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In Silico Approach to Cancer Therapy
Published in Anjana Pandey, Saumya Srivastava, Recent Advances in Cancer Diagnostics and Therapy, 2022
Anjana Pandey, Saumya Srivastava
The utilization of NGS for genomic, transcriptomic, and epigenomic profiling of tumors is constructing the primary data source enabling the identification of hallmark characteristics. The employment of these tools on bulk tissue and the computational tools that will allow the NGS data to be extracted have been previously studied (Thorsson et al., 2018). In addition, the microbiome studies (Knight et al., 2018) have progressed faster and now deliver the methods to study the microbiota composition and function from 16S ribosomal RNA sequencing, metagenomics, and metatranscriptomics data. The recent critical advancements include the development of new tools for single-cell analysis and multiplexed spatial cellular phenotyping. These tools are of precise significance for cancer immunity as they permit the detailed analysis of cancer immunity, involving the depiction of the cellular composition of cancerous and normal tissue, quantification of the immune contexture, and coupling of α chains and β chains of individual T cell receptors (TCRs) and B cell receptors (BCRs). The data types, the transitional studies, and the immunogenomic studies are shown. A fair comparison of the data is necessary to understand the practical steps that resulted in the generated data to analyze the origins of the critical characteristic and possible biases of the resultant data.
“Omics”
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
Culture-based enumeration is often performed in clinical settings and remains relevant for diagnostics. The full range of omic technologies is readily applied to the microbiome and achieved at a lower cost because of their relatively compact size (Aguiar-Pulido et al. 2016). Historically, the diversity of the human microbiome was characterized using amplicon sequencing targeting the 16S ribosomal gene used for taxonomic classification (McDonald et al. 2018). The 16S ribosomal RNA has nine hypervariable regions targeted for taxonomic classification of the microbiome. In this approach, primers hybridize to conserved regions flanking one or more of the nine highly variable regions of the 16S ribosomal RNA (V1–V3, V3–V4, V3–V5, etc.), are used to amplify genomic DNA and then sequenced. The main advantage is relatively low-cost taxonomic classification of the bacterial communities and historical use in the human microbiome project. 16S amplicon sequencing has known limitations in terms of bacterial diversity captured by amplicon choice, inability to capture fungal and viral representation, inability to detect certain genera, inability to identify to species and sub-species levels, and most metabolic predictions are based on operational taxonomic units (OTUs) rather than direct identification of the genes. However, these limitations can be overcome by whole-genome shotgun metagenomics sequencing (Ranjan et al. 2016, Knight et al. 2018).
Leaching, bioleaching, and acid mine drainage case study
Published in Katalin Gruiz, Tamás Meggyes, Éva Fenyvesi, Engineering Tools for Environmental Risk Management – 4, 2019
H.M. Siebert, G. Florian, W. Sand, E. Vaszita, K. Gruiz, M. Csővári, G. Földing, Zs. Berta, J.T. Árgyelán
Quantitative Polymerase Chain Reaction (qPCR) offers another option for monitoring and quantifying microbial communities. The technique is based on the amplification of bacterial or archaeal 16S ribosomal RNA genes. This method is used after the extraction of nucleic acids from solid or liquid samples as described earlier (e.g. Kock & Schippers, 2008; Liu et al., 2006). A community study of different coal mine drainage treatment systems using cultivation-based and molecular-based qPCR techniques has indicated the predominance of heavy metal resistant fungal and bacterial strains. The fungi were identified using morphological characterization and the amplification of the 18S rRNA gene, 28S rRNA gene, and ITS1–5.8S rRNA-ITS2 region (Santelli et al., 2010). Another study that used Denaturing Gradient Gel Electrophoresis (DGGE) analysis of amplified 16S rRNA genes and sequencing showed that the microbial diversity in the oxic sediment zone of a constructed wetland is very low (Nicomrat et al., 2006). Iron- and sulfur-oxidizing acidophiles A. ferrooxidans and A. thiooxidans as well as the bacteria Alcaligenes sp. and Bordetella sp. were identified. Another study on samples from three layers of surface sediments from a mining lake showed high microbial diversity. Depending on the physicochemical properties of the samples, cultivation- and molecule-based techniques identified acidophilic iron-reducing or -oxidizing or neutrophilic iron-reducing bacteria (Porsch et al., 2009).
