VL8 Analysis of Prokaryotic Communities PDF

Analysis of prokaryotic communities Mitja Remus-Emsermann KöLu 12-16 Raum 129 [email protected] Why are we interested in the study of prokaryotic communities? What do you know about bacterial habitats?...

Analysis of prokaryotic communities Mitja Remus-Emsermann KöLu 12-16 Raum 129 [email protected] Why are we interested in the study of prokaryotic communities? What do you know about bacterial habitats? What do you know about microbial communities? Time to discuss Omnipresent in the environment  Omnipresent in the environment Soil Air Water  Omnipresent in the environment Mono lake High in arsenic Boulder spring Boiling hot Ice Subterranean lake San Francisco bay salt ponds High in saline  Microbial communities in humans The oral cavity skin Brock, Biology of microorganisms Bacteria on humans match the number of somatic cells => Human microbiome project The gastrointestinal tract The respiratory tract The urogenital tract  Microbial communities in the soil 40% inorganic mineral matter 5% organic matter 50% air and water 5% microorganisms and macroorganisms Brock, Biology of microorganisms  Plant-associated microbial communities Plant health Plant growth Pseudomonas control Bacillus control http://www.apsnet.org/ Innerebner et al. 2011 Sphingomonas Plant-Growth Promoting Rhizobacteria Microbes are globally abundant  Microbes are globally abundant ~1031 viruses on Earth 1040 ~1030 bacteria on Earth 1030 count 1020 Distribution of bacteria: ~ 3×1029 on ocean floor 1010 ~ 1.5×1029 top ocean 100 ~ 2.5×1029 terrestrial soil h h xy e h rs rt rt rt la Ea Ea Ea ve ~ 10×1026 on plant leaf surfaces ga ni on on on he U es s ria e t an bl in ag te si um rs ac ph vi a H B St e d th an in s se s ar ru St Vi The big questions of Microbial ecology Who is out there? and What are they doing?  The big questions of Microbial ecology Who is there? How many of each species? ->Biodiversity Methods for identification and quantification What are the organisms doing in their native habitats? ->Microbial activity Methods to measure activity  Sources Amann, R. and Fuchs, B.M. (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nature reviews. Microbiology, 6, 339-348. Handelsman, J. (2004) Metagenomics: Application of genomics to uncultured microorganisms. Microbiol Mol Biol Reviews, 68, 669-685. Rastogi, G. and Sani, R.K. (2011) Molecular Techniques to Assess Microbial Community Structure, Function, and Dynamics in the Environment. In Microbes and Microbial Technology. Agricultural and Environmental Applications (Ahmad, I., Ahmad, F. and Pichtel, J. eds). New York, NY. USA: Springer, pp. 29-54. Zak, D.R., Blackwood, C.B. and Waldrop, M.P. (2006) A molecular dawn for biogeochemistry. Trends Ecol Evol, 21, 288-295. Dees M.W., Lysøe E., Nordskog, B., Brurberg M.B. (2015) Bacterial Communities Associated with Surfaces of Leafy Greens: Shift in Composition and Decrease in Richness over Time. Appl. Env. Microb., 81, 1530-1539 Madigan, M., Martinko, J., Stahl, D. and Clark, D. (2018) Brock Biology of Microorganisms 15th edition edn. San Francisco, CA, USA: Pearson Education, Inc. Question: Do you know of any methods to study bacterial communities?  We will talk about several methods to study biodiversity 1. Microscopy 2. Cultivation dependent studies 3. Amplicon sequencing and fingerprinting methods 4. Clone libraries and next generation sequencing 5. Metagenomics 6. Fluorescence in situ hybridization (FISH)  Spotting the first microorganisms MICROORGANISMS AND MICROBIOLOGY 13 II Microbiology in Historical Context UNIT 1 T he future of any science is rooted in its past accomplishments. Although microbiology claims very early roots, the science did not really develop in a systematic way until the nineteenth century because technology such as microscopes and culturing techniques had to catch up with the already strong scientific curi- osity. In the past 150 years or so, microbiology has moved forward Robert Hooke in a way unprecedented by any other biological science and has spawned several new fields in modern biology. We retrace some Antoni highlights in the history of microbiology now and describe a few of the major contributors. 