There is a growing demand to develop biocontainment strategies that prevent unintended proliferation of genetically modified organisms in the open environment. We found that the hypophosphite (H3PO2, HPt) transporter HtxBCDE from Pseudomonas stutzeri WM88 was also capable of transporting phosphite (H3PO3, Pt) but not phosphate (H3PO4, Pi), suggesting the potential for engineering a Pt/HPt-dependent bacterial strain as a biocontainment strategy. We disrupted all Pi and organic Pi transporters in an Escherichia coli strain expressing HtxABCDE and a Pt dehydrogenase, leaving Pt/HPt uptake and oxidation as the only means to obtain Pi. Challenge on non-permissive growth medium revealed that no escape mutants appeared for at least 21 days with a detection limit of 1.94 × 10−13 per colony forming unit. This represents, to the best of our knowledge, the lowest escape frequency among reported strategies. Since Pt/HPt are ecologically rare and not available in amounts sufficient for the growth of the Pt/HPt-dependent bacteria, this strategy offers a reliable and practical method for biocontainment.

Fluorescence microscopy (FM) is one of the most important analytical tools in modern life sciences, sufficiently sensitive to allow observation of single molecules. Here we describe the first application of the FM technique for the detection of inorganic environmental pollutants-airborne asbestos fibers that can cause asbestosis, mesothelioma, and lung cancer. In order to assess FM capabilities for detecting and counting asbestos fibers, we screened E. coli lysate for proteins that bind to amphibole asbestos. In combination with the previously discovered E. coli protein DksA (Kuroda et al., Biotechnol. Bioeng. 2008, 99, 285-289) that can specifically bind to chrysotile, the newly identified GatZ protein was used for selective and highly sensitive detection of two different asbestos types. Our novel FM-based method overcomes a number of limitations of the commonly used phase-contrast microscopy (PCM) method, offering a convenient alternative to PCM for airborne asbestos monitoring.

Fluorescence microscopy-based affinity assay could enable highly sensitive and selective detection of airborne asbestos, an inorganic environmental pollutant that can cause mesothelioma and lung cancer. We have selected an Escherichia coli histone-like nucleoid structuring protein, H-NS, as a promising candidate for an amphibole asbestos bioprobe. H-NS has high affinity to amphibole asbestos, but also binds to an increasingly common asbestos substitute, wollastonite. To develop a highly specific Bioprobe for amphibole asbestos, we first identified a specific but low-affinity amosite-binding sequence by slicing H-NS into several fragments. Second, we constructed a streptavidin tetramer complex displaying four amosite-binding fragments, resulting in the 250-fold increase in the probe affinity as compared to the single fragment. The tetramer probe had sufficient affinity and specificity for detecting all the five types of asbestos in the amphibole group, and could be used to distinguish them from wollastonite. In order to clarify the binding mechanism and identify the amino acid residues contributing to the probe’s affinity to amosite fibers, we constructed a number of shorter and substituted peptides. We found that the probable binding mechanism is electrostatic interaction, with positively charged side chains of lysine residues being primarily responsible for the probe’s affinity to asbestos.

Fluorescence microscopy (FM) has recently been applied to the detection of airborne asbestos fibers that can cause asbestosis, mesothelioma and lung cancer. In our previous studies, we discovered that the E. coli protein DksA specifically binds to the most commonly used type of asbestos, chrysotile. We also demonstrated that fluorescent-labeled DksA enabled far more specific and sensitive detection of airborne asbestos fibers than conventional phase contrast microscopy (PCM). However, the actual diameter of the thinnest asbestos fibers visualized under the FM platform was unclear, as their dimensions were below the resolution of optical microscopy. Here, we used correlative microscopy (scanning electron microscopy [SEM] in combination with FM) to measure the actual diameters of asbestos fibers visualized under the FM platform with fluorescent-labeled DksA as a probe. Our analysis revealed that FM offers sufficient sensitivity to detect chrysotile fibrils as thin as 30-35 nm. We therefore conclude that as an analytical method, FM has the potential to detect all countable asbestos fibers in air samples, thus approaching the sensitivity of SEM. By visualizing thin asbestos fibers at approximately tenfold lower magnifications, FM enables markedly more rapid counting of fibers than SEM. Thus, fluorescence microscopy represents an advanced analytical tool for asbestos detection and monitoring.

An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.