from the environment to the genes and back
Biomonitoring: a microbe’s perspective
Alexandre Poulain Université d’Ottawa
Life Sciences and Mining Workshop Explore - Extract - Exchange
Vale for Living Lake Center, Sudbury, ON May 7th, 2014
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Nutrients: C, H, O, N, S
Metals: Cu, Zn, Fe, Mn, U, Pb, Hg, As, Cr, Ag, Cd
Microbes are key players in biogeochemical cycles
Anoxic - Sulfidic - Oxic
Newman, 2010
Microbes are the oldest miners on
earth
Anbar, 2008
Microbes metabolic and
genetic diversity was shaped
through geological times
Falkowski, 2008
Organic chemicals
Inorganic chemicals
Chemotrophy Phototrophy
Chemicals Light
Energy Sources
(glucose, acetate, etc.) (H2, H2S, Fe2+, NH4+, etc.)
(glucose + O2 CO2 + H2O) (H2 + O2 H2O) (light)
Chemoorganotrophs Chemolithotrophs Phototrophs
Microbes exhibit incredible metabolic diversity eating and breathing minerals
Brock, 14th edition
Present over wide gradients of: - salinity - Temperature - pH - [O2] - [toxic metals]
Biomonitoring of microbes and microbial processes at several levels
Ribosomes
rRNAmRNA
DNA
Proteins
community FUNCTION
community STRUCTURE
feasibility
Functional relevance
Metabolites
A signature of the contamination can be observed in community structure
Mosher et al. 2012 Aqu. Microb. Ecol. Characterization of the Deltaproteobacteria in contaminated and uncontaminated stream sediments (Hg, U)
Microbial community function: what is active?
Metal resistance genes Cytochrome genes
Kang et al. 2013. FEMS Microb. Ecol. Functional gene array–based analysis of microbial communities in heavy metals-contaminated lake sediments
At the protein level
Mueller et al. 2011, Environ. Microb. Proteome changes in the initial bacterial colonist during ecological succession in an acid mine drainage biofilm community,
Mercury resistance operon
Boyd and Barkay, Frontiers in Microb., 2012
A B
[Mz+] total
insoluble
complexed with organic ligand
chlorocomplexes
Metal speciation matters
The total concentration of a metal is a poor predictor of its effect => what is/are the bioavailable form(s) of a metal?
• Bioanalytical tools applied to monitoring or to the discovery of microbe-metal interactions
!- Improving ecological risk
assessment of metal mining !
- Monitoring of effluents originating from metal mining activities
!- Monitoring of potential
remediation efforts
Biosensors
Sorensen, 2006
Response/ Metal Specific biosensors
sensing element gene
reporter gene
promoter regionvector bacterial
chromosome
bacterium cell wall
(a) (b)
biosensor = reporting/sensing element + reporting gene
Response specific biosensor (e.g., oxidative stress)
H2O$+$O2$
Pro)oxidants$
superoxide$dismutase$(sodA)$
Pro)oxidants$
$ catalase$(katG)$
Promoters$of$genes$that$deal$with$H2O2$stress$(i.e.$katG)$
OxyR$
O2•)$ H2O2$
katGp)
Morin and Poulain, unpublished
Metal specific biosensors
Chemical or Stressor
Measurable signal
Sensing component
Reporter gene
Transcription
Reporter protein
Translation
merR o/p
R
lux C lux D lux A lux B
merR o/pR lux C lux D lux A lux B
merR R lux C lux D lux A lux BPHg
lux E
lux E
lux E
P
x
Arsenic Mercury
Harnessing metal detoxification pathways
Bacteria emit light in the presence of the metal
How does Hg bioavailability change while Hg and NOM are reaching equilibrium?
HgII HgII
reactive Hg species
HgII HgIIhydrophobic Hg species
time required to reach equilibrium
t=0
t=24h
Chiasson-Gould et al. 2014, ES&T
Mercury specific biosensor (e.g., toxic metal)
Indu
cibl
e lig
ht p
rodu
ctio
n (C
PS)
102
103
104bioavailable HgII, t = 0h
95% Confidence Band
[DOC] mg.L-1
0 10 20 30 40
Cons
titut
ive
light
pro
duct
ion
(CPS
)
104
105
106
6
Constitutive light expression t = 0h
[DOC] mg.L-1
0 10 20 30 40
Chiasson-Gould et al. 2014, ES&T
Metals for which there exists a known microbial sensory regulatory network
harnessing the power of microbial experimental evolution
• Provide a biologically relevant perspective to metal(loid) speciation in the environment
!• Can be used for early detection !
• Have increased sensitivity !
• Can be tailored to be site-specific (e.g., by using bacteria hosts exhibiting natural adaptation to a wide range of pH); very robust
!• More cost-effective than current practices: most of the cost is to
develop the biosensor, the cost of its application is minimal
Advantages
Feasibility and HQP training
The NSERC CREATE Mine of Knowledge is an interdisciplinary training program, in a multi-institutional setup designed to train highly qualified individuals capable of fulfilling the demands of the mining industry in the field of technological innovation and environmental management.
outcomes. This will require integrating frameworks forunderstanding what is possible from mutations (howthey change functional properties of proteins and howthese alterations propagate to physiological traits underselection) with how the interplay of selection and driftact in populations to shape the distribution of observedoutcomes.