1636-1703 van Leeuwenhoek 1.6 The Discovery of Microorganisms Although the existence of creatures too small to be seen with the 1632-1723 naked eye had been suspected for centuries, their discovery had to await invention of the microscope. The English mathematician and natural historian Robert Hooke (1635–1703) was an excellent microscopist. In his famous book Micrographia (1665), the first book devoted to microscopic observations, Hooke illustrated, among many other things, the fruiting structures of molds (Figure 1.13). This was the first known description of microorganisms. The first person to see bacteria, the smallest microbial cells, was the Dutch draper and amateur microscopist Antoni van Leeuwenhoek (1632–1723). Van Leeuwenhoek constructed extremely simple microscopes containing a single lens to exam- ine various natural substances for microorganisms (Figure 1.14). These microscopes were crude by today’s standards, but by careful manipulation and focusing, van Leeuwenhoek was able ”Animalcules” to see bacteria. He discovered bacteria in 1676 while study- ing pepper–water infusions, and reported his observations in Figure 1.13 Robert Hooke and early microscopy. A drawing of the microscope a series of letters to the prestigious Royal Society of London, used by Robert Hooke in 1664. The lens was fitted at the end of an adjustable bellows (G) and light focused on the specimen by a separate lens (1). Inset: Hooke’s drawing which published them in English translation in 1684. Drawings of a bluish mold he found degrading a leather surface; the round structures contain of some of van Leeuwenhoek’s “wee animalcules,” as he referred spores of the mold. to them, are shown in Figure 1.14b, and a photo taken through a van Leeuwenhoek microscope is shown in Figure 1.14c. closing flasks and tubes. These methods were later adopted by Because experimental tools to study microorganisms were Robert Koch, the first medical microbiologist, and allowed him to crude at this time, little progress in understanding the nature and  Cultivation-dependent studies Example of bacterial community in plant leaves Wash leaves Dilution plating Plate on several media Store (e.g. in 96-well plates)  Limitations of cultivation Minimal medium + methanol Many reasons: Lack of obligate symbionts Lack of necessary nutrients or surfaces Inhibitory compounds Incorrect combination of trace combinations, nitrogen and carbon sources, or atmospheric gas composition For example, methylotrophs are Slow growth rate vs. fast growth rates visible on plates with methanol, but they grow slowly (3 days) Etc. … => The cultivation conditions determine which bacteria grow  “The great plate count anomaly” Microscopy counts > CFU counts Habitat Culturability (%) Seawater 0.001-0.1 Freshwater 0.25 Sediments 0.25 Soil 0.3 Activated sludge 1-15 Plant leaves 50-60 Root surfaces 50-60 Amann et al 1995, Microbiol Rev 59, 143-169; Bai et al 2015, Nature 528, 364-369 Þ The vast majority of cells are not cultivable under laboratory conditions Þ or are they?  Culturomics Human gut Arabidopsis leaf https://www.nature.com/articles/nmicrobiol2016203/figures/1 https://www.nature.com/articles/nature16192  Cultivation-independent methods Analysis of marker molecules, i.e. cell components allows identification and quantification of microbial cells and/or give insight into their physiology under in situ conditions. DNA/RNA proteins Fatty acids  DNA-based methods: to PCR or not to PCR? Cells Fistulated cow DNA extraction Soil Microbial mat (bottom hypersaline pond) Genomic DNA PCR no PCR metagenomics Hydrothermal vent (black smoker) PCR based methods  The 16S rRNA gene as molecular marker Universal phylogenetic tree determined from rRNA sequence comparisons Woese (1997) Bacterial evolution Red = most variable Secondary structure 16S rRNA Case et al. (2007) Applied and Environmental Microbiology Neefs et al. (1990) Nucleic Acid Research  Other marker genes Requirements Present in all organisms of interest Phylogeny of the gene should reflect evolution (no horizontal transfer) Two goals 1) Improve /confirm 16S rRNA gene-based phylogeny Examples: recA – DNA recombination protein, rpoB – RNA polymerase, gyrB – DNA gyrase 2) Characterize species with specific physiological traits such as biogeochemical conversion processes  Functional genes to study biogeochemical cycles