Functional metagenomic analysis of dust-associated microbiomes above the Red Sea.

Nojood Aalismail, David K Ngugi, Ruben Diaz Rua, Intikhab Alam, Michael Cusack, Carlos M. Duarte

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Atmospheric transport is a major vector for the long-range transport of microbial communities, maintaining connectivity among them and delivering functionally important microbes, such as pathogens. Though the taxonomic diversity of aeolian microorganisms is well characterized, the genomic functional traits underpinning their survival during atmospheric transport are poorly characterized. Here we use functional metagenomics of dust samples collected on the Global Dust Belt to initiate a Gene Catalogue of Aeolian Microbiome (GCAM) and explore microbial genetic traits enabling a successful aeolian lifestyle in Aeolian microbial communities. The GCAM reported here, derived from ten aeolian microbial metagenomes, includes a total of 2,370,956 non-redundant coding DNA sequences, corresponding to a yield of ~31 × 106 predicted genes per Tera base-pair of DNA sequenced for the aeolian samples sequenced. Two-thirds of the cataloged genes were assigned to bacteria, followed by eukaryotes (5.4%), archaea (1.1%), and viruses (0.69%). Genes encoding proteins involved in repairing UV-induced DNA damage and aerosolization of cells were ubiquitous across samples, and appear as fundamental requirements for the aeolian lifestyle, while genes coding for other important functions supporting the aeolian lifestyle (chemotaxis, aerotaxis, germination, thermal resistance, sporulation, and biofilm formation) varied among the communities sampled.
Original languageEnglish (US)
JournalScientific reports
Issue number1
StatePublished - Sep 24 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was funded by King Abdullah University of Science and Technology through baseline funding the C.M.D. We thank Razan Yahya and Jesus M. Arrieta for help with sampling. We also thank Abdallah M. Abdallah and Xiang Zhao for help with sequencing and Mr. Allan Kamau for help in computational aspects. The authors are grateful to the KAUST Supercomputing Laboratory (KSL) for the computational resources provided to complete this work. We acknowledge the use of imagery from the NASA Worldview application (, part of the NASA Earth Observing System Data and Information System (EOSDIS).


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