<p>In the oil and gas industry, internal corrosion represents one of the major threats to asset lifetime and integrity. Of the types of internal corrosion, microbiologically influenced corrosion (MIC) is the most difficult to predict and monitor due to the unpredictable nature of microbial growth and the minimal metal loss resulting in through wall failure (pitting). MIC results from biofilm communities interacting directly and indirectly with the metal. Due to the structure and nature of these pipelines, directly monitoring sessile growth is impossible. As a result, most MIC monitoring is done through planktonic cells retrieved from fluid samples as a proxy for sessile populations.</p>
<p>Growth curves are one of the most fundamental methods of quantitatively assessing microbial growth. In the lab, pure cultures are measured using optical densities, biomass staining, direct microscopic counting and counting colony forming units (CFU) on specialized media while more advanced techniques involve quantitative PCR (qPCR) of key genes. While PCR technologies are more easily transferred from the field to the lab, CFU counts are impossible in the field. Alternatives to the CFU are colorimetric activity assays such as &#8220;bug bottles&#8221; or biological activity reaction test (BART) bottles but aren&#8217;t sensitive and require long incubation times. More sensitive assays such as ATP measurements are also used but can be misleading as high metabolically active samples will give higher cell count equivalents than a metabolically slow community of an identical size.</p>
<p>To systematically evaluate a best practice, we conducted growth curves in a lab scenario using six pure cultures and techniques predominantly used in the field to determine how these techniques compare and accurately measure microbial growth. The six species used are Acetobacterium woodii, Bacillus subtilis, Desulfovibrio vulgaris, Geoalkalibacter subterraneus, Pseudomonas putida and Thauera aromatica. The techniques used are optical density at 600 nm, ATP activity measurements using a luciferase-based assay, DNA concentration and 16S rRNA copy numbers.</p>
<p>It was found that most lines of data follow the expected sigmoidal growth curve to varying degrees for all species. OD<sub>600</sub> readings follow the expected sigmoidal curves, exhibiting a lag phase, log growth phase and a stationary phase. ATP peaks during mid log phase and quickly declines, never showing a distinct stationary phase, while DNA concentrations closely follow the OD<sub>600</sub> readings but decline to death phase more rapidly. qPCR of the 16S rRNA genes revealed this data followed the same trends but was less susceptible to fluctuations.</p>
<p>Assessing microbial biofilms in the environment and on anthropogenic industrial infrastructure is extremely challenging given sampling, storage and transportation to the lab.&#160; This work begins to establish best practices for growth of environmental communities to be followed.&#160; Cumulatively, this work shows that each approach supports the expected growth curve. Considerations should be made if all field data is of a single type, e.g. ATP, as it measures activity and not total cell count. Collecting even two lines of evidence in the field will greatly improve the quality of assessment and strengthen any conclusions regarding assessment of microbial growth.</p>
Acinetobacter baylyi Starvation-Induced Genes Identified through Incubation in Long-Term Stationary Phase