Transcript No Slide Title

SYMPOSIUM IN HONOR OF
DR. GEORGE KENNY
Mycobacterium avium complex:
Biology of an environmental pathogen
Jerry Cangelosi
Seattle Biomedical Research Institute
Dept. of Pathobiology, School of Public Health
University of Washington
Mycobacterium tuberculosis
Mycobacterium avium complex (MAC)
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
Annual frequency of isolation of
M. tuberculosis and
M. avium complex (MAC)
1400
1200
1000
TB
800
600
MAC
400
200
0
A research and teaching centre affiliated with UBC
Courtesy of Kevin Elwood, BC-CDC
Comparing the genomes of M. avium subsp. avium
and M. tuberculosis:
Predictions based on ecological niche
Ecological niche
Predictions for MAA:
M. tuberculosis:
•Mammalian tissues
• Larger coding capacity
M. avium:
•Water
•Soil
•Plants
•Biofilms
•Tissues of diverse animals
•Etc.
• Horizontally acquired genes?
• Greater heterogeneity
Mycobacterium genome sizes
0
Approximate genome size
Environmental species
M. smegmatis:
~7 mb
M. marinum:
6.5 mb
M. avium subsp. avium: 5.5 mb
Professional pathogens
M. avium subsp.
paratuberculosis:
4.8 mb
M. tuberculosis:
4.4 mb
M. leprae:
3.3 mb
M. avium ssp. avium
104 genome
5.48 mB
(www.tigr.org)
ssGPL
gene
cluster
IS1245
IS999
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)
Genomic diversity of MAA:
Comparison to M. tuberculosis
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)
S. coelicolor
A3(2)
MAA 104
MAP K10
M. tuberculosis
H37Rv
5,475,491
~4,800,000
4,411,532
G + C (%)
72.12
68.99
~69
65.61
Coding
sequences
7825
4480
~~4030
3959
Predicted
regulatory genes
(% of total)
965 (12.3%)1
265 (5.9%)2
191 (4.8%)2
Predicted lipid
metabolism genes
436 (9.7%)2
233 (5.8%)2
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
1Bentley et al., 2002
et al., submitted
8,667,507
2Semret
Total size (bp)
How do people get MAC disease?
•
•
•
1.
2.
•
IS999-RFLP
Water (sometimes)
Not known (usually)
Models
Colonized early in life,
immunocompromised
later
Immunocompromised
first, then infected
Genomic variability a
challenge
N
15
6
24
1
3
9
1
1
Making sense
of MAC
epidemiology:
Deligotyping
identifies a
hospital-based
cluster
Strain
Site
101
UCLA-MC
103
UCLA-MC
104
UCLA-MC
503
UCLA-MC
504
UCLA-MC
505
UCLA-MC
501
UCLA-MC
105
UCLA-MC
502
UCLA-MC
506
UCLA-MC
MVH14
Little Rock
102
UCLA-MC
110
UCLA-MC
113
UCLA-MC
W2008
Boston
100
UCLA-MC
107
UCLA-MC
108
UCLA-MC
HMC02
Seattle-HMC
HMC34
Seattle-HMC
W2001
Boston
NWH201
Seattle-NWH
MVH21
Little Rock
HMC04
Seattle-HMC
HMC36
Seattle-HMC
MVH20
Little Rock
MVH15
Little Rock
23 additional isolates of MAA from
Washington, Quebec, the Netherlands,
and Australia3
DA21
DA71
HSD1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
na
na
-
na
ND
RFLP clade
(>60% similarity)2
B
B
B
G
G
G
unique
unique
unique
unique
unique
D
D
unique
unique
unique
A
A
A
A
unique
unique
unique
unique
unique
unique
unique
ND
Rep-PCR clade
(>90% similarity)2
4B
4B
4B
4B
unique
unique
4A
4A
4B
unique
ND
unique
1B
1A
1A
3A
3A
3A
3A
ND
5A
5A
1B
ND
ND
ND
unique
ND
Hypotheses
1.
UCLA-MC AIDS patients were infected from a shared
environmental source
•
2.
RFLP patterns diverged during and after infection
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
Diversity of MAC:
Implications for risk assessment
• Are all environmental isolates virulent to
humans?
or
Homogeneous, moderate virulence
Heterogeneous
• If heterogeneous, we need “virulence
markers”
How do we identify “virulence
markers”?
• Comparative genomics
• Mutational analysis
Mutational analysis of virulence
1.
Shotgun mutagenesis with EZ::TN transposon
Laurent 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)
EZ::TN
transposon
mutagenesis
RW-A
0
RW-J
RW-E
WR2.58
RRg3
M. avium 104
5.48 mB
RRg5
RW-I
RW-F
WR2.55
Rough
RW1, RW2
RRg1, RRg2,
RRg6, RRg-B,
RRg-D, RRg-G,
WRg1, WRg2
RRg4
RW
WR
Example
(affected
gene)
Parent
morphotype
Mutant
morphotype
Drug
susceptibility
Growth in
THP1 cells
Genetic
confirmation
Red wild type Red
-
S
No
-
White wild
type
White
-
R
Yes
-
WRg
(pstA)
White
Rough
R
(no ssGPL)
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
SBRI
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
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