Abstract The Frasassi Caves are a currently forming limestone karst system in which biogenic sulfuric acid may play a significant role. High concentrations of sulfide have been found in the Frasassi aquifer, and gypsum deposits point to the presence of sulfur in the cave. White filamentous microbial mats have been observed growing in shallow streams in Grotta Sulfurea, a cave at the level of the water table. A mat was sampled and used in a bacterial phylogenetic study, from which eleven 16S ribosomal RNA (rRNA) gene clones were sequenced. The majority of 16S clones were affiliated with the δ- proteobacteria subdivision of the Proteobacteria phylum, and many grouped with 16S sequences from organisms living in similar environments. This study aims to extend our knowledge of bacterial diversity within relatively simple geochemical environments, and improve our understanding of the biological role in limestone corrosion.

Georef Keywords: Sulfide oxidation, sulfate reduction, caves, bacteria, phylogeny, karst 1

Introduction Karst is the name given to large-scale limestone porosity—caves, sinkholes, and

other effects of significant limestone dissolution. Karst usually occurs as water

percolates through limestone. The water dissolves calcium carbonate, CaCO3, and reacts

- with it to form bicarbonate, HCO3 ; eventually pockets and large cavities form. However,

karst processes are not as simple as this popular explanation. In many cases, geologists

have found evidence of sulfur in limestone karst cave systems—often as gypsum

crystallized on the cave walls (Northrup and Lavoie, 2001). Some of the largest

limestone caves in the world, including Cueva de la Villa Luz in Tabasco, Mexico and

Lechuguilla Cave of Carlsbad Caverns National Park in New Mexico, seem to have been

formed through processes involving both bicarbonate and biogenic sulfuric acid

(Cunningham et al., 1995; Northrup and Lavoie, 2001).

Sulfuric acid, as a strong acid, can dissolve limestone more effectively than can

bicarbonate or its protonated cousin, carbonic acid. Sulfuric acid forms through the

2- oxidation of hydrogen sulfide, H2S, to sulfate, SO4 (Fig. 1a). When sulfate reacts with

water, it forms sulfuric acid, H2SO4. When sulfuric acid reacts with calcium carbonate,

CaCO3, it makes gypsum, CaSO4 (Fig. 1b). Water and carbon dioxide released through the formation of gypsum react with calcium carbonate to make bicarbonate or carbonic acid (Fig. 1c) (Galdenzi et al., 1999).

The Frasassi cave in Italy is a well-known and unusual karst limestone system, in

that, unlike most other well-known limestone caves, it is currently undergoing formation.

Observations of microbial biofilms covering the limestone surfaces within the cave and of macroscopic microbial mats floating in cave streams have led researchers to postulate

that H2S oxidation is the driving factor behind karstification (Galdenzi et al., 1999). 2

Understanding the role of microorganisms—in particular, sulfur and sulfide oxidizers and sulfate reducers—in the Frasassi cave may unearth biogeochemical interactions with a broader application to karst systems.

A valuable aspect of working in Frasassi is that the geochemistry is simpler than

in many other environments (such as a lake or soil sample), and microbial metabolism is

limited by the absence of sunlight and organic material from the surface. Stable isotope

ratio analysis conducted by Galdenzi et al. on organic samples found a distinct

fractionation signature with consistently lighter isotopes of carbon and nitrogen in the

sulfidic sections of the caves compared with those at the caves’ entrances and the surface

(1999). This suggests that all organic compounds present in the cave are autochthonous,

and is consistent with our picture of the cave as an ecosystem completely isolated from

surface nutrients and photosynthesis. For this reason Frasassi is an ideal environment

that allows us to see discrete links between in situ organisms and the cave’s

geochemistry.

Given the geochemical and metabolic requirements of organisms such as those in

Frasassi, it is difficult to follow the traditional microbiological method of culturing organisms in the lab in order to study their morphologies. This technique places heavy selective pressure on organisms, with the result that what grows is often out of proportion with the original microbial mat. Angert et al. note that typically less than 1% of all microbes from a particular environment can be grown in the lab using standard enrichment techniques (1998). In particular, organisms that rely on symbiosis or syntrophy, whereby a substance is catabolized through the metabolic efforts of two or more organisms that could not catabolize the substance on their own, may not grow at all 3 in the lab (Madigan et al., 2003). These two relationships often produce microbial mats with intricate layering according to molecule-scale gradients of substances such as oxygen and hydrogen sulfide (Stal, 2001). Therefore, it is important to rely not only on culturing but on molecular phylogenetic work to identify organisms and determine evolutionary relationships.

Some genes change over time faster than others depending on the necessity of their function to the organism. Genes that code for the most important functions in an organism are highly conserved and thus are used for molecular work. The 16S rRNA gene codes for ribosomal RNA, a molecule that plays an important structural and functional role in the ribosome (Madigan et al., 2003). For this reason it is highly conserved and is the gene most often used for phylogenetic work. Using divergences within this one gene, researchers can create phylogenetic trees that show evolutionary relationships. On these trees, internal nodes represent common ancestors, and the lengths of branches correspond to evolutionary changes, or more specifically, base pair substitutions per site. Widespread use of 16S rDNA sequences for phylogenetic analysis has resulted in a systematic and reliable way to diagram evolutionary relationships and survey microorganisms present in natural environments (Hall, 2001a).

This paper presents a molecular phylogenetic analysis of 16S rDNA from bacteria growing in a cave stream microbial mat in the Frasassi Grotta Sulfurea. Results suggest that the mat contains organisms closely related to known sulfur oxidizing and sulfur reducing bacteria. These bacteria likely play a significant role in the production of sulfuric acid and subsequent karstification.

