impact of climate change on water quality and health: a focus on … · 2020. 4. 8. · nucleotide...
TRANSCRIPT
Impact of
climate change
on water quality
and health:
a focus on cholera
3rd International One Health
Congress
Amsterdam
March 17, 2015
Rita R. Colwell, Ph.D., D.Sc. Distinguished University Professor
University of Maryland College Park
and
Johns Hopkins University Bloomberg School
of Public Health
Water-related diseases
Amoebiasis
Arsenic
Diarrhoeal disease,
Including cholera
Dracunuliasis (guinea worm)
Fluorosis
Giardiasis
Hepatitis A
Intestinal helminths
Malaria
Schistosomiasis
Trachoma
Typhoid
48,000,000
28-35m exposed to drinking
water with elevated levels
1.5 billion
> 5000
26 million (China)
500,000
1,500,00
133,000,000
396,000,000
160,000,000
500,000,000
500,000
110,000
1,800,000
-
-
Low
-
9400
1,300,000
> 10,000
-
25,000
Cases per year Deaths per year
West Virginia University
Civil and Environmental Engineering
Cholera: A Global Disease
Acute water-related diarrheal disease Seventh pandemic started in 1960s Occurs in more than 50 countries affecting
approximately 7 million people Bengal Delta is known as “native
homeland” of cholera outbreaks Since cholera bacteria exist naturally in aquatic habitats evidence of new biotypes emerging,
it is highly unlikely that cholera will be eradicated but clearly can be controlled by provision of safe drinking water.
G. Constantin de Magny
What is reported about cholera and
macro-scale processes?
Cholera outbreaks have been linked to environmental and climate variables
precipitation (Hashizume et al. 2008)
floods (Koelle et al., 2005)
river level (Emch et al., 2008)
sea surface temperature (Colwell, 1996; Lobitz et al., 2000)
coastal salinity (Miller et al., 1982)
dissolved organic material (Worden et al., 2005)
fecal contamination (Islam et al., 2006)
chlorophyll (Lobitz et al., 2000, Magny et al., 2008)
Environmental Signatures Related To Cholera Epidemics
Dan Zimble, ESRI Inc.
Cholera and SST in the Indian Ocean
0 0.6+
R2
Six-month SST lead: R2 = 0.72
Environmental Signatures Related To Cholera Epidemics K
OL
KA
TA
M
AT
LA
B
Fitted model
Cross-validation model Constantin de Magny et al., 2008, PNAS
Results Kolkata: Significant and positive relationship between
cholera and CHL(t) and Rain(t).
Matlab: Significant and positive relationship between
cholera and Chl(t-1).
KOLKATA
+1 mg.m-3 in CHL(t) => +32.5% in number
of cholera cases (95% CI 8.3%-62.0%)
+1 mm.day-1 in Rain(t) => +6.5% in number
of cholera cases (95% CI 1.6%-11.7%)
MATLAB
+1 mg.m-3 in CHL(t-1) => +31.4% in
number of cholera cases (95% CI 13.0%-
52.7%)
Constantin de Magny et al., 2008, PNAS
West Virginia University
Civil and Environmental Engineering
Location of areas in the Indus River Basin where cholera
outbreaks were reported from 1875-1900.
Relationship between cholera
outbreaks and air temperatures
Air
Air Temperature Rainfall
Below average for
two previous
months
Below average
Low Risk
Available
and intact
Cholera Outbreak Water and
Sanitation Access
Above average for
two previous
months
Above average
High Risk Poor or
Damaged
Theoretical framework for predicting cholera outbreaks in epidemic regions
West Virginia University
Civil and Environmental Engineering
Antarpreet Jutla, Elizabeth Whitcombe, Nur Hasan, Bradd Haley, Ali Akanda, Anwar Huq, Munir Alam, R. Bradley Sack and Rita Colwell. 2013. Environmental factors influencing epidemic cholera. Amer J Trop Med Hyg 89(3) 597-607
Could we have predicted the
Haiti Cholera outbreak? Recent cholera outbreak in Haiti indicated the disease remains a global threat.
Framework for developing cholera prediction models in cholera endemic (ER)
and non-endemic regions (NER)
The sharp contrast in mortality rates between ER and NER exists not because
we do not know how to treat cholera patients, but because of a persistent
“knowledge barrier” between ER and NER.
