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Alternative Methods for Assessing the Effects of Chemicals in the Eye
Published in David W. Hobson, Dermal and Ocular Toxicology, 2020
Leon H. Bruner, John Shadduck, Diane Essex-Sorlie
Luminescent Bacteria Test (LBT) — The LBT is an assay that has been used primarily for evaluating the toxicity of environmental and biomedical samples.80,81 Recently, this system has been evaluated as an alternative for ocular irritancy testing.29,63,64 The LBT measures light produced by a luminescent strain of bacteria called Photobacterium phosphoreum. The light is produced as a byproduct of the bacterial energy metabolic pathways. A decrease in light output is an indication that these pathways have been altered by the toxic effects of a test substance. When assessing test materials, increasing concentrations of sample are added to a suspension of the bacteria and incubated for a short period. The light output is measured. The concentration of test material that decreases the light output by 50% is the endpoint. Preliminary data suggests this system may provide useful information in the ocular safety assessment process.
Animal, Human, and in Vitro Test Methods for Predicting Skin Irritation
Published in Francis N. Marzulli, Howard I. Maibach, Dermatotoxicology Methods: The Laboratory Worker’s Vade Mecum, 2019
Sunita M. Patil, Esther Patrick, Howard I. Maibach
A similar system, Microtox. has been used for years in environmental studies. This luminescent bacteria (Photobacterium phosphorium) toxicity assay examines the reduction of fluorescence normally emitted by a suspension of luminescent bacteria (Photobacterium phosphoreum) after exposure to toxins (Bulich et al., 1981). This process can be used to assess irritation because the amount of viable remaining cells correspond to the degree of fluorescence (Bulich et al., 1981).
Scombrotoxin
Published in Dongyou Liu, Handbook of Foodborne Diseases, 2018
The properties of the HDC from the main HPB involved in SFP, e.g., Morganella morganii, Raoultella planticola, Enterobacter aerogenes, Photobacterium damselae, subsp. damselae, and Photobacterium phosphoreum have been characterized. Table 99.1 shows the properties of the HDC from gram-positive and gram-negative bacteria. The PLP-HDC from M. morganii, E. aerogenes, and R. planticola is composed of two to four subunits with a molecular mass of 43, 43 and 47 kDa, respectively (13,14). As seen in Table 99.2, the pH optima of the enzymes from M. morganii, R. planticola, and E. aerogenes are 6.5, while the optimum pH is 6 for P. damselae. In addition, the optimum temperature for decarboxylase activity from these bacteria is 40°C (15). Two histidine decarboxylases produced by P. phosphoreum, are inducible and constitutive (16). The optimum activity of the inducible enzymes is at 30°C and pH 6.5 but for the constitutive is optimum at 40°C and pH 6.0. The Lys-233 residue that binds the pyridoxal-P is critical for activity of the enzyme (17). Additionally, a conserved region of Ser-X-Gly-Lys has been described in the active site of the Morganella HDC. Although the amino acid sequences of HDC from M. morganii, E. aerogenes, and R. planticola share up to 81% homology, no cross-reactivity occurs with an antibody to the decarboxylase from Morganella (13).