Genes Enzyme Process pmoA, mmoX Methane mono-oxygenase Methane oxidation mcrA, mrtA Methyl-coenzyme M reductase Methanogenesis mxaF Methanol dehydrogenase Methylotrophy cbbL, cbbM RubisCO CO2 fixation amoA Ammonium monoxygenase nitrification nifH nitrogenase reductase Nitrogen fixation nirK, nirS Nitrite reductase Denitrification dsrA, dsrB Dissimilatory sulfite reductase Dissimilatory sulfate reduction Lcc Phenol oxidase Plant litter decay (lignin) Bgl Betaglucosidase Plant litter decay (cellulose) Zak et al. (2006) TREE. A molecular dawn for biogeochemistry  PCR-based methods for the analysis of the microbial community composition DNA extraction PCR cells Genomic DNA PCR product DNA melting Length Sequencing Probe hybridization behavior polymorphism Clone library Microarray DGGE T-RFLP Amplicon TGGE RFPL sequencing SSCP ARISA using NGS LH-PCR SAMPLE COMPARISON IDENTIFICATION and SAMPLE COMPARISON  Fingerprinting methods for the comparison of microbial community DNA extraction PCR Cells Genomic DNA PCR product Restriction digest T-RFLP: terminal DGGE = DNA denatured by urea restriction fragment and formamide gradient in the gel length polymorphism Sam 1 Sam 2 3 p le p le p le Sam Sample 1 Denaturing Sample 2 gradient Sample 3  Example of DGGE (Denaturing Gradient Gel Electrophoresis) Valencia cotton Sugar orange beet OroBlanco Navel Green Orange orange corn been Identification of members of the community: band excision, diffusion of DNA from gel into buffer, PCR to reamplify DNA, sequencing Yang et al. (2001) PNAS “Microbial phyllosphere populations are more complex than previously realized”.  Another fingerprinting method: ARISA Automated Ribosomal Intergenic Spacer Analysis 16S ribosomal RNA 23S ribosomal RNA variable length forward reverse PCR products of variable length Analysis of PCR products on a capillary sequencer red: size standard blue: PCR product  Clone libraries DNA extraction PCR cells Genomic DNA PCR product Ligation e.g. into Topo™ vector >clone1 tctcatggagagttcgatcctggct caggatgaacgctggcggcatgctt aacacatgcaagtcggacgggaagt ggtgt >clone2 agagtttgatcctggctcagaacga acgctggcggcatgcctaacacatg caagtcgaacgaaggcttcggcctt agtgg >clone3 agagttatcatggctcagaatgaac Colony PCR transformation gctggcggcatgcctaacacatgca agtcgaacgaagccttcgggtttag & sequencing tggcg 3-10 € per reaction  Clone libraries analysis Quality control Optional: Assembly (if clones sequenced from both ends) Remove chimera (1 sequence composed from 2 fragments from 2 different species) Alignment Construct distance matrix (pairwise distances between aligned DNA sequences) Cluster sequences Assign taxonomy to sequences  “operational taxonomic unit” OTU distance Sequence 1 Sequence 3 Rule of thumb Sequence 5 Cut tree 3% (SPECIES) Sequence 8 => 7 OTUs in this sample Sequence 2 Cut tree 5% (GENUS) Sequence 7 => 5 OTUs in this sample Sequence 6 Cut tree 20 % (PHYLUM) => 2 OTUs in this sample Sequence 4 Sequence 10 Sequence 9 20% 5% 3%  Next generation sequencing Sogin et al. (2007) PNAS “By adopting a massively parallel tag sequencing strategy, we show that bacterial communities of deep water masses of the North Atlantic and diffuse flow hydrothermal vents are one to two orders of magnitude more complex than previously reported for any microbial environment. “ Turnbaugh et al. (2009) Nature “Analysis of 154 individuals yielded 9,920 near full-length and 1,937,461 partial bacterial 16S rRNA sequences.” Can you give me the names of some NGS techniques?  Next generation sequencing - NGS 454 Life Sciences Illumina Miseq/ Nextera/ Hiseq … Ion Torrent Systems Inc. Pacific Biosciences (SMRT II) Oxford Nanopore MinION etc…  Illumina sequencing Illumina Sequencing http://www.illumina.com/Documents/products/techspotlights/techspotlight_sequencing.pdf Illumina sequencing  NGS amplicon library analysis distance Quality control Sequence 1 Sequence 3 Remove chimera Alignment Sequence 5 Construct distance Sequence 8 matrix Sequence 2 Cluster sequences Sequence 7 Sequence 6 Assign taxonomy to sequences Sequence 4 Sequence 10 => same process as for clone libraries, except that work with >104 sequences Sequence 9 20% 5% 3%  What factor structures the community? Hypothetical experiment Samples 1-2-3 come from a lake with low nitrogen (or from omnivorous people) Samples 