- Research article
- Open Access
Using pyrosequencing to shed light on deep mine microbial ecology
https://doi.org/10.1186/1471-2164-7-57
© Edwards et al; licensee BioMed Central Ltd. 2006
- Received: 04 November 2005
- Accepted: 20 March 2006
- Published: 20 March 2006
Abstract
Background
Contrasting biological, chemical and hydrogeological analyses highlights the fundamental processes that shape different environments. Generating and interpreting the biological sequence data was a costly and time-consuming process in defining an environment. Here we have used pyrosequencing, a rapid and relatively inexpensive sequencing technology, to generate environmental genome sequences from two sites in the Soudan Mine, Minnesota, USA. These sites were adjacent to each other, but differed significantly in chemistry and hydrogeology.
Results
Comparisons of the microbes and the subsystems identified in the two samples highlighted important differences in metabolic potential in each environment. The microbes were performing distinct biochemistry on the available substrates, and subsystems such as carbon utilization, iron acquisition mechanisms, nitrogen assimilation, and respiratory pathways separated the two communities. Although the correlation between much of the microbial metabolism occurring and the geochemical conditions from which the samples were isolated could be explained, the reason for the presence of many pathways in these environments remains to be determined. Despite being physically close, these two communities were markedly different from each other. In addition, the communities were also completely different from other microbial communities sequenced to date.
Conclusion
We anticipate that pyrosequencing will be widely used to sequence environmental samples because of the speed, cost, and technical advantages. Furthermore, subsystem comparisons rapidly identify the important metabolisms employed by the microbes in different environments.
Keywords
- Acid Mine Drainage
- Iron Acquisition
- Metabolic Potential
- Black Sample
- Subsystem Analysis
Background
Banded iron formations started appearing ~3,700 million years ago when localized sea floor cyanobacterial photosynthesis raised oxygen concentrations high enough that dissolved iron precipitated. That iron powered the industrial revolution. The Soudan Iron Mine in Minnesota, USA was active from 1884 to 1962, and during this period 17.9 million tons of iron ore, primarily hematite, were removed. Nowadays the mine is used as a state park and as a facility for high-energy physics experiments.
Metagenomics is a term used to describe "the functional and sequence-based analysis of the collective microbial genomes contained in an environmental sample"[1, 2]. Random shotgun sequencing of DNA from natural communities has been used to characterize seawater, sediment, and fecal viral communities [2–5], as well as the microbial communities in soil, whale falls, seawater and the Iron Mountain acid mine drainage (AMD) [6–10]. Comparative metagenomics was introduced recently[6], identifying those sets of genes that distinguish environmental samples. For example, samples from the surface of the ocean contain significantly more photosynthetic genes than soil or other samples[6, 8, 10]. We have used comparative metagenomics to characterize the metabolic potential of different environments, and identify those genes, pathways, and subsystems that are more common in any particular environment [11].
Most current sequencing is a modification of the classical Sanger method, where extending DNA fragments are stopped by the random incorporation of a fluorescently labeled ddNTP. The different-sized fragments are then separated using capillary gel electrophoresis and detected with a LASER. Pyrosequencing is a fundamentally different methodology because only one dNTP is added into the reaction at a time [12–14]. If there is a complementary base, then the DNA polymerase catalyzes the reaction and releases pyrophosphate. ATP sulfurylase uses the pyrophosphate to produce ATP in the presence of adenosine 5' phosphosulfate (APS). A Charge-Coupled Device (CCD) measures the light produced when the ATP is used by luciferase to convert luciferin to oxyluciferin. 454 Life Sciences has scaled this process up to be massively parallel, determining the composition of more than 300,000 sequences at once, for approximately the same price as 96 to 192 sequencing reactions performed using traditional chemistries[12]. In addition to the massive parallelization, the 454 technology does not require cloning of the environmental samples, thus eliminating many of the problems that are associated with this step of metagenomics[2].
