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Genomics in Public Health

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Slides from my January 20, 2017 talk at the Cesky Krumlov genomics workshop

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Genomics in Public Health

  1. 1. genomics in public health Tracking & treating disease with DNA sequencing Dr. Jennifer Gardy, BC Centre for Disease Control @jennifergardy jennifer.gardy@bccdc.ca
  2. 2. HelLO! Senior Scientist (Genomics),BCCDC Asst.Professor,UBC SPPH Canada Research Chair in Public Health Genomics
  3. 3. agenda of fun Where does microbiology fit in public health? What are we doing now? Diagnostics & outbreaks The genomics revolution Current genomics approaches in public health microbiology Future directions
  4. 4. Goal: show you a very cool domain where interesting genomics is maybe going to prevent us all from dying of something awful
  5. 5. microbiology in public health
  6. 6. public health (noun, ˈpə-blik ˈhelth): the organized efforts of society to keep people healthy and prevent injury, illness and premature death, combining programs, services and policies that protect and promote people’s health.
  7. 7. our health is governed by a number of factors
  8. 8. health protection surveillance prevention healthassessment health promotion emergency preparedness
  9. 9. the public health lab
  10. 10. patient sample for diagnosis study sample for surveillance environmental sample
  11. 11. patient sample for diagnosis what does this patient have? how should I treat them? study sample for surveillance what is circulating in our population? what does this mean for PH? environmental sample what is circulating in our water, food? what does this mean for PH?
  12. 12. What are we doing now?
  13. 13. 1. What does this patient have? Culture-based diagnostics.
  14. 14. 2. What does this patient have? serology
  15. 15. 3. What does this patient have? nucleic acid amplification testing.
  16. 16. a representative example: mycobacterial diagnostics
  17. 17. 1 Clinician suspects TB DAY 0
  18. 18. 1 2 Sample collected, submitted DAY 0
  19. 19. Sample collected, submitted Courtesy Mabel Rodrigues
  20. 20. 1 2 3 AFB smear microscopy DAY 1-2
  21. 21. Slides are prepped Courtesy Mabel Rodrigues
  22. 22. Smears are read Courtesy Mabel Rodrigues
  23. 23. AFB smear microscopy
  24. 24. 1 2 3 Nucleic Acid Amplification Testing (NAAT) 3* DAY 2-3
  25. 25. Prelim report issued
  26. 26. 1 2 3 4 Inoculate into culture DAY 1
  27. 27. Solid culture on LJ Courtesy Mabel Rodrigues
  28. 28. Liquid culture on Bactec MGIT Courtesy Mabel Rodrigues
  29. 29. 2nd report issued with culture results
  30. 30. Success! I have diagnosed my patient. Now what do I treat them with?
  31. 31. AMR occurs through multiple routes
  32. 32. 1. what do i treat this patient with? phenotypic dst.
  33. 33. 2. What do i treat this patient with? molecular testing.
  34. 34. a representative example: mycobacterial dst
  35. 35. Liquid culture on Bactec MGIT Courtesy Mabel Rodrigues
  36. 36. Third report issued after DST results
  37. 37. Cool. Thanks. I’m done. Over to you, public health.
  38. 38. Public health: what’s going around and why?
  39. 39. communicable disease surveillance • multiple data streams: lab positives, other reports, physician billing codes, alert healthcare workers • what is out there? • is there more than usual? • are blips due to an outbreak?
