This document discusses how genomics is revolutionizing public health microbiology. It provides three examples: 1) Rapid WGS-based diagnosis identified Leptospira bacteria in a patient's cerebrospinal fluid, allowing targeted penicillin treatment that resolved his illness within two weeks. 2) WGS-based drug sensitivity testing allows personalized therapy by predicting resistance from a pathogen's genome within 1 day, compared to weeks for conventional methods. 3) Genomic epidemiology tracks person-to-person disease spread by comparing whole genomes from outbreak isolates. This approach helped determine the key locations and time period fueling a TB outbreak in BC, and later demonstrated transmission had ended.

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. HelLO!
Senior Scientist (Genomics),BCCDC
Asst.Professor,UBC SPPH
Canada Research Chair in Public Health Genomics

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. Goal: show you a very cool domain where
interesting genomics is maybe going to prevent
us all from dying of something awful

5. microbiology in public health

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. our health is governed by a number of factors

8. health
protection surveillance prevention
healthassessment
health
promotion
emergency
preparedness

9. the public health lab

10. patient sample for diagnosis
study sample for surveillance
environmental sample

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. What are we doing now?

13. 1. What does this patient have? Culture-based diagnostics.

14. 2. What does this patient have? serology

15. 3. What does this patient have?
nucleic acid amplification testing.

16. a representative example:
mycobacterial diagnostics

17. 1
Clinician suspects TB
DAY 0

18. 1 2
Sample collected, submitted
DAY 0

19. Sample collected, submitted
Courtesy Mabel Rodrigues

20. 1 2 3
AFB smear microscopy
DAY 1-2

21. Slides are prepped
Courtesy Mabel Rodrigues

22. Smears are read
Courtesy Mabel Rodrigues

23. AFB smear microscopy

24. 1 2 3
Nucleic Acid Amplification Testing (NAAT)
3*
DAY 2-3

25. Prelim report issued

26. 1 2 3 4
Inoculate into culture
DAY 1

27. Solid culture on LJ
Courtesy Mabel Rodrigues

28. Liquid culture on Bactec MGIT
Courtesy Mabel Rodrigues

29. 2nd report issued with
culture results

30. Success! I have diagnosed my patient.
Now what do I treat them with?

31. AMR occurs through multiple routes

32. 1. what do i treat this patient with? phenotypic dst.

33. 2. What do i treat this patient with?
molecular testing.

34. a representative example:
mycobacterial dst

35. Liquid culture on Bactec MGIT
Courtesy Mabel Rodrigues

36. Third report issued after
DST results

37. Cool. Thanks. I’m done.
Over to you, public health.

38. Public health: what’s going around and why?

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. ?
How do we investigate an

41. SURVEILLANCE IDENTIFIES CASES

42. MOLECULAR EPIDEMIOLOGY IDENTIFIES
POTENTIALLY RELATED CASES

43. Restrictiondigest(RFLP) Multilocussequencetyping(MLST)
Multilocusvariablenumber
tandemrepeatanalysis(MLVA/VNTR)

44. RFLP MLST/MLVA
Enzymescleavechromosome
intolargefragments
PCRprimersamplifyspecific
regionsofthechromosome

45. RFLP MLST/MLVA
Thewholechromosome,
brokenintoafewpieces
Afewshortfragmentsofthe
chromosome

46. RFLP MLST/MLVA
Thewholechromosome,
brokenintoafewpieces
Runonagel
Afewshortfragmentsofthe
chromosome
Compareagainstdatabase

47. SizeStandard
SizeStandard
SizeStandard

48. SizeStandard
SizeStandard
SizeStandard

49. FIELD EPIDEMIOLOGY
SUGGESTS TRANSMISSIONS

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. BUTWAIT.WEHAVEAPROBLEM.
• Genotypingmethodsonlytellyouaclusterofcasesexists,nottheorder/
directionoftransmission
• Size/membershipoftheclustervarieswiththetypingmethod(s)used
• Epidemiologicalinvestigationisrequiredtoderivethelinksbetweencases,
andmaynotbeavailableorofsufficientqualitY

52. a representative example:
mycobacterial GENOTYPING

53. MycobacterialInterspersedRepetitiveUnitVariableNumberTandemRepeat

54. AmapofallMIRU-VNTRTBgenotypesinBC,2005-2014

55. asummaryofourroadblocks
diagnostics:time-consuming,requiresuspicion,variable
performance,stillmany“unknown”samples
phenotypicdst:sloooooooooooooooooow
surveillance:low-resolutiontypingtechniques,heavily
reliantonfieldepidemiologicaldata
thewholeprocess:multipleparallelsteps,reportingat
variousstagesthroughouttheprocess

56. ourchainsaw:genomics

57. THE FIRST SEQUENCED GENOME
1995. 1 GENOME, 13 MONTHS, $600,000, ROOM FULL OF MACHINES

58. with the current technology
800-1000 GENOMEs, 5 days, <$100 each, one single machine

59. with the current technology
20 GENOMEs, 8 hours, <$150 each, one single machine

60. the future?

61. STARTING MATERIAL:
~weeks immediate

62. genomics is revolutionizing
public health microbiology

63. pathogen genomes contain nearly everything we need for
diagnosis, phenotyping, and surveillance

64. aretheseroadblocksanymore?
diagnostics:time-consuming,requiresuspicion,variable
performance,stillmany“unknown”samples
phenotypicdst:sloooooooooooooooooow
surveillance:low-resolutiontypingtechniques,heavily
reliantonfieldepidemiologicaldata
thewholeprocess:multipleparallelsteps,reportingat
variousstagesthroughouttheprocess

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. Story #1: Rapid WGS-based diagnosis

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. Culture?Serology?NAAT?Otherassay?
Bacteria?Virus?Parasite?Fungus?Autoimmunity?

