Mycobacterium avium complex:Biology of an environmental pathogen

Jerry CangelosiSeattle Biomedical Research Institute

Dept. of Pathobiology, School of Public Health University of Washington

SYMPOSIUM IN HONOR OF DR. GEORGE KENNY

Mycobacterium tuberculosis

Mycobacterium avium complex (MAC)

• Slow-growing mycobacteria, related to M. tuberculosis

• M. avium ssp. avium

• M. avium ssp. paratuberculosis

• M. intracellulare

• Environmental, drinking water, biofilms

• Growth within phagocytic protozoa and human cells

• Opportunistic pathogens

• Chronic, intrinsic drug resistance

• Genetic, phenotypic instability

Mycobacterium avium complex (MAC)

A research and teaching centre affiliated with UBC Courtesy of Kevin Elwood, BC-CDC

Annual frequency of isolation of M. tuberculosis and M. avium complex (MAC)

0

200

400

600

800

1000

1200

1400

Years

83-84

85/86

87/88

89/90

91/92

93/94

95/96

97/981999

TB

MAC

Predictions for MAA:

• Larger coding capacity

• Greater heterogeneity

• Horizontally acquired genes?

Comparing the genomes of M. avium subsp. avium and M. tuberculosis:

Predictions based on ecological niche

Ecological niche

M. tuberculosis:•Mammalian tissues

M. avium:•Water•Soil•Plants•Biofilms•Tissues of diverse animals•Etc.

Mycobacterium genome sizes

Approximate genome size

Environmental speciesM. smegmatis: ~7 mbM. marinum: 6.5 mbM. avium subsp. avium: 5.5 mb

Professional pathogensM. avium subsp. paratuberculosis: 4.8 mbM. tuberculosis: 4.4 mbM. leprae: 3.3 mb

M. avium ssp. avium104 genome

5.48 mB(www.tigr.org)

0

IS1245

IS999

ssGPL gene

cluster

Genome of M. avium ssp. avium (MAA) strain 104

• Sequence in “minor editing” stage (TIGR)

• Annotation by Semret and Behr, McGill Univ.

• MAC vs. M. tuberculosis

– TB: 4.4 mB, ~65.6% G+C, ~3900 ORFs

– MAC: 5.5 mB, ~68.5% G+C, ~5100 ORFs

• Extra coding capacity in MAA:

– Repeating elements

– Unique cell wall structures, e.g. ssGPL

– Capacity to live in the environment

– Horizontally acquired genes (MAP)

M. tuberculosis (4.4 mb genome, ~3900 genes)

•Deletions in 19 clinical isolates relative to H37Rv

•Kato-Maeda et al., Genome Res. 11:547-554, 2001

No. of deletions: Mean 2.9, range 0-6

No. of deleted ORFs: Mean 17.2, range 0-38 (<1% of genome)

M. avium ssp. avium (5.5 mb genome, ~5100 genes)

•Deletions in 1 clinical isolate, HMC02, relative to strain 104

•Criteria: Z-value >2.0, >2 contiguous ORFs, quadruplicate

•Confirmation by PCR

•Preliminary results

No. of deletions: ~33

No. of deleted ORFs: ~520 (~10% of genome)

Genomic diversity of MAA:Comparison to M. tuberculosis

Total size (bp) 8,667,507 5,475,491 4,411,532

G + C (%) 72.12 68.99 65.61

Coding sequences

7825 4480 3959

Predicted lipid metabolism genes

436 (9.7%)2 233 (5.8%)2

  S. coelicolor A3(2)

MAA 104 M. tuberculosis H37Rv

1Bentle

y e

t al., 2

00

22S

em

ret e

t al., su

bm

itted

~4,800,000

~69

~~4030

MAP K10

Predicted virulence genes

148 (3.3%)2 99 (2.5%)2

PE/PPE 53 (1.2%)2 170 (4.3%)2

Cell wall and cell processes

662 (14.8%)2 710 (17.9%)2

unknown 280 (7.1%)2 93 (2.1%)2

Predicted regulatory genes (% of total)

265 (5.9%)2 191 (4.8%)2 965 (12.3%)1

How do people get MAC disease?