4-5-6 come from a lake with high nitrogen (or from vegan people) OTU1 OTU2 OTU3 OTU4 OTU5 OTU6 OTU7 OTU8 OTU9 OTU10 OTU11 OTU12 OTU13 OTU14 sample1 19 10 8 4 2 2 2 2 1 1 1 1 1 1 sample2 4 22 3 0 0 0 5 9 0 0 0 2 0 0 sample3 0 10 6 2 0 13 15 2 0 0 2 2 1 1 sample4 5 4 1 20 9 5 1 1 2 1 0 0 0 0 sample5 7 8 2 10 5 4 2 1 0 1 2 1 3 1 sample6 2 5 10 15 4 2 1 5 1 0 1 2 1 0 OTU table numbers in the tables = number of sequences / area of ARISA peaks / DGGE peaks, etc… Question: Is nitrogen/obesity a factor that affects bacterial community? Þ compare abundances of each OTU Þ compare community diversity and composition  Ecological diversity analysis Richness = number of different OTUs Evenness describes the distribution of individuals among OTU types the flatter the curve => the more evenly distributed Hypothetical data set of microbial lakes with low and high nitrogen Same numbers of sequences for each sample evenness OTU rank OTU rank Richness questions for you: Both samples have the same number of individuals Which sample has the higher richness? Which sample has the higher evenness?  Ecological diversity analysis Richness = number of different OTUs Evenness describes the distribution of individuals among OTU types the flatter the curve => the more evenly distributed evenness OTU rank OTU rank Richness Number individuals = 63 Number individuals = 63 Higher richness (20 OTUs) Lower richness (16 OTUs) Less evenness More evenness  Estimating species richness with rarefaction curves Describes the number of OTUs observed as a function of sampling soil effort Number of OTUs rhizosphere Random re-sampling multiple times and plot root the average number of species found If curve becomes flat => Number of sequences More sampling will only Bulgarelli et al. 2012 Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota yield few additional species  Methods for the analysis of microbial community composition FISH metagenomics DNA extraction PCR Cells Genomic DNA PCR product DNA melting Length Sequencing Probe hybridization PCR-based methods behavior polymorphism Clone library Microarray DGGE T-RFLP Pyrosequencing TGGE RFPL Other NGS SSCP ARISA techniques LH-PCR SAMPLE COMPARISON IDENTIFICATION and SAMPLE COMPARISON  Disadvantages of PCR-based methods PCR bias ÞPrimers amplify some phyla preferably ÞPrimers might fail to amplify some phyla Test primers against a ribosomal databases Primer design based on sequences in the databases => can’t detect unknown bacteria that do not hybridize to primers  Metagenomics What is a “meta”genome? Metagenome: the combined genome of all organisms in an environment Metagenomics : Genomic analysis of a community of microorganisms by direct isolation of genomic DNA from an environment  Metagenomic library construction Fosmid plasmid DNA extraction Clone DNA Cells Genomic DNA transformation E. coli 1. Sequence driven analysis 2. Function driven analysis Screening large insert libraires for clones of interest=> SEQUENCE Stein et al 1996. “Characterization of uncultivated prokaryotes“ By PCR By hybridization Positive control: archaeal genomic DNA Filter replica with 2304 fosmid clones Negative control: bacterial genomic DNA probed with insert from clone B7 => No other contigs similar to B7 on this => Fosmid B7 is archaeal DNA filter  Screening large insert libraries for clones of interest => PHENOTYPE Rees et al 2003 “Detecting cellulase and esterase enzyme activities” Novel antibiotics Antibiotic resistance genes Transporters Degradative enzymes Plate screening for cellulase activity using a Congo red assay  One of the first shotgun metagenomic study Surface water samples (170-200 liters) Water filtered and DNA extracted from the filters Small insert genomic libraries constructed Sanger sequencing of >1 million clones Craig Venter et al. 2004. Science. “Environmental Genome Shotgun Sequencing of the Sargasso Sea” Phylogenetic diversity of Sargasso Sea sequences using multiple phylogenetic markers Craig Venter et al. 2004. Science. “Environmental Genome Shotgun Sequencing of the Sargasso Sea”  Phylogenetic tree of rhodopsin-like genes in the Sargasso Sea data along with all homologs of these genes in GenBank At the time, about 67 proteo-rhodopsin genes known This study identified 782 rhodopsin homologs = > 10 fold increase! Craig Venter et al. 2004. Science. “Environmental Genome Shotgun Sequencing of the Sargasso Sea”  Next generation sequencing and metagenomics DNA extraction Clone DNA Cells Genomic DNA transformation NGS lots and lots of sequences! E. coli “Analysis of 154 individuals yielded 9,920 near Turnbaugh et al. (2009) Nature full-length and 1,937,461 partial bacterial 16S rRNA sequences, plus 2.14 gigabases from their microbiomes.”  