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Mycobacterium avium complex: Biology of an environmental pathogen

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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Mycobacterium avium complex: Biology of an environmental pathogen

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  1. 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

  2. Mycobacterium tuberculosis Mycobacterium avium complex (MAC)

  3. 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

  4. 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

  5. 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?

  6. 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

  7. 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)

  8. 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)

  9. 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

  10. 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

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

  12. 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

  13. 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”

  14. How do we identify “virulence markers”? • Comparative genomics • Mutational analysis

  15. 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)

  16. 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

  17. 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

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