Metagenomic Analysis of Microbial Communities from Coal Waste

Nunung Eni Elawati(1), Catur Retno Lestari(2), Martha Aulia Marco(3), Sintia Puspa Dewi(4), Bimastyaji Surya Ramadan(5),


(1) Biomedical Science Study Program, Faculty of Health, Universitas IVET, Semarang, Central Java, Indonesia 50233
(2) Biomedical Science Study Program, Faculty of Health, Universitas IVET, Semarang, Central Java, Indonesia 50233
(3) Biomedical Science Study Program, Faculty of Health, Universitas IVET, Semarang, Central Java, Indonesia 50233
(4) Biomedical Science Study Program, Faculty of Health, Universitas IVET, Semarang, Central Java, Indonesia 50233
(5) Graduate Programs in Environmental Systems, Graduate School of Environmental Engineering, The University of Kitakyushu, Fukuoka, Japan 808-0135

Abstract

Coal waste contains few macro-and micronutrients, which makes it less likely to become a growth site for microorganisms. One way to screen coal waste resources is to identify the diversity of microbes in them to study the relationship between these microbial communities in contributing to improving environmental pollution using metagenomics to determine microbial diversity. The purpose of this research is to study the diversity of microorganisms in areas contaminated with heavy metals using a metagenomic method. This study was performed using next-generation sequencing techniques, including DNA extraction, 16S rRNA amplification, and gene sequencing analysis. The results of this research found that the 10 most commonly found species were Baekduia soli, Nocardiodes iriomotensis, Nocardioides mesophilus, Nocardioides pakistanensis, Propionibacterium cyclohexanicum, Solirubrobacter ginsenosidimutans, Gemmatirosa kalamazoonensis, Roseisolibacter agri, Kosakonia saccahri, and Dickeya fangzhongdai. Based on this research, it can be concluded that most of the microbial communities from coal waste are dominated by the phylum Actinobaceria. The results of this study can be used as an adaptive microbial germplasm for industrial waste management strategies.

Keywords

Coal waste; Metagenomic; Next Generation Sequencing; 16S rRNA

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References

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