This report describes the first application of pyrosequencing to environmental samples. From this sequence data, we identify the 16S rDNA sequences present in the sample, and apply new annotation methods to this data using the SEED database[15]. This paper also describes a comprehensive statistical treatment of the genes identified in each sample using a completely novel methodology that exploits the differences between metagenome sequences. We demonstrate that completely unique microbial communities inhabit proximate environments joined by a common watercourse, and that using metagenomics we can identify the unique metabolic potentials prevalent in each environment such as their mechanisms of iron acquisition and respiration. The integration of pyrosequencing, subsystems analysis, comparative metagenomics, statistics, hydrogeology, and chemistry provides a comprehensive systems analysis of the Soudan Mine.
Results and discussion
Description of the environmental samples
Sampling from the Soudan Mine. The Soudan Mine is an Algoma-type Iron Formation rich in hematite. Panel A shows a cross-section of the mine looking East-North-East at 78.5°. Panel B depicts a three dimensional view of the mine, including the cross-section shown in Panel A, and with the sampling sites shown for the "Red" and "Black" samples. Panel C shows the overall flow of water in the mine at level 27, located 714 meters below the surface (Panel D). Panels E and F show a close up of the two sampling sites.
The first two pyrosequences of environmental samples
Summary of pyrosequence data from the Soudan Mine
Red Sample | Black Sample | |
---|---|---|
Number of Sequences | 334,386 | 388,627 |
Total Length of Sequences | 35,439,683 bp | 38,502,057 bp |
Average Length of Sequences | 106.0 bp | 99.1 bp |
Average Quality Score1 | 26.2 | 25.8 |
Skew2 | 2.53 | 2.44 |
The two samples produced more than 70 Mbp of sequence data from over 700,000 sequences, and there was no significant skew in the sequence data (as measured by dinucleotide frequency) when the data generated by pyrosequencing was compared to complete genome sequences.
16S rDNA analysis of the samples
Composition of the 16S rDNA sequences from the two samples and comparison of 16S sequences from the 454 libraries and a traditional clone library. The percentage of all sequences from each library in each of the orders is shown for the 454-sequenced Black sample (solid black bars; n = 24), the 454 sequenced red sample (solid red bars; n = 76), and the PCR amplified clone library (hatched red bars; n = 91).
A16S clone library was created from the Red sample to validate the 454 sequencing approach. Ninety-six clones were sequenced using traditional techniques, and compared to the 16S rDNA database from the Ribosomal Database Project [16]. The congruity between the 16S genes sequenced in the 454 library and the 16S sequences from the clone library, as shown in Fig. 2, is quite remarkable.
We also used the 16S sequences to evaluate the randomness of the library. An analysis of 160 bacterial genome sequences in the SEED database [15, 19] with annotated 16S genes showed that about 1 in 105 bases is from a 16S gene. Based on this estimate, as a rule of thumb the Soudan samples are expected to contain approximately 3,000 bases of 16S sequence in total, or approximately 30 sequences. Twenty four sequences were found to have significant similarity (with an E value less than 1 × 10-5 and a match of 50 bp or more) to 16S rDNA from the Black sample and seventy six sequences were found to have significant similarity to 16S rDNA from the Red sample.
Metabolic potential from the metagenome library
Subsystems in the Red and Black Samples. The occurrence of classes of subsystems is shown as a percent of all subsystems in each sample for the Red and Black samples. Notes and abbreviations: The subsystem class "Glu, Asp" also contains Gln and Asn. The subsystem class "Lys, Thr" also contains Met and Cys. CHO: Carbohydrates; sacch: saccharides; Extracell. Poly: Extracellular polysaccharides; Myco: Mycobacterial cell wall; Gm: Gram stain positive (+) or negative (-); Clust: clusters; RFN: Riboflavin; T: Transporters; Mot: Motility; N: Nitrogen; Resp: Respiration; e-: electron; S: Sulfur.
Subsystems enriched in the Black or Red samples
Subsystems statistically more likely to be present in either the Red or Black samples. These subsystems are more frequently found among sequences from either the Red or Black samples with a sample size of 5,000 proteins, 20,000 repeated samples, and P < 0.05.
