Is bioaugmentation a feasible strategy for pollutant removal and site remediation?

Microorganisms can degrade numerous organic pollutants owing to their metabolic machinery and to their capacity to adapt to inhospitable environments. Thus, microorganisms are major players in site remediation. However, their efficiency depends on many factors, including the chemical nature and the concentration of pollutants, their availability to microorganisms, and the physicochemical characteristics of the environment. The capacity of a microbial population to degrade pollutants within an environmental matrix (e.g. soil, sediment, sludge or wastewater) can be enhanced either by stimulation of the indigenous microorganisms by addition of nutrients or electron acceptors (biostimulation) or by the introduction of specific microorganisms to the local population (bioaugmentation). Although it has been practiced in agriculture and in wastewater treatment for years, bioaugmentation is still experimental. Many factors (e.g. predation, competition or sorption) conspire against it. However, several strategies are currently being explored to make bioaugmentation a successful technology in sites that lack significant populations of biodegrading microorganisms. Under optimal local conditions, the rate of pollutant degradation might increase upon addition of an inoculant to remediate a chemical spill; however, the most successful cases of bioaugmentation occur in confined systems, such as bioreactors in which the conditions can be controlled to favour survival and prolonged activity of the exogenous microbial population.

DNA microarray detection of nitrifying bacterial 16S rRNA in wastewater treatment plant samples.

A small scale DNA microarray containing a set of oligonucleotide probes targeting the 16S rRNAs of several groups of nitrifying bacteria was developed for the monitoring of wastewater treatment plant samples. The microarray was tested using reference rRNAs from pure cultures of nitrifying bacteria. Characterization of samples collected from an industrial wastewater treatment facility demonstrated that nitrifying bacteria could be detected directly by microarray hybridization without the need for PCR amplification. Specifically, the microarray detected Nitrosomonas spp. but did not detect Nitrobacter. The specificity and sensitivity of direct detection was evaluated using on-chip dissociation analysis, and by two independent analyses--an established membrane hybridization format and terminal restriction fragment length polymorphism fingerprinting (T-RFLP). The latter two analyses also revealed Nitrospira and Nitrobacter to be contributing populations in the treatment plant samples. The application of DNA microarrays to wastewater treatment systems, which has been demonstrated in the current work, should offer improved monitoring capabilities and process control for treatment systems, which are susceptible to periodic failures.

Discrimination of shifts in a soil microbial community associated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays.

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro transcribed 16S rRNA derived from uncontaminated and 2,4,6-trinitrotoluene contaminated soils to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and mismatch (MM) variants, thermal dissociation was used to analyze the nucleic acid bound to each PM-MM probe set. Functional ANOVA of the dissociation curves generally discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greaterthan the Td. Comparison of signal intensities measured prior to dissociation analysis from hybridizations of the two soil samples revealed significant differences in domain-, group-, and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridizations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary.

Parallel characterization of anaerobic toluene- and ethylbenzene-degrading microbial consortia by PCR-denaturing gradient gel electrophoresis, RNA-DNA membrane hybridization, and DNA microarray technology.

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis.

Environmental genomics: exploring the unmined richness of microbes to degrade xenobiotics.

Increasing pollution of water and soils by xenobiotic compounds has led in the last few decades to an acute need for understanding the impact of toxic compounds on microbial populations, the catabolic degradation pathways of xenobiotics and the set-up and improvement of bioremediation processes. Recent advances in molecular techniques, including high-throughput approaches such as microarrays and metagenomics, have opened up new perspectives and pointed towards new opportunities in pollution abatement and environmental management. Compared with traditional molecular techniques dependent on the isolation of pure cultures in the laboratory, microarrays and metagenomics allow specific environmental questions to be answered by exploring and using the phenomenal resources of uncultivable and uncharacterized micro-organisms. This paper reviews the current potential of microarrays and metagenomics to investigate the genetic diversity of environmentally relevant micro-organisms and identify new functional genes involved in the catabolism of xenobiotics.

Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses.

The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.

Direct profiling of environmental microbial populations by thermal dissociation analysis of native rRNAs hybridized to oligonucleotide microarrays.

Oligonucleotide microarrays were used to profile directly extracted rRNA from environmental microbial populations without PCR amplification. In our initial inspection of two distinct estuarine study sites, the hybridization patterns were reproducible and varied between estuarine sediments of differing salinities. The determination of a thermal dissociation curve (i.e., melting profile) for each probe-target duplex provided information on hybridization specificity, which is essential for confirming adequate discrimination between target and nontarget sequences.

Optimization of single-base-pair mismatch discrimination in oligonucleotide microarrays.

The discrimination between perfect-match and single-base-pair-mismatched nucleic acid duplexes was investigated by using oligonucleotide DNA microarrays and nonequilibrium dissociation rates (melting profiles). DNA and RNA versions of two synthetic targets corresponding to the 16S rRNA sequences of Staphylococcus epidermidis (38 nucleotides) and Nitrosomonas eutropha (39 nucleotides) were hybridized to perfect-match probes (18-mer and 19-mer) and to a set of probes having all possible single-base-pair mismatches. The melting profiles of all probe-target duplexes were determined in parallel by using an imposed temperature step gradient. We derived an optimum wash temperature for each probe and target by using a simple formula to calculate a discrimination index for each temperature of the step gradient. This optimum corresponded to the output of an independent analysis using a customized neural network program. These results together provide an experimental and analytical framework for optimizing mismatch discrimination among all probes on a DNA microarray.