Disinfection of Antibiotic-resistant Bacteria in Sewage and Hospital Effluent by Ozonation
Published in Ozone: Science & Engineering, 2021
Takashi Azuma, Tetsuya Hayashi
Genomic DNA was extracted from the samples by using an Extrap Soil DNA Kit Plus v.2 (Nippon Steel Eco-Tech Corporation, Tokyo, Japan). The concentrations and purifications of DNA were determined by Qubit® 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) using Qubit® dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) (Szekeres et al. 2017). The V1-V2 region of the 16S ribosomal RNA (rRNA) gene of bacteria was used to characterize bacterial communities (Jeon et al. 2015; Zhang et al. 2018). For PCR amplification, the universal bacterial primers 27 F/338 R (Deng et al. 2018; Quartaroli et al. 2019) were used. PCR was carried out in a T100 Thermal Cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) in accordance with previous studies (Sun et al. 2014; Yu et al. 2020). The PCR cycle consisted of 3 min initial denaturation at 95 °C; 25 cycles 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s; and a final extension at 72 °C for 5 min. Electrophoresis in 1.5% agarose gel was then conducted in a Mupid-2plus System (Advance Co. Ltd., Tokyo, Japan) to examine the quality of PCR products (Yu et al. 2020), and genes were sequenced on a MiSeq platform (Illumina Inc., San Diego, CA, USA) according to the manufacturer’s instructions and as described previously (Azuma and Hayashi 2021b).
Geographical distribution and risk assessment of heavy metals: a case study of mine tailings pond
Published in Chemistry and Ecology, 2020
Mingjiang Zhang, Minjie Sun, Jianlei Wang, Xiao Yan, Xuewu Hu, Juan Zhong, Xuezhe Zhu, Xingyu Liu
Contamination of heavy metals in soils by tailings residues poses risks to the ecosystem and to humans. In this study, we found that large amounts of elements (Pb, Zn, Cd, Cu) may be released into the environment from the tailings. To identify pollution problems, the anthropogenic contributions should be distinguished from the natural sources. Geochemical approaches, such as the geo-accumulation index (Igeo) [5,6] are often used to distinguish anthropogenic contributions from the natural sources. Microorganisms are sensitive to heavy metals, and heavy metals have some influence on bacterial community structure, microbial biomass and microbial diversity [7]. Microbial ecology is an important indicator of heavy metal pollution. Furthermore, microorganisms also affect the pH of the environment, the bound form and the mobilisation of heavy metal elements. The application of molecular biology techniques, especially 16S ribosomal RNA high throughput sequencing technology, has progressed significantly in the field of microbial ecology [8].
Molecular biological tools in concrete biodeterioration – a mini review
Published in Environmental Technology, 2019
Vinita Vishwakarma, Balakrishnan Anandkumar
Molecular methods, such as 16S ribosomal RNA (16S rRNA) gene analysis, have been used to identify microbial communities from a variety of environments [35–38]. Vincke et al. initiated both conventional as well as molecular techniques to determine the microbial communities present on the concrete walls of sewer pipes [39]. Hernandez et al. had studied on an in-situ assessment of active Thiobacillus sp. in corroding sewers using fluorescent RNA probe [40]. To find the microbial components of the biofilms DGGE analysis was made and this identified the cyanobacteria, green microalgae, bacteria and fungi by targeting the 16S and 18S ribosomal RNA genes [41]. Sometimes, the DNA-based molecular biology techniques are used to identify the components of microbial biofilms. Isolation, molecular identification and phylogenetic analysis of dominant species of bacteria in the biofilm on the three types of concrete cube specimens (normal concrete, concrete with fly ash and superplasticizer and concrete with only superplasticizer) were carried out to understand the diversity of different types of bacteria [16]. Verdier et al. reviewed both sampling and analysis methods on building materials [2]. The author reported different microbial sampling methods such as in-situ and laboratory experiments and found that laboratory testing gave reliable information on microbial development on building materials.