Microbial community structure and exploration of bioremediation enzymes: functional metagenomics insight into Arabian Sea sediments.

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.

Vertical Microbial Profiling of Arabian Sea Oxygen Minimal Zone Reveals Complex Bacterial Communities and Distinct Functional Implications.

Arabian Sea harbours one of the largest oxygen minimal zones (OMZs) among the global oceans wherein biogeochemical cycles are regulated through dominant and complex microbial processes. The present study investigated the bacterial communities at various depths of the Arabian Sea OMZ using high-throughput sequencing of the v3-v4 hyper variable region of 16S rRNA gene. A total of 10 samples which included water samples from 8 different depths and 2 sediment samples were analyzed in this study. About 2.7 million sequences were obtained from all the samples. The sequence analysis revealed high bacterial diversity at deep waters and sediment samples and comparatively less species richness at the core OMZ depths. Number of OTUs ranged from 114 to 14441.Taxonomic assignments of the obtained OTUs showed dominant presence of Proteobacteria, Bacteriodetes, and Chloroflexi across all the samples. The identified OTUs were further affiliated to the phyla Marinimicrobia, Colwellia, Nitrospina, Tepidicaulis, Shewanella, Pseudoalteromonas, Woeseia at various depths along the water column. Correlation with abiotic factors suggested distinct variation in bacterial community composition with change in depth and dissolved oxygen (DO) levels. Predictive functional annotation based on bacterial phylotypes suggested presence of active nitrogen, sulphur, carbon, and methane metabolic cycles along the vertical transect of the studied region. Presence of nitrogen reduction bacterial group below the core OMZ depths may potentially provide insight into the expansion of OMZ region in Arabian Sea. Functional profiling further revealed presence of genes related to xenobiotic degradation in the water and sediment samples indicating a potential hotspot for bio-prospection.

Deep-sea sediment metagenome from Bay of Bengal reveals distinct microbial diversity and functional significance.

Bay of Bengal (BoB) has immense significance with respect to ecological diversity and natural resources. Studies on microbial profiling and their functional significance at sediment level of BoB remain poorly represented. Herein, we describe the microbial diversity and metabolic potentials of BOB deep-sea sediment samples by subjecting the metagenomes to Nanopore sequencing. Taxonomic diversity ascertained at various levels revealed that bacteria belonging to phylum Proteobacteria predominantly represented in sediment samples NIOT_S7 and NIOT_S9. A comparative study with 16S datasets from similar ecological sites revealed depth as a crucial factor in determining taxonomic diversity. KEGG annotation indicated that bacterial communities possess sequence reads corresponding to carbon dioxide fixation, sulfur, nitrogen metabolism, but at varying levels. Additionally, gene sequences related to bioremediation of dyes, plastics, hydrocarbon, antibiotic resistance, secondary metabolite synthesis and metal resistance from both the samples as studied indicate BoB to represent a highly diverse environmental niche for further exploration.

RemeDB: Tool for Rapid Prediction of Enzymes Involved in Bioremediation from High-Throughput Metagenome Data Sets.

Environmental pollution has emerged to be a major hazard in today's world. Pollutants from varied sources cause harmful effects to the ecosystem. The major pollutants across marine and terrestrial regions are hydrocarbons, plastics, and dyes. Conventional methods for remediation have their own limitations and shortcomings to deal with these environmental pollutants. Bio-based remediation techniques using microbes have gained momentum in the recent past, primarily ascribed to their eco-friendly approach. The role of microbial enzymes in remediating the pollutants are well reported, and further exploration of microbial resources could lead to discovery of novel pollutant degrading enzymes (PDEs). Recent advances in next-generation sequencing technologies and metagenomics have provided the impetus to explore environmental microbes for potentially novel bioremediation enzymes. In this study, a tool, RemeDB, was developed for identifying bioremediation enzymes sequences from metagenomes. RemeDB aims at identifying hydrocarbon, dye, and plastic degrading enzymes from various metagenomic libraries. A sequence database consisting of >30,000 sequences proven to degrade the major pollutants was curated from various literature sources and this constituted the PDEs' database. Programs such as HMMER and RAPSearch were incorporated to scan across large metagenomic sequences libraries to identify PDEs. The tool was tested with metagenome data sets from varied sources and the outputs were validated. RemeDB was efficient to classify and identify the signature patterns of PDEs in the input data sets.