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? 1 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 2 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 3 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 4 5 6 7 8 Phylogeny of Bacterial Pathogens Pathogenic Representatives Archaea 0.10 Changes per base 9 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? 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 10 Restriction Length Polymorphism Sorting of 16S rRNA Clones * ** ** * * * * * * * * * * * * * ** * * * * * * * ** OP9 Bacteria in EPS Commonly cited uropathogens Archaea, Eucarya 0.10 11 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 12 Shower Curtain #3 Methylobacterium spp. 3% Sphingomonas spp. 13% other 44% unaffiliated 40% 13 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% 14 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 15 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 16 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. 17 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 18