IN SILICO COMPARATIVE METAGENOMICS ANALYSIS OF HEAVY METALS’ AFFECTED MICROBIAL COMMUNITIES

Abstract

Plethora of microorganisms is present on Earth and play crucial role in geochemical cycles. A major proportion of microorganisms is uncultured and therefore culture independent methods are most suited to study the microbial diversity comprehensively. This has led to microbial molecular ecology fields including metagenomics. In this study, metagenomics data of microbial communities of different sites contaminated with different heavy metals such as chromium, arsenic, lead, selenium, copper and cadmium were compared in silico to determine the microorganisms which are common in different samples and the microorganisms that are exclusive to certain samples only. For this purpose, 16S rRNA gene sequences of heavy metal contaminated sites was downloaded from NCBI Short Read Archive (SRA). The sequences were analyzed in Mothur tool through Galaxy server. The sequences were classified using Ribosomal Database Project (RDP) reference dataset and Operational Taxonomic Units (OTUs) were generated. Heatmaps, Venn diagrams, phylogenetic tree, and NMDS plots were generation. Diversity indices such as Shannon, Simpson and Chao, as well as relative abundance was determined. It was found that Proteobacteria, Actinobacteria, Acidobacteria, Fermicutes, Bacteriodetes, and Bacilli were the most abundant bacteria and were present in all heavy metal contaminated samples. Proteobacteria, and Beta-Proteobacteria, were present in all samples but were most abundant in cadmium affected samples. Xanthomonadales were most abundant in lead contaminated samples. Firmicutes were most abundant in chromium affected samples. Bacteriodetes were present in all arsenic contaminated samples but were not detected in any other samples. Rhizobiales were present in all arsenic and lead contaminated samples. Analysis of molecular variance (AMOVA) test was performed and it was found that the microbial communities of all the samples contaminated with different samples were found to be statistically different (p-value ?0.001). The bacteria which are present in all samples might have resistant against all these heavy metals and thus should be explored further for possible applications of bioremediation.