Multivariate statistical analyses of rDNA and rRNA fingerprint data to differentiate microbial communities in swine manure

Abstract Fingerprint data from swine manure microbial community rRNAs and rRNA genes were treated by multivariate statistical and diversity analyses to differentiate swine manures. Microbial communities from finishing pig manure and from a mixture of manure slurries from maternity confinement and fi... Full description

Abstract Fingerprint data from swine manure microbial community rRNAs and rRNA genes were treated by multivariate statistical and diversity analyses to differentiate swine manures. Microbial communities from finishing pig manure and from a mixture of manure slurries from maternity confinement and finishing pigs were characterized using a combination of amplicon length heterogeneity PCR (LH-PCR) and terminal restriction fragment length polymorphism (T-RFLP), using PCR primers targeting Bacteria and Archaea, respectively. Unweighted pair group method with arithmetic mean clustering, principal components analysis (PCA), indicator species analysis (ISA), and diversity analyses showed that rRNA-based fingerprinting methods [reverse transcription (RT)-LH-PCR and RT-T-RFLP] were more effective than rDNA-based fingerprinting methods for distinguishing the manure samples. Multiresponse permutation procedure from fingerprint data showed that all manure samples had distinct microbial communities. PCA and ISA showed that the major phylotypes differentiating the LH-PCR or the RT-LH-PCR profiles were distributed differently between manures, suggesting that the bacterial community structure was different from the metabolically active bacterial community. The detection of minor archaeal populations was greater using RT-T-RFLP instead of T-RFLP. The findings indicated that the analysis of microbial community rRNAs could differentiate each manure sample from the others and would be appropriate for the monitoring of metabolically active populations.