Methods Cave description and sampling Le Grotte di Frasassi (Frasassi Caves) are located in the Marche region of Italy, approximately 150 km NE of Rome, in the Sentino river gorge near the village of Genga.

The cave, at about 200-360 m above sea level, includes four main layers within the massive Calcare Massiccio limestone formation (Galdenzi and Maruoka, 2003). Frasassi includes over 100 caves in a network of about 35 cave passages (Fig. 2). The highly permeable Massiccio (which is 600-1000 m thick) rests on the Anidriti del Burano, an evaporite formation estimated to be 2000 m thick (Galdenzi and Maruoka, 2003). This is thought to be the sulfur source for the Frasassi caves (Galdenzi and Maruoka, 2003).

Sodium, chloride and sulfur ions (i.e., sulfate and sulfide) are brought to the cave by groundwater (Fig. 3) (Galdenzi et al., 1999). Sulfide concentrations of up to 0.4 mmol/l and sulfate concentrations of up to 2.5 mmol/l have been found in the Frasassi aquifer

(Galdenzi et al., 1999). The cave streams keep a constant temperature of 13˚ C and range in depth from 10 cm to several meters, with flow rates ranging from 0.5 to 50 l/s

(Vlasceanu et al., 2000). Galdenzi et al. found that conductivity of the streams was 1200-

1900 µS and that limestone dissolution occurred both in the streams and in the air at a rate of 50 µm/yr over the study period of 5 years (1999).

In 2002, Jenn Macalady1 collected samples (“GS tm”) from a macroscopic

filamentous white mat in a cave stream of Grotta Sulfurea, at about 200 m elevation (the

water table) (Fig. 4). Samples were frozen at -20˚ C until transport to the U.S. on dry ice.

Upon arrival in the laboratory, they again were stored at -20˚ C.

1 Assistant Professor, Department of Geology, Carleton College. 5

DNA extraction, PCR amplification, and cloning of 16S rRNA genes

Total community DNA was extracted from approximately 30 g (wet wt) frozen

sample using a MoBio Ultraclean Mega Prep Soil DNA kit, following the manufacturer’s

procedure. Presence of DNA was confirmed using agarose gel electrophoresis (1% agar)

with a secondary ethidium bromide staining solution. 16S rRNA genes were amplified using the polymerase chain reaction (PCR) with 8-27 forward (5´-

AGAGTTTGATCCTGGCTCAG) bacterial and 1510-1492 reverse (5´-

GGTTACCTTGTTACGACTT) universal primers (Fig. 5). The PCR cycle was as follows: initial denaturation at 94° C for 5 min., denaturation at 94° C for 1 min., annealing at 45° C for 45 sec., extension at 72° C for 1.5 min., final extension at 72° C for 20 min. and storage at 4° C; the PCR ran for 30 cycles. Tests were run against archaeal and blank controls, and against bacterial DNA extracted from a local stream microbial mat as a positive control. Clones were transformed with a TOPO TA Cloning®

Kit for Sequencing, according to the manufacturer’s procedure. I used chemically

competent Escherichia coli, which were cultured in Ampicillin LB broth overnight and

then inoculated onto agarose plates.

Colonies from these smear plates and from later streak plates were sampled randomly. Plasmids were extracted from cells in each colony and 16S rDNA inserts purified using a Wizard® Plus SV Minipreps DNA Purification System (Promega). I

checked for concentration using agarose gel electrophoresis with 3.6 mL culture,

following the manufacturer’s procedure (Fig. 6). PCR with 27f and 1492 primers was used to amplify the inserts, with the following PCR conditions: initial denaturation at 4°

C for 10 min., denaturation at 94º C for 30 sec., annealing at 47º C for 1 min., extension 6

at 72º C for 2 min., final extension at 72º C for 10 min. and storage at 4º C; the PCR ran

for 30 cycles.

The 16S gene inserts were sequenced at the University of Wisconsin-Madison’s

Biotechnology Center, using cycle sequencing with thermostable polymerases and fluorescently labeled dideoxy terminators. The primers used were T3 (5’-

ATTAACCCTCACTAAAGGGA) and T7 (5’-TAATACGACTCACTATAGGG).

Phylogenetic analysis The T3 reverse-complement and T7 ends of each sequence were matched

manually using the multiple alignment program Se-Al (Rambaut, 1996). Sequences were

submitted to the Ribosomal Database Project’s (RDP) online CHECK_CHIMERA

software to test for possible PCR artifacts (sequences recombined from more than one

organism’s 16S gene) as mediated by Taq polymerase (Maidak et al., 1997). In order to

compare the BK clones with evolutionarily similar sequences, my advisor Jenn Macalady

and I searched the ARB 16S rDNA database, which contains over 5000 bacterial

sequences (Ludwig and Strunk, unpublished data). Sequences similar to each of the BK

clones were used for automatic and manual alignments in Seaview and BioEdit (Galtier

et al., 1996; Hall, 2001b; Ludwig and Strunk, unpublished data). Aligning sequences

well is crucial to creating reasonable phylogenies, as poor alignments can lead to

misleading or meaningless trees (Hall, 2001a). I used the Blast program on the website

for the National Center of Biotechnology Information (NCBI) to calculate percent

similarity between cultivated species and my clones (Altschul et al.).

Tree-building methods vary depending on their assumptions about genetic evolution. Since we as scientists do not know exactly how evolution works, it is 7

important to use multiple methods to construct trees and analyze phylogenetic

relationships. I used both the neighbor-joining algorithm in ARB and Bayesian analysis

to analyze my sequences (Huelsenbeck and Hall, 2000; Ludwig and Strunk, unpublished

data). Neighbor-joining uses a distance matrix derived from a multiple alignment. The

matrix records the calculated distance, or fraction of differences, between each pair of

sequences or lineal groups. In this way it can give a value and a corresponding branch

length to the calculated evolutionary distance (Hall, 2001a). Neighbor-joining is the only

practical tree-building algorithm to use with large data sets, such as the one in ARB.

