(Macbeth et al. 2004; Fahy et al. 2005). The use of T-RFLP showed that the chronic
presence of benzene in groundwater reduced bacterial range and network composition in comparison with that of available groundwater resources. The use of automatic detection systems and capillary electrophoresis in T-RFLP analysis permits
high throughput and greater correct quantitative analysis of microbial community
samples than with any of the opposite genetic fingerprinting strategies.
5.8 Ribosomal Intergenic Spacer Analysis (RISA)
and Amplified Ribosomal DNA Restriction Analysis
(ARDRA)
RISA makes use of the length and sequence heterogeneities present within the
intergenic spacer (IGS) among the small (SSU) and large subunit (LSU) rRNA
genes inside the rRNA operon (Van Elsas et al. 2007). Similar to RFLP and T-RFLP,
RISA and ARDRA provide ribosomal-based fingerprinting of the microbial network. In RISA the IGS location between the 16S and 23S ribosomal subunits is
amplified through PCR, denatured, and separated on a polyacrylamide gel under
denaturing conditions. This location may encode tRNAs and can differentiate
between bacterial lines and closely related species by the heterogeneity of the IGS
duration and series (Fisher and Triplett 1999). Sequence polymorphisms are detected
through silver staining in RISA. RISA has been used to evaluate microbial range in
soil (Borneman and Triplett 1997), inside the rhizosphere of plant life (Borneman
and Triplett 1997), in contaminated soil (Ranjard et al. 2000), and in response to
inoculation (Yu and Mohn 2001). RISA is a very rapid and easy fingerprinting
approach but its application in microbial community evaluation from contaminated
assets is restricted, partly because the database for ribosomal intergenic spacer
sequences is not as large or as complete as the 16S collection database (Spiegelman
et al. 2005). Limitations of RISA include requirement of massive portions of DNA,
the longer time requirement, insensitivity of silver staining in some instances, and
acrylamide choice (Fisher and Triplett 1999). Banding patterns in ARDRA may be
used to display clones or the degree of bacterial network shape (Kirk et al. 2004).
ARDRA is easy, rapid, and cost-efficient and thus has been utilized in microbial
identification (Vaneechoutte et al. 1995; Kita-Tsukamoto et al. 2006; Krizova et al.
2006) and microbial network research (Weidner et al. 1996; Bai et al. 2006; Babalola
et al. 2009). Microbial network composition and succession in an aquifer exposed to
phenol, toluene, and chlorinated aliphatic hydrocarbons have been assessed by
means of ARDRA to identify the dominant microbial community involved in the
biodegradation of trichloroethylene (TCE) following biostimulation (Fries et al.
1997). In another study, (Gich et al. 2000) used ARDRA to examine the microbial
differences in activated sludge from remedial plants consumed at home or from
industrial wastewater. Bacterial groups in activated sludge could be distinguished
between commercial and domestic wastewater treatment plant life. Hohnstock-Ashe
18 Molecular Technologies for Assessment of Bioremediation and. . .
453
presence of benzene in groundwater reduced bacterial range and network composition in comparison with that of available groundwater resources. The use of automatic detection systems and capillary electrophoresis in T-RFLP analysis permits
high throughput and greater correct quantitative analysis of microbial community
samples than with any of the opposite genetic fingerprinting strategies.
5.8 Ribosomal Intergenic Spacer Analysis (RISA)
and Amplified Ribosomal DNA Restriction Analysis
(ARDRA)
RISA makes use of the length and sequence heterogeneities present within the
intergenic spacer (IGS) among the small (SSU) and large subunit (LSU) rRNA
genes inside the rRNA operon (Van Elsas et al. 2007). Similar to RFLP and T-RFLP,
RISA and ARDRA provide ribosomal-based fingerprinting of the microbial network. In RISA the IGS location between the 16S and 23S ribosomal subunits is
amplified through PCR, denatured, and separated on a polyacrylamide gel under
denaturing conditions. This location may encode tRNAs and can differentiate
between bacterial lines and closely related species by the heterogeneity of the IGS
duration and series (Fisher and Triplett 1999). Sequence polymorphisms are detected
through silver staining in RISA. RISA has been used to evaluate microbial range in
soil (Borneman and Triplett 1997), inside the rhizosphere of plant life (Borneman
and Triplett 1997), in contaminated soil (Ranjard et al. 2000), and in response to
inoculation (Yu and Mohn 2001). RISA is a very rapid and easy fingerprinting
approach but its application in microbial community evaluation from contaminated
assets is restricted, partly because the database for ribosomal intergenic spacer
sequences is not as large or as complete as the 16S collection database (Spiegelman
et al. 2005). Limitations of RISA include requirement of massive portions of DNA,
the longer time requirement, insensitivity of silver staining in some instances, and
acrylamide choice (Fisher and Triplett 1999). Banding patterns in ARDRA may be
used to display clones or the degree of bacterial network shape (Kirk et al. 2004).
ARDRA is easy, rapid, and cost-efficient and thus has been utilized in microbial
identification (Vaneechoutte et al. 1995; Kita-Tsukamoto et al. 2006; Krizova et al.
2006) and microbial network research (Weidner et al. 1996; Bai et al. 2006; Babalola
et al. 2009). Microbial network composition and succession in an aquifer exposed to
phenol, toluene, and chlorinated aliphatic hydrocarbons have been assessed by
means of ARDRA to identify the dominant microbial community involved in the
biodegradation of trichloroethylene (TCE) following biostimulation (Fries et al.
1997). In another study, (Gich et al. 2000) used ARDRA to examine the microbial
differences in activated sludge from remedial plants consumed at home or from
industrial wastewater. Bacterial groups in activated sludge could be distinguished
between commercial and domestic wastewater treatment plant life. Hohnstock-Ashe
18 Molecular Technologies for Assessment of Bioremediation and. . .
453