Elizabeth Wellington -Simposio Microbiología: Transmisión
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Los días 7 y 8 de mayo organizamos en la Fundación Ramón Areces con la Fundación General CSIC el Simposio Internacional 'Microbiología: transmisión'. La "transmisión" en microbiología hace referencia al proceso por el que material genético es transferido de una célula a otra, de una población a otra. Es un proceso clave para entender el origen y la evolución de los seres vivos. El objetivo de esta reunión era conocer mejor la logística de la transmisión para ser capaces de modular o suprimir algunos procesos de transmisión dañinos.
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Elizabeth Wellington -Simposio Microbiología: Transmisión
- 1. Host transmission to the environment
- 2. Flow of antibiotic resistance genes, antibiotics and pathogens in the environment Built environmentSoil People Water Food Farm animals Wild life
- 3. Flow of antibiotic resistance genes, antibiotics and pathogens in the environment Built environmentSoil People Water Food Farm animals Wild life • Viability/ infectious dose ? • Detection/ culturable ? • Rates of transmission and drivers ? • Alternative hosts for replication ? • Impacts on human health ? • Models and mitigation
- 4. The connectivity of potential sources of pathogens Wellington et al., 2013 Lancet ID
- 5. The connectivity of potential sources of pathogens Host transmission: HOW WHEN WHERE IMPACT Mycobacterium tuberculosis complex MTBC Antibiotic resistance genes and pathogens
- 6. Transmission Routes Direct transmission Indirect transmission Unboiled milk Drink blood Meat Contact Aerosol Urine, Faeces Water use Intensive system Aerosol meat Urine, Faeces Water use Water use Faecal and urine contamination
- 7. Transmission Routes Aerosol Bite wounds Aerosol Aerosol Faeces Urine Sputum Setts Feed Latrines Pasture ? Direct transmission Indirect transmission UK & RoI
- 8. Mechanisms driving transmission Brooks-Pollock et al Nature 2015 Use of a dynamic stochastic spatial model
- 9. HOW IMC : M.bovis (green) in faeces DAPI FITC Overlay White light Scale bar 3 protozoan
- 10. 0 25 50 75 100 H eadC arcase Abdom enThoraxTrachea U rineFaeces Lung U rinesw abLesions Positive(%) Diagnostic potential of qPCR: proportion of badger clinical samples testing +ve Travis et al., 2015 RoI badgers
- 11. qPCR vs. Culture King et al., Sci Rep 2015 Immunoassay all test prevalence correlated with qPCR spring/summer Spearman's rho=0.92, p=0.007
- 12. Bubble plot of shedding • Small number of social groups responsible for most of the bacteria shed into the environment • High prevalence social groups represent biggest risk for transmission King et al., Sci Rep 2015
- 14. When should you sample? King et al., J Clin Micro 2015
- 15. Bacterial load • Samples with the highest M.bovis loads usually social groups with the highest prevalence • Bacterial loads in faeces (proxy for respiratory shedding) robust surrogate of onward transmission • Mycobacterial loads and their distributions in the environment are important in our understanding of risk • Spatial-temporal data show the social groups that have the highest bacterial loads and, by implication, represent the biggest risk for transmission
- 16. Determining optimal sample number • Randomised subsampling (whole year) without replacement: 10,000 bootstraps • High prevalence groups between 5 – 20 samples per year at 95 % probability of detecting positive • Lower prevalence groups between 30 – 50 samples per year at 95 % probability
- 17. Bovine Tuberculosis in the UK • Mycobacterium bovis is the causative agent of tuberculosis in cattle • Incidence in cattle herds ~4% • Spreading geographically • Cost taxpayers £500 million in the past 10 years (Defra 2014) IMPACT
- 18. Rainy and dry seasons 18 Faecal and dust sampling in Tanzania
- 19. C: cattle G: goat 0% 10% 20% 30% 40% C G C G C G C G C G C G Malinzanga Itunundu Mboliboli Kinyika Kitisi Tungamalenga Prevalence of Mb shedding 14.0% 9.2% Prevalence of Mb shedding in cattle faeces Positive Putative Negative N=390 11.2% 10.3% Prevalence of Mb shedding in goat faeces Positive Putative Negative N=120 Faecal shedding of M. bovis in cattle and goats
- 20. bTB and TB in Tanzania Faecal shedding Cattle herds RD4 scar assay (bTB) Goat herds RD4 scar assay Household dust RD9 assay (TB)
- 21. Patterns of household shedding of M. tuberculosis in the Tanzania case control NIH study Mann-Whitney statistical test at the 0.05 p-value RD9 assay No M.bovis shedding detected RD4 assay
- 22. Distribution and extent of M. tuberculosis shedding in TB case households and control households in the study region in Tanzania IMPACT ?
