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New Tools for Bioprocess Analysis and Optimization of Microbial Fuel Production
Published in Farshad Darvishi Harzevili, Serge Hiligsmann, Microbial Fuels, 2017
Isabelle France George, Philippe Bogaerts, Dimitri Gilis, Marianne Rooman, Jean-François Flot
For this reason, people have long contented themselves with “gene-centric” metagenomic analyses in which the main aim is to produce a catalogue of the genes present in a given environment (Tringe et al. 2005). Complete or near-complete genome assemblies of noncultivable members of microbial communities have been sometimes obtained using iterative assembly procedures (Pelletier et al. 2008); however, this only works for members that are largely overrepresented in the community. Recently, the availability of new methods to “bin” metagenomes into sets of groups of contiguous sequences (“contigs” [Staden 1980]) hypothetically coming from different species has triggered a shift from gene-centric to “genome-centric” approaches, where the aim is now to assemble and characterize the genome of each member of the community in order to understand its metabolic activities (Waldor et al. 2015). The most popular binning approaches are based on the guanine + cytosine (GC) content and/or on coverage (i.e., how often a given stretch of DNA is represented among the sequence reads), assuming that the abundance of each species in the mix should endow it with a characteristic coverage signature (Albertsen et al. 2013; Alneberg et al. 2014). However, microbial genomes have varying and overlapping GC contents, and the coverage signal can be obscured by the presence of repeated genome regions or by natural and/or artefactual variations in coverage along genomes, notably as a function of the distance to its origins of replication (Semova et al. 2012; Hawkins et al. 2013).
Omics Approach to Understanding Microbial Diversity
Published in Jyoti Ranjan Rout, Rout George Kerry, Abinash Dutta, Biotechnological Advances for Microbiology, Molecular Biology, and Nanotechnology, 2022
Shilpee Pal, Arijit Jana, Keshab Chandra Mondal, Suman Kumar Halder
In the case of uncultured microbes, the qualitative understanding of their physiology can be possible by analyzing binned draft genomes. Their taxonomy and complete genome can be evaluated by identifying conserved genes in the contig bins (Sangwan et al., 2016). Moreover, their metabolic pathways, functional potentials, and interactions with the environment can be gained by matching methanogenic (draft) assemblies against pathway or gene databases (Truong et al., 2017; Hahn et al., 2017). Sometimes, microbes isolated from unexplored ecological niches have no closely related species in the database. In that case, assembly and binning are of two essential steps in the metagenomics analysis.
Functional Diversity of Microbial Communities in Hydrocarbon-Polluted Ecosystems
Published in Wael Ahmed Ismail, Jonathan Van Hamme, Hydrocarbon Biotechnology, 2023
R.M. M. Abed, H. Mahmoud, N. Sivakumar
Conversely, continual developments in high-throughput sequencing analysis have made direct WMS more practical. For samples with low levels of DNA, WMS may require a random amplification step (Khodakova et al., 2014), or a PCR-independent multiple displacement approaches to amplify DNA using phi29 DNA polymerase (Abulencia et al., 2006). Following sequencing, reads may be mapped to a reference genome, or de novo assembly approaches can be used in an effort to construct contigs and scaffolds of groups of microbial genes (Aguiar-Pulido et al., 2016). This approach provides a high-level picture of the microbial ecosystem, instead of focusing on specific genes, pathways, or individual organisms. The WMS approach has been used to characterize microbial communities with diverse metabolic potential in various oil-contaminated terrestrial and marine environments (Mason et al., 2012, 2014; Joshi et al., 2014; Jung et al., 2016; Abbasian et al., 2016; Salam et al., 2017). The genetic diversity in these environments was found to be dominated by genes related to amino acid, carbohydrate, cofactor, vitamin, prosthetic group and pigment metabolism (Salam et al., 2017), motility, chemotaxis, aliphatic HC degradation (Mason et al., 2012), and genes involved in denitrification (Mason et al., 2014). The dominance of these genes suggested an adaptation of the microbial community in polluted environments to stressors including HC hydrophobicity, heavy metal toxicity oxidative stress, imbalances in carbon, nitrogen, and phosphorous ratios, and nutrient starvation. In diesel-contaminated microcosms, WMS analysis showed significant changes in a large proportion of functional gene categories, especially those related to HC degradation and tetracycline biosynthesis, and microbial diversity decreased in the environments where alkane monooxygenase and P450 genes increased between 2 to 13-fold (Jung et al., 2016).
