PCR-based DNA fingerprinting, REP-PCR(repetitive element PCR), RAPD(randomly amplified polymorphic DNA) and16 S r DNA sequence analyses were used to characterize 23 Acidithiobacillus ferrooxidans strains isolated from different environments.(GTG)5 and BOXA1 R primer were selected for REP-PCR. Twenty arbitrary primers were used for RAPD to acquire DNA profiles from A. ferrooxidans. Both RAPD and REP-PCR produce complex banding patterns and show good discriminatory ability in differentiating closely related strains of A. ferrooxidans. The strains are clustered into 4 or 5 major groups and reveal genomic diversity using(GTG)5-PCR, BOX-PCR and RAPD analysis. Phylogenetic tree based on 16 S r DNA sequences of 23 strains and related strains shows that they are clustered into two distinct groups. Twelve strains are highly related to a new Acidithiobacillus named Acidithiobacillus ferrivorans. The results indicate that PCR-based methods are effective in revealing genetic diversity among A. ferrooxidans.
Eleven acid mine drainage (AMD) samples were obtained from southeast of China for the analysis of the microbial communities diversity, and the relationship with geochemical variables and spatial distance by using a culture-independent 16S rDNA gene phylogenetic analysis approach and multivariate analysis respectively. The principle component analysis (PCA) of geochemical variables shows that eleven AMDs can be clustered into two groups, relative high and low metal rich (RHMR and RLMR) AMDs. Total 1 691 clone sequences are obtained and the detrended correspondence analysis (DCA) of operational taxonomic units (OTUs) shows that, γ-Proteobacteria, Acidobacteria, Actinobacteria, Cyanobacteria, Firmicutes and Nitrospirae are dominant species in RHMR AMDs. In contrast, α-Proteobacteria, β-Proteobacteria, Planctomycetes and Bacteriodetes are dominant species in RLMR AMD. Results also show that high-abundance putative iron-oxidizing and only putative sulfur-oxidizing microorganisms are found in RHMR AMD. Multivariate analysis shows that both geochemical variables (r=0.429 3, P=0.037 7) and spatial distance (r=0.321 3, P=0.018 1) are significantly positively correlated with microbial community and pH, Mg, Fe, S, Cu and Ca are key geochemistry factors in shaping microbial community. Variance partitioning analysis shows that geochemical variables and spatial distance can explain most (92%) of the variation.
