Source tracking and community profiles of antibiotic resistant bacteria in wastewater samples

1. Source tracking and community profiles of antibiotic resistant bacteria in wastewater samples Jesús Sigala and Felly Rose Montelya Mentor: Dr. Adrian Unc Plant and Environmental Sciences, NMSU NEW MEXICO AMP ALLIANCE FOR MINORITY PARTICIPATION

2. Research background and relevance • Wastewater treatment plants treat waste generated from various sources including residential, industrial, schools, hospital and medical centers: • screening and removal of non-degradable solids, • physical removal of suspended solids to produce biosolids, • biological treatment • chemical treatment (chlorination) • Treated wastewater is ultimately discharged to surface waters • However, some microbes survive treatment; these may include pathogens and antibiotic resistant microbes • Discharge of antibiotic resistant microbes affect water quality, has ecological concerns, and poses public health concerns

3. Wastewater samples Pretreatment—influent After treatment—effluent

4. Previous results Two observations: 1) For all antibiotics, the proportion of antibiotic resistant E. coli generally increases throughout the treatment with significant selection during aeration stage 2) Chlorination is most effective at eliminating resistant E. coli (but not perfect)

5. Research goals • Assess a source tracking method that allows identification of pre-treatment sources • Profile the antibiotic resistant bacterial community in wastewater using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) • Determine the impact of wastewater sources on the microbial quality of effluent discharge

6. Microbial Source Tracking • Common method to identify source of pollution in food and water contamination sources • Compare source and environmental samples • Specific characteristics to compare include antibiotic resistance, mutation, or DNA fingerprint (Pillai and Vega, 2007)

7. Wastewater sampling • Four lifting stations representing distinct sources • Residential • Industrial • University • Hospital • Six stages of treatment • Influent • Primary clarifier • Trickling (roughing) filters • Aeration basin • Secondary clarifier • Chlorination tank • Three samples from each location over a period of two days for composite samples

8. Lifting stations and wastewater treatment plant University wastewater lifting station Primary clarifiers at the treatment plant

9. Lab methods • Selection of antibiotic resistant populations by plating on MH agar using antibiotic agar dilution method at four concentrations according to EUCAST (www.eucast.org) • Antibiotics included: Erythromycin, Doxycycline, Cefaclor, Ciprofloxacin • Extraction of DNA from the antibiotic selected populations (MoBio extraction kit) • Quantification of DNA by UV absorbance at 260 nm • PCR amplification • rpoB primers (rpoB 1698F, with GC clamp at 5’ end, and rpoB 2041R) • Thermocycler program as described by Peixoto et al. (2002) • Polyacrylamide gels (6%) cast for DGGE (40% to 60% denaturing gradient) • DGGE performed for 14 hours at 85 V • DGGE gels were silver stained

10. Results and discussion • Microbial growth from all samples and antibiotic concentrations • DGGE and analysis for all samples to be completed • DGGE will allow comparison of population diversity between different samples with interest in • Dissimilarity between lifting stations • Similarity between lifting stations and wastewater treatment samples

11. DGGE • Lane 1: DGGE standard • Lanes 2-7: samples • Lane 8: (empty) • Lane 9: DGGE standard

12. Future goals • Complete DGGE for all samples • Analyze DGGE fingerprints using statistical methods • Propose a novel source tracking method for identification of sources of antibiotic resistant microbial contaminants • Propose future research aimed at improved targeting of the actual treatment protocols

13. Conclusion • Sampling of lifting stations and wastewater treatment plant allows us to separate different sources • PCR/DGGE can be used to profile bacterial diversity and develop a source tracking method • Source impact on antibiotic resistance microbial load and community profile in wastewater treatment can be determined

14. Acknowledgements • NM AMP NSF Grant #NSF HRD 083171 and NM WRRI for their support and interest in our research • Dr. Unc for the support, guidance, review, and assistance during sampling and lab work • Jeanne Garland, Polina Chemishanova, Gloria Vasquez, Joy Pugh, and other AMP staff who contributed in various ways to the research project

15. References • Bitton, G. 2005. Wastewater microbiology, 3rd ed. Wiley, Hoboken, New Jersey. • Dahllöf, I., H. Baillie, and S. Kjelleberg. 2000. rpoB-Based microbial community analysis avoids limitations inherent in 16S rRNA gene intraspecies heterogeneity. Applied and Environmental Microbiology. 66: 3376-3380. • Felske, A., and A. M. Osborn. 2005. DNA fingerprinting of microbial communities. In A. M. Osborn and C. J. Smith (ed.), Molecular microbial ecology. Taylor & Francis Group, New York. • Peixoto, R. S., H. L. da Costa Countinho, N. G. Rumjaneck, A. Macrae, and A. S. Rosado. 2002. Use of rpoB and 16S rRNA genes to analyse bacterial diversity of a tropical soil using PCR and DGGE. Letters in Applied Microbiology. 35: 316-320. • Pillai, S., and E. Vega. 2007. Molecular detection and characterization tools. In J. W. Santo Domingo and M. J. Sadowsky (ed.) Microbial source tracking. ASM, Washington. • Wiegand, I., K. Hilpert, and R. E. W. Hancock. 2008. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nature Protocols. 3: 163-175.