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between laboratories working with bioaugmentation. It is unclear if the low diversity within the
Dehalococcoides is indicative of the level of diversity to be expected within the Chloroflexi.
Recently, the bioremediation field has been actively searching for other, non- Dehalococcoides
organisms with the potential to contribute to bioaugmentation efforts. Much of this effort has
been focused on expanding current knowledge of the Chloroflexi, including searching for near-
neighbors to the Dehalococcoides . In 2008, a pair of Chloroflexi bacteria capable of degrading
1,2,3-trichloropropane was isolated from a contaminated site in Louisiana (Moe et al., 2009 ;
Yan et al., 2009 ). The 16S ribosomal DNA (rDNA) genes for these isolates share only 90%
nucleotide identity with Dehalococcoides , indicating that they are a new lineage within
the Chloroflexi. This lineage has been named Dehalogenimonas lykanthroporepellens
(Moe et al., 2009 ), and represents the first discovery of a non- Dehalococcoides Chloroflexi
capable of dechlorination.
The identification of closely related Chloroflexi to the Dehalococcoides group is exciting,
as it indicates that further expansion of the known Chloroflexi is likely to yield industrially
relevant organisms with the potential to address recalcitrant contaminants. This has already
been borne out, as a novel Dehalogenimonas was recently identified that dechlorinates trans-
DCE (Manchester et al., 2012 ). Additionally, the added diversity that the Dehalogenimonas
provide will allow for deeper examination of Dehalococcoides genome evolution, including
examination of reductive dehalogenase movement and inheritance within and between gen-
omes. This could shed light on the mechanisms for acquisition of reductive dechlorination in
these bacteria, a current mystery.
A recently announced research effort in Germany seeks to discover and characterize
novel Chloroflexi from marine systems (Adrian, 2009 ). While it is difficult to predict
the outcome of a search for unknown organisms from uncharacterized environments utilizing
novel metabolic pathways, this style of large-scale environmental screening has the potential
to yield numerous interesting and novel organisms. Efforts of this nature will need to utilize many
different culture conditions in order to successfully enrich for unknown organisms. The future of
bioaugmentation lies in the discovery of novel biodegradation pathways, which will only be found
through this style of broad-scale search with varied culture conditions.
Outside of the Chloroflexi, several organisms capable of dehalogenation of chlorinated
substrates are known. Dehalobacter ( Dhb ) species are members of the Clostridia (Holliger
et al., 1998 ), while Geobacter and Shewanella belong to the delta- and gamma-Proteobacteria,
respectively (Lovley et al., 1993 ; Macdonell and Colwell, 1985 ). While these phyla are signifi-
cantly better characterized than the Chloroflexi, there still exists the potential for expansion of
the known species within these groups, including identification of organisms relevant to
bioaugmentation. The advantage to discovering novel organisms within better-characterized
phyla lies in the potential for generating a genetic system for examination of dehalogenation
and other processes more directly. Moreover, genes with high similarity to known dehalo-
genases have been identified in the genomes of organisms not known for this activity, including
Mesotoga and even in the Archeaon Ferroglobus (Dr. C. Nesbo, personal communication, 2010;
Hafenbradl et al., 1996 ). While the substrates for these putative dehalogenases have not been
identified, these findings suggest that dehalogenating activity is more widely distributed than
previously thought.
12.2.2.2 Genome Scale Reconstructions and Mathematical Models
of Microbial Metabolism
With the growing number of completely sequenced microbial genomes, interest has grown in
developing computational tools that use this genome-scale information to reconstruct metabolic
pathways and develop models of microbial growth and activity. Constraint-based models
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