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Microbial Reservoir Souring
Published in Kenneth Wunch, Marko Stipaničev, Max Frenzel, Microbial Bioinformatics in the Oil and Gas Industry, 2021
Matthew Streets, Leanne Walker
From Principle Coordinate Analysis (PCoA), it was observed that the incubation method (bottle test or bioreactor effluent) was the most influential factor in separating the communities, having a greater effect than either incubation time or operating pressure. The dataset also described further 16S rRNA gene profiling comparing the microbiological communities from the effluent water samples with the communities associated with the core material from the injector and producer ends of the bioreactors. The distances between the communities were calculated by weighted UniFrac, which considered the phylogenetic distances and relative abundances. From alpha diversity analysis, it was demonstrated that microbiological communities associated with each bioreactor section were significantly different from one another, with favorable growth at the injector end.
Time-Dependent Effect of Graphene on the Microbial Activity of the Soil Under Single and Repeated Exposures
Published in Soil and Sediment Contamination: An International Journal, 2023
Wenjuan Liu, Yufeng Guo, Zihan Wang, Wenbo Deng
In order to compare similarities of soil bacterial community compositions under different exposure method, the Bray-Curtis distance of soil samples was compared relative to the control. From Figure 3, the Bray-Curtis distance of the single exposure of GR was 0.40 which was nearly twice times compared with that of the repeated exposures after 4 days incubation, indicating single exposure of GR played a stronger effect on soil bacterial communities, while their dissimilarity tended to be smaller gradually with the extension of incubation time. The principal co-ordinates analysis (PCoA) based on Unweighted-UniFrac distance which incorporates phylogenetic distance into relative abundance measurement was also conducted to study the difference of soil bacterial community. The PCoA results showed that, the soil bacterial community after exposure to GR in a short-term time were totally separated from the control, especially for the soil under single exposure. And the soil bacterial communities changed constantly with the growth of culture time, and the distinction between the single exposure and repeated exposures got smaller. In addition, after a longer period, the control, the repeated GR exposure were clustered with single GR exposure, suggesting similar microbial community structures. And adonis analysis on the bacterial community further confirmed that the difference in soil bacterial communities under different incubation time to GR was significant (R2 = 0.573, p = .001).
Response of Rhizobacterial Community to Biochar Amendment in Coal Mining Soils with Brachiaria Decumbens as Pioneer Plant
Published in Soil and Sediment Contamination: An International Journal, 2020
Karina Rios Montes, Nancy J. Pino, Gustavo A. Peñuela, Alberto Mendoza
The hierarchical clustering tree obtained using the UniFrac metric with the Jackknife validation is shown in Figure 5a. UniFrac measures phylogenetic distances between communities based on lineages that communities contain. Jackknifing method determines how the number and uniformity of the sequences in the different samples affected the grouping, with a 95% confidence level. The rhizobacterial communities were grouped in a separate branch according to the sampling time and treatments. Phylogenetic analysis showed a correlation between the treatments with biochar (MB, MBA and MBF). Furthermore, it revealed a significant difference between these treatments and those of the control and MM. The PCoA plot (Figure 5b) clearly identified variations in rhizobacterial community composition between the treatments. Abundance in each treatment varied over time, and was significant in the biochar treatments, where a smaller distance was observed in the distribution of the MB, MBA, MBF groups. These results can be attributed to the homogeneity and stability of the populations, particularly during the last sampling time.
Identification of sulfate-reducing and methanogenic microbial taxa in anaerobic bioreactors from industrial wastewater treatment plants using next-generation sequencing and gene clone library analyses
Published in Journal of Environmental Science and Health, Part A, 2020
Krittayapong Jantharadej, Wuttichai Mhuantong, Tawan Limpiyakorn, Skorn Mongkolsuk, Kwanrawee Sirikanchana, Benjaporn Boonchayaanant Suwannasilp
The microbial communities in sludge samples from the anaerobic bioreactors of industrial wastewater treatment plants were investigated using 16S rRNA gene amplicon sequencing (MiSeq system, Illumina, USA). The V4 region of the 16S rRNA gene was amplified using universal primers for bacteria and archaea, F515 (5′-CACGGTCGKCGGCGCCATT-3′) and R806 (5′-GGACTACHVGGGTWTCTAAT-3′).[12] Each PCR amplification was performed in a 50 µL volume and contained each primer (1 µM), 1.25 U of Tag polymerase, 4 mM MgCl2, 5 µL of 10× buffer (Fermentas, Thermo Scientific), 0.2 µM dNTPs, and 2 µL of template DNA. Thermal cycling (BIO-RAD, USA) was performed with an initial denaturation step of 95 °C for 3 min followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 30 s, with a final extension step at 72 °C for 5 min. Next, the amplicons were labeled using a Nextera® XT Index kit (Illumina, USA). The DNA concentrations were measured using a Qubit® 2.0 fluorometer (Life Technologies) and adjusted to 4 ng µL−1 before pooling the samples to obtain a composite sample for sequencing. Next-generation sequencing was conducted using a MiSeq sequencer at the Faculty of Medicine, Chulalongkorn University. The quality of the resulting sequences was assessed using the FastQC application in the BaseSpace program (https://basespace.illumina.com). The analysis was initially conducted by joining paired-end sequences using the command “make.contig” in mothur[13] and by removing the chimeric sequences using UCHIME.[14] The entire procedure used to analyze the data was performed in QIIME version 1.9.1 (Quantitative Inights Into Microbial Ecology).[15] The sequences were clustered into operational taxonomic units (OTUs) with an open reference method using UCLUST.[16] A representative sequence of each OTU was performed using the Greengenes database.[17] Alpha diversity indices, including the observed OTUs, Shannon–Weaver index, and Chao1 richness estimator, were calculated from the OTU table. Unweighted UniFrac[18] was calculated to measure the differences among microbial communities.