PUMA
Istituto di Scienze Marine     
Molari M., Manini E. Reliability of CARD-FISH Procedure for Enumeration of Archaea in Deep-Sea Surficial Sediments. In: Current Microbiology, vol. 64 pp. 242 - 250. Springer Science+Business Media, 2012.
 
 
Abstract
(English)
The enumeration of Archaea in deep-sea sediČment samples is still limited, although different methodoČlogical procedures have been applied. Among these, catalysed reporter deposition-fluorescence in situ hybridČisation (CARD-FISH) technique is a promising tool for estimation of archaeal abundance in deep-sea sediment samples. Comparing different permeabilisation treatments, the best results obtained both on archaeal pure cultures and on natural assemblages were with hydrochloric acid (0.1 M) and proteinase K (0.004 U/ml) treatments. The application of CARD-FISH on deep-sea sediments revealed that Archaea reach up to 41% of total prokaryotic cells. Specific probes for planktonic Archaea showed that marine Crenarchaea dominated archaeal seafloor commu-nities. No clear bathymetric trends were observed for ar-chaeal abundances and the morphology of continental margin (slope vs. canyon) seems not to have a direct influence on archaeal relative abundances. The site-specific sediment habitat-both abiotic environmental setting and sedimentary organic matter quality-explain up to 65% of variance of archaeal, crenarchaeal and euryarchaeal relaČtive abundance, suggesting a wide ecophysiological adapČtation to deep-sea benthic ecosystems. The findings demonstrate that Archaea are an important component of benthic microbial assemblages so far neglected, and hence they lay the groundwork for more focused research on their ecological importance in the functioning of deep-sea benČthic ecosystems.
DOI: 10.1007/s00284-011-0056-5
Subject CARD-FISH
Archaea
deep-Sea


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional