Modelo de distribución de flujos piroclásticos

Hydrogen production is characterized by the presence of the H2 forming micro- organisms present in the microflora (Wirth et al., 2012). Therefore, establishing a enriched H2 producing microflora for sustainable bio-H2 production is of great importance (Koskinen et al., 2007). An understanding of the microbial composition of the heterogenous microflora present in mixed anaerobic communities will assist in optimizing the parameters for stable operation of the bioreactor.

Numerous methods based on molecular biology have been employed in characterizing these microorganisms. Most of the molecular biology methods are based on nucleic acid based assays, which are employed to examine the diversity of the microbial community (Zoetendal et al., 2004). The identification of species present in the microflora will assist us in understanding the metabolic activities associated with the microflora, including the characteristics of the identified microflora under different operational conditions and interactions between different groups of micro-organisms in mixed communities.

All molecular biology methods begins with extraction nucleic acid from microbial samples. These molecular technique involve cell lysis, contaminant removal, solvent extraction, precipitation and purification (Miller et al., 1999). The extracted DNA is subjected to polymerase chain reaction (PCR) amplification using primers which are designed based on either the relative DNA sequences or adapted from published findings according to the source of culture been used and target of interest (i.e., targeting specific group of microbial population). The amplified DNA is then cloned and sequenced to identify the species present in the microflora. The most commonly used gene sequence is the 16S rRNA gene (16S RDNA). The 16S rRNA gene has a huge database of over 3 million sequences available at GenBank (RDP, http://rdp.cme.msu.edu/). For this reason, the 16S rRNA gene-based technique is widely used for monitoring changes in microbial communities under different conditions.

Several studies have described molecular techniques used for characterizing H2 producing cultures and presented their advantages and disadvantages (Li et al., 2011; Nocker et al., 2007). The most widely used fingerprint techniques for identifying the diversity profiles of the microflora are denaturing gradient gel electrophoresis (DGGE)

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and terminal restriction fragment length polymorphism (T-RFLP). T-RFLP is preffered for comparing complex communities when high throughput and high sensitivity are required. In comparison, the DGGE method is widely used because of visualization, ease of sequencing of DGGE bands and its affordability. In addition, the separate bands may be isolated and sequenced to identify a specific species. The disadvantages of DGGE includes less sensitivity, long time to conduct the analysis (involving many intermediate steps), highly diversed communities are not easily identified and the method produces less resolved bands in samples containing small quantities of biomass. Therefore, T- RFLP is preffered for characterization of complex mixed microbial system.

The T-RFLP method is a high throughput community profiling technique with high reproducibility in terms of both qualitative and quantitative analysis of the microbial genome. The other main advantage of the T-RFLP method is that it can be standardized and used to compare data published by other researchers. The phylogenetic information (i.e. taxonomic identification) can be inferred from the T-RFs sizes by comparing them with sequences of known bacteria from standard databases available such as T-Align, PAT, MiCA, TRFMA etc. A background of T-RFLP used in the current research work is described in section 2.10.1.

2.10.1 Terminal restriction fragment length polymorphism

Terminal restriction fragment length polymorphism (T-RFLP) is a fingerprint technique used to identify the composition of bacterial communities through the use of restriction enzymes. Moeseneder et al. (1999) studied optimization of the T-RFLP method and compared that to DGGE. These authors observed that results obtained from T-RFLP had better or similar outcomes in comparsion to the DGGE. In T-RFLP, the PCR amplification is carried out by labeling one end (5’end) of the primer with fluorescence to amplify the targeted region of the 16S rRNA gene. The PCR amplified product is then treated with restriction endonuclease which generates fragments of different sizes based on the specificity of the restriction enzyme used. The terminal restriction fragments (TRFs) generated by the restriction enzymes are used for both qualitative and quantitative analyses of the microbial diversity of the cultures (Liu et al., 1997). A schematic representation of the T-RFLP technique is shown in Figure 2.7.

63 Microbial consortia 1 DNA Extraction

DNA extraction using extraction buffers, chloroform extract etc.,

5’ 3’ 3’ 5’ 5’ 3’ 3’ 5’ 5’ 3’ 3’ 5’ 2 PCR with fluorescently labeled forward primer

5’ 3’ 3’ 5’ 5’ 3’ 3’ 5’ 5’ 3’ 3’ 5’ 3 Restriction of PCR product Loading fragment samples in gel electrophoresis 4 Fragment separation in the gel 5 Analysis

Figure 2.7 Schematic representation of the steps involved in T-RFLP analysis

In T-RFLP analysis, only the labeled end fragment is detected and this makes for an easier analysis of a complex microbial communities. Each labeled end fragment refers to a single operational taxonomic unit (OTU) present in mixed microbial cultures with a restriction site at the same location (Avaniss-Aghajani et al., 1994). Thus, the pattern of the fragments depicted in the T-RFLP profile represents the number of taxonomical units present in the microbial population.

Advantages of T-RFLP, which include higher resolution than other molecular techniques involving gel electrophoresis that use capillary electrophoresis has been outlined by Marsh (1999). Marsh (1999) also reported that the output of T-RFLP (a profile comprised of digital data) can be used readily in statistical analyses by converting the information to numerical data based on the size of the fragments obtained from T- RFLP. Other advantages of using T-RFLP in the analysis of mixed microbial communities include the capability to identify rare species within the population and the phylogenetic information that can be obtained from the size of the restriction fragments that were generated. The sizes of the terminal restriction fragments of the known bacteria can be obtained from databases, such as those maintained by T-align, TRFMA, and TAP as discussed previously (Li et al., 2011).

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Limitations of using T-RFLP Include the primers and salts must be removed from the PCR-products using clean up systems prior to analysis because the presence of these charged molecules can be misleading and bias the selective detection of charged molecules (Hoshino et al., 2006; Osborne et al., 2005). Thus, the assessment of phylognetic information obtained from the T-RFLP profile of the diversity of the microbial community becomes difficult (Nocker et al., 2007). Furthermore, using a single restriction enzyme for the analysis of a complex microbial community may reduce and over-simplify the data set leading to errors, therefore, using more restriction enzymes to obtain a diverse dataset is preferred.

Nevertheless, T-RFLP has been used widely for evaluating and identifying the dynamics and variability of mixed microbial communities present in H2 producing systems because of the reproducible characterization of the microbial cultures (Castello et al., 2009; Ueno et al., 2006). Chaganti et al. (2012b) conducted analyses of mixed anaerobic communities using clone library sequencing and T-RFLP and found that the T- RFLP technique (applied to three different sources of H2 producing mixed microbial culture to assess variation in the samples) produced findings that were reproducible. Hartmann et al. (2005) conducted studies using T-RFLP and ribosomal intergenic spacer analysis (RISA) and revealed that although T-RFLP is in principle more demanding, this technique offers the benefit of phylogenetic information about microorganisms detected in the soil sample.