HABILITACIÓN Requisitos:

RISA and ARISA examine length variation of the intergenic spacer regions (ITS) between 16S rRNA and 23S rRNA. RISA uses manual electrophoresis to quantify fragments present (Kirk et al. 2004), whereas ARISA does this using automatic DNA sequencers after labelling the forward primer with a fluorescent dye (Fisher and Triplett 1999). The peak in the electropherogram is sized using a size standard and OTU abundance estimated from peak areas or heights (Cardinale et al. 2004). Large DNA fragment sizes of up to 1,400 base pair (bp) in length can be separated by this technique (Fisher and Triplett 1999).

ARISA is inexpensive, rapid and reproducible, and can track and characterize microbial diversity and composition in different environments over temporal and spatial variations (Fisher and Triplett 1999; Brown et al. 2005). Because it is automated, bacterial composition, diversity and structure can be easily analysed in a large number of samples (Crump et al. 2003; Cardinale et al. 2004). The number of OTUs detected per sample by ARISA ranges from 38 to 232 (Fisher and Triplett 1999; Ranjard et al. 2000). However, it is limited by PCR biases like other fingerprinting tools (Kirk et al. 2004) and it is difficult to identify the organisms responsible for particular ARISA fragments as the majority of ITS sequences deposited in the National Centre for Biotechnology Information (GenBank, http://www.ncbi.nlm.nih.gov/) represent cultivated microorganisms and clinical strains (Brown et al. 2005). Also, the same lengths of ITS region can be found in unrelated organisms or multiple ITS lengths may found in the same species or even within different rRNA operons within the same bacterial isolate. However, ARISA patterns are reproducible. Multiple amplifications of the same sample give the same peak intensity and OTU number, and this pattern is not altered by changes in PCR cycle numbers (Brown et al. 2005; Kara and Shade 2009) .

49 Several studies have investigated the efficiency and robustness of ARISA in characterizing bacterial communities in aquatic environments. Diversity and composition of three different communities of freshwater bacteria were evaluated by Fisher and Triplett (1999) who found different patterns in the three communities but the same number of fragment sizes. These results led the authors to propose that ARISA was an effective and rapid tool for estimating bacterial community diversity and for tracking temporal and spatial variations in composition. Schwalbach et al. (2004) assessed the changes of the abundance of some marine bacterial phylotypes exposed to different viral treatments using ARISA and TRFLP. Both methods detected changes in composition, but gave the same number of taxa. Lear and Lewis (2009) used ARISA to reveal impacts of different land use on bacterial communities in freshwaters. Danovaro et al. (2006) found that ARISA and T-RFLP were equally effective in discriminating between Pseudomonas isolates in different aquatic habitats. However, some studies have observed higher abundance and diversity of bacteria using ARISA.

Some modifications of ARISA may increase its sensitivity. Quantitative-ARISA aims to estimate the number of DNA fragment sizes present by making different dilutions of samples. Ramette (2009) used this to investigate microbial community richness in marine sediments. Nested ARISA can be used to test a large number of low volume samples or that might contain a small biomass of microbes. Lear and Lewis (2009) used this to investigate the influence of anthropogenic activities on bacterial community structure in four streams in New Zealand.

1- Intergenic transcribed spacer (ITS) region

Ribosomes are essential for protein synthesis and all bacteria have ribosomal operons containing 16S rRNA genes, 23S rRNA genes and 5S rRNA genes (Brown and Fuhrman 2005; Wolska and Szweda 2012). These contain approximately 1650, 3300 and 120 base pairs, respectively, and are highly conserved during evolution (Rossello-Mora and Amann 2001). The internal transcribed spacer ITS region between the 16S rRNA and 23S rRNA genes can contain 0, 1 or 2 tRNAs (Barry et al. 1991), but apart from these sections, it is less conserved than 16S or 23S (Brown and Fuhrman 2005). The ITS region contributes to the correct folding of nascent rRNA as it contains anti-termination motifs (Dall'Agnol et al. 2012). The ITS region varies greatly in length and nucleotide sequences because of the fact that the sequences of this region are less conservative among bacteria. This high variability makes it suitable for detecting differences between bacterial strains, and closely related species (Fisher and Triplett 1999; Fisher et al. 2000; Brown and Fuhrman 2005). For example, closely related strains of Cyanobacteria, Prochlorococcus or Synechococcus have been successfully delineated using ITS sequences by Rocap et al. (2002) and Dall'Agnol et al. (2012). Man et al. (2010) investigated the efficiency of three 16S rRNA, ITS and 23S rRNA in identifying and differentiating species and strains of Campylobacter and found that the ITS region had the best discriminatory power. They concluded that ITS genes require less effort and time to be amplified, sequenced and assembled compared with others. For example, to amplify and

50 sequence the complete ITS genes (~ 1000 base pair in length), just one pair of primers could be used compared with 4 and 8 different primers for 16S rRNA and 23S rRNA. However, more than a number of copies can be produced from a single bacterial cell and in turn may lead to the overestimation of bacterial diversity in the targeted environment (Dall'Agnol et al. 2012). Brown and Fuhrman (2005) compared the efficiency of ITS genes and 16S rRNA in revealing the diversity of marine bacteria. They found that ITS genes gave information on fine-scale phylogeny and allow more detailed discrimination of spatial patterns than 16S rRNA gene analysis. Large numbers of bacterial lineages have been identified using ITS genes. They concluded that the ITS region was the best marker for microbial diversity and biogeographical research.

