Development of a New Taqman Environmental Exposure Assessment Tool for Diarrheal Pathogens and helminths

Transcription

1 Development of a New Taqman Environmental Exposure Assessment Tool for Diarrheal Pathogens and helminths Kelly K. Baker University of Iowa In partnership with: Ananya Sen Gupta (UI), Jane Mumma (Great Lakes University of Kisumu), Oliver Cummings (LSHTM), Rick Rheingans (UFL)

2 Diarrhea incidence patterns characterized by dynamic spatial-temporal patterns Following major Flood Weekly admissions of diarrheal patients to the ICDDR,B hospital in Dhaka (06 08) Etiology- Vibrio cholerae (35%), Rotavirus (%), and Escherichia coli (%); No pathogen identified for 45% of samples Alam M, et al. Clonal transmission, dual peak, and off-season cholera in Bangladesh. Infection Ecology & Epidemiology. ;1:.

3 Global Enteric Multicenter Study suggests co-infection of child gut is complex in scope Diagnostics: enteric viruses, bacteria, parasites Isolated median of 4 pathogenic organisms from children with diarrhea AND asymptomatic controls Infection by enteric pathogens: Occurs very early in life Multifactorial Influenced by, but not conditional on disease state 0 - months - months - 59 months

4 Possible explanations? The Environment Outside: evidence of exposure patterns The Environment Within: persistent coinfection pathogenic bacteria Giardia cysts

5 Knowledge Gaps How should we measure the patterns underlying complex disease transmission systems? What is the value of a non-detect? Does a more comprehensive exposure assessment tool improve accuracy in quantifying exposure and infection risks for diarrheal disease? How many targets to minimize information loss? Can we develop more flexible modeling approaches that can utilize rich microbial systems information? (Next talk!) Positive Negative

6 Why is understanding human-microbiological patterns of exposure risk important? WASH interventions may have differential effects on how they reduce disease Reduce multi-pathogen exposure Reduce frequency of exposure Pathogen-specific effects Reduce environmental contamination with specific organisms capable of causing persistent infection or increased susceptibility to co-infection Spatial-effects Prevent propagation of environmentally-durable organisms (helminths, Cryptosporidium)

7 Objectives for this study 1. Develop a quantitative environmental exposure assessment tool that efficiently detects the majority of important fecally-transmitted microorganisms; 2. Characterize complexity of exposure in sanitation-poor Kisumu, Kenya and identify sanitation fingerprint (presence, concentration, and etiological profile of fecal contamination) for environmental samples 3. Test for correlation between organisms to test for potential generalizable and interchangeable exposure indicators; 4. Quantify the informational gains in risk detection from using additive combinations of microbial indicators. 5. Spatially map sanitation fingerprints and analyze association with observed risk factors (ex. Presence of latrines, cows, etc.)

8 Adaption of TAC Array Enteric Assay for Exposure Assessment 48 amplifications = microbiological targets + controls = species of virus, bacteria, parasites = 28 pathogenically distinct pathogen - MS2 and PhV external controls - Bacterial_s - Salmonella - Adenovirus - Astrovirus - NoroGII - Rotavirus - Sapovirus - C_difficile - C_jejuni-C_coli - Salmonella - V_cholera - EIEC-Shigella Liu et al.. A laboratory-developed TaqMan Array Card for simultaneous detection of enteropathogens. - EAEC_aaiC - EAEC_aatA - EPEC_eae - EPEC_bfpA - ETEC_ST - ETEC_LT - STEC_stx1 - STEC_stx2 - Cryptosporidium - Giardia_Lamblia - E_histolytica - Ascaris - Trichuris

9 Time and Logistical Efficiency of TacArray versus standard qpcr DNA and RNA extraction Advantages: 1. All data generated using same methods 2. MAJOR differences in user error influencing results 3. Cost per amplification 1. $7.50 TAC vs. $5.50-$8.00 monoplex qpcr 4. Time 1. 4 hours TAC vs. TAC hours

10 Optimize Extraction Protocol Cross-reactivity between pure standards and assays Rotavirus N, N - N, N - Y, Y.3,.993 Adenovirus N, N - Y, Y.336,.1 Y, Y , Cryptosporidium Y, Y.8,.856 N, N - Y, Y , 35.9 Evaluated 3 easy-to-use kits for DNA/RNA extraction MPBio FastDNA plus FastRNA kits MOBio Powerviral RNA/DNA kit MOBio Powersoil DNA plus RNA kits Spiked Iowa River water samples and soil samples with ~^3 Cryptosporidium, Rotavirus, Adenovirus in duplicate, and extracted DNA and RNA Evaluated inhibition and amplification Quantifast IC Pathogen kit TAC array card

