Aeras schofield 21112013

Collaborating Institutions

The Walter and Eliza Hall Institute
of Medical Research

Kenya Medical Research Institute

PNG Institute of Medical Research

Mozambique
Centre for Clinical Health Research

Malaria Immunology and Epidemiology studies in PNG

Understanding the targets and mechanisms of immunity to malaria to
rationalize the development of vaccines.
Examine both antibody and cellular compartments of the immune
response to the malaria.
Studies:
-Mugil study (2004)
-Alexishafen pregnancy study (2006-11)
-Ilaita (R03) mixed infection study (2007)
-MALGEN severe malaria study (2006-10)
-Cellex P. vivax study (2008)
-IPTi study (2008-2010)
-Albimana study (2009)
-ICEMR study (2010-17)

Funders:
US Dept. Veterans Affairs
MiP Consortium
NIH, HHMI Lab funds
Gates Grand Challenges, NH&MRC
Cellex Foundation with CRESIB
IPTi Consortium
Lab funds
NIH


Studies:
-Mugil study (2004)
-IPTi study (2008-2010)
Observational cohorts studies (n = ~ 500)

Funders:
MiP Consortium
Lab funds (HHMI)
IPTi Consortium
Lab funds
NIH


Studies:
-Mugil study (2004)
-IPTi study (2008-2010)
Intervention studies (n = ~ 1500-2000)

Funders:
MiP Consortium
Lab funds (HHMI)
IPTi Consortium
Lab funds
NIH

Population biology: platform for discovery
Malaria

Tuberculosis?

Stats
Epidemiology
Population biology
Systems biology (‘omics’)

Population platforms (field sites, cohort studies)

Biology and Clinical insight

Clinical examination

10mL blood taken

drug treatment

(baseline)

Immune cells purified

Passive case detection

0 days
Baseline

30

12 months

60

Active case detection
-Clinical exam
-Blood films (LM)
-Finger prick (PCR)
-Symptomatic children
taken to Mugil Health Centre

90

120

150

Longitudinal Study Design

180

Parasitological and clinical outcomes
~95% of children were reinfected with P. falciparum

Proportion uninfected

Proportion uninfected

~50% of children
experienced a clinical
episode of P. falciparum

80

80

Clinical episode: fever +
5000 parasites/μL
Michon et al., 2007

Moving beyond serology to define targets of naturally
acquired immunity
•

Development of functional assays measuring relevant host/pathogen
interactions
– Growth inhibitory antibodies
– Opsonising antibodies

Anti-Rh5 antibodies associated with reduced risk of
high parasitemia

There Are Conflicting Reports as to the Cellular
Source of Innate IFNg in Humans
gd-T

SSC

SSC

CD56

NK

CD3

ab-TCR

gd-TCR

gdT cells shown to be a major
source of innate IFNg (Hensmann
& Kwiatkowski, 2001)

NK cells also shown to be a major
source of innate IFNg (ArtavanisTsakonas & Riley, 2002)

•A comprehensive phenotypic characterization of all innate IFNg producing cells
had not been done

Major Questions
• What cells produce IFNg in response to
malaria in humans?
– Do they express NKC &/or KIR receptors?

• Is IFNg production by these cells associated
with altered risk of disease in individuals
living in malaria-endemic areas as it is in
mice?

Malaria IFNg Elicitation Assay

Blood from
Malaria
naive donors

or
Purify
PBMC

P. falciparum uRBC
iRBC
16 hour incubation
at 37ºC

Measure IFNg
in supernatants
by ELISA

Determine which
PBMC produce
IFNg by FACS

gdT Cells Are The Predominant Source of IFNg in
93% of Donors
gdTCR

uRB
C

93% of
donors

iRBC

gdT cells
CD3

IFNg

IFNg

CD56

7% of
donors

IFNg+ PBMC from 15
donors phenotypically
characterized

NK cells
CD3

D’Ombrain et al., 2007

gdT Cells Are The Predominant Source of IFNg in
93% of donors
gdT cells

% of Total IFN-g + Cells

NK cells
abT cells
other cells
100%
80%
60%
40%
20%
0%
H

N

G

I

L

F

K

M

D

A

E

J

B

O

C

Donors

•Cell frequency & the % contribution of each cell type were not associated with
heterogeneity in IFNg responsiveness