Methods for the analysis of microbial community composition FISH metagenomics DNA extraction PCR Cells Genomic DNA PCR product DNA melting Length Sequencing Probe hybridization PCR-based methods behavior polymorphism Clone library Microarray DGGE T-RFLP Pyrosequencing TGGE RFPL SSCP ARISA LH-PCR SAMPLE COMPARISON IDENTIFICATION None of the previous techniques allows to put microbial activity into a spatial context Movement of microbes is limited Immediate surrounding is what matters for microbes Sometimes, to understand microbial activity, it needs to be put into spatial context  A matter of scales Range of perception Trinidad Bean leaf 5000 km2 50 cm2 Remus-Emsermann and Schlechter New Phytologist 2018  The Microscope… again Microscopy developed tremendously Fluorescence widefield microscopy Confocal microscopy -> 3D observations Computer programs allow 3D reconstruction and (semi-) automatic image analysis  FISH – Fluorescence in situ hybridization Amann and Fuchs (2008) Nature Reviews  C A B D Remus-Emsermann et al. 2014  Eukaryotes on plant leaves Fungi, Yeasts, Algae Green = Bacteria Red = Eukaryotes  FISH to study consortia red = anaerobic methanotrophic archae (ANME) green = sulfate reducing bacteria Anaerobic methanotrophy Knittel and Boetius (2009) Annu. Rev. Microbiol.  Advantages of FISH Direct observation, context to environment Metabolically active cells (low or no signal for dead cells) Measure activity: the more active, the brighter Other transcripts than rRNA can be targeted Compatible with FACS (Fluorescence-activated cell sorting) => quantification  Limitations of FISH Limit of detection: 103 -104 target cells per ml of sample Number of phylogenetic targets is limited Cell envelope may be impermeable to the labeled oligonucleotide probes after fixation Samples that contain material or cells with a high autofluorescence are difficult to analyze. For spatial context, samples have to fit on a microscope 1000 or more rRNA molecules required for FISH signal => solution: signal amplification techniques, e. g. CARD-FISH (catalyzed reported deposition) Low throughput Analysis of prokaryotic communities Mitja Remus-Emsermann KöLu 12-16 Raum 129 [email protected] 1  From diversity to activity Methods to study diversity 1. fingerprinting methods (DGGE, t-RFLP, ARISA) 2. clone libraries and 454 pyrosequencing 3. metagenomics 4. FISH => Presence of organisms and their metabolic potential Who is active in the community? What are they doing? 2  Outline Microbial activity: What are the organisms actually doing in their native habitats? – mRNA based approaches: qPCR Metatranscriptomics – Metaproteomics – Metabolomic approaches Stable Isotope Probing NanoSIMS – Bioreporter technology – Outlook “WGA”-”MDA” 3  Further reading Dumont, M.G. and Murrell, J.C. (2005) Stable isotope probing - linking microbial identity to function. Nat Rev Microbiol, 3, 499-504. Musat, N., Foster, R., Vagner, T., Adam, B. and Kuypers, M.M.M. (2012) Detecting metabolic activities in single cells, with emphasis on nanoSIMS. Fems Microbiol Rev, 36, 486-511. Siggins, A., Gunnigle, E. and Abram, F. (2012) Exploring mixed microbial community functioning: recent advances in metaproteomics. FEMS Microbiol Ecol, 80, 265–280. Wagner, M. (2009) Single-Cell Ecophysiology of Microbes as Revealed by Raman Microspectroscopy or Secondary Ion Mass Spectrometry Imaging. Annu Rev Microbiol, 63, 411-429. Warnecke, F. and Hess, M. (2009) A perspective: Metatranscriptomics as a tool for the discovery of novel biocatalysts. J Biotechnol, 142, 91-95. Weinstock, G.M. (2011) The Impact of Next-Generation Sequencing Technologies on Metagenomics. In Handbook of Molecular Microbial Ecology (de Bruijn, F.J). ed. Oxford, UK: John Wiley & Sons, Inc. 4  Cultivation-dependent method Cultivate strains in the laboratory Community e.g.: soil Characterize