Applications of molecular microbiological methods

Microbiologically influenced corrosion (MIC) is an enormous problem in different industrial sectors with large economical consequences. Although research has been performed on microorganisms associated with MIC, the causative microbes have not been identified yet. Traditional microbiological techniques have been used, but they only detect 1% of the microbes in nature and so are not suited for this purpose. Molecular methods, based on DNA and RNA, are more efficient to characterize microbial communities in different environments, as they are sensitive and reliable, and not dependent on cultivation. In this chapter we give an overview of nucleic acid-based molecular methods that have been used to study microbial communities associated with MIC and give an outlook on the use of novel, state-of-the art methods that might be applied in the future. Introduction For decades, metal corrosion, and particularly aqueous corrosion, has led to an enormous economic loss, as well as to different safety (e.g. nuclear power plants) and conservation (e.g. cultural heritage) issues. The total costs for metal corrosion in the United States were estimated at $276 billion per year (Schmitt et al., 2009). From these figures, the costs represented by microbiologically influenced corrosion (MIC), or corrosion associated with the action of microorganisms (‘biocorrosion’) would represent 20–30%, affecting many industrial sectors (e.g. gas and oil industry, drinking water industry, wastewater treatment industry, as well as the aviation and maritime sectors). Theoretically, the occurrence of principally electrochemical reactions on a metal surface can be predicted by thermodynamics laws: the metal (the anode, where oxidation reaction with loss of electrons takes place) in contact with a conductive fluid or vapour (mostly water) and a reducing compound (the cathode, where the reduction reaction takes place, with gain of electrons), usually oxygen or hydroxyl ions depending on the pH of the aqueous solution, will be oxidized, a process called ‘corrosion’. However, the corrosion rates controlled by charge-transfer processes at the metal surface, which scale, multicity and dynamics are largely unknown in natural environments, are still very difficult to predict. The task becomes even more challenging when microbiological factors are involved, such as in MIC. In principle, any microorganism present on a metal surface may cause local electrochemical changes near the metal surface, influencing corrosion kinetics, either passively (their presence producing physical surface heterogeneity with different local potentials) or actively (as the result of physiological properties, e.g. the metal binding nature of exopolymeric substances, and metabolism, e.g. respiration, metal reduction) (Little and Lee, 2007). So far, pioneered by the work of Von Wolzogen Kuhr and Van der Vlugt (1934) sulphidogens, those organisms that can reduce sulphate, thiosulphate, and/or sulphur to sulphide, have been the focus of most studies, and one of the most often MIC key players incriminated (e.g. Larsen et al., 2010; Almahamedh et al., 2011; Lee et al., 2012). This metabolic group, affiliated to the domain Bacteria (in particular to the Deltaproteobacteria and Firmicutes), and Muyzer and Marty 8 | to the Archaea (Muyzer and Stams, 2008), are physiologically and ecologically versatile owing to their proliferation in different industrial contexts (e.g. harbour structures, gas and petroleum pipelines) (Davidova et al., 2012; Kjellerup et al., 2009; Ravot et al., 2005). Organic sulphur moieties of petroleum compounds have also been assumed to be used as electron acceptors in the absence of inorganic sulphur molecules (Orphan et al., 2003). Nevertheless, bacteria from other phylogenetic groups (e.g. Citrobacter within the Gammaproteobacteria) have been associated with sulphide production and MIC (Agrawal et al., 2010; Angeles-Ch et al., 2002). Moreover, other causative microorganisms have been consistently evidenced and classified as slime-producing bacteria (e.g. Pseudomonas and other bacteria from the Gammaproteobacteria) (Lopez et al., 2006; (Pavissich et al., 2010), sulphur-oxidizing bacteria (e.g. Thiobacillus within the Betaproteobacteria, Thiomicrospira within the Gammaproteobacteria) (Okabe et al., 2007; Korenblum et al., 2010), metal-oxidizing bacteria (e.g. Gallionella and Leptothrix from the Betaproteobacteria, Mariprofundus from the Zetaproteobacteria (McBeth et al., 2011), and Bacillus from the Firmicutes) and metalreducing bacteria (e.g. Shewanella, Geobacter, Bacillus, respectively from the Gamma-, Deltaproteobacteria, and the Firmicutes) (Teng et al., 2008; Almahamedh et al., 2010) as well as acid-producing bacteria, such as fermenters, acetogens (e.g. Clostridium within the Firmicutes), and even fungi (Oliveira et al., 2011). Additionally, methanogenesis (Zhang et al., 2003), reduction and oxidation of nitrogen compounds (e.g. ammonium, nitrite, nitrate), aerobic respiration/photosynthesis, hydrogenotrophy and hydrogen production have also been associated with MIC events (Little and Lee, 2007). However, it is generally observed that although a single type of organism can lead to MIC, the most severe cases of MIC have been reported in the presence of microbial consortia with different interacting metabolic groups (e.g. Kan et al., 2011). The nature and dynamics of these interactions are however largely unknown. Furthermore, a lot of bacteria are metabolically versatile and therefore may not accelerate corrosion by a single mechanism, but via different pathways depending on the local physico-chemical environment (e.g. hydrogenotrophy, sulphate and iron reduction for some SRB). Until recently, most of the research and monitoring of microbial populations involved in MIC have been based on culturing approaches intended to select for different metabolic groups. An example of a routinely used method is the MostProbable-Number (MPN) approach in which bacterial numbers in a sample are determined based on positive growth detected over replicate serial dilutions. However, it is well acknowledged that only a limited number (less than 1%) of microorganisms present in the environment are amenable to current culturing approaches making these techniques unreliable for detection and identification of microorganisms involved in MIC (e.g. Price et al., 2010). Furthermore, culturing media are usually not selective enough to target a specific type of bacteria (e.g. Zhu and Kilbane, 2004). In addition, in situ activities, dynamics, and interactions between community members cannot be approached with culturing techniques. Methods based on the detection of molecular markers, such as the 16S rRNA gene or genes encoding proteins (see Table 1.1 for an overview of these ‘functional’ genes), are today more relevant tools for a reliable detection, identification and enumeration of microorganisms in environmental samples, due to better accuracy, reproducibility and time-efficiency. Although these methods are routinely used in microbial ecology and environmental microbiology, their application in monitoring MIC-related processes is still relatively scarce. One of the reasons might be the lack of expertise and/or the relatively high costs for equipment and materials, although the latter might be negligible in comparison to the enormous financial costs caused by MIC. In this chapter we give an overview of the nucleic acid-based molecular methods and associated taxonomic/functional markers that have been used to study microbial communities associated with MIC (focusing on both Bacteria and Archaea) and give an outlook on the use of novel, state-of-the art methods that might be applied in the future (see Fig. 1.1 for a schematic overview). We have grouped the different nucleic acid-based approaches on the basis of three Methods in Corrosion Research, Monitoring and Control | 9 method principles: (i) PCR-based techniques, (ii) hybridization techniques, and (iii) meta-omic techniques. PCR-based techniques Community fingerprinting PCR-based techniques have often been used in combination with genetic fingerprinting techniques, such as denaturing gradient gel electrophoresis (DGGE; Muyzer, 1999), single strand conformation polymorphism (SSCP; Lee et al., 1996), and terminal restriction fragment length polymorphism (T-RFLP; Marsh, 1999). In DGGE, a mixture of DNA fragments of identical size is separated based on their difference in melting behaviour (determined by conformational and GCrichness of DNA sequences). The DNA fragments differing in their sequences (presumably corresponding to different community members) are electrophoresed in a polyacrylamide gel Table 1.1 Overview of functional genes that might be used in MIC studies Category Targeted gene Function References