Large data sets are valuable because they provide a broad context for the placement of

new sequences.

My sequences were aligned using the ARB aligner, checked manually, and added to the database using ARB’s “QuickAdd” parsimony method. Parsimony is a “minimum change” method that assumes that the most likely tree is the one with the least changes between related taxa (Hall, 2001a). It produces trees that show approximate placement of the organisms and is useful when adding a limited number of sequences to a neighbor- joining tree. The ARB tree was rooted using several archaeal species.

Trees specific to each bacterial lineage were created from the ARB libraries using

Bayesian inference in MrBayes and were formatted graphically using PAUP*

(Huelsenbeck and Hall, 2000; Swofford, 1999). Bayesian analysis uses posterior

probabilities to find the best set of trees for the given sequence alignment and a

postulated model of evolution that maximizes the probability of observing the given data.

Rather than arriving at a single best tree, Bayesian inference samples trees using the

Markov chain Monte Carlo method, which produces a set of converging consensus 8

trees—trees for which accepting or rejecting any particular change becomes essentially

random. The frequency of any particular tree being sampled is related to the probability

that it is the best tree for the data. Bayesian analysis is quite powerful and is becoming

increasingly popular among phylogeneticists (Hall, 2001a).

I used MrBayes to run Bayesian inference searches for 105 cycles with four

Markov chains to check for convergence. Trees were saved every 100 generations; the

first 350 trees from each cold chain (the Markov chain used for sampling) were discarded. These were rooted using monophyletic bacterial outgroups.

Results Sampling environment and mat structure On the sampling expedition of October 2003 to Grotta Sulfurea, Jenn Macalady and I measured the geochemistry of the shallow flowing cave stream containing the white microbial mat sampled in 2002. We found cave water temperature to be 13.9-14.6˚ C.

The water had pH = 7.0 ± 0.03. The white filamentous microbial mat was floating on or below the surface of the stream, and in places was directly on top of the black sulfidic mud that lined the streambed. The strong “rotten egg” smell of hydrogen sulfide was quite recognizable.

Phylogenetic analysis of 16S rRNA genes derived from mat From the Grotta Sulfurea cave microbial mat samples, eleven 16S rRNA clones

were sequenced. None of the sequences were positively identified as chimeric using the

RDP software CHECK_CHIMERA. Two sequences (clones BK9 and BK111) were over

99.5% identical and so only one of these (BK9) was used for phylogenetic work. The

remaining ten sequences fit into four phyla, all in the Bacterial domain (Fig. 7):

Proteobacteria (6 sequences), Cytophaga/Flavobacteria/Bacteroides (CFB) (2

sequences), WS6 (1 sequence), and Termite Group 1 (1 sequence). Of the bacteria in the 9

Proteobacteria phylum, 1 fit in the γ-proteobacteria subdivision, and the remaining 5 in

the δ-proteobacteria subdivision (Table 1). Preliminary results of further phylogenetic

work on clones from cave wall microbial films in Grotta Sulfurea (“GSgm”) are also

shown on ARB trees (Figs. 8, 10, 12, and 14).

Clone BK1 fit well with the γ-proteobacterium “Thiobacillus baregensis,” a sulfur oxidizing bacterium found in sulfurated thermal waters in Bareges, France (Hedoin et al., unpublished 1996). It also grouped with a number of Sulphur River clones, clones isolated from a white filamentous microbial mat in Sulphur River, Parker Cave, Kentucky

(Angert et al., 1998). Clone BK1 grouped most notably with clone SRang 2.5, which itself grouped closely with “T.barengensis” (Figs. 8 and 9).

The clones BK4, BK5, BK7, BK9, and BK10 grouped in the δ-proteobacteria, a

lineage of predominantly sulfur-reducing bacteria (Figs. 10 and 11). Clones BK4 and

BK10 clustered together, with their closest cultivated neighbor being Desulfosarcina

singaporensis, a sulfate-reducing bacterium isolated from sulfide-rich black marine mud

(Lie et al., 1999). Clone BK5 was closely related to Geobacter sulfurreducens and to

Geobacter metallireducens, both metal ion reducers (Methe et al., 2003). Clone BK7 fit

near Desulfonema ishimotoei, a sulfate reducer isolated from organic-rich sulfidic marine

and freshwater sediment (Fukui et al., 1999). Clone BK9 was similar to Syntrophobacter

wolinii, a sulfate reducer isolated from a culture enriched from anaerobic granular sludge

(Harmsen et al., 1993).

Clone BK2 and a partial sequence of BK108 (T3) fit in the CFB group (Figs. 12

and 13). Clone BK2 was most similar to Cytophaga fermentans. Clone BK108T3 was

closely associated with the Sulphur River clone SRang 1.29, the contaminated aquifer 10 clone WCHB1.32, and the Cesspool Cave clone CC.8. The only cultivated bacteria in the same group as clone BK108T3 were the fermenter Bacteroides putredinis and the termite gut bacterium Rikenella microfusus, both anaerobes (Madigan et al., 2003; Ohkuma et al.,

2002).

Clone BK6 fit in the WS6 phylum, near the OP11 clone OPB92 (Figs. 14 and 15).

Clone BK8 fit in Termite Group 1 (Figs. 15 and 16). Since I had only a partial sequence of Clone BK8 (T3), I could not do rigorous phylogenetic work on it. The phyla WS6 and

Termite Group 1 are newly defined and contain no cultivated species.