Red Sample (Oxidized, pH4.37, E h -8) | Black Sample (Reduced, pH 6.70, E h -142) |
---|---|
Amino Acids and Derivatives | |
Arginine biosynthesis | Urea decomposition |
Tryptophan synthesis | Chorismate synthesis |
Asp-Glu-tRNA(Asn-Gln) transamidation | Branched-chain amino acid biosynthesis |
Histidine biosynthesis | Isoleucine degradation |
Leucine biosynthesis | |
Leucine degradation and HMG-CoA metabolism | |
Valine degradation | |
Methionine salvage | |
Carbohydrates | |
Glyoxylate synthesis | |
Cell Division and Cell Cycle | |
Cytoskeleton | |
Cell Wall and Capsule | |
N-linked glycosylation in Bacteria | |
Teichoic acid biosynthesis | |
Cofactors, Vitamins, Prosthetic Groups, Pigments | |
Folate biosynthesis | Coenzyme A biosynthesis in pathogens |
Methylglyoxal metabolism | Molybdopterin biosynthesis |
Pyruvate metabolism I: anaplerotic rx, PEP | Carotenoids |
Ubiquinone biosynthesis | Polyisoprenoid biosynthesis |
Ubiquinone menaquinone-cytochrome c reductase | NAD and NADP cofactor biosynthesis global |
Riboflavin metabolism | Coenzyme PQQ synthesis |
Pyrroloquinoline quinone biosynthesis | |
Siderophore enterobactin biosynthesis | |
Siderophore enterobactin biosynthesis and ferric enterobactin transport | |
Thiamin biosynthesis | |
DNA metabolism | |
DNA repair, bacterial | |
Fatty Acids and Lipids | |
Fatty acid metabolism | Glycerolipid and glycerphospholipid metabolism |
Fatty acid oxidation pathway | |
Membrane Transport | |
ABC transporter maltose | ABC transporter ferrichrome |
ABC transporter heme | |
CbiQO-type ABC transporter systems | |
Sodium hydrogen antiporter | |
Metabolism of aromatic compounds | |
Phenylacetate pathway of aromatic compound degradation | Homogentisate pathway of aromatic compound degradation |
Motility and Chemotaxis | |
Bacterial chemotaxis | |
Flagellum | |
Nitrogen Metabolism | |
Denitrification | |
Nucleosides and Nucleotides | |
De novo purine biosynthesis | |
Ribonucleotide reduction | |
Protein Metabolism | |
Ribosome LSU bacterial | Phenylpropionate degradation |
Ribosome SSU bacterial | |
Translation factors bacterial | |
Universal GTPases | |
Protein degradation | |
Respiration | |
F0F1-type ATP synthase | NiFe hydrogenase maturation |
Terminal cytochrome C oxidases | |
Hydrogenases | |
Membrane-bound Ni, Fe-hydrogenase | |
Na(+)-translocating NADH-quinone oxidoreductase and rnf-like group of electron transport complexes | |
Respiratory complex I | |
Respiratory dehydrogenases 1 | |
RNA metabolism | |
Polyadenylation bacterial | |
RNA polymerase bacterial | |
tRNA aminoacylation | |
Stress response | |
Glutathione redox metabolism | |
ppGpp biosynthesis | |
Sulfur Metabolism | |
Sulfate assimilation | |
Virulence | |
Resistance to fluoroquinolones |
Water chemistry from Soudan Mine. No significant differences were found for Ca, Mg, Na, K, Li, Al, Mn, Sr, Ba, Si, Cr, Co, Ni, Cu, Zn, As, Se, Rb, Cd, Cs, Pb, total alkalitity, lactate, acetate, formate, chlorate, oxalate, and trace elements.
