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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
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Mycobacterium tuberculosis
Mycobacterium avium complex (MAC)
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• 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)
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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
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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.
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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
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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)
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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
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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
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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
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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
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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
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• 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
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How do we identify “virulence markers”?
• Comparative genomics• Mutational analysis
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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)
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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
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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
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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