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COMPETITIVE METAGENOMIC DNA HYBRIDIZATION IDENTIFIES HOST-SPECIFIC GENETIC MARKERS IN HUMAN FECAL MICROBIAL COMMUNITIES
SHANKS, O. C., J. SANTO-DOMINGO, J. LU, AND C. A. KELTY. COMPETITIVE METAGENOMIC DNA HYBRIDIZATION IDENTIFIES HOST-SPECIFIC GENETIC MARKERS IN HUMAN FECAL MICROBIAL COMMUNITIES. Presented at 158th General Meeting of the Society for General Microbiology, Warwick, UK, April 03 - 06, 2006.
Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for most laboratories. Here we report the application of a genome fragment enrichment (GFE) method that identifies genomic regions that differ between the metagenomes of two fecal microbial communities. In this study, human fecal microbial community DNA was hydridized against a pig fecal DNA background. Three hundred fifty one individual clones were sequenced and screened for redundancy. Dot blot analysis of 296 non-redundant sequences confirmed that 98% of the sequences were specific for the human fecal microbial community. Functional annotation of the enriched metagenomic sequences suggests that the majority of the genetic variation between the human and pig fecal DNA communities resides in genes encoding for unknown (33.1%), hypothetical (10.7%), and mobile (8.8%) functions. In addition, secretion and transmembrane prediction analyses indicated a preponderance of DNA sequences (42.6%) predicted to encode membrane-associated and secreted proteins. Oligonucleotide primers capable of annealing to 26 enriched DNA sequences did not amplify pig fecal DNA and exhibited different levels of specificity with fecal DNA from other animal sources. Five PCR assays were human-specific and demonstrated a broad distribution of corresponding genetic markers among different human populations. These data demonstrate that direct metagenomic DNA analysis using GFE is an efficient method for identifying useful microbial community-specific genetic variation, and for characterizing differences between metagenomes.