Black | Red | |
---|---|---|
Temp (°C) | 10.9 | 10.9 |
pH | 6.70 | 4.37 |
redox (mV) | -142 | -8 |
Fe (ppm) | 161.5 | 146.3 |
Total N (ppm) | 1.510 | 1.280 |
• NH4 | 1.22 | 0.91 |
• NO3 | 0.29 | 0.36 |
• NO2 | <0.10 | <0.10 |
SO4 (ppm) | 27.4 | 29.4 |
PO4 (ppm) | 4.1 | 1.8 |
B (ppm) | 186 | 70 |
Mo (ppm) | 2.59 | 0.68 |
W (ppm) | 3.82 | 0.91 |
Tl (ppm) | 1.90 | 0.52 |
U (ppm) | 1.01 | 0.20 |
Cations and Anions found in the Soudan Mine. The pie chart shows the abundance of cations and anions found in the mine. The numbers in parentheses are the concentrations (in ppm) of each ion in the "Black" and "Red" samples respectively. The minor ions are shown expanded in the rightmost pie.
Respiration in aerobic and anaerobic environments. Among other potential pathways in the Soudan mine, electrons are transferred from hydrogenases to either cytochromes and then to oxygen to produce water in an oxidative environment, or via nitrate and nitrite reductases (denitrification) in anaerobic environments. Genes encoding the hydrogenases, respiratory complexes, and terminal cytochromes of the aerobic sample were significantly more abundant in the Red (oxidized) sample, while genes encoding the hydrogenases and denitrification genes were more abundant in the Black (reduced) sample. After Vassieva, O. [25]
This analysis demonstrates that by combining pyrosequencing, subsystems analysis, and comparative metagenomics the microbiology of different environments can be correlated with the chemistry and hydrogeology of those environments to identify significant ecological differences between them.
Comparisons between Soudan and Iron Mountain communities
A previous study used Sanger sequencing to determine the metagenome of the Iron Mountain community[7]. The environmental differences (such as the difference in temperature) account for the predominant differences between the microbial communities. The organismal differences are reflected in the individual biochemistries of the samples [see Additional files 4 and 5]. For example, the AMD metagenome contains significantly more occurrences of Archaea-specific subsystems such as those involved in protein biosynthesis than the Soudan samples. The AMD sample has a preference for CO2 fixation and simple carbohydrate metabolism when compared to either of the Soudan samples. There are also many currently unexplained differences between subsystems found in these environments that must relate the biology of the organisms to the chemistry of the environment.
Comparisons between Soudan and other metagenome sequences
Subsystems present in different metagenome sequences. The subsystems present in the Soudan samples, the Iron Mountain AMD sample, the Minnesota Farm and the Sargasso Sea are shown grouped by family. The red x corresponds to very low abundance or complete absence of that family of subsystems. The size of the circle represents the proportion of sequences seen within that family of subsystems.
Conclusion
This is the first metagenome analysis performed using pyrosequencing, which is approximately 10 to 30 times cheaper than current Sanger sequencing. Pyrosequencing also eliminates the need for cloning, thus removing the potential for both aberrant recombinants in the surrogate host and for cloning-related artifacts such as counterselection against potentially toxic genes such as those found on phages[2]. The main concerns with current pyrosequencing technology are the short length of sequence fragments (average of 105 bp in this study), and the requirement to use whole genome amplification to generate sufficient DNA for sequencing from environmental libraries The former may make it difficult to accurately assemble genomes in the absence of a scaffold, while the later may bias these analyses. Our preliminary unpublished data suggests that the whole genome amplification bias is minimal, and is preferentially towards the ends of linear pieces of DNA (Haynes, Rayhawk, Edwards, Rohwer; unpublished). Since these biases are applied equally to both libraries, they will be negated during the comparative study to highlight differences between metagenomes. Nonetheless, the short fragments are sufficient to determine statistically significant differences between metagenomes that reflect the most likely biology occurring in each environment. The low cost, high yield of pyrosequencing combined with statistical analyses on the abundance of subsystems in the samples allows the rapid identification of key processes driving the metabolism of different environments.
The systems approach of integrating biology, chemistry, and geology has yielded significant insights into the metabolism of two environments in the Soudan Mine. The oxidized sample is using aerobic respiratory pathways while the reduced sample is using anaerobic pathways. Nitrogen assimilation, iron acquisition, and sulfur metabolism are all differentiated between these two samples from close proximity within the same mine. However, many more significant differences between the samples remain unexplained by our current knowledge of bacterial physiology and metabolism. Explaining these differences will be a grand challenge for the future. By combining pyrosequencing, subsystems analysis, comparative metagenomics, and statistics, Occam has used his razor on metagenomics.