• Water (sometimes)• Not known (usually)• Models

1. Colonized early in life, immunocompromised later

2. Immunocompromised first, then infected

• Genomic variability a challenge

IS999-RFLP

N 15 6 24 1 3 9 1 1

Strain Site DA21 DA71 HSD1 RFLP clade (>60% similarity)2

Rep-PCR clade (>90% similarity)2

101 UCLA-MC + + - B 4B 103 UCLA-MC + + - B 4B 104 UCLA-MC + + - B 4B 503 UCLA-MC + + - G 4B 504 UCLA-MC + + - G unique 505 UCLA-MC + + - G unique 501 UCLA-MC + + - unique 4A 105 UCLA-MC + + - unique 4A 502 UCLA-MC + + - unique 4B 506 UCLA-MC + + - unique unique MVH14 Little Rock + + + unique ND 102 UCLA-MC + - + D unique 110 UCLA-MC + - + D 1B 113 UCLA-MC + - + unique 1A W2008 Boston - - + unique 1A 100 UCLA-MC - - + unique 3A 107 UCLA-MC - - + A 3A 108 UCLA-MC - - + A 3A HMC02 Seattle-HMC - - + A 3A HMC34 Seattle-HMC - - + A ND W2001 Boston - - + unique 5A NWH201 Seattle-NWH na - + unique 5A MVH21 Little Rock - - + unique 1B HMC04 Seattle-HMC - - + unique ND HMC36 Seattle-HMC - - + unique ND MVH20 Little Rock - - + unique ND MVH15 Little Rock na + na unique unique 23 additional isolates of MAA from Washington, Quebec, the Netherlands, and Australia3

- - ND ND ND

Making sense of MAC epidemiology: Deligotyping identifies a hospital-based cluster

Hypotheses

1. UCLA-MC AIDS patients were infected from a shared environmental source

• RFLP patterns diverged during and after infection

2. UCLA-MC AIDS patients were infected from diverse point sources, all of which were colonized members of a “regional” clade

• RFLP patterns diverged prior to infection

Next steps

1. Analysis of additional isolates (SoCal & elsewhere)

2. Identification of additional genomic markers

3. Molecular epidemiology

• Are all environmental isolates virulent to humans?

• If heterogeneous, we need “virulence markers”

Diversity of MAC: Implications for risk assessment

or

Homogeneous, moderate virulence Heterogeneous

How do we identify “virulence markers”?

• Comparative genomics• Mutational analysis

Mutational analysis of virulence

1. Shotgun mutagenesis with EZ::TN transposonLaurent et al., J. Bacteriol. 185:5003-5006, 2003

2. Screen for alterations in phenotypes that correlate with virulence

– White colony type on Congo red plates

– Multi-drug resistance

– BSA independence

Mukherjee et al., J. Infec. Dis. 184:1480-1484, 2001

Cangelosi et al., Microbiology 147:527-533, 2001

3. Identify disrupted gene

4. Test in disease models (THP1 cells, mice)

Rough

R W

W R

M. avium 1045.48 mB

0RW-A

RRg3

RRg5

RW-I

RRg4RRg1, RRg2, RRg6, RRg-B, RRg-D, RRg-G, WRg1, WRg2

RW1, RW2

RW-F

RW-J

RW-E WR2.58

WR2.55

EZ::TN transposon mutagenesis

Example (affected gene)

Parent morpho-type

Mutant morpho-type

Drug suscep-tibility

Growth in THP1 cells

Genetic confir-mation

Red wild type Red - S No -

White wild type

White - R Yes -

WRg(pstA)

White Rough(no ssGPL)

R Yes Yes

WR2.55(PKS)

White Red R No Not yet

WR2.58 (PPIase, STPKase)

White Red S No Not yet

RW1 Red White S N/D Yes

Elsewhere– Luiz Bermudez, Kuzell

Institute, Oregon State– Carolyn Wallis, HMC– Tim Ford, Montana State

Univ.– David Sherman, UW– Delphi Chatterjee & Julie

Inamine, Colorado State University

– Makeda Semret and Marcel Behr, McGill University

SBRI– Chad Austin– Kellie Burnside– Richard Eastman– Shawn Faske – Kirsten Hauge– Jean-Pierre Laurent– Devon Livingston-Rosanoff– Joy Milan– Anneliese Millones – Sandeep Mukherjee– Christine Palermo– Kambiz Yaraei

Thank you– NIAID– EPA– Murdock Charitable Trust