Discussion The macroscopic filamentous white microbial mat found in the cave stream of

Grotta Sulfurea represents a distinct community isolated from photosynthetically derived carbon. The observed high sulfide and sulfate levels in the water and air are sufficient to support a sulfide-metabolizing community (Madigan et al., 2003). Given the sequence similarities of 88-94% between the Grotta Sulfurea BK clones and known sulfur/ide oxidizers and sulfate reducers (Table 1), it is reasonable to believe that my 16S clone sequences came from respective sulfur/ide oxidizers and sulfate reducers, and that the microbial community in turn includes both these types of bacteria. Since there is no input of organic material from the surface to Grotta Sulfurea, chemolithoautotrophy likely forms the base of the food chain. Sulfur oxidizing autotrophic bacteria thus likely comprise a significant portion of the base-level biomass, a biomass capable of supporting fifteen invertebrate species, of which seven are endemic to Frasassi (Galdenzi et al.,

1999).

A sulfur-rich environment such as Frasassi would be expected to support both types of metabolism—especially where sulfur/ide oxidizers and sulfate reducers may 11

form close symbiotic relationships. This has been seen in hydrothermal vent communities, where bacteria (especially sulfur/ide oxidizers and sulfate reducers) play an important role in mineralization of vent chimneys (Lovley et al., 2000; McCollom and

Shock, 1997). In fact, sulfur isotope ratios analyzed by Galdenzi and Maruoka (2003) suggest that sulfate reducing bacteria are present in Frasassi. They found sulfur isotope

34 34 34 values of δ S ~ -19.60 ‰ in gypsum deposits in the cave, δ S ~ -14 in H2S and δ S ~

+21.00 ‰ in sulfate within sulfidic groundwater (Galdenzi and Maruoka, 2003).

Galdenzi and Maruoka (2003) propose that the size of these isotope fractionations can best be explained by the sulfur cycling through sulfate reducing bacteria, which are known to produce high fractionations. The presence of sulfate reducers, shown through phylogenetic work, fits with the sulfur isotope analysis.

Not all clones from similar microbial mats in sulfidic streams have this split between sulfur/ide oxidation and sulfate reduction. The Sulphur River clones from a white filamentous mat in Parker Cave, Kentucky are predominantly ε- and γ- proteobacteria, both lineages known for their sulfur-oxidizing metabolism (Angert et al.,

1998). Similarly, clones from a white filamentous microbial mat in the sulfidic waters of

Lower Kane Cave, Wyoming are almost entirely from the ε-proteobacteria (Engel et al.,

2003). It would seem that these results would be more expected for the Frasassi Grotta

Sulfurea BK clones given the similarities in limestone geology and microbial mat morphology. There was one γ-proteobacteria, but the majority of clones (5) were δ- proteobacteria, putative sulfate reducers.

The differences between the Frasassi Grotta Sulfurea clones and those of Sulphur

River and Lower Kane Cave may be due to a difference in sampling technique or an 12

introduced experimental bias. In the DNA extraction there may be lysing bias, whereby

certain cells are more easily opened and their DNA extracted. Similarly, the PCR

primers may anneal with certain sequences better than others. This latter potential source

of difference can be eliminated entirely from the Lower Kane Cave clones, as they were

PCR amplified using the same primers as for the BK clones (27f, 1492r) (Engel et al.,

2003). The Sulphur River clones were amplified using a 515 forward primer and the

same1492 reverse primer (Angert et al., 1998). It is also possible that differences in the

PCR programs used could have affected the results.

Even considering these possible sources of disparity between the Grotta Sulfurea

BK clones and the Sulphur River and Lower Kane Cave clones, a difference between the phylogenetic results found in each location is evident. Why were there no sulfate reducers found in either the Sulphur River or the Lower Kane Cave clones? Since sulfur oxidizers produce sulfate, it would seem that their presence would provide the sulfate necessary for sulfate reduction metabolism. This apparent difference between the sulfur- cycling species present in each environment needs to be investigated further.

The presence of only Bacteria in my clone libraries can be attributed to the use of bacterial PCR primers, which amplify only the bacterial 16S rRNA gene. We would need to use archaeal primers and sequence a greater number of bacterial clones in order to explore the full diversity of the Grotta Sulfurea stream microbial mat. Since 16S genes evolve slowly relative to most other genes, they often do not keep up with changes in the genes that code for proteins such as those involved in metabolism (Woese, 1987). It would therefore be germane to sequence protein-coding genes—and even better, entire genomes. Another important and ongoing aspect of this research is Fluorescence In-Situ 13

Hybridization (FISH), which will help us visualize the population numbers and

distribution of organisms between the two prokaryotic domains.

This study expands the known diversity of the Frasassi Grotta Sulfurea cave

stream microbial mat community based on phylogenetic analysis, and is an excellent

launching point for further physiological and phylogenetic study. The high percent similarities between my 16S clones and the 16S genes of known sulfur/ide oxidizers and sulfate reducers point to a biological source of sulfuric acid through biogenic sulfur oxidation, which implies a crucial role played by bacteria in carving the Frasassi caves.

The presence of both sulfur/ide oxidizers and sulfate reducers fits with sulfur isotope

fractionation data and testifies to the complex sulfur cycle of the Frasassi cave system.

Acknowledgments Alessandro Galdenzi accompanied Jenn Macalady and me to Grotta Sulfurea in

October 2003, and for this I am grateful. Alessandro Montanari kindly shipped our

samples from Italy. The computer analysis work would have been impossible without the help of Doug Foxgrover, who worked hard to install ARB on Jenn’s computer. Leah

Morgan answered my many questions about computer programs and let me use her computer for BioEdit. My academic advisor Bereket Haileab provided moral support and perspective. Cam Davidson tutored me in Adobe Illustrator. Tim Vick offered constant technical support. Funding for this project came from the Bernstein Student Research

Endowment and the NSF Biogeosciences Program. This work builds on research done by

Jenn Macalady, Alessandro Galdenzi2, and Teruyuki Maruoka,3 among others. Finally, I

would like to thank Jenn Macalady, my advisor on this project. Without her vision,

expertise, and support, this research would not have happened.