Methods
Sample collections, microbial enumeration, and DNA extraction
Samples were collected from several sites in the Soudan Mine. This analysis concerns the sample collection at two sites on Level 27 (714 m below the surface; Figure 1). Water and sediments were sampled from the two locations shown in Figure 1 giving the "Black" (reduced) sample and "Red" (oxidized) sample. Microbes were concentrated from these samples by filtration with 0.22 μm Sterivex units. Microbial counts were enumerated by staining the samples with SYBR-Gold (Invitrogen, Carlsbad, CA) and visualization with an epifluorescent microscope [21]. DNA was extracted from the microbial sample using either the Ultra Clean Soil DNA Kit or Power Soil Kit (MolBio, Boulder, CO). The DNA was amplified with GenomiPhi (GE Healthcare, Piscataway, NJ) in an Eppendorf thermal cycler (Eppendorf, Westbury, NY) using multiple reactions containing 50–100 ng of the isolated DNA as template and the manufacturer's recommended protocols. After amplification, the resulting DNA was purified with silica columns (Qiagen, Valencia, CA) and concentrated by ethanol precipitation. The DNA was resuspended in water to a final concentration of 0.3 mg/ml. Approximately 10 μg of each sample was sequenced using the pyrosequencing technology (454 Life Sciences, Branford, CT).
Bacterial-specific primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and the universal 1492R primer (5'-TACGGYTACCTTGTTACGACTT-3') [22] were used to amplify the 16S rDNA genes. PCR products were cloned into the pCR®4-TOPO® vector as recommended by the manufacturer (Invitrogen, Carlsbad, CA).
Water and mineral analyses
Water samples were collected by filtering the water through 0.2 μm filters into clean bottles. Field measurements of pH, Eh, temperature and conductivity were conducted in situ. The sediment samples were collected as slurries with a pipette resting on and in the sediments. Those slurries were transferred to clean centrifuge tubes, allowed to settle by gravity and then the fluid was decanted.
Major anions in the water were determined by GC (Dionex IGS-2000, Sunnyvale, CA) and major and trace elements by ICP/MS (Thermo Electron PQ ExCell, Franklin, MA). The mineral identifications are based on XRD (Bruker-AX D500 X-ray Diffractometer, Germany) measurements. The X-ray peaks were relatively small. Much of the sediment was apparently not well crystallized.
Sequence analysis
The unassembled sequences provided by 454 were compared to the SEED database using the BLASTX algorithm on the Teragrid cluster at Argonne National Laboratories[15, 23]. All BLAST searches were performed using an expect value cutoff of 1 × 10-5. At this cutoff approximately 3 of the observed hits would be expected to occur at random[23].
The BLASTN algorithm was used to identify 16S genes from release 9 of the RDP database [16, 24]. These BLAST searches were also performed using an expect value cutoff of 1 × 10-5 and a minimum sequence match length of 50 nt.
Statistical analyses of metagenome datasets
The statistical analysis of subsystems present in each sample was performed essentially as described elsewhere [11]. The presence or absence of subsystems between two data sets was determined using 20,000 replicates of samples of 5,000 subsystems each. The 95% confidence interval for the median was constructed using the 0.025 and 0.975 percentiles.
Declarations
Acknowledgements
The authors are grateful to Bill Miller, Director of the Soudan Facility, for arranging the sampling trips, ploughing through paperwork, and bringing these fascinating microbial communities to our attention. Thanks to Jim Essig, for guiding us to Level 10, arranging our sampling protocols, and generally helping out. Tony Zavodnick, Paul Paulisich, and Jack Zorman provided invaluable guidance and explanations of mining at the Soudan Mine site and the whole Soudan mine and facility crews for making our sampling trip so enjoyable. In addition, we thank Robert Olson (Argonne National Labs) for assistance with the computational analysis. This work was supported by a grant NSF DEB-BE 04-21955 from the NSF Biocomplexity program (to FR).
Authors’ Affiliations
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