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Microbial Biofilm in Remediation of Environmental Contaminants from Wastewater
Published in Vineet Kumar, Vinod Kumar Garg, Sunil Kumar, Jayanta Kumar Biswas, Omics for Environmental Engineering and Microbiology Systems, 2023
Pallavi Singh, Akshita Maheshwari, Varsha Dharmesh, Vandana Anand, Jasvinder Kaur, Sonal Srivastava, Satish Kumar Verma, Suchi Srivastava
Next-generation sequencing is a revolutionized DNA sequencing technology, where sequencing by synthesis principle is applied. In NGS, sequencing of millions of small fragments of DNA occurs. This technology provides an insight into microbial ecology with exploration of deeper layers of communities of microbes and gives impartial outlook of diversities and composition of communities. The steps involved in NGS are (1) DNA extraction from the sample biofilms; (2) checking the extracted DNA’s purity and quantity using NanoDrop spectrophotometer; (3) PCR amplification of the samples using 16SrRNA gene along with universal primers, i.e. 28F and 519R, with the different barcodes incorporated between the forward primer and 454 adaptor; (4) PCR products which are purified are further used for pyrosequencing and then short adaptors are ligated to both ends for sequence segregation; (5) modified products are attached to the DNA beads; (6) clonal amplification; (7) pyrosequencing for 16S rRNA gene sequence, pre-processing at Ribosomal Database Project (RDP) for trimming of barcodes and primers are removed from the partial ribo tags along with discarding short and low-quality sequences; (8) generation of the FASTA file data sets; (9) analysis of these sequence through analysis pipeline (MOTHUR) and R-Scripts. The NGS technique has the potential utility in confirming the sequencing and removing the conventional technique of characterizing of microbes because it has the advantages of flexibility, accuracy, and easy automation (Ronaghi, 2001).
The Application of Bioinformatics as a Diagnostic Tool for Microbiologically Influenced Corrosion
Published in Kenneth Wunch, Marko Stipaničev, Max Frenzel, Microbial Bioinformatics in the Oil and Gas Industry, 2021
Nora Eibergen, Geert M. van der Kraan, Kenneth Wunch
The objective of the qPCR program in combination with the bug bottle program was to estimate the absolute quantification of contamination in the water and coupon samples obtained from the different locations. Also, a selective set of samples was chosen for a more detailed next generation sequencing (NGS) analysis. The selection of these samples was based on qPCR data and their system relevance. NGS uses the 16S rRNA gene as a common classifier for the determination of microbial diversity. The extracted DNA for the selected sample locations was subjected to MiSeq Illumina sequencing and the retrieved sequences were aligned using the open source software MOTHUR against the available SILVA database. To discuss all the results in detail is beyond the scope of this chapter, and a summary of the most relevant results is provided in Table 7.5. Taxon levels 5 and 6 are shown; these levels corresponded to two sequential depth levels of the phylogenetic tree, class, and genera, respectively. Where further taxonomic information was available, the classification of taxon level 5 was further refined by division into taxon level 6.
Biological treatment of selenium-laden wastewater containing nitrate and sulfate in an upflow anaerobic sludge bed reactor at pH 5.0
Published in Lea Chua Tan, Anaerobic treatment of mine wastewater for the removal of selenate and its co-contaminants, 2018
L.C. Tan, Y.V. Nancharaiah, S. Lu, E. van Hullebusch, G. Gerlach, P.N.L. Lens
Granular sludge samples from the UASB reactor at the end of each period and from the inoculum were analyzed for microbial community composition. Prior to sampling, vigorous biomass mixing within the reactor was performed by briefly increasing the upflow velocity. Genomic DNA was extracted from the samples following the protocol of Lueders et al. (2004). Extracted DNA was quantified using a NanoDrop-1000 spectrophotometer and amplified by PCR using the primers Pro 341 forward and Pro 805 reverse targeting the V4 region of the 16S rRNA gene of bacteria and archaea (Takahashi et al. 2014). Amplicons were checked by agarose gel electrophoresis and sequenced using the Illumina MiSeq sequencing platform following the standard protocol. Sequences produced were analyzed using the standard operating procedure of the bioinformatics platform Mothur (Schloss et al. 2009). Further details on the microbial community analysis procedure can be seen in Appendix 7.