References
Atwood, K.C., Schneider, L.K., and Ryan, F.J. (1951) Periodicselection in Escherichia coli. Proc Natl Acad Sci USA 37:146–155.
Chou, H.H., Berthet, J., and Marx, C.J. (2009) Fast growthincreases the selective advantage of a mutation arisingrecurrently during evolution under metal limitation. PLoSGenet 5: e1000652.
Counago, R., Chen, S., and Shamoo, Y. (2006) In vivomolecular evolution reveals biophysical origins of organis-mal fitness. Mol Cell 22: 441–449.
Elena, S.F., and Lenski, R.E. (2003) Evolution experimentswith microorganisms: the dynamics and genetic bases ofadaptation. Nat Rev Genet 4: 457–469.
Novick, A., and Horiuchi, T. (1961) Hyper-production of beta-galactosidase by Escherichia coli bacteria. Cold SpringHarb Symp Quant Biol 26: 239–245.
Stanier, R.Y. (1970) Some aspects of the biology of cells andtheir possible evolutionary significance. In Organizationand Control in Prokaryotic and Eukaryotic Cells. Charles,H.P., and Knight, B.C.J.G. (eds). Cambridge, UK: Cam-bridge University Press, pp. 1–38.
Vishniac, W., and Santer, M. (1957) The thiobacilli. BacteriolRev 21: 195–213.
Woese, C.R. (1987) Bacterial evolution. Microbiol Rev 51:221–271.
Where reductionism meets complexity: a call forgrowth in the study of non-growth
Dianne K. Newman (Email: [email protected]) andMaureen L. Coleman (Email: [email protected]),Divisions of Biology and Geological & Planetary Sciences,California Institute of Technology, Pasadena, CA, USAWith the advent of metagenomics, we have unprec-edented access to the genetic blueprint of the microbialworld. Yet as metagenomic databases keep growing, ourability to interpret the information contained within themhas not kept up. This conundrum arises from the fact thatwe cannot assign functions to the vast majority of theirgenes. As Jo Handelsman pointed out in a Crystal Ballpiece two years ago, ‘the glory of the last 50 years ofmicrobiology is founded, in large part, on genetic analysis’(Handelsman, 2009). Amen. Yet as enticing as the pros-pect of environmental genetics or ‘metagenetics’ seems,how can we hope to interpret the unchartered world ofenvironmental metagenomes when after more than a half-century of rigorous genetic and biochemical analyses, the
functions of roughly a quarter of the genes in Escherichiacoli – arguably the most well-studied organism on theplanet – are still unknown (Karp et al., 2007)? Where havewe gone wrong? Perhaps it is time to re-examine ourassumptions about how to assign gene functions in lightof lessons from the field.
Genetic analysis provides a powerful way to learn whatgenes are required for a phenotype of interest. Inventedby physicists, it is steeped in reductionism, permittingclear insights into biological phenomena through theapplication of simple logical rules. If we want to knowwhich genes are involved in a specific process for anorganism that is genetically tractable, we make mutantsand then design a screen or a selection that will permit usto assign a ‘yes’ or ‘no’ (or sometimes ‘partial’) level ofinvolvement to any given one. Thus, a key question wemust answer at the beginning is: what phenotype(s) do wecare about? What conditions are most relevant for ourfavourite model organism or favourite uncultured micro-bial community in the environment? Clearly, there is noone answer. Even the concept of ‘the environment’ ismisleading, because organisms reside in a dynamicworld, with changing physical, chemical and biologicalparameters. Given this complexity, is it even reasonableto think that reductionist approaches can be of value?Absolutely.
So where to begin? Recent work performed in CarolGross’ laboratory at UCSF provides an example. Theseinvestigators took a high-throughput approach to growinga collection of E. coli mutants under a battery of stressfulconditions to assign roles to genes whose functions wereunknown (Nichols et al., 2010). The idea was simple: ifmany conditions were tested, some of the unknown geneswere bound to be involved in growth on some of them.The results bore this out, and, comfortingly, strains con-taining mutations in genes that had previously beenshown to be involved in the response to particular stres-sors performed as expected. This type of approach is astart, and one can imagine doing this with any modelorganism for which a collection of mutant strains exist. Nodoubt such approaches will significantly reduce thenumber of genes of unknown functions in pure cultures.Yet, will they be enough to bring this number down tozero? Almost certainly not, as it is difficult, if not impos-sible, to capture every environmental variable in responseto which genes have evolved. Moreover, some genes maynot be useful at all. When we pluck an isolate from anatural community, we are capturing not an evolutionaryend-point but a work-in-progress, a snapshot of ongoinggene gain and loss complete with pieces that have not yetbeen, and may never be, integrated into the networks ofthe cell.
