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Freshwater is a vehicle for the emergence and dissemination of antibiotic resistance. Cyanobacteria are ubiquitous in freshwater, where they are exposed to antibiotics and resistant organisms, but their role on water resistome was never evaluated. Data concerning the effects of antibiotics on cyanobacteria, obtained by distinct methodologies, is often contradictory. This emphasizes the importance of developing procedures to understand the trends of antibiotic susceptibility in cyanobacteria. In this study we aimed to evaluate the susceptibility of four cyanobacterial isolates from different genera (Microcystis aeruginosa, Aphanizomenon gracile, Chrisosporum bergii, Planktothix agradhii), and among them nine isolates from the same specie (M. aeruginosa) to distinct antibiotics (amoxicillin, ceftazidime, ceftriaxone, kanamycine, gentamicine, tetracycline, trimethoprim, nalidixic acid, norfloxacin). We used a method adapted from the bacteria standard broth microdilution. Cyanobacteria were exposed to serial dilution of each antibiotic (0.0015-1.6 mg/L) in Z8 medium (20 ± 1°C; 14/10 h L/D cycle; light intensity 16 ± 4 µEm-2s-1). Cell growth was followed overtime (OD450nm/microscopic examination) and the minimum inhibitory concentrations (MICs) were calculated for each antibiotic/isolate. We found that β-lactams exhibited the lower MICs, aminoglycosides, tetracycline and norfloxacine presented intermediate MICs; none of the isolates were susceptible to trimethoprim and nalidixic acid. The reduced susceptibility of all tested cyanobacteria to some antibiotics suggests that they might be naturally non-susceptible to these compounds, or that they might became non-susceptible due to antibiotic contamination pressure, or to the transfer of genes from resistant bacteria present in the environment. [ABSTRACT FROM AUTHOR]

Omics technologies have long since promised to address a number of long standing issues related to environmental regulation. Despite considerable resource investment, there are few examples where these tools have been adopted by the regulatory community, which is in part due to a focus of most studies on discovery rather than assay development. The current work describes the initial development of an omics based assay using 48 h Pimephales promelas (FHM) larvae for identifying aquatic exposures to pyrethroid pesticides. Larval FHM were exposed to seven concentrations of each of four pyrethroids (permethrin, cypermethrin, esfenvalerate and bifenthrin) in order to establish dose response curves. Then, in three separate identical experiments, FHM were exposed to a single equitoxic concentration of each pyrethroid, corresponding to 33% of the calculated LC50. All exposures were separated by weeks and all materials were either cleaned or replaced between runs in an attempt to maintain independence among exposure experiments. Gene expression classifiers were developed using the random forest algorithm for each exposure and evaluated first by cross-validation using hold out organisms from the same exposure experiment and then against test sets of each pyrethroid from separate exposure experiments. Bifenthrin exposed organisms generated the highest quality classifier, demonstrating an empirical Area Under the Curve (eAUC) of 0.97 when tested against bifenthrin exposed organisms from other exposure experiments and 0.91 against organisms exposed to any of the pyrethroids. An eAUC of 1.0 represents perfect classification with no false positives or negatives. Additionally, the bifenthrin classifier was able to successfully classify organisms from all other pyrethroid exposures at multiple concentrations, suggesting a potential utility for detecting cumulative exposures. Considerable run-to-run variability was observed both in exposure concentrations and molecular responses of exposed fish across exposure experiments. The application of a calibration step in analysis successfully corrected this, resulting in a significantly improved classifier. Classifier evaluation suggested the importance of considering a number of aspects of experimental design when developing an expression based tool for general use in ecological monitoring and risk assessment, such as the inclusion of multiple experimental runs and high replicate numbers. [ABSTRACT FROM AUTHOR]