Identification of swine- specific microbial genetic markers using competitive DNA hybridization - FAN Lihua; MO Hongfei; ZHANG Xiaofeng; SHUAI Jiangbing; CHEN Qing (浙江大学学报. 农业与生命科学版)
Due to the rapid development of livestock breeding and poultry raising, non-point source pollution has become a significant threat to environmental management and aquaculture industry development, as well as to human health in the last few decades. Therefore, it is particularly urgent to establish a monitoring method that can be used as the efficient indicator of fecal pollution with high sensitivity and strong specificity. In animal guts, genes which are directly involved in bacterium-host interactions may display increased level of host-associated genetic variation, making them promising candidates for fecal source tracking. Specific markers targeting bacterium- host interaction genes for human, cattle and chicken were reported previously; however, swine-specific marker for fecal source tracking has not been found yet.We applied a genome fragment enrichment (GFE) method to enrich swine- specific metagenomic regions that differ from those of other animal species. Briefly，a portion of swine fecal DNA was labeled with biotin and pre-hybridized with a composite fecal DNA pool of other animals including cow (n=20)，goat (n=20)，chicken (n=8)，duck (n=20) and goose (n=5) to block nonunique fragments. Then the pre-hybridized product and another portion of swine fecal DNA labeled with K9 primer were taken together to perform a competitive DNA hybridization. After streptavidin enrichment and long- linker PCR amplification by K9 primer，the products that were assumed as swine- specific fecal DNA were cloned into vectors and were sequenced. Dot blot hybridization with negative control fecal DNA (composite fecal DNA pool of other animals) was used to identify the cloned GFE sequences which were not swine-specific. The cloned GFE sequences were assigned to bacterial class annotations based on the top BLASTX hit (the lowest E-value score) with the GenBank non-redundant database. The putative protein transcript of each sequence was analyzed based on the similarity of gene sequences by using BLASTX with the GenBank non-redundant database，and their biochemical functions were therefrom predicted.Sequence analyses of five hundred randomly selected clones from the libraries obtained by three rounds of metagenomic GFE revealed that this subset contained a total of 384 non-redundant sequences and most sequences (87%) ranged from 400 bp to 600 bp in size. Dot blot hybridization using DNA composite of non- target animals as probes showed that only eight clones exhibited cross-reaction，indicating a very low false-positive rate of 2.8%. BLASTX searches identified homologous sequences in GenBank database for 315 non- redundant DNA inserts，with other 69 (17.9% of 384 swine fecal DNA sequences) inserts showed no homology with any previously reported genes. Based on top BLASTX hits，the sequences were putatively grouped into 20 bacterial classes including the predominant group of Bacteroidetes- like sequences (43.2%)，among which，120 sequences were similar to Bacteroidetes-Prevotella. Clostridia-like sequences were the second most abundant group (19.5%)，and Bacillilike sequences represented 8.6% of the clones. Moreover，three sequences exhibited identity to genes in Archaea. Biochemical function annotation revealed that 38.5% of the total analyzed sequences were predicted as genes with unknown functions. Among the fragments associated with characterized function genes (61.5%)，the sequences were most frequently assigned to putative proteins associated with metabolism (22%，e.g.，carbohydrate metabolism and amino acid metabolism)，cellular process (12.8%，e.g.，membrane transport and DNA repair/replication/recombination) and information storage and processing (7.6%).It is concluded that gene encoding surface proteins，membrane associated proteins，secretary proteins and carbohydrate metabolism proteins of dominant bacterial classes could be regarded as putative targets for swine- specific microbial genetic markers.