2 Instituto Italiano di Speleologia, Frasassi Section. 3 Department of Geological Sciences, University of Vienna. 15

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+ 2- (a) H2S + 2 O2 2 H + SO4 + 2- (b) 2 H + SO4 + CaCO3 + H2O CaSO4 * 2 H2O + CO2 2+ - (c) CO2 + CaCO3 + H2O Ca + 2 HCO3

Figure 1. Various pathways for limestone corrosion: (a) oxidation of sulfide to sulfate, which can bond with water to form sulfuric acid, H2SO4; (b) formation of gypsum, which frees CO2 to form bicarbonate; (c) typical karst-style formation of bicarbonate. This can become protonated to form carbonic acid, H2CO3. Adapted from Galdenzi et al., 1999. 18

10° 16° 45° Adriatic Sea Firenze Ancona *

Tyrrhenian Sea Roma Napoli 41°

Sentino River Grotta Sulfurea

Artificial access tunnel Grotta del Fiume

Grotta Grande del Vento

0 250 m

Legend Sampling site

Figure 2. Map of Frasassi Caves showing sampling site in Grotta Sulfurea. Adapted from Vlasceanu, et al., 2000. 19

~100 m elevation Figure 3. Schematic cross section of Frasassi Gorge showing water table and movement of ground water. Adapted from Galdenzi et al., 1999. 20

e) d)

Figure 4. Photographs from Frasassi Gorge and Caves: a) entrance to Grotta Sulfurea from SS 76 in Genga; b) Sandro Galdenzi lowering the rope into the first chute of Grotta Sulfurea; c) sampling the white mat; d) white mat (scale bar is about 0.3 m); e) measuring pH of drips from wall gypsum crystals. Photos by Jenn L. Macalady. Table 1. Summary of 16S rDNA results. ------Clone name Division (Subdivision) Nearest taxon* (% identity**) Inferred physiology ------BK1 Proteobacteria (gamma) "Thiobacillus baregensis" (94%) Sulfur/ide oxidizer BK4 Proteobacteria (delta) Desulforhopalus singaporensis (93%) Sulfate reducer BK5 Proteobacteria (delta) Geobacter sulfurreducens (95%) Sulfate reducer BK7 Proteobacteria (delta) Desulfonema ishimotoei (88%) Sulfate reducer BK9 (BK111) Proteobacteria (delta) Syntrophobacter wolinii (93%) Sulfate reducer BK10 Proteobacteria (delta) Desulforhopalus singaporensis (94%) Sulfate reducer BK2 CFB Cytophaga fermentans (88%) Anaerobe BK108T7 (partial) CFB Sulphur River clone SRang 1.29 (92%) Anaerobe BK6 WS6 OP11 clone OPB92 (79%) BK8T3 (partial) Termite Group 1 Termite gut clone TG13 (79%) Anaerobe

*Nearest cultivated taxon found in public databases **Percent identity calculated using number of base pair matches per alignment in the NCBI Blast program (Altschul, S.F., et al.) 24 LacZα initiation codon T3 priming site

201 CACACAGGAA ACAGCTATGA CCATGATTAC GCCAAGCTCA GAATTAACCC TCACTAAAGG GTGTGTCCTT TGTCGATACT GGTACTAATG CGGTTCGAGT CTTAATTGGG AGTGATTTCC Pst I Pme I EcoR I EcoR I GACTAGTCCT GCAGGTTTAA ACGAATTCGC CCTT PCR A GGGC GAATTCGCGG 261 CTGATCAGGA CGTCCAAATT TGCTTAAGCG GG AA Product TTCCCG CTTAAGCGCC T7 priming site

CCGCTAAATT CAATTCGCCC TATAGTGAGT CGTATTACAA TTCACTGGCC GTCGTTTTAC 311 GGCGATTTAA GTTAAGCGGG ATATCACTCA GCATAATGTT AAGTGACCGG CAGCAAAATG

P lac LacZα-ccdB

pUC ori

Figure 5. Map of pCR® 4-TOPO® plasmid, showing nucleotide sequence surrounding the cloning site with base pair numbers given on the left. Kanamycin Plasmid contains 3954 base pairs without the PCR product. Adapted from Invitrogen (2003). Ampicillin 21 ank 11 11 1:10 ater bl LadderBK1 BK2 BK6 BK7 BK8 BK10BK108 BK1 BK1 1:B1K0 2 1B:K106 1B:1K07 1:1B0K8 1:B1K0 10B 1K:11008B 1K:10CultureB baclatenrkWial control

Figure 6. Gel of PCR product (27f, 1492r) showing BK clones 1550 bp from plasmid prep template at full 1400 bp concentration and 1:10 dilution. I used 10 µl Hi-Lo DNA Marker as my ladder, with 5 µl DNA template per well. Note size of band at ~1500 bp. Clones BK4, BK5, and BK9 were amplified during a later PCR run and are not shown. 2 a

