Dataset Information

Sediment Microbiota in Response to Circuitry of Sediment Microbial Fuel Cells, Revealed by 16S rRNA Gene Amplicon Sequencing.

ABSTRACT: Information about sediment microbiota affected by sediment microbial fuel cells (SMFC) is limited. A laboratory-scale SMFC was applied to a eutrophic lake sediment under closed-circuit/open-circuit conditions. We analyzed the prokaryotes in the sediment adhering to the anode material. The archaeal family Methanoperedenaceae was a predominant group under closed-circuit conditions.

SUBMITTER: Takemura Y

PROVIDER: S-EPMC8601140 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Sediment Microbiota in Response to Circuitry of Sediment Microbial Fuel Cells, Revealed by 16S rRNA Gene Amplicon Sequencing.

Takemura Yasuyuki Y Aoki Masataka M Tran P Thao T Tomioka Noriko N Kubota Keiichi K Matsuura Norihisa N Syutsubo Kazuaki K

Microbiology resource announcements 20211118 46

Information about sediment microbiota affected by sediment microbial fuel cells (SMFC) is limited. A laboratory-scale SMFC was applied to a eutrophic lake sediment under closed-circuit/open-circuit conditions. We analyzed the prokaryotes in the sediment adhering to the anode material. The archaeal family <i>Methanoperedenaceae</i> was a predominant group under closed-circuit conditions. ...[more]

PMID: 34792378

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Project description:BackgroundRabbit can produce meat, fur and leather, and serves as an important biomedical animal model. Understanding the microbial community of rabbits helps to raise rabbits healthily and better support their application as animal models.ResultsIn this study, we selected 4 healthy Belgium gray rabbits to collect the microbial samples from 12 body sites, including skin, lung, uterus, mouth, stomach, duodenum, ileum, jejunum, colon, cecum, cecal appendix and rectum. The microbiota across rabbit whole body was investigated via 16S rRNA gene amplicon sequencing. After quality control, 46 samples were retained, and 3,148 qualified ASVs were obtained, representing 23 phyla and 264 genera. Based on the weighted UniFrac distances, these samples were divided into the large intestine (Lin), stomach and small intestine (SSin), uterus (Uter), and skin, mouth and lung (SML) groups. The diversity of Lin microbiota was the highest, followed by those of the SSin, Uter and SML groups. In the whole body, Firmicutes (62.37%), Proteobacteria (13.44%) and Bacteroidota (11.84%) were the most predominant phyla. The relative abundance of Firmicutes in the intestinal tract was significantly higher than that in the non-intestinal site, while Proteobacteria was significantly higher in the non-intestinal site. Among the 264 genera, 35 were the core microbiota distributed in all body sites. Sixty-one genera were specific in the SML group, while 13, 8 and 1 were specifically found in the Lin, SSin and Uter groups, respectively. The Lin group had the most difference with other groups, there were average 72 differential genera between the Lin and other groups. The functional prediction analysis showed that microbial function within each group was similar, but there was a big difference between the intestinal tracts and the non-intestinal group. Notably, the function of microorganism in uterus and mouth were the most different from those in the gastrointestinal sites; rabbit's coprophagy of consuming soft feces possibly resulted in little differences of microbial function between stomach and large intestinal sites.ConclusionOur findings improve the knowledge about rabbit microbial communities throughout whole body and give insights into the relationship of microbial communities among different body sites in health rabbits.

Project description:BackgroundSequential membrane filtration as a pre-processing step for capturing sediment-associated microorganisms could provide good quality and integrity DNA that can be preserved and kept at ambient temperatures before community profiling through culture-independent molecular techniques. However, the effects of sample pre-processing via filtration on DNA-based profiling of sediment-associated microbial community diversity and composition are poorly understood. Specifically, the influences of pre-processing on the quality and quantity of extracted DNA, high-throughput DNA sequencing reads, and detected microbial taxa need further evaluation.ResultsWe assessed the impact of pre-processing freshwater sediment samples by sequential membrane filtration (from 10, 5 to 0.22 μm pore size) for 16S rRNA-based community profiling of sediment-associated microorganisms. Specifically, we examined if there would be method-driven differences between non- and pre-processed sediment samples regarding the quality and quantity of extracted DNA, PCR amplicon, resulting high-throughput sequencing reads, microbial diversity, and community composition. We found no significant difference in the qualities and quantities of extracted DNA and PCR amplicons, and the read abundance after bioinformatics processing (i.e., denoising and chimeric-read filtering steps) between the two methods. Although the non- and pre-processed sediment samples had more unique than shared amplicon sequence variants (ASVs), we report that their shared ASVs accounted for 74% of both methods' absolute read abundance. More so, at the genus level, the final collection filter identified most of the genera (95% of the reads) captured from the non-processed samples, with a total of 51 false-negative (2%) and 59 false-positive genera (3%). We demonstrate that while there were differences in shared and unique taxa, both methods revealed comparable microbial diversity and community composition.ConclusionsOur observations highlight the feasibility of pre-processing sediment samples for community analysis and the need to further assess sampling strategies to help conceptualize appropriate study designs for sediment-associated microbial community profiling.

Dataset Information

Sediment Microbiota in Response to Circuitry of Sediment Microbial Fuel Cells, Revealed by 16S rRNA Gene Amplicon Sequencing.

Publications

Sediment Microbiota in Response to Circuitry of Sediment Microbial Fuel Cells, Revealed by 16S rRNA Gene Amplicon Sequencing.

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