11 Comparison of DNA and RNA extraction kits Fast DNA & RNA PowerViral PowerSoil Cryptosporidium S IC Adenovirus Rotavirus.5,.2, , Y, Y.4 Y.353 Y, Y.077 Y, Y ,.5, 28.4, Y, Y Y.381 Y, Y.4 Y, Y , , , Y, Y 28.0 Y Y, Y 31.4 Y, N 0.6,.034, , Y, Y.8 Y.83 Y, Y.3 Y, Y , Y,Y.535 N - N, NA NA N, NA NA N NA Y N, NA NA N, NA NA

12 Pilot Study in Sanitation-poor Kisumu, Kenya

13 Data collection process and tools: 1. Random selection of gps coordinates for unbiased site identification (Batchgeo, Google) 2. Upload coordinates and select route in GPSWaypoint app 3. Design app-based sanitation risk assessment tool (Field Logs, Trekea) Survey data enhanced by audio-visual tools Evidentiary documentation interfaces with smartphone gps for spatial-temporal stamping of all data Flexibility of data-on vs data-off mode ($$$) Simplified data management/quality issues Easy sharing of protocol and Share ( or QR code

14 Implementation of sanitation risk assessment Sanitation Risk Assessment Tool to collect quantitative and qualitative information at 0 sites in 3 sanitation-poor neighborhoods. Altitude Landscape and infrastructure Presence of fecal sources (latrines, open defecation indicators, animals, open defecation) Human demographics and activities Sampling information Collected 1 4 soil and/or water samples

15 When a picture says a thousand words

16 TAC Array Output

17 Etiological Diversity in fecally-spread GI pathogens in soil and water in 1 neighborhood of Kisumu, Kenya Gastrointestinal Microbes detected in 0.5 grams Soil grams of Soil 97% contained S rrna 58% contained >= 1 pathogen types virus, bacteria, parasites 3 inhibited samples ml surface water 0% contained S rrna 0% contained >=1 pathogen types virus, bacteria, parasites Gastrointestinal Microbes detected in ml Water

18 Etiological scope and distribution over space S C. jejuni/coli Salmonella EIEC/Shigella Vibrio Ascaris Cryptosporidium Giardia E. histolytic Adenovirus Astrovirus NoroGII Rotavirus Sapovirus EAEC_aaiC EAEC_aatA EPEC_eae EPEC_bfpA ETEC_LT ETEC_ST STEC_stx1 C. difficile Trichuris STEC_stx2

19 To be completed: Remaining Objectives 1. Develop a quantitative environmental exposure assessment tool that efficiently detects the majority of important fecally-transmitted microorganisms; 2. Characterize complexity of exposure in sanitation-poor communities and identify sanitation fingerprint (presence, concentration, and etiological profile of fecal contamination) for environmental samples 3. Test for correlation between organisms to test for potential generalizable and interchangeable exposure indicators; 4. Quantify the informational gains in risk detection from using additive combinations of microbial indicators. 5. Spatially map sanitation fingerprints and identify spatial, environmental, observed risk factors (ex. Presence of latrines, cows, etc.)

20 Applications More accurate cumulative and disaggregated exposure and disease risk prediction Fecal source tracking of fecal pathways Interventions Objective evaluation of fecesmanagement or water source protection interventions

21 Policy-relevant Research Questions Will this tool measure changes in human behavior, such as open defecation? Will our disease probability estimates correlate with selfreported diarrhea? How does seasonality influence patterns of risk propagation? What is safe sanitation?

22 Acknowledgements Great Lakes University of Kisumu Dr. Jane Mumma and team University of Iowa Kayla Dreeszen, Reid Senesac, Gocale Nicoue, Lena Swander London School of Hygiene and Tropical Medicine Oliver Cummings University of Florida Rick Rheingans Communities in Kisumu Trekea Jean-Marc DeBaud and Team Funding University of Iowa Global Development Pilot Grant Environmental Health Sciences Research Core (EHSRC) Career Development Grant Water Sustainability Initiative seed grant