latency: does it exist? - aphl · university of pittsburgh jonathan carney, brian lopresti, joanne...

LATENCY: DOES IT EXIST?

JoAnne L. FlynnDepartment of Microbiology and Molecular Genetics

Department of ImmunologyCenter for Vaccine Research

University of Pittsburgh School of Medicine

National TB Lab Meeting 2011

Course of Mycobacterium tuberculosis infection

Activated MØ

Destroy bacillus

NO INFECTION

No initial destruction

Bacteria replicate

IL-12

IL-1

MØ

Bacterial

replication

and spread

Recruitment of

T cells and MØGRANULOMA

macrophage

T cell

LUNGSMØ

TNF

PRIMING

T CELLS

DC

LUNG

chemokines

airways

parenchyma

Lymph node

Granuloma

Control of replication

Latent TBinadequate control

of replication

ACTIVE TB

No disease

(control of replication

for lifetime)

Bacterial replication

Immunosuppression?

REACTIVATION

COURSE OF INFECTION

fibrosis

Tuberculosis from a clinical perspectiveA bimodal distribution of outcomes: latent or active

Primary TBLatent infection

No disease

Reactivation

90-95%

5-10%

5-10%

Active TB

P = pro-inflammatory A = anti-inflammatory

Can we define this spectrum by animal models and imaging?

Instead….M. tuberculosis infection may exist as a spectrum of outcomes

Lin and Flynn, JI, 2010

How can we assess the spectrum of M. tuberculosis infection?

Need a model with variable outcomes (like humans)

Need a model of latent M. tuberculosis infection

Need to be able to manipulate the model

Need to be able to study the infection serially and in real time

Animal models for tuberculosis research

Mouse: convenient, low variability, excellent reagents, well-characterizedbut no latent infection, pathology is different from humans

Guinea pigRabbit

Zebrafish: excellent genetic resources (M. marinum)

Non-human primates: close to humans, excellent reagentscons: cost, size, containment, animal-to-animal variability

Mimic some aspects of human pathologyLimited immunologic reagents

+ M. tbErdman25 cfuvia bronchoscope

Tuberculin Skin Test +Other immunologic tests +2-6 weeks

ACTIVE TB LATENT TB

6-8 months

Positive Chest x-ray Mycobacterial culture

•repeated + GA or BAL cultureClinical signs

•weight loss, •appetite loss•cough

No signs of disease CXR negative between 2-6 monthsMycobacterial culture

negative after 2 monthsClinical signs--none

Clinical definitions for classification of monkeys following Mtb infection

100%

Cynomolgus macaque

Progress to TB or remain stable

Reactivation

Spontaneousreactivation

Induced reactivation

(SHIV, SIV, TNF neutralization,CD8 or CD4 depletion)

stable

Active TB*N=69 (43%)

N=159 @ 6-8 months

Latent infectionN=83 (52%) Percolators

N=7 (4.4%)

Drug treatment

Outcome of low dose infection in cynomolgus macaques

*All manifestations of active TB seen in humans has been seen in macaques: Pott’s disease, cerebral TB, miliary TB, scrofula, cardiac TB, etc

Spectrum of granulomas in macaques

Caseous necrotic

Solid cellular Solid fibrotic

Suppurative necrotic

mineralized

Active vs latent monkeys: Clinical classifications validated by quantitative outcome measures at necropsy

*p=.001*p<.0001

*p=.0004 *p=.001

*Mann-Whitney, two tailed

Gross pathology CFU score

% positive tissue samples Sum of CFU/gram

PET/CT: Imaging modality for serial tracking of lesions and disease

BSL3 imaging suite Regional Biocontainment Lab (RBL)University of Pittsburgh

[18-F] FDG: fluorodeoxyglucose: incorporated into metabolically active cellsmarks areas of inflammation

Co-registration of PET/CT images: structural and functional map of disease

PET/CT in BSL3 facilityCT: structural map of lesions in organs

PET: functional map of lesions in organs

PET probe: specifically marks a cell with a particular property or function, probe is tagged with a positron emitter

University of Pittsburgh Jonathan Carney, Brian Lopresti, JoAnne Flynn, Jim Frye, and Jamie Tomko

Visualization of Small (1 mm) Tuberculous Lesions

Fusion of High Resolution microPET and Diagnostic Helical CT

1 cm 1 cm

SUV

Tuberculosis granulomas

PET CT

Fusion

Detection of small lesions (~1mm) in lungs

Not all lesions are FDG+

RLL 1 RLL 2

SUV max size CFU

RLL 1 4.6 2.7 mm 7000

RLL 2 3.4 1.7 mm 1333

The challenge: Matching lesions from scan to lesions at necropsy

Pre-treatment 1 Mo RIF 2 Mo RIF

PET/CT: track changes in lesions during drug treatment

21608

How does a drug affect a specific lesion over time?