Studies based on RNA are more accurate than those based on DNA in describing the active members of a microbial community. Active cells contain higher concentrations of ribosomes, and RNA is degraded much more rapidly after cell death (Revetta et al. 2010). However, ARISA fingerprints cannot be obtained from RNA, as ribosomal RNA is rapidly cleaved from a transcribed spacer after synthesis.

2- Primers

ARISA, like other approaches involving PCR amplification of heterogeneous targets, requires an appropriate choice of primer pairs that efficiently amplify sequences from a broad range of organisms, ideally without taxonomic bias (Gurtler and Stanisich 1996). Cardinale et al. (2004) assessed the efficiency of three different ARISA primer sets (1406F/23S, ITSF/ITSReub and S-D-Bact-1522-b-S-20/L-D-Bact-132-a-A-18). They found that the ITSF/ITSReub primer pair that they had designed generated a higher number of more reproducible peaks, with a wide range of spacer sizes. The ITSF/ITSReub primer set was also more sensitive, and could detect OTUs that represented only 0.1% of the total DNA. Jones et al. (2007) examined the efficiency of different primer sets (ITSF/ITSReub, cITSF/ITSReub and 1406f/23Sr) in determining bacterial community composition in Lake Mendota. The different primer pairs generated different community profiles but the conserved ecotypes of the bacterial community were observed and all pairs showed similar patterns. There is however, no universal primer set that can be used to amplify all microbial taxonomic groups by PCR with the same efficiency. As a consequence, no primer sets give an entirely unbiased picture of microbial communities (Jones et al. 2007).

51 2.4.4.2.2 DGGE and TGGE

DGGE and TGGE are similar techniques, introduced by Muyzer et al. (1993) and used to characterise complex microbial communities in natural environments. Distinct bands were successfully obtained, allowing them to characterise community diversity and shifts in community composition (Muyzer et al. 1993; Muyzer 1999).

16S rDNA regions are amplified by PCR using universal primers. Then, the resulting DNA fragments are electrophoresed on polyacrylamide gels, which either contain a gradient of denaturants, such as formamide and urea, or maintained at a temperature gradient. The DNA fragments are then separated based on their sequences. Bands can be re-amplified, cloned and sequenced to identify the organism that corresponds to each band (Muyzer et al. 1993; Muyzer 1999) or used as probes in hybridization methods such as FISH (Kirk et al. 2004).

DGGE has been used to study bacterial community composition and diversity in reservoirs (Yan et al. 2008), estuaries (Castle and Kirchman 2004), seawater (Riemann et al. 2008), sediments (Lai et al. 2006) and soil (Gelsomino and Cacco 2006).

DGGE and TGGE are claimed to be rapid, reliable and reproducible, allowing large numbers of samples to be processed (Muyzer 1999). However, they have low sensitivity in detecting rare members of bacterial communities, are subject to PCR and extraction biases and can be affected by the formation of heteroduplex molecules (Muyzer 1999). Primers need to include a 35-40 bp GC clamp to keep the part or most of the DNA as double stranded during separation on the gel and this may produce artefacts during the PCR annealing steps (Lee et al. 1996). In addition, different sequences may show similar migration patterns, meaning that one band may represent more than one bacterial species (Kirk et al. 2004). Large DNA fragments sizes cannot be separated on DGGE gels, so the methods are limited to DNA fragments between 300 to 500 base pairs long (Vallaeys et al. 1997).

2.4.4.2.3 SSCP (single strand confirmation polymorphisms)

Like TGGE/DGGE, SSCP depends on the separation of DNA fragments of the same size, but with DNA sequences on polyacrylamide gels. The separation is based on the formation of secondary structure in single stranded DNA, and the method is simple and does not require the use of radioactivity (Lee et al. 1996). SSCP was originally developed to discover mutations in DNA and to determine known and novel polymorphisms (Kirk et al. 2004). It can detect small changes in DNA sequences and the low percentage of bacterial population in a whole community can be detected as well. Unlike DGGE, no GC clamp is required and unlike T- RFLP, does not require the use of restriction digestions (Lee et al. 1996). It can be used as an alternative to RFLP for improving the discriminatory power of the ITS-PCR method distinguishing DNA fragments with different sequences but having similar lengths (Wolska and Szweda 2012).

52 However, SSCP can be affected by different factors, and small differences in gel matrix and temperature can alter the resulting fingerprints (Liu and Sommer 1994). It also suffers many of the disadvantages of DGGE, including problems caused by heteroduplex formation (Kirk et al. 2004).

2.4.4.2.4 ARDRA (Amplified ribosomal DNA restriction analysis/ restriction fragment length polymorphism (RFLP)

Microbial diversity can be studied using RFLP or ARADA to detect DNA polymorphisms (Kirk et al. 2004). After amplifying 16S rDNA and digesting with restriction enzymes, restriction fragment polymorphism is displayed using agarose electrophoresis (Clement et al. 1998). The method was first used on cultivated isolates before sequencing became routine. The method can be applied to the whole microbial community, when the RFLP profiles reflect all restriction fragments for the dominant members of the whole community, and common bands can be cloned and sequenced. Alternatively, it can be used to select 16S clones for sequencing, to avoid duplicates of common OTUs (Tiedje et al. 1999). Smit et al. (1997) used ARDRA to determine microbial community structure of soil environment.

However, variable numbers of fragments per strain can be obtained using ARADA, while with T-RFLP, one fragment per unique ribosomal operon can be obtained, which is often equivalent to one per strain (Clement et al. 1998; Tiedje et al. 1999).