An unexpected butadiene diolepoxide-mediated genotoxicity implies alternative mechanism for 1,3-butadiene carcinogenicity

Use of Biomarker Data and Relative Potencies of Mutagenic Metabolites to Support Derivation of Cancer Unit Risk Values for 1,3-Butadiene from Rodent Tumor Data

Unit Risk (UR) values were derived for 1,3-butadiene (BD) based upon its ability to cause tumors in laboratory mice and rats. Metabolism has been established as the significant molecular initiating event of BD’s carcinogenicity. The large quantitative species differences in the metabolism of BD and potency of critical BD epoxide metabolites must be accounted for when rodent toxicity responses are extrapolated to humans. Previously published methods were extended and applied to cancer risk assessments to account for species differences in metabolism, as well as differences in mutagenic potency of BD metabolites within the context of data-derived adjustment factors (DDEFs). This approach made use of biomarker data (hemoglobin adducts) to quantify species differences in the internal doses of BD metabolites experienced in mice, rats, and humans. Using these methods, the dose–response relationships in mice and rats exhibit improved concordance, and result in upper bound UR values ranging from 2.1 × 10⁻⁵ to 1.2 × 10⁻³ ppm⁻¹ for BD. Confidence in these UR values was considered high based on high confidence in the key studies, medium-to-high confidence in the toxicity database, high confidence in the estimates of internal dose, and high confidence in the dose–response modeling.

Whole-cell bacterial biosensor for volatile detection from Pectobacterium-infected potatoes enables early identification of potato tuber soft rot disease

Half of the harvested food is lost due to rots caused by microorganisms. Plants emit various volatile organic compounds (VOCs) into their surrounding environment, and the VOC profiles of healthy crops are altered upon infection. In this study, a whole-cell bacterial biosensor was used for the early identification of potato tuber soft rot disease caused by the pectinolytic bacteria Pectobacterium in potato tubers. The detection is based on monitoring the luminescent responses of the bacteria panel to changes in the VOC profile following inoculation. First, gas chromatography-mass spectrometry (GC-MS) was used to specify the differences between the VOC patterns of the inoculated and non-inoculated potato tubers during early infection. Five VOCs were identified, 1-octanol, phenylethyl alcohol, 2-ethyl hexanol, nonanal, and 1-octen-3-ol. Then, the infection was detected by the bioreporter bacterial panel, firstly measured in a 96-well plate in solution, and then also tested in potato plugs and validated in whole tubers. Examination of the bacterial panel responses showed an extensive cytotoxic effect over the testing period, as seen by the elevated induction factor (IF) values in the bacterial strain TV1061 after exposure to both potato plugs and whole tubers. Moreover, quorum sensing influences were also observed by the elevated IF values in the bacterial strain K802NR. The developed whole-cell biosensor system based on bacterial detection will allow more efficient crop management during postharvest, storage, and transport of crops, to reduce food losses.

1,3-Butadiene: a ubiquitous environmental mutagen and its associations with diseases

1,3-Butadiene (BD) is a petrochemical manufactured in high volumes. It is a human carcinogen and can induce lymphohematopoietic cancers, particularly leukemia, in occupationally-exposed workers. BD is an air pollutant with the major environmental sources being automobile exhaust and tobacco smoke. It is one of the major constituents and is considered the most carcinogenic compound in cigarette smoke. The BD concentrations in urban areas usually vary between 0.01 and 3.3 μg/m³ but can be significantly higher in some microenvironments. For BD exposure of the general population, microenvironments, particularly indoor microenvironments, are the primary determinant and environmental tobacco smoke is the main contributor. BD has high cancer risk and has been ranked the second or the third in the environmental pollutants monitored in most urban areas, with the cancer risks exceeding 10^-5. Mutagenicity/carcinogenicity of BD is mediated by its genotoxic metabolites but the specific metabolite(s) responsible for the effects in humans have not been determined. BD can be bioactivated to yield three mutagenic epoxide metabolites by cytochrome P450 enzymes, or potentially be biotransformed into a mutagenic chlorohydrin by myeloperoxidase, a peroxidase almost specifically present in neutrophils and monocytes. Several urinary BD biomarkers have been developed, among which N-acetyl-S-(4-hydroxy-2-buten-1-yl)-L-cysteine is the most sensitive and is suitable for biomonitoring BD exposure in the general population. Exposure to BD has been associated with leukemia, cardiovascular disease, and possibly reproductive effects, and may be associated with several cancers, autism, and asthma in children. Collectively, BD is a ubiquitous pollutant that has been associated with a range of adverse health effects and diseases with children being a subpopulation with potentially greater susceptibility. Its adverse effects on human health may have been underestimated and more studies are needed.

Overcoming stochastic variations in culture variables to quantify and compare growth curve data

The comparison of growth, whether it is between different strains or under different growth conditions, is a classic microbiological technique that can provide genetic, epigenetic, cell biological, and chemical biological information depending on how the assay is used. When employing solid growth media, this technique is limited by being largely qualitative and low throughput. Collecting data in the form of growth curves, especially automated data collection in multi-well plates, circumvents these issues. However, the growth curves themselves are subject to stochastic variation in several variables, most notably the length of the lag phase, the doubling rate, and the maximum expansion of the culture. Thus, growth curves are indicative of trends but cannot always be conveniently averaged and statistically compared. Here, we summarize a simple method to compile growth curve data into a quantitative format that is amenable to statistical comparisons and easy to graph and display.