Depletion of gdT Cells Abrogates IFNg Production
Depleted
PBMC

PBMC

1500
1500

96%

1200
IFNg (pg/mL)

89%

CD3

uRBC

1200

gdTCR
CD56

iRBC

900
900
600
600

300
300

*

0

0

PBMC
97%

gdT
depleted

NK cell
depleted

abT
depleted

abTC
R

Malaria Responsive gdT Cells Are Mainly of
the Vg9Vd2 Subset
iRBC

iRBC

83.6

Vg9
45.8

Vd1

54.2

90.5

9.5

iRBC

+

IFNg

gdTCR

gdTCR+

16.4

Vd2

IFNg+

&
PBMC
phenotypically characterised

CD4
95.3

4.7

70.1

CD8

29.9


The Majority of Malaria Responsive gdT Cells
Express NKC Receptors
NKG2D
16.8
83.2
iRBC

NKG2A
31.8
68.2

CD94

CD161

iRBC

+

iRBC

gdTCR

IFNg

10.6

89.4

26.5

73.5

gdTCR+ & IFNg+ PBMC
phenotypically characterised


NKC & KIR Receptors Are Differentially
Expressed on IFNg+ & IFNg- gdT Cells
NKG2A p<0.01

iRBC

+

gdTCR

iRBC

IFNg

gdTCR+ & IFNg- or IFNg+
PBMC compared by
Wilcoxon Signed-Rank
Tests

75%
50%
25%
0%
E

F

G

D

K

H

I

H

I

Donors
% IFNg- & IFNg+ gdT cells
expressing KIR2DL1

iRBC

% IFNg- & IFNg+ gdT cells
expressing NKG2A

100%

KIR2DL1 p<0.01

30%

20%

10%

0%
E

F

G

D

Donors

K

IFN-g- gdT cells
IFN-g+ gdT cells

Summary: PART 1
 There is heterogeneity among donors in the innate
IFNg response to iRBC
 gdT cells that express NK receptors, not NK cells,
are the major cellular source of IFNg
 NK receptors are differentially expressed on IFNg+
& IFNg- gdT cells
 Are gdT cell involved in risk of disease?

Heterogeneity in IFNg Responses Among Semiimmune PNG Children
High
14000

12000

8000
6000

Medium
4000

Low

2000

31

6

421

244

245

427

243

205

413

307

141

14

302

PNG Children

222

210

425

303

9

40

121

56

146

138

108

50

401

41

231

109

0
143

IFNg (pg/mL)

10000

Heterogeneity in IFNg Responses is Malaria
Specific

iRBC

≤

PHA

≤

High IFNg Responses Are Associated With a Lower
Risk of High Density Infections
Higher risk
(pathogenic)

Hazard Ratio

No change
in risk
Lower risk
(protective)

low density
infections

(clinical)
high density
infections

Hazard Ratio: Ratio of hazard of having a P. falciparum
infection to high IFNg responsiveness

A case-control study of severe malaria in PNG
Cases

Controls
Severe Malaria
n=202

•
•

•

Uncomplicated malaria controls
n=174

>1000 P.f./μL
WHO (2000) definition of severe
malaria
Admitted to Modilon Hospital

•
•
•

>1000 P.f./μL
No severe disease
Health / immunization clinics

Healthy community controls
n=164
•
•
•
Groups matched by age, sex, and province
of parent’s birth

No acute illness
No severe malaria within 2 weeks
Immunization clinics

Cytokine responses to pRBC are associated with disease
severity

Cytokine responses to pRBC are associated
with disease severity

Cytokine responses are associated with
specific severe malaria syndromes
Respiratorycoma
Deep distress
Hyperlactataemia

gd T cells in severe malaria produce TNF and monokines

Vg9Vd2 T Cells
•Vg9Vd2T cells are activated by phosphoantigens
•Evidence for 2 mechanisms of activation:
Vg9Vd2TCR

phosphoantigens

1. Vg9Vd2TCR can function as a PRR

IFNg

2. Vg9Vd2TCR can also be MHC
restricted
APC
Expansion

Cohort study questions
• How do malaria-specific CD4+ T cell and gd T cell
responses differ in phenotype, frequencies and
function in relation to age, parasitological and
clinical outcomes?
• What responses are associated with differential
clinical and parasitological risk i.e correlates of
immunity or susceptibility?
• Are CD4+ ab T cell and gd T cell responses
correlated?
• How do CD4+ T cell responses to EBV and CMV
differ to malaria-specific CD4+ T cell responses?

