Risk Assessment and Microbial Ecology Questions Common to Microbial Ecology 1

Risk Assessment and Microbial Ecology
Norman R. Pace
MCD Biology
University of Colorado, Boulder
[email protected]
Questions Common to Microbial Ecology
and Risk Assessment:
1. What organisms are present?
2. In what quantities?
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Detection of Microbes:
1. Specific tests (e.g. antibodies) - Need to know
what you are looking-for
2. Culture - Uncertain (EOP <0.1% in environment);
tests expensive, complex, often ambiguous.
3. Gene sequences - In principle comprehensive
and quantitative
Making Sense of Sequences:
Molecular Phylogeny
1. Align sequences so that “homologous” residues
are juxtaposed.
2. Count the number of differences between pairs of
sequences; this is some measure of “evolutionary
distance” that separates the organisms
3. Calculate the “tree”, the relatedness map, that most
accurately represents all the pairwise differences
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What Gene sequence to use to relate all life?
Ribosomal RNA
1. rRNA is ubiquitous.
2. Sufficiently highly conserved to relate all life.
(E.g., human-E. coli ca. 50% identity!)
3. Has resisted “lateral transfer” - tracks the
“genetic line of descent.
4. Abundant in all active cells
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Some Lessons from the Big Tree
• Three main relatedness groups: Eucarya, Bacteria and
Archaea.
• Origin of life, the “root” of the Big Tree, is on the bacterial
line of descent - Archaea and Eucarya are related to the
exclusion of Bacteria.
• Many consistent biochemical correlates, e.g.
transcription machinery.
• The eucaryal nuclear line of descent is as old as the
archaeal line.
• The major organelles, mitochondria and
chloroplasts, are of bacterial ancestry.
• The biological clock, the rate of sequence-change,
is not constant. You can’t date the deep past by
sequences.
• The sequence-based framework is a quantitative
articulation of biodiversity; most biodiversity is
represented by microbial organisms.
• The sequence-based framework means that
microbial organisms can be identified without the
traditional requirement for culture
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Phylogeny of Bacterial Pathogens
Pathogenic
Representatives
Archaea
0.10
Changes per base
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Prostatitis:
ƒ Inflammation of prostate; pain in scrotum, pelvis,
abdomen
ƒ 50% of males expected to espress at some time
ƒ Etiology not understood
ƒ Diagnosed as bacterial or “nonbacterial” depending on
culture results (positive ca. 10% of cases)
ƒ What rDNAs in expressed prostatic secretion?
PCR of extracted DNA from EPS
27F/805R
Patient
B
E
L
D
515F/1391R
M
(-)
B
E
(-)
basepairs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 M
- 2176
- 1766
- 1230
- 1033
- 653
- 517
A
B
C
A: 18S rDNA (515F/1391R)
B: 16S rDNA (515F/1391R)
C: 16S rDNA (27F/805R)
Lanes 19 and 20: Positive control
Lane 21: Negative control
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Restriction Length Polymorphism Sorting of 16S rRNA Clones
* ** ** * * * * * *
* * * * * * * ** * * * * * * *
**
OP9
Bacteria in EPS
Commonly cited
uropathogens
Archaea,
Eucarya
0.10
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Prostatitis study conclusions
ƒ All patients tested positive by rDNA for bacteria,
regardless of culture success.
ƒ Predominant organisms Actinobacteria and Low G+C
bcteria.
ƒ Corynebacteria prominent; four relatedness groups <98%
identical to known organisms; likely new species.
ƒ No clonal type identical between patients.
ƒ Note potential for probe design for diagnostics
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Shower Curtain #3
Methylobacterium
spp.
3%
Sphingomonas spp.
13%
other
44%
unaffiliated
40%
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Shower Curtain #2
2%
2%
Methylobacterium spp.
2%
2%
Sphingomonas spp.
35%
25%
other
unaffiliated
other a proteobacteria
g proteobacteria
32%
d proteobacteria
Pool water, early Spring
5%
3%
3%
Mycobacterium spp.
3%
Other Actinomycetales
Sphingomonadaceae
39%
26%
Other alphaProteobacteria
Bacillus/Clostridium group
CFB group
Other Bacteria
21%
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Pool Water Plus Side Biofilm
1% 4%
1%
Mycobacterium spp.
34%
Other Actinomycetales
Sphingomonadaceae
Beta-Proteobacteria
1%
59%
Gamma-Proteobacteria
Bacillus/Clostridium group
Inside Air above Pool, early Spring
Mycobacterium spp.
1% 5%
2%
2%
1%
3%
Other Actinomycetales
Sphingomonadaceae
3%
1%
Other alphaProteobacteria
Gamma-Proteobacteria
Cyanobacteria
Bacillus/Clostridium
group
CFB group
82%
Other Bacteria
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Inside air, early Fall
Mycobacterium spp.
5%
6%
Other Actinomycetales
14%
Sphingomonadaceae
Other alpha-Proteobacteria
Beta-Proteobacteria
35%
31%
Delta-Proteobacteria
Gamma-Proteobacteria
2%
Bacillus/Clostridium group
3% 3%0%
1%
CFB group
Oth
B t i
Outside air, early Fall
2%
2%
Actinomycetales
27%
Alpha-Proteobacteria
42%
Beta-Proteobacteria
CFB group
27%
Other Bacteria
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Some bacteria encountered in pool study [BLAST ID]:
Mycobacterium ulcerans [99%] (262/325 clones in one air sample)
Mycobacterium avium [99%] (36/357 clones, inside air
Mycobacterium asiaticum [98%] (62/183 clones in poolwater/side biofilm
Mycobacterium malomense [99%] (2/51 clones, pool water)
Uncultured oral rDNA [99%] (15/51 clones in pool water sample)
Uncultured archaeon [83%!] (11/51 clones in pool water)
Blastomonas ursincola [99%] (107/183 clones in pool water/side biofilm)
Problems with the Molecular Approach
ƒ Requires significant material (> a few hundred
bacterial cells).
ƒ Contaminants in reagents, enzymes, etc. a BIG problem.
(Less problem with organism-specific primers.)
ƒ General primers may not work with some rDNAs.
ƒ Clone/Sequence/Phylogenetic analysis is cumbersome.
ƒ Information on rRNA phylotype may not reflect phenotype.
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Thanks to Geothermal studies:
Phil Hugenholtz
Anna-Louise Reysenbach
John Spear
Prostatitis:
Mike Tanner
Dan Shoskes (UCLA)
Shower curtains
Ulrike Theissen
Scott Kelley
Pool mycobacteria
Lars Angenent
Mark Hernandez
Support over the years from:
NIH, NSF, DOE, NASA Astrobiology Institute
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