Isolation and characterization of phenanthrene-degrading bacteria from urban soil
Published in Bioremediation Journal, 2023
Garima Sharma, Pooja Gokhale Sinha, Kavita Verma, Deevanshi Walia, Milinda Lahiri, Vartika Mathur
Pure cultures of bacterial isolates showing positive utilization of PAH were inoculated in 10 ml of nutrient broth and incubated overnight at 28 °C. These inoculates were then centrifuged at 8500 rpm for 15 min. The pellets were used to isolate genomic DNA by CTAB method (Wilson 2001). The isolates were further identified using 16S rDNA Sanger sequencing using ABI 3730 DNA Analyzer (Applied Biosystems, performed by Barcode Biosciences Pvt. Ltd, Bangalore, India). Forward and reverse sequences of the cloned fragments obtained via 16S sanger sequence were used to prepare consensus sequence using Finch TV version 1.4.0 (Washington, USA) and BioEdit version 7.1.3.0 software (Pennsylvania, USA) through ‘cap contig assembly program’ and these sequences were compared using BLASTN (NCBI) for identification. Phylogenetic analysis was constructed by the neighbor-joining method using MEGA version 11 software (Danton, Texas, USA), with confidence tested by bootstrap analysis (1000 repeats) (Tamura, Stecher, and Kumar 2021).
Indigenous Bacillus paramycoides spp. and Alcaligenes faecalis: sustainable solution for bioremediation of hospital wastewater
Published in Environmental Technology, 2022
Aneeba Rashid, Safdar A. Mirza, Ciara Keating, Sikander Ali, Luiza C. Campos
The three bacterial isolates showing maximum decolourisation potential (> 90%) were selected for identification using 16S rDNA sequencing [45]. Polymerase chain reaction (PCR) was carried out on the three isolates using the following forward and reverse primer set (see Supplementary Material, S1.2): 27F (AGA GTT TGA TCM TGG CTC AG) and 1492R (TAC GGY TAC CTT GTT ACG ACT T) [46]. The DNA samples with the extension products were then added to Hi-Di formamide (Applied Biosystems, Foster City, CA). This mixture was incubated for 5 min at 95°C, placed on ice for 5 min and subsequently the sequencing reaction was carried out in an ABI Prism 3730XL DNA analyser (Applied Biosystems, Foster City, CA). The forward and reverse sequence chromatograms (abi files) were initially viewed in FinchTV version 1.5.0 and then interrogated using MacVector version 17.5.4. The forward and reverse reads were imported into BioEdiT version 7.2. A consensus sequence per strain was subsequently assembled using the contig assembler program (CAP) [47] using the forward read and reverse complement of the reverse read. The full sequence information and raw chromatogram details are presented in the Supplementary Material (S1.2, Figure S4 – S25). Basic Local Alignment Search Tool (BLAST) analysis was carried out on the assembled sequences. The sequences of the three isolates were deposited in GenBank with accession numbers [GenBank: MT477810], [GenBank: MT477812] and [GenBank: MT477813].
Biotransformation of chromium (VI) by Bacillus sp. isolated from chromate contaminated landfill site
Published in Chemistry and Ecology, 2020
Md. Ekramul Karim, Shamima Akhtar Sharmin, Md. Moniruzzaman, Zeenath Fardous, Keshob Chandra Das, Subrata Banik, Md. Salimullah
The contig sequence was generated from the raw sequences of forward and reverse primer using DNA Baser sequence assembly software v5.15. The contig sequence was searched for similarities in the NCBI nucleotide BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) search programme and multiple sequence alignment of the highly similar sequences was done by using Clustal Omega integrated in the Molecular Evolution Genetic Analysis v.X (MEGA X) software. The phylogenetic analysis was conducted using MEGA X [26]. The evolutionary history was inferred using the Neighbor-Joining method [27]. The evolutionary distances were computed using the Maximum Composite Likelihood method [28] for 16S rRNA gene and the Poisson correction method [29] for ChrR gene. The bootstrap testing was done with 1000 replicate samples [30]. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There were a total of 1560 positions for 16S rRNA gene whereas a total of 270 positions for ChrR gene product in the final dataset. The nucleotide sequence of the 16S rRNA and chromate reductase (ChrR) gene were submitted to the NCBI gene bank database under the accession number MN447637 and MN646078, respectively.