We propose a pragmatic and adaptive framework which hypothesizes that
convergence of three enabling situations - Inception, Environmental
Conditions, and Transmission - are necessary for a cholera outbreak to become
an epidemic.
Grande Saline
Gonaives
Saint-Marc
Drouin
Chansolmes
Bassin Bleu
Carbaret
Croix-des-Bouquets
Petion Ville
Arcahaie
Tabarre
En Plein Delmas
Cite Soleil
Port-au-Prince
Léogâne
Presumed location of
original contamination
Montrouis
Patient Town Arrondissement Department
Grande Saline Dessalines Artibonite
Gonaïves Gonaïves Artibonite
Saint-Marc Saint-Marc Artibonite
Drouin Saint-Marc Artibonite
Chansolmes Port-de-Paix Nord-Ouest
Bassin Bleu Port-de-Paix Nord-Ouest
Arcahaie Arcahaie Ouest
Cabaret Arcahaie Ouest
Croix-des-Bouquets Croix-des-Bouquets Ouest
En Plein Gonaïves Ouest
Plaine Gonaïves Ouest
Léogâne Léogâne Ouest
Tabarre Port-au-Prince Ouest
Port-au-Prince Port-au-Prince Ouest
Delmas Port-au-Prince Ouest
Cite Soleil Port-au-Prince Ouest
Petion Ville Port-au-Prince Ouest
Montrouis Ouest
18 8 3
Cange
Jacmel
Source and Distribution of isolates collected from Haitian outbreak
Air temperature in Haiti in 2010 compared
with historical air temperature data
Monthly rainfall in Haiti in 2010 compared
with historical rainfall data
Source: The Institute for Genomic Research
Small Chromosome Large Chromosome
Vibrio cholerae
RC9 O1 Ctx+ Kenya
2740-80 O1 ElTor Env US
NCTC8457 O1 ElTor 1910
B33 O1 ElTor Mzb
MJ-1236 O1 hybrid biotype
MO10 O139
623-39
1587 O12
VL426 albensis
O395 O1 Classical
AM-19226 O39
TMA21 non-O1 Brazil
MAK757 O1 ElTor 1937
MZO-2 O14
MZO-3 O37
RC385 O135 Csp Bay
V51 O141 US
V52 O37 Sudan
Chromosome I (2,961,149 bp, 2,742 ORFs)
Chromosome II (1,072,315 bp, 1,093 ORFs)
Missing ORFs in V. cholerae strains (Reference: N16961; cutoff = 70% DNA similarity)
Mosaic genomic structure of V. cholerae
revealed by comparative genomics
RC9 O1 Ctx+ Kenya
2740-80 O1 ElTor Env US
NCTC8457 O1 ElTor 1910
B33 O1 ElTor Mzb
MJ-1236 O1 hybrid biotype
MO10 O139
623-39
1587 O12
VL426 albensis
O395 O1 Classical
AM-19226 O39
TMA21 non-O1 Brazil
MAK757 O1 ElTor 1937
MZO-2 O14
MZO-3 O37
RC385 O135 Csp Bay
V51 O141 US
V52 O37 Sudan
Clinical Sample: ● 81 stool samples
V. cholerae O1 41 samples
V. cholerae Non-O1/O139 21 samples
● Both V. cholerae O1 and Non-O1/O139 have been isolated from 6 stool samples
A. From Cange in the Central Plateau: ca. 199 meters above sea level, is located near the
Artibonite River.
• From the hospital: tap water, greywater , and a latrine sample
• From the school a latrine sample was also collected.
• V. cholerae non-O1/O139 were isolated from all samples, all are ctxA negative (by PCR).
B. From Jacmel in the Sud-Est Department in southern Haiti:
• Water samples were collected from community tube well, river, and ocean
• All samples were negative for V. cholerae by toxR PCR,
• Some presumptive V. alginolyticus were isolated from these samples which showed
positive bands of different size for ctxA and the O1 rfb by Hoshino PCR.
C. Surface water samples from the south of Haiti, Grand'Anse, Nippes, and Sud.
• V. cholerae non-O1/O139 have been isolated and are currently being investigated.