  40. 40. ? How do we investigate an
  41. 41. SURVEILLANCE IDENTIFIES CASES
  42. 42. MOLECULAR EPIDEMIOLOGY IDENTIFIES POTENTIALLY RELATED CASES
  43. 43. Restrictiondigest(RFLP) Multilocussequencetyping(MLST) Multilocusvariablenumber tandemrepeatanalysis(MLVA/VNTR)
  44. 44. RFLP MLST/MLVA Enzymescleavechromosome intolargefragments PCRprimersamplifyspecific regionsofthechromosome
  45. 45. RFLP MLST/MLVA Thewholechromosome, brokenintoafewpieces Afewshortfragmentsofthe chromosome
  46. 46. RFLP MLST/MLVA Thewholechromosome, brokenintoafewpieces Runonagel Afewshortfragmentsofthe chromosome Compareagainstdatabase
  47. 47. SizeStandard SizeStandard SizeStandard
  48. 48. SizeStandard SizeStandard SizeStandard
  49. 49. FIELD EPIDEMIOLOGY SUGGESTS TRANSMISSIONS
  50. 50. Basic Principles of Field Epidemiology • Identify cases and controls • Structured or semi-structured interview • Data collated into a line list • Combined with clinical data to infer likely exposures and transmissions
  51. 51. BUTWAIT.WEHAVEAPROBLEM. • Genotypingmethodsonlytellyouaclusterofcasesexists,nottheorder/ directionoftransmission • Size/membershipoftheclustervarieswiththetypingmethod(s)used • Epidemiologicalinvestigationisrequiredtoderivethelinksbetweencases, andmaynotbeavailableorofsufficientqualitY
  52. 52. a representative example: mycobacterial GENOTYPING
  53. 53. MycobacterialInterspersedRepetitiveUnitVariableNumberTandemRepeat
  54. 54. AmapofallMIRU-VNTRTBgenotypesinBC,2005-2014
  55. 55. asummaryofourroadblocks diagnostics:time-consuming,requiresuspicion,variable performance,stillmany“unknown”samples phenotypicdst:sloooooooooooooooooow surveillance:low-resolutiontypingtechniques,heavily reliantonfieldepidemiologicaldata thewholeprocess:multipleparallelsteps,reportingat variousstagesthroughouttheprocess
  56. 56. ourchainsaw:genomics
  57. 57. THE FIRST SEQUENCED GENOME 1995. 1 GENOME, 13 MONTHS, $600,000, ROOM FULL OF MACHINES
  58. 58. with the current technology 800-1000 GENOMEs, 5 days, <$100 each, one single machine
  59. 59. with the current technology 20 GENOMEs, 8 hours, <$150 each, one single machine
  60. 60. the future?
  61. 61. STARTING MATERIAL: ~weeks immediate
  62. 62. genomics is revolutionizing public health microbiology
  63. 63. pathogen genomes contain nearly everything we need for diagnosis, phenotyping, and surveillance
  64. 64. aretheseroadblocksanymore? diagnostics:time-consuming,requiresuspicion,variable performance,stillmany“unknown”samples phenotypicdst:sloooooooooooooooooow surveillance:low-resolutiontypingtechniques,heavily reliantonfieldepidemiologicaldata thewholeprocess:multipleparallelsteps,reportingat variousstagesthroughouttheprocess
  65. 65. agenda of fun Where does microbiology fit in public health? What are we doing now? The genomics revolution Current genomics approaches in public health microbiology Future directions
  66. 66. Story #1: Rapid WGS-based diagnosis
  67. 67. “Joshua Osborn, 14, lay in a coma at American Family Children’s Hospital in Madison, Wis. For weeks his brain had been swelling with fluid, and a battery of tests had failed to reveal the cause.” –Carl Zimmer, New York Times, June 2014
  68. 68. Culture?Serology?NAAT?Otherassay? Bacteria?Virus?Parasite?Fungus?Autoimmunity?
  69. 69. Charles Chiu, UCSF
  70. 70. “Joshua’scerebrospinalfluidcontainedDNAfromapotentiallylethal typeofbacteriacalledLeptospira…readilytreatedwithpenicillin… Thatafternoon,Joshuastartedgettinglargedosesofpenicillin.The swellinginhisbrainalmostimmediatelystartedsubsiding,andtwo weeksafterthefirsttestresults,Joshuawaswalking.”
  71. 71. genomics for diagnostics • FROM CULTURE OR DIRECT FROM SPECIMEN • SEQUENCE SAMPLE, REMOVE HUMAN READS, COMPARE TO DATABASE OF KNOWN SEQUENCES (BUT WHO’S WHO?) • FASTER THAN NAAT IN SOME CASES, BUT NOT ALL
  72. 72. Clinicalmetagenomicstoolbox: • Nanoporefornear-patientsequencing • Illuminaforslightlylongerturnaround • Metagenomicspipelineforbinningreads(e.g.SURPI) • IDphysiciantomakethecalloncommensalvspathogen
