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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.
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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
A research and teaching centre affiliated with UBC Annual frequency of isolation of M. tuberculosis and M. avium complex (MAC) 1400 1200 TB 1000 800 600 MAC 400 200 0 1999 83-84 85/86 87/88 89/90 93/94 95/96 97/98 Years 91/92 Courtesy of Kevin Elwood, BC-CDC
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. • Predictions for MAA: • Larger coding capacity • Greater heterogeneity • Horizontally acquired genes?
IS1245 IS999 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
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) MAA104 MAP K10 M. tuberculosis H37Rv Total size (bp) 8,667,507 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) 265 (5.9%)2 191 (4.8%)2 965 (12.3%)1 Predicted lipid metabolism genes 436 (9.7%)2 233 (5.8%)2 Predicted virulence genes 99 (2.5%)2 148 (3.3%)2 1Bentley et al., 2002 2Semret et al., submitted 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
IS999-RFLP N 15 6 24 1 3 9 1 1 How do people get MAC disease? • Water (sometimes) • Not known (usually) • Models • Colonized early in life, immunocompromised later • Immunocompromised first, then infected • Genomic variability a challenge
Making sense of MAC epidemiology: Deligotyping identifies a hospital-based cluster
Hypotheses • UCLA-MC AIDS patients were infected from a shared environmental source • 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 • Analysis of additional isolates (SoCal & elsewhere) • Identification of additional genomic markers • Molecular epidemiology
or Homogeneous, moderate virulence Heterogeneous Diversity of MAC: Implications for risk assessment • Are all environmental isolates virulent to humans? • If heterogeneous, we need “virulence markers”
How do we identify “virulence markers”? • Comparative genomics • Mutational analysis
Mutational analysis of virulence • Shotgun mutagenesis with EZ::TN transposon Laurent et al., J. Bacteriol. 185:5003-5006, 2003 • 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 • Identify disrupted gene • Test in disease models (THP1 cells, mice)
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 R W RRg1, RRg2, RRg6, RRg-B, RRg-D, RRg-G, WRg1, WRg2 RRg4 W R EZ::TN transposon mutagenesis
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