71. Charles Chiu, UCSF

73. “Joshua’scerebrospinalfluidcontainedDNAfromapotentiallylethal
typeofbacteriacalledLeptospira…readilytreatedwithpenicillin…
Thatafternoon,Joshuastartedgettinglargedosesofpenicillin.The
swellinginhisbrainalmostimmediatelystartedsubsiding,andtwo
weeksafterthefirsttestresults,Joshuawaswalking.”

74. 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

75. Clinicalmetagenomicstoolbox:
• Nanoporefornear-patientsequencing
• Illuminaforslightlylongerturnaround
• Metagenomicspipelineforbinningreads(e.g.SURPI)
• IDphysiciantomakethecalloncommensalvspathogen

76. Story #2: WGS-based tailored therapy

77. SPECIMEN

78. SPECIMEN
DIAGNOSIS

79. SPECIMEN STANDARD THERAPY
DIAGNOSIS

80. SPECIMEN STANDARD THERAPY
DIAGNOSIS DRUG SENSITIVITY TESTING

81. SPECIMEN STANDARD THERAPY
DIAGNOSIS DRUG SENSITIVITY TESTING
PERSONALIZED THERAPY

82. SPECIMEN STANDARD THERAPY
DIAGNOSIS DRUG SENSITIVITY TESTING
PERSONALIZED THERAPY

83. SPECIMEN
DIAGNOSIS

84. SPECIMEN
DIAGNOSIS
ACGTACGATCG
ACGTACGATCG ACGTACGATCG
ACGTACGATCG

85. ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG
ATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACG
TACGATCGACGTACGATCGACGTACGATCGACGTACGATCGCGCCGGACGTACGATCGACGTACGATCGACGT
ACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCG
ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG
ATCGACGTACGATCGACGTACGATCG

86. ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG
ATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACG
TACGATCGACGTACGATCGACGTACGATCGACGTACGATCGCGCCGGACGTACGATCGACGTACGATCGACGT
ACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCG
ACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACGATCGACGTACG
ATCGACGTACGATCGACGTACGATCG

87. SPECIMEN
ACGTACGATCG
ACGTACGATCG ACGTACGATCG
ACGTACGATCG

88. SPECIMEN
ACGTACGATCG
ACGTACGATCG ACGTACGATCG
ACGTACGATCG
PERSONALIZED THERAPY

89. SPECIMEN STANDARD THERAPY
DIAGNOSIS DRUG SENSITIVITY TESTING
PERSONALIZED THERAPY

90. SPECIMEN
diagnosis
& dst
PERSONALIZED THERAPY

92. 1-day positive MGIT

93. BATCH & SEQUENCE

94. UPLOAD & ANALYSE

95. speciation via metagenomic analysis
M.tuberculosis
H.sapiens
R.randombacterium

96. resistotyping via mutation catalogue

101. Personalizedtherapytoolbox:
• Illuminasequencingatreferencelab
• Rapidresistancepredictionfromgenomicdata(e.g.Mykrobe)
• Growingcatalogueofresistance-associatedmutations

102. Story #3: Tracking outbreaks with WGS

106. 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.

107. AAAAAA

108. AAAAAA
AAAAAA
AACAAA

109. AAAAAA
AAAAAA
AACAAA
AACAAA
GACAAA
AAAATA
AAAAAA

110. AAAAAA AACAAA
AACAAA
AACTAA AACTAA
AACAAG

111. TELEPHONE
ARTBYDEVIANTARTUSERSCUMMY

112. TB TRANSMISSION IN BC IS
CONCENTRATED IN OUR MOST
VULNERABLE POPULATIONS

113. December 2010: a phone call

114. May2008-personwithafb4+pulmonarytbvisitsshelter

115. december2008-shelterscreeningidentifiessixfurthercases

116. december2010-outbreakhasgrownto30cases,largelywithinafewblocks

117. are the outbreak management
activities directed at the right
persons and locations?

119. TELEPHONE
ARTBYDEVIANTARTUSERSCUMMY

121. index case
second case

122. WE FORGOT ABOUT WITHIN-
HOST GENETIC DIVERSITY

123. detour: reconstructing a
transmission network in the
light of within-host diversity
With xavier didelot & caroline colijn

126. ¯_( )_/¯
direct observation of within-host diversity may not be possible
due to the nature of infection and specimen collection

127. math modelling

129. startbymakingloadsof
thesecoloured“infection”trees

130. createatransmissionsocial
networkfromthesuiteofinfectiontrees

131. combinewithyourepidemiological
datatoarriveatafinalreconstruction

133. m
iner
M
INOR
variants

134. m
iner
M
INOR
variants

135. how did the shelter contribute to
infection?
With ANAMARIA CRISAN

137. INDEX CASE
ACTIVE TB
LATENTTB
UNINFECTED
NO DATA
VISIT 1: DECEMBER 2007
VISIT 2: MAY 2008

138. TIME IN THE SHELTER WAS ASSOCIATED WITH INCREASED RISK OF
INFECTION (OR 1.26), ESPECIALLY STAYS OF 5+ NIGHTS (OR 4.97)

139. September 2014: another phone call

140. 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

141. can we truly say
that the outbreak is over?

143. An updated model to better infer time of infection

144. An updated model to better infer time of infection

148. 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

150. BUT WAIT!there’s more!

151. 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

152. 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

153. 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

154. PMC4556809
PMC4587932
4. understand
the drivers and
evolution of
within-host and
population-
level evolution
of resistance.

155. 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

156. 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

157. PMID: 27150362 (OA)
7. understand pathogen
population structure

158. 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

159. 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?

160. 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?

161. @jennifergardy