Abstract
Deep-sea sediments provide important information on oceanic biogeochemical processes mediated by the microbiome and their functional roles which could be unravelled using genomic tools. The present study aimed to delineate microbial taxonomic and functional profiles from Arabian Sea sediment samples through whole metagenome sequencing using Nanopore technology. Arabian Sea is considered as a major microbial reservoir with significant bio-prospecting potential which needs to be explored extensively using recent advances in genomics. Assembly, co-assembly, and binning methods were used to predict Metagenome Assembled Genomes (MAGs) which were further characterized by their completeness and heterogeneity. Nanopore sequencing of Arabian Sea sediment samples generated around 1.73 tera basepairs of data. Proteobacteria (78.32%) was found to be the most dominant phylum followed by Bacteroidetes (9.55%) and Actinobacteria (2.14%) in the sediment metagenome. Further, 35 MAGs from assembled and 38 MAGs of co-assembled reads were generated from long-read sequence dataset with major representations from the genera Marinobacter, Kangiella, and Porticoccus. RemeDB analysis revealed a high representation of pollutant-degrading enzymes involved in hydrocarbon, plastic and dye degradation. Validation of enzymes with long nanopore reads using BlastX resulted in better characterization of complete gene signatures involved in hydrocarbon (6-monooxygenase and 4-hydroxyacetophenone monooxygenase) and dye degradation (Arylsulfatase). Enhancing the cultivability of deep-sea microbes predicted from the uncultured WGS approaches by I-tip method resulted in isolation of facultative extremophiles. This study presents a comprehensive insight into the taxonomic and functional profiles of Arabian Sea sediments, indicating a potential hotspot for bioprospection.
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Availability of data and materials
All the sequence datasets generated in this study have been submitted to NCBI under the accession numbers from MW624546 to MW624655 for bacterial isolates and whole metagenome shotgun nanopore data with NCBI SRA submissions Bioproject Id (PRJNA749673) and Biosample ids (SAMN20396396, SAMN20396399, SAMN20396444, and SAMN20396445) and SRA accessions SRR15372773, SRR15372772, SRR15372771, and SRR15372770 for NIOT_S1, NIOT_S2, NIOT_S3, and NIOT_S4 samples, respectively.
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Acknowledgements
The authors are grateful to the Director, National Institute of Ocean Technology, and the Ministry of Earth Sciences, Government of India, for providing necessary facilities to carry out this study. The authors also express their gratitude to the NIOT Vessel Management Cell and crew members of ORV Sagar Nidhi for their help during collection of deep-sea water and sediment samples.
Funding
This study is funded by the Ministry of Earth Sciences, Govt. of India.
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KRSB: methodology design, microbial characterization, data analysis, and manuscript preparation; SHSS: sequence analysis, manuscript preparation; MPD: gas chromatography analysis; AR: microbial isolation and characterization; MC: sample analysis and methodology design; VRR: conceptualization, experimental design, and manuscript editing; DG: overall supervision and review of manuscript.
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Supplementary file1 (TIF 496 KB) Figure S1 Total ion chromatogram(TIC) of Arabian Sea sediment samples A. NIOT_S1 B.NIOT_S2 C.NIOTS4
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Supplementary file2 (PNG 742 KB) FigureS2a MultipleSequence Alignment of predicted Arylsulfatase enzyme (RemeDB) withits closest relatives from the NCBI nr database. Marinobacter sp. wasfound to be closely associated with predicted enzyme from metagenomic dataset.Gaps are indicated by dashes. Asterisks indicate identical amino acids.
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Supplementary file3 (PNG 681 KB) FigureS2bMultiple Sequence Alignment of predicted 6-monooxygenase enzyme (RemeDB) withits closest relatives from the NCBI nr database. Halomonas sp. was foundto be well aligned with the predicted amino acid sequence. Gaps are indicatedby dashes. Asterisks indicate identical amino acids.
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Supplementary file4 (PNG 677 KB) FigureS2c Multiple Sequence Alignment of predicted 4-hydroxyacetophenone monooxygenaseenzyme (RemeDB) with its closest relatives from the NCBInr database. Close association of all three strains of Marinobacter sp.was observed with the predicted amino acid sequence. Gaps are indicated bydashes. Asterisks indicate identical amino acids.
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Supplementary file5 (TIF 308 KB) Figure S3 Prediction of Antibiotics Resistance genes (ARG’S), Mobile Genetic Elements (MGE’s), Metal Resistance Genes (MRG’s) using NanoARG.
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Supplementary file6 (PNG 1147 KB) Figure S4 I-tipMethodology and Design along with colonies isolated using the same method.
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Supplementary file9 (XLSX 255 KB) Table S8 KEGG annotationof hydrocarbon degradation genes from xenobiotic degradation pathway.
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Balachandran, K.R.S., Sankara Subramanianan, S.H., Dhassiah, M.P. et al. Microbial community structure and exploration of bioremediation enzymes: functional metagenomics insight into Arabian Sea sediments. Mol Genet Genomics 298, 627–651 (2023). https://doi.org/10.1007/s00438-023-01995-6
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DOI: https://doi.org/10.1007/s00438-023-01995-6