Dataset Information

Metagenomic analysis revealed higher microbial and functional gene diversities in deep landfill

ABSTRACT: Waste decomposition in landfills is a complex and microbe-mediated process. Understanding the microbial community composition and structure is critical for accelerating decomposition and reducing adverse impact on the environment. Here, we examined the microbial communities along with landfill depth and age (LDA) in a sanitary landfill in Beijing, China using 16s rRNA Illumina sequencing and GeoChip 4.6. We found that Clostridiales and Methanofollis were the predominant bacteria and archaea in the present landfill, respectively. Interestingly, in contrast with the decreasing trend of microbial diversity in soil, both phylogenetic and functional diversities were higher in deeper and older refuse in the landfill. Phylogenetic compositions were obviously different in the refuse with the same LDA and such difference is mainly attributed to the heterogeneity of refuse instead of random process. Nevertheless, functional structures were similar within the same LDA, indicating that microbial community assembly in the landfill may be better reflected by functional genes rather than phylogenetic identity. Mantel test and canonical correspondence analysis suggested that environmental variables had significant impacts on both phylogenetic composition and functional structure. Higher stress genes, genes for degrading toxic substances and endemic genes in deeper and older refuse indicated that they were needed for the microorganisms to survive in the more severe environments. This study suggests that landfills are a repository of stress-resistant and contaminant-degrading microorganisms, which can be used for accelerating landfill stabilization and enhancing in situ degradation.

ORGANISM(S): Bacteria

PROVIDER: GSE68712 | GEO | 2016/06/15

SECONDARY ACCESSION(S): PRJNA283488

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: Lung is the largest mucosal area of the human body and directly connected to the external environment, facing microbial exposure and environmental stimuli. Therefore, studying the internal microorganisms of the lung is crucial for a deeper understanding of the relationship between microorganisms and the occurrence and progression of lung cancer. Methods: Tumor and adjacent nontumor tissues were collected from 38 lung adenocarcinoma patients and used nanopore sequencing technology to sequence the 16s full-length sequence of bacteria, and combining bioinformatics methods to identify and quantitatively analyze microorganisms in tissues, as well as to enrich the metabolic pathways of microorganisms. Results: the microbial composition in lung adenocarcinoma tissues is highly similar to that in adjacent tissues, but the alpha diversity is significantly lower than that in adjacent tissues. The difference analysis results show that the bacterial communities of Streptococcaceae, Lactobacillaceae, and Neisseriales were significantly enriched in cancer tissues. The results of metabolic pathway analysis indicate that pathways related to cellular communication, transcription, and protein synthesis were significantly enriched in cancer tissue. In addition, clinical staging analysis of nicotine exposure and lung cancer found that Haemophilus, paralinfluenzae, Streptococcus gordonii were significantly enriched in the nicotine exposure group, while the microbiota of Cardiobactereae and Cardiobacterales were significantly enriched in stage II tumors. The microbiota significantly enriched in IA-II stages were Neisseriaeae, Enterobacteriales, and Cardiobacterales, respectively. Conclusion Nanopore sequencing technology was performed on the full length 16s sequence, which preliminarily depicted the microbial changes and enrichment of microbial metabolic pathways in tumor and adjacent nontumor tissues. The relationship between nicotine exposure, tumor progression, and microorganisms was explored, providing a theoretical basis for the treatment of lung cancer through microbial targets.

Dataset Information

Metagenomic analysis revealed higher microbial and functional gene diversities in deep landfill

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets