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Omics Approaches for Microalgal Remediation of Wastewater
Published in Vineet Kumar, Vinod Kumar Garg, Sunil Kumar, Jayanta Kumar Biswas, Omics for Environmental Engineering and Microbiology Systems, 2023
Edwin Hualpa-Cutipa, Richard Andi, Solórzano Acosta, Xiomara Gisela, Mendoza Beingolea, Ingrid Maldonado Jimenez, Evelin Yana-Neira, Isabel Navarro Zabarburú
Proteomics has the advantage of high-throughput proteome analysis, illustrating key genes relationships with biological mechanisms of organisms in consortia and processes in connection, as well as their linkage in enzymatic studies; however, it can present troubles and complexity in data analysis (VerBerkmoes et al., 2009); despite this, efforts are underway. Patel et al. (2015) introduced the first non-ecological proteomic analysis of microalgae on wastewater, characterizing changes in the proteome of Chlamydomonas reinhardtii after culture in a synthetic wastewater medium using MS-based proteomics. Burch et al. (2021), by comparing Phaeodactylum tricornutum culture in dilute DMW versus synthetic media, discovered modifications in protein regulations associated with protein metabolism, signal transduction, transcription, protein trafficking, and oxidative stress management pathways, providing insight into how P. tricornutum reconfigures its own proteome in dilute DMW versus artificial media. In the study by Elleuch et al. (2021), on the other hand, the alga Dunaliella sp. was subjected to a comparative proteomic analysis using nano-HPLC coupled to LC-MS/MS on cells from pre- and post-zinc treatments for zinc removal in wastewater, and it was demonstrated that the target proteins are involved in various metabolic processes; photosynthesis and antioxidant defensive systems are the most important.
Integrated Omics Technology for Basic and Clinical Research
Published in Jyoti Ranjan Rout, Rout George Kerry, Abinash Dutta, Biotechnological Advances for Microbiology, Molecular Biology, and Nanotechnology, 2022
Kuldeep Giri, Vinod Singh Bisht, Sudipa Maity, Kiran Ambatipudi
Currently, there are many protein-based diagnostic and therapeutic available for clinical applications. Along with continuous development in the technology, there are structural, technical, and data-related challenges in proteomics. For instance, tissue samples and body fluids generally used for pathological screening are complex in protein content and possess several challenges. For instance, protein isoforms and different secondary and tertiary structures result in protein variability including posttranslational modifications adding further heterogeneity to proteins. Similarly, a high abundance of proteins creates difficulties during isolation of medium and low copy number of proteins from healthy and pathological samples (Ambatipudi et al., 2009). Furthermore, complex instrumentation and analytical algorithms make proteomics more challenging for translational and clinical application including the cost of instrumentation. The necessity of a huge cohort of clinical samples for unbiased clinical validation and the progression in clinically significant output is another major challenge. Similarly, integrating proteomic results with biological specimens is another challenge in proteomics (Schubert et al., 2017). Although there are many advanced tools to study large proteins, there are calls for desperate measures in the advancement of large protein isolation, solubility and MS-based studies including complex data analysis algorithms (Gregorich and Ge, 2014).
Analytical Methods for MIC Assessment
Published in Richard B. Eckert, Torben Lund Skovhus, Failure Analysis of Microbiologically Influenced Corrosion, 2021
Torben Lund Skovhus, Richard B. Eckert
Proteomics is the study of the proteins (gene products) expressed by microorganisms in response to the environmental conditions that are present, including energy sources, physical conditions and other microbiological activities occurring within a microbial consortium. The rationale for employing proteomics is the idea that the final product of a gene better describes its function than identifying the gene itself. For example, real time (RT)-qPCR can identify RNA that could be used in gene expression, but without identifying whether it was actually used, where proteomics can identify the protein a gene actually expressed. Methods applied to proteomics include the use of antibodies (immunoassays), mass spectrometry, and 2D electrophoresis (Graves and Haystead 2002). The application of proteomics to industry and MIC has been fairly limited thus far due to the complexity of the methods used and challenges in isolation of proteins; however, it is more broadly applied in bioremediation.
Hepatic proteomic assessment of oral ingestion of titanium dioxide nano fiber (TDNF) in Sprague Dawley rats
Published in Journal of Environmental Science and Health, Part A, 2022
Worlanyo E. Gato, Ji Wu, Isaac Appiah, Olivia Smith, Haresh Rochani
To explore the effects of TDNF ingestion in Sprague Dawley rats, a proteomics approach was used. Proteomics is a useful tool to evaluating the complete structure and function of proteins in an organism.[22] More than 400 hundred proteins were identified to be involved in TDNF effects in the liver. Some of these include Acyl-coenzyme A synthetase ACSM2, mitochondrial (Accession#: O70490), Betaine–homocysteine S-methyltransferase 1 (Accession#: O09171), Acyl-CoA dehydrogenase family member 11 (Accession#: B3DMA2) and Ornithine transcarbamylase, mitochondrial precursor (Accession#: P00481) among many more. These proteins are involved in such processes as catalysis of fatty acids by CoA, homocysteine metabolism, beta oxidation and the condensation of carbamoyl phosphate in the urea cycle.[23–25]
Inflammatory bowel disease: why this provides a useful example of the evolving science of nutrigenomics
Published in Journal of the Royal Society of New Zealand, 2020
Now that the genetic characteristics of IBD are emerging more clearly, it becomes easier to select (or develop) appropriate animal models to test effects of foods or dietary supplements, before suggesting them for use in the very vulnerable people, either susceptible to or currently showing symptoms of the disease. As well as analytical developments in genotyping, transcriptomics. proteomics and metabolomics are now available to nutritional research, and used increasingly in these studies (van Ommen and Stierum 2002; Barnett et al. 2015; Ferguson and Barnett 2016; Figure 2). Barnett et al. (2015) emphasised the value of all of these nutrigenomics technologies in various animal models. Proteomics (the study of proteins), combining two-dimensional gel electrophoresis with liquid chromatography-mass spectrometry (LCMS), provides an analysis of peptides. Metabolomics is formally defined as the study of chemical processes involving metabolites, the small molecule intermediates and products of metabolism. Metabolomics using both gas chromatograph-MS (GCMS) and LCMS have been combined with transcript-omics (gene expression analysis), proteomics and analyses of microbiota to give a comprehensive picture of which foods may be beneficial in individuals carrying susceptibility genes. These tools can non-invasively be applied to animal or human studies.
Current opinion on risk assessment of cosmetics
Published in Journal of Toxicology and Environmental Health, Part B, 2021
Kyu-Bong Kim, Seung Jun Kwack, Joo Young Lee, Sam Kacew, Byung-Mu Lee
Omics technology is an emerging technology developed to identify interactions and correlations by analyzing intracellular molecules, such as genes, proteins, and metabolites (Lee et al. 2020b; Pirih and Kunej 2017). Exposure of the body to chemicals might initiate various changes at the molecular level. Omics technology has been determined as an important technique for identifying functional or structural changes in cells (Brockmeier et al. 2017). Transcriptomics, proteomics, and metabolomics are typical omics technologies. Transcriptomics provides information about expressed genes in cells or tissues. Proteomics provides information on the proteins expressed in cells and tissues. Metabolomics provides information on all of the metabolites produced by cells or tissues. The integrated analysis of omics has been observed to improve the assessment of hazardous chemical reactions (Lee et al. 2020a). A method to evaluate skin sensitization by analyzing the transcriptome of the MUTZ-3 human cell line has been proposed (Johansson et al. 2011). This method provides insight into the gene expression of cells exposed to chemicals and can further predict the potential for skin sensitization. The integrated analysis of omics can group or cluster chemical substances on the basis of their similarity in exerting a biological response and can provide information as to whether chemicals share the same MOA. Furthermore, it is possible to determine the concentration at which the level of differential gene expression does not appear compared with the control. This level can be set to No-Observed-Transcriptional-Effect-Level in an invitro test similar to the NOAEL used in animal experiments (Baltazar et al. 2020). A metabolomics profiling was applied for data gap filling of read-across submitted under the REACH regulation (Sperber et al. 2019). The whole plasma metabolite profile of animals treated with 3-aminopropanol was correlated with the metabolite profile of 2-aminoethanol and it was concluded that metabolome data from 2-aminoethanol and 3-aminopropanol added confidence concerning mechanistic similarity in read-across approach (Sperber et al. 2019). Therefore, it is expected that such omics technology may provide important information for the evaluation of risk and safety for cosmetic ingredients that cannot be tested in animals.