bacteria at the physiological, biochemical and molecular level Infer potential roles Infer Disadvantages: ecophysiology in nature – “the plate count anomaly” Isolate – function in pure culture might be very strains different from community in the laboratory 5  RNA-based studies Global approach Metatranscriptomics Sample preparation Cells RNA extraction Targeted approach => Analyze selected transcripts of interest RNA Community profiling qPCR DNA melting behavior Reverse (e. g. DGGE) transcription Length polymorphism (e. g. ARISA) Microarrays Sequencing (e.g. pyrosequencing) cDNA 6 Can one of you explain the principle of qPCR? 7  principle of qPCR dsDNA 5’ 3’ SYBR green assays 3’ 5’ DENATURATION Primer and probe ANNEALING ELONGATION DENATURATION ETC… 8  Who is there ? => Template DNA Primers 16S Functional genes Fierer et al. (2005) Assessment of Soil Brankatschk et al. (2012) Simple absolute microbial community structure by use of quantification method correcting for quantitative taxon-specific quantitative PCR Assays PCR efficiency variations for microbial community samples 9  What are they doing? => Template cDNA day night day Issues Primer design RNA extraction efficiency mRNA not necessarily related to real activity => Method most suitable to investigate effect of environmental factors on Church et al. (2005) Temporal Patterns of the Nitrogenase Gene (nifH) Expression in the expression of functional genes Oligotrophic North Pacific Ocean 10  Metatranscriptomics Metagenome => genetic potential of a community Metatranscriptome => Genes that are expressed in a given environment (realized potential!) cDNA Clone libraries + Sanger sequencing Next generation sequencing 11  Microbial community Metatranscriptomics Total RNA extraction technical challenges: mRNA enrichment e.g. magnetic bead capture of rRNA, preferential polyadenylation of mRNA, preferential digestion if RNA extraction rRNA mRNA is less stable than DNA cDNA synthesis e.g. random priming, priming with poly-dT primers (after polyadenylation) mRNA enrichment Only 1-5% of total bacterial RNA Optional: Amplification RNA polymerase, multi strand displacement amplification, emulsion PCR Sequence analysis Preparation for high many reads cannot be classified throughput sequencing Metatranscriptome After Hess and Warnecke (2009) J Biotechnol 12  Example of metatranscriptomics study Stewart, F.J., Ulloa, O. and DeLong, E.F. (2012) Microbial metatranscriptomics in a permanent marine oxygen minimum zone. Environ Microbiol, 14, 23-40. metatranscriptome metagenome 13  Metaproteomics 2 different methods for protein separation and fractionation 2-D PAGE analysis Liquid-chromatography-based analysis database searches easier if metagenomic data is also available Siggins et al. 2012 FEMS Microbiology Ecology 14  Example of metaproteomics study Goal: Compare metaproteome of the phyllosphere of 3 plants Examples of core phyllosphere proteome: 1. glutamine synthetase 2. Fasciclin (cell-cell adhesion) 3. TonB-dependent receptor Examples of enriched proteins 10. bacterial flagellin 19. phycobilisome protein (light harvesting) Delmotte, Knief et al. (2009) Community proteogenomics reveals insights into the physiology of phyllosphere bacteria 15 From whole community analysis to metabolic activity Disadvantages of meta -transcriptomis/proteomics: “Data storm” Annotation by homology Some enzymes might be present but inactive Those techniques do not answer questions such as: – are bacteria or archaea functionally more important for nitrification? – What is the ecophysiology of Acidobacteria? => Directly observe and quantify metabolic activity of microorganisms in their natural environment 16 Uncertainty paves the way of the microbial ecologist DNA shows us the genetic potential RNA shows us transcribed potential Proteins show us the translated potential What is the actual function/activity? How can we measure function/activity? 17 Stable isotope probing (SIP) PLFA (phospholipid Goal: detecting DNA or RNA SIP fatty acid) SIP microbial activity via uptake of labeled substrate Dumot and Murrell (2005) Nature Rev. Microbiol. 18  DNA/RNA SIP: step 1 Incubation with labeled substrate Use substrate concentration similar to what bacteria encounter in situ Keep incubation time short First substrates used for SIP: 13CH OH and 13CH 3 4 Þ Answer question: Who is metabolizing methanol or methane? 