Biodegradation performance and diversity of enriched bacterial consortia capable of degrading high-molecular-weight polycyclic aromatic hydrocarbons
Published in Environmental Technology, 2022
Dongqi Wang, Lu Qin, Enyu Liu, Guodong Chai, Zhenduo Su, Jiaqi Shan, Zhangjie Yang, Zhe Wang, Hui Wang, Haiyu Meng, Xing Zheng, Huaien Li, Jiake Li, Yishan Lin
Processing and bioinformatics analysis of the sequencing data was performed using Mothur (v1.31.2) and QIIME pipeline (v1.8.0). Briefly, all raw reads were preprocessed to eliminate the adapter pollution and low-quality reads with a minimum window quality score of 20. The clean reads were merged to tags using FLASH (v1.2.11) and then clustered to operational taxonomic units (OTU) at 97% sequence similarity using USEARCH (v7.0.1090). All taxonomic classifications were assigned to OTU representative sequence using Ribosomal Database Project (RDP) Classifier (v2.2) and Greengenes (v201305) database (80% confidence threshold). The sequences of amplicons were deposited at the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov): Sequence Read Archive (SRA) with accession numbers of SRR6961561-SRR6961565. Alpha diversity indices, including Chao1, ACE, Shannon, and Gini-Simpson indices, were calculated by Mothur (v1.31.2). Hierarchical cluster analysis (HCA) and principal coordinate analysis (PCoA) were conducted based on weighted UniFrac distance metrics to compare the community structures across samples.
Endophyte-enhanced phytoremediation of DDE-contaminated using Cucurbita pepo: A field trial
Published in International Journal of Phytoremediation, 2018
N. Eevers, J. R. Hawthorne, J. C. White, J. Vangronsveld, N. Weyens
The FASTA files containing the raw pyrosequencing data were accessed using Mothur bioinformatics software (Schloss et al.2009) for processing and analysis according to Schloss et al. (2011). The obtained sequences were denoised before barcodes and primers were removed. The remaining sequences were aligned and classified along known sequences in the SILVA rRNA database (Pruesse et al.2007). Chimeric sequences, mitochondrial, and chloroplast sequences were deleted and the remaining sequences were grouped into operational taxonomic units (OTUs) based on a 97% similarity criterion. Rarefaction curves were starting to level off (Figure 5), but sequencing at a greater depth could have revealed more OTUs. The similarity between samples and their resemblance to the cultivated communities were visualized using Primer7 (Version 7.0.5, Primer-E Ltd.). Clustering of samples was based on S17 Bray–Curtis similarity of the group average of the species after square root transformation of the samples. The nMDS was based on S17 Bray–Curtis similarity as well, with square root transformation, Kruskall stress formula 1 and minimum stress 0.01. ANOSIM (analysis of similarities) with 999 permutations was used to test the spatial separation of the samples in nMDS.
Enrichment of microbial communities for hexavalent chromium removal using a biofilm reactor
Published in Journal of Environmental Science and Health, Part A, 2020
Masataka Aoki, Taisei Kowada, Yuga Hirakata, Takahiro Watari, Takashi Yamaguchi
Sequence processing was performed using the Mothur bioinformatics package[22], version 1.39.5, according to MiSeq standard operating procedures (MiSeq SOP; https://mothur.org/wiki/miseq_sop/)[23] with slight modifications. Briefly, raw sequence reads were combined to contigs with the “make.contigs” command and low-quality sequences were removed with the “screen.seqs” command using the following filtering parameters: maxambig = 0, minlength = 225, and maxlength = 275, maxhomop = 8. Chimeric sequences were identified and removed with the VSEARCH algorithm (“chimera.vsearch” command with the dereplicate option set to “true”). The remaining sequences were then classified with a naïve Bayesian classifier against the SILVA SSURef 132 reference database[24] with an 80% bootstrap threshold. Any sequence classified as “Chloroplast,” “Mitochondria,” “unknown,” or “Eukaryota” were removed. Remaining sequences were clustered into operational taxonomic units (OTUs) using a distance limit of 0.03 (i.e., equivalent to a 97% sequence similarity). Species richness estimators (Chao1 and abundance-based coverage estimator [ACE]),[25,26] Shannon’s and Simpson’s diversity indices,[27,28] and Good’s coverage values[29] were calculated with the “summary.single” command. In these calculations, the number of sequences was normalized to the sequence count of the smallest samples by random subsampling. The raw sequence data obtained in this study was deposited in the DNA Data Bank of Japan (DDBJ) Sequence Read Archive under accession code DRA009883.