P bi l Fir a cro nc ae i Bacteria i m t omycet A icu c myd tinob t a Cyanobacteria es Spirochaetes Figure 7. Phylogenetic tree showing e Verrucom a Chl s s cteria P3 ste the three domains of life. Highlighted OP9 O rgi Aqu eferribacteres bacteriayne D phyla denote clades in the Bacteria Chl ific S Thermod or T ae uso oflexi he F esu rmo TM6 that contain BK clones. Candidate lf togae Deinococcus-Tobhaecrmt u Termite group I eria Acidobacteria phyla Termite Group 1 and WS6 not s Fibrobacteres shown. Tree adapted from Macalady Gemmimonas and Banfield, 2003. OP10 Nitrospira TM7 Chlorobi Bacteroidetes δ Pro ε Proteob Methanomicrobia α, teobacteri act β, eria Thermoplasmata γ P rot a eo pMC1 bac teri SAGMEG1 a Thermococci Methanopyrus Archaeoglobi ccales oco eria than bact Me ano Meth pMC2 ria 0.10 nucleotide te Eucarya a changes per site alobac FA H C2 -1 C1 NPF Y rchaeot otei r diment ra Se o

K ermop

2 Th G FCG1 Archaea FC 23 56 Pseudomonadaceae Terredinibacter species, Z31658, 1494 24 Halomonadaceae Oceanospirillum minutulum, 1500 9 Oceanospirillaceae Flectobacillus sp., U14584, 1470 Marinobacter hydrocarbonoclasticus, X67022, 1461 Oceanospirillum japonicum, 1498 7 SAR86 3 Symbionts acid mine drainage clone MIM4138, 1484 Coxiella burnetii, M21291, 1471 Rhipicephalus sanguineus symbiont, D84559, 1460 acid mine drainage clone MIM451, 1485 31 Moraxellaceae

7 Francisellaceae Psirickettsia 7 Symbionts Leucothrix mucor, X87277, 1505 6 Thiothrix

84 Legionellales

13 Xanthomonadales GSgm clone EL58 GSgm clone EL9 GSgm clone EL2 GSgm clone EL54 GSgm clone EL70 GSgm clone EL8 GSgm clone EL42 8 environmental clone group PCB8contaminated soil clone WD284, AJ292675, 1489 rape rhizosphere clone wr0008, AJ295469, 1341 5 Thiotrichaceae coal tar8contaminated groundwater clone 4825, AF351230, 1447 acid mine drainage clone MIM444, 1467 "Thiobacillus ferrooxidans" strain m81 (BG), 1329 GSTM clone BK1 SRang clone 1.33, AF047619, 961 SRang clone 1.28, AF047617, 963 GSgm clone EL5 GSgm clone EL77 "Thiobacillus baregensis", Y09280, 1405 SRang clone 2.5, AF047624, 961 3 Cardiobacteriales Frasassi snottite isolate 1C ("Thiobacillus sp."), AF213054, 629 Thiobacillus sp., X97534, 1446 Halothiobacillus kellyi, AF170419, 1494 Thiobacillus hydrothermalis, M90662, 1504 20 Methylococcales Cycloclasticus pugetii, U12624, 1492 Cycloclasticus pugetii, L34955, 1492 5 Thiomicrospira 5 Methylophaga Solemya reidi symbiont 2, L25709, 1407 8 Symbionts Achromatium oxaliferum, L42543, 1479 4 Methylococcus aquaculture pond clone SF40.049, 1498 symbiont of Solemya velum, M90415, 1459 Solemya occidentalis gill symbiont, U41049, 1374 acid mine drainage clone MIM486, 1495 "Thiobacillus prosperus", unpublis, 1419 Ectothiorhodospira shaposhnikovi, M59151, 1451 6 Chromatiaceae 4 Ectothiorhodospiraceae Nitrosococcus oceanus, M96395, 1392 Nitrosococcus oceanus , M96398, 1347

0.10 substitutions/site

Figure 8. Partial ARB neighbor-joining tree showing relative position of clone BK1. Tree contains gamma-proteobacterial sequences and is rooted with a monophyletic archaeal outgroup. Numbers on tree are Genbank accession numbers for organisms in the public data bases. 25 26

Thiobacillus hydrothermalis

100 "Thiobacillus baregensis"

100 BK1 clone

Sulphur river clone 2.5

Beggiatoa alba

Acid mine drainage clone MIM444

"Thiobacillus ferooxidans"

Agrobacterium tumefaciens 0.05 substitutions/site Figure 9. Bayesian inference phylogram showing relative position of clone BK1. Tree rooted with Agrobacterium tumefaciens. Bootstrap values displayed as percentages of 1000 replications. 11 Desulfuromonadaceae/Pelobacter "Geobacter chapelleii", U41561, 1465 Pelobacter propionicus, X70954, 1549 GSTM clone BK5 Geobacter sulfurreducens, U13928, 1408 "Geobacter hydrogenophilus", U46860, 1438 "Geobacter hydrogenophilus", U28173, 1391 Geobacter metallireducens, L07834, 1537 sulfate reducing bioreactor clone SRBAC37, 1477 GSTM clone BK10 GSTM clone BK4 GSgm clone EL3 GSgm clone EL62 contaminated aquifer clone WCHB1767, AF050536, 1481 Desulfofustis glycolicus, X99707, 1545 aquaculture pond clone SF39.458, 1444 Desulforhopalus singaporensis, AF118453, 1498 aquaculture pond clone SF40.063, 1504 Desulforhopalus vacuolatus, L42613, 1482 GSgm clone EL28 Desulfobulbus elongatus, X95180, 1453 Desulfobulbus rhabdoformis, U12253, 1498 4 Syntrophaceae GSgm clone EL21 GSgm clone EL49 4 Desulfomonile Desulfoarculum baarsii, M34403, 1355 Desulfoarculus sp. BG74, U85477, 1515 GSgm clone EL4 Desulfobacterium anilini, AJ237601, 1527 Desulfobacterium sp. mXyS1, AJ006853, 1528 GSgm clone EL38 hot spring clone OPB16, AF026998, 1490 hot spring clone OPB55, AF026993, 1495 delta-proteobacteria Desulfobacter halotolerans, Y14745, 1472 Desulfobacter species, M34416, 1436 Desulfobacter species, M34415, 1373 Desulfobacter hydrogenophilus, M34412, 1358 Desulfobacter curvatus, M34413, 1344 Desulfobacter vibrioformis, U12254, 1515 Desulfobacter postgatei, M26633, 1499 delta proteobacterium WN, U51844, 1372 Desulfobacula toluolica, X70953, 1550 "Desulfobacterium niacini", U51845, 1427 "Desulfobacterium vacuolatum", M34408, 1345 Desulfobacterium autotrophicum, M34409, 1319 GSTM clone BK7 Desulfococcus multivorans, M34405, 1347 Desulfonema limicola, U45990, 1496 GSgm clone EL22 Desulfosarcina variabilis, M26632, 1499 Desulfosarcina variabilis , M34407, 1504 GSgm clone EL75 Desulfofaba gelida, AF099063, 1484 Desulfobotulus sapovorans, M34402, 1339 Desulfonema ishimotoei, U45991, 1440 Desulfonema ishimotoei, U45992, 1440 Desulfobotulus sp. BG14, U85470, 1500 Desulfocella halophila, AF022936, 1544 GSTM clone BK9 Desulfovirga adipica, AJ237605, 1524 Desulforhabdus amnigenus, X83274, 1515 unidentified sulfate7reducing bacterium, U49429, 1518 Unknown, X82875, 1475 Unknown, X82874, 1486 Syntrophobacter wolinii, X70905, 1424 Desulfoacinum infernum, L27426, 1526 hot spring clone OPB33, AF026987, 1486 Thermodesulforhabdus norvegicus, U25627, 1559 sludge clone H16, AF234748, 1555 acid mine drainage clone MIM418, 1520 12 Myxococcales 2 Bdellovibrionales 9 TM6