Even if we could wave a magic wand and somehowcapture all the relevant parameters and test them in
14 Crystal ball
© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology Reports, 3, 1–26
outcomes. This will require integrating frameworks forunderstanding what is possible from mutations (howthey change functional properties of proteins and howthese alterations propagate to physiological traits underselection) with how the interplay of selection and driftact in populations to shape the distribution of observedoutcomes.
References
Atwood, K.C., Schneider, L.K., and Ryan, F.J. (1951) Periodicselection in Escherichia coli. Proc Natl Acad Sci USA 37:146–155.
Chou, H.H., Berthet, J., and Marx, C.J. (2009) Fast growthincreases the selective advantage of a mutation arisingrecurrently during evolution under metal limitation. PLoSGenet 5: e1000652.
Counago, R., Chen, S., and Shamoo, Y. (2006) In vivomolecular evolution reveals biophysical origins of organis-mal fitness. Mol Cell 22: 441–449.
Elena, S.F., and Lenski, R.E. (2003) Evolution experimentswith microorganisms: the dynamics and genetic bases ofadaptation. Nat Rev Genet 4: 457–469.
Novick, A., and Horiuchi, T. (1961) Hyper-production of beta-galactosidase by Escherichia coli bacteria. Cold SpringHarb Symp Quant Biol 26: 239–245.
Stanier, R.Y. (1970) Some aspects of the biology of cells andtheir possible evolutionary significance. In Organizationand Control in Prokaryotic and Eukaryotic Cells. Charles,H.P., and Knight, B.C.J.G. (eds). Cambridge, UK: Cam-bridge University Press, pp. 1–38.
Vishniac, W., and Santer, M. (1957) The thiobacilli. BacteriolRev 21: 195–213.
Woese, C.R. (1987) Bacterial evolution. Microbiol Rev 51:221–271.
Where reductionism meets complexity: a call forgrowth in the study of non-growth
Dianne K. Newman (Email: [email protected]) andMaureen L. Coleman (Email: [email protected]),Divisions of Biology and Geological & Planetary Sciences,California Institute of Technology, Pasadena, CA, USAWith the advent of metagenomics, we have unprec-edented access to the genetic blueprint of the microbialworld. Yet as metagenomic databases keep growing, ourability to interpret the information contained within themhas not kept up. This conundrum arises from the fact thatwe cannot assign functions to the vast majority of theirgenes. As Jo Handelsman pointed out in a Crystal Ballpiece two years ago, ‘the glory of the last 50 years ofmicrobiology is founded, in large part, on genetic analysis’(Handelsman, 2009). Amen. Yet as enticing as the pros-pect of environmental genetics or ‘metagenetics’ seems,how can we hope to interpret the unchartered world ofenvironmental metagenomes when after more than a half-century of rigorous genetic and biochemical analyses, the
functions of roughly a quarter of the genes in Escherichiacoli – arguably the most well-studied organism on theplanet – are still unknown (Karp et al., 2007)? Where havewe gone wrong? Perhaps it is time to re-examine ourassumptions about how to assign gene functions in lightof lessons from the field.
Genetic analysis provides a powerful way to learn whatgenes are required for a phenotype of interest. Inventedby physicists, it is steeped in reductionism, permittingclear insights into biological phenomena through theapplication of simple logical rules. If we want to knowwhich genes are involved in a specific process for anorganism that is genetically tractable, we make mutantsand then design a screen or a selection that will permit usto assign a ‘yes’ or ‘no’ (or sometimes ‘partial’) level ofinvolvement to any given one. Thus, a key question wemust answer at the beginning is: what phenotype(s) do wecare about? What conditions are most relevant for ourfavourite model organism or favourite uncultured micro-bial community in the environment? Clearly, there is noone answer. Even the concept of ‘the environment’ ismisleading, because organisms reside in a dynamicworld, with changing physical, chemical and biologicalparameters. Given this complexity, is it even reasonableto think that reductionist approaches can be of value?Absolutely.
So where to begin? Recent work performed in CarolGross’ laboratory at UCSF provides an example. Theseinvestigators took a high-throughput approach to growinga collection of E. coli mutants under a battery of stressfulconditions to assign roles to genes whose functions wereunknown (Nichols et al., 2010). The idea was simple: ifmany conditions were tested, some of the unknown geneswere bound to be involved in growth on some of them.The results bore this out, and, comfortingly, strains con-taining mutations in genes that had previously beenshown to be involved in the response to particular stres-sors performed as expected. This type of approach is astart, and one can imagine doing this with any modelorganism for which a collection of mutant strains exist. Nodoubt such approaches will significantly reduce thenumber of genes of unknown functions in pure cultures.Yet, will they be enough to bring this number down tozero? Almost certainly not, as it is difficult, if not impos-sible, to capture every environmental variable in responseto which genes have evolved. Moreover, some genes maynot be useful at all. When we pluck an isolate from anatural community, we are capturing not an evolutionaryend-point but a work-in-progress, a snapshot of ongoinggene gain and loss complete with pieces that have not yetbeen, and may never be, integrated into the networks ofthe cell.
Even if we could wave a magic wand and somehowcapture all the relevant parameters and test them in
14 Crystal ball
© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology Reports, 3, 1–26
Acknowledgements