  73. 73. Story #2: WGS-based tailored therapy
  74. 74. SPECIMEN
  75. 75. SPECIMEN DIAGNOSIS
  76. 76. SPECIMEN STANDARD THERAPY DIAGNOSIS
  77. 77. SPECIMEN STANDARD THERAPY DIAGNOSIS DRUG SENSITIVITY TESTING
  78. 78. SPECIMEN STANDARD THERAPY DIAGNOSIS DRUG SENSITIVITY TESTING PERSONALIZED THERAPY
  79. 79. SPECIMEN STANDARD THERAPY DIAGNOSIS DRUG SENSITIVITY TESTING PERSONALIZED THERAPY
  80. 80. SPECIMEN DIAGNOSIS
  81. 81. SPECIMEN DIAGNOSIS ACGTACGATCG ACGTACGATCG ACGTACGATCG ACGTACGATCG
  82. 82. ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG ATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACG TACGATCGACGTACGATCGACGTACGATCGACGTACGATCGCGCCGGACGTACGATCGACGTACGATCGACGT ACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCG ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG ATCGACGTACGATCGACGTACGATCG
  83. 83. ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG ATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACG TACGATCGACGTACGATCGACGTACGATCGACGTACGATCGCGCCGGACGTACGATCGACGTACGATCGACGT ACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCG ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG ATCGACGTACGATCGACGTACGATCG
  84. 84. SPECIMEN ACGTACGATCG ACGTACGATCG ACGTACGATCG ACGTACGATCG
  85. 85. SPECIMEN ACGTACGATCG ACGTACGATCG ACGTACGATCG ACGTACGATCG PERSONALIZED THERAPY
  86. 86. SPECIMEN STANDARD THERAPY DIAGNOSIS DRUG SENSITIVITY TESTING PERSONALIZED THERAPY
  87. 87. SPECIMEN diagnosis & dst PERSONALIZED THERAPY
  88. 88. 1-day positive MGIT
  89. 89. BATCH & SEQUENCE
  90. 90. UPLOAD & ANALYSE
  91. 91. speciation via metagenomic analysis M.tuberculosis H.sapiens R.randombacterium
  92. 92. resistotyping via mutation catalogue
  93. 93. Personalizedtherapytoolbox: • Illuminasequencingatreferencelab • Rapidresistancepredictionfromgenomicdata(e.g.Mykrobe) • Growingcatalogueofresistance-associatedmutations
  94. 94. Story #3: Tracking outbreaks with WGS
  95. 95. ge·no·mic ep·i·de·mi·ol·o·gy (jē ˈnōmik ˌepiˌdēmēˈäləjē/) n. reading whole genome sequences from outbreak isolates to track person-to-person spread of an infectious disease.
  96. 96. AAAAAA
  97. 97. AAAAAA AAAAAA AACAAA
  98. 98. AAAAAA AAAAAA AACAAA AACAAA GACAAA AAAATA AAAAAA
  99. 99. AAAAAA AACAAA AACAAA AACTAA AACTAA AACAAG
  100. 100. TELEPHONE ARTBYDEVIANTARTUSERSCUMMY
  101. 101. TB TRANSMISSION IN BC IS CONCENTRATED IN OUR MOST VULNERABLE POPULATIONS
  102. 102. December 2010: a phone call
  103. 103. May2008-personwithafb4+pulmonarytbvisitsshelter
  104. 104. december2008-shelterscreeningidentifiessixfurthercases
  105. 105. december2010-outbreakhasgrownto30cases,largelywithinafewblocks
  106. 106. are the outbreak management activities directed at the right persons and locations?
  107. 107. TELEPHONE ARTBYDEVIANTARTUSERSCUMMY
  108. 108. index case second case
  109. 109. WE FORGOT ABOUT WITHIN- HOST GENETIC DIVERSITY
  110. 110. detour: reconstructing a transmission network in the light of within-host diversity With xavier didelot & caroline colijn
  111. 111. ¯_( )_/¯ direct observation of within-host diversity may not be possible due to the nature of infection and specimen collection
  112. 112. math modelling
  113. 113. startbymakingloadsof thesecoloured“infection”trees
  114. 114. createatransmissionsocial networkfromthesuiteofinfectiontrees
  115. 115. combinewithyourepidemiological datatoarriveatafinalreconstruction
  116. 116. m iner M INOR variants
  117. 117. m iner M INOR variants
  118. 118. how did the shelter contribute to infection? With ANAMARIA CRISAN
  119. 119. INDEX CASE ACTIVE TB LATENTTB UNINFECTED NO DATA VISIT 1: DECEMBER 2007 VISIT 2: MAY 2008
  120. 120. TIME IN THE SHELTER WAS ASSOCIATED WITH INCREASED RISK OF INFECTION (OR 1.26), ESPECIALLY STAYS OF 5+ NIGHTS (OR 4.97)
  121. 121. September 2014: another phone call
  122. 122. by 2014, the outbreak had grown to 52 cases and 2310 screened clients. COULD THE OUTBREAK BE DECLARED OVER? 2008 2009 2010 2011 2012 2013 7 7 11 12 8 6 1
  123. 123. can we truly say that the outbreak is over?
