Impact of climate change on water quality and health: a focus on … · 2020. 4. 8. · nucleotide...

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

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



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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• Types of markers

– Unique to the organism – Shared with other neighboring organisms in the tree

• Provide identification at or near sub-species and strain level (natural concept)

4/7/2015 CosmosID Proprietary & Confidential

Uses entire genome

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Typically different strains of a species elicit different impacts, critical to human health or business.

Most E. coli are commensal, only some are pathogenic.

Pathotypes:

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Lineages:

Strain:

ETEC EPEC EHEC EIEC EDEC EAEC

Lineage I Lineage II Lineage I/II

EC4501

O157:H7

Pathogens Commensal

Escherichia coli

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responsible for product recalls

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CO

SM

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Microbes Extracted DNA DNA Sequencing

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ctxA

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Post-sequencing Bioinformatics Analysis of Clinical Specimens Process:

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sequencefile.fasta

Microbiome Analysis of Acute Diarrheal

Patients Compared with Healthy Individuals

HEALTHY CONTROL

KNOWN ETIOLOGY

HMP

UNKNOWN ETIOLOGY

A SUBPOPULATION OF THE MICROBIOME IS OVER- REPRESENTED IN THE DIARRHEAL PATIENTS COMPARED TO HEALTHY INDIVIDUALS

DIA

RR

HEA

L PA

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HEA

LTH

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DIV

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PATHOGENIC

STRAINS ONLY

MULTIPLE PATHOGENS CAN READILY BE IDENTIFIED FROM

DISEASE PATIENTS

|-----------HMP----------|

THE POPULATION OF KOLKATA, INDIA MAY TOLERATE A SMALL NUMBER OF PATHOGENIC

MICROORGANISMS THAT WOULD COMPRISE A DISEASE STATE IN WESTERN EUROPEANS

IDENTIFIED BACTERIAL COMMUNITIES AT STRAIN LEVEL

Known Enteric

Pathogens

FDA/UVA/CosmosID Hospital Biofilm Study

• Data – NGS reads

– Biofilm samples collected from sink traps

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Identification Resistant Bacterial Strains from Biofilms

4/7/2015 38

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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




Klebsiella_pneumoniae_1762

Klebsiella_pneumoniae_1699

Serratia_marcescens_1605


Citrobacter_youngae_1720

Citrobacter_amalonaticus_1698

Enterobacter_cloacae_1697





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


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


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

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