Our more recent approaches
• Multiparameter flow cytometry consisting of:
Viability dye to exclude non-viable cells;
5 population markers to identify T cell populations
of interest and memory status;
3 markers to identify differentiation/antigenexperience and immunosenescence;
4 markers to assess functional activity of cells and
polyfunctionality.

Immune regulation of gd T cell responses
• Rationale: Conventional T cells are tightly regulated by
expression of receptors that suppresses cell activation.
Prolonged and inappropriate expression of these receptors occur
during chronic disease and immune exhaustion resulting in
dysfunctional cell functions
• Study questions:
- Are gd T cell responses regulated in a similar manner as
conventional T cells?
- How does expression of regulatory receptors on gd T cells effect
functional capacity and cytokine profiles?
- How does expression of regulatory receptors on gd T cells relate
to protective immunity during malaria?

Immune regulation: Programmed cell death-1 (PD-1)
gd T cell PD-1 expression
• PD-1 is a cell surface receptor
that suppresses activation of
immune cells.
• PD-1 is expressed by
dysfunctional cells in chronic
diseases such as HIV and HCV.
Porichis et al Blood 2011, Razziorrouh et al Gastroeneterology
2011

• Blocking of PD-1 interaction
with it’s ligand results in
restored cell function. Day et al Nature
2006

• In malaria blocking of PD-1
result in reduced parasite
burden in an animal model. Butler
et al Nat Immunol 2012

Immune regulation: and T-cell immunoglobulin and
mucin domain-containing protein 3 (Tim-3)
gd T cell Tim-3 expression

• Tim-3 engagement in mice
result in apoptosis and loss
of effector T cells.
•

Zhu et al. Nat Immunol 2005

• In humans Tim-3 is
associated with functional
exhaustion.
•

Jin et al PNAS 2010, Jones et al JEM 2008

• Blocking of Tim-3 partially
reverse cell dysfunction.
•

Sakuichi et al JEM 2010

• Very few studies have
investigated Tim-3 on nonconventional T cells.

Innate lymphocytes – unexplored players in defense against
novel non-classical pathogen ligands?

Mucosa = largest exposed surface of the body – 1st barrier
 Up to 1012 bacteria/cm3 – not all of which are friendly
 humans exposed to multitude of organisms daily
250 m2 gut
70m2 lungs

The mucosal defense system – a central motor

Coordinates cross-talk among epithelium, immune system and
endogenous microflora of gut
Implicated in a wide range of diseases
Intestinal disease – inflammatory bowel disease,
Crohn’s disease
Lung disease – asthma, TB
Autoimmune disease – mediated by ILC and alterations of gut
microbiota

Innate lymphocytes – new players in lung defense

Diverse populations form an intricate innate
lymphocyte network

Spits et al. Nature Reviews Immunology 13, 145-149 (February 2013) | doi:10.1038/nri3365

ILC subsets, functions and disease associations

Highly conserved gene program between mice and man

Spits et al. Nature Reviews Immunology 13, 145-149 (February 2013) | doi:10.1038/nri3365

Bioinformatics at WEHI: world leading in transcriptomic statistics
Eliminating background noise by focusing on gene sets linked to cell populations or
processes of interest
Gene set testing is a powerful method to identify coordinated changes in genes
associated with a particular population or process of interest.

Terry Speed

It is particularly useful in complex processes where the timing of up- and downregulation of genes can occur at different rates.
We successfully used Gordon Smyth’s rotational gene set testing (ROAST) and
competitive gene set testing (Camera) methods in Achtman et al. 2012 Effective
adjunctive therapy by an innate defense regulatory peptide in preclinical severe
malaria. Science Translational Medicine Sci Transl Med. 4 (135):135ra64. doi:
10.1126/scitranslmed.3003515.

Gordon Smyth

Deconvolution of data based on additional information on population
sizes
The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous
with respect to cell subsets - which can confound interpretation.
Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with
the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially
limiting new discoveries.
An attractive alternative solution is to extract cell subset-specific information directly from
heterogeneous samples via computational deconvolution techniques, thereby capturing both cellcentered and whole system level context.

