Publications by authors named "S E Shideler"

After extraction of bitumen from oil sands deposits, the oil sand process-affected water (OSPW) is stored in tailings ponds. Naphthenic acids (NA) in tailings ponds have been identified as the primary contributor to toxicity to aquatic life. As an alternative to other analytical methods, here we identify bacterial genes induced after growth in naphthenic acids and use synthetic biology approaches to construct a panel of candidate biosensors for NA detection in water.

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Here, we report the complete genome sequence of strain OST1911, recovered from oil sand process-affected water accumulated in tailing ponds. This water contains numerous organic and inorganic compounds of environmental significance. The genome size is 6,435,955 bp with a G+C content of 61.

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We report the draft genomes of seven bacterial strains (six spp. and one sp.) isolated from environmental water samples from oil sands tailings ponds that have accumulated a wide variety of organic compounds, salts and metals.

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New approaches are needed to discover novel antimicrobials, particularly antibiotics that target the Gram-negative outer membrane. By exploiting bacterial sensing and responses to outer membrane (OM) damage, we used a biosensor approach consisting of polymyxin resistance gene transcriptional reporters to screen natural products and a small drug library for biosensor activity that indicates damage to the OM. The diverse antimicrobial compounds that cause induction of the polymyxin resistance genes, which correlates with outer membrane damage, suggest that these LPS and surface modifications also function in short-term repair to sublethal exposure and are required against broad membrane stress conditions.

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Multivariate modelling techniques are used by a wide variety of investigations in environmental chemistry. It is surprisingly rare for studies to show a detailed understanding of uncertainties created by modelling or how uncertainties in chemical analysis impact model outputs. It is common to use untrained multivariate models for receptor modelling.

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