19  Density gradient separation Same principle used for Meselson and Stahl Experiment ÞProve of semi-conservative mechanism of DNA replication Does anybody remember the experiment? 20  Meselson and Stahl experiment 1) Grow E. coli in 2) Transfer to 3) Harvest 4) Extract 15NH4 medium 14NH4 medium every 20 DNA min 5) Ultracentrifugation in CsCl gradient DNA 50% light 75% intermediate 100% 50% 25% heavy 100% 0 generation 1 generation 2 generations 3 generations 21  DNA/RNA SIP: step 2 Extract DNA/RNA Separate on CsCl density gradient (DNA) or CsTFA gradient (RNA) 12C-DNA = light DNA, organisms Nucleic acid visualized by which did not incorporate labeled ethidium bromide staining substrate Collection of “heavy” DNA 13C-DNA = heavy DNA, organisms or RNA incorporated labeled substrate 22 DNA/RNA SIP: step 3 DNA/cDNA template for all the methods we discussed in the class: sequencing of PCR products microarrays metagenomics etc… 23  DNA SIP or RNA SIP? Advantages of DNA SIP: Whole genomic DNA is available for downstream analysis (genome sequencing) DNA is more stable than RNA DNA separated better during gradient centrifugation Advantages of RNA SIP RNA labeling occurs much faster (shorter incubation time) mRNA can be sequenced to identify which genes are expressed Physiologically active cells that are not replicating can be detected 24  From metabolic activity to single-cell approaches Stable-Isotope Probing To answer “who (which microbe) is eating what, where, and when” Disadvantages of DNA/RNA SIP incorporation of label into nucleic acid => long incubation time general idea of activity, at the community level, not single cell resolution Þ Need methods that look at single cells + shorter incubation time nanoSIMS coupled with FISH for identification (fluorescent-) bioreporter approaches 25 Single-cell vs. population measurements 26  Fluorescent bioreporters promoter-probe construct on plasmid or chromosome promoter Fluorescent protein gene (e.g. GFP) Medium without inductor Medium with inductor 27 Fluorescent bioreporter for fructose 28  Fructose bioreporter on leaf surfaces A B C D 29  nanoSIMS (secondary ion mass spectrometry) mass spectrometric technique that determine elemental, isotopic, molecular composition of a solid sample surface energetic primary ion beam=> expel secondary particles (under high vacuum) Charged particles of one polarity are extracted with an electric field secondary ions focused by an extraction lens secondary ion beam is analyzed by mass spectrometry 30 nanoSIMS (secondary ion mass spectrometry) Wagner (2009) Annu. Rev. Microbiol. 31  nanoSIMS and FISH Addition of substrates, e.g. 15N, 13C- labeled Chemical fixation and spreading on conductive surfaces Hybridization with fluorescently labeled FISH probe Microscopy => identity SIMS=> activity FISH 32  Example of nanoSIMS and FISH Behrens et al. (2008) Linking microbial phylogeny to metabolic activity at the mutualistic interaction between cyanobacteria (organic carbon single-cell level and nitrogen) and heterotroph alphaproteobacteria (decrease local O2 and produces CO2) A microbial consortium consisting of filamentous cyanobacteria and Alphaproteobacteria attached to heterocysts (specialized nitrogen fixing cell) A. FISH Fluorescence image of the microbial consortium (Alphaproteobacterial probe) B. NanoSIMS secondary-electron image C. Localization of fluorine relative to carbon D. Distribution of 15N-nitrogen enrichment 33 E. Distribution of 13C-carbon enrichment.  The kid on the block: “WGA” via “MDA” whole genome amplification by multiple displacement amplification ϕ29 polymerase Lasken 2012 Nature Rev. Microb. 34  Roundup Microbial ecology is suffering from the uncertainty principle! a.k.a things to know for the test!!! Method Identity Activity Spatial Find context something new? Culturing X X - X 16S amplicon X - - +/- sequencing FISH X +/- X - Metagenomics X - - X SIP X X - - Proteomics +/- X - - nanoSIMS X (with FISH) X X Bioreporter X X X ---* WGA X - X X *bacteria need to be culturable and we need to know their sequence 35  Other techniques that we did not talk about Raman-microscopy Microarray-techniques Other FISH-flavours Maldi-imaging … 36