Our plan to define cellular immunity to P.falciparum Malaria
Study design Key Questions:
How do malaria-specific T cells phenotypes differ in relation to: (i) age; (ii) exposure; (iii) parasitological
outcomes; (iv) clinical outcomes; and (v) elicitation stimuli?
Analyses:
Whole blood RNAseq transciptomes at base-line, clinical episode and convalesence
PBMC isolation, 16hr in vitro elicitation with whole parasites in presence/absence of autologous plasma.
Multiparameter flow cytometry for:
Surface Markers:
gd TCR
Live dead
CD3 (stained intracellularly)
CD4
CCR7 (memory marker)
CD45RA (memory marker)
CD27 (differentiation)
CD28 (differentiation)
Functional Markers:
IFNg (intracellular cytokine)
IL-2 (intracellular cytokine)
TNFa (intracellular cytokine)
CD154 (surface activation marker (stained in culture))
CD57 (surface exhaustion marker)
Post elicitation RNAseq transciptomes
Rotational gene set testing (ROAST) and competitive gene set testing (Camera), computational deconvolution.
Statistical model building, logistic regression, risk factor analysis.

ACKNOWLEDGMENTS
Schofield lab.
Krystal Evans
Ramin Mazhari
Ariel Achtman
Emily Eriksson
Marthe D’Ombrain
Leanne Robinson
Danika Hill
Stephanie Tan
Natalia Sampaio
Thuan Phuong
Amandine Carmagnac
Wasan Forsyth

Australian Institute of Tropical Health
and Medicine
Brenda Govan
Natkuman Ketheesan

Other WEHI labs.
Ivo Mueller
Diana Hansen
Marc Pelligrini
Gabrielle Belz

Mahidol University
Jetsumon Prachumsri

Bioinformatics
Terry Speed
Gordon Smyth

Queensland Mycobacterial Reference Laboratory
Chris Coulter

Case Western Reserve University
Jim Kazura
Chris King
CRESIB, Barcelona
Pedro Alonso
Carlota Dobano
Alfredo Mayor

Massachussetts Institute of Technology
Peter Seeberger
Mike Hewitt

PNGIMR
Peter Siba
Inoni Betuela
Andrew Valleley
Suparat Phuanukoonnon

Centers for Disease Control
John Barnwell
Swiss Tropical Public Health Institute
Marcel Tanner
Sebastian Gagneaux

Merck Inc.
Jan ter Meulen
Craig Pryziecki

Ancora Pharmaceuticals Inc.
Stew Campbell
University of Melbourne
Stephen Rogerson
STUDY CHILDREN & GUARDIANS
Health Centre staff &
Teachers at Mugil and Megiar schools.

AITHM
•
•
•
•

- State and Federal Agreements

Queensland Government
Queensland Government
Federal Government
Total

$19.8 million – Oct. 2011
$42.2 million – June 2013
$42.2 million (Sept 2013)
$103 million

Budget breakdown
•
•
•
•
•

State Government Budget $42 million
72% for Infrastructure – including equipment
Infrastructure – AITHM Townsville
Infrastructure – AITHM Torres Strait
28% for Operations and Research

•
•
•
•

Federal Government Budget $42 million
62% for Infrastructure – including equipment
Infrastructure – AITHM Cairns
38% for Operations and Research

Wosera

Karkar

Kikori
Balimo

Torres Strait

Hiri

Institutional Partnerships in our new TB program
•
•
•
•
•
•
•
•

Papua New Guinea Institute of Medical Research (Peter Siba)
Papua New Guinea Department of Health incl. Central Public Health Laboratory
Australian Institute of Tropical Health and Medicine (Louis Schofield)
The Walter and Eliza Hall Institute (Schofield, Mueller, Speed, Smyth, Belz)
Queensland Health, including Local Health and Hospital Boards;
Queensland Tropical Health Alliance (Louis Schofield);
Swiss Tropical Public Health Institute (Marcel Tanner, Sebastian Gagneaux);
Queensland Mycobacterial Reference Laboratory (Chris Coulter);

•
•

International research institutions/agencies with a focus on tropical health;
Key community groups and representatives.

Value proposition (for discussion)
Observational and interventional study designs in DSS, GPS
mapped, population-based longitudinal cohorts with nested clinical
case controls, in communities with v. high incidence of TB and
inadequate BCG coverage;
Integrated transcriptomic, multiparameter flow and statistical
analyses of diverse lymphoid lineages within intuitive conceptual
frameworks;

Collaborative ethos, community, institutional and political support.
Objectives:
Define natural and BCG-induced correlates of immunity and
susceptibility to TB
Improved public health tools and interventions
Vaccine trials

Aeras schofield 21112013

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