La comunidad El Condado y la cultura popular de ascendencia

In order to assess Mycobacterium species diversity in environmental samples it was necessary to evaluate techniques for the extraction of community DNA and to assess the efficiency of specific target primers in combination with a molecular detection method. DGGE was optimised for the detection of the Mycobacterium genus and the long helix 18 containing SG. The DGGE method has been previously employed to determine the diversity of mycobacteria (Leys et al. 2005; Niva et al. 2006; Uyttebroek et al. 2006), however this is the first time it has been used specifically to examine SG diversity.

In this study the specificity of both primer sets was evaluated using extracted DNA from cultured Mycobacterium species. However it was not possible to differentiate between a number of Mycobacterium species as some shared identical band positions. Consequently further steps, such as seque ncing of the bands extracted from the DGGE, were required as the method could not solely rely on a library of reference bands corresponding to Mycobacterium species. DGGE was shown to have a detection limit of 105 cells per gram and 102 cells per gram for the Mycobacterium

genus and SG primer sets using a nested approach. This was comparable to a

Mycobacterium 16S rRNA gene specific DGGE which had a detection limit of 106 CFU per gram of soil and was improved to 102 CFU per gram using a nested approach (Leys et al. 2005). The SG primer set was likely to have had a better sensitivity compared to the Mycobacterium genus primer set as it targets a smaller group with less diversity, therefore M. bovis was not likely to be outcompeted by other species in the PCR reaction.

A comparison of three water DNA extraction methods clearly demonstrated that the commercial PowerWater kit was the most sensitive with a limit of detection of at

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least 102 cells per ml and was not affected by the storage of filters for 14 days. The adapted Griffiths method had a detection limit of 104 cells per ml and the adapted Pickup method was the least sensitive with a limit of 105 cells per ml. It is possible that the Pickup method did not perform as well due to the inefficiency of the centrifugation procedure for cell lysis and the lack of a mechanical bead-beating step. The detection limit for water extraction using the PowerWater kit is comparable to, if not better than, the detection limit for soil extraction using the FastDNA spin kit. This was expected as water is less complex than soil, containing less inhibitory compounds and less microbial diversity (Torsvik 2002). As a result of the findings from this study the PowerWater kit was employed for DNA extractions from subsequent water samples.

The pilot study has shown a high diversity of Mycobacterium species in soil samples. Both methods rely on PCR of metagenomic DNA which has inherent limitations such as the bias of primer sets towards the amplification of certain species, especially where there are mismatches in the primer sequence and where species are more abundant than others (Hong et al. 2009; Kanagawa 2003; von Wintzingerode et al. 1997). Therefore a second primer set targeting SG was employed to target

Mycobacterium diversity, as although the Mycobacterium genus primer set was shown to amplify DNA from cultures of SG this group was not commonly detected in environmental community DNA.

The DGGE and pyrosequencing methods were both used to determine the alpha diversity of the mycobacteria present in the pilot soil samples. There was some agreement between Shannon diversity estimates for both methods, where sample 1111 consistently had lower diversity estimates for both datasets and sample 1108 had the highest SG diversity estimates. However there were some discrepancies, for

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example Cryfield had the highest Shannon diversity estimate for the Mycobacterium

genus PCR-DGGEs but had one of the lowest for pyrosequencing. One would expect differences in the alpha diversity estimates between DGGE and pyrosequencing due to the differences in sampling scale, where DGGE is likely to underestimate the alpha diversity. Similarities were observed between beta-diversity results for DGGE and pyrosequencing. For the Mycobacterium genus the pyrosequencing CCA and the UPMGA DGGE cluster analysis were in complete a greement, with similarities found between 1110 and Cryfield and 1109 and 1111. However, discrepancies were observed for the SG CCA; only 1110 and 1111 were consistently similar for both methods, as estimated by UPMGA DGGE cluster analysis and Jaccard pyrosequencing cluster analysis.

Close agreement was ascertained from the BLAST results of the DGGE bands and the pyrosequencing reads. All sequences from the DGGE bands were also detected in the pyrosequencing, apart from the DGGE sequence matched to uncultured Mycobacteriaceae bacterium. Agreement between the DGGE approach and pyrosequencing has been observed previously, the study found consistency between several molecular methods however overall pyrosequencing provided a several orders of magnitude more data than the other methods (Oakley et al. 2010). Overall it is generally accepted that DGGE mostly detects the relatively abundant species (Chan et al. 2002) and this was the case in the pilot study. For example M. moriokaense was matched to a DGGE band from sample 1110 and this species was the most prevalent pyrosequencing BLAST match for sample 1110. The environmental species Mycobacterium spp. DCY42 was matched to a DGGE band from sample 1111 and represented 17.71% of sample 1111 pyrosequencing BLAST matches. The SG M. colombiense was matched to DGGE sequences from samples

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1108, 1109 and 1111 and represented 32.8%, 94.9% and 3.7% of pyrosequencing BLAST matches respectively. Therefore M. colombiense was found to be abundant using both methods, particularly in samples 1108 and 1109. M. riyadhense was matched to a DGGE sequence from sample 1110 and was also the most abundant pyrosequencing BLAST match for this sample (46.9%). However, although M. haemophilum was detected in Cryfield using both pyrosequencing and DGGE, it only represented 0.1% of pyrosequencing BLAST matches. As not all DGGE bands were sequenced it is possible that other more abundant pyrosequencing BLAST matches to Mycobacterium species were present. Overall there was a good level of agreement between the two molecular methods; the relatively most abundant pyrosequencing BLAST matches also were detected using DGGE.

In summary the combination of molecular methods and the agreement between them particularly for the BLAST matches has provided a clearer and more confident picture of the Mycobacterium species diversity present within the pilot soils. The PCR-DGGE provided a snapshot of the relatively abundant Mycobacterium species. However, the limitation of DGGE is that less abundant but potentially important species are unlikely to be detected. In contrast the pyrosequencing has provided a far more comprehensive insight into the diversity of Mycobacterium species and as such the pilot study has illustrated this method would be most appropriate for a large-scale environmental survey.

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Chapter 4: Mycobacterium species diversity in the Ethiopian