Overall PET signals are reduced when drugs are working

pre-treatment 1 month HRM

1 mm 5 mm

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25

PET

FD

G m

ax S

UV

(co

rre

cte

d)

Weeks PI

19608: Granulomas

RLL 2

LLL gran A

RLL 6

RLL 1

RLL 5

Scale

RLL 6'

Individual lesions are dynamic in monkeys with active TBIndependent changes in granulomas in a single monkey

PET

FD

G S

UV

max

Weeks PI

Can imaging be used to study latency and reactivation?

Latent TB: a spectrum of lesions in lungs (8 different monkeys)

54-CJK

58 - LJK

Humans with latent M. tuberculosis infection also have a spectrum of lesion types

Clif BarryRay Cho

Spectrum of latent infection as quantified by PET/CT imaging

28 latently infected monkeys: 18F-FDG PET/CT characteristics

Lung lesions

Mediastinal Lymph nodes

Total number of lung lesions/latent monkey

What happens during reactivation?

Where does reactivation start?

How does it spread?

Do all lung or lymph node granulomas reactivate?

Using PET/CT imaging to track reactivation

Lung lesion grows and is “hotter” following anti-TNF mediated reactivation

latent

2 Weeks anti-TNF Ab

4 Weeks anti-TNF Ab

Reactivation with anti-TNF: lymph nodes and dissemination

0

5

10

15

20

25

30

35

40

45

50

55

-1 0 1 2

PET

FD

G m

ax S

UV

(co

rre

cte

d)

Months anti-TNF

16606: Lymph Node

Carinal LN

0

5

10

15

20

25

30

35

40

-1 0 1 2

PET

FD

G m

ax S

UV

(co

rre

cte

d)

Months anti-TNF

16606: Granuloma

L1

SCALE

Baseline 3 WK anti-TNF 5 WK anti-TNF

CFU 2.2x104/gm

CFU 2.6x104/gm

Anti TNF Tx

CFU score 19.9% positive 23.8

0

5

10

15

20

25

30

35

40

-1 0 1 2

PET

FD

G m

ax S

UV

(co

rre

cte

d)

Months Humira

L3

L4

L1

L2

L5

SCALE

Granulomas

0

5

10

15

20

25

30

35

40

-1 0 1 2

PET

FD

G m

ax S

UV

(co

rre

cte

d)

Months anti-TNF

11108: Lymph Nodes

R Cranial Hilar LN

R Carinal LN

SCALE

Latent, anti-TNF reactivation : not all granulomas reactivate the same

CFU score 36.3% positive 33%

L5 8000 CFUL2 0 CFU

R carinal (red) CFU 708R cran HLN (blue) CFU 17500

Spectrum of M. tuberculosis infection: includes “percolators”

Fulminant (sepsis)

miliary

extrapulmonary

Pulmonary TB

Chronic TB

Low grade TB

Dormant infection

clearance

Subclinical infection

Active

TB

“percolating”

No signs of diseaseNormal chest x-rayOccasional positive BAL or GA culture

Is a percolator more likely to reactivate?

Higher on the spectrum of latency?

Represent “subclinical” disease?

Test this by depleting CD8 T cells in “true” latent vs percolator

Percolator monkeys (clinically latent but occasional + BAL or GA)

Mtb

8 months

antiCD8 antibody

Latent orpercolator Necropsy

Percolator monkeys reactivate when CD8 T cells are depleted.Latent monkeys do not.

p=.01p=.03

Percolators may have more “hot” lesions in lungs than “true latents”

Latent Lung Percolator Lung

Latent LN Percolator LN

N=28 latents N=4 percolators

Not all latent monkeys reactivate in response to anti-TNF AbPercolator monkeys reactivate

Percolator (reactivated)

Latent (reactivated)Latent (No reactivation)

Summary• Latent TB reflects a spectrum of infection

• PET/CT imaging can monitor infection in real-time and serially

• Reactivation can occur in lymph nodes or lung granulomas

• Not all lesions are equal when it comes to susceptibility to reactivation

• Not all reactivation triggers are equally effective– CD4 T cell depletion

– CD8 T cell depletion

– TNF neutralization

– SIV co-infection

• Spectrum may dictate risk of reactivation from any trigger—who is at risk?

• Challenge: Identify the persons most at risk so they can be treated—define in monkeys and humans with imaging and immunologic biomarkers

Acknowledgements• Chris Janssen• Brian Lopresti• Jaime Tomko• Mark Rodgers• Amy Myers

• Jim Frye• Melanie O’Malley• Paul Johnston• Dan Fillmore• Jim Mountz• Cathy Cochran• Lekneitah Smith• Carolyn Bigbee• Matt Bigbee• Jiayao Phuah• Angela Green• Tao Sun

GC11 groupDouglas Young (NIMR, Imperial)Clifton Barry, III (NIH)Rob Wilkinson (NIMR, UCT)

Ed Klein

P. Ling LinChildren’s Hospital

Teresa Coleman Chuck Scanga

Eoin Carney

FundingNIH NIAID/NHLBIBill and Melinda Gates Foundation Wellcome TrustEllison Foundation

Collin Diedrich

Josh Mattila

University of MichiganDenise Kirschner