Environmental Sample:
0.00002
HC61A1
HC81A1
HC41A1
HC7A1
HC80A1
HC46A1
HC17A2
HC22A1
HC32A1
HC68A1
HCUF01
HC48A1
HC20A2
2010-EL1786
2010-EL1792
HC38A1
HC49A2
HC21A1
HFU02
2010-EL1798
HC40A1
HC70A1
HC42A1
HC57A2
HC47A1
CP1038
CP1048
CP1050
CIRS 101
CP1042
CP1040
CP1041
MO10
MJ-1236
B33
CP1032
CP1033
RC9
INDRE 91/1 N16961
HC43A1
HC23A1
HC06A1
(c) 7th pandemic (c)
HC-2 (b)
NCTC8457
BX330286
M66-2
MAK757
2740-80
V52
O395
RC27
LMA3984-4
12129(1)
MZO-3
AM-19226
HE-25
TMA21
623-39
1587
MZO-2
HE-40
HE-46
HE-39
HE-48
HC-44C1
HC-46B1
HC-43B1
HC-41B1
HE-45
Amazonia
TM11079-80
V51
CT5369-93
RC385
VL426
HE-09
HE-16
0.002
(a) (b) HC-51A1
HC-02C1
HC-1A2
HC-2A1
HC-36A1
HC-50A1
HC-55B2
HC-55C2
HC-57A1
HC-59A1
HC-59B1
HC-60A1
HC-61A2
HC-78A1
HC-56A1
HC-52A1
HC-55A1
0.0000005
HC-1
Haiti, 2010
Bangladesh, 2010
Zimbabwe, 2009
Bangladesh, 2002
Thailand, 2010
Zambia, 2004
Mexico, 1991
Mexico, 2000
Ph
ylo
gen
om
ics
The Haitian V. cholerae O1 strains clustered with other 7th pandemic V. cholerae strains in a single monophyletic clade
Haitian strains branching separately from South Asian V. cholerae strains
10 Haitian strains (red) form a cluster cloud, distinct and yet, distant, from CP genomes (concurrent epidemic isolates form different parts of the world) (blue) and others (green).
Interestingly, one reference strain CP 1038 (from Zambia) genome falls into the Haitian cluster.
Principal component analysis:
The three-dimensional PCA projection plots based on divergence of average nucleotide identity
Haitian Cluster Cloud
Conclusion:
Genomic analysis of Haitian V. cholerae O1 strains has provided evidence of:
a distinct VNTR genotype, genetic polymorphisms of rstB and ctxB, nucleotide (GTA) deletions in rstB, an increased number (five) of ToxR binding repeats, mutations in gyrA and parC gene, and a genetically similar set of MGE’s shared with isolated elsewhere
Core gene and SNP-derived phylogenies suggest, and PCA findings reinforce, that quite quickly, i.e., within a three week period early in the cholera epidemic, significant genomic diversity accumulated in the circulating population.
Genomic analysis provided evidence that two distinct Vibrio populations, V. cholerae O1 and V. cholerae non-O1/O139, contributed to the cholera epidemic in Haiti.
Comprehensive genomic analysis showed: V. cholerae O1 populations were clonal, resembling concurrent epidemic isolates
from South Asia and Africa. V. cholerae non-O1/O139 populations were not clonal but most probably serve as
a reservoir for genomic and pathogenicity islands.
V. cholerae non-O1/O139 populations in Haiti harbor a genomic backbone similar to that of toxigenic V. cholerae O1 circulating in the Western hemisphere.