The microbiome of deep-sea fish reveals new microbial species and a sparsity of antibiotic resistance genes
Published in Gut Microbes, 2021
Fergus W. J. Collins, Calum J. Walsh, Beatriz Gomez-Sala, Elena Guijarro-García, David Stokes, Klara B. Jakobsdóttir, Kristján Kristjánsson, Finlay Burns, Paul D. Cotter, Mary C. Rea, Colin Hill, R. Paul Ross
The genes required for luciferase activity are encoded within the lux operon where the luxA and luxB genes encode for both subunits of the luciferase enzyme.24,25 Using a Hidden Markov model, it was possible to identify sequences related to the luxA luciferase genes in the metagenomic data. The distribution of these genes is much lower than anticipated, with only 12 of the fish samples and the water sample containing homologs to the luxA gene at a relatively low abundance (Supplemental Table 6). Many of the of identified luxA homologs (38.5%) were predicted to be encoded by members of the Photobacterium genus, specifically Photobacterium phosphoreum¸ well-known for its bioluminescence, and Photobacterium kishitanii – a species closely related to the former which has previously been isolated from the light organ of deep-sea fishes.26 The relatively low level of luciferase-like genes across these samples was unexpected. If the theory that bioluminescence is used by these bacteria as an aid to become established in the gut of zooplankton and fish, then it could be expected that such luciferase-like clusters would be much more prevalent in the samples analyzed in this study.27 This strategy has been shown to be effective for zooplankton and smaller fish, however at higher trophic level where predators and prey are much larger, these results suggest that the bioluminescence produced by these microbes on POM and in the GI tracts of smaller zooplankton may not be sufficient to attract these larger fishes.
Grouping of nanomaterials to read-across hazard endpoints: a review
Published in Nanotoxicology, 2019
L. Lamon, K. Aschberger, D. Asturiol, A. Richarz, A. Worth
Sizochenko et al. (2018) apply cluster analysis and self-organising maps with the same objective to identify groups of NMs with similar toxicity for read-across. This case study is also based on in vitro IC50 and EC50 data on different cell lines (from Escherichia coli, Photobacterium phosphoreum, and Vibrio fischeri, human keratinocyte cell line HaCaT, epithelial cell line A549, human epithelial colorectal cell line Caco2, murine fibroblast cell line Balb/c 3T3, a microalga Pseudokirchneriella subcapitata, and protozoan Tetrahymena thermophile). Twelve groups of NMs are identified, corresponding to three toxicity classes (low, medium, and high), plus one class corresponding to unknown toxicity. The most important parameter in predicting toxicity is the enthalpy of cation formation and this is applied for data-gap filling that leaves some of the predicted values out of range.
Biosensors for the detection of mycotoxins
Published in Toxin Reviews, 2022
Akansha Shrivastava, Rakesh Kumar Sharma
Microbes as a whole-cell have some advantages as a biological recognition element in biosensors. They can be present all over the surface and effectively metabolize a wide range of chemical compounds. Microorganisms possess an immense capacity to adapt to adverse conditions and the ability to degrade new and different molecules with time. Whole cells can be used either in viable or non-viable forms (D’Souza 2001, Xu and Ying 2011). High cell viability is generally achieved by trapping the microbial cells into the pores of synthetic or biological (cellulose) membranes. A sensitive and convenient biosensor based on E. coli as a biorecognition element was developed for the first time to detect aflatoxin (AFB1) and ZEN. It was hypothetically derived from the findings that AFB1 and ZEN affect the morphology of E. coli similarly to penicillin (an antibiotic from fungi). The reactive groups including hydroxyl and ester of the AFB1 and ZEN bind to membrane proteins to destroy the integrity of the reticulocyte wall of E. coli. Some other factors including the efficiency of bio-transformative pathways, mycotoxin-induced oxidative stress, or the presence of some specific mycotoxin receptors may also influence the toxicity response of mycotoxins. Since much has not been explored yet in this section, a detailed mechanism of other toxins and microbes is still needed for further consideration (Chen et al. 2020). Biorecognition element in the form of the bacterial cell (Photobacterium phosphoreum) immobilization in cryogel was developed for the detection of different mycotoxins under discrete and flow-through analyses. These immobilized bioluminescent cells could be used for the quantification of mycotoxins such as ochratoxin A, sterigmatocystin, ZEN, and deoxynivalenol (DON) (Senko et al. 2019). Apart from whole-cell biosensors for mycotoxin detection, different microbes including Sarcina flava, Azotobacter vinelandii, Methylomonas flagelatis, Enterobacter agglomerans, and Bacterium cadaveris have also been explored for the amino acid sensor in fermentation industry, pharmaceutical industry, healthcare, and food industry (Sharma et al. 2014).