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Microbial Ecology

, Volume 56, Issue 1, pp 168–177 | Cite as

Fungi and Macroaggregation in Deep-Sea Sediments

  • Samir Damare
  • Chandralata Raghukumar
Original Article

Abstract

Whereas fungi in terrestrial soils have been well studied, little is known of them in deep-sea sediments. Recent studies have demonstrated the presence of fungal hyphae in such sediments but in low abundance. We present evidence in this study that one of the apparent reasons for the poor detection of fungi in deep-sea sediments is their cryptic presence in macroaggregates. Fungal biomass carbon from different core sections of deep-sea sediments from ∼5000 m depth in the Central Indian Ocean was estimated based on direct microscopic detection of fungal mycelia. Treatment of sediment samples with ethylenediamine tetra-acetic acid (EDTA) enabled more frequent detection and significantly higher biomass than in samples without such treatment. Treatment with EDTA resulted in detecting various stages of breakdown of aggregates in the sediments, gradually revealing the presence of fungal hyphae within them. Experimental studies of a deep-sea, as well as three terrestrial isolates of fungi, showed that all could grow at 200 bar and 5°C in a nutrient medium and in deep-sea sediment extract. Hyphae of fungi grown in sediment extract under the above conditions showed various stages of accretion of particles around them, leading to the formation of aggregates. Such aggregates showed the presence of humic material, carbohydrate, and proteins. We suggest that fungi in deep-sea sediments may be involved in humic aggregate formation by processes very similar to those in terrestrial sediments. The importance of such a process in carbon sequestration and food web in the deep sea needs to be examined.

Keywords

Fungal Biomass Dissolve Organic Matter Fungal Hypha Total Organic Carbon Content Microbial Type Culture Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The first author wishes to thank the Council for Scientific and Industrial Research, New Delhi for a senior research fellowship. The corresponding author acknowledges the Department of Biotechnology, New Delhi for the research grant No. BT/PR 1193/AAQ/03/102/2000. This paper has benefited immensely from suggestions made by Dr. S. Raghukumar, and we are grateful for his valuable comments. This is NIO’s contribution No. 4307.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.National Institute of OceanographyDona PaulaIndia

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