], dyes such as crystal violet or 5-(4,6-dichlorotriazinyl) 49 aminofluorescein (DTAF) for total biomass staining [9,10] or metabolic dyes to determine actively 50 respiring cells such as 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) [10]. Fluorescent in situ hybridization 51 (FISH) is a target-specific approach which relies on a fluorescent reporter attached to a nucleic probe to 52 determine the presence and abundance of the target sequence. This can be used for total or genera-53 specific cell enumeration when targeting a gene such as 16S rRNA [11,12]. Now in our genomics era, 16S 54 rRNA quantification using quantitative PCR (qPCR) is gaining popularity with [13] Bakken and Olson, 1989 [33]. To convert qPCR values into cell counts, the copies of 16S rRNA genes per µL were converted to copies mL -1 , then divided by the 16S rRNA gene copies counted in the 131 NCBI sequenced genomes (see Table 1).

135
Optical Density

136
Growth of each culture was monitored using OD 600 (reference media was each species' It is noted that this formula should be confirmed by plating cells at unique OD 600 values and validating the 151 formula for each pure culture. It is acknowledged that the differences in culture turbidity of the six species 152 used in this study and the inability to grow all six on plated media means converting OD 600 to CFU values 153 will be an inaccurate conversion, but this was still done to maintain uniformity between datasets and to 154 highlight the issue of using such conversion factors without confirming and modifying the equation 155 empirically for each species. Thus we used this to convert values for time zero, a time point to represent 156 the mid log phase and a time point to represent the stationary phase, which are reported in Table 5. (1 ) * 1000 (1) 166 Calculated ME are plotted in Figure 2. Data is not displayed in log scale to better illustrate fluctuations in 167 data trends. A. woodii shows a gradual increase in ME/mL between time 0 and 24 hours, going from 8.77 168 x 10 7 ME/mL (T = 0) to 7.48 x 10 8 ME/mL at 24 hours ( Fig. 2A). There was a sharp increase after this point 169 to 1.45 x 10 9 ME/mL at 32 hours, after which readings remained relatively stable at 1.33 x 10 9 ME/mL until 170 the final time point of 48 hours. B. subtilis readings were less constant, reaching peak values at 13 hours 171 (2.64 x 10 8 ME/mL) before dipping to 8.26 x 10 7 ME/mL at 24 hours and subsequently recovering to 2.47 x 10 8 ME/mL at 37 hours. After this point readings gradually decrease to 1.58 x 10 8 ME/mL at the final 173 timepoint of 48 hours (Fig. 2B). D. vulgaris ME/mL readings did not follow a typical sigmoidal growth curve, 174 rather they peaked at 31 hours (3.01 x 10 8 ME/mL) before declining to 1.56 x 10 8 ME/mL at 44 hours 175 where it remained relatively stable for the remainder of the time points (Fig. 2C). G. subterraneus had a 176 similar trend in ME, with the peak occurring at 12 hours (2.41 x 10 8 ME/mL) before decreasing to 6.30 x 177 10 7 ME/mL at the final time point (T = 36 hours) (Fig. 2D). P. putida followed a sigmoidal curve with a short 178 lag phase in the first two hours (4.12 x 10 7 ME/mL at T = 0 to 1.17 x 10 8 at T = 2 hours) before reaching 179 5.77 x 10 8 ME/mL at 6 hours, after which it gradually increased for the remainder of the growth curve, 180 reaching 9.89 x 10 8 ME/mL at T = 49.5 hours (Fig. 2E). T. aromatica followed a sigmoidal curve with a lag 181 phase between 0 and 8 hours (5.81 x 10 7 ME/mL and 9.15 x 10 7 ME/mL, respectively) before increasing to to 21.1 µg/mL at T= 28 hours then remained stable between 23.6 µg/mL and 27.5 µg/mL (Fig. 3A). B.

261
Cell count equivalents for 16S qPCR readings are reported in Table 5 and were calculated using 262 the unique 16S rRNA gene copy numbers (per cell) as reported in Table 1.

431
While this work focused on pure cultures of diverse environmental strains, we believe these 432 results can be extrapolated to mixed species and field samples with highly diverse microbial populations.

433
The simplest and most impactful conclusion from this is that there is no true, or best method for 434 monitoring microbial growth, rather being consistent with a monitoring technique is the most important 435 factor and to understand the used approaches limitations as illustrated here.