89 Acidobacteria acid mine drainage clone MIM4123, 1516 acid mine drainage clone MIM440, 1533 44 Desulfovibrionales sludge clone 1959, AF097803, 1479 lab7scale sludge clone SBR1093, AF269002, 1485 contaminated aquifer clone WCHB1727, AF050538, 1485

0.10 substitutions/site

Figure 10. Partial ARB neighbor-joining tree showing relative positions of clones BK4, BK5, BK7, BK9, and BK10 as members of the delta-proteobacteria. Tree rooted with monophyletic archaeal outgroup. Numbers shown are Genbank accession numbers for organisms in the public databases. 27 28

Desulfobacter postgatei

Syntrophobacter wolinii 100

BK9 clone 94 Desulfosarcina sp.

100 Desulforhopalus singaporensis

100 BK4 clone 59 100

BK10 clone

Desulfomonile tiedjei 78

Nitrospina gracilis 90 100

Desulfobacterium anilini

Geobacter metaloreducens 94

100 Geobacter sulfurreducens

BK5 clone

Desulfonema ishimotoei 51

BK7 clone

0.05 substitutions/site

Figure 11. Bayesian inference phylogram showing relative positions of clones BK4, BK5, BK7, BK9, and BK10. Tree rooted with Desulfobacter postgatei. Bootstrap values displayed as percentages of 100 replications. 69 Bacteroides, Prevotella

13 Porphyromonadaceae Bacteroides distasonis, M25249 M, 1511 Bacteroides merdae, X83954, 1366 sulfate reducing bioreactor clone SRBAC29, 1449 Bacteroides forsythus, L16495, 1455 Bacteroides forsythus, X73962, 1450 CDC Group DF.3, U41355, 1467 sulfate reducing bioreactor clone SRBAC55, 1446 Bacteroides sp. sp4, AB003389, 1432 Marinilabilia agarovorans, M62422, 1454 Cytophaga fermentans, M58766, 1463 Bacteroides splanchnicus, L16496, 1458 GSTM clone BK2 7 contaminated aquifer clone WCHB1.32, AF050543, 1440 SRang clone 1.29, AF047618, 944 GSTM clone 108T3 Cesspool clone CC.8, AF207533, 626 "Anaeroflexus maritimus", 1386 contaminated aquifer clone WCHB1.53, AF050539, 1448 anaerobic digestor clone vadinHA17, U81712, 1452 GSgm clone EL26 contaminated aquifer clone WCHB1.69, AF050567, 1445 GSgm clone EL20 anaerobic digestor clone BC27, U81676, 1448 sulfate reducing bioreactor clone SRBAC60, 1446 sulfate reducing bioreactor clone SRBAC32, 1446 contaminated aquifer clone WCHB1.29, AF050544, 1446 Bacteroides putredinis, L16497, 1448 Rikenella microfusus, L16498, 1463 CFB

64 Flavobacteriales

9 Flexibacteraceae

9 Sphingobacteriaceae cooling coil coloniser isolate PC5.1A, 1381 Spirosoma linguale, M27802 M, 1423 Flexibacter elegans , 1432 Flexibacter elegans , M58783, 1432 Microscilla marina, M58793, 1425 Flexibacter ruber, M58788, 1450 aerobic sludge clone PHOS.HE21, AF314419, 1417 Runella slithyformis, M27799 M, 1444 Flexibacter litoralis, M58784, 1470 Flexibacter polymorphus, M58786, 1449 Flexibacter roseolus, M58787, 1439 Flexibacter aggregans Persicobacter diffluens, M58765, 1463 environmental isolate 47077, AF227830, 1407 hot spring clone OPB73, AF027008, 1449 hot spring clone env.OPS17, AF018199, 1437 Thermonema lapsum, L11703, 1490 11 Saprospiraceae Riemerella anatipestifer, U10877, 1504 Rhodothermus hot spring clone OPB88, AF027006, 1433 14 Chlorobi

