Transcript Risk Assessment and Microbial Ecology

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?
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
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
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
Phylogeny of Bacterial Pathogens
Pathogenic
Representatives
Archaea
0.10
Changes per base
Prostatitis:
 Inflammation of prostate; pain in scrotum, pelvis,
abdomen
 50% of males expected to espress at some time
 Etiology not understood
 Diagnosed as bacterial or “nonbacterial” depending on
culture results (positive ca. 10% of cases)
 What rDNAs in expressed prostatic secretion?
OP9
Bacteria in EPS
Commonly cited
uropathogens
Archaea,
Eucarya
0.10
Prostatitis study conclusions
 All patients tested positive by rDNA for bacteria,
regardless of culture success.
 Predominant organisms Actinobacteria and Low G+C
bcteria.
 Corynebacteria prominent; four relatedness groups <98%
identical to known organisms; likely new species.
 No clonal type identical between patients.
 Note potential for probe design for diagnostics
Shower Curtain #3
Methylobacterium
spp.
3%
Sphingomonas spp.
13%
other
44%
40%
unaffiliated
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
3%
5%
3%
Mycobacterium spp.
3%
Other Actinomycetales
Sphingomonadaceae
39%
26%
Other alphaProteobacteria
Bacillus/Clostridium group
CFB group
Other Bacteria
21%
Pool Water Plus Side Biofilm
1% 4%
1%
Mycobacterium spp.
34%
Other Actinomycetales
Sphingomonadaceae
Beta-Proteobacteria
59%
1%
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
Inside air, early Fall
Mycobacterium spp.
5%
6%
Other Actinomycetales
14%
Sphingomonadaceae
Other alpha-Proteobacteria
Beta-Proteobacteria
35%
31%
Delta-Proteobacteria
Gamma-Proteobacteria
2%
1%
3% 3%0%
Bacillus/Clostridium group
CFB group
Other Bacteria
Outside air, early Fall
2%
2%
Actinomycetales
27%
Alpha-Proteobacteria
42%
Beta-Proteobacteria
CFB group
Other Bacteria
27%
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
 Requires significant material (> a few hundred
bacterial cells).
 Contaminants in reagents, enzymes, etc. a BIG problem.
(Less problem with organism-specific primers.)
 General primers may not work with some rDNAs.
 Clone/Sequence/Phylogenetic analysis is cumbersome.
 Information on rRNA phylotype may not reflect phenotype.
Thanks to Geothermal studies:
Shower curtains
Prostatitis:
Pool mycobacteria
Phil Hugenholtz
Anna-Louise Reysenbach
John Spear
Mike Tanner
Dan Shoskes (UCLA)
Support over the years from:
Ulrike Theissen
Scott Kelley
Lars Angenent
Mark Hernandez
NIH, NSF, DOE, NASA Astrobiology Institute