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hin
gom
on
as_e
lod
ea_A
TCC
_3
14
61
Sp
hin
gom
on
as_s
p_K
C8
Sp
hin
gom
on
as_s
p_S
17
Sp
hin
gop
yxis
_ala
sken
sis_
RB
22
56
St
eno
tro
ph
om
on
as_m
alto
ph
ilia_
K27
9a
St
eno
tro
ph
om
on
as_m
alto
ph
ilia_
RR
-10
Sten
otr
op
ho
mo
nas
_sp
_SK
A1
4
Syn
ergi
stes
_sp
_3_1
_syn
1
Var
iovo
rax_
par
ado
xus_
17
6 N
od
e
Var
iovo
rax_
par
ado
xus_
EPS
Var
iovo
rax_
par
ado
xus_
S11
0
Xan
tho
bac
ter_
auto
tro
ph
icu
s_P
y2
Zym
om
on
as_m
ob
ilis_
sub
sp_
10
7 N
od
e
Strains
Bio
film
0
10
20
30
Relative Abundance (%)
CosmosID Proprietary & Confidential
Biofilms AR/V Factor Results – Biofilms & Isolates
Percent.Unique.Hits
0
25
50
75
100
Plasmid.Hit
Y
N
4/7/2015 CosmosID Proprietary & Confidential 39
Aeromonas_caviae_JHU
Aeromonas_hydrophila_1695
Aeromonas_hydrophila_1719
Aeromonas_hydrophila_1721
Aeromonas_hydrophila_1761
Klebsiella_pneumoniae_1762
Klebsiella_pneumoniae_1699
Serratia_marcescens_1605
Serratia_marcescens_1606
Citrobacter_youngae_1720
Citrobacter_amalonaticus_1698
Enterobacter_cloacae_1697
Aeromonas_hydrophila_1760
Serratia_marcescens_1602
Aeromonas_hydrophila_1700
Serratia_marcescens_1702
biofilm3183_S1
biofilm3184_S2
biofilm3185_S3
biofilm3186_S4
biofilm3187_S5
biofilm3189_S6
biofilm3192_S7
biofilm3193_S8
biofilm3197_S9
AR/V Factors
Sam
ple
s
Plasmid associated
Milestone Technologies
Year Algorithm Approach Algorithm Relative Speed
1981 Smith-Waterman
Global Sequence Alignment
Exact matches and complete alignments. Hashes the query.
1.0
1988 FASTA Local Sequence Alignment
Focuses on common sub-sequences (words) shared between query and database sequences. Hashes the query.
50x SWA
1990 BLAST Local Sequence Alignment
Focuses on high-scoring sub-sequences (words) shared between query and database sequences. Hashes the query.
50x SWA
2002 BLAT Local Sequence Alignment
Hashes the database. 50x WUBLAST
2006 ScalaBLAST Local Sequence Alignment
Utilizes parallel processing
4x BLAST using 50
processors
2013 GENIUS®
5VCE Probabilistic
Matching Hashes the database.
10,000X BLAST
A Simple, Sustainable Method for Reducing Cholera
Full Study
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
Control Sari Nylon
Test Group
Cases o
f C
ho
lera
Per
1000
Po
pu
lati
on
Full Study
Control Nylon Sari
Test Group
Cases o
f C
hole
ra P
er
1000 P
opula
tion
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
From Response to Coordinated Research Established 2011
www.gulfresearchinitiative.org
www.gomri.org
gulfresearchinitiative.org
Credit: US Chemical Safety Board
Credit: SAUL LOEB/AFP/Getty Images
Credit: AP Photo/Charlie Riedel
GoMRI is not part of the
National Academy of Science or
National Fish and Wildlife Foundation
GoMRI is not part of
National Resource Damage
Assessment or
Clean Water Act
gulfresearchinitiative.org
Credit: SAUL LOEB/AFP/Getty Images
Credit: AP Photo/Charlie Riedel
GoMRI is not part of the
National Academy of Science or
National Fish and Wildlife Foundation
GoMRI is not part of
National Resource Damage
Assessment or
Clean Water Act
gulfresearchinitiative.org
1 - ESP
1 - DEU
2 - NLD
1 - NOR
• Dispersion by physics and plankton,
• Behavior and hydrocarbon transformation of deep oil spills,
• Influences on fate and transport,
• Environmental consequences,
• Ecosystem impacts,
• Oil plume fate,
• Ecotoxicology,
• Improved dispersants,
• Modeling of fate,
• Transport, and
• Ecosystems
Year One Block Grants - $45M, 149 Projects (completed)
Summer 2011 Bridge Grants (RFP III) - $1.5M, 17 Projects (completed)
2012 - 15 Eight Consortia Grants (RFP I) - $110 M, 8 RC (NCEs)
2013 -16 Investigator Grants (RFP II) - $18.6 M, 19 projects
2015 – 17 Consortia Grants (RPFIV) @ $140 M, 12 projects
2016-18 Individual Grants (RFP V) release- Nov 14 @ $30 M
2018 – 2020 last RFP(s) and wrap up.