  124. 124. An updated model to better infer time of infection
  125. 125. An updated model to better infer time of infection
  126. 126. MEMO Bus: (250) 868-7818 Fax: (250) 868-7826 Kelowna Health Centre Email: sue.pollock@interiorhealth.ca 1340 Ellis Street www.interiorhealth.ca Kelowna, BC V1Y 9N1 Quality Integrity Respect Trust In 2008, an outbreak of Mycobacterium Tuberculosis (TB) was declared after a higher-than-expected number of TB cases were identified in the Central Okanagan. Between 2008 and 2014, 52 outbreak-related active TB cases were identified. Most cases were homeless and/or street-involved persons in Kelowna with a small linked cluster in Penticton, and several cases in Salmon Arm. Interior Health’s TB Outbreak Management Team, in partnership with community organizations and the BC Centre for Disease Control have used numerous strategies to identify and treat new cases and to minimize the public health risk. Epidemiological and genomics (genetic fingerprinting) data demonstrate that the peak of the outbreak occurred in late 2010/early 2011. There is currently no evidence of ongoing transmission and incidence of new TB cases has returned to baseline (pre-outbreak) levels. The Central Okanagan TB outbreak is declared over as of January 29, 2015. We expect to see sporadic new TB diagnoses connected to the outbreak in the coming years; early detection of these cases will be critical to preventing another outbreak. The CD Unit will disseminate further information about next steps as the outbreak response is de-escalated. Outbreaks of TB among homeless persons are strongly related to social determinants of health such as employment, income, safe housing, and access to health care. Preventing and controlling future outbreaks requires continued attention to these inequities through comprehensive policies and programs that aim to reduce health disparities in our community. On behalf of the Office of the Medical Health Officers, we thank each of you for your hard work and collaboration in controlling this outbreak and for your continued dedication to TB prevention and control. If you have any questions, please contact the Communicable Disease Unit at 1-866-778-7736 or by email CDUnit@interiorhealth.ca. To: CIHS Promotion & Prevention; Infection Control, Workplace Health & Safety, KGH Administrators, PRH Administrators, Senior Executive Team, CD Unit From: Dr. Sue Pollock, Medical Health Officer & Medical Director, Communicable Disease Date: February 4, 2015 RE: Central Okanagan TB Outbreak Declared Over
  127. 127. BUT WAIT!there’s more!
  128. 128. 1. understand epidemic dynamics Group A Streptococcus PMID: 20142485 1. Sequence & assemble genomes of pathogens sampled from an epidemic (an outbreak spanning a large region) 2. Build a phylogeny to identify clades - “subclones” 3. Plot the prevalence of subclones across space and/or time to understand why/how the epidemic is happening
  129. 129. 2. discover a brand-new pathogen Bas-Congo virus PMID: 23028323 1. Do metagenomics on a sample from a patient with an unknown disease 2. BLAST reads against a database of all known organisms 3. Look in the set of reads that didn’t match to any known organisms 4. Try some lab tricks to sequence the whole virus
  130. 130. 3. describe a novel pathogen German outbreak E. coli O104:H4 PMID: 21793740 1. Sequence & assemble genome(s) of novel pathogen 2. Build a phylogeny to see how it relates to other members of its genus 3. Do comparative genomics to identify genes/elements present or absent in new pathogen relative to other species
  131. 131. PMC4556809 PMC4587932 4. understand the drivers and evolution of within-host and population- level evolution of resistance.
  132. 132. 5. date the time of a viral infection HIV PMID: 218322936 1. Sequence all the viruses found in a patient (the viral “quasispecies”) 2. Build a phylogenetic tree where branch lengths correspond to calendar time 3. Identify the time of the TMRCA - this is the infection time
  133. 133. 6. therapeutic monitoring of DRUG resistance HIV PMID: 20628644 1. Sequence all the viruses found in a patient (“quasispecies”) at multiple times throughout their ART treatment 2. Scan the sequences for known mutations that confer drug resistance - if you see them, change the treatment plan
  134. 134. PMID: 27150362 (OA) 7. understand pathogen population structure
  135. 135. DIAGNOSIS 1. Sensitivity in sequencing directly from a clinical sample 2. Clinical metagenomics - who’s the pathogen, who’s a commensal, who’s a contaminant? 3. Building lab capacity
  136. 136. RESISTANCEPREDICTION 1. What’s a resistance-determining mutation versus a compensatory or other mutation? 2. What is the effect of rare variants on resistance phenotype? 3. What’s in the databases? Who is maintaining the databases? 4. How the @#$% are we supposed to identify resistance associated with different modes, levels of gene expression?
  137. 137. EPIDEMIOLOGY 1. How can we infer transmission from genomic data alone? 2. How can we infer transmission when it’s not just a strain but an MGE that’s moving? 3. Do we have enough spatial and temporal coverage of annotated genomes to make useful inferences about population dynamics?
  138. 138. @jennifergardy

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