Sponge-associated marine bacteria as indicators of heavy metal pollution
- PMID: 17604613
- DOI: 10.1016/j.micres.2007.05.005
Sponge-associated marine bacteria as indicators of heavy metal pollution
Abstract
Sponges invariably filter a large volume of seawater and potentially accumulate heavy metals and other contaminants from the environment. Sponges, being sessile marine invertebrates and modular in body organization, can live many years in the same location and therefore have the capability to accumulate anthropogenic pollutants such as metals over a long period. Almost all marine sponges harbor large number of microorganisms within their tissues where they reside in the extra- and intra-cellular spaces. Bacteria in seawater have already been established as biological indicators of contamination. The present study was intended to find out the heavy metal resistance pattern of sponge-associated bacteria so as to develop suitable biological indicators. The bacteria associated with a marine sponge Fasciospongia cavernosa were evaluated as potential indicator organisms. The associated bacteria including Streptomyces sp. (MSI01), Salinobacter sp. (MSI06), Roseobacter sp. (MSI09), Pseudomonas sp. (MSI016), Vibrio sp. (MSI23), Micromonospora sp. (MSI28), Saccharomonospora sp. (MSI36) and Alteromonas sp. (MSI42) showed resistance against tested heavy metals. Based on the present findings, Cd and Hg emerged as the highly resistant heavy metal pollutants in the Gulf of Mannar biosphere reserve. Plasmids in varied numbers and molecular weights were found in all the isolates. Particularly the isolates MSI01 and MSI36 harbored as many as three plasmids each. The results envisaged that the plasmids might have carried the resistance factor. No correlation was observed in number of plasmids and level of resistance. The literature evidenced that the sponge-associated bacteria were seldom exploited for pollution monitoring though they have been extensively used for bioprospecting. In this background, the present findings come up with a new insight into the development of indicator models.
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