- 23. Application of sewage sludge to land: what is the impact on antibiotic resistance soil? Sewage treatment and disposal
- 24. Waste water treatment plants as a reservoir for antibiotic resistance Waste Water treatment plants Hotspot for Horizontal Gene Transfer (HGT) as waste received from various sources Little is known about the impacts of effluent further downstream in the river or the possible role of co-selection of antibiotic resistant determinants via quaternary ammonium compounds (QACs) (Gaze et al., AAC 2005, ISMEJ 2011)
- 25. •3 sediment cores were taken at 3 x 500m intervals below (DS) and above (US) Finham sewage works on the River Sowe •Samples taken a year apart in late 2009 and early 2011 •Cultivation on Chromocult and PCR screening 3rd generation cephalosporin (3GC) resistance gene abundance and diversity WWTP effluent acts as an input and or/selects for mobile antibiotic resistance determinants
- 26. 0 0.5 1 1.5 2 2.5 3 3.5 4 DS1 DS2 DS3 US1 US2 US3 Prevalence/(%) Sample site intI1 qacE∆1 qacE qacH Integron prevalence based on real time PCR data Down stream of WWTP Upstream of WWTP
- 27. Resistance Quotients Coliforms 4.48 x 105 coliforms / g DS 2.07 x 105 coliforms / g US * P<0.05 0 5 10 15 20 25 Resistanceprevalence/(%) Antibiotic selection DS US Downstream and upstream of WWTP 2009 and 2011 * * * * * * * Flow of resistance genes into the rivers: Waste Water treatment plants Amos et al., 2014. J AC 69, 1785
- 28. 3GC resistance gene analysis A subset of E. coli and other Enterobacteriaceae were taken from 2011 samples for further analysis Sequencing of blaCTX-M revealed all belonged to the genotype blaCTX-M-15 0 10 20 30 40 50 60 70 80 90 100 CTX-M TEM SHV intI1 Prevalence/(%) Gene DS US 708 blaCTX-M carrying presumptive coliforms / g DS 141 blaCTX-M carrying presumptive coliforms / g US
- 29. Mobilisation of blaCTX-M-15 DS E. coli carrying FIA, one DS E. coli carrying HI2. US E. coli carrying FIB and HI2. DS C. Freundii carrying FIB + K and one DS C. Freundii carrying FIB and I1/IY • CTX-M-15 is carried throughout a wide range of genetic contexts and plasmids • Contexts were seen in human pathogens, including several novel genetic contexts • The environment may mobilise CTX-M-15 between plasmids and species and WWTP effluent may drive this process Amos et al., 2015 IS26 tnpa 716 bp CTX-M-15 875 bp ORF47 7 151 bp 47 bp spacer ISeCP1 tnpa 181 bp 256 bp, ISeCP1 IR CTX-M-15 promoter, spacer CTX-M-15 875bp IS26 tnpa 716 bp 124 bp spacer Is26 IRL 80 bp, IS26 IR CTX-M-15 promoter, spacer CTX-M-15 875 bp CTX-M-15 875 bp
- 30. Introduction or selection or both? • More detailed typing of the E. coli (MLST) was used to determine if E. coli were of the same origin upstream and downstream • Upstream the E. coli sequence types were mainly uncharacterized (80 %), indicative of a more environmental origin • Downstream the sequences types were split between the clinically familiar ST131, ST167, ST3103, ST1421 and environmental STs • Mshana et al., 2009 “Our data demonstrate the presence of IncFI plasmids within the prevailing E. coli population in a hospital setting and suggest that the dissemination of CTX-M-15 allele is associated to lateral transfer of these well-adapted, conjugative IncFI plasmids among various E. coli genotypes.”
- 31. Concerns over presence of ST131 in a local river IMPACT
- 32. • Low-contact water sports average volume of water ingested = 3.7ml • Risk model: inverse cumulative Poisson distribution • Assumption 1: The number of organisms needed for transient colonization of a human is less than or equal to the number of organisms need for infection. • Assumption 2: The river will contain between 1% and 10% of the resistance load in the sediment sample depending on the level of disturbance in the sediment. Human exposureIMPACT the relative risk for each activity; children swimming (consuming on average 37 ml of water) downstream of treatment plants have a >99 % chance of being transiently colonized by a 3GC resistant coliform. Adults swimming (average water consumption 16 ml) are at high risk ( 99 %) upstream and downstream if under disturbed levels of sediment. Other water-based activities such as canoeing, boating, and rowing (3 – 5 ml consumed on average (20), all carry measurable risks downstream particularly under high levels of sediment perturbation when ex posure to resistant Enterobacteriaceae will be high (> 99 %)
- 33. Collaboration with Wallingford CEH, meta-data available 13 sites samples every 3 months for a year: analysed for integron prevalence and 3GC resistance counts Contribution of WWTP effluent to integron levels in a whole river system River Thames catchment area: Amos et al., 2015 ISME J
- 34. Integron prevalence 0 0.5 1 1.5 2 2.5 3 3.5 IntegronPrevalence/(%) Sample site May August February Significant difference between summer months (May and August, and Winter months November and February P = 0.004 t-test November
- 35. Evaluating the impact of WWTPs – model development
- 36. Output WWTP only Explained 49 % of variance: R2 adjusted (0.49) P < 0.01 0.5 2.51.5 -2.0 0.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 2.0 Actuallogintegronprevalence Predicted log integron prevalence
- 37. CONCLUSIONS Faecal shedding major route for transmission - DNA optimal detection method Pathogens survive well in environment eg MTBC and Enteric bacteria which can transfer genes to both G+ and G- indigenous bacteria Pollutants, sewage, WWTP effluent associated with increased resistance- anthropogenic effects QUESTIONS • What are the risks of environmental exposure? • Which mitigation strategies are possible? • How can efficiency of interventions be measured?
- 38. Acknowledgements MTBCOrin Courtenay Emma Travis Phillip James David Porter Frank Sweeney Archer Hung Hayley King Andrew Murphy Vicky Hibberd Glyn Hewinson Jason Sawyer Jennifer Cork Dez Delahay Paul Spyvee Rudovick Kazwala, Goodluck Paul, Joseph Malakalinga (Tanzania) and Woutrina Millar (UCD, US) Eamonn Gormley Leigh Corner
- 39. Acknowledgements Ab resistance University of Warwick Dr William Gaze Dr Greg Amos Gemma Hill Dr Andrew Mead Dr Lihong Zhang Dr Leonides Calvo-Bado Helen Green Abigail Carter Shruthi Sankaranaryanan University of Birmingham Professor Peter Hawkey Claire Murray University of York Professor Alistair Boxall