128 Epsilon proteobacteria

2 Desulfurellales

0.01 substitutions/site

Figure 12. Partial ARB neighbor-joining tree showing relative positions of clones BK2 and partial BK108T3 as members of the Cytophaga/Flavobacteria/Bacteroides. Tree rooted with monophyletic archaeal outgroup. Numbers shown are Genbank accession numbers for organisms in the public databases. 29 30

Bacteroides forsythus

100 Bacteroides distasonis

64 100 sulfate reducing bioreactor clone SRBAC29

85 sulfate reducing bioreactor clone SRBAC5

contaminated aquifer clone WCHB1 100

Rikanella microfusus

58 100 sulfate reducing bioreactor clone SRBAC32

100 BK2 clone

Cytophaga fermentans

Cytophaga lytica

Flavobacterium saccharophilum

0.05 substitutions/site

Figure 13. Bayesian inference phylogram showing relative position of clone BK2. Tree rooted with Cytophaga lytica and Flavobacterium saccharophilum as monophyletic sister clade. Bootstrap values displayed as percentages of 1000 replications. sulfate reducing bioreactor clone SRBAC17, 1392 contaminated aquifer clone WCHB1-64, AF050606, 1414 sulfate reducing bioreactor clone SRBAC27, 1400 contaminated aquifer clone WCHB1-07, AF050600, 1512 contaminated aquifer clone WCHB1-26, AF050599, 1417 acid mine drainage clone MIM450, 1425 hot spring clone OPB92, AF027030, 1438 OP11 contaminated aquifer clone WCHB1-11, AF050603, 1423 contaminated aquifer clone WCHB1-56, AF050605, 1517 terephthalate-degrading consortium clone TA18, AF229791, 1318 acid mine drainage clone MIM490, 1378 aquaculture pond clone SF39.380, 1376 OP11-5 contaminated aquifer clone WCHB1-01, AF050597, 1472 contaminated aquifer clone WCHB1-15, AF050596, 1474 contaminated aquifer clone WCHB1-06, AF050595, 1470 contaminated aquifer clone CA1-115, AF172911, 971 uncultured bacterium BOL-01, AF172883, 974 WS6 contaminated aquifer clone CA1-02, AF172904, 971 contaminated aquifer clone CA1-03, AF172905, 1051 marine sediment clone BMS1-28, AF172878, 1046 GSTM clone BK6 sediment clone BOL-130, AF172896, 968 uncultured bacterial clone D1-11, AF172882, 964 lab-scale sludge clone SBR1071, AF268996, 1420 lab-scale sludge clone SBR2004, AF268997, 1394 deep-sea sediment clone BD4-8, AB015558, 1568 metal-contaminated soil clone K20-27, AF145827, 1515 soil clone C1-29, 1433 soil clone S1-197, 1429 lab-scale sludge clone SBR2013, AF269000, 1420 soil clone C1-105, 1484 metal-contaminated soil clone K20-12, AF145815, 1484 TM7 full-scale sludge clone GC1, AF268994, 1381 lab-scale sludge clone SBR2060, AF268999, 1429 oral cavity clone BE109, AY005446, 1443 oral cavity clone BS003, AY005448, 1448 arid soil clone C0-26, AF128717, 1437 lab-scale sludge clone SBR2096, AF268998, 1487 oral cavity clone F061, AF125205, 1449 lab-scale sludge clone SBR2113, AF269001, 1395 oral cavity clone I025, AF125206, 1413 pasture soil clone EB1087, 1344 GSgm clone EL59 petroleum-contaminated groundwater clone 1025, AB030611, 1384 contaminated aquifer clone WCHB1-60, WS5, AF050598, 1769 SRang clone 2.3, AF047623, 930 trickling filter biofilm clone koll6, AJ224539, 1423

0.01 substitutions/site

Figure 14. Partial ARB neighbor-joining tree showing relative position of clone BK6 as member of the WS6. Tree rooted with monophyletic archaeal outgroup. Numbers shown are Genbank accession numbers for organisms in the public databases. 31 Campylobacter jejuni

Desulfurella acetivorans hot spring clone OPB92 100

BK6 clone 71 contaminated aquifer clone CA1-03 100 89 marine sediment clone BMS1-28

84 uncultured bacterial clone D1-11

Acid mine drainage clone MIM490 92 deep-sea sediment clone BD7-4 100 CL500-13 clone

90 trickling filter biofilm clone koll6 full-scale sludge clone GC1 95 soil clone S1-197 92 87 lab-scale sludge clone SBR2004 83

95 metal-contaminated clone K20-27

52 soil clone C1-105 oral cavity clone F061 78 73 85 oral cavity clone BE109 arid soil clone C0-26 72 lab-scale sludge clone SBR2113 pasture soil clone EB1087 petroleum-contaminated groundwater clone1025

BK8T3 clone

0.05 substitutions/site

Figure 15. Bayesian inference phylogram showing relative positions of clones BK6 and BK8T3. Tree rooted with Campylobacter jejuni and Desulfurella acetivorans as monophyletic sister clade. Bootstrap values displayed as percentages of 1000 replications. 32 termite gut clone TG14, AB004576, 1342 termite gut clone TG33, AB004578, 1342 termite gut clone TG25, AB004577, 1341 termite gut clone TG13, AB004575, 1341 GSTM clone BK8T7 rumen clone 4C0d$3, AB034017, 1505 anaerobic sludge clone vadinHB76, U81754, 1446 lab$scale sludge clone SBR2095, AF269003, 1453 3 OP5 hot spring clone OPB25, OP2, AF027097, 1478

0.01 substitutions/site

Figure 16. Partial ARB neighbor-joining tree showing relative position of partial clone BK8T7 as member of the Termite Group 1 phylum. Numbers shown are Genbank accession numbers for organisms in the public databases. 3

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