Metrics as of Jan 31, 2015 • About 480 scientific peer-reviewed publications/book chapters
• Over 1930 presentations and poster sessions at conferences/scientific meetings
• Over 688 graduate students
gulfresearchinitiative.org
41 states
240 academic institutions
16 countries
“When one tugs at a single thing in
nature, he finds it hitched to the rest
of the universe.” John Muir
(1838-1914)
Collaborators and Colleagues ICDDR,B
• Dr. Munir Alam
• Dr. David Sack
• Dr. M. A. Salam
• Dr. A.S.G. Faruque
• Dr. Peter Kim Streatfield
• Dr. Carel van Mels
• Mr. Sarker M. Nazmul Sohel
• Dr. Mohammad Yunus
• A.K. Ashraful Aziz
• Dr. M. Imadadul Huq
• Dr. Sirajul M. Islam
• Huda Khan
• Rezaur Rahman
NICED, Kolkata, India
• Dr. Balakrish Nair
• Dr. T. Ramamurthy
University of Maryland • Sittipan Chayanan
• Nipa Choopun
• Jafrul Hasan
• Anwarul Huq
• Christopher Grim
• Shameem Huq
• Guillaume Constantin de Magny
• Chenyang Jiang
• James Kaper
• Erin Lipp
• Valerie Louis
• David Maneval
• Tonya Rawlings
• Janie Robinson
• Estelle Russek-Cohen
• Paul West
• Young Gun Zo
• Norma Brinkley
• Jennifer Papp Newlin
• Victoria Lord
Collaborators and Colleagues • Richard Atwell, England
• Brad Lobitz, NASA Ames
• Louisa Beck, NASA Ames
• Byron Wood, NASA Ames
• Phyllis Brayton, NIAID, NIH
• Jongsik Chun, Seoul, Korea
• Ana Gil, Lima, Peru
• Jay Grimes, Univ. of Southern Mississippi
• Sunny Jiang, Univ. of California
• Tatsuo Kaneko, San Diego
• L. Lizaragga-Partida, Mexico
• Huai-shu Xu, China
• Norma Binsztein, Argentina
• Crystal Johnson, Louisiana State University
• Carla Pruzzo, University of Genoa, Italy
• Tom Brettin, Los Alamos National Laboratory
• Nell Roberts, Louisiana State University
• Betty Lovelace, NCI/NIH
• Minnie Sochard, Catholic University, Washington, DC, USA
• Irma Rivera, Univ. of Sao Paolo, Brazil
• R. Bradley Sack, Johns Hopkins University, School of Public Health
• Fred Singleton, Gainesville, Florida
• Miguel Talledo, Lima, Peru
• Jack Dangermond, ESRI, Redlands, CA
• William Davenport, ESRI, Redlands, CA
• John Calkins, ESRI,, Redlands, CA
• Glenn Morris, Florida State University
• Ron Taylor, Darmouth, College, NH
• Matt Luck, ISciences, Burlington, VT
• Thomas Parris, ISciences, Burlington, VT
• Ric Ciccone, ISciences, Burlington, VT
• Fred Zimmerman, ISciences, Burlington, VT
• Linda Zall, Office of the Chief Scientist
Special Acknowledgement
• Dr. Bruce Budowle, University of North Texas
• Dr. Jack A. Gilbert, Argonne National Laboratory
• Dr. Christopher J. Grim, FDA
• Dr. Terry C. Hazen, University of Tennessee
• Dr. G. Balakrish Nair, Minister of Translational Biotechnology,
Government of India
• Dr. Thomas A. Cebula, CSO, CosmosID Inc.
• Dr. Huai Li, Director, Product Development, CosmosID Inc.
• Dr. Seon Young Choi, Bioinformatic Scientist, CosmosID Inc.
• Dr. Poorani Subramanian, Bioinfromatician, CosmosID Inc.
Collaborators and Colleagues
Anwar Huq,
Professor
University of Maryland,
College Park, MD
Nur Hasan
Vice-President,
Research and
Development
CosmosID, Inc.
College Park, MD
Antarpreet Jutla,
Assistant
Professor,
West Virginia
University
Morgantown, WV
Dr. Seon Young
Choi,
Bioinformatic
Scientist,
CosmosID Inc.
College Park, MD
Courtesy of GB Nair, NICED, Kolkata, India