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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
To study the impact of the 2010 Gulf of Mexico oil spill, GeoChip 4.0 was used to compare microbial communities in oil-contaminated water with that of uncontaminated water (Lu et al., 2012). After 40 days, the presence of the HC plume (1,100 m depth) caused a major shift in the microbial community’s functional structure and diversity. Many genes involved in HC degradation were significantly enriched in plume samples, including naphthalene 1,2-dioxygenase, cyclohexanone 1,2-monooxygenase, and alkene monooxy-genase (AMO) (Hazen et al., 2010; Lu et al., 2012). These findings suggest that the microbial communities in the Gulf of Mexico were capable of intrinsic bioremediation, and oil stimulated the oil-degrading community members. Hence, the functional gene array studies provide information about microbial community dynamics and the role of functional genes in oil remediation. Although DNA microarray methods are rapid and facilitate monitoring of a variety of samples (Desai et al., 2010), this method has recently become less utilized due to issues related to chip sensitivity, specificity, and quantitative ability. In addition, microarray techniques are not able to identify novel sequences, overlooking the diverse reservoir of uncharacterized sequences that are common in natural environments (He et al., 2013).
Application of Molecular Tools and Biosensors for Monitoring Water Microbiota
Published in Maulin P. Shah, Wastewater Treatment, 2022
A powerful molecular tool used to study the expression of thousands of genes simultaneously is known as a DNA microarray. This technique utilizes thousands of identified genes (oligonucleotides, cDNA, and genomic DNA) assembled on a solid support in an orderly two-dimensional fashion. The solid support used in this method could be nylon membrane, silicon chips, or a glass. The basic principle behind the DNA microarray is the Watson-Crick base pairing of the complementary sequences. The microarray chips, upon exposure of labeled nucleic acid to probes, helps in the simultaneous detection of multiple genes by the phenomenon of nucleic acid hybridization. DNA microarray comprises of four steps. The process starts with the preparation of fluorescently labeled mRNA molecule isolated from the sample. Then, the nucleotide sequence hybridizes with the complementary sequence present in the microarray chip upon incubation in optimum condition. Finally, the chip is scanned for fluorescence after vigorous washing. The fluorescence generated during the process depicts the presence of target sequence in the sample and is generally proportional to the concentration of the sequence. This technique can be further coupled with PCR techniques in order to obtain higher sensitivity. PCR microarray can further increase sensitivity up to 106-fold (44).
Genomic Approach of Nanotoxicity Evaluation
Published in Vineet Kumar, Nandita Dasgupta, Shivendu Ranjan, Nanotoxicology, 2018
A comprehensive scientific understanding of the effect of toxicants in the environment requires identification of the stresses that exert an impact, their mode of actions, and their consequences on growth, survival, and reproduction of organisms. Because gene expression is closely related to most cell responses, more insight into toxicological modes of action can be obtained by measuring the first response occurring at the transcriptional level. Hence, profiling of the transcripts, proteins, and metabolites can help to discriminate classes of toxicants and their mode of action at a particular time in an organism or a cell. Gene expression profiling is one possibility for enhancing toxicity tests. DNA microarray allows the expression of thousands of genes to be compared among samples. Using microarray, the transcriptional regulation of expression of thousands of genes can also be compared. The greatest potential ability of DNA microarrays in the field of nanotoxicology is that they may be useful for unveiling the mode of action of nanomaterials in non-model organisms as shown in Figure 16.3. By investigating the effects at the mRNA level with microarrays, biomarkers can be derived that can also be used to discriminate stress induced by nanotoxicants (Amorim et al. 2011).
Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning
Published in Cybernetics and Systems, 2023
Çiğdem Erol, Tchare Adnaane Bawa, Yalçın Özkan
DNA microarray analysis is one of the technologies that help to measure the expression levels of multiple genes simultaneously via chips. It is possible to define the gene expression profile of the tumor with DNA microarray technology (D'Angelo, Di Rienzo, and Ojetti 2014). Gene expression analysis is a study used to classify cancers, predict clinical outcomes, and discover disease-associated biomarkers (Chen et al. 2014). In general, microarray studies require experimental intensive labor, time, and cost. The data obtained as a result of all these efforts are shared in public databases such as the National Center for Biotechnology Information, Gene Expression Omnibus (NCBI GEO), as well as being used in the publication in which it was produced. In order to obtain valuable information from data in today’s data age, it is necessary to conduct research in these data stacks with different perspectives, new algorithms, and new approaches.
A New Hybrid Cuckoo Search Algorithm for Biclustering of Microarray Gene-Expression Data
Published in Applied Artificial Intelligence, 2018
R. Balamurugan, A.M. Natarajan, K. Premalatha
The DNA microarray analysis is a technology which enables the researchers to analyze the expression level of thousands of genes in a single reaction rapidly and in an efficient manner (Lockhart and Winzeler 2000). A typical DNA microarray analysis involves a multistep procedure which includes fabrication of microarrays by fixing properly designed oligonucleotides representing specific genes, hybridization of complementary DNA (cDNA) populations onto the microarray, scanning hybridization signals, image analysis and normalization of data. After a number of preprocessing steps, the low-level microarray analysis of a microarray can be represented as a numerical matrix. In this matrix, the rows represent different genes and columns represent experimental conditions. Each element of this matrix represents the expression level of a gene under a specific condition, and is represented by a real number. In gene-expression matrix, a common goal is to group the genes and conditions into subsets that convey biological significance. In its most common form, this task translates to the computational problem known as clustering.
Overview of methodologies for the culturing, recovery and detection of Campylobacter
Published in International Journal of Environmental Health Research, 2023
Marcela Soto-Beltrán, Bertram G. Lee, Bianca A. Amézquita-López, Beatriz Quiñones
The development and implementation of additional sequence-based typing methods, including multilocus sequence typing (MLST), DNA microarrays and whole genome sequencing, have facilitated the detection and characterization of Campylobacter-related outbreaks and have enabled the improved differentiation of closely related isolates (Sabat et al. 2013). In particular, the MLST scheme for C. jejuni is based on the PCR amplification of seven highly conserved housekeeping genes, followed by the sequencing of the fragments and comparison of their nucleotide sequences using standard phylogenetic analysis. The advantage of MLST is that the data obtained is unambiguous and highly reproducible by using an internationally standardized nomenclature. As a high-throughput method, DNA microarrays technology has enabled the genotyping and profiling of genomic content in campylobacteria (Parker et al. 2006; Quiñones et al. 2007). This technology consists of a collection of DNA probes, attached in an orderly fashion to a solid surface, and the presence or absence of the complementary genome sequences in the tested isolate is detected after hybridization to the different probes on the array. Moreover, microarrays have allowed the detection of extra-genomic elements in C. jejuni (Parker et al. 2006), and pathogenic strains can also be simultaneously examined for their antimicrobial resistance and virulence potential (Quiñones et al. 2007, 2008; Sabat et al. 2013). One of the limitations of the microarray-based assays is that labeling of target DNA can be inconsistent and results in highly variable hybridization patterns. Another disadvantage is that DNA microarrays allows the identification of only those known probe sequences previously attached to the array, making it difficult to identify emerging strains that are highly variable. Also, this technique is unable to distinguish highly clonal strains based on single nucleotide polymorphisms (Sabat et al. 2013).